applying repair processing in chinese homophone disambiguation
thus in computation of flt x eos and fl x eos only the lts whose sub st is null are considered
the spoken corpus shows this point
NUM arabic base template o NUM o m kut ib
let tl lcb til ti2 i tmi be the set of possible labels for variable vi
cc represents words introducing a coordination either neither both
this kind of test corpus would enable the automatic identification of mispredictions as well as counting of various performance statistics for the rules
c hi ic or c v or cs barrier nh
a day was spent on writing NUM constraints about NUM NUM words of the parser s output were proofread during the process
this enables using any constraint grammar with this algorithm although we are applying it more flexibly we do not decide whether a constraint is applied or not
a separate inference chain is created for each potential speech act performed by the associated ilt
finally all state labels can be deleted since the behavior described above is encoded in the arc labels and the network structure
table NUM calculating log likelihood values
table NUM the first data set
NUM shows the logarithms of the likelihood values
figure NUM translation lexicon entries proposed by
the corpus based filter is certainly useful in the absence of an mrd
the precise linguistic environment of adaptation determines the initial values of default parameters which evolve
null word order differences between languages
figure NUM word token pairs whose co ordinates
sg NUM be fully explicit in communicating to users the commitments they have made
indicate good replicability with values between NUM and NUM
table NUM domain specificity of filtered translation lexicon entries
table NUM effect of in context vs out of context evaluation
3rd likelihood plateau can be of help in constructing
figure NUM compilation of obligatory left to right rules using the kk algorithm
in interesting applications the number of rules can be very large
what is the reason for this performance degradation in the right context
several additional methods can be used to make this algorithm even more efficient
we show the algorithm to be simpler and more efficient than existing algorithms
an efficient compiler for weighted rewrite rules
it only depends on the alphabet of the automaton representing c
the automata determinizations needed for this algorithm are of a specific type
notice that the phoneme representation used by celex called disc is shown here instead of the more standard ipa font and that the value grouping mechanism of c4 NUM has created a mnnber of phonological categories by collapsing different phonemes into sets indicated by curly brackets
hese eklil be interpreted as sets of st ee h sounds categories e.g. the category or feature labial groups those speech sounds that involve the lips as an a tive art culator
the induced rules roughly correspond to the previous decision tree but in ad lition a solution is provided to the etje versus kje problem for words ending in in9 rule NUM making use of information about the nucleus of the
that way more concise decision trees and rules can be produced instead of sew ral different branches or rule conditions for each wflue only one branch or condition has to be detined making reference to a lass of values
unsupervised learning methods do not NUM rovide the h m ner with inforlnatioil at out the outf ut to be generated only the inputs ar i resented to the learner as experience not the target outputs
the algorithm works as a heuristic search of the search space of all possible partitionings of the wdues of a particular tbature into sets with the for ination of homogeneous nodes nodes representing examples with predominantly the same category as a heuristic guide
the first thing which is interesting in this rule set is that only tlu ee of the twelve presented features coda an NUM nllclelts of lie last syllable nllcleus of the i emlltimate syllal le m e used in the rules
similar experiments in dutch t lural formation for examt le fail to produce th atcgory of bimoraic vowels and for some tasks categori s show u t which hi vc no ontological status in linguistics
table NUM presents break even points for balancedwinnow and the other algorithms as defined in section NUM NUM
the translator is in fact a subsystem of a speech translation prototype though the experiments we describe here are for transcribed spoken utterances
the probability of a string according to the model is the sum of the probabilities of derivations of ordered dependency trees yielding the string
the transfer model defines possible mappings with associated costs of dependency trees with sourcelanguage word node labels into ones with targetlanguage word labels
i am also grateful for advice and help from don hindle fernando pereira chi lin shih richard sproat and bin wu
in the case of e the supervision effort was used only as an oracle during training not directly in the cost computations
the primary purpose is to build effective mechanisms for machine translation the oldest and still the most commonplace application of nonsuperficial natural language processing
these correspond to the relations between a head word and the sequences of dependent phrases to its left and right see figure NUM
there are two types of combination corresponding to left and right transitions of the automaton for the word acting as the head in the combination
not all combinations of parameter settings correspond to attested languages and one entire language family ovs is unattested
mapping is the process by which the scorer aligns answer key objects with a system s response objects
the tla is error driven parameter settings are altered in constrained ways when a learner can not parse trigger input
table NUM summarizes the recall and precision results for semantic filtering on these three different
ltags consist of a morphological lexicon a syntactic qexicon of lemmas and a set of tree schemata i.e. trees in which the lexical anchor is missing
the association of a canonical subcategorization frame and a compatible redistribution gives an actual subcategorization namely a list of argumentfunction pairs that have to be locally realized
when such a redistribution occurs the syntactic function of the arguments change or the argument may not be realized anymore as in the agentless passive
this is useful from a semantic point of view in the case of selectional restrictions attached to the lexical items or of a syntactic semantic interface
using this hierarchy and p inciples of well formedness the tool carries out all the relevant crossings of linguistic phenomena to generate the tree families
the difference between the two verskms lies principally in the definition of quasi trees first seen as partial models of trees and later as distinguished sets of constraints
these links are obviously useful to underspecify a relation between two nodes at a general level that will be specified at an either lower or lateral level
dimension NUM the redistribution of syntactic functions this dimension defines the types of redistribution of functions including the case of no redistribution at all
the interlingua is then passed to a generation component which produces an output string in the target language
mor present mor root mor present participle mor root ing mor present tense sing three mor root s
to make the distinction sharp we call the first type of statement extensional and the second type definitional
what is needed is a natural way of saying this lexeme is regular except for this property
to see this interpretation in action we consider an alternative analysis of the past participle form of come
datr s foundation in path value specifications means that many of the representational idioms of unification formalisms transfer fairly directly
NUM formally we require them to be finite classes but this is not of great significance here
the definition for mor past overrides default definition from and in turn provides a definition for longer paths
both the software and the produced guessing rule sets are available by contacting the author
this small lexicon contained only NUM NUM entries out of NUM NUM entries of the original brown corpus lexicon
so we performed an independent evaluation of the impact of the word guessers on tagging accuracy
we also evaluated in detail whether a conjunctive application with the xerox guesser would boost the performance
however it frequently suffers from large estimation error due to insufficient training data
for different reasons all these smoothing methods are not very suitable in our case
and brill s tagger again did better then the xerox one by NUM NUM
two types of mis taggings caused by the guessers 7articles prepositions conjunctions etc
as a result when the wsd task is defined as choosing a sense out of a list of senses in a general purpose lexical resource even humans may frequently disagree with one another on what the correct sense should be
there are a number of reasons for choosing the brown corpus data for training
what does seem to be a highest common denominator is this modules that process text or process the output of other modules that process text produce further information about the text or portions of it
there are three barriers to such integration managing storage and exchange of information about texts incompatibility of representation of information about texts incompatibility of type of information used and produced by different modules
in our view the level at which we can assume commonality of information or of representation of information between le modules is very low if we are to build an environment which is broad enough to support the full range of le tools and accept that we can not impose standards on a research community in flux
an advantage here is a degree of data structure independence so long as the necessary information is present in its input a tool can ignore changes to other markup that inhabits the same stream unknown sgml is simply passed through unchanged so for example a semantic interpretation module might examine phrase structure markup but ignore pos tags
whereas in lt nsl all information about a text is encoded in sgml which is added by the modules in tipster a text remains unchanged while information is stored in a separate database the referential approach
interestingly a tipster referential system could function as a module in an lt nsl additive system or vice versa
thus to return to the themes of section NUM gate will not commit us to a particular linguistic theory or formalism but it will enable us and anyone who wishes to make use of it to build in a pragmatic way on the diverse efforts of others
second building intelligent application systems systems which model or reproduce enough human language processing capability to be useful is a large scale engineering effort which given political and economic realities must rely on the efforts of many small groups of researchers spatially and temporally distributed with no collaborative master plan
table NUM hypothetical performance data from users of
the analyzer accesses these representations and fills the in and out slots
this architecture allows the modes to synergistically mutual compensate for each others errors
we have informally observed that integration with speech does succeed in resolving ambiguous gestures
the type of each feature structure is indicated in italics at its bottom right or left corner
the potential interpretations of gesture from the gesture recognition agent are also represented as typed feature structures
the correct interpretation is frequently determined as a result of multimodal integration as illustrated below NUM
a wide base of continuous gestural input is supported and integration may be driven by either mode
it also can be driven by either mode and enables a wide and flexible range of interactions
after NUM a description of the partoof speech tags is provided in appendix a NUM all possible instantiations of transformation templates
this involves parsing of the speech and gesture streams in order to determine potential multimodal integrations
in this section we consider the first of those and discuss how it applies to the algorithms we investigate
using normalization gives an effect that is similar to the use of negative weights but to a lesser degree
the bottom level of table NUM specifies rco which covers the preference order for multiple occurrences of the same type of any information structure pattern e.g. the occurrence of two anaphora or two unbound elements all heads in an utterance are ordered by linear precedence relative to their text position
while grosz et al assume that grammatical roles are the major determinant for the ranking on the c y we claim that for languages with relatively free word order such as german it is the functional information structure is of the utterance in terms of the context boundedness or unboundedness of discourse elements
while we shift our evaluation criteria away from simple anaphora resolution success data to structural conditions based on the proper ordering of center lists in particular we focus on the most highly ranked item of the forward looking centers these criteria compensate for the high proportion of nominal anaphora that occur in our test sets
thus it complements the phenomenon of nominal anaphora where an anaphoric expression is related to its antecedent in terms of conceptual generalization as e.g. rechner computer in lc refers to 316lt in la mediated by the textual ellipsis in lb
these are due to underspecifications at different levels e.g. the failure to account for prepositional anaphors NUM plural anaphors NUM anaphors which refer to a member of a set NUM sentence anaphors NUM and anaphors which refer to the global focus NUM
our experimental results indicate that the labels in lloce make it possible to acquire important inter sense relations i many of those relations are reflected in the cross reference information in lloce
at the conceptual level textual ellipsis relates a quasianaphoric expression to its extrasentential antecedent by conceptual attributes or roles associated with that antecedent see e.g. the relation between akkus accumulator and 316lt a particular notebook in lb and la
instead considering adjacent transition pairs gives a more reliable picture since depending on the text sort considered e.g. technical vs news magazine vs literary texts certain sequences of transition types may be entirely plausible though they include transitions which when viewed in isolation seem to imply considerable inferencing load cf
for illustration purposes consider text fragment NUM and the corresponding oh c data in table NUM in ld the pronoun er it might be resolved to akku accumulator or rechner computer since both fulfill the agreement condition for pronoun resolution
we first conduct the soft clustering
this data was randomly divided into training and test samples at a NUM NUM ratio
the family of model evaluation criteria known as information criteria have the following expression
neither aic nor bic ever selects a model that results in accuracy less than the default classifier
figure NUM error rates for each test set where
the problem of data sparseness affects all statistical methods for natural language processing
the similarity based methods perform up to NUM better on this particular task
this difference is significant to the NUM level p NUM
general if an utterance u contributes to the information goals of n different attributes each attribute accounts for NUM n of any costs derivable from u thus c2 d2 is NUM
an implementation of the multilingual lexical matrix has been realized which allows a complete integration with the english version and the availability of all the translations for the italian lemmas
hcm fails to utilize this information
table NUM probability distributions of clusters
l uckily it is possible to avoid both of these operations
the result is a large number of dependent disjunctions in the same group
this effect is achieved in our approach by defining word order domains as sets of words where precedence restrictions apply only to words within the same domain
NUM modulariz tion can l e ilsc l
still necc ss try to encode recursive informal ion
we can now start to see where redundancy in dependent disjmmtions originates
maxwell and kaplan NUM fl r instance
yet as part of this it also describes phrases such as expressions of time and prepositional phrases involving e.g.
to understand this consider for the moment the case in which all the data is perfectly linearly separable
the s type transducer tags any corpus which contains only known subsequences in exactly the same way i.e. with the same errors as the corresponding hmm tagger does
in this example we have a set of three classes cl with the two tags tn and t12 c2 with the three tags t21 t22 and t23 and c3 with one tag t31
a frequency constraint threshold may be imposed on the subsequence selection so that the only subsequences retained are those that occur at least a certain number of times in the training corpus NUM
the final subsequence of a sentence is equivalent to a middle one if we assume that the sentence end symbol or or always corresponds to an unambiguous class c
the usefulness of the first two sorts of information is evident
our investigative user study indicates that the dictionary is the most important factor in user satisfaction
the software is needed to create the store of information
locolex incorporates a stochastic pos tagger which it employs to disambiguate
the only feasible option is to use an existing dictionary
even apart from this people occasionally create new words
grammar using he special feal ure pattern
synsem locleontbm is mapped to concept
instead very general dominance sc mmat a are given
id identity the same design error case identified by both annotators
only constituents mentioned in a pattern are realize during linearizalfion
categories do n t need a att crn feature
first experiments to implement itpsg in fuf rather directly showed inetficient runtime behavior
strings wil h lexi at m egories
this mechanism flmctions analogously to the slash mechanism presented above
in the experiments describecl in sections NUM NUM NUM NUM
we thus obtained the triples in table NUM
our system is unable to detect these cases
to obtain the results in sections NUM NUM NUM NUM
section NUM NUM describes results using decision tree induction
table NUM contains a summary of these errors
we tested the system with two separate german corpora
noun o NUM verb NUM NUM
the remaining NUM NUM items were used as test data
eral megabytes of on line texts from the german newspaper
null NUM for example to move as the score requires from the lowest f major register up to a barely audible n minor in four seconds not skipping at the same time even one of the NUM fingerings seems a feat too absurd to consider and it is to the flautist s credit that he remained silent throughout the passage
part np adval on j NUM where were they all walking to pp pval to 4nil in m examples tlm capitalized vert is i h lcb one in question
no document type was selected for our tests
the others senteimes NUM NUM were tagged arbitrarily as having a prepositional phrase containing the l reposi tion with and they will be entered in the diet onary with the tag intrans ei mpsis ellii a l sthere is mso th reading st e agreed to do it that w y pp pval with
nouns were embedded within a simple copula sentence
achievement of generality therefore requires access to other systems corpora and or development processes
figure NUM a fragment of the database associated with the japanese verb tsukau
in this approach every semantic expression 2with thanks to khalil sima an for fruitful discussions and for the use of his parser and every variable has a set of types associated with it
the system s dialogue model was developed using the wizard of oz woz simulation method
our solution is to allow recognition of paths in the word graph that do not necessarily span the complete utterance
state i at time t given the acoustics
there are a few complications to multiple pass recognition
figure NUM shows an example of this insight
the mathematics of multiple pass recognition is fairly simple
this could propagate through much of the chart
this correctly rules out the containing antecedent in NUM and permits it in NUM and NUM
here vpe res selected comes immediately to mind since the pp in this connection is parsed as a sister to the vp
we tested vpe res on these examples and found that its performance was comparable to its performance on the examples that were automatically identified
both the system and the baseline are evaluated by comparison with the coder output with respect to three different definitions of success
finally we evaluated system components in an incremental fashion beginning with post filter then activating syntactic filter with post filter still activated etc
the most important system component is the composite factor which is a combination of the syntactic filter the post filter and clause rel
even if it did not how would this little world of gentle people cope with its new reality of grenades and submachine guns
this paper reports on an empirically based system that automatically resolves vp ellipsis in the NUM examples identified in the parsed penn treebank
a preference ordering is imposed upon the remaining candidate antecedents based on recency clausal relations parallelism and quotation structure
the selection power for a disambiguation mechanism basically serves as an indicator of the selection ability that includes the most preferred candidate within a particular n best region
a summary illustrating the performance improvement by using the proposed enhancement mechanisms for the lex l2 syn l2 model is shown in table NUM
the performance with the tying robust learning hybrid approach as shown in table NUM deteriorates somewhat in the training set because the tying procedure decreases the modeling resolution
where v denotes the vocabulary and c stands for the frequency count of an event in the training set
to resolve the attachment problems integrating seman null computational linguistics volume NUM number NUM tic information such as word sense collocations would be required
for investigating robustness issues in more detail a robust learning procedure and the associated analyses are provided in the following section
since a huge amount of fine grained knowledge is usually required to solve ambiguity problems it is quite difficult for a rule based approach to acquire such kinds of knowledge
additionally syntactic ature structures and v i rss are attached to every edge
6feature structures and rules are reduced to a minimum in our examples to keep the structures cleat
it is quite simt le to add the t rocessing of non compositional idioms to our parser
our sysl em starts processing a potential idiom as soon as one base lexeme was foutl NUM
einen biiren aufbindcn is substituted by the complex meaning of the simple verb phrase as jmdn
to explain this lel us now consider the problem dora i he referential point of view
base for adequate anapllora resohltion and resolution of definite deseriplions resuming em lier introduced discourse material is created
iil the so equations the category symbols are used as projections to mark the structures to be used
let the cat out of the bag as in die katze im sack km n fig
the a drs of the idiomatic edge already contains the literal referent of the part of the idiom they represent
figure NUM belief and discourse levels for NUM and NUM
ifications to the system s proposed modifications resulting in an embedded negotiation sub4iajogue
and the system s evidence against it beli s attack
response generation inferencing at this point our discourse structure contains three predicates mr james is leaving as chief executive officer mr james is leaving as chairman and mr dooner is succeeding mr
in addition the dictionary includes some multi word items which appear in th e walkthrough sentences these are reduced to single lexical units and will retire as chairman he will be succeeded by mr
the first set of patterns corresponds essentially to named entity recognition names of people names o f companies and other organizations locations dates and numeric expressions including money and percent ages
for the scenario template task we spent the first week studying the corpus and writing some of the basi c code needed for the pattern matching approach which we were trying for the first time
we did not do any work specifically for the coreference and template element tasks although ou r performance on both these tasks gradually improved as a result of work focussed on scenario templates
once this has been done the event predicates are organized based on the company and position involved since this is how the templates are structured and then converted to templates
we can hardly claim that this was the result o f a new and innovative system design since our goal was to gain experience and insight with a design which others had proven successful
it would require us to organize the grammar in such a way that limited additions could be made b y non specialists without having to understand the entire grammar again not a simple task
special tests are provided for names sinc e people and companies may be referred to by a subset of their full names a match on names takes precedenc e over other criteria
it therefore adds event predicates asserting that mr
i apologize for this abuse of terminology but have got into the habit of calling them bitstrings
this will always be possible so strictly speaking the encodings we have described are not necessary
the basic idea is that we thread an agent in out feature throughout the vp
the np subj meaning is sent down to the head v to be put in its first argument position
to illustrate the technique in the simplest possible form here is a small grammar for an artificial language
the person feature is only relevant for subject verb agreement but at least number and mass count are necessary to get the right combinations of determiner or no determiner and noun in the following we can express the appropriate generalizations quite succinctly by defining a feature whose values are arranged in a hierarchy
where no is in the position associated with cb and all other positions have an anonymous variable add to cb the feature specification right no where no is in the position associated with ca and all other positions have an anonymous variable add to ca b the feature specifications
lcb npl pl ppl rcb lcb np2 p2 pp2 rcb lcb np3 p3 pp3 rcb lcb fl f2 f3 f4 rcb there is one symbol for each possible subcat position plus an extra one to mark the end of the list
this rule unifies the in and out values to make sure that what was found was what was being sought x might typically be a vp for example and this identification of feature values would take place on the s np vp rule
as described above analogical translation relies on a database of example pairs which can encode idiomatic translation correspondences at the lexical
if a monolingual or bilingual corpus from the application domain is available these probability distributions can be estimated using iterative methods
high translation quality requires not only that the output be grammatically correct but also that the output sound natural and idiomatic
the example data is classified into different linguistic constituent levels such as clause level examples phrase level examples and word level exampies
in methods of former type the rhetorical structure is appropriate for a relatively small set of sentences such as a paragraph but it does not give enough information to create an abstract for a large set of sentences
when a full description is chosen for a subsequent reference its semantic structure contains the same property and substance information as the initial reference
by using the proposed method to calculate feature weight this system can be applied to other types of texts and gives results ntore similar to those of a human process than a set of weights based on human intuition
example NUM NUM a la sortie des tourniquets du rer tu prends sur ta gauche
we will first outline some problems that may appear while translating descriptions into graphics
another interesting problem is the form and the derivation of the conceptual representation of the described route
another aspect of modeling consists in specifying graphic objects corresponding to the entities in the route model
puis tu tournes droite tu tombes sur une sdrie de panneaux d informations
we believe that it can not be directly obtained from the linguistic material itself
our first approach to translate rds into graphic maps consisted in manually transcribing linguistic descriptions into sketches
from route descriptions to sketches a model for a text to image translator lidia fraczak limsi cnrs bpst
apart from models for linguistic and conceptual representations the rules of transition have to be defined
landmarks are represented as possible attributes among others of these two ele null ments
this approach is similar to the model based segmentation method used in image understanding systems
examples of special expressions used to determine sentence type are as follows conjecture kamosirenal may kanenai be capable of souda likely to youda likely to darou probably etc
the test set is not fully representative of the task because the word graphs are relatively simple
a transduction search engine for finding the minimum cost target string for an input source string or recognizer speech lattice
then the kullback leibler distance between words w i and w in the left right tree is t w NUM e
a test of this assumption was made using trec NUM results and again during the trec NUM evaluation
the multilingual track represents an extension of the adhoc task to a second language spanish
one of the goals of trec is to provide a common task evaluation that allows cross system comparisons
sections NUM NUM and NUM NUM give more details about the documents used and the topics that were created
the test design and test collection used for document detection in tipster was also used in trec
however there are few tools for evaluating interactive systems and none that seem appropriate to trec
a subset of the adhoc topics was used and many different types of experiments were run
like most traditional retrieval collections there are three distinct parts to this collection the documents
a measure of the effect of pooling can be seen by examining the overlap of retrieved documents
at cornell they investigated the problems with using a cosine norrealization on the long documents in trec
a contain relation is represented graphically by a straight line linking the icon that represents the container and the icon representing the object contained
this paper deals with the automatic referent resolution of deictic and anaphoric expressions in a research prototype of a multimodal user interface called edward
the sentences with NUM referring expressions entered by the five users to perform the NUM tasks were processed by the three referent resolution models
in the first place the salience of an instance at a given moment is determined by a diversity of factors of varying importance
new focus spaces are put on top of the focus stack and the referent for a np will be searched from the top down
the fact that some entity is the backward looking center is used to constrain the search for the referent of a pronoun in a subsequent utterance
real referring expressions generated by users not familiar with the internal processes of the interpreter provide a more solid empirical basis for evaluation
the subjects were to perform NUM tasks most were information retrieval tasks but some tasks involved effectuating a change in the file system
much of what i want to say below follows their anmysis with one m jor difference coercion changes tile meaning of the aspectual marker in response to the semmltic properties of the marked verb
why does this carry an overtone of temporariness assuming that live denotes a state we need to look at the interactions between the mp for the progressive aspect and the mp for the aktionsart state
we therefore find that NUM must denote a set of hiccups simply by inspecting the mps and without resorting to a process which turns hiccupping from an instantaneous act to a homogeneous sequence of acts
the fact that most sentences report singleton sets of events arises in the absence of information to the contrary by a process of implicature though the adverb once is available to reinforce this conclusion if necessary
the mi for the simph asliect given above is designed to be open to readings where some single past event is being report ed and to the possibility of a hat if ual reading
shares temporal NUM roi erties with a range of other verbs then dwse are gathered together as mps for the lass as a whol which is r ferred to as an aktionsart
based on empirical and analytical grounds we conclude that the model we propose is preferable from a computational and engineering point of view
in this paper we will go into the semantic and pragmatic processes involved in the referent resolution of deictic and deixis related expressions by edward
the referring expression to generate is required to be a distinguishing description that is a description of the entity being referred to but not to any other object in the current context set
the descriptor selection component is free to choose on c3 t2 to yield the natural flat expression the table on which there are a glass and a cup
instead we simply allow the responsible component to produce descriptors incrementally even from varying referents provided the selected descriptor is directly related to some referent already included in the expression built so far
NUM there is no control to assess the adequacy of a certain description for instance in terms of structural complexity and no feedback from linguistic form production to property selection is provided
in the predecessor algorithms attributes are taken from an a priori computed domain dependent preference list in the indicated order provided each attribute contributes to the exclusion of at least one potential distractor
in addition to an initial segmentation module that finds words in a text based on a list of chinese words chseg additionally contains specific modules for recognizing idiomatic expressions derived words chinese person names and foreign proper names
the current state of our algorithm in which only three characters are considered at a time will understandably perform better with a language like chinese than with an alphabetic language like thai where average word length is much greater
the rule based algorithm we developed to improve word segmentation is very effective for segmenting chinese in fact the rule sequences combined with a very simple initial segmentation such as that from a maximum matching algorithm produce performance comparable to manually developed segmenters
yet despite the disparity in initial segmentation scores the transformation sequences effect a significant error reduction in all cases which indicates that the transformation sequences are effectively able to compensate to some extent for weaknesses in the lexicon
this suggests that both the greedy algorithm and the transformation learning algorithm need to have a more global word model with the ability to recognize the impact of placing a boundary on the longer sequences of characters surrounding that point
in the maximum matching algorithm described above when a sequence of characters occurred in the text and no subset of the sequence was present in the word list the entire sequence was treated as a single word
for our chinese experiments the training set consisted of NUM sentences NUM words from a xinhun news agency corpus the test set was a separate set of NUM sentences NUM words from the same corpus
word lists and morphological analyzers are more readily available it is instructive to experiment with a de segmented english corpus that is english texts in which the spaces have been removed and word boundaries are not explicitly indicated
this is a very surprising and encouraging result in that from a very naive initial approximation using no lexicon except that implicit from the training data our rule based algorithm is able to produce a series of transformations with a high segmentation accuracy
the training set consisted of NUM sentences NUM words in which all the spaces had been removed the test set was a separate set of NUM sentences NUM words from the same corpus also with all spaces removed
NUM NUM does the word appear up to NUM sentences in the past but not NUM sentences in the future
monitoring the relative behavior of these two models goes a long way towards helping our segmenter sniff out natural breaks in the text
the key to efficiency we will see is for the parser to be less permissive than the grammar for it to say no redundant in some cases where the grammar says yes grammatical
even more significant is the negligible difference in processing time between the two languages despite radical differences in structure particularly with respect to head complement positioning
our algorithm for generation is similar to that of parsing in that both construct a syntactic parse tree over an unstructured or partially structured set of lexical items
moreover our design achieves a reduction in time requirements because we do not retrieve a structure until the resulting parse descriptions satisfy all the network constraints
since principar has a much broader coverage than these alternative approaches the absolute measurements do not provide a complete picture of how these three systems compare
NUM we will discuss three examples to illustrate the general idea of how gb principles are interpreted as local and percolation constraints
the current framework is well suited to an interlingual design since the linking rules between the syntactic representations given above and the underlying lexical semantic representation are well defined
a case filter violation is detected if an item containing cm is combined with another item containing ca govern
a preliminary investigation has indicated that the message passing paradigm is useful for generation as well as parsing thus providing a suitable framework for bidirectional translation
these are not necessarily geared toward demonstrating the full capability of the parser which handles many types of syntactic phenomena including complex movement types
NUM NUM NUM succession org slo t definition pointer to object that captures info on the organization with the past present future management vacancy
in the case of a retirement the depart workforce fill should be used even if someone retires from one organization to go work a t another
the general title of director is taken to mean a general member of the board of directors and is not relevant to the scenario
the event object captures the management post the company the current manager and the reason why the post is or will be vacant
NUM if a title is that of an acting officer e.g. acting president the title is treated as being the same as that of a permanent position
appendix a on the job slot fill criteria these are the main heuristics used by the annnotators in preparing the answer key fills for the on the job slot i n the in and out object
NUM an acting officer is one who is identified as acting temporary interim etc
a relevant article refers to assuming or vacating a post in a company and must minimally identify the post and either the person assuming the post or the perso n vacating the post
given the grammar in figure NUM it is possible to deduce that brown can never be part of a complete np constructed from such a substring
if the two posts are at the same company and are mutually exclusive the person is giving u p the old post to acquire the new one
thus positions such as university professor law firm partner and publishing company editor are nonrelevant whereas positions such as university president and corporate treasurer are relevant
neverthelc ss a ert in am rant of mmccesm ry work is t erformed
for example the glottal stop at the end of a stem may become w when followed by the relative adjective morpheme lcb iyy rcb as in arabic samaap iyy samaawiyy heavenly but hawaap iyy hawaa iyy of air
NUM f a p w where reap n lcb glottal change w pc p rcb the glottal change rule would be a normal morphological spelling change rule incorporating contextual constraints e.g. for the morpheme boundary as necessary
the difficulties in morphological analysis and error detection in semitic arise from the following facts supported by a british telecom scholarship administered by the cambridge commonwealth trust in conjunction with the foreign sad commonwealth office
arabic ktb for katab kutib and kutub but kaatb for kaatab and kaatib ii partially voealised texts incorporate some short vowels to clarify ambiguity e.g.
similarly tl2 states that any p e lcb vl v2 rcb on the pattern tape and v on vocalism tape with no transition on the root tape corresponds to v on the surface tape this rule sanctions vowels
a lexical string maps to a surface string if they can be partitioned into pairs of lexical surface subsequences where each pair is licenced by a or rule and no partition violates a c rule
the derivation of dhunrija m q5 passive from the three morphemes lcb clc2vlncsv2c4 rcb lcb dhrj rcb and lcb ui rcb and the suffix lcb a rcb 3rd person is illustrated in NUM
for previously unseen hanzi in given names chang et al assign a uniform small cost but we know that some unseen hanzi are merely accidentally missing whereas others are missing for a reason for example because they have a bad connotation
take the following bag ex NUM lcb dogl thcl brown big rcb corresponding to the big brown dog
no other external knowledge sources are required
accordingly the context ouestablish does not have any negative extensions
that it is a much better probability model of natural language text
if the model entropy is high then transcription results are abysmal
if there are too many states then transcription becomes computationally infeasible
combining these estimates we obtain our overall estimate NUM
only flee text examples are needed
our results highlight the fundamental tension between model complexity and data complexity
figure NUM the relationship between model order and test message entropy
section NUM NUM compares the statistical efficiency of the various context model classes
the information captured for each token is physical string token symbol token type
NUM NUM document manager process csci the document manager process csci is a set of li
the nltoolset is a framework of tools techniques and resources for building text processing applications
the analyst interface process csci processes user commands passed to it
names organizations and associations entities found within the extracted annotations are validated against existing entities
these annotations are then attached to the document and stored in the document manager process csci
abstracting captures information about the cable itself its document number source date etc
the extraction process csci extracts biographical entities found within the document using lockheed martin s nltoolset
biographical entity type connections are gender country of birth date of birth etc
if the entity exists all information is processed as an update to the existing records
now in the same way as in the single tree case we can directly read off the t constraint for the whole parse forest representing the semantics of all readings
no i table NUM gives execution times used for semantics construction of sentences of the form i saw a man on a hill n for different n
future releases of vios will have to follow this latter strategy as much as possible because it highly influences the appreciation of clients
a unified scoring function used for integrating knowledge from lexical and syntactic levels is introduced in section NUM the results of using the unified scoring function are summarized in section NUM in section NUM the discrimination and robustness oriented learning algorithm is derived
this requirement may not be met by any estimation procedure since the above two probabilities are estimated from two different outcome spaces one conditioned on quan and the other conditioned on n qua n
following the notations in the previous section the correct syntactic structure is denoted by syn fl and the syntactic structure of the strongest competitor is denoted by synj k whose score may either rank first or second
we find that the main factor affecting the tree selection is the sixth phrase level which corresponds to the reduce action quan nlm with the left two contextual symbols p and n2 for the top candidate
with this small performance difference we can not reject the hypothesis that the performance of the lex l1 syn l2 model is the same as that of the lex l1 syn l2 model
b test set performance dl and rl denote discriminative learning and robust learning respectively NUM parameter smoothing for sparse data the above mentioned robust learning algorithm starts with the initial parameters estimated by using mle method
forall and exists are encoded similarly to abstraction in that they take a functional argument and so object level binding of variables by quantifiers is handled by meta hvel a abstraction
however there is no significant performance difference achieved by using the tu rl and the bf rl approaches for all language models even though turing s smoothing formula was shown to behave better than the back off procedure before applying the robust learning procedure
in our framework it is easy to tell that the first reduce action is applied when the two left context symbols are lcb y x rcb and the second reduce is applied when the left context is two xs under an l2r1 mode of operation
the talk system handled topic shift by providing users with three sets of conversational perspectives
b eker ci candy candy seller or lover c sabah qt morning morning person
the values for these weights and costs have been assigned heuristically
the system verifies an utterance s meaning not its syntax
we now examine a context dependent strategy that takes into account specific domain information
the following example of a verification subdialog illustrates the idea
here we focus on misunderstandings caused by speech recognition errors
the example just given is an example of an over verification
this research has been supported by national science foundation grant iri NUM
consequently strategies for delayed detection and resolution of miscommunication e.g.
viewing the results of multiple diagnostic runs
provides more details on documents and document setup
individually they may or may not be tipster compliant
NUM NUM available help in building a tipster application
NUM NUM NUM NUM examples of information conveyed through text
forms NUM NUM are defined below
failure to do so will be documented and justified in the tacad
to reduce the set of patterns down to a manageable size we eliminated all concept nodes that were proposed exactly once under the assumption that a pattern encountered only once is unlikely to be of much value
similarly to identify three armed men as the perpetrators we must recognize that three armed men is the object of the preposition by and attaches to the verb kidnapped
for this reason we found it necessary to run autoslog with slightly different rule sets in these domains
however some of the concept nodes represent domain specific patterns e.g. x was bombed
for frequency filtering we simply removed all concept nodes that were proposed by autoslog ts less than n times
for example the pattern x was attacked is used to extract both victims and physical targets
given the sentence john smith was killed with john smith tagged as a victim autoslog generates a concept node that recognizes the pattern x was killed and extracts x as a victim
the main difference between palka and autoslog is that palka is given the set of keywords associated with each concept essentially its trigger words and then learns to generalize the patterns surrounding the keywords
however determining which concept nodes are ultimately useful depends on how one intends to use them
the current english s ending may be analogical
the actions of the aggregation module affect as a rule the resulting syntactic structure
after the lexicon converges step NUM is repeated one last time keeping track of how many times each english source word is linked to each french target word
note that remove a fragment always implies move a fragment
gold developed his theory for formal languages it is argued that similar considerations apply here
the results from using these nets are given in table NUM
the individual tags are converted to a bipos and tripos representation
these NUM tags are then mapped onto the small customised tagsets
if the desired node wins then no action is taken
the input layer potentially has a node for each possible tuple
however some words will have more than one possible tag
around NUM of sentences are left with a single string
for example consider the sentence still waters run deep
a fast partial parse of natural language sentences using a connectionist method
the parser thus had to consider every postion in the input as a potential head trace location just as if no prosodic information about syntactic boundaries were available at all
furthermore c since the l sh percolation mechanism ensures structure sharing between the verb and its trace a verb trace always comes with a corresponding overt verb
the tags of the words can also be shown or hidden
coordination long distance subject verb relation preposition determiner resolution
it will be a closed demo
a single update parameter c NUM is used and a weight is promoted by adding c to its previous value and is demoted by subtracting o from it
the demonstration will consist in displaying the syntactic structure of sentences from novels scientific texts newspapers the syntactic structures are computed by our syntactic parser ad the output shows in a human friendly graphic interface NUM word features as computed by pos tagger NUM non recursive phrases as computed by shallow parser and NUM their relations the functional structure
syntactic structures of sentences from large corpora
in a near future a graphic comparison between two outputs will be available to control the modification of the rule base and to point out easily the differences
this principle allows to concentrate on word level or on dependency level and allows to check precisely the behavior of our parser on specific relation types
macos windows nt and windows NUM
our viewer developped with java allows to display in a graphical way see fig NUM the dependency tree between non recursive phrases and the tags of words
instead they can run in the presence of a large number of features and allow for discarding features on the fly based on their contribution to an accurate classification
NUM if the algorithm predicts NUM and the received label is NUM negative example then the weights of all the active features are demoted the weight wit is multiplied by ft
that is s f d NUM if the feature is present in the document active feature and s f d NUM otherwise
the obvious one may be to filter out all features whose weight is very close to NUM but there are a few subtle issues involved due to the normalization done in the positivewinnow algorithm
the main contribution of this work however is that we have presented an algorithm balancedwinnow which performs significantly better than any other algorithm tested on these tasks using unigram features
table NUM that employing prosodic information reduces the parser runtime for the corpus by about parsing batch jobs with and without the use of prosodic information resp
brooklyn college students have an ambivalent attitude toward their school
i just hope it does n t work on earthmen too
a non terminal node represents a discourse relation holding between its two daughter nodes
none of the examples in the brown corpus contains an embedded expectation
suppose it is something right on the planet native to it
suppose you tell me the real reason he drawled
b on the other suppose you needed some money
that expectation is then resolved in clause c
b on the other hand suppose you needed money
incerc m inc de pe acum o precizare a obiectului lui
if we had changed the first condition to if a derives then earley parsers would be excluded since they do not keep track of all substring derivations
we add nonterminal w to v for generating arbitrary non empty substrings of w thus we need finally we complete the construction with productions for the start symbol s
observe that theorem NUM translates the running time of the standard cfg parsers o gn3 into the running time of the standard bmm algorithm o m3
we will use the notational shorthand of using subscripts instead of the functions fl and f2 that is we write il and i2 for fl i and f2 i
NUM turning the starter switch to the auxiliary position the pointer will then return to zero
the total size of the NUM networks we currently use is about NUM kbytes of disk space
below are some parsing samples where the output is slightly simplified to make it more readable
in particular morphosyntactic tags are hidden and only the major functions and the segment boundaries appear
as a whole the parser is constructive it makes incremental decisions throughout the parsing process
the linguistic modularity of the system makes it tractable and easy to adapt for specific texts e.g.
failure reports or on cigarette packs with inscriptions like nuit gravement la santg NUM
the acceptable readings result from the intersection of the initial sentence network with the constraint networks
this confirms the first sentence hypothesis
the result is shown in figure NUM
the similarity between the policy de
graphs have fewer than NUM sentences
NUM sentences both created by a human
null two evaluations were conducted to confirm these points
null if the constraints stipulated in a given transducer are not verified the string remains unchanged
the replace operators allow one not only to add information but also to modify previously computed information
in the middle the ill is given in the form of a list of ill records animal mammal mane bob with relations to the language modules the domains and the top concepts
although there is a theoretical distinction between homonymy and polysemy it is not always easy to tell them apart in practice
but it is possible to alert the user to the potential problem
we argue that it is indeed possible to separate semantic from morphological phenomena that blocking or pre emption can be explained pragmatically and that barring syntactic and cultural differences the range of polysemous usage is not language specific
but as stated in their paper they only considers the special case of classifying nouns according to the distribution as direct objects of verbs
for the dictionary d lcb a b c d ab bc cd abc bcd rcb both abc d and a bcd are st tokenizations in td s lcb a b c d a b cd a bc d a bcd ab c d ab cd abc d rcb
moreover we have proven that the three widely employed tokenization algorithms namely forward maximum matching backward maximum matching and shortest computational linguistics volume NUM number NUM length matching are all subclasses of critical tokenization and that critical tokenization is the precise mathematical description of the principle of maximum tokenization
in other words critical tokenization is the precise mathematical description of the commonly adopted principle of maximum tokenization
for example concrete nouns indicate a not heavy sense of light except that concrete nouns that are color indicate its not dark sense
this example also illustrates that many of the individual nouns that we are treating as separate independent cases actually manifest a smaller number of underlying semantic categories e.g. color
this semantic ambiguity in the noun sense to which the adjective applies can therefore be resolved by the same rules formulated for unambiguous cases once the relevant noun sense is identified
for example the noun side is projected to occur equally often with each sense of right not left NUM NUM not wrong NUM NUM
this gave us ten subcorpora one for each antonym pair of NUM examples to use as a database for studying the extent to which modified nouns disambiguate their modifying adjectives
however the not long characterization of shortness of texts refers explicitly or implicitly to the duration of the performance e.g. reading or reciting of the text
thus when an adjective has different sense specific antonyms their co occurrences computational linguistics volume NUM number NUM as modifiers of different instances of the same noun reliably disambiguate that adjective
katz t ibm t j watson research center recent corpus based work on word sense disambiguation explores the application of statistical pattern recognition procedures to lexical co occurrence data from very large text databases
example NUM given the english alphabet the tiny dictionary d lcb the blue print blueprint rcb and the character string s theblueprint there are to s lcb the blueprint the blue print rcb and co s lcb the blueprint rcb
consider for example the sentence a piece will seldom bake uniformly even with the most loving attention that is it will vary from light to very dark
in some cases however nouns provided very little assistance when the pertinent semantic and syntactic features do not apply the same noun is often simply consistent with alternative senses
the word string this is his book is both the minimal element and the least element of both td thisishisbook lcb this is his book rcb and td thisishisbook lcb th is is his book this is his book rcb
in particular we argue that what have been claimed to be rules of lexical sense extension operating on lexical items are instead to be accounted for by a process of reference transfer by which the referents of lexical items and phrases can be used to refer to related things as governed by pragmatic rules of conversation and contextual knowledge
NUM type h in a sequence of chinese NUM characters s al aibl by if al ai bl bj and s are each a word then there is conjunctive ambiguity in s the segment s which is itself a word contains other words
the component words were always related to the meaning of the compound as a whole e.g. britain and great britain
since any finite nonempty poset has at least one minimal element there is co s i NUM since co s c to s there is to s co s if ico s i NUM
if we expand the query with words from a thesaurus we must be careful to use the right senses of those words
the user is generally not interested in retrieving documents with exactly the same words but with the concepts that those words represent
because this latter area is significantly less developed we ruled out concepts about taxons
hence we eliminated explanations of concepts that were sparsely represented in the knowledge base
it stands in contrast to a machine learning task such as rule induction from examples
first we can construct large scale knowledge bases such as the biology knowledge base
for both objects and processes knight scored within half a grade of the biologists
they also provide four types of knowledge base access robustness as discussed in section NUM
NUM this produced NUM explanations of which NUM described objects and NUM described processes
by the end of the study we had amassed a large volume of data
NUM evaluation traditionally research projects in explanation generation have not included empirical evaluations
unfortunately because of the tremendous cost of construction large scale knowledge bases are scarce
in this case the person element will have two different new status values dependin g on the position being discussed
such seed words can be selected by any of the following strategies use words in dictionary definitions extract seed words from a dictionary s entry for the target sense
also for an unsupervised algorithm it works surprisingly well directly outperforming schiitze s unsupervised algorithm NUM NUM to NUM NUM on a test of the same NUM words
4this latter effect is actually a continuous function conditional on the burstiness of the word the tendency of a word to deviate from a constant poisson distribution in a corpus
but for n NUM all but the most confident local classifications tend to be overridden by the dominant tag because of the overwhelming strength of the one sense per discourse tendency
column NUM shows its effectiveness 1degthe number of words studied has been limited here by the highly time consuming constraint that full hand tagging is necessary for direct comparison with supervised training
12this difference is even more striking given that schiitze s data exhibit a higher baseline probability NUM vs NUM for these words and hence constitute an easier task
thus contexts that are added to the wrong seed set because of a misleading word in a dictionary definition may be and typically are correctly reclassified as iterative training proceeds
in essence our algorithm works by harnessing several powerful empirically observed properties of language namely the strong tendency for words to exhibit only one sense per collocation and per discourse
we conclude that the head transducer model is more effective according to measures of accuracy computational requirements model size and development effort
pustejovsky91 stepped further into formulating useful intrinsic information in nouns with the notion of eo compositionality among others so as to recover from an elliptical sentence such as he began the book a default verb such as reading
an nlp system is supposed to be able to recognize the differences and commonalities between almost the same set of words in two different syntactic structures such as mary bit the dog vs mary was bit by the dog
an sdl grammar is defined exactly like a lambek grammar except that ksd k replaces kl
in order to generate committee members for bigram tagging we sample the posterior distributions for transition probabilities p ti tj and for lexical probabilities p t w as described in section NUM
this gives the following parameter free two member sequential selection algorithm executed for each unlabeled input example e NUM draw NUM models randomly from p mis where s are statistics acquired from previously labeled examples ment can be avoided
for example complete training requires annotated examples containing NUM NUM ambiguous words to achieve a NUM NUM accuracy beyond the scale of the graph while the selective methods require only NUM NUM NUM NUM ambiguous words to achieve this accuracy
applying committee based selection to supervised training for such tasks can be done analogously to its application in the current paper s rthermore committee based selection may be attempted also for training non probabilistic classifiers where explicit modeling of information gain is typically impossible
complete training of our system on NUM NUM NUM words gave us an accuracy of NUM NUM over ambiguous words which corresponds to an accuracy of NUM NUM over all words in the curacy achieved versus batch size at different numbers of selected training words
note that we do not look at the entropy of the distribution given by each single model to the possible tags classes since we are only interested in the uncertainty of the final classification see the discussion in section NUM
statistical methods for these tasks typically assign a probability estimate or some other statistical score to each alternative analysis a word sense a category label a parse tree etc and then select the analysis with the highest score
NUM of katakana tokens are mis recognized affecting NUM of test strings but accuracy only drops from NUM to NUM
we would like to thank alton earl ingram yolanda gil bonnie glover stalls richard whitney and kenji yamada for their helpful comments
extending this notion we settled down to build five probability distributions NUM p w generates written english word sequences
we have performed two large scale experiments one using a full language p w model and one using a personal name language model
the transfer and head transduction derivation models can be formulated as probabilistic generative models such formulations were given in alshawi 1996a and 1996b respectively
it has a special phonetic alphabet called katakana which is used primarily but not exclusively to write down foreign names and loanwords
so the way to write gol bag in katakana is NUM roughly pronounced goruhubaggu
this algorithm will not terminate when applied to transducers representing nonsubsequential functions
the sequence of contextual rules is automatically inferred from a training corpus
then the next function is applied
the algorithm may therefore perform unnecessary computation
the state transition function is defined by
two issues are addressed in this section
we have shown how the contextual rules can be implemented very efficiently
the dialogue manager supplies the handle enquiry dialogue intention with details of the selected domain expert
obviously the most important measure is recall since we want all possible categories for a word to be guessed
let us take for instance a lexicon entry developed jj vbd vbn
the mechanism of action ofr ietion enz3anes
both error types could be resolved in future research
this yielded incomplete csrs and degraded scoring resulted
an automatic scoring system for advanced placement biology essays
in these cases essay scores were degraded
metonyms for concepts in the domain of this test question were selected from the example responses in the training data this paradigm was used to identify word similarity in the domain of the essays
the purpose of this work is to develop computer based methods for scoring so that computer administered natural language constructed response items can be used on standardized tests and scored efficiently with regard to time and cost
treatment i describe NUM bands fragments treatment ii describe NUM bands l agments treatment lll describe NUM bands fragments treatment iv describe NUM band fragment part ci
NUM u there is no wire on connector one zero four
is it the case that suspectl6 had access to the poison
is it the case that suspectl0 had access to the poison
nately flashing one and the top corner of a seven
hopefully a proposed algorithm will do better than random
singleselection and continuous modes perform significantly better than random mode
f awatson gives control of the investigation over to holmes
a similar initiative setting mechanism is fired if agent a1 announces that it can not satisfy goal g
if the highest score is equal to NUM no class is assigned
there are two reasons for this that i will discuss briefly here
we now reduce all of wordnet s sense assignments to these basic senses
corelex is designed around the idea of systematic polysemons classes that exclude homonyms
corelex currently covers NUM underspecified semantic types
figure NUM a selection of polysemous classes
this comparison is done at the top levels of the wordnet hierarchy
gates are typically open constructions whereas doors tend to be solid
generative the lexicon should be and if one allows overgeneration of semantic objects
the representation introduces a number of objects that are of a certain type
also the narrow window gives consistently higher accuracy than the other sizes
each bitext space contains a number of true points of correspondence tpcs other than the origin and the terminus
for each bitext the true bitext map tbm is the shortest bitext map that runs through all the tpcs
in the generation phase simr generates all the points of correspondence that satisfy the supplied matching predicate explained below
you may wonder how simr will fare with languages that are less closely related which have even more word order variation
we ordinarily mean just the part concerning attachments in parallel
thus the two estimated probabilities may not differ so greatly
the lowest estimates for simr without the translation lexicon are an rms error of NUM NUM for the easy bitext and NUM NUM for the hard bitext
thus syntactic preference can be represented by a monotonically decreasing function of d
this is precisely the length probability we propose in this paper
our experimental results indicate that our method is quite effective
first there were some mistakes by syn
we call this kind of conditional probability the length probability
table NUM represents this result as lex3 lex2 pcfg
we use the definition of lexical likelihood described above to avoid this problem
we propose combining the use of three word probabilities and that of two word probabilities
the classification trees and results of the experiment are shown in figure NUM
the same basic format is used for subsequent experiments on more refined rules
if we only consider the match rates become NUM NUM NUM
thus the percentage of overall matched cases is NUM
thus we let anaphora of the nonsalient type be nonzero
zero and nonzero anaphora within the selected texts are identified
we adopted this concept to determine the animacy of anaphora
thus we first choose full descriptions for all n s
i it NUM pulls the string j down
table NUM word classes and lexicon to encode vertex cover problem
from the ci choose one word as the head h0
the probability of a parse tree is equal to 1here t o u o v o w should be read as t o u o v o w the probability that any of its distinct derivations is generated which is the sum of the probabilities of all derivations of that parse tree
this feature leads to a a NUM complete recognition problem
figure NUM simple graph with vertex cover lcb c d rcb
section NUM advances our proposal which cleanly separates dominance and precedence relations
all the more the empiricist may be interested in convincing counterexamples
two versions query dependent and generic for each of the texts with the trec NUM full text results as a baseline multiple lteratlous of the the human and machine generated extracts to compare retrieval effectiveness
the implementation briefly described in section NUM will be part of the first vodis prototype which will be evaluated by users
for potentially dangerous or expensive actions i.e. undoing them is relatively costly time consuming include a validation step
a third way to optimize speech recognition is based on the fact that not all the keywords need to be available all the time
the sr unit returns an ordered tuple of results to the dm call phil call bill
lea NUM NUM hix hartson NUM subsumes error prevention iv a and error handling iv b
the dm now proposes the first candidate of speech recognition via a validation feedback e.g. the system says call phil
naturally the user has to know what is expected from him and this puts high demands on feedback and prompt design
thus call phil is a signed a higher confidence score than the designated second candidate call bill
it is well known that people are not only sensitive to the content of a message but also to the way it is sent
the fact that machine performance mirrored human performance in trec NUM makes the decrease in automatic system performance more acceptable but still requires further analysis into why both types of query construction were so affected by the very short topics
system b however has higher precision at the high recall end of the curve and therefore will give a more complete set of relevant documents assuming that the user is willing to look further in the ranked list
this averages to NUM new relevant documents found in the second NUM documents for each system and this is a high estimate for all systems since the NUM runs sampled for additional judgments were from the better systems
the shorter topics do not create a problem for the muting task as experience in trec NUM and NUM has shown that the use of the training documents allows a shorter topic or no topic at all
null the three dominant themes in the runs using manually constructed queries are manual modification of automatically generated queries inqi02 manual expansion of queries brkly7 and assctv1 and combining of multiple retrieval techniques or queries
pircsl queens college cuny trec NUM ad hoc routing retrieval and thresholding experiments using pircs by k l kwok l grunfeld and d d lewis used a spreading activation model on subdocuments NUM word chunks
two particular factors were used in the trec NUM trec NUM topics a time factor current before a given date etc and a nationality factor either involving only certain countries or excluding certain countries
a series of additional runs see paper for details confirmed that the best method was to combine the results of the best two query techniques the long vector space and the p NUM p norm
an analysis of those topics shows that many more relevant documents were in the top NUM documents and the top NUM documents probably caused by manually eliminating much of the noise that was producing higher ranks for nonrelevant documents
pattern the cluster based method rejects four of the thirteen senses with error rate NUM i e NUM out of NUM occurrences in the y part of the brown corpus will be assigned wrong tags
much in the same way as subcategorization frames alternations are constrained by the sense of the word for example the verb appear allows there insertion and locative inversion in its senses of come into being or become visible but not in its senses of come out or par null ticipate in a play
lenge and dispute question1 asks questiou asks questions asks questions inquire and question1 challenge
in addition we believe that the two methods can be interleaved in the following manner both methods rely tot a few predominant senses that can perhaps be disambigu ted using syntactic constraints as we discuss below
the relatively high error rate for these verbs may be due to their low frequency in our corpus or may indicate that their predominant senses axe not associated with the predominant senses of show and describe as we hypothesized
was the constellation shown in figure NUM intended as ternary antosemy
we will try to show how both deictic and anaphoric references can be resolved using a single model
to determine the size of the hidden layer in the neural network that produced the lowest output error rate we experimented with various hidden layer sizes and obtained the results in table NUM from these data we concluded that the lowest error rate in this case is possible using a neural network with two nodes in its hidden layer
palmer and hearst multilingual sentence boundary indicating that in the corpus on which the lexicon is based the word well occurred NUM times as an adjective NUM as a singular noun NUM as a qualifier NUM as an adverb NUM as an interjection and NUM as a singular verb deg NUM NUM NUM
as described in section NUM NUM NUM the output of the neural network after passing through the sigmoidal squashing function is used to determine the function of a punctuation mark based on its value relative to two sensitivity thresholds with outputs that fall between the thresholds denoting that the function of the punctuation mark is still ambiguous
NUM although this is an impressive error rate the amount of training data NUM million words required is prohibitive for a problem that acts as a preprocessing step to other natural language processing tasks it would be impractical to expect this amount of data to be available for every corpus and language to be tagged
these errors can be decomposed into the following groups NUM NUM false positive at an abbreviation within a title or name usually because the word following the period exists in the lexicon with other parts of speech mr gray col north mr major dr carpenter mr sharp
therefore when we refer to the satz system we refer to the use of machine learning with a small training corpus representing the word context surrounding each punctuation mark in terms of estimates of the parts of speech of those words where these estimates are derived from a very small lexicon
note the similarity of this tree to the algorithm used by the style program as discussed in section NUM
an interesting feature of this tree is that it reduced the NUM input attributes to just NUM important ones
we next compared the satz system error rate obtained using the neural network with results using a decision tree
we suggested two ways of approximating the part of speech distribution of a word using prior probabilities and binary features
the generator component processes this information along with data from the user model and possibly the history module in order to decide which errors to correct in detail and how each should be corrected including what language level should be used in generating any required instruction
in particular through its lexical choice component it selects references to medical concepts that are shorter and more colloquial than the text counterpart
thus as the domain semantically expands the size of the semantic grammar tends to substantially grow
in addition to speech recognition we have done some preliminary development of our translation components for etd
the vocabulary size of the esst system is NUM words which includes all unique words in the esst training set
with this growth significant new ambiguities are introduced into the grammar and these tend to multiply
janus is a multi lingual speech to speech translation system which has been designed to translate spontaneous spoken language in a limited domain
scheduling dialogue typically consists of opening greetings followed by several rounds of negotiation on a time followed by closings
for example speech recognition ap null pears to indicate that the etd domain has a higher out of vocabulary rate
we have constructed a simple sub domain classifier that is based on a naive bayesian approach and trained on the available etd data
since the sub grammars are separated from each other the ambiguities between them will add and not multiply
in addition the relatively small range of meanings that could be conveyed make parsing and understanding tractable
the ne and te modules themselves are now available for any information extraction task and the object oriented template generator allows the system to easily pro duce any new template based on the task specifications
therefore we will report two sets of results the official scores for the incomplete responses and the unofficial scores for the complete responses which were generated after the bugs were fixed
applicability semantics internal generation goals figure NUM a mapping rule for transitive constructions ture at np1
iii we show how the semantics is systematically related to syntactic structures in a declarative framework
a person object consists of NUM the person s nam e NUM any aliases for that name in the article and NUM any titles for that individual which appear in the text
cer lcb NUM rcb on july NUM and future will vacancy reason out head refire as post chairman lcb NUM rcb at the end of the year
the te task was more code intensive because of its reference resolution component i e that task requires an assembling of information gathered up from throughout the article for each organization and per son object
by forcing louella to accept the match that starts with a known first name instead of another part of speech we threw out this match and raised the documen t total score to 97r 94p
slot pos actt cor par inc spu mis nonirec pre und ovg err sub all objects NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM f measures p r
if no match is recognized a content filter for the phrase i s run against a content filtered version of each known organization name if there is a match the link is made
in the previous example the reference to mccann erickson in the headline of the walkthrough text i s found only after the body of the text is processed during the variation matching phase
the person and company name passes also use the previous informatio n to identify contexts which indicate the presence of a company name such as abc stock ros e percentage
lachen x x lachte analogy a lso explains what is called the produc l ivity of bmguage i.e. the fact that underst md a ble words cml be coined which are not registered in dictiona ries
NUM the previous word is w
figure NUM transformation based error driven learning
transformation templates the learner currently has four transformation templates
figure NUM combining unsupervised and supervised learning
in the cours de linguistique gdn ul which dates back t o NUM saussm e lnetltions a phenoruenon of tremendous iml ortance in language analogy given some series of three words human beings arc able to oii a fourth one
recall that this is completely unsupervised
in tim 7ours de linguislique g alc s d llssllr l fl ilti lls kl p tello ll ioll of tremendous itnl ortance in l mguage analogy
dependency grammars describe the structure of a sentence in terms of binary head modifier also called dependency relations on the words of the sentence
the number of such quadruples in a set of items is bounded by o igi n and there are n sets of items
initial state tagging accuracy on the test set is also NUM NUM
the evaluation measure is simply tagging accuracy
neural nets also naturally model variable interactions
case assignment is partially determined by kit theta hierarchy in that the argument phrase which bears tit highest theta role in th sense of this hierarchy always gets nominative case
to achieve our overall objective the following four points will be exemplified by joined representations of ger null man verbs drt profits from set s systematic derivations of thematic roles and of morpho syntaetic features on the basis of predicate argumentstructures
the lexical entry of leihen in its variant to lend consists of an interface list whose thematic roles are based on set and of semantic structures which include and extend versehenken s semantic components
the xarnt le inf a ences la to lb and 2a to NUM result froln differences in the lexical dli ss of leihen and verschenken
as far as the degree of speeifieal ion is on erned the des rit i ion is at leas suil able as ommon d mominator
even trec type topics can be used as a detection need
sri s fastus system can also recognize tables embedded in text
the demonstration supports document detection information extraction and document management i allows the interchange of components from different contractors i uses sharable components interfaced through standard protocols allows document detection and information extraction to work together by using standardized linguistic annotations i supports foreign language text display and processing the program had two versions known as the NUM month demo and the current NUM month demo
typically extracted information is used to build domain databases
semantic emphasis th eory set has identified princit les that allow to link a prototypical description of a situation to a number of prototypical meaning descriptions of con rete lexeines that are suitable to refer to that situation
the detection need format supports natural language queries as well as boolean keywords and fuzzy queries
the results of this processing are output in a relational template
the resulting list of documents are rank ordered by best match
by the binomial distribution are starred
the first approach would be fair
where w wi correspond to the active feature indexed by ij
finally we summarize the paper in section NUM
b she gave a few apples to several children
in the b examples this ambiguity is absent
e the remaining representations containing no f are deleted
these include word class and sem antic features such as hum an
thus it is impossible to account for the structure of the sentence without describing tfa
thus as for tfa in all such cases a characteristic difference may be found
as such the comments and the examples could not cover all the possible sentence structures
thus it should be understood as a good result if the procedure identifies such an ambiguity
NUM we are aware that our procedure does not cover all possibilities occurring in english sentences
an antecedent will be accepted if the class of the anaphor in our classification hierarchy is equal to or mor e general than that of the antecedent if the anaphor and antecedent match in number and if the modifiers i n the anaphor have corresponding arguments in the antecedent
as a first step in this direction we have coded a non interactive pattern by example procedure which takes a sentence which is prepared as an exemplar of a pattern analyzes it with the stage s of pattern matching described above and then converts the resulting units to elements of a pattern
however we were at a disadvantage with respect to groups which performed more local pattern recognition in three regards NUM our systems were quite slow in processing the language as a whole our system is operating with only relatively weak semanti c preferences
the text analysis operates in seven main stages tokenization and dictionary look up four stages of pattern matching basically for names noun groups verb groups and larger patterns reference resolution and output template or sgml generation
reference resolutio n the various stages of pattern matching produce a logical form for the sentence consisting of a set of entitie s for this scenario people organizations and positions and a set of events which refer to these entities
thus two wms which have a hyponymy relation among them and which are linked to the same ill record should have equivalence relations that parallel the hyponymy relation eq has hyperonym and eq synonym
suppose that the following word sequence represents a verb final japanese sentence with a subordinate clause where n n2k are nouns pz p2 are case marking post positional particles and vl v2 are verbs and the first verb vi is the head verb of the subordinate clause
otherwise if only the two cases ga nom and wo acc are dependent on each other and the de at case is independent of those two cases e can be regarded as generated from the following two subcategorization frames independently
for this purpose we have already invented a new feature selection algorithm which meets the above requirement on preserving high case coverage with a relatively small number of active features s we will report the details of applying this new algorithm to the task of model learning of subcategorization preference in the near future
it can be shown that there always exists a unique model p with maximum entropy in any constrained set
all tells the results when all cues are used and sel
compared with those previous works this paper proposes to consider the above two issues in a uniform way
each feature function corresponds to a subcategorization frame s which has only one case of the given verb noun collocation e
let jr be the full set of candidate features each element of which corresponds to a possible subcategorization frame
NUM labeling the clusters as positive or negative the clustering algorithm separates each component of the graph into two groups of adjectives but does not actually label the adjectives as positive or negative
procedures used in this paper applicable to any category analysis using dimap are available at http i www clres com
the cost based evaluation of different c orderings refers to evaluation criteria which form an intrinsic part of the centering model NUM
these words are a subset of the wordnet synsets headed at person in particular s n sets headed by
the categories functional roles detached roles and human roles align with subtrees rooted at particular nodes in the wordnet hierarchies
we have shown how mcca categories generally recapitulate wordnet synsets and how mcca analysis leads to thematic and conceptual characterization of texts
however the set is defined as well by including a dcrivational lexical rule to allow forms in other parts of speech
specifically we show that wordnet provides a backbone but that additional lexical semantic information needs to be associated with wordnet synsets
this category also has words fi om all parts of speech and thus will entail the use of derivational lexical rules in its definition
the NUM percent frequency is for the ana c context each of the other three contexts have expected fi equencies of about
we suggest that wordnet synsets and mcca categories be augmented with further lexical semantic information for use after text is tagged or categorized
but with these methods it is necessary to make resolution rules for zero pronouns by hand
but there is currently no method to identify zero pronouns and their antecedents automatically within bilingual corpora
the accuracy of identified antecedents of zero pronouns is shown in table NUM
this phenomenon causes considerable problems in natural language processing systems
in this paper only japanese and english language pairs have been discussed
so this problem is caused by the methodological limitation of proposed method
the effectiveness of the use of english parsers for the english analysis will also be considered
to make robust rules with wide coverage takes a lot of time and labor
table NUM identification accuracy of antecedents for types of zero pronouns
this problem is also caused by the limitations of proposed method
superficially the similarity ends there because the internal structure of segments and spans is different
however g s specifies that the only relations among intentions affecting discourse structure are dominance and satisfaction precedence
such purposes have the feature that they are achieved in part by being recognized by hearers
we discuss the relationship between ils and possible approaches to informational structure briefly in section NUM
this is an important issue but one that rst simply does not make claims about
rst s authors claim that many relations have a typical ordering of their nucleus and satellite
in contrast informational structure is concerned with domain relations among the things being talked about
each subconstituent may in turn be structured in exactly the same way as the larger constituent
the defining feature of the core is its function of expressing the purpose of the segment
the first pattern divides a word NUM c japan and the u s into h japan and 1c the u s and gives each word a noun place tag as the part of speech
the character is a wild card that can match any number of characters within the word
the pattern definition in erie was powerful enough to identify the names and expressions required in the met task
the words j a corporation and a government ministry are tagged as nounsuffixes suffix by majesty while the dictionary pattern augments it by adding organization as its sub category
the second pattern divides a word whose last character is tt a government minister into NUM and the rest of the word if the word consists of more than three characters
for instance most of the above mentioned words are listed under the same topic ld geography of the intended label ld099 or its cross reference me places
moreover function words in chinese are frequently omitted
i compiling bilingual lexicon entries from a non parallel english chinese corpus
these are especially crucial in the process of handling the index and creating tools for the developers to obtain a satisfactory result
johnston et al NUM present the details of multimodal integration of continuous speech and pen based gesture guided by research in users multimodal integration and synchronization strategies NUM
commandvu agent since the commandvu virtual reality system is an agent the same multimodal interface on the handheld pc can be used to create entities and to fly the user through the NUM d terrain
other user interfaces when another user interface connected to the facilitator subscribes to and produces the same set of events as others it immediately becomes part of a collaboration
there quickset was deployed in a tent where it was subjected to an extreme noise environment including explosions low flying jet aircraft generators and the like
natural language agent the natural language agent currently employs a definite clause grammar and produces typed feature structures as a representation of the utterance meaning
the results of these commands are visible on the quickset screen as seen in figure NUM in the modsaf simulation and in the commandvu 3d rendering of the scene
in combining the meanings of the gestural and spoken interpretations we attempt to satisfy an important design consideration namely that the communicative modalities should compensate for each other s weaknesses NUM NUM
the key to this interpretation process is the use of a typed feature structure NUM NUM as a meaning representation language that is common to the natural language and gestural interpretation agents
finally quickset is now being extended for use in the exlnit simulation initialization system for darpa s stow NUM advanced concept demonstration that is intended for creation of division sized exercises
the agents may be written in any programming language here quintus prolog visual c visual basic and java as long as they communicate via an interagent communication language
hence the sum over the constrained incomplete paths is the sought after sum over all complete derivations generating the prefix
for our purposes we do not need any higher level parse structures
in our experiments we found that about eight iterations usually worked well
there are between NUM and NUM NUM sense tagged sentences for each of the NUM words
we begin with a small set of seed words for a category
ideally the text corpus should contain many references to the category
in the next section we describe experiments to evaluate our system
squad mortars explosives gun NUM NUM limon covenas refers to an oil pipehne
however we have not observed this to be a problem so far
the order of expansion is actually irrelevant for this definition because of the multiplicative combination of production probabilities
for example tables and moons have virtually no association with animals
for example dogs and sparrows are members of the animal category
we begin with a short description of decomposable models in section NUM
for example the uno system does not need to explicitly store the entailment subsumption relation betwee n the complex disjunctive type power boat or sail boat and the lexically simple type boat because this relation is directly and cheaply computed from the uno representation of these two expressions commo n nouns in this case
possible but not very likely acquisitions while the lack of a quality parse may prevent the system from understanding a high level relation i n this case what exactly was said about possible acquisitions it understands the difference between possible but not very likely and possible acquisitions
successful techniques for developing and testing in depth processing large number of texts finally by participating in muc6 we hoped to share our experience and learn from the other participant s about successful techniques for developing and testing in depth processing for a large more than two thou sands number of texts and for developing large knowledge bases
in fact when he is asked his opinion of the ne wbat namex type organization status opt coke enamex ads from enamex type organization caa enamex m mex type person james enamex places his hands over hi smo
NUM implemented changes necessary to participate in muc6 for example before muc NUM the uno system was preserving the exact image of the input text but i t did not keep track of the correspondence between the resulting knowledge base and the actual pieces o f input text from which the knowledge base resulted
however we put a lot of effort into encoding this hierarchy because a with the existence of this hierarchy we further substantiate our claim that natural language is a powerful and efficient knowledge representation system and add geographical knowledge to th e list of uniformly represented and reasoned about types of knowledge
there is a large body of existing work on morphologically marked time and aspect but we had decided against handling this type of temporal information because it necessarily requires high recall and precision of performin g sentential level parsing a task that no nlp system including our system can perform well
one of our goals is to experimentally demonstrate that an nlp system that closely parallels the representational and inferentia l characteristics of natural language allows one to achieve an in depth processing eg querring automaticall y from texts created knowledge bases or entity classification with close to real time high recall and precisio n performance
if there are genuine dependencies in the grammar the erf method converges systematically to the wrong weights
figure NUM 3the percentage of ambiguous words in a held out test sample that are disambiguated correctly
the aim of feature selection is to choose a feature that reduces this divergence as much as possible
to estimate model parameters it is necessary to compute the expectations of certain functions under random fields
for example in tree xl rule NUM is used once and rule NUM is used twice
coreference most interesting tas k we were particularly interested in the coreference task with the ne task largely considered as a preparatio n stage for it because NUM reference resolution is critical for processing pretty much any text in any domain NUM we believe that mechanism for resolving references to named entities is basically the same regardles s of the entities types
NUM compute a position binary vector for each word using the anchor points
probabilities a and b are computed incrementally in a single left to right pass over the input
each of the eight models is due to a different combination of search strategy and evaluation criterion
where q NUM p nonprobabilistically the prediction loop at position NUM would stop after producing the states
some of the possible states in the lower layer are relax constraint asks the user to relax a certain constraint e.g. no thai restaurant found on legacy but there is one on spring creek is that ok the system needs domain specific information that legacy and spring creek are close to each other
the reliability of the anchor points will determine the accuracy of the secondary lexicon
the main task of the pragmatic stage is disambiguation and type checking
we do not see the scores as a refutation of our approach
in summary we are pleased with our first participation in muc
james stepping down which timed out
each is implemented in a rule based way
links correspond t o relationships between nodes
as table NUM shows this has some consequences on the usage of null and strong pronouns
this paper is a contribution in that dire orion
the problem is how possessives affect cb computation and cf ordering
work t ut needs to be rigorously teste d
much work still remains to t e done on entering
however this syntactic alternation does n t apl ly in subject position
the problem is whether they are part of the cf list or not
from a cognitive t erst ec tive
when the document index is character based then query processing can determine proximity constraints based on word and phrase formation
modem chinese has two graphic character sets prc simplified characters and taiwan traditional characters
a second technical challenge in chinese is in a third problem is the lack of non proprietary resources for chinese
lack of linguistic resources one of the unexpected costs in this effort arose from the lack of linguistic resources
though that was varied experience it was still limited experience with a high risk high payoff technology
the current plum architecture for chinese separates segmentation part of speech analysis and name extraction into NUM pipelined modules
the template generator must address any arbitrary constraints as well as deal with the basic details of formatting
one of the major challenges for chinese named entity extraction is the lack of explicit word boundaries in chinese text
we relied on an automatic collection driven concept relation technology called infinder described below and in jing
where the user has indicated a preferred segmentation however it will be respected by the automatic component
the first class is quite annoying for the statistical parser because it contains errors that are intuitively very clear and resolvable but which are far beyond the limits of the current statistical tagger
in a month we achieved NUM NUM accuracy
the biases serve as initial values before training
our heuristics do not resolve all the ambiguity
about NUM of the words are ambiguous
figure NUM the result in the test sample
the task was not as straigthforward as we thought
the first corpus specific word is in the 41st position
we describe the two systems and compare the results
all the rules are currently represented by NUM transducers
for ease of exposition the syutax cat of pns is represented here as the colnplex category n np sigu where the np sign is app opriately lex poriion sign speeilied to account for selectional reslrictions and transitivity of properlies between the whole and the porlion via feature reeiitraucies
coutrm y to that which some authors posit ciias8 win87 it does n t seem to be a productive linguistic gencr disation to set in a lexicon some part of link between slice and cake
snormalisation of proof terms is defined by the following conversion rules
consider next a system that includes also the non associative level nl
such a modality has been used in treatments of extraction
in the latter cases siiape and magn of portions will be a function of file corresponding values of file qualia of tile whole NUM this interpretation of the magn feature accounts for the nature of relative quantifiers of pns
instead we posit that pns select both kinds of nouns count or mass denoting both kinds of things individuals or subslances but in any case crucially surl acing as expressing cumulative l eli3rence
alternative but equivalent formalisations of the above system are possible
they arc semantically tmcomplele as they do n t allow the hearer to relricve fl om ihem the information the speaker wanls io convey l urlher information as in lohn ate a slice of cake or it was an excellent cq lee
we ca n t go further with ihe issue here but at last what the discussion above stands for is that human conceptualisation is c mside ed as ihe cause and the mass count distinction as tb e surface effect
yield terms may be restructured in ways appropriate to the different operators e.g.
our current work concentrates in further reducing the number of examples necessary for training the translation models in order to cope with spontaneous instead of synthetic sentences
in all cases a edged out the other methods
however other variations are possible such as interpolating with the unigram probability
we therefore built four base language models summarized in table NUM
moreover for such a solution a separate tagged corpus is required for each domain
the main drawback of this solution is the need for a very large tagged corpus
we expect this percentage would be even smaller had we used a larger training corpus
thus a differentiation between those two analyses can not be done using our method
a feminine form of a number the masculine form of the same number
we would also like to thank ibm which enabled us to complete this paper
there are three avenues of future research we are interested in pursuing
as pointed out above such a procedure is problematic for ambiguous words
one can see clear differences between the nearest neighbors in the two spaces
neighbors with highest similarity according to both left and right context are listed
the resulting classification was applied to all tokens in the brown corpus
it is clear from the tables that incorporating context improves performance considerably
they were separated on distributional criteria that do n t have linguistic correlates
table NUM precision and recall for induction based on word type
it shows that at least in hunlan nlachinc dialogue coopcraiivity is a fornmlly nlorc coniplcx pheuonlcnon than anticipated by gricc
NUM it should be possible for users to fully exploil the system s task domain knowledge when they need it
all the problenis of inleractioii uncovered dr ring woz wore analyzed and represented as violations of principles of cooperative dialogue
development was iterated until the dialogue model satisfied the design constraints on i a average user utterance length
our solution to these problems is the application of a singular value decomposition
this paper presents an algorithm for tagging words whose part of speech properties are unknown
since naeta comnmnication had not been simulated during the woz experiments this came as no slut rise
the user lest confirmed the broad coverage of the principles with respect to cooperative spoken user system dialogue
thus in the nominative plural kr6j style becomes kroje b6r forest becomes bory b6j combat becomes boje
dc dr b4 a train leaves at NUM p m dt key for dialogues NUM and NUM for example assume that a scenario requires the user to find a train from torino to milano that leaves in the evening as in the longer versions of dialogues NUM and NUM in figures NUM and NUM NUM table NUM contains an avm corresponding to a key for this scenario
telegraphic messages contain many instances of phrases with omission cf
therefore the wh pronoun is not indexed before the appropriate subject is linked to the verb chain which also has a verbal object
whenever a relevant new theme is established however it should reside in its own discourse segment either embedded or in parallel to another one
in particular it is shown that the number of mistakes the additive and multiplicative update algorithms make depend differently on the domain characteristics
this suggests that the temporal information given by tense acts as a weaker constraint on temporal structure than the information given by temporal adverbials
past activity precede NUM no overlap NUM no same event s no past state past perf event past perf activity past perf state
we also recommended using an underspecified representation of temporal rhetorical structure to avoid generating all solutions until higher level knowledge can aid in reducing ambiguity
using constraints we reduce the number of readings to NUM using preferences we reduce that to NUM readings for each continuation
d if not the dcu may continue any of the highest rated threads and each of these solutions is generated
we have extended the penn carpenter implementation of the hpsg grammar so that semantic aspect is calculated compositionally and stored here
b lexical items such as cue words influence the value of the rhet eln type see figure NUM
explicit relation markers such as cue words and temporal relations must be consistent and take priority over indicators such as tense and aspect
figure NUM exemplifies the operation of the multiengine strategy as well as of the preferences applied to analysis and transfer
furthermore while the branching factor of the input tree can in theory be n NUM in practice it will be much smaller
the german noun bank is translated as english bank if the subject area is finances otherwise it is translated as bench
most systems allow to set a subject area parameter for subjects such as finances electrical engineering or agriculture
this is the case if a verb is used with a prefix in both the separable and the fixed variant as e.g.
nouns that occur only in a plural form also need special treatment i.e. a plural determiner and a plural copula form
in the past the evaluation of machine translation systems has focused on single system evaluations because there were only few systems available
since gender assignment in choosing the determiner is such a basic operation all systems are able to do this in most cases
especially it is not evident whether our medium frequency NUM occurrences leads to words of similar prominence in both languages
an example of a verb compound that gets a translation via segmentation is t0 tap dance and an adjective compound example is sweet scented
working in the opposite direction all systems perform segmentatiqn of orthographic unit compounds since this is a very common feature of german
we are in the process of redesigning our translation evaluation methodology to take account of all of the above points
in order to achieve a good trade off between discrimination power and precision level we adopted an empirical process with successive steps of refinement
it is our intention to build a very general instrument that can be afterwards tuned to particular domains by identifying more specific uses
in the long version of this paper we plan to present the results of our algorithm on a richer feature set as well
for example the american pilot scott o grady downed in bosnia in june of NUM was unheard of by the american public prior to the incident
noun cat noun ing root plur root still ft
the possibility of error occurrence within noun phrases are lower than between a noun phrase and a verbal phrase a preposition phrase an adverbial phrase
the accuracy of the result on atis is lower than wsj because the parameters of the heuristics are a justed not by atis itself but by wsj
let c p j be j th component of rhs p and t i be i th word of input string
scan handles states of s i checking each input terminal against requirements of states in s i and various error hypotheses
however the percentage of sentences with constituents crossing less than NUM is higher than the wsj as sentences of atis are more or less simple
in the real text however the insertion deletion or inversion of a phrase namely nonterminal node occurs more frequently
to generate post ocr text we ocr d the printouts
we start with a katakana phrase as observed by ocr
our robust parser with recovery mechanism extended general algorithm for least errors recognition can be easily scaled up and modified because it utilize only syntactic information
because our robust parser handle extragrammatical sentences with this syntactic information oriented recovery mechanism it can be independent of a particular system or particular domain
to avoid repetitiveness such a system will have to resort to using different descriptions as well as referring expressions to address a specific entity NUM
this is clearly much better than nothing but still contains some serious methodological problems
first italian verb senses were extracted from a paper version of an italian dictionary and checked against a corpus of genereric italian texts
even distantly related languages like english and czech will share a large number of cognates in the form of proper nouns
the results were evaluated in the manner described to produce figures for comprehensibility of source and target speech respectively
depending on the character of the options form entries are either multiple choice or free text
the results for the basic versions are shown in the first column of table NUM
their significant advantage over the unnormalized version of positivewinnow is readily seen in table NUM
in the second section the judge is asked to write down the principal object of the utterance
at least one major or several minor syntactic or word choice errors but the sense of the utterance is preserved
to cast the problem of determining segment boundaries in statistical terms we set as our goal the construction of a probability distribution q b i w where b e lcb yes no rcb is a random variable describing the presence of a segment boundary in context w
the extracts used for tdt include material from the reuters newswire service and from the primary source media cd rom publications of transcripts for news programs that appeared on the abc cnn npr and pbs broadcast networks the size of the corpus is roughly NUM NUM million words
here ref is an indicator function which is NUM if the two corpus indices specified by its parameters belong in the same document and NUM otherwise similarly hyp is NUM if the two indices are hypothesized to belong in the same document and NUM otherwise
given an initial distribution q and a set of candidate features c we consider the one parameter family of distributions lcb q g rcb aer q g q for each g e c the gain of the candidate feature g is defined to be
in the experiments we report in this paper we assume that sentence boundaries are provided in the annotation and so the questions we ask are actually about the relevance score assigned to entire sentences normalized by sentence length a geometric mean of language model ratios
where q0 blw is a prior or default distribution on the presence of a boundary and a f w is a linear combination of binary features fi w e lcb NUM NUM rcb with real valued feature parameters ti
we present approximate figures for the amounts of effort devoted to each language pair in conjunction with the other results
the sixth feature which boosts the probability of a segment if the previous sentence contained the word closed is another artifact of the wsj domain where articles often end with a statement of a company s performance on the stock market during the day of the story of interest
then we have normahzed both wordnet vectors andtraining vectors to separately add up across each category
first of all differences in levels of generality are acceptable although deeper hierarchies are preferred
after a building phase all potentially new ill records are collected and verified for overlap by one site
the different wordnets can be compared and checked cross linguistically which will make them more compatible
spanish dedo which can beused to refer to bothfinger and toe
the language independent objects are connected with strings that are labels
for each attribute there is an item list containing the sequence of integer descriptors corresponding to the sequence of words in the corpus
some support is provided for user written tools but as yet there is no published api to the potentially very useful query language facilities
the use of sgml as an i NUM stream format between programs has the advantage that sgml is a well defined standard for representing structured text
if the user specifies either the flight number or the arrival and departure city and approximate arrival time or the arrival and departure city and approximate departure time
robustness of an sd system refers to the ability of the system to help the user acquire the desired information even in the presence of user and system errors
overall the coefficient of sij in eq NUM increases after a promotion
to correct the morphological analyser is executed in generation mode to generate the broken plural form of lcb kd rcb in the normal way
the accuracy 2automatically derived rules require less work than manually written ones but are unlikely to yield better results because they would consider relatively limited context and simple relations only
macro averaging assigns equal weight to every category while micro averaging is influenced by most frequent categories
once an error rule is selected the corrected surface is substituted for the error surface and normai analysis continues at the same position
the may deflclt compares with a surplus of NUM NUM billion lire an the corresponding month of NUM
other than the economic factor an important advantage of combining morphological analysis and error detection correction is the way the lexical tree associated with the analysis can be used to determine correction possibilities
there are five sets of categories topics organizations exchanges places and people
where c w is the count of the word w primes indicated counts and probabilities after the change and d l changes represents the cost of writing down the perturbations involved in the representation of w
as a simple example suppose that the composition operator is concatenation that terminals are characters and that the only perturbation operator is the ability to express the frequency of a word independently of the frequency of its parts
then to code either a sentence of the input or a nonterminal word in the lexicon the number of component words in the representation must be written followed by a code for each component word
thus the word red lcb red rcb might be represented as r o e o d red
unfortunately while surface patterns often reflect interesting linguistic mechanisms and parameters they do not always do so
but for a model with knowledge of syntax and word frequencies there is nothing remarkable about the phrase
if s has tokenization ambiguity by definition there is ito s i NUM
in other words x can not be an ft tokenization if it is not a ct tokenization
moreover if y is a critical tokenization and x is its supertokenization there is x y
let e be an alphabet d a dictionary and s a character string over the alphabet
given a typical english dictionary there are five critical points in the character string s thisishisbook
if d is uniform over the length of the text then the metric represents the probability that any two sentences drawn from the corpus are correctly identified as being in the same document or not
the basic idea is conceptually simple ambiguity exists when there are different means to the same end
for each subcategorization frame s a binary valued feature function fs v ep is defined to be true if and only if the given verb noun collocation e has exactly the same cases as s has and is also subsumed by s l
computational linguistics volume NUM number NUM that is for any character string s c NUM
in particular the starting position NUM and the ending position n are the two ordinary critical points
this algorithm requires a declarative specification of three kinds of information first what operators are available and how they may combine second how operators specify the content of a description and third how operators achieve pragmatic effects
note that while this axiom is expressed at the same level of generality as glt s qualia structures this rule is part of world knowledge and applies to all things that are photocopiers not to all occasions where things are described as photocopiers
viterbi probabilities are propagated in the same way as inner probabilities except that during completion the summation is replaced by maximization vi kx y is the maximum of all products vi jw NUM vj kx y that contribute to the completed state kx y
the last summation is over all predicted states based on production x a the quantity p s xo i lxt x is the sum of the probabilities of all paths passing through i ix a inner and outer probabilities have been defined such that this quantity is obtained precisely as the product of the corresponding of yi and fli
reiter and dale also point out that which characterizations are basic level must be adjusted to reflect the expertise of the addressee however we shall sidestep this issue here by assuming that certain lexical items are simply listed as basic level terms
must be derived compositionally from special idiomatic meanings of their parts as when strings influence pull exert privately from the oed NUM the strings she pulled did n t get her the job
based on a review of psychological experimentation and their own study of referring expressions in task oriented dialogue they argue that some referring expressions can be constructed simply by selecting properties from a prioritized list of attributes until the entity is distinguished
this results in a finite state automaton representing global lexical rule interaction i.e. the interaction of lexical rules irrespective of the lexical entries in the lexicon
while this introduces the recursion necessary to permit successive lexical rule application it also grounds the recursion in a word described by a base lexical entry
prediction ordering is based on the user s previous input and so the system can automatically adapt to the individual user
the detection of which additional specifications are intended by the linguist crucially depends on the interpretation of the signature assumed in hpsg discussed in section NUM NUM
parsing with the test grammar using the constraint propagated covariation lexicon is on average NUM percent faster than the performance with the expanded out lexicon
the lexical rule predicates called by these interaction predicates are defined as in figures NUM and NUM except that the frame predicates are no longer called
since for expository reasons we will only discuss one kind of lexical entry in this paper we will not show those indices in the examples given
an interesting aspect of the idea of representing lexical rule interaction for particular word classes is that this allows a natural encoding of exceptions to lexical rules
2deg to illustrate the steps in determining global lexical rule interaction let us add three more lexical rules to the one discussed in section NUM NUM
the lexical entries are only partially specified and various specializations are encoded via the type hierarchy definite clause attachments or a macro hierarchy
we describe some practical work on word prediction and discuss its limitations as a technique for speeding up free text entry
suffixes with epenthetic re such as teiru and aspectual nominals such as bakavioust now etc
syllabification stress and allophonic rules are achieved by programs
the proposed pronunciation varies even from one dictionary to another
computational linguistics volume NUM number NUM structure of the sentence
this process can be achieved using either one or two buffers
and the input string abce to be processed
a phonetic index is built with the vocabulary of the application
a class is a set of strings having a common property
see section NUM NUM NUM on using one or two buffers
by rules the contexts indicate if the replacement is required
by using the constraints of the applicability of the functions we can identify a unique category for each verb automatically
this paper presents a method for the automatic extraction of subgrammars to control and speeding up natural language generation nlg
in this way only lexical words a e considered as the variable letter can only he instantiated to letters branching out from the current position on the lexicon tree
this will result in a grammatically fully expanded feature structure where only lexical specific information is still missing
for example assuming that the training phase has only to be performed for the example in figure NUM then for the mrs of a man gives a book to kim a partial match would generate the strings a man and gives a book to kim NUM
this is a minimally structured but descriptively adequate means to represent semantic information which allows for various types of under overspecification facilitates generation and the specification of semantic transfer equivalences l in case a reversible grammar is used the parser can even be used for processing the training corpus
then retrieval using the path abcd will return all three templates retrieval using aabbcd will return template tl and t3 and abc will only return tl NUM interleaving with normal processing our ebl method can easily be integrated with normal processing because each instantiated template can be used directly as an already found sub solution
NUM lexical lookup from each terminal element of the unexpanded template templ the type and handel information is used to select the corresponding element from the input mrs mrs note that in general the mrs elements of the mrs are much more constrained than their corresponding elements in the generalized mrs mrs g
for convenience we will use the more compact notation lcb sandyrel h4 giveael hl tempover hl some h9 chairrel hl0 to h12 kimrel h14 rcb using this notation figure NUM see next page displays the template tempi mrs obtained from fs
as shown in the figure NUM each category is defined in terms of the ability to co occur with aspectual forms
this sense of the verb insult verbally is anchored in the process concept communicative event modified by two evaluative attitudes involving two unmapped i.e. internal for the zone variables refseml and refsem2
while acquisition with lexical rules is necessary universal and efficient for the class of deverbal adjectives our research shows that often in spite of the strong appearance to the contrary such acquisition is neither fully automatic nor cost free
the first obvious complication is that not all of its semantic cases of a verb are usable for the lr in fact only one of the three semantic cases of abuse namely agent is but beneficiary and theme are not
among the constraints listed in the sem struc zone of an entry are selectional restrictions the noun must be a physical object and relaxation information which is used for treatment of unexpected ill formed input during processing
it is equally interesting to attempt to discover a rule for each and every exception and complication mentioned in section NUM the young grammarian approach to language was that every single fact had a rule attached to it
the additional attraction for using 12i ii in massive acquisition is the facilitation of the least trivial element of lexical semantic heuristics namely the discovery of the ontological concept on which the lexical entry should be based
this paper deals with the microtheory of adjectival semantics in one specific aspect namely the optimization and facilitation of the lexical entries for deverbal adjectives with the help of lexical rules deriving such entries from those of the corresponding verbs
the contribution that the adjective makes to the construction of a semantic dependency structure tmr typically consists of inserting its meaning a property value pair as a slot in a frame representing the meaning of the noun which this adjective syntactically modifies
the metrics are recall and precision and the testcollection is as introduced before reuters NUM
this section deals with the most important concept critical tokenization
and cd s is the set of critical tokenizations
normally sentence derivation and parsing are governed by complex grammars
following this line we observed two tendencies in tokenization research
they are definitely helpful but only at a later stage
alexandra vaz hugh and ng chay hwee helped in correcting grammar
that is every st tokenization is a ct tokenization
they are called hidden or invisible because others cover them
in other words a complete dictionary is an operational must
in other words shorter word strings cover longer word strings
in this case context symbols a and b are replaced by an element of the basic tag set and the frequency table of each node then consists of the part of speech subdivision set
our experinaental results show that combining hierarchical tag context trees with the mistake driven mixture method is extremely effective for NUM incorporating exceptional connections and NUM avoiding data over fitting
let t t NUM be the last constructed tree with counts of nodes z c sl z c sn z
the distribution of the grammatical forms in these examples is shown in the following table form frequency
table NUM number of polysemous words in each part
the rest of this paper is organized as follows
however it is costly to obtain a suitable grammar from an unbracketed corpus and hard to evaluate results of these approaches
the grammar acquired by this method is assumed to be in chomsky normal form and a large amount of computation is required
the term local contextual information considered here is represented by a pair of words immediately before and after a label
NUM repeat NUM NUM until all brackets nodes in the corpus are assigned labels
the probability distribution can be simply calculated by counting the occurrence of c and word1 c words
to avoid generating a large number of grammar rules some basic grammatical constraints local boundaries constraints and x bar theory were applied
due to this it is necessary to provide a criterion for determining whether this merging process should be continued or terminated
when zxe is large the current merging process introduces a large amount of information fluctuation and its reliability should be low
the functions on words that are represented by finite state transducers are called rational functions
moreover in order to detect recognition or interpretation errors that occurred in previous turns the dialogue system takes advantage from the global history of the interaction and it only accepts interpretations of user s input that are coherent with the dialogue history
at the initial step such node is one whose lower nodes are lexical categories NUM this process is performed throughout all parse trees in the corpus
that is in each step of merging this method searches for the most plausible situation that the data in c are partitioned in the certain groups g
we also provide an algorithm for computing the local extension of a finite state transducer
finally we measured the performance of the cascading application of the induced rule sets when the morphological guessing rules were applied before the ending guessing rules prefix suffixdeg suffix NUM ending c
even if one rule has a high estimate value but that estimate was obtained over a small sample another rule with a lower estimate value but obtained over a large sample might be valued higher by rl
the second experiment stands for investigating effectiveness of contexts described in section NUM the purpose is to find out useful contexts and use them instead of all contexts based on the assumption that not all contexts are useful for clustering brackets in grammar acquisition
precision the percentage of pos tags the guesser assigned correctly jj vbd over the total number of pos tags it assigned to the word jl nn rb vbd vbz i.e. NUM NUM or NUM
applying a transformation based system consists of applying each function fi one after the other
vbp jj vbd vbn says that if there is an unknown word which ends with ied we should strip this ending from it and append the string y to the remaining part
thus if the ending s predicts that a word can be a plural noun or a 3d form of a verb the information that this word was capitalized can narrow the considered set of pos tags to plural proper noun
another important consideration for rating a word guessing rule is that the longer the affix or ending s of this rule the more confident we are that it is not a coincidental one even on small samples
first we adjust the rule estimate so that we have no zeros in positive or negative NUM NUM outcome probabilities by adding some floor values to the numerator and denominator
if for some input w more than one output is allowed e.g.
among other things it allows the range of information to be tailored to individual preference
it is clear that this improves the chance of finding examples of a given lexeme immensely
number of correct brackets in proposed parses number of brackets in proposed parses recall number of correct brackets in proposed parses number of brackets in treebank parses the parser generates the most likely parse based on context seusitive condition probability of the grammar
this type of the gr tmrnar makes all output parses of this method be in the form of binary branrmng trees and then the bracketing precision can not be taken into account because correct parses in the corpus need not be in this form
min u has studied in u s a during NUM years when we list the nouns of professional title the number of pns recognized by the local grammar presented in figure NUM will be increased
in order to take large advantage of context in clustering it is preferable to choose a context c with a high value of e c because this context trends to have a high discrlm nation for characterising labels
the next match occurred in sentence NUM resulting in the erroneous extraction of the succession event in reverse
similarity metri c hasten uses a metric to compute the similarity between an egraph and an incoming text unit
however for muc NUM significant time was spent comprehending the template s and locating the originating text units
the remainder of the effort involves determining the similarity metric weights and thresholds to maximize th e extraction performance
this module is promising but there was insufficient time to fully investigat e it for the muc NUM evaluation
nametag classified three organization names as other instead of company due to the lack of explicit company indicators
hasten demonstrated a simple flexible design using simple training examples with minimal customization for the scenario template task
the generality of hasten s design can only be tested by using other task definitions in other domains
the egraph key extraction performance is not NUM recall and precision for a variety of reasons
this experiment did not produce significantly different results than the official configuration as illustrated in figure NUM
in this paper we investigate whether the same techniques can be applied in case the grammar is a constraint based grammar rather than a cfg
although this construction shows that the intersection of a fsa and a cfg is itself a cfg it is not of practical interest
in fact the possibly enormously large parse forest grammar might define an empty language if the intersection was empty
interestingly nothing needs to be changed to use the same parser for the computation of the intersection of a fsa and a cfg
the second step specifies the header of the action schema that will be used to replace the subplan that contained the error
instead we feel that a parser with a model of the discourse and the context can determine the surface speech actions
the same techniques that are used for calculating the intersection of a fsa and a cfg can be applied in the case of dcgs
acting as the mortar are intermediate actions which have constraints that the plan construction and plan inference processes can reason about
so the mental state of an agent sanctions the adoption both of goals to express judgment and of goals to refashion
for referring plans that contain more than one modifier there will be multiple derivations corresponding to the order of the modifiers
NUM NUM a NUM um third one is the guy reading with holding his book to the left
the second rule is used to adopt the goal of replacing the current plan plan if it has an error
the subset constraint in the headnoun action is evaluated which narrows the candidate set to the antenna and the fern plant
rather if either detects an error then that conversant can presuppose that they are collaborating and make a judgment
this belief would have been inferred if it were the hearer who had proposed the current plan or the last refashioning
this makes it impossible for the discourse processor to recognize sentence NUM as an acceptance
before we make our argument we will argue for our approach to discourse segmentation
given that all of the child s speech will be produced through the computer based aid it may be possible to provide intelligent interactive training
figure s NUM we need to set up a schedule for the meeting
in this paper we focus on the task of selecting the correct speech act
figures NUM and NUM contain examples which are adapted from naturally occurring scheduling dialogues
s NUM monday tuesday and wednesday i am out of town
this work was made possible in part by funding from the u s department of defense
we demonstrated that our extension to tst yields an increase in performance in our implementation
this will come into play in the next section when we discuss our evaluation
some speech acts are not recognized by attaching them to the previous plan tree
various forms of a root see saw seeing all map to the same sememe e.g. see
figure NUM parsed world book entry
an architecture for distributed natural language summarization
figure NUM parsed muc NUM template
the talcahuano bombing did n t result in any injuries
in this sense this type of contexts is appropriate to pns if an 1n is recognized we can be assured to fred a pn near to it
the input to the system includes newswire and on fine databases and ontologies
user model it keeps information about the user s interests e.g.
in this context it would be desirable to allow pictalk users to take more control over the content of their social interactions
first each pictalk screen has few items or buttons available because each picture takes up quite a bit of space
on one hand robustness is emphasized because sentences that are syntactically correct but which are not successfully analyzed in the specific application domain can have a valid linguistic meaning
considering the graph described in the previous section generating subject boundaries is simply a matter of identifying local minima on the graph
the main task of a machinelearning algorithm is thus mainly to retrieve on a statistical basis these grapheme phoneme correspondances which are in languages like french or english accidentally obscured by a multitude of exceptional and idiosyncratic correspondances
looking at these analogs it appears that three of our errors are grounded on very sensible analogies and provide us with pronunciations that seem at least plausible even if they were not suggested in glushko s experiments
NUM a r kkeyse m kkey nom hon dat hon sikyey lul sensaha sbess e
for each experiment we measure the percentage of phoneme and words that are correctly predicted referred to as correctness and two additional figures which are usually not significant in context of the evaluation of transcription systems
in fact the properties of paradigmatic relationships notably their symetry allow to reduce dramatically the cost of this procedure since not all NUM uple of strings in psc need to be examined during that stage
for example one need an update operator to say how a context c is changed when the sentence s has been processed in c c s c also one needs several operators to compare contexts
the main trouble here comes from isolated words for these words the search procedure wastes a lot of time examining a very large number of very unlikely analogs before realizing that there is no acceptable lexical neighbout
so the local value of the constituent towatuli si ess supnikka is as shown in diagram NUM
for example the discourse model may record that a certain description e.g. this composition has occurred as the x th and x t l st word of the y th sentence of paragraph number z of the u th monologue that has occurred during a given user system interaction
wip is more concerned with content and media selection according to the user s goals whereas with postgraphe the content is almost directly determined by the writer s intentions but the structure is totally flexible as the system must build its internal representations and output from raw data
good design rules also have to be considered when choosing and generating tables NUM NUM and graphs NUM NUM NUM NUM NUM NUM as these have a direct influence on the reader s perception of a report
our research extends the work of bertin NUM and mackinlay NUM NUM on the types and organization of variables the work of zelazny on messages and goals NUM and integrates it with other theories on the use of tables NUM NUM and graphs NUM NUM NUM NUM NUM NUM
the most important sub graphs describe the following features organization nominal ordinal quantitative NUM NUM NUM domain enumeration range temporal month year format integer real mesurements distance duration and specific objects countries
unfortunately the generation algorithms described to date have been intractable
of instantiated source signs onto a bag of target language signs
if not another permutation is tried and the process repeated
the fifth is used to establish the well formedness of undetermined nodes
if they are well formed the system halts indicating success
in figure NUM we illustrate a move via conjunction
transfer specifications may be incrementally refined and empirically tested for efficiency
for each paragraph of the monologue the topic state which is another part of the context model keeps track of the topic of the paragraph which is defined as a set of attributes from the music database
so far we have not found these restrictions particularly problematic
in the setting of dyd drt could take the form of a context model containing a series of sub drss the first of which contains information extracted from the dialogue that has led up to the selection of the first composition plus the monologue following it and so on
we also apply these constraints in our experiments
from all the points on the union of the dtw paths we filter out the points by the following conditions if the point i j satisfies
note that if one chunk of noisy data appeared in text1 but not in text2 this part would be segmented between two anchor points i j and u v
we would like to emphasize that if they were chosen by looking at the lexicon output as would be in a supervised training scenario then one should evaluate the output on an independent test corpus
note that this euclidean distance function helps to filter out word pairs which are very different from each other but it is not discriminative enough to pick out the best translation of a word
however they are found to be constituent words of collocations the housing projects by the hong kong government both cross and harbour are translated to d yff sea
phase one began with a study user observation of dod analysts engaged in paper and pencil translation tasks
these steps consist of user observations task analysis interface design and participative prototyping that includes formative evaluations
null storage and retrieval of information in electronic databases can be more efficient than equivalent searches in their paper based counterparts
here users and developers can identify system problems and enhancements that if implemented can significantly improve system usability
oleada uses tipster technology to provide users with access to pertinent and authentic text and tools for manipulating this text
the higher the cross entropy the worse the parsing performance
this text is also parsed with NUM different types of grammars
we select the partial trees which satisfy the following NUM
it means that there are difference in similarity among domains
also our experiments do n t cover all domains and possible combinations
there is no overlap between training corpus and test corpus
we also find the small sampling problem in this experiment
we will call the division into fiction and non fiction as class
figure NUM shows a part of the cross entropy data
is there a general and simple method to compare domains
we have integrated the system into the developing uniform modular tipster architecture
expensive operations of transformation are not done while the text is being processed
there are two approaches that have emerged in our experience with fastus
this approach is more noun driven and its patterns are much looser
fastspec allows the definition of multiple grammars one for each phase
only a small group of cognoscenti were able to use this system
there are three places in fastus processing that coreference resolution gets done
no use is made of synonymy or of a sort hierarchy otherwise
but they do not enable ordinary users to specify their own patterns
we have begun in a small way to implement such an approach
let us illustrate this idea with a simple example
the compiler performs off line constraint inheritance and code optimization
but in our setup we clearly need a definite clause
figure NUM an example theory in a hpsgii setup
figure NUM a permitted cyclic query
thus our result should really be
which is exactly what we want
figure NUM a constraint on append c
NUM NUM the theories of hpsgii directly
compiling hpsg type constraints into definite clause programs
detailed descriptions of the system components appear in our previous publications NUM NUM NUM NUM NUM NUM
speech translation in the janus system is guided by the general principle that spoken utterances can be analyzed and translated as a sequential collection of semantic dialogue units sdus each of which roughly corresponds to a speech act
this entails checking that r s focus set contains exactly two members
in this paper we describe our plans for extending the janus speech to speech translation system NUM NUM from the appointment scheduling domain to a broader domain travel planning which has a rich sub domain structure covering many topics
sentences that are covered well by more than one grammar most likely indicate true semantic ambiguity for example as mentioned above an expression such as twelve fifteen which can be interpreted as a time flight number room number or price
o monotone increasing quantifiers are those with an at least n interpretation
our initial experience with this system especially porting it from the flights arrival departure application to the map finder application has been very encouraging
computational linguistics volume NUM number NUM reply to an immediately preceding utterance if it would logically follow given the selection of some metaplan definition NUM an interpretation of an utterance u to hearer h by speaker s in discourse context ts is a set m of instances of elements of a4 such that
NUM schegloff actually argues against representing such sequences as speech acts however as in the computational work cited above we have used the notion of discourse level speech act to represent the functional relationship between the surface form of an utterance the context and the attitudes expressed by the speaker
NUM in order to capture the linguistic intentions of pretelling we also add a new attitude knowsbetterref s h p that is true if the knowledge of s is strictly better than the knowledge of p for example because s is the expert or s has had more recent experience with p
in order to account for the repair of misunderstandings we have proposed a representation of the discourse that captures the agent s interpretation of the conversation both before and after a repair and that is independent of the actual beliefs of the participants a dynamic mental artifact that is the object of belief and repair
NUM in the discourse model this was expressed as expressed do m preteu m r whoisgoing NUM from which one can assume persists do m pretell m r wholsgoing NUM by default
this makes use of the following default default NUM pickform sl s2 asurfaceform a ts decomp asurfaceform a a try sl s2 a ts NUM utter s1 s2 asurfacefo m ts
NUM thus when a speaker produces an askref about p she expresses and thereby intends the hearer to recognize that she expresses that she does not know the referent of some description in p intends to find out the referent of that description and intends the hearer to tell her that referent
premise NUM this is a plausible mistake because the acts pretell and askref both have the same surface form surface request m r informif r m knowref r whoisgoing so ambiguous pretell m r whoisgoing askref m r whoisgoing
in the theorist framework explanation is a process akin to scientific theory formation if a closed formula representing an observation is a logical consequence of the facts and a consistent set of default assumptions then it can be explained definition NUM an explanation from the set of facts NUM v and the sets of prioritized defaults a NUM
if the selected word is livre masc sg n oun the search should find other tokens of this and also tokens of the plural form livres
when the average search time grew to several seconds on a NUM mips unix server it became apparent that some sort of indexing was needed
our goal is NUM coverage of the words lemmata found in the NUM NUM word dictionaries and 1007o coverage of the most frequent NUM NUM words
non que leurs urines de l feaion m is ell offri ent des dimen ions rent par c on quent des port es inconnues juglu alors
half of the students used glosser and the other half a paper version of the same dictionary and all read the same text and answered questions tested text comprehension and satisfaction
even if one could imagine storing all the inflected forms of a language such as french the information associated with those forms is available today only from analysis software
tfedr ts1 NUM ire bereiken geraken tot reiken tot NUM halen komen tot NUM NUM taken treffen NUM fig
a pni a ma ke ekt a bai dilen you honored null i dat one def book null give 3p hon past
unfortunately as shown in table NUM many modern indian languages lack this property almost every marker has many to one mapping
NUM the f structure fs of the sentence before processing the m structure of the verb appears as in figure NUM and the final solution is as given in figure NUM
in this squib we propose a technique aimed at efficient computer implementation of lfg based parsers for indian languages in general and bangla bengali in particular
a forward reference discussed in the previous section is encountered during locate ing the left hand side of a schemata like NUM while processing an np
null the heuristics we use for coreference resolution are very simple and easily implemented in a fastus framework
the second mentions ibm only in the context of ibm compatible peripherals and is concerned with something else entirely
this effort of applying theory to a very complex real world task can give us insights into the various problems that arise
we need a way for the user to supply a mapping from strings in the text to entries in the template
NUM the basic phrase recognizer recognizes basic noun groups that is noun phrases up through the head noun
one of the first accomplishments of the current project was the definition and development of a declarative specification language called fastspec
null for singular first person pronouns t and me we resolve to the nearest person
the research described here was funded by the defense advanced research projects agency under office of research and development contract 94f157700 NUM
late in the processing in event merging some coreference resolution happens as a side effect of merging event strutures
given the incomplete nature of knowledge about conversational structure such a model would of course be at best a good guess but it could help clarify our thinking about how to build more effective systems for helping aac users to accomplish conversational goals
similarly it may be asked which of these scenarios is more likely to result in an attribution of say competence or in the longer term to have a positive effect on an aac user s self esteem status or independence
in contrast some constituents contribute the contents of their order domains wholesale into the mother s domain
figure NUM extraposition of relative clause in nerbonne NUM vom element is subject to the same variations in linear
on nerbonne s analysis the extraposability of complements has to be encoded separately in the schema that licenses head complement structures
our approach allows an obvious extension to the case of extraposition from pps which are problematic for nerbonne s analysis
so far we have only considered the case in which the extraposed constituent is inherited by the higher order domain
one of the areas in which this approach has found a natural application is extraposition of various kinds of constituents
one important aspect to note is that on this approach the inalterability condition on domain objects is not violated
then the syntactic rules discard contextually illegitimate alternatives or select legitimate ones
naturally the dependency relations may also be followed downwards down
the pc has a pentium NUM mhz processor and NUM mb of memory
this section evaluates the success of the level of dependencies
thus a verb having three valency slots may have e.g.
the valency merely provides a possibility to have an argument
the time does not include morphological anmysis and disambiguation NUM
these include the tokeniser lexical analyser and morphological disambiguator
update system knowledge based on efforts at goal completion
its control algorithm is the highest level dialog processing algorithm
NUM user where is connector three four
consequently the computer will passively acknowledge user statements
the original hypothesized combining function for u and e was
the computation of e was simple in our project
this is the repository of information about task oriented dialogs
this is the primary application dependent portion of the system
this problem was set as an assignment on the data intensive linguistics course organised by chris brew at the hcrc edinburgh university
NUM computer turn the switch up
please follow the computer s guidance
due to the morphological productivity of spanish and french we have considered different variants of this heuristic
this heuristic uses cooccurrence data collected from the whole dictionary see section NUM NUM for more details
firstly each spanish or french word was looked up in the bilingual dictionary and its english translation was found
only NUM of noun dictionary senses have monosemous genus terms in dgile whereas the smaller lppl reaches NUM
section NUM discusses previous work and finally section NUM faces some conclusions and comments on future work
this knowledge is composed of semantic field tags and hierarchical structures and both were extracted from wordnet
association ratio for vino mtd thus produced from the dictionary is used by heuristics NUM and NUM
the best heuristics according to the recall in both dictionaries is the sense ordering heuristic NUM
these problematic words are primarily function words and low semantic content words such as determiners conjunctions prepositions and very common nouns
manual construction of lexicons is the most reliable technique for obtaining structured lexicons but is costly and highly time consuming
however there are actually any number of such syntax trees corresponding to for example the first semantic representation since the np and the s can be arbitrarily far apart
in cgs which contain functions of functions such as very or slowly the addition of composition adds both new analyses of sentences and new strings to the language
the list of arguments to the left are gathered under the feature l and those to the right an np and a pp in that order under the feature r
it is also closer to some methods for incremental adaptation of discourse structures where additions are allowed to the right frontier of a tree structure e.g.
the paper includes a brief discussion of the relationship between basic categorial grammar and other formalisms such as hpsg dependency grammar and the lambek calculus
note that incremental interpretation will be of no use here since the semantic representation should be no more or less plausible in the different languages
as we noted in the last paragraph it is the nature of parsing incrementally that we do n t know what words are to come next
secondly all transitions between states occur on the input of a new word there are no em pty transitions such as the reduce step of a shift reduce parser
the inclusion of an extra feature list the h list which stores information about which arguments are waiting for a head the reasons for this will be explained later
and the kind of equivalence relation is indicated by a preceding icon for eq synonym and for eq near synonym
equivalence relations between the synsets in different languages and word netl NUM will be made explicit in the so called inter lingual index ili
as a baseline we did k best tagging of a test corpus
accuracy of that tagger was NUM NUM
so far we have not addressed the problem of unknown words
tm NUM this learner has also been applied to tagging old english
the first NUM transformations for unknown words
the 18th learned rule fixes this problem
these results are summarized in table NUM
the following classification problem is one example
given two comparable wordnet structures visualise the matching of the ilirs i.e. draw the lines between the ill records that are the same
current word is w and the preceding following word is x
in the experiments described below processing was done left to right
furthermore the addition of a new language will minimally affect any of the existing wordnets or their equivalence relations to this index
although there are substantial difficulties to be overcome in the development of phrase storage systems their potential for outputting responses relatively quickly suggests that they may be capable of meeting at least some of the more immediate social goals of aac users
regarding the multimodal interface itself quickset has undergone a proactive interface evaluation in that the studies that were performed in advance of building the system predicted the utility of multimodal over unimodal speech as an input to map based systems NUM NUM
although we had provided a multimodal interface for use in less hostile conditions nevertheless we needed to provide and in fact have provided a complete overlap in functionality such that any task can be accomplished just with pen or just with speech when necessary
quickset interface on the handheld pc is a getreferenced map of the region such that entities displayed on the map are registered to their positions on the actual terrain and thereby to their positions on each of the various user interfaces connected to the simulation
with this highly portable device a user can create entities establish control measures e.g. objectives checkpoints etc draw and label various lines and areas e.g. landing zones and give the entities behavior
leathernet simulations are created using the modsaf simulator NUM and can be visualized in a cave based virtual reality environment NUM NUM called commandvu see figure NUM quickset systems are on the soldiers tables
unlike many previous approaches to multimodal integration e g NUM NUM NUM NUM NUM speech is not in charge in the sense of relegating gesture a secondary and dependent role
the system combines speech and pen based gesture input on multiple NUM 1b hand held pcs fujitsu stylistic NUM which communicate via wireless lan through the open agent architecture oaa NUM NUM to modsaf and also to commandvu
we have developed the quickset prototype a pen voice system running on a hand held pc communicating through a distributed agent architecture to nrad s leathernet system a distributed interactive training simulator built for the us marine corps usmc
cheyer and julia NUM sketch a system based on oviatt s NUM results and the oaa NUM but do not discuss the integration strategy nor multimodal compensation
vo and wood s system NUM is similar to the one reported here though we believe the use of typed feature structures provides a more generally usable and formal integration mechanism than their frame merging strategy
profit has been implemented in quintus and sicstus prolog and should run with any prolog that conforms to or extends the proposed iso prolog standard
they are easily and unambiguously understood if there is only one unique path to the feature which is not embedded in another structure of the same sort
however when atoms from a finite domains are combined by the conjunction disjunction and negation connectives the specification of the domain can be omitted
while a prolog program consists only of definite clauses prolog is an untyped language a profit program consists of datatype declarations and definite clauses
3these clauses assume appropriate declarations for the sort elist and for the features synsem local cat subcat head dtrs and head dtr
all facilities needed for the development of application programs for example the module system and declarations dynamic multifile etc are supported by profit
sorted feature formalisms are often used for the development of large coverage grammars because they are very well suited for a structured description of complex linguistic data
as an example semantic representations in first order terms can be used as feature values but do not need to be encoded as feature terms
profit is not a grammar formalism but rather extends any grammar formalism in the logic grammar tradition with the expressive power of sorted feature terms
say yes say no say do n t know agree disagree evaluate good evaluate bad interrupt say thanks ask for expansion say wait a minute stall for time say a mistake was made in speaking
in explaining the difficulties faced by the deaf learner of english we do not propose that asl natives are fundamentally different from other learners of english as a second language rather we want to stress the view that english is for asl natives a fundamentally different and challenging language motivating the need to adopt a second language acquisition strategy toward facilitating the learning process
this approach entails however that a corpus has first to be pre analyzed ie
this lack of intermediate constituents has the added benefit that no spurious ambiguities can arise
one strength of n gram models is that they can capture a certain amount of lexical preference information
this is the final incarnation of the formalism being the state transition grammar of the title NUM
hand parsed and the question immediately arises as to the formalism to be used for this
introduction recent years have seen a resurgence of interest in probabilistic techniques for automatic language analysis
it should be capable of capturing fully the linguistic intuitions of language users
this penalty may easily be calculated according to the lengths of states in the parsed corpus
therefore when tuned to any particular language corpus the resulting grammar will be effectively finite state r
the person object contains slots only for the string representing the person name per name for string s representing any abbreviated versions of the name per alias and for strings representing a very limited rang e of titles per title
the percentage of personal pronouns is relatively high NUM compared to the test set overall NUM as is the percentage of proper names NUM on this text versus an estimate of NUM overall
two useful attributes for the equivalence class as a whole would be one to distinguish individual coreference from type coreference and one to identify th e general semantic type of the class organization person location time currency etc
it involves a three way distinction for enamex and only a two way distinction for numex and timex and i t offers the possibility of confusing names of one type with names of another especially the possibility o f confusing organization names with person names
note that the table below shows four top scores for muc NUM one for each language domain pair english joint ventures ejv japanese joint ventures jjv english microelectronics eme and japanese microelectronics jme
recurring problems in the system outputs include the information about whether the person is currently o n the job or not and the information on where the outgoing person s next job would be and where the incomin g person s previous job was
the artifact object which was not used for either the dry run or the forma l evaluation needs to be reviewed with respect to its general utility since its definition reflects primarily th e requirements of the muc NUM microelectronics task domain
given the more varied extraction requirements for the organization object it is not surprising tha t performance on that portion of the te task was not as good as on the person object3 as is clear in the figure org locale slot is filled
these results show that human variability on thi s task patterns in a way that is similar to the performance of most of the systems in all respects except perhap s one the greatest source of difficulty for the humans was on identifying dates
all but two of the systems posted f measure scores in the NUM NUM range and four of the systems were able to achieve recall in the NUM NUM range while maintaining precision in the NUM NUM range a s shown in the figure NUM
we start by presenting an example which is based on transfer between a syntactic representation and a semantic representation of the scoping of quantified nps
by changing the order in which we add the nominal arguments at the end of the derivation we can obtain all quantifier scopes in the semantics
the attributes are shown in the form name value
each annotator is assigned a name a string
returns nil if no documents are found in the collection
underneath this appear the annotations one annotation per line
these annotations all have to be described in further detail
normally used to iterate through all documents in a collection
maxstatus indicates the maximum value status may have for dciname
the proposed system has the weak language preservation property that is the defined synchronization mechanism does not alter the weak generative capacity of the formalism being synchronized
a node representing a vector vl is immediately dominated by the node representing the vector v2 which introduced the synchronization link that the synchronous production of vl rewrites
our proposal for the synchronization of two uvg dl uses the notion of locality in synchronization but with respect to entire vectors not individual productions in these vectors
the nominal arguments in the syntax are associated with pairs of trees in the semantics and are linked to two nodes the quantifier and the variable
we now apply productions for the bodies of the clauses but stop short before the two synchronous productions for the arrive clause yielding figure NUM
g simulates gs derivations by intermixing symbols of g and symbols of g and without generating any of the terminal symbols of g
for instance in japanese any change from kanji characters to kana or to romaji reliably signals a word boundary
NUM NUM NUM NUM difficulties posed by chinese chinese appears to be much harder than many other languages where information extraction has been attempted
in order to minimize impact on the system deployment schedule all reqtfired resources should be acquired prior to system development
one of the key goals of the tipster phase ii effort was to foster sharing of resources including code reuse
the message wader module determines message boundaries identifies the message header information and determines paragraph and sentence boundaries
new combinations of technical expertise and create new opportunities from past contract experiences where all work is done by the contractor
during test dialogues using the enhanced system average response time was NUM NUM seconds
the system had an average response time of NUM NUM seconds during the formal experiment
diagnosis subdialogue d establish the cause for the errant behavior
these types of experimenter interactions occurred on average once every NUM user utterances
nevertheless it provides a good first approximation of the nature of subdialogue movement
test subdialogue t establish that the behavior is now correct
similarly most human computer dialogues are collected from systems with a particular dialogue model
we next review the work of others who have examined issues in mixed initiative interaction
in a mixed initiative interaction initiative can vary between the participants throughout the dialogue
in order to examine the combined techniques we also introduce an algorithm for optimizing the settings this material is based in part upon work supported by the national science foundation under grant no
rather than using the forward and backward probabilities of speech recognition we use the analogous inside and outside probabilities x fl nj k and a nfk respectively
NUM NUM experiment NUM shallow parsing with
the operator x creates one large lexicon out of all the sublexica
to overcome this limitation we devised the mistake driven mixture algorithm summarized in table NUM which constructs t context trees and outputs the final tag model
the approach involves collecting a suitable corpus of text analyzing that text implementing the results of the analysis in a text generator and verifying the output of the generator
for precondition expressions the most common form in our corpus is the fronted if present tense clause which occurred in NUM of the NUM precondition expressions in the corpus
NUM these tables show the percentage of NUM the training corpus included some non procedural text that was included for a pilot study done before the focus on procedural text had been determined
an example of this verification process can be found in section NUM NUM in which the imagene produced remove phone text is shown to match the original text on all four of these issues
this analysis involves determining the range of forms used in the corpus and then using an iterative cycle of hypothesis formation and testing to determine the communicative contexts in which each is used
an important task of the text generation researcher is to inform the text generation process with a specification of both the range of these forms and the contexts in which they are used
this additional testing serves both to disallow over fitting of the data in the training portion and to give a measure of how far beyond the telephone domain the predictions can legitimately be applied
this fronting of contrastive purposes occurred in our corpus in the context of three oppositional semantic situations NUM initiating ending NUM allowing preventing and NUM activating deactivating
this is a representational manifestation of the hierarchical nature of the processes themselves and is displayed graphically by extending the horizontal line of a span to cover all of its subordinate spans
this approach would be difficult in the analysis of certain more complex texts such as persuasive texts but proved to be adequate in the study of local structure in instructional text
thanks also to philip resnik for writing the spanish tokenizer and hand aligning the spanish english training bitexts
currently NUM NUM user utterances have received a full syntactic and semantic analysis
now the resulting tree bank embodies a function from cfg rules to semantic rules
note that the algorithm can not be guaranteed to achieve full semantic determinacy
iteration continues until the increase in semantic determinacy is below a certain threshold
within this program the ovis NUM tree bank is created
let us illustrate this with a very simple imaginary example
in this section we give a very short impression
regrettably the tree bank is not available yet to the public
every woman a man figure NUM imaginary corpus of two trees
semtags is mainly used for correcting the output of the dop parser
agent b at which time do you want to leave from merano to milano
users NUM and NUM correspond to the dialogues in figures NUM and NUM respectively
the mean performance of a is NUM and the mean performance of b is NUM
nounpreposition noun verb preposition noun adjectivepreposition noun links
we will argue that the paradise paradigm for dialogue system evaluation framework has several advantages over other proposals
given the current state of knowledge many experiments would need to be done to develop a generalized performance function
the factor utt was not a significant predictor of performance in part because utt and rep are highly redundant
thus the observed user agent interactions are modeled as a coder and the ideal interactions as an expert coder
the egraph similarity metric utilizes a weighted sum of factors
for language pairs in which lexical cognates are frequent a cognate based matching predicate should suffice
terminology extraction is triggered after pos tagging
the person egraphs extract and fill the title slot
the abstracts created by group b for the general article in figure NUM a the three most important sentences roughly NUM NUM of the article determined by using the weight sets NUM and NUM are listed in figures NUM b and c respectively
NUM examples of special expressions used to determine rhetorical relations are listed below example tatoeba for instance nado etc etc adverse sikasi but tokoroga however etc comparison koreni taisi while etc
encode the template specification according to the task specification
the date tagger is fast sinc e the pattern matcher itself is highly optimized and since the lex based front end does not actually tokenize th e input or fire the pattern matcher unless it suspects that a date phrase may be occurring in the text
a statistical approach to this problem is to use scfg rules extracted from treebank and set a probability score scheme for disambiguation
NUM to use scfg rules as a main disambiguation knowledge will cut down the hard work to manually develop a complex and detailed disambiguation rule base
kurt dusterhoff created training data and conducted valuable testing for nametag
the number of recognition rules for each configuration is also shown
the notion that this ambiguity problem in hebrew is very complicated and that it can be dealt with only by using vast syntactic and semantic knowledge has led researchers to look for solutions involving a considerable amount of human interaction
the same word wi in a different text may have of course a different right analysis thus right and wrong in this case are meaningful only with respect to the context in which wi appears
by propagating facts in this way we can dramatically simplify the process of collating information into templates since all the information relevant to say an individual company will have been attached to that company b y equality reasoning
adding the results of the rules to those of the speakers leads to a slight decrease in kappa for tr1 but progressively better though only from NUM NUM to NUM NUM values for kappa for tr2 and tr3
nevertheless the elements in the sw sets are not determined for each analysis separately but rather are generated automatically for each analysis by changing the contents of one or several morphological attributes in the morphological analysis
should distracting elements occur in a sentence a sufficiently distinguishable description is required for a subsequent reference within the sentence instead of a reduced one even if it has been mentioned previously in the sentence for example yuanwan the round bowl in 2d and fangwan the square bowl in 2e
these seem to involve places where the speakers were more willing to use a zero pronoun where the system used a reduced nominal anaphor and where the speakers reduced nominal anaphora less than the system did
this is due to the fact that the morpho lexical probabilities are not supposed to be used alone for disambiguation but rather are meant to serve as one information source in a system that combines several linguistic sources for disambiguation
hence there is a difference of about two hundred words between the acoustically and linguistically segmented test sets
the government reviewed the framework objectives and plans for the following two years of work
NUM p1 NUM NUM p2 NUM NUM p3 NUM NUM NUM because of technical reasons we can not decide whether a given word is ambiguous or not when we automatically generate the words for the sw sets
because of the nature of the constraints it is unclear how the expressiveness relates to for example the more powerful unification based grammars that are widespread for english
in this paper we describe an approach to parsing that has evolved as a result of the problems we have encountered in making the transition from english to chinese processing
in general we may have several occurrences of the same nonterminal and it is occasionally useful to be able to constrain those occurrences to match exactly the same string
the sentences were randomly selected in various length ranges of NUM NUM NUM NUM NUM NUM NUM and NUM NUM words such that each of the five ranges contained NUM sentences
the above monotransitive rule would still be considered by the parser since it is performing partial parsing and this rule matches the subsequence
thus the monotransitive subparse might incorrectly be chosen for the partial parse output whether this happens depends rather arbitrarily on the possible subparses found over the rest of the sentence
this is used to remember the string that was matched to a constituent a so that the string can be compared to a subsequent appearance of a i in the same production
note that this feature must not be used with rules that can cause circular derivations of the type a v a since this would lead to a logical contradiction
development workbench idwb an sgml based
many verbs can also be nouns and vice versa
we are assuming a strictly syntactic approach
take for example the word beef
furthermore facilities for user defined controlled vocabulary are available
the dictionaries support three different types of vocabulary checks
thus in the datrl theory above information about the plural suffix is stated once and for all at the abstract noun node
an example proof showing thai dog plur evaluates to dog s given the datr theory presented above is shown in figure NUM
however in the datra theory global inheritance is used to capture the relevant generalizations about the singular and plural forms of nouns in english
the process continues until the top level proposed beliefs are evaluated
based on the information obtained in step NUM select
unfortunately the definitions set out in these papers are not general enough to cover all of the constructs available in the datr language
be used to support bel and applying filtering heuristics to them
associated with each belief is a strength that represents the agent s confidence in holding that belief
our system maintains a set of beliefs about the domain and about the user s beliefs
in addition numeric entries are also removed as stopwords although one can often detect a sequence of them and have it identified as a number
this language is NUM redundant but standard left to right prediction will not work well NUM keystroke saving with a menu size of NUM with the algorithm used here
this allows minor editorial differences and choice of markup for terminal elements to have no effect in overall alignment
figure 4b reference function and best fitting score function with estimated parameters
n h and k are the initial parameters of the algorithm
sense n lo stock gillyflower flower
sense n NUM malcolm stock stock flower
sense n NUM neckcloth stock cravat
selected synsets for the words bank business market and stock
for sake of brevity the algorithm is not further explained here
categories are generated with h NUM NUM and k l NUM
sense n NUM broth stock soup
we do as if we wanted to generate the entire set of sentences at a distance less than or equal to the threshold
t saussur points out wha t he calls analogy given two forms of given word and ouly one form of a second word it is possil h
course this naive solution implies an exponential explosion but fortunately it is not ne essary to consider the entire set of sentences neith r to generate them
the proposed technique falls under the example based approaches to natural language processing hut we think it may be safer than previous methods because it relies on more information and linguistically founded information
similarly to prefixing and suffixing our formalisation accounts for linguistic examples of infixing a phenomenon well illustrated by semitic languages NUM here the replacement of an a by an i
nouns rdactionnaire reactionary adje tivc rdprcsdeg sionnaire souuds perh tly i rench but will not be tound in diction u y
in ancient greek aoto is always taken as a model for the declension of tile 1st group of masculine nouns although any other word from the same group would have been as good
montague also proposed to capture purely extensional scope ambiguities using quantifying in
NUM a every man loves a woman
particles and conjunctions and modifiers and adjuncts in addition it is possible to a sign specific lexical penalties to individual words
while the system does manufacture acronyms as potential secondary references when certainpattems match the pattern which enabled it to determine that creative artists agency was a commercial organizatio n was unfortunately not one of them
since the contents of the database is central to achieving high quality translations it is usually necessary to adjust it manually in response to errors in the translation
b every representative of a company saw most samples
izfor example a below lacks such a reading
a has three quantifiers and there are NUM different ways of ordering them
NUM p e i p e p iie p i
in practice this cost can be mitigated somewhat by clustering and indexing schemes for the example databa se
in each of these phases different information elements play a crucial role
the table contains the following information the percentage of occurrences of the particular phrase the overall accuracy for that phrasal category and the accuracy for each of the three reliability intervals
with one two three and four information elements respectively
this is because c NUM part2 is moved frequently
kim bagsa neun migug eise NUM nyengan gongbuha essda dr
NUM typology of pn contexts NUM NUM
figure NUM local grammar of type ii
by adding a few more pts cf
as shown in figure NUM this can be represented as a finite state automaton that consists of a single state with a cycle from into this state for all lexical rules
in particular in case the lexical entry has t2 as the value of c we need to ensure that the value of the feature z is transferred properly
since there is no machine that can read the generated texts and give an impartial judgement about them we rely on the opinions of human readers who are native speakers of chinese to investigate the quality of the generated anaphors
for convenience we summarise the occurrence of anaphors in the test texts in a graphical form in fig NUM in the figure each box represents a clause and at the right end is the accompanying punctuation mark
these seem to involve places where the speakers were more willing to use a zero pronoun where the system used a reduced nominal anaphor and where the speakers reduced nominal anaphors less than the system did
adding the results of the rules to those of the speakers leads to a slight decrease in kappa for tr1 but progressively better though only from NUM NUM to NUM NUM values for kappa for tr2 and tr3
however there are three topic shifts within the sentence namely clauses NUM and NUM NUM and NUM and NUM and NUM as shown in fig NUM the shifts would make the rule containing the salience constraint tr3 obtain different output from those without this constraint tri and tr2 obtain the same matching rate NUM
in the following we use cij to denote the text indexed j generated by the system equipped with rule tri where i is NUM to NUM and j is NUM to NUM and hkt to denote the resulting text indexed l of speaker k where k is NUM to NUM and l is NUM to NUM the comparison work is summarised procedurally as below
the representation of lexical information in a constraint propagated covariation lexicon makes the maximum information available at lexical lookup while requiring a minimum number of nondeterministic choices to obtain this information
the authors wish to thank thilo g6tz and dale gerdemann erhard hinrichs paul king suresh manandhar dieter martini bill rounds and the anonymous reviewers
in the evaluation work the anaphors in five test texts generated by three test systems employing generation rules with different complexities vere compared with the ones in the same texts created by twelve native speakers of chinese
once the follow relation has been obtained it can be used to construct an automaton that represents which lexical rule can be applied after which sequence of lexical rules
the phrase interpreter is controlled by a small set of lisp interpretation functions roughly one for each phrase type
finally the location label on hollywood was set by a predicate that inspects the tried and not so true tipster gazetteer
it is important to note that the search strategy in the phraser differs significantly from that in standar d parsers
each rule in the sequence is applied in turn against all of the phrases in all the sentences under analysis
table NUM summarizes slot by slot differences between our training and test performance on the te task
fully half the problem instances were due to string matching issues for short name forms
np patternst the agency with billings of NUM million NUM mis org
one thing is clear to us however and that is that rule sequences are an extremely powerful tool
of course lexical coverage by itself does not guarantee a good translation
the tagger is in fact a re implementation of brill s widely disseminated system wit h various speed and maintainability improvements
table NUM tagging a text with the lexicon line NUM and contextual rules line NUM
four scorers and seven years ago the scoring method for muc NUM
this can be concluded since these systems show bigger differences than the others
finally one member of geminate pairs is deleted
secondly many languages have undergone some spelling reform
the required output phoneme string depends on the application
NUM NUM normalization for french from graphemes to graphemes
the rules could be written as shown below
an efficient rule set had to be developed
this distribution can be compared to error
figure NUM simr s noise filter ensures that tpcs
the program begins a t the roots of the source and target trees and NUM roeeeds top down reeursively filling a matrix of scorcs
a stop list of function words is also helpful
simr builds bitext maps one chain at a time
this left output context is written between angled brackets
telegraph is on second position followed by langenscheidts t1 and power translator
words in any language are dynamic and one can never capture all chinese words in a lexicon for segmentation purposes
it has been pointed out that the paragraph size trec queries are long and unrealistic because real life queries are usually very short like one or two words
this second retrieval in general can provide substantially better results than the initial if the initial retrieval is reasonable and has some relevants within the d best ranked documents
the basic cycle followed by the dialog controller is shown below
NUM knowledge about theorems for proving completion of goals
the mechanisms for finding this least cost match will be described next
reflecting this experimentally determined result the cost computation was revised to
the central mechanism of our architecture is a prolog style theorem proving system
smith hipp and biermann an architecture for voice dialog systems
some of these include question location knob
the newly inserted subgoal causes the voice output of utterance NUM
the computer still has dialog control but not as strongly
the use of expectation can also be illustrated by the example
ats was in its final delivery phase at the same time as our muc NUM development
would not have caused it to make that decision but th e j
reduction the reduction components each consist of one or more stages of applying the nltoolset s pattern matcher to phrases
apparent counterexamples to the generalization can be explained by the well known distinction between referential and quantificational np semantics
finally lexical analysis splits the token sequence into sentences including one each for headline dateline and date
in this article the only secondary reference is caa as a reference to creative artists agency
the solution clearly is to apply some of the perso n patterns before the organization patterns
org country united states org locale hollywood city org type company org name
per name john dooner per alias dooner person NUM NUM per title mr
james would enable later recognition of the more problematic barnaby jones and james
figure NUM every dealer shows most customers three cars one sample derivation investigate two dialects of
we can show however that by making use of the obliqueness hierarchy of
some of these readings become unavailable when the sentence contains coordinate structure such as one below
figure NUM shows logical forms that can be derived in the present framework from geach s sentence
as we have pointed out earlier the reading does not generalize to quantified nps in general
here arguments are not presented in succession to their function contrary to the present generalization
but from a practical perspective subparts of category members might also be acceptable
the advantage of this approach is that words are evaluated in context
such interrelation is effected by the rule NUM which allows a bracket pair of one system oj to be replaced by that of another system oi just in case the latter s system exhibits greater freedom of resource usage as indicated by the relation which orders the sub null systems thus r lcb o e rcb r
let a denote the probability of the a production with fanout degree f
in such cases the system opens an editing window with the input sentence and asks the user to correct or modify the sentence
the cogenerator operates by combining the constraints specified by the template s with the general constraints of the grammar to produce an output sentence guided by statistical information
we propose that the agents are in a mental state that includes not only an intention to achieve the goal of the collaborative activity but also a plan that the participants are currently considering
even the apparently simple linguistic task of referring in an utterance to some object or idea can involve exactly this kind of activity a collaboration between the speaker and the hearer
this allows the agents to interact so that neither assumes control of the dialog thus allowing both to contribute to the best of their ability without being controlled or impeded by the other
peter a heeman and graeme hirst collaborating on referring expressions used to replace some of the actions in the referring expression plan with new ones and the second is to add new actions
traum and hinkelman NUM sidner NUM
the algorithm also illustrates how the model of clark and wilkes gibbs minimizes the distinction between the roles of the person who initiated the referring expression and the person who is trying to identify it
this has allowed us to do the following
next it evaluates the constraints of each derivation
topic focus identification when implemented together with a simplified parser NUM the algorithm was checked with a set of sentences having our examples NUM NUM as its core and it yielded the expected results as presented in section NUM
in particular stochastic choices are independent of other choices at the same time step each process evolves independently
there are also grammatical rules such as those concerning the positions of the verb e.g. in the second position in german of the adjective or another modifier before or after the head noun in a noun group and of clitics
pp v NUM appears in sentences NUM
figure NUM constructing a dependency structure by
the translations were compared by check
discourse appears in sentences NUM NUM NUM
step NUM joining partial parses on the basis of
table NUM shows an example of such information
the experimental results obtained by using this method with technical documents offer good prospects for improving the accuracy of sentence analysis in a broad coverage natural language processing system such as a machine translation system
table NUM gives the result of our experiments on two technical documents of different kinds one a patent document text NUM and the other a computer manual text NUM
i would also like to thank taijiro tsutsumi masayuki morohashi koichi takeda hiroshi maruyama hiroshi nomiyama hideo watanabe shiho ogino and the anonymous reviewers for their comments and suggestions
one of the most striking things about the problem is that there are generally a great many sentences which provide reasonable descriptions of any given model
for example the sentence three men lifted two boxes has an interpretation in which three men combined their efforts in a single act of lifting two boxes
the description given of the algorithm is based on binary predicates for the sake of brevity and clarity but the generalisation to predicates with three or more arguments is not difficult
mergeannotations annotationset annotationset annotationset returns the union of the annotations in the two annotationsets
alternatively the application can create a new document with a new rawtext property which incorporates these modifications
the resulting document is in a normalized sgml with all attributes and end tags explicit
this uses collection which to determine which documents to process and collection destination to record the annotations
the tipster architecture defines a number of standard annotations these are divided into structural and linguistic annotations
however a tipster system is free to create and use any other annotation types that it wishes
a paragraph may be divided into sentences the s annotation type will be used to identify sentences
functionally this operator is like an or operator the document must contain one or more arguments
thus the formal specification of a set of template objects corresponds to a set of annotation type declarations
some applications may want to link an individual slot in the template object to text in the document
first those lob tags which are similar to susanne tag are selected
the tagging set of susanne corpus is extended and modified from lob corpus
different constructions make different assumptions about the status of entities and propositions
none are because copy action is the most salient action of copiers
chomsky book the yellow book a math book
b their escape had been lucky bill found it uncomfortably narrow
in particular abstract entities are introduced to represent the scopes of operators
the experimental results show that the number of multiple tags is large
column three in table NUM denotes the correct mapping to lob tags
for the first chunked result we can obtain the following contingency table
similarly the following contingency table is obtained for the second chunked result
based on this contingency table NUM is defined as follows
its syntactic structure for the first seven words is shown in figure NUM
in the following sections we first introduce the experimental framework of our model
very few large scale treebanks are currently available especially for languages other than english
with respect to the linguistic components current efforts focus on such tasks as retrieving ponctuation and use of stochastic information to rank parses
if such words are polysemous the different meanings should be relatively easy to distinguish on polysemy in wordnet subsumes homonymy as well as polysemy however the latter is far more common in most cases the different senses of a word are semantically related
furthermore application of our model to dialogues in various other collaborative environments consistently increases the accuracies in the prediction of task and dialogue initiative holders by NUM NUM and NUM NUM percentage points respectively compared to a simple prediction method without the use of cues thus illustrating the generality of our model
however surge also includes aspects of lexical grammars such as subcategorization
the input specification received by the lexical chooser is shown in figure NUM
this notation allows for a modular notation of large grammars written in fuf
other fuf constructs are introduced as needed in the rest of the paper
NUM thematic structure involves roles such as agent patient instrument etc
these tasks indicate the kind of information the syntactic grammar needs as input
for example subject verb agreement in a sentence is described by the fd
we show control techniques we have developed within fuf to reduce overall search
surce ignores the lex cset features and recurses according to the cset declarations
elhadad mckeown and robin floating constraints in lexical choice figure NUM
for nouns we found no significant increase in the number of agreed upon choices when they were at the top of the list of alternative senses indicating that the taggers were fairly sure of their choices independent of the order in which the different noun senses were listed in the dictionary
although the system s performance varies across environments the use of cues consistently improves the system s accuracies in predicting the task and dialogue initiative holders by NUM NUM percentage points with the exception of the maptask corpus in which there is no room for improvement tm and NUM NUM percentage points respectively
however instead of determining whether an adjustment is needed the counter determines the amount to be adjusted
in addition we applied our baseline strategy which makes predictions without the use of cues to each corpus
we now describe the extensions but first define some notation used throughout the paper
if c s category does not equal to g s category return NUM
right hand side contexts we introduce right hand side contexts to improve rule applicability decisions for complex compounding phenomena
our analysis shows that the task and dialogue initiatives shift between the participants during the course of a dialogue
the user can specify an ordering on type expansion
throughout the paper we will be using hpsg style avm notation for descriptions
this is almost what we want but not quite
this simple extension also allows us to process cyclic queries
our interpreter at the moment is a simple left to right backtracking interpreter
at the moment we allow the user to specify minimal control information
but this is no problem since the hiding features get introduced anyway
we believe that this view would not simplify the overall picture however
the low and high f measure scores from the formal test held in late april represent the current performance of the systems in this experimental evaluation
nonetheless as one rough measure of progress in the area of information extraction as a whole we can consider the f measures of the top scoring systems from the for muc NUM and muc NUM st tasks note that table NUM shows four top scores for muc NUM one for each language domain pair english joint ventures ejv japanese joint ventures jjv english microelectronics eme and japanese microelectronics jme
if not enter t x into rhs
the wml account of parsing complexity predicts that a right branching svo language would be a near optimal selection at a stage in grammatical development when complex rules of reordering such as extraposition scrambling or mixed order strategies such as vl and v2 had not evolved
morphological disambiguation of a text t is done by indicating for each ambiguous word in t which of its different analyses is the right one
in particular this comprises subject object asymmetry the demarcation of local domains and surf ace
according to NUM the barber who shaved himi told the client a story
current approaches avoid gen rate and te st y resorting to different strategies
sb the barberi told the clientj a story while he shaved himz
as can t e demonstrated by fllrth r e xamples e.g.
the lnaill question concerns the adequate implementation of chomsky s i inding t rinciples
two binding constraint verification procedures are employed which differ in the handling of type a nps
the antecedents for hint and size are identified uniquely as father and daughter respectively
for each decision its dynamic compatibility with the earlier more plausible deci
on the other hand revise leaves open whether paul or someone else is the decider
this approach demonstrates a way to extract very useful and nontrivial information from an untagged corpus which otherwise would require laborious tagging of large corpora
the category sequences representing the sentence types in the data for the entire language set are designed to be unambiguous relative to thi s greedy deterministic algorithm so it will always assign the appropriate logical form to each sentence type
the resulting parse forest grammar will be too general most of the times
then we compute the intersection of the skeleton with the input fsa
lastly although the improvements exhibited in subject h were small they motivated him to increase his writing significantly
figure NUM the first NUM utterances from the childes database used to test the algorithm
each word consists of a phone sequence and a set of sememes semantic symbols
figure NUM contains the first NUM utterances from the database
the right NUM were selected randomly from the NUM entries
these text utterances were run through a publicly available text to phone engine
this research is supported by nsf grant NUM asc and ar pa under the ttpcc program
such output could be used as input to many grammar acquisition programs
it maintains a single dictionary a set of words
we have implemented a simple algorithm as an exploratory effort
the combination of pos disambiguator and morphological analysis suffice to provide the contextually most likely analysis nearly all the time
if this is the case the information of this exeme is printed in pretty form on the screen
certes le eonlntultisme r t tc ljours dsigt d comme l adversaire principal le dangt n
only the bible and the reat y of maasl richl ill bilingual orlll
whereas without such an ordering special care is needed to prevent a compositional translation in cases where a more specific noncompositional translation also exists
fhture work will include tim automatic acquisition of transfer rules fronl tagged bilingual corpora to extend tim coverage and an integration of domain specific dictionaries
alignment of terminal elements we want to compute the minimal set of differences between w l and w r i.e. a monotone bijective partial function defined as follows NUM let NUM be the largest subset of i x j for NUM i length w l and NUM j length w r such that NUM is monotone and bijective and
output of the procedure the following information may be output from this procedure in the form of tables of subtree indices indicating strict alignment of two trees a table of pairs of sequences of subtree indices indicating potential alignment of pairs of terminal element indices i.e. the function NUM and of single terminal element mismatches for later processing to detect consistent differences in markup
this produced a list of NUM words that appeared in their own definitions with a different part of speech
although the disambiguator is very accurate it does n t always assigns the right tag to a word
despite the fact that the german bei phrase is analyzed as an adjunct it is treated exactly like the argument arg3 which is syntactically subcategorized
the context might either be the local context as defined by the current vit or the global context defined via the domain and dialog model
we found NUM grouping morphological variants makes a significant improvement in retrieval performance NUM that more than half of all words in a dictionary that differ in part of speech are related in meaning and NUM that it is crucial to assign credit to the component words of a phrase
not all errors made by the tagger cause decreases in retrieval performance and we are in the process of determining the error rate of the tagger on those words in which part of speech differences are also associated with a difference in concepts e.g. novel as a noun and as an adjective
these experiments provided strong support for hypotheses NUM and NUM word meanings are highly correlated with relevance judgements and the corpus study showed that there is a high degree of lexical ambiguity even in a small collection of scientific text over NUM of the query words were found to be ambiguous in the corpus
so for example if we are distinguishing words by part ofspeech and the query contains diabetic as a noun the retrieval system will exclude instances in which diabetic occurs as an adjective unless we recognize that the noun and adjective senses for that word are related and group them together
therefore we discuss in the following how to loosen the constraint
in each run one of the m blocks of training data is set aside as test data the holdout set and the remaining m NUM blocks are used as training data
we found that for NUM words out of the NUM words the minimum expected error rate of pebls using the best k is still higher than the expected error rate of the most frequent classifier
in this section we deliberately will not go into details of training and evaluation this will be the subject for a separate paper but rather we will concentrate on the feasibility of the proposed method for real world applications
x s thus we will consider as possible constraints for the model only those nodes whose NUM y feature frequency counts are greater than a certain threshold e.g. z NUM
finally the relational slru ture is mapped onto the level defining tim constituenl slructure of lhe target language sentent e
for example if a configuration abcd comes from a training sample and it is still not in the to lattice we create a node abcd and set its configuration frequency abcd to NUM
the nodes dr NUM c and mr NUM c are the reference nodes they were observed in full NUM and have non zero configuration frequency counts ur s c
usually both the dependent element and its head are implicitly and ambiguously present in the constraint grammar type of rule
in st one could produce a succession relation even if the person name or organization name is absent
in this case both disambiguation and linking can be done at the same time with command select and keyword head
again we performed no morphology manipulations or reference resolution steps which would improve the resulting scores
for this reason we defined sentence yield as the average number of different topic keywords mentioned in a sentence
NUM the wh pronoun is a clause boundary marker but the only reliable means to find its head is to follow the links
like several other groups we are pioneering research in automatically learning to extract information based on examples
ug varies inversely with recall og varies inversely with precision and err varies inversely with f score
there is design and implementation level documentation for the system as well as a comprehensive user s manual
reference resolution is performed and the relevant semantic objects are unified in the database as a result
in both cases we compared the sentences extracted according to the opp to the sentences contained in the human generated abstracts
it is clear that only about NUM of topic keywords are not mentioned in the text directly
figure NUM plum system architecture rectangles represent domain independent language independent algorithms ovals represent knowledge bases
figure NUM cumulative coverage scores of top ten sentence positions according to the opp with win
tests of the same type were then administered at three separate sessions with two months of training between each test
the intersection of the two methods increases the number of rejected senses to five
the following section describes methods for grouping multi word term variants section NUM presents a linguistically motivated method for lexical analysis inflectional analysis part of speech tagging and derivational analysis section NUM explains term expansion methods constructions with a local parse through syntactic transformations preserving dependency relations section NUM illustrates the empirical tuning of linguistic rules section NUM presents an evaluation of the results in terms of precision and recall
edward therefore had to ask the user whom do you mean with her
NUM noun noun variations relations such as result agent fixation de l azote nitrogen fixation fixateurs d azote nitrogen fixater or container content rdservoir d eau water reservoir rdserve en eau water reserve are found in this family
eci is a subset of the european corpus initiative data composed of NUM NUM million words of the french newspaper le monde agr is a set of abstracts of scientific papers in the agricultural domain from inist cnrs NUM NUM million words
although dtable cowshed is incorrectly related to dtablir to establish it is very improbable to find a context where dtablir co occurs with one of the three words found in the three multi word terms containing dtable nettoyeur cleaner alimenration feeding and liti re litter since we focus on multi word term variants overgeneration does not present a problem in our system
it actually shows that there was only a very small interdependency among the features and not surprisingly our model hnproved only about NUM NUM over a simple bayesian classifier achieving just under NUM precision
note also that on standard x bar assumptions the attachment of post modifiers may be derived via lowering at an x i node
this means that arguments which are incorporated via simple attachment will be attached preferentially to adjuncts which are incorporated via lowering
this would suggest that a reasonable search strategy for english would be to search the set of accessible node in a bottom up direction for english
we model this decision as a search for a node in the tree at which an explicitly defined parsing operation tree lowering may be applied
with reference to english and japanese processing data we show the importance of this search for empirical adequacy of the psycholinguistic model
let m be the result of substituting all instances of n in l with r the attachment node a is unified with at
however intuitively of the above sentences it is only NUM which causes the conscious garden path effect s
japanese presents a challenge for any incremental parsing model because typically it is not possible to determine where an embedded clause begins
however even if we can incorporate the required preferences into the parser the constraint of incrementality will force us to make the decision on encountering that
an example adapted from mazuka and itoh is the following NUM yamasita ga yuuzin wo o oi houmonsita kaisyai de mikaketa
NUM pick a word from the vocabulary
the reshuffling process we adopted is quite simple
NUM put the class in the c NUM nd position into the merging region and shift each class after the c NUM nd position to its left
NUM outer clustering replace all words in the text with their class token NUM and execute binary merging without the merging region constraint until all the classes are merged into a single class
hierarchical clustering of words and application to nlp tasks
take for example the following sentences
to see the effect of introducing word bits information
each subset conditioned on the feature value
the results are plotted at zero clustering text size
we can then calculate conditional probability distribution of tags for
the document handling step has four main flmctions null format preservation input to docuinent handling is a text from a text processing system which has been marked up in sgml
this means that the output from one rule application or one application cycle is used as input to a new cycle which starts at the beginning of the rule set
therefore the docmnent handler attempts to arrive at a meaningflfl partition of the sentences by identifying sentence internal boundaries and submitting the individual subparts for translation
care has been taken o t resent lhe mosl frequenl and therefore ntosl t robable answer on tim lop of the
the transfer representation is a reflection of tile argument structure of the predicates where iuformation about surface syntactic realization appears as features on the individual nodes
since patrans is based on the transfer translation model tile surface strings of the text are sequentially transformed into an interinediate representation defined by several mapping principles
patrans is making the translation process faster and more efficient and it has proven to be a good business for lingteeh saving around NUM of the raw translator cost
if the sentence is fail sorted one intermedi null ate analysis is picked from the chart which means that all words may not have been disambiguated properly by the grammar rules
if however the words have been disambiguated and impossible readings have been discarded prior to parsing the best fit parse is considerably better than it would otherwise have been
NUM that galoot in the corner that thinks john likes mary s for a chart parser where each chart cell stores the analyses of some substring this strategy says that all analyses in a cell are to be semantically distinct
the management of cached nfs in steps NUM NUM and especially NUM ensures that duplicate nfs never enter the oldnfs array thus any alternative copy of a nfhas the same array coordinates used for a nfitself because it was built from identical subtrees
karttunen s in NUM method must therefore add NUM NUM representative parses to the appropriate chart cell first comparing each one against all the previously added parses of which there are NUM NUM NUM on average to ensure it is not semantically redundant
that is whenever the lexicon contains an entry of a certain cate null gory x with semantics x it also contains one with say category t t x and interpretation ap p z
if we slightly constrain the use of the grammar rules the parser will still produce 5c and 5d constituents that are indispensable in contexts like NUM while refusing to combine those constituents into 5f
it is not hard to see that 7a eliminates all but right branching parses of forward chains like a b b c c or a b c c d d e f g g h and that tb eliminates all but left branching parses of backward chains
however it is still a perfectly good data structure that can be maintained outside the parse chart to serve 5for the proof to work the rules s and t must be available in the restricted grammar given that r and q are
however we can take advantage of the core result of this paper theorems NUM and NUM to do karttunen s redundancy check in o NUM time no worse than the normal form parser s check for fc and be tags
computational linguistics volume NUM number NUM representing discourse knowledge for a broad range of discourse genres and domains
NUM example behavior to illustrate the behavior of the system consider the concept of embryo sac formation
we conjecture that most implemented explanation generators would meet with serious difficulties when applied to a large scale knowledge base
to thoroughly exercise knight s organizational abilities we were most interested in observing its performance on longer explanations
kb accessor arguments description of view as kind of concept finds view of concept as a kind of reference reference concept
error handling when they detect an irregularity they return appropriate error codes to the explanation planner
term accommodation they tolerate specialized and possibly unanticipated representational vocabulary by exploiting the relation taxonomy
since the time of aristotle a central tenet of rhetoric has been that a rich structure underlies text
during sperm cell transport NUM angiosperm sperm cells are transported from the pollen tube to the embryo sac
for example the concept photosynthesis can be viewed as either a production process or an energy transduction process
the family typ e control is also used determined during semantics by inference possible values include human organization temporal quantity and location hence these values are used to distinguish the type of concept which ha s been created and subsequently the kind of markup added to the input text
the amount of interaction and timing of translation trigger is completely up to the user and s he can even proceed without any modification to the system s initial choice
if the updated graph is not conneeted then the proposed wfss can not form part of a complete sentence
from the grammar the only constituents that can combine with dog are vp vtra and p
un tteccssitry wiss it will i e l ossible to use indices in lexical signs
the abow pruning tcchnique has been tested on bags of different sizes including different combinatkms of modifiers
assume that the next wfss to be constructed by the generator is the np the dog
ills idea is to mainl a il a queue of rood liable constituents e.g.
exploratory work employing adjacency constraints during generation has yielded further improvements in execution time when applied in conjunction with the pruner
null when a new wfss is constructed during genera ion say by application of the modified fimdame ntal
rhe technique relies on a connectivity constraint between the semantic indices associated with each lexical sign in a ba r
each type is responsible for building one of the relations shown in table NUM
create a context vector to formally model the context
the user supplies an answer inform which the system believes to be homburg
above we discussed which structures can be successfully aggregated into more abstract goals and more compact utterances
g sys what is the destination of your call
d usr the rate of a call to frankfurt
hamburg figure NUM a parse tree and a continuation
queries about the cost of telephone calls is currently being implemented
e sys frankfurt am main or frankfurt an der oder
j usr yess k sys frankfurt am main or frankfurt an der oder
user how much is a call from bonn to homburg at NUM o clock
the planning cycle is implemented with an augmented finite state machine
in our opinion a definition is complete only if any phenomenon in the problem domain can be properly described defined
we have proven that under a complete dictionary assumption critical points in sentences are all and only unambiguous token boundaries
if a grammar revision in turn leads to additional syntactically ambiguous points such a revision would be in the wrong direction
the category also says that for this kind of vp match NUM the term in the antecedent whose category identifies it as being the subject should be treated as parallel to the explicit term in the ellipsis
the ellipsis can be represented as NUM p term s NUM ay name y simon which is conjoined with the antecedent
the order in which substitutions apply instead depends on the order in which the expressions occur when making a top down pass through NUM such as one would do when applying semantic evaluation rules to the formula
briefly the standard view within formal semantics which dsp inherit identifies semantic interpretation with composition interpretation is the process of taking the meanings of various constituents and composing them together to form the meaning of the whole
we do not get a sixth implausible reading provided that in the first clause his is resolved as being coindexed with the term for john i.e. that john and his do not both independently refer to the same individual
the coverage is basically the same as dsp s antecedent contained deletion a sloppy substitution for every person that simon did in the sentence john greeted every person that simon did results in re introducing the ellipsis in its own resolution
we consider german to be an sov language i.e.
das kind hat die alte frau besuchen wollen
in ease it is not available the new argument is inserted into the provisional argument table and its interpretation can be checked only later when the argument structure is available
the second modification occurs after argument trace insertion
the subject die frau is inserted into this argument table as the surface subject of scheirtt without a thematic role and in addition as an uninterpreted argument of a following infinitival complement
chomsky lasnik NUM haegeman NUM for a presentation of government and binding theory gb and berwick et al NUM wehrll NUM for a possible implementation of the theory
obviously any entities realized in un that are not realized in un t including the cb un as well as the highest ranked element of cdun do not affect the applicability of rule NUM
in NUM taking the boat to be the highest ranked cf and hence the most likely referent for the silly thing in the second clause of utterance d yields a coherent and easily comprehensible discourse
however because the performance function is developed on the basis of testing the correlation of performance measures with an external validation criterion user satisfaction significant metrics are identified and redundant metrics are eliminated
such a violation occurs in the following sequence presumed to be in a longer segment that is currently centered on john cf also examples NUM and NUM in section NUM NUM a
it also can not be attributed solely to a change from grammatical subject to grammatical object position as variant NUM involves such a change and yet is better than variant NUM which does not
either susan or betsy might be the referent of the subject pronoun in the fourth utterance however there appears to be a strong preference for susan i.e. for the reading susan told betsy
we examine the interactions between local coherence and choices of referring expressions and argue that differences in coherence correspond in part to the different demands for inference made by different types of referring expressions barbara j grosz et al
she gave algorithms for tracking immediate focus and rules that stated how the immediate focus could be used to identify the referents of pronouns and demonstrative noun phrases e.g. this party that party
an initial formulation of some such principles is given in section NUM NUM the forward looking centers of un depend only on the expressions that constitute that utterance they are not constrained by features of any previous utterance in the segment
segment NUM figure NUM is an example of such a subdialogue with agent a as in the initial estimation of a performance function our analysis requires experimental data namely a set of values for and el and the application of the z score normalization function to this data
NUM when there is no agreement other than that which would be expected by chance NUM
paradise represents each cost measure as a function ci that can be applied to any sub dialogue
suppose that NUM was subserted into a and the root of r is labeled by the name of some c e d only components of a will have been marked as substitutable in NUM thus in this subsertion some component cj j will have been substituted at a node in a with address n
two general trends are the most important features of this graph
sentence 6d constitutes a retain in which cf u6d is tony and cb u6d is terry
within the bfp algorithm however the ways in which these follow ons are analyzed differ radically as summarized in table NUM
an apparently popular misconception attributes this utilization to gjw however neither the draft nor final versions of gjw put forth such a proposal
rule NUM sequences of continuations are preferred over sequences of retaining and sequences of retaining are to be preferred over sequences of shifting
roughly speaking cf u contains all entities referred to in u among these is cb un
kehler centering for pronoun interpretation is by definition the most highly ranked element of cf u realized in u i
NUM for instance rule NUM makes no NUM gjw do not make any specific proposals for using rules NUM and NUM for pronoun interpretation
if it is too dense there may be too many clusters activated by a context
the approach elegantly captures the interaction between pragmatic and syntactic constraints on descriptions in a sentence and the inferential interactions between multiple descriptions in a sentence
the modifier entry for the lexical item syntax can apply to the new n node its tree adjoins there giving the tree in figure NUM
we combine ltag syntax with declarative specifications of semantics and pragmatics of words and constructions so that we can build the syntax and semantics of sentences simultaneously
our system is unique in the streamlined organization of the grammar and in its evaluation both of contextual appropriateness of pragmatics and of descriptive adequacy of semantics
the goal of distinguishing book19 is still not satisfied however as far as the hearer knows both book19 and book2 could match the current description
many van noord efficient head corner parsing of the techniques that will be described in the following sections can be applied to a left corner parser as well
thus even though the head corner parser proceeds in a bottom up direction it can run into left recursion problems just as the left corner parser can
for left corner parsers this can be achieved by partially evaluating all rules that can take gap s as their left most daughter s
therefore we generally can not use all information available in the grammar but rather we should compute a weakened version of the linking table
thus for clauses of the form head link x b y b
very often it is much cheaper to solve this single but more general goal than to solve each of the specific goals in turn
for an input sentence timeyqies like an arrow this module may produce the following set of clauses lexical analysis NUM i verb
we have also implemented a version of the system in which acoustic scores and bigram scores are used to select the best path through the word graph
currently the best results are obtained with a scoring function in which bigram scores acoustic scores and the number of skips are included
we have just completed the sixth in a series of message understanding conferences which have been organized by nrad the rdt e division of the naval command control and ocean surveillance center formerly nosc the naval ocean systems center with the support of darpa the defense advanced research projects agency
each of these can been seen in part as a reaction to the trends in the prior mucs
id NUM ref i42 his coref desk as a personal reminder
additional work on the definition will he necessary and it may be necessary to narrow the task fllrther
much of tile energy for the current round however went into honing the definition of the task
one has to identify descriptions of entities a distributor of kumquats as well as names
furthermore while so much effort had been expended a large portion was specific to tire particular tasks
the text we are striving to have a strong renewed creative partnership with coca cola mr dooner says
resea ch based on a treebank is active for many natural language applications
the stochastic parsers are also developed in NUM NUM
note how the company name is heard correctly each word is in the vocabulary but the person s last name barbakow is misheard as barr now
the substantive differences between the system generated output and the answer key are indicated by underlining in the system output
the output of an lvcsr may be a single transcription NUM best the n highest scoring transcriptions n best or a chart of high scoring alternatives at each point
he uses the progressive and rate null ambiguous NUM NUM NUM NUM NUM total NUM NUM NUM NUM NUM adverbs constructions in combination with some sort of statistical smoothing technique
the interval NUM in figure3 designates a state which is a part of the state described by a lex ical stative verb
the te task takes the entity names found by the ne system and merges multiple references to the same entity using syntactic and semantic information
a set of training data must be provided training data consists of sentences and annotations that represent correct output i.e. an answer key
the best filter we found was simply to select those shogun templates that had the highest percentage of frame matches to the frames produced by ejv
NUM xtended events take time th re ix some instant NUM etween their start an l end points instantaneous events do llot tiote that this may or not lneall that the
carrier dependent argument realisation the argument is realized in a different way in func null tion of the selected carrier
in the following sections more details will be provided about the combination of fixed and variable information templates and arguments
each breakpoint consists of a location value in ms relative to the beginning of the phoneme followed by a pitch
the mts described above is realized in the context of a dialogue system that places a heavy burden on its hardware environment
all mus are prosodic units that can not be combined in an arbitrary way to form messages syntax specifies how to combine different mus units into a message
in this paper we present a message to speech system for natural language generation that is to be integrated in a dialogue system
figure NUM example of a carrier with a morpho logical restriction on the slot onset is not vocalic on co
therefore a good and practical compromise has to be found for the trade off between storage space on the one hand and flexibility and prosodic quality on the other
although the epts as such do not support linguistic variation the combination of pts with a template driven system provides linguistic flexibility as well as natural prosody
as speech rate can vary from one message to another a slot specific speech rate coefficient provided by the carrier can also be taken into account
four specific questions are addressed NUM are pause units reliably shorter than whole utterances
l i figure sample inst ance of the tsni p mmotation schema br one test item the umotations are giwm
a tesl s01 is a group f test items containing typically one i ositive examl le lid one or nlore negative examples
therefore a number of guidelines for this purpose such as use declarative sentences and avoid modifiers and adjuncts is provided
for the alternative intt lententation tsdb NUM the coml etitively priced dat a l asc l a ckage
additionally substantial parts of the implementation work at dfki and the university of essex have been carried out by tom fettig fred oberhauser and martin rondell
sentences s clauses c n mn NUM hrases np et al
a fi n ther sul elassifieation of phenomena is made according to the relevanl ynl actie domains in which a i henonmnon occurs e.g.
local and global constraints the third feature have been illustrated and they are often expressed in practice by dropping into full scheme code the fourth feature
the string texas i s identified as a place sub category center and sub sub category state and it is both a name an d capitalized
a number of applications would be very well served simply with automated and accurate identification of the data classes that occurred in their texts of interest with interpretations left to experts
extremely rapid processing is not a requirement in the belief that achieving the quality goals first could be followed subsequent speed u p enhancements e g parallelization
instances of data classes in text are phrases which identify factual content such as names of people or organizations products financia l amounts quantities and so forth
technical information retrieval foreign technology and politica l assessments tracking financial and other resource transactions in the written media and various types of link analyses based on text correlations
the underlying text processing syste m was adapted from an ongoing saic navy project to convert legacy technical manuals into a particula r data base form suitable for interactive electronic use on a portable computer NUM
the focus of the effort is dataextraction the identification of instances of data classes in commercial text e.g. newspapers technical reports business correspondence intelligence briefs
single word organizations or ones without a prefix or suffix title e g pilsbury birds eye required context sensitive semantics to pull in
the thing that most needs reworkin g the leading candidate for this prize is the user function library to keep the rule writer out of th e bowels of programming
since the methods discussed in this paper describe techniques for analysis of semantic information in text presumably this application could be extended to public informational settings in which people might key in requests for information in a number of domains
value may be atomic or it may he a boolean expression
also both analysis and generation of word forms are required
morphology also needs to be well integrated with other processing levels
if this session was in directive mode the subjects were told the system would act like a teacher and that they should follow its instructions
however there are exceptions such as zb6j bandit becoming zbgje
this technique helps a lot in the onstruclion of tree banks
i istanees are given in words
the distances are given on the arrows
they were taken from the tree bank
we thus posit the following equivalence
but the third equation may not always be verified
the only possihle solution is then the third term
throughout a written document and during the course of spoken conversation the topic evolves affecting local statistics on word occurrences
some examples are as follows a the referent of pronouns in the implemented system the only pronoun is it
the purpose of the tree generator module is to generate html documents in different languages from job database entries i.e.
the vertical bars simply separate the parts of the string and do not themselves match letters
a model which could adapt to its recent context would seem to offer much over a stationary model such as the trigram model
the partial or completed proof of a subgoal is not erased or popped from any stack when processing moves to another part of the proof tree
it would be possible to hypothesize all sensible lengths and compile separate spelling patterns for each
people at high risk of stroke include those over age NUM with a family history or high blood pressure diabetes and smokers
the next interesting action occurs in utterance NUM when the user answers a request to connect a wire with the question which knob
the architecture of vios is such that the dialogue management component and the text generating component work in sequential order
the wml at each step is shown for this derivation
segmentation whether at the word or sentence level is about identifying boundaries between successive units of information in a text corpus
the inputs to the voice input system for each utterance are the set of expectations from the dialog controller and the speech utterance from the user
the parser is a deterministic bounded context stack based shift reduce algorithm
limsi italy cselt and the u k ccir
model segments segments p precision data from NUM NUM not included in tdt corpus and the top NUM features were selected
tables NUM NUM and NUM show the quantitative facts that underly our description
table NUM shows the amount of information elements for each utterance of the information service
nevertheless the scenario must also contain the right given new combinations for the individual utterances
the table also gives a possible linguistic form of the separate lines in the scenario
it will arrive at art in poc
figure NUM shows the performance of the wsj segmenter on a typical collection of test data in blocks of NUM contiguous sentences
the figures above bear out the impression that trees in the penn treebank are more highly articulated than those in susanne even leaving aside the additional structure induced by the treatment of punctuation and preterminals in the treebank
we leverage long and short range language models as well as automatic feature induction techniques in the design of this model
there are many future experiments of obvious interest particularly those to do with examining potential factors in cases of agreement or disagreement analysis of consistency of annotation by markup label certain phrase types may be more consistently annotated than others so that we can be more confident in our analyses of such phrases
n n rather than cnn and the network identification information is often given at the end and beginning of news segments c
the unset learner represents a pure principles and parameters learner
roughly speaking if two corpora bracket off the same stretches of words in their structural analysis of a text the corpora agree that that stretch of text should be considered a single unit at some level of structure
trees in the right corpus which we can think of as the target are represented as elements in a hash table whose key is computed from the terminal indices of the start and end of its yield
the noun company can not be disambiguated because the matched nearest quadruple q4 contains the same noun and such a disambignation is not allowed the description million is monosemous
verb hierarchies are more shallow than those of nouns as nouns tend to be more easily organised by the is a relation while this is not always possible for verbs
when splitting the set of training examples by the attribute a according to its values a w the emerging subsets contain those quadruples whose attribute a value is lower in
the relatively low accuracy of the word sense disambiguation is compensated by the fact that the same sense disambiguation error is present in both the training set and the classified quadruple
we feel that a bigger corpus would provide us with an increase of accuracy of certainty NUM attachments which partly includes attachments based on the small leaves
we believe however that a bigger corpus would provide better word sense disarnbiguation which in turn would allow to increase the homogeneity limit for the termination of the tree expansion
we have conducted an experiment in which the disambiguated senses of the testing set were replaced by the most frequent senses i.e. the fast senses as defined in wordnet
there are two quadruples which satisfy the similarity threshold for verbs q2 and q4 q6 is not considered because its verb is identical and therefore not similar
the table NUM shows that the incorrect attachments usually occur with a lower certainty than the correct ones i.e. most of the incorrect attachments are marked as less certain
on the other hand in aggregate markov models the hidden state at time t NUM is predicted via the matrix p ct llwt from the word at time t
it is interesting to note that the size of the before and after corpora are very similar
be the joint good turing probability mass of all types with frequency f in the sample of m NUM NUM tokens and let mp f be the joint probability mass of exactly the same word types but now in the population n NUM NUM tokens
the replace plan schema is used by the speaker to replace some of the primitive actions in a plan with new actions
the other participant would then pass judgment on it either accepting it rejecting it or postponing his decision
no lemmatization has been attempted first because the probabilistic aspects of the problem considered here are not affected by whether or not lemmatization is carried out and second because it is of interest to ascertain how much information can be extracted from texts with minimal preprocessing
their model consists of conversational moves that express a judgment of a referring expression and conversational moves that refashion an expression
if the speaker of the refashioning is the agent who initiated the referring expression then this choice is obviously pre determined
we extend the earlier approaches of cohen and appelt by accounting for the content of the description at the planning level
by doing both we will give our work generality in the direction of a complete model of the collaborative process
second we address the act of referring and show how it can be better accounted for by the planning paradigm
likewise the clarifications that agents propose need not result in a successful plan in order for them to be accepted
the real world fact that to give something to someone you first must have it leads to a strong preference for the sloppy reading
an example to illustrate that the theory has applicability well beyond the problem of vp ellipsis we present an example of semantic parallelism in discourse
step NUM count ne occurrences by subtype tagged names were searched by ne type person location organization using a concordance tool nmsu s xconcord then copied to files representing each of the posited subtype classes or to a catch all residual class
we provide a general account of parallelism in discourse and apply it to the special case of resolving possible readings for instances of vp ellipsis
walkthrough errors in order to quantify alembic s performance on the walkthrough message we compiled an exhaustive analysis of our errors on this text
NUM represents the v NUM elements of the transition matrix p w21wa in terms of the 2cv elements of p w2 c and p clwl
for the te task this means identifying person and organization individuals by matching on person x o r organization y
abriqu abric fabricate or additional allomorphic rules
to dispense quickly with the latter we admit to failing to filter lines beginning with NUM in the body of the message
entity type table NUM te errors on walkthrough message organizational np phraser rules which is consistent with trends we noted during training
we argue that this synchronous system can deal with quantifier scoping in the desired way
pebls contains a number of parameters that must be set before running the algorithm
this rule changes the label of a phrase from none to person if the phrase is bordered on its left by a ttl phrase
descriptors are terms from the edf thesaurus that are automatically recognized in the documents
the scothag criteria are limited to those that can be measured without access to anything more than the annotations the systems generate NUM and those applied by human taggers to the answer keys
this accessibility is certainly one of the prerequisites for the massive use of natural language processing nlp techniques
the method chosen for this experiment combines an initial linguistic extraction with statistical filtering sta 95b
many diverse subjects are dealt with nuclear energy thermal energy home automation sociology artificial intelligence etc
indexing consists of recognizing the terms in a text that belong to a reference system this is called controlled indexing
mcca tags each word with a category and examines the distribution of categories against norms representing general usage of categories
the linguistic intentions of askref are compatible with those already expressed
assume that for some i we have q ei e e r pl and a linktr q does not immediately dominate pi
when executed once on nodes p and if shift link uses time NUM NUM if s link p and s link p are both defined
the negative evidence for the transformation is the number of positions at which factors ux and ux are aligned within the corpus x x c as above
in all other cases it uses an amount of time proportional to the number of implicit nodes visited by function faslscan which is called through function up link down
proper names the general NUM class language model is most of the time unable to choose between the different categories of proper names
the latter transformations with highest score if any can be easily recovered by visiting the implicit nodes that immediately dominate the nodes of tx recorded at step NUM
in addition to the inconsistent dates we also e.g.
therefore predictions are computed for both speakers
special thanks to reinhard for karger s machine
NUM NUM experiments with the most probable parse
an awk program extracts tallies that appear in the score report output by the scoring software and puts them in a file to be fed to the c program for approximate randomization
each date to be negotiated serves as a root while the nodes represent the information about years months weeks days days of week period of day and finally time
this paper gives the results of the statistical testing for the three muc NUM tasks where a single metric could b e associated with a system s performance
such research could revea l strengths and weaknesses in extracting certain information and lead to test designs that focus research in areas tha t will directly impact operational value
results are depicted in lists of systems that are all equivalent i e the differences in thei r scores were due to chance
in addition to looking at the scores evaluation research on a mor e granular level is needed to understand the differences in the systems performance
however if there is overlap and there is a lot of it in these results the n the ranking of the grouped systems is impossible
the two systems above them form a group which are significantly different from the other systems but not from each other
these three all use the f measure as the single measur e for systems as defined in NUM and in the muc NUM test scores appendix to this proceedings
what this method does not tell us is a numerical range withi n which f is not a significant distinguisher such as plus or minus NUM
of course we can expect famous names like zhou enlai s to be in many dictionaries but names such as t shi2 jil lin2 the name of the second author of this paper will not be found in any dictionary
we thank hector levesque mike gruninger sheila mcilraith javier pinto and stephen shapiro for their comments on many of the formal aspects of this work
by contrast our approach and that of the plan based accounts use expectation to refer to agents beliefs about how future utterances might relate to prior ones
the segmenter handles the grouping of hanzi into words and outputs word pronunciations with default pronunciations for hanzi it can not group we focus here primarily on the system s ability to segment text appropriately rather than on its pronunciation abilities
in the account speakers select speech acts on the basis of both their goals and their knowledge of which speech acts are expected to follow upon a given speech act
we divide our specification of a participant into three subtheories a set NUM of prior assumptions about the beliefs and goals expressed by the speakers including assumptions about misunderstanding
the act informif s p asserts the truth value of the proposition named by p i.e. informif is equivalent to inform v inform not
the strategies for displaying understanding suggest performing speech acts that have an identifiable but defeasible relationship to other speech acts in the discourse or to the situation
we also describe our account of belief and intention distinguishing the beliefs agents actually have from the ones they act as if they have when they perform a discourse act
initially russ interprets t1 as expressing mother s desire to tell that is as a pretelling or preannouncement but finds this interpretation inconsistent with her next utterance
according to the ethnomethodological account of human communication known as conversation analysis ca agents design their behavior with the understanding that they will be held accountable for it
adjacency pairs are sequentially constrained pairs of utterances such as question answer in which an utterance of the first type creates an expectation for one of the second
anote that this is one of the cases where church s chunker allows separate np fragments to count as chunks
in these tests punctuation marks were tagged in the same way as words
one change in the algorithm is related to the smaller size of the tag set
since training set size has a significant effect on the results values are shown for three different training set sizes
table NUM shows the results for the basenp tests and table NUM shows the results for the partitioning chunks task
he uses a lexicon that lists all the possible chunk tags for each word combined with hand built constraint grammar patterns
the same NUM patterns can also be used to match against part of speech tags encoded as p0 p l etc
unfortunately such complete indexing proved to be too costly in terms of physical memory to be feasible in this application
these brackets were placed using a statistical model trained on brown corpus material in which np brackets had been inserted semi automatically
similarly there is no compelling evidence that either of the syllables of binl lang2 betelnut represents a morpheme since neither can occur in any context without the other more likely NUM binl lang2 is a disyllabic morpheme
t non concious uung relational processes process t mental processes
the text structure evolves by the expansion of leaves top down and left to right
the following example illustrates the mechanism of aggregation and its effect on resulting text
now let us examine another semantic grouping rule where and are no longer identical
if the input indicates that an action to achieve or observe a physical state was completed then conclude that the user knows how to perform the action
what this research has contributed is a theory of usage for these inferences which is usage by the theorem prover as it attempts to complete task goals
the main role of the semantic categories is to provide vocabularies which specify type restrictions for nodes
we demonstrate why paraphrasing and aggregation significantly enhance the flexibility and the coherence of the text produced
it is organized using the part of relationship with each node that represents a part of a subsystem connected as a child node below that subsystem
when this occurs an efficient interaction requires that the machine yield control so that the more competent partner can lead the way to the fastest possible solution
the user has dialog control but the computer is free to mention relevant though not required facts as a response to the user s statements
if the user does not know according to the user model where the switch is the model releases the system to so inform the user
that is the user has achieved find knob knows how to find the knob and no further consideration of this subgoal is needed
the subjects were asked to listen to and repeat four sentences spoken by the dectalk system this exercise was repeated until they overcame any difficulties in understanding
it was similar to the second except that the system was placed in the mode that was not used in session NUM either declarative or directive
thus in a problematic debugging situation specifications with the smallest count will be checked again and again until all have been checked the same number of times
cotnllh ntenl tntldilict dot c t l iii ci i ic l i ol it lcb t ll illct t pol t ioll lcb lit o pol it ioll spond to the senmntic translation of the subtrees rooted in NUM and NUM respectively
he can take advantage of such paral hrasing potential in cerlain cases of synlacljc divergence belween languages l or instance french does not have a syn tactic equivalent to the dative lnoven etlt passive configuration o1 rachel was given a book by claude so that a direct syntactic translation is not possible
the topic of implicit logical junctors in value sets of wordnet s generic and meronymic relations was also treated by bloksma et al but the therapy they propose we could not get to like
this requirement however is di fficult to reconcile with the flexibility needed for handling quantilier scope ambiguities
fhe process of semantic translalion tin coeds in this way bottom ul on the b form
we also use the notation fv ibf for the set of the free variables in ibf
by permuting several dependent lines along their head line this incorporation order is changed and gives rise to different scopings
centrate on their types l efltype righttype result l ype then the rules can be seen as itnl osin constraints on what can count is validly typed trees these constrahlts can flow from nlother to daugthers as veil as in the opposite direction
demd labeled n ary tree which can be obtained from a b form through the inverse application of the s form bform encoding
l is thai the nodes of our new tree are considered ordered whereas they were considered tinordered in the i lorm the convention is now that tepetlttent sister nodes are interpreted is having ttil l 2retlt scopes with llarrower scope correspondillg to a position iilore it tile right
well formedness conditions tot b forms a complement incorporation d x it is only possible when h contains x among its fi ee variables the syntactic dependent d is seen as semantically filling the place that x occttpies in the syntactic head h
in such cases we assign all of the estimated probability mass to the form with the most likely pronunciation determined by inspection and assign a very small probability a very high cost arbitrarily chosen to be NUM to all other variants
if the context does not allow the analyzer to select one of those a coarsergrain solution is preferred
these are the results of the respective database search for noun concepts after we have corrected that irresponsibility was an antonym of itself there is exactly one hit shown in figure NUM
as file default property connecting file modifier to the modifiexl the mikrokosmos analyzer uses file catchall relation pertain to
for verb concepts if we subtract the two cases produced by the error treated in subsection NUM NUM the check detects exactly six pairs or two constellations shown in figure NUM and figure NUM
nevertheless we report results on sparser test sets to show how our algorithm scales up
where c is the complement of cluster c i.e. the other member of the partition
they show that positive and negative orientation are objective properties that can be reliably determined by humans
they agreed with us that the positive negative concept applies to NUM NUM of these adjectives on average
furthermore there are words so inflected which have positive orientation e.g. independent and unbiased
in the following sections we first present the set of adjectives used for training and evaluation
we then measured the percentage of conjunctions in each category with adjectives of same or different orientations
the results are extremely significant statistically except for a few cases where the sample is small
adjectives related in form e.g. adequate inadequate or thoughtful thoughtless almost always have different semantic orientations
thus we add to the predictions made from conjunctions the different orientation links suggested by morphological relationships
NUM NUM and store them in an array called adverbs cf
the corpus contains NUM NUM sentences from various genres of text
their aspectual properties are changed by the complements they take
table NUM the verb classification obtained by the ex
figure NUM the relation between categories of verbs and features
table NUM the restdts of the evaluation ex eriment
a number of aspectually oriented lexical semantic representations have been proposed
NUM gradual process verbs are those that have graduality
gojikan seizasita sit straight for NUM hours etc
finally we shall mention time in the past adverbs
this may have the fortuitous consequence that the child s next utterance depends more on the child s last utterance which is known by the system rather than on the partner s last utterance which is not known
to our knowledge the wordnet lexicographers were not supported by dynamic checking on update or by an easy to use database query language for batch checking nor by a graphical browser editor for visual feedback
second the parameters estimated from the smoothing techniques give the robust learning procedure a better initial point and are more likely to reach a better solution when many local optima exist in the parameter space
although both the adaptive learning procedures and the smoothing techniques show improvement the robust learning procedure which emphasizes dis null crimination capability rather than merely improving estimation process achieves a better result
significant improvement compared with the ml rl mode has been observed computational linguistics volume NUM number NUM by using the smoothed parameters at the initial step before the robust learning procedure is applied
in other words the smoothing techniques indirectly prevent the learning process from being trapped in a poor local optimum although reducing the estimation errors by using these methods does not directly improve the discrimination capability
in evaluating the robust learning procedure on a corpus of NUM NUM sentences NUM NUM of the sentences are assigned their correct syntactic structures while only NUM NUM accuracy rate is obtained with the mle approach
the optimization criteria are thus not compromised by the topologies of the parse trees because the number of shift actions i.e. the number of input tokens is fixed for an input sentence
respectively where nw NUM NUM stands for the number of words in the lexicon and nt NUM denotes the number of distinct terminal symbols parts of speech
to overcome this problem a novel approach is proposed in this paper to train the null event parameters by tying them to their highly correlated parameters and then adjusting them through the robust learning procedure
then the first syntactic approach is not very useful because it only takes into account the previous word
and the second one is very hard to implement because of the number of variations a word may have
in inflected languages the complexity in making the changes is very high because of the number of possibilities
y has property last name smith
the adaptation of the system for the new words is made increasing the word frequencies and the weights of the rules
nevertheless in all our experiments the ml aggregate markov lit is worth noting in this regard that individual zeros in the matrices p w2 c and p c wl do not necessarily give rise to zeros in the matrix p w21wt as computed from eq NUM
the user can specify an ordering on feature ex null pansion
the remaining symbols are set to expand uniformly among their possible expansions
and where we denote the training data as o for observations
p oig requires a parsing of the entire training data
this smoothing is also performed on the inside outside post pass of our algorithm
the parameter a is trained through the inside outside algorithm on held out data
from this starting point the inside outside algorithm is run until convergence
these new rules replace the first three rules listed in table NUM NUM
in the adjacency model the structure would be decided by looking at the adjacency association of information retrievaf and retrieval technique
we rely on the post pass described later to refine parameter values
in particular we used a probabilistic grammar to generate the data
in human human negotiation efficiency is a major goal
the empty position is substituted for by the second phrase marker dp
for every type of information e.g.
a second problem is that sequential composition does not allow us to insert new words inside old lattices as needed to generate sentences like john looked it up
in this example the rule gives four ways to make a syntactic s two ways to make an infinitive and one way to make an np
however it is possible that not all the alternatives actually share the same level of fluency or currency in the domain even if they are rough paraphrases
for example penman defaults article selection to the and tense to present so it will produce the dog chases the cat in the absence of definiteness information
choosing the is a good tactic because the works with mass count singular plural and occasionally even proper nouns while a does not
for example adding identifiability q t forces the choice of determiner while the lex feature offers explicit control over the selection of open class words
all alternatives are ordered by the gram null mar writer so that the topmost lattice path corresponds to various defaults
we can generate a lattice with eight paths instead of one the deficit stands for the empty string
first we have replaced the narrow band NUM bit NUM khz sampled acoustic models included with the nuance recognizer and designed for telephone applications with wide band NUM bit NUM khz sampled acoustic models that take advantage of the higher quality audio available on computer workstations
to make it possible to derive an equivalent finite state grammar we restrict the gemini grammars used as input to our gemini to nuance compiler as follows all features in the gemini grammar that are compiled into the recognition grammar must allow only a finite number of values
it accepts messages from the sr agent containing the words that were recognized messages that the user has stopped speaking for click to talk and messages from any agent that contain confirmation or error messages to be displayed to the user
some of the problems which must be solved by the ci agent are noun phrase resolution predicate resolution temporal resolution vagueness resolution a noun phrase denoting an object in the simulation must be resolved to the unique modsaf identifier for that object
to transform a restricted gemini grammar into this format we first transform the gemini rules over categories with feature constraints into rules over atomic symbols and we then transform these rules into a set of definitions in terms of regular expressions
similarly the parameter value indicating a column formation for tanks is different from the one indicating a column formation for infantry and the parameter that controls the speed of vehicles has a different name than the one that controls the speed of infantry
by retraining the statistical component on a different domain we can automatically pick up the peculiarities of the sublanguage such as preferences for particular words and collocations
we can improve on them by enhancing the statistical model or by incorporating more knowledge and constraints in the lattices possibly using automatic knowledge acquisition methods
figure NUM unified hash table example
to be is used to show that the phrase is a be verb and its complement
by the following constraints NUM head coimt i aints tha nontermina l
a lgorithm a nd sea lability in r menta l
the a deqtmcy of cvg for des ribing na tura NUM la ngua ge
null we assume that the input is a logical formula whose predicates are input predicates
the output will be a logical formula consisting of output predicates
given an ldt this conjunctive context determines how the predicate will be translated by aet
we shall call the set of all such nce t s a normalized r ldt
as an example suppose that the lexical representation of the sentence is there a student who takes
althcugh the preprocessing is computationally intensive it is done off line during the delevopment of the nli
that is we can determine which words can be modified or complemented by which other words
an rldt therefore also athe predicate unknown will be discussed in the next section
spanish text is processed using the same approach
the length of a path is defined as the number of scanned states on it
from now on we will use derivation to imply a left most derivation
NUM NUM current matchplus context vector learning law
notice that the scanning and completion steps are deterministic once the rules have been chosen
b follows from a by using s as the start nonterminal
the constrained paths ending in scanned states represent exactly the beginnings of all such derivations
x is used for prediction in a complete earley path generating x
note that the computation makes use of the inner probabilities computed in the forward pass
adjoin t to t as the right most child at the root and return t
in this section we examine the computation of production count expectations required for the e step
target i and neighborj figure NUM
the romanian text that has been closely analyzed for explicit expectation raising constructions is t vianu s aesthetics
figure NUM learning law vector magnitude
it is interesting to compare the intuition of the human labeller with results actually produced most of the time differences may be attributed to the fact that available analyzers do n t yet match our expectations for state of the art analyzers because they produce spurious parasite ambiguities and do n t yet implement all types of sure linguistic constraints
for example an expert interpreter monitoring several bilingual conversations could solve some ambiguities from his workstation either because the system decides to ask him first or NUM according to a study by cohen oviatt the combined success rate sr is bigger than the product of the individual success rates by about NUM in the middle range
in the case of a classical context free grammar g shall we say that a representation of u is any tree t associated to u via g or that it is the set of all such trees
this is done in the second part where we define formally the notion of ambiguity relative to a representation system as well as associated concepts such as kernel scope occurrence and type of ambiguity
however the same spoken language analyzers may be able to produce sets of outputs containing the correct analysis in about NUM of the cases structural consistency NUM NUM
for example attachment ambiguities are represented differently in the outputs of various analyzers but it is always possible to recognize such an ambiguity and to explain it by using a skeleton flat bracketing
for instance please state your phone number shoukl not be deemed ambiguous as no complete analysis should allow state to be a noun or phone to be a verb
this means that any strictly smaller fragment w of u has strictly less than n associated sub representations or equivalently that at least two of the representations of v are be equal with respect to w
we suppose an architecture flexible enough to allow the above three extralinguistic processes to be optional and in the case of interactive disambiguation to allow users to control the amount of questions asked by the system
for lack of space we can not give here the context free grammar which defines our labeling formally and illustrate the underlying principles by way of examples from a dialogue transcription taken from NUM
to that end we assume that every node in the parse forest u has associated with it a variable xv that is used for constraining the partial semantic structure of u
therefore root is equivalent to just a big conjunction of all rule constraints for the inner nodes and all leaf constraints
the precision of NUM roughly can be interpreted as that for words which take on three different boss in their bos class the ending guessing rules will assign four but in NUM of the times recall the three required boss will be among the four assigned by the guess
for example if the estimate for the word ending o was obtained over a sample of NUM words and the estimate for the word ending fulness was also obtained over a sample of NUM words the later case is more representative even though the sample size is the same
first we evaluated the guessing rules against the actual lexicon every word from the lexicon except for closed class words and words shorter than five characters NUM was guessed by the different guessing strategies and the results were compared with the information the word had in the lexicon
this actually gave us the search space of four combinations the xerox tagger equipped with the original xerox guesser brill s tagger with its original guesser the xerox tagger with our cascading ps0 s60 e75 guesser and brill s tagger with the cascading guesser
for example ifa rule was detected to work just twice and the total number of observations was also two its estimate NUM is very high NUM or NUM NUM for the smoothed version but clearly this is not a very reliable estimate because of the tiny size of the sample
to collect such rules we set the upper limit on the ending length equal to five characters and thus collect from the lexicon all possible word endings of length NUM NUM NUM NUM and NUM together with the pos classes of the words where these endings were detected to appear
our experiments with the lexicon and word frequencies derived from the brown corpus which can be considered as a generm model of english resulted in guessing rule sets which proved to be domain and corpus independent NUM producing similar results on test texts of different origin
there are two kinds of morphological rules to be learned suffix rules a rules which are applied to the tail of a word and prefix rules a p rules which are applied to the beginning of a word
the most important ones are that the brown corpus provides a model of general multi domain language use so general language regularities can be induced from it and second many taggers come with data trained on the brown corpus which is useful for comparison and evaluation
in our approach guessing rules are extracted from the lexicon and the actual corpus frequencies of word usage then allow for discrimination between rules which are no longer productive but have left their imprint on the basic lexicon and rules that are productive in real life texts
abbot uses a neural network to model standard phonemes
pants within the limits of a common budget
this extension of the formalism poses a serious problem every discourse getsuyoubi wa seminaa ga haitte iru node zikan ga na i noda monday top seminar nora insert asp pres conj time nora fail prcs aux pres monday is n t good because i do n t have any time since some seminars have been inserted then figure h three discourse relations in a sentence relation introduces a partition into the antecedent and the conclusion part for the sentence in which it occurs
this not only corresponds to the intuition in NUM but is also the case in fig NUM gogo wa yamada ga i ru noda afternoon top pn nom be pres aux pres as or the afternoon yamada will be here on the other hand scope underspecification among discourse relations can not be disambiguated straightforwardly if they are of the same type according to the above classification
keeping track of the assumption that all discourse relations in a sentence take a wider scope than the other scope taking elements in a sentence we are confronted with the l since the verbmobil project deals with spoken languages the unit treated is in reality not a sentence but an utterance which constitutes a turn in a dialogue and includes ellipsis mm other typical phenomena which need special treatments
al fa i5 pron NUM NUM NUM aeg i9 h8 NUM inc NUM NUM NUM role i2 tloc i4 NUM dm NUM NUM inc NUM NUM modifies ll0 pred haitte i2 ll9 dm i8 NUM role i2 arg3 i3 NUM pred zikan i8
the parsing module takes the best recognition hypothesis
here are two examples of scoring and filtering
practically the alignments previously calculated are scanned if the two bordering neighbors of a word w are once adjacent and well aligned in an hypothesis w is marked as an insertion
now in pass two it is observed that in the sure part of the sentence whom is this chair two words whom and be are the beginning of several multi anchor elementary trees
filtering errors and repairing linguistic anomalies for spoken dialogue systems
the nuance communication recognizer system exploits phonemes in context
table NUM comparison of average cpu time required
we consider context effect only from the immediate neighbors
similarities and differences between manual and automatic rule formation the automatic rule learuing procedure uses a generateand test approach to learn a sequence of rules
it turns out that for the first two of these kinds of precision errors the manual corrections are extremely quick to perform
in the case of utterances that evoke more than one temporal unit a separate entity is added for each to the focus list in order of mention
there is no unconstrained forward chaining using a soup of rules as in a standard production or rule based system
the generation of reliably tagged text corpora requires that a human annotator read and certify all of the annotations applied to a document
we intend to provide a welldocumented api in the near future for external utilities to be incorporated smoothly into the corpus system development environment
while its name indicates its lineage we do not view the alembic workbench as wetkkxt to the alembic text processing system alone
the philosophy motivating our work is to maximally reuse and re apply every kernel of knowledge available at each step of the tailoring process
the goal of the alembic workbench is to dramatically accelerate the process by which language processing systems are tailored to perform new tasks
initial empirical studies using the alembic workbench to annotate named entities demonstrates that this approach can approximately double the production rate
the ultimate goal of this project is to enable end users to generate a practical domain specific information extraction system within a single session
example NUM g where the dead tree is on the other side of the stream there s farmed land
g straight down and curve to the right til you re in line with the pirate ship
instructions that keep them on land f so i m going over the bay
this suggests the disagreement is general rather than arising from problems with the written instructions
unlike the other coding schemes transaction coding was designed from the beginning to be done solely from written instructions
note that it is only possible to measure reliability of move classification over move segments where the boundaries were agreed
first some coders marked a ready move but the others included the same material in the move that followed
if the transferer is sufficiently confident that the transfer has been successful a question such as ok
game and move coding have been completed on the entire NUM dialogue map task corpus transaction coding is still experimental
using the speech would probably help but most uses of transaction coding would not require boundary placement this precise
handling deletion of a space or substitution of another character for a space are straightforward additions to the morphological process
and to date grammar checkers and other programs which deal with ill formed input usually step directly from spelling considerations to a full scale parse assuming a complete sentence
in each case output is a best guess of the category for the word in its current context
church NUM derose NUM cutting et al NUM merialdo NUM etc
e.g. the similarity between the tags rb in nn and rb in should be bigger than the similarity between rb in and vb nn
the first letter is encoded as well because it contains information about prefix and capitalization of the word
in rule based approaches words are assigned a tag based on a set of rules and a lexicon
this work is supported by a british telecom scholarship administered by the cambridge commonwealth trust in conjunction with the foreign and commonwealth office
to investigate issues involved in shallow processing and cooperative error handling the pet processing errors in text system is being built
at present this phase postulates only one grapheme at a time although all its possible categories are passed along together to later stages
allowing this change would necessitate a more complex backtracking mechanism as there would be a focus lag between morphological processing and later phases
the ideal data would be records of peoples keystrokes when interacting with an editor while creating or editing a piece of text
below follow an outline and discussion of the linguistic components of pet and discussion of testing and evaluation of the system
tag checks though the current set of categories stop when one category passes but backtrack and continue if parsing then fails
the effectiveness of the sub domain approach described above will most likely depend heavily on our ability to choose appropriate sub domains
let t be the i th subtree in the derivation d that yields tree t then the probability of t is given by p t d NUM ii p tid
this gives the knowledge engineer a means to quickly and relatively accurately classify the most frequent vocabulary used in a particular domain
these data structures are initally produced by the procedures described above and refined by the analysis refinement tool to become a conceptual type lattice and a set of frames
therefore the term myocardial infarction might become body part adj disease noun s as might gastrointestinal obstruction or respiratory failure
a data extraction module provides the knowledge engineer with manageable units of lexical data words phrases etc grouped together according to certain semantically important properties
some of the tools axe already implemented while others still need implementation or reimplementation in terms of the open architecture of the workbench
such patterns are then presented for a conceptual characterization to the knowledge engineer and some predefined generic conceptual structures are suggested for specialization
an acute myocardial infarction a true posterior myocardial infarction an interior infarction a small anterior myocardial infarction an extensive myocardial infarction in NUM
once this is achieved we can again apply the same methodology to find patterns of higher level which include patterns themselves
for instance it states explicitly that a body component which is a location of a disease belongs to the person who is an experiencer of that disease
from the nl processing point of view lexico semantic patterns provide a way for going about without the definition of a general semantics for every word in the corpus
in theory we want to constrain only some general hidden nodes so they would accurately predict the reference nodes and we hope that they will be good as well for the unseen configurations
combining raw language data with linguistic intormation offers a promising basis for the development of new efficient and robust nlp methods
the performance of this component is shown in table NUM
these constructions are a source of error in appositive recognition
table NUM la hack NUM and syntactic pattern performance
table NUM performance with lower case string matching added
this means that two candidate antecedents can be equally salient
performance for this component alone is shown in table NUM
therefore the evidence from wordnet is weighted on a scale of plausibility
databases are shown in drums and system modules are shown i n rectangles
proper nouns which are portions of longer noun phrases may b e annotated
these counts can be used to estimate the probabilities of wordnet word senses
in p2 llnl v n2 one combines the evidence for nl as the subject of v with any object with that of n2 as the object of v with any subject
as can be seen in figure NUM the interface is quite complex at present
the proximity operator required that the narue terms occur within two non stopwords of each other in the text
such a capability would be important for achieving appropriate speed and maintenance of flow
an interesting cross section a exhibition in the central children s and youth library in the community center bornheim organized by the french culture institute with support of the bsrsenverein shows an interesting cross section sentence NUM below was the source for the test tuple altersgrenze nennen gesetz age limit mention law
a facility for adding unique text for speaking during a conversation was then added
from the development of a number of previous prototype systems some important lessons had been learned
this is of course an oversimplification since overlapping talk is in fact the norm
conversely a basically transactional conversation with a shop assistant may include some social chat
if the user does not provide query name recognition then the system must do so automatically
similarly conversations are not necessarily exclusively concerned with either social goals or transactional goals
another way in which co operation is evident in natural social conversation is in the sharing of control of topic direction
step NUM compute similarity sim d s based on dice coefficient for all clef tuitions d defh and labels s seth
descriptions of trees the hierarchy is a strict multiple inheritance network whose terminal classes represent the elementary trees of the ltag
the tool starts from the syntactic hierarchy and principles of well formedness and carries out all the relevant combinations of linguistic phenomena
the passive rules simply discards the first complement representing the canonical direct objet the other complements moving up
certainly the lexical rules are proposed as a tool for generation of new schemata or new classes in a inheritance network
this solution not only addresses the problem of redundancy but also gives a more principle based representation of an ltag
furthermore the automatic generation from the hierarchy guarantees the well formedness of the families with all possible conjunctions of phenomena
we will call them meta features as opposed to the features attached to the nodes of the tag trees
these terminal classes are not written by hand but automatically generated following principles of well formedness either technical or linguistic
figure NUM two trees of the strict transitive family for french the relativized subject and the cliticized object
the idiosyncratic features attached to the anchor or upper in the tree are introduced in the syntactic lexicon
the pronoun resolution preferences that result from an addressee s immediate tendency to interpret a pronoun motivate pursuing a centering based approach
center continuation cb un l cb un cp un l
in this case cb un l is not the most likely candidate for cb un NUM
again however this strategy would treat follow ons 6el and 6e2 quite differently
these varied results are inconsistent with the aforementioned facts concerning these passages in both empirical and theoretical respects
in particular they suggest that hearers assign referents to pronouns before interpreting the remainder of the sentence
passage NUM is presumed to be in a longer segment that is currently centered on john
empirically the results are counter to the more consistent preferences associated with the subject pronouns in each case
in this case cb un NUM is the most likely candidate for cb un NUM
the examples gjw give to illustrate rule NUM are shown in passages NUM and NUM
this will be explained in more detail below
goal weakening is discussed in the following subsection
the start symbol of this grammar is nt6
two sets of data were collected in this experiment one for the baseline run and one for the experimental run
we are now shifting the discussion to the prediction of open class elements instead
since u4 does neither contain any anaphoric expression which co specifies the cv NUM ua block NUM nor any other element of the NUM NUM u3 block 2a and as there is no hierarchically preceding segment block 2c applies
b close the embedded segment and open a new parallel one if none of the anaphoric expressions under consideration co specify the cp NUM NUM u NUM l end then the entire c at this segment level is checked for the given utterance
in table NUM e.g. the first two discourse segments at level NUM ranging from u5 to u5 and us to ull are closed while those at level NUM ranging from u1 to u3 level NUM ranging from u4 to ut and level NUM ranging from u12 to u13 are open
anaphora resolution can then be performed a with the forward looking centers of the linearly immediately preceding utterance b with the forward looking centers of the end point of the hierarchically immediately reachable discourse segment and c with the preferred center of the end point of any hierarchically reachable discourse segment for a formalization of this constraint cf
as far as standard installations are concerned gets one well by without any manual
NUM gerasterte grauflftchen erzeugt der brother sehr homogen raster mode grey scale areas generates the brothervery homogeneously
we like to thank our colleagues in the clif group for fruitful discussions and instant support joe bush who polished the text as a native speaker the three anonymous reviewers for their critical comments and in particular bonnie webber for supplying invaluable comments to an earlier draft of this paper
the category of errors covers erroneous analyses the algorithm produces while the one for false positives concerns those resolution results where a referential expression was resolved with the hierarchically most recent antecedent but not with the linearly most recent obviously the targeted one both of them denote the same discourse entity
the algorithm presented in this paper improves the run time of the recent result using an entirely different approach
for our purpose we will assume that every internal node in an elementary tree has exactly NUM children
NUM reduce problem size to az an zal NUM n NUM an and find closure of this input
we have also demonstrated with our implementation work that matrix multiplication techniques can help us obtain efficient parsing algorithms
if the only adjunction was by an auxiliary tree on node m spanning tree q j k lx as shown in figure 5d then the set of minimal nodes will include both m and the root ml of the auxiliary tree and the nodes in their respective assoc lists
exemplar based word sense disambiguation some recent improvements
by the induction hypothesis the algorithm correctly computes all nodes spanning trees i j k i within the first NUM NUM i e lcb i l rcb e lcb rt r2 r2 p d NUM rcb and i l
the concept of last operation is useful in modelling the steps required in a bottom up fashion to create
this represents NUM NUM of all word tokens in the collection not counling stopwords
the first three features are concatenation of two words
we have to find the highest probability of placing a non aligned word e between a predecessor word e and a successor word e
p ele p ele NUM skip transition non aligned word
we have to keep in mind that in the search procedure both the language and the translation model are applied after the text transformation steps
table NUM word error rates ins del wer and sentence error rates ser for different transforma tion steps
we have tested the model successfully on the eutrans traveller task a limited domain task with a vocabulary of NUM to NUM words
this testifies not only to rst s usefulness but also to the direct applicability of the results of the current study to the field of natural language generation
the grammatical form selection sub networks can then be seen as operating on the appropriate relations included in this representation and producing the full trl structure shown in figure NUM
text for this example is shown here NUM when you are instructed remove the phone by grasping the top of the handset and pulling it
imagene s realizations for linker form slot and clause combining that matched those in the corpus differentiating between the training set and the testing set
NUM no more descriptors are globally available c19 c22 NUM NUM
this approach is applicable not just to the problem of expressing procedural relations in instructional text but rather to any lexical or grammatical aspect of any linguistic genre
the slot of these forms is always initial and is determined here rather than in the slot selection sub network just discussed
next property c19 NUM selects a cognitively motivated candidate property to be included next
besides the preference list needs to be fully instantiated for each referent to be described which constitutes a significant overhead
links within the clause table are used to indicate subordinate relations and links between the clause and phrase tables are used to represent relative clauses and predicate argument relations
the nucleus satellite schema relates two spans of text one designating a more central span called the nucleus and a more peripheral one called the satellite
this issue should be of interest for any researcher developing a parsing system that will need to deal with unknown words
they would be given both missing and spurious scores for all mismatching fills instead of one incorrect for eac h mismatched fill in the response with a correlate in the key
the examinee is theasked to give reasons as to why this might have occurred
a second lexically based statistical approach performed poorly for the same reasons described above
these grammars were based on the conceptual structural representations identified in the training response set
test developers had categorized both responses under the trained for self defense safety category
responses were parsed and then input into the phrasal node extraction program
each examinee s response set would then typically be scored by two human graders
in the layered lexicon approach words are linked to definitions within some hierarchy
we claim that grammatical role criteria should be replaced by indicators of the functional information structure of the utterances i.e. the distinction between context bound and unbound discourse elements
we would like to thank our colleagues in the 8pszy r group for fruitful discussions and jon a1cantara cambridge uk for re reading the final version via interact
the distinction between context bound and unbound elements is important for the ranking on the c i since bound elements are generally ranked higher than any other non anaphoric elements cf
the results show that the largest number of erroneous classifications occurred due to lexical gaps
these systems did not use structure or domain specific lexicons in trying to analyze response content
a common topic of criticism relating to focusing approaches to anaphora resolution has been the diversity of data structures they require which are likely to hide the underlying linguistic regularities
the second one concerns an empirical issue in that we demonstrate how a functional model of centering can successfully be applied to the analysis of several forms of anaphoric text phenomena
in this paper we provided an account for ordering the forward looking centers which is entirely based on functional notions grounded on the information structure of utterances in a discourse
with this placement we will have a model of NUM what features we expect the student has mastered and is using consistently these are features below the user s level in the model NUM what features we expect the user to be using or attempting to use but with limited success these are features at the user s level and NUM what features we NUM
consider the following we went to see senator biden s office then we go to see the vietnam memorial this example is a particularly good illustration of a difficulty due to a question of when to mark tense since the writer clearly knows how to form the past tense of go because the appropriate past tense form appears in the sample
it is important to note that although the default levels i.e. cross hierarchical connections for the process of second language acquisition will be somewhat predeffined the model is flexible enough to allow and account for individual variations beyond those represented by the initial model and its filters
while our analysis so far has been restricted to proficient asl signers samples from other deaf writers might help us determine what the asl influence filter for example might look like since it would apply to one group of samples but not to another
the system called icicle interactive computer identification and correction of language errors is designed to be a general purpose language learning tutor however we have focused on its application to deaf users of asl acquiring written english essentially as a second language
one of the few areas of general agreement among most sla researchers is that linguistic input at or near the user s current second language proficiency is beneficial for the acquisition learning process kra82 tar82 vyg86 hat831
on the other hand instruction or corrective feedback dealing with aspects outside of the zpd will likely have little effect and may even be harmful to the learning process in the sense that the user may become bored or confused by information that they are unable to comprehend or apply
null prising since the components of asl grammar and written english grammar are very different sto60 bp78 pad82 hs83 kb79 bpb83
consider the sentence my brother like to go a final area of language transfer occurs when one language say l1 has two or more words or phrases which co espond to a single word phrase in the other language and vice versa
figure NUM effect of search strategy
one is related to the limitations of estimation using untagged corpora
the reestimation algorithm was iterated for five to twenty times
for the model evaluation a stochastic tagger was implemented
the scaled forward probabilities are defined with the above definitions
he applied this algorithm to bigram model training from untagged japanese text for new word extraction
in the initial estimation of a model an equivalent credit factor is used for estimation
the connections of dotted lines constitute noise for the estimation algorithm
in the case of this confusion set using NUM factors increases the performance by NUM
we gratefully acknowledge the comments and suggestions of thomas landauer and the anonymous reviewers
for example we chose somewhat arbitrarily to retain NUM factors for each lsa space
the predicted word is compared to the correct word and a tally of correct predictions is kept
we have chosen to use the same NUM confusion sets and the brown corpus in order to compare
we ll identify these matrices as t s and d see figure NUM
as previously mentioned we can tune the number of factors to a particular confusion set
in other words the bigrams fill their own row in the lsa matrix
smaller context sizes did n t seem to contain enough information to produce good predictions
ect ccamthology s np dekplaat cc anatomy s houri weggenomen cc surg deed cs remove s ycrb t p dirobject de osteofytaire randen win de dekphmt i p means met een beiteltie NUM ill
the definition o1 the cases of case hammar is similar to the definition of cic n semantic i inks both of them expressing relations between w ious parts of the sentence
in other words if the i value of an element in the surgical deed clause satifies one of the i values rcquired for an ind slot then the element will be linked
given the textual information explosion in particular in though not restricted to specialized domains there is an urgent need for tools enabling to exploit the information available in natural language texts
because of the neutral character of tim fiamc no priority information can be given so every constraint is labeled with the same degree o1 prinrity l
d if necessary adaptation of the fl ame will be carried out il the surgical deed concept is a noun o a non finite tbr n of the verb
8c and section v NUM ex
multitale linking medical concepts by means of frames
the next example illustrates the cen tc251 model
l he syntactical module functions as a preprocessor
changes in tense aspect and modality are promising clues for recognizing subdialogs in this data which we plan to explore in future work
it parses the speech acts encountered so far tests their consistency with the dialogue model and saves the current state
the modifying noun in italian complex nominals with the preposition di describes the result that is achieved by performing the particular function associated with the head noun
representations such as that in NUM are intended to be the values of a content attribute which specifies the semantic content of a lexical item
in the gl representation all of the participants which show up in the predicates in qualia are listed as default argument parameters in the argstr
the first default argument d arg1 has been specialized from physobj to bread and this value is structure shared with the third argument position in the cut act predicate
in the following three sections we will show how the free classes of compounds considered so far can be treated as instances of telic agentive and constitutive qualia modification respectively
english compounds are worse than italian post modified forms in this respect since in italian the preposition gives at least some indication of the relation involved
unlike the operation which derives bread knife by associating the modifier to an argument position in the telic role of bread the compositional operations which involve events produce a more complex structure
in order to illustrate our approach we will start with examples such as bread knife la in which the modifying noun relates to the purpose of the head noun
we argue that compounds where the modifying noun describes an event such as those in NUM involve co composition of the qualia structures of the head and the modifier
the fact that a knife is an object whose inherent purpose is to cut things is encoded by the predicate cut act in the telic role see NUM above
first it can generate and link aliases of names automatically using language specific alias generators
we plan to port it to other unsegmented languages such as chinese and thai in addition to other european languages
figure NUM rightward complete link outside probability
figure NUM rightward complete sequence outside probability
figure NUM leftward complete link outside probability
figure NUM leftward complete sequence outside probability
the value of m varies from i to j NUM
this is exactly what is needed as a semantic domain bias of the later classification process
third raw contexts of words provide a significant bundle of information able to guide disambignation
the relation of NUM orders dependencies in terms of which can be established earlier on i.e. NUM NUM if the later arriving assumption of NUM arrives before the later arriving assumption of NUM note however that NUM NUM may have the same later arriving assumption i.e. if this assumption is involved in more than one dependency
availability of source information for semantic tagging or disambignating words in corpora is problematic
applying semantic disambignation as soon as possible is useful to improve later la and other linguistic tasks
figure NUM rightward complete sequence inside probability
figure NUM rightward complete link inside probability
since noisy features will receive low ig weights this also implies that it is much more noise tolerant
words in the lexicon were hyphenated based on the assumption that all remaining candidates do split
in this paper we propose a markov radom field mrf model based approach to the tagging problem
furthermore the category is expanded to include candidates whose second part is a double vowel blend
all diphthong and excessive diphthong candidates included in these words were collected and designated nonsplitting sequences
this holds for all vowel sequences in greek independently of whether they exist within acceptable words
from the remaining vowel sequences of lemma NUM a few may be identified as non existent
identification of certain patterns would presumably be based on an empirical rather than an analytical process
in our experience this method has proven extremely effective for avoiding missegmentation pitfalls essentially erring only in pathological cases involving coordination constructions or lexicon coverage inadequacies
NUM given a sequence of word and phrase categories t t1 tk and a parent category q we calculate the sequence of grammatical functions g
two levels of automatic annotation level NUM assigning grammatical functions and level NUM assigning phrase categories have been presented and evaluated in this paper
to deal with this problem formally we shall determine weaker boundaries of diphthongs and excessive diphthongs
let cl be the first obligatory consonant in the consonant sequence of that expression
in order to examine the domain dependence of parsing in this paper we report NUM comparison of structure distributions across domains NUM examples of domain specific structures and NUM parsing experiment using some domain dependent grammars
these two results and the evidence that fiction domains are close in terms of structure indicate that if you have a corpus consisting of similar domains it is worthwhile to include the corpus in grammar acquisition otherwise not so useful
however we do n t know which of the following two types of grammar produce better performance a grammar trained on a smaller corpus of the same domain or a grammar trained on a larger corpus including different domains
in the romance and love story domain the precision of the grammar acquired from NUM samples of the same domain is only about NUM lower than the precision of the grammar trained on NUM samples of the same domain
for example figure NUM shows the five most frequent partial trees in the format of production rule in domain a press re null portage and domain p romance and love story
so it may be plausible to say that the grammar of the fiction domains is mainly representing k l and n and because it covers wide syntactic structure it gives better performance for each of these domains
the assumption that the fiction domain grammar represents domains of k l and m may explain that the parsing result of domain p strongly favors the grammar of the same domain compared to that of the fiction class domains
if p s is the statistical model of s the probability can be approximated by the n gram probabilities
the analysis of the hit rate shows also a large variation in the structure of the dialogues from the corpus
to find out whether there might be an additional interpretation the plan recognizer relies on information provided by the statistics module
also since the dialogues in our corpus are rather unrestricted they have a big variation in their structure
as can be seen and as could be expected the prediction rate drops heavily when unforseeable deviations occur
this is the case especially in the first application of vv rbmobil the ondemand translation of appointment scheduling dialogues
needs accurate estimates of life cycle support required cost and
the speech recognizer uses a frame synchronous one pass viterbi algorithm and earley like parser for context free grammar while using hmms as syllable units
the dialogue manager is a component which carries out some operations such as dialogue management control of contextual information and query to users
irus in the examples below are capitalized and their antecedents are italicized
when this happens the relevant material is displaced to main memory
consider dialogue c in figure NUM
in the cache model popping only occurs via displacement
this locality assumed is rcfercd t o as t mm kov imlcl endence rlsstlllll liioil
NUM the cache model includes specific assumptions about processing
however the inference requires more effort to process
let us consider the first possibility
section NUM contains an example for which such a type apply tm tm tm o
in other words object level function application is handled simply by the meta level function application
they will both have the category fs bs np s s
for example the following query hate xl x
ethe i notation is used because of the combinatory logic background of ccg
also the predicate atomic type is declared to be true for the four atomic categories
each ccg lf is represented as an untyped a term namely type t
NUM NUM each of the three operations have both a forward and backward variant
each has a different set of interests
in this sense parts of speech seem to differ from morphology and syntax their status as an independent level of linguistic description appears doubtful
this resnlt tells us that it is reasonable to select contexts with large values of raxiance to ones with small v 4riance and a relatively axge number of contexts are enough for the clustering process
the model incorporates various recent techniques for incorporating and manipulating linguistic knowledge using finite state transducers
there are two weaknesses in chang et al s model which we improve upon
z japanese octopus c no how say NUM NUM NUM NUM NUM NUM NUM NUM figure NUM
there are thus some very good reasons why segmentation into words is an important task
NUM in various dialects of mandarin certain phonetic rules apply at the word level
an initial step of any textanalysis task is the tokenization of the input into words
classical metric multidimensional scaling of distance matrix showing the two most significant dimensions
under this scheme n human judges are asked independently to segment a text
the last affix in the list is the nominal plural lcb r meno
sproat shih gale and chang word segmentation for chinese handled given appropriate models
separate whenever possible between the needs of novice and expert users user adaptive dialogue
otherwise we might get wrong results by erroneously crediting the high number of occurrences of such a word NUM to one of the analyses
the set of rules used by the algorithm for automatic generation of sw sets for each analysis in the language are of a heuristic nature
to clarify this point consider the word mcby xr2 which has the following two morphological analyses
given a corpus in which every word is tagged with its right analysis we can find the morpho lexical probabilities as reflected in the corpus
this assumption holds for most of the hebrew verbs since all hebrew nouns and not only animate ones have the gender attribute
we can now use the models to do a sample backtransliteration
in this case it seems that adding information reduces nondeterminism
this suggests that lexical tokens do not provide any selective information
if the verb is intransitive rule NUM can not apply
as an illustration consider the following set of context free rules
whoi did you think ti seemed t i to like mary
NUM disjoint chains are nested as in 6a
d whoi did john think t i that mary loved ti
however in no case do they clearly disconfirm the hypothesis
some features of the corpora can be seen in table NUM
finally a multispeaker speech corpus for the task was acquired
for these adverbs the appropriate head is asp0 which we find only with finite verbs
the second clause selects a chain when the foot is seen
figure NUM general schema of the treatment of categories in the learning and translation processes
spanish deseaha reservar una habitacisn tranquiia con teldfono y teievisidn hasta pasado mal ana
dependencies such as cross serial depe ndencies
by defining a few such nota tions
list for english or french ex mls
the basic strategy for choosing a candida te
not written in a foreign hmgua ge
table NUM presents the performance of the competitive backed off estimation algorithm on the test data
he concludes that although no mathematical analysis for the algorithm is available the complexity appears to increase exponentially with the input size
conceptually the latter organization encodes x theory directly and it maintains a general design which makes it applicable to several languages
however experiments with parsers that are tightly related to linguistic principles have often been a disappointment largely because these parsers are inefficient
i differ from other investigations on the import of principle based parsing in not drawing on cognitive issues or psycholinguistic results to justify my assumptions
abney alleviates this problem by attaching lr states to the constructed nodes thus losing much of the initial motivation of the licensing approach
modularity if it exists is to be found in the linguistic content and not in the organization of the theory
this confirms the intuition that the results reflect some structural property of the grammar and are not an artifact of the lr compilation
grammar NUM is larger than grammar NUM as it contains category and some co occurrence information but its average of conflicts is smaller
these properties interact so that when the subject np is reduced infl is always the next token in the parsing configuration
these algorithms deal in detail with the somewhat neglected problem of what to do when more than one chain has to be constructed
null NUM a er wird seiner tochter ein m irchen erzghlen mfissen
but exactly those constituents that have to be avoided in the mittclfeld are needed in the vorreid
his daughter a fairy tale he will have to tell his daughter a fairy tale
there is no problem with sentences like those in NUM for the standard nonloc mechanism
outperformed that of any frequency estimation methods
aud NUM NUM f measure NUM NUM
the former moves a word boundary keeping the number of words unchanged
this is a major flaw of our word model using character unigram
the third type is erroneous longest match
NUM NUM the nature of the word unigram model
there are two syllabaries hiragana and katakana
NUM NUM comparison of various word frequency estimation methods
fizst we will clarify the nature of the word unigram model
we used the word unlgram model because of its computational efficiency l
heuristics NUM kinds of terminal symbols some terminal symbols like punctuation symbols conjunctions and particles are often misused
this paper presents a trainable rule based algorithm for performing word segmentation
null table NUM shows a summary of the four chinese experiments
this greedy algorithm produced an initial score of f NUM NUM
this greedy algorithm gave an initial segmentation score of f NUM NUM on the test set
learning the rule sequences can be achieved in a few hours and requires no language specific knowledge
we can use these resources to provide an informed initial segmentation approximation separate from the greedy algorithm
the thai corpus consisted of texts s from the thai news agency via nectec in thailand
this often resulted in words containing NUM or more characters which is very unlikely in chinese
this score was improved further when combining the character as word caw and the maximum matching algorithms
for the novel event priors we used a simple variant of the good turing method which could be easily implemented online with our data structure
a node s has a probability c of being a leaf and a probability NUM a of being an internal node
as a simple test of for applicability of the model for language modeling we checked it on text which was corrupted in different ways
therefore such a sequence can never be a reasonable plan or a reasonable expectation el out a townhil race
in this mode we also checked the performance of the single most likely maximum aposteriori model compared to the mixture of psts
the likelihood values of the mixture of subtrees equation NUM are returned from each level of that recursion up to the root node
the grammar network construction algorithm consists of two steps the first defines the basic structural description i.e. bar level nodes and the second defines the satellites i.e. adjunct and specifier nodes
if principar had an average case complexity that was exponential relative to sentence length but had only managed to be efficient because of the implementation language the sentence length vs performance curve would clearly be different from the curves for cfg parsers which are known to have a worst case complexity that is polynomial relative to sentence length
roughly a preposition assigns oblique case to a prepositional object np a transitive verb assigns accusative case to a direct object np and tensed infl ection assigns nominative case to a subject np
the subjacency condition is implemented by the percolation constraints attached to the barrier links which block any message with barrier and changes barrier to barrier i.e. it allows the message to pass through
every phrasal constituent is considered to have a head x deg x which determines the NUM for the purpose of readability we have omitted integer id s in the graphical representation of the grammar network
a phrase potentially contains a complement resulting in a one bar level x xbar projection it may also contain a specifier or modifier resulting in a double bar level x xp projection
while the principles described in the previous section are intended to be languageindependent the structure of each grammar network in figure NUM is too language specific to be applicable to languages other than the one for which it is designated
john i phal i pwureciessta nom arm nom was broken john is in the situation that his arm has been broken the grammaticality of the above example suggests that nominative case in korean must be assigned by something other than tensed infl ection
the reason the message passing paradigm is so well suited to a pararneterized model of language parsing is that unlike head driven models of parsing the main message passing operation is capable of combining two nodes in any order in the grammar network
the generalized backed off estimation approach which we have presented constitutes a practical solution to the problem of multiple pp disambiguation
the recognition of a certain input v is obtained if starting from the initial configuration for that input we can reach the final configuration by repeated application of transitions or formally if qin v i q aria e where t denotes the reflexive and transitive closure of b
for any q c ilk closure q is the smallest set such that i q c closure q and ii b c aft e closure q and a NUM e pt together imply a NUM e closure q
in a stack symbol of the form x q the x serves to record the grammar symbol that has been recognized last cf the symbols that formerly were found immediately before the dots
transitions in i above are again called shift transitions in ii are called initiate those in iii are called gathering and transitions in iv are called goto
if the top most symbols of the stack are NUM then these symbols may be replaced by NUM provided that either z e or z a and a is the first symbol of the remaining input
null our implementation is merely a prototype which means that absolute duration of the parsing process some of their dimensions and the average length of the test sentences NUM sentences of various length for each grammar
suppose now that the administrator finds the texts useful but insufficient
the broader value of this approach however remains suspect until it can be demonstrated to apply more generally
it uses an application specific input file called the application schema which describes all the relevant fields in that application and lexico semantic patterns that indicate their presence
matchplus computes the dot product of every other word vector in the vocabulary to the selected word
words that are never used in a similar context will retain their initial condition of quasiorthogonality
additionally the current volumes of information would overwhelm any organization who attempted to perform bulk translation
however it should be noted that these approaches are extensible to many languages being processed simultaneously
language learning tools could exploit the technology to analyze the relationships between word usage s across languages
additionally this approach can be utilized for ideographic languages such as japanese chinese and korean
these desired dot products are found as a function of co occurrence statistics for word stems i and j
the factors at b are the desired dot products for the trained set of context vectors
this technique called symmetric learning is based upon the use of tie words
what is proposed is to use the tie word list to provide references for common context vectors
parameters ai e are tied together for similar c to prevent data sparsity
for the inside outside algorithm we follow the methodology described by lari and young
however in our work we do not wish to limit the size of the grammars considered
presents a bayesian grammar induction framework that includes such a factor in a motivated manner
where c w denotes the count of the word sequence w in the training data
in addition we still achieve the goal of parsing each sentence but once
when we find a superior grammar we make this the new hypothesis grammar
large can prevent the objective function from preferring grammars that overfit the training data
consider the move of creating a rule of the form a bc
for the left hand side of a rule we always create a new symbol
NUM inconsistent user or system errors may sometimes lead the dm to this state where the system s knowledge of the various fields violates some consistency rule
for example in a flight arrivai departure application the user might say i said dallas not dulles to correct a misrecognition by the speech recognizer
aac systems complain more about the slowness of their speech output than about any restrictions limiting the precision with which their thoughts can be expressed
there are two means by which this can be achieved
the input is tagged using a standard tagger e.g.
np x pn x a lcb a rcb
np x n x a lcb a rcb
it can not consequently be included in the generated output
for example some slots and their fillers can be quite ambiguous cf
the biggest baggage is that someone has to write them
NUM some knowledge of spanish would be helpful
multilingual generation and summarization of job adverts the tree project
busy near the seaside or the employer e.g.
transitions phrases are combined bottom up to form progressively larger phrases
altogether the entries in these lexicons made reference to around NUM structurally distinct head automata
transfer search maintains a set m of active runtime entries
ci is the cost of the match specified by the parameter table
the translation runs were carried out with parameters from method a
it is also somewhat sensitive to peculiarities of the distance function h
invited talk head automata and bilingual tiling translation with minimal representations
after all this is the primary function of natural language
we informally refer to the transcribed utterances as sentences
a reasonable lower bound seems to be NUM NUM as scored by the most likely for each preposition method
the above estimate is undefined in this situation which happens extremely frequently in a large vocabulary domain such as wsj
however later in this paper it is shown that estimates based on low counts are surprisingly useful in the pp attachment problem
we illustrate this analysis by constructing a drs in figure lb for sentence NUM
the decision was made as followsz an experiment was implemented to investigate the difference in performance between these two methods
in particular quadruples and triples seen in test data will frequently be seen only once or twice in trmning data
NUM NUM are normalisation constants which ensure that conditional probabilities sum to one
a nucleus is defined as a structure containing a preparatory process culmination and consequent state
this reference time is represented as r0 in the top sub drs
ta when mary telephoned sam was always asleep
this state is a result of the event e in which mary met the president
the eventualities described by the perfect of a verb refer to the consequent state of its nucleus
a greedy search is used to learn a sequence of transformations which minimize the error rate on training data
because the temporal connective is before rl is restricted to lie before el
such processing could be included in an application but outside the architecture compliant portion
the external tipster interfaces are the links between the outside world and the tipster environment
the end user is the person whose needs the application is designed to meet
this cooperation will be in the form of an engineering review board erb
this document may contain specifications for some of the modules he needs to implement
in that case he may look in the architecture design document for guidance
the selection of which architecturallyspecified modules to use is the responsibility of the application
these sub modules may be modules in themselves or complete systems or databases
under these circumstances conformance to the tipster architecture can not be rigidly defined
the tipster architecture describes a common framework for the development of text processing applications
in the commandtalk grammar the digit category has features singular vs plural zero vs nonzero etc that would generate at least NUM combinations if all instantiations were considered
once we have transformed the gemini unification grammar into an equivalent grammar over atomic nonterminals we then rewrite the grammar as a set of definitions of the nonterminals as regular expressions
thus if there is a rule that does not constrain a feature on a particular daughter category an atomic category will be created for that daughter that is under specified for the value of that feature
for instance if no unit is explicitly addressed by a command it is assumed that the addressee is the unit to whom the last verbal command was given
the loxy formula which is to be paraphrased is processed step by step to natural language by the different modules to a deep structure
the natural module is also called sentence planner i.e. it plans the length and the internal order of the different sentences
further on it is difficult to express the nl paraphrase in a similar fashion as the user expresses him herself therefore are some improvements suggested
the quasi logical form qlf of cle uses already dcl ynq and whq and could be extended to also treat np
c when the anaphor is included in a pp particular case of b pp attachment rules need semantic information about the object of the pp when it is a pronoun no semantic information is available so that the attachment rules can not be applied
the objective was to emphasise more than it has been done until now the fact that pp attachment and anaphora resolution could interact in the same system in order to produce a complete conceptual analysis instead of slowing down each other
three main cases are faced by the algorithm a when the anaphor occurs before a given preposition in the sentence its resolution does not depend on where the preposition is to be attached except for cataphors that are quite rare
in both cases too a little less than NUM appear difficult to recover given the current filtering last two lines of the table
due to the limit of paper space we show only f measure in figure NUM the graphs tell us that the case of top NUM seems superior to the case of NUM random contexts in a11 merging step
each text in the evaluation suite was tested for each facet
gpi while ignoring the interlocutor s background knowledge
some applications benefit from categorization according to facet not genre
and letters to the editor will be of roughly equal interest
we represent ratios implicitly as sums of other cues by transforming all counts into natural logarithms
ratios correlate in certain ways with genre and have been widely used in previous work
the baseline column tells what percentage would be got correct by guessing no for each level
NUM NUM structural for all variables and NUM NUM surface vs NUM NUM
one place where sr errors might arise is in the recognition of names in the user s personal phone book
for the current application the fact that the car is an acoustically hostile environment is an extra complication
for purposes of automatic classification they have the limitation that they require tagged or parsed texts
it is also an example of a narrative as opposed to a directive e.g.
each recognized phrase is mapped to a set of representations as found in the lexicon
in the dm module this list is unwrapped and the phrases are parsed
the dm can give feedback to the user via two modalities sound and vision
we have tried to summarize these guidelines in NUM metaguidelines or commandments
these evaluation temporal units were assigned by an expert working on the project
it relies on a set of elementary trees defined in the lexicon which have at least one terminal symbol on its frontier called the anchor
fewer than NUM cumulative errors result from these primary areas
here the phrasal less than and the company coke are identified and marked as multitokens mt i would be mt lcb less than rcb less than honest to say i m not disappointed not to be able to clai m creative leadership for mt lcb coca cola rcb coke mr dooner says
using the link frequencies f s t and the frequencies f s of each english source word s the maximum likelihood estimates of pr t s the probability that s translates to the french target word t can be computed in the usual way pr tls f NUM NUM f s
cmo while the remaining two contain multi sentence elaborations of the original supposition
a retired professional killer if he was just a nut no harm was done
of these NUM contain imperative suppose or let us suppose
the expectation raised in a is not resolved until clause d
but this is just a right frontier rf rooted at that left sister
the latter would we believe have to be coupled with incremental sentence level processing
but if he was the real thing he could do something about lolly
the following examples illustrate the creation of expectations through discourse markers example NUM a
with respect to parser design this result implies that the well known polynomial time complexity of chart or tabular based parsing techniques can not be achieved for these dg formalisms in general
in addition the instructions serve to document the annotations
the brown corpus contains only NUM examples of adverbial on the one hand
it is only the latter that are of interest from the point of discourse expectations
for instance the contrast in NUM is based on the knowledge that kicking the ball into the wrong goal implies scoring a goal for the opposing team
however both are subtypes of a more general event type and if regarded at this higher event level the structures might be considered as contrastible after all
acknowledgements this research was carried out within the priority programme language and speech technology tst sponsored by nwo the netherlands organization for scientific research
after six minutes nilis scored a goal for psv
1besides the situation aspect described by the semantic entry of the verb many other sentential constituents e.g.
there are two main problems with this approach
if the resulting representations are unifiable the two sentences stand in a contrast relation and the parallel elements from the most recent one receive a pitch accent or another focus marker
this caused ajax to fall behind
in english the perfective view sets the end point NUM and no cancellation is allowed afterwards
a similar value is the ambiguity of nouns in the set of their unique beginners
we will break matrix indices into two parts our grammar will check whether the first parts of k and are equal and our string will check whether the second parts are also equal as we sketched above
however in order to preserve time bounds we desire that o ig i o gi and we also require that theorem NUM holds for g as well as g
thus given two boolean matrices we need to produce a string and a grammar such that parsing the string with respect to the grammar yields output from which information about the product of the two matrices can be easily retrieved
a boolean matrix multiplication algorithm takes as input two m x m boolean matrices a and b and returns their boolean product a x b which is the m x m boolean matrix c whose entries c j are defined by
if we had written the second condition as if a does not c derive ur i then then cky parsers would not be c parsers since they keep track of all substring derivations not just c derivations
however since g contains no epsilon productions or unit productions it is easy to see that we can convert g simply by introducing a small o n number of nonterminals without changing any c derivations for the cp q
lang in fact argues that parsing means exactly the production of a shared forest structure from which any specific parse can be extracted in time linear with the size of the extracted parse tree lang NUM pg
finally we note that if a lower bound on bmm of the form f m NUM a were found then we would have an immediate lower bound of n NUM 3a on c parsers running in time linear in g
menus are supported as a useful way of getting help on commands and labels
however additionally smith assumes a so called neutral viewpoint which contains the initial point and at least one internal stage
it may be concluded from this analysis that discourse structure differs depending on the language
after receiving feedback they annotated four unseen test dialogs
ow c is the number of words in the corpus appearing in at least one of the synsets of w that belong to c the synonymy depends both on wordnet and on the corpus
a requirements analyst builds a formal object oriented oo domain model
this allows the user to edit human authored annotations of the object model
a user domain expert validates the domain model
aberdeen ab9 2ue scotland ereiter csd abdn
it has the ta sally blake figure NUM description used for validation
modex is implemented in c on both unix and pc platforms
kittredge t korelsky d mccullough a nasr and m
the certainty factor of an ailt is calculated as follows
no bilingual concordances were made available to them
lr and lt are not defined on word sequence of NUM length zvi
applicable to word prediction and imply that much better keystroke savings could be achieved in principle
however if an out of domain sentence is automatically detected as such by the parser and is not translated at all it is given an ok grade
as hoped both the evidential and merging decision approaches outperformed the uniform and greedy approaches with respect to cross entropyj deg interestingly and perhaps surprisingly the evidential approach outperformed the merging decision model even though in many respects the latter is more natural and elegant
for korean english simr takes advantage of punctuation and number cognates but supplements them with a small translation lexicon
in the extreme case where i u we know that the text between j and v is noise
in the recognition phase simr calls the chain recognition heuristic to find suitable chains among the generated points
texts that are available in two languages bitexts are immensely valuable for many natural language processing applications z
next the angle of each chain s least squares line is compared to the arctangent of the bitext slope
those procedures attempted to align texts by finding matching word pairs and have demonstrated their effectiveness for chinese english and japanese english
thanks to gary adams cookie callahan bob kuhns and philip resnik for their help with that project
we therefore decided to use a weighted finite state transducer machinery which is the technological framework for the text analysis components of the bell labs multilingual tts system
estimated time estimated time main informant for spent to build spent on language pair matching predicate new axis generator hand alignment
injectivity no two points in a chain of tpcs can have the same x or y co ordinates
the NUM th one has the same morphological features except for the root
knight s explanation planner uses the following resources the biology knowledge base explanation design packages the kb accessing system and an overlay user model
some revisions involved NUM while we have not explored this hypothesis in the work described here the edp framework can be used to test it empirically
the recursion bottoms out when the system encounters the leaves of the edp i.e. content specification nodes in the edp that do not have elaborations
the edp library has an indexing structure that maps a query type to the edp that can be used to generate explanations for queries of that type
these findings demonstrate that an explanation system that has been given a well represented knowledge base can construct natural language responses whose quality approximates that of humans
parses which are not disallowed by the syntactic context it appears in
these can only be disambiguated usually on semantic or discourse constraint grounds
turkish allows sentences to consist of a number of sentences separated by commas
table NUM number of choose and delete rules learned from training texts
nlearning iterations have been stopped when the maximum rule score fell below NUM
for example the feature structure above is projected into a feature structure such
the first text labeled ark is a short text on near eastern archaeology
for example in the bnc diet has probability of about NUM NUM of occurring in the food sense and NUM NUM in the legislature sense the remainder are metaphorical extensions e.g. diet of crime
a distinctive feature of sdrt is that if the dice axioms yield a nonmonotonic conclusion that the discourse relation is r and information that s necessary for the coherence of r is n t already in the constituents connected with r e.g. elaboration a NUM is nonmonotonically inferred but e3 c eo is not in a or in NUM
for example to reflect the fact that compounds such as apple juice seat which are compatible only with general nn are acceptable only when context resoh es the compound relation we assume that the drs conditions produced by this schema are rc y x rc NUM and made o y x
if the lexicon produces a small number of very underspecifled senses for a wordform the ambiguity problem is apparently reduced but pragmatics may have insufficient information with which to resolve meanings or may find impossible interpretations
schemata can be re null garded formally as lexical grammar rules lexical rules and grammar rules being very similar in our framework but inefficiency due to multiple interpretations is avoided in the implementation by using a form of packing
for example elaboration states if NUM is to be attached to a with a rhetorical relation where a is part of the discourse structure r already i.e. r a NUM holds
suppose that the sentence NUM is misparsed as an active rather than a passive sentence due to the omission of the verb was and that the prepositional phrase NUM nm is misparsed as the direct object of the verb destroy
once we allow the parser to take part of speech as the input the parts of speech rather than actual words will appear as the terminal symbols in the parse tree and hence as the vocabulary items in the semantic frame representation
considering the low parsing coverage of a semantic grammar which relies on domain specific knowledse and the fact that the successful parsing of the input sentence ks a prerequisite for producing translation output it is critical to improve the parsing coverage
we have given experimental results of the proposed grammar and compared them with the experimental results of a syntac tic grammar and a semantic grammar with respect to parsing coverage and misparse rate which are summarized in table NUM and table NUM
prepositions and domain specific phrases providing the accurate subcategorization frame for the verb intercept by lexicalizing the higher level category vp ensures that it never takes a finite clause as its complement leading to the correct parse as in figure NUM
each word in the tagged training corpus has an entry in the lexicon consisting of a partially ordered list of tags indicating the most likely tag for that word and all other tags seen with that word in no particular order
no of misparsed sentences NUM NUM NUM mar evaluation results of the two types of grammar on the test data given in table NUM and table NUM are similar to those of the two types of ammar on the test data discussed above
while most stochastic taggers require a large amount of training data to achieve high rates of tagging accuracy the rule based ethe parsing coverage of the semantic grammar i.e. NUM NUM is after discounting the parsing failure due to words unknown to the rammar
he is kind to her but he is n t her husband
for example consider the following sequence NUM a
he s the president s key man in negotiations with congress
this underdetermination may continue in a subsequent utterance with the pronoun
he was sick and furious at being woken up so early
satisfaction of the dsps contributes to the satisfaction of the dp
centering was proposed as a model that accounted for this phenomenon
some citations to other work have dates between NUM and NUM
three pieces of previous research provide the background for this work
this paper presents an unsupervised algorithm that can accurately disambiguate word senses in a large completely untagged corpus the algorithm avoids the need for costly hand tagged training data by exploiting two powerful properties of human language NUM
the training procedure computes the word sense probability distributions for all such collocations and orders them by r NUM pr sensealcolloeationi x NUM the log likelihood ratio gt prisenseblcolloeationi with optional steps for interpolation and pruning
if one begins with a small set of seed examples representative of two senses of a word one can incrementally augment these seed examples with additional examples of each sense using a combination of the one senseper collocation and one sense per discourse tendencies
the process employed here is sensitive to variables including the type of collocation adjacent bigrams or wider context coliocational distance type of word content word vs function word and the expected amount of noise in the training data
a used to strain microscopic a zonal distribution of a close up studies of a too rapid growth of aquatic a the proliferation of a establishment phase of the a that divide life into a many dangers to a mammals
keep a manufacturing molecules found in union responses to animal rather than many dangers to company manufacturing growth of aquatic automated manufacturing animal and discovered at a st louis plant profitable without plant and animal tissue plant closures
with this model structure we tried a number of methods for assigning cost functions
where snv denotes the strength of a noun verb pair snn the strength of a noun noun pair and d x y represents the distance between x and y
first we concentrated on finding the best thresholds NUM for the rule sets
we tagged several texts of different origins except from the brown corpus
the results of this tagging are summarised in table NUM
NUM estimate is a good indicator of rule accuracy
here is a typical example of tagging a text of NUM words
word leading and trailing characters to figure out its possible pos categories
for every word we computed its metrics exactly as in the previous experiment
thus the most frequent words had the greatest influence on the aggreagte measures
under the topic coherence postulation in a paragraph we compute the connectivities of the nouns in each sentence with the verbs and nouns in the paragraph
for example 1consider an example try tried
so first words are looked up in the lexicon
under the topic coherence postulation the nouns that have the strongest connectivities with the other nouns and verbs in the discourse form the preferred topic set
the experimental test has been difficult as a precise notion of what is a relevant term in a domain is very vague and subjective
alter the first set of parameters is generated the remain NUM NUM lob corpus is run until par and pv converge using equations NUM NUM and NUM
we solved these problems by offering the user the possibility to name constraints e.g. principle1 c NUM c
because a noun with basic form n may appear more than once in the paragraph say k times its strength is normalized by the following recursive formula
NUM the size of the dictionary in plain text ascii form is 742kb
we will conclude with a discussion of the inter coder reliability
figure NUM the drafter dialog box for setting the local parameters
figure NUM drafter screen with the procedural structure for the example
this indicates that the reader should avoid damaging the service cover
figure NUM shows the drafter interface after this has been done
we ran the three programs on large files and piped their output into a file
this module basically follows the same techniques as the ones used to implement the lexicon
dr after allows authors to set generation parameters on individual actions using a dialog box mechanism
the main user goal of repairing the device is represented by the largest enclosing box
next we added the etd training corpus to the esst training corpus and used the merged corpus for language model training
in the press reportage experiment the grammar acquired from the same domain does not make the best performance when the size of the training corpus is small
we do not regard the result in section NUM NUM as a negative one since general transformations specified as in NUM seem too powerful for the proposed applications in natural language processing and learning might result in corpus overtraining
for each node q of tx dominating some node in f p we store in v p the triple q e e since a linktr q necessarily dominates p
in addition we will show that critical tokenization forms a sound mathematical foundation for categorizing critical ambiguity and hidden ambiguity in tokenizations which provides a precise mathematical understanding of conventional concepts like combinational and overlapping ambiguities
it helps to clarify that the only difference between the definition of tokenization ambiguity and that of critical ambiguity in tokenization lies in the tokenization set while tokenization ambiguity is defined on the entire tokenization set td s critical ambiguity in tokenization is defined only on the critical tokenization set cd s which is a subset of to s
as the former pair of operations is well established and has great influence in the literature of sentence tokenization many researchers have either consciously or unconsciously been trying to transplant it to the latter
for all edges d if g is the active symbol in the rhs of d and match g c returns NUM then call extend d c and add the resulting edge
by convention we will use a and b for symbols that can be either terminals or nonterminals c for terminal symbols only d for the semantic domain of a terminal and i for an integer index
in fact this is not the correct rule for the ditransitive phrase the vp is not but rather g j but we would not be able to distinguish the monotransitive and ditransitive cases g and because both g and can have part of speech vn
nom np vn lcb np the new right condition np prevents rule NUM from being used within cases such as rule NUM where the immediately following constituent is an np
we would like to note at the outset that from the formal language standpoint the complications introduced by the form of our production rules have so far hindered theoretical analyses of the formal expressiveness characteristics of this grammar
NUM is parsed as np locph modph np np because ps g is a locative phrase where locph stands for locative phrase and modph stands for modifier phrase
a traditional context free grammar cfg is a four tuple g n p s where n is a finite set of nonterminal symbols is a finite set of terminal symbols such that n n o p is a finite set of productions and s e n is a special designated start symbol
given two sentences NUM a b zki gu ngd6ngsh ng de t6uz c in guangdong investment d the investment in guangdong province NUM a j e b z i xi ozh ng de ji c in xiaozhang house d in xiaozhang s house they have the same surface structure
if the return value is NUM add an initial edge e for that rule to chart for all the chart entries subtrees d beginning at end e l if g is the active symbol in the rhs right hand side of e and match g c returns NUM then call extend e cl
integrated with other nlp systems the task of our verb sense disambiguation system is not only to output the most plausible verb sense but also the interpretation certainty of its output so that other systems can vary the degree of reliance on our system s output
the training utility of an example a is greater than that of another example b when the total interpretation certainty of examples in x increases more after training using the example a than after using the example b
this sentence generates the following message ambiguous attachment of verb phrase using the same application program
secretary nom sleeping car acc in this example one may consider hisho secretary and shindaisha sleeping car to be semantically similar to joshu assistant and hikski airplane respectively and since both collocate with the to reserve sense of toru one could infer that toru may be interpreted as to reserve
NUM given the fact that example based natural language systems including our system search the example database database hereafter for the most similar examples with regard to the input the computational cost becomes prohibitive if one works with a very large database size NUM
one of our greatest concerns has been to provide a system that is both useful and acceptable to the user
easyenglish works with sgml bookmaster or ip formats as well as with plain text
user dictionaries for restricted words acronyms and controlled vocabulary have been built for the idwb for certain domains
who what is using the same application program different system users or different objects
the evaluation of transcribed input allows us to assess how well our translation modules would function with perfect speech recognition
i i d v j d cornpute the corresponding entry in a d v x d v
the clustering program relies on ldoce domain code grammar code and NUM types of semantic relations exu acted from definitions
a similar heuristic lex match was incorporated into our program as the following preprocessing steps NUM for each source node v all possible lexical matches are identified in the target tree if
null in most of the above mentioned works experimental results are reported only for some senses of a couple of words
NUM the implicit links between instances of many of these relations are available in a thesaurus such as lloce
the summation in NUM ranges over all pairs denoted by i j which appear in a given palnng p e p v v
nearly NUM NUM sets of related words in lloce are organized according to NUM subjects and NUM topics top
the era rent implement tion aligns trees NUM times faster than our previous program grishman 199d with a NUM NUM inq rovement in pre ision
initially s is filled with undefined values
the algorithm seeks an alignment with maximal score
NUM earth which is heaped up in a l eld or garden often making a border or division
we are currently trying to get a licence s t the full lloce entries in order to ccmduct a more complete test
more thorough and challenging methods of evaluation are now feasible
this principle may be expressed in different ways but the idea is essentially the same
thanks also goes to karen hamilton for her implementation of the database used for our error analysil
when subject verb agreement is marked in asl it involves NUM
to be is not lexicalized using a standard sign
second languages may differ in how they mark a feature
thus these mal rules capture expected language generation pattems from this population
it is what he she is currently in the process of acquiring
this has several implications on the responses given by the system
a student stops making some errors and begins making others
the situation of subject verb agreement is more complex in asl
NUM go loc thing NUM toward NUM loc thing NUM at loc thing NUM thing NUM by NUM
the reference classification is highly domainand task dependent
line or reformulating a candidate term e.g.
in this paper we focus on np clustering
in the remainder of this article we describe the way a ke uses lexiclass to build conceptual fields and we also compare the clusterings obtained from the two different data sets
NUM relevant clusters that can be labeled
NUM relevant clusters that can not be labeled
on the theoretical side we think that this result for s01 is also of some importance since sdi exhibits a core of logical behavior that any lambek based logic must have which accounts for non peripheral extraction by some form of permutation
however the morpho lexical probabilities can not serve as the only source of information for morphological disambiguation since they are imperfect by definition they always choose the same analysis as the right one regardless of the context in which the ambiguous word appears
the performance of the method for full disambiguation is measured by the recall parameter which is defined as follows no of correctly assigned words recall no of ambiguous words in addition to this parameter we present two additional performance parameters applicability and precision
tr1 corresponds to our rule NUM together with an animacy test to distinguish between pronouns and nominal anaphora
a formal description of the algorithm written in a pseudo code is given in figure NUM NUM this is done mainly in order to handle cases where a certain analysis has an empty sw set since it does not have naturally similar words
the perplexity results of table NUM indicate that the ling seg model does better than the acoustic seg model for hypothesizing segment boundaries
for instance the word xlw n has two such morphological analyses the verb xlh n n fem masc plural third person past tense they became ill
since the approximation we acquire depends on the corpus we have been using texts taken from the hebrew newspaper ha aretz NUM we have to computational linguistics volume NUM number NUM calculate the test corpus probabilities from texts taken from the same source
morpho lexical probabilities estimated over a test corpus in order to avoid the laborious effort needed for the manual tagging of all the occurrences of an ambiguous word in a large corpus we estimate the morpho lexical probabilities by calculating them from a relatively small corpus
this is since the ambiguous word at is very frequent in the corpus while the counters in the sw sets for the second and third analyses indicate that these analyses are not the reason for the high frequency of at in the corpus
the problem stems from the fact that we have been able to use the morphological analyzer on personal computers only while both the corpus and the program that automatically generates the sw sets for each analysis could have been used only on our mainframe computer
a hypothesized model is accepted if the significance i.e. probability of its reference g value is greater than in the case of fss or less than in the case of bss some pre determined cutoff a
summarist is an attempt to create a robust automated text summanzauon system based on the equation summarization topw ment ficatwn mterpretatwn generatwn we descnbe the system s arclutecture and provide detmls of some of its modules
mclude a rudimentary generator that composes noun phrase and clause stzed umts into stmple sentences it wdl extract the noun phrases and clauses from the mput text by following hnks from the fuser concepts through the words that
then by definition any dependency tree of a sentence wi n can be uniquely represented with either a lr bos eos or a sl NUM eos as depicted in figure NUM
arise because exmtmg robust nlp methods tend to operate at the word level and hence miss conceptlevel generalizations which are provided by symbolic world knowledge whale on the other hand symbolic knowledge is too difficult to acqmre m large enough scale to provide coverage and robustness
true compilation is the logical development in a maturing field that has hitherto relied on interpreters in high level programming languages such as prolog and lisp
figure NUM sample retrieved classifications of adjec
the results of training the maximum entropy models are discussed in section NUM to illustrate the approaches described in the next section we will use the probabilities for the templates from the example passage in section NUM shown in table NUM which were produced from the parameters induced from one of the training sets
the third term is for the larger lt i h from the combination of sr i si j NUM h and the dependency link from wh to wi
inference in alembic is actually performed directly on interpretation structures an d there is no need for a separate translation from interpretations to more traditional looking propositions
one such rule distributes the meaning of certain adjectives such as retired across coordinated titles as in retire d chairman and ceo
on the other hand these rules simplify semantic characteristics of distributivity by deferring questions of scope and non compositionality to a later stage i.e. inference
turning now to the template element task we note that the largest fraction of te errors are repercussions of errors committed while performing the ne task
this was due to the fact that earlier training data had these lines marked with NUM tags whereas the official test data did not
for example james in the walkthrough headline field should have been merged with robert l james in the body of the message
nonetheless this lexicon by itself got less than half of the organizations in the official named entity test corpus organization recall was NUM and precision NUM
there were many lessons learned and there will continue to be as we further analyze our results and mak e improvements to the system
note the pers NUM term in this proposition it designates the semantic individual denoted by the phrase and is generated in the process of interpretation
but the bragging rights to org coke org s ubiquitous advertising belongs to org creative artists agency org the big location hollywood location talent agency
nevertheless the results of the evaluation of these methods with the basque language are not as good as the ones obtained with non inflected languages
so far word prediction methods have been developed in order to increase message composition rate for people with severe motor and speech disabilities
even some of these underlying rule patterns however are questionable since their incidence is very low maybe once in the whole corpus or their form is so linguistically strange so as to all into doubt their correctness possibly idiosyncratic mis parses as in NUM
the reestimation and bfp algorithms utilize cyk style chart and the non constituent objects as chart entries
in this way the prediction system is able to offer the most recently used words among the most probable ones beginning by a
as an alternative this article presents an efficient trainable system for sentence boundary disambiguation
one active area of research is the development of algorithms for aligning sentences in parallel corpora
a neural network is trained by presenting it with input data paired with the desired output
a vector or descriptor array is constructed for each token in the input text
in addition heuristic approaches depend on having a NUM this work has not been published
another useful evaluation technique is the comparison of a new algorithm against a strong baseline algorithm
added to the semantic formalism are pragmatic operators corresponding to denial confirmation correction and assertion NUM that indicate the relation between the value in its scope and the information state
the f skeleton variant peter s mother praised x is actually entailed by the question 17a thus the f marking of ihn
when organization names are recognized they can often be directly linked to their appositives or prenominal phrases
the italicized words represen t the rule which is being matched and the variables which are being bound
this method allows us to check our progress frequently and to backtrack quickly if a regression is noticed
ten percent of the NUM development messages were set aside as a blind set for the developmen t phase
she also had very high f measure for the locale and country slots and for the descriptor slot
this link is used in the te system to identify aliases found in the text for that entity
over the years the toolset has evolved into a robust set of aids for tex t analysis
the lockheed martin group s louella parsing system participated in three of the fou r from a text
for the unaccented gab the background linking principle applies giving rise to a defensible link s link n bg
one function of the slot is to contain any descriptor phrase which is related to an organization s name
once an organization noun phrase or personal pronoun is identified the reference resolution module seeks to find its referent
two semantical partitions for focus foc and background bg are assumed each of them a set of semantic conditions
NUM this condition allows to explain data like NUM a puzzle for theories based on the question test for focus cf
the first is the generative capacity of a feature structure of a rule schema i.e. a rule schema can generate infinite variety of signs
transition arc the role of which will be discussed in section NUM NUM is the rule schema used to create this arc
we also showed the el feet of our optinfization te hniques by a series of exl erinwats m a real world text
our fonmdism has only one type of compolmnt g ts lloll 10xic l ollll ollellt q of rallllnar i.e. rule schemata
NUM definition NUM dcp a definite clause program dcp is a finite set of feature structures each of which has the following form
the states in automat l are possible signs derived fl om h xical entries and contaili information raised fl om the lexical entries
no matter how we eventually arrive at the compound terms we hope they would let us to capture more accurately the semantic content of a document
we would also like to thank ralph weischedel and constantine papageorgiou of bbn for providing and assisting in the use of the part of speech tagger
this paper is based upon work supported by the advanced research projects agency under tipster phase NUM contract NUM fi57900 NUM and the national science foundation under grant iri NUM NUM
the challenge is to obtain semantic phrases or concepts which would capture underlying semantic uniformity across various surface forms of expression
one way to regularize syntactic structures is to transform them into operatorargument form or at least head modifier form as will be further explained in this paper
while this description is meant to be self contained the reader may want to refer to previous trec papers by this group for more information about the system
one popular term weighting scheme known as tf idf weights terms proportionately to their inverted document frequency scores and to their in document frequencies to
this way if a corresponding full name variant can not be found in a document its component words matches can still add to the document score
in this case there are four possible outcomes shown in figure NUM but only two of them are allowed under the constraint that there can be no carets outside the brackets
in a stochastic itg sitg a probability is associated with each rewrite rule
the overall architecture of the system is essentially the same for both years as our efforts were directed at optimizing the performance of all components
the extracted phrases are statistically analyzed as syntactic contexts in order to discover a variety of similarity links between smaller subphrases and words occurring in them
system presented result solely frorn research concerning relevant l rol erties of the syntax of a particular language ze ll in t art also bulgarian and herlce they are strongly languagedependent
experiments were run on associated press articles which were manually tagged at the university of lancaster
when training on one million words of text test set accuracy peaks at NUM NUM
next we explore weakly supervised learning where a small amount of human intervention is permitted
being a processor it can be applied to the output of any initial state annotator
below are some of the transformation templates used by the learner
percentage of all possible transformations when searching for the best one
initial state tagging accuracy on the training set is NUM NUM
a limit is also placed on the possible parts of speech of unknown words
that is a pseudo random number generator is used to assign each node in the map a random unit vector of dimension n
the algorithm employs a rebalancing strategy reminiscent of balanced tree structures using left and right rotations
there is therefore a great need for formal models of corresponding levels of representation and for corresponding algorithms for transduction
no changes were necessary neither to the chart parser itself nor to the fundamental rule
they are annotate d with equations the solutions of which result in syntactic feature stractures
by paraphrasing decomposable idioms the identifiable parts of nmaning are taken into account
only the semantic structures differ one drs represents the literal idiomatic and one the idiomatic reading
similm examples an be bttnd in other languages too
NUM tom hat mtf der sitzung cinch groflen dock geschossen
this way the expenditure of translation c m be reduced in this paper
reference identity between bear and tall tale is established by the equation u z
if for example in a text of one language two words a and b co occur more often than expected from chance then in a text of another language those words which axe translations of a and b should also co occur more frequently than expected
the monotonically increasing chaxacter of the curves in figure NUM indicates that in principle it should be possible to find word correspondences in two matrices of ditferent languages by randomly permuting one of the matrices until the similarity function s reaches a minimum and thus indicates maximum similarity
regardless of the formula applied the english and the german matrix where both normalized NUM starting from the normalized english and german matrices the aim was to determine how far the similarity of the two matrices depends on the correspondence of word order
if now the word order of the english matrix is permuted until the resulting pattern of dots is most similar to that of the german matrix see table lc then this increases the likelihood that the english and german words axe in corresponding order
tot this end to process a substring of length which ca nnot be set in a dvance in generm the whole input string on the other hand it is not necessary t o use the power of the fldl fledged parser al d in particular it is sufficient to use the power of a finite state autolnaton or only slight augmenta lion
user interface automatic region fmding and region labeling information retrieval and document highlighting and temporal analysis of the information space
all sentences containing only singular verbs or plural verbs but in present tense or in neuter gender or infinite verb tbrms as containing no detectable error without any actual grammar checking taking place it is however obvious that this does not necessarily mea n that the sentences are truly correct they just do not contain the kind of error the system is able to detect
among education there are subtopics on scholarship school and school entrance
the simplest way to construct a tree structured representation of words is to construct a dendrogram as a byproduct of the merging process that is to keep track of the order of merging and make a binary tree out of the record
when we extend this notion of two level word clustering to many levels we will have a tree representation of all the words in the vocabulary in which the root node represents the whole vocabulary and a leaf node represents a word in the vocabulary
one of the fundamental issues concerning corpus based nlp is that we can never expect to know from the training data all the necessary quantitative information for the words that might occur in the test data if the vocabulary is large enough to cope with a real world domain
finally by tracing the path from the root node to a leaf node and assigning a bit to each branch with zero or one representing a left or right branch respectively we can assign a bit string word bits to each word in the vocabulary
never occurred in the training data then sentence b must look to the system very much like c and it will be very hard for the parsing system to tell the difference in sentence structure between c and d
the reason is that after classes in the merging region are grown to a certain size it is much less expensive in terms of ami to merge a singleton class with lower frequency into a higher frequency class than merging two higher frequency classes with substantial sizes
class295 yes wordbits word NUM NUM NUM smember word NUM set and or nor no tag prep types rcb wordbits questions isprefix word NUM
the quality of the result of the indexing process depends in large part on the quality of the terminology completeness consistence
thus the vocabulary used is either very technical with subject field terms and candidate terms or very general with stylistic expressions etc
a current problem is the automated construction of these resources e.g. terminologies thesauri glossaries etd from a corpus
this indicator highlights a certain number of terms that are not transverse to the corpus but rather concentrated in documents that are close to each other
this includes general expressions stylistic effects etc statistical methods can thus be used in a second step to discriminate terms from nonterminological expressions
at the end of these transformations the decomposed text is compared to the list of documented terms of the thesaurus in order to supply the descriptors
the most effective model is the one based on mutual information between terms which typifies the fact that two terms often appear together in the corpus but rarely apart
variance this is based on the idea that the more the occurrences in a document of an expression are scattered the more likely it is to be a term
this functionality was inherited from another system developed at hnc called matchplus NUM
furthermore the system will automatically generate an appropriate name or label for the region
a consequence of this type of training is that some map nodes may never win the competition
using higher dimensioned vectors NUM is possible but adds to the timely computation problem
before conducting the second competition the conscience mechanism creates a bias factor for each node
the value of the bias is determined by the running statistics kept on the first competition
the summed dot products for each node are used to determine the size of the node
a self organizing training process is used to adjust the node vector components in an iterative fashion
a typical interior design scenario deals with composition of pieces of furniture equipment and decoration in an office room by several partici the training corpus for the trigram was generated artificially by the context free grammar of the first recognizer mentioned
typically the som consists of a collection of nodes arranged in a regular two dimensional grid
the system is built around two separate neural network methodologies context vectors and self organizing maps
both directions of the lemma can be proved by induction on the height of derivation
yet the recovery lets the information introduced by wrong filtering sentence rightly rejected by the parsing wrong filtering sentence falsely analyzed as well formed wrong filtering sentence analyzed through the robust parsing for different parsing options the preposition undefined
in order to decide we rely on idfi and tij as follows
ultimately the position method can only take one a certain distance
here only selected snapshots are highlighted
state of the workspace at cycle NUM
the problem should not arise with lattice or word graph as they keep temporal information where al wi is the alignment value of word wi between the first best hypothesis h1 and the n best hypothesis h aln wj wi is the context effect of word wj on word wi equation NUM
it triggers the genera null tion of joker trees the candidates a the this these are represented by a single joker tree while it in the NUM h best hypothesis involves a different joker tree it is in fact its own tree but with semantic features marked as uncertain
we then removed all closed class words from the texts
oov word nb c1 c2 c3 c4 tchdtch ne NUM afs ams nfs nms NUM NUM NUM NUM this frequency information allows us to filter the lexicon according to NUM criteria number of occurences of each word percentage of occurences for each label given to a word
in particular whenever a noun is associated with a verbal argument the isa function is triggered to check whether the synset of the noun is subsumed by the selectional restriction of the corresponding verbal argument
but because we want to process oov words we use a NUM gram model specific to proper names where some categories of words are represented by their classes all the proper names as well as punctuation and non alphabetical words while others are represented by their graphical form all the other classes
the results are based on straight field by field comparisons of the temporal unit representations introduced in section NUM thus to be considered as correct information must not only be right but it has to be in the right place
time of day then for each non empty temporal unit tui from focuslist starting with most recent if specificity tu specificity tu1 then let f be the most specific field in starting fields tu
otherwise the system architecture is similar to a standard production system with one major exception rather than choosing the results of just one of the rules that fires i.e. conflict resolution multiple results can be merged
in all cases below after the resolvent has been formed it is subjected to highly accurate trivial inference to produce the final interpretation e.g. filling in the day of the week given the month and the date
the structure relevant for the task addressed in this paper is the following corresponding to their figure NUM there are four temporal units mentioned in the order tu1 tu2 tu3 tu4 other times could be mentioned in between
the errors remaining in the seen unambiguous nmsu data are overwhelmingly due to parser error errors in applying the rules errors in mistaking anaphoric references for deictic references and vice versa and errors in choosing the wrong anaphoric relation
all the real work of analyzing texts in a gate based le system is done by creole modules or objects we use the terms module and object rather loosely to mean interfaces to resources which may be predominantly algorithmic or predominantly data or a mixture of both
second building intelligent application systems systems which model or reproduce enough human language processing capability to be useful is a large scale engineering effort which given political and economic realities must rely on the efforts of many small groups of researchers spatially and temporally distributed
in addition modules can be reset i.e. their results removed from the gdm to allow the user to pick another path through the graph or re execute having altered some tailorable data resource such as a grammar or lexicon interpreted by the module at run time
when the user initiates a particular creole object via ggi or when a programmer does the same via the gate api when building an le application the object is run obtaining the information it needs document source annotations from other objects via calls to the gdm api
multilingual nametag tm multilingual internet surveillance system multimedia fusion system
editors although the servers minimize the amount of linguistic work that needs to be done to develop an application they do not eliminate it
in addition the toolkit provides tools for automatically generating rules in the special case of applications which do not control a dialog
actions include saying something to the user retrieving information from a database and resetting the dialog to a new state
the main reason for this is that a particular application will make use of words that do not exist in the dictionary
this is a useful generalization but the correspondence between the different qualia roles and different choices of preposition in italian is not as clear cut as this suggests
tlrtis89 olie of lilllll rica analysis nietilod
we used the statistical distribution of the sixty sll lixes thztt are ilk st frequently used ill english
this difference stems from the fact that this cooccurrence score overestimates rare events and underlines the collocations specific to each form
but it has many difficulties integrating heterogeneous information coping with data sparseness prohlem and adapting to new environments
on the basis of this information how can we determine the probability value of the function pi x
linear interpolation is so advantageous because it reconciles the different information sources in a straightforward and simple minded way
NUM NUM me maxlmum entropy principle there is very powerful estimation method which combines information sonrces objectively
NUM describes a two tier timeline consistent with NUM
since there are only finitelymany argument categories the argument s being passed can be encoded in afinite store
i would like to thank michael ksnyves tdth who developed the parser engine used in the experiment described in this paper for his support
when the generator is used as part of the translation system the dependency parameter costs are not in fact applied by the generator
we also present a model and algorithm for machine translation involving optimal tiling of a dependency tree with entries of a costed bilingual lexicon
the transfer algorithm described in section NUM searches for the lowest cost til ing of the target dependency graph with entries from the bilingual lexicon
each head automaton defines a formal language with alphabet r whose strings are the concatenation of the left and right sequence pairs written by the automaton
let the probability of generating an ordered dependency subtree d headed by an r dependent word w be p d w r
backed off costs can be computed by averaging over larger equivalence classes represented by shorter sequences in which positions are eliminated systematically
is the mean value of h t t for solutions t produced by derivations including the choice eic
the nature of the training methods and their corresponding cost functions meant that different amounts of training data could be used as discussed further below
d normalized distance in this fully automatic method normalized distance costs were computed from reflexive translation of the sentences in the unsupervised training corpus
the first measure meaning and grammar gives the percentage of sentence translations judged to preserve meaning without the introduction of grammatical errors
the probabilities associated with new states will be computed as sums of various combinations of old probabilities
in fact if it were n t for left recursive and unit productions their computation would be trivial
the library routines of the document manager process provides all csci s of the canis prototype with a standard interface apd for accessing docmnents and communicaring annotation information about those documents
the process will link the following entity information family persons to family employment persons to organizations and affiliation persons to associations
the analyst interface process csci allows an analyst to review and modify all information index records addressees subject line and fding locations about a document
an analyst may select a given entity from the list and review the enfity s detailed information delete the entity from the list or lookup a new entity found in the body of the document
their daily process will involve more analysis than data entry and they will be able to process a larger number of documents in a single day
we are currently in the testing phase and developing the evaluation criteria in conjunction with the government these phases are scheduled to complete july NUM
the document name lookup and processing allows the review and modification of named entities personnel company and associations of the selected document
when a cable has been abstracted and indexed its index record s are placed in a queue so that they will be stored
some of the data such as gender citizenship or relationship types for example have alternative choices available on a pulldown menu to minimize key strokes necessary to make changes
cables with useful data are abstracted and indexed
viewed as production it would be described in terms of its raw materials and products during photosynthesis a chloroplast uses water and carbon dioxide to make oxygen and glucose
for example he or she should be able to express the condition that the content associated with the output actor fates topic should be included only if the process being discussed is a conversion process
the explain process edp has four primary topics process overview explains how a process fits into a taxonomy discusses the role played by its actors and discusses where it occurs
node of the edp to be applied the newly created exposition node that will become the root of the explanation plan the verbosity and a list of the loop variable bindings
each judge was given NUM explanations to evaluate
third explanation generation is an ill defined task
these decisions are delegated to the realization system
finds view of steps of process
question what happens during pollen tube growth
in order to assure the expected generative capacity we place a condition on the use of rules
in the mlm taggers the word occurrence threshold that isolates the less probable words and the tag probability threshold used to reject the less probable tags from the unknown words tagset are the manually defined parameters
due to the large number of analyses it is useful to discard unplausible constituents as soon as possible to cut the search space
v and np list contains content words found in this dcu and is used to compare the content words of the current dcu with 3semantic closeness ratings wo n t help in examples NUM NUM because there is as strong a relationship between door and bell as there is between door and key
in NUM for example the default interpretation would be that john s being in detroit overlaps with his being in boston but the phrase the previous thursday overrides this giving the interpretation that john s being in detroit precedes his being in boston NUM john was in boston
the problem for practical systems is twofold we could assume that in the case of narrative the kamp hinrichs partee algorithm is the default but each time the default is applied we would need to check all our available world knowledge to see whether there is n t a world knowledge postulate which might be overriding this assumption
in what follows the event variable associated with dcoi is e and the tempfoc of el is the most recent event activity processed possibly el itself null e2 can overlap with el if dcu NUM describes a state or dcu1 describes a state and dcu2 describes an activity
charts such as table NUM provide the observations we use to fill in the vmue of i het reln
a a semantic distance rating between the new dcu and each previous thread is determined
lexical items and phrases such as cue words stored in cue word affect the value of this slot
this is probably also the reason for the awkwardness of the well known example max poured a cup of coffee
it has been suggested that only world knowledge allows one to detect that the default is being overridden here
we also built a maximum entropy model for the task of extraction of the most informative sentences for automatic document abstracting
NUM this makes it possible to evaluate any potential dialogue strategies for achieving the task as well as to evaluate dialogue strategies that operate at the level of dialogue subtasks subdialogues
table NUM attribute value matrix instantiation scenario
since dreamland is the subject of employed its meaning is determined by maximizing the similarity between one of lcb person organization locaton rcb and the words in table NUM
if common a b consists of two independent parts then the sim a b is the sum of the similarities computed when each part of the commonality is considered
what is abusive is either the event e itself as in abusive speech or abusive behavior or the agent a of the event as in abusive man or abusive neighbor
above and leave in the same sense of leave somebody or something at location because the latter s own deverbal left is not comfortable in adjectival use especially attributively
the result can be gen y eralized to all real numbers because f is continuous and for any real number there are rational numbers that are infinitely close to it
the interlingua language is called the text meaning representation tmr language and the tmr of a text is its representation in this particular type of interlingua
except for types viii and xv most of the types have few examples in the corpus and perishable is alone in its type v
NUM is a partial lexical entry for big with just two of the NUM lexical zones represented NUM big big adj cat adj
NUM we have the same situation in the cow does not eat with a knife the cow does not cat grass with a knifc thc cow does not eat meat in paris in these examples only the choice of the last argument seems to bc concerned by the negation
in this section we briefly review the basis of our approach to adjectival meaning and illustrate it on three examples of adjectival lexicon entries i.e. one each for the three major classes of adjectives
obviously the denominal adjective lr and the deverbal adjective lr 12i ii respectively exemplified in NUM and NUM NUM are precisely such lrs
just about all of them mean something than can be verb ed thus readable means something that can be read
these are based on both objective and subjective measures used in previous work such as the frequency of diagnostic or error messages inappropriate utterance ratios or the proportion of repair utterances
this paper discusses the range of ways in which spoken dialogue system components have been evaluated and discusses approaches to evaluation that attempt to integrate component evaluation into an overall view of system performance
using tei we have changed the existing markup of utterances to make each utterance unique across the entire corpus
in section NUM we briefly outline the properties of an underlying semantic framework that are required by centering
the tree project aims to address this problem by providing a system on the internet where employers can deposit job ads and which users can browse each in their own language
the forward maximum tokenization operation or ft operation for short is a mapping fd NUM d defined as for any s e fd s lcb w i w is a ft tokenization of s over g and d rcb
under a the sentences with the largest amount of deictic and anaphoric expressions are given under b the sentences with the least amount of deictic and anaphoric expressions are given
however the noun gesetz occurred more frequently as the object of the verb nennen than as its subject leading to the erroneous decision
devising optimal tagsets for given tasks is a field in which further work is planned
this highly redundant code will aid the processing of sparse data typical of natural language
for instance if al ai bl by cl ck and al aibl bjcl ck are all words in a dictionary the character string s al aibl bjcl ck while intuitively in type i conjunctive ambiguity is in fact captured neither by type i nor by type ii
a fast partial parse of natural language sentences using a connectionist method comqrgd hert s ac
table NUM performance on text from perkins manuals after NUM sentences have been excluded
there is a small set of NUM extensions to the grammar or semi local constraints
he examined the process of learning the grammar of a formal language from examples
table NUM performance on text from perkins manuals using improved representation and larger training
however some will only be connected to one output see figure NUM
there are several measures of correctness that can be taken when results are evaluated
concerning the problem involving mtknown constructions we could easily generalize the gramram to extend its coverage
with the context fi ee granunar rules of fnglish as input the system produces the parse tree of an inlmt sentence
this work is an initial step toward the ultimate goal of text and speech translation for enhanced nmltilingual and multinational operations
fire analysis module produces a semantic frame which is an interlingua representation of the input sentence
somewhat owdr f kn the corresp6nditig figure for data
the parse tree is then mapped onto a semantic frame which plays the role of an interlingua
the reason simply is that users seeking flight information do not make any commitments they merely ask for information
when applying fmm we used our proposed method of creating clusters and set NUM to be NUM NUM NUM NUM NUM NUM NUM
by accepting input in left to right order and dealing with best only substructures the explosion of structural ambiguity is restrained and an efficient translation of a lengthy input sentence can be achieved l re liminary experimentation has shown that average translation times are reduced from NUM NUM seconds to NUM NUM seconds for input of NUM words in length and from NUM NUM seconds to NUM NUM seconds for input of NUM words in length
a mechanism for spontaneous speech translation must be consistent with a mechanism for handling associative knowledge such as translation usage examples and word co occurrence information for rnemory b ed processing and with a mechanism for logical structure analysis according to detailed rules for each processing phase in the transfer driven mt processing
the system makes use of semantic frame representation so as to paraphrase a recognized speech input utterance into a concrete and simple expression that contbrms with one of the system s internal representations and makes the utterance meaning easy to handle
null NUM handling spoken language fragmental phrases isolated phrases a gradient of case role changing complex topicalization metonymical phrases idiomatic expressions for etiquette and inconsistent expressions in one utterance are main characteristics of spoken language
there were days in which we had as much as a NUM reduction in error rate to borrow the performance measure used by the speech community where error rate NUM fmeasure
when decoding if either word of the bigram is unknown the model used to estimate the probabilities of equations NUM NUM NUM is the unknown word model otherwise it is the model from the normal training
the input is a string of graphemes the output a string of phonemes and occasionally the allophones themselves
expert systems are used to facilitate the transfer of the knowledge of a specific domain from an expert to a computer
throughout most of the model we consider words to be ordered pairs or two element vectors composed of word and word feature denoted w f
for example in many cases NUM and i can be considered equivalents
and since the a priori probability of the word sequence the denominator is constant for any given sentence we can maxi mize equation NUM NUM by maximizing the numerator alone
to our knowledge our learned name finding system has achieved a higher f measure than any other learned system when compared to state of the art manual rule based systems on similar data
although the part of speech tagger used capitalization to help it determine proper noun tags this feature was only implicit in the model and then only after two levels of back off
using the viterbi algorithm we efficiently search the entire space of all possible name class assignments maximizing the numerator of equation NUM NUM pr w nc
in this way users can define their own code and the formalism can be used for different languages
we report the results for english and for spanish and then the results of a set of experiments to determine the impact of the training set size on the algorithm s performance in both english and spanish
these clauses comprise NUM segments in which NUM relations were analyzed
figure NUM the rda analysis of the example in fig
a tutor may offer an explanation in multiple segments the topmost constituents of the explanation
the set of intentional relations in rda is a modification of the presentational relations of rst
the final source of disagreement reflects more of a theoretical question than a question of reliable analysis
the core of this subsegment is c NUM because it most directly expresses this purpose
to assess inter coder reliability of rda analyses we compared two independent analyses of the same data
the second question we report on here concerns whether segment embeddedness affects cue selection
second our subjects coders are not naive about their task they are trained
a text can be segmented in at least two different ways into turns resp
the fact that the bfp algorithm predicts the garden path effect exhibited by sentence 3e is particularly indicative that it embodies the motivations for centering theory
one of these other test variables mentioned by church and gale is burstiness
secondly frequent words tend to receive lower significance scores
in this section we provide an analysis of the capabilities and current limitations of profile
all queries are cached and the descriptions retrieved can be reused in a subsequent query
null figure NUM shows the profile associated with the key john major
we use each of these strings as the key in the database of descriptions
the construction of a database of phrases for re use in generation is quite novel
such descriptions may not be present in the original text that is being summarized
we are investigating algorithms that will decide the order of generation of the different descriptions
NUM NUM technical terminology strengths and limitations
but which results m an extended phrase set containing an exhausbve hstmg of the objects mentmned m the text second overgenerabon
typ cally sentences of the souse document are deemed to be hlghly representahveof
a spamsh b est was charged here today with attempting to murder the pope
coherence constraint on elaboration constrains the semantic content of constituents connected by elaboration in coherent discourse
dice specifies how various background knowledge resources interact to provide clues about which rhetorical relation holds
furthermore it gives no hint of likely interpretations leaving an immense burden to pragmatics
summing the heights of the bars for n NUM through n NUM indicates that for an average narrative whose length is NUM phrases there will be about NUM boundaries identified by three or more subjects
she put her skirt into the bag made out of cotton
and some discourse contexts favor interpretations associated with less frequent senses
u o mary x clothes y bag z
we let c represent the attachment event where c NUM indicates that the pp attaches to the verb and c NUM indicates attachment to the object np
ft w weight w NUM NUM f w NUM
note that we present hard classification and soft classification results in word class based language model respectively
our rule modification reflects this issue
not all misrecognitions required experimenter interaction
using t term weighting method nouns ii a new article would be represented by vector of the form
NUM a phr sal lexicon which walker suggested in his method gives a negatiw influence for classification
a possible improwmmnt is that we use all definitions of words in the dictionary
a group with a smmler value of NUM is considered semantically less deviant
however these works do not seriously deal with the NUM roblem of polysemy
this shows that for these words there is only one meaning in the dictionary
where wi orresl on ls to the weight of the noun i
the weight is used to the fr lu mcy of noun
the di tionary we have used is collins english dictionary in acl dci cd rom
farm m figure NUM the results of method experiment
hence a particular claim of centering theory is that the resource demands of this inference process are affected by the form of expression of the noun phrase
they used centering to determine an almost monadic predicate representation of an utterance in discourse they then used this representation to reduce the complexity of inference
in contrast immediate focusing referred to a more local focusing process one that relates to identifying the entity that an individual utterance most centrally concerns
no matter how rich a model of context one has it will not be possible to fully constrain the interpretation of an utterance when it occurs
for example the second utterance in the following sequence prefers a vf interpretation but allows for the vl interpretation that is needed in the third utterance
the most highly ranked element of cf u that is realized in un i is the cb u l
in the remainder of the paper we will use a notation such that the elements of ct are ranked in the order in which they are listed
the actual skipping heuristics need to be different for organizations persons locations dates and numbers
here all the values which always contrib uted to a positive outcome are used as the primary decision
quinlan s original paper suggests the range of values of th e attributes should be small
the autolearn system was developed by one graduate student specifically for muc NUM also one man month
sequences like those in NUM seem to suggest that there might be multiple cb s analogous to the partially ordered set of cf s
overall performance was recall NUM and precision NUM giving an f measure of NUM NUM
once a frequently mentioned name is ignored in its full form the syste m unfortunately misses all abbreviated forms
this we hope will provide us with some feedback on patterns and errors in the data files
this is demonstrated in our use of the match insertion and deletion numbers above
the insertion rate can be drastically reduced with only a moderate increase in the deletion rate
the parse forest is returned or the sentence fails completely
if the parse fails the parser moves to pass two
morphological recognition can also be helpful in predicting possible parts of speech for many unknown words
in addition we designate irregular verbs as closed class words for this research ince
also the possible failure of the parser clue to insufficient rule coverage is not considered
for each successive run the next dictionary is used from dict9 down to dictl
for example a word directly following a determiner is typically a noun or noun modifier
both these methods work well but they ignore the global syntactic content of the sentence
the advantage of a separate text preprocessing module is that it does not clutter up the letter to sound rules
we tested several tag models by keeping all other conditions i.e. dictionary and word model identical
to resolve the first question we fix ti at subdivision level as is done in other tag models
tag models is the set of collocational sequences of words that can not be captured by just their tags
in a typical american telephone book for example are names that originate from hundreds of languages
we set the iteration number t to NUM the results of our experiments are summarized in figure NUM
after the next symbol whose subdivision is x t is observed generate the next tree t t as follows follow the t t NUM starting at the root and taking the branch indicated by each successive symbol in the past sequence by using basic tag level
this technique significantly improves the practical time efficiency of the parser especially if the resulting code is compiled
a possible approach is to compile the grammar into an equivalent grammar in which no such epsilon rules are defined
the fact that prolog is a high level language has a number of practical advantages related to the speed of development
as in the left corner parser a linking table is maintained which represents important aspects of the head corner relation
this technique is used in the mimo2 grammar and the alvey nl tools grammar both discussed in section NUM
x f x x were weakened into x f
we have evaluated a experimental tdmt system with NUM model sentences about conference registration
therefore a robust processing technique that collects the remnants of the parsing process in a meaningful way seems desirable
the possible extra cost of allowing impossible partial analyses is worthwhile if the more precise check would be more expensive
that is it is almost true that the normalization procedure can be considered as a one to one mapping which indicates p o rcb lj tk w i in our task
in nlp systems especially for spoken language many possibile syntactic structures are produced
c phrase structure rules the grammar is composed of NUM NUM phrase structure rules expressed in terms of NUM terminal symbols parts of speech and NUM nonterminal symbols
with the formulation each transition probability between two phrase levels is calculated by consulting a finite length window that comprises the symbols to be reduced and their left and right contexts
d case set in the current system the case set includes a total number of NUM cases which are designed for the next generation behaviortran mt system
in addition errors for case identification is one of the problems that make the deep null structure disambiguation system unable to achieve a high accuracy rate of normal form
the basic idea for the robust learning algorithm to achieve robustness is to adjust parameters until the score differences between the correct candidate and the competitors exceed a preset margin
to evaluate the performance of the proposed case identification models the recall rate and the precision rate of case assignment defined in the following equations are used
where c and correspond to the begin of sentence and the end of sentence symbols respectively i i and r stand for the left and right contextual symbols to be consulted in the i th phrase level
to compute the semantic score the normal form is first decomposed into a series of production rules in a top down and leftmostfirst manner where each decomposed production rule corresponds to a case subtree
it seems therefore useful to underspecify the lexical meaning of fast to a representation that captures this primary semantic aspect and gives a general structure for its combination with other lexical items both locally in compositional semantics and globally in discourse structure
the testing was done using NUM anaphora of which NUM were one of the four anaphoric types in NUM blind test texts for both the mlrs and the mdr
nm rod bluepoint capon clam cockle crawdad crawfish crayfish duckling fowl grub hen lamb langouste limpet lobster monkfish mussel octopus panfish partridge pheasant pigeon poultry prawn pullet quail saki scallop scollop shellfish shrimp snail squid whelk whitebait whitefish winkle other the act and relation aspects are intimately connected
the success rate is NUM for a test data set consisting of NUM NUM unseen sentences in the same domain
disjunction is an alternative case form such that there is one alternative for every disjunct of every disjunction in the group and there is one case for each disjunct in the group which is a co onetion of the alternative variables for that disjunct
this is equivalent to tile compact alternative forin NUM a c a a x a d x and tile following case fornl ease
essary ondition t rcb tat if l he fl ee combination of the confinements is the same as the original case form then the i roduet of tile number of disjun ts i ea h conflneme t
sometimes it is difficult to distinguish them from names of other types especially from person names
all but one of the development teams udurham had members who were veterans of muc NUM
suppose for instance that the input word is w aabcab as shown in figure NUM and that the factors that are in dom f6 1deg can be found according to two different factorizations i.e. w i a w2
thus a transition a a is to be added to the identity state NUM which refers to NUM because of the transition a b of t7 and to i NUM because it is possible to start the transduction t7 from any identity state
for example if we choose to create a pseudo word out of the words make and take we would change the test data like this make plans lcb make take rcb plans take action lcb make take rcb action the method being tested must choose between the two words that make up the pseudo word
fl a b we want to extend it to a function f2 such that f2 w w where w is the word built from the word w where each occurrence of a has been replaced by b
distribution of ne tag elements in test set figure NUM subcategories of enamex in test set
usheffield udurham umanitoba we sometimes have expectations of deep understanding cf
as a case study we applied these techniques to the problem of part of speech tagging and presented a finite state tagger that requires n steps to tag a sentence of length n independently of the number of rules and the length of the context they require
a y x for instance if y dom fa lcb aaa rcb the set of y decompositions of x daaad is lcb d aaa ad da aaa d rcb
many of the sites have emphasized their pattern matching techniques in discussing the strengths of their muc NUM systems
with the following two definitions definition the left distance between two strings u and v is i u v i u iv NUM u v
if an antecedent expression is nonreferential can it nonetheless be considered coreferential with subsequent anaphoric expressions
in addition we present transformations of the taggers calculations to a fixed point arithmetic system which are useful for machines without floating point hardware
the closed and functional grammatical classes can be estimated automatically as the less probable grammatical classes of the less probable words in the tagged text
when the model parameters are estimated from a limited amount of training data tagging errors appear because of unknown or inaccurately estimated conditional probabilities
in this section we present techniques to speed up the tagging process and avoid underflow or overflow phenomena during the estimation of the optimum solution
therefore frequency measurements are defined or updated as model parameters instead of conditional probabilities that are computed afterwards by using the corresponding relative frequencies
as shown in the above figures the close relation between the tested probability distributions is evident for all sizes of training and testing text
the measurements were carried out on newspaper text and split into two parts of the same size the training and the open testing text
several taggers based on rules stochastic models neural networks and hybrid systems have already been presented for part of speech pos tagging
the floating point multiplications of these probabilities are transformed into an equal number of floating point additions by computing the logarithm of the optimum criterion probability
the tagger speed exceeds the rate of NUM word sec in a NUM 33mhz for all languages and tagsets in text with known words
there are many metrics we can use to measure the closeness of two worms
the longer the chain the less probable it is that a set of false points of correspondence will take on a valid looking arrangement
the minimum confidence at which gsa trusts the length based re alignment is a gsa parameter which has been optimized on a separate development bitext
table NUM compares simr s error distribution on these bitexts with that of the previous front runner char al i gn
those points that are generated are extremely unlikely to be sufficiently linear and to have the proper slope to fool the chain recognition heuristic
figure NUM shows a segment of the tbm trace that contains a vertical gap an omission in the text on the x axis
note that these bitexts are so named because one was easier than the other for the alignment algorithm that was first evaluated on them
if no suitable chains are found the search rectangle is proportionally expanded up and to the right and the generationrecognition cycle is repeated
the first step in most corpus based multilingual nlp work is to construct a detailed map of the correspondence between a text and its translation
this is a desirable property because even languages with similar syntax like french and english have well known differences in word order
in a region of the scatterplot containing n points there are NUM n possible chains too many to search by brute force
in our word relation matrix worm representation we use the correlation measure w ws wt between a seed word ws and an unknown word tot
we present a statistical word feature the word relation matrix which can be used to find translated pairs of words and terms from non parallel corpora across language groups
in addition to the evaluation results we have also discovered that the content words in the same segment with a word or term all contribute to the occurrence of this word
w w wo as an initial step all pr w NUM are pre computed for the seed words in both languages
the wall street journal tends to focus on u s domestic economic and political news whereas the nikkei financial news focuses on economic and political events in japan and in asia
we use correlations both between monolingual lexical units and between bilingual or multilingual lexical units to find a consistent pattern which is represented as statistical word features for translation
we selected a test set of NUM set a by NUM set b single words with mid range frequency from the wsj texts
to increase the candidate numbers test ii is carried out on NUM japanese terms with their english counterparts plus NUM other english terms giving a total of NUM possible english candidates
the two are shown to be qualitatively different asymptotically but nevertheless to be instances of a common class of reestimation formula asymptote pairs in which they constitute the upper and lower bounds of the convergence region of the cumulative of the frequency function as rank tends to infinity
in addition otp can refer naturally to the edges of syllables or morphemes
otp appears capable of capturing virtually all analyses found in the phonological ot literature
modify the arc labels of ml rcb so that they no longer restrict mention k
the key observation here is that also for real valued o x in general z c i ii l k o x NUM o this means that we have a single reestimation equation
differentiating this w r t z the lower bound of the integral yields dr x d f x dx dx n y dy n x and using the chain rule for differentiation yields
i wish to thank mark lauer for helpful comments and suggestions to improvements seif haridi for constituting the entire audience at a seminar on this work and focusing the question session on the convergence region of the parameter NUM and ke
most of the work was done while the author was visiting ircs at the university of pennsylvania at the invitation of aravind joshi and a number of new york pubs at the invitation of jussi karlgren both of which was very much appreciated
r NUM f r e NUM note that reassuringly the relative frequency of the most populous species fx is preserved NUM x f NUM n1 n fx
the reason that these formulas are of interest in computational linguistics is that they can be used to improve probability estimates from relative frequencies and to predict the frequencies of unseen phenomena e.g. the frequency of previously unseen words encountered in running text
as soon as the accumulated penalty exceeds the total penalty of the best alignment found so far
if the two words to be aligned are identical the task of aligning them is trivial
the third of these has the lowest penalty and is the etymologically correct alignment
tables NUM to NUM show how the aligner performed on NUM cognate pairs in various languages
third the tree search algorithm lends itself to modification for special handling of metathesis or assimilation
more about this later first i need to sketch what the aligner is supposed to accomplish
a different methodological choice is required for verbs and nouns
table NUM shows the three classes the eight cue types their subtypes if any whether a cue may affect merely the dialogue initiative or both the task and dialogue initiatives and the agent expected to hold the initiative in the next turn
in this case the first clause is the antecedent for both ellipses
in an analogical system the process that matches the input expression against examples can be very robust and can always return the best matching output expressions instead of failing completely
as a result a segment delimited by two punctuations is used as the context window size
for example table NUM indicates that when the polysemous nouns are ordered from the most frequently occurring noun to the least frequently occurring noun the top NUM polysemous nouns constitute NUM of all noun occurrences in the brown corpus
wrap up learns to discard as irrelevant any persons and organizations that are not attached to an in and out or a succession
the output generator which was required to handle potentially nested coref sgml tags required nearly a week long effort
most notably the te task fo r muc NUM could be effectively tackled without the benefit of a sentence analyzer
for example the training corpus contained a past tense instance for a specific verb but no present progressive instance
the st training corpus was undoubtedly much too small to support an inductive algorithm designed to lear n relational decisions
from this table we see that the organization specialist actually made the fewest classification errors misclassifying only seven locations
badger currently recognizes NUM p o s tags and it took NUM hours to manually create a p o s dictionary fo r muc NUM
tic semantic structures incorporate both syntactic and semantic information about an utterance
it takes as a parameter the following list of actions NUM s attrib rel entityl entity3 xx
however NUM of the time the system failed to predict a shift in task initiative s this suggests that other features need to be taken into account when evaluating user proposals in order to more accurately model initiative shifts resulting from such cues
this research was supported by national science foundation advanced research projects agency grant iri NUM
semantic annotation task we predicted that three properties of the words that were to be matched with specific wordnet senses would result in differences among the individual taggers annotations and between those of the taggers and the more experienced lexicographers
during the first pass of the inside outside algorithm assuming near uniform initial rule probabilities each of these parses will have equal posterior probabilities
we have implemented a statistical parser and training mechanism based on the above notions but results are too preliminary to include here
more recent experiments however indicate that expanding the corpus size by an order of magnitude has little affect on our results
however the vocabulary is so much larger that is is not possible to gather useful statistics over such a small sample
here we describe some tests that explore the interaction of the head driven tanguage models described above with this parser and training method
creation of a standardized architecture could help in many ways
information about an entire collection could be recorded as attributes on the collection
the viewpoint is understood in this representation as a focus on parts or on the whole situation figure NUM
type c can be seen as the part of an episode of the complete event type c which is focussed by the neutral viewpoint
it may be concluded from NUM that we can not assume a perfective viewpoint because this view includes the end point of a situation
it could speed initial application development by providing standardized pre existing modules
to sum up these two discourses can be seen to show that the german aspect system for the preterite offers only a neutral view on every situation
this cross linguistic account gives prominence to the underlying concepts instead of focussing only on the surface structure which is unalterably bound to the peculiarity of a single object language
the author gratefully acknowledges the helpful comments of sheila glasbey lex holt and the three anonymous reviewers of this paper
furthermore the proposed formalisation provides an account which can handle the discussed phenomena within an implementation this is ongoing work
this data shows that the use of the preterite in german does not commit the speaker to saying anything about the end point
chinese speakers are able to recognize most japanese technical terms since they are very similar to chinese
we expect that by taking a sample of dialogues whose task dialogue initiative distributions are more representative of all dialogues we will lower the value of p e the probability of chance agreement and thus obtain a higher kappa coefficient of agreement
we use the term property to cover all of these cases
etd cross talk utterances average NUM NUM words
book a hotel room that is conveniently located
however it is possible to draw some comparisons
breaking these down syntactically would be an unnecessary complication
similarly the types of dialogues are naturally limited
initial evaluations on travel planning data are also presented
the translation system is based on an interlingua approach
the measure is trained on hand segmented transcriptions of dialogues
sdus are semantically coherent pieces of information
our current sub domain classification has two dimensions
two properties are inferentially independent if neither can be derived from the other
the parser described here can judge the grammaticality of simple declarative transitive and intransitive sentences and of subordinate clauses
in utterance 3a a directly responds to s s question thus the conversational lead remains with s on the other hand in 3b and 3c a takes the lead by initiating a subdialogue to correct s s invalid proposal
the t NUM row corresponds to the models in table NUM
for performing the decomposition in eq
figure NUM histogram of the winning assignment
there are other grammars in the svo family in which all modifers follow heads there are postpositions and so forth
however existing models can not explain the difference in the two responses namely that in 3c a actively participates in the planning process by explicitly proposing domain actions whereas in 3b she merely conveys the invalidity of s s proposal
this is a fairly typical case in the constraint grammar framework but relatively rare in the new dependency grammar
null roughly one could say that the remove rules of the constraint grammar are replaced by the index rules
this is modified to yield the following new database entry NUM act loc thing NUM by march NUM the modified entry is created by changing go to act and removing the toward NUM constituent
the overall result should be considered good in the sense that the output contains information about the syntactic functions see
the following rule establishes a dependency relation of a verb and its object complement if the object already exists
in such a case we can remove some readings even if we do not know what the correct alternative is
however the comparison to other current systems suggests that our dependency parser is very promising both theoretically and practically
this is a property that will be crucial when we will apply this framework to a language having free word order
the most important result is that the new framework allows us to describe non projective dependency grammars and apply them efficiently
based on the results of our evaluation we make the following observations
we call this reading the retardation reading r
we call this reading the first of a sequence reading fs
in NUM this is the canonical order of the numbers
we call this reading the exclusion of preceding alternativesreading epa
drawing inferences from the context can be a means for resolving semantic ambiguities
we add that nothing of the ersgargument is new information against this background
NUM iqrst den ib ief g ab l ctcr maria
examples are NUM c and NUM d
this may explain why the example is felt to be a bit odd
sin the case of suboptimal actions we encounter the sparse data problem
the new initiative indices then determine the initiative holders for the next turn
the final type includes utterances that satisfy an outstanding task or discourse obligation
thus after likes is absorbed the state category will need to expect an np
this is relatively trivial using a non curried notation similar to that used for aacg
to ensure full coverage these categories may be added to ci or alternatively they can be replaced by their direct ancestors but clearly a good selection of c i is one that minimizes this problem
therefore we may conclude that the method is robust in the sense that it correctly identifies a range of reasonable choices for the set of categories to be used eventually leaving the final choice to a linguist
p thinks mary p john ap aq
the following tree is suitable for the sentence mary thinks john shaves but not for e.g.
there are however problems with this kind of approach when features are considered see e.g.
the limitations of the parsing approaches become evident when we consider grammars with left recursion
however unlike bar hillel we allow one argument to be absorbed at a time
however there is a corresponding problem of far greater non determinism with even unambiguous words allowing many possible transitions
where nc c i is the number of words that reach at least one category of ci and for each word wj in this set cwj c i is the number of categories of c i reached
null the second feature is the most important since as we remarked so far assigning semantic characteristics to words is very useful in lexical learning tasks but overambiguity is the major obstacle to an effective use of thesaura in semantic tagging
for a better understanding of the importance of term expansion we now compare term indexing with dchange ionique ionic exchange n to a cultures primaires de cellules primary cell cultures modif
the first parenthesis c a t n a p represents a coordinated head noun the second a c p and third p d
the contribution of this research is the successful combination of parsing over a seed term list coupled with derivational morphology to achieve greater coverage of multi word terms for indexing and retrieval
cat n lemma modernisation reference NUM derivation cat n derivation lemma modernisateur derivation reference NUM derivation history on eur
NUM substitution modification a substitution is the replacement of a content word by a term a modification is the insertion of a modifier without reference to another term
the tagger takes the output of inflectional morphological analysis and through a combination of linguistic and statistical techniques outputs a unique part of speech for each word in context
the key issue in capturing gb theories within l NUM k p is the fact that the mechanism of free indexation is provably non definable
x is closed wrt the link relation note that every node will be a member of exactly one possibly trivial chain
while minimality ensures that every trace must have a unique antecedent we may yet admit a single antecedent that licenses multiple traces
models for the language are labeled tree do null by the fact that it is couched in terms of an algorithm for checking models
grammars in this approach are purely declarative definitions of a class of structures completely independent of mechanisms to generate or check them
in fact the second order quantification of l NUM allows us to capture any monadic k p first order inductively or implicitly definable property explicitly
thus these principles do not introduce features into the trees but rather propagate features from one node to another possibly in many steps
privileged x 3x privsety x a x z
NUM consequently languages are definable in l2k p iff they are strongly context free in the mildly generalized sense of gpsg grammars
NUM if we change the tagger to tag all unknown words as common nouns then a number of rules are learned of the form change tag to proper noun if the prefix is e a b etc since the learner is not provided with the concept of upper case in its set of transformation templates
each known word in the test corpus was tagged with all tags seen with that word in the training corpus and the five most likely unknown word tags were assigned to all words not seen in the training corpus
although adverbs are more likely than prepositions to follow some verb form tags the fact that p as in is much greater than p as rb and p jj in is much greater than p jj rb lead to as being incorrectly tagged as a preposition by a stochastic tagger
however in order to make progress in corpus based natural language processing we must become better aware of just what cues to linguistic structure are being captured and where these approximations to the true underlying phenomena fail
so while the first rule specifies the english suffix s the rule learner was not constrained from considering such nonsensical rules as change a tag to adjective if the word has suffix xhqr
it has recently become clear that automatically extracting linguistic information from a sample text corpus can be an extremely powerful method of overcoming the linguistic knowledge acquisition bottleneck inhibiting the creation of robust and accurate natural language processing systems
given a sequence of characters classify a character based on whether the position index of a character is divisible by NUM querying only using a context of two characters to the left of the character being classified
in the dependency model however the structure would be decided by looking at the dependency between information and retrievap i.e. the tendency for information to modify retrievat and the dependency between information and technique
finds view of process with respect to another process of which process is a step
auxiliary process process finds temporal causal or locational view type information about process as specified by view type
we then transcribed their handwritten explanations and put them and knight s explanations into an identical format
to promote high quality human generated explanations we assigned the NUM most experienced experts to the writing panel
rather than evaluating the explanations directly subjects were given a quiz about the concept under consideration
object process division each judge received explanations that were approximately evenly divided between objects and processes
for example given an object the substructural accessor inspects the object to determine its parts
fourth the kb accessors exhibit immunity to modifications of the representational vocabulary by the domain knowledge engineer
the final two aspects of expressiveness ordering and grouping of propositions are concerned with organization
we formulated constraints of different accuracy
we also tested the tuggers with more difficult text
palo alto research center NUM NUM training
we did not expect this result
they represent kinds of lexical probabilities
we started our project by doing so
NUM NUM errors of principled and heuristic rules
the results are shown in figure NUM
for such cases we introduce ad hoc heuristics
by contrast the feature b distinguishes the set lcb xl x2 rcb from lcb x3 x4 rcb
NUM the acceptance probability a y xn reduces in our case to a particularly simple form
the functions fl and f2 represent the frequencies of features NUM and NUM respectively as in figure NUM
account of context dependencies that the erf distribution fails to capture incrementally improving the fit to the empirical distribution
but now rather than taking rule applications to be features let us adopt the two features in figure NUM
markov chains are stochastic processes corresponding to regular grammars and random branching processes are stochastic processes corresponding to context free grammars
we hence associate different weights to the upwards and downwards traversal of links with the NUM unique be ers of word net being the topmost nodes
the most frequent word net sense is chosen simply because current word sense disambiguation algofithm still can not beat the most frequent baseline consistently for all words
for the convenience of discussion some formulae are given an identifying label such as l1
when tested on the muc NUM terrorism domain the approach is shown to outperform the most frequent heuristic substan lly and achieve comparable accuracy with human judges
to dis m iguate the semantic class of the word plane the sentence is fed to the word sense disambiguation module
for words which the algorithm failed to disambiguate when no senses or more than one sense is returned we relied on the most frequent heuristic
the disambiguation of the semantic class of words in a particular context facilitates the generalization of semantic extraction patterns used in information extraction from word based to class based forms
if the noun to be disambiguated is the first noun of the passage the window will include the subsequent n nouns of the same passage
these reflections of the human judges seem to point towards the need for an effective method for selecting only particular nouns in the surrounding context as evidence
figure NUM b shows the relative positions of the concept node plane l and the three semantic cl q nodes in word net
nouns are extracted from the first NUM passages dev muc4 NUM to dev muc4 NUM of the corpus of news wire art ides to form our test corpus
we generalize here transformations in NUM by letting NUM be a string over e u f more precisely we assume NUM has the form
the main problem with vr is that the verbs occur on the right of the uppermost final auxiliary while their maximal vp constituents remain on the left and contain a head trace
however to account for the surface word order the heads besuchen and wolleu must be extracted and attached to the right of hstte as shown in 9c
for the hypothesis of haben as a main verb port german speakers assign an interpretation to arguments even before the predicate is available el
the task of our dips parser consists of not only building one or more trees for an input sentence but also of determining the grammatical function and the thematic interpretation of arguments
since restructuring allows arguments and adjuncts to be attached to the clause containing a coherent verb while being interpreted with respect to the infinitival clause the ais needs to be extended
take for instance the vp in 9a and the complex vp in 9b the latter is attached as the complement of the former i.e. to its left
beesley for editorial advice on the first draft of the paper
a trace is inserted into the specifier of ip for the subject and another trace into the complement of vp for the direct object as illustrated in NUM
vote entropy is maximized when all committee members disagree and is zero when they all agree
in particular the simplest method which has no parameters to tune gives excellent results
there are NUM different syntactic categories in the ovis tree bank that appear to cover the syntactic domain quite well
the well formedness and validity of an expression is decided on the basis of a type lattice called a frame structure
in this way we implicitly model the distribution of model parameters used for classifying input examples
b accuracy versus number of words examined from the corpus for different batch sizes
this procedure assumes that one can sample from the models posterior distribution at least approximately
more generally it is possible to break the text at any point where tagging is unambiguous
NUM classify e by each model giving classifications cl and c NUM
we first review the basic approach of committee based sample selection and its application to part of speech tagging
on the other hand as n decreases batch selection becomes closer to sequential selection
we varied the size of the training set and the maximal depth of the subtrees
which uses an atelic verb estar NUM in colnbination with a telnporal adjunct durance el m es de diciembre john estuvo en la cabafia nueva durance el mes de diciembre literally john was in lhe new cabin during lhe month of december
m NUM language model rid word segmentation algorithm
most of the evaluations described in the literature have centered around one mt system
second it takes a lot of redundant translating to find missing lexical items
there is no information for systran since the built in lexicon can not be accessed
null systran has NUM topical glossaries all on the same level
it is also noteworthy that the systran results differ only slightly between table NUM and table NUM
a special purpose translation list could be incrementally built up in the following manner
as more translation systems become available there is an increasing demand for comparative evaluations
it is shown how these word lists can be compiled and used for testing
we mentioned above that some systems subclassify their lexical entries according to subject areas
a proof however can not be expected from any black box testing method
the results are for the m NUM mixed order markov model
the non nominals are verbs adverbs adjectives prelixes aud suf ixes
the candidate nouns n of the best sequence are then added to the noun dictionary
mary put a skirt in a cotton bag
first each possible decomposition of a compound noun is identified
egory of current word given the category of the previous word
we define the distribution of a terln as the hleati ing of the terin
the proposed method assumes a dictionary of nouns that is automatically constructed from the document set
the decomposition of a compound noun is particularly problematic because of the severe ambiguity of segmentations
a document is represented as a list of term weight NUM wi pairs
a send up my suitcases to my room please
r we booked two double rooms with a bathroom
the approach is motivated by the need to handle productive word use
a explain the bill for room number three two four
we are able to apply an efficient dp based search algorithln
the objective is an architecture utilising a general purpose lexicon with domain dependent probabilities
of these sentences NUM are failed in normal parsing and are processed again by the robust parser
moreover only one word error was considered though several word errors can occur simultaneously in the running text
we extend the general algorithm for least errors recognition to adopt it as the recovery mechanism in our robust parser
next we describe the implementation of the system and the result of the experiment of parsing real sentences
so the substring comma v comma should be dealt with as a constituent in extended completer
in fact we implemented the algorithm to allow substring insertions well as insertions of nontermlnal nodes
so we assign less error value to the deletion error hypothesis edge than to the insertion and mutation errors
if the sentence is within the grammatical coverage of the system the normal parser succeed to analyze it
the result of the robust parser is the parse trees which are within the grammatical coverage of the system
next we will show how to do it depending on the resource used
that is a dcgs are called pure if they do not contain any calls to external prolog predicates
then the top node is examined for membership in a set of atelicity indicators act be stay if there is a match the lcs is further examined for inclusion of a telicizing component i.e. to toward fortc p
for computational purposes these degrees of knowledge for each factor can be quantified the agent a may know percentage q of the knowledge about diseases that cause dizziness while the collaborator c knows percentage qc of the knowledge about these diseases
thus in the medical domain the agent may know that the collaborator knows more about diseases that account for dizziness and nausea less about diseases that cause fever and headache and nothing about diseases that cause itchy feet
assuming independence the expected number of branches which satisfy all n factors is expaun n l ii l xi given that a branch satisfies all n factors the likelihood that the collaborator will know that branch is rzin l qc
efficiency our model of best first search assumes that for each goal there exists a set of n factors fl f which are used to guide the search through the problem solving space
the advanced maintenance assistant and trainer amat currently being developed by research triangle institute for the u s army allows a maintenance trainee to converse with a computer assistant in the diagnosis and repair of a virtual mia1 tank
when an agent ai asks another agent a2 to satisfy a goal g agent a2 gains initiative over goal g and all subgoals of g until agent a2 passes control of one of those subgoals back to agent a1
rather than selecting a branch at random intelligent behavior involves evaluating by some criteria each possible branch that may lead toward the solution of a goal to determine which branch is more likely to lead to a solution
each user response was annotated with an update representing the meaning of the utterance that was actually spoken
where the user has the initiative throughout the dialogue such as in command and control applications the user has greater expressibility and freedom of choice
f language perplexity the ability to express oneself as one wishes and still be understood is an important factor which contributes to naturalness in dialogue
we are now ready to construct the tagger
section NUM NUM formally defines the notion of transducer
w2 was delayed for ql resp
for instance consider t6 of figure NUM
this step can also be made very efficient
speeds of the different parts of the program
this intuition will be formalized in section NUM NUM
the new tagger therefore operates in optimal time
these procedures have the following possible effects they can cause an immediate breaking of the current sentence into clauses
to the beginning of a paragraph the higher its dhit score is
the sixth seventh and eighth columns of table NUM show the word segmentation accuracy f measure of each estimation method using different sets of initial words d1 d200
system discourse markers are considered unambiguous with respect to the relations that they signal
for example when the cue phrase although is identified it is also assigned a discourse usage
i am grateful to melanie baljko phil edmonds and steve green for their help with the corpus analysis
when we began this research no empirical data supported the extent to which this ambiguity characterizes natural language texts
the relative position of the textual unit that the unit containing the marker was connected to before or after
we believe that there are two ways to evaluate the correctness of the discourse trees that an automatic process builds
discourse is ambiguous the same way sentences are more than one discourse structure is usually produced for a text
in both conditions significantly more tagger expert matches occurred for all parts of speech when the expert choice was in first position than when it occurred in a subsequent position NUM NUM vs NUM NUM
confidence was slightly higher for inter tagger than expert tagger matches supporting the reality of a naive lexicon as opposed to representation of polysemous words in the mental lexicon of practiced lexicographers or linguists
in the frequency condition taggers overall chose the same sense as the experts NUM NUM of the time in the random condition the overall agreement was NUM NUM
we reasoned that the greater the sense number in wordnet was the harder the taggers task of evaluating the different sense distinctions in terms of the target word became
we predicted that a greater degree of polysemy would lead to greater discrepancies between the taggers matches and those of the experhnenters as well as among the taggers themselves
in the first condition frequency condition NUM taggers were given a dictionary booklet listing the wordnet senses in the order of frequency with which they appear in the already tagged brown corpus
a strong tendency towards picking the first sense in the random order would point to a reluctance to examine and evaluate all available senses independent of whether this sense represented the most salient or core sense
the difference between the tagger expert matches for words in the first position and words in subsequent positions was particularly strong for verbs and in the frequency order condition for words with eight or more senses
weighing all available senses against each other and against the given usage can be a difficult task especially for novice taggers and we expected a general tendency to gravitate towards the first choice for this reason
experiments using wall street journal data show that our approach achieves a relatively high accuracy NUM recaj1 NUM precision and NUM NUM crossing brackets per sentence for sentences shorter than NUM words and NUM recall NUM NUM precision and NUM NUM crossing brackets for sentences between NUM NUM words
our wfst has NUM states and NUM arcs
the latter module see section NUM NUM specifically takes care of assimilation and the prosodic integration of the slot values with the rest of the template
table NUM shows the number of words words and the number of NUM digit class codes classes with respect to each part of speech
in order to have useful predictive power it would be best to assign semantic types to the elements of the complex nominal and determine the probability that a complex nominal consisting of words of types a and b involves modification relation c given the sparsity of data to support a statistically based approach we believe that the way forward in this area is to pursue the integration of a rule based approach with a statistical model
the two subevents namely the process and the resulting state in the event structure representation of the verb are encoded in the nominalized form as separate events in the agentive and formal roles and they are related by the relation of temporal precedence o
the fact that the content of the compound always comes from the head noun is captured by having all of the compound phrase structure schemata which are themselves implemented as types all inherit the constraint specified by the structure sharing index e
as the result we obtain three grammar rules np dt nn np pr p nn and np dt cl
the algorithm involves the following steps asome systems produce soft s clusters where words can belong into more than one group
in section NUM we outline our first method based on domain independent lexical knowledge presenting results from an analysis of thousands of verbs
by applying the two methods in tandem and intersecting the sense sets produced by them we can reduce the size of the final tag
the look up method can tag distinct tokens of the same verb with distinct senses if the subcategorization patterns are distinct and correlate with distinct senses
it is indirect because the actual result is groups of word forms but we presume each group to represent a relatively homogeneous semantic class
table NUM number of words in a semantic group linked with each sense of each word in it and associated reduction in ambiguity
in this paper we propose a two pronged approach to an initial step in lexical semantic tagging pruning the search space for polysemous verbs
table NUM shows the highest rated rules from the induced prefix and suffix deg rule sets
rules which have scored lower than the threshold are merged together into more general rules
table NUM displays the tagging results on the unknown words obtained by the four different combinations of taggers and guessers
we evaluated the taggers with the guessing components on all fifteen subcorpora of the brown corpus one after another
next we cut out the most infrequent rules which might bias further learning
documents can be news stories emailmessages reports and so forth
cal dialogue model rule based or statistical dialogue act idenfication rules and semantic representations of user utterances
only recently other kinds of information e.g. phonetic transcriptions of unknown words has been used
this attempt fails several times since the incoming semantic representations are inconsistent due to recognition errors
more advanced systems exploit additional information to generate more intelligent feedback for the user
turn off the radio and call again how acoustic clues can improve dialogue management
one binary tree is produced to represent the word relation direction from the left to the right and the other is to represent the word relation direction from the right to the left
null no i also do n t want to go to offenburg but to hamburg
based on samples from each category topic keywords which are most useful in identifying this topic would then be extracted automatically
combined with contextual knowledge they account for the acceptability of different alternative responses
thematic relatedness is based on the types of relationships which occur in the domain
maintain the initiative if the response is related give the initiative if unrelated
maintain the initiative if the response is expected give the initiative if non expected
NUM everything that is related to cc is motivated if not already known
current interests concern the extension of the communicative principles into different activities and agent roles
this paper has presented a new way to formulate system goals in intelligent dialogue systems
this is similar to our basic assumption of how domain tasks give rise to eonlinunication
also speech act classification is abandoned in favor of contextual reasoning and rationality considerations
po g h po giigi NUM gi NUM NUM i NUM
pq tic argmax a pq t argm xpq a
the main advantages of the model are it is relatively theory independent and closely related to semantics
ghter nodes correspond to the outputs of the markov model while grammatical functions correspond to states
the extended tagger using a combined model as described in section NUM was applied
table NUM shows the NUM mostfrequent errors which constitute NUM of all errors
the following rows show the relative percentage and accuracy for different levels of reliability
figure NUM parts of the markov models used in selbst besucht hat peter sabine hie cf
this aspect of the work has led to a new model of human supervision
since keyboard input is most efficient for assigning categories to words and phrases cf
the various semantic filters in turn reduce the number of assignments further
the results for both of these filters are shown in table NUM
we will address this point further in section NUM
for example the verb scatter is a synonym of break in wordnet
for example there are NUM senses of the verb break in wordnet
but the correct class for scatter is NUM NUM spray load verbs
let us now look at the behavior of the synonymy based semantic filter
r t r u u u while there certainly is difference in approach and emphasis between f structures qlfs and udrss the motivation foi flat underspecified representations in each case is computational
it is straightforward to extend the definition to take account of subordinat ion constraints if that is desired but as we remarked above the translation image the resulting f structures can not in all cases reflect the constraints
NUM NUM of these assignments NUM NUM are correct
the performance of the various filters is shown in table NUM
in the second case all the relevant reference nodes will be already correctly decided by the time we will try to add another co founded feature
table NUM confusion matrix agent b
the evaluation model presented here has many applications in apoken dialogue processing
in general there is no analytical solution to such equations and the most popular numerical method is newton s method where we fit aa iteratively
dr u2 what are the options
in the latter case e.g. a genitival attribute may instantiate dependlug on the np either the subject qenitiwts subjectivus or the object qenitiwts objectivus for german cf
this tendency is strongly supported by the case role inertia heuristic which promotes a complementary distribution of preferred antecedents for type b pronouns cooccurring in a domain of binding
c what is the led displaying
the at proach presented below is sealsitive to these decision interdependencies while avoiding the exponential time comi lexity of an immedl ate l inding constraint implem mtatioil
according to NUM NUM most colnmon tectmiquc for anat horic nps a separate antecedent search is t ertbrmed resulting in a quadratic time complexity e.g.
lies des being efficiently searchable the simplified surface structure has to represent the stru t ural details wtfich are necessary for th verifica
check whether the binding l rinciples of y and x are satisfied for the proposed eoindexing i verify that the binding principle of y is satisfied constructively ii
to allow for m efficient detection of intex let endencies store the selected antecedent separately fl om corefercnt occurrences contributed by earlier invocations of the algorithm
in general it is necessary to determine the instantia tion of its participants but this at least in certain ases involves pragmatic inferencing
chomsky states merely as a the rctical device a flee inclexing rule wlfich ran lomly assigns reference in lexes to surface structure np nodes
figure NUM hypothetical agent c dialogue interaction
c the circuit is working correctly
it then uses an untagged training corpus in which all lexical items have been annotated with all possible morphological analyses incrementally proposing and evaluating additional possibly corpus dependent constraints for disambiguation of morphological parses using the constraints imposed by unambiguous contexts
our results indicate that by combining these hand crafted statistical and learned information sources we can attain a recall of NUM to NUM with a corresponding precision of NUM to NUM and ambiguity of NUM NUM to NUM NUM parses per token
support interruption and recovery use the normal manners for interrupting the user in his current activities i.e. only interrupt in critical or urgent situations and provide the user with a justification for the interruption
this noise pattern is illustrated in figure NUM
in the formula the first term means the original estimated probability and the second term expresses a uniform distribution where the probability of all events is estimated to a fixed form number
we will now look at two examples of selected trees in action
person is role yes this feature was never used in the walk through text
we learned that NUM documents do not provide enough training for our system
our official co scores were NUM recall and NUM precision
but keep in mind that resolve was not trained on perfec t data
crystal and badger neither helped nor hindered they just were n t needed
the complete tree is large containing NUM nodes or possible decisions points
a walk with resolve resolve is needed here to determine that mr
the pairwise examination of nps is handled by a c4 NUM decision tree
simr makes errors of omission and errors of commission
the last experiment is carried out for evaluating the whole grammar which is learned based on local contextual information and indicating the performance of our statistical parsing model using the acquired grammar
when a goal has been answered satisfied the problem solving stack is popped
contexts axe selected in the order of their varlznce and a context wi NUM be accepted when its variance is more than NUM of the average v iance of the previously selected contexts
this enables us to decrease computation time and space without sacrificing the accuracy of the clustering results and sometimes also helps us to remove some noises due to useless contexts
to give the reader a global idea of our approach we focus on those aspects of the compiler that are crucial to the presented conception of lexical rules
in the subsequent step of word class specialization section NUM NUM this finite state automaton is fine tuned for each of the natural classes of lexical entries in the lexicon
when c is the common information and d1 dk are the definitions of the interaction predicate called we use distributivity to factor out c in
in this section we describe criterion named differential entropy which is a measure of entropy perplexity fluctuation before and after merging a pah of labels
NUM as discussed in section NUM NUM the parsing times with a covariation lexicon without constraint propagation suffer significantly from the lack of information directly available upon lexical lookup
the use of constraint propagation however makes it possible to exploit the covariation encoding of lexical rule application such that it results in an increase in speed
thus part of the defining characteristics for this category is a specification for lexical items that have a contentdisjunct feature
the analyst identifies the contextual focus traditional practical emotional or anmytic and the ways in which the texts differ from one another
the category the contains one word with an average expected fiequency of NUM percent with a range over the four contexts of NUM NUM to NUM NUM
thus the heuristic detached roles is like a hearst schcttze super category but not constructed on a statistical metric rather on underlying semantic components
we found that the mcca categories were generally internally consistent but with characteristics not intuitively obvious as a result we needed to articulate firm principles for characterizing the categories
the remaining NUM or so categories consist primarily of open class words nouns verbs adjectives and adverbs sprinkled with closed class words auxiliaries subordinating conjunctions
they note that their system needs further refinement suggesting that adding information to lexical entries about diathesis alternation possibilities and semantic selectional preferences on argument heads is likely to improve their results
the meaning that is the underlying concepts of the mds graph is then described in terms of category and word emphases
this approach differs from the standard approach for joint distribution equation NUM in plugging in the empirical marginal estimate x
unlike other content analysis techniques or classification techniques used for measuring the distance between documents in information retrieval mcca uses the non agglomerative technique of multidimensional sealing mds
n ireq debito publieo x y log NUM freq debito freq publico
ideally we would like to be able to pick a performance level in terms of either entropy or precision and recall and find the best set of thresholds for achieving that performance level as quickly as possible
then after the best ranked feature has been established it is added to the feature space and the weights for all the features are recomputed
the sample sentence gives rise to the following collision sets lcb ufliiciale di flna nza guardia di finanza rcb
the second kind of thresholding we consider is a novel technique global thresholding described in section NUM global thresholding makes use of the observation that for a nonterminal to be part of the correct parse it must be part of a sequence of reasonably probable nonterminals covering the whole sentence
this section describes three types of patterns introduced in the previous section
the pattern development was mainly done by hand which is very time consuming
they computed a score for each sequence as the minimum of the scores of each node in the sequence and computed a score for each node in the sequence as the minimum of three scores one based on statistics about nodes to the left one based on nodes to the right and one based on unigram statistics
although our previous information extraction system textract performed well in muc NUM the pattern matching engine which was written in awk language was slow NUM
figure NUM shows an example of a dictionary pattern
segmentation lcb noun place noun place lcb rcb noun organization suffix position lcb len NUM rcb rcb
a pattern name is written on the left side of the pattern and the word sequence to be searched for is defined on the right side
figure NUM example of a name recognition pattern
this is several times faster than textract
figure NUM shows an example of the segmentation patterns
figure NUM example of a dictionary pattern
a d tree is a tree with two types of edges domination edges d edges and immediate domination edges i edges
in particular the foot nodes of these trees are always daughters of the root and either the leftmost or rightmost frontier nodes
dtg like other formalisms in the tag family is lexicalizable but in addition its derivations are themselves linguistically meaningful
we use the node label vp throughout and use features such as top for topic to differentiate different levels of projection
a derivation graph for NUM e t g results from the addition of insertion edges to a sa tree r for NUM
a sac is a finite set of pairs each pair identifying a direction left or right and an elementary d tree
practice and annotate nodes with lexemes and arcs with grammatical function by distinguishing between the adjunction of modifiers and of clausal complements
note that sister adjunction involves the addition of exactly one new immediate domination edge and that severm sister adjunctions can occur at the same node
this is handled by a sac associated with the n node that allows all trees rooted in adjp to be left sister adjoined
this is done by either using appropriate feature constraints at nodes or by means of subsertion insertion constraints see section NUM
in this paper focus is on the description of the dialogue component which processes the interaction of the two dialogue partners and builds a representation of the discourse
the speech acts which in our approach are embedded in a sequential model of interaction can be additionally classified using the taxonomy of dialogue control functions as proposed in e.g.
this is an interval on the timeline throughout which certain events one or more specified edges or interiors are all present and certain other events zero or more specified edges or interiors are all absent
in fig NUM we give two snapshots showing how the dialogue memory looks like after processing the turns de006 NUM and el007
since the statistical model always delivers a result and since it can adapt itself to unknown structures it is very robust
when an inconsistency occurs fall back strategies using for instance the statistical layer are used to select the most probable state
robust processing will be another issue to be tackled the possibility to process gaps in the dialogue will also be integrated
once an item is accepted nd mutual agreement exists either the dialogue can be terminated or another appointment is negotiated
the main reason is that the dialogues in our corpus frequently do not follow conventional dialogue behavior i.e. the dialogue structure differs remarkably from dialogue to dialogue
dialogue dependent language models for the disambiguation of diflhrent readings of a sentence or for guiding the dialogue planner
the dialogue component computes the most probable speech act type of the next utterance in order to selects its typical key words
in reading NUM every girl admired an arbitrary saxophonist and most boys also detested an arbitrary saxophonist
c two representative of at least three companies touched but of few universities saw most samples
since any np referential or quantificational requires quantifying in to outscope another quantifying in consequently confounds referential and quantificational np semantics
9one can replace most samples with other complex np such as most samples of at least five products to see this
in the other more than two women outscopes every man and a few boys which together outscope most boys
computational linguistics volume NUM number NUM the user model must change continuously during a dialog as the interactions occur
each rule is associated with a label such as or b etc shown at the end
the purpose of the architecture is to deliver to users in real time the behaviors needed for efficient human machine dialog
its purpose is to indicate what needs to be said to the user to enable the user to function effectively
questions from the user may indicate lack of knowledge and result in the removal of items from the user model
NUM computer connect the end of the black wire with the large plug to connector one two one
then the system will achieve observeposition swl up and answer its question without interaction with the user
when a vocalized response comes back parsing and error correction will be biased to recognize one of these meanings
the other major component of a node is a set of status flags for the subsystem represented by the node
this is the one in which three companies outscopes most samples which in turn outscopes two representatives cf
buthe bragging rights tnamex type oanization creative artists agency enamex the hi namex type location hollywood enamex alent agency
in additio namex type person peter kim e mex was hired fro m up for the headaches of running one o fthe is d wide agencies
he has he says fon dmemories of working wit1 enamex type organization coke enamex executives soon
having a lub of top means that the two types do not share any information
in our case however the bitstring will always coincide with one row exactly
notice how easy it is to get a lattice that does not obey our constraints
the second statement restricts the application of the default to members of the category noun
value must be any atom feature subcat list category
lists of lists of atoms represent the subsets whose product forms the space of values
briscoe et al NUM arnold et al NUM kleene operators have been included
an initial implementation of this idea resulted in the resolution of NUM
lcb renat a simone rcb c cogsci ed
NUM NUM of the cases based on names
at the top level is the template object of which there is on e instantiated for every document
it is of course also possible that a text may identif y an organization solely by name
but the exact nature of the difference is not known and th e performance differences are very small
motor vehicles international is the biggest american aut o exporter to latin america
the interannotator variability test provides reference points indicating human performance on the different aspects of the ne task
the type slot however is a more difficult slot for enamex than for the other subcategories
the decision to minimize the annotatio n effort makes it difficult to do detailed quantitative analysis of the results
many of the sites have emphasized their pattern matching techniques in discussing the strengths of their muc NUM systems
table NUM ne subcategory scores err metric in order of decreasing overall f measure p r
the amount of agreement between the two annotators was found to be NUM recall and NUM precision
we present preliminary results concerning robust techniques for resolving bridging definite descriptions
entity types can be identified by complements like mr co inc etc
first we segment the training corpus by the character type based word segmenter and make a list of words with frequencies
the reason is that in addition to full underspecification udrt allows partial underspecification of scope for which there is no correlate in the original lfg f structure formalism
although the estimates by poisson distribution are not so accurate they enables us to make a robust and computationauy efficient word model
the initial word list is au nented by identifying words in the training text using a heuristic rule based on character type
the total number of words in the corpus is derived simply by summing the string frequency of each word in the dictionary
it involves applying the above segmentation algorithm to a training corpus using a set of initial estimates of the word frequencies
among word frequency estimates the longest match string frequency method lsf consistently outperformed the string frequency method sf
surprisingly the best word segmentation accuracy is achieved when the very small initial word list of NUM words d100 is augmented
our em training goes like this NUM
the varying concentration of identical tokens suggests that more localized fence a were switched during translation resulting in a non monotonic segment
i where iw l and c w are the length and the frequency of word i respectively
it is formally defined as the joint probability of the character sequence c ck if wi is an lmkaown word
examples include stock market crash the markets and discount packages the discounts
for other names if we can infer their entity type we could resolve them using wn
table NUM precision and recall for induction for natural contexts
the tag prd stands for predicative uses of adjectives
within this search 1since distances in the bitext space are measured in characters the position of a token is defined to be the mean position of its characters
even for natural contexts performance varies considerably
several researchers have worked on learning grammatical properties of words
the members of classes NUM and NUM function as subjects
a closer look reveals that many clusters embody finer distinctions
their right similarity according to the cosine measure would be zero
rare words are difficult because of lack of distributional evidence
hence their vector would be zero in the word based scheme
this procedure was applied to all tokens of the brown corpus
each graph appearing in the rule has a single node the semantic head which acts as a root indicated by an arrow in figure NUM
if this additional information is within the input semantics then information can propagate from the input semantics to the mapping rule the shaded area NUM in figure NUM
moreover if the semantic input comes from other applications it is hard for these applications to determine the most prominent concepts because linguistic knowledge is crucial for this task
this allows us to investigate a more general version of the sentence generation problem where one is not pre committed to a choice of the syntactically prominent elements in the initial semantics
if we evaluate in a bottom up fashion the semantics of the s node we will get the same result as the input semantics in the result is fred limped quickly
the semantic annotations of the syntactic nodes are either conceptual graphs or instructions indicating how to compute the semantics of the syntactic node from the semantics of the daughter syntactic nodes
because of the minimality of the mapping rule the syntactic structure that is produced by this initial stage is very basic for ex mple only obligatory complements are considered
this explicit marking of the semantic head concepts differs from NUM where the semantic head is a prolog term with exactly the same structure as the input semantics
many generators expect their input to be cast in a tree like notation which enables the actual systems to assume that nodes higher in the semantic structure are more prominent than lower nodes
if the initial goal has a more elaborate syntactic structure and requires parts of the semantics to be expressed as certain syntactic structures this has to be respected by the mapping rule
NUM NUM topic interpretation concept fusion
figure NUM encoding a solution to the vertex cover prob
j ill phrases baxendale NUM word frequency and evaluation
figure NUM coverage scores for top ten opp sentence posiuons window sizes NUM to NUM
NUM NUM rule NUM adding topic continuity
NUM NUM rule NUM adding discourse structure
NUM NUM rule NUM adding syntactic constraints
for some applications one may be more appropriate than another e.g. the scores produced by a neural net may be useful for another processing step in a natural language program so we do not consider either learning algorithm to be the correct one to use
the number in the training epochs column is the number of passes through the training data required to learn the training set the number in the testing errors cohnnn is the number of errors on the NUM NUM item test set the system made after training with the corresponding context size
for a binary vector all categories with a nonzero frequency count are assigned a value of NUM and all others are assigned a value of NUM in addition to the NUM category frequencies the descriptor array also contains two additional flags that indicate if the word begins with a capital letter and if it follows a punctuation mark for a total of NUM items in each descriptor array
the system called satz makes simple estimates of the parts of speech of the tokens immediately preceding and following each punctuation mark and uses these estimates as input to a machine learning algorithm that determines whether the punctuation mark is a sentence boundary or serves another purpose in the sentence
to avoid this circularity we approximate each word s part of speech in one of two ways NUM by the prior probabilities of all parts of speech for that word or NUM by a binary value for each possible part of speech for that word
however more context may be necessary such as when punctuation occurs in a subsentence within quotation marks or parentheses as seen in example NUM or when an abbreviation appears at the end of a sentence as seen in 5a NUM a
what sets spud apart is its simultaneous construction of syntax and semantics and the tripartite lexicalized declarative gram null matical specifications for constructions it uses
the occurrences n ill both tal rcb le NUM an lcb l table NUM apparently sul rcb port the conditions NUM all lcb NUM
in the case of figure NUM to limit the amount of illegible strings to not exceed NUM of the total extracted strings we set the threshold to NUM
our method is applied to n gram text data using statistical observation of the change of frequency of occurrence when the window size of string observation is extended character cluster wise
we generate both rightward and the leftward sorted n gram data then determine the left and right boundaries of a string using the methods of competitive selection and unified selection
in this paper we examine the result of applying our medlod to thai tex corpora and also introduce conventional thai spelling rules to avoid e xtracting invalid strings
null further define that a is the right observation of the string a and a is the left observation of the string a
very few illegible strings are extracted though the threshold of the difference value is set to be as low as NUM in figure NUM and NUM in figure NUM
we can see that the result is always the best when the grammar acquired from either the same domain or the same class fiction or non fiction is used
for the string a n a l decreases significantly from n a when a is a frequently used string in contrast to a l
from this it can be seen that a is a rigid expression of an open compound when it satisfies the condition n a NUM n a
for NUM different domains domain dependent grammar or the grammar of the same class provide the best performance if the size of the training corpus is the same
one might claim that the ready salience of the information state naturally different across languages is what makes idioms different from metaphors
in order to acquire grammar rules in our experiment we need a syntactically tagged corpus consisting of different domains and the tagging has to be uniform throughout the corpus
the list is too large to show in this paper a part of the list is shown in appendix b it obviously demonstrates that each domain has many idiosyncratic structures
in the press reportage domain one needs a three to four times bigger corpus of all domains or non fiction domains to catch up to the performance of the baseline grammar
we believe that the reason why the performance in this domain saturates with such a small corpus is that there is relatively little variety in the syntactical structure of this domain
under a strict criterion the method proposed in this paper may not be suitable for short fat trees
for example lob tags nn and nns in row three of table NUM appear three and one times respectively
susanne corpus is adopted as the source of testing data for evaluating the performance of our probabilistic chunker
verb is presented in the form of third person and singular past tense or base form
the above derivation tells us the local minimums of the b NUM distribution denote plausible boundaries of two chunks
the second step is to find the corresponding lob tags from lob corpus for each word collected at the first step
rather than using a treebank as our training corpus a corpus which is tagged with part of speech information only is used
but the tagging sets NUM NUM of lob corpus and susanne corpus are different
note that only tags which have the same first character as iw are considered that is
linguistic and statistical techniques are used to extract a candidate term from a corpus or to recognize a term in a document
when we see a structure of a simple type we do n t need to apply any constraints neither on the top node nor on any substructure
the descriptions which make up the theory are also called constraints since these descriptions constrain the set of objects which are admissible with respect to the theory
since the values of the features of append c are of type list a hiding type those features are hiding features and need to be considered
this for the first time offers the possibility to express linguistic theories the way they are formulated by linguists in a number of already existing computational systems
an object is admissible with respect to a certain theory iff it satisfies each of the descriptions in the theory and so does each of its substructures
b elseif t is a hiding type then check if its hiding features and the hiding features of all its hiding subtypes are identical
they are all embedded under node which is being checked anyway so listing them here in the rhs is entirely redundant
NUM compute hf the set of hiding features on the type of the current node then insert these features with appropriate types in the structure
the theory the linguist proposes distinguishes between those objects in a domain which are part of the natural language described and those which are not
now for the recursive disjunct we start therefore we do n t enter that node in the rhs but proceed to look at its features
in particular dependency parameters and context dependent transfer parameters give rise to an implicit graded notion of word sense
the intransitivity of the metric detex mines this metric can not be used in clustering words to equivalence classes
because the average mutual information is little it is possible that pc is less than l pc
the probability contained in classes c and c in the left right and right left trees respectively
NUM p o lcb k introduces misspellings caused by optical character recognition ocr
the basic idea is to consume a whole syllable worth of sounds before producing any katakana e.g. this fragment shows one kind of spelling variation in japanese long vowel sounds oo are usually written with a long vowel mark but are sometimes written with repeated katakana
in the first experiment we extracted NUM unique katakana phrases from a corpus of NUM short news articles
so this model also serves to filter out some ill formed katakana sequences possibly proposed by optical character recognition
figure NUM english sounds in capitals with probabilistic mappings to japanese sound sequences in lower
these subjects were native english speakers and news aware we gave them brief instructions examples and hints
we use the english phoneme inventory from the online cmu pronunciation dictionary NUM minus the stress marks
p c wi ic w can be estimated by
the base lexical case holds by definition of the grammars
finally it should also be possible to embed our phonetic shift model p jle inside a speech recognizer to help adjust for a heavy japanese accent although we have not experimented in this area
the only approximation is the viterbi one which searches for the best path through a wfsa instead of the best sequence i.e. the same sequence does not receive bonus points for appearing more than once
finally u5 illustrates a user request for an agent action and is tagged with the rt attribute
it turns out that the use of tables defined in the previous subsection can lead to a problem with cyclic unifications
since the normal form can not be correctly constructed without selecting the correct parse tree errors of this type deteriorate system performance most seriously
for those words which are not included in the longman dictionary their sense are defined according to the system dictionary of the behaviortran system
besides trivial cases of temporal constraint resolution such as guessing the endpoint of an appointment from its startpoint and its duration our inference engine performs disambiguation of domain actions by comparing intervals referred to by different dialogue utterances
in case of a rejection of one or more participants the initiator would continue to propose new time slots to all partners until everyone agrees to a common date or there is no such slot within the interval
il expressions have been designed with the goal of representing both a domain action that is easily mapped onto an agent system s cooperation primitive and the associated temporal information which should be fully specified due to contextual knowledge
for instance if an utterance u describing an interval i is ambiguous between a refinement and a modification and the previous utterance refers to an interval j including i then u can be disambiguated safely as denoting a refinement
here ranking depends not only on informative null ness but also on dialogue expectation sentence structures are favored that contain a domain action compatible with the il expression previously stored in the discourse memory
we assume a three party e mail negotiation between a human h who does not use a scheduling agent system and two machine agents a b that schedule appointments for their respective owners
analogous inferences are drawn by just checking the possible combinations of domain actions across the current dialogue a rejection can hardly be followed by another cancellation a fixing can not occur after a rejection etc
the rules are coded in a transparent and declarative language that allows for a possibly underspecified description of the sines input represented as a feature structure with its associated information gathering action
with the present version of the nl server these problems are solved by adopting a shallow analysis approach which extracts meanings from those portions of a text that are defined as interesting and represents them in an agent oriented way
the repair provided by h NUM is underspecified with respect to clock time see also NUM hence the agents offer free time slots in accordance to their calendars NUM NUM NUM
a scalable summarization system using robust nlp
based on the integrated score function the lexical score function the syntactic score function and the semantic score function are derived
the templates and the words for stations and times are prerecorded and their accoustic representations are concatenated to form complete sentences
up NUM were hearing a lot these days about selling abroad about the importance of britain exporting abroad
the dialogue fragment displayed in figure NUM shows an example of an information presentation in an ovr dialogue
this position paper sketches the author s research in six areas related to speech translation interactive disambiguation system architecture the interface between speech recognition and analysis the use of natural pauses for segmenting utterances dialogue acts and the tracking of lexical co occurrences
a i j l so far there has been no basic restriction of the approach
using the same basic principles we can rewrite the probability by introducing the hidden alignments
this model will be referred to as ib m NUM model
as with the ibm2 model we use again the max
thus in a responding utterance hai sou desu meaning literally yes that s right the segment sou desu may be most naturally translated as he can you will she does etc depending on the structure and content of the prompting question
NUM does the string contain NUM NUM NUM or NUM commas
where a comma is anything tagged as or
in principle the model could condition on any structure dominated by h r1 or r2
for example knowledge that week is likely to be a temporal modifier
table NUM results on section NUM of the wsj treebank
for brevity we omit the pos tag associated with each word
table NUM the conditioning variables for each level of back off
the verb has been selected and the clause structure has been built
ulfills both the s c and gap requirements in rc
this section describes a probabilistic treatment of extraction from relative clauses
identify constituents in result i.e. read the lex cset attribute
the test may also refer to the positions somewhere in the sentence without specifying the exact location
the bottom shows the result of key paragraphs extraction
for example the following rule discards the syntactic function NUM qi 0bj indirect object null
in addition another test may be added relative to the unrestricted context position using keyword link
the object complement c pcompl NUM is linked to the verb readings having the subcategorisation sv0c
after the morphological disambiguation all legitimate surface syntactic labels are added to the set of morphological readings
in addition both systems use the front parts of the engcg system for processing the input
as already noted the dependency grammar has a big advantage over engcg in dealing with ambiguity
according to figure NUM when the extraction ratio was NUM the paragraphs NUM and NUM were extracted and the paragraph NUM was not extracted although it is a key paragraph
table NUM our method and a vector model
these titles are classified into NUM different domains
we call each paragraph context in the paragraph
table NUM the results of key paragraphs experiment
in figure NUM general signal corp
an automatic extraction of key paragraphs based on context dependency
this regularity is a clear benefit to users since only one syntax must be learned
cqp requires users to transform the corpora which will be searched into a fast internal format
this means that the character data corresponding to each distinct corpus token need only be stored once
but to do this requires only a convention for the encoding of meta information about text corpora
similarly a tipster architecture like gate could read sgml and convert it into its internal database
each attribute NUM can be thought of as a total function from corpus positions to attribute values
there is a logically separate index for each attribute name in the corpus
sgml is human readable so that intermediate results can be inspected and understood
we will discuss two such systems and compare them with the lt nsl approach
this may however be a problem for very large corpora such as the bnc
the 2nd nearest neighbor of x is the nearest occurrence of the same word ignoring the 1st nearest neighbor
the particular thematic roles found in the output are characteristic of surce
for example the interface between speech recognition and analysis can be supplied entirely by the user who can correct sr results before passing them to translation components thus bypassing any attempt at effective communication or feedback between sr and mt
ken wa kesa kara zutto axto kimono wo ki te i ru
the default window size is fifteen sentences and this works well for all but the most homogeneous of texts
the baseline was then compared against a version of the query in which all variations were eliminated except for the part of speech that was correct i.e. if the word was used as a noun ill the original query all other variants were eliminated
for example attentional space u2 has two subordinate spaces u3 and u4
for combining the heuristics each heuristic assigns each candidate hypernym sense a normalized weight i.e. a real number ranging from NUM to NUM after a scaling process where maximum score is assigned NUM c f section NUM NUM
vote o e weight o e max e weigth o ei s the values thus collected from each heuristic are added up for each competing sense
for instance in dgile heuristic NUM has the worst performance see table NUM precision NUM but it has the second larger contribution see table NUM precision decreases from NUM to NUM
for instance when accessing the semantic fields for vin french we get a unique translation wine which has two senses in wordnet wine vino as a beverage and wine wine coloured as a kind of color
we have also shown that such a simple technique as just summing is a useful way to combine knowledge from several unsupervised wsd methods allowing to raise the performance of each one in isolation coverage and or precision
this work would not be possible without the collaboration of our colleagues specially jose mari arriola xabier artola arantza diaz de ilarraza kepa sarasola and aitor soroa in the basque country and horacio rodr guez in catalonia
it is precisely this information that is needed to judge the content of a particular segment of text
minimal parameter adjustment window size cooccurrence weigth formula and vector similarity function should be done to fit the characteristics of the dictionary but according to our results it does not alter significantly the results after combining the heuristics
in our case combining informed heuristics and without explicit semantic tags the success rates are NUM and NUM overall and NUM and NUM for two way ambiguous genus dgile and lppl data respectively
table NUM aspectual categories of verbs
the maximum entropy method requires very long training times e.g. NUM cpudays in rosenfeld s experiments
an ideal architecture for high road or integrationintensive speech translation systems would allow global coordination of cooperation between and feedback among components speech recognition analysis transfer etc thus moving away from linear or pipeline arrangements
the output of this stage is a normalised significance score NUM NUM for each word in the text
i am on vacation at the beginning of april
table NUM correspondence between NUM and b3
first pictalk holds very few typically two dozen utterances
however since these techniques are likely to remain insufficient a new technique for semantic smoothing is proposed and supported researchers can track co occurrences of semantic tokens associated with words or morphs in addition to co occurrences of the words or morphs themsel es
anfang april bin ich in urlaub
ende april habe ich noch zeit
NUM the explicit form where a reason is literally repeated
figure NUM shows a partial description representing a sentence with a nominal subject in canonical position giving no other information about possible other complements
without large dominance links an order of inheritance of the classes describing a subject in canonical position and a cliticized complement should be predefined
define crossing bracket to be the percentage of true words that violate the segmentation tree structure s the search algorithm was applied to two texts a lowercase version of the million word brown corpus with spaces and punctuation removed and NUM million characters of chinese news articles in a twobyte character format
it starts from a compact hierarchical organization of syntactic descriptions that is linguistically motivated and carries out all the relevant combinations of linguistic phenomena
the three cited solutions give an efficient representation without redundancy of an ltag but have in our opinion two major deficiencies
in the case of a clitic the path between the s and v nodes can be specified with the description of figure NUM
problems with ltags this extreme lexicalization entails that a sizeable ltag comprises hundreds of elementary trees over NUM for the cited large grammars
i using nlp in the design of a conversation aid for non speaking children
we have added atomic features associated with each constant such as category index canonical syntactic function and actual syntactic function
using this hierarchy and principles of well formedness the tool carries out automatically the relevant crossings of linguistic phenomena to generate the tree families
in other words it suffices to track the status high alone
and cnn interactive at www cnn com as well as others such as clarinet which is propagated through the nntp protocol
we adopt a new primitive act to characterize certain activities such as march which are not adequately distinguished from other event types by jackendoff s go primitive
december holiday summer weekend etc template to telic we make use of the measure phrase for terap which was previously available
this list structure recursively associates arguments with their logical heads represented as primitive field combinations e.g. actloc becomes act loc with a thing NUM argument
in either case the top node is further checked for membership in sets that indicate dynamicity d and durativity r
in this case the top node is further checked for membership in sets that indicate dynamicity d and durativity r
the lexical aspect of verbs and sentences may be therefore determined from the corresponding lcs representations as in the examples provided from machine translation and foreign language tutoring applications
events are characterized by go act stay cause or let whereas states are characterized by go ext or be as illustrated in NUM
we show how proper consideration of these universal pieces of verb meaning may be used t o refine lexical representations and derive a range of meanings from combinations of lcs representations
we show how proper consideration of these universal pieces of verb meaning may be used to refine lexical representations and derive a range of meanings from combinations of lcs representations
for example in the following sentences a posthn il nei s family house appears with a pn alone or with a pn followed by an n here NUM ssi mr
while these expert sense selections constituted the standard for evaluating the taggers performance they should not be re null garded as the right choice implying that all other choices are wrong
x1 projections x1 x0 argl argn
grishman et al NUM see figure NUM
p v i NUM lanlt verbs in elass il ipatterns f degr il lanlt verbsl ipatternsl
currently we simply select the pattern supported by the highest ranked parse
then the patternset extractor locates the subanalyses containing attribute and constructs a patternset
of the system that are new the extractor classifier and evaluator
in future work we intend to extend the system in this direction
the extractor takes as input the ranked analyses from the probabilistic parser
wide coverage is important since information is acquired only from successful parses
he does not attempt to rank different classes for a given verb
the system can now be run on a large number of articles from the domain scanning process
with pictalk pictures and labels are used to indicate the content of utterances that the user can choose to speak using a speech synthesiser
content utterances are stored within an organisational framework provided for the user that is designed to enable their prompt retrieval in real time conversations
using a to represent a list of categories possibly null we arrive at the following transitions with their corresponding categorial types alongside
in other words using the formalism one should be able to characterize the structural regularities of language with at least the sophistication of modern competence grammars
accounting for linguistic structure if a finite state grammar is chosen however the third criterion that of linguistic adequacy seems to present an insurmountable stumbling block
these include the use of pre loaded text a menu controlled organisational structure that models conversation flow and additional items specifically designed to keep the flow of conversation going
null not only would this allow the modelling of the restriction on centre embedding but it would also allow many other processing phenomena to be accurately characterized
the actual formalism used was much fuller than the rather schematic one given above including many additional features such as case tense person and number
fortunately if extensions are made to the formalism necessarily taking it outside weak equivalence to a context free grammar natural and general analyses present themselves for such constructions
consider the pair of sentences NUM and NUM identical in interpretation but the latter containing a discontinuous noun phrase and the former not
unknown words in the input of which there were obviously many were assigned to one of seven orthographic classes and given appropriate transitions calculated from the corpus
as it stands the formalism is weakly equivalent to a context free grammar and as such will have problems dealing with phenomena like discontinuous constituents non constituent coordination and gapping
in this paper we describe the sentence planner in the healthdoc project
note that coreference information is here encoded by using the same variables
related work falls into two areas
the fragment d1 is not changed
figure NUM the architecture of the sentence
developraent of each module using any formalism
these constraints are the following NUM
u s a tel NUM NUM NUM NUM ext
individual sentence planning tasks have been the focus of much previous research
we do not discuss this issue here
question the speaker has the initiative unless the utterance is a response to a question or command
spoken inputs are frequently ungrammatical but must still computational linguistics volume NUM number NUM be interpreted correctly
correspondingly the average number of utterances between control shifts is reduced by a factor of almost NUM NUM
in this case the user s assertion does change control as it is a change of topic
had users speaking longer utterances NUM of the user utterances were multiword versus NUM in directive mode
this is due to the fact that on average users spoke longer utterances in declarative mode
woz studies such as the ones cited above have been particularly useful in obtaining data on discourse structure and contextual references
the system is taking a long time to respond or please remember to start utterances with verbie
in particular they had not taken a class in ai and they had not interacted with a natural language system
simultaneously we could more readily monitor the effects of the change in initiative setting while holding other system features constant
since the vocabulary items are entered in the grammar as part of lexicalized grammar rules if an input sentence contains words unknown to the grammar parsing fails
telegraphic messages with numerous instances of omission pose a new challenge to parsing in that a sentence with omission causes a higher degree of ambi6uity than a sentence without omission
one is the parsing results on two sets of unseen data test and test discussed in section NUM using the syntactic grammar defined purely in terms of part of speech
in this paper we describe a technical solution for the issue and resent the performance evaluation of a machine transtion system on telegraphic messages before and after adopting the proposed solution
ger as a preprocessor to the language understanding system null tagger achieves performance comparable to or higher than that of stochastic taggers even with a training corpus of a modest size
unknown words are first assumed to be nouns and then cues based upon prefixes suffixes infixes and adjacent word co occurrences are used to upgrade the most likely tag
the results are given in table NUM tagging statistics before training are based on the lexicon and rules acquired from the brown corpus and the wall street journal corpus
tag ing statistics after training are divided into two categories oth of which are based on the rules acquired from training data sets of the muc ii corpus
table NUM indicates that even for sentences constructed to be similar to the training data the grammar coverage is about NUM again excluding the parsing failures due to unknown words
the misparse NUM rate with respect to the total parsed sentences ranges between NUM NUM and NUM NUM which is considered to be highly accurate
the detail of the process will be discussed next
this paper concentrates only on the word filtering process
however an implicit spelliug error may stiu exist
an overview of thai morphological analysis
shows tag ambiguity in our corpus
figure NUM two parts of word filtering
the chi square distance between the tag probability distributions is minimized for low values of the word occurrence threshold
in the following we demonstrate experimentally that this approximation is valid and independent of the training text size
in all other languages the hmm ts2 tagger gives the correct solution for three out of four unknown words
the training process has been designed to estimate or update the model parameters from fully tagged text without any manual intervention
in the english text verbs adjectives and conjunctions are more frequent than in the french text
the text coverage by prepositions is NUM NUM percent for the english and NUM NUM percent for the french corpus
this situation appears when uncorrected tags or analysts mistakes remain in the text used to estimate the stochastic model parameters
correct classification rates up to NUM NUM percent have been achieved in the latter case by testing on the teleman swedish corpus
this technique solves the underflow problem which arises when many small probabilities are multiplied and accelerates the tagger response time
in some languages taggers based on hmms almost reduce the prediction error to the half compared to the mlm approach
for the prediction phase it then outputs a final tag model by mixing all the constructed models according to their performance
the more specific b is the more s and p isb differ
it generalizes the basic tag context tree and avoids over fitting the data by replacing excessively specific context in the tree wi4h more general tags
in the same way the node of depth NUM in t NUM represents an order NUM tri gram context model
the tagging problem is formally defined as finding a sequence of tags tl that maximize the probability of input string l
section NUM presents a new probability estimator that uses a hierarchical tag context tree and section NUM explains the mistake driven mixture method
tile basic tag set is a set of tile most detailed context elements that comprises the words selected above and part of speech subdivision level
on the contrary the tag model is a set of probabilities that a tag appears after the preceding words and their tags
use these analyses to develop a corpus of plausible utterances together with an indication of which features are likely to be most helpful for children with different conversational styles or in different conversational settings
similar observations can be made about the errors in predicting dialogue initiative shifts when analytical cues are observed table NUM b
in other words if an honorific morpheme attaches to a subject np the honorific infix si must appear in a verb as shown in NUM
first the local value of the constituent j kkeyse is as shown in NUM because the honorific nominative case marker kkeyse attaches to the subje6t np j
NUM higher x y if x y not equal x y xc y equal higher x y
it is possible to recognize whether a sentence in a dialogue is consistent with the previous sentence s with respect to the honorification of a certain person
second if an honorific case marker is used the referent of the np to which the honorific case market attaches is respected by speaker
the two facts however are not compatible because higher ra sin means not equal higher sm m
the prolog facts in 42a and 42b are obtained from the feature structures in 41a and 41b respectively
without considering the extra sentential individuals such as speaker and addressee it is not possible to compute relative social status of the persons involved in a sentence
the value of the feature s statijs is also a set whose elements provide the information about relative social status of the persons involved in a dialogue
second the local value of the constituent w nimul is as illustrated in NUM because the honorific suffix nim attaches to the object np w
tables NUM a and NUM b summarize the results of our analysis with respect to task and dialogue initiatives respectively
word splitting in spanish the personal pronouns in subject case and in object case can be part of the inflected verb form
similarly if the spoken command described an area for example an anti tank minefield it would only unify with an interpretation of gesture as an area designation
one of the factors contributing to this is that routes and areas do not have signature shapes that can be used to identify them and are frequently confused figure NUM
however we believe that unification of typed feature structures provides a more general formally well understood and reusable mechanism for multimodal integration than the frame merging strategy that they describe
for example if the system spuriously recognizes m 1a1 platoon but there is no overlapping or immediately preceding gesture to provide the location the speech will be ignored
however in the sixteen years since then research on multimodal integration has not yielded a reusable scalable architecture for the construction of multimodal systems that integrate gesture and voice
vo and wood NUM present an approach to multimodal integration similar in spirit to that presented here in that it accepts a variety of gestures and is not solely speech driven
if a given speech or gesture input has a set of interpretations including both partial and complete interpretations the integrator agent waits for an incoming signal from the other mode
in the future n best recognition results will be available from the recognizer and we will further examine the potential for gesture to help select among speech recognition alternatives
it also inserts certain pcas to mediate between parts we adopt for proof tree the notation of gentzen
i would like to thank graeme hirst as well who carefully read the final version of this paper
the smooth injective map recognizer simr has five advantages over previous bitext mapping algorithms
a complete set of tpcs together with appropriate boundary information guarantees a perfect alignment
various knowledge sources can be brought to bear on the decision
so the first search rectangle is anchored at the origin
not surprisingly simr can not perform well on such text
such chains can be recovered by re searching regions between accepted chains
figure NUM two text segments at the end of sen
furthermore switched segments are almost always adjacent and relatively short
sets of points with a roughly linear arrangement are called chains
the linearity property leads to a constraint on the chain size
for example if art noun and pron noun are in the same label group we replace them with a new label such as np in the whole corpus
the combined complexity of the test rewrite cycle is thus o n3
structural transfer operations only affect the efficiency and not the functionality of generation
figure NUM figure NUM equivalent tncbs
we define five operations on a tncb
the move operation maintains the same number of nodes in the tree
null we thus see that rewriting and re evaluating must improve the tncb
the structure is essentially a binary derivation tree whose children are unordered
we enter the rewrite phase then with an ill formed tncb
deletion a maximal tncb can be deleted from its current position
we begin by describing the fundamentals of a greedy incremental generation algorithm
this is done as a default and is also performed for bigrams
a relational head acceptor writes or accepts a pair of symbol sequences a left sequence and a right sequence
looking forward the relative simplicity of head transducer models makes them more promising for further automating the development of translation applications
these timings are for an implementation of the search algorithms in lisp on a silicon graphics machine with a 150mhz r4400 processor
despite he simple direct architecture the head transducer model does embody modern principles of lexicalized recursive grammars and statistical language processing
under such a formulation negated log probabilities can be used as the costs for the actions listed in sections NUM and NUM
the following nondeterministic actions are involved in the tiling process null selection of a bilingual entry given a source language word w
we can characterize the language models used for analysis and generation in the transfer system as quantitative generative models of ordered dependency trees
head transducer models consist of collections of finite state machines that are associated with pairs of lexical items in a bilingual lexicon
in the case of text translation for publishing it is reasonable to adopt economic measures of the effectiveness of translation systems
phonological rewrite rules our definition of replacement is in its technical aspects very closely related to the way phonological rewrite rules are defined in kaplan and kay NUM but there are important differences
the prefix operators and bind more tightly than the postfix operators and which in turn rank above concatenation
the relations NUM and NUM that introduce the brackets internal to the composition at the same time remove them from the result
this paper introduces to the calculus of regular expressions a replace operator and defines a set of replacement expressions that concisely encode alternate variations of the operation
we consider four versions of the same replacement expression starting with the upward oriented version a b x ii a b a applied to the string abababa
we write the replacement part upper lower as before and the context part as left right where the underscore indicates where the replacement takes place
the left oriented version of the rule shows the opposite behavior because it constrains the left context on the upper side of the replacement relation and the right context on the lower side
another sort of conversion is illustrated by mass nouns denoting virtues
the constituency of a collective can change without the collective changing
if this ambiguity is not desirable we may write two replacement expressions and compose them to indicate which replacement should be preferred if a choice has to be made
when the component relations are composed together in this manner upper gets mapped to lower just in case it ends up between left and right in the output string
these nouns behave both as mass nouns and as count nouns
suppose for example one has an eight foot rope
NUM NUM the numbers NUM and vf are identical
judgments of acceptability are not on proposals but on the current plan or a part of it
example we examine a simple logic formula derive para c1 c2 b
therefore their expressive power is restricted see NUM for an extensive discussion
this mental state sanctions the acceptance of clarification plans and sanctions the adoption of goals to clarify
a fully expanded text structure will be traversed again to choose the appropriate lexical items
the first difference is that our aggregation rules are defined in terms of manipulations of the upper model
according to our analysis there are at least two linguistic phenomena that call for appropriate microplanning techniques
each node is typed both in terms of the upper model and the hierarchy of textual semantic categories
the realization class associated to the concept however contains several alternative resource trees leading to different patterns of verbalization
an increasing interest in more sophisticated microplanning techniques can be clearly observed NUM NUM however
one important feature of our work is the integration of microplanning knowledge specific to our domain of application
in this case some of them may be converted to embedded structures as is done by the following rule
in their model of understanding information seeking dialogs they propose a distinction between problemsolving activities and discourse activities
as a result the system adds the following belief after applying rules i and NUM
pick one object set pick one object object of the members of set
we may take these to be inferentially independent and look for no further properties once properties inferrable from these have been used in establishing the parallelism
the structure of this paper is then as follows first we will describe the general architecture of the database with the different modules
the latter is in its turn linked to the ili synset food l any substance that can be metabolized
since wordnet senses can not be consistently assigned to these occurrences we use a new tag int in place of a sense number or in addition to one when there is an appropriate sense creating a new category as we did with the auxiliaries
even accounting for differences in processor speed this amounts to a significant mismatch in overall training time
for example but the new york stock exchange chairman said he does n t support reinstating a collar on program trading arguing that firms could get get around verb NUM around such a limit
for efficiency explicit lists of hypotheses at not generated instead evaluation is performed during a reeursive search over the portion of the trie below the current coinpletion prefix
in contrast with previous interactive approaches the translator is never expected to perform tasks that are outside the realm of translation proper such as advising a machine about common sense issues
figure NUM shows the performance of the system for various values of the trigram coefficient a a noteworthy feature of this graph is that interpolation improves performance over the pure trigram by only about NUM
the main challenge in generating hypotheses is NUM o balance the opposing requirements of completion accuracy and speed the former tends to increase and tile latter to decrease with tile nmnber of hypotheses considered
the output distribution of any state tie the set of probabilities with which it generates target words deitends only on the correspondit g source text word and all next state transition distributions are uniform
for etticiency this distribution is modeled as a silnple linear combination of sol re ate tn edietions fl om tit target text and the sottree text
because of the restricted form of the state transition ualong with source and target text lengths in l town et al s fornmlation lint these are constant for arty particular hmm
the results NUM resented in this palter are optimistic in that the target text lengl h was assumed to be known in advance which of course is unrealistic
the work described in this paper constitutes a rudimentary but concrete first step toward a new approach to imt in which the medium of interaction is simply the target text itself
NUM complel ions m e free in this i ccoilltl hlt any spaces or punctuation characters in handtyped prefixes are assessed a one keystroke escape penalty
the corrections we would suggest are cutting of an antonym link or a merging or a splitting of concepts and all these cases belong to the type characterized in the next subsection
the experience gained from that evaluation will serve as critical input to revising the engish version of the task
null dtg assumes the existence of elementary structures and uses two operations to form larger structures from smaller ones
pattern matching has given us very robust very portable technology but has not broken the performance barrier al l systems have run up against
the nodes in the syntactic structure will be feature structures and we use unification to combine two syntactic nodes
this is particularly useful for multilingual generation and in practical generators which are fed input from non linguistic applications
the blind test set was used to measure our progress at least once a week with the frequency increasing as the end of the evaluation approached
the representation of the relevant semantics will forrealized more rigorously
the theory will also be implemented in an adequate lexical knowledge
each verb has a complex feature prefix located at synsemilocicat
suc s the set of morpheme numbers that the s th morpheme connects to
morphidtrs the internal structure and morphib kse the base form
fig NUM presents the prefix related part of the sort hierarchy
frequently the prefix modifies features of the base verb such as valency or aspect NUM
on q the set of morpheme numbers that are associated with synchronous point q
in the stage of building the skeletal structure the mapping rule i in figure NUM is used
the estimation algorithm can approximately maximize the modified likelihood that is weighted by the credit factors
such an initial mapping rule will have a syntactic structure that will provide the skeleton syntax for the sentence
the text plan tree for our example
NUM plum s performance had not peaked on te since we put the least effort on it of any of the evaluatio n dimension
however there are some important differences
the text of the example claim generated by
NUM the division of objects into broadly applicable ones te and domain specific ones st was a plus in ou r opinion
a few sample rules are illustrated below
ally used the words with broadest senses
this is a property of any sublanguage
the following considerations guided our lexicon work
in this paper we describe how the use of similarity between patterns embodies a solution to the sparse data problem how it we illustrate the analysis by applying mbl to two tasks where combination of information sources promises to bring improved performance pp attachment disambiguation and part of speech tagging
in the previously presented prediction methods the ones using probabilistic information mainly work with the words as isolated entities
the caller will apply a checking sequence if he wants to check extra information about the current plan that he suspects to be true
reconfirmations and corrections directly concern problems in processing the previous utterance while wh questions and checks mainly ask or check extra information about the travel plan
as a result the travel plan will have to be divided in manageable chunks of information which follow the temporal order of the travel schedule
the dialogue manager will probably have to incorporate extra contextual information into its instructions in case several repair sequences will appear between two information chunks
since the dialogue manager has knowledge concerning known and new information it can instruct the text generator to present the text in a natural way
the table confirms our observation that departure and arrival places are generally introduced during the query phase and serve as given information during the information phase
on the contrary places of change and the directions of train are mostly introduced in the information phase and become given information after introduction
NUM repair in the presentation phase figure NUM shows an information phase where the caller has no problems in processing the presentation of the travel plan
while centering our attention on the presentation of information by the automated speech processing asp system we describe the implications of this corpus based approach on the implementation of our prototype
the surface speech action of expand plan is s actions which takes the surface speech actions of the expansion listed below as its parameter
this is done by evaluating the constraints of reject plan and so finding the action whose yield is the surface speech actions that were rejected
in our model the acceptance of gate NUM would be presupposed and so it would be incorporated into the take train trip plan
when the user makes a contribution to a conversation the system assumes that the user believes that the plan will achieve its intended goal
the human subject is also allowed access to a copy of ldoce which the system also uses
the complexity of recognition of linguistically adequate dependency grammars
f y using the maximum likelihood estimator
it is found to be effective in acquiring semantic coherence knowledge from a relatively small corpus
therefore the sentence usually does not contain enough identified salient words to provide enough contextual information
we are currently developing a computer based device called pictalk to support their casual conversation and hence their social development
the discourse plans that we described in the previous section can now be seen as plans that can be used to further the collaborative activity
a second avenue for future work is to further investigate collaborative behavior and protocols for interaction
in this work sidner presents a number of speech actions for use in collaborative tasks
so what constitutes grounds for accepting a judgment or clarification
the modifier plan thus accounts for individual components of the description
the goals that we are interested in achieving are communicative goals
speaker speaker hearer hearer world world
an important part of our model is the surface speech actions
however such reasoning is beyond the scope of this work
each clarification action includes a surface speech action in its decomposition
third it would be extendable to other forms of interaction such as information seeking dialogs
null the above datasets were then split into training and test sets by automatically extracting stratified samples
mul null tiple pp attachment presents two challenges to the approach NUM
for a single pp the model must make a choice between two structures
definition NUM edition distanee let v be a vocab ulary dist is defined on v as a commutative operation in the following way v a c v v o v
the constraints guiding this disambiguation procedure are encoded as filters on the output of imas and reduce the set of pragmatically adequate il expressions
this allows us to use a siml le non determinist version of the aho orasick algorithm aho k rasick NUM which only cheeks the possible presenc e of patterns on an array of integer triples
the dtg operation of subsertion is designed to overcome this limitation
v tag retains adjoining to handle topicalization for this reason
d edges and i edges express domination and immediate domination relations between nodes
d trees can be composed using two operations subsertion and sister adjunction
after that we subsert the nominal arguments and function words
to see this consider the following putative alternate derivation
in this section we have defined raw dtg
in purely cfg based approaches these relations are only implicit
this would be necessary if we wanted to describe for example the formation of plurals in malay indonesian orang orang orang NUM here a speculative remark would link the power of analogy with some class of languages our proposal seems not to go beyond regular languages
a d tree containing n d edges can be decomposed into n NUM components containing only i edges
it is used to ensure the a tree is a tree
brown et al proposed the following 1a tually it is the first term of equation NUM times l that appeared in their paper but we believe that it is simply due to a misprint
another notable point in the figure is that introducing word bits constructed from wsj texts is as effective for tagging aq r text s as it is for tagging wsj texts even though these texts are from very different domains
lb see the effect of introducing word bits information into the tagger we performed a separate experiment in which a randomly generated bit string is assigned to each word NUM and basic questions and word bits questions are used
because equation NUM is expressed as the summation of a fixed number of q s its value can be cmculated in constant time whereas the cmculation of e tuation NUM requires o v NUM time
the sizes of tile texts are NUM million words mw t0mw 20mw and 50m w he vocabulary is selected as the NUM NUM most fl eqneutly occurring words in the entire co pus
french dleindrai leindre viendrai x x viendre our goal is to give one possible account of this phenomenon in compul ationm t erms rod to show that given n tree brink possible pl lica t ion
we first make v singleton classes out of the v words in the vocabulary and arrange the lasses in descending order of frequency then define the merging region as the first c l positions in the sequence of classes
a simple example with a five word vocabulary is shown in figure NUM if we apply this method to the above o c 2v algorithm however we obtain for each class an extremely unbalanced mmost left branching subtree
to learn a transformation the learner in essence tries out every possible transformation 1i and counts the number of tagging errors after each one is applied
by selecting one each of these three perspectives the user gets access to pre loaded content appropriate to that combined perspective e.g.
this property should make transformation based learning a useful tool for further exploring linguistic modeling and attempting to discover ways of more tightly coupling the underlying linguistic systems and our approximating models
in the transformation based tagger the lexicon is simply a list of all tags seen for a word in the training corpus with one tag labeled as the most likely
corpus based methods are often able to succeed while ignoring the true complexities of language banking on the fact that complex linguistic phenomena can often be indirectly observed through simple epiphenomena
part of speech tagging is also a very practical application with uses in many areas including speech recognition and generation machine translation parsing information retrieval and lexicography
1deg half cd dt jj nn pdt rb vb this entry lists the seven tags seen for half in the training corpus with nn marked as the most likely
since the goal is to label each lexical entry for new words as accurately as possible accuracy is measured on a per type and not a per token basis
a simple way of disambiguating this is to look at the preceding dialogue act s
it concludes in the closing phase where the negotiated topic is confirmed and the locutors say goodbye
insights into the dialogue processing of verbmobil jan alexandersson norbert l lcb eithinger elisabeth maier
we also annotated the utterances with the dialogue acts as determined by the semantic evaluation module
figure NUM overview of the dialogue module figure NUM a part of the sequence memory
one problem that has to be tackled in the future is the segmentation of turns into utterances
if the construction of parts of the structure fails some functionality has been developed to recover
the data from these parallel tracks must be consistently stored and made accessible in a uniform manner
no restrictions are put on the locutors except for the limitation to stick to the approx
the remainder of the degradation in performance compared to text input is due to other problems such as the lack of punctuation note how important commas are in information extraction
bbn particularly would like to investigate how statistical algorithms over large unmarked corpora can effectively extrapolate from a few training examples such as in st in muc NUM to prrovide greater coverage
compared to plum s previous performance in muc NUM NUM and NUM our progress was much more rapid and our official score was higher than in any previous template fill task
semantic model editor sme maintains the semantic model factbase a data file containing the concepts which the nlu application uses to represent the information contained within messages
the root and infl lines show the features that differ between the root and inflected forms while the both line shows those that they share
all rules and lexieal entries in the cle are compiled to a form that allows normal prolog unification to be used for category matching at run time
spell boundary ill i bmarker NUM
the compiler is optimized for a class of languages including many or most european ones and for rapid development and debugging of descriptions of new languages
we believe that development of other broadly applicable information extraction functionality such as ne and te will be a win maximizing the value of defining reusable knowledge bases for information extraction
this process results in a set of spelling palterns one for each distinct application of the spelling rules to each affix sequence suggested by the production rules
the only differing feature is that for gender shown as the third argument of the c agr macro which itself expands to a category
lexical and after instantiation surface strings for the unspecified roots are therefore represented in a more complex but less redundant way as a structure
for full generality and minimal redundancy lm and r1 are constrained not to match the default rule but the other li s and ri s may
simple examples of generation are follows NUM NUM heat gently to soften the coating
generation and enablement are relations that can hold between pairs of states events processes or actions
note that the two actions can bc presented in either order generating first or generated first
in example NUM the action of heating gently ha s the effect of softening the coating
inal infinitive rcb imperative i mperative imperative imperative portuguese enablement NUM NUM NUM
de fa oi l d inlinitive limperative figure NUM french enablement
these single characters are often stopwords that hopefully are in our lexicon
sequence on the other hand is signalled by temporal connectives such as antes de before depois de after or apes afte NUM by the connective e and or implicitly by the use of a comma between the elements of a string of imperatives
NUM NUM itesult if when ing e la s by in and ted o subject complement condition if ei fiit te i NUM figure it english generation generating imperative imperatiw
phrase categories summed over reliability levels
in the second condition random order condition the remaining NUM taggers were given a dictionary booklet with the same wordnet senses arranged in random order generated by means of a random number generator
from the algorithm descrbed above we can conclude that the computation time is to be order o hv NUM for tree height h and vocabulary size v to move a word from one class to another
nonetheless as one rough measure of progress in the area of information extraction as a whole we can consider the f measures of the top scoring systems from the muc NUM and muc NUM evaluations
the task definition is now under review by a discourse working grou p formed in NUM with representatives from both inside and outside the muc commuity including representative s from the spoken language community
in addition to miscategorization errors the walkthrough text provides other interesting examples of syste m errors at the object level and the slot level plus a number of examples of system successes
similar tradeoffs and upper bounds on performance can be seen i n the tst2 and tst4 results see score reports in sections NUM and NUM of appendix g in NUM
note that for each task sites were assigned different code names that were used in lieu of the site names to identify systems up to the time of th e conference
the subcategory error scores were NUM on organization NUM on person an d NUM on location NUM on date and NUM on money and percent
and must depend on one or more deep pieces of information such as world knowledge pragmatics o r inferences drawn from structural analysis at the sentential and suprasentential levels
common mistakes made by the systems included missing the date expression the 21st century and spuriously identifying NUM pounds which appeared in the context mr
the approximate NUM NUM split between relevant and nonrelevant texts was intentional and is comparable to the richness of the muc NUM tst2 test set and the muc NUM tst4 test set
as a result a few peripheral facts about the event were include d that were difficult to define in the task documentation and or were not reported clearly in many of the articles
however if plain dop1 were used the accuracy would have been NUM for these sentences
figure NUM another derivation generating the same tree for she displayed the dress on the table
the following table shows the means of the results for the three accuracy metrics with their standard deviations
in the following we will refer to the extension of dop3 with an external dictionary as dop4
there will be domain specific words and word senses abbreviations proper nouns etc that are not found in a dictionary
to make such an estimation feasible we use the following assumptions the size of the vocabulary is known
treating all words as potential unknown category words would certainly lead to an impractically large number of subtrees in the parse forest
no is equal to the difference between the total number of types and the number of observed types
since during constraint deselection at every point we have a fully fit maximum entropy model we rank the constraints on the basis of their weights in the model
otherwise we update the model s weights as ax axk aaxk and go to step NUM to obtain a better fit of the model
imagine that b and c in the example above are two mutually exclusive features and we never see them on their own
now we can compute the maximum entropy model taking the reference probabilities which are configuration probabilities as in equation NUM
the presented above method provides us with an efficient way of selecting only important atomic features from the in tim set of candidate atomic features without resorting to iterative scaling
the current version of the demonstration took four months to build and operates on a sun unix system
it however does not guarantee that the features employed by the model are good features and the model is useful
simple and complex features together with their overlaps are naturally incorporated into the model and all the interactions are naturally accounted for
the examples above would be rewritten as the following under this approach
these features of cps can be used to encode cfgs as follows
in the demonstration name spotting capability is shown by the sri bbn and lm systems
each case consists of a word or a lexical representation for the word with preceding and following context and the corresponding category for that word in that context
automatic tagging is useful for a number of applications as a preprocessing stage to parsing in information retrieval in text to speech systems in corpus linguistics etc
for example suppose v gp and s are already bound to fv fwp and fs and the grammar contains the following productions with vp on the left hand side
in general the top levels of the tree represent the morphological information the three suffix letter features and the prefix letter while the deeper levels contribute contextual disambiguation
a cfg agmentdefined using the highe orderconstructors
now we turn to the problem of left recursion
squibs and discussions memoization in top down parsing
a feature relevance ordering technique in this case information gain see section NUM NUM is used to determine the order in which features are used as tests in the tree
succinctly using scheme s call cc continuation constructing primitive
we present two fertility models which attempt to capture this phenomenon
the probability NUM h1c of a string h given the model c h e is calculated as a chain of conditional probabilities NUM
in contrast stories that mention the word somewhat could be about practically anything
this generalizes the standard notion of clause by allowing the head h0 to consist of more than one atom
now we turn to the specification of the algorithm itself beginning with the basic computational entities it uses
in this derivation vpi abbreviates s npi a is a lexieal rule which
the tag associated with a clause in an item identifies the operation that should be performed on that clause
for example memoized goals in our gb parser consist of conjunctions of x and ecp constraints
such equality constraints are inherited via resolution by any clause that resolves with the completed clause
this is necessary because the number of adjuncts that can be associated with any given verb is unbounded
delay division x y var l var y
control gso control memo g select g ceo gs control table g gs member g gso delay g control program control solution
item NUM is tagged table since it contains a x NUM literal because this goal s second argument i.e. the left string position differs from that of the goal associated with table NUM a new table table NUM is constructed with item NUM as its first item
the word gweilo is not a conventional english word and can not be found in any dictionary but it appeared eleven times in the text
to i avoid distributing the less probability to the word assigned to this class than the probability to the word not assigned to this class we distribute the probability NUM to the word assigned to the class
to improve the computation speed we constrain the vector pairs further by looking at the euclidean distance g of their means and standard deviations
figure NUM and NUM show the learning curves averaged over these NUM words and NUM words with at least NUM and NUM training examples respectively
in addition the calculation of a wl w requires summing only over those w2 for which p w2iwj and p w2 w are both non zero which for sparse data makes the computation quite fast
we found that it is more advantageous to increase the overall reliability of anchor points by keeping the highly reliable points and discarding the rest
we will also describe the implementation of these models in the form of a finite state transducer
how can the remaining problems be solved and what are the topics for future work
it did not have any other lexical information at its disposal
weights are a convenient way to describe and predict linguistic alternations
we extracted graphemic substrings of different lengths from all city names
table NUM compares the performances of the two text analysis systems
multiple mistakes within the same name were considered as one error
these data were not used in building the name analysis system
using the procedure described above we selected NUM street names
as already mentioned this is partly because few pictures can be accommodated on a fixed size of computer screen
NUM reconstruction of the left arteria mammaria on the lad
for all the NUM sentences pseudo html code was generated
NUM single venal bypass from the aorta to the avsulcusbranch
also for the medico administrative activities such a tool can also be helpful
several variants on this base scheme can be thought of
important for the present discussion is the semantic selection level of the lsp mlp
these codes indirectly serve for statistical and financial purposes
currently the set of pdss is limited to nine texts
the method is based on explanation based learning ebl
the system is capable of handling follow up questions requesting further information and generating responses in context of previously supplied information
a sul sl antial morl hologiea l
the first part is an index to generate a key for every word
sys enl provided by i lcb ank xerox
the system is partieulm ly dependent on advanc ed
as mentioned in the l revhms section l he
ibr analysis look up tm l exa mples
windows for morphological nalysis dictionary and further examples
since indeed most words are multiply ambiguons a problem looms
this index is used for all files in the corpus
fhe disambiguator assigns every word of the sentence a tag
to perform this task we adopted a linear interpolation method using semcor the semantically tagged brown corpus as a reference corpus
the purpose of the method is to automatically select a domain appropriate set of categories that well represent the semantics of the domain
wordnet and roget s thesaura have not been conceived despite their success among researchers in lexical statistics as tools for automatic language processing
the model parameters are tuned against a reference correctly tagged corpus but this is not strictly necessary if correctly tagged corpora are not available
the method presented in this paper has been developed within the context of the ecran project le NUM funded by the european community
the algorithm was applied to the NUM NUM different nouns of the wall street journal hereafter wsj corpus that are classified in wordnet
one of the main research objectives of ecran is lexical tuning being semantic tagging and sense disambiguation two important and preliminary objectives
most sense disambiguation or semantic tagging methods evaluate their performances manually against few very ambiguous cases with clear distinctions among senses
for bank and market we observed that the less plausible for the domain senses ridge and market grocery store are pruned out
stated prove theoretically for gentzen style systems this amounts to disallowing the left rule for o
sd grammars are able to generate each context free language and more than that
taken for themselves these variants of i are of little use in linguistic descriptions
the usual formal framework of these logics is a gentzen style sequent calculus
in our example the relative pronoun has such a complex type triggering an extraction
we need strong np completeness of NUM partition here since our reduction uses a unary encoding
the concatenation of sequences u and v is denoted by u v
also a later processin g stage removes indefinite cases from those proposed as appositives
we study the computational complexity of the parsing problem of a variant of lambek categorial grammar that we call semidirectional
hence only strings containing the same number of a s b s and c s may be produced
put tiers v g to lcb stem s rcb
i still have time at the end of april
NUM a gestern reparierte er den wagen
in conclusion we suggest that the demonstrated advantages of the winnow family of algorithms make it an appealing candidate for further use in this domain
first the training algorithm is run for several iterations until the number of mistakes on the training data drops below a certain threshold
if done correctly discarding irrelevant features may also improve the accuracy of the classifier since irrelevant features contribute noise to the classification score
the results of the comparative evaluation appear in columns NUM NUM and NUM of table NUM corresponding to the three alternatives above
null NUM a ich glaube du sollst nicht tgten
the time alignment is done by a standard hidden markov model word recognizer
NUM a sollen wir uns dann im monat mpsr
means that the classifier l roposes an x0 tt this position
hence no further need for a syntactic representation of empty elements emerges
the empty verbal head in NUM carries syntactic and semantic information
let us first examine the impact of replacing unigram models by aggregate models at the root of the aggregate markov models with different numbers of classes c
in our experiments the perplexity gain of the mixed order model ranged from NUM to NUM depending on the amount of truncation in the trigram model
note that smoothing with the c NUM aggregate markov model has nearly halved the perplexity of unseen bigrams as compared to smoothing with the unigram model
we denote by p y i z the probability that the model assigns to y in context x
the purpose of a statistical language model is to assign high probabilities to likely word sequences and low probabilities to unlikely ones
the eight subjects were duke university undergraduates who met the following criteria
the entries represent values of less than NUM
this separation of plans permits greater flexibility in the plan recognition process
smith and gordon human computer dialogue NUM dialogue processing model an integrated approach
these problems were also balanced for difficulty
problems NUM through NUM were similarly balanced
furthermore we balanced the subjects also
this was interpreted as the phrase faster
this logging information included the time the words were received or sent
the led is supposed to he displaying alternately flashing one and seven
however a look at compatible aligned words in the n best hypothesis can instanciate the joker once an analysis is found
there is a theoretical case that would result in a loss of information the false rejection of an optional word
a rule language for constructing task specific phrase tagging and or pre tagging rule sets
a scorer that allows arbitrary sgml markup to be selected for scoring
high performanc e on these slots may be due to the attention given to reference resolution during the development of louella for muc NUM
for persons louella uses the simple heuristic of assigning the last person mentioned as the referent keeping in mind gender constraints
we randomly divided approximately NUM documents of varying sizes into five groups
we envision two classes of external utilities tagging utilities and analysis utilities
unfortunately acquiring such data has usually been a labor intensive and time consuming exercise
this has focused increased attention on the importance of obtaining reliable training corpora
by this definition we refer to the pattern matcher as a finite state machine which matches against bot h syntactic and semantic features of text
louella uses eight rule packages to reduce elements ranging from the most primitive tim e expressions and organization noun phrases to the more complex in and out objects
during the 1980s general electric corporate research and development began the design and implementation of a set of text processing tools known as the nltoolset
it has been used to build a variety of applications and the knowledge gained from each application has been utilized to improve the toolset
once an article s information has been extracted it is then organized into a sensible account based on a model of the domain
inclusion of linguistic theory in addition to other techniques that have been successful for the coref erence participants is a possibility
the ne system generates all possible variations for each person and compan y name it finds another pass tries to find these variations
if there is a tie file position is considered as a factor and the closest name is the most likely refer ent
commands such as group words group phrases ungroup change labels re attach nodes generate postscript output etc are available
the sundial corpus was analyzed by two of the det tool developers
he is an experienced computational linguist but with little experience in slds development
it would of course be encouraging if this proved to be the case
in the present case this number would be NUM NUM guideline violation types
one central reason why this is the case is the problem of generalisation
generality and objectivity central issues in putting a dialogue evaluation tool into practical use
for the first generality test of det we have selected NUM dialogues
the remaining NUM dialogues were split into two sub corpora of NUM dialogues each
this is illustrated in figure NUM in which the case of offer give phone no
the verb flames attached to wordnet verb synsets are not sufficiently detailed to cover the granularity necessary to characterize an mcca category
has marked das the ap bonusprogramm and the pp as a constituent and the tool s task is to determine the new node and edge labels marked with question marks
april at the corner of 60th and 48th streets in western medellin only NUM me ters from a metropolitan police cai immediate attention center the antio quta department liberal party leader had left his house without any body guards only minutes earlier as he waited for the traffic light to change three heavily armed men forced him to get out of his car and into a blu e renault hours later through anonymous telephone calls to the metropolitan polic e and to the media the extraditables claimed responsibility for the kidnap ping in the calls they announced that they will release the senator with a new message for the national government last week federico estrada velez had rejected talks between the govern ment and the drug traffickers
the vsm promotes recall and precision based evaluation but there are several ways of calculating or even defining them
unfortunately our experience with pictalk users suggests that this menu structure is very difficult for them to understand
content on something i like doing me present happy
the hookah system will analyze each dea NUM offline doing an initial match of dea NUM subjects against naddis records extracting information for each subject and performing a provisional merge against any existing naddis information to provide an initial set of suggested database updates
several factors make this an ideal task for the application of tipster information extraction technology a constrained domain makes information extraction feasible high traffic volume increases the payoff for reducing manual processing an established base of analysts are available to support system development and perform testing the naddis database already exists and has high value to the customer the need for timely database update is an appropriate match for state of the art tipster technologies
in figure NUM this is shown for a four featured pattern
extraction processor the bulk of hookah processing is done off line by the extraction processor which is responsible for processing of the indexing section of the document to determine which subjects should be processed automatic non interactive matching of subjects against naddis and extracting information from the body of the text
for example from the start state NUM the empty string is emitted on input vbd while the current state is set to NUM if the following word is by the two token string vbn by is emitted from NUM to NUM otherwise vbd is emitted depending on the input from NUM to NUM or from NUM to NUM
the result in table NUM is obtained using dudani s weighted voting method
NUM NUM domain dea 6s the focus of project hookah is to improve the processing of the dea NUM report a semi formatted report generated primarily by field agents as well as legal staff analysts and others
the local extension of f6 will be the transduction that takes each possible factorization and transforms each factor according to f6 i.e. f6 w2 bc and f6 w3 dca and leaves the other parts unchanged here this leads to two outputs abccbc according to the first factorization and aadcab according to the second factorization
we have found that users are readily able to correct erroneous proposals from the system improving precision but they are less likely to skim the document for themselves to ensure no important information has been omitted improving recall
the resulting ig values can then be used as weights in equation NUM
it consists of sentences containing the possibly ambiguous sequence verb noun phrase pp
in such cases we wish to include some of the lower ranked schemata
in addition if either of the noun phrases involves conjunction as in president of general motors and forme r ceo of ford both minimal noun phrases president andformer ceo would be recovered
while regular expressions could catch many of the phenomena we have described they will become increasingly comple x as they attempt to capture long range dependencies in the text and will also become increasingly inaccurate
each step consists of a back off to a lower level of specificity
bride of cogniac also handles lower case definite descriptions using various knowledge sources to do semantic classification of noun phrases into categories such as person male female place thing singular an d plural
the final stage is an upper case substring match which is targeted at finding coreference chains which were missed by the named entity tool and the other stages as well
the tag actually used by the muc system is determined by a majority voting scheme in which a tag is chose n as the winner if at least two of the taggers postulate it
as a result syntactic patterns of th e type np is np are also recognized as are constructions involving the verbs remain or become which function in a similar way to be
we were also given access to a large acronym dictionary which peter flynn maintains for a world wide web site in iceland http curia ucc ie info net acronyms acro html
this simple data structure which was inspired by a pretty printing convention used by lexis nexis t o display multiple levels of textual annotation also allowed people to write software in the programming language of their choice
first most of u s are accustomed to working alone and we enjoyed the opportunity to work as a team especially since this fostere d research contacts which might not have otherwise been made
the xtag morphology database NUM was originally extracted from the NUM edition of the collins english dictionary and the oxford advanced learner s dictionary of current english and then edited and augmented by hand
then we compared the output of the system with the coder s selections
for this evaluation we used the exact match criterion
in this sample there were NUM valid vpe occurrences that were missed
in the brown corpus vpe occurrences are not labeled in this way
in the case of vpe the subject and auxiliary are parallel elements
parse tree for all felt freer to discuss things than students had previously
in france when a person is asked to provide a proper name he or she is also often asked to spell it
in english whether british or american there are many different ethnic groups represented in a telephone book or database of names
where x w and z are grapheme sequences and y is a phoneme or phone sequence
nevertheless a large superset of rules has to be added to obtain very high accuracy since the phonotactics change from language to language
both anglophone and francophone countries these patterns of immigration have been sufficient to make this a serious problem for any automatic phoneticization algorithm
translating the root scandal could be done either from left to right or from right to left in one or more blocks of rules
czech for example underwent spelling reform fairly recently and the orthographic system was brought into line with the phonological and phonetic system
however the disparate nature of different languages argues for a brief mention of our experience in developing letter to sound rule sets in other languages
the number of applications of each of the NUM rules has been calculated on the NUM NUM words to give an indication of its weight
in the domain of appointment scheduling the german phrase geht es bei ihnen is ambiguous bei lhnen can either refer to a location in which case the translation is would it be okay at your place
the implementation of this method for the muc evaluations was firs t described in NUM and later the concepts behind the statistical model were explained in a more understandable manne r in NUM
we have to be careful to not confuse the numerical order of the f measures with a ranking of systems and to instead loo k at the groupings on these charts
in our example dialogue turn a13 the utterance ich wiirde ahm vierzehn uhr vorschlagen i would hmm fourteen o clock suggest contains the proposal of a time which is characterized by the dialogue act suggest support date
obtain suggested goal from the domain processor
in addition two similarly acting systems could use very differen t approaches to data extraction so there may be some other value that distinguishes these systems that has not been measured in muc NUM
on the other hand the segmentation as computed by the syntactic semantic construction module and the dialogue acts as computed by the semantic evaluation module are very often not the ones a linguistic analysis on the paper will produce
based on the average of the strength of word chain and the most likely sequence of parts of speech ic will be selected as the solution of word segmentation and tagging
one of the major problcrns in many languages such as japanese chinese korean and thai is word boundary ambiguity because these languages do not have any delimiters between words
in this work we attempt to provide a computational solution to handle these three nontrivial problems for making ejob of a parser much easier
if there is any explicit error the second step that is spell checking will give a suggestion about a set of most likely words
using word fonnation rules and a dictionary look up algorithm in the first step all possible word groups with all possible tags will be given
thai seutences are simile to the japanese s and chinese s in terms of having no blank space to mark each words within the same sentence
another probleau is implicit spelling error that occurs because some incorrect words can be found in a diotionm3 this problem is very hard to solve with a simple approach such as dictionary approach
since an implicit spelling error affects both meaning and tag such as fly v j on preposition the special process is needed
assume the chunk length is the number of tags in a chunk
the easiest way to construct these gold standards is to extract them from pairs of hand aligned text segments the final character positions of each segment in an aligned pair are the co ordinates of a tpc
if the matching predicate generates a reasonably strong signal then the signal to noise ratio will be high and simr will not get lost even though it is a greedy algorithm with no ability to look ahead
instead initiative of the interaction shifts among participants in a primarily principled fashion signaled by features such as linguistic cues prosodic cues and in face to face interactions eye gaze aad gestures
the test metric was the root mean squared distance in characters between each tpc and the interpolated bitext map produced by simr where the distance was measured perpendicular to the main diagonal
they are hard to avoid unless they become l opular enough to be added to the lexicon
as discussed earlier at the end of each turn new task dialogue initiative indices are computed based on the current indices and the effect of the observed cues to determine the next task dialogue initiative holders
the prefix and tile infix are stored in the bin table for NUM character words with clear indications of their status
for instance the first row in table NUM a shows that when the cue invalid action is detected the system failed to predict a task initiative shift in NUM out of NUM cases
each time the system makes an incorrect prediction the value for the actual initiative holder is incremented by a NUM cdeg z and that for o decremented by the same amount
on the other hand when a character is khmtifie d as a single cha ra tcr
if the answer is negative then the possibility of abed being a word is considered
based on forward icnaxinnlln matching and word binding force is t roposed in this pai er
its diagonal remains parallel to the main diagonal
this extension to simr is next in line
yet the three cases are clearly different
the aligned blocks are outlined with solid lines
there are usually very few such intersections that are also large enough to accommodate new chains so the second pass search requires only a small fraction of the computational effort of the first pass
slope of the tbm between the end of chain c and the start of chain d must be much closer to the slope of chain x than to the slope of the main diagonal
simr s rms error is lower by more than a factor of NUM simr is also much more robust it rarely errs by more than half the length of an average sentence
errors given errors not given bitext algorithm hard constraints hard constraints least some of these sentences are mutual translations despite simr s failure to find any points of correspondence between them
when the translation lexicon was used in simr s matching predicate the largest aligned block that needed to be re aligned in the easy and hard test bitexts was 5x5
we denote by ct wl wn
we have also carried out a preliminary evaluation on the
now there is a different grouping with the original whether if what wit s comt lement and how how s pi how to inf augmented by where when how much how inany where when s
mc l ury NUM rcb mb asso lcb iate lcb t press NUM NUM mb mis lcb ejlaneous trc NUM rcb ank litcrat ure NUM NUM mb etc adding up l lcb i about NUM lcb rcb NUM mb of rex
t lcb rcb t e lcb onsistent with tim original at t roa h the omt rcb lenmnt s have been r x onsl ru lcb who r t lcb rcb ssil le
the rccov ry lcb f th c rcb mt hmmnt in passivization and opicalizati lcb nl is reasonably straig lt forwar l lcb hough passivizadon may lead t lcb misintert r tat ion of dm omi rcb lement
NUM these verbs occurred t redoininately in environments like the price increased NUM to to percent
the cxist n missinp lcb omplenmnl s f r s one into dm uncomfortable NUM osidon rcb t tagging items t hat lo not app a r
we tagged NUM exmnples for each of NUM common verbs which had previously been entered in the comlex lexicon
cr rcb apsion of the original c mi ex tra lit ional di lcb ti mary pr cedur was followc lcb l y classifying verbs as having the lcb omt h nmnts wid which hey can appear in isolation in simple declarative sentences
we have acquired not only statistical data on the occurrence of colnplements in texts but information on possible gaps in comlex s syntactic coverage which we moved to rectify when it seemed justified and we have a record in our tagged data of those instances which we did not add to c mm x classes
nametag also assigns country codes to place names
table NUM enamex performance measures recall precision
hasten generated one of the three organization descriptors usin g an egraph to extract the appositive the big hollywood agency which then enabled it to extract the locale and countr y fills
the natural language group is led by chinatsu aone and key members include kevin hausman sharon flank scott bennett john maloney hamid bacha job van zuijlen and arcel castillo
in this sentence the reference resolver correctly resolved the pronoun he to the last mention of james thus resulting in the extraction of the person2391 representation for james
the fast configuration drops the performance to NUM NUM due to its failure to learn mccann erickson as an organization which consequently causes nametag to miss NUM mccann aliases
this figure also shows the adjusted performanc e results labeled scenario only that factor out the slots of the person andorganization objects since these slots confuse the evaluation of the scenario event extraction
the nametag only configuration achieved a performance of NUM NUM
the last run labeled freq consists of running only those egraphs that matched at least tw o training text units NUM total approximately one third of the total egraphs
we also extend psts with a wildcard symbol that can match against any input word thus allowing the model to capture statistical dependencies between words separated by a fixed number of irrelevant words
our learning algorithm has however the advantages of not being limited to a constant context length by setting d to be arbitrarily large and of being able to perform online adaptation
furthermore the required theory independence means that the form of syntactic trees should not reflect theory specific assumptions e.g.
paradise is a general framework for evaluating spoken dialogue agents that integrates and enhances previous work
the results also show is significant at p NUM
dt u5 please reserve me a seat on that train
thus notions such as head should be distinguished at the level of syntactic flmctions rather than structures
training was over NUM sentences NUM of which NUM were skipped because of length NUM words testing over NUM sentences NUM NUM skipped NUM words
for the whole dialogue d1 in figure NUM el d1 is NUM utterances
the second heuristic operates in much the same way as the first but at the level of sentences
for example the semantic components of both modules know about points in time space only and not about durations
the fragments of natural language that represent time and location are by no means trivial to recognize let alone interpret
a text segmentation can be represented as a grid with clauses down one side and events along the other
figure NUM contains a representation of a sample news text and shows how this maps onto a clause event grid
if we are to distinguish between events it is important that we know what they look like
this is applied in the context of terrorist news articles and a technique is suggested for evaluating the resulting segmentations
the results show that fragments of time oriented language play an important role in signalling shifts in event structure
we demonstrated why paraphrasing and aggregation will significantly enhance the flexibility and the coherence of text produced
they remove redundancies by combining the linguistic resources of two adjacent apos which contain redundant content
taggers working in the frequency condition probably realized that the sense ordering resembled that of most standard dictionaries and chose the first sense that seemed at all to be a good match rather than examining all senses carefully as they would have to do in the random order condition
although the management post and information associated with it are represented in the succession event object that object does not actually represent an event but rather a state i.e. the vacancy of some management post
thc rule c5 differs from the previous rules in ll e list in that it introduces the logical connective a which does lint originate in functional material already present in either of the arguments
from the NUM test articles a subset of NUM articles some relevant to the scenario template task others not was selected for use as the test set for the named entity and coreference tasks
which permits to reconcile the notalien of predicateargnnlenl structnre with the notation of syntactic depondoilcy so in the i fornl considered above while semantically tile wellqan node is an irglnl oni of tile hate node syntactically tile hate node is a dependent of tile woman node
but the gain of one bit in word prediction is offset by a loss of at least two bits from uncertainty in the expansion of ap
one then considers tile predication tree t i made by forming the collection of edges a lx where i is positive and either a l x or x inverse l a is a predication edge of a each predication tree denotes a predicate argtnnent rehition among ij fornl nodes
such mechanisms are powerful but they tend to be algorithmically complex to be non lncal and also to give rise to spurious antbiguities superficial variations in the proof process which do not correspond to di fferent semantic readinos t here we will prefer to use a less general tnecha nism but one which has two advantages
this effect was found for all parts of speech but it was especially strong for adverbs where performance dropped from a mean NUM NUM tagger expert agreement for adverbs with two senses to NUM NUM for adverbs with NUM NUM senses and to only NUM NUM for the most polysemous adverbs
in the case of the management succession scenario a proposal was made to eliminate the three slots discussed above and more including the relational object itself and to put the personnel information in the event object
the mix of challenges that the scenario template task represents has been shown to yield levels of performance that are smilar to those achieved in previous mucs but this time with a much shorter time required for porting
in our approach the process of generalization is immediate once we have the output of the parser since the elementary trees anchored by the words of the sentence define the subtrees of the parse for generalization
to construct a complete parse the stapler performs the following tasks identify the nature of link the dependency links in the almost parse are to be distinguished as either substitution links or adjunction links
the difference in the response times between this experiment and experiment l a is due to the fact that we have included here the times for morphological analysis and the pos tagging of the test sentence
the bottom fs contains information relating to the subtree rooted at the node and the top fs contains information relating to the supertree at that node NUM the features may get their values from three different sources such as the morphology of anchor the structure of the tree itself or by unification during the derivation process
number a signed number that indicates the direction and the ordinal position of the particular head elementary tree from the position of the current word or an unsigned number that indicates the gorn address i.e. the node address in the derivation tree to which the word attaches or if the current word does not depend on any other word
given an ltag parse the generalization of the parse is truly immediate in that a generalized parse is obtained by a uninstantiating the particular lexical items that anchor the individual elementary trees in the parse and h uninstantiating the feature values contributed by the morphology of the anchor and the derivation process
so if there is an auxiliary tree that is used in an ltag parse then that tree with the trees for its arguments can be repeated any number of times or possibly omitted altogether to get parses of sentences that differ from the sentences of the training corpus only in the number of modifiers
the simple solution is to address this problem when utterances are constructed for the user with a particular conversation partner in mind or when utterances are selected during a conversation
the fifth word in table NUM belongs to this category
ambiguity of a word in the sw set of a given analysis
a morphological generator for hebrew that was written especially for this project
in another experiment we examined NUM words with more than two analyses
table NUM shows how our algorithm reduced the ambiguity of these words
case ll au ked oblique o ject does not denote something edible but rather a container then the sense maps to to eat oul of with the optio naldirect cdil le ol ject denoting the ol iect eat
our model of the language is based on a large fixed hebrew corpus
several high quality morphological analyzers for hebrew have been developed in the last decade
ii indica tes the voice marker is not m en iii nil indic ltes the voice mm ker must not be taken this is used only it the sense detiuitions in the lexicon m d cm unify with but
a number of observations tha t we have made on turkish tmve indicated that we have to go beyond the traditional transitive and intransitive distinction and utilize a damework where verb valence is considered as the obligatory co existence of an arbitrary subset of possible arguments along with the obligatory exclusion of certain others relative to a verb seuse
for the five categories reported in this paper we arbitrarily chose a few words that were central members of the category
aq llle i ns to devialc j rom NUM a dative case marked oblique object and with uo other object a menns to be s a lt riscd at NUM m accusative casc mar ked direct object with no other objecl qaq llleg ms lo be coco used aboul
there are still numerous issues to address in integrating extraction technology into useful operational systems in general extraction has not been the hardest problem for hookah
the paper overviews project hookah describes the system architecture and modules and discusses several lessons that have been learned from this application of tipster technology
fortunately most of the unknown words were proper nouns with relatively unambiguous semantics so we do not believe that this process compromised the integrity of the experiment
for this reason the user interface displays proposed naddis updates not templates or other d ect representations of extraction results
in particular the analyst s focus is updating the database and extraction output is merely a tool to support that activity
our experience so far indicates that the user interface can affect analyst throughput and performance even more than the quality of the extraction itself
function of our lexicon is to m q bidirectionally i etween a case frame containing information that is sy fl acti and sem mtic dame wifich c pl ures the predication denoted by the case fr mw along with information d out who fills what thematic role in that predication
there is a semi formatted index which contains references to most subjects to be to the database and some information about them
in addition the fact that much information in the text already exists in the database and that the text and database can disagree provides additional challenge
this paper describes project hookah a tipster implementation project with the drug enforcement administration to extract information from the dffa NUM field report
since the database information is normalized according to different standards than are used in preparing the reports this aspect is challenging
we did n t formally evaluate this category because most of the category words were proper nouns and we did not expect that our judges would know what they were
for example some of the words rated as NUM s for the vehicle category include flight flights aviation pilot airport and highways
the left hand column characterises the aspect of dialogue addressed by each principle
if an incompatible dialogue act is encountered an alternative dialogue act is looked up in the statistical module which is most likely to come after the preceding dialogue act and which can be consistently followed by the current dialogue act thereby gaining an admissible dialogue act sequence
because the prediction of NUM dialogue acts from a total number of NUM is not sufficiently restrictive and because the dialogue network does not represent preference information for the various dialogue acts we need a different model which is able to make reliable dialogue act predictions
the representation of intentions in the verbmobil system serves two main purposes utilizing the dialogue act of an utterance as an important knowledge source for translation yields a faster and often qualitative better translation than a method that depends on surface expressions only
since the verbmobil system is not actively participating in the appointment scheduling task but only mediating between two dialogue participants it has to be assumed that every utterance even if it is not consistent with the dialogue model is a legal dialogue step
in the verbmobil corpus we found that dialogue act combinations like suggest and reject can never be attributed to one utterance while init can often also be interpreted as a suqgest therefore getting a typical follow up reaction of either an acceptance or a rejection
an example of the internal use namely the treatment of unexpected input by the plan recognizer is described in section NUM outside the dialogue component dialogue act predictions are used e.g. by the abovementioned semantic evaluation component and the keyword spotter
following the assignment rules which also served as starting point for the automatic determination of dialogue acts within the semantic evaluation component we hand annotated over NUM dialogues with dialogue act information to make this information available for training and test purposes
to illustrate this principle we show a part of the processing of two turns fmwl NUM NUM and mpsl NUM NUM see figure NUM from an example dialogue with the dialogue act assignments as provided by the semantic evaluation component
it involves a three way distinction for enamex and only a two way distinction for numex and timex and it offers the possibility of confusing names of one type with names of another especially the possibility of confusing organization names with person names
this evaluation was performed by inspection hence it is purely empirical
the results illustrated in this paper are very interesting though not conclusive
the first set defines a prior probability distribution over all possible psts in a recursive manner and is intuitively plausible in relation to the statistical self similarity of the tree
finite state methods for the statistical prediction of word sequences in natural language have had an important role in language processing research since markov s and shannon s pioneering investigations c e
one indication of immaturity of the task definition as well as an indication of the amount of genuine textual ambiguity is the fact that over ten percent of the linkages in the answer key were marked as optional
unfortunately extending model order to accommodate those longer dependencies is not practical since the size of n gram models is in principle exponential on the order of the model
the perplexity obtained in the batch mode is clearly higher than that of the online mode since a small portion of the data was used to train the models
the low perplexity achieved by relatively small pst mixture models suggests that they may be an advantageous alternative both theoretically and practically to the widely used n gram models
however low order n gram models fail to capture even relatively local dependencies that exceed model order for instance those created by long but frequent compound names or technical terms
when we know in advance a large bound on vocabulary size we represent the root node by arrays of word counts and possible sons subscripted by word indices
when rare words are removed from the suffix tree the estimates of the prediction probabilities at each node are readjusted reflect better the probability estimates of the more frequent words
this model is found by pruning the tree at the nodes that obtained the highest confidence value ln s and using only the leaves for prediction
the pst learning algorithm allocates a proper node for the phrase whereas a bigram or trigram model captures only atruncated version of the statistical dependencies among words in the phrase
an example of a formal and natural language pair is f list flights from new orleans to memphis flying on monday departing early morning e do you have any flights going to memphis leaving new orleans early monday morning here the evidence for the formal language concept early morning resides in the two disjoint clumps of english early and morning
contains the beliefs selected to be addressed in order to change the user s belief about beli and its value will be nil if the system predicts that insufficient evidence is available to change the user s belief about bell
rather we give representative examples and appeal to the taggers intuitions asking them to generali from the examples to new situations encountered in the text or dialog
from the point of view of measuring corpus homogeneity or similarity it is desirable to use a method which m r6mi qes the significance of the division of a corpus into texts
related work is resumed in chapter NUM our method is examined in chapter NUM by some experiments
graph can not be decomposed by duplication of i b figure i NUM
figure NUM m n transitive graph scribes the anchor distance n
m n transitive graphs can be extracted as the subgraphs of the input graph
in the previous section the procedure to obtain subgraphs should begin from every branch in the input
null performing the above procedure starting from every branch in the input graph we obtain many subgraphs
the algorithm halts since the input graph is finite and the output is unique for an input
esst data was collected by giving marked up calendars to two speakers and asking them to schedule a two hour meeting at a time that was free on each of their calendars
words in class NUM is not ambiguous they should connect two subgraphs into one see section7
we have discussed a method to duster a co occurrence graph obtained from a corpus from a graph theoretical viewpoint
there are many ways in which the question could be addressed but the one we take here is to take texts from each newspaper and compare the frequencies of words used
thus in the bnc the shortest file which approximates to a text contains NUM words and the longest a hundred thousand times that many
there is a large body of work aiming to find words which are particularly characteristic of one text or corpus in contrast to another
in relation to content we should be n counting word senses or lexical units since any list will be compromised if money bank and river bank are counted together
the first line of the table then states that the average of these values for the first NUM items on the list the first of which was the was NUM NUM
subscripts e.g. vl v2 cl c2 are used in order to distinguish more than one vowel or consonant of the same pattern
a sequence of two consonants between two vowels is hyphenated with the succeeding vowel if a greek word exists that begins with such a consonant sequence
in order to avoid confusion hyphenation is applied to those vowel sequences corresponding to the category currently being explained and not to the entire word
our preliminary experiments with english travel domain data indicate that it is characterized by higher out of vocabulary rates and greater levels of semantic complexity compared with english scheduling domain data
thus these rules are capable of completely hyphenating at least NUM NUM NUM NUM NUM NUM NUM NUM of the NUM NUM sequences
specifically they may or may not be labeled as diphthongs depending on the specific dialect or on the personal preference of the native speaker
hyphenation issues pertaining to modern greek have been analyzed and correct and thorough machine hyphenation has been achieved as a result of the present study
for the scheduling domain we have been using semantic grammars in which the grammar rules defi e semantic categories such as busy free phrase and schedule meeting in addition to syntactic categories such as np and vp
to define this point formally let cc be the set of consonant sequences two characters in length that can begin a greek word
commonly fulfilling the second requirement depends on the development of extensive subword patterns associated with hyphenation rules as in liang NUM for example
comparison of travel and scheduling domains in this section we compare some characteristics of the english travel domain etd and the english spontaneous scheduling task esst
therefore we have not included a log term in the definitions of d1 d2 and d3 above
furthermore since we expect to experiment with various sub domain classifications it would be useful to devise an automatic method for dividing a large comprehensive grammar of the entire travel domain into sub domain grammars
it also demonstrates very effectively that one sided information measures are much better than symmetric measures at utilising context properly
the etd and esst databases are not comparable in some ways etd has been under development for less than one year whereas the esst database was collected over a three year period and is much larger
a document has persistence by virtue of being a member of a collection and can be accessed only as a member of a collection
conclusions in this paper we described our plans for extending the janus speech to speech translation system from the appointment scheduling domain to a broader domain travel planning which has a rich sub domain structure
in spite of the differences in the size of the two databases we can compare the out of vocabulary rates in order to get some idea of the difference in vocabulary sizes of the two domains
if we look at spoken communication between human beings with different native languages very often the main success criterion for this communication is not whether or not the individual sentences produced by the participants have been expressed or understood without errors which will rarely be the case but rather whether the intended goal of the communication has been attained hotel room reservation airline information etc
to summarize although many of the fundamental problems of mt and speech have not been solved the movement towards more specialized systems the redefinition of the notion of success and the potential of dialogues taken together give us reason to believe that we will see many successful spoken translation systems in the near future and we hope that this workshop will contribute to this
the probabilistic model employed is in the general class of hidden markov models hmm though the hmm used currently is more complex than those traditionally used in speech recognition and in part of speech tagging
they present an example based hybrid approach containing aspects of both corpus based and rule based styles of translation architecture this move towards hybrid architectures seems to represent a strong tendency in current work within the field of slt finally pascale fung et al present a paper focussed on the problems which a speech recognizer has to contend with in a multilingual environment where people typically speak using a variety of languages and accents
this new focus will be reflected in a special issue of the journal machine translation devoted to slt for which a call for papers will be issued soon and already we can see in other mt related conferences and publications a clear inclination towards this problem area
languages with a rich morphology may be more difficult than english since with fewer tokens per type there is less data on which to base a categorization decision
dd l describe an algorithm called improved iterative scaling iis for selecting informative features of words to construct a random field and for setting the parameters of the field optimally for a given set of features to model an empirical word distribution
the preponderance of word pairs that exhibit only one direction of significant influence eg according to shows that no symmetric score could have captured the correlations in all of these phrases
if we ignore failed derivations the process of dag generation is completely analogous to the process of tree generation from a stochastic cfg indeed in the limiting case in which none of the rules contain constraints the grammar is a cfg
for our target stories we selected the muc NUM story set one hundred english newspaper articles dealing with joint ventures
this was one of the reasons that prior to muc NUM semantic inferencing was added to plum s discourse processor
this initial system matched the whole templates produced by the two systems for each story
in parallel with this data conversion we built an initial version of a datacombining program
for the recognition algorithm the viterbi algorithm which is typically used in hidden markov models is applied
the template merging experiment provided a substantial range in recall versus precision i.e. in undergeneration versus overgeneration
adds and edits word meanings based on concepts and their attributes in the semantic model
the nlu shell provides a way for non programmers to build and maintain language processing applications
we believe this offers the opportunity to again try heavyweight techniques to attempt deeper understanding
we represented each instance where a job position is mentioned as a job situation semantic object
the reason for this is that mixed order models assign finite probability to all n grams wlw wn for which any of the k separated bigrams wkwn are observed in the training set
this suggests that for large vocabularies there is a useful regime NUM c v in which aggregate models do not suffer much from overfitting
for typical models e.g. n NUM v NUM this number exceeds by many orders of magnitude the total number of words in any feasible training corpus
for example the baum welch algorithm for hmms requires forward and backward passes through each training sentence while the em algorithm we use does not
these results confirm that aggregate markov models are intermediate in accuracy between unigram c NUM and bigram c v models
varying the number of classes leads to models that are intermediate between unigram c NUM and bigram c v models
in hmms the hidden state at time t NUM is predicted via the state transition matrix from the hidden state at time t
because many n grams occur very infrequently a natural question is whether truncated models which omit low frequency n grams from the training set can perform as well as untruncated ones
the overall perplexity decreased by NUM a significant amount considering that only NUM of the predictions involved unseen word combinations and required backing off from the trigram model
for a particular parameter setting we can run m fold cross validation to determine the expected error rate of that particular parameter setting
the final estimating equation is then
the method being described henceforth st
the average agreement among the human judges is NUM
the result of this is shown in figure NUM
sproat shih gale and chang word segmentation for chinese
we have argued that the proposed method performs well
one hopes that such a corpus will be forthcoming
a stochastic finite state word segmentation algorithm for chinese
two issues distinguish the various proposals
two objections can be raised to these claims first the use of tag elementary trees to restrict the working space of the parser amounts to a precompilation of phrase structure and locality constraints so that locality is not computed in the course of the parse but basically done as template matching
algorithm NUM input node label s ordered list of chains output chain or empty set m if label e lcb ah ah rcb then start new chain m if label ai then choose nearest unsaturated chain unless it is the immediately preceding element in the stack
for an illustration under the name of chain intersection problem and chain composition problem see merlo NUM
however this is not true if we use the number of entries as the relevant measure of size
this might make one think that there is some sort of relation between grammar size and nondeterminism after all
even introducing filtering lexical information co occurrence restrictions and functional complementation does not appear to help
partial evaluation and variable substitution can increase performance but as usual a space time trade off will ensue
in NUM the chains are john t and the children t
it is not clear that linear time complexity can actually be claimed if all factors are taken into account
one obvious example of this type of violation is inflection
another further work is expanding the dictionary especially idiomatic expressions
finally we discuss some remaining problems and direction of further work
in section NUM the method is compared with former approaches
the system wakes up when the user toggles on japanese input
a translation equivalent selection and translation area are shown
after translation trigger the system pauses showing c
we also have to mention some ambiguities difficult to resolve though basic operations of the method
suitable usage will be balanced between the user s skill and the capability of the system
according to the target language skill of the user useful support function will be different
when two defaults conflict the stronger one i.e. the one having the lower priority value takes precedence
during a conversation agents can easily come to have different beliefs about the meaning or discourse role of some utterance
interpretation and repair attempt to apply this process in reverse working back from an observed utterance to the underlying goal
the third utterance if it occurs immediately after an utterance such as the first one would be a challenge
in appendix a we show the system s output for a third turn repair interleaving the perspectives of its two participants
if s2 had performed asimitar then anew would be expected NUM sl may have mistaken aintended for asirailar
theorist is called to explain the utterance and returns with a list of assumptions that were made to complete the explanation
the portion of the output from the update describes russ s interpretation of this explanation see figure NUM
NUM mother s credulousness about russ s goals explains her belief that he wants her to perform the expected informref
the intuition behind the constraints is clear and new rules are easily added since the principles apply to any rule
the lexicon and phrase structure rules are encoded in the wfs well formed sign relation shown in fig NUM and the implicational principles in fig NUM
this compiler is still under development but it is reasonable to expect speed improvements of at least an order of magnitude
given that cuf already offers the control strategies required by our scheme the changes to the run time system would be minimal
if c is a complex conjunctive description then the result of normaiising c might be highly disjunctive
the lexical entries for the verbs loves and sleeps are specified in clauses NUM and NUM respectively
the hfp requires that in a headed construction the head features of the mother are identified with the head features of the head daughter
thus the model predicts from wt NUM with probability a1 wt NUM from wt NUM with probability NUM al wt NUM a2 wt and so on
this is not the case for other surface cues e.g. distributional cues exist for every word in a corpus
we trained mixed order markov models with NUM m NUM figure NUM shows a typical plot of the training set perplexity as a function of the number of iterations of the em algorithm
it then introduces morphological cueing a type of surface cueing and discusses an implementation
the next section explores the possibility of using derivational affixes as surface cues for lexical semantics
examples from the brown corpus include clasp coil fasten lace and screw
among the conforming words were equalize stabilize and federalize
NUM finally for some nlp tasks NUM per null cent reliability may be adequate
a combination of cues should produce better precision where the same information is indicated by multiple cues
the part of speech of the base is used to disambiguate these two senses of ize
one sense of the suffix ize applies to adjectival bases e.g. centralize
some examples are blockage seepage marriage payment restatement shipment and treatment
with respect to these desiderata derivational morphology is both a good cue and a bad cue
to verify this we ran a series of simulation experiments
is cyclic we have a a and b b in its cfbackbone and the stack schemas in production rl NUM indicate that an unbounded number of push actions can take place while production r3 NUM indicates an unbounded number of pops
its language is lcb r deg NUM r9 NUM r4 r NUM rcb which shows that the only linear derivation in l is s s c r t tc c r
this is due to the fact that in the cf case a tree that is derived a parse tree contains all the information about its derivation the sequence of rewritings used and therefore there is no need to distinguish between these two notions
though this is not always the case with non cf formalisms we will see in the next sections that a similar approach when applied to ligs leads to a shared parse forest which is a lig while it is possible to define a derivation grammar which is cf
this compositions rely on one side upon property NUM recall that the productions in pl must be produced in reverse order and on the other side upon the order in which secondary spines the rlf2spines are processed to get the linear derivation order
in this section we illustrate our algorithm with a lig l lcb s t lcb a b c rcb lcb NUM NUM o c rcb pl s where pl contains the following productions
intensionally speaking the definition of the function that groups verbs semantically would have something to do with the actual meaning of the verbs
to start off we conducted a preliminary evaluation on the etd test set using the original esst acoustic and language models
the etd speech vocabulary was constructed by augmenting the esst vocabulary with NUM new words that appeared in the etd training set
the supervised learner learns a second ordered list of transformations
in addition informal observations of the grammar developers point out sources of ambiguity in etd that do not exist in esst
the baseline algorithm should perform better than the lower bound and should represent a strong effort or a standard method for solving the problem at hand
thus it is an empirical question whether easily identifiable abundant surface cues exist for the needed lexical semantic information
in particular if a the ltig procedure is applied to a cfg in chomsky normal form b the ltig is converted into a cfg as specified in theorem NUM and c any resulting empty rules are eliminated by substitution the result is always the same cfg as that produced by the gnf procedure
the prediction rules NUM NUM NUM NUM can apply o igi2n NUM times because they are triggered by a chart state and grammar node NUM and for each of o igin NUM possible values of the former there can be o gi values of the latter
the earley style tig parser collects states into a set called the chart c a state is a NUM tuple p i j where p is a position in an elementary tree as described below and NUM i j n are integers indicating a span of the input string
as a more dramatic example some important kinds of text consist only of upper case letters thus thwarting any system that relies on capitalization rules
in particular consider l n lcb sa rcb s lcb bs rcb x lcb sa rcb ns lcb bs rcb nx this intersection corresponds to all the paths from root to x in the trees that are generated by recursively embedding the elementary auxiliary tree in figure NUM into the middle of its spine
the set i of initial trees in g is constructed by converting each rule r in p into a one level tree t whose root is labeled with the left hand side of r if r has n NUM elements on its right hand side then t is given n children each labeled with the corresponding right hand side element
the top of the chain tm is either not substituted anywhere i.e. only if x s or substituted at a node that is not the leftmost nonempty node of a tree in t the bottom tree in the chain tl has some tree u t substituted for its leftmost nonempty frontier node
further no adjunction whatever is permitted on a node that is to the right left of the spine of an elementary left right auxiliary tree t note that for t to be a left right auxiliary tree every frontier node dominated by must be labeled with
from this tree it can be seen that context farther away from the punctuation mark is important and extensive use is made of part of speech information
each rule substitution in g becomes a tree substitution in gl as a result exactly the same trees are generated in both cases and there is only one way to generate each tree in g t because there can not be two ways to derive the same tree in a cfg
thus our speech recognizer for read speech was improved to deal with spontaneous speech
semicorrect recog means the recognition rate that permitted some recognition errors of particles
the corrected semantic representation is the result of analysis end of analysis
we also developed a multi modal dialogue system based on the robust spoken dialogue system
and we found that the multi modal interface with spontaneous speech and touch screen was user friendly
human agents can recover illegal sentences by using general syntactical knowledge and or contextual knowledge
alternative plan is the rate that the system proposed the alternative plan
so we adopt an heuristic strategy to determine an optimal level
the unknown word processing part uses hmm s likelihood scores for arbitrary syllable sequences
user can only use the positioning selecting input and speech input at the same time
the work is supported by national science foundation of china
the resulting initial distribution the erf distribution is not the maximum likelihood distribution as we know
thus given a set of parses the one that is most likely to best describe the input is the one that contains mal rules corresponding to errors in that realm of constructions and that does not use constructions well beyond the student s current acquisition level
the probability of a dag is proportional to the product of weights of rules used to generate it
also shown in the table are the perplexities on the entire test set
the method generally suffers from the problem of data sparseness
we add two more features process and gradual
section NUM discusses the disambiguation procedure based on the contexts
we can classify verbs by means of different combinations of the five features
this means that the erf method does not converge to the correct weights as the corpus size increases
the content of the response itself will be derived from the annotations on the errors that were passed from the error analysis component additional content for the responses may be provided by the asl english expert language model and influenced by the acquisition model
just fifty years since warren weaver in his letter to norbert wiener later reproduced in his famous memorandum first expressed realistic hopes for mechanical translation see hutchins in press we find ourselves realistically discussing the possibility of using computers to translate the spoken word
the fit for x2 improves but that is more than offset by a poorer fit for xl
i he argues the need for interactive disambiguation a view that the authors of the other papers in this section would probably reject and NUM for a particular kind of system architecture a variant of the blackboard architecture incorporating a supervisory coordinator program which may also be controversial
abney stochastic attribute value grammars since log is monotone increasing maximizing likelihood is equivalent to maximizing log likelihood
a term sometimes seen used is machine interpreting but it seems that this might apply to only one aspect of slt implying some activity similar to that of human interpreters i.e. simultaneous or consecutive translation of spoken language often in the context of a meeting or someone addressing a group of people
in tagging the focus word to be assigned a category is obviously more relevant than any of the words in its context
retrieval by search in the tree is independent from the number of training cases and therefore especially useful for large case bases
on the wsj corpus unknown words can be predicted using context and word form information for more than NUM
although the workshop session itself did not leave space for poster presentations we felt that it was important to dedicate a small section of the proceedings to short poster papers where researchers can communicate to others what they are doing so that people who are interested in the same or related research topics know where to go
e.g. once would get a new tag representing the category of words which can be both adverbs and prepositions conjunctions rb in
during tagging the control is the following words are looked up in the lexicon and separated into known and unknown words
word correlations w w wt are computed from general likelihood scores based on the co occurrence of words in common segments
items in the cache can be preferentially retained and items in main memory can be retrieved to the cache
direct personalized interaction in a non threatening non human package coupled with constructive input in the form of specific example utterances that address issues the student is currently learning could go a long way toward bringing satisfactory english literacy within reach of the deaf population
in dialogue c a version of the dialogue without the irus is possible but is harder to interpret
these knowledge sources allow us to determine parts of speech for unknown words without using domain specific knowledge in the timit corpus
since the baseline parser assigns every open class part of speech to an unknown word a combinatorial explosion of parses occurs
although john is very generous b if you should need some money c you d see that he s difficult to find
in one case there was no explicit target contrast and the expectation raised by on the one hand was never satisfied
for example milne NUM makes use of morphological reconstruction to resolve lexical ambiguity while parsing
we have experimented with various segment sizes ranging from phrases delimited by all punctuations a sentence to an entire paragraph
consulting a list of affixes the recognizer determines which affix if any are present in the word
since the nikkei is encoded in two byte japanese character sets the latter is equivalent to about 60m bytes of data in english
this would allow the parser to reparse the sentence changing only a few words definitions providing better all around performance
since it is not feasible to evaluate the incremental profit of every subset of e extend w uses a greedy heuristic that repeatedly augments the set of profitable extensions of w by the single most profitable extension until it is not longer profitable to do so
the simplest models of natural language are n gram markov models
each round of presenting the same input data is called an epoch of course it is desirable to require as few training epochs and as little training data as possible
the state to state versus word tostate dynamics lead to different learning algorithms
the context and extension models are all of order NUM and were estimated using the true incremental benefit and a range of fixed incremental costs between NUM and NUM bits extension for the extension model and between NUM and NUM bits context for the context model
wasson s reported processing time can not be compared directly to the other systems since it was obtained from a mainframe computer and was estimated in terms of characters rather than sentences
one example follows dar de i trebuie s l parcurgem in intregime pentru a orienta cercetarea este nevoie s
l t c i l c i l tic since conditioning on the model class i is always understood we will henceforth suppress it in our notation
the adverbial on the one hand is taken as signalling a coherence relation of contrast with something expected later in the discourse
for each symbol that occurs on the right hand side of a rule but which was not one of the most frequent NUM symbols we create a rule that expands that symbol to a unique terminal symbol
in figure NUM NUM the fact that the symbols atalks and aszo ozv occur adjacently is indicative that it could be profitable to create a rule b at t sasto olv
a e no d lcb s x rcb nold denotes the set of nonterminal symbols acquired in the initial grammar induction phase and x1 is taken to be the new sentential symbol
from a million words of parsed wall street journal data from the penn treebank we extracted the NUM most frequently occurring symbols and the NUM most frequently occurring rules expanding each of these symbols
delaying the parsing of a sentence until all of the previous sentences are processed should yield more accurate viterbi parses during the search process than if we simply parse the whole corpus with the initial hypothesis grammar
most work in language modeling including n gram models and the inside outside algorithm falls under the maximum likelihood paradigm where one takes the objective function to be the likelihood of the training data given the grammar
yet n gram language models can only capture dependencies within an nword window where currently the largest practical n for natural language is three and many dependencies in natural language occur beyond a three word window
the grammar induction algorithms most successful in language modeling include the inside outside algorithm NUM a special case of the expectation maximization algorithm and work by
we have implemented only a subset of the moves that we have developed and inspection of our results gives reason to believe that these additional moves may significantly improve the performance of our algorithm
however we feel the largest contribution of this work does not lie in the actual algorithm specified but rather in its indication of the potential of the induction framework described by solomonoffin NUM
it is interesting that the system performs so well using only estimates of the parts of speech of the tokens surrounding the punctuation mark and using very rough estimates at that
to investigate the effects of parameter smoothing on robust learning both these techniques are used to smooth the estimated parameters and then the robust learning procedure is applied based on those smoothed parameters
the fifth class of error may similarly be addressed by creating a new classification for ellipses and then attempting to determine the role of the ellipses independent of the sentence boundaries
table NUM shows that better performance both in terms of accuracy rate and selection power can be attained when more contextual information is consulted or when more parameters are used
the network accepts k NUM input values where k is the size of the context and NUM is the number of elements in the descriptor array described in section NUM NUM
a segment at x NUM for instance spans the 40th to the 49th word of text
tony broke the piggy bank open
training of the learning algorithm is therefore necessary only once for each language although training can be repeated for a new corpus or genre within a language if desired
if a word is not present in the lexicon the satz system contains a set of heuristics that attempt to assign the most reasonable parts of speech to the word
since it does not affect points of the discussion the english translation is used here for the convenience
where a is the instance distance function i is the distance function for parameter i n is the total number of parameters by which a and b are defined and ai and bl are the values of parameter i for instances a and b respectively
the matching process then involves sliding one phrase past the other identifying strong matches word and tag or weak tag only matches and allowing for gaps in the match in a method not unlike dynamic programming
however both these procedures can be identified as essentially rule based in the sense that linguistic data used to match whether fixed patterns or syntactic rules must be explicitly listed in a kind of grammar which implies a number of disadvantages which we will mention shortly
using such rules in combination with other rules enables us to produce simple html documents or if required quite complex and deeply nested documents incorporating links to other ads or buttons to expand information or clarify terminology e.g. to get a definition of an unfamiliar jobtitle
inversion transduction grammar g there exists an equivalent inversion transgrammar g where t g t g such that the right hand side of any proof g t contains either a single terminal pair or a list of nonterminals
NUM where jj is t the e secretary of finance llq when l needed
each production is either of straight orientation written a ala2 ar or of inverted orientation written a ala2 ar where ai e a u x and r is the rank of the production
the specific semantic selection of mental adjectives follows from the headcdness system
it is subtyped as an intellectual act
mental state adjectives the perspective of generative lexicon
un livre ing6nieux an clever book c
it also explains possible divergences between french and english
un livre furieux a furious book c
in this paper we focus on the representative members of these classes in i and ih we would like to thank james pustejovsky for extensire discussions on the data presented in this article
in the following we will first focus on the two different kinds of headedness applying to the state or one of the events and then show the consequences of an headless structure
section NUM will then focus on emotion adjectives
je suis ing nieux de partir i m clever to leave the qualia representation is rich enough to explain the syntactic polyvalency shown in NUM and NUM
ways in which this manual effort an be partly replaced by corpus based training
this pa per des ril cs an efficient algorithm for bilingual tree alignment
which is at least NUM times faster than before using this algorithm
in preliminary testing penalty values of NUM and NUM yielded iinprovenmnts in precision
the optimization w riables have different effec ts on the different texts
these fimctions have the value NUM if there is no lexica match
i i this paper the coh h ell e is co sider d within the framcw rlc of kn wh lgc representati n texts
in the intensional universe of a world the in lividuals also named types are nodes of a lattice the lattice o1 types the hierarchy l eing represente d by t he ingredient fun tot
in the second domain we derived the grammar from manually parsed text
a factor in the objective function that favors smaller grammars over
moreover reentrancy as a notational device to express common features seeks the same type of representational economy that is expressed by the use of traces in gb theory
additionally there are NUM characters in one key pad see figure NUM
where the bulk of effort was spent knight ridder information inc did only the ne part
football teams were to be considered companies while publications trade unions and governmen t organizations were not
we are striving to have a strong renewed creative partnership with coca cola mr enamex type person dooner enamex says
an obvious question was what kind of value can be added to the information
this should help to implement automati c training see below in the future
the flags corresponding to different meanings of a word are mixed in the same entry
NUM the title field was not processed at all for no good reason
i m going to focus on strengthening the creative work he says
the system used for muc NUM is exactly the same as is used in production
in the future such rule sets should b e constructed using automated tools
we compared the recognition accuracies of a pure language recognizer with a mixed language recognizer
note that in example NUM the move marked is not a check because it asks for new information f has only stated that he ll have to go below the blacksmith but the move marked is a check because f has inferred this information from g s prior contributions and wishes to have confirmation
coders disagreed on where to place boundaries with respect to introductory questions about a route segment such as do you have the swamp when the route giver intends to describe the route using the swamp and attempts by the route follower to move on such as where do i go now
it is also theoretically possible at any point in the dialogue to refuse to take on the proposed goal either because the responder feels that there are better ways to serve some shared higher level dialogue computational linguistics volume NUM number NUM goal or because the responder does not share the same goals as the initiator
in general the results of the game cross coding show that the coders usually agree especially on what game category to use but when the dialogue participants begin to overlap their utterances or fail to address each other s concerns clearly the game coders have some difficulty agreeing on where to place game boundaries
example NUM g ok after an instruction and an acknowledgment example NUM g you should be skipping the edge of the page by about half an inch ok example NUM g then move that point up half an inch so you ve got a kind of diagonal line again
finally coders had a problem with grain size one coder had many fewer transactions than the other coders with each transaction covering a segment of the route which other coders split into two or more transactions indicating that he thought the route givers were planning ahead much further than the other coders did
in addition participants occasionally overview an upcoming segment in order to provide a basic context for their partners without the expectation that their partners will be able to act upon their descriptions for instance describing the complete route as a bit like a diamond shape but a lot more wavy than that
however it is also possible to make such refusals explicit for instance a participant could rebuff a question with no let s talk about an initiation with what do you mean that wo n t work or an explanation about the location of a landmark with is it said with an appropriately unbelieving intonation
grammar NUM has a slightly smaller average of conflicts while it has three times the number of rules and twelve times the number of entries compared to grammar NUM
this process repeats itself until a head corner is constructed that dominates the whole string NUM
the difference with a left corner parser which was derived from the head corner parser is small
this strategy works but turns out to be considerably slower than the original version given above
q e parse right ds q q
this paper describes an efficient and robust implementation of a bidirectional head driven parser for constraint based grammars
the memory requirements for an implementation of the earley parser for a constraint based grammar are often outrageous
moreover the nature of the application also dictates that the parser proceeds in a robust way
most importantly the selection of rules to be considered for application may not be very efficient
since the number of rules is expanded but no filtering constraint is incorporated in grammar NUM with respect to grammar NUM this result might not seem surprising
in linguistic terms i will argue for a model in which only maximal projections are memorized
of course in order to make this approach feasible certain well chosen search goals are memorized
sometimes the detection of some recognition errors for example the substitution of an uttered word with another one of the same class is outside of the capabilities of both the parser and the dialogue modules
we applied the above classification in a different experimental set up in may NUM a laboratory test was designed to study the reaction of users to different speech technologies and dialogue strategies applied to the railway information domain
this peculiarity directly impacts on the performance of the language models that is in these applications language modeling predictions are weaker when the dialogue prediction says that next user s utterance is likely to be about a departure place this does not exclude that the recognizer substitutes the actually uttered name with a phonetically similar one
in the dialogos corpus we observed that while subjects were usually able to correct errors in confirmation turns that concern a single information or two semantically related information such departure and arrival on the contrary some errors were not corrected when the feedback was offered together with a system initiative or when the system asked to confirm information that had not strong relationships
each class is further specialized with the indication of the focused parameters for example request date of departure if the system is asking the user to provide a departure date request plusconfirmation departure plus arrival when the system is addressing the user with a feed back about the departure city and a request of the arrival city and so on
the approach is based on pragmatic based expectations about the semantic content of the user utterance in the next turn
then we have transcribed the best decoded sequence in all caps that is the recognizer output
for example some of the task oriented systems that give information about railway timetable or flight scheduling have large vocabularies that contain an huge number of words belonging to the same class for example dialogos vocabulary is NUM NUM words including NUM NUM proper names of places another example is the
this process may need to be iterated based on any new auxiliary trees produced in the last phase
the second phase begins with lexical types and considers the application of sequences of rule schemata as before
this algorithm has been implemented in lisp and used to compile a significant fragment of a german hpsg
note that the subj feature is only reduced in the former but not in the latter structure
raising head features would block its application to non finite verbs and we would not produce the trees required for raising verb adjunction
there are also several techniques that we expect to lead to improved parsing efficiency of the resulting tag
lp constraints could be compiled out beforehand or during the compilation of tag structures since the algorithm is lexicon driven
two nodes standing in this domination relation could become the same but they are necessarily distinct if adjoining takes place
the second is a general nonparametric fertility model
if the formal language for this sentence were
for general lm results increased by NUM NUM
each clump is required to have a headword
moreover both systems were trained using english annotated by hand with segmentation and labeling and both systems produce a semantic representation which is forced to preserve the time order expressed in the english
the unannotated corpus also shows a comparable gain
this does not involve a loss of generalization but simply means a further refinement of the type hierarchy
the number of words in q is denoted by g ci cl begins at the first word in the sentence and ct c ends at the last word in the sentence
this took about NUM minutes of the processor time
indeed part of the spirit of this work is to explore how far one can go in advocating principle based parsing in the absence of motivations given by cognitive modelling
the treebank consists of a correct parse for each sentence it contains with respect to the atr english grammar NUM every non terminal node is labeled with the n rae of the atr english grammar rule NUM that generates the node and each word is labeled with one of the NUM tags in the gramm r s tagset
transition between states is accomplished by one of the following steps NUM assigning syntax to a word NUM assigning semantics to a word NUM deciding whether the current parse tree node is the last node of a constituent NUM assigning a rule label to an internal node of the parse tree
for instance in she did n t attend because she was tired and did n t call for the same reason the phrases because she was tied and for the same reason should probably postmodify their entire respective verb phrases did n t attend and did n t call for maximum clarity
for instance as noted in NUM NUM the parse base of the atr english grammar which generates the parses of the atl lancaster treebank is NUM NUM which means that on average the grammar generates about NUM parses for NUM word sentence NUM parses for a is word sentence and NUM NUM parses for a NUM word sentence
at the other extreme it may already have been modified in a way that tends not to permit further modification such as a noun phrase followed immediately by a postmodffying comparative phrase such as can understand the topic may attend more reasons than you can imagine were adduced
not for a golden staudard test set as des ibed in NUM NUM in which all parses are indicated for each test sentence 23it seems worth mentioning that future large scale treebank creation efforts would probably benefit from constmcting parallel data with respect to other large eeb k right from the start
NUM NUM words included in the ibm lancaster treeb n NUM NUM words of associated press newswire and NUM NUM words of canadian hansard le slative proceedh s were treebanked with respect to the atr english grammar in the exact same manner as the data in the atl lancaster treeb nk
NUM i choose one or more most likely parts of speech estimate the probability for each tag for the first word given the part of speech decision s made above choose one or several likely tag s repeat the steps above for each word in the sentence
i or instance tile spanish phrase iiacer pan to make bread means baking bread whilst iiacer ri banadas de pan to make slices ofbread means slicing bread
figure NUM a tongue box cake limon cont i b things substances plurals aggregates a container of wheat sugar water paper cakes tongues boxes
this surfaces in the language as a zero determiner plus tile noun in singular in the case of substances a glass of wine and either in singular or plural in the case of individuals a slice of lemon a basket of lemons
the initial lattice was of NUM NUM nodes
for this new approaches are being explored like reordering the words in the translations the use of new inference algorithms and automatic categorization
instead of imposing the determinism conditition we will only enforce the existence of at most one valid path in the transducer for each input string nonambiguity
the use of ssts to model limited domain translation tasks has the distinctive advantage of allowing an automatic and efficient learning of the translation models from sets of examples
we firstly establish when a singleton lemma is a relevant concept by using distributional properties of nouns
in the extend step of the a lgorithm we need to evaluate the mutual information values of phrase
when terminology is available many complex nominals are retained as single tokens and several ambiguity disappear
denotes its absence any dictionary d can thus be evaluated by measuring precision i.e.
this has a strong implication on shallow or robust ms widely accepted in literature parsing
the success of this method allow to design automatic methods for taxonomic thesaurus like knowledge generation
we decided to estimate the mutual information of such structures in a left to right fashion
distributional properties of complex terms nominals differ significantly on those of their basic elements
for sake of completeness we selected two large hand coded thesaura for the environment the cnr dictionary
complex terms a complex nominal cn u m1 u2
this house is the cb 19b it is realized but not directly realized in 19b
2deg the use of different types of transitions following the rankings in rule NUM are illustrated by the discourse below
section NUM provides the basic definitions of centers and related definitions needed to present the theoretical claims of the paper
in section NUM we state the main properties of the centering framework and the major claims of centering theory
center continuation cb un l cb un and this entity is the most
susan s expertise null in the c utterance of each sequence susan is the cb
these differences in inference load underlie certain differences NUM this example and the others in this paper are single speaker texts
discourse NUM centers around a single individual describing various actions he took and his reactions to them
to avoid confusion with previous uses of the term focus in linguistics they introduced the centering terminology
joshi kuhn and weinstein were concerned with reducing the inferences required to integrate utterance meaning into discourse meaning
NUM features names are mnemonic argument positions are not
l i e following sliititii tt izes ttw l roc hlre o extra ting shnple tiolll s roil i col tl ounct llolllts
figure NUM and l tble NUM summarize the perfor umnce of the indexing me t hods mauum nnmy sis tim propose i i rolmbilisl ie method and the bigr mi utet lod
lit proposed mel ho l showed a slightly bct ter pet f riilatlee around NUM NUM j them nlmnlm indexing or bigr mi tildextrig
the words in non nominal dictionaries do not include those that can also be used as notins whi h is not a probleni since unlike in english the lmllti eategorial words in korean telld to be invariant of meaning
the set of profit programs is a superset of prolog programs
to see the potential of the component nouns of the decomposition we observe how the component nouns are distributed over the total document set and also examine how the simple and componnd nouns of the current document are distributed over the same document set
to deal with the notion of consistency we have to deiiue tile nwaning of a term or a set o terms it is a well recognized practice to regard the discriminating power of a terln as the value of the term
one determiner of whether attentional or propositional effects are dominant is the type of information provided by the accented constituent
the attentional interpretations are constrained by what has been mutually established in the prior discourse or is situationally evident
both attentional cf and propositional mutual beliefs structures are updated throughout
the relevant claims in ph90 and gjw89 are reviewed in the next two sections
however pronominals with little intrinsic semantics perform primarily an attentional function
if the hearer is hasty she might select bill as the new cb
centering theories would be hard pressed to predict pitch accents on pronominals on grounds of redundancy
NUM pitch accents on pronominals are primarily interpreted for what they say about attentional salience
is preferred over a sequence of retentions which is preferred over a sequence of shifts
therefore pitch accented pronominals are mainly interpreted with respect to cf for their attentional content
because of the granularity of the presuppositional event sequence that develops from the presupposition constrnction in such cases in NUM the iteration must satisfy to a one day rhythm at least the temporal adjunct can not truly act as a restrictive referential constraint and becanse of what we have said above about novelty it is not the best attributive information also
in the context NUM c the recipient under null stands the event as element of a sequence of events and the realization of the sequence in particular the reported realization of the event at the textual perspective time seems to be in retardation with regard to some previous expectation about the realization dates of the sequence
therefore an extension is needed to check if there are any empty categories wait17 note that here i am assuming that checking a feature and checking a chain have the same computational cost which is an approximation as a chain can not be checked with a single operation
principle based grammars engender compactness given a set of principles p1 p2 pn the principles are stored separately and their interaction is computed on line the multiplicative interaction of the principles p1 x p2 x x pn does not need to be stored
for example descriptions of character objects include their morphological category stem affix and whether they are bound or unbound
we have only just begun to experiment with the possibility of building the entire network automatically from a more exhaustive corpus analysis
one way to address this problem is to use machine learning techniques to automate the derivation of planning resources for new domains
in this section we will briefly discuss the nature of our corpus and the fimction and form features that we have coded
although the analysis can not be fully automated we noted that the derivation of decision networks from coded corpus examples can
for example the main goal repair device represents the action of the reader repairing an arbitrary device
although this switch has entailed some additions to the domain model drafter s input and generation facilities remain as they were
within this box there is a single method called repair method which details how the repair should be done
these are all shown in terms of a pseudo text which gives an indication albeit ungrammatical of the nature of the action
this did not happen in figure NUM but occasionally one of the features is independently used in different sub trees of the network
it accurately predicts the grammatical form of NUM NUM of the NUM training examples and NUM NUM of the NUM testing examples
we have also seen that as a result of step NUM all the minimal nodes w r t the gap r p x NUM rl NUM p l NUM with the desired property as required in part c have been computed
we propose an o m n2 time algorithm for the recognition of tree adjoining languages tals where n is the size of the input string and m k is the time needed to multiply two k x k boolean matrices
thirdly the technique of getting rid of the mid NUM NUM and focusing on the reduced problem size alone does not work as shown in figure NUM suppose NUM is a derived tree in which NUM a node rn on which adjunction was done by an auxiliary tree ft
note that z is an additional symbol not a variable
left and by pereira and wright s algorithm right
when references are resolved corresponding semantic variables are unified
the scoring program served as a guide for our development
the three muc NUM systems represent three different levels of complexity
te st the te st system uses a more complex message reader
ovals represent declarative knowledge bases rectangles represent processing modules
the plum architecture is presented in figures NUM and NUM
each trigger fragment contains one or more words whose semantics triggered the ddo
an automated tool generated n tuples based on part of speech tag to aid this process
NUM s i aijbj wr tr flr j as i aijbj wr tr fl j NUM pk
although all possible p w t s on combinations of w and t can not be estimated there are some particularly useful approximations such as the n gram model and the hmm
an example of simple alias improvements is to recognize mr
furthermore this parsing is done using essentially domain independent syntactic information
beginning with trec NUM several additional specialty subtasks referred to within trec as tracks were added
a few examples are listed below to demonstrate the degree to which this statement has been played out
so how well has tipster adhered to the evaluation driven research paradigm as described in this preceding section
however tuning the number of factors to select the best number for each space shows an average of NUM improvement over all the results and up to NUM for some confusion sets
an approximation of the original matrix is created by eliminating some number of the least important singular values in s they correspond to the least important and hopefully most noisy dimensions in the space
the pair of sentence and confusion word vectors with the largest cosine is identified and the corresponding confusion word is chosen as the most likely word for the test sentence
the word predicted most likely to appear in a sentence is determined by comparing the similarity of each test sentence vector to each confusion word vector from the lsa space
a relatively simple method for estimating this distribution is described in me196b
this has a small number of possible semantic representations the exact number depending upon the grammar e.g.
as shown in table NUM verbs can have high entropies for several reasons
this research was supported by sun microsystems laboratories inc and by arpa contract n NUM 94c NUM
the semantic entropy e s of each word s accounts for both the null links and the non null links
while some of these advances would have happened without tipster tipster was probably instrumental in accelerating their emergence
these papers make for exciting and interesting reading and the reader is happily directed to them for further details
we also experimented that by changing slightly the model parameters and or the definitions of the four performance figures in the NUM in any case the peak performance of the obtained scoring function falls in the NUM NUM NUM NUM interval and the function stays high around the peak with local maxima
global reduction of ambiguity for each c i let s w i be the total number of wordnet leaves synsets reached by the words in wr ci and scon i s w i the set of these synsets that reach some category in c i
ades and steedman noted that the use of function composition allows cgs to deal with unbounded dependency constructions
tend to be larger in the middle of the frequency range germans and smaller at both ends fromm which
if a document is about boycotts we should n t be surprised to find two boycotts or even a half dozen in a single document
in particular a NUM gram or NUM gram may have the following relationships with its substrings NUM compositional the n gram can be decomposed into legal words e.g. this afternoon l announce today k intervene the election
where tj is the j th possible set of lexical tags parts of speech for the segmentation pattern w the tagging process can thus be optimized based on the product of the pos tag transition probabilities p t ill i NUM and the distribution for p w ilt the viterbi training process for pos tagging based on this optimization function is shown in figure NUM
the practice implicitly assumes that words and ngrams are distributed by a single parameter distribution such as a poisson or a binomial
NUM cx of the documents have no chance of mentioning boycott NUM NUM because they are totally irrelevant to boycotts
they may not not be large but they are too large to be due to chance and they all point in the same direction
katz k mixture katz personal communication the solid line labeled k fits the data better than the poisson
we will use inverse document frequency idf a quantity borrowed from information retrieval to distinguish words like somewhat and boycott
NUM document frequency is similar to word frequency but different word frequency is commonly used in all sorts of natural language applications
this indicates a large amount of variability in the data reflecting wide differences across narratives speakers in the training set with respect to the distinctions recognized by the algorithm
in this article we have discussed how we apply predictions from our plan based discourse processor to the problem of disambiguation
despite this variation we found statistically significant agreement among subjects across all narratives on location of segment boundaries NUM z NUM NUM p NUM z NUM NUM
one example of a change to clause coding is that formulaic utterances having the structure of clauses but which function like interjections are no longer recognized as independent clauses
the learning NUM tree not shown due to space restrictions is more complex than the tree of fig NUM but has slightly better performance
by asking subjects to segment discourse using a non linguistic criterion the correlation of linguistic devices with independently derived segments can then be investigated in a way that avoids circularity
NUM zibro pronouni falls over before after pause duration cue NUM word NUM cue word coref infer e lobal pro
now a pronoun e.g. it that this in ci referring to an action event or fact inferrable from ci NUM links the two clauses
the second input is the training data i.e. a set of examples for which the class and feature values as in fig NUM are specified
hence part of the bias in the estimation may be overcome
the data structure is a word laltice an acyclic state transition network with one start state one final state and transitions labeled by words
the procedure dep defined in NUM returns the set of dependencies established within a subproof
in this case arbitrarily gives precedence to the dependency whose two assumptions occur closer together in delivery order
however the treatment of indexation in the above system is one that does not readily adapt to flexible combination
thus we believe the above system to exhibit maximal incrementality in relation to allowing semantically contentful combinations
linear deduction methods on the other hand work with unordered collections of formulae
to build normal form proofs we only need to limit the order of selection of dependencies using i.e.
note that this ordering restriction makes the selection process deterministic from which it follows that normal forms are unique
given lexical look up there will then be an order of delivery of lexical formulae to the parser
whether movement is to the left or to the right does not affect structural licensing which is here performed by the conditions that apply to the reduction of an rule
practically this amounts to NUM k at most as the number of active chains is not more than NUM because of the restriction requiring that the nearest unsaturated chain be selected
he shows that an initial version of the parser where the phrase structure rules were expressed as a dcg and interpreted on line spent NUM of the total parsing time building structure
the treatment of aspectual adverbs is more complex
this is because the former theory is smaller
the logic of why this happens is clear
rules NUM and NUM cover the same string
it also stores compatible continuations based on subcategorization
figure NUM a symmetrical system as traveling business person s query translator figure NUM speech recognizers perform better on native speakers
for aac devices the templates are designed for particular dialogue situations
in what follows we describe the development and in house testing of the tool section NUM present ongoing work on testing its generality and objectivity section NUM and conclude the paper taking a look at the work ahead section NUM
this is our first example of how symbolic and statistical knowledge sources contain complementary information which is why there is a significant advantage to combining them
pictalk provides a few other utterances that are outside of the menu structure and are always available
nr s respectively be the counts of occurrences of words wl w2 w at a given context node s where r is the total number of different words that have been observed at node s
combining equations NUM and NUM we see that the prediction of the whole mixture for next word is the ratio of the likelihood values lmi n e and lmixn l e at the root node p wnlwl
finally we trained a depth two pst on randomly selected sentences from the nab corpus totaling approximately NUM NUM million words and tested it on two corpora a separate randomly selected set of sentences from the nab corpus totaling around NUM NUM million words and a standard
there are two cases of novel events ca an occurrence of an entirely new word that has never been seen before in any context b an occurrence of a word that has been observed in some context but is new in the current context
more extensive pruning may be useful for such large training sets but the most promising approach may involve a batch training algorithm that builds a compressed representation of the pst final from an efficient representation such as a suffix array of the relevant subsequences of the training corpus
we then fed ciaula with groups of verbs belonging to each of these categories
on average we were satisfied with one half of the tags assigned to clusters
the verbs in this category take with local and global degree of membership
locative relations are treated in wordnet as lexical adjuncts of the verb to record
in a second set of experiments we ran ciaula on a more homogeneous set of verbs
finally the algorithm must avoid the selection of excessively general categories like create make
members of class NUM NUM are verbs that take an abstraction abs as a direct object
this rough labeling is then improved by applying a rule sequence
the two below come from the actual sequence for spanish met
such applications include extraction of bibliographic information document indexing competitiv e analyses based on open sources
token chars not recognized as data classe s are analyzed morphologically to determine their part of speech word stem and number
in addition new attributes are added includin g part of speech the word stem number singular or plural
last october NUM we started designing a new pattern recognition language to be developed for just the purpose of data extraction
in this section we summarize the elements of our design objectives and provide extensive detail s of the general system implementation
the last four types have a part of speech attribute plus several classification attributes plus a number of other features
the first feature is illustrated by figure NUM which shows a dxl rule for one of the person canonical forms
a token whose chars value is bank will not have these attribute values alread y associated with it
attributes such as being capitalized not a name referring to an organization whose activity i s commercial
thirdly the principles were tested against the dialogue corpus fi om the user test of the implemented system
a similar process would be difficult to conceive in languages in which the orthography and pronunciation are significantly different
in conclusion 2v and vc are disjoint thus tokenization results in a unique list of tokens
vowel splitting is quite common in modern greek and is usually handled in grammar books with prohibitive rules
theorem NUM rules presented in table NUM are sufficient to completely hyphenate all words containing no consecutive vowels
the present paper will encapsulate the standard grammar hyphenation rules and the general principles used in this study
in addition the initial step toward the development of lists of hyphenated words is commonly rule based hyphenation
we will also prove that rules c1 c3 are not sufficient to provide complete hyphenation coverage of greek words
let c2 and c3 be the second and the last optional consonants of the same sequence
for example candidate diphthongs located between the members of compound words prefixed by a preposition do not split
the degree of completeness calculated above does not represent completeness in terms of hyphenated words of real text corpora
in this section we analyze the relationship between grice s nmxiins and our principles el dialogue cooperativity
make your contribution as informative as is required for the current purposes of the exchange
we first show that the set of classifications that can be provided via decision trees is a proper subset of those that can be provided via transformation lists an ordered list of transformation based rules given the same set of primitive questions
insofar as tagging can be seen as a prototypical problem in lexical ambiguity advances in part of speech tagging could readily translate to progress in other areas of lexical and perhaps structural ambiguity such as wordsense disambiguation and prepositional phrase attachment disambiguation
for instance the following sentence specification is identical to the previous except the cause relation is now taken as the head producing a substantially different sentence we will now turn to the textual component of the input specification which is responsible for tailoring the expression of the ideational content
paris NUM lexico grammatical choices can be constrained by reference to attributes specified in the speaker and hearer roles NUM this has not however been done at present while the implementation is set up to handle this tailoring the resources have not yet been appropriately constrained
therefore in training we find transformations that maximize the function number of corrected errors number of additional tags in table NUM we present results from first using the one tag per word transformation based tagger described in the previous section and then applying the k best tag transformations
the main systems in this network are as follows null initiation the grammatical form used to realize a particular utterance depends on whether the speaker writer is initiating a new exchange or responding to an existing exchange e.g. an answer to a question
in many sentence generation systems direct specifications of grammatical choices or forms is often needed or in the case of penman the user needs to include arcane inquiry preselections interventions in the interstratal mapping component perhaps more arcane than grammar level intervention
to demonstrate this integrated approach to sentence generation we show below the generation of some sentences in two stages firstly assertion of knowledge into the kb and secondly the evaluation of a series of speechacts which selectively express components of this knowledge
this unfortunate error had the following effect on total locale slot scor e third problems with persons
with the propositional content of the text structured in terms of processes mental verbal material etc the participants in the process actor actee etc and the circumstances surrounding the process location manner cause etc
to allow for this wag allows input specifications to directly constrain the surface generation either by directly specifying the grammatical feature s a given unit must have or alternatively specifying grammatical defaults grammatical features which will be preferred if there is a choice
a move with features and elicit negotiate action would be realized as a request for action e.g. will you go now while a move with features and propose negotiate action would be realized as a command e.g. go now
such a structure is specified by providing two sets of information for each entity as in the propositional slot of fig null ure NUM type information a specification of the semantic types of the entity derived from the um or associated domainmodel
the surface syntactic component linearizes the nodes of the ssynts which yields the deepmorphological structure or dmorphs
this means that realpro gives the developer control over the output while taking care of the linguistic details
these tasks we assume are handled by a separate component such as a sentence planner
the deep morphological component inflects the items of the dmorphs yielding the surface morphological structure smorphs
realpro is currently distributed with a socket interface which allows it to be run as a standalone server
realpro can be run as a standalone server and has c and java apis
the input to realpro is based on syntactic dependency roughly predicate argument and predicate modifier structure
these sentences are all of the form this small girl often claims that that boy often claims that mary likes red wine where the middle clause that that boy often claims is iterated for the longer sentences
NUM NUM mutual infi rmatlon clust rhig algorithm
portm ility of word t il s
both col pora use the atr syntactic tag set
figure NUM examt le of a n event
the results are plotted at zero clustering text size
a new approach we adopted is as follows
the tagger employs a set of NUM syntactic tags
to it in terms of occurrence measure
the conscience mechanism is employed as a second competition based on the outcome of the first competition described above in the self organizing algorithm
while the current rule language has a simple syntax as well as an extremely simple control regimen we do not imagine all users will want to engage directly in an exploration for pre tagging rules
these statistics can be used to bootstrap a bilingual dictionary compilation algorithm
in section NUM we go into the process of interpreting deictic and anaphoric expressions in some detail
in figure NUM the user has requested the system to copy all reports except for this one
first edward s context model and the simplistic model do not make any predictions about discourse intention
the one with the highest sv being the most likely referent is put at the front
table NUM shows how the salience of some individual instances changes in the course of a short dialogue
the decay function of the linguistic cfs subtracts NUM from a cf s weight at each successive update
unlike deictic expressions anaphors can be interpreted without regard to the spatiotemporal context of the speaking situation
personal deixis involves first and second person pronouns e.g. i we and you
instead of conducting full parsing on the texts several heuristics were used in order to obtain dependencies between nouns and verbs in the form of tuples frequency noun postposition verb
a transfer rule for such a case is given in NUM
11b and 11d show possible german and english lexicalizations
so although the signature for the change of state class has NUM frames the verb break has NUM frames from the other classes it appears in
by following the top NUM entity name link the user sees the list of entity names in order of frequency cf figure NUM
for example if international business machine and ibm appears in the same document the system records in the database that they are aliases
the first step for constructing a signature is to decide what syntactic information to extract for ttre t asic syntactic patterns that make up the signature
in this paper we describe our multilingual or cross linguistic information browsing and retrieval system which is aimed at monolingual users who are interested in information from multiple language sources
the overall results of the suite of experiments illustrating tile role of disambiguation negative evidence and prepositions is shown in table NUM
a speaker s theory of language
heuristics are applied to discriminate among alternatives
NUM NUM turn NUM russ performs a repair
this is interpreted as a discourse level informnot knowref
NUM NUM the difference between misunderstanding and misconception
NUM NUM the reasoning framework prioritized theorist
NUM NUM the characterization of a discourse participant
null figure NUM how mother interprets t2
NUM NUM building a model of the interpreted discourse
some of these tasks may also involve an action that may change the state of the underlying database e.g. making a reservation for an event making transactions on an account etc
it is our contention that for ia tasks the dialogue between the user and the system proceeds in a domain independent manner at a higher level and can be described by a set of domain independent states
the main limitation of querying cgi scripts is that if the web site being queried is modified by its creators slight modifications will have to be made to the query generator to accommodate those changes
several web sites permit database queries where the user types in the search constraints on an html form and the server submits this form to the cgi script which generates a response after searching a local database
the upper layer states are tried in the order in which they are described below since if the dialogue is in any of the earlier states there is no point in trying later ones
we further contend that if one encounters a dialogue state that is not covered by our state set it can be abstracted to an upper level state which may later be useful in other applications
for example if a system is designed to access american airlines flight information and the user says what time does delta flight NUM reach dallas the system enters the out of bounds state
preprocessor this component is responsible for identifying domain independent e.g. time place name date and domain specific semantic patterns e.g. airport name book title in the input utterance
an important feature of this recognizer is that based on the dialogue state certain grammars may be switched into or out of the dynamic vocabulary NUM thereby leading to better speech recognition accuracy
further although modeling a speaker s intentions and the relations between them is informative about the structure of the discourse their recognition in an actual system may be non trivial and prone to errors
this corresponds to k NUM different specific rules that might be created where k is the current number of symbols in the grammar
the universal a priori probability has many elegant properties the most salient of which is that it dominates all other enumerable probability distributions multiplicativelyj
in order to quantify its im portance we asked one of the pilot annotators to repeat the evaluation on the same items this time giving her access to context in the form of the bilingual concordances for each term pair
NUM if the pair can be of help in constructing a glossary choose one of the following NUM v the two words are of the plain vanilla type you might find in a bilingual dictionary
since entries obtained from the hansard corpus are unlikely to include relevant technical terms we decided to test the efficacy of a second filtering step deleting all entries that had also been obtained by running sable on the hansards
thus in total we evaluated four lexicons derived from all combinations of two independent variables cutoff after the 2nd plateau vs after the 3rd plateau and hansards filter with filter vs without
resulting values for inter rater reliability are shown in table NUM the six annotators are identified as a1 a2 a6 and each value of reflects the comparison between that annotator and the group annotation
for tile first experiment below we construct a verb based syntactic signature while for the second exl eriment we constructed a class based signature
for english to chinese translation we can decrease the complexity of the transducers i.e.
we took our lexicon with the new tagset a corpus of french text and trained the tagger
some are quite reasonable e.g. the determiner reading of des is preferred at the begining of a sentence
the errors are the following fifteen errors are due to the heuristics for de and des
seven errors seem to be beyond reach for various reasons long coordination rare constructions etc
this is a short time for the manual tagging of a corpus and for the training of the tagger
we had a strict time limit of one month for doing the tagger and no tagged corpus was available
there are two words between the pronoun and the verb that do not carry any information about the person
this means that the errors of the first class are probably easiest to resolve by means other than statistics
the result was that the problematic sentence was disambiguated correctly but the changes had a bad side effect
but the constraint based tagger seems to be superior even with the limited time we allowed ourselves for rule development
the difference between the two algorithms appears still clearly for these two sets of rules
we briefly described a new algorithm for compiling context dependent rewrite rules into finite state transducers
moreover the corresponding determinization is independent of which can be very large in some applications
it is then crucial to give a representation of these rules that leads to efficient programs
this both makes the use of the transducer time efficient and reduces its size
these weights are then used at the final stage of applications to output the most probable analysis
figure NUM shows the equivalent data for the right context
time and space efficiency of the compilation are also crucial
we describe a new algorithm for compiling rewrite rules into fsts
the resulting transducer representing a rule is often subsequentiable or p subsequentiable
the likelihood of distribution q is the probability of the training corpus according to q
unfolding the interaction predicates with respect to the lexical entries basically expands out the lexicon off line
in the ale system for example a depth bound can be specified for this purpose
we illustrate the encoding with the finite state automaton of figure NUM
3deg the successive unfolding steps are schematically represented in figure NUM
using unification right away would significantly complicate the algorithm in particular for automata containing cycles
this is because most of nouns do not contribute to showing the characteristic of each domain for given articles
for example NUM NUM shows that there is one article which consists of three paragraphs
according to table NUM recall and precision values range from NUM NUM NUM NUM to NUM NUM NUM NUM
for example the x NUM value of word237 is NUM NUM and slightly higher than NUM NUM
in our method the correct ratio of key paragraphs extraction strongly depends on the results of wsd
table NUM shows that the results using our method are highly than those of using the vector model
in our method when the extraction ratio was more than NUM the correct ratio decreased
from the observation for these articles there are limitations to our method based on context dependency
consider a hypothetical language in which personal proper names have one of two genders masculine or feminine
we evaluate those extensions and demonstrate the advantage of exploiting context based predictions over a purely non context based approach
perhaps surprisingly datr turns out to be an excellent language for defining finite state transducers fsts
NUM we can of course use the same technique to define many valued logics if we wish
we observed that some classes of ambiguities can be more l erspieuously dealt with in one way or the other
it can mean either ihc second al j o a lhc second lo the jburlh or lwo go four
preliminary experiments show that both t roposals help reduce the adverse effect of the cumulative error problem
in most cases inference chains for ilfl s with this ambiguity have tit same focusing scores
in addition NUM applies again s link in bg
to provide the input for the resolution routine the representation was enriched in sec
three special symbols are used in regular expressions NUM zero represents the empty string often denoted by c
entaihnent is extended to expressions of other type than propositions by existentially binding unfilled arguments
it takes multiple ambiguous li ts fi om the parser and computes three quantified discourse scores for each ambiguity
the significance of this syntactic notion arises from the following property NUM every functional datr description is consistent
NUM karl hat ein buch elesen f f
the vp can e.g. either belong to foc via a chain of arrows or to bg
time of day then let f be the most specific field in starting fields tu return lcb when next tu rf todaysdate certainty NUM NUM rcb rules for anaphoric relations rule hl all cases of anaphoric relation NUM
but at this point a problem arises NUM is not a probability distribution
figure NUM simplified transitive verb head transducer
however they do not address the problem of cue occurrence
this information is then used to rank the set of possible inferences left after the elimination constraints are i rdegcessed
this architecture imposes no constraints on the components programming language or software architecture since communication is based on the smtp protocol
this paper reviews two nlp engineering problems reuse and integration while relating these concerns to the larger context of applied nlp
no constraint is placed on the type of linguistic processing but a small library of data structures for nlp is provided to ease data conversion problems
the sun java idl system with its door orb implementation is used to interface client programs to the document server implementation
the document server itself is accessed via its api and is running as a java door orb supporting requests from the component s servers
the corelli project has started collaborating with the university of sheffield with the aim to merge the corelli document architecture and the gate architecture
any client api call made on a resolved object reference is then transparently to the client invoked on the corresponding server side object
in such cases it is more efficient to run the morphological analyzer as a server that can be accessed by various client processes
in the head corner parser a parse goal is provided either with a begin or end position depending on whether we parse from the head to the left or to the right but also with the extreme positions between which the category should be found
NUM a parser in which application of a rule is driven by the left most daughter as it is for instance in a standard bottom up active chart parser will consider the application of this rule each time an arbitrary constituent arg is derived
nd that the grammar associates with each local tree a semantic rule that specifies how to construct the mother node s semantics from those of its children
the generate and test behavior discussed in the previous section examples NUM and NUM is avoided in a head corner parser because in the cases discussed there the rule would be applied only if the vp is found and hence arg is instantiated
moreover the goal table will be smaller both in terms of number of items and the size of individual items which can have a positive effect on the amount of memory and cpu time required for the administration of the table
the re processing consists of removing various high frequency words and splitting all nmlti word definitions into a list of single words needed to find one to one associations
first a head driven shift reduce parser is presented which differs from a standard shift reduce parser in that it considers the application of a rule i.e. a reduce step only if a category matching the head of the rule has been found
this enables the construction of a term for each local tree in the head corner predicate consisting of the name of the rule that was applied and the list of references of the result items which is a pointer to either a lexical entry or a gap
rather than a simple integer comparison we now need to check that a derivation from p0 to p can be extended to a derivation from e0 to e by checking that there are paths in the word graph from e0 to p0 and from p to e
the main difference between the head corner parser in the previous paragraph and the left corner parser is apart from the head driven selection of rules the use of two pairs of indices to implement the bidirectional way in which the parser proceeds through the string
in other words if you want to select a solution from the second table it must not only be the case that the solution matches your goal but also that the corresponding goal of the solution is more general than your current goal
i found myself becoming one of that group of people who in carlyle s words are forever gazing into their own navels anxiously asking am i right am i wrong
five common english adjectives have such antonyms yielding ten antonymous adjective pairs hard easy hard soft light dark light heavy old new old young rightleft right wrong and short long short tall
the disambiguated subcorpora can be used to assess the extent to which target adjectives when modifying a given noun are specific to a single sense rather than being usable in either sense
similarly when old modifies house it almost always has one of its not new senses as in in the fashionable suburb of kingston full of beautiful old houses
overwhelmingly old is used in the sense aged not young when it modifies man e.g. in the man was very old and very frail a widower
since we are interested in discriminating between the two antonym related sets of senses of the targets we limited attention to those instances of that target occurring in a sense for which an antonym exists
whether the antonym co occurrences involve contrastive opposition or a range of attribute values they call forth the semantic dimension designated by the antonym pairs and guarantee concordance in adjective sense of the co occurring antonyms
thus any noun or semantic attribute that is associated with the alternative senses of these adjectives would be wrongly interpreted when a type of s usage of the noun is not recognized
the NUM newly covered instances are incorrectly assigned in only NUM cases even when every noun from the co occurrence sentences is treated as an indicator reliability remains high NUM NUM
there are several mechanisms we are exploring in acquiring the kind of user model information necessary for the previously described dialogue mode algorithms
an additional source of user model information can be dynamically obtained in environments where the user interacts for an extended period of time
therefore the expected number of branches for which the collaborator knows all n factors is expalln i i NUM qg
this means that the dialogue initiative should always pass immediately to the participant who is best able to handle the current task
however in utterance NUM the user takes the initiative and suggests a different wire than has been proposed by the computer
in general if agent NUM is working on its ith ranked branch and agent NUM is working on its jth ranked branch we compare
oracle in oracle mode an all knowing mediator selects the agent that has the correct branch ranked highest in its list of branches
ambiguity labeling may also be considered as part of the specification of present and future state of the art analyzers which means that it should be compatible with the representation systems used by the actual or intended analyzers
in this paper we will present a theory explaining how initiative changes between participants and how computational agents can evaluate who should be in control of solving a goal
such an evaluation requires knowledge of the collaborating agent s capabilities as well as an understanding of the agent s own capabilities
we take it for granted that for each considered representation system we know how to define r r each fragment v of an utterance u having a proper representation p tile part of p which represents v
the control panel see fig NUM still in progress allows the choice of the corpus the sentence to be displayed
a large scale corpus will be available to prove that the results are not hand made and open demos will be possible during informal demo sessions
selected contextual properties of the sentence are represented by f1 fn NUM and the sense of the ambiguous word is represented by s our task is to induce a classifier that will predict the value of s given an untagged sentence represented by the contextual feature variables
finally our labeling should only be concerned with the final result of analysis not in any intermediate stage because we want to retain only ambiguities which would remain unsolved after the complete automatic analysis process has been performed
rather than having to observe the complete feature vector ft f f3 si in the training sample to estimate the joint parameter it is only necessary to observe the marginals ft si and f2 f3 si
an alternative to using a x NUM approximation is to define the exact conditional distribution of g NUM the exact conditional distribution of g NUM is the distribu tion of g values that would be observed for comparable data samples randomly generated from the model being tested
the discourse cues for identifying major subtopic shifts are patterns of lexical co occurrence and distribution
tilebars allows users to specify different sets of query terms as discussed later
this section has discussed why multi paragraph segmentation is important and how it might be used
a typical example is a NUM page science magazine article or a NUM page environmental impact report
however important and interesting discourse phenomena also occur at the level of the paragraph
its contents can be described as consisting of the following subtopic discussions numbers indicate paragraphs
for example in the stargazers text introduced in section NUM a discussion of
yet the basis for the identification of topic is rarely made explicit
there are also potential applications in some other areas such as text summarization
these model structures were coded by hand as a head transducer lexicon
where the old system outperformed the new one
but often these locations are the names of towns or districts whereas a user might want to search for jobs in a wider area a user looking for work in flanders for example should be presented with jobs whose location is identified as antwerp
the algorithm is fully implemented and runs in o n4log n in sentence length if the grammar meets some reasonable normality restrictions
since the system currently treats three languages with the prospect of extension to more we decided to codify in a languageneutral fashion the information extracted from the ads converting equivalent linguistic terms into codes and vice versa via the analysis and generation modules described below
a second type of user is the potential employer who provides job announcements to the system in the form of free text via an e mail feed or it is planned via a form filling interface though we shall not discuss this latter input mode here
not all the vocabulary that the system needs to recognize and handle can be structured in the way just described so we recognize a second type of lexical resource which for want of a better term we call simply the lexicon
our approach is based on the idea that canned text approaches template based approaches and grammar based approaches to natural language generation while they are often contrasted may in fact be regarded as different points on a scale from the very specific to the very general
that is a value must have a distance of zero to itself NUM a positive distance to all other values NUM distances must be symmetric NUM and must obey the triangle inequality NUM
in the first case there is a way of assembling the individual leaves into aggregates of leaves and the individual wires into aggregates of wires such that each aggregate of leaves that is each pile of leaves is touching some aggregate of wires that is some bundle of wires and each aggregate of wires is being touched by some pile of leaves
this paper has three aims to present the principal morphological and semantic properties of the mass count distinction to formulate in terms of lexical inference rules the empirical generalizations pertinent to systematic connection between english mass and count nouns and to show how such rules fit with a syntactic and semantic theory of english common noun phrases
NUM np all men np some women y is np z yp endorsed np y next the following two sets are aggregations formed from the denotation of men and women respectively
this can be handled by a simple rule the feature of a conjoined noun phrase is the sum of the features of the conjuncts where the sum of xpli is pl if i NUM and pl otherwise where x ranges over and and i enumerates the i th conjunct in the conjunction
the semantic requirement imposed on a count noun in its conversion to a mass noun means that its denotation be the largest aggregate or mereological sum of the parts of each atom of the denotation of the count noun where what constitutes a relevant part may and typically does vary from count noun to count noun
it is well known that demonstrative noun phrases quantified noun phrases and interrogative noun phrases in english exhibit different patterns interrogative noun phrases form overtly discontinuous structures i.e. move at s structure while demonstrative and quantified noun phrases do not quantified noun phrases exhibit different scope like interpretations while demonstrative noun phrases do not NUM
the backed off estimate is a method of combating the sparse data problem
NUM NUM naming schemes for streets in german
what is crucial to collectives is that they are subject to constituting conditions which determine how the members of the collective constitute the collective of which they are members whereas pluralities do not have such constituting conditions
a plurality is not the same as a collective or a group a plurality is nothing more than the sum of its atomic constitutents whereas a collective is more than the sum of its atomic consitutents
in examples NUM NUM and NUM above bought is a transitive verb but without knowledge of traces example NUM in training data will contribute to the probability of bought being an intransitive verb
the author is also very grateful to four anonymous reviewers for their insightful comments on earlier versions of the paper
the issue of when to apply the lexical rules in a computational environment is relatively new
figure NUM partial entry for the spanish lexieal item comprar
the reported research concentrated on lexical rules for derivational morphology
consequently derivational lrs are even more prone to overgeneration than inflectional lrs
the first three and the last rule are truly large scope rules
the lexical entries are conceptual phonological frames rather than word entries and a number of expansion rules are used to generate entries of actual words from these frames
as we do not have very much to say about phonological representations here we assmne in the following that tim realizational part is a simple graphemic representation
the morpho syntactic augmentation rule shown in figure NUM a for example derives the basic entry for the verb paintv from tile minimal sign paint
the omi osition part says that there are two structures involved a main stru ture and a s u j l x
starting with the minimal sign paint we use the rules in figure NUM a and NUM b to generate a simple entry for paintedy
a non repetitive resultative ons ruction is always completed whereas constructions like ion is painting and jon paints eve ry day are incompleted
assuming that tile lexical entry has already been given a word class and a paradigm the inflectional rule expands the graphemic representation into a particular inflected word form
that is the word string the blueprint is the only st tokenization
correspondingly argument NUM is the entity on which the force is used limit and the entity being controlled by argument NUM goal
the search space for this is restricted since the rules are semantically conditioned and monotonic and wellformedness conditions decide when to stop expanding the structure
top level categories such as sentence subject etc are syntaxbased whereas lower level categories such as ship name time expression etc are semantics based
as for mapping from parse tree to semantic frame we reduce all the major parse tree constituents into one of the three syntactic roles i.e. clause topic and predicate
in figure NUM we have additional categories like time expression whether or not we add more categories to the semantic frame depends on how elaborate a translation output is desired
since the tagging result is quite promising despite the fact that lie training data is of modest size we are planning to integrate the tagger into the analysis module
for riffs puipose we have adopted an interlintlua approach with natural language understmlding tina and generation genesis modules at the core
opinions interi retations conclusions and recommendations are those of the authors and are not necessarily endorsed by the united states air force
however there is no limit to the number of semantic frame categories and we can easily create new categories for a more elaborate representation
however an absence of x y does not imply the existence of guo critical tokenization y x
we now have a schematic defining clause of the form t fs
also as a first research attempt an n gram model captures the most general significance of the words in each name class without presupposing any specifics of the structure of names i la the person name class example above
because it seemed that capitalization would be a good name predicting feature and that it should appear earlier in the model we eliminated the reliance on part of speech altogether and opted for the more direct word feature model described above in ss3
number correct in key put informally recall measures the number of hits vs the number of possible correct answers as specified in the key file whereas precision measures how many answers were correct ones compared to the number of answers delivered
as one might imagine it would be useless to have the first factor in equation NUM NUM be conditioned off of the end word so the probability is conditioned on the previous real word of the previous name class i.e. we compute
furthermore name finding can be useful in its own right an internet query system might use name finding to construct more appropriately formed queries when was bill gates born could yield the query bs NUM
the views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies either expressed or implied of the defense advanced research projects agency or the united states government
given this maximum size of training available to us we successfully divided the training material in half until we were using only one eighth of the original training set size or a training set of NUM NUM words for the smallest experiment
a system may have excellent performance on a given task but if it takes long to compile and or run on test data the rate of improvement of that system will be miniscule compared to that which can run very efficiently
the unknown word model can be viewed as a first level of back off therefore since it is used as a backup model when an unknown word is encountered and is necessarily not as accurate as the bigram model formed from the actual training
also ideally we would have sufficient samples of that upon which each conditional probability is conditioned e.g. for pr nc i nc w we would like to have seen sufficient numbers of nc i w
for instance dialog act hypotheses are available with the first input word although good hypotheses may only be possible 2this is also motivated by our additional goal of receiving noisy input directly from a speech recognizer
this good performance is partly due to the distributed representation in the dialog plausibility vector at the input layer
all other often occurring dialog act categories performed very well as the individual percentages and the overall percentage show
in order to support the unforeseeable errors and variations of spoken language we have concentrated on robust data driven learning
input to our dialog component are utterances h om a corpus of business meeting arrangements like tuesday at NUM is for me now again bad because i there still train i think we should delay the whole then really to the next week is this for you possible NUM
furthermore we have developed tim segmentation parser and dialog act network as very robust components lit fact both are very robust in the sense that they will always produce the best possible segmentation and dialog act categorization hi the future we plan to explore how the output from a speech recognizer can be processed by our dialog conlponent
the result of select focus modification is a set of user beliefs in bel focus that need to be modified in order to change the user s belief about the unaccepted top level belief
if attacking the evidence for bel does not appear to be sufficient to convince the user of bel the algorithm checks whether directly attacking bel will accomplish this goal
our model is capable of selecting the most effective aspect to address in its pursuit of conflict resolution in cases where multiple conflicts arise and of selecting appropriate evidence to justify the need for such modification
this indicates that the focus of modification could be either teaches smith ai or on sabbatical smith next year since the evidential relationship between them was accepted
if no single justification chain is predicted to be sufficient to change the nser s beliefs new sets will be constructed by combining the single justification chains and the selection is repeated
NUM NUM else select focu modificatioa bel and select focus modification supports beli bel NUM choose between attacking the woposed evidence for bel
since collaborative agents are expected to engage in effective and efficient dialogues the system should address the unaccepted belief that it predicts will most quickly resolve the top level conflict
they identified strategies that a system can adopt in justifying its beliefs however they did not specify the criteria under which each of these strategies should be selected
our endorsements are based on the semantics of the utterance used to convey a befief the level of expertise of the agent conveying the belief stereotypical knowledge etc
the discussion of example NUM shows that in the approach proposed here no world knowledge is needed to determine contrast it is only necessary to compare the data structures that are expressed by the generated sentences
described informally pulman s focus assignment algorithm takes the semantic representation of a sentence which has just been generated looks in the context for another sentence representation containing parallel items and abstracts over these items in both representations
this guarantees that in NUM b the proper name ajax which expresses the value of the team field of b is accented despite the fact that the contrasting team was not explicitly mentioned in NUM a
consequently pulman s theory also faces the problem of determining when two items are of the same type
the fact that it is not inappropriate to accent kluivert in NUM c shows that NUM c may be regarded as contrastive to NUM b otherwise it would be obligatory to deaccent the second mention of kluivert due to givenness like psv in NUM c
figure NUM shows that all the fields of b have different values from those of a this means that each phrase in NUM b which expresses the value of one of those fields should receive contrastive accent NUM even if the corresponding field value of a was not mentioned in NUM a
still contrary to prevost pulman can explain the lack of contrast accent in NUM c because obviously the representations of sentences NUM b and NUM c will not unify
this also means that the prediction of contrast does not depend on the linguistic expressions which are chosen to express the input data the data can be expressed in an indirect way as in NUM a without influencing the prediction of contrast
in particular we want to study how the model i handles fragments with local ambiguities such as those in sentences NUM and NUM when they appear in different sentential contexts and ii handles fragments with global ambiguities such as those in sentences NUM and NUM when there is no discourse information
distinguishing this tuple in the tree bank leads to the greatest increase in semantic determinacy that could be found
we expect NUM to represent perfection indeed an algorithm scores NUM with respect to some data if and only if it predicts its segmentation exactly
in the above 7t is a normalization chosen so that d is a probability distribution over the range of distances it can accept
other applications that we have not explored in this paper include automatic inference of subtopic structure for information retrieval document summarization and improved language modeling
we hasten to add that these results were obtained figure NUM randomly chosen segmentations of tdt test data in NUM sentence blocks using model b
having concluded our discussion of our overall approach we present in figure NUM a schematic view of the steps involved in building a segmenter using this approach
inspecting the sequence of features selected by the induction algorithm reveals much about feature induction in general and how it applies to the segmenting task in particular
the set of candidate word based features we use are simple questions of the form does the word appear up to NUM sentence in thefuture NUM sentences
this explains how a model is chosen once we know the features fl fn but how are these features to be found
the personal pronoun i appears as the second feature if this word appears in the following three sentences then the probability of a segment boundary is discounted
the spatter full parser of english and the new semantic inference procedure are examples
the weights are configuration parameters that can be adjusted
a parameter table giving the costs of actions for head transducers and the recursive transduction process
this involved the addition of target relations including some epsilon relations to automaton transitions
the actual left and right target sequences are formed by concatenating these subsequences
we can use the following notation to number these additional positions
simply mean assignment of the cost functions for fixed model structures
one possible cause that the results of freq is worse than dis is that these polyselnous words which are high frequencies are not recognized polysemy in freq
in order to col e with the relnainiug i rol lems inentioned in section NUM and apply thin work to practical use we will conduct further experilnents
a dissimilarity measure is the degree of deviation of the grout in an n dimensionm euclidean space where n is the number of nouns which co occur with t NUM and
our disalnbiguation method is based on niwa s method which used the similarity NUM etween a sentenee containing a t olysemous noun and a sen tence of dictionary definition
freq link dis and method show the nulnlmr of sets which are clustered orrectly in ea h experiment
the other is that in order to cope with the problem of a phrasal lexicon linking which links words with their semantically similar words in articles is introduced in our method
oj om we call them basic words are selected the 1000th most frequent words in the reference collins english dictionary lil erman NUM
examining the results shown in figure NUM bvg and hrd are correctly classified into food restaurant and market news respectively
in linking method there are NUM nmmn in ern and NUM nouns in hrd whi h tre repl tccd for representative words
the title tagger marks personal titles making distinctions along the lines drawn by the ne and st tasks
the language of phraser rules the language of the phraser rules is as simple as their control strategy
on the sample sentence this rule causes the following relabeling of the phrase around james
to illustrate this process consider the following walkthrough sentence as tagged by the ne rule sequence
the phras e person izobert l james person for example is mapped to the following propositional fact
all of the muc NUM ne training set was used to generate a list of i NUM o8 distinct organization name strings
james for robert l james then the short form is merged as an alias of the longer
rules that require contextualized facts however crucially rely on the chronological order of th e sentences underlying these facts
for ward top is not a topic marking construction at all
the following for example is how a sample walkthrough sentence is passed to the part of speech tagger
an initial preprocessor the punctoker makes decisions about word boundaries that are not coincident with whitespace
this preference holds regardless of syntactic position in the e utterances
centering is proposed as a model of the local level component of attentional state
we must leave this as a topic for future work
the case is analogous for pair of retentions and a pair of shifts
centering for example the sequence NUM a
the man you re referring to is n t her husband
he called up mike yesterday to work out a plan
he called john at NUM am on friday last week
of course he had n t intended to upset tony
of course terry had n t intended to upset tony
in this way mu c participants could develop code for these low level objects once and then use them with many different type s of events
counting scheme the algorithm obtains recall and precision scores by determining the minimal perturbations required to align the equivalence classes in the key and response
the tie up relationship object points to the activity object which in tur n points to the industry object which describes what the joint venture actually did
the mucs are notable however in that they have substantially shaped the research program in informatio n extraction and brought it to its current state NUM
most participants worked on the tasks for NUM months a few the tipster contractors had been at work on the tasks for considerably longer
the final task specification which also involved time currency and percentage expressions used sgm l markup to identify the names in a text
the following objects were to be generated although we can not explain all the details of the template here a few highlights should be noted
the results of the muc NUM evaluations are described in detail in a companion paper in this volume overview of results of the muc NUM evaluation
there were NUM participants NUM participated in the named entity task NUM in coreference NUM in template element and NUM in scenario template
the texts used for the formal evaluation were drawn from the NUM and NUM wall street journal an d were provided through the linguistic data consortium
in the third part some sort of book keeping is carried out evidence about the used lexical resources is updated is NUM descriptors that are likely to be expressible by yet empty slots are determined is NUM and relations between the context sets of all referents considered and partial descriptions are maintained s19
throughout processing the algorithm maintains a constraint network n which is a pair relating a a set of constraints which correspond to predications over variables properties abstracted from the individuals they apply to to b sets of variables each of which fulfill these constraints in view of a given knowledge base the context sets
NUM the algorithm enables one to control the processing aspect of building the referential description and its complexity
basically these parts are evaluated in sequence which is repeated iteratively NUM
NUM and NUM constitute an extension to previous approaches
some interesting cases are NUM achieving global rather than local goal satisfaction if t NUM is the intended referent and on bl o is the descriptor selected next adding the category of the entities on top of t3 here books is sufficient to identify t3 uniquely
an informal schematic view in figure NUM that abstracts from technical details is complemented by a detailed pseudo code version in figure NUM in both versions the lines are marked by is in the schematic view and by c in the pseudo code version to ease references from
motivated by this deficit we present a new algorithm that NUM allows for a widely unconstrained incremental and goal driven selection of descriptors NUM integrates linguistic constraints to ensure the expressibility of the chosen descriptors and NUM provides means to control the appearance of the created referring expression
most algorithms dedicated to the generation of referential descriptions widely suffer from a fundamental problem they make too strong assumptions about adjacent processing components resulting in a limited coordination with their perceptive and linguistics data that is the provider for object descriptors and the lexical expression by which the chosen descriptors is ultimately realized
in the scope of some context set an attribute or a relation applicable to the intended referent can be assigned its discriminatory power NUM that is a measure similar to the number of potential distractors that can be removed from the contrast set with confidence because this attribute or relation does not apply to them
NUM relevant types of organizations are limited to those that are categorized as company in th e organization object
or who is described as keeping the post only until a successor is found
reassignment vacancy created because of someone s leaving a post voluntarily or involuntarily
minimum instantiation conditions the name or description of the organization must be given
this slot is reserved for use in the answer key templates it is not to be generated by the extraction systems
other org slot definition pointer to organization object that captures information on the io person s past o r future employer organization
when a new company is formed including a company formed by a merger all posts are new
post slot definition the management post at succession org where there was is or will be a vacancy
for this reason other org will never point to an organization object that represents a board of directors
in terms of the template definition this slot indicates th e corporate relationship between other org and succession org
currently we use NUM hand crafted choose rules
however context based models proved unnecessary case as learned by estimation maximization
a explain tile bill for rooln number three two four for me
we have built also context based models using decision trees receded as wfsts
for n a node p a path and c a possibly empty sequence of value descriptors an equation of the form n p NUM is called a definitional sentence
the rule for evaluable paths provides a general statement of this process if a sequence of value descriptors c evaluates to a and n a evaluates to NUM then n c also evaluates to
the basic idea underlying datr s default umchanism is as follows any definitional sentence is applicable not only to the path specified on its left hand side but also for any rightward extension of that path for which no more specitic definitional sentence exists
each intermediate stage is a wfsa that encodes many possibilities
therefore the development of aac devices capable of supporting a reasonable approximation to natural social conversation should be a high priority for aac designers
a bigram model would prefer orren hatch over olin hatch
for example the input string is clc2 and the dictionary includes three words c1 cl c2
we then describe the initial word frequency estimation method and the initial word identification method
that foundation was built tipster style out of an evaluation driven paradigm heavily borrowed from darpa s speech r d program and it has continued to grow and evolve over the past seven years in a tipster unique way
a portion of each workshop has been devoted to each contractor describing the technical details of their underlying algorithms and approaches the results of their internally conducted evaluations and experiments as well as their successes and failures on the tipster sponsored formal evaluations
and the conditional maximum entropy model will also have the greatest log likelihood l value
the collection annotation tagging and formatting of the base document collections along with the creation of the appropriate answer keys to support each separate evaluation program has beeen a costly time consuming human analyst intensive process
most of our frequent day long planning meetings during the first year of our tipster program planning were held at darpa headquarters in arlington va and were chaired by the program manger of darpa s speech and text r d efforts
so my objective for the remainder of this paper is to give a high level summary response to each paradigm component and maybe in the process to give a perspective with which you can read and interpret these individual papers
NUM practical applications were limited to highly constrained domains with high enough priority to warrant the development expense associated with a highly tailored system solution in response to these conclusions phase i of tipster established multiple inter related tasks
the manner in which the tipster program has incorporated this component is most easily seen in the design of the multiple tasks that underwrote phase i our evaluation of the pre tipster state of the art in document detection systems concluded that there was specific applications
all of the proceedings listed in the reference section directly below are filled with excellent papers which describe in full detail what each tipster text program participant has discovered learned and accomplished while investigating tipster tasks under an evaluation driven research paradigm
looking back now from the perspective and vantage point of seven years of rich history it is very clear to me that those of us who participated in these early formative tipster text program discussions collectively laid a very solid foundation
all participants were required to demonstrate language portability by performing the same basic tasks in both english and in japanese and system robustness by successfully handling and processing text documents which contained ungrammatical usage garbles new words and structures
in contrast the specification dog lcb root rcb noun root rcb does not follow by default from the definition of dog even though it can be obtained by extending left and right paths in the required manner
compared with turing s procedure the back off procedure is NUM NUM worse in all cases
however correct disambiguation only depends on the ranks rather than the likelihood values of the candidates
approach fails to achieve satisfactory performance because the discrimination and robustness issues are not considered in the estimation process
as discussed in the previous section the discriminative learning approach aims at minimizing the training set errors
the probabilities of the events that never appear in the training corpus can thus be trained more reliably
for example mle gives a zero probability to events that were never observed in the training set
an accuracy rate of NUM NUM for parse tree selection is attained after this robust learning algorithm is used
the mle for the probability of a null event is zero which is generally inappropriate for most applications
for example syriac md nt city is pronounced mdit
if this fails analysis is attempted again without the no error restriction
ktab while the root lcb nht rcb takes the vowel e e.g.
for example the syriac root lcb ktb rcb takes the perfect vowel a e.g.
its root and pattern phenomenon not only poses difficulties for a morphological system but also makes error detection a difficult task
plc and prc above are the left and right contexts of both the lexical and correct surface levels
for this reason rules are marked as to whether they can occur more than once
NUM gives three general rules r0 allows any character on the first lexical tape to surface e.g.
verbs are classified according to their measure m there are NUM trilateral measures and NUM quadrilateral ones
for example kidaa would be analyzed as root lcb kd rcb with a broken plural vocalism
in this sentence more changes from its initial jjr comparative adjective to rbr comparative adverb
most of the rule sequences that drive the tagger were automatically learned from hand tagged corpora rather than hand crafted by human engineers
on the template elements task our initial te score was p r NUM NUM and our revised official score was NUM NUM
finally note that the phrase interpretation machinery maintains pointers between semantic individual s and the surface strings from which they originated
in particular we were missing a large number of head nouns that would have been required to identify relevant descriptor nps
in particular the people name companies that wer e treated as persons during ne processing in turn led to spurious person templates
we have applied the same general error reduction learning approach that brill designed for generating part of speech rules to the problem of learning phraser rules in support of the ne task
while we regret no t participating in the st task we do believe that the framework was up to it especially in light of our te scores
predicate implements a simple discourse model it is true just in case its second argument is the most immediate job out fact in the context of its first argument
the answer came in the form of rule sequences an approach eric brill originally laid out in his work o n part of speech tagging NUM NUM
we will first give a quick description of the system and then discuss in more details the speech modules and their interfaces with other components of the system
NUM j vudr iin gbr av k du av k vii sir lo jardi those word hypotheses constitute the input for the linguistic component
the phonetic lexicon is organized as atrie structure knuth NUM that is a tree structure in which nodes correspond to phonemes and subtrees to possible continuations
itsvox is currently restricted to some subsets of french english in the speech to speech mode french english in the written mode with speech output
those developments however will not affect the basic guideline of this project which is that speech to speech translation systems and text translation systems must be minimally different
we next consider combination strategies that look at both parse cost and context
if utterance in the main expectation then do not engage in a verification subdialog
to improve information presentation further dialogue management and text generation will have to collaborate intensively
rs111 NUM c i am familiar with that circuit
several utterances in each dialog concerned a discussion of the led display
these expectations can be for phonemes morphemes words or meanings
NUM c add a wire between connector NUM and connector NUM
we define expectations based on an abstract representation of the current task goal
in this way the dialog system responds much as a person would
figure NUM lists these expectations for the major task goals of the model
an assertion that obj now has the value propvalue for property propname
NUM NUM an implementation based on the fuf surge package
the resulting index is the offset in the stem hash table
this puzzle is explained by the expansion factor as follows
for that reason extension models should be amendable to efficient online implementation
the equation for this learning is shown in figure NUM
let y and xy be two distinct contexts in a model c
our algorithm repeatedly refines the accuracy of our model in increasingly long contexts
lid lz n log n m lm l
nem outperforms all other model classes for all orders using significantly fewer parameters
the extension model class is the clear winner by this criteria as well
an extension model c is valid iff it satisfies the following constraints
the induction step is true by lemma NUM NUM and definition NUM
under this class of models there is no benefit to grouping two words with high mutual information together in the same minimal phrase it is sufficient for both to be the heads of phrases that are adjacent at some level
assigns higher probabilities to the corpus even though it fails to model the dependency between a and d this is a general problem with scfgs there is no way to optimally model multiple ordered adjunction without increasing the number of nonterminals
even if p ap a bp p ap ap cp NUM NUM the probability of the structure abcd under the above grammar is onequarter that assigned by a grammar with no expansion ambiguity
multiple expansions of a nonterminal for this test the sentences were four words long abcd and we chose a word distribution with the following characteristics i a b lbit
unfortunately there is anecdotal and quantitative evidence that simple techniques for estimating phrase structure grammars by minimizing entropy do not lead to the desired grammars grammars that agree with structure a for instance
on the other hand an application will also need some functionalities not covered by the architecture
architecture related services include maintenance of a catalog of previously built tipster modules and persistent knowledge bases
these changes will be managed by a configuration control process administered by the tipster program se cm contractor
extraction encompasses the technology which identifies specific entities and the relationships between entities in free text
form NUM document the document as the end user s site receives it
the architecture will provide standards for handling the following types of end user interactions
easier and better testing will contribute to an application which better meets the end user s needs
the tipster architecture is general and designed for use in a variety of software and hardware environments
it has been saved and it is in machine readable form
the experimental results are shown in table NUM
thus their determination is also demanded in the future
figure NUM incorrect partitioning for beau e spell change au lll ill lalu e spell change au ll2 ill alul e here change au lll rules out a a partition in figure NUM and change au ll2 rules out the u u one
analysis of french words using this rule set and only an in core lexicon averages around NUM words per second with a mean of NUM spelling analyses per word leading to a mean of NUM NUM morphological analyses the reduction being because many of the roots suggested by spelling analysis do not exist or can not combine with the affixes produced
the required values of m may be calculated similarly with reference to the left contexts of rules NUM during rule compilation the spelling pattern that leads to the run time analysis of chore given above is derived from m NUM and n NUM and the specified rule sequence with the variables r1 r2 matching as in figure NUM
however although it would be possible to analyze words directly with individually compiled rules see section NUM below it can take an unacceptably long time to do so largely because of the wide range of choices of rule available at each point and the need to check at each stage that obligatory rules have not been broken
in table NUM include denotes that the correct tag belongs to the remaining multiple tags and exclude denotes that the correct tag is not mcluded in the remaining tags
table NUM experimental results after applying two heuristic
however although surface targets of any length can usefully be specified it is in practicea good strategy 1the cdouble feature is in fact used to specify the spelling changes when e is added to various stems cher e chdre achet e ach te but jet e jette
the type of compilation appropriate for rapid development and acceptable run time performance depends on at least the nature of the language being described and the number of base forms in the lexicon that is on the position in the three dimensional space defined by a b and c
the cause of the or u intrasenten al deictic explic t total free translation NUM NUM NUM pass ve voice NUM NUM NUM pre dicate
this paper proposes a method to identify zero pronouns within a apansse sentence and their antecedent equivalents within the corresponding english sentence from aligned sentence pairs
but it is very difficult to make rules for every domain because of the time cons rni g labor and the need for expertise
this paper proposes a powerful method for the automatic identification of japanese zero pronouns and their antecedents from japanese and english aligned sentence pairs
i am also grateful to francis bond and several anonymous reviewers of wvlc NUM for helpful comments on earlier drafts of the paper
one typical method for this purpose is to use a corpus for extracting resolution rules by analyzing each sentence hi the corpus
the remaining case is caused by the fact that the verb of the zero pronoun and the verb of their antecedents could not be aligned
this result shows that even without using anaphora resolution at english analysis this method achieves relatively high accuracy for zero pronouns with intrasententiai antecedents
for the sentence pairs which contain zero pronouns antecedents of each zero pronoun within the text set were automatically identified section NUM
one is targeted a t high performance and uses some knowledge about the structure of english financial newspaper text which may not be applical le t o text from other genres or in other languages
p that maximizes the entropy h p
for each potential sentence boundary token and we estimate a joint probability distribution p of the token and it s
performance on the wsj corpus was as we expected higher than perforlnance on the brown corpus since we trained the model on financial newspaper text
table NUM shows performance on the wsj corpus as a flmction oft raining set size using the best performing system and the more portable system
for example riley s performance ot the brown corpus is higher than ours but his sysl era is trained on the brown corpus and uses thirty i ilnes as much data as our system
table NUM also shows the number of sentences in each corpus the lmmber of candidate punctuation marks the accuracy over potential sentence boundaries the nmnber of false positives and the number of false negatives
all experiments use a simple decision rule to elassi y each potential sentence boundary a potential sentence boundary is an actual sentence boundary if and only if p yeslc NUM
coriv NUM p a l l c features of the word left of the candidate features of the word right of the candidate the templates specify only the form of the information
since NUM training sentences is considerably more than might exist ill a new dolnail or a language other than english we experimented with the quantity of training data required to maintain perforlnance
computational linguistics volume NUM number NUM both ivan and james are salient enough that the referent of his in the target is ambiguous in exactly the manner required to yield both the strict and sloppy interpretations
a second consideration in multilingual text retrieval is where the translation is done
1degwhether such syntactic phrases are more effective than simple statistical phrases e.g. high frequency word there are two lines of future work first the results from information retrieval experiments often show variances on different kinds of document collections and different sizes of collections
one way to change spanish queries is to add and remove terms
for brevity only the first two queries are shown in table NUM
crl evaluated five methods for query translation in tipster ii
this process is diagrammed in figure NUM d
this process is shown in figure NUM a
s np argmaxsp slnp argmax p np s p s argmaxs h p u v p s u v em np s
yet the terms that occur with moderate frequency are sometimes significant
this produced a parallel corpus of around NUM NUM aligned sentence pairs
but the operator concatenates constituents on output stream NUM while reversing them on stream NUM so that c1 a b but c2 b2a2
performance therefore suffers if the probabilities are not appropriate a serious problem given that the syntactic production probabilities above are manually and arbitrarily set to be uniform
our first strategy is to expropriate a very simple coarse monolingual grammar of english as the backbone for a bilingual english chinese sitg which is then used for bracketing parallel text
the resulting parse may be incompatible with the parse chosen for the other half of the sentence pair causing a matching error even though some alternative parse might in fact been compatible
the first approach borrows a coarse monolingual grammar into our bilingual formalism in order to transfer knowledge of one language s constraints to the task of bracketing the texts in both languages
the result is that the maximum likelihood parser selects the parse tree that best meets the combined lexical translation preferences as expressed by the bij probabilities
the methods coarse bilingual grammars expropriated from monolingual grammars with em parameter estimation are grounded upon a firm theoretical model and preliminary experiments show promising behavior
in particular if the sub constituents of any constituent appear in the same order in both languages lexical matchings do not provide the discriminative leverage to identify the sub constituent boundaries
in typical cases we can assume a sort of pairwise dependence by considering all word pairs fj ei for a given sentence pair f el
these tags are either positive indicating that a rule applied or negative indicating that it did not
the characteristic feature of this approach is to make the aligmnent probabilities explicitly dependent on the mignment position of the previous word and t o assume a monotony constraint for the word order in both languages
many sentences are acceptable and semantically correct translations see the example translations in table NUM as can be seen in table NUM the translation errors can be reduced systen at ically
these candidate rules are then evaluated as described above
experiment results on this NUM megabyte document collection have shown that using different kinds of syntactic phrases provided by the noun phrase parser to supplement single words for indexing can significantly improve the retrieval performance which is more encouraging than many early experiments on syntactic phrase indexing
in addition the parser confuses some instances o f conjunction with appositives
as noted earlier the recognition of intentional structure is crucial for anaphora resolution among other discourse processing tasks
in either theory more research is needed to understand how informational relations are used to achieve discourse intentions
the claims of g s and rst discussed so far have been we argued either equivalent or compatible
when there are multiple embedded segments in g s each subsegment will be analyzed as an rst satellite
the span b c forms a satellite that stands in a motivation relation to a
a purpose i dominates another purpose in when satisfying i is part of satisfying ira
however due to the grammar definition and since weisen is also a verb without a separable prefix in german c er weist die kritik der prinzessin is still accepted as a valid clause leading to the erroneous training tuple er weisen kritik NUM he point criticism
ii the case in which p takes time linear in the grammar size is of the most interest since in natural language processing applications the grammar tends to be far larger than the strings to be parsed
in case one of the nouns in the tuple is a pronoun it does not make sense to predict that it is subject object of a verb based on how often it occurred unambiguously as such in a sample text
in order to increase the coverage for these cases as well as the overall performance of the procedure the sample space should be reduced by morphologically processing german compound nouns and the size of the training set should be increased
that is many of the claims in the two theories although formulated differently are essentially equivalent
each theory has some consistent ground additional claims that concern issues simply not addressed by the other theory
NUM of these NUM sentence pairs were such exact matches is finally in an experiment to determine accuracy of our team members parsing using the grammar the atr grammarian scored for parsing and tagging accuracy some NUM sentences of tr eebank data from randomly selected treebank documents
a measure of the performance of a search is whether it 2in a parallel experiment to determine consistency on tagging we asked each of the three team members to choose the first correct tag from a raaked list of tags for each word of each sentence of test data
specifically starting from a sequence of words we first tag the sentence as follows estimate the probability for each part of speech of the first word 4for efficiency we break down the semantic model further into a set of models one for each syntactic category
NUM for cosmetic reasons the parameters am w are actually fixed to unity so that the model never looks further than m words back
the combined performance of this component in conjunction with above components is show n
this heuristic correctly handles cases such as acme s president bill jones
further far from featuring a rudimentary set of lexicat tags and non termlnal node labels the atl lancaster treebauk utilizes gaud presumably their source grammar as well rougbjy NUM NUM lexica tags and about NUM NUM d erent non terminal node labels sdeg as mentioned in NUM NUM
the test results seem to indicate that the most significant keystroke savings are furnished by the grammatical bigrams at least NUM NUM over the grammatical unigrams whose minimum savings amount to a mere NUM NUM compared to prediction without any grammatical information
we can compute dhit for a sentence several sentences or several paragraphs
finally we sorted the paragraph and sentence position by decreasing yield hang scores
for each paragraph and sentence number position we computed the average havg score
a window matches when it contains the same words as a topic keyword ti
he introduced four clues for identifying significant words topics in a text
figure NUM cumulative precision recall scores of top ten opp selected sentence positions of window size
each input token is assigned a single tag generally representing the part of speech and some limited morphological information e g the number but not the gender of nouns
post nominal adjectives are not attached to the immediate noun on their left and coordinated segments are not systematically merged until strong evidence is established for their linkage
hence the main steps in segmentation are tag potential beginnings and ends of a segment use these temporary tags to mark the segment remove the temporary tags
marking transducers are compiled from regular expressions of the form a c t1 t2 that contains the left to right longest match replace operator c
with tbeginvcs we assume that the sentence has a main finite verb as is usually the case but this is just an assumption that can be corrected later
we would like to thank kenneth it beesley and lauri karttunen for their editorial advice and gregory grefenstette for the valuable discussions we had about finite state parsing and filtering
null a l interpr tation des sentiments pr sidentiels s ajoute l atmosphdre de surench re politique qui prdc de tout congr s du patti socialiste
the speed of analysis is around NUM words per second on a spap cstation NUM machine running in a development environment that we expect to optimize in the future
the constraints are mainly syntactic they are about subject uniqueness unless there is a coordination the necessary sharing of the subject function among coordinated nps etc
primary segmentation subject tagging segment expansion optional other syntactic functions tagging the input text is first tagged with part of speech information using the xerox tagger
this paper proposes a new approach for word similarity measurement
lcb fuj ii take t anaka cs t pstsch
figure NUM a fragment of the simultaneous equation as
the correlation of various prosodic features with their independently obtained consensus codings of segmental structure codings on which all labelers agreed is analyzed using t tests the results support the hypothesis that discourse structure is marked intonationally in read speech
wbm then views a document as a sequence of words d wl w n NUM and assumes that each word is generated independently according to a probability distribution of a category
suppose that there is another category c3 skiing in which the word ball does not appear i.e. ball will be indicative of both cl and c2 but not cs
l dlc i i x p ktlci x p wtlkt NUM it p ktlci x l it lp wtlkt lo
in particular we learn the distribution for each category and that for its complement category from the training data and then determine whether or not to classify into each category the documents in the test data
earn acq crude money fx gr ain interest trade ship wheat corn give the results of classifying into the ten categories having the greatest numbers of documents in the test data see tab NUM
then we assign w to ki the cluster related to ci where f wlci denotes the frequency of the word w in category ci and f w denotes the total frequency ofw
we experimentally used the japanese morph syntax parser ethe edr corpus was originally collected from news articles
sim nc e stands for the similarity between nc and an example case filler
more specifically as the mus and underlying carriers take arguments it is possible to generate several variants of the same basic message
while such results may not be useful in guiding syntactic theory they are not irrelevant
the principles then distinguish particular sub languages the head final or the pro drop languages for instance
the nature of language theoretic complexity hierarchies is to classify languages on the basis of their structural properties
this again captures a presumably dynamic aspect of the original theory in a static way
second while this is a first order inductive property the definition is a second order explicit definition
thus the insights offered by formal language theory might actually be misleading in guiding theories of syntax
NUM the most prominent example is the definition of the chains formed by move a
this distinction is one particularly interesting question is whether it has empirical consequences
the fundamental problem here is identifying each trace with its antecedent without referencing their index
in addition it is very important to study how such phrase effects interact with other useful ir techniques such as relevancy feedback query expansion and term weighting
we treat the noun phrases with their possible structures as the complete data and the noun phrases occurring in the corpus without the structures as the observed incomplete data
null second it is desirable to study how the parsing quality e.g. in terms of the ratio of phrases parsed correctly would affect the retrieval performance
in most current ir systems documents are primarily indexed by single words sometimes supplemented by phrases obtained with statistical approaches such as frequency counting of adjacent word pairs
when a large corpus is available which is true for an ir task statistical preference of word combination or word modification can be a good clue for such disambiguation
noun phrase parsing or noun phrase structure analysis also known as compound noun analysiss is itself an important research issue in computational linguistics and natural language processing
pc sj and pc u v are subject to the constraint of summing up to NUM over all modification structures and over all possible word combinations respectively
information retrieval ir is an important application area of natural language processing nlp where one encounters the genuine challenge of processing large quantities of unrestricted natural language text
in synchronous rewriting the productions of two rewriting systems are paired and applied synchronously in the derivation of a pair of strings
integral thresholds are searched for the conceptual distance meetri whilst the thresholds of the other mettics are searched in steps of NUM NUM
it thus requires a metric which can effectively represent the semantic distance bet veen two nodes in a taxonomy such as word net
the sense of a word is disambiguated by choosing the sense which is most highly supported by the other nouus of the noun group
in this paper we address the problem of s mantic class disambiguation with a view towards applying it to information extraction
hence in our hnplementation we ign a large conceptual distance of NUM to the virtual edges between two unique beginners
link probability is defined as the difference between the probability of instance occurrences of the parent and child of the i k
we hence turn to dictionary based approaches focusing on wordnet based algorithms since they fit in snugly with our wordnet based semantic class disambiguation task
these NUM nouns are hand tagged with their sense and semantic class in the particular context to form the answer keys for subsequent experiments
in addition to capture the context appropriate use of word order the formalism must associate information structure components such as topic and focus with the appropriate sentence positions regardless of the predicate argument structure of the sentence and be able to handle the information structure of complex sentences
as motivated from the data a formalism for free word order languages such as turkish must be flexible enough to handle word order variation among the arguments and the adjuncts in all clauses as well as the long distance scrambling of elements from embedded clauses
in this domain the focus is the new or important part of the answer to a wh question while the topic is the main entity that the question and answer are both about that can be paraphrased using the clause as for x
the post verbal positions are influenced by the given new status of entities within the discourse postverbal elements are always evoked discourse entities or are inferrable from entities already evoked in the previous discourse and thus help to ground the sentence in the current context
the information structure is is distinct from predicate argument structure as in languages such as turkish because adjuncts and elements long distance scrambled from embedded clauses can take part in the is of the matrix sentence without taking part in the as of the matrix sentence
several studies on aggregating text based on text structure appear in the literature
the percent sign here means that the following blank is to be taken literally that is parsed as a symbol
without the additional complexities introduced by contexts the directionality and 3upper is the same language as upper except that carets may appear freely in all nonfinal positions
at that point it selects the longest matching substring which is rewritten as lower and proceeds from the end of that substring without considering any other alternatives
on the other hand in our text processing applications the upper language may involve a large network representing for example a lexicon of multiword tokens
in a phonological or morphological rewrite rule the center part of the rule is typically very small a modification deletion or insertion of a single segment
for example the bracketing a a is not allowed if the upper language contains aa as well as a
to make the notation less cumbersome we systematically ignore the distinction between the language a and the identity relation that maps every string of a into itself
for example we may insert a string before and after each substring that is an instance of the language in question simply to mark it as such
instead of replacing the instances of the upper language in the input by other strings we can also take advantage of the unique factorization in other ways
if the component has an api written in one of the languages supported by the java native interface currently c and c it can be dynamically loaded into the server at runtime and accessed via a java front end
a persistent store version uses a persistent store back end for storing and retrieving collections attributes and annotations this version supports the persistent object service which provides greater efficiency for storing and accessing persistent objects as well as enhanced support for defining persistent application objects
these problems are application dependent and need to be resolved on a case by case basis such integration is feasible as demonstrated by the various tipster demonstration systems and use of the architecture reduces significantly the load of integrating a component into the application
the corelli plug n play layer aims at filling this gap by providing a dynamic model for component integration this framework provides a high level of plug and play allowing for component interchangeability without modification of the application code thus facilitating the evolution and upgrade of individual components
section NUM presents the corelli document processing architecture a new software architecture for nlp which is designed to support the development of a variety of large scale nlp applications information retrieval corpus processing multilingual mt and integration of speech with other nlp components
architecture the corelli document processing architecture is an attempt to address the various problems mentioned above and also some other software level engineering issues such as robustness portability scalability and inter language communication for integrating components written in lisp c or other languages
this is the solution adopted in the verbmobil architecture which makes use of a special communication software package written in c and imposing the use of c and unix at the process level and uses a chart annotated with feature structures at the data structure level
direct reuse of nlp software components e.g. using an existing morphological analyzer as a component of a larger system is still very limited but is nevertheless increasingly attractive since the development of large scale nlp applications a focus of current nlp research is prohibitive for many research groups
deletion error hypothesis NUM lyon said thzlt e is an ezzor count if c p j is terminal then add p j l l f
these mistakes usually take place rather between small constituents such as a verbal phrase an adverbial phrase and noun phrase than within small constituents themselves
so the robust parser assigns less error values NUM to the error hypothesis edges with these symbols than to the other terminal symbols
of these rules we remove rules which occurs fewer times than the average frequency in the corpus and then only NUM rules are left
in table NUM 5th 6th and 7th raw mean that the percentage of sentences which have no crossing constituents less than one crossing and less than two crossing respectively
the specific se tic class disambiguation accuracy is hence the stricter measure
the upper layer of nkrl consists of two parts
nkrl is a two layer language
suspectl0 had access to the poison
i i i i i figure i semantic class dis mhiguation
figure NUM an analytical comparison of dialogue initiative setting schemes
table NUM a dialogue fragment in the circuit fix it
again we will rely on a probabilistic analysis
we have investigated four initiative setting schemes using this analysis
this scheme provides a baseline for initiative setting algorithms
methods can typically be delineated along two dimensions corpns based vs dictionary based approaches
assumption NUM the similarity between a and b sire a b is a function of their commonality and differences
substituting for x in this equation f z v since z is rational there exist m and n such that
we also showed as a baseline the performance of the simple strategy of always choosing the first sense of a word in the wordnet
since table NUM contains many organization words the support for the organization sense is nmch higher than the others
we can then use the distance between a pattern and a schema to weight its vote in the nearest neighbor extrapolation
the attachment can be viewed as being generated by the following conditional distribution null
we now consider how to specify the syntactic preference function in NUM
in disambiguation we simply rank the interpretations according to their lexical likelihood values
in this subsection we formalize a syntactic preference based on rap and alpp
moreover since lpr overrides rap the preference is solely determined by lpr
first let us compare the left hand length probabilities of NUM and NUM
experimental results indicate that our method improves upon or is at least as effective as existing methods
the average number of interpretations obtained in the analysis of a sentence was NUM NUM
first let us compare the left hand length probabilities in NUM and NUM
the csrs in the computer rubric categories exemplify the information required to receive credit for a sentence in a response
at this point in the process each computer rubric categow is an electronic file which contains finetuned csrs
the csr in NUM was then used for the rule generation process described in the next section
computer rubric categories are created for the bulleted categories listed in the human rater scoring guide illustrated in figure NUM
for part c2 the categories were change in l change in ii alternate and detail point
each computer rubric category exists as an electronic file and contains the related concept grammar rules used during the scoring process
a total number of points is assigned to the essay after the program has looked at all sentences in an essay
part a explain how the principles of gel electrophoresis allow for the separation of dna fragments NUM point maximum
for part a the categories were electricity charge rate size calibration resolution and apparatus
speed our system is reasonably fast
distribution of the main grammatical classes of the known and unknown words and the words occurring only once in english text
wordnet can be described as a lexical matrix with two dimensions the lexical relations which hold among words and so are language specific and the conceptual relations which hold among senses and that at least in part we consider independent from a particular lan null guage
a hand tagged corpus is of course very useful for performing the third of these processes in a rigorous manner
the methods described in this paper mcquitty s similarity analysis ward s minimum variance method and the em algorithm assign each instance of an ambiguous word to a known sense definition based solely on the values of automatically identifiable features in text
NUM mm clarets of surfa featmw
deverbal adjectives turn out to be the largest single subclass in the adjective lexical category
we will see that there are other factors which push this latter decision forward
enamex type quot person quot dooner enamex declines to identify possibl e acquisitions
the common case of deverbal adjective acquisition includes most deverbal adjectives and it is discussed first
denominal adjectives are relative and as such are expected to be non scalar and non predicating
an even stronger case of suppletivism involves processes which do not have single word verbs denoting them
the other alternative is to check each verb manually i.e. by a qualified human
even so there are several aspects of its application which require human judgment
an ontology is thus a necessary prerequisite for building a tmr language
the architecture of mikrokosmos is described in onyshkevych and nirenburg NUM and beale et al
the lr however works with the same ease as in typical morphologically derivative cases
for the same verb and subcal egorization
it also prunes senses without loss of correctness
figure NUM distribution of verbs according to number
m m investigating complementary methods for verb sense pruning
it recognizes abbreviated phrases contractions punctuation etc
low frequencies are not drawn to scale rather the presence of a bar for a category corresponding to more than NUM senses indicates that at least one verb falls in that category
after hypothesizing the subcategorization pattern for a specific verb token we use our sense restriction matrices as in table NUM to tag the verb token with a pruned set of senses
the syntactic context can partly disambiguate the semantic content
for a specific example consider the verb appear
the work with unambiguous input symbols allows fast parsing in the phase a cyk is polynomial with respect to the length of the input but creates some problems in the context of constraint relaxations used in subsequent phases
let us suppose that we have the following three input words the actual lexical value of these words may be neglected preposition accusative or locative adjective animate or inanimate gender genitive or accusative sing
after we had deleted this phrase the processing time went down to NUM NUM s the same number of syntactic representations as in the previous case was derived NUM and the number of items was slightly lower NUM
from the point of view of iocalisation of grammatical inconsistencies we can proceed even farther the group title surname in fact represents only one item if we remove titles preceding surnames we do not change syntactic structure of the sentence
every time a new branch or subtree is created it is compared with the other branches or subtrees with the same structure and coverage and if it contains more errors than those already existing it is not parsed further
in this basic form of the sentence which is an exact transcription of the text from the corpus the processing by the positive projective phase of our parser takes NUM NUM s and it provides NUM different variants of syntactic trees
the main window of grammar is able to provide either the complete list of errors the statistics concerning for example the number of different syntactic trees built during grammar checking or even the result in the form of a syntactic tree
it is possible to change values of the resulting item x by means of an assignment operator the constraint relaxation technique is implemented in the form of so called soft constraints the constraints with an operator
as shown in table NUM there are NUM NUM NUM full descriptions used among NUM matched nominal anaphora
let us take the last clause of the sentence namely p edseda kpest anskych demokrato pan benda v telefonick6m rozhovoru s petrem pithartem prosazoval in en ra dejmala do funkce ministra ivotniho prost edi
although this division into phases worked fine for short sentences for the sentences not more than NUM words long the first phase usually took about NUM second on pentium NUM mhz long and complicated sentences were unacceptably slow even tens of seconds
the system was designed to be easily portable to new natural languages assuming the accessibility of lexical part of speech information
the part of speech tags used were identical to those from the english lexicon and the descriptor array mapping remained unchanged
common morphological endings are recognized and the appropriate part s of speech is assigned to the entire word
in this case the sum of all items in the vector is not predefined as it is with probabilities
the two methods are almost equally effective for this task and both train and run quickly using small resources
the output of the learning algorithm is then used to determine the role of the punctuation mark in the sentence
we present results of testing our system on several corpora in three languages english german and french
furthermore no estimates of scalability were given so we are unable to report results with a smaller set
satz is very fast both in training and sentence analysis and its combined robustness and accuracy surpass existing techniques
possible end of sentence punctuation marks and all references to ptmctuation marks will refer to these three
g1 gk that link t and q as argmaxpq git NUM g pq a
the prediction is classified as unreliable if the probability of an alternative is larger than of the most probable tag
for example because the greedy algorithm always looks for the longest string of characters which can be a word given the character sequence economicsituation the greedy algorithm first recognized economics and several shorter words segmenting the sequence as economics it u at io n
typically the coneval module is asked to determine the class of a particle wherupon transfer chooses a target word
tracted verb co occurring with a noun w is vs
this paper focused on only NUM digit class codes
generalizing individual nouns by constructing clusters remedies this problem
the threshold was set to NUM NUM in this experiments
some words are assigned more than one class code
further tuples containing nouns in bgh were selected
conditioning p clw on each possible event gives
which also should not be translated literally do i see that correctly
in this section we look in more detail at the design of the dm module within vodis with special attention to the specific conditions posed by the vehicle context and the relation with the NUM commandments
this parser is trained on a language model and differs from classical parsers in that it does not only use syntactic information but also domain dependent semantic information
after the user s input has been analyzed in the speech recognition unit the dm receives a message consisting of a list sr results which contains the recognized phrases and their respective confidence scores
at this stage the user can do three things NUM cancel the sr results NUM challenge the first candidate or NUM accept it
during the evaluation attention will be paid to i the speech recognition performance and ii the user system interface with the emphasis on security safety acceptability and effectiveness
the results of these experiments will constitute the input for the development of the second prototype which also aims at 50f course the limited control and command language will not give rise to many ambiguities
however as in natural human speech the system must recognize and accommodate spontaneous shifts from one script to another and be able to cope with changes in the detailed content and structure of a script in different circumstances
inheritance and association relationships will be used to ensure that generic functionality which can be shared by more specialised system components need be defined only once and can be introduced into the dialogue flow in real time as and when required
each domain expert class regardless of the specific domain its subclass addresses typically provides the following functionality NUM request template structure for the domain NUM enquiry processing algorithms for the do null main typically if then else constructs including recommended use of any skills expert for specialised but nondomain specific processing e.g.
to make three broad distinctions one may view these set pieces as occurring at a meta level a domain level and a skill level and these levels are reflected in the system architecture we are evolving
firstly how can developers exploit the commonality that exists between different application domains to make the development task easier on the one hand and on the other hand to make systems as computationally efficient and as functionally wide ranging as possible
dialogue intentions may themselves encapsulate heuristics that allow them to instantiate a dialogue model and by extension the associated dialogue objects discourse states and request templates for relatively high level processing tasks greet find enquiry type for example
as an exception in some contexts a pragmatic adverb is suppressed in the translation
for example the following sentence in an english remote sensing domain hereafter rsd the satellite produced information with high accuracy originates the instance NUM produce manner property
in both the poisson and general fertility models the computation ofp clf in equation NUM uses a unigram model
this schema is one of a number which are used to license this kind of modification of default arguments
in this paradigm formal language words first generate a clumping or partition of the word slots of the english expression
this enquiry is well supported in terminologyframework by its easy tohandle facility to define virtual i.e.
both of these systems require significant rule based transformations to produce disambiguated interpretations which are then used to generate the sql query for atis
the factorial terms combine to give an inverse multinomial coefficient which is the uniform probability distribution for the alignment a of f to c
these counters suggest that if we manually tagged the NUM occurrences of the string hqph in the corpus we would find that the first analysis of hqph is the right one NUM times out of the NUM times that the word appears in the corpus that the second analysis is the right one NUM times and that the third analysis is the right analysis only twice
intriguingly the headword model is more strongly biased towards the likely translations and has a smoother tail than the unigram model
in constructing the similarity between x2 and xl we can either take them to be coreferential case a or prove them to be similar by having similar properties including having similar dependencies estab
be appropriate to the immediate needs tit each stage of the transaction
but notice also that the recall decreases with disambiguation in the NUM study recall drops from NUM NUM for undisambiguated verbs to NUM NUM for disambiguated verbs
by right assignments we mean cases in which the system assigns a verb to a given levin class when that verb appears in that class in levin s book
parallelism is characterized in terms of a co recursion in which the similarity of properties is defined in terms of the similarity of arguments and the similarity of arguments is defined in terms of the similarity of properties NUM two fragments of discourse stand in a parallel relation if they describe similar properties
thus if we build our semantic field on the basis of the synonymy relation all synonyms of verbs in a particular class would be legal candidates for membership in that class
of the NUM NUM assignments of ldoce verbs to levin classes given by the syntactic filter NUM NUM pass through the semantic filter
would deteriorate further even though NUM out of NUM possible assignments would be correct these correct assignments constitute only NUM NUM of the total number of assignments made by the algorithm
it is important to point out that even though the semantic filter is based on words in levin it still sometimes categorized the levin verb incorrectly
verbs that do not occur in the semantic field of a particular class fail to pass through the semantic filter for that class by definition
precision on the other hand is the number of correct categorizations that the algorithm gives divided by the total number of categorizations that it gave
briefly if the first ellipsis is resolved to the strict reading then the jjjj reading is possible
this feature gives NUM points for fact NUM for conjecture and NUM for insistence
this feature gives NUM point for reason NUM for example and NUM for others
for each sentence in a given japanese newspaper article the following features NUM are analyzed
abstracts created by humans tend to differ according to their creators background knowledge and interests
l blest of these features were proposed in the previous studies
the testers were divided into two groups a and b each consisting NUM people
therefore the feature weight wi is calculated by multiple regression analysis
the steps in creating an abstract are as follows NUM
in japanese ta implies the past tense completion and so on
an important keyword is defined as a keyword that appears in another sentence or in a title
students would be placed within a model of climbing literacy with language concepts rated as above below or within their current realm of acquisition and the tutorial interaction tailored to this model
one potential drawback of an exemplar based learning approach is the testing time required since each test example must be compared with every training example and hence the required testing time grows linearly with the size of the training set
machine learning comprehension grammars for ten languages
the robotic framework and the associated corpora we test our program on are certainly restricted although we have implemented our learning program on robotworld a standard robot used in academic settings for development purposes
however this begins to suggest how users withou t detailed system knowledge might be able to create suitable patterns
and the approach did indee d mitigate the shortcomings of the full parsing approach which we outlined in the introduction
in processing the succeeds predicate the inferencing component notes that we have explicit information on the positions that mr
fred was succeeded by harry we need to infer that harry is becoming the president of legal beagle
here a certain amount of inferencing is needed to extract the actual events from those explicitly stated in the article
each of these stages uses one or more set s of finite state patterns to perform some reductions on the input string
then we define the contezt x of an event z y as the verb v and the output NUM as the nominal part ep of e and each event in the training sample is denoted as v ep
in this feature structure e cc c and cj represent the leaf classes in the thesau us of the nouns odomo child rotten park and juus fldce
cani cbev e cliq h we additionally allow these superordinate classes as sense restriction in subcategorization frames we can consider several additional patterns of subcategorization frames which can generate the verb noun collocation e along with those patterns described in the previous section
in addition to the requirement that s subsumes e we can also put an assumption that all the cases in the given verb noun collocation e are independent of each other and that a subcategorization frame s which has only one case of e can generate e
next in addition to the requirement that s subsumes e we put another assumption that all the cases in the given verb noun collocation e are dependent on each other and that a subcategorization frame s which can generate e should have exactly the same cases as e has
as well as a subcategorization frame a verb noun collocation e can be divided into two parts one is the verbal part containing the verb v while the other is the nominal part ep containing all the pairs of case markers p and thesaurus leaf classes c of case marked nouns
then we can find a division of s into a tuple sl s of partial subcategorization frames of s where any pair si and si i i do not have common case markers and the unification sl a
in ma dmum entropy modeling approach this is done by constraining that the expected value of each fi with respect to the model p y x left hand side be the same as that of fi in the training sample right hand side
a dependency tree a and a p s
dependency and constituency frameworks define different syntactic structures
the total number of states of all the transition graphs for a grammar g is at most o igi where igi is the sum of the lengths of the dependency rules
in this section we introduce a dependency formalism
such that cat c0 is a dependency rule
the following algorithm constructs the transition graph for the
each category until all states in v are marked
each string is prefixed with a dot
the phase completer executes at most one action per pair of items
the a structure is then considered as the application of an argumentative structure made of modifiers operators and connectives to a vector of tss s
connectives constrain a pair of sentences or a sentence and a discursive environment operators constrain argumentative power and modifiers constrain only argumentative orientation and strength
given a sentence we identify operators connectives and modifiers and build the a structure of the sentence linking these linguistic clues to the tss s
ducrot s integrated pragmafics also claims that many phenomena usually described at the pragmatic level must be described in the signification such as argumentation
the signification of a sentence is viewed as the application of an argumentative super structure to the signification of tss s free of operators or connectives
the talking subject introduces the speaker to whom the words are attributed different from the talking subject in some cases such as indirect speech
modifier a little the signification of a little p is the one of p where the strength of all cells is attenuated
in addition to more global or flexible relations we also try explicitly define compatibility of configurations
it does not tell that lhere is a blue departure at NUM NUM
only through careful evaluation and full reporting of the results can the community of researchers as well as the general public gain an understanding of the current abilities and the future potential of snlds
yet the correlation is not perfect simply sorting the words by frequency would produce a suboptimal result
the second option b is to link the languages through an structured language neutral inter lingua
transaction coding gives the subdialogue structure of complete task oriented dialogues with each transaction being built up of several dialogue games and corresponding to one step of the task
it is also possible to formulate complex queries in which any piece of information is combined
in the next section each transformation will be associated with several strings
attempting to overwrite a feature specification yields an error
tgl rules are presently written using a text editor
practical experience must show whether this approach saves effort
NUM tuning a classification framework to a domain the wide spectrum classification adopted within wordnet is very useful on a purely linguistic ground but creates unacceptable noise in nlp applications
additional uses could include some rhetorical structuring e.g.
thus causing v2 to have two elements
after the first solution has been found i.e.
zweig will sic am freitag treffen prof
for each of the systran is not to be confused with systran professional for windows
figure NUM a sample gil input structure prof
like spl gil assumes only little grammatical information
we wish to see how well the morphological recognizer can replicate the performance of a parser with a full dictionary
a match occurs when the sentence has generated a parse that occurs within the control parse forest for that sentence
for example acts is one word but it has two definitions as a noun and a verb
by creating a dictionary containing all the closed class words some words in any sentence will very likely be known
so in this case butterfly is mistakenly assumed to have the ly suffix
consider the unknown word smolked used in this sentence the cat smolked the dog
with NUM of the open class dictionary missing there are NUM deletions out of NUM total possible parse matches
since every possible part of speech is assigned to each unknown word all the original parses should be generated
in the case of a large corpus especially one with no specific domain a comprehensive lexicon is prohibitive
less confident transferers can ask for confirmation of some fact that the transferee should be able to infer from the transferred information since this provides stronger evidence of success
predictions about the preferred referents of the pronouns in sentence 3d nor does it predict the garden path effect in sentence 3e in each case the rule is satisfied assuming either possible assignment of referents to the pronouns
lt nsl lifts this restriction allowing tools access to streams which are sequences of tree structured text a representation of sgml marked up text
thus different features will be appropriate for the special case versus other uses
they augment the transition hierarchy by replacing the shift transition with two transitions termed smooth shift and rough shift which are differentiated on the basis of whether or not cb u i is also cp u i NUM
lt nsl implements a textual inclusion semantics for such links inserting the referenced material as the content of the element bearing the linking attributes
to address this issue lt nsl includes a number of text based tools for the conversion of sgml textonly sgmltrans and sgrpg
in conclusion the sgml and database approaches are optimised for different nlp applications and should be seen as complimentary rather than as conflicting
notice that for the two non context based approaches the performance figures for
normally however normalised sgml will be created on the fly and passed through pipes and only the final results will need to be stored
the subquery and regexp allow one to specify that the matching element has a subelement matching the subquery with text content matching the regular expression
segment index is always identical to the currently valid segment level since the algorithm in table NUM implements a stack behavior
a word in chinese can be made up of a single character such as ok f m rice or it can be a combination of two or more gan palmer and lua a statistically emergent approach characters such as shu gu6 fruit
these problems cause a lot of difficulty to the parser due to the alternative or erroneous chain of word
thus the information obtained here can be used for various applications
we believe that the separation of word identification from the task of analysis accounts for the difference in performance
nodes also spread activation to their neighbors and thus concepts closely associated with relevant concepts also become relevant
first all possible words in a sentence are identified and assigned initial probabilities based on their usage frequency
the test set covers the four types of word boundary ambiguities de null computational linguistics volume NUM number NUM scribed in section NUM when the sentential contexts of locally ambiguous fragments both the overlap and combination type were varied our system was able to identify the correct word boundaries
where a a b is the affinity relation between the character objects a and b p a b is the probability that the two character objects co occur consecutively p a and p b are the probabilities that a and b occur independently
the constituent characters of the word still exist in the workspace but they become less explicit in the figure
some default bottom up word codelets are also posted to determine whether monosyllabic words could be constructed from character objects
NUM there is no need for external specialists such as knowledge representation systems or temporal reasoners
he will be succeeded bymr namex type person dooner enamex NUM
the ne muc NUM task is a good test for such experiments
we have recently extended the uno model to incorporate temporal reasoning
second handling such short forms resemble s handling semantic ambiguity
whenever possible we illustrate our system s capabilities with the examples from the walkthrough article
our temporal reasoner automatically extracts explicit temporal expressions from on line textual documents and creates their representation
in advertising he does n t want to talk about the disappoi meats
i m going to focu s on strengthening the creative work he says
we devoted a considerable effort to this task because such a change directly affects every processing stage
the second process detects and corrects the implicit spelling error by generating the new words for the detected error
in using a list of controlled terms coupled with a syntactic analyzer the method is more precise than traditional text simplification methods
we do not have these scenarios
our strategy for ad hoc retrieval involves two stages
in contrast to conventional dialogue systems it mediates the dialogue while processing maximally NUM of the dialogue in depth
to incorporate constraints in dialogue processing and to allow decisions to trigger follow up actions a plan based approach has been chosen
during the planning process tree like structures are built which mirror the structure of the dialogue
if processing breaks down verbmobil has to initiate a clarification dialogue in order to recover
it explains certain ph nolilella of local discourse eohe reuce
table NUM illustrates the pour transitions that are detined according to diese constraints
the finite state machine provides an efficient and robust implementation of the dialogue model
the cp represents a prediction about die cb of the following utterance
the null subject is considered part of the system of weak pronouns
the resull s are as follows
evidence t han as slrong indicators as i hc
deictics such its i you etc
projects currently underway at sheffield may be more appropriately described by the term language engineering than the well established labels of natural language processing or computational linguistics
the multext architecture is based on a commitment to tei style NUM sgml NUM encoding of information about text
the extensive work done on sgml processing in multext could usefully fill a gap in the current tipster model in which sgml capability is not fully specified
this facility supports hybrid systems ease of upgrading and open systemsstyle module interchangeability NUM figure NUM shows the launchpad for a muc NUM ie system
figure NUM ggi the gate graphical interface
gdm is based on the tipster document manager
to this end sheffield has produced gate a tipster compatible general architecture for text engineering providing an environment in which a number of sheffield projects are currently being developed
note that we use the terms module and object rather loosely to mean interfaces to resources which may be predominantly algorithmic or predominantly data or a mixture of both
we will now describe the robust parsing module in more detail
we have analyzed the relationship between back off smoothing and memory based learning and established a close correspondence between these two frameworks which were hitherto mostly seen as unrelated
because the cosine metric fails to group the patterns into discrete schemata it is necessary to use a larger number of neighbors k NUM
we can then define the ordering between schemata in the following equation where a x y is the distance as defined in equation NUM
information gain ig weighting looks at each feature in isolation and measures how much information it contributes to our knowledge of the correct class label
the same results are measured in the french text
rule like behavior results from the linguistic regularities that are present in the patterns of usage in memory in combination with the use of an appropriate similarity metric
since the underlying k nn classifier is a method that does not necessitate any of the common independence or distribution assumptions this promises to be a fruitful approach
notice that we did not actually compute the 21deg terms of naive back off in the pdddaaasss condition as ib1 is guaranteed to provide statistically the same results
if an action on the active path is a repeating action rather than only the rightmost instance being included on the active path all adjacent instances of this repeating action would be included
so the other problem is that it makes it impossible to resolve anaphoric referring expressions adequately in the case where there are multiple threads as in the case of parallel suggestions negotiated at once
in this paper we will present our ongoing work on a plan based discourse processor developed in the context of the enthusiast spanish to english translation system as part of the janus multi lingual speech to speech translation system
in section NUM we will argue that our proposed extension to standard tst is necessary for making correct predictions about patterns of referring expressions found in dialogues where multiple alternatives are argued in parallel
if the expression can only refer to an entity on the stack then the discourse segment purpose NUM of the current discourse segment must be attached to the rightmost frontier of the intentional structure
that are not discourse segment purposes in lochbaum s theory since they can not form the basis for a shared plan having not been decided upon yet and being associated with only one agent
when the chain of inference for the current sentence is attached to the plan tree not only is the speech act selected but the meaning representation for the current sentence is augmented from context
that s can not be discourse segment purposes
that s each corresponding to different alternatives
this is because the interpretation relation d c g2 e holds intuitively the putative event corresponding to the situation described in NUM NUM would have to include e since joe s visual state in fact comprises the complement of the outer see that clause
nevertheless capturing generalizations of this type does seem desirable
alon itait technion uzzi ornan t technion
first a corpus is enriched by tagging each word unambiguously and then expanded by linking each word with all its possible derivatives
a typical verb for a category c is one that is either non ambiguously assigned to c in wordnet or that has most of its senses syneets in c
the final tag model is constructed by mixing all the models according to their performance
we thus have a circularity problem in order to perform the morphological analysis we need the short context computational linguistics volume NUM number NUM to identify the short context we have to find anchors but in order to find such words we need first to perform the morphological analysis
words with reasonable approximation words that do not fall into the previous category but cat ptest cat papp holds for all their analyses using lower threshold NUM NUM and upper threshold NUM NUM the fourth word in table NUM belongs to this category
however the morpho lexical probabilities can also be used in order to reduce the ambiguity level in the text
following are the main components in our project that were used in order to conduct the experiment NUM
thus the comparison we describe in what follows serves for evaluation of the quality of the approximated probabilities
since NUM deals with speaker s knowledge it can not be subsmncd by gpll
for a given instance si sis si the algorithm predicts NUM iff
i will address only logics or levels having three connectives a product connective a form of conjunction corresponding to matter like addition of substructures plus two implicational connectives the left and right residuals of the product notated as deg l and o for a product o
note however that word order determination must be sensitive to the specific modes of structuring and their properties e.g. the non associativity of r implies an integrity for y z in x y z excluding y x z as a possible order despite the permutativity of r
for example by having a lexieal element subcategorise for a complement that is some non associative functor i.e. of the form at b or b a we could be sure that the complement taken was a natural projection of some lexical head and not one built by composition or other associativity based combination
recent work within the field of categorial grammar has seen the development of approaches that allow different modes of logical behavior to be displayed within a single system something corresponding to making available differing modes of linguistic description
our argumentation is that this is possible since the dialogue manager can use acoustic clues on the one hand to establish better recognition conditions and on the other hand to generate more co operative interactions
structural modalities allow that stronger logics may be embedded within weaker ones via embedding translations i.e. so that a sequent is derivable in the stronger logic iff its translation into the weaker logic plus relevant modalities is also derivable
generalising from this ease we expect that for any two sublogics in a mixed system with products oi and oj where the former is the stronger logic including more structural rules we will observe transformations
obvious requests or follow up questions like those examplifled above are one option more clever questions like our communication may proceed more smoothly if the system adapts to your acoustic conditions shall this be done
we describe one approach to build an automatically trainable anaphora resolution system
abe can be an anaphor of abcde
but still exceeds that of the mdr
by the syntax module and discourse maxkers are created for them
lr core m o quot rcpe rb i quot cor iii b
distance between anaphor and antecedent features
the worst case asymptotic time complexity of the analysis algorithm is o min n NUM iy12 n3 where n is the length of an input string and ivi is the size of the vocabulary
left transition if in state qi NUM m can write a symbol r onto the right end of the current left sequence and enter state qi with probability p qi rlqi NUM m
the effect oll performance of grouping the compounds is related to the relative distribution of the open and closed forms
transfer configurations in order to apply target language model relation costs incrementally we need to distinguish between complete and incomplete arcs an arc is complete if both its nodes have labels otherwise it is incomplete
NUM for each dependent node wi select a lexical entry with cost c mi qilri j wi and recursively apply the machine rni from state ql as in step NUM
with these considerations in mind we have started to experiment with a version of the translator described here with even simpler representations and for which the model structure not just the parameters can be acquired automatically
keeping the arcs p separate in the configuration allows efficient incremental application of target dependency costs cv during the search so these costs are taken into account in the pruning step of the overall search control
given a set of solutions from executions of a process let n e e be the number of times choice e c was taken leading to acceptable solutions e.g.
apart from the words themselves the only symbols used are the dependency relations r in our experimental system these relation symbols are themselves natural language words although this is not a necessary property of our models
the recursive process of generating this subtree proceeds as follows NUM select an initial state q of an automaton m for w with lexical probability p m q r w
reflexive training if we have a manually translated corpus we can apply the mean and normalized distance models to translation by taking the ideal solution t for translating a source string s to be the manual translation for s
each system translated the sentence lists and the target document was saved
we will show the proof of correctness of the algorithm by induction on the length of the sequence of symbol positions
NUM find the closure of the last NUM NUM i.e. all nodes spanning trees which are within the last NUM NUM
there are about NUM NUM of thai words that can cause this kind of the confusion to typist
this research was supported in part by an nsf research initiation award ccr NUM NUM and an aro grant daal03 NUM c NUM
a previous version of the authoring interface which relied on the resource editor of the neuron data gui builder proved unsatisfactory as it i required excessive clicking for the author to navigate from snippet to snippet and ii failed to provide sufficient context making it unnecessarily difficult for the author to adhere to the cogenthelp authoring guidelines
in a one pass top down approach this need would force the various inclusion conditions to be cumbersomely centralized with revisions in contrast one can simply add the paragraph and italics element during the first pass then check during a second pass whether any of the optional messages for this paragraph did in fact appear removing the superfluous html elements if not
individual and lass an object can be an individual or a class
this model can be used in the context of man machine dialogue or for information retrieval
l his c onmmnical ion aims t clarify NUM NUM
while several current guidevelopment environments include tools which generate an initial skeleton help system for an application with topics that correspond to the widgets in the gui to our knowledge cogenthelp is the first operational prototype to implement the evolution friendly properties of a tool like javadoc in a system for generating end user level documentation
the solution seemingly substitutes a positive fact by a negative fa ct one
while we did not originally think of cogenthelp s collection of input help snippets as a phrasal lexicon a la milosavljevic et al in retrospect it becomes evident that this collection can be viewed as tantamount to one of course since these snippets vary in size from phrases to paragraphs the term phrasal is not entirely accurate
the solution to this problem was to develop as part of our ikrs a method of recovering these func null tional groups using spatial cues as heuristics the reason this approach might be expected to work is that in a well designed gui functionally related widgets are usually clustered together spatially in order to make the end user s life a bit easier
it indicates a relation between the object considered and another object
other important benefits stem from supporting the separation of the content of the document to be generated descriptions of individual components from the structure of the document how the content is distributed and formatted this allows the author to focus on writing accurate component descriptions and avoid much of the drudgery involved in creating a complex hypertext document manually
and each such object is in accord with its un lerlying tyl e
for mixed language input we found out that a straight forward implementation of a mixed language model based speech recognizer performs less well than the concatenation of pure language recoguizers due to the increase in recognition candidate uumbers
the assignment of part of speech to the segmented word is also effected by the word boundary ambiguity
it seems that we should first have a large scale sense tagged corpus in order to build semantic space but establishing such a corpus is obviously too timeconsuming
consider two possible ways to implement a mixed language recognizer NUM use two pure monolingual recognizers to recognize different parts of the mixed language separately NUM use a single mixed language model where the word network allows words in both languages
word accuracy is defined as NUM where n is the length of the actual utterance and d is the distance as defined above
for instance the update below is semantically not equivalent to the one given in section NUM as the ground focus distinction is slightly different
this picture becomes more detailed when we look at the second question
brill addresses the problem of finding a valid metric for distituency by using a generalized mutual information statistic
after training we find that the weight range is bounded total time for training is measured in seconds
the factor should be greatest in the central region least as it moves away in either direction
here are a few more examples null
NUM n NUM hypertags are always inserted in pairs so that closure is enforced
these are now extended to NUM words in the pre subject NUM in the subject see section NUM
if during the generation of a candidate string a prohibited tuple is encountered then the process is aborted
in these cases negative information is not required but they are not plausible models for unbounded natural language
however the head of the subject is then found and number agreement with the verb can be assessed
using this representation a hierarchical language structure is converted to a string of tags represented by a linear vector
however even then the nlonotouy constraint is satisfied locally for the lion s share of all word alignments in such sentences
by taking the mean of the scores the features can be ordered as follows most important first the initial tentative ranking of features indicates that anaphora and ellipsis are important whilst functional perplexity and interaction strategy are least important
in these cases theorem NUM does not define additional hyphen points and lemma NUM does not indicate impermissible hyphen points
word pairs other than self triggers for example can be discovered automatically from training data using the techniques of mutual information employed by our language model
parser models are trained on NUM NUM r nn ngwords of atr lancaster treebank alone
third we may decide to implement the more laborious two model approach desribed in NUM NUM
it is frequently assumed that the speakers using a system are native speakers belonging to the same accent group
the nswers to these questions are made available to the models in our parser
vvhen words have multiple senses these may have very different frequencies
NUM the result of this scoring was a NUM NUM expected parsing error rate
in this paper we have presented an hmm based approach to handling word alignlnents and an associated search algorithm for autonaatic translation
figure NUM three atr lancaster english treebank sentences one from credit union brochure
these deletion errors are often caused by the omission of word groups like for me please and could you
the job of the generative model is to model the original process which generated the name class annotated words before they went through the noisy channel
necessarily then the system knows about all words for which it stores bigram counts in order to compute the probabilities in equations NUM NUM NUM NUM
a finite state pattern rule attempts to match against a sequence of tokens words in much the same way as a general regular expression matcher
we use bayes rule pr nc w pr w nc NUM NUM pr w
the tags were taken at face value there were not k best tags the system treated the part of speech tagger as a black box
contrary to our expectations which were based on our experience with english spanish contained many examples of lower case words in organization and location names
in order to generate the first word we must make a transition from one name class to another as well as calculate the likelihood of that word
we present our justification for the problem and our approach a detailed discussion of the model itself and finally the successful results of this new approach
the generation of words and name classes proceeds in three steps NUM select a name class nc conditioning on the previous name class and the previous word
we then assigned an orientation label either or to each adjective using an evaluative approach
NUM informational relations do not appear as often as intentional relations their discriminatory power seems more relevant for clusters
NUM o trib pos i inten rel NUM info rel NUM above NUM core type NUM below NUM NUM NUM NUM
when the core is first and a cue is associated with the relation the cue never occurs with the core
the constituent that makes the purpose obvious in this case l b is the core of the segment
NUM figure NUM shows the tree for core space constraints prevent us from including figures for each tree
NUM table NUM summarizes our main results concerning cue occurrence and includes the error rates associated with different feature sets
among these trees we choose the one that we deem the most perspicuous in terms of features and of complexity
one strategy we utilize is to identify contexts strongly associated with a given semantic event
using the three attributes extracted by the parser we constructed a cross classification of the conjunctions in a three way table
in more complex cases however considerations of grammaticality such as overloading and even interference due to scoping ambiguity may become a serious concern
this hyphenation however resulted in certain words whose stress appeared on a syllable to the left of the antepenultimate position
for many of the applications we envision for segmentation however the user will not correct the output but will rather browse the returned text to extract information
these works consisted of tables of correspondences and examples of words containing these correspondences
o g this procedure is equivalent to a different view on the same problem involving one large combined markov model that enables a very efficient calculation of the maximum
sample lexical entries are shown in figure NUM they associate a word with the semantics of the word special pragmatic restrictions on the use of the word and a set of trees that describe the combinatory possibilities for realizing the word and may impose additional pragmatic restrictions
we have developed a program that automatically generates rules from csp s by generating permutations of each csr the example rules in NUM were generated from the csr in NUM
to the lexicon used by the lexical chooser to determine if such a choice exists
in a hospital setting it can be difficult for caregivers to obtain needed information about patients in a timely fashion
it attempts to include as many other propositions as it can as adjectives or other modifiers of information already included
this means that spoken references must be coordinated with graphical references to the same information
currently we are using at t bell laboratories text to speech system
furthermore the domain labels can directly be used in information retrieval also in language learning tools and dictionary publishing to group concepts in a different way based on scripts rather than classification
the sequence it was a dark and stormy night is a pattern in the sense it occurs in text far more frequently than the frequencies of its letters would suggest but that does not make it a lexical or grammatical primitive it is the product of a complex mixture of linguistic and extra linguistic processes
null the system identifies and uses this indirect information in the following stages NUM
in this setting one would expect a long range model to outperform a trigram or other short range model which does n t avail itself of long range information
table NUM includes a category marked possessive which refers to hill nps that inchtde a possessive adjective referring to an animate entity such as i suoi sforzi his cfforts
in almost any conceivable application a segmenting tool that consistently comes close off by a sentence say is preferable to one that places boundaries willy nilly
for the verbs we see far more ands than ors among the latter also undifferentiated verbs which are troponyms e.g. of both change NUM undergo a change and of change NUM cause to change el also below section that again subsumption is of the type of the harmfully implemented disjunctwe hypernym
in this way further processing could discover the what and how of a sentence or body of text
more formally we must find the most likely sequence of name classes nc given a sequence of words w
table NUM shows the distribution dat of simple and complex sentences in the training and test sets
formally a labeled constituent mc i n may be looked as a syntactic tree
on a 80mhz NUM personal computer with NUM megabytes ram the parser can parse about NUM NUM sentences per second
tagged word sequence NUM how to select the best tree from all of the possible parse trees
in order to build a complete syntactic tree based on the boundary prediction information two basic problems must be resolved
the key of our approach is to simplify the parsing problem as two processing stages
this kind of data provides the basis for matching brackets and labeling the matched constituents
therefore a statistical model for the automatic prediction of constituent boundary is set up
the first one is how to find the reasonable constituents among the partially bracketed sentence
the left hand side could be treated by can end possibilities but a better idea should be to keep within the purview remember previously derived constituents that involve current words
cooperative processing deals with errors that one backtracks to catch if not a different class or range these at least might have a different distribution of error types
however initial tests over a small file of constructed errors showed that the error rules did just as well slightly better in fact at choosing the correct correction
currently processing will just backtrack to the intraword correction level but particularly if there has been no correction yet made pet should consider here the possibility of a simple phrase error
work underway focuses on shallow processing how far error detection and correction can proceed when the system purview is set to a stretch of text which does not admit complete sentential analysis
this would 2microsoft word NUM NUM autocorrect wizard allow one measure of the linguistic feasibility of cooperative error processing the effectiveness of shallow processing over errors revealed by the keystroke record data
cle partial parsing using left corner analysis combined with top down prediction on the results of the phrasal phase looks for complete phrases and breaks down a wordstring into maximal segments
work described below is aimed at evaluating the effectiveness of shallow sub sentential processing and the feasibility of cooperative error checking through building and testing appropriately an error processing system
a system under construction is outlined which incorporates morphological checks using new two level error rules over a directed letter graph tag positional trigrams and partial par tng
pet will give one of three responses to this word it will accept the word suggest a correction or indicate that it found an error it could n t correct
to distinguish between the effects of an utterance and the intentions behind it austin s concept of illocution is split up into two ezpvession of the speaker s attitude and evocation of a reaction in the partner perlocution corresponds to what is actually achieved by the act the evoked respot se cf
for example is kicking the bucket a proper lexical unit
the answer depends on factors external to the unsupervised learning framework
of the resulting word sequences were compared with the true meanings
symbol accuracy was again NUM NUM recall was NUM NUM
the utterances are taken from dictated wall street journal articles
all bits necessary for exactly reproducing the input were counted
furthermore the crossing bracket brown corpus ranked by frequency
the search algorithm described above avoids many of these problems
the first is a means of hypothesizing candidate new words
any statistical definition of pattern depends on an underlying model
we then submitted the explanations to the panel of eight judges
hence we assigned each judge a set of NUM explanations
empirically exploring this hypothesis presents an interesting line of future work
knight has generated hundreds of explanations from the biology knowledge base
process details explains the steps of a process
they combine a frame based representation language with embedded procedural constructs
subjects posed questions to edge about the operation of four circuits
ana and streak were both subjected to quantitative corpus based evaluations
schemata are atn like structures that represent naturally occurring patterns of discourse
we gratefully acknowledge the support of the british council and the swiss national science foundation on grant 83bc044708 to the first two authors and on grant NUM NUM NUM
in this section we describe the larger research context of this work and then briefly discuss the previous work that led to it
we have also greatly shortened the discussion of criteria for and constraints on a possible semantic theory as a foundation for this work
we consider only those verbs that are formed by productive synchronic rules
there are several alternative explanations of such examples involving various accounts of speaker s intentions mutual belief and the like
the more highly ranked an element of cdun the more likely it is to be cb un l
however it appears that such uses are best when the full definite noun phrases that realize the centers do more than just refer
the second case concerns the use of a pronoun to realize an entity not in the cdun such uses are strongly constrained
the variation in aboutness they exhibit arises from different choices of the way in which they express the same propositional content
grosz and sidner distinguish among three components of discourse structure a linguistic structure an intentional structure and an attentional state
the connection between the backward looking center of utterance un NUM and the forward looking centers of utterance un may be of several types
one of the tasks a hearer must perform in processing a discourse is to identify the referents of noun phrases in the discourse
in the following section we briefly illustrate the structure of those parts of the lexicon entry in mikrokosmos which bear on the description of the three types of adjectival meaning scalar denominal and deverbal
in this section we offer a bird s eye view of large scale acquisition of deverbal adjectives with the help of lrva both in the general common case with its non trivial complications deviations and exceptions and in the particular case of the single largest and seemingly most regular subclass of deverbal adjectives namely those ending in able ible
abusivele NUM is then the eventive sense of the adjective formed from abuse v1 NUM and abusive la NUM is the agentive sense of the adjective in the same sense of abuse
artifact permits evaluation of uses component of a system functions role duties ornamentauon purposes food pleasurability and healthfulness there are many others
thus many denominal and deverbal adjectives in fact most adjectives can form a comparative or superlative degree form with the help of more or most respectively e.g. more medical 3ii more aeronautical NUM more employable NUM more abusive NUM NUM
to accomplish that we use a simple criterion that applies only to pairs or groups of words of opposite orientation
surely participles may have syntactic dependents and be used postpositively in english more so than adjectives but this is easily accounted for in the appropriate patterns not demonstrated in the adjective syn struc zone
these are part of any model formulation procedure even if not broken out as separate steps so the tradeoffs explored in this paper are relevant to a wide variety of methods
the basic premise of a probabilistic approach to classification is that the process of assigning object classes is non deterministic i.e. there is no infallible indicator of the correct classification
other nlp tasks that have been performed using probabilistic classifiers include part of speech tagging NUM assignment of semantic classes NUM cue phrase identification NUM prepositional phrase attachment NUM other grammatical disambiguation tasks NUM anaphora resolution NUM and even translation equivalence NUM
the measures are demonstrated in a large experiment in which they are used to analyze the results of roughly NUM classifiers that perform word sense disambiguation
these determinants are the appropriateness for the test set of the results of NUM feature selection NUM formulation of the parametric form of the model and NUM parameter estimation
the committee based selection algorithms work because they tend to select examples that affect parameters with the above three properties
null it may sometimes be difficult to sample p m s due to parameter interdependence
the latter was found to work well only for small batch sizes where the method mimics sequential selection
null finally property NUM is addressed by independently examining input examples which are drawn from the input distribution
the plots shown are for the best parameter settings that we found through manual tuning for each method
the model expresses the relationships among the classification variable the variable representing the classification tag and var ables that correspond to prop this research was supported by the office of naval research under grant number n00014 NUM NUM NUM
b accuracy versus number of words examined from the corpus both labeled and unlabeled
here we present and compare results for batch randomized thresholded and two member committee based selection
first we describe the simplest committee based selection algorithm which has no parameters to tune
the tagger then assigns the sentence to the tag sequence which is most probable according to the hmm
the total number of occurrences is NUM NUM
chi square test for the main grammatical classes distribution of the unknown and the less probable words in the english text for various training text sizes
to hmm t lcb tio tio argmaxp ti w rcb ti i NUM m NUM
the italian and greek corpora have the greatest number of unknown words followed by the spanish corpus for the available results with restricted training text
the significant influence of the training text size on tagger speed is proven by comparing the experimental results in the english corpus newspaper and eec law
the segment bl bj is known as an overlap which is usually one character long
the taggers handle both lexical and tag transition information and without performing morphological analysis can be used to annotate corpora when small training texts are available
in figure NUM the percent occurrence of unknown words in an open testing text of NUM NUM words is shown versus the size of the training text
the actual tagger error rates for all experiments are given in appendices a and b in this section we present a discussion of these error rates
when a new text is processed some words are unknown to the tagger lexicon i.e. they are not included in the training text
furthermore the tags probability distribution of the words that are not included in the training text and are characterized as unknown words is shown
the rule transforms the grapheme into phonemes and stress marks used by the stress module
the set of rules for french consists of about NUM rules and NUM classes
an exception dictionary has been defined for words not correctly translated by these rules
algorithms for grapheme phoneme translation for english and french applications for database searches and speech synthesis
in text to speech systems these rules are typically used to create phonemes from computer text
in any case we apply albeit unconsciously rules to read text aloud
NUM this elision sometimes does not occur as in poetry reading for example
this morpheme decomposition is difficult and is sometimes based on a large dictionary of morphs
there are two kinds of word guessing rules employed by our cascading guesser morphological rules and nonmorphological ending guessing rules
after applying the k and v operations to the training lexicon we obtained rule collections of NUM NUM NUM NUM entries
clearly ending guessing rules have wider coverage than morphologically oriented ones but their predictions can be less accurate
this system divides a string into three regions and infers from training examples their correspondence to underlying morphological features
when we compared this distribution to that of the xerox guesser we saw that the accuracy of the xerox guesser itself was only about NUM NUM lower than that of the cascading guesser NUM and the fact that it could handle NUM fewer unknown words than the cascading guesser resulted in the increase of incorrect assignments by the default strategy
if we keep the sample space for each rule separate from the others we have a binomial experiment
the rule estimate then will be taken at its lowest possible value which is the l limit itself
such rules account for the regular prefixation as for instance un q screw unscrew computational linguistics volume NUM number NUM ending ending guessing rules for hyphenated words ending c ending guessing rules for capitalized words ending ending guessing rules for all other nonhyphenated and noncapitalized words
when the operator is applied to a pair of entries from the lexicon w c i and w c j first it segments the last or first n characters of the shorter word wj and stores this in the m element of the rule
we have described a prediction system which can adapt to a user s vocabulary and syntax with fairly small amounts of data
finally sentence external individuals set the criteria for all relations of social status and thus they should be available in the computation of social status
for instance although the is much more frequent than a an in most corpora following buy the reverse is usually true
during the last year of phase i the government began planning phase ii
prediction also has the side effect of reducing misspellings and typographic errors which is useful because these often make the synthesized speech incomprehensible
in order to experiment with different algorithms we ran simulations using NUM NUM words of data logged from the daily speech of one user
this paper suggests consistently representing these as separate subclasses
where bdy is a special marker to delimit the beginning and end of the clump
wordnet proved to be a valuable and useful tool
the identity of a clump s headword is hidden hence it is necessary to sum over all possible headwords
we will transform these indexed formulae to another form which better suits our needs using the compilation procedure NUM
NUM i ii iii iv xo y yo w wo z z at x az t
in this paper we have demonstrated however that the relatively rich training data obtained for the first preposition can be exploited in attaching subsequent pps
the theme rheme hierarchy of un corresponds to the ranking in the c s
and the effect is less pronounced
for st a typical expectation represents an event with the person organization date etc mtokens in the clause that was matched being used to fill its slots
this meant we had to prevent the secondary organization reduction from matching what are clearly person names eg primary schecter group secondary mr
dooner alan gottesman peter kim walter thompson marti n puris and alas mccann
two out of the three spurious dates are due to our apparentl y mistaken belief that yesterday and tomorrow were supposed to be marked
one step to take is to add to the patterns to allow modifier phrases after the head noun in a descriptor noun phrase such as the agency with billings of NUM million
after those two improvements we turn to the problem of org descriptors although we had th e highest f measure it was only NUM NUM which shows that there is still room for improvement
a tncb is composed of a sign and a history of how it was derived from its children
at the deletion site a new undetermined node is created which may or may not be ill formed
dominance monotonicity merely requires all nodes that are disrupted under this top down control regime to be well formed when re evaluated
conjunction a maximal tncb can be conjoined with another maximal tncb if they may be combined by rule
and nodes dominating the maximal disrupted node which were previously ill formed may become well formed after reevaluation
this can never exceed the number of interior nodes of the tncb formed from n lexical signs i.e.
in this case the first move will be past to bark by conjunction figure NUM
this will produce the correct answer in the test phase of generation without the need to rewrite at all
the implication of this equivalence is that if say we are translating into a s v o language from a head finm language and have isomorphic dominance structures between the source and target parses then simply mirroring the source parse structure in the initial target tncb will provide a correct initim guess
if we interpret the s o and v as subject object and verb we can observe an equivalence between the structures with the bracketings s v o s o v v o s and o v s
the results from the shorter topics may be so poor that the top documents provide misleading expansion terms
this query language uses boolean operators and proximity constraints to create intervals of text that satisfy specific conditions
these systems include most of the major text retrieval software companies and most of the universities doing research in text retrieval
by opening the evaluation to all interested groups tipster has ensured that trec represents many different approaches to text retrieval
the first measure the non interpolated average precision corresponds to the area under an ideal noninterpolated recall precision curve
this also created a subcouection of the longer more structured federal register documents for later use in the research community
three runs can be compared to a baseline run to check the effects of manual versus automatic query construction
topic expansion was done by allowing activation from the top NUM documents in addition to the terms in the original topic
the last two groups in the top six systems using manual query construction used some form of combination of retrieval techniques
less clear is why the manual modifications in the inqi02 run showed superior performance to the automatic nan with no modifications
the remaining nouns and proper nouns in english and all words in chinese are represented in a nonlinear segment binary vector form from their positions in the text
in stage NUM we represent the positional and frequency information of low frequency words by a binary vector for fast matching
its binary vector is v2 i NUM where i NUM NUM NUM NUM NUM NUM NUM NUM
for a noisy corpus without sentence boundaries the primary lexicon accuracy depends on the robustness of the algorithm for finding word translations given no a priori information
since each pronunciation may be derived from different source dictionaries or via different rules each pronunciation of a word may contain multiple derivations each consisting of the list of rules which applied to give the pronunciation from the base form
for each word pron pair p e pron from forced viterbi alignment let derivs p be the set of rule derivations of p for every d q derivs p for every rule r NUM d if r rule
following is a sample entry from this lexicon for the word adams which shows the five derivations for its single pronunciation adams ae dz az m z count NUM each pronunciation of each word in this lexicon is annotated with rule tags
figure NUM computing most likely phone paths in a forced viterbi alignment of of the new york city s fresh nyuw yaorkclk sihtcltiyz frehsh kills landfill on kclkihlz laendclfihl aan staten island for one steltaetclten aylaxndcl faor wahn dumps four million
let derivs p rcb be the set of all derivations of a pronunciation p posr ules p r d be NUM NUM if derivation d of pronunciation p uses rule r else NUM
hh hv voice voice NUM phonological rules are optional the surface lexicon must contain each underlying pronunciation unmodified as well as the pronunciation resulting from the application of each relevant phonological rule
since we have no prior knowledge we make the zero knowledge initial assumption that p d p NUM the algorithm can the be run as a derivs p i successive estimation maximization to provide successive approximations to p dlp
for instance frequency effects which tend to favor the more frequent lexical neighbors need to be properly model if we wish to make a more realistic account of the human performance in the pronunciation task
this second series of experiment is intended to provide us with more realistic evaluations of the paradigmatic cascade rnodeh glushko s pseudo words have been built by substituting the initial consonant or existing monosyllabic words and consl itute
all but one pseudo words of glushko s test set could be pronounced by the paradigmatic cascades algorithm and amongst the NUM pronunciation suggested by our program only NUM were uncorrect that is were not proposed by human subjects in glushko s experiments yielding an overall correctness of NUM NUM and a precision of NUM NUM
at the end of the learning stage we have in hand a set a lcb ai rcb of functions exchanging suffixes or prefixes in the graphemic domain and for each ai in a i a statistical measure pi of its productivity defined as the likelihood that the transform of a lexical item be another lexieal item
in fact one of our main goal was to define in a sensible way the concept of a lexical neighborhood it is therefore important to check that our model manages to keep this neighborhood relatively small
this is especially obvious in the context of example based learning techniques where the inference of some unknown linguistics property of a new object is performed on the basis of the most similar available example s
as a consequence the definition of neighborhoods implicitely incorporates a great deal of linguistic knowledge extracted fl om the lexicon especially regarding morphological processes and phonotactic constraints which makes it much for relevant for grounding the notion of analogy between lexical items than say any neighborhood based on the string edition metric
the second stage of the pronunciation procedure is to adapt the known pronunciation of y and derive a suitable pronunciation for x the idea here is to mirror in the phonemic domain the series of alternations which transform x into y in the graphemic domain using the statistical pairing between alternations that is extracted during the learning stage
the task of the annotator is to correct the output of a parser i.e. to eliminate wrong readings complete partial parses and adjust partially incorrect ones
for example 12a c define the following candidate sets for r and s
an inner quantifier is also returned together with a flag which indicates scoping choices within the restriction
for example the descriptions 13a c are licensed by the partitions 12a c respectively
not all sentences provide equally good descriptions of the model but they are all true in it
this example actually meets all three criteria
predictor predict cat state corresponds to a prediction of the category cat as a modifier of the category cat and to the transition to state in case a substructure headed by cat is actually found
this is modeled by introducing two new items in the set a cat NUM i which represents the initial state of the transition graph of the category cat which will span a portion of the input starting at i
in the sample grammar below this extension allows for several prepositional modifiers under a single verbal or nominal head without introducing intermediate symbols the predicate arguments structure is immediately represented by a one level flat dependency structure
the transition graphs obtained for the five categories of g1 are in fig NUM conventionally we indicate the non final states as h and the final states as sk where h and k are integers
the leftward or rightward orientation of the arrows in the dependency tree represents the order constraints the modifiers that precede the head stand on its left the modifiers that follow the head stand on its right
the other four items are the result of a double application of the predictor which in a sense builds a chain that consists of a noun governed by the root verb and of a determiner governed by that noun this is the only way according to the grammar to accomodate an incoming determiner when a verb is under analysis
but the pairs considered are not all the possible pairs one of the sets has the index which is the same of one of the positions and the complexity of the completer is o igi NUM n3
if a display of misunderstanding occurs in the turn immediately following the one that was misunderstood and the speaker notices the problem immediately and acts to resolve it then we say that they have made a third turn repair see example NUM
the analysis stages are fairly standard and are arranged in a
markers for reported speech are distributed over all sentences inside the quotes
parsing of whole sentences using the tomita algorithm NUM
summary templates where the article does not contain a prominent event
the output consists of a reference to another template enabling hypertemplates
a fairly sophisticated facility existed pre muc as had a few template
figure NUM block diagram of the lolita core plus some application s
like vp and np are treated according to x theory
the sisters of the head are parsed recursively
several key points of the article are also identified despite weak parses
there is some evidence of it happening in the walk through article
the minimalist program is a generation oriented framework
event event adj ng org l compl vg active resume word NUM ng talk word lcb with ng org NUM i event adj rcb type talk parties list obj i obj NUM talk status bargaining
in describing the system we will say what it does given as input the following paragraph from the management succession domain of muc NUM a c nielsen co said george garrick NUM years old president of information resources inc s london based european information services operation will become president and chief operating officer of nielsen marketing research usa a unit of dun bradstreet corp
prep with NUM pobj org semantics type talk parties list obj i obj NUM talk status bargaining
we have implemented a number of parameterized metarules that specify the possible linguistic variations of the simple active clause expressed in terms of the subject verb and object of the active clause and having the same semantics
as part of tipster ii v we are engaged in a joint effort with the university of massachusetts to determine ways in which information extraction technology can improve the performance of document retrieval systems such as the inquery system
we search for noun groups of the right number and gender first from left to right in the current sentence then from left to right in the previous sentence and then from right to left in two more sentences
in our view the principal current problems are the need to handle broader domains and applications the need to continue to make new domains easier to implement and the need to use the technology in a wide variety of new applications
we have made it substantially easier to use in new domains by implementing the fastspec declarative specification language and the compile time transformations and we believe our work on adapting rules from examples will make the system yet easier to use in new domains
for example there is a general rule allowing a noun hyphen past participle sequence in the adjective position of noun groups and there is a specialized version of this for a location followed by based as in london based
the second important characteristic and modifier referents of the subject phrase NUM NUM NUM referents of noun phrase modifiers NUM NUM e.g. prepositional phrase relative clause relations expressed by subject NUM NUM NUM NUM prepositional phrase and relative clause of salience is its gradedness
NUM association for computational linguistics computational linguistics volume NUM number NUM the location and identification of persons objects events processes and activities being talked about or referred to in relation to the spatiotemporal context created and sustained by the act of utterance and the participation in it typically of a single speaker and at least one addressee
it could mean that the bali s location is referred to in relation to the car from the speaker s point of view deictic use or with respect to the orientation of the car itself intrinsic use or with respect to the actual direction of motion of the car extrinsic use
when combined with the three categories of interaction modes unimodal graphical unimodal linguistic and multimodal this results in the four types of referring expressions listed in table NUM NUM the basic principle that is used by edward to solve referring expressions is the same for all four types of referring expressions shown in table NUM
we currently use the following tentative heuristic for associated individual instance retrieval all relations are taken into account between the referent in context in this example department NUM having a name relation with nici and a referent of the requested class that can be expressed by the lemma van of
the role set restrictions specify for example that the filler of the recipient role in a send relation is not at least not in our current domain allowed to be identical to the filler of the agent role NUM the interpreter could use these restrictions to exclude certain referents from the set of potential referents
deixis and anaphora implies that if a particular individual instance has the highest sv the user need not be very specific and can use for example het it die that one die file that file or dat ding that thing
henceforward we will say that an individual instance is in context if its sv is more than NUM the elegance of this particular notion of salience is that it allows for a unified measure of salience which is determined by an indefinite number of independent factors that can be monitored separately
in the context model on the other hand influences originating from different levels and types of processing are modeled by individual cfs which are created and managed locally i.e. by these processes themselves
if the highest sv is shared by several instances a tie edward will ask the user to indicate which of the candidates is intended e.g. do you mean donald report
second the second pass is more complicated than the first typically ineaning that it has more states
the computer has complete dialog control
the user has complete dialog control
subdialogs and effective movement between them
these modules will be described next
our system dynamically modifies the active rule
can you turn the switch up
subdialogs are entered in several ways
weight log number of classes with term NUM the class score is calculated by the following equation ll
for example one topic NUM document described arms control efforts in france and this was always misclassified as topic NUM
if the two characters prior to the ed are the same and not s remove the second one
this is approximated by taking the frequency of the term occurrence in the training set divided by the size of the training set
in this paper we both extend the classification scheme to include any number of topics and modify the scheme to also perform routing
topics NUM and NUM had a significant overlap in distinguiqhing words and this created the most difficulty in choosing the proper class
if the last character is s and the next m last is any consonant except s remove the s
but making the assumption that the training set and the corpus have the same distribution of words the following weights wonld be calculated
vocalic consonant sonorant rhotic advanced front high low back rounded tense voiced w offglide y offglide coronal anterior distributed nasal lateral continuant strident syllabic silent flap stress primary stress to be an invariant of the unmodified ostia algorithm but it is not essential to the working of the algorithm
in order to resolve this problem and the related cases of arbitrary phone deletion we saw above we need to appeal to the fact that theories of generative phonology have always assumed that all things being equal surface forms tend to resemble underlying forms
in linguistics theories of such prior knowledge are referred to as universal grammar ug nativist linguistic models of learning assume implicitly or explicitly that some kind of prior knowledge that contributes to language learning is innate a product of evolution
finally in a deterministic transducer there is no need to computational linguistics volume NUM number NUM table NUM a slightly expanded arpabet phoneset including alveolar flap syllabic nasals and liquids and reduced vowels and the corresponding ipa symbols
a transduction of an input string to an output string corresponds to a path through the transducer where the input string is formed by concatenating the input symbols of the arcs taken and the output string by concatenating the output symbols of the arcs
in these rules the transducer must wait to see the right hand context of a rule before emitting the rule s output and the rule applies to a general enough set of phones that additional states are necessary to store information about the pending output
the simple rules we used in our experiment contain no feeding the output of one rule creating the necessary environment for another rule or bleeding a rule deleting the necessary environment causing another rule not to apply relationships among rules
our model is rather intended to suggest the kind of biases that may be added to other empiricist induction models and the way in which they may be added in order to build a cognitively and computationally plausible learning model for phonological rules
the algorithm begins by constructing a tree transducer that covers all the training samples according to the following procedure for each input pair the algorithm walks from the initial state taking one transition on each input symbol as if doing a transduction
taking p l7 i l6 as an example the first probability corresponds to the event that lcb f g rcb are the constituents to be reduced and the second probability corresponds to the event that they are reduced to c the transition probability can thus be expressed as follows
one of the challenges in processing chinese is the difficulty of word segmentation
the activation of the clusters in cluw by the context c demonstrates that c is similar with the contexts of the clusters in cluw so there should be at least one cluster in clu in which the senses are similar with the correct sesne of w in c
results were very positive in english
this approach was applied to both chinese and spanish
experimentally we find that this significantly reduces the perplexity of unseen word combinations
computational linguistics volume NUM number NUM where e t is a positive function which usually decreases with time to control the convergence speed of the learning process u is a positive definite matrix which is assumed to be an identity matrix in the current implementation and NUM is the gradient operator
NUM the schema also has two constraints
figure NUM modifier schema for absolute modifiers
NUM NUM constructing in the corner
next the plan derivation is evaluated
this feedback allows the system to exploit complex term extraction before activating syntactic recognition in order to prune out significant components of grammatical ambiguity
NUM as an example a term like debito pubblico public debt receive a mutual information score according to the following figure
however their model is not computational
this does not mean that the context free assumption is wrong
we choose rule NUM to expand the first a node
the probabilities assigned to the states implicitly imply different preferences for left hand side contextual environment of the reduced symbol since a computational linguistics volume NUM number NUM state in general can indicate part of the past parsing history i.e. the left context from which the current reduced symbol follows
with the frequency of proof trees in the training corpus
gibbs sampling is possible for the application that dd l consider
then we can encode the bitstring directly into a boolean vector term of the kind we discussed earlier
in the usual colmerauer encoding an impossible boolean combination is signaled by all the arguments being shared
having a glb of computational linguistics volume NUM number NUM btm is read as failure of unification
this will in fact be the case whether or not the lattice has the properties we are assuming
we often use underscore as a variable if we are not interested in its value
i say without necessarily sacrificing efficiency because some compilation strategies may actually make matters worse
in this case the idea behind the compilation just described can be incorporated into the analysis directly
in some cases the extra level of embedding that this method gives might actually be linguistically motivated
it might seem that something like p a np a np would accurately describe the possibilities
we describe it again here because we will need to know how it works in detail later on
we separate these words into the following classes family name first name town name company name country name
the choice of this feature aff ts the structure in four ways
the variable elimination process for color variables is very simple it allows to transform a set g u lcb a c d rcb of equations to d a g u lcb a c d rcb making the equation lcb a c d rcb solved in the result
in order to be legal a g substitution such a mapping a must obey the following constraints if a and b are different colors then a xa a xb i.e. the color erasures have to be equal
brent s cues are very primitive but because hc only picks up frmnes when the indicators are mmmbiguous his results are very reliable albeit sparse unless a very large training corpus is used
such a heuristic should have the following three characteristics null NUM verbs that are close to the word being examined should carry more weight in the decision process than verbs that are closer to the perimeter
there are several rules that decompose the syntactic structure of formulae we will only present two of them
the role of colors in this is to restrict the logically possible solutions to those that are linguistically sound
ideally the name of the type phrase that the verb occurred in should be used as a clustering feature but since this information is unavailable the non terminals in the trees implicit in the bracketing are unlabelled the next best thing is used and each boundary is marked by a pair of tags occurring on either side of the bracket
incidentally the projection is only a c unifier of our colored example if c and d axe identical
where the first equation determines the interpretation of the ellipsis whereas the second fixes the value of the fsv
clearly something must be done to separate the wheat from the chaff the problem is twofold getting the grammar and lcxicon to a ccrtain level of competence was a laborious and timc consmning process and undoing this i.e. eliminating unwanted options is ahnost as difficult and painfifl as the constant augmenting in the first place
the problem is that what is grannnatical depends on the tmwritten rules of a certain domain
this produces a flat tree with phrase boundaries marked and identified by type but without much internal detail
the range of numerical and temporal expressions covered by the task was als o limited one notable example is the restriction of temporal expressions to exclude relative time expressions suc h as last week
the measures used for information extraction include two overall ones the f measure and error pe r response fill and several other more diagnostic ones recall precision undergeneration overgeneration and substitution
the evaluation metrics used for ne are essentially the same as those used for the two template filling tasks template element and scenario template and are discussed in the paper by chinchor in this volume on the scoring software
for each np in the equivalence class it would be useful to identify its grammatical type proper noun phrase definite common nou n phrase bare singular common noun phrase personal pronoun etc
the author would also like to acknowledge the critical behind the scenes computer support rendered at nrad by ti m wadsworth who passed away suddenly in august NUM leaving a lasting empty spot in my work and my heart
there may in fact be just one organization involved the person could be leaving a post at a company in order to take a different or an additional post at the same company
also the descriptor is not always close to the name and some discourse processing may b e required in order to identify it this is likely to increase the opportunity for systems to miss the information
the university of durham reported that the y had intended to use gazetteer and company name lists but did n t because they found that the lists did not hav e much effect on their system s performance
as indicated in the table below all systems performed better on identifying person name s than on identifying organization or location names and all but a few systems performed better on location name s than on organization names
the mix of challenges that the scenario template task represents has been shown to yield levels of performance that ar e smilar to those achieved in previous mucs but this time with a much shorter time required for porting
the retrievedocuments operation will not fail due solely to the absence of a nil monitor argument
if the type is not supported a reasonable default shall be provided with the type indicated
athe same is true fl r practically all accounts of focus projection that i am aware of of e.g. selkirk utterance is the question test where the tbcus in the answer corresponds to the interrogative constituent in the question
although it is very comprehensive and explicit no manual can ever foresee and cover all the tricky instances that will occur in unrestricted language
the latter ones may lead us to questions about the nature of language and to what extent natural language really is exact and welldefined
informally speaking during a synchuvg dl derivation the two synchronous productions in a pair of synchronized vectors must be applied at the same time and must rewrite linked occurrences of nonterminals previously introduced
another possibility would be to amalgamate tile two readings into one bivalued or underspecified depending on how one chooses to see it
i also address some objections to wsd research
the relevant expressive patterns and the contexts within which they are found have the great virtue of being objectively observable and assuming the use of these patterns is common to all native speakers it should be possible to reach a consensus classification of the patterns according to their contextualized meaning and use
adherence to evolving architecture standards and commitment to reusing shared software impacted negatively on the demonstration systems
how these resources are managed most effectively provides new challenges to both the government and contract groups
anticipate that unforeseen challenges of a new language will probably drive system performance down to some degree
each filler found in the text is assigned a confidence score based on distance from trigger
the semantic interpreter contains two subcomponents a rule based fragment interpreter and a pattern based sentence interpreter
it applies finite state patterns to the input which consists of word tokens with part of speech
we have previously observed that two character sequences perform much better than single character selection in relevance feedback
the database used was the chinese peoples daily collection containing more than NUM megabytes of text
then the performance of a system can be evaluated based on the subset of judged documents
for reasonably large sets a subset of documents is identified and judged for each query
actually only about NUM NUM of words were not fully disambiguated after step NUM
the final heuristics favor tags that have survived all conditions that restrict their use
the non contextual rules that eliminate the remaining NUM ambiguities produce an additional NUM errors
altogether the lexicon mismatches produced NUM NUM errors to the input of the tuggers
a new language specific tagger can therefore be built with a minimal amount of work
the result was not good NUM of the words were tagged incorrectly
for instance the word avions may be a noun or an auxiliary verb
our constraint based tagger is based on techniques that were originally developed for morphological analysis
as illustrated in figure NUM the generation process amounts to the iterative selection of b out of word level subdivision part of speech and null no expansion
this tsstllnption results in the robustness mid ada ptability of the inodel eveli though unti a ined events ocolir
wc can rcwvit the prolmldlily of0ach scqttcnc as a prodltct of NUM he cotmil iona l
the more state of random variables are near to llie solution the niore the system becomes stable and energy function has lower vahie
unfortunately our corpus only contains tagged senses for NUM words and this set of words does not constitute a sufficiently large fraction of all occurrences of content words in an arbitrarily chosen unrestricted text
topping the list are the english possessive suffixes which have no equivalent in french or in most other languages
in this section we very briefly discuss some previous approaches to the text segmentation problem
for instance they can try to distinguish segments expressing informs and yn questions according to the f0 curves associated with them a distinction which would be especially useful for recognizing yn questions with no morphological or syntactic markings
clearly the high road is the most desirable for the longer term integration of knowledge sources is a fundamental issue for both cognitive and computer science and maximally automatic use is intrinsically desirable
the methodology is successfully applied to this portion showing that there are in fact patterns of expression in cordless telephone manuals that can be identified and implemented
in rst these spans have typically been clauses as is the case in the remove phone passage but certain phrases with propositional content may be considered as well
in the hypothesis phase the analyst hypothesizes a feature of the communicative context that appears to correlate with the variation of some aspect of the lexical and grammatical forms
the issues of procedural planning user modeling and content selection although of unquestionable importance to the broad goal of generating instructions were not specifically addressed here
in example 6a there is a precondition on the high level purpose of removing the phone a feature that correlates well with the use of the by form
it was useful for analyzing expressions of actions or situations that are expressed as being the result of other actions as in place the handset in the base
currently imagene uses simple algorithms for pronominalization and determiners which are not based on a detailed corpus study of the forms and functions of the object reference domain
theorem NUM any c parser p with running time o t g t n on grammars of size g and strings of length n can be converted into a bmm algorithm mp that runs in time o max m NUM t m2 t mu3
in figure NUM the links are shown as lightfaced directed arrows marked with either new sentence or continue sentence in this segment only the latter is used
the next question to be asked is whether this is the only explanation of fronted purpose expressions or whether there are other fronted purpose expressions that are not global
breaking out equation NUM as NUM 2x sb is represented as the product of the frequencies of all subdivision symbols at node sb and kullback leibler kl divergence
according to the definition of unit element u ltr u
the mean segment length in the training and test data was NUM p NUM sentences
in vp ellipsis strict identity corresponds to copying the entire role assignment from the antecedent
it is also possible for a qlf to be uninterpretable to specify no possible evaluation
there are alternative ways of referring to both the reasons and the method
the writing of this paper was supported in part by the fracas project lre NUM
the first reading involves scoping the book quantifier before ellipsis resolution
one must apply the knowledge that austrians speak german to correctly interpret the ellipsis
the syntactic structure underspecifies the intended composition so that the meanings of some constituents e.g.
tense in vp ellipsis illustrates how categories can be put to work
the third from sloppily identifying both the books and the pronouns
the substitutions can be built up in an order independent way i.e.
the matching rate as shown in table NUM increases from NUM to NUM for tr2 which shows that the constraint on discourse segment beginnings in tr2 is effective
the speakers as a whole agreed with kappa greater than NUM NUM on only NUM out of the NUM anaphora with complete agreement only NUM times
in the annotation stage each of NUM native speakers of chinese is given five test sheets corresponding to five texts generated by our generation system
although the algorithms would be refined due to the introduction of more discourse structure they would essentially still serve the purpose of distinguishing potential referents
according to the above algorithms a single description would be produced for both anaphora if the context sets at both places contain the same elements
null the language model relevance statistic appears for the first time in the sixth feature
the breakdown of the matched nominal anaphora in the test data in terms of the above clas NUM sification is shown in table NUM
therefore we enhanced rule NUM by adding the above syntactic constraints on zero anaphora which becomes rule NUM as shown in figure NUM
let us examine the situation when the proof below is awaiting presentation
it is natural to expect that in a segmenter close should count for something
at present we have chosen only one intuitively plausible way to generate increasingly complex rules with refinements introduced as they occurred to us though not motivated by the data
text NUM contains three topic shifts that would make the rule containing the salience constraint tr3 obtain different output computational linguistics volume NUM number NUM from those without this constraint
nevertheless during this process a systematic method of identifying additional nonsplitting sequences was discovered based on a rule for stressing that states that a stress mark can only be applied to the ultimate penultimate or antepenultimate position of a word
lemma NUM the rules presented in table NUM are sufficient to completely hyphenate all words in which each vowel sequence is included in set vl iv2 such that vl iv2 e v u 2v u vc
for words containing at least one n gram vowel sequence with n NUM it is not apparent which vowel pairs if any will constitute a double vowel blend or a vc so that the associated negative rules can be applied
furthermore diphthongs and excessive diphthongs comprised of either digrams consisting of two vowels or trigrams consisting of a vowel and a double vowel blend or tetragrams consisting of two double vowel blends need to be precisely separated before the rules are applied
the remaining strings are vlclc2 c c3 v2 and as indicated by c2 and c3 their hyphen point is the point preceding cl if clc NUM e cc or the point between cl and c2 otherwise
all combinations are made up of two parts both parts of double vowel blends and combinations c v cv etc of rule v2 are vowels while both parts of diphthongs and excessive diphthongs can be either vowels or double vowel blends
NUM however tokenization is achieved unambiguously because vowels are examined from left to right and a concrete token of the v type is extracted if it does not form a double vowel blend or an element of the vc set with its subsequent vowel
taking into account the initial specification that the hyphenator should never generate non acceptable hyphens and in order to pare down the enormous sets of candidate diphthongs and excessive diphthongs we need to isolate the subset of sequences for which splitting is always permitted
they posit a more complex representation of attentional state to meet these challenges
in this paper we propose a reestimation algorithm and a best first parsing algorithm for pdg
they report improved results on speech act resolution in a corpus of scheduling dialogs
to investigate the possibility of automating annotation experiments were performed with the cleaned part of the treebank NUM approx
the proposed reestimation algorithm is a variation of the inside outside algorithm adapted to probabilistic dependency grammars
precontrol attentional spaces are attentional spaces that contain the controlling attentional space
the fast mode reduces the analysis to increase speed with minimal degradation in performance
proverb must select referring expressions for methods of inference in pcas as well
the list of labels given to each word of the lexicon is classified by frequency as shown in the example below
we parked the car in the garage
when two adjacent spans are merged into a larger span some conditional tests must be satisfied
other occurrence of the verb require as they have been found in the rsd corpus are
all sentences below stem from the corpus
as lexical ambiguity pervades language in texts the words used in dictionary are themselves lexically ambiguous
since g does not derive the empty string the label of the root of t is not s
the points labeled NUM are the results of running the egrap h keys on the training data
the rate which the economist expects
figure NUM some useful filter cascades for training corpora as large as NUM sentence pairs
the average of these NUM fl actions was NUM NUM with a standard deviation of NUM NUM
the system was implemented and tested on a NUM million word newspaper corpus
the french half was kindly tagged by george foster of citi
a cognate filt er is another kind of oracle filter
this is called the longest common subsequence ratio lcsr
NUM of the source tokens were found in the mrbd
the oracles lists often supplied more than one match per word
this kind of automatic quality control is indispensable for an engineering approach to better machine translation
a simple strategy was adopted to demonstrate the practical utility of filters presented in this paper
for each translation wordnet yielded its senses in the form of wordnet concepts synsets
the system performed with an overall accuracy of NUM NUM
still many words with frequency NUM seem exotic or idiosyncratic uses
unfortunately the celex data contain some noise especially for the german entries
this meant that the extracted word lists had to be manually checked
it is weu known for its long standing employment with the european commission
null NUM what percentage of the test words is correctly translated
NUM adjectives nouns verbs with frequency NUM or less
the method is based on lists of words from different frequency classes
but now there are several commercial systems for the same language pair
define the terminal alphabet r s s i o s s i l s s i NUM s s i z s s NUM o s s NUM z s a s b sigma
a more linguistically valid factor would be differences in morphosyntactic language typology
using part of the mlcc corpus part of speech tagged and sentence aligned using lt nsl tools they explored various techniques for finding word alignments
NUM the manual annotation is wrong and a correct tagger prediction is counted as an error
the tag statistics for the database were derived from the suc subset
the experimental results show that the precision rate of NUM NUM and the recall rate of NUM NUM can be achieved for the repair processing
because this paper corrects repairs based on acoustic and prosodic cues the chinese characters in the spoken corpus are converted into the corresponding syllables manually
figure NUM the relation between the number of equations used and the accuracy
typical examples are interjections e.g. o2 oh and phrase final particles e.g. i a5 a
from table NUM we find that glottal stop is a more reliable cue than unfilled pause but it does not occur as frequently as unfilled pause
although our method can perform well in repetition repairs other kinds of repairs such as addition replacement and abandon repairs are not addressed in this paper
when the unfilled pause information and the glottal stop information are all applied to the baseline model the experimental results for two conversations are listed in table NUM
these points are verified by the high precision rate NUM NUM and the low recall rate NUM NUM
r a scs NUM NUM scs NUM s s NUM o s s NUM z a
a second reason is that on line analysis being shallow or surface bound should be relatively quick as opposed to plan based analysis
the rest NUM NUM in conversation l and NUM NUM in conversation NUM are the most complex type of repairs i.e. abandon
that is if many utterances issued by other speakers are inserted between two utterances of the same speaker the repetition repairs usually do not occur
in one possible partly distributed control structure the coordinator would oversee a set of agendas one or more for each component
in this experiment we used the function proposed by kurohashi et ai
in some cases the tagger was able to abstract from these errors during the training phase and subsequently assigned the correct tag for the test data
no matter what part of the world the documentation is written in it is normally first written in english and then translated into all the other supported languages
arbortext s adept editor is used with idwb NUM easyenglish summarizes the problems encountered in a given document by giving an overall rating the clarity indez ci
the size of such supervision increments varies from local trees of depth one to larger chunks depending on the amount of training data available
user friendliness is attained by integrating easyenglish with suitable editing environments in such a way as to make changes easy and to keep the easyenglish information upto date with these changes
possible rephrasing which the header file did not include the parse supplies the information necessary to decide on the correct word order and tense used in the rephrasing
that is these figures show how we i each system does what it tries to do rather than how useful what it tries to do is
NUM the cl checks of easyenglish do work better when the text is not too ill formed grammatically since ill formedness reduces the chances of the parser making good sense of the input
there are easyenglish commands for compiling a user maintainable format of these different kinds of dictionaries into efficiently useable forms and for creating abbreviation dictionaries from terminology dictionaries in maintainance form
however the more grammatical the text is the easier it is to read and translate so it seems that this concept of a cl checker is too narrow
the animacy constraint checks whether the anaphor in question is animate
in this experiment the reliability of assigning phrase categories given the categories of the daughter nodes they are supplied by the annotator was tested
to derive a finite state automaton representing global lexical rule interaction we first determine which lexical rules can possibly follow which other lexical rules in a grammar
when forced to make a decision even in unreliable cases NUM errors occured during the NUM test runs NUM NUM error rate
let q be the set of all grammatical functions that can occur within a phrase of type q assume that these sets are pairwise disjoint
in contrast an unusual compound such as apple juice scat may only be compatible with general nn and would be assigned the most underspecified interpretation
indeed it is unclear what meaning is conveyed by the weights and consequently the means by which they can be computed are not well understood
update a a is not well defined because the cotton bag can not be one of the bags in 4a
let s consider the representation of 3b with the highest probability i.e. the one where cotton bag means bag made of cotton
a discourse is incoherent if NUM update t a NUM holds for every available attachment point a in
we describe a formal framework for interpretation of words and compounds in a discourse context which integrates a symbolic lexicon grammar word sense probabilities and a pragmatic component
this is not the usual possessive compare his blacksmith s hammer with his blacksmith s hammer
the syntax of datri is as given in see ion NUM except of course that the three forms of global inheritance descriptor are omitted
given that the values of the sequences c and c are known then the value of c c can be obtained simply by concatenation
at the other end of the scale there are judges who will accept any translation which conveys the approximate meaning of the sentence irrespective of how many grammatical or stylistic mistakes it contains
ill contrast the node path pair dog cat rcb obtains its value indirectly by local inheritance from the value of noun cat
intuitively the required value is obtained by concatenating the values of the descriptors dog root and noun surf rcb yielding dog s
we wish to provide an inductive definition of an evaluation relation denoted between sequences of datr descriptors in desc and sequences of atoms i.e.
two sinfilar rules are required for sentences contmning quoted descriptors of the forms n lcb c and qs
for a given datr theory t rules of this kind lllay e used to deduce additional sentences as theorems of
given the premise s the rule lieenees the conclusion the value of path l at node nj is t
in fact it only makes sense to talk about the value of a global descriptor relative to a given context of evaluation or 91obal context
when one does ir in the chinese language with its peculiar property then one would assume that accurate word segmentation is also a crucial first step before other processing can begin
if the example of figure NUM is interpreted in this way we also loose simple symmetry of antosemy from the mere fact that the antosem of stt down NUM is artse NUM one can no longer infer that the antosem of arise NUM is sit down NUM
from the results presented in the following one can infer that redundancy free data was the aim of the wordnet lexicographers but apparently if by insertions redundant data was generated this occasionally was missed if the redundant data was out of sight of the human checker or the checking program i.e. distant by some hierarchical levels from the point of update
it is an abstraction from one possible correction of the constellation the antonyms of the synonyms v energue and v smnulate NUM should be synonyms i.e. v de energize and v sedate should be synonyms and in order to achieve that the concepts v deenergtze and v sedate need to be merged
it is well known that a sentence in chinese or several other oriental languages consists of a continuous string of characters without delimiting white spaces to identify words
however because these two antonyms are synonyms the value set of the induced antosemy relation of the concept belonging to trust NUM has cardinality i therefore we say that trust i has no genuine multi value antonymy or synonymously it has binary antonymy although its antonym set has cardinality NUM
this rule may be based on the feature model of concepts if we assume a concept representation by features then hypernymy entails inclusion of all features of the superconcept and this can not be compatible with an antosemy between superconcept and subconcept which may for example be based on a meta antosemy relation between features
asking for a check which would be adequate in more generality we draw the reader s attentmn to the two superconcept chains which before they end up in a common ancestor are headed by hfe form and object NUM in the sense of the negation of life form see glosses
returning to table NUM and table NUM i indicates the size of segment and i the length of text
the vertical dimension gives the probability that a segment at a particular position is chosen as most similar to the title
the information returned consists of a fuzzy match list containing all of the words beginning with ma
a most serious problem is that as the length of a story increases the model s performance quickly degrades
finally it is shown that information on text structure is more effective for large documents than for small documents
here we tried two approaches one is based on a fixed length segment and the other on a proportional length segment
behind this is an assumption that parts of the text most similar to its title would best represent its content
here two nonterminal grammar means a grammar which uses only s sentence and np noun phrase as actual non terminals in the grammar and other grammatical nodes like vp or pp are embedded into a rule
however a way of calculating the optimal value of credit is not yet available so a preliminary method described in this section was used for the experiments
the costs of candidates outputted by a rule based tagger were used as the source of information related to the credit
translation in speech to text mode evaluation of the system s performance on a given utterance proceeds as follows
in one failure there was confusion by the subject about when the circuit was working and in another failure there were problems with the system software
in addition time information was recorded for when the parser finished its processing of the input and when the computation of the input interpretation was complete
furthermore we have reported the results from the analysis of NUM dialogues collected during experimental use with a system based on the overall dialogue processing model
the message reader module determines message boundaries identifies the message header information and determines paragraph and sentence boundaries
the two primary measures reported by walker and whittaker are average number of utterances between control shifts and percent of total utterances controlled by the computer
the ability to yield the initiative as users gain experience is essential if a dialogue system is to be useful in practical applications involving repeat users
NUM if the dialogues follow the transition model then the largest entries should be in the values in the diagonal just above the main diagonal
this particularly increased as users gained more experience with only NUM of the NUM declarative dialogues of the final session containing no unusual transitions
each of these dialogue types has advantages as a model for system building in terms of the relevance of the data to the final model
in the models the n gram and the hmm are used to model tag sequence and p wlt is used for another part of the model
in particular ax NUM x NUM represents the initial state probability trx NUM of x NUM
how would these systems perform in upper case only or in languages where initial capitalization does not signal a name
without specifying more closely what is meant by acceptable it is difficult to compare evaluations
dialogue continuations the dialogue model provides all possible continuations of a dialogue while the dialogue history defines the context in which to calculate the continuations
another example would be the abbreviation of acts for acquiring specific information as in what are the source and the destination of your call
finally we will have to build a much more powerful task model in order to support the disambiguation and abstraction procedures and the generation process
this goal can be further classified with respect to the kind of information under discussion i.e. interpreted in the context of the state of the task model
consider the following dialogue fragment a sys where do you want to call request b usr i want to call hamburg
in our approach we apply this observation to the design of information systems hoping that it results in an interaction as illustrated in dialogue NUM
this is a realization of the dominating goal acquire uval starttime and the subordinate goal acquire confirmation of svai destination
because these metrics are based on human judgements such judgements need to be reliable across judges in order to compete with the reproducibility of metrics based on objective criteria
hence in this kind of dialogue we can not predict what information the user chooses to provide and hence can not predict the system s response
here some goals are expressed implicitly e.g. confirmation in utterance c while others are omitted e.g. asking for the destination
a dcg is a simple example of a family of constraint based grammar formalisms that are widely used in natural language analysis and generation
luckily ordinary recognizers parsers for cfg can be easily generalized to construct this intersection yielding in typical cases a much smaller grammar
even if some promising results have been obtained there are still some problems to solve in this approach
let n be the number of features of the current category
in both cases all other weights maintain the same value
where wit is the weight corresponding to the active feature indexed by ij
thank to michal landau for her help in running the experiments
tpartly supported by a grant from the israeli ministry of science
the algorithms we study are all learning algorithms for linear functions
the data is split into training set and test set based on lewis s
multiplicative update algorithm are known to tolerate a very large number of features
similarly editing such texts using a normal text editor becomes tedious and error prone
it is recommended that future work should include attention to allowing interfacing between both approaches
it stands in need of extension to provide more flexible access to hierarchical structure
in addition all of them make use of the markup created by earlier steps
the implication of this is that corpus components can be hyper documents with low density i.e.
on words while the latter can index on any level of the corpus annotation
solaris NUM NUM and linux and a windows nt version will be released during NUM
lt nsl is a tool architecture for sgml based processing of primarily text corpora
null the gate idea of providing formal wrappers for interfacing programs is a good one
the idea of having formalised interfaces for external programs and data is a good one
the context free grammar defining this intersection is simply constructed by keeping track of the state names in the non terminal category symbols
additional dialogue act readings can be proposed and the dialogue history can be changed accordingly
the most common reason is gross misrecognition but translation problems can sometimes be the cause as well
additionally we investigate methods to cluster training dialogues in classes with a similar structure
we expect that wrap up would tell a similar story if we were to compute the learning curve for key relationa l decisions
part of this drop in recall and precision comes from low recall for new status which is critical to this scenario
although this would ease the problem of diverging systems it might als o suppress imaginative new perspectives on the coreference problem
we will limit most of our discussio n to a single sentence as it was handled by resolve crystal and wrap up
as such the resolve decision trees represent crucial capabilities along with routines designed to analyze appositives and other complex noun phrases
defl extracts a person from any syntactic buffer as long as the people string specialist identified a person in that buffer
we are frankly surprised to see resolve operatin g as well as it does on the basis of only NUM training documents
thanks to jan alexandersson for valuable comments and suggestions on earlier drafts of this paper
results are presented by simply counting the number of translations in a run which fall into each category
therefore we developed a statistical method which is described in detail in the next section
a conversational game is a sequence of moves starting with an initiation and encompassing all moves up until that initiation s purpose is either fulfilled or abandoned
krippendorff also points out that where one coding distinction relies on the results of another the second distinction can not be reasonable unless the first also is
as a result krippendorff warns against taking overall reliability figures too seriously in favor of always calculating reliability with respect to the particular hypothesis under test
the information can be some fact about either the domain or the state of the plan or task including facts that help establish what is mutually known
more discussion between the expert and the novices might also improve agreement on segmentation but would make it more difficult for others to apply the coding systems
all four coders were postgraduate students at the university of edinburgh none of them had prior experience of the map task or of dialogue or discourse analysis
for this study they simply segmented and coded four dialogues using their normal working procedures which included access to the speech as well as the transcripts
for instance it would be odd to consider a classification scheme acceptable if coders were unable to agree on how to identify units in the first place
for example we expect the semantic categories described in the previous section to be useful in prediction outside the context of a full cogeneration system and also to allow better output from the speech synthesizer e.g. in the pronunciation of homographs such as bow in stress placement for compounds etc
it may sometimes be necessary to do lexical disambiguation to support generation but often this is not required
there are also limitations in compatibility with particular software or hardware and restrictions in the physical interfaces
this scoring method is an idealization but is a reasonably accurate predictor of keystroke savings for our user
we aim to build the cogeneration system in a modular fashion that allows the reuse of knowledge sources
one possibility would be to use n grams based on words or word stems but even assuming that we could extract useful information from one of the existing large text corpora rather than an aac specific one the data is still likely to be too sparse for all but the most frequent words
the simplest way to add contextual information is to temporarily increase weights of recently seen words recency
the alternative strategy which we have adopted is to back off to semantic classes for infrequent or unseen collocations
this rule applies to punctuation and numbers as well as to lexical cognates
a set of spanish to english speaker independent translation experiments were performed integrating in our speech input system as described in ror rates wer and real time factor rtf for the best spanish to english transducer
in a first phase the scenario has been limited to some human tohuman communication situations in the reception of a hotel asking for rooms wake up calls keys the bill a taxi and moving the luggage
the viterbi search for the most likely path was speeded up by using beam search at two levels independent beam widths were used in the states of the sst empirically fixed to NUM and in the states of the hmms
the symbol a represents the empty string first letters a b c represent individual symbols of the alphabets and last letters z y x represent strings of the free monoids
this predicts that the distribution of preposition occurrences changes from pp1 to pp3 with an increase in the proportion of low attaching pps
when the judge has determined the acceptability of the recognition hypothesis the text version of the translation is presented
this measure expresses that the number of useful contexts should be diverse for different labels
at the initial step such node is one whose lower nodes are lexical categories
the assumption is that a context with has the highest variance is the most effective
reducing the number of contexts will help us to improve the computation time and space
table NUM displays the detail results of our statistical pexser evaluated against the wsj corpus
to compute the similarity of labels the concept of local contextual information is applied
the larger de is the larger the information fluctuation before and after merging becomes
om this result the proposed parsing model is shown to succeed with high bracketing recalls to some degree
this phase produces an early td dictionary of simple terminological elements
it can be shown that each round in the algorithm produces a likelihood that is at least as high as the previous one the em algorithm is therefore guaranteed to find at least a local maximum of the likelihood function
the similarity goes further as both kupiec s and our approach is based on state transitions and dotted productions earley states turn out to be equivalent to rtn states if the rtn is constructed from a cfg
earley s algorithm allows for an adjustable amount of lookahead during parsing in order to process lr k grammars deterministically and obtain the same computational complexity as specialized lr k parsers where possible
all of the applications listed above involve or could potentially make use of one or more of the following speech technology and research laboratory sri international NUM ravenswood ave menlo park ca NUM
for the purpose of this definition we allow scanning to operate in generation mode i.e. all states with terminals to the right of the dot can be scanned not just those matching the input
a the forward probability oq kx NUM a d is the sum of the probabilities of all constrained paths of length i that end in state kx
the analysis of the text above on the basis of the local grammar presented in figure NUM type ii pn spec pt posq e in NUM NUM
all full name ssi sequences here appear attached whereas in the preceding text they all appear with a blank i.e.
o chinese language in fact the distinction between these two categories might be arbitrary
poyn mily let us examine the following sentences here several of the noun phrases we have examined so far occur piled together
in the following sections we will classify in five types the contexts where proper names can appear and describe their characteristics in detail
pns are understood in general as phonic sequences associated with one referent without any intrinsic meanings such as socrates bach or paris
we can eliminate these interpretations since these forms precede the complex sequence that requires necessarily a pn
boy dropped his wallet somewhere in the parse structures each terminal category leaf node is given a name tag while there is no label for each nonterminal category intermediate node
for two words w and w i let pa w wl be the probability of word w occurring to the left of w i within the distance d
this property is very useful in word class based language modeling used in speech recognition for it allows the system to have several powerful candidates to be matched during recognition
and another shortcoming is that a small number of words in almost every resulting class does n t belong to the part of speech categories which most of words in that class belong to
so NUM is too long for dimensions and the search in searching space is computationally prohibitive and i is so small for dimensions that much information will be lost
during the splitting process especially at the bottom of the binary tree some classes may be empty because the classes higher than them can not be splitted further more
it is an excellent metric for comparing two language models because it is entirely independent of how each language model functions internally and also because it is very simple to compute
many casks in computational linguistics whether they use statistical or symbolic methods reduce the complexity of the probl m by dealing with classes of words rather than individual words
since the splitting procedure is restricted to be trees as opposed to arbitrary directed graphs there is no mechanism for merging two or more nodes in the tree growing process
the distinction between the open a and closed ct has almost disappeared in france in favor of the open a
the closed phoneme is used for instance before a phoneme s pose chose oser or at the end of a word abricot escargot
swahili for example was written in arabic script until NUM when krapf a german missionary introduced the roman alphabet to the bantuspeaking peoples of the east african coast
in certain languages as diverse as spanish and swahili letter to sound rule sets are extremely easy to produce due to the extremely close fit between orthography and its phonemic phonetic equivalent
the grapheme string is matched using a simple right to left text compare and the context strings are matched by a recursive procedure that interprets the pattern string built by the rule compiler
in this study we took two different corpora NUM a NUM NUM word corpus originally used by bill huggins bb n and eventually by dennis klatt mit
using the same formalism a different set of rules has been defined for proper names found in a typical telephone book in the us and could be extended to other languages
these differences are only due to a mismatch between open or closed phonemes for phonemes a e and o
as suggested above discourse relation elements have the following characteristic in lud
on the other hand nodes x y and z will be thresholded out because none is part of such a sequence
rather than having to examine NUM values in a single dimensional space we might have to examine NUM combinations in a two dimensional space
to remove a symbol means to substitute it by e a regular operation
the techniques used here should allow similar advantages for a variety of such theories
the parser selects a word NUM and proves that the category associated with this word is the head corner of the goal
furthermore these cyclic structures lead to practical problems because items containing such a cyclic structure may have to be put in the table
for this reason we will assume that parse trees are not built by the grammar but rather are the responsibility of the parser
the structure of the parse forest in the head corner parser is rather unusual and therefore we will take some time to explain it
note that all nodes except for substitution nodes are associated with a rule or lexical entry of the original grammar
if we assume that the grammar constructs logical forms then it is not clear that we are interested in parse trees at all
the composition of an input sentence with these transducers produces a weighted finite state automaton which is then input for the parser
NUM xp sem pp sem xp sem advp sem
while we do n t know of other systems that have used exactly our techniques our techniques are certainly similar to those of others
the head corner parser is one of the parsers that is being developed as part of the nwo priority programme on language and speech technology
head corner parsing is a radical approach to head driven parsing in that it gives up the idea that parsing should proceed from left to right
a user can work with annotations produced by any tipster compliant language processing software e g nama recognizers phrase spotters
crl is engaged in the development of document management software and user interfaces to support government analysts in their information analysis tasks
the software developed at crl is based on crl s extensive experience of user interface support for government analysts developed during phase i of tipster
the goal of the project is to reduce the amount of manual time that an analyst will spend reviewing and processing each cable NUM ftm the free text management project is a demonstration system that will be located in the
the database contains NUM noun and only NUM verb concepts with more than one direct superconcept
however not all cases of cardinality NUM of antosemy are caused by deliberate ternary antonymy
for morahty NUM and immorahty NUM weak commutativity holds
if we normalize the probabilities of possible combinations by distributing the sum of the probability assigned to all impossible combinations the result is the same as that gotten by iteratively combining the pairwise distributions using dempster s rule
so if the speaker wants to achieve goal she will attempt to construct a plan whose effect is bel hearer goal speaker goal
NUM the first schema shown in figure NUM is used to terminate the recursion and its constraint specifies that only one object can be in the candidate set
this would take the form of either repairing the expression by correcting speech errors expanding it by adding further qualifications or replacing the original expression with a new expression
one way is to work together or collaborate in formulating a plan of action with other people who are involved in the actions or who know the relevant information
in the case of a refashioning the hearer might not view the proposed referring expression plan as being sufficient for identifying the referent but would nonetheless understand the refashioning
in our formalization of the conversational moves we have equated the first case to reject plan and the second case to postpone plan and their constraints test for the abovementioned conditions
if there is more than one then this constraint is peter a heeman and graeme hirst collaborating on referring expressions postponed and the evaluator moves on to the subset constraint
in inferring a plan derivation we first find the set of plan derivations that account for the primitive actions that were observed without regard to whether the constraints hold
in collaborating to achieve a mutual goal participants sometimes propose an action that is not believed by the other participant or even by the participant that is proposing it
we need to formalize what it means for agents to be collaborating in a theory that takes account of rational interaction and the beliefs and knowledge of the participants
because every suggestion rejection acceptance or appointment confirmation is also giving information about the schedule of the speaker state constraint is considered to be a weaker form of suggest reject accept and confirm appointment
in a separate evaluation with the same set of dialogues performance in terms of attaching the current chain of inference to the correct place in the plan tree for the purpose of augmenting temporal expressions from context was evaluated
it is assumed that the input string is represented by the trans NUM predicate
note that off line parsability is one possible way of ensuring that this is the case
to solve this problem we construct the suffix trees t t for w w respectively
when an attachment to the active path is attempted a regular expression evaluator checks to see that it is acceptable to make that attachment according to the annotations in the plan operator of which this new action would become a child
edges are labeled by factors of w which are encoded by means of two natural numbers denoting endpoints in the string
this is because attaching a leaf node corresponds to pushing a new element on the stack adjoining a node di to a node dj corresponds to popping all the stack elements through the one corresponding to dj and pushing di on the stack
in this paper we investigate how we can generalize this approach for unification grammars
it is shown that existing parsing algorithms can be easily extended for fsa inputs
in a straightforward approach this would also lead to a finite state automaton with cycles
in the parse forest grammar complex symbols are non terminals atomic symbols are terminals
to evaluate the performance of our algorithm we nmst first determine what the expected baseline or lower bound on performance would be
whatever theory is chosen syllabification should serve as an accurate input into the module that handles stress
it consists of NUM NUM utterances in spanish
to describe another copier c43 which is the fastest copier to fill with paper spud would describe not only its rate but also the relevant action in order to distinguish it from c42 i.e.
besides the above problem the critical points for local minimum are not obvious in some cases
note that n is the length of the tag sequence and the last chunk is always a one tag
obviously the attributes by which we describe abstractions like events and states typically time location and manner or quality are quite distinct from the natural attributes by which physical objects are distinguished
whether a hard idea is hard to formalize to communicate or to understand depends on the topic to be clear a natural language system must model how its audience arrives at such understandings
this means that collocations in definite descriptions either will arise only by accident or by generate and test search or by a secondary specificatioo that ensures the preference for semantics that can ultimately be realized using collocations
three tags say fa fb and gg must be treated in particular
then pi is input to the probabilistic chunker and a chunk sequence c is produced
thus a function magn determines the realization of a concept very intense intensely NUM a magn escape a narrow escape to magn bleed to bleed profusely
ignoring these three special tags only nineteen susanne tags have wrong mapping in uniq0e tag case
all these applications employ the syntactic information extracted from different treebanks and show the satisfactory results
susanne tag iw can be mapped to lob tags in or ri in the above experiment
the tag mapper in this figure is used to transform the susanne part of speech into lob part of speech
besides the simple but effective chunker can also be applied to many natural language applications
consequently the growing phase is terminated before the training samples assigned to the leaf nodes are entirely homogeneous
schedule the prototype will be completed by september NUM
the prototype is being built and evaluated at the federal intelligent
use the tipster architecture in the development and work closely with the
ndic has strong needs for retrieval and extraction from large multi source textual databases
management of free text for ndic an overview of the ftm project
information on lessons learned is shared among all the participants and sponsors
the database will serve as the basis for analytical tools
hardware environment is a dec alpha NUM server and two celefis 5100dp dual pentium workstations
the current grammar containing e.g. NUM rules is applied to the ambiguous input in a trace mode in which the parser also indicates which rule discarded which analysis NUM the grammarian observes remaining ambiguities and proposes new rules for disambiguating them and NUM
however if the grammar should be of a very high quality extremely few mispredictions high degree of ambiguity resolution a large test corpus formally similar to the input except for the manually added extra information about the correct analysis should be used
the syntactic parser s main task is disambiguating rather than adding new information to the input sentence contextumly illegitimate alternatives should be discarded while legitimate tags should be retained note that also morphological ambiguities may be resolved as a side effect
given a set of variables a set of possible labels for each variable and a set of compatibility constraints between those labels the algorithm finds a combination of weights for the labels that maximises global consistency see below
rl fd inf r cr x pk m x x pkd m is the product of the current weights NUM for the labels appearing in the constraint except vi tj representing how applicable the constraint is in the current context multiplied by cr which is the constraint compatibility value stating how compatible the pair is with the context
he also tries to identify misanalyses cases where the correct tag is discarded and using the trace information corrects the faulty rule this routine is useful if the development time is very restricted and only the most common ambiguity types have to be resolved with reasonable success
the NUM NUM word corpus of journalese from which these constraints were extracted was analyzed using the following modules engcg morphological tagger module for introducing syntactic ambiguities the np disambiguator using the NUM rules writ null ten in a day no human effort was spent on creating this training corpus
utterances like 8c may be used to provide a basis for a shift in cb NUM
in translation the information on discourse function is important for deciding whether to translate a particle at all and how to do that by inserting a corresponding target language particle or by modifying the syntactic structure or intonation contour of the target utterance
there exists undoubtly strong historical evidences supporting the view that the orthographical system of most european languages developped from a such phonographical system and languages like spanish or italian still offer examples of that kind of very regular organization
given the youth of the field plus the fact that particles at first sight do not exactly seem to be the most important challenge for translating spoken language it comes as no surprise that there are no satisfactory solutions in implemented systems yet
future work will include how to decrease the number of equations without degrading the performance and application of our framework to other nlp tasks for the further evaluation
the dismiss sense of fired matches with boss at NUM levels of subject domain coding thus scoring NUM NUM NUM NUM for both sentences
in verbmobil the deep analysis is undertaken in the context evaluation coneval module which constructs a conceptual representation based on a domain model coded in a description logic language from the output of the syntactic semantic analysis module
as such overly many equations are redundant and the time complexity to solve the simultaneous equation becomes a crucial problem
x is a list of variables which represents the statistics based length sbl for the corresponding branch in the thesaurus
as fillers we often find phrases like lch wiirde denken or ich muff sagen in english the translation i must say is not wrong but not conventionally used in this context
simr can be ported to a new language pair in three steps
in analysis the deep representation holds all the information required for successful processing the transfer and generation components can then decide whether discourse functions get realized in the target language and if so by what means
thus depending on the s ze of the lattice we can use either the first or the second way
thus we have the types of antecedent anaphor pairs shown in figure NUM since in the new rule the condition of topic continuity in clause will be considered to refine the zero leaf node in the decision tree of rule NUM we focus on investigating the corresponding anaphora in the classification trees
given the wide variety of information required for determining discourse functions listed in section NUM NUM the task is best performed in tandem with building up the conceptual representation of the utterance i.e. in the coneval module
the main strength of the gibbs distribution is that it can handle complex overlapping features and therefore account for feature interaction
therefore we do n t have to use the iterative scaling during constraint ranldng and apply it only for linear model regression
therefore we do n t have to use the iterative scaling for constraint ranking and apply it only for linear model regression
we set a complex constraint which is a logical conjunction or collocation of the two atomic features NUM and cap
by support here we mean the c relation and the direct support is that there is no intermediate nodes between two
since there are no distracting elements for the string in the discourse the use of full descriptions at the beginning of sentences e and g can be interpreted as emphasizing that a new discourse segment sentence has begun
we considered as features bigram and trigram combinations together with unigrams of possible parts of speech for words in question
this gave us NUM NUM atomic features but the lattice itself was not surprisingly fiat and had NUM NUM nodes
this type of expectation differs from the type defined above in that it depends on the real beliefs of the agent performing the first rather than the second part of an adjacency pair and it does not depend on the activity of any suppositions or actions
although we have not yet considered the problem of indirect utterances in detail we anticipate that such explanations might include as a subtask the kind of plan based inference that has been proposed but this inference would be limited by the hearer s own goals and expectations
if during a turn t a supposition is expressed by an agent through the utterance of some speech act or the display of misunderstanding then we say it becomes active in the turn sequence that has t as its focus see section NUM NUM NUM
this says that the fact that the surface form asurfaceform can be used to perform discourse act a in some context and the apparent occurrence of a would be a reason for agent sl to utter asurfaceformdeg NUM the model does not discriminate between equally acceptable alternatives
mcroy and hirst the repair of speech act misunderstandings the theory includes the discourse level acts inform informif informref assert assertif assertref askref askif request preteu testref and warn which we represent using a similar notation
thus the test for subj is carried out first followed by iobj and obj if any
however for certain verb forms for example ones in pseudopassive voice it also marks the subject
thus we would generalize the above interpretation to be that a full description is preferred for a subsequent reference if it is at the beginning of a sentence or the first mention in the sentence otherwise a reduced description is preferred
NUM a zhangsan i jinghuang de wang wai pao zhangsan frightened nom towards outside run zhangsan was frightened and ran outside b zhuangdao yige renj he bump to a person he bumped into a person
both methods have approximately a NUM success rate in pairing the senses of morphological variants if those problems are removed
the data in table NUM and table NUM shows that many words are related in meaning despite a difference in partof speech
it is not always possible however to provide phrases in which the word occurs only with the desired sense
however there are also distinctions that are important in information retrieval that are unlikely to be important in machine translation
these are typically proper nouns there are approximately NUM words in the longman dictionary which have more than one part of speech
a lexical phrase is a phrase that might be defined in a dictionary such as hot line or back end
that is we had a false positive rate of NUM because the tagger indicated the wrong part of speech
for example cook as a noun is defined as a person who prepares and cooks food
we manually examined the word tokens in the corpus for each query word and estimated the distribution of the senses
evidence the next set of experiments were concerned with determining the effectiveness of different sources of evidence for distinguishing word senses
a sample scenario template is shown in the appendix
to avoid one zero count of p vj ci nullifying the effect of the other non zero conditional probabilities in the multiplication we replace zero counts of p vj ci by p ci n where n is the total number of training examples
in addition the contribution of a word should be evenly distributed between all the senses of a word and the contribution of a sense should be evenly distributed between all the concepts in a sense
dbcc defmition bascd conceptual cooccurrence and human mark the columns with the results of our system and the human subject in disambiguating the occurrences of the NUM words in the brown corpus respectively
to disambiguate a polysemous word a system can select the sense with a dictionary definition containing defining concepts that co occur most frequently with the defining concepts in the definitions of the other words in the context
NUM the evidence mutual information score from multiple defining concepts words is averaged rather than summed NUM NUM NUM
noun that is at the head of a sentenced to life prepositional phrase following a verb nouns that are subject and the hawk found a object of the same action perch given a pair of words and the adjacency relationship the disambiguator applies all heuristics corresponding to that category and those word senses that are rejected by all heuristics are discarded
we introduce a degree of context dependency into the structure of newspaper articles shown in figure NUM in order to extract keywords
we recall that if i satisfies both NUM and NUM in section NUM the word i is regarded as a keyword
some heuristics look for specific hypernyms such as person or place in the input words e.g. if a noun is followed by a proper name as in tenor luciano pavarotti or pitcher curt schilling those senses of the noun that have person as a hypernym are chosen
our method and vector model shows the results of our method and using vector model respectively
in key paragraphs experiment the overall results were positive especially when the ratio of extraction was NUM NUM
according to table NUM the average ratio of our method and method a was NUM NUM and NUM NUM respectively
from the observation in a corpus such as wall street journal utilising a location heuristics is useful for extracting key paragraphs
as a result the result of the extraction of key paragraphs shown in table NUM was also worst NUM NUM
in our method every sense of words in articles for extracting key paragraphs is disambiguated in advance and linking method is performed
as a result paragraph NUM and NUM are the most semantically similar paragraphs and NUM was not extracted as a key paragraph
the test article and the results of it was shown in figure in figure NUM the headline shows the title name
for example w e correctly marked both expressions problematic for most other systems the 21st century and hollywood
we can make further improvements in terms of the perception o f loss of the key creative assignment for the prestigious coca col aclassic account
the same machinery is used as a metalanguage for describing and propagating arbitrary boolean constraints including dictionary entries describing morphological and grammatical constraints
moreover holes for scope domains are discerned from other labels
leq relations read that labels are always less or equal to labels in the given order
lud grouping and lud mota show among others which labels are to be treated together to construct drss
the paper outlines a treatment of multiple discourse relations on the sentential level in two aspects
this form can be seen as a participle form re form of noda mentioned above
the interpretation of a possible plugging at the top hole is the interpretation of the matrix drs
first modifiers share its instance with the modified drs and show no different scopal behavior
this treatment reflects the fact that each element can introduce a different partition of the same sentence
it should be read such that a label is bound to plugged into a hole
to the extent that the c command relation is unclear between them the resolution remains unclear here
NUM coordination a coordination the combination of two terms with a common head word or a common argument
la production et surtout la diffusion des semences the production and particularly the distribution of the seeds
conclusion this paper has proposed a syntax based approach via morphologically derived forms for the identification and extraction of multi word term variants
for instance the following set of constraints express that the noun modernisateur is morphologically related to the word modernisation NUM
1as quantifier noun denotes an arbitrary set n of individuals d such that d has the property noun and that the cardinality of n is determined by quantifier and NUM a n ap vz e s every n p b a encodes wide scope type raising and b narrow
NUM improved handling punctuation it is fair to say that before muc NUM we largely ignored as opposed to handled punctuaction
table NUM scenario template processing statistics
dooner who recently lost NUM pounds over three and a half months as a monetary value rather than ignoring it as a weight
unknown word accuracy on the test corpus was NUM NUM and overall tagging accuracy on the test corpus was NUM NUM
the system maintains a mechanism for indicating what is reasonable to ask and what is not as described below
we show here that there exist transformation lists for which no equivalent decision trees exist for a fixed set of primitive queries
in transformation based learning the objective function used in training is the same as that used for evaluation whenever this is feasible
if we instead used a lexicon where the is listed unambiguously as a determiner the baseline accuracy would be NUM NUM
when transformations are allowed to make reference to words and word pairs some relevant information is probably missed due to sparse data
NUM we found it a bit surprising that the addition of lexicalized transformations did not result in a much greater improvement in performance
NUM when training contextual rules on NUM NUM words an accuracy of NUM NUM was achieved on a separate NUM NUM word test set
their responsabilities properties behavior and interface are determined by the classes they belong to
the key idea consists in adopting a manager based object based view of the architecture shown in figure NUM
moreover gaps between appointments may be specified in order to permit sufficient time between meetings
tation for temporal expressions as is shown in figure NUM
temporal information is partitioned into range appointment and duration information
figure NUM semantic annotation of pps and nps an
5the recall r and precision p for the three variations axe vax1 r NUM NUM p NUM NUM
agent agent interaction usually relies on an initiating agent being responsible for the success of a negotiation
figure NUM named entity test results
most misrecognitions were corrected automatically and thus resulted in no such messge
figure NUM vtw tcc vtt configuration for automatic construction of an electronic dictionary
its major domain is news articles and reports from the china times daily news
we consider here the replacement operation in the context of finite state grammars
it seems that the performance is not significantly different between the two different models
the global system achieves a precision rate of NUM NUM at the recall rate of NUM NUM
furthermore there are only NUM trigram words and NUM NUM gram words in the training seed corpus
the vtt module will be estimated in terms of several weighted tag precision and recall rate measures
the remaining n grams then form the word candidates for expanding the various segmentation patterns
the constant NUM is used for training an appropriate threshold
a particular segmentation pattern can be expressed in terms of the words they have
therefore the corpus size may not be a critical issue in this task
to specify that c a p holds for all phrases while the hfp only holds for headed phrases we now have to manually split the definition into two clauses the subcase we want to attach the hfp to and the other one
all other tags had to be manually inserted
while in ttpsg theories the principles usually form the main part of the grammar and relations such as append are used as auxiliary constraints called by the principles a more traditional kind of grammar for which one prefers a relational organization can also be expressed more compactly by adding some universal principles constraining the arguments of the relations to the relational core
instead of permitting the grammar writer to express universal wellformedness constraints directly the systems require the grammar writer to express relational constraints and attach them locally at the appropriate places in the grammar NUM we believe there are several reasons why the advances in the linguistic data structures should entail the development of systems offering more expressive means for designing grammars
firstly if there is another constraint t NUM with disjunctive then the compiler will need to normalise the expression c v c ac a
a good example for this behavior was shown in fig NUM the system does not instantiate the phon and the head values of the solution since the existence of grammatical values for these attributes is independent of the query
as grammar size increases it becomes very difficult to track down bugs or termination problems without it since these problems are often the result of some global interaction and thus can not be reduced to a manageable sub part of the grammar
the unconditional replacement of upper by lower is
this paper proposes a corpus based language model for topic identification
this paper will touch on its feasibility in topic identification
that is their 1df values are reset to zero
this is the spatial locality of events in a discourse
the performance can be measured as the table NUM shows
the discourse topic is usually the form of topic sentence
this paper adopts a corpus based approach to process discourse information
thus nouns play the core part in the underlying language model
row NUM gives the rank of assumed topic
we have demonstrated on muc NUM and on cdis that we have an excellent approach to both entity an d event extraction on a range of document types
the most knowledge intensive module is the plan recognizer
a cooperation request protocol this prot oeol allows atl agen to ask one or nlore agents to operate with it in order to solve the conflict it has crt ated it has l roduc ed
an interaction i rotoeol is a set of rules containing t he NUM ossible intt ractions during a conversation it provides strategies for t rol leln solving due to the co existence of several agents in tile same system
sian s protocol will be sin NUM litied aim det omposed for better understanding into three l roi o ols mt assertion proto oh this l rotoc l allows a gents to send t artial or eomt lete results to the concernetl agents it is use t
among them one can find systems for english analysis such as ask thomson NUM loqui binol al NUM team pereira NUM and for prench analysis such as saphir erli NUM or leader benoit el
there are different types of decomposition knowledge decomposition by abstraction pret morph segm synt task decomposition by type of input coord nega task decomposition by type of output ellip
so we have defined the talisman architecture that includes linguistic agents that corrost ond either to classical levels in linguistics mori hology syntax semantic or to coml lex language phcnolnena analysis
repair subdialogue r establish that the correction for the errant behavior has been made
posterior probability from god and over wrath grace shall abound NUM NUM NUM NUM from god but over wrath grace shall abound from god and over worth grace shall abound from god and over wrath grace will abound before god and over wrath grace shall abound from god and over wrath grace shall a bound from god and over wrath grape shall abound
such error messages from the experimenter occurred on average once every NUM user utterances throughout the experiment
problems NUM through NUM of sessions NUM and NUM consisted of NUM missing wires for each problem
c the switch is connecting to the battery when there is a wire between connectors NUM and NUM
the remaining control shifts were due to requests for repetition of the previous utterance or requests for other information
in these cases there was a greater need for the experimenter to notify the user of the misunderstanding
to evaluate the likelihood of the whole mixture we build a tree of maximal depth d containing all observation sequence suffixes of length up to d thus the tree contains a node s iff s wi k l wi with NUM k d NUM i n
there are a few exceptions e.g. appositive and conjunctions modifying plural nouns are evenly split between same and different orientation
we identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives
here we present an overview of the experiment sufficient for understanding the environment in which the data were collected
as described in section NUM NUM our view of initiative concerns which participant s task goals currently have priority
advanced motif based multi lingual user interface capabilities supporting chinese japanese korean arabic and other writing systems
figure NUM gives theoretical upper bounds on the matching flexibility as the lengths of the sequences increase where the constituent structure constraints are reflected by high flexibility up to length NUM sequences and a rapid drop off thereafter
we adhere here to a purely task driven definition of what a correct segmentation is namely that longer segments are desirable only when no compositional translation is possible
local collocation was shown to be the most indicative knowledge source for lbxa8 and these NUM features are the common features used in both lf x as and teo et al s bayesian algorithm
currently only one is used at a time
in addition this couple presents some original desirable features that we intend to push further
in practice the number of parameters in a head automaton language model is dominated by the dependency parameters that is o v NUM ri parameters
NUM power tr dieses ist hart
we think this success is due to several factors
an utterance such as the meeting starts at NUM is represented as an interval rather than as a point in time reflecting the orientation of the coding scheme toward intervals
the recognition hypothesis are displayed in table NUM the recognizer is nuance
nevertheless we are pleased with our early results
s the context and hence a corresponding node in the tree used for predicting the word wn l with a given pst t wildcards provide a useful capability in language modeling since syntactic structure may make a word strongly dependent on another a few words back but not on the words in between
we distinguish them thanks to segmental cues and to local word variations between competitive hypothesis
it is difficult to compare our results with those in the word prediction literature because of the lack of common corpora and of an agreed standard of measurement
inserted and substituted elements are a major problem as they are a source of misunderstanding
in this illustrative example the results of the regression with all factors included shows that only and rep are significant p NUM
paradise provides a method for determining a performance function for a spoken dialogue system and for calculating performance over subdialogues as well as whole dialogues
to calculate costs over subdialogues and for some of the qualitative measures it is necessary to be able to specify which information goals each utterance contributes to
note that to calculate such costs each utterance in the corpus of dialogues must also be tagged with respect to the qualitative phenomenon in question e.g.
given the definition of success and costs above and the model in figure NUM performance for any sub dialogue d is defined as follows NUM
building text planning resources by hand is timeconsuming and difficult
section NUM NUM describes the use of linear regression and user satisfaction to estimate the relative contribution of the success and cost measures in a single performance function
the research described here concentrates on the latter aspect although the utility of various techniques is partially dependent on the interface
the repair utterances in figure NUM are a3 through u6 thus c2 d1 is NUM utterances and c2 sa is NUM utterances
it is possible however that user satisfaction data collected in future experiments or other data such as willingness to pay or use would indicate otherwise
on the other hand nlp components can be robust with respect to recognition errors
tony called him at 6am the next morning
NUM a terry really goofs sometimes
c john wanted to meet him quite urgently
he had frequented the store for many years
then ross perot slammed him on his tax policies
here we argue that this is also insufficient
he was furious for being woken up so early
NUM a terry really gets angry sometimes
the resulting transition definitions are summarized in table NUM
however their annotations are at the level of individual expressions rather than at the level of temporal units and they do not present the results of an intercoder reliability study
outputs of off the shelf machine translation mt systems are often of low quality and even high end mt systems have problems particularly in translating proper names and specialized domain terms which often contain the most critical information to the users
NUM using the one sense per discourse property the algorithm performs well using only local collocational information treating each token of the target word independently
in addition they can disambiguate types of names so that a person named washington is distinguished from a place called washington and a company apple can be distinguished from a common noun apple
in the query mode cf figure NUM a form based boolean query issued by a user is automatically translated into an sql query and the english terms in the query are sent to the term translation module
these definitions have already been implemented
cogeneration involves three different types of knowledge source a grammar and lexicon statistical information about collocations and preferred syntactic structures application and contextdependent templates
the morphology of turkish enables morphological markings on the constituents to signal their grammatical roles without relying on their order
both muc NUM and muc NUM involved sanitized forms of military messages about naval sightings and engagements
how this might be tested in the context of a muc is not entirely clear
one significant advantage of the cogeneration technique is that extra information is available to guide the speech synthesizer allowing more appropriate intonation prosody and even volume
in addition p ter kim was hired from wpp grout s i
prot lems arose with ea h of t he semeval tasks
these i roi osals to let ailed spe cifi ations
the second muc also worked out the details of the primary evaluation measures recall and precision
walter thompson org type company person NUM NUM per name peter kim
it can all be gone like that figure NUM sample coreference annotation
as shown in figure NUM we assume that the order domain within nps or pps is essentially flat and moreover that domain objects for np internal prenominal constituents are prepended to the domain of the nominal projection so that the linear string is isomorphic to the yield of the usual rightbranching analysis trees for nps
this syntactic table is smaller than the one used in the previous approach and the proportion of probabilities which are close to zero is also smaller
the first approach can produce false assumptions while the second one slows the message composition rate and demands a great knowledge of the syntax by the user
for instance in english friends is the only variation without creating composed words of friend and the verbs have a few variations too
the first area of research in support of transportability and user customization was the development of the pattern specification language fastspec
if we assume following kathol in progress that topicalized constituents are part of the same clausal domain as the rest of the sentence NUM then an extraposed domain object inherited via partial compaction from the topic will automatically have to occur clause finally just as in the case of extraposition from regular complements
non monotonic tbm segments result in a characteristic map pattern as a consequence of the injectivity of bitext maps
thus if p is the only point in its row and column its ambiguity level is zero
second the angle of each chain s least squares line is compared to the arctangent of the bitext slope
injectivity no two points in a chain of tpcs can have the same x or y co ordinates
nevertheless in order to facilitate comparison of the geometric approach with other alignment algorithms i have designed the geometric sentence alignment gsa algorithm to reduce 3the techniques presented in this section can be applied equally well to paragraphs lists of items or any other text units for which boundary information is available
for example if a token at position p on the x axis and a token at position q on the y axis are translations of each other then the coordinate p q in the bitext space is a tpc NUM tpcs also exist at corresponding boundaries of text units such as sentences paragraphs and sections
the asynchronous productions of the two synchronized gram null mars are not subject to the synchronization requirement and they can be applied at any time and independently of the other grammar but of course subject to the grammar specific dominance links
we claim that each set n has size bounded by the number of nodes in this can be shown using the fact that all derivation trees represented at a node of rq employ the same multiset of productions of g
when starting at a root node and walking through the graph if we follow exactly one of the outgoing arcs at each or node and all of the outgoing arcs at each and node we obtain a tree in t modulo the removal of the or nodes
in the first stage we construct the vector derivation tree NUM associated with t let q be the number of nodes of we also construct a parse forest 7rq representing the set of all parse trees in g with no more than q vectors
if gs is lexicalized such a parse forest has size bounded by a polynomial function of i t i despite the fact that the size of t can be exponentially larger than the size of t in fact we have a stronger result
we call family the set lcb n rcb and any nonempty subset of out n n e f the main idea is to associate a set of families n to each node n of 7rq such that the following condition is satisfied
parts of the present research were done while rambow was supported by the north atlantic treaty organization under a grant awarded in NUM while at talana universit6 paris NUM and while satta was visiting the center for language and speech processing johns hopkins university baltimore md
a convenient normal form is shown to exist
the paper is divided into two main parts
the common underlying idea in all of these formalisms is to combine two generative devices through a pairing of their productions or in the case of the corresponding automata of their transitions in such a way that right hand side nonterminal symbols in the paired productions are linked
selection between multiple possible arrangements may be arbitrary
the same then applies recursively to each subtree
the key is that for any given subtree if the outermost bracket involves a singleton that should be rotated into a subtree then exactly one of the singleton rotation properties will apply
moreover we also show how postprocessing using rotation and flattening operations restores the rank flexibility so that an output bracketing can hold more than two immediate constituents as shown in figure NUM
the problem is particularly acute for english and chinese because word boundaries are not orthographically marked in chinese text so not even a default chunking exists upon which word matchings could be postulated
parsing must overcommit since the algorithm is always forced to choose between a bc and ab c structures even when no choice is clearly better
individual quantifiers are selected for generation on the basis of dependency function partitions
l he a rc lal ns l ticn help ix retilie i llosc chlsses t y acldiu s solile of the sllrl olilidhl words whicli axe li l pa i l t lie clkluc bul
the algorithm is as follows where the choose construct indicates non determinism
most of these approaches however need large or even very large corpora in order for word classes to be discovered NUM whereas it is often the case that the data to be processed are insufficient to provide reliable lexical intbrmation
for instance in the whole set of words connected to etude study in a strongly connected component of the ntc graph analyze evaluation resultat presentation principe calcul travail some subsets form cliques with etude
medium size corpora in companies with a wide range of activities such as edf the french electricity company the rapid evolution of technical domains the huge amount of textual data involved its variation in length and style imply building or updating numerous terminologies as nlp resources
at each node we visit we compute a partial score consisting of a tuple s p a where s is the number of transitions on the path not part of a maximal projection the skips p is the number of maximal projections a is the sum of the acoustic scores of all the transitions on the path including those internal in maximal projections
in the ideal case the parser will find one or more paths in a given word graph that can be assigned an analysis according to the grammar such that the paths cover the complete time span of the utterance i.e. the paths lead from the start node to a final node
because each sub domain grammar should be able to parse well only sentences that fall in its domain of coverage we expect that in many cases it should be relatively easy to select which among the parses produced by the different sub domain grammars is most appropriate and or correct
through the mapping of the donzapsuspecific semantic hierarchy onto wordnet and the application of general purpose word sense disambiguation and semantic distance metrics the approach proposes a portable wide coverage method for disambiguating semantic classes
a number of different similarity measures can be used
information should be grouped into convenient clusters and presented in a natural order
graphs can also be embedded within one another
this paper presents a technique for sentence generation
i lcb NUM uses rvalual io funclion i y suunuali while iimm do s i y umltil lic tion
inverting word order english he saw the boy
individual phenmnenn are often further sub classified according to phenomenon internm dimensions
figure NUM status of the tsnlp data l ecember
e produc d sllt sl tntiztl i.e.
inc udilip r ia l ion
vocabulary is an aspect of the test data that needs to be controlled
both grmnmatical and ungrammatical test items togeth0r with some part of the mnotations
tsni p achieves this by restricting the vocabulary in size as well as in domain
on the other hand the interaction of universal principles and relations tends to get very complex for realistic linguistic theories
to be able to interpret referring expressions edward uses three knowledge sources a knowledge base a context model and a lexicon
pietra used his owu algorithm iis hnt roved lterative scaling based on g is to induce the features and parameters of random field automatically l ietra95
learning probabilistic subcategorization preference by identifying case dependencies and optimal noun class generalization level
we thank christian pavoni who developed much of the software used in this experiment as well as all our partners in the ecran project
a workbench for finding structure in texts
a number of verbs take very particular noun phrases
sntool hing proc ss is ahnost sscntia in tlmm tmcattse iimm has sevet c d at t sparseness prol hmt
the top five nouns that are not already seed words are added to the seed word list dynamically
do not say what you believe to be false
this led to a much simpler picture as shown in figure NUM
all symbols are predicted using that context and those predictions are estimated using the same set of histories
l here is no critical point of postcriori l robal lil y in mri i while iimm has cril ical poi NUM in zero value
on the other hand one can experiment with individual principles without having to change the other principles or rules
two times NUM sundial dialogues will be used for the purpose
however the reliability of the algorithm decreases with the increased resolving power
during det development we never tested for objectivity of annotation
three dialogues were used for initial discussions among the two analysers
this lllelllls t hat ellergy funcl ioli urill be obliained rolll each clique funtion which splits l ie set of ralldolll viu iables to slibscls
a i1 i ara liiclxu s used in the iiio e are esljmated from tra ining data a ul omatica lly
but such simpliticy is also the source of its weaknesses linearly interpolated information is generally inconsistent with their information sources because information sources are heterogeneous for each other in general
m ii f provides the i heoretical bi cliground about the probal ility of the system bes tg74 leijfla ii84
as part of tipster phase iii the tipster r d investigations will be expanded into the field of text summarization
NUM orang human being singular orang orang plural
in a rectangle opposite sides and diagonals are equal see
mathematics mathematical physics x NUM NUM NUM x
let NUM be a non empty finite set called the vocabulary
in other words no material from outside should be used
unfortunately our proposal does not render an account of reduplication
the supposition of an intention to make q true when q is already true in the agent s interpretation of the discourse
before concluding in section NUM discussion is added in section NUM
a general view of the process can be seen in figure NUM
unfortunately we could not repeat these experiments with chinese and thai due to the small amount of hand segmented data available
this assumes that each category appears at most once within each sentence
language dependent differences can be maintained in the individual wordnets
such mistakes are possible when the surface form of the earlier act might be used to accomplish either aobserved or a intende d NUM
having a look at rooms complaining about and changing them
we will explain the method used to compute ccd later in this section
the price to pay is the introduction of non determinism in the model
this companion evaluation program became known as the text retrieval conference trec
asking and complaining about the bill
planning is already underway to determine an appropriate metric based evaluation strategy for text summarization
the new state emission function is
the lockheed martin approach to information extraction is to build sets of floating phrases i.e. rules which can glide over each sentence binding to the right configuration of information
matches orgnp rule NUM but the bragging rights to coke s ubiquitous advertising belongs to creative artists agency orgnp lcb NUM rcb the big hollywood talent agency
create a copy of the csst corresponding to the category label
this is due to the fact that louella was no w producing four succession events instead of one each with its own in and out object
this task is called the scenario template st and requires a deeper anlysis of th e text than the other tasks
for example it is permissible to have more than one in and out object participating in a succession event bu t there must be one succession org involved
much of the information related to the entity name is found during the initial phases of the ne module in the context surrounding the entity name
louella s ne system makes a basic assumption that any organizations that appear in th e headline are the same as or variations of the organizations found in the text
this package will use knowledge about the use of products in text i e how they are referred to and when they include the company name as a premodifier
mr enamex type quot person quot james enamex NUM years old is stepping down as chief executiv e officer on timex type quot date quot july NUM timex and will retire as chairman at the end of the year
this increased the number of correct slots from NUM to NUM even though the additional succession event s had no post and no succession organization
NUM last pass a set of dxl rules which checked the token stream for any remainin g tokens which could be part of place org or person
numerous modifications have been made to this system including a new data base access capability developed in september fo r rapid access to many millions of words and facts
washington were recognized by straight forward person rules the first by one which looks for known first names the second by one which uses prefix titles
if so the tokens corresponding to the known data class entry are replaced with a single token whose type is set to the name of the data class
it is this rich set of features that are used as conditions on the dxl pattern elements and largely underlie the potential for very high accuracy in target detection
in may NUM the first version of the language dxl for data extraction in our view data classes comprise ids and quantifications
discontinuous constituents are easily specified using the kleene star operator as in first pattern any second pattern where any
the numeric suffix on the rule sets indicates that data class rules were clustered into groups of canonical forms that were increasingly complex as indicated by the number value
if no match was found on any of the three words combination the algorithm backed off to a combined match on two words i.e. one of the content words with a preposition
we can argue that the insufficient disambiguation context sparse data problem and empirically set iteration step in the disambiguating algorithm lead to an unreliable disarnbiguation
the nearest quadruple in this set is q2 dqn q4 q2 ffi0 and the noun company in q4 is disambiguated to the sense of the noun in q2
there are many ways that this research could be extended
only if the homogeneity termination condition is satisfied before all three content words are compared the decision is based on less than a full quadruple
it does not exhibit the highest accuracy because many of the homogenous leaves are formed from only very few examples and many of these are erroneous
where p ppadvia aw and p padjia aw represent the conditional probabilities of the adverbial and adjectival attachments respectively
NUM return a tree whose root is a and whose subtrees are s w and links between a and s w are labeled a w
table NUM some examples of sentence pairs from the traveler task
at the moment we are working on an implementation of the algorithm to work on with a wider sentential context and on its incorporation within a more complex nlp system
a final assumption that we adopt concerns the nature of the semantic rules
NUM this makes it possible to evaluate any potential dialogue strategies for achieving the task as well as to evaluate dialogue strategies that operate at the level of dialogue subtasks subdialogues
table NUM attribute value matrix circuit domain
danieli and gerbino found that agent a had a higher transaction success rate and produced less inappropriate and repair utterances than agent b and thus concluded that agent a was more robust than agent b
paradise supports comparisons among dialogue strategies by providing a task representation that decouples what an agent needs to achieve in terms of the task requirements from how the agent carries out the task via dialogue
paradise supports comparisons among dialogue strategies with a task representation that decouples what an agent needs to achieve in terms of the task requirements from how the agent carries out the task via dialogue
then this value of is normalized using data from comparable subdialogues with both agent a and agent b based on the data in tables NUM and NUM the mean is NUM
this assumes that the factors that are predictive of global performance based on us generalize as predictors of local performance i.e. within subdialogues defined by subtasks as defined by the attribute tagging
in general if an utterance u contributes to the information goals of n different attributes each attribute accounts for NUM n of any costs derivable from u thus c2 d2 is NUM
these approaches are also limited in that they currently do not calculate performance over subdialogues as well as whole dialogues correlate performance with an external validation criterion or normalize performance for task complexity
a particular robust parsing model implemented in ovis is described
for some grammars naive top down prediction may even fail to terminate
an efficient implementation of the head corner parser gertjan van noord rijksuniversiteit groningen
the interface between the speech recognizer and the parser consists of word graphs
of course in a left corner parser certain simplifications are possible
revleftds is reverse of leftds and e0 NUM
the basic idea of the head corner parser is illustrated in figure NUM
recognition checks whether a given sentence can be generated by a grammar
for practical implementations the use of goal weakening can be extremely important
in this section we provide the basic solution technique for simple sentences i.e. consisting of a single verb only in two parts
rather it can only try to accept a plan that the other agent contributed for it is just such plans for which it will have the belief by way of rule NUM that the peter a heeman and graeme hirst collaborating on referring expressions user believes the plan achieves the goal
the expand plan schema shown in figure NUM is similar to the replace plan schema shown in figure NUM
testing of symbol table entries with m structure schema and resulting binding of nameholders to actual function names are carried out by a newly introduced operator search
the decomposition of the refashioning plans encodes how a new referring expression can be constructed from the old one
this goal would be achieved by an instance of accept plan which decomposes into the surface speech action s accept
the constraints require that the error occurred in an action instance whose yield includes at least one primitive action
will believe that the speaker has the goal that it be mutually believed that the plan achieves its goal
since the conversational moves update the referring expression and its judgment they are presented as functions
it uses the surface speech action s attrib rel and also includes a step to refer to the object of comparison
with f i x the frequency of the i th type in a sample of x tokens
by sufficiently powerful we mean that there must be a formalization of the notion of inference strength of inference and inferential independence and there must be a reasonable knowledge base
other examples test the generality of an analysis in terms of its ability to account for phenomena similar to vp ellipsis and to interact with other interpretation processes that may come into play
in brief our theory of parallelism is not something we have introduced merely for the purpose of handling vp ellipsis it is needed for a wide range of sentential and discourse phenomena
when seeking to establish the paral null lelism between two clauses we must begin with the top level properties this is generally determined by the syntactic structure of the clause
first it has to be determined what is going to be said
in the implementation domain schemata NUM works quite well if the mapping from case marker to function is nearly one to one
suppose we want to define the unary relation wf phrase to hold of all grammatical phrases
the operator search takes the entire set m structure schema for a particular gf and carries out the process described in the previous paragraph
other bitext mapping algorithms mitigate this source of noise either by assigning lower weights to respondence that line up in rows and columns
in the case oflexical cognates the axis generator typically needs to invoke a language specific tokenization program to identify words in the text
a word that has another word as a substring should result in one axis position for the substring and one for the superstring
therefore for languages like chinese and japanese which are written without spaces between words tokenization boils down to string matching
figure NUM shows a segment of the tbm that contains a vertical gap an omission in the text on the x axis
macklovitch NUM multitexts in more than two languages are even more valuable but they are much more rare
the purpose of a bitext mapping algorithm is to produce bitext maps that are the best possible approximations of each bitext s tbm
non monotonic tpc chains are quite common because even languages with similar syntax like french and english have well known differences in word order
the four parameters described in section NUM interact in complicated ways and it is impossible to find a good parameter set analytically
the most tedious part of the porting process is the construction of tbms against which simr s parameters can be optimized and tested
the use of rather broad categories also reduces the extent to which words are ambiguous with respect to semantic category
in the second step we formulate the transition between phrase levels as a context sensitive rewriting process
moreover the preference scores related to various types of linguistic information may have different dynamic ranges
for rule based approaches knowledge is induced by linguistic experts and is encoded in terms of rules
eterms identifies candidates for new terms by looking for words not found in any of the dictionaries NUM as well as multinoun terms
similarly in parsing the query if the name has not been identified as a name by the syntax of the query then it will be necessmy to recognize it
therefore a rule based approach in general fails to attain satisfactory performance for large scale applications
the null hypothesis we are testing is that both frequency lists were the outcome of random selections from the s me source
most adaptive learning procedures stop adjusting the parameters once the input training token has been classified correctly
a manually marked up case law name recognition test collection of NUM test documents was created for evaluating name recognition and name t equency analysis
this paper describes a series of experiments exploring the retrieval application and draws some tentative conclusions about it and how it differs from database name matching and information extraction name recognition applications
the experimental results of using the discriminative learning procedure with NUM iterations are shown in table NUM
of course high precision and recall alone are not enough to ensure the usefulness of an authoring tool such as easyenglish
the standardized template structure minimizes the amount of idiosyncratic programming required t o produce the expected types of objects links and slot fills
the basis of the third event comes halfway through the two page article in addition peter kim was hired from wpp group s j
common errors in these three organization objects included missing the descriptor or locale country o r failing to identify the organization s alias with its name
errors of these kinds result in a penalty at the object level since the extracted information is contained in the wrong type of object
there was just one system that posted a higher error score on the body than on the headline the nmsu crl ives basic
what would performance be on data where case provided no reliable clue s and for languages where case does n t distinguish names
the results should also be qualified by saying that they reflect performance on data that makes accurate usage of upper and lower case distinctions
template element te extract basic information related to organization and person entities drawing evidence from anywhere in the text
the system generated outputs are from three different systems since no one system did better than all other systems on al l three events
this object points down to one or more succession event objects if th e document meets the event relevance criteria given in the task documentation
thematic inferences in scheduling dialogues referring expressions like the german word ndchste occur frequently
the thematic structure is also used to check whether the time expressions are correctly recognized
currently turns are very often split up into too many and too small utterances
when given back to the transfer and generation modules this will enhance translation quality
examples are the german words for thirteenth dreizehnter and thirtieth dreifligster
however they are defined manually and have not been tested on larger data sets
figure NUM shows parts of the thematic structure after the processing of turn b10
the need for and use of this structure is highlighted by the following example
inspecting our corpus we can distinguish three phases in most of the dialogues
of the same month in the box above from t0 NUM NUM
the question is both of interest in its own right and is a preuminary to any quantitative approach to corpus similarity
however here we focus on how incorporating similar ideas into spud gives a general framework for specifying conventional uses of words and remain neutral about achieving similar speedups
an overview of the microplanner s architecture is provided in figure NUM
apart from that the microplanner also handles sentence scoping and layout
i h2 in con lplement mode
fig NUM is indeed a b form and consequently the rep
or instance the previous representation now becomes tile tree of l ig
thanks to alain l ecomtc and frdddrique segond fol coitiilloiiis NUM NUM discussions
a second advantage is that they represent a good compromise between paraphrasing potential and semantic precision
ter hates i f we consider tile six l mmulalions of lhe nodes under like
john ll oma l like peler womalg figure NUM predicate argument relations in a u form
forms in order to be well formed a u iorm uf has to respect tile following condition
therefore it distinguishes clearly f om the rules in section NUM NUM
NUM therein de maupertuis meylan NUM france lcb dyme tman copperman rcb xerox
with some minor technical changes to function up link down we can align a suffix tree with itself w r t a given homomorphism
in this work we consider the computational problem of automatically learning from a given corpus the set of transformations presenting the best evidence
throughout the paper we assume that the rightmost symbol of w is an end marker not found at any other position in the string
we also show that the same learning problem becomes np hard in cases of an unbounded use of do n t care symbols in a transformation
a total function h e e and e two alphabets is called a restricted homomorphism
fast scan p1 sufflzab l p p i l label pl p
let w w be an aligned pair and let NUM be some transformation of the form u v
they thus consider o nn NUM factor pairs where each pair takes time o n to be read stored
this paper argues that there is a class of grammars which allows the use of linguistically motivated form of type raising involving variables while it is still weakly equivalent to ccg std
we need s axb kakb NUM which can be the result of unboundedly long compositions to simulate NUM without depending on the gtrcs
the special cases in this direction involves the feature wrap and or the new categories of the form z lcb rcb which record the argument s being passed
this paper shows that a class of combinatory categorial grammars ccgs augmented with a linguistically motivated form of type raising involving variables is weakly equivalent to the standard ccgs not involving variables
as g corresponds to a unique g e ta we extend g from g to simulate g then show that the languages are exactly the same
in languages like japanese multiple nps can easily form a non traditional constituent as in subj i objl subj2 obj2 verb
thus c which originatedin skakb c in a may be passed onto another category in b after a possibly unbounded number of compositions as follows
for example b NUM on a b t tkc can not realize the unification of the form a b trite ttitz c
in term based representation a document as well as a query is transformed into a collection of weighted terms derived directly from the document text or indirectly through thesauri or domain maps
the lexicon is an lkb which is used by all components
we submitted NUM official runs in automatic adhoc manual ad hoc and automatic routing NUM and were ranked NUM or NUM in each category out of NUM participating teams
then shortly before the conference participants are given a set of test messages to be run through their system without making any changes to the system the output of each participant s system sundheim poj ke
the following table shows the runtime for sentences of different lengths
figure NUM analysis of give me is a budget recovery from a substitution
with respect to port bility custoiners would like to have systems which can be ported in a t ew hours or at most a few days by someone with less expertise than a system developer
relative clauses both on subject and object
a further filtering process maps these similarity links onto semantic relations generalization specialization synonymy etc after which they are used to transform a user s request into a search query
intentional information can rule out some of these readings for example a belief that the speaker already knows the time might rule out the request interpretation
when one participant produces a response that is consistent and coherent with what the other has just said then the other will take it as a display of understanding
this utterance is appropriate independent of whether or not russ actually wants to know who is going to the meeting because it displays acceptance of mother s pretelling
thus try m r inform m r not knowref m whoisgoing ts NUM is explained
NUM it is possible that the same surface form might accomplish several different discourse acts in which case it might be desirable to evaluate the likelihood of alternative choices
figures NUM NUM NUM and NUM will show the output of the system for each of the four turns of this dialogue from russ s perspective
for example one rule says that if a speaker wants to know the truth value of some proposition then she might want the proposition to be made true
NUM a statement about related task steps i.e. subgoals of a tasks that contain a as a step or tasks that might follow a
when theorist is called to find a coherent discourse level act i.e. by using the default for intentional acts it finds that russ can perform a fourth turn repair
NUM an informref by russ is expected see section NUM NUM NUM in the reconstructed dialogue because there is a linguistic expectation corresponding to the adjacency pair askref informref
this problem defines a relation r n x l where n belongs to the set of nodes and l belongs to the set of labels for the elements of chains
i show the i o of each algorithm given the sentence who did you think that john seemed to like where a multiple a chain and an a chain must be recovered
however it is important that such covering be done in such a way that accidental properties of a particular grammar which would not hold under counterfactual changes are not used
in the parser structural information is separate from information about co occurrence rules NUM NUM functional selection rules NUM NUM and subcategorization rules NUM NUM
finally note that all the augmentation necessary to make the ls grammar work make it equivalent to a phrase structure grammar possibly with the disadvantage of being procedurally instead of declaratively encoded
out of NUM target verbs we could uniquely identify categories for NUM verbs
the assignment of the feature barrier depends on l marking which in turn requires that the head is lexical and that marking occurs in the complement configuration
NUM my proposal combines these two approaches it adopts the idea of compiling the grammar at least partially off line but it attempts to find a principled way of doing so
for sentences of this length grammar size becomes a relevant factor for grammars that contain more than approximately NUM rules in his algorithm an lr parser with parallel stacks
for example according to gb theory the same set of principles are at work in the raising construction la and in passive lb
this allows easy modification if needed
the fronted phrase is a maximal projection with the missing constituents moved to slash
so if we combine c q0 with 3j the result is cql j j and the negative condition i l j is added as a filter to the mother node
for instance node NUM which expresses dogs covers the first NUM facts hence its array is NUM node NUM which expresses bark contributes the 4th fact bark e d and accordingly its array is NUM
in parsing this is straightforward there has to be a top node from string position NUM to string position n
nodes NUM and NUM are disjunctive with choices represented by the proposition variables pl NUM and ql NUM respectively
this paper describes a new generation method that produces multiple paraphrases from a semantic input which may contain ambiguities
the events and entities are represented as variables that appear in the predicates and connect the various facts together
in parsing identifying the coverage of the input is straightforward since phrases consist of consecutive items and combine at common end points the coverage of each edge is uniquely defined by its string posilnote that the traditional representation of charts as transition diagrams is not suitable for generation charts essentially because of the absence of fixed positions
for example the logical form chase e d c dogs d young d cats c young c denotes a chasing event e in which young dogs d chase young cats c
these two interpretations are given in the following disjunctive logical form filter f oil o lcb hydraulic o i hydraulic f rcb figure NUM shows the packed generation forest that encodes the two incidentally identical strings that express this piece of semantics
structure sharing is expressed using secondary links
by incorporating content into descriptions of a variety of entities until the addressee can fill in the details this procedure results in short natural and unambiguous sentences
another axiom similar to NUM ensures that states that specify a given attribute are equally salient across copiers when the copiers involved are equally salient
the pragmatic specification for the book syntax and topicalized have appear under the semantics for each tree in figure NUM
however the mle is notoriously unreliable when there is insufficient training data
the linguistic uncertainty problems can thus be resolved on a solid mathematical basis
clearly the poisson does not fit our data very well especially for good keywords like boycott
attempts to replace idf with fw or some simple transform offw have not been very successful
computational linguistics volume NUM number NUM is a normalized factor such that
detailed discussion on this hybrid approach is addressed in the following section
the shaded rows indicate the different patterns between the two parse trees
the maximum likelihood approach achieves disambiguation indirectly and implicitly through the estimation procedure
the advantages of this approach are two fold
the results with the tu rl hybrid approach are also listed for reference
this kind of improvement is desirable when the training data is limited
the accuracy rate of NUM NUM is attained by using this approach
however performance is an improvement over our previous best results condition NUM in table NUM and is comparable to learning NUM or very slightly better than learning NUM the hand tuning results condition NUM in table NUM
cue prosody which encodes a combination of prosodic and cue word features was motivated by an analysis of ir errors on our training data as described in section NUM cue prosody is complex if athe cue phrases that occur in the corpus re shown as potential values in fig NUM
condition NUM results are better than condition NUM in table NUM and condition NUM in table NUM
we predict discourse segment boundaries from linguistic features of utterances using a corpus of spoken narratives as data
its vahle is a synscm objeet if the verb embeds another verb and none otherwise
np a performed better than the other unimodal algorithms and a combination of np a and pause a performed best
the NUM training narratives range in length from NUM to NUM phrases avg NUM NUM
the first is the wide variation in the number of boundaries that subjects used as discussed above
defined in section NUM co occurred represented as the first conditional statement of fig NUM
therefore we have to determine the minimum wflue of the difference when there is more than one branch extended from a string
the result of extraction was improved as a result but the deterinination of longest strings is always made consecutively from left to right
they extended their winning streak to three games
you do not have experience in writing essays
each such knowledge source is represented as a fug
computational linguistics volume NUM number NUM 1993c
it must be able to handle floating constraints
this is a benefit in several different scenarios
ments are specific to the advisor ii domain
to identify the correct starting position of a string we apply the same observation to the leftward extension of a string
a possessive thematic structure for that main process
note also the syntactic variety of these dependent elements
the accuracy depends mostly on word entries in the dictionary and the priority for selecting between candidate words when there is more than one solution for word segmentation
when the data area is exhausted during the parsing a fitted parsing technique is used to build the most plausible parse tree from the partially parsed trees
to obtain more meaningful strings fl om a large file we have to set a relatively high threshold of extraction
recall that introduction inferences of the original formulation are associated with abstraction steps
it is of course also possible that a text may identify an organization solely by name
figure NUM indicates the relative amount of error contributed by each of the slots in the organization object
NUM one effect of this bias is simply the number of entities mentioned in the articles for the
the decision to minimize the annotation effort makes it difficult to do detailed quantitative analysis of the results
the type slot however is a more difficult slot for enamex than for the other subcategories
apart from the initial set of codelets present at the onset of processing new codelets are sometimes created by old codelets to continue working on a task in progress and these codelets may in turn create other computational linguistics volume NUM number NUM codelets and so on
the choice of which codelet instance to execute depends on three factors i its urgency ii the number of codelet instances in the coderack that are of the same type as the individual instance and iii the current temperature
the difference that recourse to lists can make in performance is seen by comparing two runs made by sra
supposing that the coderack contains the same types of codelets with the same quantities but the temperature is NUM the probability of selecting an instance of a word codelet an affinity codelet and an affix codelet becomes NUM NUM NUM NUM and NUM NUM respectively
translation proceeds from a smaller unit as word to a larger unit as sentence step by step in a bottom up manner
to see alternatives for another word the user has only to move the cursor to that word on the main window
do not rely on baseline evaluation results to predict the success of a technology effort in a new language
this corpus is used to gain statistical information about the dialogue structure namely unigram bigram and trigram frequencies of speech acts
then s he notice that he ga buy ta book is treated as one phrase against his her interpretation
by choosing the fifth alternative the user can specify the result to be a complement of an adjective possible
null then we check if each group is feasible and determine the best schema to express it
after this the topic of the conversation is introduced usually the fact that one or more appointments have to be scheduled
the new company plans the launching on february
in that case further approximations are required
if verbmobil is inactive shallow processing by a keyword spotter takes place which allows the system to follow the dialogue at least partially
most importantly the two level generation model allows us to indirectly apply lexical constraints for the selection of open class words even though these constraints are not explicitly represented in the generator s lexicon
perkin elmer co s japanese subsidiary holds majority of stocks and as for the beginning production of steppers and dry etching devices that will be used to construct microcircuit chips are planned
we process semantic subexpressions in a bottom up order e.g. a2 g p and finally a the grammar assigns what we call an e structure to each subexpression
of course the results are not perfect
there are many such inflectional and derivational patterns
NUM you may be obliged to eat chicken
NUM you might be required to eat chicken
NUM you may be required to eat chicken
a p q or universal conditional equivalences v p1 p ri rz f or existential equivalences
db take e x y a db course y crept710 v rib take e x yz a db course y1 crept720
ldt was introduced for a system where input is a logical formula whose predicates approximately correspond to the content words of the input utterance in natural language lexical predicates
formulas student x take e x y1 unknown y cmpt710 and unknown yl cmpt720 are translated similarly
the result of the preprocessing is a normalized kldt the collection of the lexical predicates their meanings in terms of the database and the patterns of the conjunctive contexts
future work will explore other uses of normalized rldts to construct a sophisticated help system to lexicalize some small database domains and to develop more complex lexical entries
an approach is described for supplying selectional restrictions to parsers in natural language interfaces nlis to databases by extracting the selectional restrictions from semantic descriptions of those nlis
for tagging unknown words each word is initially assigned a part of speech tag based on word and word distribution features
however we found many cases where the tagger was incorrect
grouping related morphological variants makes a significant improvement in retrieval performance
we therefore repeated the procedure but allowing for inflectional variants
this low success rate was almost entirely due to tagging error
such distinctions are unlikely to have an impact on information retrieval
NUM phrases are an important and poorly understood area of ir
i am grateful to dave waltz for his comments and suggestions
for example consider the grouping of police and policy
this will be followed by a discussion of our experiments
our results support the need to distinguish homonymy and polysemy
support verb sei n such t iktl tile comparative construction lieber sein g ist x liebet is translated as a whoh to the english verb prefer x prefers y
in NUM a light verb construction like einen terminvorsehlag aachen is translated into su qgest a date by decomposing the compound and light verb to a simplex verb and its modifying noun
such children require a relatively simple device that will enable them to select and produce suitable content for a conversation in real time
the main motivation for using a senmntic based at proach for transfer is the abilil y to abstract aww froln morplioh gical and syntactic idiosyncrasies of individual languages
instead of using a specific lexical item like passen the rule should be abstracted for a whole class of verbs with similar properties by using a type definition e.g.
in general a pure interlingua approach results in very application and domain specific knowledge sources which at e difficult to maintain atm extend to new languages anti domains
coindexation of labels and markers in the source and target parts of transfer rules ensures that the semantic entities are correctly related and hence bey any semantic constraints which may be linked to dram
a semantic approach is much more independent of different syntactic analyses which axe the source of a lot of classical translation problems such as structural and categorial divergences and mismatches
the current transfer implementation consists of a transfer rule compiler which takes a set of rules likc the one presented in section NUM and compiles them into two executable prolog programs one for each translation direction
the names de and en are the sl and tl modules in which the lass is deiined temp loc is the lass natne and the list denotes the extension of the class
to solve these problems finite state transducer technologies are often employed and investigated
slt systems are often heavily restricted to specific domains and in their vocabulary
in the proceedings one will also find three poster papers
the papers in this section address problems of efficiency in two senses
seligman suggests that this is promising though there are technical problems
since then the world has changed
parsing to the level of phones
this applies both to spoken and written language translation
notice that such speech may or may not be wholly spontaneous
in a dialogue system such an approach is unthinkable
each line presents a morphological and signs mor
besides the effort of discoveriug and implementing them there is also the significant time and effort expenditure on the procedure of semi automatic checking of the results of the application of lrs to the basic entries such as those for the verbs
NUM for instance comprable adj lr3feasibilityallribulel is morphologically derived from comprar scope of this paper and is discussed in viegas et al and adds to the semantics of comprar the shade of meaning of possibility
if the lr inventory approach is used or if the lhs constraints are very good see below then the overgeneration penalty is minimized and the advantage of a large run time lexicon is combined with efficiency in look up and disk savings
the temptation to mark all the verbs as capable of assuming the suffix able or ible and forming adjectives with this type of meaning is strong but it can not be done because of various forms of blocking or preemption
thus the user is guided through the application building process
for instance the morpho semantic rule lrpolarity negative is at least attached to all verbs belonging to the aa class of spanish verbs whose initial stem is of the form con tra or fir with the corresponding allomorph in
can a system reliably identify the topic of a speech segment
however forms not found in the dictionaries are not discarded outright because the mrds can not be assumed to be complete and some of these rejected forms can in fact be found in corpora or in the input text of an application system
plum had higher precision while shogun had better recall
programmers to build and maintain information extraction systems based on plum
for muc NUM the task was change in corporate officers
each system produces an overall structure for each input story
NUM NUM tools for porting and maintaining the plum information extraction system
figure NUM identifinder system architecture rectangles represent domain
value pair succeeds only when the imir is either NUM or i l
to acid we the best l ossible average rllntinle and ac llrtl y
se luen i ink constra ints are prol aga ted
and scha bes NUM is propos d this ambigmty r ma ins
colnl lcxity of pa rsing a lgo rithms fin these formalisms a nd the workl m d of building st
rules before pa rsing e the combined process is a lso known as offlineparsing in ltac
style at present it may be possible to support some variations in conversational style as a function of who the conversational partner is e.g.
initially the ill will only contain all wordnetl NUM synsets but eventually it will be updated with language specific concepts using a specific update policy a site that can not find a proper equivalent among the available ili concepts will link the meaning to another ill record using a so called complex equivalence relation and will generate a potential new ill record see table NUM
each wordnet represents a language internal system of synsets with semantic relations such as hyponymy meronymy cause roles e.g.
however their coverage is relatively low NUM NUM
in the training system the computer using one speech synthesised voice converses with the child using another speech synthesised voice
in section NUM multiple discourse phenomena arc presented in terms of an example
the treatment of discourse relations should thus be modified at least in these respects
it is shown that the underspecification is to be represented for them too
in the verbmobil project a spoken language machine translation system is being developed
it is sometimes possible to resolve scopal underspecifications of discourse relations on several grounds
in this case the resolution seems to depend on the syntactic c command information
another problem is the treatment of multiple occurrences of discourse relations in a sentence
secondly each discourse relation element introduces a different partition of the given sentence
NUM subjects d e f g NUM NUM well anyway NUM subjects b c l NUM NUM so u m tsk all the pears are picked up NUM NUM and he s on his way again sample of subjects responses
for such applications with a diverse set of features it is not necessarily the case that terms can be excluded beforehand
we evaluate three algorithms each of which uses features pertaining to only one of these linguistic devices in order to see whether linguistic associations proposed in the literature can be used by natural language processing systems to perform segmentation and to compare the utility of different knowledge sources
for the t NUM boundary set learning NUM recall was NUM as good as humans precision was NUM as good fallout was better than humans and error NUM was almost as low as that of humans NUM
in this way a lud representation describes a set of possible pluggings at once
it consists of a functional noun for the senteutial nominalization no and the copula
the histogram in figure NUM gives a different view of the same point showing the relative frequency of cases where n subjects place a boundary at a given location for n from NUM to NUM the y axis is normalized to represent the average narrative length of NUM phrases thus the bar at n NUM indicates that on average NUM NUM of the NUM phrases were not classified as boundaries
the top half of the table reports results for boundaries that at least three subjects agreed upon t NUM and the lower half for boundaries using a threshold value of NUM t NUM where np duplicates the figures from table NUM going by the summed deviations the overall performance is about the same although variation around the mean is lower for t NUM
these figures are each calculated from NUM trials to a NUM error rate they suggest that in general the default learner is more effective than the unset learner
a practical message to speech strategy for dialogue systems
in addition to the strict synonymy relation which holds between synset variants there is also the possibility to encode a near synonym relation between synsets which are close in meaning but can not be substituted as easily as synset members e.g.
the relationship between gcg as a theory of ug gcug and as a the specification of a particular grammar is captured by embedding the theory in a default inheritance hierarchy
the advantage of a graph over a trie is that it allows for comparison from the end of the word and well as the beginning
it might be possible to introduce intelligence into the script to allow the computer to respond appropriately to a wider range of user s contributions
unless a keyboard user is particularly proficient a frustrating amount of time is usually spent backtracking to pick up mis typed or otherwise mistaken input
figure NUM architecture of the tagger generator flow of control
table NUM accuracy of igtree tagging on known and unknown words
again this compression does not affect igtree s generalisation performance
this extra information is essential when using the tree for classification
a considerable compression is obtained as similar cases share partial paths
this way noise in the training material is filtered out
table NUM shows part of the case base for unknown words
the altered word is different but usually semantically somewhat similar
it is robust enough to tolerate extragrammaticalities disfluencies and the like in the input
phrases which occur more often than a threshold computed using zipf mandelbrot law are saved for post analysis
also note that the date constituent is optional which is expressed by
these higher level patterns can be clustered in the same way to yield longer semantic sequence paradigms
figure NUM this figure shows results of generalization for the types sbody part and disease
if modules are to communicate flexibly then an inter module information representation format needs to be specified
another important requirement for spoken language translation is that the system has to be very robust
we now describe this method for inferring a syntactic and semantic classification of words from scratch
thus the majority of word tokens appear in a small fraction of the possible word types
statistical clustering can be automatically checked and subcategorized with the help of external linguistic and semantic sources
in the rest of the paper we will embark on a more detailed characterization of tools themselves
for the result label s subterm x z y the directionality of applications suggests the ordering x z y
figure NUM example of a parse forest
NUM a set of word meanings across languages have a simple equivalence relation but they have diverging language internal semantic relations
the rhyme of the last syllabic of tim noun is necessary and sutlicienl t predict i rcb m col l cl a lomort h
be mation by providing a new tool for the evaluation of linguistic hypotheses for the extraction of rules front corpora and for the discovery if useflll linguistic categories
for ass re in ion placu of mti ulation f atllr s arc t est ilse l
the extracted system can of course also be used in language technology as a data oriented system for solving particular linguistic tasks in this case diminutive format on
in order to test the usability of the approach for this application we compared the performance of the extracted rule system to tile performance of the hand crafted rule system proposed by trommelen
there are several robustness issues arising from the multilingual characteristics of many spoken language translation systems which have not studied by the speech recognition community since the latter tends to focus on monolingual recognition systems
statistical connectionist and machine learning induction dataoriented approaches are currently nsed mainly in language engineering at t lications in order to alleviate the
if t contains one or more cases all belonging to the same class cj then the decision tree for NUM is a leaf node with category cj
only the change of state feature will be discussed here
expanding the tagset in this way improves prediction performance for instance distinguishing between personal pronouns allows greatly improved prediction of verb agreement
a given the above linguistic principles a special purpose grammar for potential terminological structures can be sketched
the reason for such a performance degradation of a mixed model is not difficult to deduce the dictionary of a mixed model has more candidates
ill these cases also simultaneous compilation including binary relational labeling can provide additional advantages
lambek showed cut elimination for both calculi i.e. every theorem has a cut free proof
by way of orientation we review the propositional features of clausal programming
it seems that only the first of these difficulties can be overcome from a gentzen sequent perspective
unification must be carried out according to the structural axioms but is limited to one way matching i.e.
in the case of l we have first interpretation in semigroups l i.e.
figure NUM groupoid relational execution for the references are missing from this book
anchor builds up a feature value pair
in some sense this is true
we call these lexical entries anchors
for first order programlning the set cjo aps of goals is defined by
i.e. the unit agenda is a consequence of any database containing its atomic clause
they report an accuracy of NUM NUM
furthermore the conceptnet toolkit makes it possible to visualize the semantic relations as a tree structure which can directly be edited
figure NUM overall recall and precision on the co tas k
the precision figure is supported by evidence from the ne evaluation
r recognizes that the situation presented in n could be a cause for the action or situation presented in s the result relation is a simple amalgam of rst s volitional and non volitional results
by adopting the complete dictionary assumption it has been proven that critical points are all and only unambiguous token boundaries while critical fragments are the longest substrings with all inner positions ambiguous
intuitively a position in a character string is ambiguous in toke zation or is an ambiguous token boundary if it is a token boundary in one tokenization but not in another
in addition it was proven that every tokenization has at least one critical tokenization as its supertokenization and only critical tokenization has no true supertokenization
as has been observed the tokenization of about NUM of the text can be completed in the first parse of critical point identification which can be done in linear time
while they all agree that a certain form of extremes must be attained they nevertheless have their own understanding of what the form should be
NUM every tree reading is a
this study provides detailed prescriptions concerning how such testing should be performed i.e. what forms should be tested and what contexts controlled for but it does not actually perform the tests
theorem i spells out a simple way of making any dictionary complete which calls for adding all the characters of an alphabet into a dictionary as single character words
by definition for any tokenization y y e to s there is a critical tokenization x x e co s such that x y
if no match is found then a is assumed a NUM character word and maximum matching moves on to b
if that fails again then c in the table of NUM character words is examined to see if it can be a suffix
each prefix points to a linked list of associated infixes and each infix in turn points to a linked list of associated suffixes
otherwise one has to accept that ab is a NUM character word and moves on to start inatching at c
then it is decided that the lexicon should be fllrther enriched with new words and adjusted word binding forces over a number of generations
in that case every t ossil h w rd so lll n
word fl following another word t in th line one annot he ltl wondca ing
a necessary condition to successflfl collection of n gram statistics is the existence of a coinprehensive le xicon and a large text corpus
use the off position for charging the batteries
here is a very blatant example vars snumber singular plural
here e appears on the left hand side of two conflicting definitions
and indeed this turns out to be valid
examples of this general type are quite common in the world s languages
computational linguistics volume NUM number NUM of seven atoms
there is no longer a unique path representing morphological form
datr sentences represent the statements that make up a description
the morphological form of word1 is now given by mor present participle
similarly word2 s morphological form is given by mor passive participle
note the tension here the first point identifies a centrifugal tendency pushing researchers into ever greater theoretical diversity the second a centripetal tendency forcing them together
work is underway to integrate the lt nsl api with gate and provide sgml i o for tipster and we acknowledge valuable assistance from colleagues at edinburgh in this task
in many cases the technologies being developed are assistive rather than fully automatic aiming to enhance or supplement a human s expertise rather than attempting to replace it
creole wrappers encapsulate information about the preconditions for a module to run data that must be present in the gdm database and post conditions data that will result
gdm imposes constraints on the i o tbrmat of creole objects namely that all information must be associated with byte offsets and conform to the annotations model of the tipster architecture
the system was a pipelined architecture which processes a text sentence at a time and consists of three principal processing stages lexical preprocessing parsing plus semantic interpretation and discourse interpretation
this work was supported by the uk engineering and physical sciences research council grant number gr k25267 and the ec dg xiii language engineering program grant number le1 NUM
in tip ster the column of a table can be represented as a single object with multiple references to parts of the text an annotation with multiple spans
the model also describes elements of information extraction ie and information retrieval ir systems relating to their use with classes representing queries and information needs
there are several reasons for doing so
while dimogue NUM fits very well in the statisticm model acquired from the training corpus dialogue NUM does not
the above assumption guarantees that all three measures are always well defined in particular it guarantees that the marginal probabilities p x NUM and p y NUM and the conditional probabilities p x NUM i y NUM and p y NUM i x NUM are all nonzero
and indeed the value of the lcb 2x2 NUM NUM intuitively corresponds to that assessment of similarity NUM dice coefficient y4 NUM now suppose that the NUM s and l s in x and y are exchanged so that the situation is now described by the last column of table NUM
these new goals are added to the current goals and then the algorithm repeats
this goal is satisfied as long as the sentence implies p given shared common sense knowledge
second goals of the form communicate p instruct the algorithm to include the proposition p
treating sentences as referring expressions allows us to encompass the strengths of many disparate proposals
in section NUM we review research on generating referring expressions and motivate our treatment of sentences as referring expressions
however unlike systemic networks our system derives its functional choices dynamically using a simple declarative specification of function
an important task of the text generation researcher is to specify both the range of these forms and the contexts in which they are used
it will conclude with a discussion of how well imagene s predictions match the text in the training and the testing portions of the corpus
a somewhat different approach that may turn out to be more efficient is to use the ordinary comparison operator that we used in the original definition of the head corner parser
we simply assume that in the first phase the parser only refers to syntactic information whereas in the second phase both syntactic and semantic information is taken into account
if a parse goal has been solved then this list containing the history information is asserted in a new kind of table the history item NUM table
such a list thus represents in a bottom up fashion all rules and result items that were used to show that that lexical entry indeed was a head corner of the goal
the set of derived trees for this tree substitution grammar equals the set of derivation trees of the parse ignoring the nonterminal symbols of the tree substitution grammar
a possible approach to deal with this situation is to index the items of the second table with the item of the first table from which the solution was obtained
here the device s action of lighting the indicator is a result of the reader s action of placing the handset in the base
collect a corpus of text from the relevant genre and encode a full range of the lexical and grammatical features of all of the text
counting single occurrences of transition types in general does not reveal the entire validity of the center lists
the main difference between grosz et al s work and our proposal concerns the criteria for ranking the forward looking centers
this claim however has to be further substantiated by additional cross linguistic empirical studies
the second column contains the results of this modification with respect to the naive approach
null the status of the accumulator is to the user indicated
also is the charge time of NUM NUM hours quite short
we distinguish between two types of transition pairs cheap ones and expensive ones
hence we claim that no one particular centering transition is preferred over another
first we examine the error data for anaphora resolution for the five cases
as before we need sublemmas to handle each case
the full parser does not take advantage of the dependency information present in the almost parse however it benefits from the elementary tree assignment to the words in it
figure NUM as and s elementary trees a derived tree b derivation tree and c dependency tree for
figure NUM finite state transducer representation for the sentences show me the flights from boston to philadelphia show me the flights from boston to philadelphia on monday
the drop in coverage is due to the fact that for NUM of the sentences the generalized parse retrieved could not be instantiated to the features of the sentence
in this section we introduce a device called stapler a very impoverished parser that takes as input the result of the ebl lookup and returns the parse s for the sentence
we have also introduced a highly impoverished parser called the stapler that in conjunction with the ebl resuits in a speed up of a factor of about NUM over a system without the ebl component
then mthough the index of the test sentence matches the index of the training sentence the generalized parse retrieved needs to be augmented to accommodate the additional modifier
an elementary tree serves as a complex description of the anchor and provides a domain of locality over which the anchor can specify syntactic and semantic predicate argument constraints
some characteristics of this test set are given in table NUM
we assume that the additional modifiers along with their arguments would be assigned the same elementary trees and the same substitution and adjunction links as were assigned to the modifier and its arguments of the training example
the three aspects of ltag a lexicalization b extended domain of locality and c factoring of recursion provide a natural means for generalization during the ebl proce88
the grammar for ovis describes grarnrnat ical user utterances i.e. whole sentences are described
we discuss test results suggesting that grammatical processing allows fast and accurate processing of spoken input
this demonstrator is called ovis openbaar vervoer informatie systeem public transport information systern
bigrams attach a measure of likelihood to the occurrence of a word given a preceding word
11a define s lambda args apply seq np vp args with a macro definition such as NUM named to remind us of this deficiency in the current scheme specification and perhaps encourage the language designers to do better in the future the definition of functions such as 11a can be written as 11b
figure NUM states in the two layered dialogue management architecture self organizing system
un examen ing nieux an clever exam NUM a un homme irritfi ennuyfi an irritated bored man b un livre irrit ennuy6 an irritated bored book c une destruction irritde ennuyfi an irritated bored destruction finally the third interesting property manifested by these adjectives is their pattern of polysemy
when the event is saturated we get the eventual sense in 17b cleverness is predicated of the manner of playing chess structure NUM when the object of the event is saturated we get the faetive sense so that in 17c cleverness is predicated of the fact of leaving structure NUM
des enfants tristes de la mort de leur inere to finish notice that in NUM NUM and NUM the role of the suffix appears clearly for emotion adjectives the d suffix constrains the head to be on the state and anti able on the causative event
they denote n cntal state el examljes NUM to NUM but they are also able to make reference to events the cause of the state c2 and or its manifestation ca as shown in examples NUM to NUM
remember however that all emotional state adjectives which combine a stative and a eventual meaning will not be able to get the three meaniugs the emotion adjective furieuz for example can not have the head on the agentive as shown in llb un livre furieu can not get the causative meaning
finally the t lic role is inherently a tcinporal consequence of the folt mai cf
to do this it call use four possible different roles the formal role encoding the basic semantic type s of the word the constrru riw role its constitutive elements the telic role its purpose or function and the agentive role the factors involved in bringing it about
in the case of ambiguity it is striking to see that french syntax distinguishes clearly the two senses
those in NUM will have the head on the state and will get only the stative sense
this observation should not be surprising since the set of dependency relations returned for a proof is in essence just a rather unstructured summary of its functional relations
conflict resolution may be accomplished by a sub dialogue in the lower layer
query generator this component is responsible for generating a database query
the consistency rules specific to an application are provided in an input file
it is often cumbersome and sometimes impossible to pre specify such a dialogue graph
a sample linking table may be head link s verb
in this notation the structures describing semantic types are the values of an attribute con tent and orth specifies the orthographic form
the phrase structure schemata presented contain full lexical entries which have a content attribute as well as an orth and a dtrs attribute
these english forms will be accounted for by another schema licensing default argument type specification like that in NUM above
as we saw before if the modifier specifies an argument in the telic qualia role the preposition in italian is da
nominals such as destruction credit and so on in NUM above describe the result of an activity
all of the representations for single words and complex nominals throughout the rest of the paper consist only of the value of content
the latter in turn expresses four aspects of the meaning of the lexical item formal constitutive telic and agentive
conversely the reading of the compounds in NUM makes explicit the result which is achieved by using a particular object
through the application of phrase structure schemata which constrain this co composition we obtain the representation in NUM for hunting rifle
we presented an algorithm for hierarchical has tering of words and conducted a clustering experiment using large texts of varying sizes
NUM ml clustering make c classes using the mutual information clustering algorithm with the merging the classes are merged into a singe lass
the widespread diffusion of wordnet and its large scale as well have motivated in several recent studies to start using it as a common source and adapt it for the purpose of the target la task
recall and precision have been measured against a manual classification carried out by three human judges about NUM cases received the same tag by all the judges this suggesting a certain complexity ot the task
the reference information i.e. the wordnet taxonomy is a well known sharable resource with an explicit semantics i.e. the hyperonimy hyponimy hierarchy this has a beneficial effect on the possibility to extract further lexical phenomenon e.g.
whenever an early tuning of the potetial semantic classes of a given verb in a corpus has been applied and local disambiguation has been carried out as corpus semantic annotation more precise verb clustering can be applied first local ambiguities have been removed during corpus tagging second clustering is applied with an intraclasses strategy and not over the whole set of verbs
a methodology to customize the general purpose tag system i.e. the high level chases of the wordnet hierarchy to a domain is described and a semantic disambiguation model to semantically tag source raw texts is defined
experience in wordnet sense tagging in the wall street journal
the relevance of ata NUM in the writing and exploiting process of the document encouraged us to consider the corpus from a quite new point of view in the nlp domain the pragmatic approach
the study of one chapter of the maintenance manual of the super puma helicopter made it possible to identify the pragmatic characteristics relevant in the choice of the morpho syntactic structures and translation processes actually used
the presence of a reference annotated translation is also of great help for keeping the evaluator as impartial as possible even if a r ference human translation may not always be the best one
the production and exploitation environment having a strong impact on the way the documents are written it was quite natural to first characterise the corpus we were intending to study from a pragmatics point of view
this is particularly true when dealing with terminology
on the long run future mt systems could take advantage of the pragmatic information contained in the sgml tags of the source text to drive both the analysis and the transfer phases
NUM building an annotated test set the corpus study allowed us to get a large number of information concerning the french text on one hand and the english text on the other hand
another example in the case of automatic analysis is the resolution of anaphora more than NUM of the pronouns in our corpus refer to the object complement of the last sentence
the results nevertheless seem interesting for some other purposes
it is interesting to see that the grammar acquired from all domains is not the best grammar in any tests
it may not be so useful to use different domain corpus even if the size of the corpus is relatively large
when we try to parse a text in a particular domain we should prepare a grammar which suits that domain
we need to further quantify these trade offs in terms of the syntactic diversity of individual domains and the difference between domains
because only NUM samples are used a single domain grammar tends to covers relatively small part of the language phenomena
in other words it is a small sampling problem which can be seen in the next experiment too
this could be the explanation that the grammar of fiction domains are superior to the own grammar for the three domains
note that this partial tree definition is not the same as the structure definition used in the parsing experiments described later
a partial tree is a part of syntactic tree with depth of one and it corresponds to a production rule
in order to represent the syntactic structure of each domain the distribution of partial trees of syntactic structure is used
that the semantic structures associated with syntactic nodes will be updated appropriately during the subsertion and sister adjunction operations
null progressivity progressivity is the principle of starting h om simple test items and increasing their complexity
proach as well as tim quality of the sys cms being l est d
systematicity in tsnlp is achieved for well formed items by the explicit classification of test items according to phenomena and sub phenomena
suital le for s nw sl iti tn sk
these are necessary qualities for an adequate reusable test suite which are difficult to find in test corpora
test sets lpest items emt optkmally be groul ed into test sets
tit folh wing major NUM rforlna lt c
the tsnlp project has laid tile tbundations for buihting large scale reference data for diagnostic and evaluation imrposes
systematicity systematicity refers to the depth of coverage of a test suite with respect to both well formed and ill formed items
for ea h of the three languages some NUM l esl items are l rovided
where l g is the length of the description of the grammar in bits
the more corresponding pairs of inferentially independent properties that are found and the more contextually salient those properties are the stronger the similarity
the block diagram for generation of a system using the symmetric approach is shown in figure NUM
hence our algorithm should be less prone to suboptimal local minima than the inside outside algorithm
to the initial grammar as well as the concomitant rule x b
the less likely nonterminal will probably not be part of either the correct parse or the tree returned by the parser so removing it will do little harm
unfortunately there is no good way to quickly compute the outside probability of a node during bottom up chart parsing although it can be efficiently computed afterwards
nodes a b and c will not be thresholded out because each is part of a sequence from the beginning to the end of the chart
we choose n to yield approximately equivalent performance for each algorithm
finally the actual algorithm we used also contained a simple annealing schedule in which we slowly decreased the factor by which we changed thresholds
on the other hand all nodes in the agenda parser were compared to all other nodes so in some sense all the priority functions were global
the errors fall into the following classes NUM
approximately NUM additional sentences have been annotated this way
the procedure is repeated NUM times with different partitions
table NUM the NUM most frequent errors in assigning
table NUM the NUM most frequent errors in assigning
table NUM levels of reliability and the percentage of
the overall accuracy of this approach is approx
the tagger described in section NUM was used
NUM the program suggests partim or complete parses
the predictions of the tagger are correct in approx
although the name k nearest neighbor might mislead us by suggesting that classification is based on exactly k training patterns the sima x fimction given by the overlap metric groups varying numbers of patterns into buckets of equal similarity
the average is computed as the mean value over the three word classes
only the user is able to detect such kinds of errors
the system turns have been only reported in their english translation
who knows tell me if there is something at eight
voglio partire da mils no milano di sera
in next section we will elaborate more on users error
the dialogos corpus consists of NUM NUM dialogues including NUM NUM utterances
figure NUM example of miscommunication due to mis conception
as it was explained above telephone recognition is error prone
the dialogue shown in figure NUM is a typical example
the ts rate was NUM on the NUM NUM dialogues
while this approach is theoretically intriguing it has yet to be shown to be computationally feasible in practice
in addition to computational efficiency we also consider a factor that might be called linguistic efficiency
more specifically each word is represented in the dictionary as a sequence of arcs starting from the initial state of d and labeled with an element s of hxp which is terminated with a weighted arc labeled with an element of e x p the weight represents the estimated cost negative log probability of the word
while gan s system incorporates fairly sophisticated models of various linguistic information it has the drawback that it has only been tested with a very small lexicon a few hundred words and on a very small test set thirty sentences there is therefore serious concern as to whether the methods that he discusses are scalable
many hanzi have more than one pronunciation where the correct pronunciation depends upon word affiliation is pronounced deo when it is a prenominal modification marker but di4 in the word i mu4 di4 goal is normally ganl dry but qian2 in a person s given name
this is because our corpus is not annotated and hence does not distinguish between the various words represented by homographs such as which could be adv jiangl be about to or nc jiang4 military general as in xiao3 jiang4 little general
so xue2 shengl meno student pl students occurs and we estimate its cost at NUM NUM similarly we estimate the cost of jiang4 meno general pl generals as in d lcb r xiao3 jiang4 meno little generals at NUM NUM
for example in northern dialects such as beijing a full tone NUM NUM NUM or NUM is changed to a neutral tone NUM in the final syllable of many words dongl gual winter melon is often pronounced dongl guao
the use of weighted transducers in particular has the attractive property that the model as it stands can be straightforwardly interfaced to other modules of a larger speech or natural language system presumably one does not want to segment chinese text for its own sake but instead with a larger purpose in mind
in our prototype implementation we are using the pc kimmo system for generating english morphology ant worth
several commercial aac systems which take text input exist but we found that these had a variety of drawbacks for our user
the primary reason is to perform more accurate tagging
these content dependent features are needed together with the speed dependent features to meet longer term goals such as those concerned with the development of relationships participation in activities status self esteem and independence
given the information the system has access to such errors can not be avoided
this paper describes a procedure to automatically assign grammatical subject object relations to ambiguous german constructs
and calculate p2 llnl v n2 with these new definitions
the model below is similar to that in icollins and brooks NUM
NUM and NUM are sentences to which this rule applies
we proceed analogously in case nl but not n2 is a pronoun
the learning procedure produced a total of NUM NUM test tuples and NUM NUM training triples
not only spelling errors in the source text are the source of incorrect tuples
the system incorrectly considered the noun altersgrenze to be the subject of the verb
a generalized clause ho NUM bo is an ordered pair of generalized goals where fro contains at least one relational atom
now we define items which are the basic computational units that appear on the agenda and in the lemma tables which record memoized subcomputations
the algorithm manipulates a set t of lemma tables which has the property that the first components of any two distinct members of t are distinct
the three program clauses for x NUM are used to resolve the selected literal in item NUM just as in item NUM yielding items NUM NUM
thus the lemma table proof procedure generalizes earley deduction in the following ways NUM memoized goals are in general conjunctions of relational atoms and constraints
note that the definitions of add adjuncss NUM and division NUM are recursive and have an infinite number of solutions when only their first arguments are instantiated
however the memoizing clp interpreter presented below has also been applied to gb and hpsg parsing both of which benefit from constraint coroutining in parsing
as the examples discussed below show some linguistic constraints can not be effectively resolved during parsing at the location in which they are most naturally introduced
because items NUM and NUM contain memo literals the control rule tags them table there already is a table for a variant of these goals after abstraction
we do not claim to understand exactly what types of categories will work well and which ones will not but our early experiences did shed some light on the strengths and weaknesses of this approach
the parser returns a new list parses to the interpreter
once this message has been synthesized the user can do various things
finally the responses will be displayed to the user who then has an opportunity to enter corrections to the text and have it re checked
therefore fox tinite forms the subject is in luded into the comps list from where extraction is possible l br nonfinite forms the subject does not appear on comps hut stays in the subj list NUM schema NUM licenses verb vird
i would also like to thank martin bsttcher and the anonymous reviewers for many helpful comments on an earlier version of the paper
w mm fin m al s j verb vcompv lexjv bse subj comps u vcomp nofte cat NUM luster structures
we experimented with a number of different types of comment and though the set that finally went into talk was by no means a definitive one the list of comment categories given
a problem with ill formed signs that are admitted by all hpsg accounts for partial verb phrase fronting known so far will be explained and a solution will be suggested that uses the difference between combinatoric relations of signs and their representation in word order domains
it shall attract exactly the arguments of the fronted verbal projection that were not saturated by this projection i.e. the matrix verb shall perform the argmnent attraction that would take place in base position abstracting away from tile value of lex
i will argue that the presented account is more adequate than others made during the past years because it allows the description of constituents in fronted positions with their modifier remaining in the non fronted part of the sentence
i will suggest a solution to the problem that is very simple if it is the case that an embedded verb or verbal complex has to be lex when verb and complement are tombitted locally and if it is the case that this does not hold if a nonlocal dependency is involved than the simplest solution is to view lex not as a local feature
when a hearer of a sentence hears the words that have to be combined with a trace or introduce the nonlocal dependency in another way he or she has already heard the phrase actually located in the vorfeld
i her i knew that peter hit her in NUM sic is extracted from the complement sentence of gewuflt and than inserted into the comps list of babe and saturated in the mittelfeld
each clause is segmented into phrase like constituents including nominative nc prepositional pc and verbal vc constituents
edward never needs to figure out the type of an expression that is being analyzed for all referring expressions the most salient referent is chosen
the subjects all had some previous experience with the system but this was limited to NUM or NUM hours and dated from NUM to NUM months before
computational linguistics volume NUM number NUM both expressions the file and the directory are ambiguous and would force edward to start a clarifying user consult
koen lives in nijmegen is an open ended time interval starting at the machine time at the time of interpretation and ending at now
depending on the number of words and ambiguities in a linguistic expression interpretation takes between NUM NUM and NUM NUM seconds when running on a personal decstation NUM
contrary to kl one relations have a time interval associated with them which represents the period of time during which the relation is assumed to hold
in edward we presently use seven cfs see table NUM four serve to model linguistic context effects and three to model perceptual context effects
notice however that demonstrative phrases are not necessarily accompanied by pointing gestures they can be used anaphorically as well see section NUM NUM NUM
notice that all simulated pointing gestures are in principle ambiguous they can refer either to the positions themselves or to the objects located at these positions
nevertheless it is doubtful that a segmentation with a score of NUM NUM would be useful in too many applications and this result will need to be significantly improved
while the scores themselves were not as high as the chinese performance the error reduction was nevertheless very high which is encouraging considering the simple rule syntax used
the results of such experiments can help us determine which resources need to be compiled in order to develop a high accuracy segmentation algorithm in unsegmented alphabetic languages such as thai
since we had a large amount of english data we also performed a classic experiment to determine the effect the amount of training data had on the ability of the rule sequences to improve segmentation
in this experiment when such a sequence of characters was encountered each of the characters was treated as a separate word as in the caw algorithm above
our rule based algorithm learned a sequence of NUM transformations from the training set applied to the test set they improved the score from NUM NUM to NUM NUM a NUM NUM reduction in the error rate
the greedy algorithm starts at the first character in a text and using a word list for the language being segmented attempts to find the longest word in the list starting with that character
with the above algorithm in place we can use the training data to produce a rule sequence to augment an initial segmentation approximation in order to obtain a better approximation of the desired segmentation
to gloss a few of these in the first rule here determiners with part of speech tag dt which usually begin n chunks and thus are assigned the baseline tag bn have their chunk tags changed to hl if they follow a word whose tag is also bn
the power of that approach is dependent on the fact that the confusion matrix for part of speech tagging partitions the space of candidate rules into a relatively large number of classes so that one is likely to be able to exclude a reasonably large portion of the search space
the large increase in the number of rule templates in the text chunking application when compared to part of speech tagging pushed the training process against the available limits in terms of both space and time particularly when combined with the desire to work with the largest possible training sets
this kind of partial static index proved to be a significant advantage in the portion of the program where candidate rules with relatively high positive scores are being tested to determine their negative scores since it avoids the necessity of testing such rules against every location in the corpus
word NUM to left word NUM to right current word and word to left current word and word to right word to left and word to right two words to left two words to right word NUM or NUM or NUM to left word NUM or NUM or NUM to right
by keeping track of the rule with maximum benefit seen so far one can be certain of having found one of the globally best rules when one reaches candidate rules in the sorted list whose positive score is not greater than the net score of the best rule so far
in those comparisons the stochastic methods outperformed the hand built finite state models with claimed accuracies of NUM NUM clauses and NUM NUM nps for the statistical models compared to to NUM clauses and NUM NUM nps for the finite state methods
one interesting direction here would be to explore the use of chunk structure tags that encode a form of dependency grammar where the tag n NUM might mean that the current word is to be taken as partof the unit headed by the n two words to the right
furthermore template specification itself wa s rather cumbersome due to the in and out object which really was a pseudo object for grouping relate d information
this mode can test how well the collector is merging information how well the reference resolver is working or test the format produced by the generator
for the formal muc NUM test data hasten had three official configurations one to maximize recall one to maximize precision and one to maximize both
p now mr james is preparing to sail the sunset and mr dooner is poised to r hasten uses that example to analyze subsequent text
this step not only supports th e creation of the generator output scripts but also enables the loading of answer key templates fo r analysis and evaluation
nametag uses its own tag specification that classifies names and other key phrases and can either generate sgml annotated tex t or a table of extracted entities
nametag tagged coca col a within coca cola classic as an organization since it failed to recognize the larger product name
since the reference resolver provides some of the organizational descriptors which may include some location or nationality information the second configuratio n resulted in lower recall
figure NUM shows the results of the three official configurations for both the training and the test data as well as additional data points for other threshold settings
the training examples did not contain a sentence involvin g the word hire and thus the egraphs were not similar enough to result in a match
phonetic syncopation a consonantal segment may be omitted from the phonetic surface form but maintained in the orthographic surface from
vocalisms the quality of the perfect and im null perfect vowels of the basic forms of the semitic verbs are idiosyncratic
1we have used the cv model to describe pattern morphemes instead of prosodic terms because of its familiarity in the computational linguistics literature
vowels in non stem and stem morphemes respectively note that the lexical contexts make sure that long vowels are not deleted
short vowels can legitimately be omitted from an orthographic representation it is this fact which contributes to the problem of vowel shifts
where p1 e lcb c2 c3 c4 rcb resuming the description of the grammar NUM presents spreading rules
the error rules capture the correspondence between the error surface and the correct surface given the surrounding partition into surface and lexical contexts
hence in the next pass of normal analysis the partition is analyzed as a legitimate omission of the expected vowel
morphosyntactic issues of broken plurals diminutives and deverbal nouns can be handled by a complementary correction strategy which also depends on morphological analysis
morphosyntactic issues in broken plurals diminutives and deverbal nouns the user may enter a morphologically sound but morphosyntactically ill formed word
null this view of social conversation may however be misleading in its disregard of the range of goals that motivate such interactions
valuable criticism has come from poul andersen susan armstrong and serge yablonsky
the first requirement is to develop a basically phrase storage system that emphasizes speed of output and models other speed dependent features of natural conversation i.e.
it seems reasonably clear that aac systems based on phrase construction are unable to meet some of the social goals that users are likely to have
more accurately we could say that the conversationalist is either leading the direction of the conversation or is following the other person s lead
it quickly proved very difficult particularly on the information extraction side to maintain sufficient training and testing data throughput and at the same time maintain high data consistency
as indicated earlier in this paper the optimal situation is one in which the data collection effort is NUM completed prior to the start of the associated research task
the rest of us around the table had little or no previous exposure to either the details of an evaluation driven research paradigm or to the inner workings of darpa programs
since its creation in NUM tipster has developed grown and evolved into its current role as a major driving force within both the information retrieval and information extraction r d communities
since october NUM the introductory briefing of the tipster text program has regularly been given as a joint briefing by dr sarah taylor of the office of research and development and myself
early on we opted to focus the tipster program on two core problems which seemed to be central to a large number of different operational problems
in phase iii we will add a third enabling technology area text summarization while continuing to pursue natural extensions of these phase ii goals
NUM the implementation of the tipster phase ii architecture demonstration system required extensive detailed coordination between all seven of the tipster phase ii contractors
in trials the talk system has shown that incorporating the modelling of pragmatic features of conversation can produce improved results in computer aided communication
we present a self organized method to build a stochastic japanese word segmenter from a small number of basic words and a large amount of unsegmented training text
we approximate the joint probability p w by the word unigram model which is the product of word unigram probabilities p wl
summary during the past seven year history of the tipster text program there has been dramatic improvements in the current state of the art in text handling processing and exploitation
NUM one participant in the document detection component of tipster has participated in all three tipster phase i evaluations in trec NUM to trec NUM and is currently participating in trec NUM
NUM the following results relate to the prolog implementation ldeg fig NUM shows the resulting packed udrs for the example forest in fig NUM fig NUM displays the sem part as a graph
the reason for this presumably lies in the fact that meta logical operations the algorithm needs like generalise and copy term have been modeled in oz and not on the logical level were they properly belong namely the constraint solver
to be more precise we assume a constraint language c over a denumerable set of variables x that is a sublanguage of predicate logic with equality and is closed under conjunction disjunction and variable renaming
the leaf constraints together with the rules define a semantics constraint z for every node u and the semantics of the full sentence is described by the t constraint of the root node root
a c NUM a n a def n c r NUM ac NUM a c r NUM ac 2s ac NUM the packed semantic representation as constructed by the method described so far still calls for an obvious improvement
therefore let us abstract away this size by employing a function fa n that bounds the size of semantic structures respectively the size of its describing constraint system in normal form that grammar g assigns to sentences of length n
hence input to the algorithm is a parse forest an associated semantic rule for every local tree and node together with its children therein and a semantic representation for each leaf coming from a semantic lexicon
we assume that any associated instantiated semantic rule r u of a local tree and branching u ul u determines u s semantics z u as follows from those of its children
the disjunctive binding environment only encodes what the variable referents b and d in conjunction with the corresponding labels a and c may be bound to to one of el x2 or x3 and likewise the corresponding label
all these differences however are not statistically significant
the discrimination function is thus modified into the following form
this in turn leads to the following equation
basically the reasons for using this approach are two fold
unfortunately this problem occurs frequently in statistical language modeling
scores and the l1 mode of operation in computing syntactic scores
initial estimation for an m gram model the conditional probability
a promising result has been observed by applying tung hui chiang et al
robust learning smoothing and parameter tying on syntactic ambiguity resolution
not all such uses are covered by wordnet entries
the content of that location in the hash table is a pointer to the context vector data structure
in theory the summation on the right hand side extends over all word stems in the vocabulary
the contents of that location in the hash table is a pointer to the context vector data structure
a description of this system the training corpus and the preliminary results are provided in section NUM
inroute is an inference network system tailored for document filtering
the second form annotates a collection or a subset thereof
an overview of the prototype information dissemination system prides
a user describes his dissemination need in an interest profile
prides end users use a web browser to communicate with the web server
insertion project sponsored by the office of research and development ord
in addition fbis analysts publish analyses of trends and patterns across articles
extraction therefore does not require any operations and classes beyond those already presented
any formscompatible web browser can be used to access prides
optimize concurrency control to allow frequent updates of the document index
prepositional phrase attachment through a backed off model
NUM a grammatical representation morphosyntactic descriptors and their application guidelines can be specified with a reasonable effort
in this article we report on a double blind experiment with a surface oriented morphosyntactic grammatical representation used by a large scale english parser
a nil argument means that no progress monitoring is required
unfortunately they give very little empirical evidence for their position e.g. in terms of double blind experiments
in the rare cases where two analyses were regarded as equally legitimate both could be marked
the central task of a parser is to assign grammatical descriptions onto input sentences
generally the engcg morphological tag set avoids the introduction of structurally unjustified distinctions
consider the following sentence fragment NUM that managers gn keeping
the returned document will also tell how to analyze own samples using the engcg server
NUM these tagged versions were compared to each other using the unix sdiff program
in this case comments were added to distinguish it from the other two types
likewise the style of the texts from which the lexica were built must be taken into consideration
however there are of course inherent limitations of any approach that relies entirely on crossing and fanout constrained lexical matching
an example for the first and second point is found in a translation equivalent set for an auxiliary rareru which is known to be highly ambiguous
contrary to the ej direction the major task in je japanese to english direction will be writing short original documents such as e mail
this representation step in which english words content words and japanese words functional words are mixed separates steps for word translation and
in this article we present an interactive translation method and its implementation which has advantages of both a dictionary look up tool and a machine translation system
if the user needs only the result of dictionary lookup s he can signal the end of translation at this point just after choosing the translation equivalent
at the same time as initial translation equivalent selection the system predicts an appropriate area for translation as shown by an underline b
before each calculation the system pauses to show an interpretation of the underlying structure and allows the user to examine and change it if necessary
for many users however the translation function will be considered helpful to produce a result of the quality level that matches their english reading skill
precision of initial prediction of translation equivalent and translation area is crucial to the performance of the system since they determine the quality of default translation
x is assoeia ted with a head g h g and t has no se quenee of nonterminal symbols q rcb that derives exactly the same set of strings a s x does
the notion of head constra ints may havo to hc ext mdo d into that of a set ln md wshil constraint if we need to ha ndlc coor lina ted structures
ii llt rminal symbol or a hart x and a sul s quen ill of ini ut string s is bounded by h
for full template output the output generator takes the ddos produced by discourse processing and fills out the application specific templates
cs ui a ii l with a mriliary vcrh caux a ild it spa ns the word l ositions NUM to NUM o we ca ii mso find several h ad cortstrained pa tterns there
i assume that there is one head structure for each mc tag structure and that the a g place holder structure is the head structure for each at g structure
however in sentence NUM the at go np pair should be used instead for translating the scrambled argument jerry i.e. figure NUM a
therefore using the initial restrictions in a sentence of NUM words or more counting punctuation marks as words there could be NUM alternative placements
however in order to disambiguate the tag and place the subject markers it is only necessary to know that it is a noun or else a verb
actually the restriction on whether flat golf can be adjoined onto a certain node does not come from the formalism of synchronous tags but purely from the grammar of korean tags figure NUM b shows the final derived trees for both korean and english after applying NUM a to the partially derived trees
for mapping korean to english the simple object np structure of english e.g. the right structure of NUM pair in figure NUM can be mapped to two structures i.e. aa o and at go thus generating two possible lexical pairs
similarly flat go denotes a pair of structures for representing a scrambled object argument
a s shows a pair of structures for representing the scrambled subject argument
the activations at the input layer are fed forward through the weighted connections to the output nodes where they are summed
however for some processing steps we need to reduce the number of candidate tag strings presented to the neural network to manageable proportions see section NUM
the linear separability of data is related to its order and this system uses higher order pairs and triples as input
weighted links input nodes prep represents the start of the subject
relationship between the neural net and prohibition table the relationship between the neural net and the rules in the prohibition table should be seen in the following way
if it is not linked to any node of the target language the structure can be freely adjoined onto any available node of the partially derived tree of the source language which is approximately what scrambling is about
the node preposition would not occur in a correct string so it is not connected to the yes output node
the procedure for extracting key paragraphs has the following three stages stage one representing every paragraph as a vector the goal of this stage is to represent every paragraph in an article as a vector
for the same reason query time segmentation should include the raw characters or at least the bigrams in the query
every indefinite noun phrase that can not be associated by context becomes an un named entity
corporate name changes another five missed aliases were found in scenarios of changing corporate identity
this approach was evaluated by examining the source of the answer key s locale fillers
when the system reveas to preferring the longest descriptor the following scores are achieved
the scoring system must then decide which object to map to the answer key
as the table shows this gives an average improvement in performance of about NUM over the unsegmented query
this may also benefit from a survey of typical contexts over a large corpus
five areas were identified in which improvement to the name variation code is needed
these scores were generated for inter experiment comparison proposes using the muc6 scoring program v NUM NUM
first the phrase is checked to mske sure it has n t already been associated by context
bbn conducted a comparative test in which the experimental configuration used a larger lexicon than the baseline configuration but the exact nature of the difference is not known and the performance differences are very small
NUM indicates that a word has length NUM c indicates that a word is capitalized mr indicates that a word has spelling mr3 and dr indicates that a word has spelling dr2
with respect to how often expectation raising constructions appear in text we have brown corpus data on two specific types imperative suppose and adverbial on the one hand as well as a detailed analysis of the romanian text by vianu mentioned earlier
these include reference and ellipsis resolution inference e.g. inferential processes associated with focus particles such as in english even and only and identification of those structures underlying a discourse that are associated with coherence relations between its units
figure 4a illustrates the incremental analysis of example NUM figure 4a i shows the elementary tree corresponding to sentence 2a on the one hand the interpretation of john is very generous i corresponds to the left daughter labeled a
on the one hand the public health service declared as recently as october NUM that present radiation levels resulting from the soviet shots do not warrant undue public concern or any action to limit the intake of radioactive substances by individuals or large population groups anywhere in the aj
figure NUM a illustrates adjoining midway down the rf of tree a while figure NUM b illustrates adjoining at the root of a s rf figure NUM c shows adjoining at the degenerate case of a tree that consists only of its root
examples NUM and NUM illustrate that such an expectation need not be satisfied immediately by the next clause in example NUM clause b partially resolves the expectation set up in a but introduces an expectation that the subsequent discourse will indicate what happens in such cases
figure NUM depicts the general architecture of the vodis system
this paper employs acoustic and prosodic cues to correct the repetition repairs
leiser is specifically concerned with speech interfaces in the car
however two practical guidelines apply for vodis NUM
for example the german navigation computer knows NUM NUM city names
keywords spoken dialogue management errorprevention error recovery design issues
grice NUM should hold for speaking systems as well
however we would like to claim that the basic dm methodology can remain largely unchanged
even though state of the art speech recognition modules perform well speech recognition errors can not be precluded
moreover this should be done in a way which is not disturbing for the driver
from here on we will identify complements by attaching a c suffix to non terminals figure NUM gives an example tree
in a pcfg for a tree derived by n applications of context free re write rules lhsi rhsi NUM i n
for example in figure NUM the trace is an argument to bought which it follows and it is dominated by a vp
for reasons we do not have space to describe here model NUM has advantages in its treatment of unary rules and the distance measure
the gap is then passed down through the tree until it is discharged as a trace complement to the right of bought
we specify a parameter NUM c gip h h where g is either head left or right
when parsing the pos tags allowed for each word are limited to those which have been seen in training data for that word
jointly figures NUM and NUM show that NUM guideline violation types were found by both analysers NUM types were found by one analyser only one type in fact a single case was undecidable on the evidence provided by the transcription NUM types were disagreed upon and NUM types were rejected during the consensus discussion no types were found that demanded revision or extension of the guidelines
the other analyser took the user s question to be an incredulous request for more information did you say there are n t any flights leaving crete today in which case the system s subsequent reply yes would have been a violation of gg NUM
until the user has built up a flail query which of course may be done in a single utterance but sometimes takes several utterances to do the system would only respond by asking for more information or by correcting errors in the information provided by the user
a cky style dynamic programming chart parser is used to find the maximum probability tree for each sentence see figure NUM
we intend to perform experiments to compare the perplexity of the various models and a structurally similar pure pcfg 1deg
when as in many realistic cases in which det might be used no scenarios exist or are available an additional problem arises of whether the corpus analysers are actually able to detect the same problems in a dialogue prior to classifying them
deb in figure NUM what we need as slds developers is not a tool which tells us many times of the same dialogue design error but a tool which helps us find as many different dialogue design errors as quickly as possible
if it says so up front this is an sg4 but if it later demonstrates that it has said too little this should be an sg8 but it is comparatively innocuous if an analyser happens to classify the violation as an sg4
each detected problem was a characterised with respect to its symptom b a diagnosis was made sometimes through inspection of the log of system module communication and c one or several cures were proposed
k p a p e NUM p e where p a is the proportion of times that the coders agree and p e is
one caveat about the meaning of the difference between reply y and reply n rarely queries include negation e.g. you do n t have a swamp you re not anywhere near the coast
although kappa corrects for chance expected agreement it is still susceptible to order of magnitude differences in the number of units being classified when the absolute number of units placed in one of the categories remains the same
second although other coding schemes may distinguish many categories for utterances segmented according to the discourse goals they serve by showing game and transaction structures this coding scheme attempts to classify dialogue structure at higher levels as well
these dialogue structure distinctions were developed within a larger vertical analysis of dialogue encompassing a range of phenomena beginning with speech characteristics and therefore are intended to be useful whenever an expression of dialogue structure is required
on the other hand this is a common failing of coding schemes and in some circumstances it can be more important to get the ideas of the coding scheme across than to tightly control how it is done
to test how well the scheme would transfer it was applied by two of the coders from the main move reliability study to a transcribed conversation between a hi fi sales assistant and a married couple intending to purchase an amplifier
in principle check moves could cover past dialogue events e.g. i told you about the land mine did n t i or any other information that the partner is in a position to confirm
for instance instructions signal that the speaker intends the hearer to follow the command queries signal that the speaker intends to acquire the information requested and statements signal that the speaker intends the hearer to acquire the information given
although this is not the same as agreeing on the category of an initiating move because not all initiating moves begin games disagreement stems from the same move naming confusions notably the distinction between query yn and check
to fit the model to its reference distribution we use the improved iterative scaling algorithm NUM we initialize all weights lambdas of the features from our constraint space with some initial values
NUM when NUM NUM i.e. when we conduct clustering the best of fmm almost always outperforms that of hcm
num of doc in training data NUM num of doc in test data NUM num of type of words NUM avg
we then applied fmm hcm wbm and a method based on cosine similarity which we denote as cos NUM to conduct binary classification
in this section we describe the results of the experiments we have conducted to compare the performance of our method with that of hcm and others
from the perspective of number of parameters hcm employs models having very few parameters and thus may not sometimes represent much useful information for classification
we then classify the document into category c2 as log NUM l d c2 is larger than log NUM l dlcl
for a given training sequence wl wn the maximum likelihood estimator of NUM is defined as the value which maximizes the following log likelihood func
we ignore in document classification those words which can not be assigned to any cluster using this method because they are not indicative of any specific category
for the first data set fmm0 attains the highest score at break even point for the second data set fmm0 NUM attains the highest
in section NUM we will study tokenization ambiguities and explore the concepts of critical points and critical fragments
the first line cat clause indicates that what follows will be some type of verbal phrase in this case a sentence
this is mainly due to the lack of sufficient unambiguous contexts to bootstrap the whole disambiguation process
gold standard disambiguated versions for ark c270 were prepared manually to evaluate the automatically tagged versions
unless an explanation generator has access to a sufficiently large knowledge base the first step and hence the second and third cannot be carried out enough times to evaluate the system empirically
despite the complex and possibly malformed representational structures that an explanation system may encounter in its knowledge base it should be able to cope with these structures and construct reasonably well formed explanations
it introduces a new evaluation methodology and builds on the conceptual framework that has evolved in the nlg community over the past decade particularly in techniques for knowledge base access and discourse knowledge representation
although there was a significant difference between knight and biologists on explanations of processes knight and the biologists did not differ significantly on explanations of objects tables NUM and NUM
table NUM average parses recall and precision for text ark after applying learned rules
similarly the delete rules find some interesting situations which would be virtually impossible to enumerate
we finally conclude after a discussion and evaluation of our results
table NUM average parses recall and precision for text NUM after applying learned rules
there has been a large number of studies in tagging and morphological disambiguation using various techniques
these are rules which impose very tight constraints so as not to make any recall errors
whenever an unknown word had more than one parse it was counted under the appropriate group
the following is a list of multi word constructs for turkish that we handle in our preprocessor
in the lcs telic verbs contain a path of a particular type or a constant in the right most leaf node argument
we are therefore able to describe not only the lexical aspect at the sentential level but also the set of aspectual variations available to a given verb type
the marker indicates a variable position i.e. a non constant that is potentially filled through composition with other constituents
notation is used as a wildcard which is filled in by the lexeme associated with the word defined in the lexical entry thus producing a semantic constant
the feature specification of this compositionally derived accomplishment is therefore identical to that of a sentence containing a relic accomplishment verb such as produce in NUM
this work shows 11the maximum possible error rate reduction is NUM NUM or the mean applicability discussed in section NUM
bird and machine for the word crane and using for seeds only those contexts containing one of these words
each kind of template object corresponds to a type of annotation
in general the decision list algorithm is well suited for the task of sense disambiguation and will be used as
using the decision list algorithm these additions will contain newly learned collocations that are reliably indicative of the previously trained seed sets
our algorithm exceeds this accuracy on each word with an average relative performance of NUM vs NUM
new data are classified by using the single most predictive piece of disambiguating evidence that appears in the target context
this would indicate that the cost of a large sense tagged training corpus may not be necessary to achieve accurate word sense disambiguation
and in some cases actually achieves superior performance when using the one sense per discourse constraint NUM NUM vs NUM NUM
such a bridge to the sense a collocate cell is illustrated graphically in the upper half of figure NUM
additional details aimed at correcting and avoiding misclassifications will be discussed in section NUM figure NUM sample final state
proof on the one hand every single character is also a character string of length NUM
given a typical english dictionary there is no extraordinary critical point in the character string s fundsand
the best thresholds were detected for ending rules NUM points for suffix rules NUM and for tries out of NUM NUM entries of the original lexicon
thus with certain confidence we can assume that if we used more training data the rule estimate NUM would be no worse than the l limit
in the first experiment we tagged the text with the brown corpus lexicon supplied with the taggers and hence had only those unknown words which naturally occur in this text
they do n t appear on a real window
the score of the resulting rule will be higher than the scores of the merged rules since the number of positive observations increases and the number of the trials remains the same
the first pos set in a guessing rule is called the initial class class and the pos set of the guessed word is called the resulting class r class
first setting certain parameters a set of guessing rules is acquired then it is evaluated and the results of evaluation are used for re acquisition of a better tuned rule set
to perform such rule merging over a rule set first the rules which have not been included into the final set are sorted by their score and best scored rules are merged first
if the subtraction results in an non empty string it creates a morphological rule by storing the pos class of the shorter word as the class and the pos class of the longer word as the r class
usually this is the case with irregular words like for example cattle which was wrongly guessed as a singular noun nn but in fact is a plural noun nns
the system was also evaluated on the data which were collected from an in house experiment
when compared with a syntactic grammar this grammar achieves a lower degree of ambiguity misparsing without decreasing the parsing rate
ambiguity misparses without decreasing the parsmg coverage when compared with a purely syntactic grammar
nm in the muc ii corpus which frequently occur in phrases with omitted elements
in this section we discuss typical misparses for the syntactic grammar on experiments in the muc ii corpus
secondly after the most likely tag for each word is assigned contextual transformations are used to improve the accuracy
lexical resources e.g. dictionaries and glossaries on the other hand are currently maintained in a database and are accessed via calls to a c library api
the rationale for this architecture is that many nlp tools are themselves rather large software corn null ponents and embedding them in servers helps to reduce the computation load
this data model does not provide any support for communication between components i.e. for executing and controlling the interaction of a set of components nor for rapid tool integration
also relevant to this presentation are topics such as integration of heterogeneous components for building hybrid systems or for integrating speech and other higher level nlp components section NUM
the server also acts as a filter by translating the document data structures stored in the document server in a format appropriate as input for the component and conversely for the component output
the process or communication layer involves for example communication between different components that could be written in different programming languages and could be running as different processes on a distributed network
some projects have concentrated on developing lexical resources directly in a format suitable for further use in nlp software e.g. genelex multilex
in this architecture components do not talk directly to each other but communicate through information so called annotations attached to a document
if the component is an executable the server must issue a system call for running the program and data communication usually occurs through files
in this example each component of the application is embedded in a server which is accessed through the corelli component integration api as described above
the critical finding was that the faster her speech output in a particular conversation the more pleasurable that conversation was rated by both the user and her conversational partners
however in evainating the effectiveness of attacking the proposed evidence for bel the system must determine whether or not it is possible to successfully refute a piece of evidence i.e. whether or not the system believes that sufficient evidence is available to convince the user that a piece of proposed evidence is invalid and if so whether it is mote effective to attack the evidence itself or its support
object standard dependents of transitive verbs in passive voice transverb n passive word noun n subject
however as shown above dug is a hybrid grammar although dependency rules are the backbone of the formalism it allows the introduction of quasi non terminals that are integrated into the grammar via references
and by modifying both the preprocessor and the accept predicate so that the input sentence is split at the position of the dependent accepted and left and right remainders are passed to the next rules separately
n sleep verb n n noun n n sleep verb n
NUM NUM ptionality many dependents are optional
the applicability conditions NUM of correct node specify that the action can only be invoked when sl believes that node is not acceptable while s2 believes that it is when sl and s2 disagree about the proposed belief represented by node
NUM agents involved in collaborative negotiation are open and honest with one another they will not deliberately present false information to other agents present information in such a way as to mislead the other agents or strategically hold back information from other agents for later use
this paper extends that work by i ting into the modification process a slrategy to determine the aspect of the proposal that the agent will address in her pursuit of conflict resolution as well as a means of selecting appropriate evidence to justify the need for such modification
collaborative negotiation differs from non collaborative negotiation and argum entation mainly in the attitude of the participants since collaborative agents are not selfcentered but act in a way as to benefit the agents as this material is based upon work supported by the national science foundation under grant no iri NUM
since beliefs NUM and NUM above constitute a warranted piece of evidence against the proposed belief and beliefs NUM and NUM constitute a strong piece of evidence against it the system will not accept on sabbatical smith next year
text phenomena e.g. textual forms of ellipsis and anaphora are a challenging issue for the design of parsers for text understanding systems since imperfect recognition facilities either result in referentially incoherent or invalid text knowledge representations
accordingly any context bound expression in the utterance u i is given the highest preference as a potential antecedent of an anaphoric or elliptical expression in in while any unbound expression is ranked next to context bound expressions
in the case of textual ellipsis the missing conceptual link between two discourse elements occurring in adjacent utterances must be inferred in order to establish the local coherence of the discourse for an early statement of that idea cf
the results for the latter approaches become only slightly more positive with the modification of ranking the antecedent of a textual ellipsis above the elliptical expression but they do not compare to the results of the functional approach
at the methodological level we develop arguments that at least for free word order languages grammatical role indicators should be replaced by functional role patterns to more adequately account for the ordering of discourse entities in center lists
note that rechner computer is the subject of the sentence though it is not the preferred antecedent since akku accumulator precedes rechner computer and is anaphoric as well
when the function is triggered the longest possible string starting at that position is transformed according to this function
applying one function consists of looking for the first position in the input at which the function can be triggered
proof lemma NUM shows that the algorithm always terminates if it is subsequential
since f is subsequential it is of bounded variations therefore there exists k s t
since only a few transitions are allowed from many states this table is very sparse and can be compressed
figure NUM NUM when multiple output symbols are emitted a comma symbolizes the concatenation of the output symbols
since the dictionary is the largest part of the tagger in terms of space a compact representation is crucial
when dealing with medium sized corpora a few hundred thousand words the terminological network is too voluminous for analysis by hand and it becomes necessary to use data analysis tools to process it
knowledge acquisition ka from technical texts is a growing research area among the knowledge based systems kbs research community since documents containing a large amount of technical knowledge are available on electronic media
we asked the ke to evaluate the quality of the clusters by scoring each of them assuming that there are three types of clusters NUM non relevant clusters
moreover the length probabilities for cfg rule vp vp pp and those for cfg rule np np pp show different distribution patterns suggesting that syntactic preference is a function of a cfg rule
it also means that it is easy for programs to access the information which is relevant to them while ignoring additional markup
the lexical likelihood values pt NUM of the two interpretations were calculated as NUM the lexical likelihood value pl NUM of the interpretation of attaching under phrase to child was higher than that of attaching it to reclaimed as there were many expressions like a child under five observed in the training data
while both represent an attachment of pp to np the length of np of the former is NUM and that of the latter is NUM thus the second length probability in NUM is likely to be higher than that in NUM as in training data there are more phrases attached to nearby phrases than are attached to distant ones
moreover the difference between the right hand probabilities is likely to be higher than that between the left hand probabilities and thus the syntactic likelihood value of the former interpretation will be higher than that of the latter
pt x i1 p ileat argl x p ice creamleat arg2 x p spoonleat with NUM NUM and pzex i2 p ileat argl x p ice creamleat arg2 x p spoon ice cream with NUM NUM
for subproblem b we point out that the notion of the length of a syntactic category NUM is important and propose to use a length probability to perform structural disambiguation
if a verb or a noun has a strong tendency to require a certain noun as the value of its case frame slot the estimated three word probability for such a co currence will be very high
it can be argued therefore that the computer with its complete morphological knowledge is facing a much more complex problem than that of a human who may be ignorant of some rare analyses reading a hebrew text
show me flights from boston on lcb f uh rcb from denver on monday a restart is repaired by deleting the material between the open bracket and the interruption point
for example in table NUM which shows the counts for the top NUM words pronouns such as t and it rows NUM and NUM are much more frequent in the before set
it is clear from the definitions of the given vs new parts of the sentence that the vocabularies in the corpora resulting from the division will have different distributions given information will be expressed with a larger number of pronouns whereas the new portion will have more complex descriptive noun phrases and thus a wider ranging vocabulary
an experiment to test the usefulness of the morpho lexical probabilities for morphological disambiguation in hebrew yielded the following results a recall of NUM for full disambiguation and a recall of NUM for analysis assignment
these results can be explained by the following properties of the ambiguity problem in hebrew in many cases two or more alternative analyses share the same category and hence these alternatives satisfy the same syntactic constraints
NUM p1 NUM NUM p2 NUM NUM p3 NUM NUM in this example the similarity assumption holds and the words in the sw sets excluding the word hqph itself are also unambiguous
the quality of the approximated probabilities we acquire using our method is now measured by examining the proportion of words for which the estimated category for each of their analyses agrees with the category defined by the approximated probabilities
the main idea was to use the rich morphology of the language to learn the computational linguistics volume NUM number NUM frequency of a certain analysis from the frequency of other word forms of the same lexical entry
oh can be treated as a filled pause if it appears along with other words for example oh yeah oh really as in example NUM otherwise oh is treated as a regular word unit of language if it appears by itself as a reply as in example NUM
the set of rules defined for hebrew would enable us to observe that in the domain of daily newspaper articles the first analysis probably has a high morpho lexical probability while the second analysis has a very low probability
this addresses the need for increased granularity of the units of reuse as noted in section NUM
this work concerns either reusable resources which are primarily data or those which are primarily algorithmic i.e.
so for example c NUM v c NUM v ca v c NUM is satisfiable just in case at c NUM a a2 c NUM a
4q ht se lexical rules air simplitied versions of those presented in polb rd md sag NUM
however only doing this might leave us with duplicate disjuncts so converting the result to dnf removes any such duplicates
this method takes dependent disjunctions within a constraint formula and factors them into non interacting groups whenever possibh by determining their independence
however all of these algorithms sutfer from a common problem thc ir performance is highly deternfined by their inputs
so the dis iunets in ease case must be every conjunction of possible a s and a s
the engineering and physical science research council uk funding body
the creole apls may also be used for programming new objects
mistrust of foreign code NUM integration overheads
gdm is based on the tipster document man null ager
the modularity of gate based systems should however contribute to cutting the engineering overhead involved
this facility supports hybrid systems ease of upgrading and open systemsstyle module inte rchangeability
ec proje ct demonstrators can use gdm and creole without ggi see below
a parser developer for example can replace the parser sut plied with vie
evaluatmn smce the two manual extracts for an amcle are chfferent the amount of overlap between an automatic and a manual extract depends on which manual extract as selected for comparison the opumisuc evaluauon for an algonthm as done by selecung the manual extract wath winch the automatic extract has a ingher overlap and
notion of an adeal extract s more chss rmlar to the automatac extract as our user tins in some sense
measuring tins overlap tins as the same as using the human whose nouon of an deal extract s closer to the automauc extract as our user NUM pesszrmstw evaluanon analogously a pessarmstic evaluation s done by selecting the manual extract with winch the automauc extract has a lower overlap thas as the same as using the human whose
research and commercial msmut ons to pushthe stareof the art m text processing technologies
ments m languages other than enghsh for summanzatlon either into then native language or into enghsh
the author is grateful to donna harman and beth sundhelm for then support and assmance m designing the evaluauon the views expressed m this paper are those of the author and do not necessarily reflect the views of the department of defense or any of its agen
the modifying noun must be of semantic type individual and its content value is structure shared with the d arg1 in the argstr of the resulting compound
for example the schema in NUM which accounts for bread knife requires the modifying noun to be typed as individual
complex nominals play an important role in the encapsulation and expression of nominal concepts and are frequent in a wide variety of types of texts
our goal is to identify the features that predict cue selection and placement in order to devise strategies for automatic text generation
these results are based on the portion of our corpus that is analyzed and entered into the database approximately NUM clauses
another kind of intentional relation is evidence in which the contributors are intended to increase the hearer s belief in the core
we analyze the way in which each contributor relates to the core from two perspectives intentional and informational as illustrated below
here we report the percentage of instances for which the reliability coder agreed with the main coder on the various aspects of coding
the reliability coder coded one quarter of the currently analyzed corpus consisting of NUM clauses NUM segments and NUM relations
because rda analyses capture the hierarchical structure of texts we were able to explore the effect of embedding on cue selection
second the cue selection for one relation was found to constrain the cue selection for embedded relations to be distinct cues
since and because were two of the most frequently used cues in our corpus occurring NUM and NUM times respectively
this result shows that the choice between since and because is determined by something other than the attributability of contributor to hearer
we feel that our two layered architecture should make the system more portable
although this is more powerful i.e. one is not restricted to tree structures it does make validation of annotations more difficult
also let cs be the result label p e cl p e c2 and p e c3 are probability distributions over contexts e of cl c2 and respectively p cl p c2 and p c3 are estimated probabilities of cl c2 and ca respectively
based on what the user said an appropriate response is generated
occurs at beginning of sentence length of word preceding
it was due friday by NUM p m saturday would be too late
training was performed in less than five minutes on a next workstation
this catches common sequences like and
if any abbreviation class of words with
upper lower cap numbers punctuation after
at translation time f anei mt back substitutes tile appropriate target language word into any translation which involves any tokenized words
number next to the source language chunk in the output indicates the wdue of the scoring flnlction where higher values are worse
in that sense our incremental parser is nonmonotonic earlier decisions may be refined or even revised
the te system identifies the primitive template elements person and organization involved in a particu lar scenario
for generic phrases like the company reference is currently determined solely by file position and type
a difficulty occurs with this method when a sentence identifies a person as leaving one position and entering another
portions of images are recognized easily and their configuration is ultimately used to iden tify the complete image
template generating louella has a template generator which uses an object oriented mapping script for generat ing the final template
therefore the system does not examine the headline for organizations until it processes the body of the text
below we describe the processing stages that are used in louella s ne te an d st systems
this is a justified assumption for our model since we can not say that two words or word groups will not occur in the same sentence or in a sentence and its translation such an event may well happen by chance or because the words or word groups are parts of different syntactic constituents even for unrelated words and word groups
another interesting ambiguous phrase which our system did not handle correctly is mr enamex type quot person quot dooner enamex who recently lost numex type quot money quot NUM pounds numex over three and a half months says now that he has rein vented himself he wants to do the same for the agency
changes in post processing while prompted by performance on the walk through message affect system performance as a whole
NUM champollion the algorithm and the implementation champollion translates single words or collocations in one language into collocations including single word translations in a second language using the aligned corpus as a reference database
the numbers are the associated similarity score using the dice coefficient for the best translation at each iteration and the number of candidate translations that passed the threshold among the word groups considered at that iteration
we also wish to thank the reviewers for their very helpful comments
in other cases the range of values has a strong influence
we then give an overview of a statistical report generator called postgraphe
postgraphe a system for the generation of statistical graphics and text
the user chooses the intentions to be conveyed in the graphics e.g.
the inheritance mecanism is much simpler than the one used for types
intentions are constraints on the expressivity of the chosen text and graphics
postgraphe does not use a list of variables as its main input
the next step is the low level generation of graphic primitives and text
figure NUM schema barresl comparison of the
run in parallel to propose translations of various portions of the input dora which the final translation is selected by a statistical language model
the corpus used by panebmt consists of a set of source target sentence pairs and is flflly indexed on t he source language sentences
phase NUM for comt leted parse trees compute sub structures by dfss sub fs r for each schema r and frozen NUM c1 programs
goals arg2 fl arg3 freeze this means the resolution of this query is not performed if NUM is
this means that NUM is equivment to s except ff r the attributes head dtr mm non head dtr whose root is the head dtr non head dtr value in s
p uni f y fl f NUM r s is a partial unification routine where fl and f2 are feature structures and rs is a restriction schema used in generation of las
the b part here we use two techniques tie is dependency analysis which is eml odied by the function dep in expressed by p nnify in the figure
definition NUM paths for arty node n in a feature structure f paths n f is a set of all the paths that reaches n from the root of f
in each learning iteration the system learns that transformation whose application results in the greatest reduction of error
instead we can try to use information from the distribution of unambiguous words to find reliable disambiguating contexts
let n argmaxz freq y freq z incontext z c
so all learned transformations will have the form change the tag of a word from x to y in context c where x is a set of two or more part of speech tags and y is a single part of speech tag such that y e x below we list some transformations that were actually learned by the system
where freq y is the number of occurrences of words unambiguously tagged with tag y in the corpus freq z is the number of occurrences of words unambiguously tagged with tag z in the corpus and incontext z c is the number of times a word unambiguously tagged with tag z occurs in context c in the training corpus
in the case where the tag of a word is not fully disambiguated by the tagger a single tag is randomly chosen from the possible tags and this tag is then compared to the gold standard
another approach is to obtain the initial probabilities for the model directly from a manually tagged corpus instead of using random or evenly distributed initial probabilities and then adjust these probabilities using the baum welch algorithm and an untagged corpus
earley NUM billotlang NUM tomita NUM
it also describes the possible queries that may be made in that application
second there is a group which shows more syntactic and semantic flexibility
it is implemented with the helt of unification on the feature structures
all features concerning idioms are handled in the lexicons or the grammar
the only result will be ttm literal meaning of the elltenee
logical form make x y mistake y
the category symbols are also used in the senmntic operations on dllss
only when initializing the chart this information is spread over several edges
the learning algorithms are described in the next two sections and the results obtained with the algorithms are presented in section NUM
NUM adding syntactic constructs needed for a new scenario was har d having a broad coverage linguistically principled grammar meant that relatively few additions wer e needed when moving to a new scenario
however when specialized constructs did have to be added th e task was relatively difficult since these constructs had to be integrated into a large and quite comple x grammar
it appeared plausible although not certain that problem s NUM and NUM could be overcome within such an approach by adopting a strategy of conservative parsing
in particular chunking parsers which built up small chunks using syntacti c criteria and then assembled larger structures only if they were semantically licensed might provide a suitabl e candidate
reference resolution first seeks an antecedent in the current sentence then in the preceding sentence the n in the one before that etc
in the pattern matching approach we no longer have a monolithic grammar bu t we are now able to take advantage of the syntactic regularities of both noun phrases and clauses
starting at each word we identify the longest matching pattern if any use it to reduce the input sequence and then continue with the nex t unmatched word
we exaggerate of course the radicalness of our change since muc NUM NUM and since the muc NUM dry run which was conducted with our traditional syntactic system
the clausal patterns play the main role in this scenario recognizing the basic events of executive succession having jobs starting jobs leaving jobs succeeding other people in jobs
if it has information about the job person2 has or is leaving but no information about personl it adds information about the job s person l is starting
figure NUM depicts the modules of the dm which are involved in the subsequent handling of the input from the user
parallel mata further sarani in addition etc reason karada because tameda because etc NUM process of creating an abstract null the basic method for creating an abstract in most previous studies has been to analyze the sentences of a text in terms of some surface features and a heuristic to determine the most important sentences on the basis of these features
the sentence type is determined by checking special expressions in the last phrase a for instance if the final phrase contains bekida should or nakerebanaranai must then its sentence type is insistence if it contains darou probably then its type is conjecture otherwise its type is fact
more formally a route is structured into a list of segments each segment consisting of a relay and of a transfer
we have not considered those parts of linguistic description contents which are not representable by images such as comments or evaluations e.g.
prendre la rue to a relay which will coincide with a turn or with the beginning of a way landmark e.g.
continuer aller corresponds to a transfer and the actions of the type change of direction e.g.
such transcription constraints once defined and analyzed should be taken into account in order to obtain a faithful graphic representation
these latter will enable during the processing of a rd to extract and represent information concerning actions and landmarks and their attributes
the core of the process of translating rds into graphic maps will thus consist in the transition from the linguistic representation to the conceptual one
such systems should not only use different modes to ensure better communication but should also be able to pass from one to the other
consider the fragment about turning to the left tu prends sur ta gauche and the downgrade descente
once trained these word stem context vectors are used as building blocks for creating document and query context vectors
this text is preprocessed in the same exact manner as the training text was preprocessed
this process results in the fact that context vectors for documents with similar subject content will point in similar directions
words like driver driving drives driven and drove are all stemmed to the word drive
in sum we found that matching word usages to word senses in a dictionary is a hard task whose dit culty depends on the part of speech of the target word and increases with the number of senses given in the dictionary
given the slowness of operation and the potential complexity of a system which could handle large amounts of text some sort of predictive or assistive mechanism will be necessary to make topic shifting a realistic possibility
to demonstrate this robustness we converted the training cross validation and test texts used in previous testing to a lower case only format
while this performance was achieved on news text and may not necessarily generalize to other types of text it is a very strong result
the set of training and test tuples for a given corpus is obtained as follows
their study explores the usefulness of multiple windows for organizing the contents of long texts hypothesizing that providing readers with spatial cues about the location of portions of previously read texts will aid in their recall of the information and their ability to quickly locate information that has already been read once
see information extraction task definition document for further information
should be excluded from the fill
minimum instantiation conditions see section NUM NUM NUM
NUM boards of directors are treated specially
out the person is vacating the post
x s subordinates would report temporarily to mr
we make use of the semantic codes given in the chinese thesaurus to nnnno lnno NUM
sense tagged corpus we try to outline the semantic space by only taking into consideration the mona sense words instead of locating all word senses in the space
x took over april NUM mr
information extraction task scenario on management succession
additionally each topic can have a definitions section and or a factors section
all of the category b groups used automatic construction of the queries
table NUM shows the breakdown for the NUM adhoc topics in trec NUM
this lessthan optimal testing was required by the last minute unavailability of new data
the use of these curves assumes a ranked output from a system
an important component of trec was to provide a common evaluation fonun
for this reason it produces a guaranteed evaluation point for each system
this measure is useful because it reflects a clearly comprehended retrieval point
this run was a base run for theft experiments in manual query editing
clearly this is a very promising approach and more experimentation is needed
but if items are allowed to change their meanings as a consequence of the semantic properties of other items then the principle of compositionality that the mealfing of the whole is made out of simple l onlbinations of the meanings of the parts t if we alh w arbitrary rules of combination then we an include rules which make arbitrary changes becomes rather ineffectual
mp eat ve type e eat extended relic action e mp hiccup ve type e hiccup inst event e mp eat says that eating events take time and mp hiccup says that hiccuping events doift or
a speaker who is committed to the existence of a state then may not be concerned about the existence of the start or end point of that state they may not know when it started they may not care whether it has ended as far as they are concerned it may have been going on since the beginning of time and it may continue to the emt of time
if we back ut the labels representing lexical and predexical items with appropriate sets of meaning postulates then we may well find that different things can be inferred from a single itein in different semantic contexts without being forced to conclude that those items themselves mean different things
if for instance we were considering allan was living in bray rather than allan is living in bray then we would assume that the speaker knew enough about the end of this state to place it before the reference point marked by the past tense of the auxiliary
they do not exhaust thai sl a e an l they do not ne essarily botl om lit in sense dal a l ased primitives carnap NUM quine NUM NUM
analysis l rogranls ca u l rovided in ol lna t ion m ou h s since most are fornm l accordi g o very gcmcral t l i hologi al i r css s
this is reflected in tile semantics principle i assume which specifies the semantic value of a phrase as the application of a two place function compose to the semantic values of both daughters
if e.g. a begriiflen event is contextually given like in a question who greets whom the arrow from begriiflt to bg will become an obligatory arrow
ii the function variable a of a daughter with max f j and o sem o for both daughters
the waiter greeted a friend context iv does not entail der direktor der x begriiflte y so ii is ruled out according to schwarzschild s system
the resolution process proposed in this paper is based on the maximality assumption and thus checks givenness for the complete sentence only once with the complete subject f marked
to rescue the difference between ii and iii it would have to be enforced that resolution of the subject np takes place before the resolution of the focus projected from the object
and ki number of contexts for rule i NUM i n figure NUM statistics of complex operation s
in the second and third phases of the compilation process we need to incorporate members of c i freely throughout the contexts
however since in ss4 NUM above the symbol p appears freely we need to introduce it in the above expression
tufoai ar ll0 s l h 4t a iiotioll analogous to empathy arises in wesl ern languages as well e.g. with i erception verbs it is the experichter which is ofl en in ot jec t
gb principles are implemented as local constraints attached to nodes and percolation constraints attached to links
efficient parsing for korean and english phrasal heads precede their complements
the structure in i represents the relative order observed in korean
case theory requires that every np be assigned abstract case
an attribute named barrier is used to implement this principle
linear ordering is indicated by the starting points of links
a technique for automatic precompilation of parameter settings is described
we provide a language independent processing mechanism that accommodates dorr et al
in practice the present implementation sometimes fails to give an analysis to heavily ambiguous inputs regardless of their grammaticality
part of speech disambiguation the system was tested against a NUM NUM word test corpus consisting of previously unseen journalistic scientific and manual texts
there is an additional collection of NUM optionally applicable heuristic constraints that are based on simplified linguistic generalisations
they resolve about half of the remaining ambiguities increasing the overall error rate to about NUM NUM
if any of these context patterns are satisfied during disambiguation the tag is deleted otherwise it is left intact
the overall success of the system is very encouraging NUM NUM of all words retained the correct morphological analysis
finally we compare the efficiency of the cbar
we are currently incorporating the parser into a machine translation mt system called princitran
fig NUM showes mp f t which is defined for the neighborhood system with distance NUM the arrows represent that the random variable ti is affected by the neighbors ti NUM ti NUM ti t j
the student s placement in the model of acquisition can further direct our decisions regarding actions because if this agreement is too far above the student s current level to be intellectually attainable at this time we do not want to act on the error at all
f ven if a cliqm flmcl ion wd e is very bad o hcr cliqnc function ca n conll ensate dequa ely lmca use the clique functions are coime l ed by summatiou
various mttds o inlol ntal ion sf ttr s and differout knowledge sources must he colnl incd l o s iv the l a gging prol l m
filially mi lcb l NUM show itnliroveinent as the size of l raining hfl grows i ul collvel g is l o l ile inlit oll sol l
most readers will undoubtedly be at least somewhat familiar with the nature of the chinese writing system but there are enough common misunderstandings that it is as well to spend a few paragraphs on properties of the chinese script that will be relevant to topics discussed in this paper
the breakdown of the different types of words found by st in the test corpus is given in table NUM clearly the percentage of productively formed words is quite small for this particular corpus meaning that dictionary entries are covering most of the NUM gr is NUM
to evaluate proper name identification we randomly selected NUM sentences containing NUM NUM hanzi from our test corpus and segmented the text automatically tagging personal names note that for names there is always a single unambiguous answer unlike the more general question of which segmentation is correct
as indicated in figure NUM c apart from this correct analysis there is also the analysis taking ri4 as a word e.g. a common abbreviation for japan along with NUM wen2 zhangl essay and yu2 fish
for ff the good turing estimate just discussed gives us an estimate of p unseen i the probability of observing a previously unseen instance of a construction in its given that we know that we have a construction in
2deg in the table are the typical classes of words to which the affix attaches the number found in the test corpus by the method the number correct with a precision measure and the number missed with a recall measure
in the pinyin transliterations a dash separates syllables that may be considered part of the same phonological word spaces are used to separate plausible phonological words and a plus sign is used where relevant to indicate morpheme boundaries of interest
figure NUM shows a small fragment of the wfst encoding the dictionary containing both entries for just discussed l zhongl hua2 min2 guo2 china republic republic of china and i rcb nan2 gual pumpkin
the probability of the next word given the past n observations is provided by bayes formula
the results show that the combination of heuristics is useful even if the performance of some of the heuristics is low
together these techniques result in language models that have few states even fewer parameters and low message entropies
in NUM NUM we formally define the class of extension models and prove that they satisfy the axioms of probability
for example our techniques achieve a message entropy of NUM NUM bits char on the brown corpus using only NUM NUM parameters
we introduce three new techniques for statistical language models extension modeling nonmonotonic contexts and the divergence heuristic
we note that the test message entropy of the n gram model class is minimized by the NUM gram at NUM NUM bits char
extension mixing allows us to remove the uniform flattening of zero frequency symbols in our parameter estimates NUM
given the tremendous risk of overfitting the most important property of a model class is arguably its statistical efficiency
the third term encodes the actual tree without labels using an enumerative code
in the second state the source generates the string NUM with certainty
the canis customer receives cables from sites world wide indexes the entities mentioned in these cables and stores that information for access by analysts at a later date
the following functions are available to an analyst document details review canis prototype process logs review name lookup and processing and system filing
biographies relationships id numbers locations phone numbers etc the analyst reviews modifies the informatiou if necessary and checks off the information
analysts must read every cable and extract the infcmnation that should be placed in the new index records or update existing records
the information captured by these logs includes document identifiers for documents processed error messages system generated messages ie
reduction performs multiple passes through the document buffer looking for sequences of tokens that can be simplified into a single identifiable unit
the canis customer indexes large quantifies of information mostly manually and wishes to reduce the human rescmrces applied to this task
finally the analyst data setup process csci adds the document to an analyst working queue for processing by an analyst through the analyst interface process csci
the canis prototype as illustrated in figure NUM NUM will take as input cable text cable header and cable delivery system server fields
a head automaton m of a lexical entry w m defines possible ordered local trees immediately dominated by w in derivations
select a node with word label w having a finite start of derivation cost c w m ql t
the model uses dependency tree fragments which are the same as unordered dependency trees except that some nodes may not have word labels
this means that side by side comparison of these methods has practical relevance even though the methods exploited different amounts of data
discriminative model the costs in this model are likelihood ratios comparing positive and negative solutions for example correct and incorrect translations
the cost parameters of the model are defined as c elc ln p elc
head automaton models admit efficient lexically driven analysis parsing algorithms in which partial analyses are costed incrementally as they are constructed
we can now express the probability p do for an entire ordered dependency tree derivation do headed by a word w0 as
a secondary motivation is to test the extent to which a non trivial language processing task can be carried out without complex semantic representations
this initial word score is reevaluated on the basis of concordances between the different recognition hypothesis
for reasons of space it is difficult to give examples of the sign based output of the grammar or of the ale rules so we will restrict ourselves here to a summary of the algorithm and to a very limited rendition of the system output
because of considerations like these our aim in the implementation work was to treat tense aspect cue words and rhetorical relations as mutually constraining with more specific information such as explicit cue words having higher priority than less specific information such as tense
d if there is no temporal expression or cue phrase tense and semantic aspect also influence the vmue of the i het reln type see table NUM so that rhetorical relations tense and aspect constrain each other
table NUM lists the possible temporal relations between the eventualities described by two consecutive sentences without temporal expressions or cue words where the first sentence s1 may have any tense and aspect and the second sentence s expresses a simple past event
e2 can elaborate on el if dcu1 describes an event or dcu1 describes an activity and dcu2 describes an atelic or dcu and dcu2 describe states and either dcu2 describes a simple tense state or dcu1 describes a complex tense state
for example in NUM the order of the events is understood to be the reverse of that in NUM due to the cue word because which signals a causal relationship between the events NUM john entered the room because mary stood up
his keyring b okej we store semantic patterns between words as a cheap and quick form of world knowledge these 2we will not discuss the additional problem that if the final sentence in llb is the end of the text the text is probably ill formed
in our ale implementation a dcu contains the following slots for temporal information fwd center existing threads bkwd center the thread currently being followed closed threads threads no longer available for continuation temp expr relns stores the semantic interpretation of temporal expressions associated with this dcu
for example if dcu1 represents a simple past eventive sentence and dcu2 a past perfect eventive sentence then in spite of the lack of rhetorical cues we know that e2 precedes el as in NUM NUM sam rang the doorbell el
we found NUM instances of clausal on the other hand occuring without an explicit source contrast cued earlier
to illustrate let us have another look at figure NUM
in section NUM we discuss the software engineering goals for cogenthelp
cogenthelp nlg meets se in a tool for authoring dynamically generated on line help
robin NUM wanner and hovy NUM
in section NUM we give a brief overview of the cogenthelp system
thus there is no single a priori position for the vp node NUM
these points will be elaborated upon below
to enable them to the shared message to yield what appears in figure NUM
tipster applications will be associated with a particular version of the architecture
the executive summary of the configuration management plan is reprinted below for reference
an application need not provide all of these capabilities to be architecturally compliant
the architecture will make it easier for the researcher to evaluate component interactions
a module is a logical element in the design of a component
the tipster architecture is a software architecture for providing document detection i.e.
maintenance of the architecture itself through the configuration control board
again the availability of standards will provide a common ground for discussions
annotation allows these two components to share information at a modular level
this study suggests that the name recognition accuracy of name searching software is reasonably good and it seems safe to assume that that accuracy can be improved using domain specific heuristics and tuning
if speech does not follow within the three to four second window or following speech does not integrate with the gesture then the unimodal interpretation is chosen
the interpretation of these percentages is by no means straightforward as there is no straightforward way of combining these different measures into a single one
lexicalist grammar formalisms such as head driven phrase structure grammar hpsg have two characteristic properties NUM lexical elements and phrases are associated with categories that have considerable internal structure and NUM instead of construction specific rules a small set of generic rule schemata is used
constraints NUM NUM and NUM suggest that the sp employ a blackboard architecture
existing treebank annotation tools are characterised by a high degree of automation
we then used the model in the batch mode to evaluate the likelihood of each of the alternatives
other words are tagged using suffix information or else defaults are invoked
the function of the network is briefly explained and results are given
ali declarative sentences have been extracted for processing about half were imperatives
table NUM and figure NUM show some of the characteristics of the corpus
then the performance of the pump must be monitored
we also have the NUM arbitrary limits on length of pre subject and subject
here in contrast anything that is not expressly prohibited is allowed
figure NUM the single layer net showing the feed forward process
this process has now been improved by going further along the sentence
this version of claws has a dictionary of about NUM NUM words only
the tool is currently used to add trees for some elliptical coordinations
one may want to express generalizations despite a few more specific exceptions
first it is local simple and efficient
because of the high number of variables in our experiments there is a danger that overfitting occurs
tables NUM and NUM present aggregate results when all texts are classified for each facet or level
in particular these functions include boolean disjunctions and conjunctions on k n variables and r of k threshold functions NUM r k n
vv e plan to improve those labelings in future experiments by classifying brow on an article by article basis
in addition many of the text features in karlgren and cutting are structural cues that require tagging
particularly in contrast with properties of structure or topicality which for all their complications involve well explored territory
if genre classification is so useful why has n t it figured much in computational linguistics before now
in information retrieval genre classification could enable users to sort search results according to their immediate interests
in this paper we want to consider texts from the point of view of genre that is
in principle a given text can be described in terms of an indefinitely large number of facets
the first two characterize two types of articles from the daily or weekly press reportage and editorials
this interaction remains unnoticed by the dialogue server
although agent systems allow users to automate their scheduling tasks to a con this work has been supported by a grant from the german federal ministry of education science research and technology fkz itw NUM
the following persons have contributed significantly to the development and the implementation of the nl server system and its components thierry declerck abdel kader diagne luca dini judith klein and g inter neumann
using distributed rather than centralized calendar systems they not only guarantee a maximum privacy of calendar information but also offer their services to members or employees in external organizations
appointment scheduling is a problem faced daily by many individuals and organizations and typically solved using communication in natural language nl by phone fax or by mail
to overcome this drawback we have designed and implemented cosma a novel kind of nl dialogue system that serves as a german language front end to scheduling agents
two are using autonomous agent systems that partially automate the negotiation of appointment scheduling and manage their users private electronic calendars
the client ensures that the server has available to it linguistically relevant information about the interlocutors such as names sexes etc
in particular the systems to be demonstrated can process counterproposals which form an important part of efficient and cooperative scheduling dialogues
using a thick separator is even more important when documents are ranked rather than simply classified that is when the actual score produced by the classifier is used in the decision process
in some cases we adopt solutions that are well known in the ir literature to the class of algorithms we use in others we modify known algorithms to better suit the characteristics of the domain
in section NUM NUM we demonstrate how to cope with wrong predictions
this could be an advantage in the perspective of machine translation for instance
interactive disambiguation by speakers does not necessarily converge a correct interpretation
NUM NUM NUM handling spoken language spoken language includes many phenomena here howew r
conventional approaches to machine translation are mostly concerned with written text such as technical documents
first a well known difficult problem in japanese to english translation was selected as a test
table NUM shows the number of target expression patterns corresponding a japanese particles in je and jk
ple is properly translated in our ti mt pro null totype system
ebmt tdmt the remaining requirements are handled effectively by an example based approach as explained here
spoken language translation the following new design features are critical for success in spoken language translation
high quality translation this is necessary in order to ensure correct information exchange between speakers
we do not consider simplex nps joined by relative clauses
zthis is an actual example from a u s patent document
parsing of simplex noun phrases is done in multiple phases
second it may improve the reliability of statistical decisions
suppose the pair to test is w1 w2
these rules are especially useful when integrated into database searches on names and addresses since they can complement orthographic search algorithms that make use of permutation deletion and insertion by allowing for a comparison with the phonetic equivalent
in the case of the english rules above words such as call cell cilia cool would be handled as well as cure cute assuming that palatalization issues are handled by another rule
for cities like caen ka rennes rcn reims r s etc the pronunciation differs substantially from the spelling
in french we observe a similar situation where the name smith is pronounced smis and thatcher as sat or as french does not have a NUM phoneme
the s in tournesol entresol tolosi ge vraisemblable contresens antisocial must be considered the beginning of a morpheme and although it occurs between two vowels is pronounced s
this value is given by m trace i k p with trace as defined in NUM
although there are a lot of erroneous words in the augmented word list most of them are filtered out by the re estimation
the accuracy of the significance tests vary greatly depending on the choice of c
these conditions together imply that s w
we can sketch the behavior of the grammar as follows
s instead the above system will allow precisely those combinations that establish functional relations that are marked out in lexical type structure i.e.
so as it stands the class of c parsers includes tabular parsers e.g.
it takes o m NUM time to read the input matrices
thanks to les valiant for pointing out the folklore reduction
the substrings derived by ail k
by definition of c parsers each such query takes constant time
and bkl jl lie right next to each other
for short sentences NUM NUM words the parser achieves up to NUM recall and NUM precision with only NUM NUM crossings
the difference between this and the earlier model is one of perspective when a global descriptor is encountered one can either bring the global context to the current evaluation context first model or take the new descriptor back to the global context and continue from there second model
slightly weaker in that it allows the left hand side to be defined when the right hand side is undefined but even in datr if both sides are defined they must be the same so in principle the value of the left hand side does semantically constrain the value of the right hand side
to enhance the results of this method an indication about the recency of use of each word may be added
it is not possible say to combine assumptions i and if together first as part of a derivation
tile data clearly indicates that the tf idf method is superior to this default approach in terms of relevance ldeg the computation of these precision recall values is based on the sentences which were chosen by the human subjects from the experiinent i.e. an average was built over the precision recall between the machine system and each individual subject
while our system currently produces abstracts offline it is feasible to extend it in a way where it uses the user s query in an ir environment to determine tile relevant sentences of the retrieved documents tiere instead of producing a general abstract the resulting on line abstract would reflect more of the user s perspective on the respective text
the global analysis shows a surprisingly good correlation across the hunmn subjects for the sentence scores of all six articles see table in the pearson r correlation matrix NUM coefticients are significant at the NUM NUM level NUM at the NUM NUM level and only NUM are non significant n s
NUM b t cilitate their ask the subjects should first give each of the sentences in an artme a relewmce score from l barely relewmt to NUM highly relevant and finally choose tit trust scored sentences for th ir abstracts
all these artmes are at out a single topic i robably becmme of our choi e al out a ret resenl ative text lengdl we lo not address ttm issue of multi topicality here however it is well known that texts with more hall olle tel it are
an experiment shows that recall and precision for the extracted sentences taking the sentences extracted by human subjects as a baseline is within the same range as recall precision when the human subjects are coinpared amongst each other this means in fact that tile performance of the system is indistinguishable from the performance of a human abstractor
therefore we argue that it is not only an easy way but indeed an appropriate one for an automatic system to choose a number of the most relevant sentences and present 1by satisfying we mean at least indicative for the content of he respective text if not also informative about it
this means that if we compare the output of the automatic system to the output of an average human subject in the experiment there is no noticeable ditference in terms of precision recall the machine l erforlns as well as human subjects do given the task of selecting the most relevant sentences from a text
further issues concerning the human inaehine interface are highlighting passages containing the query words listing of top ranked keywords in tile retrieved text s indicating the relative position of the extracted sentences in the text allowing for scrolling in the main text starting at an arbitrary position within the abstract null
first it allows us to test the other components of the system
in subsection NUM NUM we showed the precision of the extraction of entity names
there are two occasions on which we extract descriptions using finite state techniques
all NUM entity names retrieved by the system are indeed proper nouns
in addition some researchers have explored the use of both lo
at the current stage of implementation profile has the following coverage
of the NUM descriptions NUM NUM NUM were correct
the web based interface is accessible publicly currently within columbia university only
our system generates finite state representations of the entities that need to be described
we show counts for multiple and unique occurrences of the same noun phrase
NUM global inheritance that is inheritance relative to the global context is indicated in datr by using quoted descriptors and we can use it to extend our definition of verb as follows verb syn cat verb syn type main mor form mor root ing
the higher the precision the better
this semantic notion induces a notion of consistency for datr descriptions we say that a datr description is consistent if and only if it has a coherent interpretation as a function that is if the extensional sentences defined explicitly or implicitly for each node constitute a partial function from paths to values
so we get blacksmith s hammer and not blacksmith hammer to mean hammer of a type conventionally associated with a blacksmith also driver s cab widow s allowance etc
for example the representm ions of sa b in sireplified form are respectively a and t3 NUM a mary put her clothes into various large bags
where n is the number of pairs of senses which match the schema input and m is the number of attested two noun output forms we ignore compounds with more than two nouns for simplicity
because of the difficulty of resolving lexical ambiguity it is usual in nlp applications to exclude rare senses from the lexicon and to explicitly list frequent forms rather than to derive them
thus for any compound there may be some context in which it can be interpreted but in the absence of a marked context only compounds which instantiate one of the subschemata are acceptable
established compounds may have idiosyncratic interpretations or inherit from one or more schemata though compounds with multiple established senses due to ambiguity in the relationship between constituents rather than lexical ambiguity are fairly unusual
again there are restrictions it is not usually possible to form a compound with an agentire predicate taking an argument that normally requires a preposition contrast water seeker with water looker
in particular if the context makes the usual meaning of a compound incoherent then pragmatics should resolve the compound to a less frequent but conventionally licensed meaning so long as this improves coherence
our initial experiments were designed to investigate the following two hypotheses hypothesis NUM word senses provide an effective separation between relevant and non relevant documents
most of the lexically based checks
these considerations help us to refine our use of the adjective noun relation itself and to put it on a firmer linguistic footing
since he s doing this for his physical welfare it would n t be right of me to let him be bothered
also the verb be may be deleted and the entire construction subordinated to a higher verb such as seems or becomes
human analysis and understanding are simply richer than the mechanical statistical tools and data sources presently available for arriving at such rules
all the example sentences above involve one of these ten pairs and they exemplify the concordance of the antonyms senses
furthermore a small number of semantic attributes supply a compact means of representing the noun clues in a very few rules
when old modifies house then this is a good indication that old is being used in one of these senses
to get enough sentences containing antonym co occurrences of antonyms to address disambiguation issues adequately we used the NUM NUM million sentence aphb corpus
in one sentence in contrast to his rangy sons he was a short heavy oaken barrel sort of man
the last two columns of table NUM present the results of adjective disambiguation by a combination of syntactic and semantic indicator attributes
the next section will present the overview of the system
the above multiple tagged words give NUM combinations of word chain
figure NUM two level key pads for thai character
these problems generate a lot of unuecessary work for the parser
once a perspective is selected romper includes in its explanations only those attributes whose salience values are the highest
a discourse knowledge engineer can use edps to encode discourse knowledge for his or her application
local variables provide a means of decomposing more complex content specification expressions into simpler ones
first these systems representation of discourse knowledge should be easily inspected and modified
lester and porter robust explanation generators writing style the quality of the prose
this is an inherently information losing process as english r and l sounds collapse onto japanese r the NUM english vowel sounds collapse onto the NUM japanese vowel sounds etc
of course this performance is far short of what is needed for a practical extraction system but it already constitutes a major source for labor savings since NUM to NUM percent of the annotations that need to be moused or clicked in are already there
to map japanese sound sequences like m o o NUM a a onto katakana sequences like t we manually constructed two wfsts
moreover the correspondence of japanese katakana writing to japanese sound sequences is not perfectly one to one see next section so an independent sound inventory is well motivated in any case
bilingual glossaries contain many entries mapping katakana phrases onto english phrases e.g. aircraft carrier t NUM i NUM NUM
given a katakana string o observed by ocr we want to find the english word sequence w that maximizes the sum over all e j and k of
in all of these cases the most reliable method for detecting these human machine interactions is probably to use some representative sub population of the corpus documents to measure and analyze the inter annotator agreement between human annotators who have and who have not been exposed to the machine derived heuristics for assigning annotations
since the precision at this early stage is only around NUM percent there will be extra phrases that need NUM to be removed NUM their assigned category changed from say organization to person or NUM their boundaries adjusted
and suppose we build an english pronouncer that takes a word sequence and assigns it a set of pronunciations again probabilistically according to some p plw
names that slipped past the organization specialist and were claimed by th e person or location specialists were charged against organization precision by the scoring program
as shown empirically it also exhibits considerable effectiveness
note that this rule introduces some non determinism since in general there is more than one way to break up a sequence of value descriptors
i same string no i i most recent compatible subject yes i i i name NUM no NUM NUM NUM NUM
concept node cn definitions are still used to create case frame instantiation s and multiple cn definitions can apply to the same text fragment
ilere the relationship between the nodes dog and noun has effectively been collapsed into just a single statement dog NUM noun
the muc NUM co task was defined to include nearly all noun phrases not just those that were relevant to eithe r the te or st tasks
this variation roughly corresponds to the variety of english auxiliary verbs and higher predicate verbs such as dekiru can rareru be pp can kotoga dekiru be able to tai want to seru make let garu feel complement etc a x ga y wo tabe ru b
NUM bic removes too many interactions and results in models of too low complexity
these codes the contents of which are to be elaborated in the fig NUM NUM are assigned on the extended category of auxiliary verbs dekiru can rareru be pp can kotoga dekiru be able to tai want to seru make let garu feel complement
in this system the input for the parser is not a simple list of words as we have assumed up to now but rather a word graph a directed acyclic graph where the states are points in time and the edges are labeled with word hypotheses and their corresponding acoustic score
to account for the acoustic score of a derivation defined as the sum of the acoustic scores associated with all transitions from the word graph involved in the derivation we assume that the predicate lexical analysis represents the acoustic score of the piece of the word graph that it covers by an extra argument
without any loss of generality i assume that no external prolog calls the ones that are defined within lcb and rcb are used and that all lexical material is introduced in rules that have no other right hand side members these rules are called lexical computational linguistics volume NUM number NUM goal
but since most versions of prolog do not implement the occur check it is worthwhile investigating this potential problem
each time a result is found the table is checked to see whether that result is already available
this technique ensures that the lookup of the head corner table can be done in essentially constant time
suppose the parser predicted for a goal category s a category v from position NUM to NUM
the computation that is carried out in order to obtain such a chunk uses a depth first backtrack search procedure
each of persons from this text who are not involved in a change of status were correctly classified as irrelevant b y wrap up and were discarded
such syntactic variants may result from a writing convention function words or non discriminative syntactic information such as punctuation markers
with such a formulation the capability of context sensitive parsing in probabilistic sense can be achieved with a context free grammar
all other specialists were developed in the nlp lab and the location specialis t accessed a dictionary based on a subset of the gazetteer entries
as these errors are examined it is found that more than NUM of the incorrect normal forms have only one erroneous case
again the number of parameters required to model such a formulation is still too many to afford unless more assumptions are made
our solution is to have a lexical rule that changes the subcategofization frames of verbs to handle cases where objects may be case marked nps or unmarked ns
grammatical role changes type constraints on word subtypes and noun to np promotions as in non referential objects control the proliferation of lexical entries
this will enable the system to rule out for instance affixation of two free forms or impose selectional restrictions on the stems of affixes
uzun 9igek li g6mlek long fiower adj shirt long shirt with flower patterns NUM a kalem ler i b kalem ler i c kalem leri
in this view morphology is not isolated from syntax but similar to the modular organization bound morphemes are not considered lexical items
out the lexical rules seems to be impractical since generating every possible form for a large lexicon of roots causes exponential growth in the lexicon
another source for economy of representation can be seen in example NUM where attributive adjectives are used as nouns in 2b and 2d
for instance a causative suffix will demote an agent to a patient or a recipient and it will add a new grammatical role for the causer the new agent
the child read a book the child did book reading d kitap 9ocuk okudu non referential objects are not inflected and they must occupy the immediately preverbal position
aic and bic reward good model fit and penalize models with large numbers of parameters
now we just need to test whether two confined case ibrms are independent with respect to the original
the subscript sub may be t x or d x indicating that the function represents the task or dialogue bpa under scenario x
e we can see that this would have hell ed us in th
tim use o h iron hint disjunctions t rovid s
this paper describes a nlethod for compiling a constraint based grammar into a potentially inore efficient form for processing
maxwell and kaplan s goal in doing this was to have an efficient method for solving disjunctive constraints
to this end a somewhat different notion of contexted constraint will be used as show in lemma NUM
of formulae c i respectively where each i is a member of the set of indices n
while m and m are derived dora m the elealeuts of the ns are arbitrary
here we consider the results of each stage of the sequential model selection for interest
prior to parsing each sentence must be converted to a string of terms holding the features derived through lexical analysis
NUM notes on performance the presented dug formalism with free word order has successfully been employed to parse latin sentences
if desired phrase structure rules can thus easily be combined with ordinary dependency rules
references account for the fact that many words are similar in terms of the dependents they take
its built in resolution and unification mechanisms are well suited to both accept and generate sentences of artificial and natural languages
the start rule s n verb
n give verb n n noun n n give verb n n noun n noun
if however the word partially specified as n verb in the body of the start rule is accepted before the next rule is selected an intelligent parser can exploit the fact that the sentence s verb is sleep and immediately call the appropriate rule
the crucial problem for current p p lu p wi can not be consid 2in p w t p t ered to be identical for ll segmentations
to put the above idea into our learning algorithm the mistake driven mixture method attaches a weight vector to each example and iteratively performs the following two procedures in the training phase NUM constructing a context tree based on the current data distribution weight vector NUM updating the distribution weight vector by focusing on data not well predicted by the con null structed tree
during the splitting process especially at the bottom of the binary tree it may be empty for some classes because the classes at higher level than it can not be splitted further more according to the rule of maximum average mutual information
rather than merge two words we merge the two classes which belong to the resulting classes generated by left right binary tree and right left binary tree respectively and select the merged class which can lead to maximum value of similarity metric
so the probability word w which belongs to class can be presented as follows where i is the mutual information between the class and the i other class which is in the same binary branch with
mles of the model parameters are simply the marginal frequencies normalized by the sample size n
we refer to a syntactic tree and its corresponding case frame as obtained in an analysis an interpretation
the construction of such a system is extremely difficult however and we need to adopt a more realistic approach
instead of the back off method we used the product of lexical likelihood values and syntactic likelihood values to rank interpretations
it is very likely however that this kind of ambiguity could be resolved satisfactorily by using the three word probabilities
next let us consider a simple example illustrating how the operation of this model indicates the functioning of rap
i thank greatly mr k nakamura mr t fujita and dr k kobayashi of nec for their constant encouragement
preference value will be high when ll equals NUM and syntactic preference based on alpp s can be defined as
suppose that an attachment is obtained after the application of c fg rule l r1 r2
although each of the disambiguation methods proposed to date has its merits none resolves the disambiguation problem completely satisfactorily
alpp prefers categories forming a coordinate structure to be of equal length see figure l b
the main difference between the two functions is that there the t value was implicitly assumed to be NUM which corresponds to a confidence level of NUM on a very large sample
the task of assigning a set of pos tags to a word is actually quite similar to the task of document categorization where a document is assigned a set of descriptors that represent its contents
this is not surprising the higher the threshold the fewer the inaccurate rules included in the rule set but at the same time the fewer the words that can be handled
to see how well the guesser performs we can compare the results of the guessing with the pos tags known to be true for the word i.e. listed in the lexicon
it has two parameters c the level of confidence and dr the number of degrees of freedom which is one less than the sample size dr n NUM
usually this was the case with irregular words such as cattle or data which were wrongly guessed to be singular nouns nn but in fact were plural nouns nn8
our guessing rule induction technique uses the training and test data prepared as described above and can be seen as a sampling for the best performing rule set from a collection of automatically produced rule sets
a pos tag stands for a unique set of morpho syntactic features as exemplified in table NUM and a word can take several pos tags which constitute an ambiguity class or pos class for this word
in section NUM we start with the asymptotic behavior stipulated by zipf s law and derive a recurrence equation similar to that associated with turing s formula and from this induce a corresponding reestimation formula
in section NUM similar techniques are used to establish the asymptotic behavior inherent in a general class of recurrence equations parameterized by a real valued parameter and then to rederive the recurrence equations from their asymptotes
this could potentially be used to improve sparse data estimates by assuming a geometric distribution tail and introducing a ranking based on direct frequency counts frequency counts when backing off to more general conditionings order of appearance in the training data or to break any remaining ties lexicographical order
since eq NUM implies eq NUM we start from the latter and establish that further note that if a h y b on a b then a b a b h y dy b b a
all of the zipf simon mandelbrot distributions exhibit the same basic asymptotic behavior c f r r parameterized by the positive real valued parameter NUM comparing this with eq NUM we find NUM NUM that NUM z NUM NUM and thus NUM NUM NUM
ce NUM NUM although this correspondence was derived with the requirement that NUM x we can in view of the discussion in section NUM NUM assume that x is not only considerably larger than NUM but also greater than any fixed value of NUM the extension to the negative real numbers is straightforward although perhaps not very sensible
another direction would be to enrich the vocabulary of chunk tags so that they could be used during the learning process to encode contextual features for use by later rules in the sequence
if there is just one derivation and it is invalid the action containing the constraint that is the source of the invalidity is noted
so we evaluate the constraints in order of mention in the derivation but postpone any constraints that have multiple solutions until the end
the third is s attrib rel entity otherentity predicate and is used for describing an object in terms of some other object
otherwise there will be an action that includes a constraint that is unsatisfiable and the hearer construes the action as being in error
the describe action shown in figure NUM is used to construct a description of the object through its decomposition into headnoun and modifiers
the physical sounds are handed down from earlier generations but the system of contrasts is constructed anew by every child learning to talk
often when the hearer can not do so the speaker and hearer collaborate in making a new referring expression that accomplishes the goal
the principles that emerge are that syllabicity is paramount consonants matter more than vowels and affixes tend to be contiguous
as for the hearer the explicit encoding of the adequacy of referring expressions allows referent identification to fall out of the plan inference process
so it is these rules that specify how plan inference and plan construction affect and are affected by the mental state of the agent
the following representation for type nimalofood describes interpretations that can not occur simultaneously but are however related
the resulting representations can be seen as underspecified lexical meanings and are therefore referred to as underspecified semantic types
be mined restructured and extended which makes it a good starting point for the construction of corelex
the semantic lexicon we generated for the pdgf corpus covers NUM noun stems spread over NUM corelex types
in this paper i discuss the construction of a new type of semantic lexicon that supports underspecifled semantic tagging
traditional semantic tagging assumes a number of distinct senses for each lexical item between which the system should choose
corzlex provides such knowledge representations and as such it is fundamentally different from existing semantic lexicons like wordnet
it therefore uses a o instead of a as a type constructor null
consider for example the following classes some of these classes are collections of homonyms that are ambigtzotz s in similar ways but do not lead to any kind of predictable polysemous behavior for instance the class act anm art with the lexical items drill ruff solitaire stud
whereas the first group of nouns express two separated but related meanings the act of clearing repair etc takes place at a certain location the second group expresses two meanings that are not related the charleston dance which was named after the town by the same name
f department of artificial intelligence NUM south bridge edinburgh eh1 1hn scotland
tr3 obtains a NUM matching rate on average which is NUM lower than its predecessor tr2
another middle sized test text text NUM is broken into three sentences and contains three topic shifts
this dg can characterize at least some context sensitive languages such as anbnc n i.e. the increase in complexity corresponds to an increase of generative capacity
NUM the vi in reading ui are governed by s through the valencies uj j NUM iwl k
before we prove that this encoding can be generated in polynomial time we show that lemma NUM the dg recognition problem is in the complexity class alp
an edge is represented by two linked words one for each end point with the governing word corresponding to the node included in the vertex cover
typically a minimal subset language dominated although full and intermediate languages did appear briefly they did not survive against less expressive subset languages with a lower mean wml figure NUM is a typical plot of the emergence and extinction of languages in one of these runs
since in many cases these conditions will include the presence of constraints working memory limitations expressivity the learning algorithm etc which will remain causally manifest further testing of any conclusions drawn must concentrate on demonstrating the ac null curacy of the assumptions made about such constraints
the permutation operation each time step lc is visited during the reduce step permutation is applied to one of the categories in the top NUM cells of the stack until all possible permutations of the NUM categories have been tried using the binary rules
instead a vso language emerged at cycle NUM which has the same minimal expressivity of the vos language but a lower wml by virtue of placing the subject before the object and this language dominated rapidly and eclipsed all others by cycle NUM
all lagts were initialized to be age NUM with a critical period of NUM interaction cycles of NUM random interactions for learning a maximum age of NUM and the ability to reproduce by crossover NUM NUM probability and mutation NUM NUM probability from NUM NUM
after every ten interactions in which the adult randomly generated a sentence type and the learner attempted to parse and learn from it the state of the learner s p settings was examined to determine whether the learner had converged on the same grammar as the adult
for english gendir is default right but the node of the intransitive functor category where the directionality of subject arguments is specified overrides this to left reflecting the fact that english is predominantly right branching though subjects appear to the left of the verb
german is a more complex sov language in which the parameter verbsecond v2 ensures that the surface order in main clauses is usually svo NUM there are NUM p settings which determine the rule schemata available the atomic category set and so forth
in all these runs the population settled on subset languages of low expressivity whilst the percentage of absolute principles and default parameters increased relative to that of unset parameters mean change from beginning to end of runs NUM NUM NUM NUM and NUM NUM respectively
we do this by distinguishing two varieties of concatenation operators on string pairs depending on tim odeatation
however the following theorem simplifies our algorithms by allowing us to get away with degree NUM btgs
a wansduction grammar is a bilingual model that generates two output streams one for each language
with this simple proviso the transduction grammar of figure NUM straightforwardly generates sentence pair NUM
unlike these models however the btg aims m model constituent structure when determining distortion penalties
ignoring capitalization an example of a valid parse that is consistent with our linguistic ideas is
for example after rebalm cing sentence NUM is bracketed as follows
the model nonetheless retains a high degree of compatibility with more conventional monolingual formalisms and methods
i t we will see how postprocessing restores the fanout flexibility section NUM NUM
several additional extensions on this algorithm were found to be useful and are briefly described below
a wildcard matching capability is also available
by viewing words and expressions in context
the approaches realizing this framework however have not so far addressed the task of incremental parsing a key issue in earlier work with flexible categorial grammars
the second is to characterize workers tasks
the third is to identify cognitive bottlenecks
figure NUM the annotation list window with
NUM NUM text display edit and annotations
there are several on line dictionaries available
it draws attention to particular passages
humans are good at using language
the benefits of oleada are numerous
the main system modules are speech recognition parsing discourse processing and generation
this method allowed us to collect speech in a limited domain that was nevertheless spontaneous
instead translations must be provided for the phrase as a whole
champollion then continues with the next step
consider the example given in table NUM
analysis of the effects of our thresholds
our system design will be modified to facilitate working with multiple sub domain grammars in parallel
in this case the result is correct
the correct translation is shown in bold
two tasks must therefore be considered
in addition such collocations are flexible
for both ibi ig and naive back off a NUM fold cross validation experiment was run using both pdass and pdddaaasss patterns
here we briefly show a method of statistically identifying the dependencies of the cases in verb noun collocations from corpus NUM then by incorporating the identified case dependencies into the generation model we introduce a model of generating a verb noun collocation from a tuple of independent partial subcategorization frames
as can be seen in the definitions of the above three models the basic idea of defining the model of generating a verb noun collocation from subcategorization frame s lies in identifying the dependencies of the cases in the given verb noun collocation and expressing the dependencies within a subcategorization frame
for each casep and the leaf class ct marked byp of at least one subcategorization frame corresponding to a feature in s has the same case p and its sense restriction cs subsumes c i.e. cl de cs according to this factor vl e i is preferred to v2 NUM if and only if the following condition holds
let t c e t be the set of verb noun collocations e for which co q holds and esnco t c e t be the set of verb noun collocations v e for which v e co does not hold
then we consider a subcategorization frame s which can generate e and assume that s subsumes e e f s we denote the generation of the verb noun collocation e from the subcategorization frame s as s e NUM when considering a subcategorization frame which can generate a verb noun collocation e there are several possibilities of the case dependencies in the subcategorization frame
the following columns represent the tags for the token vedouc and their frequencies in the training data for example vedoucf was tagged twice as adjective feminine plural nominative first degree affirmative
in order to compare two different approaches to text tagging statistical and rule based we modified eric brill s rule based part of speech tagger and carried out two more experiments on the czech data obtaining similar results in terms of the error rate
tagged corpus manually tagged training corpus untagged corpus collection of all untagged texts lexruleoutfile the list of transformations to determine the most likely tag for unknown words tagged corpus NUM manually tagged training corpus tagged corpus entire czech modified corpus the entire manually tagged corpus context rulefile the list of transformations to improve accuracy based on contextual cues
token frequency tags in train data in train data jejich NUM NUM NUM jeho NUM NUM NUM jeho NUM NUM jejich NUM NUM vedoucl NUM NUM table NUM NUM in the czech modified corpus the token vedouc appeared NUM times and was tagged by twenty two different tags NUM tags for adjective and NUM tags for noun
to illustrate the results of our tagging experiments we present here short examples taken from the test data
verbs infinitives vta verbs transgressives vwntsga verbs common vpnstmga pronouns personal pppnc pronouns 3rd person pp3gnc pronouns possessive prgncpgn svfij his referring to psgnc subject reflexive particle se pec pronouns demonstrative pdgnca not all possible combinations of morphological categories are meaningful however
4we use the same names of files and variables as eric brill in the rule based pos tagger s documentation
the experiments show not surprisingly that the more training data the better is the success rate
docuverse is based on the context vector technology foundation developed at hnc over the past few years and additionally provides a visual interface that allows the user to browse the information space in a visually appealing fashion
tools like the virtual reality modeling language vrml are well suited for use with this technology for visualizing information on the world wide web and will aid us as we strive to improve information visualization
the concept of self organizing maps was first developed by tuevo kohonen in NUM at the university of helsinki
related information themes contained in the corpus depicted graphically are presented in spatial proximity to one another
hnc software inc has developed a system called docuverse for visualizing the information content of large textual corpora
the adjustment comes in the form of moving the winning node vector in the direction of the input vector
furthermore as part of hnc s involvement in the us intelligence communitysponsored p1000 visualization effort hnc has applied a secondary neural network process the self organizing map som NUM which uses the document context vectors to build a visual representation of the information content of the corpus
therefore the user is given the ability to edit the labels to put them in correct grammatical form
this is done by taking the weighted average of the context vectors belonging to the nodes in the region
the documents are news reports taken directly off the ap news wire during a four month span in NUM
each of these transducers adds syntactic information represented by reserved symbols annotations such as brackets and names for segments and syntactic functions
we first mark possible beginnings and endings of a segment and then associate each beginning tag with an ending if some internal constraints are satisfied
adjective phrases are marked by a replacement transducer which inserts the ap and ap boundaries around any word sequence that matches the
however all the transducers can in principle be composed into a single transducer which produces the final outcome in a single step
the parser has four main linguistic modules each of them consisting of one or several sequenced transducers null NUM seriously endangers your health
for example labels for functional heads such as c sub j c 0b j c i 0bj mark the word which is a head of a noun phrase having that function in the clause but the parent is not indicated
because both systems leave some amount of the ambiguity pending two figures are given the success rate which is the percentage of correct morphosyntactic labels present in the output and the ambiguity rate which is the percentage of words containing more than one label
but obviously any recognition grammar should deal with non projective phenomena to the extent they occur in natural languages as for example in the analysis shown in figure NUM our system has no in built restrictions concerning projectivity though the formalism allows us to state when crossing links are not permitted
the default is a nominal type of complement but there might also be additional information concerning the range of possible complements e.g. the verb say may have an object sv0 which may also be realized as a to infinitive clause wh clause that clause or quote structure
the suffix tree associated with w is a compressed trie of all strings suffi w NUM i iwl
when word objects are constructed nodes denoting relevant relations between words will be activated
note that there is no top level executive deciding the order in which codelets are executed
this equation is used to magnify differences in urgency values when the temperature is low
the affinity relation is a quantitative measure that reflects how strongly two characters co occur statistically
it may happen that a structure being constructed is in conflict with an existing structure
the strength of a structure is an approximate measure of how promising the structure is
therefore efforts towards building different structures are interleaved sometimes co operating and sometimes competing
in other words the system s high level behavior arises from its low level stochastic substrate
note that this section would be overwhelmed with details if a step by step explanation were given
eight instances of affix codelet are posted to identify and construct affix relations between characters
quantitative results for a NUM word test set are reported
if x is the current tag and y is the correct tag then the transformation will result in one less error so we increment the number of improvements caused when making the transformation given the part of speech tag of the previous word lines NUM and NUM
a stochastic trigram tagger would have to capture this linguistic information indirectly from frequency counts of all trigrams of the form shown in figure NUM where a star can match any part of speech tag and from the fact that p n t rb is fairly high
the features hl and h2 are used for height simply because there are three possibilities clustering of phoneme data NUM x NUM
these probabilities can be estimated directly from a manually tagged corpus s these stochastic taggers have a number of advantages over the manually built taggers including obviating the need for laborious manual rule construction and possibly capturing useful information that may not have been noticed by the human engineer
emmanuel roche and yves schabes deterministic part of speech tagging the determinization algorithm of figure NUM computes the above subsequential transducer
thanks to stephen isard of edinburgh cstr and linda shockey of the linguistics department reading university for help with diphones and related matters
in the proposed system this middle stage is replaced by the som stage which introduces a learned notation based on acoustic data
we now turn our attention to lexical assignment the step that precedes the application of the contextual transducer
intuitively speaking this transducer has to look ahead an unbounded distance in order to correctly generate the output
the corresponding dag takes 360kb of space and provides an access time of NUM NUM words per second
this dialogue system was integrated with a touch screen input method
the result of applying this procedure to the sample dictionary of figure NUM is the dag of figure NUM
we decided to compare this strategy with one that uses a large lexicon of organization names
we call the token containing the symbol which marks a putative sentence boundary the candidate
f w lcb wl w2 rcb then f is called a rational transduction
the most recent work will be described in pa lmer and hearst to appear
we also conducted tests using wider contexts but performance did not improve
viation if it is preceded and followed by whitespace and it contains a
palmer and hearst achieved performance of s NUM with the neural network
the training procedure requires no hand crafted rules lexica part of speech tags or domain specific information
its performance on the same two corpora is shown in table NUM the highly portable system
all the results we will present for our a lgorithms are on their initial larger test
wojciech skut and christian braun were a great help in testing and improving the system
disjunction in the general case can not be encoded in a prolog term representation
abstraction through templates is also useful for defining interfaces between grammars and processing modules
a path is minimal if it does not contain any repeated features or sorts
any subset of this set of possible values can be encoded as one prolog term
in addition to being an abbreviatory device the template mechanism serves three other purposes
if a sort has subsorts and introduces features these are combined in one declaration
int ernal node intro left daughter binary tree right daught er binary tree
one such dimension is whether the quantities entered into the parser chart are defined in a bottom up cyk fashion or whether left to right constraints are an inherent part of their definition NUM the probabilistic earley parser shares the inherent left to right character of the lri algorithm and contrasts with the bottom up i o algorithm
lexical items v parsing with an hpsg grammar to provide a mix of the above tasks
in case of extensional sorts see section NUM NUM this variable is omitted
NUM NUM using context statistics to delete parses
it helps to interpret these quantities in terms of an unconstrained earley parser that operates as a generator emitting rather than recognizing strings instead of tracking all possible derivations the generator traces along a single earley path randomly determined by always choosing among prediction steps according to the associated rule probabilities
now that word senses occur in accordance with their contexts we measure their similarity dissimilarity by their contexts
we suppose that the senses form some clusters and the senses in each cluster are similar with each other
instead of locating all word senses in the space we only make use of mono sense words to outline it
in each step two closest nodes are selected and merged into a new one
but ideal resources from which to learn exemplars are not generally available for any languages
totally NUM possible segmentations will be found if we simply match the sentence with a dictionary
this module guesses a tag for a word according to its suffix e.g. a word with an ing suffix is likely to be a verb its prefix e.g. a word starting with an uppercase character is likely to be a proper noun and other relevant properties
these functions are responsible for seeding the sentences with likely candidat e phrases of various kinds
moreover huge corpora especially sense tagged or aligned ones are not generally available in all domains for all languages
we randomly select NUM ambiguous words contained in the dictionary and there are altogether NUM words listed as their collocations
therefore we are developing a training system to help such children develop their conversation skills with pictalk
as a result the emphasis in generative grammar has turned from formalisms with restricted generative capacity to those that support more natural expression of the observed regularities of languages
to rule out this possibility we require chains to be closed wrt the link relation i.e. every chain must include every node that is related by link to any node already in the chain
having interpretations both of gpsg and of a gb account of english in l NUM provides a certain k p amount of insight into the distinctions between these approaches
in fact it fails only in cases like head raising in dutch where there are potentially unboundedly many chains that may overlap a single point in the tree
while this provides a model theoretic interpretation of the systems of constraints produced by these formalisms those systems are typically built by derivational processes that employ extra logical mechanisms to combine constraints
using such encodings we can define a predicate free x which is true at a node x iff the feature f is compatible with the inherited features of x
the idea now is to define chains as sequences of nodes that are linearly ordered by link but before we can do this there is still one issue to resolve
finally the fact that these complexity classes have automata theoretic characterizations means that results concerning the complexity of natural languages will have implications for the nature of the human language faculty
thus with this framework we get both the advantages of the model theoretic approach with respect to naturalness and clarity in expressing linguistic principles and the advantages of the grammar based approach with respect to language theoretic complexity results
at least initially there was hope that this relationship would be informative for linguistics that by characterizing the natural languages in terms of language theoretic complexity one would gain insight into the structural regularities of those languages
most arcs also have inverses e.g. the subject arc has the inverse subjectof which allows determination of the events in which a particular concept played the subject role
if successful a wor d is linked to lexical and semantic nodes allowing access to lexical and semantic information during the res t of morphology parsing and semantics
this is a good debugging aid showing what text gave rise to particular nodes and also allowin g us to trace the semantics produced from certain parts of the text
haskell has some similarity to lisp such as building programs by writing functions a garbage collected heap list s as a basic type and full higher order use of functions
the following example rule handles phrases like alan gottesman an analyst with painewebber which is a propernoun phras e followed by a noun phrase describing the propernoun
some is a events need to be treated as co referential others do not an d the distinction is often based on the type of surface form that produced the event
since an arc is also a node the concepts of the different kinds of relationshi p possible between nodes can be represented in the same formalism as more concrete concepts
a state value the context is passed around during traversal this holds possible referents in order of occurrence and is used to resolve anaphoric expressions
during development we have seen several examples of goo d scores being obtained when the system works to its full potential and we are much encouraged by it
e.g. dog u has a link english to the noun form of dog an d a link italian to the italian word cane
the program matches this pattern into the following entries myocardial infarction infarction of myocardium stenosis at the origin of left coronary artery
the system assumes that if there is at least one word in common in wordnet entries for two different adjectives they can be clustered together
thus we map the term bank to a set of paradigms and we choose the set of paradigms which appear most frequently for clustering
note that it is the latter theorem and its variants that most crucially bear upon what is gained by the move to a mixed system given that the lexical encoding of linguistic information predominantly involves the assignment of functional types
the sequent rules shown in NUM may still be used for each of the levels with o serving as a placeholder for the various product operators as may the axiom and cut rule in NUM
consider how we might formulate a mixed logic of the kind just suggested what i term a hybrid system one which includes the logics that arise by choices from just a and p
mula a can be derived from the structured configuration of antecedent formulas f f i NUM represents represents the result of replacing i with l in f i
x y z gives orders xyz and yzx since its yield term is x r y r z whose only variant is y r z r x
it is well known that parsing theorem proving with sequent formalisms suffers efficiency problems as a consequence of derivational equivalence or spurious ambiguity i.e. from the existence of multiple proofs that assign the same meaning for a given type combination
some categorial treatments of non constituent coordination have depended crucially either implicitly or explicitly on associativity allowing for example subject and verb to be combined without other verb complements making possible a like with like coordination treatment of non constituent coordination as in e.g.
NUM the word order consequences of proofs are instead determined from the normal forms of proofs terms s which encode all the relevant information from the proof and in particular the directional etc information encoded by the connectives of the types combined
if that meaning of credit exists where credit NUM an offer has been transformed into a debt by drawmg on the offer should it not be differentiated from credtt NUM
here are examples of these forms
NUM NUM more examples of imagene s output
a structural view of purpose demotion
return to seat to place calls
this work was supported by the national science
the phone is now ready to use
note that the match must be exact
probability distributions with nearly no specific assnmptions
only NUM NUM sentences out of the training corpus
figure NUM word aligmnents for spanish english sentence pairs
ing word error rate was only NUM NUM v
in both cases we obtained a correct translation
the language model used is a standard bigram model
however by performing simple word reorderings it
this monotony requirement limits the applicability of our approach
a similar type of questionable synecdoche pertains to cases such as lcb drumhead head rcb a synset which is a hyponym of membrane but also creates a new sense of head total number of senses NUM
the degree of semantic determinacy reached depends on the consistency of annotation annotation errors the granularity of the type system peculiarities of the language in short on the nature of the tree bank
the analysis uses the formula schemata discussed in section NUM NUM but here the interpretations of daughter nodes are so called update expressions conforming to a frame structure that are combined into an update of an information state
as in the purely syntactic version of dop we now want to compute the probability of a semantic analysis by considering the most probable way in which it can be generated by combining subtrees from the corpus
and in many application contexts it probably makes sense to use an a i style language which highlights domain structure frames slots and fillers while limiting the use of quantification and negation see section NUM
though charniak only uses corpus subtrees smaller than depth NUM which in our experience constitutes a less than optimal version of the data oriented processing method he reports that it outperforms all other non word based statistical parsers grammars on this corpus
given a partially annotated corpus as defined above the multiset of corpus subtrees consists of all subtrees with a well defined top node semantics that are generated by applying to the trees of the corpus the decomposition mechanism described above
their algorithm however requires a procedure which can inspect the semantic formula of a node and determine the contribution of the semantics of a lower node in order to be able to factor out that contribution
the maximal depth of subtrees involved in the parsing process was varied from NUM to NUM results in figure NUM concern a match with the total analysis in the test set whereas figure NUM shows success on just the resulting interpretation
NUM tp with linear thematization of rhemes an element of the c ui NUM which is not the cp ui NUM appears in ui and becomes the cp ui after the processing of this utterance
as far as anaphora resolution is concerned e.g. the model requires to consider those discourse entities as potential antecedents for anaphoric expressions in the current utterance ui which are available in the forward looking centers of the immediately preceding utterance ui NUM
competency evaluation will be based on how likely an agent s branch will be successful based on a weighted factor analysis and how likely the collaborator s branch will be successful based on a weighted factor analysis and a probabilistic model of the collaborator s knowledge
c i have proven that suspect9 has a motive to murder lord dunsmore and suspect9 had access to the poison d i have proven that suspect7 had access to the poison suspect7 had an opportunity to administer the poison and suspect7 has a criminal disposition
given a reformulation of the tp constraints in centering terms it is possible to determine referential segment boundaries and to arrange these segments in a nested i.e. hierarchical manner on the basis of which reachability constraints for antecedents can be formulated
according to the segmentation strategy of our approach the cp of the end point i.e. the last utterance of a discourse segment provides the major theme of the whole segment one which is particularly salient for anaphoric reference relations
the model constitutes a template tool for designing integrated systems it specifies the standard components and how they fit together
turning to the research community we asked those who had designed systems incorporating dialogue management for their experiences and opinions
user orientated over informativeness is an important feature to have and is directly related to the degree of freedom of expression
in contrast a system which allows the user to take the initiative has less control of the user s language
in human to human conversations for example an utterance can perform more than one illocutionary or speech act
this form of deixis is applied to words which can only be interpreted in the given context of the dialogue
despite there many differences every one contains a common process an evaluative cycle
in natural dialogue a speaker can provide more information than is actually requested
a dialogue management system must be able to recover from any deviations which occur
correspondingly there are a number of design methodologies for building such a system
we believe that the explanation of this observation is that text processing is essentially a left to rightprocess usually people write texts so that the most important ideas go first both at the paragraph and at the text level the more text writers add the more they elaborate on the text that went before as a consequence incremental discourse building consists mostly of expansion of the right branches
is the case equivalent to figure NUM we do not think so and this may be backed also by the fact not shown in this figure that v sedate is a top concept while v de energize is not
on average we randomly selected approximately NUM text fragments per marker having few texts for the markers that do not occur very often in the corpus and up to NUM text fragments for markers such as and which we considered to be highly ambiguous
the convention that we use is that nuclei are surrounded by solid boxes and satellites by dotted boxes the links between a node and the subordinate nucleus or nuclei are represented by solid arrows and the links between a node and the subordinate satellites by dotted lines
to better understand this problem the corpus analysis described in section NUM was designed so as to also provide information about the types of rhetorical relations rhetorical statuses nucleus or satellite and sizes of textual spans that each marker can indicate
hypotactic relations are those that hold between a span that is essential for the writer s purpose i.e. a nucleus and a span that increases the understanding of the nucleus but is not essential for the writer s purpose i.e. a satellite
the differences between the two analysts came mainly from their interpretations of two of the texts the discourse trees of one analyst mirrored the paragraph structure of the texts while the discourse trees of the other mirrored a logical organization of the text which that analyst believed to be important
NUM although the atmosphere holds a small amount of water and water ice clouds sometimes develop NUM most martian weather involves blowing dust or carbon dioxide each winter for example a blizzard of frozen carbon dioxide rages over one pole and a few meters of this dryice snow accumulate as previously frozen carbon dioxide evaporates from the opposite polar cap
the best tree for text NUM has weight NUM and is fully represented in figure NUM the postscript file corresponding to figure NUM was automatically generated by exemplification forexample i
no i had assembled the desk myself
she had been happy taking it on
edinburgh eh8 9lw scotland j hitzeman ed
the drawers only took me ten minutes
the possible temporal rhetorical relations are constrained
the type hierarchy used for constraints
the drawers only todk her ten minutes
she was building a dog house
the previous thursday he was in detroit
a balanced for the domain set of categories i.e.
with hindsight and foresight we are emphasizing the following the logic of the generic subsumption demands that every instance of a subeoncept is also an instance of its superconcepts otherwise the logic supposed to be started from is changed
after having consulted the data in the railway database the system realized that the number of connections between milano and roma in the evening was high and it suggested the user to choose a more precise departure time t5 s
since the percentage of users that was not able to detect recognition errors is around NUM we may hypothesize that a part of the subjects that experienced clarification subdialogues would have failed to give the correct values of the task parameters
the feature structure associated with bread knife will be as in NUM
we assume the vendlerian distinction between activities states accomplishments and achievements
this preposition da has a different meaning from the one associated with the telic
the basic layout of the lexical entries we employ is given in NUM
in other words da selects for any type while di is restricted to events
we turn now to consider some of the applications of this work in more detail
aside from translation the phrase structure schemata can also be used for multi lingual generation
for italian the nature of the modification can alternatively be directly encoded in the lexical entry for the preposition
a number of phrase structure schemata are used each specifying linking to a different argument position in the telic
in order to account for the italian forms as in the english case we utilize phrase structure schemata
in the conclusion we discuss how the combination of the two methods increases the performance of our system and enhances the robustness of the final results
the first part of the word mila was misrecognized as a noise and the last syllable was recognized as no that the parser interpreted as the negation adverb no
our method tags each of these tokens with two or three possible senses and in all but one case the sense tag includes the valid sense
the average error rate is NUM NUM this average is driven up by the inclusion of present prove and introduce in our test set
these can be converted to non overlapping groups for the purposes of this discussion by assigning each word to the group for which it has the highest membership coefficient
our initial results indicate that domain independent syntactic information reduces potential verb senses for multiply polysemous verbs five or more wordnet senses by more than NUM
we present an approach for tagging verb sense that combines a domain independent method based on subcategorization and alternations with a domain dependent method utilizing statistically extracted verb clusters
our ultimate goal is to develop methods to tag lexical semantic features in discourse corpora in order to enhance extraction of constraints of the sort just listed
the rule for definitions was briefly discussed in the previous section
the main change in the definitions rule lies in the conditions under which it is applicable
in this work an attempt is made to set out a logic of datr statemerits
inheritance descriptors are fllrther distinguished as being local unquoted or global quoted
thus the rule captures a logical relationship between datr sentences
this varianl will be refl ared to as datrl
thus dog cat also has the value noun
to be useful for working translators methods for searching retrieving and presenting information must be done in ways that are familiar
the following proof illustrates the use of tt lcb e quoted path rule qu
the rules for values definitio ns and sequences are modified in an entirely similar inanner
to evaluate the results of the classification more objectively we focus on one evaluation metric namely the automatic examination of the meaning of teiru which can represent several distinct senses as described in the introduction
at any stage all subproofs so far constructed are in normal form and the result of any combination is admitted only provided it is in normal form otherwise it is discarded
as a ollse ltlellce our roposal linds its t lnee in i he xa tul le l as d
in this experiment we compared the following three methods for word similarity measure the bunruigoihyo thesaurus bgh the similarity between case fillers is measured by a function between the length of the path and the similarity
this is the case when prefixes and sulfixes are dissimilar enough as in our example with mathema i cs and phys i cal but in the general case only dist u v
figure NUM shows the relation between the number of equations used and the accuracy we divided the overall equation set into n equal subsets NUM see section NUM NUM and progressively increased the number of subsets used in the computation
to integrate the advantages of these two approaches we aim at calculating a statistical weight for each branch of a thesaurus so that we can measure word similarity simply based on the length of the path between two words in the thesaurus
besides this as we pointed out in section NUM sbl allows us to reduce the data size from o n NUM to o n in our framework given that n is the number of word entries
in this case if word a is more closely located to b than c is to d and vsm a b vsm c d that trial measurement is taken to be successful
further investigation of this issue will be presented in the long version of this paper
in all the results presented from this point on positive winnow is normalized
ih re we lefine the recall as the number of times the exact structure was computed by analogy divided by the number of sentence t airs having the same structure in the tree bank
in the on line learning model learning takes place in a sequence of trials
in sections NUM NUM and NUM NUM we discuss how we incorporate those ideas in our setting
thus i and space are both assigned nearly zero probability in the context e establish simply because m and e get nearly all the probability in that context
similarly we say that the algorithm predicts NUM when the score exceeds NUM
it is therefore important to consider the frequency of a feature when determining its strength
the results of classification with feature filtering appear in the last column of table NUM
here is part of that table this model outputs a superset of the NUM katakana symbols including spurious quote marks alphabetic symbols and the numeral NUM
according to the divergence heuristic the decision to add an extension w is made relative to that context s maximal proper suffix lwj in d as well as any other extensions in the context w
the approach just outlined is unsuited to incremental processing
however computationally speaking the relation between the similarity namely the semantic length of the path and the physical length of the path is not clear NUM
on the other hand the local syntactic context may help reduce some of the ambiguity above as in NUM
the first example chooses parses with case feature ablative preceding an unambiguous postposition which subcategorizes for an ablative nominal form
our experience is that these rules improve precision by about NUM to NUM additional percentage points with negligible impact on recall
these factors are combined into a score by calculating their weighted sum
others are close tanya harding nickel simpson danger washington world cap
this will either drive those technologies underground at least with rrespect to muc meetings or it may discourage a whol e line of research which we feel holds great promise
at the foundation of all our system configurations are the string specialists these are the pattern matchin g routines that attempt to recognize proper names dates and other stylized noun phrase descriptions
since the default classification for an ambiguous leaf node a leaf node that contained both positive and negative instances was to take the majority class the tree returned
this may seem to be an unintuitive and risky pattern for coreference classification but in fact this processin g turned out to be correct in the walk through text whenever resolve received correctly extracted nps
in summary our greatest concern after muc NUM is that the preparation of adequate training corpora may be to o expensive or too labor intensive to enable a fair evaluation of trainable text processing technologies
in previous muc evaluations wher e NUM NUM or more training documents were provided our dictionary construction tool picked up the more important morphological variants because the training corpus contained examples of them
although umas s has participated in previous muc evaluations all of our information extraction software has been redesigned an d rewritten since muc NUM so we are evaluating a completely new system this year
a breakdown of our recall and precision for each specialist is shown below our string specialists were organized in a serial architecture which allowed upstream components to clai m strings in a non negotiable manner
NUM NUM we also note that the precision of the money and percentage specialists would have been perfect had w e succeeded in filtering out a data table in one of the test texts
resolve was designed to work in conjunction with an information extraction system as such it s expected input is a set of phrases that are relevant to a specified information extraction task
in the fourth compilation step the finite state automata produced in the last step are encoded in definite clauses called interaction predicates
and second relative to a specific lexical entry many sequences of lexical rules that are bound to fail are tried anyway
NUM note that it is only possible to eliminate the frame predicates since they are never called independently of the covariation encoding
whereas unfolding can be viewed as a symbolic way of going forward in computation folding constitutes a symbolic step backwards in computation
in addition computationally treating lexical rules on a par with phrase structure rules fails to take computational advantage of their specific properties
one possible reduction of the above automaton consists of taking into account the propagation of specifications along each possible path through the automaton
both of these computational treatments of lexical rules however have significant shortcomings with respect to lexical rules as used in hpsg
a deletion occurs when the sentence has failed to produce a parse that occurs in the control parse forest for that sentence
there is information in the parse forest of failed parses that may allow single words to be identified as problem words
there are four separate files that constitute the lexicon for this parser and corpus nouns contains all irregular noun plurals
dictl0 contains all the words from the corpus that are not contained in the other three files and are thus all open class words
the information common to all solutions to the interaction call is lifted up into the lexical entry and becomes available upon lexical lookup
in the following we show that the additional specifications on the extended lexical entry needed to guide processing can be deduced automatically
unknown words could be learned by discovering their part of speech and feature information during parsing and storing that information in the lexicon
in foul up a strong top down context in the form of a script is needed to provide the expected attributes of unknown words
one fact is immediately striking even with such simple sentences and rule sets more often than not the inside outside algorithm converges to a suboptimal grammar
organization org NUM title ttl NUM retired ttl ttl NUM job ttl NUM org NUM job NUM person pers NUM holds job pers NUM job NUM h j NUM
has location org NUM geo NUM hasloc NUM i.e. creative artists agency is located in hollywood this propagation of facts from one individual co its co designating siblings is the heart of our coreferenc e mechanism
learned rules from brill s release NUM NUM NUM lexical rules NUM contextual rules for which brill has measure d accuracies that are NUM NUM percentage points higher than in our own smaller scale experiments
in our phraser only the current rule in a rule sequence is tested the rule is applied wherever this test succeeds and the rule is never revisited at any subsequent stage of processing
the simple minded strategy we adopted here is to proceed backwards from the current sentence searching for the most recent sentence containing a n occurrence of ajob out phrase and returning the semantic individual it denotes
as noted above it has somewhat less recognition power than a finite state machine and as such shares many characteristics of pattern matching systems such as circus NUM or fastus NUM
in fact these rules are organized as a brill style rule sequence where each rule is allowed to run to quiescence at only one point in the sequence before the nex t rule becomes active
however since the merging takes place in th e inferential database with propagation of relevant facts as a side effect the process is greatly simplified an d obviates the need for explicit template comparisons
it is further fleshed out by looking up related facts that hold of the matched individual e.g. has location y z for organizations or has title x w for persons
it is our hypothesis though that alldomain inference rules can be so organized not jus t contextualized ones and that by this organizational scheme rules can be automatically learned from example
the system requires rich descriptions of language and of the world which for now must be specified by hand
we assume that information states are recovered from the context just like other parameters of interpretation like states and actions
we consider that the whole aspectual meaning of verb phrases is determined in the following order verbs arguments adverbs aspectual forms adverbs and aspectual forms are defined as indicators of such cognitive processes as zooming and focusing which operate on the time line representation
reiter and dale suggest that the prioritized list of attributes their algorithm uses is domaindependent
books can be described by author by physical characteristics or by content e.g.
to see how we can generate ordinary collocations consider describing parts of a library
specifications of world knowledge can be used for generation in many languages while linguistic specifications apply across many domains
between these extremes are three classes of constructions of particular concern for natural language generation
in particular transduction presupposes that the content of referring expressions has already been established
this means that the algorithm generates the most marked licensed form for the particular context
these new goals are added to the current goals and then the algorithm repeats
updating the distribution by focusing on data that are not well predicted by the constructed model
the experimental results show that the proposed method significantly outperforms both hand crafted and conventional statistical methods
in a more general form we introduce a tag set that has a hierarchical structure
the ctw method computes probability by mixing subtrees in a single context tree in bayesian fashion
the total number of words in the hand revised corpus was NUM
before describing the algorithm we prepare some definitions and notations
in this section we construct a basic tag context tree
null the second option is to devise a new tag model
to addless the problems and utilize the advantages of the methods presented above we put forward a new algorithm to automatically classify the words
k sb z n alsblldegg NUM aca
figure NUM exemplifies two context trees comprising binary symbols a and b
the rules themselves are stated in terms a linguist would be familiar with such as the following
using these criteria as a working basis we developed a set of highly accurate letter to sound rules
the lookup procedure could then strip some of the affixes to retrieve the root in the dictionary
we consider incorrect stress placement to be a more serious error than one incorrect segmental phoneme
the word scandalousness could be decomposed by the following rules begin rl rl for right to left
many languages are somewhat more complex and fit into a second category of languages of mid level difficulty
the reason we have not tested extensively on much larger corpora is that using head features but no bracketing constraint statistics must be recorded for every word pair in every sentence
some problems are similar in both languages others are specific to one language or the other
in this framework we consider tagging as a process carried out in two phases NUM selection of the semantic tag system specific to the domain tnning wordnet NUM use of the specific classification to tag the corpus in v vo
our aim is thus to provide a systematic bootstrapping framework in order to assign sense tags to words induce class based models from the source corpus null use the class based modesl that have a seman null tic nature within a nlp application
where n is the total number of synsets of a word w i.e. all the wordnet synonymy sets including w n c is the number of synsets of w that belong to the semantic category c i.e. synsets indexed with c in wordnet
the wsd algorithm wsd godot can be sketched as follows i let k be a context of a noun verb to in the source corpus and lcb ci c2 c rcb be the set of domain specific classifications of w as they have been pre selected by c godot NUM for each class ci the normalized contextual sense ncs is given by
given the above reference tag system our method works as follows step NUM select the most typical words in each category step NUM acquire the collective contexts of these words and use them as a distributional description of each category step NUM use the distributional descriptions to evaluate the corpus dependent membership of each word to the different categories
after the tuning phase local tagging is obtained in a similar fashion given a context k for a word w and the set of the proposed classes lcb c1 c2 cn for w a tag c e c1 c2 cn rcb is assigned tow in k itf adherence of k to the probabilistic model of c is over a given threshold and it is maximal
dipartimento di informatica sistemi e produzione universita di roma tor vergata italy lcb bas ili dellaroc pazienza inf o ut ovrm it
table i lists the mean semantic entropies ep for each part of speech p sorted by p and the variance of each ep
a verb with a high degree of synonymy in c is one with a high number of synonyms in the corpus with reference to a specific sense synset belonging to c salient verbs for c are frequent typical and with a high synonymy in c the salient words to for a semantic category c are thus identified maximizing the following function that we call score
initially all word bigrams are initialized to uniform distributions and context free rule probabilities are initialized to a small random perturbation of a uniform distribution
closed class parts of speech are those parts of speech that may not normally be assigned to new words
formalizing a theory of what constitutes appropriate inferences could be a separate research project
a statement about ancestor task steps of which accomplishment of s is a part
repetitions in either of these sessions were used as needed to obtain acceptable recognition rates
experimentation was thus to be limited to just two modes even though four were operative
the first was to train the subject and register subject pronunciations on the verbex machine
this function selects the meaning with minimum utterance cost and uses expectation to break ties
the dialog controller of course will provide an expectation for each received input
determine next operations to be performed by the domain processor in providing a suggested goal
it also maintains all dialog information shared by the other modules and controls their activation
it formulates goals at the top level to be passed on to the theorem proving stage
space prohibits a full explanation but essentially the fact that aprolog is a typed language leads to a good deal of formal clutter if this method is used
in this paper we use the generalized rule to illustrate the elegance of the representation but it is an easy change to implement a bounded coordination rule
NUM with no external specialists no interfaces to access them are needed and therefore there is no need t o translate between incompatible representations
a simple list of NUM or so standard abbreviations and the sensitivity to the most commo n occurences of periods in numbers was largely responsible for this good performance
we certainly did not need to do this for muc NUM a simple gazetteer list would do an approach adopte d by most muc NUM participants
also the declarative nature of prolog programs opens up the possibility for applications of program transformations such as partial evaluation
the four arguments to cooed are a category and three terms that are the object level lf representations of constituents of that category
identifying certain named entities and therefore resolving references to them may be facilitated by the availability of extra constraints on the beginning or the end of the name
nd concentrate on his duties as rear commodore a tth it promises to be a smooth process which is unusual given the volatile atmosphere of the advertising business
developed and tested a general approach to handling abbreviations acronyms and aliases we spent much more effort on abbreviations acronyms and aliases than originally planned
this buggy piece of code is about the easiest to fix but at the same time it is the most damaging in terms of the score
in our previous example it means that only the output dbbbad is generated
null stands for a blank tag representing the beginning or ending mark of a sentence
in the rest of this section we first describe distributional analysis in subsection NUM NUM
NUM replace labels in each label group with a new label in the corpus
as the result of this step a certain set of label groups is derived
to perform this task our grammar acquisition algorithm operates in five stages as follows
therefore we can use the following measure to select the best group pair to merge
in this condition we can not calculate the divergence of two probability distributions
each group cluster gj is a set of data and the groups are mutually exclusive
a is applied as a balancing weight between the observed distribution and the uniform distribution
the first term in the right part of the formula is the original estimated probability
word correlations are important statistical information which has been successfully employed to find bilingual word pairs from parallel corpora
however the result we obtain from this corpus gives us a lower bound on the performance of our algorithm
however none of them specializes in economics or finance which is the domain of the wsj nikkei corpus
it is also a significant initial result for lexical translation from truly non parallel corpora particularly across language groups
from our experiment results we conclude that the right segment size is a function of the frequency of the seed words NUM segment size cc frequency ws if the seed words are frequent and if the segment size is as large as a paragraph size then these frequent seed words could occur in every single segment
this evaluation is a difficult test case because NUM the two languages english and japanese are across language groups NUM the i two texts wall street journal and nikkei financial news do not focus on the same topics and NUM the two texts are not written by the same authors
next pr w i is computed for all unknown words z in both texts
with large segments such seed words are too biasing and thus smaller segment size must be used
if only the NUM content words manually selected are kept from the NUM word set the precisions at different top n candidates for the NUM word set are higher as shown in figure NUM by the dotted line
the most correlated seed word w will have the top scoring as an example using NUM seed word pairs in the wsj wsj corpus we obtain the following most correlated seed words with debentures in two different years of wall street journal as shown in figure NUM in both texts the same set of words correlate with debenture closely
it is np hard on the size of the grammar which for human languages is likely to be quite large
i then show the following i good news generation in otp can be solved attractively with finke state methods
thus when it is impossible to satisfy all constraints at once successive filtering means early constraints take priority
however it appears possible to limit these cases to the forms in NUM NUM
the input representation must specify only v iv not v v
cost o NUM apiece rather than k NUM intersections that cost up to NUM NUM k apiece
in NUM the constraint ci l needs to be intersected with only certain factors of si
sit is important to take not as our indication that we have been inside constituent
otp specifies the class of autosegmental representations the universal generator gen and the two simple families of permissible constraints
yet intuitively they are similar with respect to their right syntactic context despite the lack of common right neighbors
in order to investigate whether the application of differentia entropy to cut off the merging process is appropriate we plot values of these measures at all merging steps as shown in figure NUM from the graphs we found out that the best solution is located at around 44th 45th merging steps
this generalization could not be exploited if left and right context were not treated separately
first we are planning to apply the algorithm to an as yet untagged language
generalized left context vectors were derived by an analogous procedure using word based right context vectors
there are arguably fewer different types of right syntactic contexts than types of syntactic categories
the right context neighbors of onto contain verbs because both prepositions and verbs govern noun phrases to their right
we chose m NUM reduction to a NUM dimensional space for the svd s described in this paper
i m also indebted to michael berry for svdpack and to the penn treebank project for the parsed brown corpus
the two context vectors of a word characterize the distribution of neighboring words to its left an d right
the generation of the subject noun phrase is not discussed here
the newly built structures are also mixed syntactic semantic representations an
the input for generation systems varies radically from system to system
sister adjunction involves the addition of exactly one new immediate domination link
the initial semantics need not be marked for its semantic head
this reduces the search space and speeds up the generation process
we have presented a technique for sentence generation from conceptual graphs
protector is implemented in life NUM
a graph is a set of concepts connected with relations
in addition several sister adjunctions can occur at the same node
we will also study the formal properties of dtg and complete the design of the earley style parser
the label of the maximal projection is we assume determined by the morphology of the anchor
the sa tree r for NUM has root labeled by the name for a and k subtrees rt
dtg provide a mechanism called subsertion insertlon constraints to control what can appear within d edges see below
however this was found to be insufficient for treating both long distance scrambling and long distance topicalization in german
this variable will become bound when the system develops a plan to achieve this goal by using the action schema replace plan see figure NUM above
since there are no further belief or goal adoption rules that can be applied the system next checks for any goals that it can try to achieve
since the error in the referring plan is in the terminating instance of modifiers the plan constructor builds an instance of postponeplan which it names p26
first our work addresses not only understanding but also generation and how these two tasks fit into a model of how agents collaborate in discourse
replacements can be used if the referring expression either overconstrains or underconstrains the choice of referent while the expansion can be used only if it underconstrains the choice
these actions serve as the basis for communication between the two agents and so they must convey the information that is dictated by clark and wilkes gibbs s model
in the latter case we need to determine which action in the plan is to blame so that this knowledge can be shared with the other participant
it uses the modifier plan which adds a component to the description and updates the candidate set by computing the subset of it that satisfies the new component
the first ensures that the referent is of the chosen category and the second determines the candidate set cand associated with the head noun that is chosen
we will implement a language identifier and carry out more experiments to compare the output from the recognizers
robustness is a critical issue which must be addressed for this technology to be useful in real applications
there are a total of thirteen possible speech acts which we identify with our discourse processor
as figure NUM indicates in many of these cases the discourse processor guesses correctly
we will discuss our proposea extension to tst which handles these structures in a perspicuous manner
notice that in both of these examples the speakers negotiate over multiple alternatives in parallel
a graph structured stack is a stack which can have multiple top elements at any point
that which represents an agent s intention that some proposition hold
in section NUM we will present our implementation of extended tst
how about thursday at twelve NUM sounds good
from the re null suits of our preliminary experiments we find that accent difference causes recognizers performance to degrade
they are shown in figure NUM and in figure NUM which contains two cases the pair exhale NUM breathe NUM and the pair inhale NUM breathe l NUM
our approach makes good use of contextual information such as information about social status and sentence external individuals
we used NUM or NUM of the pairs so derived for building base bigram language models reserving NUM o NUM for testing purposes
this is so since the lexical rule in figure NUM like all lexical rules in hpsg preserves all properties of the input not mentioned in the rule
fourth honorific verbal endings indicate that speaker shows honor or courtesy to addressee
NUM the pruned finite state automaton constitutes valuable feedback as it represents the interaction of the set of lexical rules possible for a word class in a succinct and perspicuous manner
in query 3l both speaker and addressee arc specified for each sentence
each transition in the automaton is translated into a definite relation in which the corresponding lexical rule predicate is called and each final state is encoded by a unit clause
we felt that significant improvements could be gained by combining the input features in more complex ways rather than by simply combining the outputs of independent algorithms
although all of these approaches have involved detailed analyses of individual discourses or representative corpora we believe there is a need for more rigorous empirical studies
however usually it is the case that lexical entries resulting from lexical rule application differ in very few specifications compared to the number of specifications in a base lexical entry
on the other hand the writer s intention for figure NUM is totally different
these include the phrases let s see let me see i do n t know you know when they occur with no verb phrase argument
in a statistical report graphics show the data that is analyzed in the text
the years are sorted in ascending order also to give the impression of evolution
the input of postgraphe consists of NUM special annotations followed by the raw data
we analyzed linear segmentations of NUM narratives performed by naive subjects NUM new subjects per narrative where speaker intention was the segment criterion
we wish to thank michel gagnon for his help in adapting pr texte
they were at their lowest in NUM with about half their NUM value
in this section we present a simple example of input and output from the postgraphe system
the prolog input can be seen in figure NUM lines starting with NUM are comments
the finite state automaton representing global lexical rule interaction can be used as the backbone of a definite clause encoding of lexical rules and their interaction see section NUM NUM
on accepting an input goal from the user the system invokes the text planner which uses the operators in the plan library to build up a plan which is a hierarchical discourse structure to satisfy the input goal
lack of subcategorization leads to errors when verbs occurring with an ambiguous dative nc are mistaken for verbs which subcategorize for an accusative nominal phrase
up the search time considerably especially when the database is very large
thus they can identify names which they have never seen before
therefore confirmation and disambiguation questions are necessary and hence we have a larger number of communicative goals to satisfy than the afore mentioned systems
figure NUM person names co occurring with peru
in particular we discuss system utterances whose primary goal is to acquire information of various kinds since these occur frequently in our domain
the type menu allows the user to disambiguate types of query terms
name tagging can reduce such errors by identifying names as single units
when japanese texts are retrieved indexed terms are translated into english
such planning is a major source of paraphrasing power and since it is controlled by pragmatic factors as explained in section NUM it also increases the sensitivity of the generator to the situation of enunciation
if lcb he lcb lcb imi lement ca n be recov u ed st raighd orwardly from the surrom ling s iii iico he verb was marked far that omphmmnl
cross ranking constraints which arise from the fact that an input network of content units is not isomorphic with the resulting linguistic structure allowing a single content unit to be realized by surface elements of various linguistic ranks cross ranking proper or multiple content units to be realized by the same surface element merging
the role of the syntactic grammar is to NUM map the thematic structure onto a surface syntactic one NUM enforce syntactic rules such as agreement NUM choose the closed class words NUM inflect the open class ones and NUM linearize the surface syntactic tree into a natural language string
in summary the first step of the mapping from conceptual network to clause is NUM to select a perspective among the conceptual relations of the network which deter null mines a head clause and NUM to attach the remaining relations as either embedded or subordinate modifiers of the head clause
the advisor ii system expects in its input up to four semantic relations the highest number of relations that we observed expressed by a single sentence in our NUM note that while an object can not in general serve as a verb a relation can serve as clause noun and a variety of different modifiers
the ability to realize relations by compact constituents such as predicative adjectives or noun noun modifiers allows for the fluency of the sentences of figure NUM realizing all relations in the figure NUM input as clauses would result in rather cumbersome sentences such as programming is the kind of assignments of the class whose topic is ai and the number of these assignments is six
if every relation is to be realized as a clause then the only option for lexicalizing the relations NUM and NUM in example NUM of figure NUM is to generate two separate sentences as in NUM or to embed one of the relations as a relative clause modifier of the shared argument as in NUM or NUM
as a result many different paraphrases of this content can be generate d as illustrated by the five given at the bottom tier of figure NUM note that while in NUM the relation assignt type surfaces as the main element in the syntactic structure in NUM NUM it appears as a dependent element
however words that are less frequent or exhibit diverse translations generally do not have statistically significant evidence for confident alignment thereby leading to incomplete or incorrect alignments
as mentioned in the previous section collocation is one of the reasons why in context translation usually deviates from the dictionary translation
this section thoroughly analyzes the alignment results from the experiments described in section NUM and in particular the data relating to cases where the algorithms failed
mark johnson memoization in top down parsing NUM define terminal x
the union and the company met
this method could help in both directions
null if we retaliate against terrorists
the topic was actual retaliation against terrorists
events in the domain of interest
our implementation of the compiler does in fact perform this pruning as an integrated part of the compilation not as an additional step
in general if a node is listed in the rhs then no other node below it needs to be there as well
for every type the relation specifies its only argument to bear the type information and the consequents of the type definition for that type
when we want to compute the defining clause for a minimal type we first of all check what sort of type it is
by reasoning with the different kinds of types we can drastically reduce the number of goals that need to be checked on line
whenever we encounter a structure of a constrained type we need to check that the structure conforms to the constraint on that type
the body of a definition for a non minimal type is just a disjunction of the relations defining the minimal subtypes of the non minimal type
clearly it needs to be checked but what about nodes and e
append c on node is a constrained type and also has to go onto the rhs
not only does this approach allow the processes of building referring expres department of computer science rochester new york NUM
case of domain questions errors occur when NUM the response requires more reasoning than do typical domain questions causing the hearer to take over the dialogue initiative or NUM the hearer instead of merely responding to the question offers additional helpful information
here we describe an abductive account of the interpretation of speech acts and the repair of speech act misunderstandings
each participant will use the subsequent discourse itself in order to judge whether previous discourse has been understood correctly
we will not consider these two types of repairs further because they do not involve misunderstanding per se
this unification is achieved by treating production as default reasoning while using abduction to model interpretation and repair
the second is conventions for each speech act about what act should follow we call these linguistic expectations
speakers will expect each other to display their understanding of these conventions and how they apply to their conversation
the set of acts itself is not necessarily exhaustive but sufficient to handle the examples that we consider
in poole s implementation facts are given by fact w where w is a wff
in particular we want to develop a general model of conversation that is flexible enough to handle misunderstandings
while the latter relates the rank and relative frequency as asymptotically inversely proportional the former states that the frequency declines exponentially with rank
the set of intentional relations in rda is a modification of the presentational relations of rst while informational relations are similar to the subject matter relations in rst
NUM the best individual features whose predictive power is better than the baseline as table NUM makes apparent individual features do not have much predictive power
our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanations
our experiments enable us to identify the features with most predictive power and show that machine learning can be used to induce decision trees useful for text generation
the purpose of this segment is to inform the student that she made the strategy error of testing inside part3 too soon
thus below we will report the average estimated error rate on the test set as computed by NUM fold cross validation experiments
where x is the count of the most populous species and f is the relative frequency of any species with frequency count x
we build the set of trees that are statistically equivalent to the tree with the best error rate i.e. with the lowest error rate upper bound
in this case the best tree see figure NUM results from combining the two best individual features and reduces the error rate by NUM
class NUM is absent because it is not the most probable class for any of the selected words
we will return to this point in section NUM when we consider how to smooth n gram language models
the number of classes c can be small or large depending on the constraints of the modeler
the value of k determines how many words one skips back to make the prediction
in these models the probability of each word depends on the n NUM words that precede it
the language models in this paper were evaluated on the arpa north american business news nab corpus
new word editor nwe updates the dictionary of words lexicon to be recognized by the nlu application
it enables aspects of the maintenance and construction of such systems to be performed without knowledge of specific programming languages and environments
we examined the score reports for plum and shogun and found that their performance in almost every category matched their overall performance
by this organization the knowledge bases of te do not include domain specific knowledge and domain specific knowledge is localized only in st
schematically the architecture being investigated is given in figure NUM below a system with this architecture has already been demonstrated
text as a managing director of donaldson lufkin jenrette mr barbakow also was an investment banker for national medical
unlike other groups which have focused on case based reasoning or on binary decision trees we are focusing on statistical learning techniques
in the ideal the only knowledge of the language required for the learned system would be the examples of correct output
two terms which are of an extensional sort are only identical if they have a most specific sort which has no subsort and if all features are instantiated to ground terms
s ign var lexphras phon synsem qstore retriev the following declaration introduces two sort hierarchy dimensions for subsorts of phrasal and one new feature
in this introductory section we discuss the advantages of sorted feature formalisms and of the logic grammar paradigm and show how the two developments can be combined
abstraction and interfacing by providing a fixed name for a value that may change partial evaluation functional notation that can make specifications easier to understand
profit compiles all sorted feature terms into a prolog term representation so that the built in prolog term unification can be used for the unification of sorted feature structures and no special unification algorithm is needed
for the development of profit programs and grammars it is necessary to give input and output and debugging information in profit terms since the pro og term representation is not very readable
sorted feature terms consist of a specification of the sort of the term NUM or the specification of a feature value NUM or a conjunction of terms NUM
the corresponding prolog term representation instantiates the representation for the sort sign further and leaves argument positions that can be instantiated further by the subsorts of phrasal and for the newly introduced feature daughters
sort checking can be turned off for debugging purposes and feature search and handling of cyclic terms can be turned off in order to speed up the compilation process if they are not needed
however we will see that these spurious derivations do not translate into spurious ambiguity in the parser which maps from strings of words directly to semantic representations
sue s np s where sue np this can be generalised to the following rule which is similar to function application in stan
the complement adjunct distinction and traces increase the number of rules compounding this problem
we then extend the model to include a probabilistic treatment of both subcategorisation and wh movement
a generative model uses the observation that maximizing 7v t s is equivalent
figure NUM shows a tree which will be used as an example throughout this paper
applicative categorial grammar is the most basic form of categorial grammar with just a single combination rule corresponding to function application
john mary the second representation is appropriate if the sentence finishes with a sentential modifier
the result is a new state expecting an argument which given an np could give an s i.e. an np s
situation NUM may indicate a mistake or it may indicate that equivalent meanings have been encoded in an alternative way in terms of the language internal relations
very can be treated as a function of a function and given the type n n n n when used as an adjectival modifier
it also includes a discussion of some of the issues which arise when parsing lexicalised grammars and the possibilities for using statistical techniques for tuning to particular languages
in the next section the comparison with other reseaxches will be discussed
these trees can be expanded and shrunk by clicking on word meanings and by specifying so called filters indicating the kind and depth of relations that need to be shown
iorl is the number of possible contexts and a is an interpolation coefficient
learning stops when no transformations can be found whose application reduces errors beyond some prespecified threshold
recently there has been a rebirth of empiricism in the field of natural language processing
i i and the bound variable or sloppy reading
of these NUM NUM words were used for training and NUM NUM words were used for testing
the high baseline accuracy is somewhat misleading as this includes the tagging of unambiguous words
NUM the word two before after is tagged z
we then give some practical differences between the two learning methods
testing with no unknown words might seem like an unrealistic test
antecedent clause which has a parallel counterpart in the target i.e.
it is crucial for our system that colors annotate symbol occurrences i.e.
in contrast the following take x to z s mother
not just source parallel elements are taken to be primary occurrences
a trigram tagger will correctly tag this collocation in some instances due to the fact that
NUM from vbp to vb if one of the previous two words is n t
pa iyans consists of a core grammar and translation module and a host of peripheral utilities terin databases general databases editors for pre and postediting document handling facilities facilities for creating and updating term databases
though insufficient for the task at hand the pa lk ans development eould buil l on the english and danish grammars and dictionaries as well as on the transfer module from english into daifish
the NUM templates schematized in NUM replace the two templates of NUM
no semantics is necessary simply block any rule use that would violate NUM
n NUM ever serves as the primary left argument to s
composition bn n NUM x y ot y inz 12z2 ilzl ot d
theorem NUM remains true NUM nf per reading
future work should continue by eliminating the spurious ambiguities that arise from grammatical or lexical type raising
transfer where the strucl ure of the input representation is altere t is kept at a minimum complex transfer is costly inasmuch as the general applicability of the rules is usually very restricted
as patrans is used for a numl er of ditferenl subject fields the priorily of the databases is user defined and flexible the user specifies which term bases are to be used for a translation job
dynamic context knowledge allows the server to reconstruct a full time specification that is interpreted by the agents as an alternative proposal
when h notices that tuesday is promising she chooses to refine her proposal by suggesting a clock time NUM
in contrast to the planning layers the behaviour bascd layer consists of the agent s basic reactive behavior and its procedural knowledge
moreover the surface semantic representations derived by the grammar were too close to nl for an agent system to deal with
moreover we consider it indispensable to have agents understand and generate counter proposais to avoid inefficient plain rejections like NUM
either the agent or its owner is referred to as actor in the agent s e mail messages see section NUM
the measures used for this evajuation are bracketing recall precision and crossing
in each iterative step of the merging process differential entropies are calculated
but as the corpus size increases the fit between and ql becomes ever better
if the corpus was generated by a stochastic context free grammar then this dependency is accidental
as probability distributions ql and NUM should have the same total mass namely one
by contrast the set of graphs admitted by an attribute value grammar g is highly constrained
computational linguistics volume NUM number NUM NUM NUM selecting the initial weight
the sampler for p proposes a new item y
there is an obvious fix for this problem we can simply normalize NUM
we may formalize an attribute value grammar as a context free grammar with attribute labels and path equations
in addition we demonstrated the generality of our model by applying it to dialogues in different application environments
the problem of interest was how to combine a stochastic context free grammar with n gram language models
for example let ql be as before the distribution determined by model m1
row NUM in the table shows the prediction results when applying our training algorithm using the constant increment with counter method
it will be shown that it is possible to exploit the connectivity assumption NUM above in order to achieve a reduction in the number of redundant wfss constructed by both types of generator described in section NUM
updating the graph involves three steps l irstly every node in the graph which is a leaf of tit new wfss is deleted toge t lmr with its associated ares
note that deciding whether a lexical sign can appear outside a phrase is determined purely by the grammar and not by whether the lexical elements share the same index or not
ht these frameworks the unordered natllfe f predicate or relation sets makes the ai plict tion o bag generation techniques attra ctiw
lb show that this condition indeed holds consider a grammatical ordering of some input bag b represented as the string w ce t w
consider sollle wfss w constructed from a bag b and with category c this category in the form of a sign will include syntactic and lexical semantic information
this graph is built by nsing the outer domain of each lexical element to decide which of the remaining elements could possibly share an index with it in a complete sentence
this paper presents an algorithm for the compilation of regular formalisms with rule features into finite state automata
chunker then partitions this sequence into several chunks
this general notion can also be applied to other algorithms which compile regular rewrite rules into automata
we see that while all selective methods are less efficient in terms of examples examined than complete training they are comparable to each other
this section presents results of applying committee based sample selection to bigram part of speech tagging as compared with complete training on all examples in the corpus
note that this type of uncertainty regarding the identity of the appropriate classification is different than uncertainty regarding the correctness of the classification itself
the flexible degree of detail of the tsl will allow either more semantic or more surface oriented sentence planning
property NUM is addressed by selecting examples for which committee members highly disagree in classification rather than measuring disagreement in parameter estimates
finally we studied the effect of sample selection on the size of the trained model showing a significant reduction in model size
in general a selected training example will contribute data to several statistics which in turn will improve the estimates of several parameter vmues
this means that many counts in the data are less useful for correct tagging as replacing them with smoothed estimates works just as well
NUM the administrator the top level process that invokes modules updates pre spl expressions manages parallel alternative expressions etc
NUM the sp must be extensible allowing new modules to be introduced as a need for them is identified
NUM alternative lexical choice in some instances an implant wears out loosens or fails
sampling this distribution yields a set of estimates scattered around assuming a uniform prior whose variance decreases as n increases
NUM the module must make decisions somewhat blindly and allow backtracking when a decision turns out later to be incompatible
the exophoric lexical choice module chooses lexical units for those entities specified in the pre spl that are new in the discourse
to facilitate this the rules and knowledge resources employed by the sp modules should be represented as declaratively as possible
supplant x process situation y x y
the above algorithm was implemented in prolog and was tested successfully with a number of sampletype grammars
when the coding process was completed all discrepancies were resolved to the satisfaction of both authors
a second drt iteration is required to detect and add the missing wire that completes the repair
if it is not correct perform zero or more diagnostic steps to further isolate the problem
NUM for brevity dialogue NUM represents one of the simplest directive mode interactions that could occur
NUM c is the one on the led displaying for a longer period of time
NUM c is the seven on the led displaying for a longer period of time
NUM c is the one on the led displaying for a longer period of time
an important feature of any spoken natural language dialogue system is the ability to perform robust parsing
the main source of ungrammatical inputs in our experiments was the misrecognition of the user s input
the fact that these features are available does not entail that they are consistenly set at every appropriate reading
the second dimension of lexical depth is about the amount of syntactic and semantic knowledge attributed to every reading
the marker m in the langenscheidts t1 translation indicates that the translation has been found via compound segmentation
during this time we do not expect to see any relative clauses
nlp systems can widen the coverage of their lexicon considerably if they employ word building processes like composition and derivation
we also present the results of using our method on NUM mt systems that translate between english and german
after all the systems had processed the sentence lists the resulting documents were merged for ease of inspection
f missing interfix nouns only the source word was segmented into units and correctly translated
the four elements of the tuples are surface pattern root and vocalism
we have depicted parts of four hierarchies in the figure morphological syntactic features noun phrases verb complements and various relative clauses
1st condition substrings can be extracted in the order of the number of matching character string length
we would like to express our special appreciation to the stuffs of csli
our proposed improvement does not require any analytical knowledge as initial condition
araki eli hokkai s u ac
fourth in the feedback process the system determines the fitness value of translation rules used in the translation process and performs the selection process of erroneous translation rules
therefore we consider that our proposed improvement can remove many erroneous translation rules by utilizing only the given translation examples without the requirement of analytical knowledge
in this paper we describe an improvement in the selection process of ga ilmt and confirm the effectiveness of improvement in the selection process of ga ilmt
and the system determines the rate of error based on the number of erroneous combinations and removes the translation rules for which the rate of error is high
namely it determines whether a combination of the english word and the japanese word in a translation rule is true or false by utilizing the given translation examples
in the learning process new translation examples are automatically produced by crossover and mutation and various translation rules are extracted from the translation examples by inductive learning
by the definition of the cover relation there is iyi ixi
p is an unresolved mete variable
it becomes coindexed with s instead of j
on the sloppy reading simon loves simon s mother
a strict substitution substitutes the term by its index
kehler blocks this reading in a similar manner
further contextual information is required to fill the gaps
a generalized quantifier representation equivalent to the above is
but there is no way of discharging them
in this study however names were not tagged
a portable algorithm for mapping bitext correspondence
stochastic approaches to natural language processing have often been preferred to rule based approaches because of their robustness and their automatic training capabilities
as we will see in the next section these two aspects are the source of local nondeterminism in brill s tagger
NUM for evaluation purposes we randomly selected NUM of the brown corpus for training purposes and NUM for testing
for the following it is useful to note that if it i is a function then NUM is a function too
NUM a transducer defines an automaton whose labels are the pairs input output this automaton is assumed to be deterministic
line NUM of the algorithm builds the first state and instantiates it with the pair lcb NUM e rcb
lines NUM NUM build the transitions from and to the identity states keeping track of where this leads in the original transducer
the algorithm treats each rule as a template of tags and slides it along the input one word at a time
definition a function f on g has bounded variations iff for all k NUM there exists k NUM s t
not claiming that avms determine an agent s behavior or serve as an utterance s semantic representation
the tbm consists of a set of tpcs
however only a NUM NUM error reduction rate is observed in the test set from NUM NUM to NUM NUM
a referring expression should help a reader to identify an object from a pool of candidates this section presents a classification of the possible forms with which mathematicians refer to conclusions previously proved called reasons or to methods of inference available in a domain
a piece of argumentative text such as the proof of a mathematical theorem can be viewed as a sequence much of this research was carried out while the author was at dept of cs univ of the saarland supported by dfg german research council
lcb tan l dd k NUM NUM l d NUM otherwise
unlike other bitext mapping algorithms simr allows crossing correspondences to account for word order differences
therefore we use only two values to denote the level of focus of individual intermediate conclusions which is calculated from textual distance between the last mentioning of a reason and the current sentence where the reason is referred to
note that the result of applying rule NUM and rule NUM depends on the availability of an implicit form which often interacts with the verbalization of the rest of a pca in particular with that of the inference method
below are the three reference forms identified by the author which are analogous to the corresponding cases for reasons NUM the explicit form this is the case where a writer may decide to indicate explicitly which inference rule he is using
the input proof in figure NUM is an nd style proof for the following theorem2 theorem let f be a group and u a subgroup of f if i and lv are unit elements of f and u respectively then NUM 1u
if a reason for instance is last mentioned or proved in the active attentional space the subproof which a reader is supposed to concentrate on it is likely that this reason still remains in his focus of attention
complete link outside probability atr a this is the probability of producing the words before i and after j of a sente ace while complete link i j
NUM the noun hqp the feminine possessive suffix h n qpn her perimeter
as we have pointed out already because of technical reasons we have not been able to apply the morphological analyzer to the words in the sw sets and thus we have not been able to automatically observe that a given similar word is ambiguous by itself
however it fails to reject semantically unacceptable dependency structures
the average number of dependency structures per sentence is NUM NUM
NUM search cod for the particle nmdificant subpattern in the corresponding positions
rdg is designed to determine dependency relations among words and phrases in sentences
if there is no entry search for the modificant only
with the particle modificant sub pattern in the co occurrence dictionary cod
the next figure shows the result for the first example sentence
the method we used selected the correct structures for NUM sentences
the dependency structure with highest probability of being correct is the one with the highest score
our approach automatically extracts the occurrences from the dictionary as well as builds the taxonomic hierarchy
after the parameters are estimated and tied through the tying procedure the robust learning algorithm is applied on the tied parameters
given a text t with n words wl wn for each morphologically ambiguous word wi e t with k analyses a1 ak there is one analysis NUM af e lcb a1 ak rcb that is the one
in addition it reduces the large number of parameters and thus greatly eases the memory constraints for implementing the system
in this paper non constituent objects complete llnk and complete sequence are defined as basic units of dependency structure and the probabilities of them are reestimated
for reestimation of dependency probabilities of pdg eight kinds of chart entries are defined based on three factors inside outside complete link complete sequence and leftward rightward
a is the sum of all the probabilities that sl is to become a subentry of larger entries st l and lt
the need to manually tag the texts used for evaluation limited the number of words in the test texts we used
efficient implementations of the table and entry manipulation procedures would be specialized for the particular types of arguments and results used by the unmemoized procedures
yet as norvig notes in passing using his approach the resulting parsers in general fail to terminate on left recursive grammars even with memoization
if a nominal anaphor n is the first mention in a sentence then a full description is preferred otherwise if n is within a sentence and has been mentioned previously in the same sentence without distracting elements then a reduced description is preferred otherwise a full description is preferred
these results have to be interpreted considering that the focus of the experiment is on selectional restrictions which of course is just one among the various kinds of information occurring during lexical discrimination
other cases such as japan honshu ex NUM u s congress ex NUM requires splitting between the same two characters
instead we aim at segmenting texts into short words of one to three characters long that function like english content terms
we used our segment hypothesizing scheme for scoring an n best list corresponding to these lattices n NUM
while the initial context was provided for the n best lists we had to throw away the final segment boundary
rule NUM if an entity e in the current clause was referred to in the immediately preceding clause does not violate any syntactic constraint on zero anaphora is not at the beginning of a discourse segment and is salient then a zero anaphor is used for e otherwise a nonzero anaphor is used
yeah row NUM occurs almost exclusively in the npc set which is comprised mainly of replies
this record which is called the knowledge state will be part of dyd s context model
metrical structure determines which leaves of the tree are most suitable to carry an accent on syntactic grounds
this record will be called the discourse model which is also a part of the context model
the discourse model presents itself as a natural candidate to implement this idea since it contains all the relevant information
this is a useful exercise which leads to a better understanding of the peculiarities of linguistic context
de stressing and pronominalization occur in roughly the same environments namely those in which an expression contains given information
a template can be used in principle if there is enough information in the database to fill its slots
these definitions are combined with a version of focus accent theory to determine the exact word at which the accent must land
the basic idea is to apply clustering analysis to find out a number of groups of s m ar brackets in the corpus and then to sign each group with a same nonterminal label
from the internal semantic structure of the idiom encoded in piiraseo lex as shown above and translated into the a drs
drs NUM shows the result of processing the in this case senseless literal reading of sentence NUM without any idiom handling procedures a drs NUM represents a non compositional solution after analyzing the structure syntactically the literal meaning of the multi word lexeme jmdm
eille liigenges hieltte erzithlen lit tell a tall tale to sb lilt ix evident for tim t m al hrase and the idiom to hnve at least the same syntactic stru ture as showll ill the next table
eq pull sb s leg spin sb a yarn in the following examples sew ral modifications 2since a high degree of language competence is necessary when judging about grammaticality of idiom constructions we as german native speakers choose german idioms as examples
inct dible z tall tale z tcll to x y z NUM xyzuvw kim x incredible z tall talc z tcll x y z v 2g t z bdicve w v u
instead of logical clmlses a s b x aufbindcn x y z or bdiiqen x y we present the sentence meaning with bear x tie on x y z or lic to x y
a french decomposable i liom is lever u n li n e lit raise a hm e fig touch a lelieate subjeet p cndcre una cantonata lit take a cor iler incmling to make a mistake is it italian one
case nominativeagrm number singular kperson two stem sehieben vpll3 head stem bdegck vple31 NUM rest nil fcase nominative number singular null agrm
whe n parsing a sentence where a part of a non compositional idiom is modified the corresponding rules fail t e ause no discourse referent can be found this modification mw be bound to
we would hke to thank our six jurors not only for hawng accepted to take pan m thts experiment but also for thetr comments on certain aspects of the abstracts
dialogue management systems particularly those which replace a graphical user interface with a spoken language one have become increasingly popular
certain sentences that apparently escape this generafization will be discussed in the next section
known first names and contextual clues such as known occupations like president analyst etc
the system generates claim texts from the input specified partly by the stored conceptual text schemata and partly by the input from the user
the first segment is realized in a standard fashion the predicate is always realized as the present participle and no pronominalization or ellipsis occurs
this activity is based on the expectation that the exposition in a patent claim is one coherent entity without a possibility of unconnected threads
these frequencies are marked in the system s dictionary only for verbs and take the form of the verb s rank in its semantic class
traditional approaches to quantifier scope typically need stipulation to exclude readings that are unavailable to human understanders
among the remaining five that the uvc allows which in fact does not appear to be available
the next three items will be left for further research
table NUM shows the quantitative facts that underly our description
in the last section we will describe future research
the concatenated phrases exhibit differences in speech tempo and loudness
if a misinterpretation is detected the system will first start a correction sequence
after each line an acknowledgement or a short repair sequence may follow
table NUM the amount of utterances in each turn of
table NUM the place of the repair sequences in the
our work is also inspired by other related research projects
in the next section we will discuss related work
instead it provides lists of similar systems
all of wrap up s decision s are handled by trained decision trees so discrepancies in the incoming data are managed on the basis of simila r situations encountered during training
badger also relies on a p o s dictionary as well as semantic feature tags and we do customize a p o s dictionary and a semantic feature hierarchy for specific domains
our preparations for muc NUM began on june NUM at the release of the call for participation and ended on octobe r NUM when we began our test runs
template entities te in moving from ne to te we add badger s processing and a trainable cn dictionary to support badger s case frame instantiations
these problems do not reflect any essential weakness on the part of crystal s learning algorithm they merely illustrate the importance of adequate training materials for machine learning algorithms
since resolv e was already handicapped with respect to potential recall focusing only on person and organization references w e decided to use unpruned trees in the final evaluation
the resulting rip scores are as follows with p1 th e precision for the type as reported by the scoring program and p2 the precision for the individual specialists
to see how each specialist was performing individually we broke down the actual column of the scor e report into three columns one for each of the three specialists
a post mortem o f the official ne test set shows that it contained a large number of government organizations which represented a weak spot in our organization dictionary
a walk with wrap u p with these cns in hand wrap up can then apply its trained d trees to the cns in order to establish relationa l links between objects
by doing such an extensive analysis and representing the results in a database we are able to identify patterns of cue selection and placement in terms of multiple factors including segment structure and semantic relations
it is not difficult to see that in this way each symbol of w is charged at most once
the tutoring system gives the student a troubleshooting problem to solve allows the student to solve the problem with minima tutor interaction and then engages the student in a postproblem critiquing session
the main drawback of these approaches is their requirement of a sizable sense tagged corpus
a word in the testing set need not have occurred in the training set
also different thresholds axe used for different levels of the domain specific hierarchy
in this case all three classes correspond to their first sense in wordnet
l ble NUM word sense dls mhiguation using surrounding nouns
however it is not a descendant of attack y in word net
the definitions of general and specific semantic class disambigttation accuracy are detailed in section NUM NUM
their responses are then compared ag t the tagged answers of the test corpus
the s ic class disambiguation results are compiled and tabulated in table NUM
in particular our ideal metric would be strictly increasing as our thresholds loosened so that every loosening of threshold values would produce a measurable increase in performance
for i NUM let g be the union of the set i g with the set of all d trees NUM that can be produced as follows
the primary resources that are loaded are lexicon ontology gazetteer abbreviation special term list set of lexiccr semantic rule the extraction process csci passes a document through a series of nltoolset functions to perform the extraction
when a d tree a is sister adjoined at a node y in a d tree fl the composed d tree NUM results from the addition to of a as a new leftmost or rightmost sub d tree below NUM
only the new components of NUM that came from a are marked as substitutable in NUM let vl k be the sa trees for NUM NUM k respectively
in contrast since both the subject and the object of to adore have been moved out of the projection of the verb the path to these arguments do not carry any sic at all NUM
the components a NUM a NUM and a NUM of a above a NUM drift up the path in NUM which runs from the substitution node
the resulting phrase structure tree would be the same as in the previously discussed derivation but the derivation structure is linguistically meaningless since to adore world have been subserted into both seems and claims
infarction v is a disease expr person y loc body comp has person y cul time point
where the exact nature of the function f is a problem to be solved
where u n is the smallest observed utterance cost for the given utterance
attempt to complete the goal using the ipsim system possibly invoking voice interactions
it then receives the results of the interaction and appropriately updates its data structures
they then were asked to speak NUM sentences to train the system for coarticulation
the last two problems were repeats of the practice problems from the first session
the grammar used by the parser consists of NUM rules and NUM dictionary entries
they were told they would receive NUM NUM for participating in the three part experiment
this paper presents a single self consistent mechanism capable of achieving simultaneously the above described behavior
each implicit node corresponds to some node in the original trie having only one child
function slow scan p u starting at p scan u symbol by symbol
we also need a function that shifts a links to a new pair of aligned paths
the proof of the linear time result is rather long we only give an outline here
the second author is a member of the center for language and speech processing
the notation we use in the remainder of the paper is briefly introduced here
these functions are briefly introduced here and will be exploited in the next subsection
in our domain calculation of the price of phone calls the system must acquire several variables with sometimes ambiguous values
this problem is in part due to the fact that some of the plans they look at are quite complex and correspondingly difficult to express but it is also attributable to the lack of a detailed corpus study of the linguistic tools used by technical writers in instructional text
in a manner of speaking one could say that the objective is to create a pure portable usable robust extensible system
in the second example the by form would similarly be prescribed by imagene because of the condition that the handset be returned to wall unit from which NUM the distinction between conditions and optional purposes is under the purview of rhetorical status selection and is yet to be addressed
dictionaries likewise often agree among each other on the most central core senses of words but differ in the number and kinds of subtle distinctions
the upper right part shows the intentional structure built by the plan recognizer
it is evident that we can not assume as we did in dop1 that the space of training set subtrees represents the total population of subtrees since this would lead to a zero probability for any unknown subtree
we knew that by translating to c we could get a sizable gain in speed
likewise a response object may be similar to more than on e key object
the format required a different parser than the templates or the low level template objects
as we have seen the set of possible np subtrees of maximal depth three consists of NUM NUM types which is a factor NUM larger than the set of seen np subtrees NUM x NUM
emacslisp does not allow applications programmers to manage memory usage but c does
as basis for storing context information we developed the dialogue sequence memory
among others it is shown that dop3 displays a preference for parses constructed by generalizing over a minimal number of words and that dop3 prefers parses that generalize over open class words to parses that generalize over closed class words
the lisp versions of named entity an d coreference were used for the muc NUM scoring
additional metrics are calculated for named entity template element and scenario template
this left however two important questions unanswered NUM how does dop perform if tested on unedited data and NUM how can dop be used for parsing word strings that contain unknown words
various relations such as entailment or its dual subsumption set inclusion and negation set complement are automatically computed and the intuitively and formally correct results are guaranteed to hold
automated deduction for lambek calculi is of interest in its own right but solution of the parsing problem for categorial logic allowing significant linguistic coverage demands automated deduction for more than just individual calculi
this limitation combines with the other difficulties with groupoid labeling of worst case of even one way associative unification for l and the need for a priori hypothesis of non associative structure for nl
points in v intuitively corresponds to string positions as in definite clause grammars and charts and ordered pairs to the vertices of substrings pertaining to the categories to which they are assigned
the sharing of a skolem constant between a and b in NUM ensures that b can and must be used to prove a so that a mechanism for the lazy splitting of contexts is effected
a ax NUM i.e. an atomic agenda is a consequence of its unit database all program clauses must be used up by the resolution rule
then the final tag seqtlellce is the solution
xi a constraint function or fealure
the definition of mrf is presented as following
t t i and the feature f illtctioll o ii gralll inchiding bigram trigram s
all we know is the expectation value of the flmction
this can significantly improve overall efficiency
if none of the above apply the control rule returns solution
content words introduce concepts and are the means for the expression of ideas and facts for example nouns proper nouns adjectives and so on
this allows constraint eoroutining within a memoized subcomputation
the selected literal s are shown underlined
definition NUM to add an item an item e
figure NUM the lemma table algorithm
solution add e to its table
all remaining errors are our own
it turns out that there are three distinct sources of this variation and they correspond to three different kinds of choices which are made in the generation algorithm
by preprocessing we mean lemmatizing stemming converting upper to lower case etc testing this assumption on her algorithm indeed seems not to change the results
the hand crafted rules are linguistically motivated and tuned to improve precision without sacrificing recall
on the other hand it is shown that additive update algorithms have advantages when the examples are sparse in the feature space another typical characteristics of the ir domain which motivates us to study experimentally an additive update algorithm as well
for supervised training of conditional models the sample space consists of configurations which include features from two non overlapping sets factor features x and behavior features y
thus in the feature lattice we have nodes with non zero configuration frequencies which we call reference nodes and nodes with zero configuration frequencies which we call latent or hidden nodes
we built a model out of NUM most frequent atomic features which gave us the collocation lattice of NUM NUM nodes in NUM minutes of processor time on sun ultra NUM workstation
we can set a certain threshold on the weights so all the candidate nodes whose as differ from NUM less than this threshold will be unconstrained in one go
this can be seen as the union of the observed and logically implied configuration spaces which still usually will be much smaller than the total possible configuration space w
instead of using total possible configuration space w as required for iterative scaling by equation NUM we restrict the configuration space to that actually observed during the lattice building
the sentence number x axis is plotted against the correspondence y axis between the two windows of text on either side of that sentence
those in NUM are based on the scoping choices r c and r s with the partition choice shown in NUM
thus with high likelihood we discard features which have not contributed to many mistakes those that were promoted or demoted at most once possibly with additional promotions and demotions which canceled each other though
the acceptability of each quantifier is as defined by a call to the following prolog goal q inc fmin nc quant
in the array l j we get a situation where in one or node the ct disjunction we need to select the ith branch and in another the NUM disjunction we need to choose the jth branch
with that consider the following logical form john j move e j into e r room r quick e and the packed generation forest representing its various derivations figure NUM
the idea is that in such cases the generator will attempt to find an expression that conveys or honors all the specifications but if such an expression is not admitted by the grammm it would still produce a grammatical result covering the crucial parts of the input
dog d lcb small d i young d rcb we assume that this semantics licenses small dog young dog and puppy but not young puppy or small puppy
admittedly computing a satisfiable assignment to the various propositional variables can be hard exponential complexity in the general case however certain computational properties which are likely to exist independence between sets of variables will tend to make the computation much more efficient
now if our goal is to enumerate the paraphrases corresponding to the first interpretation we satisfy the condition in the second slot small d and dissatisfy the condition in the third slot young d
likewise the top most s node NUM is disjunctive since there are two ways to form a sentence either using the vp of node NUM or the one of node NUM which also expresses the fifth fact about the barking event being loud
in order to simplify the exposition we choose to represent the packed generation forest as an and 0r tree in which or nodes represent equivalent alternations and and nodes represent combination of daughters into larger constituents or nodes are distinguished by the little arcs between their branches
automatically acquired class based alignment rules are used to compensate for what is lacking in a bilingual dictionary such as the english chinese version of the longman dictionary of contemporary english lecdoce
third what distinguishes a topic from a subject is that the subject must always have a direct syntactic and semantic relation with the verb but the topic does not need to
the thesaurus provides classification that can be used to generalize the empirical knowledge gleaned sue j ker and jason s chang word alignment from a corpus
first a significant majority of words have diversified translations that are not found in a bilingual dictionary or statistically derived lexicon but that are largely bounded within the word classes in thesauri
as shown in table NUM over NUM of the source words in both test sets are connected to a target and over NUM of the connections are true ones
consider the following logical form dog d plural d big d bark e d loud e and the chart forest that would be constructed from it by the generation algorithm in this drawing the branches of the or nodes are labeled with propositional variables and below each edge is the array that indicates its coverage
given this semantics as its input the generator creates nominal edges with indices d and c as a realization of dogs d young d and cats c young c respectively and verbal edges with index e as a realization of chase e d c
at a less esoteric level annotations can be used to record the overall structure of documents including in particular documents which have structured headers as is shown in our third example6 NUM lncounting characters count one character for the newline between lines
here is a simple example based on the mini muc organization template more elaborate template examples are given in section NUM type package organizations annotation type organization lcb org name org aliases org type org location annotation type typed location lcb location type
when it is necessary to distinguish among two or more detectionneeds for example when they are stored in an ascii file the detection need sgml tag indicates the beginning of a detectionneed and the detection need sgml tag indicates the end of the detectionneed
typically this involves the creation of a term index but it may also involve the gathering of various statistics about the set of documents such as term document frequencies term co occurrence frequencies and even term similarities based on cooccurrence
for example an event might be described at the beginning of an article and again later in the article but not in the intervening text using a set of spans allows us to have an annotation for the event refer to these two passages
all of these linguistic annotations would be optional the architecture would be used to establish standards whereby people who want to generate or use these annotations could communicate but except possibly for name recognition this would not obligate anyone to produce these annotations
if an application system involves extractions for multiple scenarios multiple classes of events it will be necessary to distinguish the annotations corresponding to different extraction scenarios so that for example one can display all the annotations for one scenario
the additional argument the first of the two arguments is of enumerated type one of lcb string sequence collectionreference documentreference annotationreference attributereference rcb and specifies the type of the second argument which is the value itself
clientdata is optional user data for the monitorprogress operation monitorprogress monitor dciname string status integer maxstatus integer type one of lcb numdocs time percent rcb boolean dciname is the name of the documentcollectionlndex which is being monitored
length bytesequence integer returns the number of bytes in bytesequence converttostring bytesequence string createbytesequence string bytesequence in fact the simplest implementation of a bytesequence will probably be as a string so the conversion will be an identity operation
another way of viewing this would be to see the actual compilation step as being much simpler just check every possible feature and to subsequently apply program transformation techniques some sophisticated form of partial evaluation
it means that a structure of type append c is well formed if it unifies with the argument of the head of the above clause and whatever is under arg2 and ar g3 is a well formed list
relational level a simple picture there are three characteristics of hpsgii theories which we need to model on the relational level one needs to be able to NUM express constraints on any kind of object NUM use the hierarchical structure of the type hierarchy to organize the constraints and NUM check any structure for consistency with the theory
for each type we compute a unary relation that we just give the same name as the type
NUM let t be the type on the current node and x its tag a variable
if t is not a constrained type and subsumes a type to that has a feature f appropriate s t
since the value of the feature hd may be of a type which is constrained by the grammar
our experiments show that by utilizing the constant increment with counter adjustment method in determining the basic probability assignments for each cue the system can correctly predict the task and dialogue initiative holders NUM NUM and NUM NUM of the time respectively in the trains91 corpus compared to NUM NUM and NUM NUM without the use of cues
a hpsg grammar consists of two components the declaration of the structure of the domain of linguistic objects in a signature consisting of the type hierarchy and the appropriateness conditions and the formulation of constraints on that domain
in addition to experimenting with different adjustment methods we also varied the increment constant a for each adjustment method we ran NUM training sessions with a ranging from NUM NUM to NUM NUM incrementing by NUM NUM between each session and evaluated the system based on its accuracy in predicting the initiative holders for each turn
the dempster shafer theory is a mathematical theory for reasoning under uncertainty which operates over a set of possible outcomes o associated with each piece of evidence that may provide support for the possible outcomes is a basic probability assignment bpa a function that represents the impact of the piece of evidence on the subsets of o
weighted constituent precision i.e. the percentage of incorrectly identified syntactic constituents
out of nearly NUM NUM sentences i NUM NUM words we extracted NUM NUM sentences i NUM NUM words as possible material source for traiuing a grammar and NUM sentences NUM NUM words as source for testing
in addition to evaluating the taggers annotations against those of the experts we examined the degree of inter tagger agreement which would shed some light on the representation of meanings in the lexicons of novice taggers unpracticed at drawing a large number of fine grained sense distinctions and their ability to deal with potentially overlapping and redundant entries in wordnet
for instance although the dialogue initiative is distributed approximately NUM NUM between the two agents in the trains91 corpus and NUM in the airline dialogues the prediction rates in row NUM shows that in both cases the distribution is the result of shifts in dialogue initiative in approximately NUM of the dialogue turns
algorithms follow naturally as a consequence of the representational features
goals concerned with getting things done in the world the transactional goals may range from getting a snack prepared to your liking through planning an outing to gaining a qualification
3b a you ca n t take nlp because you have n t taken ai which is a prerequisite for nlp 3c a you ca n t take nlp because you have n t taken ai which is a prerequisite for nlp you should take distributed programming to satisfy your requirement and sign up as a listener for ni
signed to facilitate natural expression of conditions for reducing ambiguities
NUM set start e to start e
into definite clauses after this intuitive introduction to the problem we will now show how to automatically generate definite clause programs from a set of type definitions in a way that avoids the problems mentioned for the simple picture
if in the frequency condition there was a significant tendency to chose the first sense which was usually also the most inclusive general one it would indicate that the taggers adopted a safe strategy in picking the core sense rather than to continue searching for more subtle distinctions
aa disjunction of the immediate subtypes of t compared to the hierarchy relation of a type which collects all constraints on the type and its subtypes the last kind of relation additionally references those constraints which are inherited from a supertype
we therefore use an extension of this parsing approach that permits partial parsing
up to this point all we are doing is standard earley parsing
discrete dictionary senses could be particularly iu suited to usages where core senses have been extended beyond what the dictionary definitions cover and where taggers must abstract from a creative usage to a more general inclusive sense
in some cases homonymy the division between different senses seems fairly clear and agreed upon among different lexicographers while for others it is not at all obvious how many senses should be distinguished
today it is easy to obtain a 10k 100k word list from either commercial or public domain on line japanese dictionaries
initial word frequencies are estimated by counting all possible longest match strings between the training text and the word list
for example in the manually segmented corpus we found the string
for instance murayama murayama can be a person s last name or a city name and foodo 2yoshio eriguchi and tsuyoshi kitanl personal communication ford can be a person s last name or company name
the mixed sequence of ascii and juman tokens is then input into the sgml handler which recognizes the document structure based on sgml tags and outputs a fastus document object with slots for the headline text and other sgml fields
mimi was also grasper based but its input was ascii character romaji with spaces between words and it had a NUM NUM word dictionary in the domain of conference room scheduling NUM NUM NUM
for example in zidousya seizou gaisya no papiyon papillon an automaker the word papiyon papillon may be unknown but the immediate linguistic context makes it a company name
note in NUM that the lambda terms of assumptions are written below their indexed types simply to help the proof fit in the column
upon the context in which this collocation appears
with a robust working prototype system in hand we are encouraged to look for new interesting results
on the other hand neither can we use the very top concept everything is a thing
clearly we can not just use the leaf concepts since at this level we have gained no power from generalization
in order to choose an adequate wavefront with appropriate generalization we introduce the parameter starting depth l
we ran the algorithm on NUM texts and for each text extracted eight sentences containing the most interesting concepts
we scored how many sentences were selected by both the system and the professional abstracter
the closer these two measures are to unity the better the algorithm s performance
for each text we obtained a professional s abstract from an online service
using a hierarchy the question is now how to find the most appropriate generalization
NUM a baseline was established in which all variants of a word were present in the query regardless of part of speech variation the baseline did not include any morphological variants of the query words because we wanted to test the interaction between morphology and part of speech in a separate experiment
the current corpus size is NUM mb
this approach requires less computational load than the one developed in della pietra et al NUM at a price of being not yet suitable for building models with a very large hundreds of thousands set of parameters
when this way we add atomic features to the optimized lattice some of the features raight turn out not to contribute or contribute only on a very small scale to the probability distribution on the lattice
the sources of complexity uncovered here are thus a forteriori present in all these richer systems as well
m n s be an arbitrary NUM partition problem and gr the corresponding sdl grammar as defined above
we have defined a variant of lambek s original calculus of types that allows abstracted over categories to freely permute
the korean english bitexts were provided and hand aligned by young suk lee of mit s lincoln laboratories
a remarkable point about sdl s ability to cover this language is that neither l nor lp can generate it
discharging the hypothesis indicated by index NUM results in bill misses being analyzed as an s np from zero hypotheses
we show that the parsing problem for semidireetional lambek grammar is np complete by a reduction of the NUM partition problem
we show that the parsing problem for sdl grammars is np complete by a reduction of the NUM partition problem to it
louella s ne system was developed using the spotters from the nltoolset
in fact many of the automata in these entries had the same structure and are independent of the atis domain
we have also argued that allowing multiple target positions for transitions increases the flexibility of transducers without an adverse effect on efficiency
this approach allows the most basic elements of the scenario to be identified first
stop actions are not shown though states allowing stop actions are shown as double circles the usual convention for final states
performance on this message reveals two areas in which our system can be improved
the nodes in the figure correspond to states a bilingual lexical entry would specify q0 as the initial state in this case
in that case a separate estimation phase is needed to automatically determine the values of the thresholds
reduce the number of states and transitions they have by allowing multiple target positions to the left and right of the head
louella s strategy is to repeatedly simplify the text before information extraction take s place
the algorithm maintains optimal active edges spanning a segment of the input string or two states in the recognition word lattice
the template generator uses a heuristic to choose the descriptor which is most likely correct
a fourth category might be added to cater for those systems that provide communication and control infrastructure without addressing the text specific needs of nlp e.g.
gate promotes reuse of component technology permits specialisation and collaboration in large scale projects and allows for the comparison and evaluation of alternative technologies
this is a recursive process in which the dependency relations for corresponding nodes in the two trees are derived by a head transducer
a head transducer m is a finite state machine associated with a pair of words a source word w and a target word v
thus the subject s back was to the experimenter
NUM table NUM summarizes the results of the statistical analysis
figure NUM provides a rough sketch of the room layout
figure NUM an iterative search algorithm
consequently the selected goal must be a relevant fact
NUM c glad to have been of assistance
NUM c i am familiar with that circuit
the remaining meaning can be written down more succinctly
the first two columns can be coded
fortunately simple approximations of the change are adequate
figure NUM sections of the lexicon learned from the
the composition and perturbation encompasses this application neatly
common irregular forms are compiled out
the user controls the dialogue but still requires computer assistance
to the final stage of the dialogue almost every time
the scope among them is underspecified again
figure NUM a relation with auaphoric antecedent
figure NUM two relations with anaphoric force
this predicts a narrower scope than that of the subordinate relation noda
actually there seems to be only one
we will be concentrated on the latter problem in the following sections
the mode predicate can be seen as a secondary sentence mood predicate
in figure NUM we give pseudocode for the learning algorithm in the case where there is only one transformation template change the tag from x to y if the previous tag is z
NUM vn livre fpsch ennuy irrit an angry bored irritated book the ones in NUM will have the head on the agentive and will receive only a causative sense
in NUM the qualia structure specifies that emotional states are caused by a causal event and can have a further manifestation in NUM that the agent oriented state can have a fnrther manifestation
they exhibit different senses depending on the semantic type of the item modified when they predicate of an individual they normally denote the mental state of this individual NUM but see example NUM
form that means yes however that is expressed
a query w is any query not covered by the other categories
an instruct move commands the partner to carry out an action
where actions are observable the expected response could be performance of
when the event structure is headed on one of these the adjective is projected via the agentive or the relic role i.e. the template p e2 z or p e3 z
f do you want it to go below the carpenter
f so you want me to go above the carpenter
however if the system has access to a predefined set of classes of words and if car and bus are in the same class and house and apartme nt
the merit of the second type for the purpose of constructing hierarchical clustering is that we can easily convert the history of the merging process to a tree structured representation of the vocabulary
NUM if initially the word to is not reliably tagged everywhere in the corpus with its proper tag or not tagged at all then this cue will be unreliable
with this algorithm the time complexity becomes o c2v which is practical for a workstation with v in the order of NUM NUM o and c up to NUM NUM
since a is in the training data we know that the prepositional phrase by car is attached to the main verb went not to the noun phrase the house
here h is the entropy of the NUM gram word distribution and i is the average mutual information ami of adjacent classes in the text and is given by equation NUM
NUM inner clustering let lcb c NUM c NUM c c rcb be the set of the classes obtained at step NUM
then as an attempt to combine the two types of clustering methods discussed in section NUM we performed an experiment for incorporating a word reshuffling process into the word bits construction process
in the test phase the system looks up conditional probability distributions of tags for each word in the test text and chooses the most probable tag sequences using beam search
suppose for example that we have two sets of clusters one is finer than the other and that word NUM and word NUM are in different finer classes
we thank the audiences at edinburgh and pennsylvania for their useful comments
the descriptive observation has been made that when nominalized forms of a verb exist in the lexicon they tend to be used
the early indications are that at the very least this integration can significantly increase the productivity of the corpus annotator
an interface to alembic s phrase rule learning system for generating new application specific rule sets
bootstrap method with NUM document training set NUM awb rule learning bootstrap method with NUM document trainin set
once a text has been marked up the user s annotations are highlighted in colors specified by the user
the alembic phrase rule interpreter provides the basis for developing rule based pre tagging heuristics in the workbench
we anticipate providing an api for integrating other nlp systems in the near future
this tool also measures the degree to which a word occurs in different markup contexts
for an overview and history of muc6 and the named entity task
as the human annotator continues generating reliable training data she may at convenient intervals reinvoke the learning process
initial experiments indicate an significant improvement in the rate at which annotated corpora can be generated using the alembic workbench methodology
spelling correction is based on a three way match algorithm which slides a small window simultaneously across both the unknown input word and a candidate word from the lexicon
for example the entry for uncle is not a descendant of the entry for man although an uncle is clearly a type of man
the evidence for uncle is considered more plausible than that for end because both senses of uncle in wordnet have the entry person among their ancestors
unfortunately because of the nature of the training data used by the noun phrase detector bare hyphen s cause serious noun phrase detection errors
first because the system was built using many small tools the number of files grew to be quite large nearly NUM per article
it is trivial to add new patterns to the system since the parser has effectively abstracted away many of the complications of the surface text
the parser was used to spot syntactic patterns which signaled coreference of noun phrases withi n sentences such as appositive relations and predicate nominative constructions
basal noun phrase detection to identify noun phrases the system uses lance ramshaw and mitch marcus basal noun phrase detecto r NUM
matching the f structure functional representation of the student s utterance to the logic form of the tutor s question is largely performed by ad hoc code
i end of sentence detector i tokenizationi iadwait s taggeri eric s tagger1 ix tag tagger tag voting noun phrase detector i inamed entity tool
processing input files in groups allowed the overhead for loading dictionaries and statistical models to be reduce d because it could be averaged over many articles
since null is the most frequent value for all the fields this is equivalent to using a naive algorithm that selects the most frequent value for a given field
after the last word is generated the last state of layer i should be reached
each class has a set of distinguishing terms which are those individual terms which occur more often in the class than in other classes and which can be used to distinguish the class from the other classes
we would note that every time a troponymy relation between two verbs holds an isa rela
moreover the computation of outside probabilities can be made only on the valid parse space once a chart is prepared
for a boundary case of the outside probability where f is the first state of a layer in the above equation
the internal structures of the examples above are respectively 2a pn type i type NUM posthn noun postp 2b pn type NUM type iii posthn postp 2c pn type if posthn postp hence by providing information about the combinations of these strings we could rise the accuracy in recognizing pns
for a girl the nouns above can all be used as fts that is a term one can use to indicate some social or familial relations between himself i.e. the speaker and his interlocutor s or to call on somebody paying due respect to his social status honorific terms
the multinomial distribution method works best if the distingalishlng terms for each class are more lilfely to be in the class than in another class so the method which worked best was to choose the words which occur more often in one list than in all other lists combined until the sum of the probabilities of the chosen words was at least NUM
the structure can be formalized in the following graph figure NUM figure NUM type iii of norm phrases containing pns the strings n gen e fr do not automatically guarantee existence of proper names since common nouns that have a human feather can also appear with a fr like
semantically pts designate professions the list of which we can determine a priori while its are more vague and non predictable without examining pragmatic situations the latter are closer to the nouns of family relation fr since as mentioned above they imply familial or social relations between a speaker and his interlocutor s
besides ssi strings with two pts daitonglyeng the president and susa g the prime minister we could recognize NUM of pns that is NUM occurrences of NUM i.e.
on the contrary some proper nouns such as baudelaire or napoleon can be used as well as common nouns in contexts where they occur in metonymic or metaphorical relations with common nouns like i read some baudelaire poems of baudelaire he is a real napoleon general moreover they often allow like common nouns the derivation of adjectives e.g.
the computation of an inside probability may be improved further using a similar technique introduced in this paper
the following methods were used to determine the distingui qhing terms calculate the weights associated with those terms and to compare documents to the distingulqhing terms to get class scores and classification and routing determinations
after experimenting with the distinguishing term selection methods it was found that using the most frequent NUM words which were not the most frequent NUM words in any other class worked best for the ff idf method
figure NUM shows that in the vast majority of cases our prediction methods yield better results than making predictions without cues
furthermore as will be shown in table NUM section NUM the task and dialogue initiative distributions in trains91 are not at all representative of collaborative dialogues
we argue that this view of initiative fails to distinguish between task initiative and dialogue initiative which together determine when and how an agent will address an issue
for each a we cross validated the results by applying the training algorithm to seven dialogue sets and testing the resulting bpa s on the remaining set
this is because such dialogues are constrained by the goals therefore there are fewer digressions and offers of unsolicited opinion as compared to the switchboard corpus
for instance the utterance any suggestions indicates the speaker s intention for the hearer to take over both the task and dialogue initiatives
we analyzed our annotated trains91 corpus and identified additional cues that may have contributed to the shift or lack of shift in task dialogue initiatives during the interactions
our model predicts the initiative holders in the next dialogue turn based on the current initiative holders and the effect that observed cues have on changing them
NUM example NUM such rules of course handle only those forms that constitute the set of assimilated or partially assimilated loanwords
that is if allophonic rules can be done in one pass here we include them along with rules that output phonemes
spanish for example is a simple system in that there is an almost iconic relationship between graphemes and their phonemic equivalent
in fact even lexical stress is marked in many forms and where it is not it is almost always predictable
text normalization i.e. replacing numbers abbreviations and acronyms by their full text equivalents is done in a preprocessing section
other similar uses are under investigation for the pronunciation of names from on line telephone books in particular and telecommunications applications in general alcatel
it can be done in the first syllable pesanteur retard teneur except if there are two consonants as in premier
generic stress rules in this module assign primary stress if and only if NUM stress has not yet been assigned
the same formalism could be used for both english and french with a slight modification for instance of the french formalism
for example the oregon graduate institute is currently investigating letter to sound rules done in more traditional ways and comparing them to neural network learning
the propensity of the system to verify can be adjusted so as to pro null vide any required level of speech understanding accuracy
a simple definite clause specification of the head corner parser is given in figure NUM the predicate visible to the rest of the world will be the predicate parse NUM
a head corner parser for a grammar in which for each rule the left most daughter is considered to be the head will effectively function as a left corner parser
that is the goal is to only verify utterances that need verifying and to verify as many of these as possible
moreover there are unary rules such as max gem np sem for np s pp advp
the rule name is always the name of a rule without daughters i.e. a lexical entry or a gap the lexical head
in this case the administration of the goal table can be simplified considerably the table consists of ground facts hence no subsumption checks are required
another possibility is to use the linking table only as a check but not as a source of information by encapsulating the call within a double negation
in the first step the head corner table is weakened such that for a given goal category and a given head category at most a single matching clause exists
the definition of the smaller equal predicate therefore reflects the possibility that a string position is a variable in which case calls to this predicate should succeed
NUM she always abbreviates a very annoying habit
NUM school where their delicate transformation began
as a group three year ohl children hit
these arc divided into nadvp time nadvp dir np nadvp loc and nadvp manner
the problem is to forecast how to find the light
coerced into being nunits in this structure NUM the
figure NUM partial comlex syntax dictionary entry for adjust
all in all our tagging has been interesting and informative
with each other examples not from the corpus
tags l hat were not deemed worthy to become comlex complements for various reasons e.g.
main expectation NUM completion acknowledgement e.g. okay desired property now exists e.g. the switch is up
t NUM i c is the one on the led displaying for a longer period of time
this paper presents paradise paradigm for dialogue system evaluation a general framework for evaluating spoken dialogue agents
section NUM describes paradise s performance model and section NUM discusses its generality before concluding in section NUM
depart city dc arrival city ac depart range dr depart time dt request type r r
and the combination of and rep account for NUM of the variance in us the external validation criterion
the chances of NUM or NUM false points of correspondence satisfying the maximum point dispersal maximum angle deviation and maximum point ambiguity level thresholds are negligible
a cognate based matching predicate will generate more points for more similar language pairs and for text genres where more word borrowing occurs such as technical texts
if the languages involved have similar alphabets then it may be possible to construct a matching predicate with very little effort using the method of cognates
this means that the ambiguity level of a given point can increase as the search rectangle expands the set of points that simr ignores can change dynamically
if this confidence level is sufficiently high gsa accepts the length based re alignment otherwise the alignment indicated by simr s points of correspondence is retained
a tag grammar consists of a finite set of elementary trees which can be combined by these substitution and adjoining operations to produce derived trees recognized by the grammar
for uniformity with auxiliary verbs we represent it as a separate tree in this case with a null head which assigns a morphological feature to the main verb
sorry i did n t think of that question how about you specific feedback that s really interesting these context sensitive comments like the quick fire phrases helped with speed maintenance of flow and having a share of the control of the conversation
chafe points out that the format of a message is only partially related with the content of the message information packaging has to do primarily with how the message is sent and only secondarily with the message itself just as the packaging of toothpaste can affect sales in partial independence to the quality of the toothpaste inside
we then identified words that were related in spite of a difference in part of speech this was based on the data that was produced by tagging the dictionary see section NUM NUM NUM
the more evident problem with wordnet is that it is a lexical knowledge base for english and so it is not usable for other languages
the filtering track represents a variation of the current routing track
when another is speaking the conversationalist needs a quickly available supply of feedback remarks to express their reactions
an egraph contains three components an extracted concept structural elements attached to the original regions of text and semantic labels attached to the structural elements as illustrated in figure5
experimental results demonstrated the speed of customization the relationship between the number of examples an d performance the predicted potential performance and performance on just the core scenario event
for the muc NUM scenario template task the analyzer first extracted person concepts then organization concepts then management post concepts then succession events
extraction by example the key module of hasten is the analyzer which matches the extraction examples to incoming text an d decides what to extract
nametag tagged goldman within kevin goldman as an organization since that is a company alias in its static list of names
sra submitted four official configurations the base configuration the two speed configurations and the configuration without the use of personal and organizational names
the extractor compares the similarity values of all extraction examples selects the most similar example tha t exceeds the threshold and then converts the maximal annotated sentence into a semantic representation
the major characteristics of this representation are that it is straight forward to create conducive to automated learning easy to compare to each other and easy to match against text
the five point drop in recall for the base configuration does demonstrate that structural differences in example s may interfere with the extraction of semantic content
since the ultimate goal of hasten is to minimize the customization effort hasten must strive to maximize its performance from as few examples as possible
replace expressions denote regular relations defined in terms of other regular expression operators
our system takes two types of goals
NUM NUM simple regular expressions the replacement operators are defined by means of regular expressions
transitions that differ only with respect to the label are collapsed into a single multiply labeled arc
x lower possibly alternating with strings not containing upper that are mapped to themselves
two level rules our definition of replacement also has a close connection to two level rules
the difference is in the intelpretation of etween left and right
the right bracket marks the beginning of a complete right context
within this region replacement operates just as it does in the unconditional case
the trees of figure NUM respect this generalization
these tur frames in turn organize many smaller frames that describe the organizations people places and activities involved in the joint venture
analysis of consistency of annotation by depth in tree from the above discussion we can see that alignment of maximal trees approximates NUM while that for terminals approximates NUM
a backup mechanism could then be provided which attempted a slower but more complete direct application of the rules for the rarer cases
we tried another experiment in which we selected the shogun turs entire joint venture descriptions having the highest percentages of individual frames that matched the key
in particular parsing requires a powerful mechanism for lexical discrimination in order to select the appropriate lexical readings for each word in the input sentence
example NUM taken from the instructions for a household smoke alarm shows the enabling action appearil g tirst closing the cover enables testing to take place but does not automatically result in a test
in section NUM we give a brief definition of generation and enablement before going on in section NUM to describe how the two relations are realized in the corpus of portuguese english and french instructions
as was the case for generation french enablement shows a strong ordering preference when an imperative is used as enabled it must be placed second if expressing generated it must be placed first
as in portuguese though both rhetorical relations are clearly marked and there is a similar mthough less marked tendency to view the semantic content of the enablement relation as being one of temporal sequence
puiu osf is the only relation that is expressible in both ed first and ing first order in fact it is only infinitives and for with a nominal that can appear either before or after their main chmse tdeg
the two actions must be performed or perceived to be performed by the same human agent and the two actions must be asymmetric i.e. if a generates fl then fl can not generate a
since our notion of semantic content is based on a formal model of the task plan to be conveyed to the instruction user the significance of the approach is clear for developing natural language generation applications within this limited domain
a satisfactory level of congruence requires the use of syntactic and pragmatic rules appropriate to each target language mat ping fi om the semantics to appropriate expression in a way that is frec from influence from any source language NUM
not have a meronymy link to wall whereas building does
table NUM results for the category based approach total NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
a possible adjustment to the decision tree approach to capture some of these generalizations would be to augment the decision tree with information about the features of the output segment or about features of more distant phones perhaps about nearby syllables
we still have to look in more detail at compound nouns
the semantics of the generic subsumption is that every instance of a subconcept is also an instance of its superconcept otherwise the subsumption is not justified
it is not necessary that the end user be familiar with the architecture requirements document in order to benefit from it the availability of this document whether or not the end user is aware of it will reduce the chances of misunderstanding or overlooking critical requirements
for example if the markups were in the standard generalized markup language sgml then the syntax and definitions e.g. embedded tags name name mark a name would need to be available in the form of document type definitions dtds
in order for an application or vendor product to successfully acquire a tacad the following conditions must be met for tipster application development the tipster application development complies with the tipster cm process the details which are contained in this document
other documents which describe the architecture and its associated concepts include tipster text phase ii architectural requirements tipster text phase ii architecture design document tipster text phase ii interface control document icd tipster text phase ii configuration management plan
the architecture will facilitate the development of text processing applications and new text processing applications will contribute to the development of the architecture as noted above the tipster icd which defines the architecture specifies inputs and outputs to components and modules
the architecture will assist the developer in communicating with the end user technology transfer officer team because everyone will have the same reference points for conceiving and evaluating the capabilities of tipster modules and everyone will be using the same terminology for those modules
in preparation for these control gates it is expected that the developing contractor and the se cm will work together to prepare the documentation and to identify any discrepancies between the architecture design as detailed in the interface control document icd and the tipster application s design
from may NUM to may NUM the architecture will be tested by use in a number of applications and the lessons learned fed back into the architecture for the purpose of refining those details which have been determined and specifying those interfaces which remain underspecified in may NUM
this document addresses the following subjects the mission and goals of the tipster architecture the concept of the architecture benefits of the architecture for various users inputs and outputs to the architecture maintenance of the architecture
it is the application s responsibility to select the appropriate capabilities to build the user interface component including user commands screen layout and sequence of user operations and to ensure that the output of this component meets the tipster icd specifications
the dnp anaphora with dou and ryou prefixes are characteristic of written but not spoken japanese texts
the nlp system sometimes fails to create discourse markers exactly corresponding to anaphora in texts due to failures of hxical or syntactic processing
another advantage is that unlike the mdr whose features are hand picked the mlrs automatically select and use necessary features
we plan to analyze further the features which the decision tree has used for zero pronouns and compare them with these theories
the features that we employed are common across domains and languages though the feature values may change in different domains or languages
in this approach we tag corpora with discourse information and use them as training examples for a machine learning algorithm
thus if there are possible antecedents in the text which are not in the c b a transitive closure say d c d and b d are negative training examples
we wanted to develop however truly automatically trainable systems hoping to improve resolution performance and reduce the overhead of manually constructing and arranging such discourse data
then we evaluate and compare the results of several variants of the machine learning based approach with those of our existing anaphora resolution system which uses manually designed knowledge sources
it is however checked whether the anchors appear in given competitive recognition hypothesis at compatible positions NUM
even though implementii g tim part el speech tagger and extending the analysis grantiltar to accept parts of speech as terminal strings will increase tile granmmr coverage it is an ahnost impossible task to write a grammar which covers all freely occurring natural language texts let alone haw a re
since the semantic frame uses english as its specification language and is the basis for constructing the target language grammar and lexicon entries in the lexicon contain words and concepts found in the semantic frame expressed in english with corresponding surface realization forms in korean
but with connected speech the system can not easily pinpoint the source of errors and may not provide satisfactory guidance
our model class comparison is based on three criteria of statistical efficiency total codelength bits parameter on the test message and bits order on the test message
it is possible to reduce the test message entropy of the nem and ncm to NUM NUM and NUM NUM respectively by quadrupling the number of model parameters
therefore it is necessary to have a a fine grained model along with a heuristic model selection algorithm to guide the expansion of the model in a principled manner
in this section we compare the statistical efficiency of three model classes context models extension models and fixed length markov processes ie n grams
accordingly the context blish has three positive extensions lcb e i m rcb of which e has by far the greatest probability
although the mdl framework obliges us to propose particular encodings for the model and the data our goal is not to actually encode the data or the model
let mi be the number of contexts that have exactly i extensions ie mi j lcb w je w l i rcb l
considering the inclusion relation between subgraphs they constitute a partial order figure NUM
it contained twenty wires and used a number of components on the board a switch potentiometer light emitting diode led battery and two transistors
the tightest constraint is when anchor distance is NUM as in figure NUM NUM
since they will be unordered in the lattice their ordering in the sequential coding is arbitrary
the number of clusters whose sizes are more than NUM is plotted against the threshold value
the output naturally gives a partial order of clusters which can be compared with conventional thesauri
we defined that a word co occurs with NUM words ahead of the word within a sentence
in NUM dusters there were NUM different words out of NUM in the input graph
we proposed an algorithm to extract subgraphs whose branches are transitive co occurrence relations and discussed its features
a theory of voice dialog systems this paper presents a theory of voice dialog systems that integrates into a single self consistent architecture a variety of capabilities necessary for successful dialog
the wml algorithm is in accord with existing psycholinguisticallymotivated theories of parsing complexity e.g.
despite the many areas for improvement that were identified our system still had the second highest recall measure in organization alias confirrning the basic soundness of our approach
after noun phrase recognition those phrases which have not already been associated with a name are compared against known names in the text in order to fred the correct referent
table NUM selecting pos candidates on the basis of discourse information
in the work described in this paper our goal was to evaluate the contributions of various techniques for associating an entity with three types of information NUM namev atious
the corpus for the muc6 template element task consists of approximately NUM documents for development pre and post dry run and NUM documents for scoring
when the key s descriptive phrases were added directly to the system s knowledge base as a hard coded rule package to eliminate this variable the following scores were produced
breaking this down further our system found NUM of those kxmle fillers which originated in prenominals appositives and post modifiers and NUM of the other NUM
find the three organizations in the following list of phrases of course presentation of the names as a list is unfair to the reader because it eliminates all context cues
if not a content filter for the phrase is run against a content filtered version of each known organization name if there is a match the link is made
first the abbreviation portion of the name should be included within an acronym for example arco as alias for atlantic richfield company and rla as alias for rebuild l a
the following results show that the falter did help the system link the correct descriptors without it the system lost five points of recall and seven points of precision
the procedure for extracting discourse information is as follows NUM
however the resulting unified parses were not always correct
whether the last letter of the lemma is a vowel or a consonant different tables of declensions are also used
two common measures of performance are recall and precision where recall is defined as the percent of words in the hand segmented text identified by the segmentation algorithm and precision is defined as the percentage of words returned by the algorithm that also occurred in the hand segmented text in the same position
NUM how can we build a wfst to perform the se null quence mapping
the precision at different number of documents retrieved a user oriented measure are also comparable in both cases
it combines different probabilistic methods of retrieval that can account for local as well as global term usage evidence
the process is like having a dynamic thesaurus bringing in synonymous or related terms to enrich the raw query
these rules allow us to employ a small lexicon of only NUM NUM entries and provide quite admirable retrieval results
fig lc shows the results of processing the trec NUM query NUM based on these rules after step a
given an input string we scan left to right and perform longest matching when searching on the lexicon
we do not have such a large resource besides maintenance of such a list is not trivial
first coordination accepts a substitution which replaces the noun n3 with a noun phrase d
however in the recent 5th text retrieval conference trec NUM where a fairly large scale chinese ir experiment was performed kwok and grunfeld 199x we have demonstrated that a simple word segmentation method couple with a powerful retrieval algorithm is sufficient to provide quite good retrieval results
this makes it difficult to do machine studies on these languages since isolated words are needed for many purposes such as linguistic analysis machine translation etc automatic methods for correctly isolating words in a sentence a process called word segmentation is therefore an important and necessary first step to be taken before other analysis can begin
we also built a separate word sequence model containing only english first and last names
where fli is the weight for feature i and f is its frequency function that is fi x is the number of times that feature i occurs in configuration x for most purposes a feature can be identified with its frequency function i will not always make a careful distinction between them
in brief the difficulty is that the iis algorithm requires the computation of the expectations under random fields of certain functions in general computing these expectations involves summing over all configurations all possible character sequences in the orthography application which is not possible when the configuration space is large
we might define the distribution q for an av grammar with weight function b as q x z x where z is the normalizing constant xel g in particular for NUM we have z NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
rule NUM instructs us to create two children both labeled a the edge to the first child is labeled NUM and the edge to the second child is labeled NUM the constraint NUM NUM NUM NUM indicates that the NUM child of the NUM child of x is identical to the NUM child of the NUM child of x
the approach that dd l adopt is to assume a consistent prior distribution p k over graph sizes k and a family of random fields qk representing the conditional probability q x i k the probability of a tree is then p k q x i k
fib NUM NUM in parsing we use the probability distribution ql x defined by model m1 to disambiguate the grammar assigns some set of trees lcb xl xn rcb to a sentence or and we computing the probability of a parse tree
we can sample from q by performing stochastic derivations each time we have a choice among rules expanding a category x we choose rule x i with probability fli where fli is the weight of rule x g now we can sample from the initial distribution p0 by performing stochastic derivations
we have just seen for example that the best weights for grammar g1 yield distribution ql yet d ql NUM NUM NUM a closer inspection of the divergence calculation in table NUM reveals that ql is sometimes less than but never greater than
each weight measures theimportance of a term in a natural language expression which can be adocument or a query
we expect to complete an initial prototype implementation of the above methods and have additional preliminary evaluations of their effectiveness by late summer NUM
with this important improvement the algorithm gives exact approximations for the left linear gram null in space bounded by n and time bounded by n NUM it is easiest to test this empirically with an implementation though it is also possible to check the calculations by hand
each module is language independent in the sense that it consists of a general processor that can be loaded with language specific knowledge sources
to aid in such cases we plan on developing a sub domain topic identification and tracking component that will be independent of the semantic grammars
for both parsers segmentation of an input utterance into sdus is achieved in a two stage process partly prior to and partly during parsing
we also tried to build the language model just based on the etd corpus which was smoothed by interpolation with the esst language model
the same statistical measure used to find the most likely sdu boundaries during pre parsing segmentation is used to filter out unlikely segmentations during parse time
in addition to the scheduling domain the janus speech recognizer has also been trained and developed for switchboard a broad domain lvcsr task
in order to assess the overall effectiveness of the translation system we developed a detailed end to end evaluation procedure NUM
the travel domain includes negotiations information seeking instruction giving and dialogues that accompany non linguistic domain actions such as paying and reserving
for example x theory a condition on graphs the case filter an output filter on strings and the NUM criterion a bijection relation on predicates and arguments all fall under the label of principles
the perplexities pp based on different n gram for word and class are presented in table iii
the fact that grammar NUM is larger than grammar NUM with only a slightly smaller average of conflicts confirms the prediction made by the icmh that compiling x theory with categorical information will increase the size of the grammar without decreasing nondeterminism
the qualitative inspection of the tables confirms the clustering of conflicts suggested by table NUM grammar NUM and grammar NUM show the same patterns of conflicts as the lr and ll tables conflicting actions cluster with the bar level of the category
in other respects however this design lends itself readily to extensions the structure building and chain formation routines do not rely on characteristics that are found only in english or in a head initial language as was discussed in the previous section
the former is from the left to the right is the default circumstance mentioned in NUM NUM NUM
secondly precomputation of syntactic features o roles case etc results in efficient computation of chains because it reduces several problems of chain formation to a local computation thus avoiding extensive search of the tree for an antecedent or extensive backtracking
however researchers have been trying hard to find sub optimal strategies which lead to useful classification
first one can notice that adverbs although they are analyzed as maximal projections because they can be modified never take a complement thus they are usually limited to a very short sequence of words and they do not have a recursive structure
mutual information can be explained as the ability of dispelling the uncertainty of information source
in the process of left right the left branch contains more words than the right branch
tags on the other ha nd are known to be mildly context sensitive gra mma rs and they ea n
have no more deseriptiw power than cfg they c an provide considerably better descriptions of the domain of locality than ordinary cfg rules
then t accet ts a context free la nguage cfl de noted by l NUM such tha t
NUM a p has a head constraint h x for some nonterminal symbol xi i NUM NUM h
also no one particular slot identifies an object after several trials of different weightings over the las t few years it was decided that each slot should be given equal emphasis when considering an object s mapping and that the threshold should be set so that getting one slot fill in an object correct is enough for the object to b e considered as a candidate for mapping
l a tterns as fbllows ach noilt rmirull node in a pattern can be a s socia t0d
sectionize the input data consists of three files one file contains the set of source text articles used in the task one file contains the set of human generated answer keys derived from the source articles and one file contains the system generated responses also derived from the source articles
the key and response files are composed either o f annotated text articles as in the case of the named entity and coreference text annotation tasks or of relevant dat a extracted from those articles as in the case of the template element and scenario template tasks
in the event that an object has multiple candidate mappings a key can be possibly mapped to any o f several responses and a response can be possibly mapped to any of several keys that object is eventually mapped to the best remaining unmapped partner
another consequence of this approach is manifested in the score results an object slot may be discredite d because its content does not match the corresponding slot content of the finally aligned object although it does matc h with the corresponding slot of an alternate object
during the scoring phase the scoring tallies for all slot fills are totaled for all similar slot fills for all similar object types within the document as well as for all similar slot fills for all similar object types across all th e documents
the resulting scores are used for decision making over the entire evaluation cycle includin g refinement of the task definition based on interannotator comparisons technology development using training data validating answer keys and benchmarking both system and human capabilities on the test data
however if the two objects were connected then the slots for which both the key and the response provided data will be scored a s incorrect thus reducing the scoring penalty from two missing and spurious to one incorrect
the identification of acting officers is captured in the new status slot rathe r than being represented at a higher level via separate succession event objects
this reference system was created manually from corporate documents and was validated with the help of many experts
when tested against upper case legal text the system still performed very well achieving error rates of NUM NUM and NUM NUM on test data of NUM NUM and NUM NUM punctuation marks respectively
the method was developed specifically for the tageszeitung corpus and hoffmann reports that success in applying her method to other corpora would be dependent on the quality of the available abbreviation lists
these initial trees are converted into right auxiliary trees as specified by lemma NUM the applicability of lemma NUM in this case is guaranteed since after step NUM there are no auxiliary trees no interior nodes and tig prohibits adjunction at frontier nodes
in particular if the cfg does not have any empty rules or sets of mutually left recursive rules involving more than one nonterminal then the size of the ltig created by the procedure of theorem NUM will be smaller than the size of the original cfg
the last rule NUM specifies that the input is recognized if and only if the final chart contains a state of the form s go NUM n where s is the root of an initial tree
this procedure operates in a completely different way from greibach s procedure simultaneously eliminating all leftmost derivation paths of length greater than one rather than shortening derivation paths one step at a time via substitution and eliminating left recursive rules one nonterminal at a time
again since tigs do not treat the roots of initial trees in any special way there is no problem converting any operation applied to the interior node of u that corresponds to the root of t into an operation on the root of t
preposition i article i noun NUM verb NUM pronoun i verb l noun NUM verb NUM
since each of the completion rules requires that the chart states be adjacent in the string each can apply at most o igi2n NUM times since there are at most n NUM possibilities for NUM i j k n
the initial states encode the fact that any valid derivation must start from an initial tree whose root is labeled s the addition of a new state to the chart can trigger the addition of other states as specified by the inference rules in figure NUM
the chain as a whole can be replaced by the simultaneous adjunction of the corresponding trees in t t tl t NUM t m on the root of u with u used in the same way that tm was used
further if there is only one way to derive a given tree in g there is no ambiguity in the mapping from derivations in g to g because there is no ambiguity in the mapping of the t to trees in g
nevertheless the problem of giving semantic categories to the words is very complex and it results difficult to be programmed
in order to make formal sense of the informal notion filling the place of x in a where the notation a means that a contains the free variable x we introduce the variable binding rules of fig NUM
these rules tell t s how to get rid of the free vailable being bound during complement or tnodifier incorporation namely by forming the abstraction xx a before actually performing the semantic composition between tile dependent and tile head
sotllc t t them can be seen as special cases of general type raising principles others such as c5 are necessary it one accepts that the type of intersectivc adjectives and restrictive relative clauses has to be e t
we use the notation d label h the labeled binary tree obtained by taking h as the right subtree d as the left subtree and by labeling the left edge with label
with each node a in tile tree one associates its set of predication edges that is the set pal of edges of the form a i x or x i a
however at tile level of u form this sentence is equivalent to the french sentence claude a donne un livre t rachel and this equivalence can he exploited it provide a translation of the first sentence
and that the result of this application is ah2 woman h ahate l eter h e it is a matter for further research to propose principles lk l ploducing such ill on
we apply the vat table binding rules to the snbtree ph peter hl hate hi h2 of fig NUM we lind that we mtisl compose the semantic transhttions peter and h NUM hate h
in the same way a modifier incorporation d x h is only possible when d contains x among its fiee wuiables outscoping most trashcans and which is not obtained from a u form in this simple way
however l igurc NUM b form interpretation l or cvcry we make use of the gcner tlized quantilier notation qmm l n st ri cl i ou scol e
the core algorithm itself and output formats are completely independent of the markup used for the different corpora
it appears that there is a wide variety of sources of error that impose limits on system effectiveness whatever th e techniques employed by the system
for generation of a person object the text must provide the name of the person full name or part o f a name
dooner who recently lost NUM pounds over three and a half months as a monetary value rather than ignorin g it as a weight
the error scores for persons dates and monetary expressions was less than or equal to NUM for the larg e majority of systems
in particular problem s remain with normalizing various types of date expressions including ones that are vague and or require extensiv e use of calendar information
for person objects this challenge is small since the only additional bit of information required is the person s title mr
however there were at least three factors that might lead one t o expect higher levels of performance than seen in previous muc evaluations NUM
of the NUM texts in the test set NUM were relevant to the management succession scenario including six tha t were only marginally relevant
after the atomic features have been selected we using the iterative scaling compute a fully saturated model for the maximal constraint space and then the algorithm starts to eliminate the most specific constraints
these higher order functions can be used to provide simpler definitions such as 2a or 2b for the function vp defined in NUM above
of course in the left recursive cases this seems to lead to an inconsistency since these are cases where the value of an expression is required to compute that very value
third turn and fourth turn NUM repairs address actual misunderstandings
the repair of speech act misunderstandings by abductive inference
NUM NUM using social conventions to guide interpretation and repair
thus in general these progams fail to terminate when faced with a left recursive grammar for essentially the same reason the procedures that correspond to left recursive categories involve ill founded recursion
in turn NUM russ produces a surface request
linguistic expectations capture the notion of adjacency pairs
agents distinguish between intended actions and misunderstandings
NUM a statement about the accomplishment of a
NUM we also presume that a parser can recognize surface informref and surface informif syntactically when the input is a sentence fragment but it would not hurt our analysis to input them all as surface inform
scheme s first class treatment of functions simplifies the functional abstraction used in this paper but the basic approach can be implemented in more conventional languages as well
our tests were performed using NUM templates these included almost all of brill s combinations and extended them to include references to chunk tags as well as to words and part of speech tags
table NUM partitioning chunk results without lexical templates
table NUM apparent errors made by voutilainen s nptool
the search for the best scoring rule can then be halted when a cell of the confusion matrix is reached whose maximum possible benefit is less than the net benefit of some rule already encountered
the portions of the text not involved in n type chunks were grouped as chunks termed vtype though these v chunks included many elements that were not verbal including adjective phrases
the goal of the basenp chunks was to identify essentially the initial portions of non recursive noun phrases up to the head including determiners but not including postmodifying prepositional phrases or clauses
when this approach is applied to part of speech tagging the possible sources of evidence for templates involve the identities of words within a neighborhood of some appropriate size and their current part of speech tag assignments
member result entry results entry as claimed above the memoized version of the cps top down parser does terminate even if the grammar is left recursive
table NUM patterns used in templates
the predictions of the model on new
we may also choose at this point to normalize other aspects of markup known to consistently differ between the two corpora
first ipsim may discover that there could be outside information that could be used to supply missing axioms as described above
thus the theorem prover needs to be immensely more flexible than ordinary prolog and this is the topic of the next section
in our studies we have found such requests for clarification to be routine and have designed a special mechanism for handling them
for example the mention of an object is assumed to indicate that the user can recognize and find that object if needed
other flags give for each individual specification its status and a counter for the number of iterations that the specification has been checked
in the implemented system the domain processor assists in electronic equipment repair and contains a debugging tree that organizes the debugging task
general dialog knowledge includes knowledge about the linguistic realizations of task expectations as well as discourse structure information maintained on the current dialog
if erroneous information has been entered after any observation that observation will eventually be repeated enabling progress and guaranteeing ultimate success
the ability of the system to respond to silence as a legitimate input was disabled because it had earlier confused our pilot subjects
in the example the path no a wire matches the grammatical no wire with the deletion of only an article a
for example arabic kuttib cause d to write perfect passive is composed from the root morpheme lcb ktb rcb notion of writing and the vowel melody morpheme lcb ul rcb perfect passive the two are arranged according to the pattern morpheme lcb cvccvc rcb causative
tiffs paper describes our approach to detecting rod recording adjectival meaning compares it with the body of kalowledge on adjectives in literature and presents a detailed practically tested methodology for the acquisition of lexical entries for adjecfives
we have seen above a formal characterization and implementation of an algorithm for determining the extent of agreement between two corpora
though hidden markov models have been successful in some applications such as corpus tagging they are limited to the problems of regular languages
tiffs work belongs to a family of research efforts called nficrotheories and aimed at describing the static inemfing of all lexical categories in several languages in the fr unework of tile mikrokosmos project on computational semantics
is that disagreement evenly distributed or are there factors to do with the complexity of analysis at play
notice that our predicted viterbi parse can stray a great deal from the actual viterbi parse as errors can accumulate as move after move is applied
as it is too computationally expensive to consider each of these rules at every point in the search we use heuristics to constrain which moves are appraised
the third task involves naturally occurring data and in this task our algorithm does not perform as well as n gram models but vastly outperforms the inside outside algorithm
the search space is taken to be some class of grammars for example in our work we search within the space of probabilistic context free grammars
firstly recall that our grammars model a sentence as a sequence of independently generated symbols however in language there is a large dependence between adjacent constituents
this is the actual heuristic we use for moves of the form a bc and we have analogous heuristics for each move in our move set
in this paper we describe a corpus based induetion algorithm for probabilistic context free grammars that outperforms n gram models and the inside outside algorithm NUM in medium sized domains
furthermore the only free parameters in our search are the parameters p a all other symbols except s are fixed to expand uniformly
NUM a rigger is a phenomenon in the viterbi parse of a sentence that is indicative that a particular move might lead to a better grammar
the gom of grammar induction is taken to be finding the grammar with the largest a posteriori probability given the training data that is finding the
we were unable to reach a unique label out of context for several adjectives which we removed from consideration for example cheap is positive if it is used as a synonym of inexpensive but negative if it implies inferior quality
thus a revised but still simple rule predicts a different orientation link if the two adjectives have been seen in a but conjunction and a same orientation link otherwise assuming the two adjectives were seen connected by at least one conjunction
we then classified several sets of adjectives according to the links inferred in this way and labeled them as positive or negative obtaining NUM accuracy on the classification task for reasonably dense graphs and NUM accuracy on the labeling task
table NUM shows the results of these experiments for a NUM to NUM our method produced the correct classification between NUM of the time on the sparsest test set up to more than NUM of the time when a higher number of links was present
we counted types and tokens of each conjoined pair that had both members in the set of pre selected labeled adjectives discussed above NUM NUM NUM NUM of all conjoined pairs types and NUM NUM NUM NUM of all conjunction occurrences tokens met this criterion
the analysis in the previous section suggests a baseline method for classifying links between adjectives since NUM NUM of all links from conjunctions indicate same orientation we can achieve this level of performance by always guessing that a link is of the same orientation type
although the log linear model offers only a small improvement on pair classification than the simpler but prediction rule it confers the important advantage 5when morphology is to be used as a supplementary predictor we remove the morphologically related pairs from the training and testing sets
for most connectives the conjoined adjectives usually are of the same orientation compare fair and legitimate and corrupt and brutal which actually occur in our corpus with fair and brutal and corrupt and legitimate or the other cross products of the above conjunctions which are semantically anomalous
our approach relies on an analysis of textual corpora that correlates linguistic features or indicators with NUM exceptions include a small number of terms that are both negative from a pragmatic viewpoint and yet stand in all antonymic relationship such terms frequently lexicalize two unwanted extremes e.g. verbose terse
its basic strategy for headlines was a conservative one tag a string in the headline as a name only if the system had found it in the body of the text or if the system had predicted the name based on truncation of names found in the body of the text
this specific view extends and generalizes the classical notion of terminology as used in information science
sra satie base system james out dooner in as chairman of mccann erickson as a result of james departing the workforce james is no NUM on the job as chairman any more dooner is already on the job as chairman and his old job was with ammirati puris
the training set and test set each consisted of NUM articles and were drawn from the corpus using a text retrieval system called managing gigabytes whose retrieval engine is based on a context vector model producing a ranked list of hits according to degree of match with a keyword search query
when the outputs are scored in key to response mode as though one annotator s output represented the key and the other the response the humans achieved an overall f measure of NUM NUM and a corresponding error per response fill err score of NUM
in addition there are plans to put evaluations on line with public access starting with the ne evaluation this is intended to make the ne task familiar to new sites and to give them a convenient and low pressure way to try their hand at following a standardized test procedure
an extended experimentation has been carried out on a subset of NUM sentences of the corpus
the tag elements are enamex for entity names comprising organizations persons and locations timex for temporal expressions namely direct mentions of dates and times and numex for number expressions consisting only of direct mentions of currency values and percentages
event james out dooner in as chairman of NUM mccann erickson as a result of james departing the workforce james is still on the job as chairman dooner is not on the job as chairman yet and his old job was with the same org as his new job
this result is more general ti method produces more indexes
almost without exception systems did more poorly on those two slots than on any others in the succession event and in and out objects the best scores posted were NUM error on other org median score of NUM and NUM error on rel other org median of NUM
the method has been tested over two corpora of italian documents
words can be sorted by syntactic categories to facilitate the selection process
this has useful implications on more complex text processing tasks e.g.
to solve this problem there are some possibilities
inflected languages application to basque language
so poorer keystroke savings are expected
different approaches have been studied to overcome this problem
this could increase the hint rate of the predictor
both cases are in the present of the indicative
we then selected every 50th entry from the four files yielding a total of NUM street names thus the training set also reflected the respective size of the cities
media specific tailoring generation must take into account that one output medium is speech as opposed to the more usual written language producing wording and sentence structure appropriate for spoken language
these results are obtained in recall tests on a manually transcribed training corpus it remains unclear however whether the error rates are reported by letter or by word
consonant quality chemnitz kcmnits not e mnits in analogy to chemisch e mif
the size and geography criteria were also applied to the selection of the test material which was extracted from the cities of frankfurt am main and dresden see evaluation
in general such a description can be based on an expert s analysis of linguistic data and his or her intuition or on statistical probabilities derived from annotated corpora
the arc which describes the transition from the initial state start to the state root is labeled with c epsilon the empty string
semantic aggregation is the first category of operators applied to the set of related propositions in order to produce concise expressions as shown in lower portion of fig NUM
the first system contained the regular text analysis modules including a general purpose module that handles words that are not represented in the system s lexicon typically compounds and names
in order to synchronize duration of the spoken and graphical references the lexical chooser invokes the speech synthesizer to calculate the duration of each lexical phrase that it generates
language generation in magic addresses its user s needs for a brief yet unambiguous briefing by coordinating spoken language with the accompanying textual references in the graphical illustration and by combining information into fewer sentences
in the following sections we show how we meet this constraint both in the speech content planner which organizes the content as sentences and in the speech sentence generator which produces actual language
it would be possible for medical history to be presented after all other information in the sentence by generating a separate sentence e.g. she has a history of hypertension and diabetes
our current work involves modifying the fuf surge language generation package so that it can produce prosodic and pause information needed as input to a speech synthesizer to produce a generic spoken language sentence generator
continuous speech systems required speaker dependent training and restricted vocabularies but still had such a large number of misrecognitions that this tended to be the limiting factor in the success of the system
be determined and not that the definition of terminal element correspond to some notion of say word
in our domain the system has the role of a librarian answering patrons queries
prediction and completion loops do not come into play since no precise inner or forward probabilities are computed
this idea arises in our formulation out of the need to compute probability sums given as infinite series
our experiments showed that the computation is dominated by the generation of earley states during the prediction steps
it accounts precisely for those derivations that expand the rhs prefix y1 wi NUM without consuming any of the input symbols
x y1 wi lyi then yi is a left corner of x iff y1 yi NUM all have a nonzero
this is the justification for the way these probabilities can be used in modified prediction and completion steps described next
the states involved in parsing the string aaa are listed in table NUM along with their forward and inner probabilities
for the computation of probabilities however this would mean truncating the probabilities resulting from the repeated summing of contributions
the nonprobabilistic earley algorithm can stop recursing as soon as all predictions completions yield states already contained in the current state set
scanning does not involve any new choices since the terminal was already selected as part of the production during prediction
another advantage is that the material in a computer manual is observed to be written as clearly as possible in a relatively narrow area which will hopefully ease the difficult job of understanding and representing the input sentence
another major difficulty encountered with this approach is that the language specific attributes neeessary to define the translation equivalents in the lexical and structural levels are neutralized in the interlingual representation thereby complicating the task of generation considerably
this data was distributed electrically via a www server NUM the first two texts clarify the systems s performance on shorter texts
by using the new set of anchors a new asm is constructed using the same method as used for initial asm construction
the tu language project sponsored by the nato science for stability programme was started in NUM to establish computational foundations for the natural language processing research on the turkish language with the collaboration of the computer engineering department of middle east technical university the computer science department of bilkent university and ttalici computing inc
the cle system has been trained to meet the lexical syntactic and semantic demands of the ibm corpus
helpful comments of asst prof cem bozsahin and assoc prof mehmet r tolun are gratefully acknowledged
the english to turkish mt system under development uses a structural transfer approach which has the following components
the system is very economical because it assumes only online dictionaries of general use and does n t require the labor intensive construction of domain specific dictionaries
the gradualism of the algorithm makes it robust because anchor setting errors in the last stage of the algorithm have little effect on overall performance
figure NUM alignment process sadding to the bilingual dictionary of general use users can reuse their own dictionaries created in previous sessions
in this section we will demonstrate how well the proposed method captured domain specific word correspondences by using text NUM as an example
if wjpn and weng are good translations of one another a should be large and b and c should be small
japanese has three types of characters hiragana katakana and kanji each of which has different amounts of information
the reason for this is that text NUM concerns brain science and the bilingual dictionaries of general use did not contain domain specific keywords
in this paper some issues in translating from english to turkish languages the translation domain the outline of the machine translation system under development and a detailed description of the transfer component will be presented
in fact soundness is easy to show since all of the operations are resolution steps
the abstraction operation here unbinds the first and third arguments of x NUM goals as discussed above
the ability to learn these relationships is confirmed by the results in table NUM
each file contains approximately NUM words
the algorithm consists of two subroutines
experiments led to the hypothesis that the most improvement came by assigning a boundary if the cue prosody feature had the value complex even if the algorithm would not otherwise assign a boundary as shown in figure NUM
that is approximately orthogonal with a dot product of approximately zero
a context effect is introduced in the potential in the form of penalties and bonus which are proportional to the direct neighbouts alignment values see equation NUM so that
to infer as much information as possible from the retained sequence of words we propose a bottom up syntactico semantic robust parsing relying on a lexicalized tree grammar and on integrated repairing strategies
the third pass succeeds in inferring an analysis by inserting a generic prepositional tree that meets the syntactic and semantic expectations see figure NUM
three consequences follow from this property
in particular the np algorithm which used three fea null passonneau and litman discourse segmentation tures outperformed both the cue phrase and pause algorithms each of which used only a single feature
this is an important dimension of difference between the two sets of segments we use segments identified by a minimum of four subjects are larger and fewer in number than those identified by a minimum of three
unlike most previous work which typically considers each linguistic device in isolation we also evaluate a simple additive method for combining linguistic devices in which a boundary is proposed if each separate algorithm proposes a boundary
this type of joker tree has a full syntactic structure but undefined semantic features some semantic features can be added along the syntactico semantic operations
the window is slid through each document in the corpus
generalise should operate on solved forms but when we try to eliminate the names introduced for subtree constraints in order to solve the corresponding constraints we end up with constraints that are exponential in size
the algorithm adopted is primarily a linear recency based approach that does not include a model of global focus
otherwise we shift our attention one word tothe left and repeat the process
let p clwl denote the probability that word wl is mapped into class c
recall that for the generalisation operation it is usually meaningful to operate on input parse forest leaf and rule constraints as described above array of variables x indexed by node s t
the same situation was detected with brill s tagger which in general was slightly more accurate than the xerox one NUM
first we tagged the text by the four different combinations of the taggers with the wordguessers using the full fledged lexicon
in the second experiment we tagged the same text with the lexicon which contained only closed class NUM and short NUM words
so we did not worry too much about tuning the taggers for the texts and used the brown corpus model instead
in this case however we do not account for the known words which were mis tagged because of the guessers
the direct evaluation of the rule sets gave us the grounds for the comparison and selection of the best performing guessing rule sets
as in the previous experiment we measured the precision recall and coverage both on the lexicon and on the corpus
the method for setting up this threshold is based on empirical evaluations of the rule sets and is described in section NUM
this however by no means restricts the described technique to that or any other tag set lexicon or corpus
somehow we made this transcription compile and work in dialog s system and used it as the core of vanf
the obvious answer was that the added value shoul d have the same nature as the original it should be information
these assertions are not treated as the ultimate truth but all the evidence is collected and combined by an evidence combiner
type date NUM timex the agency still is dogged by the loss of the key creative assignment for the prestigious coca col a classic account
a possible explanation i s that dialog s original requirements explicitly advised against splitting the names which might mak e sense in the first of the two examples
there is no formal differentiation between primary n v and secondary sing trans morphological features no r between grammatical and semantic flags
also the reason we could get anywhere at all was the fast turn around cycle modifying the grammar and rerunning a document take s about NUM minute
one enamex type organization mccann enamex account i ca n t believe it s not butter a butter substitute is in NUM countries for example
experience gained from vanf and the future our experience with vanf has proved that a core cascaded ndfsm approach is suitable for man y intelligent text processing tasks
one can also write n v meaning any word which is both a verb and a noun NUM appendix
the NUM types disagreed upon and the NUM rejected types illustrate we suggest that dialogue design is not an exact science
we do not have the scenarios used in sundial and do not have access to the early design specification of the sundial system
each observed problem was considered a case in which the system in addressing the user had violated a guideline of cooperative dialogue
the corpus was produced by NUM subjects who each performed NUM or NUM dialogues based on scenarios selected from a set of NUM scenarios
the diagnostic analysis may demonstrate that new guidelines of cooperative dialogue design must be added thus enabling continuous assessment of the scope of det
secondly det can be used to guide early dialogue design in order to prevent dialogue design errors from occurring in the implemented system
the tool has generalised well to the sundial corpus and some amount of objectivity has been demonstrated with respect to type identification and classification
it appears to be a simple fact that there will always be data on guideline violation which legitimately may be classified in different ways
let us now examine the recursive use of mixed order models to obtain smoothed probability estimates
in addition the tool supports the repair of those problems preventing their occurrence in future user interactions with the system
it should be added that it is not accidental that exactly these guidelines are not likely to be violated in the transcribed dialogues
our example grammar consists of some universal principles phrase structure rules and a lexicon
NUM 3c can mean any of these but indirect passive the whole sentence of which is shown in e.g.
taberu eat may let goal be added to the agent
the proposal is to serve as a solution to the empirical difficulties with japanese verbs and case elements described above
considering this requirement the contents in verb subcategorization frames play a major role of disambiguations in many nlp systems
a whole subcategorization frame is described and stored in a s block coupled with corresponding other syntactic features such as aspect features
there are other cases in which the above criteria require to separate the intuitively single word sense as is shown in e.g.
nj b NUM gives a solution to this kind of case by introducing some deeper conceptual primitives than our deep cases
the japanese lexicon developed by this design has comprehensive there are four other special ones that only replaces deep case labels
this inconsistency in the case assignment does not allow the lexicon to allocate the same c block to both e g NUM NUM a and b
nagaos0 NUM are currently used in the deep frame of our japanese verb frame system fig NUM
contacts to the region a series of states seek contacts to the region
it is these context vectors that are given to the self organizing map for visualization
interleaving universal principles and relational constraints over typed feature logic thilo giitz and detmar meurers
instead of specifying the instantiation state required for execution the delay deterministic statement 6cf
pedersen bruce NUM suggest to use alc i e s information criteria aic to judge the acceptability of a new model
as more people began attending these meetings and contributing to the project it grew to include eight graduate students
for the first few months tools were built and the system was extended at weekly hack sessions
breck baldwin and jeff reynar informally began the university of pennsylvania s muc NUM coreference effor t in january of NUM
when it is actually part of a company name it does not indicate possession of the following noun phrase
uniqueness is achieved b y eliminating competing antecedents using semantic information or by preferring some candidate antecedents ove r others
moreover no t all of the senses of end are equally likely to occur
this information is used when performing type checking prior to positin g coreference between entities
in addition we manually added some transformations to the set learned from the treebank
table NUM system performance without formatting errors but with optional elements treated as required
end of sentence detectio n the first step in our processing pipeline is end of sentence detection
our experiments compared a baseline no stemming against several different morphology routines NUM a routine that grouped only inflectional variants plurals and tensed verb forms NUM a routine that grouped inflectional as well as derivational variants e g ize ity and NUM the porter stemmer porter NUM
we not only have to know the sense of the word in the query in this example the sense of the word term but the sense of the word that is being used to augment it e.g. the appropriate sense of the word sentence chodorow et al NUM
import markup from one corpus to another if one corpus contains richer information than another for example in terms of annotation of syntactic function or of lexical category the markup from the first may be interpreted with respect to analyses in the second
l h text th user is reading is lisl htycd in the maia window
on the left is a text in which in which information for the word
in case tit disamt igmm r r orl hologicala imlyser
but such ties undermine ot s idea of strict ranking they confer the power to minimize linear functions such as c1 c1 c1
is allowed only if it corresponds to a path in g finally 23f forces the grammar to minimize the number of such chains
dep voi or fill voi voi voi voicing features appear on the surface only if they are a so underlying
example input which is a NUM state automaton and c1 is f x which says that every foot bears a stress mark
any factor that mentions no tiers at all goes onto l0 NUM if k NUM then return mlk as our desired i
such edges are tricky to define in 5a because a syllable s features are scattered across multiple tiers and perhaps shared with adjacent syllables
if the minimum is NUM i.e. an arbitrarily selected output candidate violates 23f only once then g has a hamilton path
in the first experiment the church tagger was used to identify part of speech of the words in documents and queries
the general requirement for speed of output bears on some other features of natural conversation that are speed dependent to varying degrees
by altering one of these perspectives the user called up a new set of candidate texts for speaking reflecting that perspective
where a has been absorbed into the effective clump score q c i f
the simplicity arises from the fortuitous cancellation of n between the poisson distribution and the uniform alignment probability
a particular clump is denoted by ci where i NUM lcb NUM g c rcb
the individual words in a clump c are represented by el el
the rationale behind a clumping model is that the input english can be clumped or bracketed into phrases
these fertility models can be used to impose clump fertility structure on top of preexisting clump generation models
the resulting model can be trained automatically from a bilingual corpus of english and formal language sentence pairs
the summation regions of i s in equation NUM are illustrated in figure NUM brown et al seem to have ignored the second term of the right hand side of equation NUM and used only the first term to calculate l i j l m lk l m NUM
in order to measure the performance of the markedness tests discussed in the previous section we collected a fairly large sample of pairs of antonymous gradable adjectives that can appear in howquestions
in a later version of the classifier we employed cross validation separating our training data in NUM equally sized subsets and repeatedly training on NUM of them and validating on the other
we consider the problem of determining semantic markedness as a classification problem with two possible outcomes the first adjective is unmarked and the second adjective is unmarked
this is mainly because of the data sparseness of co occurrence data
the percentage of nonzero differences which correspond to cases where the test actually suggests a choice is reported as the applicability of the variable
under the null hypothesis of equal performance of the two methods that are contrasted this test statistic follows the binomial distribution with p NUM NUM
note that y e NUM NUM each of the two ends of that interval is associated with one of the possible choices
the immediately preceding segment is ultimately closed and a parallel segment is opened at ui cf
their single theme property e.g. NUM in the sample text
we specify an algorithm that builds up a hierarchy of referential discourse segments from local centering data
this is an approach to centering in which issues such as thematicity or topicality are already inherent
this is to some extent implied by the structural patterns we find in expository texts viz
the numbers in these tables indicate at which segment level anaphors and textual ellipses were correctly resolved
the output of this stage is a table with two strata corresponding to the two groups and containing measurements on NUM variables for the NUM pairs with a semantically unmarked member
this coincides with our supposition that the overall structure computed by the algorithm should be rather fiat
we will use the labels transactional and social to refer to this broad distinction
the opening and closing of a conversation can be done according to a fairly well set out routine
words in the h xi in are divided into NUM groups a ccording to wom h ngths
tions as cornpared with each other and las the following characteristics
however by introducing new words into and adjusting word binding threes in the lexicon such difficulties can be greatly mitigated
any word longer than NUM characters will be di null vided into a NUM character prefix a NUM character infix and a suffix
if it can not then ab is a NUM character word and maximum matching moves on to c
the latter must tie lexically analyzed in order to identify all the words from which n gram statistics can be derived
ahnost every character is a word and inost words are of one or two characters long but there are also abundant wor ls longer than two characters
on the other hand an agent may take over the dialogue initiative but not the task initiative as in 3b above
due to cultural differences tile same language used in different geographical regions and difl crent applications can be quite diffferent causing problems in lexical analysis
null this word segmentor will be applied to word segment the entire corpus of NUM million characters before n gram statistics will be collected for post processing recognizer outputs
similarly the meaning of a set of terms is the distribution of terms on the dec anent set
the method for di tionary making is also us d for tokenizing
previous researches on the problem of compound noun indexing in korean have been done in two directions
automatic indexing renders a form of document representation that visualizes the content of the document more explicitly
the compound noun analyzer inw stigates if the components of compound nouns are appropriate as indexes
l et ps and c denote the sets of simple and compound nouns respectively
our approach consists in building an electronic lexicon of pns in a way more satisfactory than other existing methods such as their recognition in texts by means of statistical approaches or by rule based methods
we illustrate them in turn using a to refer to the prevented action and using agent to refer to the reader and executer of the instructions
instead additional counts were used for those possibilities
the function features which are more subjective in nature engender more disagreement among coders as shown by the k values in the following table feature k
the percentage agreement for each of the features is shown in the following table feature percent agreement form NUM intentionality NUM NUM awareness NUM NUM safety NUM NUM
clearly a corpus study french of preventatives is still needed but this does show drafter s ability to make use of kpml s language conditionalised resources
data is input from the split output file node on the left of the figure and is passed through filtering modules until it reaches the output modules on the right
the microplanner therefore is able to identify those portions of the procedure which are to be expressed as warnings and to enter the derived sub network appropriately
the condition a c b of NUM ensures that iv must contribute to the derivation of i s argument which is needed to ensure correct inferencing
NUM none of the derived trees in this test were emarkably different from the one just shown although they did order the intentionality and awareness features differently
NUM it is important to note here that although the micro planner is implemented as a systemic resource the machine learning algorithm is no respecter of systemic linguistic theory
when natural language processing and speech recognition are integrated into a single system one may have the situation of a finite state language model being used to guide speech recognition while a unification based formalism is used for subsequent processing of the same sentences
this re null duction system can be shown to exhibit the property called strong normalisation that every reduction is finite from which it follows that every proof has a normal form
when each of these sets has been subtracted from the initial approximation we can remove the auxiliary symbols by applying the regular operator that replaces them with e to give the final finite state approximation to the context free grammar
because the utterance itself does not provide a any other alternatives hel g is only felicitous and coherent if an alternate cospecifier has been placed in cf by prior discourse or by the speaker s concurrent deictic gesture towards a discourteous male
the corollaries for pitch accented pronominals are NUM when a pitch accent is applied to a pronominal its main effect is attentional on the order of items in cf NUM the obligation to accent a pronominal for attentional r asons depends on the variance between what the text predicts and what the speaker would like to assert about the order of items in cf
the adjacency relationship is obtained automatically by processing the text through the xerox parc part of speech tagger NUM and a phrase extractor
parts of speech are found through a tagger and related neighboring words are identified by a phrase extractor operating on the tagged text
wordnet is organized around a taxonomy of hypernyms a kind of relations and hyponyms inverses of a kind of and NUM other relations
our algorithm which is described in the next section is in the same spirit as vanderwende s but with two main differences
the examples from the captions also helped us identify the heuristic rules necessary for automatic disambiguation using wordnet and the webster s dictionary
the input to the disambiguator is a pair of words along with the adjacency relationship that links them in the input text
21t is standard to use the shorthand notation p x for prp x x
all languages have words that do n t translate easily into other languages and paraphrases are common in translation
this definition is more useful for his particular applications namely evaluating concept similarity and estimating selectional preferences
this paper presents a method for measuring semantic entropy using translational distributions of words in parallel text corpora
if p is a pdf over the random variable x then the entropy of p is defined as2
comas and dashes are often used for similar purposes so one is often translated as the other
ideally semantic entropy should be estimated by averaging each source language of interest over several different target languages
guage string with an initial and a nonfinal in the middle
it has proven to be an effective alternative to bayesian classifiers
terms located nearer to the confusion word are given additional weight in a linearly decreasing manner
the official results would thus have stayed the same
this is a net increase in performance of NUM
the approach taken used letter n grams to build the semantic space
svd factors the original matrix into the product of three matrices
the singular values are sorted in decreasing order along the diagonal
a vector in lsa space is constructed from the resulting terms
vector similarity is evaluated by computing the cosine between two vectors
the left half of the table lists the various confusion sets
the table provides the baseline performance information for comparison to lsa
portability of the score r the scorers are all driven by data in external files which allows the scoring software to be adapted to other similarly structured database objects without a change in code
compiling to first order formulae is to create a deduction method which like chart parsing for phrase structure grammar avoids the need to recompute intermediate results when searching exhaustively for all possible analyses i.e.
for instance in the training responses a recent push in safety training has paid off for modern day police and officers now better combat trained the terms safety training with combat trained needed to be related
in the hand scoring process test developers i.e. the individuals who create and score exams create a multiple category rubric that is a scoring key in which each category is associated with a set of correct or incorrect responses
the authors would like to thank the national science council of the roc for financial support of this research under conu act no nsc NUM NUM e NUM NUM
for instance in the response police are better skilled the phrase better skilled should be equated to better trained but this could not be done based on the training responses or dictionary sources
f h is an experimental inferencing item in which an examinee is presented with a short passage about NUM words in which a hypothetical situation is described and s he composes up to NUM hypotheses that could explain why the situation exists
so we estimate that it would take one person approximately NUM NUM hours to create the lexicon and another NUM NUM hours to do the preprocessing and post processing required in conjunction with the automatic rule generation process currently being developed
in reviewing the lexical gap errors we found that the words not recognized by the system were metonyms that did not exist in the training and were not identified as synonyms in any of our available thesaurus or on line dictionary sources
to further understand the scoring method we will look at the features and algorithms embodied in each o f the scorers showing their basic similarity and discussing the differences from task to task
there are on the average NUM NUM definitions in ldoce for each words as opposed to the average NUM NUM definitions per words in the test set
the labels can be used as a coarser sense division so unnecessarily fine sense distinction can be avoided in word sense disambiguation wsd
while at first glance one might think that problems of natural language generation only occur in the last phase of the system processing we believe that a generation perspective on the entire process is extremely beneficial
because both having and being are expressed through the same grammatical structure in asl language transfer could explain why some asl singers sometimes con se the use of the verbs be and have in english
once this initial determination is made further input from the student as well as feedback given during the correction and tutorial phases could cause the system to update the user s profile in the model
transfer can be positive in the sense that it may speed the acquisition of the l2 however it may also result in deviations in l2 production in places where the l1 and the l2 differ
the basic idea behind slalom is to divide the english language the l2 in our case into a set of feature hierarchies e.g. morphology types of noun phrases types of relative clauses
other researchers e.g. pq731 qwm761 rqp76 qps77 kk78 qp841 studied errors in deaf writing
we would like to thank john albertini of the national technical institute for the deaf ntid bob mcdonald of gallaudet university lore rosenthal of the pennsylvania school for the deaf george schellum formerly of the margaret s sterck school and mj bienvenu of the bicultural center for helping us gather writing samples
for example according to an asl informant to say the shirt is red the signer would typically sign shirt and mark it as a topic by raising his her eyebrows tilting his her head and maintaining fairly constant eye gaze on the addressee and then sign red with a different head position brow position and gaze
since data on writing skills is not well documented we note that the reading comprehension level of deaf students is considerably lower than that of their hearing counterparts with about half of the population of deaf NUM year olds reading at or below a fourth grade level and only about NUM reading above the eighth grade level str88
the coverage represents the percentage of sentences that were assigned a parse
fs are manipulated by substitution and adjunction as shown in figure NUM
as before NUM of the sentences were assigned a generalized parse
generalization of this derivation tree results in the representation in NUM
this type of generalization is called feature generalization
auxiliary trees in ltag represent recursive structures
this type of generalization is called modifier generalization
the application phase of ebl is shown in the flowchart in figure NUM
a highly impoverished parser called a stapler has also been introduced
is reduced in a specific domain and enumeration of all their senses is unnecessary
in experiments the extended algorithm could estimate the hmm as well as the n gram model from an untagged unsegmented japanese corpus and the credit factor was effective in improving model accuracy
for example a morpheme network can be derieved from the input sentence l NUM v which means not to sail fig NUM
the precision was defined as the proportion of correct morphemes relative to the total number of morphemes in the sequence which the tagger outputted as the best alignment of tags and words
given a morpheme network generated by juman with a cost width the implemented tagger selects the most probable path in the network using each stochastic model
the numbers on the lines show the credit factor of each connection that is assigned by the method described in section NUM the numbers at the right of colons are morpheme numbers
extension of baum welch algorithm i formulate an algorithm that can be applied to untagged unsegmented language corpora and estimate not only the n gram model but the hmm
the cost widths see horizontal axes in figs NUM NUM and NUM were provided to juman to generate the morpheme network used in the stochastic tagger for model evaluation
appendix b following is the set of rules used for hebrew in order to automatically generate the sw set for every morphological analysis in hebrew
a verb without an object pronoun the same verb in the same tense and person changing the gender and number attributes only
the verb xl vn fem masc plural third person past tense they occurred
the approximated probabilities obtained by our method were evaluated by comparing these probabilities with test corpus probabilities obtained by manual tagging of a relatively small corpus
we consider written languages and for the purpose of this paper a word is a string of letters delimited by spaces or punctuation
according to this table the average number of possible analyses per word token was NUM NUM while NUM of the word tokens were morphologically ambiguous
a recall shared classes of NUM denotes a very high compression i.e.
for example unless the system is to choose randomly it needs enough information to choose between different syntactic options available in the grammar
to forrealize this notion the system computes the plausibility score for each verb sense candidate and chooses the sense that maximizes the score
this is realized by equation NUM where vsm is the similarity between a and b based on the vector space model
we now briefly overview the fuf language and then the surge syntactic grammar before explaining in detail how unification is used to perform lexical choice
because paths can be used with no constraints the graph encoding an fd can include loops and does not need to be a tree
such constraints can not be considered solely from local positions within a constructed tree but require some global knowledge of interaction between semantic units
advisor ii thus essentially consists of a pipeline of two fugs a lexical fug encoding the domain specific lexical chooser and the domain independent syntactic fug surge
we illustrate through a relatively simple example that depends on a single type of constraint how fuf and unification are used for lexicalization
for example when discussing basketball the words rebound and point realize distinct concepts under the generic concept of a player performance
the general rule is the following if a phrase noun phrase or prepositional phrase has a semantic link with a surgical deed concept at least one of the words of the phrase is a cen concept
so too in tire expression oste taire ccl ath lcb rcb logy randen cc combi van de dekplaat cc anatt rcb my tile pathology concept will overrule the other colrccpts
the aneurysm is cut off with a straight clip considering a sentence like example NUM the system has to decide which noun phrase can be related to the verb and what is the nature of that relationship
NUM het intracellair gedeelte van de tumor wordt uitgecuretteerd null the intracellar part of the turnout is cleaned the prepositional phrase van de tumor modifies the noun phrase her intra cellair gedeelte
lemma wegnelllen to remove cat verb concept type cc surgical decd concept subtype cs remove endlemma b the non surgical deed lexicon a lexicon of nonsurgieameed concepts containing about NUM tokens with their concept type and part of spcech lemma beitet chisel cat north concept type cc intervent equipment
for identifying the scmantic i inks three particular kinds of information are needed NUM the surgical deed concept and its possible semantic links NUM the np and its concept type NUM the prepositions and their values l values
met een i eiteltje pl cc lnterventional equipment the task of the linking module is to combine the concepts of the sentence in order to build a colnpositc surgical procedure concept respecting the cen norms
some j agments of the discus are removed therec er the osteophytie edges of the cover plate are taken away mith a cell beiteltje cc interv eqtfip s notre de osteofytaire randen van de dekplaat r dir ob
this li ame is called cs neutral its semantic constraints the allowed concept types and its syntactic constraints the i values are less strict than the constraints which have been specified for the frames of the surgical deeds belonging to a specific snbtypc
this priority ranking hierarchy looks as follows it governs the concept type calculus so that e.g. in an expression with a surgical deed the latter overrules all other concepts as in rechler cc modifier lk rcb dyside retromasloidale icc anatomyl incisie cc surgicat deed
NUM this similarity in performance profiles may indicate a similarity in the underlying methodology of the two systems
we did not have access to other systems and care must be taken when interpreting the results which are not strictly comparable
psts are able to capture longer correlations than traditional fixed order n grams supporting better generalization ability from limited training data
let syns v denote this set i.e. lcb yns 13s c s v s is a syns rcb the is relation denote the transitive closure of wordnet is a syns v is the set of possible ambiguous wordnet hyperonims of the verb v through its senses i.e. s v
splay trees support search insertion and deletion in amortized o log n time per operation
to support fast insertions searches and deletions of pst nodes and word counts we used a hybrid data structure
for each new word frequency counts mixture weights and likelihood values associated with each relevant node are appropriately updated
specifically updates to the model structure and statistical quantities can be performed adaptively in a single pass over the data
as a simple check of the model we used it to generate text by performing random walks over the pst
in our first batch tests we trained the model on NUM of the data and tested it on the rest
for examples the verbs of cluster NUM NUM in table i are highly characterized i.e. have high local membership values by the fact that they take as object some physical property pr of a natural object
the rule says that a dependency relation o c omp1 should be added but the syntactic functions should not be disambiguated index
however only the subtree rooted at wn ls NUM s is actually affected by the update
however such a model may not and often will not exist
the features selected were similar to those in the training of the evidential model
nonetheless additional data is necessary to confirm the results of these initial evaluations
the third test set was created by combining the first and second test sets
the maximum entropy algorithm selected similar sets of features to model in each case
second the regularity between topics and sentence positions can be used to identify topic sentences in texts
with regard to the abstracts most have NUM sentences and over NUM NUM have fewer than NUM
the dictionary is similar to the one used in the previous approach with the addition of morphological information to allow concordance
we also counted how many different topic keywords each specific text unit contains counted once per keyword
although appropriate for an object oriented data base the structures frequently were not straightforwardly mappable from linguistic structures
control flexible non sequential control with all modules accessing the knowledge representation module
in fact however message processing systems typically must deal with a large number of distinct applications
as for assigning grammatical functions insufficient information in the labels is a significant source of errors cf the second most frequent error
the first run of experiments was carried out to test tagging of grammatical functions the second run to test tagging of phrase categories
the task is performed by an extension of the tagger presented in the previous section where different markov models for each category were introduced
such a representation permits clear separation of word order in the surface string and syntactic dependencies in the hierarchical structure
figure NUM cumulative coverage scores of top ten sentence positions with contribution marked for each window size
some linguistic knowledge is inherently global e.g. there is at most one subject in a sentence and one head in a vp
we also wish to thank jason eisner robert macintyre and ann taylor for valuable discussions on dependency parsing and the penn treebank annotation
this included three levels of objects the lowest level of objects is identical to the te objects
if there is an akernative between these two thresholds the prediction is classified as almost reliable and marked in the output cf
as observed from this example whether two utterances have local cohesion with one another or not is determined by coherence relations between the speech act types in the utterances coherence relations between the verbs in them and coherence relations between the nouns in them
in table NUM the default method assumed that all of the pairs of utterances in a dialogue has local cohesion and the accuracy was calculated as the accuracy in the default method the number of the pairs of utterances
when the endexpr expressions which are listed in the set of the speech act expressions represented as set endexpr are defined as a symbol endexpr we can approximate the speech act types as speech act endexpr
the words can be collected by automatically extracting fixed expressions from the parts at the end of utterances because the speech act expressions in japanese have two features as follows fl the speech act expression forms fixed patterns
the presented method consists of two steps NUM identifying the speech act expressions in an utterance and NUM calculating the plausibility of local cohesion between the speech act expressions by using the dialogue corpus annotated with local cohesion
the presented method consists of two steps NUM identifying the speech act expressions in an utterance and NUM calculating the plausibility of local cohesion between the speech act expressions by using the dialogue corpus annotated with local cohesion
formally when the speech act types are denoted by sact type we can interpolate an original plausibilhy by using these type patterns cohesion speechact type is a function giving the plausibility of coherence relations between the speech act types
for example in japanese itadake masu ka can be segmented into the smaller morpheme masu ka or ka and the original morpheme itadake masu ka is interpolated by these two morphemes as follows
thus although the learning algorithm per se is fixed a range of alternative learning procedures can be explored based on the definition of the inital set of parameters and their initial settings
table NUM shows the number of such interaction cycles i.e. the number of input sentences to within ten required by each type of learner to converge on each of the eight languages
figure NUM emergence of language s
figure NUM the grammar set ordering parameters
figure NUM sequential encodings of the grammar fragment
postpositions in which specifiers and modifiers follow heads
denotes zero or more further flmctor arguments
co evolution of language and of the language acquisition device
NUM for example the existing features can discriminate among five types of capitalized references relating to people as indicated by th e following reagan american christian irish and mr
cooed be i b abe r abe s abe t pi x coord b r x s x t x
note also that the use of the same bound variable names obj and sub causes no difficulty since the use of scoped constants meta level h reduction and higher order unification is used to access and manipulate the inner terms
a meta level a abstraction ay p is written y p s thus if wazked has type tat tat then y walked y is a aprolog meta level function with type ta tat and abe y walked y is the object level representation with type tat
it is only in the past month after the contest that the grammar writers have learned the right leve l of ambition for writing a rule testing it with the limited debugging capabilities and revising it modestly
consider again the derivation of harry found by type raising and forward composition harry would get type raised by the raise clause to produce abe p app p haxry and then composed with found with the result shown in the following query compose abe p app p harry
the lf for found shown in NUM would be represented as cabs obj abs sub found sub obj app encodes application and so in the derivation of harry found the type raised harry has the aprolog value abe p app p harry
with lhs context sensitivity and the ability to permute add and delete lhs elements and also unrestricted look ahead the rewrite power is essentially unconstrained beyond recursively enumerable context sensitiv e grammars having the power of a turing machine
then reasoning heuristics e g names of people often have appositives whose heads or modifiers are marked semantically as bein g characteristic of people are used to direct the search for discriminating contextual clues
secondly identify by pattern using a language specially developed to have all the functionality useful for this approach especially very powerful pattern matching capability coupled with the facility of placing arbitrary constraints on th e patterns or local contexts
this will match with the first clause for coord with t instantiated to be np s btos it to p app p john s to p app p bill and t a logic variable waiting instantiation
this method is hardly adaptable to include the user preferred words because the dimensions of the table can not be changed
lexicat concepts spread their information to lemmas in the mental lexicon
one purpose of an ikrs korelsky et al suggest is to provide a component in which to locate domain level inferencing not provided by the application program
this can be expressed by equation NUM where z is the vector for the word in question and t j is the co occurrence statistics of w and w
next we describe the knowledge sources edward uses to interpret deictic and anaphoric expressions section NUM
then we used this feature space for tel resenting the features of the domains
then we represented the conteut eta japanese text x as the following vector of douiain specific
since pointing to time is impossible only spatial and personal deixis is possible in multimodal referring expression
edward solves this type of referring expression simply by obtaining the most salient object of the right type
an example is the different context effects of reference by a pronoun versus reference by a definite full fledged np
the report about donald is in claassenz with a pointing gesture to the claassen directory
however edward solves referring by name the same as it does the other four types of referring expressions
referent resolution can make use of this structure to exclude referents to sub dialogues that are ended
its significance weight is initially NUM and it remains NUM for as long as the icon remains selected
as soon as the icon is deselected the weight drops to NUM and the cf will be discarded
it would be very hard to recover from this situation and the user would most likely never call again
for instance the subject of a passive tree is number l and not NUM figure NUM
the benefits of a multilingual approach to text processing extend well beyond text retrieval
refining the thesaurus or re estimating word similarities from an expanded bilingual corpus
NUM NUM examples vs syntactic or semantic grammars
deleting a feature depends only on the feature itself
the overall system is described briefly in this section
phrasal and clausal manner in a natural way
given an input and an example
this is illustrated in figure NUM
after considering different possible approaches to acquiring lexicm semantic information this paper concludes that a surface cueing approach is currently the most promising
the axiom for ize dependent must hold only for those senses of central that occur in the tokens of centralize for the central ize dependent pair to be correct
2although this definition is required for many cases in the vast majority of the cases the derived form and its base have only one possible sense e.g. stressful
since corpora with over NUM million words are common and english has over NUM common derivational affixes one would expect to be able to increase this number by an order of magnitude
speakers rely on their expectations to decide whether they have understood each other
for example an agent might challenge the presuppositions of a previous action
NUM poole s theorist implements a full first order clausal theorem prover in prolog
rather they are used to correct misconceptions misspeakings nonhearings etc
NUM NUM turn NUM russ decides that his interpretation of turn NUM was wrong
the system then removes any candidates whose implicatures are inconsistent with prior beliefs
repairs display non acceptance of a previously displayed interpretation see section NUM NUM
NUM fact believe r knowref r whoisgoing
we then translate these request forms into the discourse level actions askif and askref
nodes at the same level but having different parents represent repairs
the next two days the weekend show maps with two predominate regions pertaining to themes like music tv movies and various other entertainment themes
using the step buttons the user can manually step through each time increment or alternatively the play button will rapidly step through the time increments in succession
kohonen demonstrated that a system could be taught to organize the data it was given without the need for supervision or external intervention through the use of competitive learning
recall that each of these NUM nodes has a context vector associated with it and that the context vectors have been adjusted to represent the prevalent themes in the corpus
along with this spectacular growth has come new challenges for effectively locating on line information especially when browsing rather than performing a directed search for a specific piece of information
nodes can have similar themes and in fact the same theme if there is a relatively large amount of information pertaining to that particular theme within the corpus
to simplify matters it is often appropriate to generate an initial version naively then carry out revisions on it in a subsequent pass cf
in particular this applies to the goal acquire disambiguation when a large number of alternatives are at hand
however parameter optimization showed us that NUM is the oi tireal numl er for this parmneter
the weights of the eight scores are determined by minimizing the word error on the training data set
finally rst has been used by many researchers for the purpose of text generation e.g. moore and paris NUM hovy and mccoy NUM scott and souza NUM r6sner the rst representation of the remove phone text
we believe this small number gives our colnponent greater opt ortunity to include errors rather than improvenlents
wc may also need to reconsider the strategy for incorporating the sublanguage component into the speech recognition system
our sublanguage model works to replace word day by million but this was not the correct word
also in parentheses the numl er of possit le improveinents for each case is shown
this means that NUM NUM NUM of the possible improvement was achieved NUM out of NUM
ilowever other parts of the sentence like hyundai corporation and fuj itsu were not amended
the training data set has speech data recorded under the same conditions as the evaluation data set
we ca n t expect our sublanguage model to fix all of the NUM word errors non mne
for example the term perte au stockage storage loss is encountered in the agr corpus as pertes occasionndes par les insectes au sorgho stockd literally loss of stored sorghum due to the insects
sample dialogue NUM a sys do you want the rate or the total cost of a call
to test the influence of this effect we performed a third experiment
it is a graded lexical feature which may play a role anywhere lexical semantics plays a role
the heuristic used here is that the most frequent class in the initial training set is used
whenever a test has to be selected the feature is chosen with the highest information gain
this raises the question of the task dependence of linguistic categories
t is voiceless a coronal and a stop
table NUM error of c4 NUM on the different corpora
we provide the rule version of the inferred knowledge this time
or inspectability of the induced knowledge is usually not an issue in this type of research
in those cases where more than one rule applies a choice was made at random
when we look at the error rates for individual allomorphs a more complex picture emerges
the leaf nodes are labeled with a category name and constitute the output of the system
s0 s tier rules for f and x which require well formed feet and well formed stress marks and combine them with c1 to get a new factor that requires stressed feet
they must however be interpreted in the context of the current dialogue history to form a concrete communicative goal
the first is the initial retrieval where a raw query is used directly
stanfill waltz NUM cost salzberg NUM
a modular theory that encodes universal principles has obtained a greater degree of succinctness than a nonmodular theory and is considered more explanatory
these models are natural and direct implementations of the grammar but they are not efficient because gb is not a computationally modular theory
in grammar NUM the same patterns of actions are repeated for each left corner independently of the goal or of the input token
figure 4b which could be a continuation of the accept u in figure 4a illustrates this case
NUM i present here a simplified version of the algorithm to avoid technical linguistic details which are not relevant for the following discussion
fig NUM and NUM show evolution of the word score across n best re evaluation
NUM when using this case representation
construction de thesaurus etatd avancement genera qphase d ndus a isafion expressions an a avancemen NUM la phase d industrialisation iconstrucfion de the z
organization names person names location names are shown by colour coded highlighting of relevant words phrase structure annotations are shown by graphical presentation of parse trees
to that end the main panel of the ggi top level display shows the particular tasks which may be performed by modules or systems within the gate system e.g.
the first point means that any attempt to push researchers into a theoretical or representational straight jacket is premature unhealthy and doomed to failure
the second means that no research team alone is likely to have the resources to build from scratch an entire state of the art le application system
the ggi also has facilities to display the results of module or system execution new or changed annotations associated with the document
the principal overhead in integrating a module with gate is making the components use byte offsets if they do not already do so
clearly the pressure to build on the efforts of others demands that le tools or component technologies be readily available for experimentation and reuse
having chosen a task a window appears displaying a connected graph of the modules that need to be run to achieve the task
its results are then stored in the gdm database and become available for examination via ggi or to be the input to other creole objects
further expansion is halted at that point
the method consists of breaking down the text fragment being processed by a series of successive transformations that may be syntactical nominalisation de coordination etc semantic e.g. nuclear and atomic or pragmatic the thesaurus synonym relationships are scanned to transform a synonym by its main form
in other words a covering word string is in a more compact form than its covered word string
NUM finally context based predictions must be combined successfully with non context based ones
several graded constraints may be fired in one inference chain
table NUM reports the percentages of ambiguous sentences correctly disambiguated by each method
table NUM lists some examples of these tyt es of atn biguities
this technique can learn functions which are efficient and humanly understandable and editable
our current direction is to seek a solution to the cumulative error problem
processing begins with tim speech input in the source language
thus we extended our original discourse processor as follows
take into account legitimate partner expectations as to your own background knowledge
we now schematically specify the learning algorithm additional computational details will be provided later in the discussion of the complexity
note that nothing would prevent us from memoing other predicates as well but experience suggests that the cost of maintaining tables for the head corner relation for example is much higher than the associated profit
english and chinese lexicons of around NUM and NUM words respectively were constructed
a parameter for choice e c in the distance model
the two first points will be the object of section NUM NUM and the third one of NUM NUM
van noord efficient head corner parsing if we want to recognize all maximal projections at all positions in the input then we can simply give the following parse goah parse xp sem
which is such that gi figure NUM transfer matching and mapping functions
the main transfer search is preceded by a bilingual lexicon matching phase
the remaining NUM supervised training set sentences were hand tagged for prepositional attachment points
the equivalence relations for contexts and events may be different
the generalization from strings to weighted acyclic finite state automata introduces essentially two complications we can not use string indices anymore and we need to keep track of the acoustic scores of the words used in a certain derivation
section NUM briefly describes an english chinese translator employing the models and algorithms
consider the case in which the category we are trying to parse can be matched against two different items in the linking table but in which case the predicted head category may turn out to be the same
in parsing we will have to wait until the so position is reached
guilty came the hoarse croaking sounds of the old men
we refer to man and house as indicators for the senses of old
some attributes happen to apply to all senses of a given noun
the lexical database used by this parser must include semantic attribute tags
as shown by the immediately following sentence so did her present doctor
target adjective disambiguating rules hard NUM infinitival not easy
this strategy involved disambiguation of adjectives by their co occurrence with sense specific antonyms
some of these clues however may be hard to automate
the syntactic indicator attributes predicative and infinitival were applied first
more complex rules can be expected to be required in other cases
thus is not allowed the internal structure
but the account of disambiguation we ve offered circumscribes pragmatic reasoning as much as possible
texttiling is geared towards expository text that is text that explicitly explains or teaches as opposed to say literary texts since expository text is better suited to the main target applications of information retrieval and summarization
by contrast the document whose title begins it s hard to ghostbust a network is about computer aided diagnosis but has only a passing reference to medical diagnosis as can be seen by the graphical representation
more experiments in this vein are necessary to firmly establish this result but it does lend support to the conjecture that multi paragraph subtopicsized segments such as those produced by texttiling are useful for similarity based comparisons in information retrieval
NUM NUM computational linguistics volume NUM number NUM thus rather than identifying topics or subtopics per se several theoretical discourse analysts have suggested that changes or shifts in topic can be more readily identified and discussed
for example the query in the figure is translated by the system as patient or medicine or medical and test or scan or cure or diagnosis and software or program
when long texts are available there arises the question can retrieval results be improved if the query is compared against only a passage or subpart of the text as opposed to the text as a whole
in the test lexicon we also included the hapax words not found in the celex derived lexicon assigning them the pos tags they had in the brown corpus
the proposed technique is targeted to the acquisition of both morphological and ending guessing rules which then can be applied cascadingly using the most accurate guessing rules first
we noticed certain regularities in the behavior of the metrics in response to the change of the threshold recall improves as the threshold increases while coverage drops proportionally
computational linguistics volume NUM number NUM the amount of work required to prepare the training lexicon is minimal and does not require any additional manual annotation
thus in general each kind of guessing rule can be further subcategorized depending on whether it is applied to the beginning or tail of an un
to mirror this classification we will introduce a general schemata for guessing rules and a guessing rule will be seen as a particular instantiation of this schemata
for instance a morphologically motivated guessing rule can say that a word is an adjective if adding the suffix ly to it will result in a word
note that in a terminal drs ready for an embedding test all the auxiliary rpts disappear do not participate in the embedding
the verb tense determines the relation between the location time and the utterance time e.g. if the tense is simple past the location time lies anteriorly to the utterance time
the assymetry in using the event time for e and the location time for e arises from the interpretation rules of temporal connectives for both quantified and nonquantified sentences
in sentence NUM the location time of the event in the main clause is restricted to fall just after the event time of the event of the subordinate clause
reichenbach s well known account of the interpretation of the different tense forms uses the temporal relations between three temporal indices the utterance time event time and reference time
paraphrasing this we could say that john lights up cigarettes at all times preceding each phone call not just once preceding each phone call
the temporal relation in the sentence is inclusion between the event time of anne s coming home and the location time of the result state of paul s already having prepared dinner
in the second approach the structure is as such the existential quantification in NUM has to be stipulated whereas our analysis acquires this existential quantification for free
the drs in figure NUM describes the complex state sl that after each event of john s coming home there is a sequence of subsequent events according to his activities
thirdly we have made use of the training subset of reuters toobtain the categories representatives
second it integrates conditions on the database with other categories in the bodies of grammar rules
n x job x NUM chef
while sense disambiguation is clearly an important task it presents numerous experimental difficulties
we use their model NUM b which they found yielded the best experimental results
p w denotes the base estimate for the unigram probability of word w
we also investigated the effect of removing extremely low frequency events from the training set
we therefore consider similarity based estimation schemes that do not require building general word classes
and katz s back off method which always had an error rate of about NUM
a tie occurs when the two words making up a pseudo word are deemed equally likely
data sparseness makes the maximum likelihood estimate mle for word pair probabilities unreliable
we give the details of each of these three parts in the following three sections
the second type implement the brute force identify by look up principle of the dx system
it may seem puzzling that semantic class disambiguation does not achieve NUM accuracy even when supplied with the correct senses i.e. even when the word sense d mhiguation module is able m attain NUM NUM accuracy the overall semantic class disambiguation accuracy still lags behind the ideal
as an example suppose the rules defining the non null this completes the transformation of a gemini grammar with finitely valued categories and a finite state backbone into a nuance regular expression grammar
the sr agent accepts messages that tell it to start and stop listening and to change grammars and generates messages that it has stopped listening and messages containing the hypothesized word string
the interface supports two classes of spoken input commands and questions
questions allow the user to access information contained in the knowledge base that underlies the simulation
or set of objects do any of the friendly ships have emitters on board
however this would lead to many times more patterns being produced than are really necessary
in the film the answers consist primarily of yes no and short noun phrases
items oqen ring in the example
the sanle gralllnlar would be used in all cases
to part of speech prior to a second attenlpt to parse
the system runs on a sparc NUM workstation and
the corpus of texts used for this study was the brown corpus
washington dc NUM usa everett i wauchope c aic nrl
we chose logistic regression lr as our basic numerical method
other templates can be interpreted in a similar manner
currently the system has a vocabulary of t427 words
they too begin with a corpus of hand classified texts the brown corpus
suppose that we are presented with the unfamiliar category finan cial analysts report
but this increases errors due to unexpected vocabulary especially for highly productive derivational processes
i iajog acts depend a lot on signiticant words and word order
null how well do we perform compared to related work
so simple recurrent networks performed better than the statistical average plausibility method
the turn starts with a reiection followed by all explaining statement
however they do not occur very often
utl erance before the current word
in this paper we describe a new approach for learning dialog act processing
currently there is a pref rence for ling the earlier dame
abstract semantic category e.g. agent object recipient
all the dedr rward connections in the network are flflly connected
for the moment we exploit this by making the approximations
in the experiments reported in this paper an over simplified solution is adopted
furthermore their system deals with a single conflict while our model selects a focus in its pursuit of conflict resolution when multiple conflicts arise
thus the negations of these beliefs will be posted by the system as mutual beliefs to be achieved in order to perform the mod fy actions
thus further research is needed to determine whether or not the focus of modification for a rejected belief will ever be nil in collaborative dialogues
thus the algorithm recursively applies itself to the evidence proposed as support for bel which was not accepted by the system step NUM
if providing evidence directly against bel is predicted to be successful then the focus of modification is bcl itself step NUM NUM
NUM NUM else if beli is accepted but supports beli bel is not sdect focus modlficatlon beli bel
focus from the candidate foci tree using the heuristic attack the belief s that will most likely resolve the conflict about the top level belief
to illustrate the evaluation of proposed beliefs consider the following uttermmes NUM s NUM think dr smith is teaching ai next semester
the testing procedure assumes that a confusion word must be predicted as if the author of the text had n t supplied a word or that writers misuse the confusion words nearly NUM of the time
for instance consider the example on tuesday the sixth of april still have a slot in the afternoon is that possible versus on tuesday the sixth of april i still have a slot in the afternoon is that possible
our three week flurry of rule writing simply didn t cope
furthermore manual effort is needed in constructing grammar rules
in section NUM we discuss some extensions to these models and some open problems for future research
we can now test to what extent the underdispersed types are responsible for the divergence of e v n and its expectation by comparing the progressive difference scores d k defined in NUM with the progressive difference scores for the subset of the underdispersed words du k defined as
they can be used for example to store morphological tags produced by some tagger to represent the html structure of an html document or to store partial results of a data structure enable modular architectures and reduce the number of interfaces from the order of n NUM to the order of n
the transition probabilities in n gram models are estimated from the counts of word combinations in the training corpus
this system uses no new global rules or features nor ambiguous lexical entries but only the addition of cs to the relevant items within the lexicon
this treatment of licensing operates precisely at the syntax semantics interface since it is carried out entirely within the interface glue language linear logic
first some notation let NUM c mean that contains condition c a d assume that NUM c c stands for the sdrs which is the same as NUM save that the condition c in NUM is replaced by c
compound schemata range from the nonproductive e.g. the verb noun pattern exemplified by pickpocket to the almost fully productive e.g. made of with many schemata being intermediate e.g. has part door car is acceptable but the apparently similar sunroof car is not
update models how interpreters are allowed and expected to fill in certain gaps in what the speaker says in essence affecting semantic canter through context and pragmatics lve ll use this information flow between context and semantic content to reason about the semantic content of compounds in discourse simply put we will ensure that words are assigned the most freqent possible sense that produces a well defined sdrs update function
recent work on the syntax semantics interface see e.g.
all remaining errors are naturally my own
table NUM parsing from text which starts out correctly tagged percentage of parses which ex
we ran the algorithm on a small chinese english parallel corpus of approximately NUM unique english words
we use an extremely basic set of question language functions in querying the structure of the source treebank parse
this way takes advantage of both data sets though not as etbciently as the bayesian approach
further the parent inherits some feature values from one child and some from another
we choose to tag the entire sentence first producing an n best list of tag sequences
table NUM parsing from text which starts out correctly tagged percentage of parses which exactly
since the lexicon we computed was not perfect we get some noise in this graph
in our software environment this approach would require constructing a feature based grammar for the source treebanlc
however their target grammar NUM generates only NUM parses on average per sentence of test data
the taxonomic NUM nlc in word net are designed to capture membership of words in classes it may senn odd that the correct identification of the word sense coupled with the is a taxonomic NUM still do not guarantee correct semantic class disambiguation
the english half of the corpus has NUM unique words containing NUM nouns and proper nouns
in general language models must learn to recognize word sequences that are functionally similar but lexically distinct
this produced a more useful list of lexicon and again improved the speed of our program
we also want an algorithm that bypasses a long tedious sentence or text alignment step
we also show how the results can be used in the compilation of domain specific noun phrases
for example japanese has no distinct l and r sounds the two english sounds collapse onto the same japanese sound
null given such an underspecified timeline as lexical input gen outputs the set of all fully specified timelines that are consistent with it
c c lab jlab lab for phonetic interpretation o says to end voicing laryngeal vibration
NUM shows a candidate in which underlying nas has surfaced in place but with rightward spreading
scoring constraint r number of a NUM pairs in r such that the a overlaps the NUM
i syncope cvc cc the v is crushed to zero width so the c s can be adjacent
this is an explicit property of otp otherwise nothing that failed to parse would ever violate parse because it would be gone
null the present paper sketches primitive optimality theory otp a new formalization of ot that is explicitly proposed as a linguistic hypothesis
clash fsas are therefore just degenerate versions of implication fsas where the arcs looking for 3j do not exist because they would accept no symbol
given that the concept or meaning unit is context independent this may bring to mind that not only translation needs context but also synset membership
it is difficult to judge the feasibility of their approach given the fact that only a limited coverage has been addressed so far
b l passen e l suit e
in the walkthrough message this kind of contextualized interpretation is required early on yesterday mccann made official what had been widely anticipated mr james NUM years old is stepping down a s chief executive officer on july NUM
as a representational substrate it records propositions encoding the semantics of parsed phrases as an equational system it allows initially distinct semantic individuals to be equated to each other an d allows propositions about these individuals to be merged through congruence closure
a susan gave betsy a pet hamster
however this is not the case
the house appeared to have been burgled
section NUM provides references to such work
he was annoyed by john s call
as a point of clarification note that the inference system does not encode facts at the predicate calculu s level so much as at the interpretation level made popular in such systems as the sri core language engine NUM NUM
transfer equivalences are stated as relations between sets of source language sl and sets of target language tl senlantie entities
NUM a john thinks that the telephone is a nuisance
the vice president of the united states is also president of the senate
this tagging is changed by the following rule which roughly reads change word w from jjr to ror if the the word to w s immediate right is tagged jj jjr rbr nexttag jj table NUM below illustrates the tagging process
in the walkthrough message for example amarati purls is identified as an organization which ultimately leads to an incorrect org tag for martin purls since this person s name shares a common substring with the organization name
in standard parsing one searches for any and all rules whose antecedents might apply given the stat e of the parser s chart all these rules become candidates for application and indeed they all are applie d modulo higher order search control
we restrict the following discussion to the direction from german to english but the rules can be applied in the other direction as well
wordnet concept definitions are given by glosses in natural language not referring explicitly to the net itself thus not making use of its disambiguated polysems
the author is grateful to john local for enthusiasm and help with phonetics
developing a nonsymbolic phonetic notation for speech synthesis
any remaining mistakes are the author s responsibility
it is possible for kohonen s technique to work in 3d 3d maps have been produced by the author but are more difficult to work with and are still undergoing evaluation
relevant information is captured in the formant trajectories
department of cybernetics university of reading
the following nine binary articulatory features were used continuant voiced nasal strident grave compact vowel height i vowel height NUM and round
one possible way to introduce this kind of variability is through the development of representations that encode in a reduced dimensionality a range of examples of the phenomenon to be accounted for
the implementation of the normalization procedure includes a syntactic normalization procedure and a semantic normalization procedure
head head fs time vactn theme i goal quan head
in the syntactic normalization procedure many parse trees that are syntactically equivalent should be normalized first
different sense definitions of these words are extracted from the longman english chinese dictionary of contemporary english
however they are assigned the cases modifier and head respectively
a model using this case score function is hereby said to operate in an ansr mode
b lexicon in the lexicon there are NUM NUM distinct words extracted from the corpus
in each separate run a different open class dictionary is used
i denoting the i th part of speech in t k stands for the part of speech assigned to wi
therefore word association information can be trained and applied more effectively by considering the structural features
the property can be inferred from the description generated so far or it is prototypical for the object to be identified and may thus yield a false implicature c25 c28 s12
thus we can represent an input expression i as an example plus a set of distortion operators NUM i lcb e distortl distortffi rcb this means that we can re express the conditional probability distribution for an input expression i given that the meaning expressed by example e is intended ms follows
this language dependent procedure analyzes the functional description created so far for potential misinterpretations and scope ambiguities which may occur in connection with nested postnominal modifiers or relative clauses that depend on an np with a postnominal modifier
NUM rejection of a descriptor because of a scope problem however if the local relation in the previous example is expressed by the one to the left of a table adding a relative clause expressing the objects on t3 would still work badly because the addressee would interpret these objects to be placed on t2 check scope should recognize this reference problem
NUM producing flat expressions instead of embedded ones if t NUM is the intended referent and on gt t2 is the descriptor selected next another descriptor must be selected to distinguish t2 from t4
in this paper we have presented a new algorithm for generating referential descriptions which exhibits some extraordinary capabilities descriptors can be selected in a goal driven and incremental fashion with contributions from varying referents interleaving with one another
if the intended referent is identified un then exit with an identifying description if the complexity limit of the expression then exit with a non identifying descriptio choose property if no further descriptors are available then exit with a non identifying descriptio else call the descriptor selection componen if the descriptor does not reduce the set or the referent further described is alre or the descriptor is inferable from the d or the descriptor cannot be lexicalized w or lexicalizing the descriptor would caus then reject the proposed property and goto NUM extend description update the linguistic resources used determine properties which when being update the constraints holding between goto NUM the text
the second deficit results from ignoring that the ultimate goal envisioned consists in producing a natural language expression that satisfies the discourse goal and not merely in choosing a set of descriptors by which this goal can in principle be achieved
this is because the larger win h w sizes might be onsidered to be useful for extra ting s unanti relationshil s between ltoltlls
for the seh cted lusters if there ix a noun which belongs to xev ral ehtsters thex chlsters are grouped together
fl eq is fl equencyt a sed exlmriment i.e. we use word frequency for weighting and do not use wsd and linking methods
in order to cope with walker s l rob lem for the results of disand iguation technique semantic relativeness of words are cmeulated and semantically related words are grout ed together
the clustering algorithm is applied to the sets shown in table NUM and produced a set of semantic clusters which are ordered in the as ending order of their semantic deviation wdues
however while their pproaehes assign each oor linate of a vector to each word in artmes we use a word noun of wtfich sense is disambiguated
NUM her f x y is the munl er of total co occurrences of words e and y in this order in st window size of NUM words
we believe that structure sharing has a much stronger el bet on uurious geo ve than on el camino real because the tbrmer has longer sentences which produced more parses
when a value for s v v is required and the corresponding entry in the matrix is undefined it is recursively computed by the following formula
i lcb ecently several groul s have been exploring the possibility of aligning t armlel syntacticalhj analyzed sentences fr m the source and target languages el
thus an input expressions i consists of a sequence of words iwl iw2 iw
an example of strltetllre sharing i etween two ila rse trees ff l rcb le same input senten e is shown in fig are NUM
v has at least one possible lexical match all of those positions in the score matrix s which do not correspond to a lexical match of v are set m zero
by setting to zero those positions in the score matrix which represent unlikely matches this heuristic prevents these scores from ever being cah ulated substantially reducing the rmming time
finally due to the special characteristics of the verbs that include any kind of affixes in concordance with other words in the entire sentence their prediction requires a special treatment
since there are very few japanese spoken language corpora available we are currently adopting a word class based model for the remaining distributions that uses the categories for strong content words nouns and verbs light content words adjectives and some adverbs grammatical fimction words e.g.
it expresses the rescoring power of hypothesis hn and is calculated iteratively as
in NUM the noun neighbor being cb as a definite subject noun usually is belongs to the topic according to i and so does new as its modifier according to ii although it is nb
an automatic identification of topic and focus may use the input information on word order on the systemic ordering of kinds of complementations reflected by the underlying order of the items included in the focus on definiteness and on lexical semantic properties of words
however if a non final element carries the intonation center then all the complementations standing after this element belong to the topic for the rest of the sentence i and ii hold the bearer of the intonation center belongs to the focus
this presupposition is absent in NUM a in which the to phrase belongs to the focus the presupposition that they moved somewhere from boston is triggered only by those readings of this sentence in which the from phrase belongs to the topic
on the other hand the ambiguity of the a sentences is determined by the fact that the scale of cd is in accordance with so here and that one of the complementations thus belongs to the topic in some of the readings and to the focus in others
in this sense the examples above can be understood as corroborating the cited shape of so for some of the pairs of complementations example NUM illustrates that addressee precedes objective since only NUM a is possible as an answer to NUM
thus for example in the king of france is not bald the subject which is the topic of the sentence on its preferred reading is outside the scope of negation so that if the sentence is uttered as referring to the world we live in it is connected with a presupposition failure the existence of the king of france is presupposed entailed even by the negative sentence
to illustrate how our procedure works for the sentences differing from NUM NUM in the values of delimiting features definite indefinite in word order and so on we add a list of these sentences with simplified perspicuous results of the procedure i.e. with the values t and f produced by our algorithm added to the autonomous autosemantic lexical occurrences
b if the verb occupies the rightmost position in the sentence and its subject is ba definite including noun groups with this with oneofthe etc then the verb is nb i.e. f and its subject is cb belonging to the topic which we denote as t bb indefinite then the subject is f and the verb is t
the algorithm can also be used to learn traditional phonological rules of the form a b NUM where a b are single phonemes and is a sequence over lcb c v rcb the classes of consonants and vowels
a template contains a predefined set of slots with associated fill in rules that direct the search for appropriate information in the net
in the fourth from last paragraph one of the sections of reported speech contained two closing quotation marks mr james
once this transformation has been made the machinery from the earley algorithm carries over remarkably smoothly
recall that our application for the parsing algorithm is as the first stage of a robust bracketer
some nodes have an associated name this is usually a single word which characterises the meaning of the node
retire figure NUM example piece of semantic net for the sentence john will retire as chairman
textrefs enable more robust reporting of results as witnessed in a significan t performance improvement in our non muc template generation applications
a benefit of this approach is that the form of rules is natural and simple to write
despite the hard work muc has been an extremely useful an d enjoyable experience and we look forward to muc NUM
as well as providing impetus to develop the core system the experience has taught us much about testin g and evaluation
in the remainder of this paper we first describe our grammar framework in sections NUM
a production in our extended formalism may have left and or right context and is denoted as
to investigate the effect of re estimation we tested the combination of three initial word lists d1 d2 d100 and two initial word frequency estimation methods string frequency method sf and longest match string frequency method au nented with the word identification method lsf ct
for example from the phrase y soviet union i made suffix i l tank which means soviet union made tank the initial word identifier extracts two word hypotheses y and k where the former is written in katakana and the latter is written in kanfi
that is to build a japanese word segmenter from a list of initial words and unsegmented training text
the word based language model is then re estimated to filter out inappropriate word hypotheses generated by the initial word identification
to prefer segmentation hypothesis e c21cs over c c2cs the following relation must hold
in this experiment we randomly selected two sets of training sentences each consisting of NUM thousand sentences
to help readers understand the heuristics we have to give a brief introduction to the japanese writing system
it contains a variety of japanese sentences taken from newspapers magazines dictionaries encyclopedias textbooks etc
for each s in seth compile a set of words tops that listed under the topic of s and refs the set of words listed under it cross references
from this rule a set of expansions can be generated
this paper reports on both context independent and context dependent strategies for utterance verification that show that the use of dialog context is crucial for intelligent selection of which utterances to verify
the goal of any algorithm for selective utterance verification is to minimize the rate of under verifications while also holding the rate of over verifications to as low a value as possible
in this paper we address the problem of introducing structures into the probabilistic dependencies in order to model the string translation probability pr f e
the decision rule for deciding when to initiate a verification subdialog is specified as follows if the parser confidence score the verification threshold then do not engage in a verification subdialog
we say that an utterance is correctly understood if it is either correctly interpreted initially or is an utterance for which the system will engage the user in a verification subdialog
in experimental trials consisting of eight different users NUM problem solving dialogs and NUM user utterances the circuit fix it shop natural language dialog system misinterpreted NUM NUM of user utterances
u the led is displaying a one and seven at the same time be led a display ng one an seven at the same time NUM
for each of the best n parses an expectation cost ec is also produced according to how likely the input is to occur according to the expectations
in section NUM we report on the results of tests of various strategies for deciding when to engage in verification subdialogs within a specific dialog environment the circuit fix it shop
after a warmup session where the subject trained on the speech recognizer and practiced using the system each subject participated in two sessions where up to ten problems were attempted
the reduction above proves np hardness of the parsing problem
adjoining corresponds to identifying a discourse relation between the new material and material in the previous discourse that is still open for elaboration
however if the order instead was ii iii i iv then NUM would not be incremental since at the stage when only ii and iii had arrived they could combine as part of an equivalent alternative analysis but are not so combined in NUM
for example combination of the formulae from iii and iv of NUM requires unification of the index set expressions NUM and lcb j rcb yielding the result formula w NUM plus the single constraint equation v lcb l rcb tg lcb j rcb which is obviously satisfiable with NUM lcb j l rcb
simple indexing is almost error free but does not cover term variants
others working within the parameters framework have proposed unmarked default parameters e.g.
the grammars defined generate usually infinite stringsets of lexical syntactic categories
therefore each parameter can only be reset once during the learning process
experiments show that several experimentally effective learners can be defined in this framework
in these tests a single learner interacted with a single adult
these represent a sample of degree NUM learning triggers for the language e.g.
however neither this nor the ovs language survived beyond cycle NUM
however for the remaining five languages there was no strong preference
the proposed ebl method guarantees soundness because retaining and applying the original derivation in a template enforces the full constraints of the original grammar
two learning procedures were predefined a default learner and an unset learner
null development times are at least as important as computation times
some obligatory spelling changes in french involve more than one letter
reasonably quick compilation is required and run time speed need only be moderate
the features in a rule is a list of feature value equations
figure NUM partitioning of ehtre as chef e
figure NUM spelling pattern application to the analysis of ch re
then the partitionings given in figure NUM will be the only possible ones
this obviates the need for null characters at the surface
figure i shows three of the french spelling rules developed for this system
part of speech tagging syntactic analysis inference and even some of the set fill processing in the template elemen t task te
at that time several of us reluctantly admitted that our major impediment towards improved performance was reliance on then standard linguistic models of syntax
mr james num tagged age is identified on the basis of part of speech information as is the organization name mccann
it uses a lex based scanner as a front end for tokenizing and typing its input then a pattern matching engine finds the actual date phrases
in this example the job outin contexa predicate succeeds by binding the succ variable to j o NUM with the rule overall yielding a job in fact
these were due to the same problem a known flaw that had been left unaddressed i n the days leading to the evaluation
in other words the representation is actually a structured attribute value graph such as the following whic h encodes the age apposition above
to further improve our organization tagging it appears that we will simply have t o expend more energy writing named entity phraser rules
because these short forms went unmerged they in turn spawned incorrect person templates hence the dro p in person and person name precision
we believe that if we more intelligently took advantage of thi s knowledge source we could reduce the additional precision errors almost entirely
h NUM is an abbreviation of
dep represents the dependency of an edge and its daughter edges
j indices lcb llmy collea o
qo tile initial state which corresponds to a lezical erltry
note that the ub struet are
the rest of the features the procedure complexity structure of the dictionary adaptability are similar to the previous one
as a result no hand crafted rules or lists are required by the highly portable system and it can be easily retrained for other languages or text genres
we use information about the tollen containing the potential sentence boundary as well as contextual information about the tokens immediately to the left and to the right
admittedly the way we integrated the organization lexicon into alembic was relatively nayve thereb y leading to some of these silly precision errors
performance is comparable to or better than the performance of similar systems but we emphasize the simplicity of retraining for new domains
thus the probability of seeing an actual sentence boundary in the context c is given by p yes e
the other system uses no domain specific knowledge and is aimed at being portable across english t ext genres and roman alphabet languages
a trimmed down system which used no information except that derived from the training corpus performs nearly as well and requires no resources other than a training corpus
the model can therefore be trained easily on any genre of english and should be trainable on any other romanalphabet language
pot ential sentence boundaries are identified by scamfing the text tbr sequences of characters sepaa ated by whitespace tokens containing one of the symbols
given a corpus annotated with sentence boundaries our model learns to classify each occurrence of and as either a valid or invalid sentence boundary
uniform natural language based representation of taxonomic and temporal reasoning as we explain later this uniformity of our representation greatly simplifies our architecture and control
thus it is on ice that i walked and it is walking that i did on ice and it is ice that i walked on are sentences but there in no equivalent form for relocating walking on
in other words given knowledge of the first word in the sentence predicting the second word is as difficult as guessing between four equally likely words and knowing the second word makes predicting the third as difficult as guessing between seven words
statistical phrase structure models of language NUM such as scfgs are motivated by different assumptions about language principally that a phrase grouping several words is a constraint on co occurrence that makes it possible to better predict one of those words given another
in english the word sequence walking on ice is generally assumed to have an internal structure similar to a NUM answers on ice can move and delete as one unit whereas walking on can not
the point here is that using such a context free rule to model a sequence of two words reduces the entropy of the language from a model that treats the two words as independent by precisely the mutual information between the two words
it might seem that a minimal entropy grammar for this corpus would be sin the following derivation understand that for word bigrams p a b p bla because p a p b NUM
it could be shown that even for unrelated english and german texts the patterns of word co occurrences strongly correlate
the english corpus consists of the german matrix correspond the dot patterns of the two matrices are identical
as a measure for matrix similarity the sum of the absolute differences of the values at corresponding matrix positions was used
the simulation was continued until for each value of c a set of NUM similarity values was available
in order to limit the seaxch space translations that axe known beforehand can be used as anchor points
if morphological tools and disv mbiguators axe available preliminaxy lemmatiz tion of the corpora would be desirable
for each pair of words in the english vocabulary its frequency of common occurrence in the english corpus was counted
equivalently the german co occurrence matrix was created by counting the co occurrences of german word pairs in the german corpus
a simulation experiment was conducted in order to see whether the above assumptions concerning the similarity of co occurrence patterns actually hold
similarly seeing only one of them a NUM NUM or NUM NUM mismatch decreases our belief in their association
automatically identifying and explicitly using collocations such as new mexico at search or indexing time can help solve this problem
champollion identifies translations for the source collocations using the aligned corpora database as its entire knowledge of the two languages
consequently sometimes the results are specific to the domain and seem peculiar when viewed in a more general context
bilingual corpora in the same domain which are not necessarily translations of each other are more easily available
the grammars will usually be of unrelated origins not designed to make interlingual matching easy
then it would be possible to compute fr and the dice coefficient in linear time
the frequency of each word in sentences is also computed at this stage
thus the following are two distinct productions in an itg
preliminary experiments on parallel english chinese text are supportive of these strategies
the training method does not require syntactically annotated parallel corpora which are difficult to obtain
the weighting of the bracketing constraints and matching constraints is probabilistic
structure for both sentences simultaneously with the interlingual constituent matching criteria
furthermore how to deal with ambiguities presents another serious problem
in contrast both the average and the specific mutual information depend on both the conditional and the marginal probabilities
figure NUM single communicative goal comparison
figure NUM extreme ranges cause low readability
a picture shows but a text describes
figure NUM single communicative goal evolution
it is usually the other way around
graphics and text are very different media
henceforth all uvg dls mentioned in this paper will implicitly be assumed to be lexicalized
in uvg dl several context free string rewriting rules are grouped into sets called vectors
let t be a parse tree in g and 7r be the parse forest representing t t
NUM use of pauses for segmentation it is widely believed that prosody can prove crucial for speech recognition and analysis of spontaneous speech but effective demonstrations have been few
a nonterminal on which a synchronization link impinges is referred to as a synchronous nonterminal
the problem with non local synchronization is that the weak language preservation property does not hold
the vector derivation tree for the derivation in figure NUM is shown in figure NUM
one simple method which we use in our system is to represent a compound name dually as a compound token and as a set of single word terms
the circsim tutor v NUM input lexicon is comprised of approximately NUM lemmata
NUM conclusion the twin concepts of bilingual language modeling and bilingual parsing have been proposed
one side of the screen contains room for students to enter their predictions
at this time we are unable to explain the much smaller improvements in routing evaluations while the massive query expansion definitely works nlp has hard time topping these improvements
we report on the joint ge nyu natural language information retrieval project as related to the tipster phase NUM research conducted initially at nyu and subsequently at ge r d center and nyu
our recent results indicate that some of the critical semantic dependencies can in fact be obtained without the intermediate step of syntactic analysis and directly from lexicallevel representation of text
in trec NUM the automatic query expansion has been limited to to routing runs where we refined our version of massive expansion using relevenace information wrt the training database
these types of pairs account for most of the syntactic variants NUM for relating two words or simple phrases into pairs cartying compatible semantic content
in addition our trec NUM results with long and detailed queries showed NUM NUM improvement in precision attributed to nlp as compared to NUM NUM in tree NUM
the tx n i factor realizes our notion of hot spot matching where only top n matches are used in computing the document score
finding a proper term weighting scheme is critical in term based retrieval since the rank of a document is determined by the weights of the terms it shares with the query
ciacslm tutor is based on a qualitative model involving seven core physiological parameters
what is the correct value of hr s unchanged
yoon hee lee s leem seop shim s chong woo woo s
before training the node vectors are assigned random values
because of the stemming process some of the stem words are truncated
an iterative algorithm compares a region to every other region on the map
consider ways that a user can make use of the automatic region generation
t correct the value of hr is unchanged
at its peak the snap computes at a rate of NUM NUM gigaflops
specifically closeness in the space is equivalent to closeness in subject content
the logic forms are used to fill in abstract templates
first a context vector is computed for the region
similar entities have context vectors that point in similar directions
then we briefly review the claims of each theory that are outside this common ground
one approach to constraining informational structure is to define it as parasitic on intentional structure
this span can the rst structure assigned to the example discourse in figure NUM
the nucleus expresses a belief or action that the hearer is intended to adopt
this occurs when the multiple satellites bear the same rst relation to the nucleus
the core has an important function it manifests the purpose of the segment
without a core the segment purpose must be inferred from the subsegments alone
second what synthesis of the two theories emerges when we recognize the correspondence
second in addition to intentional and linguistic structure g s posits an attentional structure
first the two theories offer different but consistent perspectives on the ordering of segments spans
in the diagram baro baroreceptor pressure and ns nervous system response
in section NUM we will compare their approach with ours
where is her e mail to lou ok information added
these values are used for the interpretation of the next sentence
in particular their model addresses the notion of discourse coherence
NUM she sent an e mail about bos to wietske
edward scans the vicinity of both relata dir NUM and dir NUM
for example in the sequence the secretary is hil
the arrows represent the information flow between the main components
figure NUM illustrates how the user can interact with edward
the two lists for each affix were created by hand using rules described in quirk et al NUM
if we look at the papers contained in the proceedings of this workshop we can clearly see that many researchers both in academia and in industry have taken up the challenge to build systems capable of translating spoken language
the critical tokenization introduced in this paper has a similar role in string tokenization to that of the syntactic graph in sentence parsing
rather it is that the low road of forgoing integration and embracing interaction may offer the quickest route to widespread usability and that experience with real use is vital for progress
in short the understanding of critical points and fragments will significantly assist us in both efficient tokenization implementation and tokenization ambiguity resolution
moreover in practice most critical fragments are dictionary tokens by themselves and the remaining nondictionary fragments are generally very short
for instance we observed from a chinese corpus of four million morphemes a very strong tendency to have one tokenization per source
let s cl cn be a character string over the alphabet and let d be a dictionary over the alphabet
given the frequency of utterances in spontaneous speech which are not fully well formed which contain repairs hesitations and fragments strategies for dividing and conquering utterances inclusion of other levels is also possible
here the semantic constraints are made visible in the node symbol e.g.
once the word confidence scores are available the filtering still needs a threshold to point out would be errors
what is specific with this sequence is the fact that the postconditions of any of these events are snch that the preconditions of the successive events never can hold
interpretation in tile epa reading crsl is used ms a focus mdverb i.e. it structures its argument into focus and background
brill s tagger begins by tagging unknown words as proper nouns if capitalized common nouns if not
lev8a for m overview of the notions used the tests associated with them and the problems connected to them
we directly come up with tile i rss that to our opinion represc nt the impact of the different remdings
as an example NUM a and NUM b present their resulting structur d event types
the relation r has to be understood as characterizing the e i as opportunities for peter to point to specific numbers
this choice triggers the structuring of the ps into the brockground event type c 13ao i l
the frequencies of generating all the alternatives vary from one sentence to another
this feature enables the system to occasionally discover less obvious interpretations of word boundaries
for linguistic phenomena not yet covered suboptimal solutions may sometimes be generated
an overview of our classification of relations is shown in figure NUM
the mutual information approach is similar to the relaxation approach in principle
with the rapid rate at which the availability of information is increasing it is important to make access to this information easier
this section gives a brief overview of these approaches and argues that for ia tasks the frame based approaches are the most suitable
it processes this new dialogue state and constructs an interaction template that determines what feedback should be provided to the user
this provides added robustness although lack of a deep structure in the parse sometimes causes the pragmatics component to miss useful information
termlnology extraction the aim of this experiment was to test the ability of the method to capture relevant concepts in the sublanguage
the two layered architecture ensures that the overall dialogue progresses at a domain independent level and keeps the domain independent and domain specific states separate
however the decrease in temperature in our system is not necessarily monotonic
these parameters can be estimated as maximum likelihood estimates mles such that the estimate of 8i is
however if some features are of questionable value the naive bayes model will continue to utilize them while sequential model selection will disregard them
the significance of a model is equal to the probability of observing its reference g in the x NUM distribution with appropriate dof
we discuss related work in section NUM and close with recommendations for search strategy and evaluation criterion when selecting models for word sense disambiguation
suppose there is a training sample where each sense tagged sentence is represented by the feature variables f1 fn NUM s
in this paper word sense disambiguation is cast as a problem in supervised learning where a classifier is induced from a corpus of sense tagged text
each point on this plot represents the accuracy of the models selected for a word by the same evaluation criterion using bss and fss
bss aic finds the most accurate model for NUM of NUM words while fss aic finds the most accurate for NUM of NUM words
however the interactive check s action part should be a warning and not a roll back otherwise the system would exclude that polysems can be arranged in a generic hierarchy as superconcept and subconcept
term weights for document vectors can be computed making use of wellknown formulae based on term frequency
it is not at all clear what the linguistic significance of we have illustrated a general formal framework for expressing theories of syntax based on axiomatizing classes of models in l NUM this approach has a k p number of strengths
so while both theories accomplish agreement between filler and gap through marking a sequence of elements falling between them the gb account marks as few as possible while the gpsg account marks every node bf the spine of the tree spanning them
vz bar NUM x a privileged pas x pas x
while it is unlikely that every theory of syntax with an explicit derivational component can be captured in this way for those that can the logical re interpretation frequently offers a simplified statement of the theory and clarifies its consequences
a natural next step in the evolution of constraint based grammar formalisms from rewriting formalisms is to abstract fully away from the details of the grammar mechanism to express syntactic theories purely in terms of the properties of the class of structures they license
while the linguistic significance of individual results of this sort is open to debate they at least loosely parallel typical linguistic concerns closure properties state regularities that are exhibited by the languages in a class normal forms express generalizations about their structure
more importantly by examining the limitations of this definition of chains and in particular the way it fails for examples of noncontext free constructions we develop a characterization of the context free languages that is quite natural in the realm of gb
following the terminology of gkp s we can identify the set of nodes that are prohibited from taking feature f by the combination of the id rules ffp cap and hfc as the set of nodes that are privileged wrt f
but the fundamental restriction of l NUM k p is that all predicates other than monadic first order predicates must be explicitly defined that is their definitions must resolve via syntactic substitution NUM into formulae involving only the signature of lk p
in gpsg in contrast many universals are in essence closure properties that must be exhibited by human languages if the language includes trees in which a particular configuration occurs then it includes variants of those trees in which certain related configurations occur
also many part of speech tagging systems are only concerned with resolving ambiguity not dealing with unknown words
NUM NUM articles NUM million words
formally assume that each noun phrase is generated using a word modification structure
a principal difficulty in noun phrase structure analysis is to resolve such structural ambiguity
a fast statistical noun phrase parser has been developed based on the probabilistic model
moreover fagan fagan NUM found that syntactic phrases are not superior to simple statistical phrases
a fast and robust noun phrase parser is a key to the exploration of syntactic phrase indexing
then each indexing set is passed to the clarit retrieval engine as a source document set
heavy construction industry group different combinations of the three kinds of terms can be selected for indexing
the author is especially grateful to david a evans for his advising and supporting of this work
however single words are often ambiguous and not specific enough for accurate discrimination of documents
the parser works fast and can be scaled up to parse gigabytes text within acceptable time
NUM seconds for an article of th test set
the second category of oov words represents the forms which have been identified as proper names
we decided to test the module on a corpus containing forced oov words
when working with corpora we are faced by the evolutionary aspects of a given language
part of speech disambiguation has recently been tackled best with data driven techniques
compares very favourably with other systems c f NUM NUM NUM NUM
refinement of the finite state software is still underway
lexeme oriented constraints could be formulated for some of these cases
also the ambiguous sentences are represented as regular expressions
stralnts tagger d2 performed somewhat poorer than usual
note in passing that the ratio NUM NUM NUM NUM NUM NUM
recent state ofthe art part of speech taggers are based on the data driven approach
the finite state parser gave an analysis to about NUM of all words
the choice between adverbs and other cate null gories was sometimes difficult
a correct case output the semantic representation of the analysis result end of analysis
so we get long wyden instead of ton wyden
phonetic translation across these pairs is called transliteration
NUM a translator pronounces it in english
the phrase is covered by NUM where both daughters are marked max f and thus fulfil subclause ii
it has been introduced for convenience signifying that a chain of links to foe exists a condition that could be checked directly in the graph
we recognize two types of symbols unary symbols a b c etc and symbol pairs a x b NUM etc
note that here the dashed arrow from begriiflt to ihren besuch is overruled right away since the accented begriiflt is strictly tied to foc
this is a conceivable solution however declarative perspiguity would be sacrificed for a very moderate benefit considering the main point of this paper
the phrase ihren besuch is forced into tile bg partition thus the utterance is correctly predicted to be restricted to contexts where besueh is given
this paper shows that a fully expressive underspecified representation of is an be effectively composed by linguistic principles circumvening the eoinputational problems that the disjunctive analyses of existing theories pose
a sample analysis for NUM a slight simplifi ation of 25b is give n in fig NUM
in resolving underspecifieation from context it is checked for each node with access to the bg t artition t whether its f skeleton is entailed by an antecedent in the context
they can be used to locate all kinds of expressions that can be described by a regular pattern such as proper names dates addresses social security and phone numbers and the like
we present an alternative method symbolic based on the simplification of parse trees
subjects clustering ontology development robust parsing knowledge acquisition from corpora computational terminology
this merging process guaranteed the probabililty to be nonzero whenever the word distributions are
figure NUM example of a strongly connected component mc corpus
unlike a morphological rule this rule does not ask to check whether the substring preceeding the ing ending is a word with a particular pos tag
this technique does not require specially prepared training data and employs fully unsupervised statistical learning using the lexicon supplied with the tagger and word frequencies obtained from a raw corpus
way described for the NUM operator of the morphological rules and then infrequent rules with f NUM are filtered out
the a operator is applied to each entry in the lexicon in the 2usua ly we set this threshold quite low NUM NUM
to select best performing guessing rule sets we suggested an evaluation methodology which is solely dedicated to the performance of part of speech guessers
using these training data three types of guessing rules are learned prefix morphological rules suffix morphological rules and ending guessing rules
a good way to do that is to divide it by a value which increases along with the increase of the length
the acquired guessing rules employed in our cascading guesser are in fact of a standard nature and in that form or another are used in other posguessers
next from these sets of guessing rules we need to cut out infrequent rules which might bias the further learning process
the acquisition of word pos guessing rules is a three step procedure which includes the rule extraction rule scoring and rule merging phases
figure NUM some possible integration paths for het erogeneous components
the naming service supports a limited form of persistency for storing bindings
it will also be available as part of the gate system distribution
an initial version of this architecture has been developed by vani mahesh
research reported in this paper is supported by the dod contract mda904 NUM c NUM
this pre processing can not usually be fully automated and is therefore costly
figure NUM describes the java idl compiler and java door orb interaction
figure NUM java idl compiler java door orb inter action
a naming service provides access to documents and collections via their names
each component server acts as a wrapper and several solutions are possible
the number of items that user should fill in using our system in this experiment is eight where to go and what to do in first day and second day and where to stay kind of accommodation accommodation name and accommodation fee in first night
it then provides implicit feedback through answering the query
this is especially useful in applications without a display queries made over the telephone since it takes time to give more information than is necessary
so it is necessary to induce a similarity metric which reflects this directional property
NUM flight travel information dialogues concerning british airways flights
NUM actual departure date not verified
larry cochran tried to keep a discreet distance away
objects precede their predicates in their base position
keywords alignment bilingual corpus image processing
when they came to seventy ninth street he caught a real break when she crossed over to him and he realized he might be able to squeeze off full face shots
while the evaluation is based on an english chinese bitext the linguistic constraints motivating the algorithms seem to be quite general and to a large extent language independent
by using one of several ways of estimating the lexical translation probability ltp between pairs of source and target words we can turn a bitext into a discrete gray level image
NUM die kinder haben dieseu bericht gelesen
figure NUM hough transform of the test data
that would be a real coup
we expect that automatically leaxned values of parameters can upgrade the performance of the parser
the extended least errors recognition algorithm can handle not only terminal errors but also nonterminal errors
m ta o is the cost of a mutation error for a nonterminm symbol
dele is the cost of a deletion error for a nonterminal symbol
our robust parser can recover these extragrammatical sentences with NUM NUM accuracy
parameter adjustment we chose the best parameters of heuristics by executing several experiments
a deletion oeinsertion mutation deletion insergiort
to cope with this problem we adjust error values according to the following heuristics
in the limit this approach degrades gracefully into word for word translation with the most likely translation of each input word being selected
in this section some of the methods that have been used in word prediction for non inflected languages are summarised
the inevitable consequence is that rdg often produces multiple parses even for a simple sentence
to do so it classifies the phrases according to grammatical categories and syntactic attributes
for the example sentence the mutual information for the ambiguous relations are as follows NUM
we evaluate the various possible structures according to the mutual information between modifiers and particle modificants
note that in the pattern a and b l NUM the modificant precedes the modifier
each entry includes syntactic information concept identifiers a numerical code and the number of occurrences in the corpus
our method selected the most likely relation among the multiple generated in NUM of the cases
in some cases there is no particle and the modificant directly precedes the modifier see example in section NUM NUM
kay NUM NUM and kaplan NUM
planning is complete when the explanation planner has traversed the entire edp
first the results of the evaluation call for further analysis and experimentation
the explanation planner then applies the edp by traversing its hierarchical structure
NUM evaluation of all explanations by the second panel of domain experts
ideally we would like to measure the coherence of explanations
single explanation restriction no judge received two explanations of the same concept
the remaining NUM experts were assigned to the judging panel to evaluate explanations
when it visits the output actor fates topic the inclusion condition is not satisfied
applying this discourse knowledge the system retrieves views from the knowledge base
to solve the sparseness of the data he applies a singular value decomposition
this occurs when two words are substituted for a single word or when an insertion is classified as a substitution
we are given o j a source french string fl fl fj f l which is to be translated into a target english string c el ei el
we motivate the modifications to the basic algorithms and justify them experimentally by exhibiting their contribution to improvement in performance
this paper presents an analysis of temporal anaphora in sentences which contain quantification over events within the framework of discourse representation theory
when one is a state and one an event then the time index of the state includes that of the event cf
eventualities are interpreted with respect to the rpt events are taken to follow the current rpt while states include it
figure NUM shows a drs for the first two sentences of this discourse according to hinrichs and partee s analysis
this separation allows for an analysis in drt of temporal subordinate clauses in quantified sentences which avoids partee s problem altogether
when clauses for example introduce a new reference time which is ordered after the events described in the preceding discourse
an analysis of the mechanism of temporal anaphoric reference hinges upon an understanding of the ontological and logical foundations of temporal reference
the main clause triggers the introduction of an event marker e and its location time marker t with the drs condition e c t
but unrestricted before is analyzed as some time before and thus the problem arises
the three tagsets used by the annotation tool for words phrases and edges are variable and are stored together with the corpus
the kappa coefficient is calculated from a confusion matrix that summarizes how well an agent achieves the information requirements of a particular task for a set of dialogues instantiating a set of scenarios
the remainder of this section explains the measures ovals in figure NUM used to operationalize the set of objectives and the methodology for estimating a quantitative performance function that reflects the objective structure
normalization of the predictor factors and ci to their z scores guarantees that the relative magnitude of the coefficients directly indicates the relative contribution of each factor
head transducers were introduced in alshawi 1996b where the symbols in the source and target sequences are source and target words respectively
this problem is easily solved by normalizing each factor x to its z score n x o x where cr is the standard deviation for x
this research is a step in the same direction
all of these adjectives are therefore gradable
the two features are typically confused in the literature
NUM NUM the ontological approach to the meaning of adjective types
lcb raskin sergei rcb crl nmsu
given the paucity of relative adjectives in english because of the adjectival use of nouns the denominal adjective lr 12i produces around NUM entries in our english corpus
cases like impregnable above raise another significant issue
gaps of this kind abound throughout the corpus
nevertheless rottable sounds less acceptable than perishable
we also conclude that events that occur only once in the training set have major impact on similarity based estimates
the mle for the probability of a word pair wl w2 conditional on the appearance of
therefore we only ran comparisons between the measures that could utilize unsmoothed data namely the lt norm l wx w the total divergence to the average a wx w and the confusion probability pc w lwx
then the general form of similarity model we consider is a w weighted linear combination of predictions of similar words
is bounded ranging between NUM and 2log2 and smoothed estimates are not required because probability ratios are not involved
however it may be desirable to restrict NUM wl in some fashion especially if NUM NUM is large
however using consistent estimates such as the mle we can rewrite pc as follows
figure NUM shows the results on the five test sets using mle NUM as the base language model
an important observation is that all methods including rand were much more effective if singletons were included in the base language model thus in the case of unseen word pairs katz s claim that singletons can be safely ignored in the back off model does not hold for similarity based models
book x a n l x n syntax n NUM t syntax concerns x syntax b s l r havlng npj
actually the robust parsing strategy articulates around a single parser which is used iteratively according to the anomalies encountered
the algorithm is robust enough to use on noisy texts such as those resulting from ocr input and on translations that are not very literal
on the other hand we are able to filter the sentence meanings on a linguistic basis
figure NUM shows the correspondence between english and italian synsets for the verb scrivere write
the result of this phase is the extension of the english wordnet with the italian synsets
this iteratire process ended with complex selectional restritions for verbs as the figure NUM shows
we present a prototype of the italian version of wordnet a general computational lexical resource
the analyses produced by the parser are compared with the set of interpretations given by a human
algorithms for the resolution of the ambiguities in the coupling with the english wordnet have been developed
in this paper we presented the approach underlying the italian wordnet a general computational lexical resource
they are preliminary since they have been obtained on a limited number of sentences NUM
the probability is calculated based on word co occurrences
fig NUM structure of bunruigoihy6 bgh
a simple top down search in which the cluster with the highest probability is followed at each level allows only one path leading to a single leaf NUM digit class code
we conducted cross validation on nouns appearing in bgh and the judgement of correctness was done automatically while uramoto used unknown words as test cases and decided the correctness on a subjective basis
NUM obtain from the cod the concept identifiers for the modificant there may be multiple meanings and the concept identifiers with the number of their occurrences in the corpus for the modifiers which occur with the particle modificant pattern
the average number of codes assigned was NUM NUM
classification was conducted for each strategy as follows
the results were averaged over these NUM trials
but we would probably not use the term genre to describe merely the class of texts that have the objective of persuading someone to do something since that class which would include editorials sermons prayers advertisements and so forth has no distinguishing formal properties
the framework decouples task requirements from an agent s dialogue behaviors supports comparisons among dialogue strategies enables the calculation of performance over subdialogues and whole dialogues specifies the relative contribution of various factors to performance and makes it possible to compare agents performing different tasks by normalizing for task complexity
people who go into a bookstore or library are not usually looking simply for information about a particular topic but rather have requirements of genre as well they are looking for scholarly articles about hypnotism novels about the french revolution editorials about the supercollider and so forth
if this assumption is violated multiple pass parsing is still possible but some of the algorithms need to be changed
the extra time of running two passes is more than made up for by the time saved in the second pass
perhaps our lack of success is due to differences between our grammars which are fairly different formalisms
add p to chart length start figure NUM second pass parsing algorithm
in traditional beam search only the probability of a nonterminal generating the terminals of the cell s span is used
we introduce two novel thresholding techniques global thresholding and multiple pass parsing and one significant variation on traditional beam thresholding
the probability of node li n k is just its prior probability times its inside probability as before
we run two passes the first of which is fast and simple eliminating from consideration many unlikely potential constituents
it then changes this parameter in the correct direction to move towards et and possibly overshoot works
a simplified version of the algorithm is given in figure NUM figure NUM shows graphically how the algorithm
performance function estimation should be done iteratively over many different tasks and dialogue strategies to see which factors generalize
paradise uses a decision theoretic framework to specify the relative contribution of various factors to an agent s overall performance
formally we calculate the phrase category q and at the same time the sequence of grammatical functions g g1 gk on the basis of the sequence of daughters t t1 tk with argmax maxpq g t
the table shows a mother and a daughter node category the frequency of this particular combination sum over NUM test runs the grammatical function assigned manually and its frequency and the grammatical function assigned by the tagger and its frequency
the table contains the following information the number of all mother daughter relations i.e. number of words and phrases which are immediately dominated by a mother node of a particular category the overall accuracy for that phrasal category and the accuraciees for the three reliability intervals
has marked das the ap bonusprogramm and the pp as a constituent of category np and the tool s task is to determine the new edge labels marked with question marks which are from left to right nk nk nk mnr
we ignore maximal trees of depth one in both corpora as these correspond to indications of textual units rather than sentence internal structural markup
the start of the susanne corpus is shown in the table here the o s nns s
we present here a general design for and modular implementation of an algorithm for computing areas of agreement between structurally annotated corpora
in this section we describe the implementation of the above procedure which abstracts away from details of the markup used in any particular corpus
given a discourse referent x and a udrs NUM picks out components of the udrs corresponding to proper names indefinite and genuinely quantificational nps with x as implicit argument
again in the absence of scope constraints this results in n scopings for n quantifiers q everything else being equal this establishes correctness with respect to sets of disambiguations
they correspond directly to the construction principles discussed in section NUM the first one deals with genuinely quantificational nps the second one with indefinites and the third one with proper names
it provides a model theoretic interpretation and an inferential component which operates directly on underspecified representations for f structures through the translation images of f structures as udrss
textual definitions of udrss are based on a labeling indexing of drs conditions and a statement of a partial ordering relation between the labels
finally unlike qlf the udrs formalism comes equipped with an inference mechanism which operates directly on the underspecified representations without the need of considering cases
due to space limitations we can provide only a brief description of core contributor relations and omit altogether the analysis of the example into the minimal rda units of state and action units matrix expressions and clusters
as shown in figure NUM we can conclude that when a relation is going to have a cue that is semantically similar to the cue of a relation it is embedded in an alternative cue must be chosen
because it is important to detect the contrast between occurrence and nonoccurrence of cues the corpus study must be be exhaustive i.e. it must include all of the factors thought to contribute to cue usage and all of the text must be analyzed
for each contributor in a segment we analyze its relation to the core from an intentional perspective i.e. how it is intended to support the core and from an informational perspective i.e. how its content relates to that of the core
multiple segments arise when a tutor s explanation has several steps e.g. he may enumerate several reasons why the student s action was inemcient or he may point out the flaws in the student s step and then describe a better alternative
two cues may be intersubstitutable in some contexts but not semantic alternatives e.g. and and because or they may be semantic alternatives but not intersubstitutable because they are placed in different positions in a relation e.g. so and because
then for each pair of cues in the same turn it was categorized in two ways NUM the embeddedness of the relations associated with the two cues and NUM whether the two cues are the same alternatives or different
segments are internally structured and consist of a core i.e. that element that most directly expresses the segment purpose and any number of contrlbutors the remaining constituents in the segment each of which plays a role in serving the purpose expressed by the core
mathemat s and computer saence dept
each sdu is classified first as either relevant to the scheduling domain in domain or not relevant to the scheduling domain out of domain
the lexicon used is composed of NUM NUM items
various applications at lia need a large lexicon such as the automatic generation of graphical accents in a french text language models for a dictation machine the grapheme to phoneme transcription system etc
we carried out similar experiments to those presented above
a NUM gram model seems natural for solving this problem
finally it is important to point out that the approach chosen in this study remains independent of the processed language as long as the hypotheses made by the morpho syntactic devin are satisfied
NUM NUM of oov words were correctly labeled as compared to the initial reference and NUM NUM of induced contextual errors were corrected due to attributing a syntactic category to each oov word
the use of a big general dictionary allows us to limit most of the oov words to one of these categories proper names composit words unused flexions neologisms mistakes
the lexicon of oov proper names is limited to the words which have at least NUM occurences in the corpus and for which the most frequent label has a frequency of at least NUM
NUM NUM contextual tagging using the devin for
the results obtained satisfy this goal
note that this algorithm performs greedy search
computational linguistics volume NUM number NUM proper name identification
the heart of our solution to the problem of assigning context based values of topical significance to all words in a text can be summed up in the following formula this is essentially a sigmoid function with the range varying between two and ten as shown in figure NUM the constants scale and translate the function to yield the desired behavior which was derived empirically
what sets spud apart is its simultaneous construction of syntax and semantics and the tripartite lexicalized declarative grammatical specifications for constructions it uses
the processing of this example may seem simple but it illustrates the way in which spud integrates syntactic semantic and pragmatic knowledge in realizing sentences
in order to avoid having to recompute successfully generated partial results within the ego such results are stored during processing together with the part of the input structure and the current category
analyzing dependencies of criteria the solution fulfilling most criteria is generated first if sets of mutually independent criteria are applied fulfilling one criterion must not exclude the applicability of another one
on the basis of the knowledge in figure NUM a rhetorical planner might decide to answer by describing state have27 as an s and lose5 likewise
the rules are encoded in tgl a language that allows the definition of canned text items templates and context free rules within the same formalism
smood expresses sentence modalities in null cluding sentence type time a specification of which constituents to topicalize in a german declarative sentence etc
only little attention has been paid so far to the many small and simple applications that require coverage of a small sublanguage at different degrees of sophistication
the recomputation is rendered possible by adding in addition to storing terminal strings in the table the underlying calls to the inflection component as well
tg NUM implements a simple strategy that processes those backtrack points first that have conflict sets containing c rules and preferrably choses a c rule from a conflict set
in particular the context free backbone implicit in any solution and the restrictions to side effects mentioned above keep the structural effects of tgl rules local
a weight is specified by the user e.g. a feeding system and expresses the relative importance of the criterion being fulfilled in a solution
note that first and second person pronotms in the test data are classified as type bare in the table
because of its practical performance however it proved to be a satisfactory substitute for the generate and test strategy
given a NUM functional descriptions may also employ syntactic sugar for purposes of legibility
in particular one must create only left and right auxiliary trees as opposed to wrapping auxiliary trees
any derivation in g can be converted into a derivation in g by doing the reverse of the conversion above
second lexicalized grammars are finitely ambiguous because every rule introduces at least one lexical item into the resulting string
the algorithm above can be extended to take advantage of the fact that the elementary trees in an ltig are lexicalized
in this equation n is the length of the input string and ig i is the size of the grammar g
comparison with tag is that they prevent wrapping adjunction from occurring by preventing the creation of wrapping auxiliary trees
supertrees are shared by explicitly recording the fact that there are multiple alternatives for the nth child of a some node
therefore the parser can be changed so that a prediction rule is triggered at most once for any j and p
the acquired gr mrn t is normally in chomsky normal form
NUM it is important to notice that rule NUM constrains the realization of the most highly ranked element of the cf un that is realized in un t given that pronominalization is used
thus the discourses in NUM NUM suggest that grammatical role is a major determinant of the ranking on the cf with subject object s other
thus the focus of our investigation is on interactions among choice of referring expression attentional state the inferences required to determine the interpretation of an utterance in a discourse segment and coherence
centering accommodates these differences by allowing the noun phrase the vice president of the united states potentially to contribute both its value free interpretation and its value loading at the world type to cf 25a
therefore the ambiguity in the dialogs contributes much to the errors
underlying these claims is the most fundamental claim of centering theory that to the extent a discourse adheres to centering constraints its coherence will increase and the inference load placed upon the hearer will decrease
the excite sense of fired has with as a functional collocate and enthusiasm as an illustrative collocate in cide and thus scores NUM NUM NUM NUM NUM for the fu st sentence and NUM NUM NUM NUM for the second sentence
without utterance 16c this sequence like the sequence in NUM is unacceptable unless it is possible to consider the introduction of a second person named john
in discourse NUM utterances f and g exhibit the same kind of misdirection as do utterances 3d and 3e in discourse NUM
as a parameterized trained model if such a transition were never observed the model backs off to a less powerful model as described below in ss3 NUM NUM on p NUM
the number of initial rules is NUM
since we wished to test the effectiveness of using similarity for unseen word cooccurrences we removed from the test set any verb object pairs that occurred in the training set this resulted in NUM unseen pairs some occurred multiple times
recall that the accessors return views which are subgraphs of the knowledge base
this is to be expected since homograph contexts are quite distinct and hence it is a much simpler task to disambiguate among a small number of coarse sense classes
similarly the average number of senses per polysemous noun is NUM NUM for the polysemous nouns which account for the bottom NUM of noun occurrences in the brown corpus
for instance the word red might well act like a generic color word in most cases but it has distinctive cooccurrence patterns with respect to words like apple banana and so on
recent work on intelligent example selection techniques suggest that the quality of the examples used for supervised learning can have a large impact on the classification accuracy of the induced classitier
recent advances in large scale broad coverage part of speech tagging and syntactic parsing have been achieved in no small part due to the availability of large amounts of online human annotated corpora
there are basically three kinds of semantic patterns that are utilized in a corelex lexicon hyponymy sub supertype information in the formal role meronymy part whole information in the constitutive role and predicate argument structure in the telic and agentive roles
given the benefits of a wide coverage high accuracy and domain independent wsd program i believe it is justifiable to spend the NUM man years of human annotation effort needed to construct such a sense tagged corpus
however the remedy is not to throw out word senses completely but rather to work on a level of sense distinction that is somewhere in between homograph distinction and the refined worvnet sense distinction
in the corelex approach these nouns are given the same semantic type which is underspecifled for any specific sense but assigns them consistently with the same basic lexical semantic structure that expresses the regularities between all of their interpretations
the underspectfied semantic type that corelex assigns to a noun provides a basic lexical semantic structure that can be seen as the class wide backbone semantic description on top of which specific information for each lexical item is to be defined
these results clearly demonstrate the superiority of the proposed models for deep structure disambiguation
sgp consists of a training module tm an application module am and the subgram2but note our approach does not depend on a flat representation of logical forms
for instance one of the headnoun preposition headnoun patterns is the following that is used to detect part null clearly not every syntactic construction that fits this pattern is to be interpreted as the expression of a part whole relation
the core idea behind partial matching is that in case an exact match of an input mrs fails we want at least as many subparts as possible to be instantiated
different models for case identification and word sense disambiguation are further derived below
NUM expansion a successfully retrieved template templ is expanded by deterministically applying the rules denoted by the non terminal elements from the top downwards in the order specified by tempi
the parser analyzes the part of speech sequences and then produces corresponding parse trees
finally the parameters are adapted by using the robust discriminative learning algorithm
only less than NUM of errors arise due to incorrect parts of speech
table NUM summary of the performance for the deep structure disambiguation system
in total there are NUM NUM distinct senses for those NUM NUM words
when the ratio p w21wl p w2 is large we may think of w2 as being exceptional since if w2 is infrequent we do not expect p w21wj to be large
indeed the generation problem is shown np complete in this sense
questions under the new paradigm include these generation
minds used predictions based on dialog context to reduce perplexity
minimizing this sum achieves a kind of left to right iterative footing
let us fill in the details of NUM
let tiers denote the fixed finite set of constituent types
similarly sa is replaced by sb
on the one hand are they expressive enough to describe real languages
NUM c x fll and NUM cf
algorithm a dynamic programming algorithm that prunes away all but the minimum weight paths in
suppose the input is simply stem
for instance the pairs of objects that are introduced by the type informationephysical book journal scoreboard are addressed as the complex objects x information y physical in discourse
as we focus in this paper on alternatives for pslm we will not consider this approach here that is for the rest of this paper pr w2 wl pslm w21wl
both possibilities present increased opportunities for systems to undergenerate or overgenerate
in figure NUM c NUM lcb i rcb identity of line NUM states that state NUM of the transducer to be built is of type identity and refers to the initial state i NUM of t7 q represents the current state and n the current number of states
text strings that are to be annotated are termed markables
this test measures the amount of variability between the annotators
it was very small only NUM articles
all except the scenario template task are defined independently of any particular domain
in all seven sites participated in the muc NUM coreference evaluation
note that c q it where it is the number of states emmanuel roche and yves schabes deterministic part of speech tagging in t for each q c q let s q e q be a state s t
let NUM be a y decomposition of NUM y then NUM NUM NUM is a y decomposition of v strictly smaller than NUM l mdy v which contradicts the minimality of mdy v
null when the discourse processor computes a chain of inference for the current input sentence it attaches it to the current plan tree
for example consider the case where tuesday april eleventh has been suggested and then the response only makes reference to tuesday
some phrases are shorthand e.g. r NUM uaapuro should be translated as word processing
this is largely because of cases like the one in figure NUM sentence NUM is an acceptance to the suggestion made in sentence NUM
biggest difference in terms of speech act recognition between the two mechanisms is that extended tst got more correct where standard tst got more acceptable
both suggestions remain in focus as long as the node which immediately dominates the parallel suggestions is on the rightmost frontier of the plan tree
we intend to extend this research by exploring more fully the implications of our extension to tst in terms of discourse focus more generally
potential intentions are expressed within portions of dialogues where speakers negotiate over how to accomplish a task which they are committed to completing together
recently however beginning with work at atr there has been an interest in making use of discourse information in machine translation
such measures must take the overall dialog context into account
hereafter we will refer to this method as fmm
snlds can not succeed without a strong base of domain knowledge
the esst testing vocabulary contains NUM words
the most interesting ranking of semantic entropies is among the verbs including present and past participles
given a bilingual list of known translation pairs i.e.
to generate pre ocr text we collected NUM NUM characters worth of katakana words stored them in a file and printed them out
translation precision increases on the average by NUM NUM
figure NUM most correlated seed words with debentures
evaluation shows a precision of about NUM
by allowing n top candidates the accuracy improves as shown in the graphs for NUM words output in figure NUM i.e. a translation is correct if it appears among the first n candidates
since these seed words have relatively low frequencies compared to the corpus size of around NUM million words for the wsj text we chose the segment size to be that of an entire paragraph
we have described a statistical word signature feature the word relation matrix that can be used to find matching pairs of content words or terms in a pair of same domain non parallel bilingual texts
NUM evaluation NUM matching japanese terms to english m evaluations are also carried out on the wall street journal and nikkei financial news corpus matching technical terms in japanese to their counterpart in english
their output is in set a our system then proposes two sets of outputs NUM for each japanese term our system proposes the top NUM candidates from the set of NUM noun phrases
miscategorized as being an instance of i0 il il incorrect and precision and recall of the categorization of t
it may seem worrying that some of the tags are assigned a high number of clusters e.g. NUM for n NUM for adn
for example transitive verbs and prepositions belong to different syntactic categories but their right contexts are virtually identical in that they require a noun phrase
null another argument for the two step derivation is that many words do n t have any of the NUM most frequent words as their left or right neighbor
if the counts of neighbors are assembled into a vector with one dimension for each neighbor the cosine can be employed to measure similarity
this proposal was implemented by applying a singular value decomposition to the NUM by NUM matrix of left context vectors and clustering the resulting context vectors into NUM classes
while the proposed algorithm is not successful for all grammatical categories it does show that fully automatic tagging is possible when demands on accuracy are modest
the NUM recorded and transcribed dialognes were scenario based and covered the full functionality of the system
flu provides a new departure time s do you still want discount
it was developed from examples of user mistmderstandings of the system due to reasoning by analogy
NUM can not be subsumed by gpi informativeness which ignores communication faihne
do not say that for which you lack adequate evidence
provide ability to initiate repair il system understanding has failed
in this paper we have attempted to construct an algorithm for fully automatic distributional tagging using unannotated corpora as the sole source of information
finally our procedure induces a hard part of speech classification of occurrences in context i.e. each occurrence is assigned to only one category
it is indeed frequent that an ambiguity relative to a fragment appears disappears and reappears as one broadens its context
so far a word that occurs only in a and not in b contributes zero to jan
the definition captures f structures that are complete coherent and
c ql rilqi NUM m in the running total for this derivation
for example the entry for v might be for translating an idiom involving wi as a modifier
since different tasks required different configurations we summarize the relevant system components in the we also note that some major system components were designed and implemented during our preparations fo r muc NUM including all the string specialists all the consolidation routines template parsers and text markin g interfaces to translate muc NUM training documents into formats compatible with our various trainable components and a completely new implementation of wrap up
with these factors in mind we decided to run resolve on the muc NUM co task but to constrain its input s o that it only attempted to find coreferent relationships among references to people and organizations references tha t were potentially relevant to the muc NUM te and st tasks
another week was spent modifying feature extractors used in the muc NUM english joint ventures domain and adding new features designed specifically for the muc NUM co task especially resolution of pronominal references to people and organizations and the st task especially associating persons with their roles
we can see this in lexicalized grammatical theories head driven parsing and generation and statistical disambiguation based on lexical associations
although this resulted in some improvement in testing so far the improvement has not been statistically significant
NUM for each dependent wi recursively generate a subtree with probability p d iwi ri
for translation we use a model for mapping dependency graphs written by the source language head automata
for each graph component the main steps of the search process described non deterministically are NUM
since we are analyzing bottom up with generative model automata the algorithm runs the automata backwards
head automata are formally more powerful than finite state automata that accept regular languages in the following sense
drcc assigns the structural status by applying the following rules
now we can say that we can handle periods exclamations most unpaired singlequotes most commas an d some dashes
these criteria are somewhat over strict as in some cases more than one tag could be considered acceptable e.g.
this distinction is preserved even if we generalize the selection rule to select a set of candidate contexts
the nonmonotonic extension model nem outperforms all other models for all orders using vastly fewer parameters
given the logarithmic nature of codelength and the scarcity of training data this is a significant improvement
it rests on the assumption that adding a new context does not change the model s performance in the shorter contexts
the first consequence is that the context dictionary is unnecessarily large because most of these contexts are redundant
the fourth term assigns labels ie symbols from e to the edges in the tree
in such a situation the shorter context is only used when the longer context was not used
word stemming spanish morphology chinese and japanese word segmentation and multiple codeset indexing all help to ensure that every lexical form related to the search term is found
through a process of task oriented user centered design and iterative refinement computer tools have been developed that take advantage of the strengths of machines to support the strengths of humans
changes to the system included the addition of crl s concordance tool x concord improved multilingual information retrieval and enhanced chinese japanese word identification and segmentation
xconcord shows the results in a kwic display and also as seen in the smaller bottom window in figure NUM the complete sentence for the selected kwic line
the tipster ii architecture makes it possible to integrate a variety of information retrieval extraction and text processing systems in ways that help analysts address more complex problems
tipster i was an effort to find electronic methods for information retrieval and information extraction tipster uses texts from a variety of sources including newspaper articles and wire service reports
in the context of crl s work in nlp however user observations and task analysis combine to define directions for additional nlp and user interface research and objectives
further text is fully integrated with the windowing software so it is easy to copy and paste words and phases found in these resources directly into the user s target documents
the initial interface design focused on methods for displaying editing and marking up multilingual text and the identification of tools and methods for accessing electronic equivalents of paper based resources
these differences can be illustrated by a comparison with the penn treeba nk
fiat representation structures complete absence of ernl ty
in a multi speaker discourse dialogue for example a negotiation is sell able to de tide which of the two possibilities the positive or the negative fact is to be incht h in the knowledge base
m aim of the mo l l is to rot resent dynamically the knowledge associated with a list mrs at a given point time of its progression
in addition to pure annotation we can attach conlments to structures
tim framework of the rllo h l is linguistic pragmatics wc want to represent the linguistic marks of NUM ragmaties and not the pragmatics of ail al l lieation
negation about denominations negation can focus on the denominations a nd others names sub objects l enyi g a denomination or other natncs means to denying a property of the object
further differences concern the a ttachment of the degree modifier ehr
consequently the negation cart have art effect in object based knowledge representation model such as to update properties of objects but it rztrely provobe n incoherence between the objects of discourse and the objects of knowledge base
so far about NUM sentences of our corpus have been annotated
lit the sentence only peter came we pose a property mmttt peter then only introduces the class and of the same time indicates that the class contains only peter
this entails that tit negation may focus on only negation that the class contains only one individnm or on the property pcter caste then asserted about only peter
only NUM of these concept nodes were in the original autoslog dictionary
therefore we introduced a human in the loop to weed out the unreliable definitions
all of the appropriate heuristics are fired
figures NUM and NUM show the scatterplots
consequently a new training corpus must be annotated for each domain
should the entire noun phrase be tagged or just the head noun
suppose we build an english phrase generator that produces word sequences according to some probability distribution p w
NUM NUM evaluation of clustering in grammar acquisition
in addition we will explore the possibility of combining machine learning results with manual encoding of discourse knowledge
currently the mdr uses an ordered list of multiple orderer ks s for each anaphoric type cf
the anaphoric types are sub divided according to more semantic criteria such as organizations people locations etc
finally we compare our algorithms with existing theories of anaphora in particular japanese zero pronouns
our goal is to customize and evaluate anaphora resolution systems according to the types of anaphora when necessary
features of either an anaphor or an antecedent such as syntactic number values or binary features i.e.
features concerning relations between the pairs such as the positional relation between an anaphor and an antecedent
the small test results NUM sentences from NUM articles had high success rate of NUM
however the mlr results seem to indicate the limitation of the mdr in the way it uses orderer ks s
it results in poorer performance than the mlr NUM NUM NUM NUM and NUM configurations and the mdr
the detail of divergence is iuustrated below
however the algorithm was only tested on coarse level senses and not on the refined sense distinctioas of wordnet which is the required sense granularity of our approach
to circumvent the notation bottleneck faced by kenmore our approach exploits general a orithms and resources for the disambiguation of do i specific semantic classes
one is that the part that is being replaced may at the same time serve as the context of another adjacent replacement
for example in NUM the international telephone services many countries have established are very reliable
finally control changes because of the response either to NUM return from this subdialog with success NUM continue in this subdialog searching for a success having received an unsuccessful response NUM enter special processing to deal with a need for clarification or NUM interrupt processing to jump to some other subdialog
they were shown the target led displays and given suggestions on how to successfully describe such displays to the system specifically the user should tell what they see present on the display as in the top of a seven is displaying and not describe what does not appear as in the bottom of the seven is missing
so attempts to achieve measurevoltage NUM NUM v continue and involve the rule measurevoltage x y v find voltmeter set voltmeter NUM connect bw com x connect rw y vocalize read voltmeter v
thus the domain processor is asking that test NUM on circuit t1 be performed returning a voltage v zmodsubdialog begins in prolog fashion looking for a rule in the database to prove the goal tl circuit test2 v and it finds the debugging rule tl circuit test2 v set knob NUM measurevoltage NUM NUM v
it is the implementation of statement NUM given above inference NUM if we have learned that an action to achieve or observe a physical state was completed then conclude that the physical state has the appropriate status and that the user knows how to perform the action
adjust knob NUM usercan adjust knob NUM vocalize adjust knob NUM to include a clarification subdialog adjust knob NUM find knob usercan adjust knob NUM vocalize adjust knob NUM
the zmodsubdialog routine is a prolog style interpreter with a number of special features designed for the dialog processing application
we will examine sequentially NUM a theory of task oriented language NUM an implementation of the subdialog feature NUM a method for accounting for user knowledge NUM mechanisms needed to obtain variable initiative and NUM the implementation and uses of expectation
the expectation system scores the likelihood of each meaning and selects the most likely one using its scoring method
however NUM does satisfy the missing axiom for completing the main task step NUM
f wlci NUM t2 f w
as before the lexical productions will constitute the bulk of the rules set
both methods build upon a formalism we recently introduced called stochastic inversion transduction grammars
our preliminary experiments show improved parsing behavior in general compared to generic bracketing grammars
i would like to thank xuanyin xia and eva wai man fong for data conversion assistance
where the horizontal line from figure NUM corresponds to the level of bracketing
the latter is simply a parallel corpus in which both halves have been independently bracketed
verbs auxiliary verbs nouns adjectives pronouns prepositions determiners figure NUM lexical productions of a stochastic constituent matching itg
itgs impose two desirable classes of constraints on the space of possible matchings between sentences
under the stochastic formulation the objective of parsing is to find the maximum likelihood parse for a given sentence pair
this is a much more interesting kind of annotation if it can be accomplished especially for machine translation applications
for example until in the following suggests that the first utterance specifies an ending time
extra phenomena such as nominal subject inversion impersonal middle constructions some causative constructions or free order of complements have been added
adjunction allows the extended domain of locality of the formalism all trees anchored by a predicate contains nodes for all its arguments
jean made the children sit dimension NUM the canonical subcategorization h ame this dimension defines the types of canonical subcategorization
now the set of tree schemata we intend to describe hierarchically is empty of lexical idiosyncrasies which are in the syntactic lexicon cf
in the case of a clitic the path between the s and v nodes can be specified with the description of figure NUM
the canonical subject argument NUM in a passive construction even when unexpressed is still an argument of the predicate
the link between the s and v nodes is underspecified allowing either presence or absence of a cliticized complement on the verb
the training set contained NUM messages giving rise to NUM coreference sets and the test set contained NUM messages the first two test sets overlapped by two messages which gave rise to NUM coreference sets
we defined a simple greedy approach to merging similar to the one used in fastus in which merging of newlycreated templates is attempted iteratively through the prior discourse starting with the most recently produced object
temporal relations only appear in clusters thus not in the data we discuss in this paper
above belozo encode the number of relations hierarchically above and below the current relation
more specifically in figure NUM inten reldistinguishes two different speaker purposes convince and enable
the most discriminant feature turns out to be the syntactic relation between the contributor and the core
for cue placement the most important factors are syntactic structure and segment complexity
our results make apparent that the structure of segments plays a fundamental role in determining cue occurrence
inlen rel appears in all trees confirming the intuition that the speaker s purpose affects cue occurrence
initially we performed learning on all NUM instances of core contributor relations
given that we have chosen a set of such constraints to impose on our model we wish to identify that model which has the maximum entropy this is the model that assumes the least information beyond those constraints
qualitatively the algorithms produced by error analysis are more intuitive and easier to understand than those produced by machine learning
with each method we have achieved marked improvements in performance compared to our previous work and are approaching human performance
abstracting statistically significant results from the subjects responses is thus the second goal of our study of the segmentation task
as discussed in section NUM we derive discourse segmentations based on the statistical significance of the agreement among our subjects
chafe recorded and transcribed subjects who had been asked to view the same movie and describe it to a second person
however the best machine learning performance is an improvement over our previous best results ea in table NUM
furthermore the results on combining algorithms suggests that with more sophisticated methods results approaching human performance can be achieved
given that the lhs of the rule has a gap there are NUM ways that the gap can be passed down to the rhs head the gap is passed to the head of the phrase as in rule NUM in figure NUM
most importantly the probability of generating the stop symbol will be NUM when the subcat frame is non empty and the probability of generating a complement will be NUM when it is not in the subcat frame thus all and only the required complements will be generated
frames the model could be retrained on training data with the enhanced set of non terminals and it might learn the lexical properties which distinguish complements and adjuncts marks vs week or that vs because
the incorrect structures in figure NUM should now have low probability because ic lcb np c np c rcb s vp bought and prc lcb np c vp c rcb i vp vb was are small
4an exception is the first rule in the tree t0p h h which has probability prop h hltop NUM generate modifiers to the left of the head with probability rl l n l
a couple of complexities are that modification by an sbar does not always involve extraction e.g. the fact sbar that besoboru is initially generates an sbar modifier but specifies that it must contain an np trace by adding the gap feature
in fact if the derivation order is fixed to be depth first that is each modifier recursively generates the sub tree below it before the next modifier is generated then the model can also condition on any structure below the preceding modifiers
for a constituent to be correct it must span the same set of words ignoring punctuation i.e. all tokens tagged as commas colons or quotes and have the same label s as a constituent in the treebank parse
played with a ball and a bat and it is not uncommon for extraction to occur through several constituents e.g. the changes sbar that he said the government was prepared to make trace
in the first sentence of the example output the micro planner has combined the NUM input propositions shown above in figure NUM into a single sentence ms jones is an NUM year old hypertensive diabetic female patient of dr smith undergoing cabg
all the distance features except for close and mid distance received negative hi values suggesting that coreference between close and mid distance templates was more likely than coreference between templates that were very close far away and very far away
in the tipster architecture there is an architectural requirement that all annotations be ultimately associated with spans of a single base text but lt nsl imposes no such requirement
since our linking mechanism uses the sgml entity mechanism to implement the identification of target documents we can use the entity manager s catalogue as a means of managing versions
lt nsl does not require tools to obey any particular conventions for metainformation but once a convention is fixed upon it is straightforward to encode the necessary information as sgml attributes
under both models processing is essentially a loop over calls to the api in each case choosing to discard modify or output unchanged each bit or element
subsequent tools can and often will use the lt nsl api which parses normalised sgml henceforth nsgml approximately ten times more efficiently than the best parsers for full sgml
cqp uses all integerised representation in which corpus items having the same value for an attribute are mapped into the same integer descriptor in the index which represents that attribute
similarly lt nsl has been used to recode the edinburgh maptask corpus into sgml markup a process which showed up a number of inconsistencies in the original non sgml markup
the multext architecture was toolspecific in that its api defined a predefined set of abstract units of linguistic interest words sentences etc and defined functions such as readsentence
the use of normalised sgml and a compiled dtd file means that the overheads of parsing sgml in each program are small even for large dtds such as the tei
for instance indefinites will sometimes corefer with a previously described entity a typical case is illustrated by the coreference between the indefinite a rail depot and the depot introduced in the subject line in the example passage
as outlined in the beginning of this section the way to combine all the heuristics in one single decision is simple
while dgile is a good example of a large sized dictionary lppl shows to what extent the smallest dictionary is useful
table NUM shows the first eleven words out of the NUM which cooccur with vino wine ordered by association ratio
tested accuracy is above NUM overall and NUM for two way ambiguous genus terms showing that taxonomy building is not limited to structured dictionaries such as ldoce
given two definitions that of the hyponym and that of one candidate hypernym this heuristic computes the total amount of content words shared including headwords
in order to test the contribution of each heuristic to the total knowledge we tested the sum of all the heuristics eliminating one of them in turn
this work does not address all the heuristics cited in her paper but profits from techniques that were at hand without any claim of them being complete
table NUM shows an evaluation metric developed by trial and error using the NUM cognate pairs shown in the subsequent tables
to represent a function which requires an np on the left and an np and a pp to the right there is a choice of the following three types using curried notation
if desired it is possible to tune the frequency of selection by changing the variance of p mis or the variance of p i ails for each parameter where larger variances increase the rate of disagreement among the committee members
associative cgs with composition or the lambek calculus also allow strings such as boy with the to be given the type n n predicting very boy with the car to be an acceptable noun
marslen wilson NUM tanenhaus et al NUM
both trees have the same syntactic type however in the first case we want to allow for there to be an s s modifier of the lower s but not in the second
thus increases in the size of a grammar do n t necessarily effect efficiency of processing provided the increase in size is due to the addition of new words rather than increased lexical ambiguity
hence arguments with functional types had to correspond to single lexical items there was no way to form the type np s for a non lexical verb phrase such as likes mary
mary thinks john coming here was a mistake
fm is determined by a probabilistic model m in many applications fm is the conditional probability function pm cle specifying the probability of each class given the example but other score functions that correlate with the likelihood of the class are often used
arrows show the bracket matching operations
compared to the thesauri we had previously modeled and downloaded wordnet NUM NUM offers a richer set of semantic and lexical relations which give rise to new questions of redundancy or consistency cf
an overview of th6 representation used by the parser
the overview of different constituent combination cases in treebank
there are two directions to improve the prediction model
table NUM basic statistics for the chinese treebank
a the segmented and tagged sentence
for instance given 4a above the required equation will be l j s gd s with two possible values for gd ax l j x and ax l j s
let ssem and tsem be the semantic representation of the source and target clause respectively and tp NUM tp n sp NUM sp n be the target and source parallel elements NUM then the interpretation of an soe must respect the following equations
to be more formal we presuppose a finite set g lcb a b c pe pe of color constants and a countably infinite supply lcb a b rcb of color variables
in contrast if the quantified wh focused np does not precede and c command the pronoun as in NUM we only expected himi to claim that hei was brilliant there is no ambiguity and the pronoun can only give rise to a co referential interpretation
tom take sue to al s mother once again we see that the por is a necessary restriction by labeling as primary all occurrences representing a parallel element it can be ensured that only the first solution is generated
like dan golf the process of solving such equations is traditionally called unification and can be stated as follows given two terms m and n find a substitution of terms for free variables that will make m and n equal
5although we do not model information about the surface positions of the expressions from which s and t were created within their respective sentences the coreference module does take such information into account in determining likely antecedents of definite expressions
in contrast to this higher order unification tests for satisfiability by finding a substitution a that makes a given equation m n valid a m a n even if the original equation is not m z n
we will need the so called color erasure imi of m i.e. the formula obtained from m by erasing all color annotations in m we will also use various elementary concepts of the a calculus such as free and bound occurrences of variables or substitutions without defining them explicitly here
its addition will not only affect other words but may also cause other words to be added or deleted
if a NUM the word is predicted to reduce the total description length and is added to the lexicon
they usually do not have competing roles in contrast for instance to hidden nodes in neural networks
given a text document the search algorithm can be used to learn a lexicon that minimizes its description length
for example scratching her nose inherits its meaning completely from its parts while kicking the bucke does not
for example the word wanna is naturally thought of as a composition of want and to with a sound change
not shown are the perturba1a simple composition operator is concatenation but in section NUM a more interesting one is discussed
for instance the sequence the dog occurs much more frequently than one would expect given an independence assumption about letters
since all existing models have flaws patterns will always be learned that are artifacts of imperfections in the learning algorithm
this reduces the counts of water and melon accordingly though they are each used once in the representation of watermelon
speakers goals can certainly be analyzed in many ways
the system we propose addresses two key issues that face developers of speech based natural language dialogue systems
table NUM summary of prediction errors
table NUM cues for modeling initiative
this information is complete and available for each lexical entry
gsa converted these maps into alignments
NUM NUM mapping dom specific hierarchy onto word net
table NUM word sense disnmhiguation results
as is evident our approach outperforms the most frequent heuristic substantially
both algorithmg are used for learning the specific sema tic class of words
j domain specific semantic class disambiguation using wordnet
the results are shown in table i
which we felt is closest in meaning
the results are detailed in table NUM
from NUM the world knowledge of the system would be reinforced by the two stereotypical transitions
NUM NUM running words from section n of the brown corpus texts n01 n08 were hand parsed using the state transition grammar
this paper presents a grammar formalism designed for use in data oriented approaches to language processing
NUM factor out other features which are merely passed from state to state
centre embedding it should be noted that an indefinite amount of centre embedding can be described but only list eg
one or more of the transitions necessary to find a parse path was lacking even after generalizing the transitions
although essential for effective processing the smoothing operations may give rise to new problems
the process of generalizing or smoothing the transition probabilities is therefore seen to be indispensable
for example the simple transducer for a b x in figure NUM can not be sequentialized
a transducer is sequential just in case there are no states with more than one transition for the same input symbol
the size of intermediate states of the computation becomes a critical issue while it is irrelevant for simple phonological rules
additionally to circumvent problems with solely relying on the boolean query a word s definition is also examined in a rudimentary way to check for ke y words that indicate semantic features of the potential referents of this word such as the word someone which suggests a human referent
in the case at hand it means that the internal is disallowed in the context a b a
with that abbreviatory convention a composition of a simple np and vp spotter can be defined as in figure NUM
upper case strings such as upper represent regular languages and lower case letters such as x represent symbols
the number of auxiliary symbols used to encode the constraints has a critical effect on the efficiency of that computation
the formulation of the longest match constraint is based on a suggestion by ronald m kaplan p c
to make the construction simpler we can start by defining auxiliary symbols for the basic regular patterns
this time the processing time is only NUM NUM s only NUM structures are created and NUM items are derived
these semantic parameters are instantiated using a knowledge base cf
prostr3edi NUM kr3es t3rns ky2ch z31votni 2ho prosao l s i q
we consider two alternative selection criteria for step NUM
we describe here general algorithms for both sequential and batch selection
this paper investigates methods for reducing annotation cost by sample selection
additional statistics would bring the estimate closer to the true value
null ters that affect only few examples have low overall utility
the first author gratefully acknowledges the support of the fulbright foundation
our work focuses on sample selection for training probabilistic classifiers
this avoids redundantly annotating examples that contribute little new information
two different measures of agreement are useful
note however that this is a close but not entirely direct measure of the error in the input because there are a few cases of the normalization process committing errors and a few of it correcting them
the next processing step looks for date matches and those alternate forms not identified by the ibm tool
the testing of this sentence and also of all the following ones was performed on pentium 75mhz with 16mb ram
one idea is to use the example sentences listed with the different readings in a comprehensiveprint dictionary
we think that it is especially tricky to get to all the readings along the first dimension
it would be interesting to continue the work on our system towards the development of statistical methods for this task
s sense preservingly segmented the source word was segmented and the units were translated
but the resulting german compound is missing an interfix windscreenwiper windschutzscheibe wischer
judging sense preserving segmentations or other close to correct translations must be left over to the human expert
if these sentences are carefully designed they should guide an mt system to the respective translation alternatives
adjectives were tested in predicative use since this is the only position where they appear uninflected
the keyword projective indicates that the rule may be applied only in a projective way
running the tests takes some time since NUM sentences need to be translated by NUM systems
these sentence groups must then be checked manually to determine whether the given translation is correct
these results have led us to a layered design of grammar for positive projective parsing
this idea turned out to be unrealistic because the necessary interface is among the classified inside information in most companies
as an example we may take the following simple sentence karlova ena zal6vala kv tiny
tokenization is the process of mapping sentences from character strings into strings of words
it provides us with a precise understanding of what and where tokenization ambiguities are
as a result these taggers require more memory figure NUM
the experimental process was repeated for each language tagset and tagger
word for word translation charles fem pl wife watered flowers
the corpora were divided into NUM NUM word entries
this experiment has two significant results a
errors due to inadequate training data
in case that the user wants to know which sentences were not analyzed properly s he may obtain a warning
NUM NUM hidden markov model hmm approach
there is cd abcd lcb abc d ab cd a bcd rcb
computational linguistics volume NUM number NUM NUM NUM NUM
most probable tag sequence hmm ts
in order to bring the recognizer performance for the non native speaker to that of the native speaker we need to improve the models in the recognizer
first there is the question of which architecture to adopt
the process required a deep search into the woi dnet noun hierarchy
furthermore kop NUM does not match any synset in wordnet1 NUM
awe assume abney s NUM dp hypothesis according to which the head of a noun phrase is the deter
state NUM labeled lcb NUM e rcb is thus added and a transition labeled h bh that points to state NUM is also added
in this document each depicted finite state transducer will be assumed to have a single initial state namely the leftmost state usually labeled NUM
however whereas every finite state automaton is equivalent to some deterministic finite state automaton there are finite state transducers that are not equivalent to any deterministic finite state transducer
then the set of new errors caused by applying the rule is computed and the process is repeated until the error reduction drops below a given threshold
moreover the lookup process has to be very fast too otherwise the improvement in speed of the contextual manipulations would be of little practical interest
the complexity of the lookup procedure depends only on the length of the word in particular it is independent of the size of the dictionary
in order to conduct a fair comparison the dictionary lookup part of the stochastic tagger has also been implemented using the techniques described in section NUM
when the first sense was also the one the lexicographers had chosen as the most appropriate one the taggers task was relatively easy
taggers agreed with the experts and with each other significantly more often when the wordnet senses were presented in the order of frequency of occurrence
within the cooperative parallel grmmnar project pargram ims stuttgart xerox pale alto xerox grenoble the analysis and representation of stru tures in the grammars must bc viewed from a more gh bal perspective than l hat of tile individual languages german english french
somewhat surprisingly it needs little or no knowledge of phonology beyond the distinction between vowels consonants and glides
however tuning this parameter alone still leaves the performance short of the baseline predictor
null term weighting is an effort to increase the weight or importance of certain terms in the high dimensional space
the bayesian component is used to predict the correct word from among same part of speech words
each system should perform well on the most frequent confusion word in the training data
we can see that it occurred in almost NUM of the training sentences
for example consider the case of the confusion set lcb principal principle rcb
however this work focused on detecting misspelled words not contextual spelling errors
they represent a scaling factor for each dimension in the t and d matrices
for instance our ending guessing rules are akin to those of xerox and the morphological rules resemble some rules of brill s but ours use more constraints and provide a set of all possible tags for a word rather than a single best tag
our experiments with the lexicon derived from the celex lexical database and word frequencies derived from the brown corpus resulted in guessing rule sets that proved to be domain and corpus independent but tag set dependent producing similar results on texts of different origins
however using a smaller context size reduces the total number of unique terms by an average of NUM
in practice however the reduction we use had little effect on the predictions obtained from the lsa space
we train two sets of models an english model using native american english speakers as reference and a cantonese model using native cantonese speakers as references
the track followed the adhoc task but using only the category b data
the interactive track focusses the adhoc task on the process of doing searches interactively
both inquery and city combined the passage retrieval with query expansion cornell did two separate runs
the etho01 run used both topic expansion and passages in addition to a baseline vector space system
standard recall precision figures have been calculated for each trec system along with some single value evaluation measures
they also used dynamic passage retrieval in addition to the whole document retrieval in their final ranking
the best of these measures combined the standard cosine measure with the okapi measure
the questions or topics and the relevance judgments or right answers
this run is a modification of the base pircs system to use manually constructed sor boolean queries
trec NUM and trec NUM for NUM of the groups that did well in each evaluation
thus their model assumes that there is a rank scale relationship between these units halliday 196l
the first of these is monologue generation which focuses on generating monologue text typically of paragraph length
but notice that these features have attached to them same pass sp preference resetting rules i.e.
however the typical choice and the most interesting one is with satellite m
first its realization rule NUM NUM rovides for re entry to the network to fill n is this fact that provides for the recursion typical of rst relations in the present framework as we shall shortly see
to see this we must look at the new system network shown in figure NUM and also at its associated realization rules because it is through these that the chosen features are converted into structures
post editing s not an option in speech translation systems for person to person communication and real time operation is important in this context so in comparing the two translation models we looked at a variety of other measures including translation accuracy speed and system complexity
we also know that some additional improvement of the transducer system can be achieved by increasing the amount of training data with a further NUM supervised training samples for a total of NUM the error rate for the transducer system falls to NUM NUM
we looked at three measures of model complexity for the two systems with the results shown in for the transfer model this includes both monolingual entries and the bilingual entries required for the english to chinese direction there are only bilingual entries in the transducer model
after construction of the english head acceptor models common to both systems a rough estimate of the effort required for completing the models for english to chinese translation is NUM person months for the transfer system and NUM person months for the transducer system
the version used in the experiment allows additional positions including the left end of l2 and the right end r allowing additional target positions increases the flexibility of transducers in the translation application without an adverse effect on computational complexity on the other hand we restrict the source side positions as indicated above to keep the transduction search similar in nature to head outward context free parsing
for the purposes of this definition mistranslation of a source word includes choice of the wrong target word or words the absence or incorrect addition of a particle related to the word and the generation of a correct target word in the wrong position
in the dependency trees generated by these models each node is labeled with a word w from the vocabulary v of the language in question the nodes and their word labels immediately dominated by such a node are the dependents of w in the dependency derivation
another important improvement in our method is that since the generalized smaller model deviates from the previous larger model only in a small number of constraints we use the parameters of that larger model NUM as the initial values for the iterative scaling algorithm
this introduces several additional constraints namely
turing s formula reestimates population frequencies locally
relating turing s formula and zipf s law
the head can be in any position in the string and its sisters can either be to the right or to the left
the reason for this is that as was mentioned before otherwise we would be reasoning backwards with relation to movement
as the corpus grows the time for incremental search likwise grows linearly
but there are also examples of words which could have different grammatical meanings
glosser was developed with the philosophy of exploiting available nlp technology wherever possible
for example if a verb is intransitive it will not require a complement if it is transitive it will require a complement
when a lexical head corner is found an x rule is selected in which the lexical head is on the rhs
conse sin the minimalist head corner parser that is described here a head always has only one sister because minimalist trees are at most binary branching
however they claim that the semantic classification of verbs based on standard machine readable dictionaries e.g. the ldoce is a hopeless pursuit since standard dictionaries are simply not equipped to offer this kind of information with consistency and exhaustiveness
we now examine how well it performs on unknown words by constructing a semantic filter based on three different proportions of the original NUM levin verbs a NUM b NUM and c NUM chosen randomly
because the verb break occurs in each of these classes the semantic filter based on synonyms assigns scatter to classes NUM NUM cheat verbs NUM NUM split verbs NUM NUM NUM hurt verbs NUM NUM break verbs NUM NUM NUM appear verbs
we first examined different semantic relations provided by wordnet synonymy hyponymy both synonyms and hyponyms and synonyms of synonyms in order to determine which one would be most appropriate for constructing semantic fields for each of levin s NUM verb classes
for class NUM NUM using the synonymy relation would result in a field size of NUM i.e. there are NUM wordnet synonyms for the NUM verbs in the class by contrast the hyponymy relation would yield a field size of NUM
the case n NUM does not admit much reclassification because it is unclear which sense is dominant
in the book could also have been included
thus our model will have to choose a probability distribution for parts of speech which will agree with our observations and which will assign all the cases when a word which can be a noun is preceeded with neither a determiner nor an adjective with equal probabilities
the nodes in the text plan tree are labeled with the input templates not invention components
the procedure applies to simple case role values or to components of the compound case role values
in many cases the choice of aggregation is virtually arbitrary
the output of the morphological analysis involves the assignment of the word class and an inflectional form
next the procedure orders the siblings left to right in preparation for eventual linearization
notably it allows us to avoid using a deep knowledge representation language for describing the invention
in order to assign label strings to case role values the values must be analyzed morphologically
in this paper we report about an tmplemented system for supporting authoring claims for patents describing apparatuses
figure NUM illustrates a rather simple claim text claims can be over a page long
it is necessary to motivate the order of verb realization in the text at the generation stage
a large part of our input is in fact in the mind of the inventor
p i NUM is the penalty for collapsing the edge vi which may depend on the label of that edge
smith hipp and biermann an architecture for voice dialog systems NUM
what is the voltage between connector one two one and connector three four
in a real time system a theorem prover can never be released arbitrarily
the following dialog segment illustrates the behavior of the system in declarative mode
NUM knowledge about the expectations for responses when performing an action
the goal may thus be selected from one of the active subdialogs
the sequential levels of more distant subdialogs each were given higher expectation costs
this system receives the spoken inputs from the user and returns spoken outputs
the user model specifies information needed for efficient interaction with the conversational partner
acquisition of user model axioms is made from inferences based on user inputs
we can start another exploration of interesting concepts downward from this interesting wavefront resulting in a second lower wavefront and so on
similarly most people can tell that the second passage is from a sports article even though the word sport is never mentioned
parameters are estimated from the frequency of certain sets of words and phrases the distinguishing word sets found in the training collections
a weight is calculated for each of the classes for each term and the weight is the probability of the term occurrence in the class
the document words are compared to each of the distinguishing terms sets and a class score is calculated according to the selection method being used
mter removing the header material separating the words stemming sorting by frequency and calculating the probabilities the following lists would result
previous theoretical results were verified using two classes of documents and excellent recall and precision scores were achieved for distinguishing topics previous tests were conducted in both japanese and english
many systems start with prevalent but not common so that words such as the and to are not used words and phrases in the class training set
the tf idf and cosine methods all calculate the weight using the number of classes which contain the term while the multinomial distribution method calculates the weight using the term probabilities
heads of state geographical and common sense knowledge
on the other hand the way that the surface representation is put together i.e. the categories that have contributed to the ultimate string and the grammatical dependency relations head argument head adjunct etc holding among them will be called the composition structure of that sentence represented below by means of unordered trees
however the definition of the p compaction relation in NUM also holds in the case where the list of liberated domain objects is empty which amounts to the total compaction of the sign in question
for instance in figure NUM the unioned specification on the higher np occasions the vp domain to comprise not only the verb but also both domain objects of the np
one problematic aspect of nerbonne s proposal concerns the fact that on his account the extraposability of relative clauses is directly linked to the head adjunct schema that inter alia licenses the combination of nominals with relative clauses
hence the only legitimate operations involve adding elements to an order domain or compacting that domain to form a new domain object but crucially operations that nonmonotonically change existing domain objects within a domain are prohibited
thus a constituent marked unioned jr requires that the contents of its domain be shuffled into the domain of a higher constituent that it becomes part of i.e. it is domain unioned
i r einen hund der hunger hat rel s unioned np extra dom eine NUM
moreover the integrity of the remaining np s domain object is not affected as unlike in nerbonne s analysis there is no corresponding domain object in the domain of the np before the latter is licensed as the complement of the verb fattern
rayner et al ignore the inside probabilities of nodes while this may work after processing only the first level of a grammar when the inside probabilities will be relatively homogeneous it could cause problems after other levels when the inside probability of a node will give important information about its usefulness
so when the passenger asks where is it
so the passenger s question of where is it
the system next tries to apply the belief and goal adoption rules
for simplicity the plan inference process is invoked separately on each
hearer might not believe it to be
this results in the partial plan newplan
when we first began this work we were unsure about what types of categories would be amenable to this approach
a user only needs to supply a representative text corpus and a small set of seed words for each target category
the words selected by the user are added to a permanent semantic lexicon with the appropriate category label
a user then reviews the top ranked words and decides which ones should be entered in the semantic lexicon
replace plan newplan the plan newplan replaces plan
this only requires that the hearer believe the speaker to be sincere
it can be used for almost any statistical parsing formalism that uses thresholds or even for speech recognition
much of the previous related work on thresholding is in the similar area of priority functions for agenda based parsers
the selector list will not allow more than three complements of send to be found
this rule illustrates what to do when the distinguished daughter precedes the subsidiary one
the semantic subject is the value of the in part of the agent feature
therefore leihen s representat ions make use of ciiange sign s subconcept ciiange sign temp
this section describes two techniques for eliminating multiple lexical entries for the same word
as we have seen the gpsg treatment of subcategorization involves many vp rules
i have omitted as many parentheses as possible in the interests of readability
this time i have used disjunction to give a more compact encoding
these values should then be compiled to boolean combinations of the corresponding mnemonic atoms
there are several linguistic concepts which seem amenable to an analysis in terms of such a technique
therefore the total time complexity is reduced by o v
one can easily achieve this property by indexing all used grammatical functions with their associated phrases and if necessary duplicating labels e.g. instead of using hd mo use the indexed labels hds hdvp monp this property makes it possible to determine a phrase category by inspecting the grammatical functions involved
local and non local dependencies are represented in the same way the latter indicated by crossing branches in the hierarchical structure as shown in figure NUM where in the vp the terminals of the direct object oa den traum yon der kleinen gastst tte are not adjacent to the head hd aufgegeben NUM
the probabilitiesof all alternatives are much smaller than that of the best assignment thus the latter is assigned
section NUM automates the recognition of phrasal categories and so frees the annotator from typing phrase labels
they are also so equal in number and so frequent that one can not simply decide to let one reading overrule the other and live with the errors that such a happy golucky solution would give rise to
thus the position of the vp in figure NUM is defined as equal to the string position of besucht
with fairly little trimming it could well reach a level of at least NUM NUM consistence with the human annotator but now the basic idea was to test it raw
this is just as could be expected naked plurals are far more common than naked singulars in all declinations and will thus be favored by the statistics
three of them are almost neglectable and one has a strong unidirectional pattern where the reading as an adverb more precisely a verbal particle is often taken for a preposition
a remarkable fact is that the high number of different tags does not seem to influence the training and performance of probabilistic taggers negatively in the way that might have been expected
it may well be that a subcategorization of verbs might eliminate the problem but this is a large task to implement both in the lexicon and in the tagger
by systematic modifications of the tagset along these lines it is possible to decide to what extent the introduction of underspecified tags will improve tile overall performance of a tagger and or facilitate the task of human annotators
it is however important that the s tuations where underspecified tags can be used are restricted to welldefined cases and that the reasons for using them are quite clear
starting from a set of texts and a lexicon the xpost looks up all words in the texts and assigns to them a set of one or more readings
foreign names are represented phonetically in chinese by a small set of chinese characters
as the table shows relevance feedback gives a performance increase of NUM NUM
when a pattern is matched a semantic form is assigned by the pattern
the database contains all the predicates mentioned in the semantic representation of the message
first an introductory phase where the discourse participants greet i each other introduce themselves and provide information e.g. about their professional status
future extensions of the dialogue component which has been sucessfidly tested with NUM dialogues of our corpus concern the treatment of clarification dialogues
an additional resource issue is personnel management among multiple contract sites and the government site
should the results of an empirical evaluation be similar to previous results in similar languages
sem p head adj cont x adj dtr cont x or head comp or head marker or head filler coat y head dtr coat y
disjunctive terms make it possible to state facts that belong together in one clause as the following formulation of the semantics principle s em p of hpsg which states that the content value of a head adjunct structure is the content value of the adjunct daughter and the content value of the other headed structures head complement headmarker and head filler structure is the content value of the head daughter
the input also includes information from the generation component about the utterance produced in the target language and a word lattice from the keyword spotter
among the domaindependent speech acts there are low level primitive speech acts like bec ruessung for initiating and verabschiedung for concluding a dialogue
if semantic processing must access the semantic representations of different grammars this can be done if the semantic module makes use of a template defined for each grammar that indicates where in the feature structure the semantic information is located as in the following example for hpsg
tion bild where it is used to compile typed feature grammars into logic grammars for which bidirectional nlp algorithms are developed and cray systems formerly pe luxembourg with whom we had fruitful interaction concerning the future development of the alep system
pdddaaasss more left and right context fea null tures and more suffix information
the first letter provides information about capitalization and the prefix the two last letters about suffixes
a bucket is defined by a particular number of mismatches with respect to pattern x
in mbl the similarity metric and feature weighting scheme automatically determine the implicit back
the gray schemata are not used if the third feature has a very high weight
this introduces one additional parameter which has to be tuned on held out data
f x stands for the frequency of pattern x in the training set
all differences are statistically significant two tailed paired t test p NUM NUM
the basic idea in memory based language processing is that processing and learning are fundamentally interwoven
each language experience leaves a memory trace which can be used to guide later processing
these relations include part and whole subset and superset and membership in a common class
we have formulated a reverse translation from udrss back into f structures and established a one to one correspondence between subsets of the lfg and udrt formalisms
if the lexical map between argument positions in udrs predicates and grammatical functions in lfg semantic forms is a function it can be shown that for
there is a direct or indirect meronymy partof relation between the np and the dd
each of these two aspects will be discussed with reference to the rst representation for the remove phone text shown in figure NUM NUM the first aspect of this structure is its hierarchical nature
b for nominalization NUM NUM NUM NUM NUM c for gerund NUM NUM NUM NUM NUM d for goal metonymy NUM NUM NUM NUM NUM e by purpose NUM NUM NUM NUM NUM
in the remove phone text for example these statements restructure or demote the remove action in the hierarchical structure shown in figure 6a into a satellite node in the rst structure shown in figure 6b
NUM i will help you if you want me to but i will kiss you even if you do n t s deriving this reading requires a core relation between the elided event and its antecedent in the first main clause which is obtained when our algorithm bails out in event coreference see footnote 8mark gawron p c attributed to carl pollard
this covariation however does not constitute proof that the technical writer actually considers the issue of slot during the generation process nor that the prescribed form is actually more effective than any other
marking the lexical and grammatical forms of expression of the nodes mark iterative mark the realization statements also determine the grammatical form of the expression of each of the nodes in the structure
the imagene project concurs with this concern for detailed forms of expression but its methodology is geared toward determining the elements of the communicative context used to choose between equally acceptable alternative forms of expression
our experimental results also show that a common feature set parameter set and common algorithm lead to different performance output for cantonese and english speech recognition modules
in order to illustrate the basic idea we will first give a simplified graphical definition of the translation r from f structures to udrss
we are studying the issues raised above in the domain of a traveling business person s query translation system figure NUM
one important issue for multilinguality in a spoken language translator is the complexity of implementing more than one recognizer in the system
method NUM requires some sort of language identification to switch between two recognizers whereas method NUM seems to be more flexible and efficient
in our system such differences arise from the referential requirements and inferential opportunities that are encountered
although she reports just over NUM initial purpose clauses for english text in general she reports NUM initial purpose clauses for a book of recipes and NUM for an auto repair manual
this portion of the system network is capable of generating a greater range of purpose expressions than is typical in generation systems and of identifying the functional reasons for choosing one form over the other
a company spokesman said they are moving their operations to mexico in a cost saving effort
about half the systems focused only on individual coreference which has direct relevance to th e other muc NUM evaluation tasks
as it stands however the level of f structure representation does not express the full range of subordination constraints available in udrt
better economic circumstances mean less crime
bbn system and like muc NUM was carried out under the auspices of the tipster text program
the substantive differences between the system generated output and the answer key are indicated b y underlining in the system output
the org country slot is a special case in a way since it is required to be filled when th e
for percentages about half the systems had NUM error which reflects the simplicity of that particular subtask
the entity types that were involved in the evaluation are the same a s those required for the scenario template task
the answer key for the te task contains one object for each specific organization and person mentioned i n the text
with respect to performance on org descriptor note that there may be multiple descriptors or none in the text
coreference many aspects of the co task are in definite need of review for reasons of either theory or practice
in most cases the first choice is the third word in a group taking one of a large number of values
some rule based heuristics or a stochastic method are required to guess the form of a nominalisation
discourse structure another problem found in our first test with wn was the large number of false positives
the following is an example of english word l ictionary record lleadword dog cormoct
this paper outlines the specification of edr electronic dictionary and describes the current status of its utilization
the data in the concept description dictionary and the co occurrence dictionary is extracted from the edr corpus
the eoricept identifiers and concept explications are used to indentify the meaning of the polysemous headwords
they must include all the information necessary for a computer to understand a natural language
the japanese corpus contains NUM NUM sentences and the english corpus contains NUM NUM sentences
these subdictionaries are not indendent but are organically connected figure NUM
true electronic dictionaries are not simply machine readable editions of dictionries for use by people
the total number of zero cases for the nonsalient type is NUM NUM in the test data the total number of nonzeros for the same type is NUM NUM
this type of information is used to select the appropriate correspondence words in machine translation
in this section we introduce the psycholinguistic principles of disambiguation
examples that occurred more than once in the corpus were also filtered out since repetitive sequences in our corpus tend to be nongrammatical markup
the automatically extracted phrasal translation examples are especially useful where the phrases in the two languages are not compositionally derivable solely from obvious word translations
more sophisticated word alignment algorithms therefore attempt to model the intuition that proximate constituents in close relationships in one language remain proximate in the other
approximately NUM NUM sentence pairs with both english and chinese lengths of NUM words or less were extracted from our corpus and bracketed using the algorithm described
to choose the best set of matchings an optimization over some measure of overlap between the structural analysis of the two sentences is needed
consider firstly a hybrid system that includes only the two levels l and lp of which clearly l will in general be more appropriate for linguistic description
this fact tends to favor the selection of stronger systems for the base level logic a move which is associated with loss of possibly useful resource sensitivity
hnagine how an lp formula x r y might be translated into the system l plus la
hence a lexical functor constructed with l connectives may be transformed to one involving lp connectives allowing us to exploit the structural freedom of that level
the minimal set of sequent rules for any group o NUM of connectives lcb o rcb is as in NUM NUM NUM
the calculus l respects linear order so that s up or np s corresponds to a sentence missing a np at its right or left periphery
extending this proof with repeated uses of p and a we can attain any desired reordering of the component types
the autolearn system was developed to explore the possibility of using simple learning algorithms to detect specific features in text
abstraction discounts z as an orderable element leaving just x NUM y i.e. with a b preceding c b as we would expect
we then looked at the predictive power of linguistic cues for identifying the segment boundaries agreed upon by a significant number of subjects
the decision tree and classification tree are shown in figure NUM
the revision led to fewer clauses more assignments of na for the three np features and more inference relations
the overall scores were recall NUM and precision NUM giving an f measure of NUM NUM
thus despite the high standard deviations NUM narratives seems to have been a sufficient sample size for evaluating the initial np algorithm
the last example shows that the negation of a property about a class may have two interpretation the ordinary one in which the property is negated for tit individual of the class attd the negation of the lass itself or conversely of the individual to pose the property about an individual a lass
discourse structures are derived from subjects segmentations then statistical measures are used to characterize these structures in terms of acoustic prosodic features
an important area for future research is to develop principled methods for identifying distinct speaker strategies pertaining to how they signal segments
because a different tree is learned on each iteration the cross validation evaluates the learning method not a particular decision tree
n therefore the kite does not fall down
from these it selects the entry with the most specific semantic and pragmatic licensing conditions
this is inappropriate for aac since formal meaning representation languages are hard to learn and anyway tend to be more verbose than their natural language counterparts
decision tree classification tree and result for rule NUM
this admittedly crude calculation suggests that it may be unrealistic to expect much better than NUM NUM keystroke savings on free text with a usable menu size
finally the post mortem method which integrates these concepts into a parsing system is described
the alembic module was used to disambiguate the sentence boundaries in all cases except when one of the five problematic abbreviations was encountered in these cases satz in neural network mode was used to determine the role of the period following the abbreviation
for satz we used a fully connected feed forward neural network as shown in input layer is fully connected to a hidden layer consisting of j hidden units the hidden units in turn feed into one output unit that indicates the results of the function
in the resulting lexicon of NUM NUM german adjectives verbs prepositions articles and NUM abbreviations each word was assigned only the parts of speech for the lists from which it came with a frequency of i for each part of speech
the NUM attributes for mixed case english texts as seen in the induced decision tree in figure NUM are where t NUM is the token preceding the punctuation mark t NUM is the token following and so on
this denotes that at and the have a probability of NUM NUM of occurring as a preposition and article respectively plant has a probability of NUM NUM of occurring as a noun and a probability of NUM NUM of occurring as a verb and so on
integrating the decision tree induction algorithm into the satz system was simply a matter of defining the input attributes as the k descriptor arrays in the context with a single goal attribute representing whether the punctuation mark is a sentence boundary or not
the decision tree induced for the german news corpus utilized only three attributes t NUM can be an abbreviation t NUM can be a number t NUM can be a noun and produced a NUM NUM error rate in all cases
the results of testing the system on a separate set of NUM NUM potential sentence boundaries also from the hansards corpus with a baseline algorithm performance of NUM NUM are given in table NUM including a comparison of results with both probabilistic and binary feature inputs
the difference w lue d is generated separately for the rightward and legward sorted string ta bles
the purpose is to show that our method is effective regardless of the size of the data file
we propose a new method of extraction for languages which haw no specific use of punctuation to signify word boundaries
the frequency of each string is the same but the strings are lexically reversed and ordered based on this reversed state
according to o the thai spelling rules the character can never stand by itself
if an erroneous string is extracte d its error directly propagates through the rest of input
up to the present lexicographers efforts have been inhibited by insufficient corpora and limited computational facilities
following are the steps for creating n gram text data according to the fundamental features of thai text corpora
table NUM a further example of the count of a lightwm d sorted string tal le
then n t a l is the number of occurrences of the string a with one cluster added to its right and n a l is the number of occurrences of the string a with one cluster added to its left
this partly contributes to the fact that there are far fewer chinese words than english words in two texts of similar lengths
the number of such unique bigrams indicate a degree of heterogeneity of this word in a text in terms of its neighbors
points NUM NUM and NUM contribute to the fact that the chinese text of our corpus has fewer unique words than in english
in some other languages such as french and english word order for trigrams containing nouns could be reversed most of the time
our texts each have about NUM million words which is much smaller than the parallel canadian hansard used for the same purposes
moreover in some cases our chinese tokenizer groups frequently co occurring characters into a single word that does not have independent semantic meanings
as we have mentioned our chinese text has many acronyms and idioms which were identified by our tokenizer and grouped into a single word
we use the ordered pair based on the assumption that the word order for nouns in english and chinese are similar most of the times
on the other hand gg has more similar context heterogeneity values as those of air even though its occurrence frequency in the chinese text is much lower
in our experiment the co occurrence happens within the same text and therefore we got a candidate list for that is a cluster of words similar
this study reviews the accuracy of personal name recognition as shown in the named entity task of the
name recognition is the precess of identifying that a given character string is in fact a name
the experimental results show that our method can
the above formula therefore becomes as follows
glottal stop has the similar functions to unfilled pause
it can be represented as follows
the distribution of length of the repeated
the aim of this study was the automatic production of a lexicon from corpus dedicated to some specific areas
compared to the two methods described above this method attempts to optimize the clustering using perplexity as a global measure
before a part of speech tagger can be built the word classifications are performed to help us choose a set of part of speech
corpus based statistical oriented chinese word classification can be regarded as a fundamental step for automatic or non automatic monolingual natural processing system
for probabilistic classification we define the word as belonging to certain class in which this word has the largest probability
the goal of this module is to give a probability to syntactic labels which can represent the oov common words
so we extract a part of it which contain the news published in april from the original news texts
this is a useful advantage compared with agglomerative clustering techniques that need to compare individual objects being considered for grouping
the distribution of the probability is not optimal but it reflects the degree a word belonging to a class
these rules are shown in figure NUM
figure NUM the evaluation semantics for datr
we may ask which of these two contradicting views of linkage is correct
context descriptor sequence path extension triples to atom
inforinally n p c specifies a prolmrty of the node n nalnely that the value of the path p is given by the sequence of value descriptors c
let node and atom be finite sets of symbols
consider for examt le the quoted path rule
figure NUM evaluation of quoted descriptors
this paper describes an operational semantics for datr theories
figure NUM evaluation semantics for datrl
then we show that ccg gtrc can actually be simulated by a ccg std proving the equivalence
since p w is tho same for all possible lexical sequences this term can be ignored without m tl k n affecting the final disambignation results
these constraints and condition also tell us how we can implement a ccg gtrc system without overgeneration
first wrapping may affect unboundedly long chunks of categories as exemplified in NUM
where both have an error the errors can sometimes be of the same type sometimes of different types
iri95 NUM stc sbr NUM arpa grant no n66001 NUM c6043 and arid grant no daah04 94g0426
therefore an automatic method for determining markedness values can also be used to determine the polarity of antonyms
NUM g may include the rule schemata in NUM
the results support the use of type raising involving variables in accounting for various linguistic phenomena
k represents the number of arguments being passed
all errors on this word were machine induced except NUM cases where human annotators took a subjunction to be a preposition
client systems usually want to express in nl a cooperation primitive and a date expression
flexible and reliable client server communication is made possible by the generic server interface module gsi
this permits efficient resource sharing as several ccms can be associated to one component
the implementation of the annotation procedure based on the sines output format is underway
the agent may poll its owner s mailbox or have one of its own
cosma allows human and machine agents to participate in appointment scheduling dialogues via e mall
a novel type of object oriented architecture is needed to treat multiple dialogues in parallel
virtual partial system instances are maintained as long as a dialogue is going on
in speech synthesis the grapheme to phoneme transcription phase uses morphological and syntactical information to constjrain the phonetic transcription of the graphemes
what would be needed for cosma is a mapping between strategies implemented in such languages
the generic server interface invokes the necessary server processes and maintains interaction with the client
however existing text corpora do not make good models for the speech of our user and the amount of training data which we can collect is insufficient to extract reliable word based trigrams
as the parser before coupling with the semantic interpreter is considered the performance is improved from NUM NUM to NUM NUM which corresponds to NUM NUM error reduction
it is possible to build prosthetic devices for such users by linking a suitable physical interface with a speech synthesizer so that text or other symbolic input can be converted to speech
another norm is the intuition of the working linguist with the possibility of consulting other people to get their intuitions
in section NUM we discuss previous research in more detail
we attribute this to the fact that these features encode partly overlapping information
thus for each dataset wc considered only the following subsets of features
NUM one of the best induced tree s
in three of them we predict cue occurrence and in one cue placement
we quickly determined that this approach would not lead to useful decision trees
other hypotheses about cue usage derive from work on discourse coherence and structure
we ran the same trials discussed above on this dataset
cmmot decide which mw lysis is more likely the user is allowed to select which hc is inl eresl ed in this fea tm e toggh s for sers who l r fer fewer choices
ea eh of the three sorts of infor mati m is disl la yed in scl a ra le windows moi i iloi ogy life lyestlll s of morphological ma lysis
cases like these suggest a po elltially crippling problem for the glossi r i lcb ug concept if words are in general ambignous then providing morphological analyses for them may be too tiresome to be of genuine use to language learners
wc chose to include the i hird sorc as well because orpora seemed likely o bc vahmbh in providing exauq les rlto concrel ely an l certa inly m r ex i ensively h her sollr s
l ossil h base forms of t he seh c ed word namely in order to get the right entry in this case elltry NUM one has to consider te whole sentence
this is made possible i y lemmatizing the entire corpus in t t ret rocessing st el a n l ret a in i g the results in an index of temma a
sont au fond d un bonnet par billets entass s l e colonel chabet h de balmc NUM http wab ct am frlabu abu xerver h i igure NUM some oiu ora i xami i t s
mats le diverges figurcs d un nafionalisme e x acer b6 ont elics aussi l tloiic coillll e nlelllionger e eontrairt all plan de l ieu
the dm module is a separate block in the vodis architecture which interacts 3this relates to the notion of information packaging cf
in both cases using lexicons which have the maximum information about the subject is an important benefit
then the dm proceeds with the the next candidate call bill which corresponds with the actual utterance
in general a statistical parsing model defines the conditional probability NUM t s for each candidate parse tree t for a sentence s the parser itself is an algorithm which searches for the tree tb st that maximises NUM t i s
NUM is similar NUM np c bill vp c funding i vp vb was p np c bill i vp vb was NUM vp c funding i vp vb was is a bad independence assumption
these problems are not restricted to nps compare the spokeswoman said sbar that the asbestos was dangerous vs bonds beat short term investments sbar because the market is down where an sbar headed by that is a complement but an sbai t headed by because is an adjunct
the penn treebank annotation style leads to a very large number of context free rules so that directly estimating NUM r r1hl1 lm i p h may lead to sparse data problems or problems with coverage a rule which has never been seen in training may be required for a test data sentence
NUM NUM attaching the remaining relations as modifiers
NUM major french bank opens office in kiev
figure NUM a rhetorical structure for the newswire texts
an implementation of the iterative algorithm that calculates the probabilities
importantly we used texts which are not explicitly structured
as a conclusion let us mention a few points
all of the articles appeared in the first half of the year NUM
both the flm and plm approaches produced an improvement over the full text model
the feasibility of the idea is explored in the paper
predicting important terms involves numerical weighting of terms in document
table NUM proportional length model plm a summary
the star indicates that the relevant items are wrongly tokenized
under categorial grammars that have powerful rules like composition a simple n word sentence can have exponentially many parses
no constituent produced by bn any n NUM ever serves as the primary right argument to bn any n NUM
as an example consider the effect of these restrictions on the simple sentence john likes mary
thus every functor will get its arguments if possible before it becomes an argument itself
one might worry about unexpected interactions involving crossing composition rules like a b b c a c
significantly it turns out that NUM really does suffice the proof is in ss4 NUM
any cfg style method can still parse the resulting spuriosity free grammar with tagged parses as in NUM
the standard ccg theory builds the semantics compositionally guided by the syntax according to NUM
in practice this means that the number of locations where the parser has to assume the presence 2xo position means that the relewlnt position is occupied by a xo gap xo prop
however in order to balance for the a priori probabilities of the different classes during training the mlp was presented with an equal number of feature vectors from each class
the empty element fills tile po sition occupied by the finite verb in subordinate j clauses leading to the structure of main clauses exemplified in NUM
thus in a second experiment we examined how the syntactically correct verb trace position is ranked among the positions proposed by the prosody module w r t its s3 boundary probability
where extraposition takes place involve a highly characteristic categorial context we expect a further improvement if the trace notrace classification based on prosodic information is combined with a language model
during training the desired output for each of the feature vectors is set to one for the node corresponding to the reference label the other one is set to zero
this says that the nf parser is complete generating only normal forms eliminates all spurious ambiguity
suppose the ccg grammar includes not some but all instances of the binary rule templates in NUM
there will always remain a set of doubtful cases which do not necessarily depend on deficits in the linguistic description
the right analysis should be one of the remaining analyses
NUM NUM selecting the head relation and building its argument structure
this is because the repair process was always the addition of a missing wire to the circuit a process that users quickly became able to do without explicit guidance
the model attaches an initiative level to each task goal and a competency evaluation based on user model information is used to decide who should be given the initiative for a given task goal
once the errant system behavior is described the dialogue goes through one or more cycles of diagnosis repair and test until the system behavior is correct
unless explicitly noted the results on human subjects linguistic behavior that will be reported throughout this section are based only on the NUM dialogues that were successfully completed
given that our first priority in experimentally evaluating the system was to demonstrate that behavior varied as a function of initiative it was necessary to fix the level of initiative for the duration of a session
their analysis distinguishes between advisory dialogues and task oriented dialogues but they do not allow for the possibility that the novice in a task oriented dialogue can gain knowledge over time and want more control of the dialogue
while our problem domain is more similar to the advisory dialogues the nature of our dialogues is more similar to the task oriented dialogues as the task of circuit repair is being completed concurrently with the dialogue
for example it is difficult to simulate and test the computer s error recovery strategies for speech recognition or natural language understanding errors because the natural language understanding of the computer is only a simulation
one group of subjects worked with the system in directive mode during the second session and in declarative mode during the third session while the other group worked with the same modes but in opposite sessions
in particular it is expected that the user will initiate most of the transitions to the final test phase for confirming circuit behavior since an experienced user would have learned how the circuit should function
b he pphs1 attribute vvd his app fail ure nn1 he pphs1 say vvd to ii no blank one pn buy vvg his app book nn2
we have also demonstrated that a subcategorization dictionary built with the system can improve the accuracy of a probabilistic parser by an appreciable amount
the sentences containing these verbs were tagged and parsed automatically and the extractor classifier and evaluator were applied to the resulting successful analyses
there are only NUM true negatives which the system failed to propose each exemplified in the data by a mean of NUM NUM examples
our system needs further refinement to narrow some subcategorization classes for example to choose between differing control options with predicative complements
it locates the subanalyses around the predicate finding the constituents identified as complements inside each subanalysis and the subject clause preceding it
these verbs listed in figure NUM were chosen at random subject to the constraint that they exhibited multiple complementation patterns
none of them are returned by the system because the binomial filter always rejects classes hypothesised on the basis of such little evidence
b const ruct a pair of potentially independent as forms we first need to partition the set of alternative vm iablts from the original ca qe form into two sets
the first subset contains all of and only the variables corresponding to some subset of the original disjunctions and tile second subset of variables is the complement of the first corresponding to all of and only the other disjunctions
in these examples both extraposed elements are associated with the same antecedent
in german in contrast the aelr can apply in full generality
ther fronting nor further extraposition is possible from extraposed phrases
note that 35b allows for periphery marking to be specified lexically
however our account of extraposition involves no traces cf
NUM NUM where we formulate a parochial restriction for german
therefore fronting but not extraposition is allowed from extraposed phrases
for english and german pps have to precede sentential material
c the pmc also entails a nesting requirement for extraposed elements with distinct antecedents
this paper investigates the syntax of extraposition in the hpsg framework
fastspec and metarules constitute the basis for ongoing efforts to improve system transportability and customization further
the rules expand these heads leftwards to incorporate lexemes that satisfy a set of part of speech constraints
in the case of hand crafted rules it facilitates the process of designing a rule sequence
note in particular that the overal l person age apposition is itself parsed as a person phrase
these rules are responsible for finding phrases denoting events relevant to the muc NUM scenario templates
this could certainly be larger but seemed a reasonable size
person pers NUM has age pers NUM age NUM ha NUM age age NUM
for each so matching semantic individual we create a skeletal template
alias coca cola classic treated as organization NUM inc org
the post ttl and date phrases were identified by the title and date taggers
the parasenter zones text for paragraph and sentence boundaries the former being unnecessary for muc NUM
thus the length of a path is always the same as the length of the input string it generates
NUM in a left most derivation each step replaces the nonterminal furthest to the left in the partially expanded string
as a result complete derivations have joint probabilities that are simply the products of the rule probabilities involved
one might choose to simply preprocess the grammar to eliminate null productions a process which is also described
earley s algorithm is appealing because it runs with best known complexity on a number of special classes of grammars
the definite relations representing the pruned finite state automaton of figure NUM
the result is a pruned finite state automaton
schematic representation of the partial unfolding transformation
this disjunction thus constitutes the base lexicon
NUM NUM word class specialization of lexical rule interaction
a computational treatment of lexical rules in hpsg as covariation in lexical entries
NUM in the example q7 and q9 are such identical nodes
as a result the grammar does not have an infinite lexicon
in the news domain a summary needs to refer to people places and organizations and provide descriptions that clearly identify the entity for the reader
it operates in real time allowing for connections with the latest breaking online news to extract information about the most recently mentioned individuals and organizations
a summarizer that has access to different descriptions will be able to select the description that best suits both the reader and the series of articles being summarized
as a result we keep separate profiles for each of the following robert dole dole and bob dole
the fd generation component uses this interface to send a new fd to the surface realization component of surge which generates an english surface form corresponding to it
we then turn to a discussion of the system components which build the profile database followed by a description of how the results are used in generation
the first case is when the entity that we want to describe was already extracted automatically see subsection NUM NUM and exists in profile s database
note in this case the ct tokenization a bc d is not in so s
trivial differences of the tagger learning rates between languages and tagsets show the efficiency of the training method in estimating the model transition probabilities for the tested languages and the validity of the stochastic hypothesis for the unknown words
similar results have been achieved by testing the dutch german greek italian and spanish texts both with the tagset of the main grammatical categories and with the common extended set of grammatical categories
in the extended set of grammatical classes the distance is minimized in all cases for the threshold value one i.e. when only the words occurring once in the training text are regarded as less probable words
in section NUM the influence of the training text errors and the dermatas and kokkinakis stochastic tagging sources of stochastic tagger errors are discussed followed in section NUM by a short presentation of the implementation
figures NUM and NUM show the probability distributions of the tags in the training text known words and that of the words occurring only once in this text for the english and french language respectively
thus a total number of NUM tagsets NUM taggers NUM languages NUM test on english eec law text NUM experiments was carried out
five language and tagset independent stochastic taggers handling morphological and contextual information are presented and tested in corpora of seven european languages dutch english french german greek italian and spanish using two sets of grammatical tags a small set containing the eleven main grammatical classes and a large set of grammatical categories common to all languages
when the corresponding lexical probabilities p w i t are not available in the dictionary that specifies the possible tags for each word a simple tagger can be implemented by assuming that each word wi in a sentence is uncorrelated with the assigned tag ti e.g. p wi l ti p wi
computational linguistics volume NUM number NUM specifically the grammatically labeled text of NUM NUM word entries of the english language was separated into two parts the training text where the tag probabilities distribution of the less probable words was estimated and the open testing text where the tag probabilities distribution of the unknown words was measured
n mod i push rar down water unffi leg expensive the implicit spelling errors can occur much easier in thai than in english and japanese in hiragana because the errors ah ays involve using aword that has a similar pronunciation
because of the sparse data problem in trigram model rather than equation NUM we instead use t l t l thus we can compute the better probabildes although the relevant trigram or bigram data are missing
while the above equation is perfectly valid in theory sparse data means it is rather less useful in practice
of these NUM subsequence NUM is the best chain
diagonal will be contiguous subsequences of the sorted point sequence
translators create a bitext each time they translate a text
to a first approximation tbms are monotonically increasing functions
the origin of the bitext space is always a tpc
lastly chains that lack the injectivity property are rejected
a non hansard reference set was used for simr s development
figure NUM chain x is perfectly valid even though
figure NUM segments i and j switched placed dur
the slope of the main diagonal is the bitext slope
one approximation is to train the system with a mixture of separate languages so that the model parameters would capture the spectral characteristics of more than one language
subject honorification is manifested in a subject np and a verb
attend boa past dec hon k attended at the meeting
let us infer the relative social status of those four persons
each member of the list is the result of parsing each sentence occurring in a dialogue
NUM higher inf three k p s
in ale the primary predicate for parsing is rec
the latter derivation can not be compatible with the former derivation
we changed the word order of some sentences by hand
for example while danieli and gerbino found that agent a s dialogue strategy produced dialogues that were approximately twice as long as agent b s they had no way of determining whether agent a s higher transaction success or agent b s efficiency was more critical to performance
for example in the train timetable domain we might like our task based success measure to give higher ratings to agents that suggest express over local trains or that provide helpful information that was not explicitly requested especially since the better solutions might occur in dialogues with higher costs
recent advances in dialogue modeling speech recognition and natural language processing have made it possible to build spoken dialogue agents for a wide variety of applications n potential benefits of such agents include remote or hands free access ease of use naturalness and greater efficiency of interaction
this task could be represented using the avm in table NUM an extension of table NUM where the agent must now acquire the value of the attribute request type in order to know what to do with the other information it has acquired
t smith and gordon collected NUM dialogues for this task in which agent initiative was varied by using different dialogue strategies and tagged each dialogue according to the following subtask structure NUM introduction i establish the purpose of the task
preliminary studies indicate that reliability for human tagging is higher for avm attribute tagging than for other types of discourse segment tagging passonneau and dialogue interaction utterances that contribute to the success of the whole dialogue such as greetings are tagged with all the attributes
for example assume that a scenario requires the user to find a train from torino to milano that leaves in the evening as in the longer versions of dialogues NUM and NUM in figures NUM and NUM NUM table NUM contains an avm corresponding to a key for this scenario
for convenience in later expressions we incorporate features with as follows
the following modifications to the procedure given in section NUM are required
regular grammars which make use of rule features normally interact with a lexicon
i would like to thank richard sproat for commenting on an earlier draft
for example we would like to perform experiments on a larger number of speakers to determine whether training and test speaker mismatch caused such a performance degradation
this specification required changes to only hasten s generator script
sentence NUM contained the succession event for kim
description of the sra system as used for muc NUM
the analyzerhas two components as shown in figure NUM
each collector concept represents a processing phase for the analyzer
the vocabulary chosen meanwhile reflects conventional names for the structures and services of the library
trees in the tree family are shared among all lexical items that share a particular structure
similarly there are different initial trees for each clause type anchored by a particular verb
ifx is hearer new this goal is satisfied by including any constituent of type cat
in our system such differences arise from the referential requirements and inferential opportunities that are encountered
we combine possible lexical items and possible trees to give an evaluation of all applicable options
for example book is stored with a tree family that includes a book and the book
we specify the semantics of trees by adapting two principles of computational semantics to the ltag formalism
we can use this ranking to indicate the conventional importance of the eventuality in distinguishing the object
these specifications can include idiosyncratic semantic and pragmatic information grammatical processes like tense marking apply normally
it can be observed that the approach is basically non destructive toward well recognized sentences
NUM z adnired z svery girl geach s observation implies that NUM is ambiguous so that every girl can still take wide or narrow scope with respect to the unknown argument
all nodes only NUM in figure NUM which dominate the node corresponding to the new combination node NUM must be marked undetermined such nodes are said to be disrupted
whitelock s shake and bake generation algorithm attempts to arrange the bag of target signs until a grammatical ordering an ordering which allows all of the signs to combine to yield a single sign is found
in the worst case we can imagine picking an arbitrary child tncb o n and then trying to find another one with which it combines o n
note how after combining dog and the the parent sign reflects the correct orthography after finding that big may not be conjoined with the brown dog we try to adjoin it within the latter
figure NUM NUM is conjoined with NUM giving NUM adjunction a maximal tncb can be inserted inside a maximal tncb i.e. conjoined with a non maximal tncb where the combination is licensed by rule
this allows us to employ a greedy algorithm to refine the structure progressively until either a target constituent is found and generation has succeeded or no more changes can be made and generation has failed
since complete specification of transfer operations is not required for correct generation of grammatical target text the version of shake and bake translation presented here maintains its advantage over traditional transfer models in this respect
in figure NUM we see that when node NUM is deleted so is its parent node NUM the new node NUM representing the combination of NUM and NUM is marked undetermined
indeed there are obstacles that prevent asr systems to be fully reliable
the process of generating quantifiers takes place after a scoping has been chosen and a dependency function has been constructed and partitioned so that all decisions are made in the context of a particular partitioning of a particular dependency function
the progressive difference scores of the key words the deviation scores for the expected and observed numbers of new types appearing in the successive text slices reveal a pattern that is highly similar to the same scores for the vocabulary as a whole both qualitatively and quantitatively
for example if it is decided that c r and r s then clearly since scope is transitive c r in this framework the following scopings are allowed for 8a
hence sentence 13ai is generated as is some representatives saw a sample at least one representative saw a sample a representative saw some samples and other similar sentences formed by selecting from the above quantifiers
in fact sentences NUM NUM and NUM all assume wide scope for representative while sentences NUM NUM and NUM all assume wide scope for sample
in narrow scope position the check is similar to the one for monotone increasing quantifiers except that where fmin the focus minimum nc i candidate set and the q inc NUM relation is defined along the following lines
o quantifier scoping choices o choice of focus sets dependency function partitions o choice of individual quantifiers constrained by the above two choices this is not necessarily a problem in the context of language generation where only one solution is sought
for example sentence NUM assumes that every representative has wide scope while sentence NUM assumes that every sample has wide scope and the sentences are only satisfied in the model under these assumptions
the algorithm generates suitable quantifiers to complete sentences of the form qr representative s saw qs sample s where qr and qs can be arbitrary quantifiers like some two all both one of the most etc
based on the focus in 18b the quantifier exactly one might be generated for c the corresponding candidate set for r is lcb rl r2 rcb but this is not the set of all representatives who satisfy the restriction since r3 also satisfies it
for alice in wonderland minimalization of NUM for k NUM leads to p NUM NUM and according to this rough estimate of goodness of fit the revised model fits the data very well indeed x NUM NUM NUM NUM p NUM NUM
for increasing k as shown in the upper right panel of figure NUM the divergence between v n and its expectation first increaseswthe initial text slices contain the lowest numbers of underdispersed types and tokens and then decreases as more and more underdispersed words appear
when a d tree a is sister adjoined at a node NUM in a d tree NUM the corn NUM subtree which contains only i links
such constraints on builtsem are useful because in general inputsem and builtsem can happen to be incomparable neither one subsumes the other
our model is a generalisation of the paradigm presented in NUM where issues of mismatch in lexical choice are discussed
in the second stage the generator aims to find mapping rules in order to cover most of the remaining semantics see figure NUM
the boundary constraints are two graphs uppersem and lowersem which convey the notion of the least and the most that should be expressed
we keep track of more structures as the generation proceeds and are in a position to make finer distinctions than was done in previous research
the third type includes questions which based on anticipated responses are divided into domain and evaluation questions
they often require an analysis of the proposal and thus frequently result in a shift in dialogue initiative
the second type includes utterances that do not contribute information that has not been conveyed earlier in the dialogue
for instance for a randomly reordered text the likelihood that a hapax legomenon in the full text that appears in the first m tokens will reappear among the remaining n m tokens is greater than zero in a model that assumes constant probabilities contrary to fact
appendix equation NUM can be derived as follows see good NUM good and toulmin NUM kalinin NUM let f i m denote the frequency of i in a sample of m tokens m n and define
finally the investigation of the distribution of key words may turn out to be a useful tool for investigating the structure of literary texts a tool that may lead to an improved understanding of the role of lexical specialization in shaping the quantitative developmental structure of the vocabulary
using such joint configurations w x y we have to estimate a conditional model which would predict a behavior variable y given a configuration of factor variables x p y x
this restricts the constraint set only to those cases which were actually observed in the training samples for a particular value of the behavior variable y and in solving a constraint we only sum over seen conjurations rather than all possible ones
null the first is to decrease the nllmber of anchor branches
a method that gawron and peters attribute to hans kamp generates either four readings including the above three and jtjt or all six readings
despite of a medical cluster potato appears in the cluster
NUM words words of class NUM is the ambiguous words
the uniqueness of the clusters for an input is self evident
the effectiveness of our method was examined using the 30m corpus
g o are the transitive graphs of the tightest constraint
if the following condition is satisfied put v into a
thesauri and synonym dictionaries are some of its manual examples
clustering is the operation to group words by some criterion
in order to specify this set we shall proceed to a formal interpretation noussia hyphenator for modem greek of the grammar rules
given the wider aims of the project the approach taken was to put minimal effort into the development of the four new application s needed for the muc NUM tasks and maximum effort into the development and improvement of the core syste m although work was concentrated to some extent in areas of the core stressed by the muc tasks
a few transformations are done on this structure to unpac k contractions e g i ll expanded to i will expand monetary and numeric expressions eg NUM million to NUM million dollars and to transform certain surface level idiomatic phrases eg in charge of
if the ld field of annotation is filled not nil and there is an existing annotation on the document with the same id the new annotation replaces the existing annotation
this may be done through operations which retrieve substrings of a bytesequence or through operations which allow a bytesequence to be opened to a stream for subsequent read and write operations
in the terminology developed by the message understanding conferences the information extracted from a document is stored in a filled template which in turn consists of a set of template objects
it is invoked by annotate document annotatorname string or annotatecollection which collection destination collection annotatorname swing the first form annotates a single document
an even more complex attribute value would be a template object which may in turn contain pointers to several other annotations for the text elements filling various slots in the template object
a human judge or possibly an alternative source of relevance judgments such as an extraction system then reviews the retrieved documents and records relevance judgments on the collection using the relevant attribute
this caused several problems for the lolita system and was the principal factor in the low walk through article score
there are several other less important ranks used for things like encoding script lik e information or existential quantification
the org descriptor slot is filled with any textrefs that are noun phrase s and not in the above slots
the remaining concepts are converted in to chains of markups and then added to the sgml tree
mr james is recognized as the subject of the retiring event however the system has problems in deciding that th e vacated posts will be taken over by mr dooner due to the failure in the unification of the retiring and succeeding events
fixed a bug in the semantics which caused a poor analysis of some verbs which were keywords for the
the id field of the annotation is returned
since every concept in the system should be connected to some point in th e semnet hierarchy the core inference functions are used to check if the organization concept from which th e template is derived is an instance of a company or government organization and the results used to fill i n the org type slot
performance increased from 43r 64p wit h a NUM NUM
in the following after having characterized in general the status of formal checks in semantic nets of the wordnet type we present and comment a series of constellations which shall give exemplary insight into the topic
NUM the n best list 2the example chosen was the most interesting of the dozen or so in our most recent demonstration session and the intermediate results have been repro null lexicon entry using transitive verb macro for serve asin does continentalserve atlanta ir serve v subj obj serve f1yto
the lexicographers implicitly made heavy use of transitivity otherwise the data would be highly incomplete in other words if transitivity of the partof relation had not been presupposed there should be many more short cuts
the fpp is a near deterministic parser which generates one or more non overlapping parse fragments spanning th e input sentence deferring any difficult decisions on attachment ambiguities
a good example from wordnet is the noun drink in the sense of beverage drmk 2j and in the sense of alcol ohc beverage drink NUM
NUM NUM it is preferable then to instantiate an empty in and out object around each person element and then to fill in the rest of the information if an event is extracted
this paper will give an overview of our systems describe our performance on each of the task s and the walkthrough article and discuss areas where our systems need to be improved
when a semantic form for an event of interest i s encountered a ddo is generated and any slots already found by the interpreter are filled in
the number of points for this feature is tile total number of occurrences of impot tant keywords
dooner could lose NUM NUM pounds of money
liszt brahms verdi dvorak bizet borodin puccinichopin nwagnerchopin
we have developed a sentence generator called protector approximate production of texts from conceptual graphs in a declarative framework
generation is performed by finding a cyclic path in the graph which visits each node at least once
its purpose is to simulate a linguist s first look at unfamiliar data
for example the user model at the beginning of the dialog indicates that the user can find the knob find knob
the decision as to whether to send a missing axiom to the controller depends on whether the axiom represents an answerable question by the user
in its present form the aligner does not participate in this process
it is typical of such interpreters in that it lifts goals from a priority queue and applies rules from the knowledge base to try to satisfy them
it receives a gadl specification for an output and some parameters regarding the statement context and uses a grammar to generate the desired word sequence
a basic implementation of the algorithm is to use a chart and avoid doing the same computations more than once
we present a re estimation algorithm for training probabilistic parameters and show how efficiently it can be implemented using charts
the key point is to save intermediate results and avoid the same computation later on
the re estimation algorithm for prtn uses a variation of the inside outside algorithm customized for prtn
the increasing availability of corpora annotated for linguistic structure prompts the question if we have the same texts annotated for phrase structure under two different schemes to what extent do the annotations agree on structuring within the text
i j denotes the network segment between states i and j
once charts of selected insides are prepared an outside probability is computed as follows
a chart item is a function of five parameters and returns an inside probability
but on centum hekaton and centum satom the aligner performed perfectly
null there are NUM trees in the treebank of which we can eliminate NUM as trees indicating preterminals which includes NUM containing just a textual delimiter and an estimated further NUM as representing trees including sentence punctuation
it is basically the same as the inside probability except that it carries an i that indicates a stop state
at each step the aligner can perform either a match or skip
while the performance of some systems was quite impressive the best got NUM recall NUM precision overall with NUM recall and NUM precision on the NUM core object types the question naturally arose as to whether there were many applications for which an investment of one or several developers ove r half a year or more could be justified
a linguistic classification of the collocations which are correct variants brings up the following families of variations a
alignment is a neglected part of the computerization of the comparative method
table NUM shows some variants of agrovoc terms extracted from the agr corpus
variation du climat climate variation is a synonym of variation climatique climatic variation
description of the problem linguistic variation is a major concern in the studies on automatic indexing
figure NUM shows the search tree after pruning according to this principle
our system exploits a morphological processor and a transformation based parser for the extraction of multi word controlled indexes
without careful structural disambiguation over internal phrase structure these important syntactic distinctions would be incorrectly overlooked
final results are evaluated for precision and recall and implications for indexing and retrieval are discussed
remainder by taga co a company active in trading with taiwan the official s said bridgestone sports has so far been entrusting production of golf club parts with union precision casting and other taiwan companies with the establishment of the taiwan unit the japanese sports goods make r
with reape we assume that one crucial mechanism in the second type of order domain formation is the shuffle relation reape s sequence union which holds of n lists l1 l NUM l iff l consists of the elements of the first n NUM lists interleaved in such a way that the relative order among the original members of l1 through l NUM respectively is preserved in ln
hence if NUM in figure NUM were to be expanded in the subsequent derivation into a larger domain for instance by the addition of a sentential adverb the relative order of subject and object in that domain could not be reversed within the new domain
instead of deriving the string representation from the yield of the tree encoding the syntactic structure of that sentence as for instance in gpsg lfg and as far as the relationship between sstructure and pf discounting operations at pf is concerned gb these proposals suggest deriving the sentential string via a recursive process that operates directly on encodings of the constituent order of the subconstituents of the sentence
to express this more formally let us now define an auxiliary relation joinf which holds of two lists l1 and l2 only if l2 is the concatenation of values for the feature f of the elements in l1 in the function cons i.e. cons holds among some element e and two lists l1 and l2 only if the insertion of e at the beginning of l1 yields l2
null the general vocabulary is stored in a monolingual english dictionary a monolingual l anish dictionary separated into a
generally comi ounds are co led in the terminoh gical dictionari s
function words corjmwtions determiners prepositional case markers are featurized and tense aspect and negation represented in language neutral features
when the program finished in NUM it had delivered a huge amount of research 1patrans was developed for lingtech a s
for this reason complex transfer is costly and is only used for frequent phenomena considered crucial for good translation e.g.
a transfer rule applies to any object matching its left hand side and performs the mapping defined on the right hand side
we had to develop treatnmnt of lists and emmmration and conversely we could simplify the treatment of modality considerably
in addition it has been augmented with several local contextual rules developed by the linguists working with patrans
pa ans NUM is a fully automatic machine translation system designed for english danish translation of patent texts
patent texts are characterised by the vocabulary they contain terlns belonging t o the fiehl tt eated e.g.
the discourse memory is used by imas as a stack the
the annotations render a semiautomatic description of automata possible
used to annotate new material with available linguistic information
the same principle was also adopted for nl analysis
after the training on each chunk the estimation of the parameter of word modifications is smoothed to account for the unseen word modification pairs
ksnnten sie bitte den wochentag oder das datum korrigieren
machine utterances must conform to human dialogue strategies
the most influential approach to planning discourse monologues in recent years has undoubtedly been rhetorical structure theory rst
NUM NUM adapting agents to the cosma server
NUM john said that he revised his paper and bill did too
NUM we can not establish coreference between the events because their agents are distinct
inferential independence is generally undecidable but in practice this is not a problem
it is necessary to have parallelism in order to license the lazy pronoun reading
we first illustrate our approach on the simple case of vp ellipsis in sentence NUM
figure NUM flow of control of the example sampling system
the force predicates are the same so there is no need to infer further properties
because of the mutual constraints of the three parallelisms no other readings are possible
NUM NUM basic model viterbi training for words viterbi training for pos tags vtw vtt
the two class classifier is then used as a postfilter to confirm whether the candidates are real word n grams
for example in figure NUM there are three unique labels derived cl jj nn c2 dt nn and prp nn
therefore the precision performance estimated by comparing it with a general dictionary is usually underestimated
if the mutual information measure is much larger than NUM then it tends to have strong association
we then derive the word list to be included in the electronic dictionary from the segmented text corpus
this process repeats until the segmentation patterns no more change or a maximum number of iteration is reached
to get an estimation of the system performance automatically the extracted dictionary is compared against a manually constructed standard dictionary
the derived word tag dictionary contains NUM NUM entries including NUM NUM bigram words NUM NUM trigram words and NUM NUM NUM gram words
it is observed that the system could acquire pos tagged lexicon entries with a reasonably acceptable precision and recall
this may imply that the segmented text corpus passed from the various models do not have significant difference
we have explored a method of training a transformation based tagger when no information is known other than a list of possible tags for each word
this allows for an integration of implicational and relational constraints and a uniform evaluation strategy
the delay mechanism for relational goals is very close to the one used in cuf
for our previous example we might define the delay statements in fig NUM
the denotation of a relation thus is a set of objects just like the denotation of any other feature term
figure NUM shows the inheritance hierarchy of types which we will use for our example with the appropriateness conditions attached
we introduce a typed feature logic system providing both universal implicational principles as well as definite clauses over feature terms
using universal implicative constraints or universal principles as they are usually called in the linguistic literature grammatical generali1cf
a full grammar implementation would make this connection more precise
make a dendrogrmn l d out of the merging protess for each class
random word bits are expected to give no class information to the tagger except for the identity of words
illstiers of woms are then tumirmly NUM ransrorlned to a bit string representld ion of i.e.
this dendrograrn NUM ooc constitutes the upper part of the final tree
we used phdu texts from six years of tile wsj c ort us to create word bits
the first three lines show questions about identity of words around tile current word and tags for previous words
for instance in figure NUM it is reasonable to classify the brackets c2 c4 and c5 into a same group and give them a same label e.g. np noun phrase
however focus must be maintained at the object level resulting in assignin g higher value to the cumulative score of all other matched slots in the object at the expense of a few mismatched slots
we have not experienced and do not anticipate a memory problem wit h larger data sets because c s memory management capabilities have been taken advantage of in the coding
another key object may have already had a bette r rank ordered pairing with the same response object or the arbitrary order of like ranked pairs may have preclude d this alignment
an interface to the c versions of the scorers has not been completed for any language but when it is it will be extended to cover non english alphabets
the bnf of the database objects is manually translated into the form required by the software for the slot configuration file this process could be automated in the future
the overall summary score report displays the same object and slot by slot scores as the individua l document score reports but the numbers displayed are the totals across all documents
the scorers for the named entity and coreference tasks diverged from the usual approach because of th e format of the input and the special needs for mapping and scoring
the algorithm for scoring the linkages could be a simple counting process of the subsequent noun phrases in the coreference chain but that would b e computationally too expensive
towards the problems this paper proposes a new method which can learn a standard cfg with less computational cost by adopting techniques of clustering analysis to construct a context sensitive probab distic grammar from a bracketed corpus where nontermlnal labels are not annotated
ii c j figure NUM an extremely distorted alignment that can be accommodated by an itg
m i lcb i nlodel i lises unigrmn higranri and t rigraln
if the number of these is adequate the dialogue manager gives a semantic network and retrieved information to the response sentence generator
let s define the clique as the tag sequence with size l in tagging problem
typically otto nmkcs t wo silllplifying assmttp ions i o llt
w lcb call l he pair w t an alignlltotll
this problem occur seriously when the size of the training data is not large enough
that does llot occllr in l rainiug datl h althougtl the cvellt is legal
the task is complicated by the presence of both and NUM brackets with both l1 and l2 singletons since each combination presents different interactions
by an arrangement between any given pair of sentences from the parallel corpus we mean a set of matchings between the constituents of the sentences
arrangements where the matchings between subtrees cross each another are prohibited by crossing constraints unless the subtrees immediate parent constituents are also matched to each other
the sets of monolingual strings generated by g for the first and second output languages are denoted lffg and l2 g respectively
the formalism is independent of the languages we give examples and applications using chinese and english because languages from different families provide a more rigorous testing ground
however the imposition of identical ordering constraints upon both streams severely restricts their applicability and thus transduction grammars have received relatively little attention in language modeling research
the english is read in the usual depth first left to right order but for the chinese a horizontal line means the right subtree is traversed before the left
the architecture has already been successfully re deployed in the construction of multimodal interface to health care information
although encouraging results were shown in these works the derived grammars were restricted to chomsky normal form cfgs and there were problems of the small size of acceptable trai ng corpora and the relatively high computation time required for training the grandams
ordering the surface expression of the nodes order reorder insert order combine imagene uses these statements to specify the NUM penman s sentence level realization statements work with a single prespecified list of features of the sentence called grammatical functions such as actor process goal and theme
e mail knvl itri bton ac uk t department of computer science university of colorado boulder co NUM NUM usa
this paper addresses this issue in the context of the expression of procedural relations between actions in instructional text
the multi nuclear schema relates one or more spans designating no span as superordinate or subordinate to any other
the procedural sequence schema at the top of the text hierarchy for example indicates that there are two
as an example of the data structures used by imagene consider the prl representation of the actions from the remove phone text depicted graphically in figure NUM NUM note NUM return action is a child of place action because we have viewed it as the first of the sub actions of placing a call
this metonymy occurs in purposes in which the direct object or goal of the purpose clause is more important than the action as in NUM for frequently busy numbers you ll want to use redial NUM and the pause will have to be in redial memory
the resulting trl structure had to specify the identical linker either preposition or conjunction form tense aspect mood and voice or non finite verb or nominalization slot textual order and combining if the expression was combined with the following one
this methodology starts with the range of lexical and grammatical forms corresponding to each of the rhetorical relations considered
the noun phrase john gives rise to an existentially quantified term uniquely identified by the index i j
section NUM raises some open questions concerning the determination of parallelism between ellipsis and antecedent and other issues
along similar lines to dsp we can set up an equation to determine possible values for p2
this would not be the ease if the substitutions were treated as syntactic operations on qlf to be applied immediately some re entrant meta variables would be substituted out of the ellipsis and those remaining would not be subject to the substitutions which would have already been applied when they were eventually instantiated
the qlf representation is able to distinguish between the primary and the secondary pronominal reference to john
in this way the version of the term occurring in the ellipsis is directly linked to antecedent term
NUM a canadian flag hung in front of every house and an american flag did too
ka hang t erm ka NUM this has equivalent truth conditions to NUM NUM besides illustrating scope parallelism this is an example where dsp have to resort to higher order unification beyond second order matching
for this reason the context euestablish has a single positive extension lcb m rcb corresponding to the great frequency of the string the establishment
in our memory based approach we provide morphological information especially about suffixes indirectly to the tagger by encoding the three last letters of the word as separate features in the case representation
morphological analysis presupposes the availability of highly language specific resources such as a morpheme lexicon spelling rules morphological rules and heuristics to prioritise possible analyses of a word according to their plausibility
a case consists of information about a focus word to be tagged its left and right context and an associated category tag valid for the focus word in that context
table NUM lists the results in generalization accuracy storage requirements and speed for the three algorithms using a ddfat pattern a NUM NUM word training set and a NUM NUM word test set
we are not convinced that variation in the results of the experiments in a NUM fold cv set up is statistically meaningful the NUM experiments are not independent but follow common practice here
in both cases context is used in the case of unknown words the first and three last letters of the word are used instead of the ambiguous tag for the focus word
given an annotated corpus three datastructures are automatically extracted a lexicon a case base for known words words occurring in the lexicon and a case base for unknown words
the viterbi algorithm produces a score which is the sum over all possible clumpings for a fixed l this score must then normalized by the exp x t v z l aa l factor
an alignment between e and f determines which f generates each clump of e in c similarly a denotes the alignment with g a g c and the ai denote the formal language word to which each e in c align
then the most natural clumping would be i want to fly to memphis please in which we would now expect to memphis to be generated by destination loc
as a result the whole sequence is lifted up to level NUM and continues this segment which started at the discourse element lnhaltsverzeichhis list of contents
this behavior breaks down at the occurrence of the anaphoric expression sie it in uxl which co specifies the cp NUM ul o viz
this small change reduces the test message entropy from NUM NUM to NUM NUM bits char but it also quadruples the number of model parameters and triples the total codelength
in the test set NUM anaphors NUM and NUM textual ellipses NUM NUM fall out of the intersentential scope of lit those common algorithms
given this configuration the function lift lifts the embedded segment one level so the one particular is already noticed in the first approach to the big brother
c open new embedded segment if there is no matching antecedent in hierarchically reachable segments then for utterance ui a new embedded segment is opened
many of the minor defects pardons one the hl NUM when one the first print outs holds in one s hands
the suggestion is that natural pauses can play a part in such a strategy that pause units or segments within utterances bounded by natural pauses can provide chunks which NUM are reliably shorter and less variable in length than entire utterances and NUM are relatively well behaved internally from the syntactic viewpoint though analysis of the relationships among them appears more problematic
second we were concerned with domain specificity
to be able to fulfill a goal a plan operator can define subgoals which have to be achieved in a pre specified order see e.g.
the values of NUM for annotators NUM and NUM emphasize quite clearly that is measuring not the level of absolute agreement hut the distinguishability of that level of agreement from chance
leaving aside the rela null tionship between the two words your choice of p v or i the word pair would be of use in constructing a general usage glossary
NUM in light of the results of the pilot study therefore our six annotators were given access to bilingual concordances for the entries they were judging and instructed in their use as just described
in this paper we have investigated the application of sable a turn key translation lexicon construction system for non technical users to the problem of identifying domain specific word translations given domain specific corpora of limited size
for example corbeille wastebasket makes sense in the computer domain in many popular graphical interfaces there is a wastebasket icon that is used for deleting files but also in more general usage
first we were concerned with the overall accuracy of the method that is its ability to produce reasonable candidate entries whether they be general or domain specific
the japanese awk was also found to contain different programming examples from the english version
those constraints limited the number of the pairs of vectors to be compared by dtw
the union effect of all these dtw paths shows a salient line approximating the diagonal
we have been studying robust lexicon compilation methods which do not rely on sentence alignment
the words hong and kong are both translated into i4 indicating hong kong is a compound name
if they never occur in the same segments their m would be negative infinity
the pos filter throws out nouns and pronouns and makes room for high and vast
likewise separate evaluations canl be performed for each k NUM k n in n best lexicons
the effectiveness of different data filters for inducing translation lexicons crucially depends on the particular pair of languages under consideration
all the presented filters except the pos filter improve performance even when a large training corpus is available
the attitude of the present study is do n t guess when you know
recall is the fraction of the source language s vocabulary that appears in the lexicon
the evaluation method uses a simple objective criterion rather than relying on subjective human judges
to confirm this an independent second translation of NUM french hansard sentences was commissioned
if all the words in the text are considered then bible measures percent correct
this form of semantic representation has the following adwmtages for transfer it is possible to preserve the underspecification of quantifier amd operator scope if there is no divergence regarding scope ambiguity between sollrce and target languages
therefore before entering tile transfer component of our system individual lexemcs can already be decoinposcd into sets of such entities e.g. for stating generalizations on the lexical semantics level or providing suitable representations for inferences
NUM a l termin x it appointment x b l terrain x sort x temp point l date x
in contrast to a purely this work was funded by the german federal ministry of education science research and technology bmbf in the framework of the verbmobil project under grant NUM iv NUM u we would like to thank our colleagues of the verbmobil subproject transfer our ims colleagues ulrich heid and c j
the rule in 3c illustrates how an additional condition ill passen e might be used to trigger a specific translation of schleeht into not good in the context ofpassen
the translation results are summarized in table NUM
after the unificationt ased s illantic construction the logical wu iables for labels and nmrkers such as events states and individuals are skolemize l with special constant symt ols e.g.
interaction with external modules e.g. the domain model and dialog module or other inference components is done via a set of predefined abstract interface functions whidl may be called in the condition part of transfer rules
ordered according to their scores these candidates for insertion are tested for compatibility with either the previous or the current dialogue act
in our demo the whiteboard was maintained in a commercial lisp based object oriented language while components included independently developed speech recognition analysis and word lookup components written in c overall the whiteboard architecture can be seen as an adaptation of blackboard architectures for client server operations the coordinator becomes the main client for several components behaving as servers
this table is valid only for words referring to objects but there are different tables for declensions of words referring to living beings
as a starting point let us show what the declension of a word in basque may be by means of an example
there is another possibility the predictor marks the lemma and the user is asked to complete the needed information after ending the session
to implement this system a two entries table is needed to store the conditional probability of apparition of each word wj after each wi
these were added to help focus research on certain known problem areas and ineluded such issues as investigating searching as an interactive task by examining the process as well as the outcome investigating techniques for merging results from the various trec subcollections examining the effects of corrupted data and evaluating routing systems using a specific effectiveness measure
we give an example of a uvg dl in figure NUM in which the dotted lines represent the dominance links
in figure NUM top we see how the link is inherited by the heir nonterminal of the applied production
for splitting a sentence into clauses the last idea how to gain efficiency is that of splitting the sentence if possible into clauses before the processing which has a two fold positive effect on the overall process of grammar checking i it
the substrings es t and c u v both derive from the node q
pushdown transducers are a standard model for parsing and have also been used usually implicitly in speech understanding
the type of synchronization is closely based on a previously proposed model which we will call local synchronization
is less time consuming to parse two shorter strings than one longer a ssmning that l arsing is a t least cubic in t ime this fl llows trivially fronl the inequality a a b a a a c4 b aab b a a b a for a b p ar ticular also m ca ses
the basic philosophy of the technology discussed in this paper NUM is that of linguistic theoretically smmd grammar and parsing based machinery able to detect by constraint relaxation errors from a predefined set as opposed to pattern matching approaches which do not seem promising for a free word order language
since their probabilities remain impossible throughout the illegal subhypotheses will never participate in any ml bibracketing
this yielded approximately NUM NUM filtered phrasal translations some examples of which are shown in figure NUM
this approach as we will see gives us both the desired empirical coverage and acceptable computational and formal results
let nr and f be the root node and the set of all nodes of NUM respectively
hence these entities must be both hearer old and discourse old
the evaluation exercised only three of the system s eight editors
mil is then evaluated against a manually prepared answer key
plate NUM from article NUM NUM
null the following templates were to be generated
in earlier mucs each event had been represented as a single temi late in effect a single record in a data l ase with a large nuinber of attributes
although called conferences the distinguishing characteristic of the mucs are not the conferences themselves but the evaluations to which participants must submit in order to be permitted to attend the conference
tile scenario involved changes in corporate executive management personnel
message understanding conference NUM a brief history
for muc NUM the template had NUM slots
in this experinlent we if cussed on noun phrases as they are central in most terminologies
which more readily exhibit the flmdamental binary relations and to classify words with respect to these simplified trees
admittedly this is not a perfect measure
additionally fro simplitication purposes a contracted word like du is considered as a preposition determiner sequ enec
for example in np0 the adjectival modifier scrrc is removed as well as the determiner and the adjectives
the subgraph of the chirurgical acts words which is easy to identify from the syclade graph fig
parsers for the sake of reusability we chose to add a generic post processing treatment to the results of robust parsers
the network also enables to distinguish the uses of quasi synonyms such as eoronaire and coronarien in the cmc corpus
a simple extension of the earley chart allows finding partial parses of ungrammatical input
one approach to this problem is to insert complete states into a prioritized queue
the recurrence for rl can be conveniently written in matrix notation rl NUM NUM plrl
only those nonterminals can have nonzero contributions to the higher powers of the matrix p
inversion of the full matrix i p took NUM minutes NUM seconds
the example illustrates how the parser deals with left recursion and the merging of alternative sub parses during completion
the main difference is that unit productions rather than left corners form the underlying transitive relation
the benefits of such semantic smoothing appear especially in the possibility of retrieving reasonable semanticallymediated associations for morphs which are rare or absent in a training corpus
such an application can be of use for medical administrative purposes in a hospital environment
two selection boxes allow the medical encoder to choose a text and the semantic labels
figure NUM lsp mlp parse tree generated after sublanguage processing for sentence of figure NUM
a sample of NUM dutch sentences of varying length and syntactic complexity was selected
buttons in the menu page allow to display very rapidly the selected view on the pds
but the human encoder would remain responsible for the ultimate selection of the exact codes
a larger test set needs to be processed in order to provide more conclusive results
NUM venal jump graft from the aorta to the diagonalis further to the lad
the linguistic information of the dmlp and the lsp mlp systems correspond in a high degree
furthermore if words are written down in order of decreasing frequency a huffman code for a large lexicon can be specified using a negligible number of bits
given a coding scheme and a particular lexicon and a parsing algorithm it is in theory possible to calculate the minimum length encoding of a given input
this rarely happened when the computer had the initiative
we now turn our attention to the order effect
the reported data combine both computer and user utterances
a hardware failure caused termination of the final dialogue
the algorithm was also run as a compressor on a lower case version of the brown corpus with spaces and punctuation left in
of course for this representation to be more than an intuition both the composition and perturbation operators must be exactly specified
part of the encoding will be devoted to the lexicon the rest to representing the input in terms of the lexicon
in the case of both english and chinese most of the unfound words were words that occurred only once in the corpus
since parameters words have compact repre null sentations they are cheap from a description length standpoint and many can be included in the lexicon
the meanings sthis framework is easily extended to handle multiple ambiguous meanings with and without priors and noise but these extensions will not be discussed here
a second test was performed in which the algorithm received three possible meanings for each utterance the true one and also the meaning of the two surrounding utterances
smith and gordon human computer dialogue table NUM
figure NUM at step NUM of algorithm NUM triple q e e is inserted in v p if the relations depicted above are
each of these classes can be parameterized for specific predicates by for example different prepositions or particles
there are NUM distinct values for vsubcat and NUM for psubcat these are analyzed in patterns along with specific closed class head lemmas of arguments such as it dummy subjects whether wh complements and so forth to classify patterns as evidence for one of the NUM subcategorization classes
we compute this measure by calculating the percentage of pairs of classes at positions n m s t
the combined throughput of the parsing components on a sun ultrasparc NUM NUM is around NUM words per cpu second
the binomial distribution gives the probability of an event with probability p happening exactly m times out of n attempts n p m n p m n rn pro NUM p n m the probability of the event happening m or more times is
these figures are straightforwardly computed from the output of the classifier however we also require an estimate of the probability that a pattern for class i will occur with a verb which is not a member of subcategorization class i brent proposes estimating these probabilities experimentally on the basis of the behavior of the extractor
however since there are disagreements between the dictionaries and there are classes found in the corpus data that are not contained in either dictionary we report results relative both to a manually merged entry from anlt and comlex and also for seven of the verbs to a manual analysis of the actual corpus data
finally the meaning of the question becomes a salient open proposition
it shows the number of true positives tp correct classes proposed by our system false positives fp incorrect classes proposed by our system and false negatives fn correct classes not proposed by our system as judged against the merged entry and for seven of the verbs against the corpus analysis
is it in the cupboard in response to where is it
the algorithm now selects the goal of describing book19 as an np
the algorithms use the training data where each document is labeled by zero or more categories to learn a classifier which classifies new texts
overall the best variation we investigate performs significantly better than any known algorithm tested on this task using a similar set of features
the way we treat the negative weights is different though and significantly more efficient especially in sparse domains see section NUM NUM
while the basic versions of our algorithms search for linear separators we have modified those so that our search for a linear classifier is biased to look for thick classifiers
the rest of the paper is organized as follows the next section describes the task of text categorization how we model it as a classification task and some related work
the label of the document is denoted by y y takes the value NUM if the document is relevant to the category and NUM otherwise
while winnow is guaranteed to find a perfect separator if one exists it also appears to be fairly successful when there is no perfect separator
intuitively this makes the algorithm more sensitive to the relationships among the features relationships that may go unnoticed by an algorithm that is based on counts accumulated separately for each attribute
the features used are the experts and the learning algorithm can be viewed as an algorithm that learns how to combine the classifications of the different experts in an optimal way
this dialogue is part of a corpus of NUM dialogues which are all fully processed by our dialogue component
it is generally considered highly desirable to allow people with disabilities to be as independent as possible
mcca is a technique for characterizing the concepts and themes occurring in text sentences paragraphs interview transcripts books
lexical look up at this point the text is stored in tokenized form and any unambiguous names and phrase s that are stored in the lexicon are identified
throughout processing the structure holds an original and a latest version of each sentence the latest version is updated with each processing phase
in addition noun phrase recognition prompts a backward search through a stack of named entities in orde r to identify its referent
assigning the same symbol name to each instance of a template element greatly simplifies th e work of the subsequent st system
this allowed the mapping NUM NUM of the two mccann erickson organization objects which improved our score to 76r 79p fro m 71r 67p
the system also links other descriptive phrases and pronouns to the named entity and these additional descriptions are used to assist the st system in its information extraction task
the agency still is dogged by the loss of the key creative assignment for the prestigious ename x type quot organization quot coca cola enamex classic account
the system then searches for locations knowing that the entities found previously will not be par t of the location phrase
the script is based on the task specification and contains the path whic h the template generator should follow through the objects
if the noun phrase is semantically rich a content filter is constructed and compared against content filters for known named entities
it appears that the presence of stopwords have little effect on chinese ir just as noticed for bigrams
information from the merriam webster concise electronic dictionary integrated with dimap and attached lexical semantic information from other resources to entries in these sublexicons
the proportion of times that one would expect them to agree by chance
krippendorff argues that there are three different tests of reliability with increasing strength
overall agreement on the entire coding scheme was good k NUM
classification results can only be interpreted if the underlying segmentation is reasonably robust
this may involve embedding new games with subservient carletta et al
the game coding was somewhat less reproducible but still reasonable
most of the disagreement fell into one of two categories
there are two important components of any game coding scheme
all four dialogues used different maps and differently shaped routes
level of coding most useful for work in other domains
therefore the interpolation effect is usually small or negligible
no preference factors or filters are applied
it only seems as if they did
this vp is the correct antecedent
which the antecedent was more distant
here the correct antecedent is have good times
coder NUM collapsed coder NUM collapsed last week
NUM NUM she said she would not
this weight is modified by any applicable preference factors
some errors result from problems with the syntactic filter
the advantage of using this same algorithm for determination of both verbal and sentential aspect is that it is possible to use the same mechanism to perform two independent tasks NUM determine inherent aspectual features associated with a lexical item NUM derive non inherent aspectual features associated with combinations of lexical items
table NUM the naive back off smoothing algorithm
in addition this approach is broadly integrative incorporating aspects of transaction success concept accuracy multiple cost measures and user satisfaction
after each step in the information presentation the caller shows that he has processed the step by an acknowledgement
memory based learning using similarity for smoothing
semantically a description d is just an open formula
tree NUM shows a portion of a tree that considers relationships between person and status
the results are in table NUM
the manual text annotations required for resolve provide us with our final observation about muc NUM an d our muc NUM system
each word has a numeric NUM dimensional vector representation
first argument indexing now ensures that table lookup is efficient
the double negation also takes care of this potential problem
a memorization technique is applied to obtain a fast parser
head corner parsing is a mix of bottom up and top down processing
the result is a slightly larger head corner NUM
this grammar derives exactly all derivation trees of the input
correspondingly the theoretical worst case space requirements are also worse
therefore proposed that aspectual interpretation be derived through monotonic composition of marked privative features NUM dynamic NUM durative and NUM relic as shown in table NUM olsen to appear in NUM pp
in this case we say that we skip that transition
the giving itself is hm aeterized by the concept hian ie sign
etc figure NUM sample entries from the tree database
syntax and semantics as well as referential and inferential potential
drt gains a purely semantically motivated orientation towards lexical fields
the discourse referent ec and the thematic roles of the
the presupposition to ji n e 2t
according to auilg NUM NUM l NUM kiistne
utterances can also be available support the speaking conversation partner
table NUM results of the classification of adverbs adverb class label
for the sentence the soldier marched to the bridge in the next sections we outline the aspectual properties of the lcs templates for verbs in the lexicon and illustrate how lcs templates compose at the sententim level demonstrating how lexical aspect feature determination occurs via the same algorithm at both verbal and sentential levels
sentence planning as description using tree adjoining grammar
la hack NUM makes four passes through each article
this goal was met with varying degrees of success
it s performance is shown in table NUM
table NUM contains our official system performance figures
most of these tools were developed at penn
all other uppercase words were converted to lowercase
as a result disk space became a problem
tokenization once sentence boundaries are identified tokenization begins
the first is the alteration of headline word capitalization
definite cases of predicate nominative constructions are also markable
within this search rectangle simr generates all the points of correspondence that satisfy the supplied matching predicate as explained in section NUM NUM
in order for simr to generate candidate points of correspondence it needs to know what token pairs correspond to co ordinates in the search rectangle
it is likely that the accuracy of both kinds of algorithms can be improved by alternating between the two on the same bitext
if more than one chain is found in the same cycle simr accepts the one whose points are least dispersed around its least squares line
null when the matching predicate can not generate enough candidate correspondence points based on cognates its signal can be strengthened by a translation lexicon
the smooth injective map recognizer simr algorithm presented here is a generic pattern recognition algorithm that is particularly well suited to mapping bitext correspondence
the evaluation in section NUM shows that simr s error rates are lower than those of other bitext mapping algorithms by an order of magnitude
when one or both of the languages involved is written in pictographs cognates can still be found among punctuation and digit strings
more importantly bitexts often contain lists tables titles footnotes citations and or mark up codes that foil sentence alignment methods
in english the choice between expansion to the full graphemic equivalence or expansion to a full phonetic equivalence was made in favor of the latter
it then searches linearly through the rules in the order they were written until it finds a rule that matches at that current position
oin is pronounced w ps in loin poing but not in avoine where the rule for oi applies
but in french there are more interactions between words due to the linking problem nous avons and mute e chemin defer
in effect three contexts are usable the left context and right context in the input buffer and the left context in the output buffer
ef is replaced by h if on the left side of ef an element of the class c2 is found preceded by another e
for english and french the number of words having this problem is relatively small and can be dealt with by a dictionary or rules
this happens between two words in the context cc c and is again the result of the difficulty of pronouncing more than two consecutive consonants
any procedure to convert text into phonemes would necessarily make use of a lexical database or dictionary to provide for lookup of words prior to letter to sound conversion
the main advantage is that this dictionary can then be used to drive a sentence tagger and parser necessary for improving intonation and naturalness for speech synthesis
block on the table figure NUM
dop3 is very similar to dop1
accuracies for atis word strings by dop4
NUM NUM experiments with dop2 the problem of unknown category words
queries containing personal names NUM NUM name frequencies in the case law collection
such reference resolution is not generally possible without some additional context
the baseline searches treated each term in the query as a separate concept
the test statistic is the number of runs where the score of one predictor is higher than the other s as is common in statistical practice ties are broken by assigning half of them to each category
it was conducted under the auspices of the columbia university cat in high performance computing and communications in healthcare a new york state center for advanced technology supported by the new york state science and technology foundation
this indicator is exact for the case of test NUM since the formally marked word is derived from the unmarked one through the addition of an affix which for adjectives is always a prefix
in addition we included all gradable adjectives which appear NUM times or more in the brown corpus and have at least one gradable antonym the antonyms were not restricted to belong to this set of frequent adjectives
first the domain sequence must mirror the precedence of the words included i.e. words in a prior domain must precede all words in a subsequent domain
the pictalk system allows the user to pre load potential conversation fragments that may be useful in some future conversational interaction
after motivating non projective analyses for dg we investigate various variants of dg and identify the separation of dominance and precedence as a major part of current dg theorizing
to test this hypothesis we manually identified the senses of the words in the queries for two collections computer science and time
the vertex cover problem is to decide whether for a given graph there exists a vertex cover with at most k elements
thus modifiers must be inserted into an order domain of their head i.e. no mark in valencies
the dg recognition problem dgr consists of all instances g a such that a e l g
because of this early position in the ie process an event recognition program is faced with a necessarily shallow textual representation
a modular architecture is described that allows us to examine the contributions made by particular aspects of natural language to event structuring
the contributions of n gram frequencies and the cue phrase analysis module are yet to be fully evaluated although early results axe encouraging
to do this it makes use of the constraints it receives from the analysis modules combined with a number of document structuring heuristics
it is based on the reasoning that quiet clauses should be assigned to the same event as previous clauses within the sentence
the presence of a cue phrase in a sentence is used to signal the start of a totally new event
whilst the issue of evaluation of information extraction in general has been well addressed the evaluation of event recognition in particular has not
graph unification is then used to build a set of constraints determining which clauses NUM in a text can refer to the same event
the purpose of our work is therefore to investigate the quality of text segmentation that is possible given such a surface form
this is illustrated in the fourth section where we give a simple encoding of an a p complete problem in a discontinuous dg
to prove this we will encode the vertex cover problem which is known to be a p complete in a dg
head outward processing with a lexicalized model also the obvious advantage to efficiency that only the part of the model related to the source words in the input needs to be active during the search process
at the time of deciding on the ellipsis substitutions the precise composition of the antecedent may not yet have been determined
lw log wgl where v is the vocabulary
we identified how far back on the focus list one must go to find an antecedent that is appropriate according to the model
recall that the input is ambiguous the figures in table NUM are based on the system selecting the first ilt in each case
s is the entropy of class
among the first more constrained data there are twelve dialogs in the training data and three dialogs in a held out test set
the merging process might have yielded additional opportunity for making obvious inferences so that process is performed again to produce the final ailt
we also allow antecedents for which the anaphoric relation would be a trivial extension of one of the relations in the model
in the less constrmned data some error occurs due to subdialogs so an extension to the approach is needed to handle them
in the input ilt different pieces of information about the same time might be represented separately in order to capture relationships among clauses
this parser is proprietary but it would not be difficult to produce just the portion of the temporal information that our system requires
this shows that the system performs respectably with NUM accuracy and NUM precision on this less constrained set of data
thus we map from the input representation into a normalized form that shields the reasoning component from the idiosyncracies of the input representation
else if it is as shown in figure 5b we can concentrate on the tree spanned by node ml and repeat the process
the resulting tree NUM obtained by adjoining fl onto c at node m is built as follows figure NUM NUM
all other leaf nodes are labeled with symbols in e u lcb rcb at least one of which has a label strictly in e
thus if we discard the mid NUM NUM we will not be able to infer that the adjunction had indeed taken place at node m
the point to note is that in step NUM we can get rid of the mid NUM NUM and focus on the remaining problem size
the tal recognizer that uses the cubic time algorithm has a run time comparable to that of vijayashanker oshi s algorithm
if m is a leaf then quit NUM if m has children ml and me both yielding the empty string at their frontiers i.e.
null below is given a sample of a grammar tested and also the speed up using the sparse version over the ordinary version
steps la lb and 4a can be carried out in the following manner consider the composition of node ml with node me
on the other hand compared to grammar NUM both the table and the average number of conflicts are smaller
for binary rules as per equations NUM and NUM the distribution of the non head word is conditioned on the head a bigram
by choosing the nearest chain i.e. the last one in the list only nested dependencies are built
pre s the set of morpheme numbers that connect to the s th morpheme
in fact with students experienced in marking ne consistency across annotators was only NUM suggesting that the annotation rules can use further elaboration
credit factors for training data to improve the reliability of the estimated models axe also introduced
however they sometimes differ due to the fact that different heuristics or template filling strategies may result i n better performance in each of the domains
this would not be possible if the semantic rules were required to cover a wider variety o f syntactic structures before it could achieve reasonable performance
NUM to achieve an f above NUM on the other hand is likely to require significant overall improvement and heavyweight processing in particular
the tags were defined as the combination of part of speech conjugation and class of conjugation
the scaled forward probabilities can be calculated synchronizing with the synchronous points from left to right
the other extension is that the algorithm can train the hmm in addition to the n gram model
the cost width of NUM required almost all of the morphemes to be used for the estimation
in particular word groups that are important to the domain and that may be detectable wit h only local syntactic analysis can be treated here
when cases of permanent predictable ambiguity arise the parser finishes the analysis of the current phrase and begins the analysis of a new phrase
the third clause assigns ai in a condition that is more complex than the others to deal with subject oriented parasitic gaps
the second observation is based on a detailed inspection of the form of the principles of the grammar
this is also the best place to explain why we apply aggregation before choosing concrete linguistic resources
the general form of this type of rules can be characterized by the pattern as given below
of greater interest is that although use of head features improves bracketing performance it does so only by an insignificant amount though obviously it greatly reduces perplexity
however some other s andard analyze s
constituent structure serves as an exl la natory
an explicit coordinating conjunction need not be present
recognize tha t he cries anna
the corpus is stored in a sql database
the tool checks the appropriateness of the input
afor an extensive use of gr tnllnaticm functions cf
the procedure was repeated NUM times with different partitionings
argument structure represented in terms of unordered trees
nk denotes a kernel np component v
the parameter determines the fraction of relevant and irrelevant documents
katz k mixture distribution can be thought of as a mixture of
but not all equally frequent words are equally mean ngful
the NUM words are shown in table NUM sorted by dr
document frequency is similar to word frequency but different in a subtle but crucial way
the resource trees of an upper model concept are assembled in its realization class
stories that mention the word boycott for example are likely to be about boycotts
unfortunately we believe the words in the middle are often the most important words for information
the correlations of idf and log NUM y2 across years are presented in tables NUM NUM
figure NUM idf in one year of the ap is very predictive of idf in another
concepts of the hierarchy of textual semantic categories are noted in sans serif text
level for values ofj NUM where j is the number of subjects NUM to NUM on all NUM narratives NUM fig NUM shows a typical segmentation of one of the narratives in our corpus
the kind of observations that are available from the corpus are i the set of lemmas met in the texts ii the set of their well formed restrictions i.e.
comparison of tables NUM and NUM shows that as with the hand tuning results and as expected average performance is worse when applied to the testing rather than the training data particularly with respect to precision
the first input to c4 NUM specifies the names of the classes to be learned boundary and non boundary and the names and potential values of a fixed set of coding features fig NUM
understanding systems could infer segments as a step towards producing summaries while generation systems could signal segments to increase comprehensibility our results also suggest that to best identify or convey segment boundaries systems will need to exploit multiple signals simultaneously
this suggests that hand tuning is a useful method for understanding how to best code the data while mschine learning provides an effective and automatic way to produce an algorithm given a good feature representation
because machine learning makes it convenient to induce decision trees under a wide variety of conditions we have performed numerous experiments varying the number of features used to code the training data the definitions used for classifying a potential boundary site as boundary or non boundary NUM and the options available for running the c4 NUM program
fig NUM illustrates how the first boundary site in fig NUM would be coded using the features in fig NUM the prosodic and cue phrase features were motivated by previous results in the literature
our third contribution is the divergence heuristic which adds a more specific context to the model only when it reduces the codelength of the past data more than it increases the codelength of the model
backgrounding a typical function of the imperfective view where the initim point is not included is therefore not applicable for this viewpoint
we therefore will need to maintain our vigilance in protecting and enhancing the clarity of the design
rule NUM bmb system user replace plan newplan agtl e lcb system user rcb cstate system user plan goal bmb system user error plan node bmb system user bel agtl replace plan newplan
NUM the first sentence of NUM refers to a situation s where sn is of a type c
by modestly increasing the number of model parameters in a principled manner our techniques are able to further reduce the message entropy of the brown corpus to NUM NUM bits char
goal system bel user bel system achieve p104 knowref system user entityl antennal NUM the plan constructor achieves this by planning an instance of accept plan which results in the surface speech action s accept which would be realized as okay
first in section NUM NUM we use the minimum description length mdl principle to quantify the total merit of a model with respect to a training corpus
to account for this observed syntactic complexity the lexical chooser must be able to accept as input networks of several semantic relations sharing certain arguments
the head word for the linguistic constituent is selected by looking up the semantic feature in i.e. unifying the semr feature with the lexicon
they show how time and manner can be mapped to two different surface elements of different syntactic rank in the sentence among many other possibilities
it is only during the subsequent stage of lexicalization proper when the specific verb to have in this example is selected
in this broader systemic sense a process is thus a very general concept simply denoting a semantic class of verbs sharing the same thematic roles
several conceptual elements may be realized by the same linguistic constituent and conversely several linguistic constituents may be needed to realize a single conceptual element
the value of lex cset is a list of pointers to the constituents of the fd as shown in figure NUM constituents bring structure to functional descriptions
since in different contexts different constraints play more or less of a role unification can determine dynamically which constraints are triggered and in what order
rule NUM goal system bel user bel system achieve plan goal cstate system user plan goal bel system achieve plan goal bel system bel user achieve plan goal
this two level theory gives an explanation for the difference between aspectual information understood as a view on a situation and temporal features of a situation
our approach uses the functional unification formalism fu to represent a generation lexicon allowing for declarative and compositional representation of individual constraints
instead such constraints float appearing at a variety of different levels in the resulting linguistic structure depending on other constraints in the input
a y on x y s refer entity3 s attrib entity3 xx category x television the system finds two plan derivations that account for the primitive action one an instance of replace plan see figure NUM and the other an instance of expand plan see figure NUM
rule NUM bmb system user bel agtl prop bmb system user goal a gt l bel agt NUM bel agt l prop agtl agt2 c lcb system user rcb not agtl agt2
although the icmh is not so stringent as to make predictions that converge on a single parsing architecture it does provide some predictive power about the organization of the parser
note that in algorithms i and NUM features such as case and NUM role must be available as input for the correct labeling and chain assignment of the empty category
for the chain selection algorithm csel there are four main constraints first a nodes can only be inserted in a chains and a nodes can only be inserted in a chains
they are a method to schedule the on line computation of principles that are the direct translation of the theory and not a way of defining the design of the parser
nlab takes an input word and outputs a label while csel takes a triple node label chains as input and returns a new chain list
this is very much in the spirit of the current shift in linguistic theories from construction dependent rules to general principles and it separates quite clearly the grammar from the parsing algorithm
in particular interfacing with a database has required several new technology components matching information exlracted from text against existing database records and determining when no suitable match exists and a new database record must be created once a match is made extracted information must be fused with database records to isolate new information
the results of the compilation of the same grammars into ll tables are shown in table NUM grammar NUM is a modified version of grammar NUM without adjunction rules
in order to check that this is not the case the same three grammars reported in the appendix were compiled into ll and left corner lc tables
his in particular allows the program to avoid outputting all strings generable by the grammar whose lp rules are being acquired lcb notice for instance in the lh st colunm of figure NUM that no language expression involving the dictionary rule NUM det the from figure NUM is displayed to the user
the sibling list then after hierarchical sorting from lower level to higher level nodes becomes det num adj n sum vtr np aux mme vp and the lirsl element of this list is first passed to the learning engine
a ti s sot det num acid these are singleton sets which are identical and the resultant consistent generalization is there fore det num adj
thus for instance an immediate dominance rule say a i3 c d with no linear precedence rules declared stands for the mother node a expanded into its siblings occurring in any order six context free grammar rules as result of the permutations
the predicate observes two types of lp constraints tile globally valid lp rules tha t have been acquired by the system so far and the transitory lp constraints serving to produce an ordering as required by an intermediate stage of the learning process
we only use a double arrow to avoid mixing up with the often built in deftnite clause irammar notation and besides empty productions and sisters ha ving the very same nallm are not allowed since they interfere with NUM NUM rules statmnenl s
afl er processing the lirst NUM ositive tirst row tile system generalizes by varying a paraluet r imnd er or l recedenec verbalizes NUM he generalization the generated phrase is class tied by the teacher then another generalization is made depending on the classiiication it is verbalized evaluated and so on
l isposing of the ordering of constituents in the positive example the tra silive closure of these partial orderings is computed in our case from det num adj adj n num we get det num adj ad i
a general description of our task is as follows given a specific id grammar with no lp rules find those lp rules NUM in this task we also need to reason from very specific instances of lp rules language phrases like small childreu children smalt to rnore general lp rules adjective noun therefore it can be interpreted in terms of tile twospace model described above
singleton right hand sides rule NUM above and all dictionary rules are therefore left out and so are cuts and escapes to pro null log in curly brackets since they are not used to represent tree nodes but are rather coustraints on such nodes
this product is the probability of the word
furthermore they reorganize themselves to so as to decrease the cost of accessing to the most frequently accessed elements thus speeding up access to counts and subtrees associated to more frequent words
the gt method sets NUM w0 t ns where tl is the total number of words that were observed only once in that context
at each node s we keep two variablesj the first in practice we keep only a ratio related to the two variables as explained in detail in the next section
the key ingredient of the model construction is the prediction suffix tree pst whose nodes represent suffixes of past input and specify a predictive distribution over possible successors of the suffix
since the generation of these objects is independent of the relevance criteria imposed by the scenario template st task there are many more organization and person objects in the te key than in the st key
NUM the highest score for the person object NUM recall and NUM precision is close to the highest score on the ne subcategorization for person which was NUM recall and NUM precision
for each np in the equivalence class it would be useful to identify its grammatical type proper noun phrase definite common noun phrase bare singular common noun phrase personal pronoun etc
some of the shortfall in performance on the organization object is due to inadequate discourse processing which is needed in order to get some of the non local instances of the org descriptor org locale and org country slot fills
top performance on person objects came close to human performance while performance on organization objects fell significantly short of human performance with the caveat that human performance was measured on only a portion of the test set
the following two principles address dialogue parlnor asynllnelry NUM sp4
slots only for the string representing the person name per name for strings representing any abbreviated versions of the name peralias and for strings representing a very limited range of titles per title
2the travel domain grammars have been under development for only a few months
the increase in interpretations can be attributed to the larger number of sub domains
thus the out of vocabulary rate for the etd test set is NUM NUM
it is a research question to determine which words frequencies vary for a given variation in linguistic structures see the section on newspapers for an indication of how this can proceed
given an accurately part of speech tagged or parsed corpus the same method could be applied to frequency lists of parts of speech or syntactic constructions and the methodological part of the paper would still be salient
her incriminates for him to thieve an automobiles
it is not clear what if anything a measure of the similarity of a thousand word corpus and a million word corpus or a one text corpus and a thousand text corpus would mean
then we watch the statistical component make its selections
she am accusing for him to steal autos
null collocational restrictions are another example of lexical constraints
for example does a contain frequent three word sequences
tionally available to the lexical chooser
we also look at lattice properties and execution speed
some instances remain ambiguous even within precise contexts
otherwise the sequence is split into two syllables
therefore the assumption does not always hold
already included in f4 because of the stress mark
although consonant splitting is clearly determined by the grammar rules of modern greek and is thus easily expressed in terms of non exceptional formal patterns associated with specific hyphenation rules vowel splitting is not
computational linguistics volume NUM number NUM according to theorems NUM and NUM for each substring vlcl c2c c3 v2 precisely one hyphen point can be derived
other loanwords most frequently words that end in more than one consonant e.g. l film film have not completely adapted
apparently then incorrect hyphenation had been applied
we evaluate the translation modules on both transcribed and speech recognized input
optionality is indicated by placing characters inside square brackets
consequently all such words have exactly n syllables
we investigated several methods to identify related senses both across part of speech and within a single homograph and these will be described in more detail in section NUM NUM NUM
the bark of a dog versus the bark of a tree is an example of homonymy review as a noun and as a verb is an example of polysemy
this is because of the uncertainty involved with sense representation and the degree to which we can identify a particular sense with the use of a word in context
these related forms e.g. fiat as a noun and an adjective are referred to as instances of zero affix morphology or functional shift marchand NUM
grouping inflectional variants also harms retrieval performance because of an overlap between inflected forms and uninflected forms e.g. arms can occur as a reference to weapons or as an inflected form of arm
most research to date has focused on syntactic phrases in which words are grouped together because they are in a specific syntactic relationship fagan NUM smeaton and van rijsbergen NUM
we tested this with the computer science and time collections and used those results to develop an exception list for filtering the pairs e.g. do not consider special ties specialties
this was done via brute force a program simply concatenated every adjacent word in the database and if it was also a single word in the collection it prim ted out the pair
senses may also be related etymologically but be perceived as distinct at the present time e.g. the cardinal of a church and cardinal numbers are etymologically related
lexical ambiguity is a fundamental problem in natural language processing but relatively little quantitative information is available about the extent of the problem or about the impact that it has on specific applications
the lisambiguation of different readings couht require an m bitrau y amount of reasoning on real world knowledge and thus should be avoided whenever possible
the table shows the baseline and mixed order perplexities on the test set the number of distinct trigrams with t or more counts and the fraction of trigrams in the test set that required backing off
we believe fi om a grammar engineering point of view it is unrealistic to come tip with such aai interlingua representation without a strict coordination between the monolingual grammars
if necessary such additional information can be used in transfer and sem mtic evaluation for resolving ambiguities or in generation for guiding tile realization choices
the output of transfer is a semantic representation for the target language which is input to the generator and speech synthesis to produce the target language utterance
NUM a l echt a l real h
k the translation rules in NUM are applied to the semantic input in 2a they yield the semantic output in 2b
b motivates a in each case the interpretation of the current element b of a discourse depends on the accessibility of another earlier element a according to the limited attention constraint only a limited number of candidates need to be considered in the processing of b for example only a limited number of entities in the discourse model are potential cospecifiers for a pronoun
tentions and the hearer s recognition of intention NUM expectations about what will be discussed
here reinstantiation is certainly sufficient but in general these cases can not be distinguished from corpus analysis
irus in combination with selectional restrictions leave only NUM cases of pronouns in return pops with competing antecedents
the notion of processing effort for retrieval operations on main memory makes predictions that can be experimentally tested
figure NUM compares the deletion rates figure NUM shows the insertion rates and figure NUM shows the match rates
an insertion occurs when the sentence produces a parse that does not occur in the control parse forest for that sentence
finally the eleventh run is done without loading any extra dictionary files only the three dosed class files are used
the attentional multiple thread case is when tu1 is required to be an antecedent of tu3 but tu2 is also needed to interpret tu4
job ads are stored in the system in a schema which is a typed feature structure consisting of named slots and fillers
second but related to the first point the hypertext capabilities are also a mild form of tailoring to the needs of different users
it remains to be seen what are the consequences of this scaling on what has so far proved to be a simple but effective architecture
nonetheless we believe this approach is capable of giving considerable coverage at a far lower cost and higher quality than that usually associated with mt
thus objects can be seen as being located within an n dimensional parameter space where n is the number of defining parameters of the object
in the following paragraphs we explain the analysis process and discuss our reasons for preferring this over a more traditional string matching or parsing approach
the distinction between terms and items in the lexicon is discussed below but we consider first the design and implementation of the schema database
lcb item e x y job y NUM company x c rcb
one way of characterizing the integrated approacfi to generation is to say that we go from database records to sentences in just one step
to estimate the semantic entropy of english words roughly thirteen million words were used from the record of proceedings of the canadian parliament hansards which is available in english and in french
the process can be implemented very efficiently and does not affect selectional restrictions of the input language
the ldt also contains functional relationships that are used for simplifications of the translated formulas and assumption declarations
output is a logical formula consisting of predicates meaningful to the database engine database predicates
however we allow further translation of the output formula into database formulae using only existential conditional equivalences
an approach is described for supplying selectional restrictions to parsers in natural language interfaces nlis to databases
where student take and unknown are lexical predicates and db student rib course db take are database predicates NUM
assuming crept710 and crept720 are courses the input fsi g can be rewritten into fdb shown below
we assume that each atomic formula with input predicates can be translated into an atomic formula with output predicates
this correlation is to be expected since the maximum possible entropy of a word with frequency f is log f which is what equation NUM evaluates to when a word is always linked to nothing
then the user can assign a task to the new platoon by saying m1a1 platoon follow this route while drawing the route with the pen
because we use individual texts in our experiments instead of the fixed length conglomerate samples of karlgren and cutting we averaged all count features over text length
lr is a statistical technique for modeling a binary response variable by a linear combination of one or more predictor variables using a logit link function
most such combinations have six texts in the evaluation corpus but due to small numbers of some types of texts some extant combinations are underrepresented
for other purposes we will want to stress narrativity for example in looking for accounts of the storming of the bastille in either novels or histories
or are we really talking about a muhidimensional space of properties that have little more in common than that they are more or less orthogonal to topicality
similarly for pos tagging the frequency of uses of trend as a verb in the journal of commerce is NUM times higher than in sociological abstracts
that is there is less than a NUM chance that a machine guessing randomly could have come up with results so much better than the baseline
for the polytomous facets genre and brow we computed a predictor function independently for each level of each facet and chose the category with the highest prediction
to a large extent the problems of genre classification do n t become salient until we are confronted with large and heterogeneous search domains like the world wide web
in the case of tagging the error was due to idiomatic senses and example sentences and in the case of word overlap the error was links due to a single word in common
attachment site with np2 results in the derived structure with np subordinated into the lower clause
the algorithm is constructed in such a way that lowering is only attempted in cases where simple attachment fails
NUM the lamp near the paintings of the houses that was damaged in the flood
dom l p np2 and prec v np2 are not subtracted from the set
if the input subsequently continues with a verb then we have a choice of two nodes for lowering i.e.
this means that the bottom up search which we use for english will wrongly predict a maximal expulsion strategy
however presumably their parser would overgenerate on examples such as the horse raced past the barn fell
consider the following example NUM john ga oi ronbun wo kaita seitoi wo hometa
these are examples of what gorrell calls secondary relations which are not subject to the monotonicity requirement
the prediction made by the icmh is that compiling together x theory and categorial information will increase the size of the grammar without reducing the nondeterminism contained in the grammar because category subcategory information belongs to a different ic class than structural i.e. x information
this is precisely what distinguishes case assigned to the subject structural case assignment from other types of case assignments e.g. case assigned to the object by either a verb or a preposition it is assigned independently of the properties of the main verb
if b were not checked nlab would not distinguish between feet and intermediate traces even in the same type of chain thus it would output four sets of labels ah ah lcb af ai rcb lcb af ai rcb
in the course of pondering the relation between the grammar and the parser and mostly how the conceptual modularity of current linguistic theories can be implemented one learns that in fact the notion of modular theory is both true and false at least in its present incarnation
it must also decide whether to start a chain headed by an element in an argument position a chain such as the head of a passive chain or a chain headed by an element in a non argument position a chain NUM actually chains can also compose
figure NUM simple domain dependent linguistic rules
more experiments are needed to better understand whether this category is inherently difficult or whether a more carefully chosen set of seed words would improve performance
example NUM shows the representation of the literal form of example NUM the fourth turn repair example
section NUM and appendix a present machine to machine dialogues involving two instantiations of the implemented model
lintentions relate discourse acts to the linguistic intentions that they conventionally express see section NUM NUM
we will call the turn sequence whose focus is the current turn the discourse context
moreover they may both change their minds in the face of new information
NUM it is actually controversial whether an askref followed by an inform not knowref is a valid adjacency pair
thus from her perspective she need never recognize that russ has misunderstood
during interpretation procedures that test for particular features of the input suggest candidates
oh probably mrs mcowen and probably mrs cadry and some of the teachers
fact believe r knowsbetterref m r whoisgoing
the resulting classification tree was used to identify whether a word ending in a period is at the end of a declarative sentence in the brown corpus and achieved an error rate of NUM NUM
although sentence boundary disambiguation is an essential preprocessing step of many natural language processing systems it is a topic rarely addressed in the literature and there are few public domain programs for performing the segmentation task
the tokens returned by the lex program can be a sequence of alphabetic characters a sequence of digits NUM or a sequence of one or more non alphanumeric characters such as periods or quotation marks
this use of part of speech estimates of the context words rather than the words themselves is a unique aspect of the satz system and is responsible in large part for its efficiency and effectiveness
the training time on a workstation in our case a dec alpha NUM is less than one minute and the system can perform the sentence boundary disambiguation at a rate exceeding NUM NUM sentences minute
a capitalized word is not always a proper noun even when it appears somewhere other than in a sentence s initial position e.g. the word american is often used as an adjective
the output of the network is thus a single value between NUM and NUM and represents the strength of the evidence that a punctuation mark occurring in its context is indeed the end of a sentence
the lexicon itself need not be exhaustive as shown by the success of adapting satz to german and french with limited lexica and by the experiments in english lexicon size described in section NUM NUM
since the use of abbreviations in a text depends on the particular text and text genre the number of ambiguous punctuation marks and the corresponding lower bound will vary dramatically depending on text genre
in the case of a probabilistic vector described in section NUM NUM NUM the NUM category frequencies for the word are then converted to probabilities by dividing the frequencies for each by the total frequency for the word
finally we have filled the lack of an adequate control strategy for shake and bake by developing a nonmonotonic control strategy which orders more specific rules before less specific ones
intonational boundaries speech repairs and discourse markers modeling spoken dialog
thanks to the set orientation and indexing techniques we did not encounter any scaling problenls aald the average runtime pcrfornlanec for a NUM word sentence is about NUM milliseconds
the two approximations have therefore captured different aspects of the context free language
the word counts for these five groups were groupl NUM NUM group2 NUM NUM group3 NUM NUM group4 NUM NUM group5 NUM NUM for a total of NUM NUM words
clearly more experiments are called for we plan to conduct these across different annotators task types and languages to better evaluate productivity quality and other aspects of the annotation process
on the other hand if the user notices a consistent mistake being made by the machine learned rules early in the bootstrapping process the user can augment the machine derived rule sequence with manually composed rules
the large step in performance between columns three and four indicate that repeated invocation of the learning process during the intermediate stages of the corpus development cycle will likely result in acceleration of the annotation rate
while these files are generally hidden from the user they provide a basis for the combination and separation of document annotations tagsets without needing to modify or otherwise disturb the base document
in the limit if the pre tagging process performs well enough it becomes the domain specific automatic tagging procedure itself and can be applied to those new documents from which information is to be extracted
rules that include references to a single lexeme can be expanded to more general applicability by the human expert who is able to predict alternatives that lie outside the current corpus available to the machine
in order to obtain more detailed results on the effect of pre tagging corpora we conducted another experiment in which we made direct use of the iterative automatic generation of rules from a growing manually tagged corpus
this allows the user to view NUM in cases where documents use some of the more complex aspects of sgml the user supplies a document type description dtd file for use in normalization
it concerns nps occurring in a sentence s this combination occurred NUM times during testing
under this assumption the contribution to the semantic entropy of s made by each null link is f log f if f nullis represents the number of times that s is linked to nothing then the total contribution of all these null links to the semantic entropy of s is
for example rmin in german might either be translal ed as appointment or as date depending on the context
the results confirm the choice of reliability measures the lower the reliability the lower the accuracy
note that the grammar forms a single strongly connected component
tho do faxllt v np translapsion pa ttefil will assign a wrong japanese caso mamker for this phra se
languages this observation extends to sdl
key words computational complexity lambek categorial grammar
in semidirectional lambek calculus sd there is an additional nondirectional abstraction rule allowing the formula abstracted over to appear anywhere in the premise sequent s left hand side thus permitting non peripheral extraction
lemma NUM soundness if a NUM partition problem f a m n s has a solution then vwl w3m is in gr
a consequence of only allowing the o r rule which is easily proved by induction is that in any derivable sequent o may only appear in positive polarity
furthcrnlore we show tha t our fva nmwork ca n i c xttmded to incorpora te exa mt h lmsed
the siml lest wa n of integra ting the corpus b into t is just to consider the sentence pair s t rcb as a translation pa ttern
we denote this language by l g
one of the main reasons for this was a tendency to sometimes give high scores to texts that were actually too short to constitute reliable sainples the bnc attempts to maintain a standard sample size but this is not always possible
in fact it is possible to directly compare the oov rates with the performances shown above the bnc lm with the ernail vocabulary has an oov rate of NUM NUM on the vmr data and a correct of NUM NUM
this approach offers interesting possibilities regarding the development of a general methodology for corpus adaptation by attempting to grow a suitable corpus of training data for any domain using only a small sample as a seed
firstly it is necessary to determine the homogeneity of a corpus prior to performing any similarity measures since it is not clear what a measure of similarity would mean if a homogeneous corpus was being compared with a heterogeneous one
of course the whole point of this approach is to develop techniques that do not rely on ambiguous manual annotations such as title or domain so the presence of suitable floes is merely an initial indication of success
for example extracting NUM million words of text for a domain such as world affairs is trivially easy since domain information is encoded in the header of each individual file of which there are over NUM NUM
since much hp email concerns the computing business and the bnc classifies computing as a branch of applied science it would appear that the NUM million words from applied science section of the bnc may prove sufficiently similar
starting froln the initial database and agenda a proof will be represented as a list of agendas avoiding the context repetition of sequent proofs by indicating where the resolution rule retracts from the database superscript coindexed overline and where the deduction theorem rule adds to it subscript coindexation 1though nl with product is incomplete with respect to finite trees as opposed to groupoids in general
a sequent comprises an agenda forlnula a and a database f which is a bag of program clauses lcb b1 b rcb m n NUM subscript m for multiset we write f a in bnf the set of agendas corresponding to the nonterminal agps aft a and the set of program clauses corresponding to the nonterminal t cpss are defined by
for the higher order case agendas and program clauses are defined as above but the notion of goaps on which they depend is generalised to in null clude implications goaps atom oaps pcpss lo and a deduction theorem rule of inference is added f b a f v NUM a
and left and right rules are permutable n cn cn s n s may be proved by applying a left rule first or a right rule and the latter step then further admits the two options of the first example
furthermore for the particular case of associative lambek calculus an additional perspective of binary relational interpretation allows an especially efficient coding in which the span of expressions is represented in such a way as to avoid the computation of unifiers under associativity and this can also be exploited for non associative calculus
each model assigns grammatical functions and more important for this step a probability to the phrase
on man machine communication user wants to know his or machine situation what information he gets from the dialogue or how machine interprets understands his utterances as well as the speech recognition result
in this experiment the performances recognition comprehension rate dialogue time number of utterances of three systems were not seen explicit differences because the system is imperfect
in the speech input the system unterstood about NUM of the all utterances and offered the available information to user about NUM NUM NUM NUM NUM NUM NUM
further more based on that dialog system through natural language must be designed so that it can cooperatively response to users we devloped a cooperative response generator in the dialogue system
if the input pattern matches with one of the registered patterns its semantic representation is rejected and the correction procedure is applied if possible
NUM unacceptable out of NUM utterances were caused by unknown words so we considered that it was very important to solve the unknown word problem
this panel is attached on the NUM inch display of sparc NUM which has coordinate axes of NUM x NUM and a transmission speed of NUM points sec
a spoken dialogue system that can understand spontaneous speech needs to handle extensive range of speech in comparision with the read speech that has been studied so far
a multi modal response algorithm is very simple because the system is sure to respond to user through speech synthesizer and if the system is possible to respond through
if there is much retrieved information from the knowledge database for user s question the dialogue manager queries further conditions to the user to select the information
the bonus program for frequent fliers starting in NUM figure NUM example for automation level NUM the user
in the case of the first paper it is the move from smaller domains to larger more inclusive domains in the case of the second it is the move across from one domain to a distinct and separate domain of similar size
yet the final result was still a useful approximation with NUM states
extensional sentences derivable from the examples given in section NUM include do mor past participle done
as already noted the inferential core of datr is extremely simple to implement
NUM a descriptor containing an evaluable path may include nested descriptors that are either local or global
computational linguistics volume NUM number NUM simple extensional sentences take the form node path ext
indeed there are a variety of ways in which this can be done
dag2 v agr sing v agr num sing v agr per NUM v agr per num NUM
orthogonal multiple inheritance omi is a desirable property of lexical representation systems
in this section we examine the significance of this observation from a formal perspective
this however is not valid in fact v agr gen is undefined
NUM they are not freely interchangeable alternative values for a single attribute or path
the first paper in the section lavie et al focuses on the issues that arise when one transfers from a relatively narrow domain in this case appointment scheduling dialogues to a broader domain travel planning dialogues
in this pass we use the morphological recognizer to reduce the number of possible parts of speech for the word
NUM due to the n gram approach the tagger only sees a local window of the sentences
pre parsing segmentation relies on acoustic lexical syntactic semantic and statistical knowledge sources
weischedel et al NUM study the effectiveness of probablistic methods for part of speech tagging with unknown words
components used within gate will typically exist already our emphasis is reuse not reimplementation
information is stored in the database in the form of annotations
the required use of a script to provide information limits the applicability of this method to situations where scripts are available
there are three ways to provide the creole wrapper functions
given an integrated module all other interface functions happen automatically
experience so far indicates that gate is a productive environment for distributed collaborative reuse based software development
indeed alep based systems might well provide components operating within gate
the creole apis may also be used for programming new objects
table NUM collocation words and the number
open class words are comprised of words with the following parts of speech nouns verbs adjectives and adverbs
the first distinguishes between topics such as accommodation transportation restaurants events and sights
the rule set was designed first to properly parse the test corpus and second to be as general as possible
if the antecedent of an implication is not
con trib utor pos ition captures the posi null tion of a particular contributor within the larger segment in which it occurs and encodes the structure of the segment in terms of how many contributors precede and follow the core
fig NUM is a sub tree of the dendrogram we build for chinese
NUM for the tfor details cf
the reading where mary asked out bob at bob s party while readily available with light accent on the pronoun in example 10a is not available in its elided counterpart 10b
it might mean that james loves ivan s mother the so called strict reading shown in 3b or that james loves his own mother the sloppy reading shown in 3c
in general discourse principles for normal pronominal reference are more flexible than is consistent with the reference behavior exhibited by elliptical reconstructions because for instance overt pronouns allow for accent and accompanying deictic gestures
within each level the monotone alignment model can still be applied and only when moving from one level to the next we have to handle the problem of different word orders
as with the various forms of event reference a vp thus requires an antecedent to license deaccenting that either exists in the discourse or is inferrable from it
that is the readings in which the pronouns in the two coordinated constituents refer to different entities are derivable but do not exist for example NUM
thus the problem formulation is similar t o that of he time alignment problem in speech recognition where the so called hidden markov models have been successfully used for a long time jelinek
in the first example we changed the order of two groups of consecutive words and placed an a dditional copy of the spanish word euest a into the source sentence
tition this case corresponds to a target word with two or more aligned source words and therefore requires so that there is no contribution fl om the language model
word joining phrases in the english language such as would yogi mind doing and NUM would like you to do are difficult to handle by our alignment model
as expected only the first two of these readings are available for the unelided version of sentence NUM shown in example NUM again assuming that the vp is deaccented
however he states that allowing other parallel elements would represent a radical departure for the equational approach since the solution to the equation would no longer represent merely the elided material p
obviously the experimental data shows that the insertion rate has been cut drastically from the baseline performance
the following rule is needed to tell the system that it is allowed to realize the value of the feature company as the value itself i.e. the value is the name of the company
syntactically similar structures that correspond to different semantic concepts usually require separate rules in a semantic grammar
our approach is represented in figure NUM
figure NUM challenge of speech input
thus all valencies are actually filled
shogun s f score was always slightly higher
x has property history hypertension property
third the etd data is cross talk which is generally more disfluent and contains more co articulation
these two systems fell into neither category
whether this is true and just what the hard problems are will require more extensive analysis of the results of muc NUM
in this way muc participants could develop code for these low level telnplates once and then use them with many different types of events
this factor and the room that exists for improvement in performance suggest that including this task in a future muc may be worthwhile
n answcr kc lcb t esuliset i where re suit fret is the set o1 source parses to which the alignment procedure assigned the highcst score and answcrkey is the sc t of best source parses as judged by one of the exderjtnentefs
several features of the measures of similarity listed above are summarized in table NUM base lm constraints are conditions that must be satisfied by the probability estimates of the base 2actually they present two alternative definitions
the l ex m tch optimization has a greater effect on l si 7amino real than on curious ceor q because all of the words contained in el camino real are ineluded in our bilingual dictionary but only a small portion of the words in curious geovje are included
x has property last name jones
clearly we need something stricter than the first approach but more relaxed than the second
the best data source for observation of grammatical punctuation usage is a large parsed corpus
NUM he does surprisingly like fish
therefore instances of this rule application were covered by the np np s rule pattern
however this rules out correct cases like NUM
the grammar rules that correspond to the correct parse are then added to the appropriate sub domain grammar
able through columbia presbyterian medical cen
that means the large value it gets the less similarity it means
the dependency component defines four main categories and possible dependencies between them
where np means noun phrase pp prepositional phrase noun is a noun and prep a preposition
pm stands for motthological partich a label tbr german infinitival zu aml superlative am
i emotional adjectives triste sad furieuz angry furious irritg irritated heureuz happy ennuyg bored
in case of change of world a complex inheritance procedure must transmit only knowledge which insures the coherence of the new world from the old intension to the new one this also stresses tit necessity to be able to detect incoherence in a discourse
state and event depending on the event headeduess in gl the notion of head provides a way of indicating a type of foregronnding and baekgronnding of event arguments
un homme triste k voir a sad man to see which causes the sadness of the persons which see him as a result of this 32a vs
the two events are default events as the adjective emains a state even when it has a causative sense contrary to real transitions a ecomplishnw nt
moreover some adjectives will be unspecified regarding the head and will therefore be able to be headed on any of the subevents of the event structure
in the former case the object or event is the cause of the stat while in the latter it is the manifestation of the state
event structure headed on an event recall that the adjective denotes one or two events i.e. e2 or e3 in NUM and NUM
gl allows this distinction to be characterized and given a more formal representation an adjective being dynamic if it refers to the cause or its further manifestation
NUM je suis habile ttre malade i m skilful at being ill saturation of the object of the experiencing or intellectual act event
if the grammar allows the construction of a complete dependency tree cf
an mternative solution is to make argurnent structure the main structural component of the formalism
there is n o clear methodology for evaluation in the nlp field however a well established and well known event such a s muc presents an excellent challenge and provides important resources for evaluation
corpora annotated with syntactic structures are commonly referred to as trt tbauk
an example parse is given in figure NUM
realworld texts annotated with difihrent strata of linguistic information can be used for grarninar induetion
applications for muc support for hyper templates templates that can refer to other templates was added and via the textref system the ability to reproduce surface text
the encoding outlined above uses non projective trees i.e. crossing dependencies
this hybrid representation makes the structure less transparent and therefore more difficult to annotate
it is an interesting example however because i t demonstrates how small errors can have a wide impact when attempting to perform a deep analysis of the article s meaning
thus the context li ee constituent backbone plays a pivotal role in the annotation scherne
general events other kinds of information
for the linguists the negation is not a simple problem for tit mathematical logiciau a negation is a simple problem starting from a worhl where the negation is a simple problem which or example is mathetnatieal logician thc previous assertion entails tim opening of a new world in which the new fact is asserted NUM he ncgation is uot a simple problem
hyper templates have been used for muc NUM scenario templates
negation on notional sub obje ts NUM he lcavcs arc green the leaves arc not green he infer nee possibilities from the negative assertion arc of two kinds there is a finite opposition between the notion and its lexical negation blood is red 111ood is not rcd it is f another eolor
nodes correspond to concepts of entities or events
each analysis gives rise to an update of the dialogue state
updates specify the ground and focus of the user utterances
this algorithm is based on bayes theorem
in this respect speech output places heavier demands on translation quality
the results are then passed on to the robustness component
we had no heap failures during the evaluation
the second and third rows present the results for word graphs
table NUM this table lists the number of transitions
ich mschte gerne um zwei uhr nach hamburg fahren
nein nicht nach homburg sondern nach hamburg
the appropriate action of the dialogue manager thus would be to make it clear to the officer that he is wasting his time in trying to get his message through
figure NUM fms when chsnging the number of context n
null we seem to have a communica null still missing even from the more sophisticated systems like verbmobil is a flow of information all the way from the acoustic level up to the dialogue management component
to see this imagine a situation where a police officer reports from the scene of an accident in this situation the acoustic conditiohs presumably are so adverse that recognition accuracy is inacceptable
today many dialogue management components base their operations on a combination of dialogue history a rule based or statisti most of the ideas presented here were developed during work at forum technology malvern
no not to homburg but to hamburg
be brief avoid unnecessary t rolixit
except for nps we employ a default rule that takes the leftmost element as the anchor in case the phrase has no unique head
the table shows the phrase category assigned manually and its frequency and the category erroneously assigned by the tagger and its frequency
in this study we will focus on the problems of recognition of proper names
i will comment here on how deictics possessives and subordinate clauses affect centering
we present text classification results with autoslog ts in the next section
we have shown that a more coarse level of manual effort is sufficient for certain tasks
there is poor agreement on what constitutes an acceptable translation
however in a bottom up algorithm we need the extra factor that indicates the probability of getting from the start symbol to the nonterminal in question which we approximate by the prior probability
using all three thresholding methods together and the parameter search algorithm we achieved our best results running an estimated NUM times faster than traditional beam search at the same performance level
since this algorithm is run n times during the course of parsing and requires time o n NUM each time it runs the algorithm requires time o n NUM overall
in the first simple pass we record the forward probabilities c s and backward probabilities fl s of each state i at each time t
if we get either a change in the wrong direction or a change that makes everything worse then we retry with the inverse change hoping that that will have the intended effect
on the other hand if we are trying to increase our entropy we want as much time decrease as possible per entropy increase that is we want the flattest slope possible
if we use a loose beam threshold removing only those nonterminals that are much less probable than the best nonterminal in a cell our parser will run only slightly faster than with no thresholding while
in a probabilistic framework where almost every node will have some possibly very small probability we can rephrase this requirement as being that the node must be part of a reasonably probable sequence
the last technique we consider multiple pass parsing is introduced in section NUM the basic idea is that we can use information from parsing with one grammar to speed parsing with a other
our global thresholding technique thresholds out node n if the ratio between the most probable sequence of nodes including node n and the overall most probable sequence of nodes is less than some threshold to
as we can see st ggestions and exl lanatory st d ements often occur but in general all dialog acts occur reasonably of ten
nevertheless we consider the results as promising given that it is to the best of our knowledge the first attempt go integrate symbolic segmentation parsing with dialog act learning in simple recurrent networks
the drop in the performance for the query dialog act from training to test set can be explained by the higher variability of the queries compared to all other categories
the research presented here is embedded in a larger effort for examining hybrid eonnectionist learning capabilities for the analysis of spoken language at various acoustic syntactic semantic and pragmatic levels
fb e main task is the examinatiotl of learning h r limog act processing and the donlain is tc arrangement of business dates
for a fiat level of dialog act processing the incrementm output is NUM utterance boundaries within a dialog turn and NUM the specific dialog act within an utterance
figure NUM shows the intersections between the dictionaries after relevancy filtering
for instance while many conjunctions like because are good indicators for utterance borders some conjunctions like and and or may not start new coordinated subsentences but coordinate noun groups
overall the text classification results from autoslog ts are very encouraging
for the lexical function f of g from terminals to sets of categories if a e f a f may additionally include lcb a t t a a t t a rcb
every woman a man figure NUM imaginary corpus of two trees with syntactic and semantic labels
NUM systems failed hard when they encountered previously unseen vocabulary linguistic structures formats etc
in more detail source language parsing goes through successive stages of lexical morphological analysis low level phrasal parsing to identify constituents such as simple noun phrases and finally full sentential parsing using a version of the original grammar tuned to the domain using explanation based learning see section NUM above
two such units are either related or unrelated by the intent of the document author
more formally given two segmentations ref and hyp for a corpus n sentences long
suppose there is precisely one sentence in a target corpus that satisfies our information demands
based on the signature the frame predicates are automatically derived from the lexical rule predicates and they can have a possibly large number of defining clauses
the features are simple trigger pairs of words chosen on the basis of mutual information
computational linguistics volume NUM number NUM predicate is defined in terms of the parse NUM predicate
in section NUM i show how the head corner parser is generalized to deal with word graphs
hence the only candidate head corner of this phrase is to be found between NUM and NUM
as before the head corner relation is the reflexive and transitive closure of the head relation
because we use chunks of parse trees less packing is possible than in their approach
the sentence i see a man at home has two derivations according to this grammar
an answer a to a weakened goal g is only considered if a and g unify
an improvement of the head corner parser using a goal weakening technique often eliminates this occur check problem
obviously the nature of the grammar determines whether it is useful to represent such information
NUM a few potential problems arise in connection with the use of linking tables
they do not constitute autonomous units but are attached to human nouns at the syntactic level
bmb speaker hearer category object category subset world xx bmb speaker hearer category x category cand s attrib entity x category x category figure NUM headnoun schema
bmb speaker hearer pred object otherobject subset cand xx bmb speaker hearer pred x other newcand s attrib rel entity otherentity pred refer otherentity other figure NUM modifier
the former is shown in figure NUM it decomposes into the surface speech action s attrib and has a constraint that determines the new candidate set newcand by including only the objects from the old candidate set cand for which the predicate could be believed to be true
belief NUM along with NUM and NUM allows the system to apply rule NUM and so the system enters into a collaborative activity in which the goal is for it to know the referent and in which the current plan is pl
ay in x y s refer entity2 s attrib entity2 c category x corner next the system assumes the user will understand the refashioning and by way of rules i and NUM will be cooperative and adopt the communicative goal that the system believes that the new expanded plan replaces the old referring expression plan
not only must participants form mutual beliefs about what was said they must also form mutual beliefs about the adequacy of the plan for the task NUM another approach would be to have the plan inference process reason about the intended effects of the plan that it is inferring in order to decide whether it should evaluate embedded plans and whether this evaluation should affect the evaluation of the parent plan
figure NUM the accuracy of the system at each level
rule NUM bmb system user achieve plan goal cstate system user plan goal bmb system user bel agtl achieve plan goal bel system bel agt2 achieve plan goal agtl agt2 e lcb system user rcb not agtl agt2
NUM alle architekten sollen hand in hand arbeiten
the learning procedure has no access to multiword lexical units
for each language the test set perplexity has been computed by training a trigram model with simple fiat smoothing using a set of NUM NUM random sentences and computing the probabilities yielded by this model for a set of i0 NUM independent random sentences
it is important to get the right value for m in the combination fro n used to combine p q so that the correct argument of the assumption i as now inherited to the end type of p is involved
we refer to the individual elements of the strings by means of subindices as in x al an
evaluation should take account of this possibility
for example the associative lambek calculus imposes a linear order over formulae in which context implication divides into two cases usually written and depending on whether the argument type appears to the left or right of the functor
NUM noun noun term NUM noun term a p NUM adjective i past participle cong NUM e prepositional postmodifiers are modeled according to the following rules
here two specific senses of the lemma attivit6 are captured natural and biological activity as in attivitd entropies and human activities like attivitd produttiva productive activity or attivitd di costruzione building activity
nouns as seeds of a terminological structured dictionary selected according to NUM NUM complex nominal forms of some of those seeds generated by the grammar and filtered according to NUM
statistical information for term discovery the principled definitions of legal grammatical structures by which terms are expressed and the description of their distributional properties in a sublanguage are crucial for the automatic construction of a domain terminological dictionary
the extend in the experimental tests best values for g have been obtained a s a fimction of mean and variance of the distribution over the set of cn headed by h phase allows to capture all the relevant specifications of the singleton terms cornpile a more appropriate dictionary for the corpus and structure it in hierarchically organized entries
the proposed method combines principle of grammatical correctness with statistical constraints on the distributional NUM precision is the number of detected correct esl s over the total number of detected esl s while recall is the number of detected correct esl s over the number of correct esl s properties of the detected domain terms
we are convinced that these are the typical selectional constraints to be captured by corpus driven lexical acquisition methods
using a fixed training set six of the fourteen variables were selected for modeling the morphologically unrelated adjectives
furthermore the prominence of one variable can easily lead to overfitting the training data in the remaining variables
on the other hand tests based on morphological productivity are valid although not as accurate as frequency
we consider this performance reasonably good especially since no previous automatic method for the task has been proposed
we have built a large system for the automatic domain dependent classification of adjectives according to semantic criteria
when a node can not be split further it is labeled with the locally most probable category
this process was repeated NUM times giving vectors of estimates for the performance of the various methods
ptolemy s problem is to forecast where against the inverted bowl of night some particular light will be found at future times
NUM o give pref r lcb n lcb to tagging examt rcb tes k m tlrown
the conq lemeni with whi h the vea l should NUM e tagged appea rs in square brackets
we felt these cases to be different from the other cases that we have discussed above not only because of the difficulty of locating the complement but in the nature of the construction
the tags ill figure NUM are all fl rcb ln i ll lcb rcb bl wn c lcb l t ils
the doctor agreed but explained that it would be necessary first to check fred s blood to ascertain whether or not it was of the same type
the new comlex complement intrans ellipsis is added to verbs of this type and therefore comlex differentiates between true intransitives NUM and eases like the above
to select the optimal value for we initially held out a part of the training data
this means that in cases such as NUM the first preference will be to lower the verb and therefore expel both subject and object whereas the human preference to lower the object and verb and therefore expel only the subject is the parser s second choice on the bottom up search strategy
utterance 7c continues susan as cb whereas utterance 8c merely retains her
this to our opinion seems to be the explanation of why the i lcb reading is not possible in case the description in the scope of erst comes with a temporal location in the foens
grosz and sidner were concerned with the inferences needed to interpret anaphoric expressions of various sorts e.g.
ranking of ct the cf elements are partially ordered according to a number of factors
this paper is concerned with local coherence and its relationship to attentional state at the local level
participants were said to be globally focused on a set of entities relevant to the overall discourse
in contrast discourse NUM seems to flip back and forth among several different entities
attentional state models the discourse participants focus of attention at any given point in the discourse
the model of local attentional state described in this paper provides a basis for explaining these differences
discourses NUM and NUM convey the same information but in different ways
profit supports cyclic terms by being able to print them out as solutions
this method involves defining a contraction relation t l between proofs which is typically stated as a number of contraction rules of the form x t l y where x is termed a redex and y its contractum
a single synset is selected for nouns based on the hood overlap with the surrounding text
this section gives brief descriptions of some approaches that use on line dictionaries and wordnet as references
in computational linguistics considerable effort has been devoted to word sense disambiguation NUM
in addition to noun sequences the algorithm has heuristics for handling NUM other adjacency relationships
due to space considerations we will not describe the heuristic rules individually but instead identify some common salient features
the initial results have not been promising with both programs reporting deterioration in performance when the disambiguator is included
these adjacency relationships were derived from an analysis of captions of news photographs provided by the associated press
second the algorithm brings to bear both wordnet and semantic relations extracted from an on line webster s dictionary during disambiguation
this format proved awkward when an event had several participants e g several victims of a terrorist attack and one wanted to record a set of facts about each participant
therefore the committee with strong encouragement from darpa included three muc tasks which were intended to measure aspects of th e internal processing of an information extraction or language understanding system
for named entities this was relatively straightforward
the tag enamex entity name expression is used for both people and organization names the tag numex numeric expression is used for currency and percentages
for muc NUM the template had NUM slots
problems arose with each of the semeval tasks
these low level objects were named template elements
this annotation was done in the winter of NUM NUM
we capture the attempt to resolve a conflict with the problem solving action modify proposal whose goal is to modify the proposal to a form that will potentially be accepted by both agents
to repair the device NUM consult the repair manual
the two authors agreed on their coding of this feature in all cases
the realisation statements are included only at the leaf nodes of the network
must be aware that a is one of his or her possible alternatives
the workspace pane shows the procedure represented in an outline format
under this approach the three values above are significant at the NUM
we demonstrated this for the case of preventative expressions in instructional text
we therefore rely on the author to set them manually
a lexical entry e.g. morpheme which is associated with a feature structure c is simply expressed by c where k is a morpheme boundary symbol which is not in the alphabet of the lexicon
here we will work upto the rules gradually by considering which kinds of rules we might need in particular instances
the two rules which are given in figure NUM tdeg are difficult to understand in their most general form
the prototype accessible via the internet has been trained on sentences from the technical manuals slightly augmented
along with these metaplans a speaker s linguistic theory includes two diagnostic axioms that characterize speech act misunderstandings self misunderstanding and other misunderstanding
the supposition of an intention to perform some act expressing any supposition that is incompatible with the agent s interpretation of the discourse
in consequence the number of examples to be taught will decrease
we need a method that captures information from infrequent events and adopt a direct measure of misclassification
evaluation of the rule sequence is carried out on a test set of data which is independent of the training data
heeman and edmonds model this with a plan recognition and generation system that can recognize faulty plans and try to repair them
we see this work as providing some of the first steps toward a unified account of interpretation generation and repair
figure NUM the relation between applicability and precision with several a s
a reader wanting additional details is directed to one or more of these references
in bunruigoihyo and their relative similarity sire x y
NUM as shown in figure NUM theorist finds that if russ accepts mother s pretelling he should perform an askref
however many common uses of indirectness can be explained by the existence of a well accepted social convention that makes them expected
this clearly allowed the collective program to cover more ground and to move forward faster
a report outlining the details of successfully implemented techniques and approaches is made at one workshop by a single participant
text corpus complexity text corpus size template fall complexity and the overall nature of the task
in may NUM tipster sponsored a new information extraction evaluation program the multilingual evaluation task met
very often the different branches of disjunctions contain constraints that have large parts in common
this is however not as trivial as it might appear at first sight
now let us consider semantics construction for a single parse tree for the moment
one further possibility is to choose a single syntax tree and to use destructive tree operations later in the parse a
note that if the same rule is to be applied at another node we have a different rule constraint
thus the constraint z b above can be written as c r NUM a
n3 since every program step other than the generalisation operation can be done in constant time per node
however although these overlaps are efficiently handled on the representational level they are invisible at the logical level
6actually in the e b example such a factoring makes the use of the name n superfluous
katz k mixture has two parameters cc and NUM
the correlations are shown in table NUM
surprising given that empirical estimates of variance are notoriously subject to outliers
and yet boycott is a reasonable keyword and somewhat is not
clearly the non poisson phenomenon is robust
why is idf such a useful quantity
the correlations are shown in table NUM
all of the correlations are quite large
in fact the only voice interactions the system undertakes are those called for by the theorem proving machinery
for the syntactic and lexical parameters corresponding to the correct candidate t l
for the syntactic and lexical parameters corresponding to the strongest competitor t l
figure NUM the observed idf is systematically lower than what would be
each scatter plot compares idf in one year with idf in another
referring to figure NUM this rule r is a general rule resulting in the third choice branch
ble but do not occur lexically with the cited nouns
the same procedure can be applied on diminutive and deverbal nouns
the authors would like to thank their supervisor dr stephen pulman
normal prolog backtracking to explore alternative rules lexical branches applies throughout
mattes lectionis can not be omitted from the orthographic string
this results in the vowels being shifted one position e.g.
r5 sanctions the spreading and gemination of consonants
below are outlined error rules resulting from peculiarly semitic problems
subsection NUM NUM presents the formalism used and subsection NUM NUM describes the model
as with human human interaction spoken human computer dialog will contain situations where there is miscommunication
here we review the key aspects that are exploited in a context dependent strategy for verification
power a six a circuit NUM c i do not understand
computer what is the switch position when the led is off7 user up
it should be noted that on average users spoke NUM utterances per dialog
consequently assertions about the led display were often part of the main expectation
these over verifications result in extraneous dialog and if excessive will limit usability
example a command e.g. put the switch up
these strategies make use of information obtainable from dialog expectation and the error correcting parsing process
combination is allowed by the single inference rule NUM
firstly the argument requirements of a categorial functor are ordered
figure NUM local reordering of combination steps the four cases
the procedures defined above can be used to identify these dependencies
we can now state a generalised composition rule as in NUM
this eases development by automatically keeping the language that can be recognized and the language that can be parsed in sync that is it guarantees that every word string that can be parsed by the natural language component is a potential recognition hypothesis and vice versa
it is convenient to have a linear notation for writing proofs
a proof is in normal form if it contains no redexes
however as one final optimization we look for special cases where we can use the kleene plus operator which indicates one or more instances of an expression in sequence and which is handled more efficiently by the nuance recognizer than equivalent expressions using kleene star
the first order formulae are those with only atomic argument types i.e.
a tree resulting from a derivation of a string is called a parse of this string
lightfoot NUM but the bioprogram hypothesis can be interpreted as towards one end of a continuum of proposals ranging from all parameters initially unset to all set to default values
a model of the language acquisition device lad incorporates a ug with associated parameters a parser and an algorithm for updating initial parameter settings on parse failure during learning
in seven of these runs the population fixed on a full sov v2 language in two on the intermediate subset language sov v2 n and in one on the minimal subset language sov v2 n gwp comp
since the original rule however puts no constraints on any of the features of the digit category by generating an atomic category that is under specified for all features we only need a single rule in the derived grammar
these runs suggest that if a full language defines the environment of adaptation then a population of randomly initialized lagts is more likely to converge on a related full language
default learners may have a fitness advantage when the number of interactions required to learn successfully is greater because they will tend to converge faster at least to a subset language
so there are at least two learning procedures in the space defined by the model which can converge with some presentation orders on some of the grammars in this set
english without the rule of permutation results in a stringset identical language but the grammar assigns different derivations to some strings though the associated logical forms are identical
like virtually all practical speech recognizers the nuance recognizer requires a finite state grammar while the gemini parser accepts grammars that have a context free backbone plus unification based feature constraints that give gemini grammars the power of an arbitrary turing machine
global ambiguity the sentence was agreed to be globally ambiguous
the judges were encouraged to consult the documentation of the grammatical representation
some comments are in order first about morphology
neutralisation both analyses were regarded as equivalent
according to a pessimistic view e.g.
our three main findings are NUM
the experiment was conducted as follows
usually only a unique analysis was given
specifying a shallow grammatical representation for parsing purposes
we compared the two learned models with two baseline models
the current task deviates from that problem in several respects
NUM c d a b NUM
we then sought a more challenging yet straightforward baseline
this inconsistency arises from the manner in which the pairwise probabilities are estimated
NUM a b c d NUM
NUM c d a s NUM
NUM b d a c NUM
the percentage for the whole training corpus was p NUM
the percentage for the whole training corpus was p NUM
c s should ask u about priority NUM NUM is not a discount departure
the first pattern that matches is chosen for the string currently being processed
the left context vector of w
but we run the risk that the n gram model will pick a non grammatical path like a large federal deficits fell
if two or more word lattices can be created from one rule they are merged with a final or
while this experiment shows that statistical models can help make choices in generation it fails as a computational strategy
in contrast the two level model provides for the automatic collection and implicit representation of collocational constraints between adjacent words
but the default choices frequently are not the optimm ones the hybrid model we describe provides more satisfactory solutions
for example bei in japanese may mean either american or rice and sha may mean shrine or company
the result is text that is more fluent and closely simulates the style of the training corpus in this respect
as we climb up the input expression the grammar glues together various word lattices
for the remaining features we compute new e structures using the rule s right hand side
we will also show that this raises interesl ing questions about the
this architecture promotes prides evolution because older user interfaces can stay in operation while new user interfaces are gradually tested and fielded to replace them and new versions or even different types of tipster compliant search engines and routing engines can be tested without changes to the user interface
these web browsers also provide a user friendly interface to the other protocols of the internet such as file transfer protocol ftp and network news transfer protocol nntp and allow printing of text and graphics on the user s local printer
a single query can contain words and phrases in a mix of different languages with the foreign language terms entered using the native script
this approach necessitates a more generic approach to many functions to ensure that the same user interface can be tailored to differing search engine technologies
out of the set of events a decision tree is constructed whose leaf nodes contain conditionm probability distributi ms of tags conditioned by the feature values
suppose that the i dr cx i c j was cho sen to merge thai
furthermore chlstering is nnl h more iiseful if the clusl ers i i e of vnrimje grmnllarity or hierar chi al
the last air in the event is a special item which shows the answer i.e. the col rect tag of the current word
a feature can be any attribute of the context in which the current word word o appears it is conveniently expressed as a question
however for sentence NUM where the order is osv the object argument is nan additional constraint system called dominance links was added thus giving rise to mc tag dl
however in the case that it is a multi component structure e.g. at an adjunction node need not necessarily be linked to any node
our working hypothesis is that syntactic behavior is reflected in cooccurrence patterns
computational linguistics volume NUM number NUM assuming transformations are applied left to right on the sequence the above classification problem can be solved for sequences of arbitrary length if the effect of a transformation is written out immediately or for sequences up to any prespecified length if a transformation is carried out only after all triggering environments in the corpus are checked
the fiat g structure will be used only when the argument is in a scrambled position so that the aat g structure can not be used
usually the nodes in the source language should be linked to each relevant node in the target language and vice versa in stags
whenever adjunction or substitution is performed on a linked node in a source tree the corresponding operation applies to the linked node in the target tree
i present a mechanism to translate scrambled korean sentences into english by combining the concepts of multi component tags mc tags and synchronous tags stags
using mc tags tags and related formalisms due to the extended domain of locality can combine a lexical head and all of its arguments in a single elementary structure of the grammar
thus it is necessary that a korean fla rg structure mc tag be mapped to an english np structure tag to transfer a scrambled argument in korean
generally when a structure given a higher priority over others can be successfully used for the final derivation of a sentence the remaining structures will not be tried at all
we have done so for three reasons NUM to allow for a comparison with previously quoted results NUM to isolate known word accuracy from unknown word accuracy and NUM in some systems such as a closed vocabulary speech recognition system the assumption that all words are known is valid
NUM lcb ecently l vee insertiml vamm u sdud s
we choose to use relative entropy also known as the kullback leibler distance pereira et ai NUM
the allowable transformation templates are the same as the contextual transformation templates listed above but with the rewrite rule change tag x to tag y modified to add tag x to tag y or add tag x to word w instead of changing the tagging of a word transformations now add alternative taggings to a word
for example one could accurately assign a part of speech tag to the word race in NUM NUM without any reference to phrase structure or constituent movement one would only have to realize that usually a word one or two words to the right of a modal is a verb and not a noun
the recursion takes place by running a head transducer m in the second action above to derive local dependency trees for corresponding pairs of dependent words w vl
thus we can not hope that their occurrence frequencies would correspond to each other in any significant way
we could use part of speech taggers to label these words with different classes effectively treating them as different words
we plan eventually to incorporate context heterogeneity measures and other word pair similarity measures into bilingual lexicon learning paradigms
in the left figure we show that NUM words have their translations in the top NUM candidates
this is because in most asian languages there are very few function words compared to indo european languages
we have chosen chinese and english as the two languages from which we will build a bilingual dictionary
we have shown the existence of statistical correlations between words and their translations even in a non parallel corpus
chinese part of speech classes are very ambiguous many words can be both adjective or noun noun or verb
current algorithms for bilingual lexicon compilation rely on occurrence frequencies length or positional statistics derived from parallel texts
in contrast there were a total of NUM misunderstandings in directive mode NUM for which the experimenter was allowed to notify the user
the x entries along the main diagonal represent impossible exit transitions i.e. there can not be a transition from diagnosis to diagnosis
the taggers were NUM undergraduate and graduate students NUM male NUM female
a semantically enriched tree bank will generally contain a wealth of detail
the next section elaborates on what is meant by multi paragraph subtopic structure casting the problem in terms of detection of topic or subtopic shift
girill finds that divisions at the fine grained level are less efficient to manage and less effective in delivering useful answers than intermediate sized units of text
we use wordnet to group extracted descriptions into categories
although all games begin with an initiating move possibly with a ready move prepended to it not all initiating moves begin games since some of the initiating moves serve to continue existing games or remind the partner of the main purpose of the current game again
NUM reliability of coding schemes it is important to show that subjective coding distinctions can be understood and applied by people other than the coding developers both to make the coding credible in its own right and to establish that it is suitable for testing empirical hypotheses
since intonational cues can be necessary for disambiguating whether some phrases such as ok and right close a transaction or open a new one coders were instructed to place boundaries only at particular sites in the transcripts which were marked with blank lines
using a a generalized version of kappa which also works for ordinal interval and ratio scaled data he remarks that a reasonable rule of thumb for associations between two variables that both rely on subjective distinctions is to require a NUM with NUM a NUM
coding involves marking where in the dialogue transcripts a transaction starts and which of the four types it is and for all but irrelevant transactions indicating the start and end point of the relevant route section using numbered crosses on a copy of the route giver s map
although some natural dialogue is this orderly much of it is not participants are free to initiate new games at any time even while the partner is speaking and these new games can introduce new purposes rather than serving some purpose already present in the dialogue
however manually disambiguating a test corpus of a few hundred thousand words would probably require a human effort of at least a month
the support for the pair variable label expresses how compatible that pair is with the labels of neighbouring variables according to the constraint set
the remove constraints express total incompatibility NUM and select constraints express total compatibility actually they express incompatibility of all other possibilities
each iteration will increase the weight for the tag which is currently most compatible with the context and decrease the weights for the others
null NUM the input contained more than one analyses all of which seemed equally legitimate even when semantic and textual criteria were consulted
the statistical models were obtained from a training corpus of NUM NUM words of journalese syntactically annotated using the linguistic parser see above
corpus for evaluating the systems five roughly equal sized benchmark corpora not used in the development of our parsers and taggers were prepared
the compatibility value for these should be at least as strong as the strongest value for a statistically obtained constraint see below
al have devised a way suitable to documentation classification
here we give one example approach to cluster creation
for his help with the english of this text
table NUM tested categories in the second data set
figure NUM precision recall curve for category grain
figure NUM precision recall curve for category corn
figure NUM precision recall curve for the second data set
figure NUM precision recall curve for the first data set
fmm includes wbm and hcm as its special cases
experimental results indicate that our method outperforms existing methods
it is not sufficient that some method or tool has been trialled on many different cases and in widely different conditions
it is a well recognised fact that the production of a new software engineering tool or method is difficult and time consuming
following the detection of human machine miscommunication det enables in depth classification of miscommunication problems that are caused by flawed dialogue design
a generic guideline may subsume one or more specific guidelines which specialise the generic guideline to a certain class of phenomena
we also found that the guidelines generalise well to the different test type tool purpose pair of the sundial corpus
and b to which extent do the analysers classify the identified cases types in the same way
make your contribution as informative as is required for the current purposes of the exchange
firstly it may be used as part of a methodology for diagnostic evaluation of spoken human machine dialogue
of word type pairs that are not filtered out during the re estimation cycle
this is a much more conservative metric than that used by daille et al
going beyond single word correspondences however is a priority for future work
the assessment questionnaire was designed to elicit information primarily of two kinds
annotators also had the option of working electronically rather than on hardcopy
simr filters candidate points of correspondence using a geometric pattern recognition algorithm
figure NUM summary of filtered translation lexicon va lidily statistics
two versions of the dictionary booklet were prepared one for each training condition
vowel a contains twenty five rules
we have extended the algorithm to allow for this
in many areas of natural language and speech processing
figure NUM compilation times for rules of the form
figure NUM compilation times for rules of the form
figure NUM number of arcs in the non
table NUM comparison in a real example
at first glance the data looks similar to that for the left context until one notices that in figure NUM we have plotted the time on a log scale the kk algorithm is hyperexponential
we wish to thank several colleagues of at t bell labs in particular fernando pereira and michael riley for stimulating discussions about this work and bernd msbius for providing the german pronunciation rules cited herein
they are used in various areas of natural language and speech processing because their increased computational power enables one to build very large machines to model interestingly complex linguistic phenomena
moreover we expected less inter tagger agreement for verbs and modifiers than for nouns
the cost of a derivation of a solution by the process is taken to be the sum of costs of choices involved in the derivation
it is therefore tempting to conclude that an adequate treatment of these tasks requires the manipulation of artificial semantic representation languages with well understood formal denotations
to the extent that contextual resolution is necessary context may be provided by the state of the language processor rather than complex semantic representations
in this paradigm ordered dependency trees can be viewed as natural language strings annotated so that some of the implicit relations are more explicit
however other reasons led us to predict a preference for the first sense
each polysemous content word in a text was matched to a sense from wordnet
the difference however was significant p NUM NUM only for nouns
adding information to the description reduces monotonically the set of satisfying trees
these features belong to the meta formalism of i tag hierarchical organization
we do not show the feature equations for the sake of clarity
so a family contains all the schemata for a given canonical subcategorization
the arguments are numbered starting at NUM for the canonical subject
tile set of tree schemata forms the syntactic part of the grammar
the path syn form being explicitly defined is exempt from this default behavior and so retains its value definition present participle any extensions of syn form obtain their definitions from syn form rather than syn since it is a more specific leading subpath and so will have the value present participle also
our first step towards a more concise account of wordl and word2 is simply to change the extensional statements to definitional ones this is possible because datr respects the unsurprising condition that if at some node a value is specifically defined for a path with a definitional statement then the corresponding extensional statement also holds
had we begun evaluation at say a daughter of the lexeme eat we would have been directed from verb mor form back to the original daughter of eat to determine its mor root which would be inherited from eat itself we would have ended up with the value eat ing
in pursuit of this we can define a syntactic notion of functionality over datr descriptions as follows a datr description is functional if and only if i it contains only definitional statements and ii those statements constitute a partial function from node path pairs to descriptor sequences
NUM other interesting implementations that we are familiar with include the experimental reverse query implementation by langer osnabrueck duda and gebhardi s berlin implementation that is dynamically linked to patr and barg s duesseldorf implementation of a system that induces datr descriptions from extensional data sets
yet the high percentages of matches in this condition show that the taggers worked well
as such it was a standalone system that was aimed at specific tasks and while based on a modular design none of its modules were specifically designed with reuse in mind nor was there any attempt to standardise data formats passed between modules
this view breaks down when structures like tables appear these are inherently twodimensional and their representation and manipulation is much easier in a referential model like tipster than in an additive model like sgml because a markup based representation is based on the one dimensional view
our gloss on these terms is common models for the representation storage and exchange of data in and between processing modules in nlp systems along with graphical interface tools for the management of data and processing and the visualisation of data
we describe a system called gate a general architecture for text engineering that provides a software infrastructure on top of which heterogeneous nlp processing modules may be evaluated and refined individually or may be combined into larger application systems
this information is needed by ggi and is provided by the developer in a configuration file which also details what sort of viewer to use for the module s results and any parameters that need passing to the module
this observation is born out by the facts that tipster started with an sgml architecture but rejected it in favor of the current database model and that lt nsl has gone partly towards this style by passing pre parsed sgml between components
the infrastructure that ice delivers does n t fit into our tripartite classification because the communication channels do not use data structures specific to nlp needs and because data storage and text collection management is left to the individual modules
clearly the pressure to build on the efforts of others demands that le tools or component technologies parsers taggers morphological analysers discourse planning modules etc be readily available for experimentation and reuse
if the combined evidence is still predicted to fail the system does not have sufficient evidence to change the user s view of bel thus the focus of modification for bel is nil step NUM NUM
its preference is to address the unaccepted evidence be null select focus modlflcatlon bel NUM bel u evid system s beliefs about the user s evidence pertaining to bel bel s attack
if during infommtion sharing the user provides convincing support for a belief whose negation is held by the system the system may adopt the belief after the re evaluation process thus resolving the conflict without negotiation
once a set of beliefs forming justification chains is identified the system must then select from this set those belief chains which when presented to the user are predicted to convince the user of bel
NUM he postponed his sabbatical until 199z if the user accepts the system s utterances thus satisfying the precondition that the conflict be resolved modify node can be performed and changes made to the original proposed beliefs
however our research did not specify in cases where multiple conflicts arise how an agent should identify which pm of an unaccept proposal to address or how to select evidence to support the proposed modification
thus once an agent detects a relevant conflict she must notify the other agent of the conflict and initiate a negotiation subdialogne to resolve it to do otherwise is to fail in her responsibility as a collaborative agent
while cue occurrence and placement are interrelated problems we performed learning on them separately
we will develop arguments how to locate elliptical discourse entities and resolve textual ellipsis properly at the center level
therefore the preferred antecedent of er it is determined as rechner computer
the fourth column supplies the results of the same modification as was used for the naive approach viz
constraint that elliptical antecedents are ranked higher than elliptical expressions short ante express
given these basic relations we may formulate the composite relation s table NUM
his proposal concerning the centering analysis of german already referred to as the naive approach cf
despite several cross linguistic studies a kind of standard has emerged based on the study of english cf
in a graphical model variables are either interdependent or conditionally independent of one another
during both bss and fss it found that all the features were relevant to classification
it is clear that bss exact conditional is much more accurate than fss exact conditional
the presence of phrases as a NUM deg or regarded as and drastic change in topic toward the second half of the definition may be cues for identifying metonymy and metaphor
for instance the candidates for labeling senses of bank are the fouowing NUM set labels ld099 and nj295 are listed under ld geography and nj action and position respectively
merging senses via labeling has another implication as weu
acquisition of computational semantic lexicons from machine readable lexicai resources
each word in v2 contributes to exactly one pseudo word
the back off models bo NUM and bo ol also perform similarly
c wl is a normalization factor
table NUM base language model error rates
on the kl divergence to the average being the best overall
straightforward to show by grouping terms appropriately that
s wl was set equal to vt in all cases
similarity based language models provide an appealing approach for dealing with data sparseness
these initial results provide us with a baseline for quantifying improvements resulting from distinct modifications to the algorithms
the system is called gate the general architecture for text engineering
processing tools or programs or code libraries
s inv error feature wh w my01 NUM np case sub wh w agr p head vp vform v pres past agr s person NUM
in other respects solutions are possible
ggi is in development at sheffield
of gdm and creole mean only one integration nmchatiisih must be learned
tagsct to tagset mapping and in solne cases by extra processing e.g.
this will be proved in section NUM
the goal in this paper is compilin q
the modularization algorithm consists of two main steps
however the second disjmmtion is independe nt
this is shown more forlnally in theorem NUM
disjunctive constraints into more efficient ones for fllture solution
each figure shows the accuracy of lexas versus the base line most frequent sense classifier
the method provides a plausible hypothesis but it can not prove in a strict sense that one lexicon necessarily is bigger than another
it simply gives the letter d as the beginning letter of all three different forms der die das
in order to design tagging capabilities at a semantic level it is more important to design adaptation capabilities to process a given corpus in a domain driven fashion
for this we took unknown words wrong translations and missing words as negative counts and all others as positive counts
tagging is a dynamic process that aims to produce a core semantic information to support several induction processes over the same domain
further research in assessing the semantic tagging evaluation customizing the lexical acquisition models to the proposed semantic type system and evaluating extensively the acquisition results are on going
require subj theme co obj r nst ument ob
as gold standards are fairly questionable it is necessary to rely on sources that are as much systematic as possible and adapting their description to the underlying corpus
for the second system only those words will be checked where the translation differs from the translation saved in the translation list
the resulting classification is more specific to the sublanguage as the exhaustive enumeration of general purpose word senses has been tackled and potential new senses have been introduced
experimental evidences for verb and nouns tagging in different domains have been outlined and extensive data froma remote sensing medium size corpus have been reported
adding to these difficulties is the fact that asl has no accepted written form eliminating the opportunity to establish literacy skills in a fluent native language and then transfer those skills to the new language being learned
as we have seen morphological analysis is necessary if one wishes to access an online dictionary
there are four language pairs currently supported by glosser english bulgarian english estonian english hungarian and french dutch
the program is operational on unix and windows NUM platforms and has undergone a pilot user study
glosser is designed to support reading and learning to read in a foreign language
glosser is designed with four major components which are sketched in figure NUM
the copernicus program of the european commission supports the glosser project in grant NUM
a pilot study involving NUM university level students of french was conducted in feb NUM
lemma tization recovers citation forms from inflected forms and is a primary task of morphological analysis
the realization of these design goals required extensive knowledge bases about morphology and the lexicon
utilizing top n contexts we learn the whole grammar based on the algorithm given in section NUM brackets rules which are occurred more than NUM times in the corpus are considered and the number of contexts used is determ ned by the criterion described in the previous subsection
in this method nonterminal labels for brackets in a bracketed corpus can be automatically assigned by making use of local contextual information which is defined as a set of category pairs of left and right words of a constituent in the phrase structure of a sentence
by grouping brackets in s corpus into a number of sire far bracket groups based on their local contextual information the corpus is automatically labeled with some nonterm a labels and consequently a grammar with conditional probabilities is acquired
besides cases of n NUM NUM NUM NUM and ali NUM a case that NUM contexts are randomly chosen from all contexts is taken into account in order to e arnlne the assumption that vaxiance is efficient
al l le level cat phrasal category
a simph functor argumenl scheme is suflicient for the word grammar
synsem ocica rlhead is mapped to head
generator was chosen as the core component of t he system
it employs both phrase structure rules and unification of feature descriptions
to det erinine whi h substructures have
alternatives are explored sequentially until one i ranch succeeds
the tmsic ido for rcmizmg head driven pro
this will be described in more detail below
as the thresholds were moved from the initial values of NUM NUM and NUM NUM certain items that had been classified as false pos or false neg fell between the thresholds and became not labeled
in contrast to many existing systems which depend on brittle parameters such as capitalization and spacing satz is able to adapt to texts that are not well formed such as single case texts
a summary of these heuristics is listed below
it adapts easily to new text collections
the second set of texts used in training is the cross validation set whose contents are separate from the training text and which contains roughly half as many test cases as the training text
palmer and hearst multilingual sentence boundary well behaved corpus with regular punctuation and few extraneous characters and they would probably not be very successful with texts obtained via optical character recognition ocr
NUM NUM false positive or negative due to a sequence of characters including a period and quotation marks as this sequence can occur both within and at the end of sentences
we drink our response now is obvious with m u hinc tagging we would not have been abl to r ogniz m r or NUM these facts about language
we tagged these intrans habitual NUM since it seems that this ix really a grammatical question as any verb it would seem may occur an a habitual intransitive it has not been proposed as a comlex comph inent
NUM he hoped to persuade him to become his assistant in research for the labor novel if breasted agreed they would get a car and tour the country NUM spoke up plenty of it
comt hunents may l rcb e l ansfo ln lcb xl so nel inles l y nd ready r lcb ognition or lcb ontextualty z roed
we decided not to make this a separate np coniplement for several reasons NUM these verl s also take regular np complements though in some instances as in the below example the meaning of the verb changc s
nouns which can appear in quantifier phrases including a scalar adjectiw before another noun o1 as a head noun tollowed by a prel ositional phrase containing a scalar mmn a two foot long board a board two feet in length
a the l ype rcb zeroing involved in th xmnt rcb le s has l m recorded in tim ags and a lcb lded u the di lcb i lcb mary
in examining decision trees produced with anaphoric type identification turned on the following features were used for qzpro org in this order topicalization distance between an anaphor and an antecedent semantic class of an anaphor and an antecedent and subject np
in this paper we report the results of the four types of anaphora namely name org qzpro org dnp org and zpro org since they are the majority of the anaphora appearing in the texts and most important for the current domain i.e.
one of the advantages of the mlrs is that due to the number of different anaphoric types present in the training data they also learned classifiers for several additional anaphoric types beyond what the mdr could handle
the anaphoric chain parameter described above was employed because an anaphor may have more than one correct antecedent in which case there is no absolute answer as to whether one antecedent is better than the others
we suspect that the poorer performance of zpro or and dnp org may be due to the following deficiency of the current mlr algorithms because anaphora resolution is performed in a batch mode for the mlrs there is currently no way to percolate the information on an anaphor antecedent link found by a system after each resolution
because of the way in which the corpus was tagged according to our tagging guidelines an anaphor is linked to the most recent antecedent except for a zero pronoun which is linked to its most recent overt antecedent
such selection ignores the fact that even anaphora of the same type may use different orderers i.e. have different preferences depending on the types of possible antecedents and on the context in which the particular anaphor was used in the text
with this param null eter on a decision tree is trained to answer no when a pair of an anaphor and a possible antecedent are not co referential or answer the anaphoric type when they are co referential
our approach therefore differs from theirs in many ways
we tried multiple pass thresholding in two different ways
the large number of possibilities can greatly slow parsing
figure NUM precision and recall versus time in beam
figure NUM shows the tradeoff between accuracy and time
iri NUM and a national science foundation graduate student fellowship
we therefore ran our experiments using both thresholds together
we can use an analogous algorithm for multiple pass parsing
beam thresholding is a common approach
to remedy this caraballo et al
in the following sections tim structure of i hc parser will t e describe l along these lines
and the lexicon in lie following we introduce the tools we have used r r parsing idiomatic sentences
if this test was positive an additional chart edge is inserted for every idiom the word can occur in
an interface module he lps to connect lifl realt lexicons to the t arse r
null consider example NUM kim bindet tom einen unglaublichen biiren an fig kim
the result of the parsing process are two readings of the sentence the literal one and the idiomatic one
for example the words katzc and sack occur as well in die katzc aus dcm sack lassen fig
of all dive rsed syntactic semantic and pragmatic iifformal ion provided by NUM iirasi o lex
the mle summary of performance for the lex l2 syn l2 model using various performance enhancement methods
in addition this learning procedure is able to resolve problems resulting from statistical variations between the training corpus and real tasks
there is no discourse referent for that the condition incrcdible as semantic representation of the adjective unglaublich holds
q hen the questions arise which kind of inemfing do these pm ts carry
motivated by that concern a discrimination and robustness oriented learning algorithm is proposed in this paper for minimizing the error rate
this table shows that the number of parameters is greatly reduced after the tying process especially for the l2 syntactic models
with such a formulation the capability of context sensitive parsing in probabilistic sense can be achieved with a context free grammar
in addition for computational feasibility only a finite number of left and right contextual symbols are considered in the formulation
he found that in NUM of paragraphs the topic sentence was in the first sentence and in NUM the final one
our holy grail like that of many groups is to eventually get the computational linguist out of the loop in adapting an information extraction system for a new scenario
in contrast pattern matching system s assemble structure bottom up and only in the face of compelling syntactic or semantic evidence in a nearly deterministic manner
the name recognition stage records the initial mention and type of each name subsequent mentions of a portion of that name will be recorded as aliases of the name
we can expect a computational linguist to consider all syntactic variants although it may be a small burden we can not expect the same of a typical user
all these considerations led us to conclude that we should do a muc ourselves using the patter n matching approach in order to better appreciate its strengths and weaknesses
for general vocabulary we use cornlex syntax a broad coverage dictionary of englis h developed at nyu which provides detailed syntactic information but does not include any proper names
as a simple example of a clause level pattern conside r defclausepattern runs np sem c person vg c run np sem c company person at l
this is expanded into patterns for the active clause fred run s ibm the passive clause ibm is run by fred
it is required that the semantic type of this formula matches the semantic type of the unification variable
the use of clause level syntax to generate syntactic variants of a semantic pattern is even more importan t if we look ahead to the time when such patterns will be entered by users rather than computational linguists
such as fred smith president of general motors age modifiers such as fred smith NUM year s old and relative clauses
we assume a corpus that is already syntactically annotated as before with labeled trees that indicate surface constituent structure
in the other rules x acts as the distinguished daughter and y as the subsidiary daughter
however there are two reasons for maintaining the type inheritance encoding separately from the boolean feature combination
we would do well to require distributivity for otherwise operations on lattices will become order dependent
the array generated for the inverted lattice is which corresponds to the lub in the original lattice
for these cases it is genuinely important to have some way of achieving the effect of kleene operators
it is usually possible to achieve the same effect by inventing a new bool comb value feature and using disjunction
construct a values feature whose value will be a tuple of the values of psem in a canonical order
for example in derivational morphology the presence of multiple entries for verbs like send can cause unwanted ambiguity
under these circumstances it is in fact very often the case that a recursive analysis is empirically superior
unfortunately there is a price to pay for this increase in expressive power a decrease in efficiency
NUM NUM of paragraphs have only one sentence thus the first sentence is also the last and NUM NUM only two
the sub units have been further analyzed and divided into types e.g.
tourner or taking a way e.g.
computer translation of route descriptions into sketches raises some interesting issues
secondly a thorough linguistic analysis of route descriptions is necessary
you continue straight on passing alongside the tennis courts and you come to building a
the analysis has been performed at two levels the global level and the local level
for this purpose it is necessary to establish relationships between different linguistic and conceptual entities
our goal is to build a linguistic model for the text type route description
that is why we need a two stage internal representation based on specific linguistic and conceptual models
we have mentioned here only some of the problems concerning the translation of rds into graphic sketches
morphological generation research examines the ability of morphological affixation rules to generate new words from a lexicon of base roots
we retain the following criterion i ntailment criterion for the i lcb reading to l e acceptable first the l ost conditions of each event of the t resupt ositional line must he compatible with the preconditions of the successor and s cond at least for homo
l he exl tation of some l rol osition p t o be true in a specific situation s can not t e falsilied in case the wdidity of a parti ular prol osition NUM in the subsequent test situation s l confirms the wdidity of p
some implementations may also include implicit definitions of more restricted variables such as integer
this shows that roughly NUM of the cmu utterances with temporal information contain redundant temporal references while NUM of the nmsu ones do
this points to several issues that need further investigation in trec NUM
the dominant new feature in trec NUM was the very short topics
the retrieval system used in assctv1 is the smart system
the same types of modification gained only NUM NUM in trec NUM
the adhoc task is represented by new topics for known documents
both these factors led to less accurate filtering of nonrelevant documents
the relevance judgments are of critical importance to a test collection
using these lexical definitions a new possibly ambiguous tag is produced for each word type
cornell s version of this is called local global weighting
the performance has suffered for this in the middle recall range
it is furthermore compatible with the requirement that there should be an event whose start is before t and an event whose end is not before t since eating events are extended if they have end points then these are after their start points
cases like NUM are generally taken to be prototypical the present parti it le marker indicates the progressive aspect which says that sonto extended event with a recognisable end t oint is in progress and will probably reach its conclusion
note that the wide scope of the aspect oi erator si mple ineans that for f0 we are conside ring ewmt types in which there is a pea h and a hmch for every instance of the type
since hiccups are generally thought of as taking no time it is not possible to be in the middle of a single hiccup and hence we are solnehow driven to conelude that harry was in the middle of a series of hiccut s
the aim of the current paper is to show how to deal with a well known t henolnenon by relying on ombinatorim effects to infer difforent consequences from the same items in difl erent contexts without altering the contributions that these items make individually
the striking thing about these is that itt each of NUM and NUM NUM the obvious interl retadon is tsha his period of living in bray continued after the reference time so that he probatfly live d
mp state says that the characteristic property of the state is true of its patient throughout some interval but unlike mp relicevent it says nothing about the start and end points of that state not even whether or not they exist
we are alter all led to describe a word as being a homonym in exactly those cases where the meaning of what appears to be a single lexical item depends on the semantic properties of the words it is being combined with
this approach NUM referring by name is not included in this table because it is neither a deictic nor an anaphoric reference
here the referent of the pronoun her was mentioned too long ago for edward to be able to locate the referent alice
dutch word linkerknop means left button versleep means to drag and rechterknop means right button
this model is used in conjunction with a knowledge base by edward s interpretation component to solve deictic and anaphoric referring expressions
in section NUM NUM we present the inherent limitations of edward s referent resolution model as well as those of the two alternative models
in the second solution associate individual instances are not in focus as long as interpretation of referring expressions can work as described above
though it performed by far better than we anticipated too often the wrong object is taken to be the referent
unfortunately computational linguistics volume NUM number NUM table NUM a translated compilation of the sentences the subjects used to perform the tasks
task NUM particularly showed the restrictions of the simplistic model NUM which are the e mails sent by alice
we have collected some indications about the quality of the context model for referent resolution we implemented in our multimodal user interface edward
in this paper we show that a large scale application of the memory based approach is feasible we obtain a tagging accuracy that is on a par with that of known statistical approaches and with attractive space and time complexity properties when using igtree a tree based formalism for indexing and searching huge case bases
word segmentation can easily be cast as a transformation based problem which requires an initial model a goal state into which we wish to transform the initial model the gold standard and a series of transformations to effect this improvement
using our native knowledge of english as well as a short list of common english prefixes and suffixes we developed a simple algorithm for initial segmentation of english which placed boundaries after any of the suffixes and before any of the prefixes as well as segmenting punctuation characters
the simple syntax described in section NUM NUM can however be easily extended to consider larger contexts to the left and the right of boundaries this extension would necessarily come at a corresponding cost in learning speed since the size of the rule space searched during training would grow accordingly
the reasoning behind this reorganisation which is in fact a compression is that when the computation of feature relevance points to one feature clearly being the most important in classification search can be restricted to matching a test case to those stored cases that have the same feature value at that feature
table NUM shows some example translations for the best translation results
as a broad coverage system princitran is very efficient
thus we parameterize case assignment as follows
NUM unless the article makes it clear that someone is not moving directly from one job company to another assume that the transition is direct
in and out slot definition a pointer to the object that captures relational information on the person assuming a post and or the person vacating that post
the fact that a post is shared may be indicated by the title e g co chairman but may also be indicated indirectly
in the normal case other org and succession org will point to the same organizatio n object only when the succession event represents a promotion or shuffle within an organization
it indicates the past affiliation if the value of new status is in or in acting and it indicates the future affiliation if the value of on the job is no
outside org the person s old and new positions are in organizations that are not identified in th e text as having some corporate relationship with each other
the adjusted timings are plotted in figure NUM
each node locally stores a set of items
NUM there will naturally be two succession event objects involving a particular person if the person is giving up one post in order to take another
thus we achieve a reduction space requirements
efficient parsing for korean and english lcs
non linearity a semitic stem consists of a root and a vowel melody arranged according to a canonical pattern
the model handles errors vocalisation diacritics phonetic syncopation and morphographemic idiosyncrasies in addition to damerau errors
NUM NUM NUM finding the error morphological analysis is first called with the assumption that the word is free of errors
in addition to the automatic methods ag gr and st just discussed we also added to the plot the values for the current algorithm using only dictionary entries i.e. no productively derived words or names
thanks to daniel ponsford for providing data on the broken plural and nuha adly atteya for discussing arabic examples
the model presented corrects errors resulting from combining nonconcatenative strings as well as more standard morphological or spelling errors
in the case of the most common usage is as an adverb with the pronunciation jiangl so that variant is assigned the estimated cost of NUM NUM and a high cost is assigned to nominal usage with the pronunciation jiang4
for the use of moraic sad afflxational models in handling arabic morphology computationally see kiraz
the segmenter will give both analyses y d cai2 neng2 just be able and cai2 neng2 talent but the latter analysis is preferred since splitting these two morphemes is generally more costly than grouping them
error rules can also be constructed in a similar vein to deal with typographical damerau error which also take care of the issue of a vowel shift error rule will be tried with a partition on a short vowel which is not an expected lexical vowel at that position
katakana writing follows japanese sound patterns closely so katakana often doubles as a japanese pronunciation guide
therefore extragrammatical sentences should be handled by some recovery mechanism s rather than by a set of additional rules
a stateset s i where i is the position of the input is an ordered set of states
otherwise the normal parser fails and then the robust parser starts to execute with edges generated by the normal parser
also we present the heuristics to reduce the number of edges so that we can upgrade the performance of our parser
second to show the adaptability of our robust parser same experiments are carried out on NUM NUM sentences from the atis corpus in penn treebank which we have n t referred to when we propose the robust parser
there is no requirement for parallel text for the symmetric learning algorithm
example training text for english and spanish is shown in figure NUM
currently NUM gestures can be recognized resulting in the creation of NUM military symbols irregular shapes and various types of lines
one with the inclination to do so
table NUM results for the nonmonotonic extension
all stem vectors are of length NUM unit vectors
in this paradigm only the direction of the vector carries information
this section consists of four parts
hnc has developed an approach to learning stem level relationships across multiple languages
note that the summation in figure NUM is over co occurring word stems
now we encode the symbols available in each context
symbols that do not co occur are left in their quasi orthogonal original condition
if not we can not contend ourselves with the possibility of compact representation of constraints but rather need a means to enforce this compactness on the constraint level
although this constraint is only half the way to the packed semantic representation we are aiming at it is nevertheless worthwhile to consider its structure a little more closely
small greek letters c c will henceforth denote constraints open formulae and letters x y z possibly with indeces will denote variables
NUM for any node u the constraint sem v is never larger than the constraint of any single tree in the forest originating in u
hence together with the assumption that the semantic structures the dcg computes can be bounded linearly in sentence length and are acyclic we obtain a o n
nevertheless although we get better initial estimates by smoothing parameters corresponding to rare events these parameters still can not be trained well in the robust learning procedure because such parameters are seldom or never touched by the training process
as described above the probability p n quan nlm i n quan reduced l2 p li n2 is assigned a large value in the back off estimation procedure
for the lex l2 syn l2 model the accuracy rate for parse tree disambiguation in the trairting set is improved from NUM NUM to NUM NUM which corresponds to a NUM NUM error reduction rate
in other words we use context symbols explicitly and directly to evaluate the probabilities of a substructure instead of using the parsing state to implicitly encode past history which may fail to provide a sufficient characterization of the left context
for instance the test set performance for the lex l1 syn l2 model is NUM NUM while the performance for the lex l2 syn l2 model is only NUM NUM
where treex is the parse tree x and NUM correspond to the end of sentence marker and the null symbol respectively and li and ri represent the left and right contexts to be consulted in the ith phrase level respectively
they also provided a better way of responding appropriately to the unexpected than the more general purpose quick fire remarks
most current aac systems have been designed using a predominantly phrase construction approach in order to maximize the flexibility of speech output
another feature of natural conversation that has an important bearing on which general approach to the design of aac systems i.e.
in the circuit fix it shop the crucial condition was correct determination of the led display
as previously noted correctly interpreting certain utterances is crucial for efficient continuation of the dialog
figure NUM shows some of the utterances successfully handled by the implemented system during the experiment
based on the current context there is an expectation of what is to come next
clearly the use of dialog context in the verification subdialog decision rule improves system performance
ach prop obj propname propvalue achieving a property
main expectation direct answer e.g. the switch is up
examples of misrecognized inputs from interacting with the circuit fix it shop are given in figure NUM
subjects attempted a total of NUM dialogs of which NUM or NUM were completed successfully
the tipster program has undertaken to identify several types of document parts
these include some message headers and two languages english and japanese
the most common markup is probably to indicate parts of a document
then the appropriate type of processing can be applied to each part
the tipster configuration management policy is documented in the configuration management plan
markups which are not needed by the application need not be converted
responsibility for maintenance of the architecture resides with the configuration control board
for relevancy filtering we retained only the concept nodes that had n correlation with relevant texts
as a result people tend to retain many patterns that are not likely to be encountered very often
we have shown how a preclassified training corpus can be combined with statistical techniques to create conceptual patterns automatically
domain specific text annotations are expensive to obtain so our goal has been to eliminate our dependence on them
autoslog ts often proposes the same pattern multiple times and keeps track of how often each pattern is proposed
autoslog ts suggests promising directions for future research in developing dictionaries automatically using only preclassified corpora without detailed text annotations
complex noun phrases e.g. conjunctions appositives prepositional phrases are often confusing for annotators
these issues are not only frustrating for a user but can have serious consequences for the system
these systems generate extraction patterns automatically using a set of associated answer keys or an annotated training corpus
however previous approaches require an annotated training corpus or some other type of manually encoded training data
you honored will give me a book sengupta and chaudhuri delayed syntactic encoding
NUM NUM then the performance of the pump must be monitored
NUM NUM then the performance of the pump must be monitored
at this stage the tagset mode NUM includes number information and has NUM classes
correct a also requires that the words within the subject are correctly tagged
hture work will investigate the effect of training the networks on the positive examples alone
for instance if a relative pronoun occurs then a verb must follow in that constituent
thus for the subject detection task then the performance of the pump must be monitored
in order to reconcile computational feasibility to empirical realism an appropriate form of language representation is critical
the pattern matching capabilities of neural networks can be used to locate syntactic constituents of natural language
in an ep approach an initial population of queries is needed along with a mutation strategy to modify queries
the mundial demo uses a bilingual dictionary combined with several heuristics to limit the terminological expansion of the input query
this process is shown in figure NUM b several of the resulting queries are given in table NUM
the range of translation techniques that are available to a query translation system is greater than in standard machine translation systems
lexicaltransfer techniques can also be used in the same context providing wide coverage of term senses
several of the spanish trec queries and their hand translated versions are shown in table NUM below
the top NUM most significant terms are then extracted and become the new spanish query
table NUM below shows two of the resulting queries from the ep method
a primary consideration is that most modem text retrieval systems regard queries and documents as unordered bags of words
multilingual text retrieval extends the basic monolingual detection task to include retrieving relevant documents in languages other than the query language
the most basic task of a natural language interface is to map the user s utterance onto some meaning representation which can then be used for further processing
the ideal repair hypothesis for this example is one that specifies that the temporal expression should be inserted into the when slot in the busy frame
the three biggest challenges which continue to stand in the way of accomplishing even this most basic task are extragrammaticality ambiguity and recognition errors
since any string can be mapped onto any other string through a series of insertions deletions and transpositions this approach makes it possible to perform any desired repair
the underlying assumption behind the mdp approach is that the analysis of the string which deviates the least from the input string is most likely to be the best analysis
therefore this two stage process is more efficient since the first stage is highly constrained by the grammar and the results of this first stage are then used to constrain the search in the second stage
rather than placing the full burden of robustness on the parser itself i argue that it is more economical for partial parsing and combination to be separate steps in the hypothesis formation stage
this parser is capable of skipping over any portion of an input utterance that can not be incorporated into a grammatical analysis and recover the analysis of the largest grammatical subset of the utterance
an efficient two stage approach to robust language interpretation
a fitness function ranks hypotheses narrowing down on a small set
we describe these in the sections that follow
it is understood that forward maximum tokenization backward maximum tokenization and shortest tokenization are the three most representative and widely quoted works following the general principle of maximum tokenization
one tenet of our theory is that proper initiative setting requires an effective user model
another consequence of the principle of sequentiality is that the only node at which substitution is allowed in a tree with substitution sites is at the most embedded one
substituted node u t would then appear to the left of uk in the terminal frontier but to the right of it in the original discourse
in three cases the expectation is satisfied immediately by a clause cued by but or or e.g.
there is clearly more to be done including a more complete characterization of the phenomenon and development of an incremental discourse processor based on the ideas presented above
this we found we could model in terms of constraints on adjoining and substitution with respect to a suitably defined right frontier
the examples given in the introduction were all minimal pairs created to illustrate the relevant phenomenon as succinctly as possible
ch16 you could n t on the one hand decry the arts and at the same time practice them could you
substitution unifies the root of a substitution structure with an empty node in the discourse tree that serves as a substitution site
earlier we noted that in a discourse structure with no substitution sites adjoining is limited to the right frontier rf
in addition for tag and nontermi l nodes the name of the label and the values of all the gr mmar s features including those based on information propagated up the parse tree from lower down at that node are also available
in addition we are able to ask whether there is a constituent in the source treebank parse with the identical span as a given node of an atr parse and if so what its non terminal label is or how many children it has
emphasis mine NUM c there is supposed to be a wire between connector one zero four and
for example the feature and two non sequential from chinese take out food flier np modification helps to predict attachment events by carrying up to the top node of each noun phrase data as to how much more modification the noun phrase can probably take
since and if there is just one comma in the sentence and that comma occurs in the fn st quadrant then there is a good chance that the overall structure of the sentence is premodlfying phrase then main clause
in the performance results cited below however we show exact match only with the single correct parse of the test treebank rather than with any one of the correct parses indicated in the golden standard version of the test set
in fact the discourse marker that is responsible for most of our algorithm recall failures is and
we wish to thank joshua goodman and john lafferty for their contributions to the treebank conversion work reported here akira ushioda for his implementation of the brown word clustering algorithm and craig macdonald and toyomi saiga for their contributions to our work overall
then in section NUM NUM we explain that g can easily be converted to chomsky normal form in such a way as to preserve c derivations
let us now calculate the size of g v consists of o n2 NUM o m NUM NUM nonterminals
www http agora leeds ac uk amalgam abstract enough to be employed in the commercial market we present a generic template for spoken dialogue systems integrating speech recognition and synthesis with higher level natural language dialogue modelling components
we would like our results to apply to all practical parsers but what does it mean for a parser to be practical
in short we require practical parsers to output a representation of the parse forest for a string that allows efficient retrieval of parse information
encoding the indices ensures that the grammar is of as small a size as possible which will be important for our time bound results
figure NUM average yield by paragraph and sentence
figure NUM recall scores show individual contribu
NUM NUM sentence position yields and the optimal position policy
the results displayed in figure NUM are especially promising
fig ure NUM indicates that the precision score decreases
this study provides empirical validation for the position hypothesis
naturalness in dialogue is difficult to define but by examining phenomena which occur in human to human dialogue we can begin to draw some features which contribute to its definition
fast generation of abstracts from general domain text corpora by extracting relevant sentences
in principle the process of determining whether the statistically validated segment boundaries correlate with linguistic devices requires a complex search through a large space of possibilities depending on what set of linguistic devices one examines and what features are used to recognize and classify them
for example the regular expression although identifies such a discourse usage
table NUM the relation between the length of the path
after the slots for each text fragment were filled the results were automatically exported into a relational database
table NUM precision of word sense disambiguation the highest
by finding the solutions for x we can assign sbls to branches
let us take figure NUM which shows a fragment of the thesaurus
this method is expected to excel in the following aspects
the database lists several case filler examples for each case
every object can be a slot or a value
this result suggests that these two methods are complementary to each other rather than competitive and that the overall performance can be improved by combining them
quite a few analysis trees that did not exactly match with their counterparts in the test set yielded a semantic interpretation that did match
five of the ten senses are represented by fewer than NUM co occurrence sentences each and only one of these five yields any coverage at all
we demonstrated this sense specificity of modified nouns by compiling all pairs consisting of a target adjective modifying the same noun as either of its antonyms
the average length of the test sentences was NUM NUM words
katz principled disambiguation remaining sentences we further extracted a subset of sentences in which both members of the pair modify distinct instances of the same noun
discrimination among senses of adjectives based on the nouns they modify or of which they are predicated has been the subject of less intensive and systematic study
about three quarters of all instances of these adjectives can be disambiguated almost errorlessly by the nouns they modify or by the syntactic constructions in which they occur
they shells of bullets were small and light but their turnip shape and radial fins made them difficult to conceal
in other cases however the move from specific nouns to semantic features as sense indicators necessitates the formulation of more specific rules for using them
and those in which a noun object from the infinitival clause is promoted to serve as subject of the verb of being in place of it
it is therefore important to take into account the infinitival construction prior to disambiguating any adjective even those for which it does not constitute an indicator
three nouns in the co occurrence sentences emerged as significant indicators for the not heavy sense of light cruiser load and harness
depth of subtrees NUM sem synt
we see that by decomposing the tree into two subtrees the semantics at the breakpointnode n man is replaced by a variable
on the empirical side a particularly intriguing kind of experiment is an ablation study
the kb accessing system described above possesses discourse knowledge in the form of kb accessors
we define robustness as the ability to gracefully cope with the complex representational struc
content specification nodes house the high level specifications for extracting content from the knowledge base
temporal attributes explains how a process is related temporally to other processes
the majority of revisions involved the reorganization and removal of nodes in the edps
this indexing structure permits edp selection to be reduced to a simple look up operation
ifications of a given node organize the children of that node into paragraph clusters
the method is employed in our current implementation
for example training examples keyword in context company said the plant is still operating although thousands of plant and animal species zonal distribution of plant life
null although several algorithms can accomplish similar ends NUM the following approach has the advantages of simplicity and the ability to build on an existing supervised classification algorithm without modification
the classification procedure learned from the final supervised training step may now be applied to new data and used to annotate the original untagged corpus with sense tags and probabilities
the one sense per discourse hypothesis clearly the claim holds with very high reliability for these words and may be confidently exploited as another source of evidence in sense tagging
one sense per collocation NUM nearby words provide strong and consistent clues to the sense of a target word conditional on relative distance order and syntactic relationship
most of the tendency is statistical two distinct arbitrary terms of moderate corpus frequency axe quite unlikely to co occur in the same discourse whether they are homographs or not
because no alignments are possible such pairs are skipped by the learning algorithm cases like these must be solved by dictionary lookup anyway
the major difference is that in discourses where there is substantial disagreement concerning which is the dominant sense all instances in the discourse are returned to the residual rather than merely leaving their current tags unchanged
for example computer in english comes out as i l konpyuutaa in japanese
however the situation is more complicated for language pairs that employ very different alphabets and sound systems such as japanese english and arabic english
if possible our techniques should also be portable to new language pairs like arabic english with minimal effort possibly reusing resources
however these models are expensive to compute many more alignments and lead to a vast number of hypotheses during wfst composition
in a second experiment we took katakana versions of the names of NUM u s politicians e.g. jm
for language pairs like spanish english this presents no great challenge a phrase like antonio gil usually gets translated as antonio gil
we choose the most probable interpretation of
to disembiguate a word the sentence context of the word is first streamed through a general word sense disambiguation module which assigns the appropriate sense of the word
instead of considering all edges as equi distance the probability of the NUM NUM n c or edge is used to bias its distance
in the same spirit the descendant coverage mettic attempts to tweak the constant edge distance assumption of the conceptual distance metric
there are cases where the exact wording of a semantic class in the domain specific hierarchy is not pre mt in wordnet
take for instance the semantic class goveroment ot cia in the domain specific hiermx hy
accuracy on specific se mtic classes refers to an exact match of the pcogram s response with the corpus answer
NUM we compared our approach with supervised methods c o contrast their reliance on annotated corpora with our r nce on wordnet
the amount of er ining data needed for a supervised learning algorithm to achieve good performance on semantic class disambiguation may be larger than what we have used
figure NUM deriving the predicate argument and information structure for a simple sentence
the formalism compositionally derives the predicate argument structure and the information structure e.g.
in a it completely ignores the relation between the verb and the prepositional phrase save to predict that a prepositional phrase any prepositional phrase will follow the verb
this is an example of argument dependent carrier selection
the present paper explains this semantic interpretation method
likewise the intension of the function that groups verbs syntactically would be defined in terms of something strictly syntactic such as subcategorization frames
because the tokenization dictionary is complete the critical fragment can at least be tokenized as a string of single character words
in this case the student is aware that s he needs to mark subject verb agreement but does not know how to do so or believes that s he has already done so
semantic compositions in inflections and derivations are constrained by the properties of the terms and predicates
onur t ehito lu and h cem bozsahin laboratory for the computational studies of language
this will allow the locative marked noun to modify a verb
example NUM keeping morphology and syntax entirely separate forces one to stipulate different scopes for affixes
the child read the book b kitab t 9ocuk oku du c ocuk kitap oku du
multi dimensional approach allows affixes to pick out different scopes in mixed morphological and syntactic composition
this tree is part of a greater hierarchy which includes inheritance information for words and phrases
most inflections e.g. person and number markers however have grammatical functions only
null a simple example will make this clear
this implies that lexical rules are responsible for semantic composition and for the changes in syntactic requirements
the lexical approach to morphology presented here is a mid point in the design of the morphology syntax interface
to ensure that such a single character string is being tokenized the single character must be a word in the dictionary
we approximate the posterior p ai ai s by first assuming that the multinomial is a collection of independent binomials each of which corresponds to a single value ui of the multinomial we then normalize the values so that they sum to NUM
it states that the final state emission function is indeed a function
up these data are not unexpected in our account since we posit no fixed position for extraposition and hence allow that an extraposed np complement is bound inside the np itself provided that an adjunct is present to mark the right periphery of the np
basically one can manipulate finite state transducers as easily as finite state automata
the first symbol is the input and the second is the output
in addition the tagger requires drastically less space than stochastic taggers
the transition for the input symbol e is computed the same way
on line NUM d and e are then two possible symbols
NUM figure NUM gives an algorithm that computes the local extension directly
in particular we address problems such as wide variations in document sizes word repetitions and the need to rank documents rather than just decide whether they belong to a category or not
mance of both supervised algorithms lag b hl d that of our approach
these new rules if they score above the threshold can also be included in the working rule sets
the longer the length the smaller the sample that will be considered representative enough for a confident rule estimation
a suitable solution is to use the logarithm of the affix length o i
the major topic in the development of word pos guessers is the strategy used for the acquisition of the guessing rules
three complimentary sets of word guessing rules are statistically induced prefix morphological rules suffix morphological rules and ending guessing rules
this is especially important when dealing with uninflected words and domain specific sublanguages where many highly specialized words can be encountered
in english as in many other languages morphological word formation is realized by affixation prefixation and suffixation
the general schemata can also capture ending guessing rules if the class is set to be void
if we rederive the asymptotic behavior we again obtain zipf s law
for an ideal turing population we would have x z
instead of relying on corpus statistics static inforn tion
for ne the system uses the original text of the article to write a copy
we will prove that critical points are all and only unambiguous token boundaries for any character string on a complete dictionary
for instance the pairs of objects that are introduced by the type form artifact
the instances for this type only cover the class m rod
this corpus describes research carried out by the research division of edf the french electricity company
the paper defends the notion that semantic tagging should be viewed as more than disambiguation between senses
complex types are called dotted types after the dots that are used as type constructors
that is doors and gates are both artifacts but they have different appearances
null hyponymic information is acquired through the classification process discussed in sections NUM NUM and NUM NUM
the last number is much better on smaller corpora NUM on average
there are about NUM different patterns that are arranged around the headnoun of an np
however despite its shortcomings wordnet is a vast resource of lexical semantic knowledge that can
any non monotonic segment of the tbm will occupy the intersection of a vertical gap and a horizontal gap in the monotonic first pass map
the purpose of a bitext mapping algorithm is to produce bitext maps that are the best possible approximations of each bitext s tbm
simr s output has been used to align more than NUM megabytes of the canadian hansards for publication by the linguistic data consortium
it rarely got lost with a fixed chain size of NUM and never with a fixed chain size of NUM or more
typical errors of commission are stray points of correspondence like the one in cell h e in figure NUM
experiments conducted to test the effectiveness of our method demonstrate an encouraging accuracy of NUM NUM
in this sentence a human speaker would probably assume the former interpretation over the latter
it is obvious that these kinds of mistakes could be avoided if more data were available
phonetic cognates can be used to map between language pairs with dissimilar alphabets even when the languages are not closely related
valid chains that are rejected by the angle deviation filter sometimes occur between two accepted chains as shown in figure NUM
structural disambiguation is still a central problem in natural language processing
thus the data sparseness problem is unlikely to be resolved
we further examined the types of mistakes made by our method
table NUM breakdown of lex3 lex2 synj
a single without any script represents an intrasentential zero anaphor
email the superscript b is the index of the referent
in this research we confi ne ourselves to descriptive texts
the lexicon lex associates words with classes as given in table NUM
have some water will flow out come some water will overflow
in this paper we focus on the first two cases
during training we say that the algorithm predicts NUM and makes a mistake if the example is labeled positive when the score it assigns an example is below NUM
table NUM summary scores for different feature combinations
and princeton un verszty if doctors
t a van dijk and w kmtsch cognitive psychology and discourse recalling and summarmmg stones in w u dressier editor current trends
moreover since the car is an acoustically hostile environment the limits of speech recognition have to be taken special care of
this choice is more or less forced upon us since there is no fixed vocabulary from the system s point of view
broadening the range of the possible user s input by allowing more natural language like database queries for the navigation task
on the other hand it also indicates that the characteristics of a dialogue manager are largely determined by the kind of application
where possible we have related our propos ms to guidelines found in the literature summarized in our NUM commandments for spoken dialogue
on the one hand this can be seen as an indication that this is still a relatively immature area of research
thus if question is placed in the same semantic group with ask and inquire the three senses lcb NUM NUM NUM rcb survive out of the five senses of question with a preference for sense NUM if on the other hand question is classified with challenge and dispute only sense NUM survives
using the verb show of the experiment described in the previous section as an illustration we note that whenever the verb takes only a direct object the syntactic method eliminates three of the thirteen possible senses while always retaining the zassuming no gaps in the subcategorization information for this verb in comlex and wordnet
for the NUM NUM verbs present in all three databases the average reduction in ambiguity was NUM NUM for words with two to four senses NUM NUM for words with five to ten senses and NUM NUM for words with more than ten senses the overall average for all polysemous words was NUM NUM
however given that each word must be assigned to one class independently of context NUM the problem of ambiguity is solved by placing each word in the class where it fits best that is in the class dictated by the predominant sense of the word in the training text
thus if the automatically induced semantic classification indicates that the predominant sense of question is associated with dispute rather than with ask by placing question and dispute but not ask in the same group we can infer which of the wordnet senses of question is the predominant one in this domain
in all these cases our method makes a single correct prediction out of the eight possible senses
we have chosen to risk overgeneration in these cases at present rather than accidentally eliminating a valid sense
we will then incrementally evaluate the utility of tagging corpora with pruned sense sets for different types of discourse
we found that after an initial increase in the error rate which can probably be accounted for by the fact that the new training data came from a different part of the corpus increasing the size of the training and cross validation sets to NUM NUM NUM NUM reduced the error percentage to NUM NUM as can be seen in table NUM
the lexicon consisted of less than NUM NUM words assigned parts of speech by the tagger including NUM french abbreviations appended to the NUM english abbreviations available from the lexicon used in obtaining the results described in section NUM the part of speech tags in the lexicon were different from those used in the english implementation so the descriptor array mapping had to be adjusted accordingly
by using part of speech frequency data to represent the context in which the punctuation mark appears the system offers significant savings in parameter estimation and training time over word based methods while at the same palmer and hearst multilingual sentence boundary time producing a very low error rate see table NUM for a summary of the best results for each language
we constructed a training text of NUM potential sentence boundaries from the corpus as well as a cross validation text of NUM potential sentence boundaries and the training time was less than one minute in all cases
those words not present in the lexicon are assigned a certain language dependent probability NUM NUM for english of being a proper noun and the remainder is distributed uniformly among adjective common noun verb and abbreviation the most likely tags for unknown words n capitalized words appearing in the lexicon but not registered as proper nouns can nevertheless still be proper nouns
by comparison the satz approach has the advantages of flexibility for application to new text genres small training sets and thereby fast training times relatively small storage requirements and little manual effort
words in the lexicon are followed by a series of part of speech tags and associated frequencies representing the possible parts of speech for that word and the frequency with which the word occurs as each part of speech
the method uses information about one word of context on either side of the punctuation mark and thus must record for every word in the lexicon the probability that it occurs next to a sentence boundary
a decision is then made by assigning to the category c only those documents that exceed some threshold or just by placing at the top of the ranking documents with the highest such score
therefore a word starting with say a p is very likely to have a very close derivative where the initial p has been replaced by say a r
the latter also involves other static microtheories describing world knowledge and syntax semantics mapping as well as dynamic microtheories connected with the actual process of text analysis
table NUM gives the list of the phonological correlates of the alternation which consists in adding the suffix ly corresponding to a productive rule for deriving adverbs from adjectives in english
moreover this model provides us with an effective way to define the lexical neighborhood of a given word on the basis of surface orthographical local similarities
let us start with some notations given g a graphemic alphabet and p a phonetic alphabet a pronunciation lexicon ps is a subset of g x p
this set better viewed as a stack is ordered according to the productivity of the ai the topmost element in the stack is the nearest neighbor of x etc
the search procedure is therefore stopped when al rcb derivatives up to a given depth NUM in our experiments have been generated and unsuccessfully looked for in the lexicon
the arrows of the conceptual relations indicate the domain and range of the relation and do not impose a dominance relationship
NUM it has the format of a mixed structure like the representation used to express mapping rules figure NUM
so that discriminatory power is not lost and not too specific so that the referring expression is in a sense minimal
in order to constrain this approximate matching of the input we impose additional restrictions on the semantics of the generated sentence
when applying a mapping rule the generator keeps track of how much of the initial semantic structure has been covered consumed
this is done in the stage of covering the remaining semantics when the mapping rule ii is used
our technique provides flexibility to address cases where the entire input can not be precisely expressed in a single sentence
others assume all concepts are expressible and try to substitute syntactic relations for conceptual relations NUM
null the most practical way to create an abstract is thus to determine the most important portions by using surface clues
simulating this human process is clearly outside the area that can be dealt with by current computational linguistics
named entities such as organizations and people ar e stored on an active token list to allow the system to link occurrences of the same entity based o n name variations
by using this method a system can have an ability to be applied to a variety of texts
the rapid expansion of the internet enables us to easily access a lot of information sources in the world
the ability to browse information quickly is therefore a very important feature of an information retrieval and navigation system
generally an abstract can be considered to be a concise text giving an outline of the original text
the importance s of a sentence is calculated as follows r i t where a is a constant p is the number of points assigned to the i th feature which is normalized to be between NUM and NUM and wi is the weight assigned to the i th feature
now carrying on with the geometrical paralm analogy may be interpreted in terms of distances as follows the dis tanee of any term to the unknown is the same as the distance between the two remaining terms
in figure NUM we plot the difference between training and test set accuracies after the apphcation of each transformation including a smoothed curve
using the transformations learned in the above unsupervised training experiment run on the penn treebank we apply these transformations to a separate training corpus
we recall that there may be no solution one solution or several solutions as a restrict ion in this experiment we lid not consider distances between objects over half of the lengths of the objects
l he purpose of this article is to propose a possible mathemati mly sound cx i l mat on and to show the p t h to comtmtational applications
lb have a more precise idea about the power of the method we carried out some experiments on an excerpt of the tree bank of the university of pennsylwmin NUM sentences with their corre sponding analyze s
definition NUM metric let s bc a set dist a function from q x s to ir the so of non negative real n umbcrs dist is a metric on s if and only if
we give t ossi i le a count of this phenomenotl in t et tns of edition distances thus paving the way to comptlt d ionm applic tk ns
we show how it is possible to perform the a nmogical anal ysis and generation of sentences using tt tree bank m l q l roxitnall e l tt tertf ln t hitlg
an example of a learned transformation is change the tag of a word from verb to noun if the previous word is a determiner
in transformation based part of speech tagging NUM all words are initially tagged with their most likely tag as indicated in the training corpus
in the first case a detection need is passed against the entire corpus
these data elements are highlighted in color in the documents on screen
figure i outline of the components of the spoken dialogue system
relationships represent more complex extraction techniques
after the results of the previous run are examined sample relevant or non relevant documents are typically selected from the run and used to modify a detection need so as to improve the query
all the extraction components produce annotations in the same structure and format
routing is not supported in the demonstration
all components use the common document manager and common viewers for collection lists document lists documents detection needs and annotations in the graphical users interface gu1
document detection includes the selection of documents from a corpus with the output rated by relevance and the routing of documents to users based upon detection need profiles
the gui is not part of the architecture but the use of only one gui illustrates the standardization of component outputs and is another example of the sharing of common facilities by diverse components
at the basic level of name spotting the typical elements identified are person and organization names cities countries dates and numeric expressions such as monetary figures
this subconcept entails t transformation of its superconcept s prestate so so dc ec to its superconcept s poststate s ee dc sl as well as the new poststate s2 ec s e i.e. the belief in a return of the involved object
pnrtia lexi al i l ls and the on et t si e ific inf rnt tion in i he axioms
presupl ositional information is embedded in the discourse eontext by a process called justification whi h omt ines NUM inding verijication with contextual mri hment accommodation in varying prol ortions
rl ansrrion inii eua fin e iiave pji transrr on init e fin u iiave q u
however there are two important points concerning the determination of which grammatical realizations are possible firstly the predicate that takes the corresponding elementary argument directly and secondly the choice of that subset of roles of tim maximum case frame that are not blocked
based on the hypothesis that set s proi ol ypical situation descriptions ca n be interl reted in the same way as NUM l ss we have l ro ee led to a new joined ret resentation format
i ook will give NUM aek n line with the ret resentation format icy lolled by kanlt and l ot deutseher the corresl onding lexieal entries are twofohl stru tures
one of the three prototypical ineaning descriptiolm that constitute the partial field of to 9ire and tile gramrnatical case assiglmmnt of verschenke n NUM is given in figure NUM those parts of l he description t hat have emphasis are written in bold face
c is l he t t ositc of the final state fin e i.e. bec a is inl erpreted as c tii ansition a a
NUM a she gave several children a few apples
NUM a they arrived by car at the lake
but this is not the case with NUM b
in its present form however the algorithm has several limitations
this also applies to NUM NUM
prepositions are being analyzed just in one or two meanings each
b they arrived at the lake by car
NUM how does john behave towards few girls
b john talked about many problems to few girls
b john made a canoe out of every log
n significance x NUM x z arctan dx i n i l twicd NUM
vanf is an independent re implementation of fastus i
these relationships form a graph indic lting the necessary conditions for a lexical item to form part of a comt h te
italian spanish to the same ill record even when there is no english or wordnet equivalent
NUM a set of word meanings across languages have complex equivalence relation and they have di
since the dutch synset voorwerp has an equivalence relation to the ill record the top concept object also applies to the dutch synset
secondly there is the language independent module which comprises the ill the domain ontology and the top concept ontology
in addition physicians anesthesiologist and anesthesia residents enter data throughout the course of the patient s surgery including start of cardiopulmonary bypass and end of bypass as well as subjective clinical factors such as heart sounds and breath sounds that can not be retrieved by medical devices
we then copied the ill record meat NUM into the spanish wordnet yielding carne NUM as the synset linked to it
perhaps a selectional restriction on the perceiver that the type of action is an evaluative one thus providing semantic patterning
after automating the mds analysis we will examine the extent to which the lexical semantic information is correlated with the thematic analyses
a number of prnning techniqtms have i een suggested to re hwe the mnom t of redundancy in bag generators
the sl is compared with one or more other languages which will be called the reference languages rls
this combination is more useful than the bilingual corpora of similar languages
the remaining NUM instances are caused by zero pronouns with intrasentential antecedents
thus in japanese to engiish ma ne translation systems it is necessary to identify case elements omitted from the original japanese zero pronouns for their translation into engiish expressions
table NUM the cause of the error in automatic identification of antecedents
for example in a machine translation system the system needs to recognize those elements which are not present in the source language but may become mandatory elements in the target language
the method focuses on the characteristics of japanese and english in two languages from chfberent f rngles and in which distribution of zero pronouns is very d uterent
this method was implemented using the japanese to english machine translation system alt NUM e for the analysis of japanese sentences and brill s tagger for the analysis of the english sentences
it seems that a bilingual corpus consisting of sentence pairs with an original in one language and a translation is better than a monolingual corpus for the purpose of acquiring resolution rules of zero pronouns
to ex m e the effectiveness of automatically identifying antecedents of japanese zero pronouns within english translations the accuracy of the identified antecedents of each zero pronoun of the following three types were examined
to make the compatibility of hyperonyms more explicit the most frequent hyperonyms can be defined as allowable or non allowable combinations
building on the correspondence between dominance and nuclearity we raise two issues in the following sections
in the rst schemas considered thus far a span always consists of a nucleus and satellite
ils is not directly addressed in rst but is implicit in the rst concept of nuclearity
the dominance relation among intentions fully determines the embeddedness relations of the discourse segments that realize them
in g s the definition of linguistic structure does not require a segment to contain a core
specifically an embedded segment corresponds to a satellite and the core corresponds to the nucleus
the discussion in section NUM suggests that rst and g s share a large amount of common ground
to begin this section we state the common ground that emerges from relating dominance and nuclearity
when one dsp in satisfaction precedes another ira then dsn precedes ds i in the discourse
keeping track of all domain relations in a discourse is an overwhelming task and is often infeasible
by assumption NUM the lexical elements in the bag and therefore in any grammaticm ordering of it are connected
regular expressions such as and can also be used in the pattern
there are NUM dictionary patterns NUM segmentation patterns and NUM name recognition patterns defined in erie
the pattern set was developed by using a hundred newspaper articles annotated and provided to the met participants by darpa
during its official run on a sun sparcstation NUM erie processed each article in an average of NUM NUM seconds
this was achieved by separating the patterns and pattern matching engine which has made the pattern development faster and easier
the pattern matching engine recognizes organization person and place names along with time and numeric expressions in japanese text
majesty tags a part of speech such as a noun or noun suffix as the major category of the word
a pattern can be any combination of words their parts of speech character type and the pattern name
erie solves these problems by generating a pattern matching engine in c language directly from the defined patterns
the name recognition patterns recognize proper names times and numeric expressions that appear in the text
figure NUM a tree from the ovis tree bank
the string j then can not be pulled straight
for example a part of speech tagger would expect w elements inside s elements and a tag attribute on the output w elements
in addition to multimodal input unimodal spoken language and gestural commands can be given at any time depending on the user s task and preference
the comparison result is shown in table NUM
in addition to leathernet quickset is being used in a second effort called exlnit exercise initialization that will enable users to create division sized exercises
muitimodal integration agent the multimodal interpretation agent accepts typed feature structure meaning representations from the language and gesture recognition agents and produces a unified multimodal interpretation
currently for this task the language consists of noun phrases that label entities as well as a variety of imperative constructs for supplying behavior
additionally the architecture supports mobility in that lighter weight agents can run on the handheld while more computationally intensive processing can be migrated elsewhere on the network
this allows for a domain independent integration architecture in which constraints on multimodal interpretation are stated in terms of higher level constructs such as typed feature structures greatly facilitating reuse
a brief description of each agent follows in the remainder of the paper we illustrate the system briefly describe its components and discuss its application
holding quickset in hand the user views a map from the modsaf simulation and with spoken language coupled with pen gestures issues commands to modsaf
importantly the unified interpretation might not include the highest scoring gestural or spoken language interpretation because it might not be semantically compatible with the other mode
most of the words rated as NUM s are not specific to the target category but some of them might be useful for certain tasks
for example if a cartridge or trigger is mentioned in the context of an event then one can infer that a gun was used
the corpus based algorithm is especially good at identifying words that are common in the text corpus even though they might not be commonly used in general
the word m NUM would be in the context windows for both gun and rifle even though there was just one occurrence of it in the sentence
the stopwords and numbers are not specific to any category and are common across many domains so we felt it was safe to remove them
with the exception of the energy category we were able to find NUM NUM words that were judged as NUM s or NUM s for each category
we performed experiments with five categories to evaluate the effectiveness and generality of our approach energy financial military vehicles and weapons
also this reduces the chance of finding different word senses of the seed word though multiple noun word senses may still be a problem
a few inappropriate words are not likely to have much impact but many inappropriate words or a few highly frequent words can weaken the feedback process
while the anaphor belongs to an open lexical class
subsequent anaphora checks exclude any of the preceding parallel segments from the search for a valid antecedent and just visit the currently open one
the distribution of the senses in the clusters is demonstrated in table NUM
NUM the words in the definitions are called definition words
the distribution of senses in the clusters
nllpublprolog app cl97 and the world wide web at http www let
the algorithm is a depth first search procedure
another problem is about the length of the contexts to be considered
finally we get x NUM NUM and let it be the threshold dr
such access is notoriously difficult to obtain for several reasons including commercial confidentiality protection of in house know how and protection of developers time
this would also be a disadvantage c the observations of each tag would hopefully be more correct as the instances lost to the underspecified tag would be the tricky and atypical cases that otherwise might obscure the contextual patterns of the unambiguous tags
be it here sufficient to say that in general prefer the term consistent with a certain norm instead of the term correct nevertheless in the following discussion i will call the deviances from the applied noun errors
if words from these categories are ever mixed up they are mixed up in very specific patterns namely with themselves as when different inflected forms of the same stem coincide or they are mixed up with words they are related to e.g. by derivation
the configuration in which a continiie is preceded by st iletain whic h
this rule has been omt utationally int erpreted to individuaix the cb
ontinue cb john cf john mary
the discourse component then applies inference rules that may add more semantic information to the discourse predicate database
avoid depending on component software still under development without including support for coordination between main system and component developers
the xat library would allow input of chinese text which could then be communicated to a program
the modified query was resubmitted to the system and the first ten documents returned were evaluated for relevance
the difference between the two segmentation methods is largely due the presence of proper names in the queries
semantic rules are matched based on general syntactic patterns using wildcards and similar mechanisms to provide robustness
there are ambiguities in segmentation that probably ca n t be resolved without including more context in the decision
the input character representation must be matched to the document collection representation and converted if necessary
in semitic languages words are classically viewed as consonant stems with the addition of prefixes and suffixes
in chinese artificial intelligence becomes a four character phrase which could be translated literally as man made cognition able
the tagger does not require a tagged corpus for training but two types of biases can be set to tell the tagger what is correct and what is not symbol biases and transition biases
the training process of a statistical tagger requires some time because the linguistic information has to be incorporated into the tagger one way or another it can not be obtained for free starting from null
an example is les boltes the boxes where les is wrongly tagged in the test sample because the noun form is misspelled as boites which is identified only as a verb by the lexicon
we prefer the noun reading and accept the verb reading only when the first person pronoun nous appears in the left cor text e.g. as in nous ne les avions pas we did not have them
however the current simple heuristics fully disambiguate NUM instances of de and des out of NUM i.e. NUM of all the occurrences were parsed with less than a NUM error rate
some of the very frequent words have categories that are rare for instance the auxiliary forms a and est can also be nouns and the pronoun cela is also a very rare verb form
the personal pronoun may thus be too far from the verb because bi gram models can see backward no farther than le and tri gram models sthat is not case with all the french verbs e.g.
among the remaining NUM ambiguous words about NUM of the ambiguity is due to determiner preposition ambiguities words like dn and des NUM are adjective noun ambiguities and NUM are noun verb ambiguities
also as mentioned earlier resolving the adjective vs past participle ambiguity is much harder if the tagger does not know whether there is an auxiliary verb in the sentence or not
the constraint based tagger made several naive errors because we had forgotten miscoded or ignored some linguistic phenomena but still it made only half of the errors that the statistical one made
the meaning of portions is many times tightly related to such agenlive process if one has obtained slices it necessarily has been by culling somelhing there even exists the verb to slice
in languages wilh classifiers these words semantically strongly similar to deterinincrs and quantifiers have functions of individuation and enumeration making surface notions such as sorl of entity shape or measure
in any case if a lemon were n t an individuated and bounded thing it could n t be sliced and tile shape of tile portion would n t depend on that of the whole
being b such wholes bear definite shape and magnitude therefore such values for the portiou will be fuiictious of those of the whole
to assume that imon here is mass entails assuming that it has undergone a dcrivation d grinding rule which converts countable individuals in masses
on ihc other hand portions denoted by such constructions differ from their wholes in some aspects basically iudividuation quantity process of bringing about and shalx
their shape is tightly related to ihat of the whole bt t one of their dimensions is concepmalised as close to non existence jac9 l
nevertheless a round slice of lemon is always a slice of some individual lemon not a special measure of substance which some time in the past was lemon
some have assumed ihat pns select mass nouns slice of cake glass of wine being mass nouns the way in which substances tipically surface in tile language
the problem with the output had certain subtelities since the translation of a category label can appear before or after the label has been seen in the input
this has been done because a model trained with ostia dr is guaranteed to reproduce exactly those sentences it has seen during learning
the technique used for integrating categories in the system is detailed in section NUM section NUM presents the speech translation system
both speech and text input experiments are described in section NUM finally section NUM presents some conclusions and new directions
the use of automatic corpora generation was convenient due to time constrains of the first phase of the eutrans project and cost effectiveness
for the eutrans project the approach was changed so that a single usst would comprise all the information for the translation including elementary transducers for the categories
parsing is carried out in a bottom up mode
we found that the acquired probabilities are truly a good approximation for the morpho lexical probabilities
by closely looking at these words we can identify two reasons for failure
first we used the probabilities only for ambiguous words that can be fully disambiguated
we tested the performance of the method on the test texts from two different perspectives
learning morpho lexical probabilities which is really the size of the sample we use to calculate the morpho lexical probabilities
this process is iterated until the new proportions calculated are sufficiently close to the proportions calculated in the previous iteration
the sw sets for each analysis is as follows hqph n pn encirclement
the right context neighbors all take to infinitives as complements
tors NUM NUM induction based on word type only
class NUM consists of proper nouns
equal weight to precision and recall
NUM NUM induction based on word type and
the right context vector of w
you would like to go to oftenburg
it fell through a hole in his pocket
he gave her back her sfice of pizza
a further ambiguity is that when the third sentence is past perfect it may be a continuation of a preceding thread or the start of a new thread itself
an alternative is to assume that the temporal ordering between events in two consecutive sentences can be any of the four possibilities just after precede same event and overlap
in this paper we describe a method for analyzing the temporal structure of a discourse l this component was implemented as part of a discourse grammar for english
patterns are easier to provide than are the detailed world knowledge postulates required in some other approaches and result in similar and sometimes more precise temporal structures with less processing overhead
in 3b the second sentence is an elaboration of the first and they therefore refer to aspects of the same event rather than to two sequential events
b local builders constructed the ford st bridge
he gave her back her slice of pizza
some relations are not found in wn for instance mr morishita type person the NUM year old
in section NUM we explain how head transducers help satisfy the requirements of the speech translation application and we conclude in section NUM
verbs come after the modal verb miissen because it requires an infinitive and it does not distinguish between separable prefix verbs and other verbs
in addition systran allows for the selection of document types such as prose user manuals correspondence or parts lists
the translation is not correct but the meaning of the source word an be inferred unreasonableness vernunfllos heit instead of vnvernunft
null power translator and telegraph do not come with built in subject dictionaries but these can be purchased separately and added to the system
it is in the realizer that knowledge about the target language resides syntax morphology idiosyncratic properties of lexical items
systems that generate natural language output as part of their interaction with a user have become a major area of research and development
while springpferd turnpferd or simply pferd could count as correct translations of vaulting horse springen pferd can still be regarded as sense preservingly segmented
this is due to the fact that its 3rd person past tense form is a homograph of the frequent adverb heute engl
subject and object are indicated by the arc labels i and ii respectively and modification is represented by the arc label attr
it draws on information from the lexicon as well as on a default inflection mechanism currently hard coded in c
the average runtime for this input is NUM NUM seconds which is comparable to the runtime reported above for the NUM word sentence
realpro has the following characteristics which we believe are unique in this combination realpro is implemented in c
we also tested the system on the syntactically rather varied and complex input of figure NUM which is made up of NUM words
in our case the number of nodes in the input dsynts is equal to the number of words in the output string
let n be the length of the output string and hence an upper bound on the size of both dsynts and ssynts
we conclude that the uniformity of the syntactic constructions found in the sentences used in the above test sequence does not influence the results
how does this nonrandom organization of key words in the discourse as a whole influence v n
when the conversational lead is being taken they will need a way of speaking on a topic and changing topics
the notion of what constitutes a natural clumping depends on the formal language
we also fixed the maximum clump size at NUM words
to establish similar el2 e22 NUM we need to show that their corresponding arguments are similar
also the accuracy is improved from NUM NUM to NUM NUM even if the heuristics differentiate edges and prefer some edges
among NUM NUM sentences from the atis NUM sentences are processed by the robust parser after the failure of the normal parsing
also because the recovery process runs when a normal parser terminates unsuccessfully the performance of the normal parser does not decrease in case of handling grammatical sentences
with respect to the datr theory above we should expect that dog cat noun and that dog root noun surf dog s amongst other things
e1einents of node are called nodes and denoted by n elements of atom are called atoms and denoted by a elements of atom are called values and denoted by a NUM NUM the set desc of datr value descriptors or simply descriptors is built up from the nodes and atoms as shown below
noun cat noun sum sing root pint root surf dog cat noun root dog sing noun plur noun
the detinitional sente iices specify values for node path l airs where the st eeitication is either direct a particular value is exhitfited or indirect the wflue is obtained by local inheritance l r exalnpte the value of the node path pair noun lcb eat rcb is specitied directly as noun
initially the global context will be the pair dog sing bs om tile theory the value of dog sing rcb is to be inherited locally fl om noun sing which ill turn inherits its value globally from the quoted path root
to avoid this type of error the tagger should be able to take the neighborhood of phrases into account
twe know that the phrase mutation error hypothesis is not meaningful in the red text because we can not find out any example of phrase mutation error in the corpus
this technique has an advantage that there is no repeating work for the chart to prevent the parser from generating the same edge as the previously existed edge
this is the price we pay for not using supervised training data
the accuracy is then estimated by the ratio between the number of successful measurements and the total number of trials
the alignment is defined between the formal language words and the clumps
for example the wh question word when is decomposed into temp loc e x whq x r time r x lit at which time hence no additional transfer rules are required
NUM j kkeyse w nim ul nora hon hon acc towatuli si ess supnikka help hum hon past int hon did j help w speaker k addressee l since the local loc value of the constituents appearing in NUM is relevant to our discussion it is considered
using this modified probability p ele we can rewrite the overall search criterion at l i
on the other hand when a businessman talks to another businessman a formal verbal ending is used
the contextual information about social status and sentence external individuals can bc included in the attribute context conx
in the case of dialogue NUM the prolog facts shown in NUM are obtained
it is also possible to attach both the honorific suffix nim and an honorific case marker to an np
NUM inds o indsp inds o indad on the other hand if a subject referent or an object referent is not respected by speaker the social status of speaker is equal to or higher than that of the subject referent or the object referent as shown in NUM
NUM indsp inds o when a humble form of a verb is used in a sentence the social status of an object referent is higher than that of any other individuals that is speaker addressee and a subject referent involved in a sentence as represented in NUM
iea1 noun acc NUM cat subcat j
the diagram in NUM provides the contextual information that speaker respects an object referent and a subject referent that the social status of the object referent and the subject referent is higher than that of speaker and addressee and that the object referent has higher social status than the subject referent
each phrasal category s vp np pp etc is represented by a different markov model
for the translation lnodel the alignment probabilities are made dependent on the differences in the alignment positions rather than on the absolute positions
for smaller amounts of training data say NUM NUM sentence pairs the dp based search seems to be even lnore superior
as a convention of this type of tagging utterances that contribute to the success of the whole dialogue such as greetings are tagged with all the attributes
they more or less implicitly assume that the set of descriptors represented as oneand two place predicates can be expressed adequately in natural language terms
because of these reasons the incremental algorithm interpretation is generally considered best now and we adopt it for our algorithm too
from these informal evaluations the users report that using the prototype has given them conversational opportunities they would not otherwise have had
a goal driven aspect is added by encouraging the selection of descriptors whose images are candidates of filling empty slots in the expression built so far
in companion with the variables and functions explained in separated tables this description should enable the reader to understand the functionality of the algorithm
after having introduced some basic terminology we elaborate interface deficits of existing algorithms form which we derive desiderata for an improved algorithm
the prototype models several aspects of the pragmatics of conversation but no doubt there are other aspects which could be helpfully incorporated
a few names are available in wn such as famous people countries cities and languages
we then discuss the evaluation of speech translation systems
in this section a means of learning the mappings between words and artificial representations of meanings is described
for example that a queen regnant could rlte music an letter missive i.e. a kind of correspondence is one periment
we have proposed example based machine 3y anslation ebmt to deal with these difliculties sumita92 a
it found NUM of the boundaries to within an accuracy of a single sentence and NUM to within an accuracy of two sentences
furthermore this algorithm is language independent except for the preprocessing stage which can be omitted with only a modest degradation in performance
the algorithm located NUM of the article boundaries precisely and NUM of the boundaries to within an accuracy of a single sentence
a large negative value indicates a low degree of correspondence and a small negative value or a positive value indicates a high degree of correspondence
he then uses the inter document distributions to make inferences about probabilities of the repeat occurrences of content words and phrases within a single document
where x is an individual word in the document and dx i is the distance between word x and its ith nearest neighbor
the talk user had available a set of on screen buttons with which to produce quick fire responses to what another person was saying
delivered by the speech recognizer contains the sentence actually uttered could you show me an early flight please but only in fourth position
type v from id p4 w2 p linking is specified using one of the available tei mechanisms
the kinds of repeated search required by lexicographers are more of a problem since the system was not designed for that purpose
because of the presence of this list the storage cost of adding a new attribute is linear in the size of the corpus
subdirectories with catalogue fragments can thus be used to represent both increasing detail of annotation and alternatives at a given level of annotation
it provides a modular architecture which does not require a central database thus allowing distributed software development and reuse of components
these editors can fit into a pipeline of lt nsl tools allowing hand correction or disambiguation of markup automatically added by previous tools
unlike tipster lt nsl is not built around a database so we can not take advantage of built in mechanisms for version control
creole a library of program and data resource wrappers that allow one to interface externally developed programs resources into the gate architecture
in contrast the sgml parser validates its dtd and hence provides some check that annotations are being used in their intended way
however casual social conversation with it s free ranging content and its dependence on speed of responding presents a considerable challenge
the algorithm starts with a tree bank to in to the cardinality of tuples to equals the number of different syntactic categories in to
tree bank the nwo NUM priority programme language and speech technology is a five year research program aiming at the development of advanced telephone based information systems
if we extract a subtree out of a tree we replace the semantics of the new leaf node with a unification variable of the same type
this annotation convention obviously assumes that the meaning representation of a surface constituent can in fact always be composed out of the meaning representations of its subconstituents
instead the parser yields parses containing information regarding syntax and semantic types and the actual semantic rules can be determined on the basis of that information
an update expression is a set of paths through the frame structure enhanced with pragmatic operators that have scope over a certain part of a path
the coverage of the parser was rather low NUM because of the sheer number of different semantic types and constructs in the trees
then we show how this method can be straightforwardly extended into a semantic analysis method if corpora are created in which the trees are enriched with semantic annotations
for the annotation task the annotation face written by bonnema offering all functionality needed for examining evaluating and editing syntactic and semantic analyses
nonetheless the broad distinction between conversations motivated primarily by social goals and those motivated primarily by transactional goals can be sustained
null in section NUM we elaborate on the methodology of our word similarity measurement
there were NUM correct resolutions distributed over NUM dds and NUM false positives
phrase construction or phrase storage is more likely to help users to achieve their conversational goals is the imprecision of much conversational content
a language with words azzz bzzz zzzy has the same entropy but allows much better prediction performance NUM saving
recognizing kitchen window as a unit for generation so it does not get split up and preceding it with a plausible determiner
for this first approach due to the above mentioned primacy of suffies over other affixes in the basque language and to simplify the problem prediction in basque is divided in two parts prediction of lemmas and prediction of suffixes
apart from the increase of the complexity a decrease of the keystroke savings may be expected because of the need of accepting at least two proposals for completing a word while at least only one proposal is required with predictors for non inflected languages
they depend not only on the subject which normally appears as absolutive or ergative cases but also on the direct complement if the sentence is transitive this complement has the absolutive case while the subject has the ergative case and on the indirect complement the dative case
as the acceptable suffixes for a noun can be about NUM as we have seen in the table NUM only the most probable n suffixes are offered NUM as can be seen the operational way is very similar to word prediction using tables of probabilities but there is some added complexity because the system and also the user has to distinguish between lemmas and suffixes
by associating the category with a relatively general node we can automatically classify a large number of words with a fair degree of reliability
the graph in figure NUM shows the keystroke savings achieved with the basic method compared with an enhanced method which takes some account of context
prediction is only useful when text input is very slow and difficult even someone using a head stick may not find any advantage in it
furthermore as word NUM lotzg time shows they are often properly decomposed into components
thus the algorithm has done an extremely good job of learning words and properly using them to segment the input
the actual formulas used in the tests presented in this paper are slightly more complicated than presented here
the lexicon that minimizes the combined description length of the lexicon and the input maximally compresses the input
word NUM causes crossing bracket violations and words NUM and NUM have internal structure that causes recall violations
in the case of the brown corpus word recall was NUM NUM and crossing brackets was NUM NUM
this sort of grammar offers significant advantages over context free grammars in that non independent rule expansions can be accounted for
so the motivation for including a word in the lexicon must be that it function differently from its parts
this assignment method does not even need to look at the training examples
entity np app org NUM org NUM e n a NUM this ultimately causes org NUM and org NUM to become co designating through the equality system and th e following fact appears in the inferential database
by the non revised metric we achieved a performance of p r NUM NUM on the training data with an overall drop of NUM NUM points ofp r between training and official test
tests in turn can be part of speech queries literal lexeme matches tests for the presence of neighboring phrases or the application of predicates that are evaluated by invoking a lisp procedure
if any end of sentence punctuation has no t been explained by the punctoker as part of a lexeme as in abbreviations it is taken to indicate a sentenc e boundary
in these runs we used phrase rules that had been learned for the enamex expressions only we still used the hand coded pre processors and phraser rules for recognizing timex and numex phrases
the inference system also supports equality reasoning by congruence closure and this equality machinery is in turn exploited to perform te specific processing i n particular acronym and alias merging
other trends in addition to this analysis of the single walkthrough message we opened up some to of the test data t o inspection and performed a rough trend estimation
note however that all but a few of the organizations that were found in both the training name list and the test data were found by alembic from first principles anyway
in the present st task for example succession events are not always fully fleshed out bu t depend for their complete interpretation on information provided earlier in the discourse
inference is generally a non deterministic search problem with no firm guarantee as to whether facts will be derived in the same chronological order as the sentences which underli e the facts
it is possible for an utterance to prefer either a value free vf or value loaded vl interpretation but not force it
the theme of u is represented by the preferred center c p u the most highly ranked element of c un
NUM strings bp k c s4 k c c a pull bp k c e x c now we just use the ordinary meanings of pull and strings to describe this situation
adjoining is a more complicated splicing operation where the first tree replaces the subtree of the second tree rooted at a node called the adjunction site that subtree is then substituted back into the first tree at a distinguished leaf called the foot node
for different languages spud s model may vary along a number of dimensions including the exact range of objects which roughly corresponding lexical items can describe and the default salience rankings both for typical properties and actions associated with objects and for the information states licensing idioms
in NUM below a sample from the lexicon is given
NUM requisite properties of underlying semantic theory different semantic theories make different commitments with respect to the completeness or definiteness required of an interpretation
in particular section NUM provides references to subsequent investigations of additional factors that control centering and examinations of its crossqinguistic applicability and empirical validity
thus NUM considers a case where there is a part part of a library lib suppose NUM witnesses that part has some type type NUM that part provides service service and NUM that part has location loc
the need to cope with unknown words will continue to grow as new words are coined and words associated with sub cultures leak into the main stream vocabulary
we expect that these three knowledge sources will greatly improve our parser s ability to process and cope with words that are not in the system lexicon
he suggests that his system can handle unknown words by simply assigning them all possible parts of speech without using any morphological analysis of the words
viegas et al NUM show that the use of lexical rules and morphological generation can greatly aid in the task of lexical acquisition
when new words are discovered they tend to be given specialized meanings that are related to the semantic domain limiting the system to that specific domain
the use of all possible parts of speech will cause an exponential increase in the number of parses for a sentence as the number of unknown words increases
for example all past tense regular verbs end in ed third person singular regular verbs end in s and so on
since the functional approach generates a larger amount of continue transitions we interpret this as a first rough indication that this approach provides for more efficient processing than its competitors
specific houses blood family figure NUM part of wn s semantic net for buildings
this is an average of only NUM NUM deletions per sentence or NUM NUM of the total parses as shown in figure i and table NUM
these codes may be composed of one or more characters
we have seen the verb noun homographs in the previous section
kg is replaced by kilos in 5kg or trois kg
in this case the longest match is abc
these are not the only languages in this last category
papers on the subject are rarely found in linguistics journals
the grapheme pattern is encoded as a simple text string
section NUM describes the stochastic pos tagging scheme and hierarchical tag setting
this paper proposes a mistake driven mixture method for learning a tag model
the following two steps abstract the human algorithm for incorporating exceptional connections
NUM construct temporary rules which seem to well generalize given data
percentage of phonemes is obviously higher than percentage of words
it depends also on the grammatical category of both words
first construct htree generates a basic tag context tree by calling construct btree
this is not a realistic assumption in part of speech tagging and other nl applications
remember that the input of the retrieval operation is the sorted generalized mrs mrsg of the input mrs mrs
this grammar has been developed at csli stanford and kindly be provided to the author
furthermore in case exact matching is requested only the application module is needed for processing the subgrammar
note that because of the alphabetic ordering the relative order of the elements of new input mrs is immaterial
in case of exact matching strategy the decision tree must be visited only once for a new input
the method is based on explanation based learning ebl which has already been successfully applied for parsing
this is simply achieved by selecting a supertype of a mrs element instead of the given specialized type
thus described the approach seems to facilitate only exact retrieval and matching of a new semantic input
however more complex type abstraction strategies are then needed which would be able to find appropriate supertypes automatically
extended application phase for the application module only the retrieval operation of the decision tree need be adapted
despite the number of papers on the topic the evaluation and comparison of existing segmentation algorithms is virtually impossible
and of course a further complication is that im in the verb is not negative while in the adjective it is this is apparently the reason for the non existence of the positive adjective but this reason is itself an accidental gap
to derive the semantics zone of an adjectival entry from that of the corresponding verbal entry one must first identify the case or thematic role such as agent theme beneficiary etc filled by the noun modified by the adjective in question
after all 4i ii are not re null ally ill formed and it is hard to imagine a more truly relative adjective than aeronautical related to aeronautics NUM i ii his approach to the problem was aeronautical
on the surface of it these adjectives seem to be the best candidates for a fully automatic lr especially if the beneficiary theme distinction can be taken care of on the basis of the animateness or inanimateness of the modified and this can be done
while a relatively small class of true scalars is more or less easily associated with certain property concepts there are many more process and object concepts and it is much easier to relate the meaning of a verb or a noun to one of those
kjellmer NUM and also hall NUM jespersen NUM marchand NUM abraham NUM meus NUM for a discussion of such adjectives in english even though much of the discussion sheds little light on the semantic and lexicographic issues in hand
nevertheless it often makes sense and this is a human judgment to construct such verbs out of the appropriate ontological concept with necessary constraints because this is still the easiest way to construct lexical entries for some adjectives NUM
otherwise the human cost of manual checking every verb entry before applying the rule to it would render each adjective entry obtained with the help of the lr more or at least no less expensive than if it were produced manually from scratch
in other words a typical able entry is derived from the lexical entry of the appropriate verb with the positive potential attitude added and in either the beneficiary or theme role depending on the animateness inanimateness of varl respectively 17i ii
the two versions of the independently prepared manual annotations of NUM articles were scored against each other using the scoring program in the normal key to response scoring mode
an analysis by the participating sites of their system s performance on the walkthrough article provides some insight into performance on aspects of the coreference task that were dominant in that article
for past muc evaluations the formal run had been conducted using the same scenario as the dry run and the task definition was released well before the dry run
in the case of org descriptor the results of the co evaluation seem to provide further evidence for the relative inadequacy of current techniques for relating entity descriptions with entity names
the results of the evaluation give clear evidence of the challenges that have been overcome and the ones that remain along dimensions of both breadth and depth in automated text analysis
marginally relevant event objects are marked in the answer key as being optional which means that a system is not penalized if it does not produce such an event object
the succession event object points down to the ib and out object which in turn points down to person template element objects that represent the persons involved in the succession event
one of the innovations of muc NUM was to formalize the general structure of event templates and all three scenarios defined in the course of muc NUM conformed to that general structure
it represents just one style of writing journalistic and has a basic basic toward financial news and a specific bias toward the topic of the scenario template task
the latest in a series of natural language processing system evaluations was concluded in october NUM and was the topic of the sixth message understanding conference muc NUM in november
in the formula of relative entropy there is a possibility that p2 e becomes zero
an example of the parse structures of two sentences in the corpus is shown graphically in figure NUM
pc1 pc and p NUM are estimated probabilities of cl c2 and c3 respectively
that this is so is demonstrated by the fact that 25c is true in NUM whereas 26c is not
in this section we describe two techniques which utilize local context information to calculate similarity between two labels
ranking of elements in cf un guides determination of NUM shifting of the center does not in itself mark a discourse segment boundary
we also note that the silly thing conveys additional information roughly the speaker s attitude toward the bear or tugboat cf
NUM rule NUM ignores certain complications that may arise if one of the forward looking centers of un l is realized by a deictic pronoun
a can be ignored for our purpose
figure NUM architecture of proverb attention
proverb works in a fully automatic way
centering rule NUM sequences of continuation are preferred over sequences of retaining and sequences of retaining are to be preferred over sequences of shifting
figure NUM abstracted proof about unit element of subgroups
closed spaces are attentional spaces without open goals
several similar attempts can be found in previous work
the two kinds of planning operators are treated accordingly
remember that b n i is the probability that nfk is in the correct parse given as always the model and the string
otherwise they are considered as structurally distant
hence the first step is to identify applicable lexical entries these items must correctly describe some entity they must anchor trees that can substitute or adjoin into a node that describes the entity and they must contribute toward satisfying current goals
this was especially the case with the words that were guessed as noun adjective nn jj but in fact act only as one of them as do for example many hyphenated words
to compute the similarity of a pair of labels in step NUM we propose two types of techniques called distributional analysis and hierarchical bayesian cbtstering as shown in section NUM
the cascading guesser outperformed the other two guessers in general and most importantly in the non proper noun category where it had an advantage of NUM NUM over brill s guesser and about NUM NUM over xerox s guesser
quite often obvious proper nouns as for instance summerdale russia or rochester were marked as common nouns nn and sometimes lower cased common nouns such as business or church were marked as proper nouns
however some domain specific words or infrequently used morphological variants of general purpose words can be missing from the lexicon and thus their pos classes should be guessed by the system and only then sent to the disambiguation module
in our approach we do not require large amounts of annotated text but employ fully automatic statistical learning using a pre existing general purpose lexicon mapped to a particular tag set and word frequency distribution collected from a raw corpus
we also did not include the foreign word category fw in the set of tags to guess but this did not do too much harm because these words were very infrequent in the texts
from this viewpoint we apply this measure as a criterion for determining the termination of the merging process which will be given in the next section
using a bracketed corpus the grammar learning task is reduced to the problem of how to determine the nonterminal label of each bracket in the corpus
distributional analysis is a statistical method originally proposed by harris harbl to uncover regularities in the distributional relations among the features of speech
a pair of labels is considered to be identical when they are distributionaliy similar i.e. the divergence of their probability distributions over environments is low
for words that the guessing components failed to guess we applied the standard method of classifying them as common nouns nn if they were not capitalized inside a sentence and proper nouns nnp otherwise
where n a is the occurrence frequency of o ntag8 is the number of terminal categories and a is a interpolation coefficient
the similarity between two bracket groups labels g and gv can be defined by sim g gv
using a bracketed corpus the learning task is reduced to the problem of how to determine the nonterminal label of each bracket in the corpus
one nice feature of multiple pass parsing is that under special circumstances it is an admissible search technique meaning that we are guaranteed to find the best solution with it
litman and allen NUM chu carroll and carberry NUM or incrementally adding to the current plan with each accepted proposal e.g.
however they have not addressed how agents collaborate in building a plan only how agents collaborate while executing a plan
they use this formalism to explain how such elements of communication as confirmations arise when agents are engaging in a joint action
once an utterance has been acknowledged it will reside in mutual belief as a proposal of the person who initiated it
litman and allen represent the state of the discourse after the second utterance as a clarification of the passenger s take train trip plan
although the participants are not collaborating in making a referring expression the dialog will serve to illustrate our point
however we have extended their model by incorporating even the generation of the components of the description into our planning model
so the system adds the belief that it is mutually believed that the new expanded plan replaces the old referring expression
the expansion it chooses includes a relative modifier see figure NUM that describes the object as being in the corner
compromise soundness soundness in this context should be understood as the property that all parse trees in the parse forest grammar are valid parse trees
it is possible to compromise here in such a way that the parser is guaranteed to terminate but sometimes misses a few parse trees
there are two main contributions of the work we will discuss in this paper
NUM example of deliberating over a meeting time purpose of making our argument easy to follow
we will present our extension to this approach along with its implementation in our plan based discourse processor
in a more refined version of the formalism we would associate a single finite valued feature structure with each node NUM it is a matter of further research to determine to what extent sics and sacs can be stated globally for a grammar rather than being attached structures
when a d tree a is subserted into another d tree NUM a component of a is substituted at a frontier nonterminal node a substitution node of NUM and all components of a that are above the substituted component are inserted into d edges above the substituted node or placed above the root node
the results were consistent with what would have been expected given the results on speech act recognition
and when the second response is processed it can be attached to the second suggestion
we subsert the resulting structure into the seems clause by substituting its maximal projection node labeled vp fin at the vp fin frontier node of seems and by inserting the subject into the d edge of the seems tree
of course it is possible to experiment with different ways of taking the context free skeleton including as much information as possible useful
the idea is to limit the possibilities in the beginning to those that are most likely and broaden the search space later if the first methods fail
the first step produces a context tree by using tile basic tag set
intelligent example selection for supervised learning is an important issue of machine learning in its own right
this robustness is particularly important for processing spoken language since spoken language can contain constructions including interjections pauses corrections repetitions false starts semantically or syntactically incorrect constructions etc
other second best networks with additional local representations tbr abstract semantic category knowledge could perform better on the training set but failed to generalize on the test set and only reached NUM
in this paper we describe our subsequent work on bridging dds which involve more complex forms of commonsense reasoning
table NUM lists the analogous statistics for the wall street journal corpus
these examples clearly demonstrate the utility of wsd in practical nlp applications
another possibility is to tag during the learning stage alternations with one or several morphosyntactic labels expressing morphotactical restrictions this would restrict the domain of an alternation to a certain class of words and accordingly reduce the expansion of the analog set
this termination criterion is very restrictive in comparison to the one implemented in the depth first strategy since it makes it impossible to pronounce very long derivatives for which a significant number of alternations need to be applied before an analog is found
figure NUM context trees for baab and baabab
this has allowed us to precisely define and identify the content of lexical neighborhoods achieves a very high precision without resorting to pre aligned data and detects automaticmly those words that are potentially the most difficult to pronounce especially foreign words
this basic search strategy which amounts to the exploration of a derivation tree is extremely ressource consuming every expension stage typically adds about a hundred of new virtual analogs and is in theory not guaranted to terminate
if we keep the words that could not be pronounced at all about NUM of the test set apart fi oln the evaluation the per word and per phoneme precision improve considerably reaching respectively NUM and NUM
indeed if this neighborhood can be quite large typically NUM analogs for short words the number of analogs used in a pronunciation averages at about NUM NUM which proves that our definition of a lexical ncighbourhood is sufficiently restrictive
using this model it becomes possible to predict correctly the outcome in the phonological domain of a given derivation in the orthographic domain including patterns of vocalic alternations which are notoriously difficult to model using a rule based approach
now we go back to the hierarchical tag context tree construction
both figures indicate that wsd accuracy continues to climb as the number of training examples increases
the first column shows the linear text index of each utterance
the structure above it must be adjusted in order to maintain binary branching
our novel generation algorithm has polynomial complexity o n4
proponents of the shake and bake approach have employed various techniques to improve generation efficiency
a tncb records dominance information from derivations and is amenable to incremental updates
tncb nillvalue x tncb x tncb value sign i inconsistent i undetermined
the second and third items of the tncb triple are the child tncbs
when re evaluated they may remain ill formed or some may now become well formed
let us further consider the contrived worst case starting point provided in figure NUM
the crucim data structure that it employs is the tncb
concretely it is either nil or a triple
for our current application the system indexes names and s t terms but for other applications we can customize the system to index different types of names and terms
the web browser based user interface will work in any web browser supporting html NUM NUM on any platform which the web browser supports and this ensures a large user base
figure NUM shows the main browse screen where the user can browse the top NUM or NUM names of people entities locations and s t terms
for example a personal name dole in katakana was translated into a common noun doll as the two have the same katakana string in japanese
one of the biggest advantages of introducing ie technology into information access systems is the ability to create rich structured data which can be analyzed for buried information
it can disambiguate query terms to increase precision expand query terms automatically using aliases to increase recall and improve translation accuracy significantly by finding and disambiguating names accurately
as described in section NUM NUM the indexing module not only identifies names of people entities and locations but also disambiguates types among themselves and between names and non names
figure NUM positional difference signals showing similarity between governor in english and chinese
then the point i j is noise and is discarded
we can see that these two vectors share five segments in common
the average accuracy for all evaluators for both sets is NUM NUM
the interesting fact here is council is also matched to j
however there is still many pairs of words left to be compared
the two vectors have different dimensions because they occur with different frequencies
figure NUM dynamic time warping path for governor in english and chinese
its position vector is NUM NUM NUM NUM
the need to have tipster style document management for annotations and attributes became apparent early in the user task analysis
the dictionaries are easily accessible and entries can be retrieved by simply clicking on the dictionary button corresponding to the desired dictionary
this feature is shown in highlighted text in an oleada document is similar to highlighting text with a marker on paper
language instructors search through large amounts of text to find authentic examples of language use in particular contexts
the contents of a form is intended to be used to automatically generate specialized databases for information analysts
the oleada text editor makes it possible for users to display edit and annotate multilingual text
there are many problems associated with developing new technology to help with tasks previously solved with old technology
the segmentation algorithms presented in the next two sections were developed by examining only a training set of narratives
first we look for a previous noun group with the same head noun
the third type of coreference resolution occurs after complex noun groups are recognized
the microelectronics domain of muc NUM and the labor negotiations were of this character
our experience with this aspect of the fastus system has been very encouraging
our current approach is to be conservative and to experiment with various options
this approach is more verb driven and the patterns tend to be tighter
he succeeds john h costelloeer who resigned in marchd t
event adj is matched by temporal locative epistemic and other adverbial adjuncts
with this set of metarules defining the necessary patterns becomes very easy
a company can have another company as a possessive as in information
the following query is allowed by our system
the final result then is list list list append c
definition hiding feature if t is a constrained or hiding type then f is a hiding feature on t iff approp t f is a constrained or hiding type
a further problem of the encoding is that the value of an appropriate feature which is not mentioned in any type definition may nonetheless be implicitly constrained since the type of its value is constrained
our compilation procedure will adhere to this interpretation
figure NUM the signature for the append c example
some choices of partition and quantifier must be excluded
the following dependency function is constructed
consider the following dependency function and associated partition
results are presented both for a fully automatic version of the system table i and for a version with a simulated abort button table NUM
fs if they re not already there
if he argues red birds red houses and red books mean all different kinds of redness and they do how can one derive the meaning of an adj n combination from the meaning of the adjective and the noun
this is the subject of a future paper
defining an interlingua even if it is possible to do so for an increasing number n of languages really only addresses the first task
this subset stands in the realtion most to the entire set of representatives lcb rl r2 r3 r4 rcb and is the focus set for r in sentence NUM
an interesting future task would be to investigate the significance of various kinds of written language translation errors in terms of reducing comprehensibility of the spoken output
the output of each call is a set of css that represent the intermediate resuits exchanged between each call and on which both modules operate in turn
this metaplan applies because russ had misunderstood a prior utterance by mother a reconstruction of the discourse is possible and within the reconstructed discourse an informref is expected as a reply to the misunderstood askref
the method reported in NUM i is a finding out the n best segmentation candidates explicitly in terms of word frequency and length b pos tagging each of the n best segmentation candidates resulting in the n best tag sequences accordingly and c using a score with weighted contributions from a and b to select the best solution
however in the context of an a priori lexical knowledge of the verb the alternations cease to exist in most cases
next the m structure schema of the main verb are operated on with the search operator in the default phrasal order for the language
the word segmentation precision is ranging from NUM NUM to NUM NUM pos tagging precision from NUM NUM to NUM NUM and the recall and precision for unknown words are from NUM NUM to NUM NUM and from NUM NUM to NUM NUM respectively
at least two explanations are possible for the fragment o p in NUM resulting in two different segmentations correct segmentation for NUM a this classifier institute very famous this institute is very famous
the focus of the research should be no longer solely on the pure or new formal algorithms no matter what it will be instead what is urgently required is on two issues i.e. NUM what sorts of and how many knowledges are needed and NUM how these various konwledges can be represented extracted and cooperatively mastered in a system
only clear chinesesurname touching logically ill formed sentence pos tagging for chinese is similar to that of english except that an english tagger only need to tag one word sequence for an input sentence but in the case of chinese to get a correct tag sequence for a sentence a chinese tagger may be requested to tag more than one word sequences simultaneously due to the presence of segmentation ambiguities
at the same time if russ had revised any of his real beliefs on the basis of the first turn he might now reconsider those revisions however our theory does not account for this
mcroy and hirst the repair of speech act misunderstandings if we assume that mother produced the first turn as an askif she might also hear t2 as an intentional askref but for a reason different than russ would
thus they can expect each other to be consistent in the attitudes that they express and to respond to each act with its conventional reply unless they have and can provide a valid reason not to
mcroy and hirst the repair of speech act misunderstandings priority constraints require that no ground instance of d e ai can be in d i if its negation is explainable with defaults usable from any a j j i
NUM fact lexpectation do sl askref sl NUM d knowref s2 d do s2 informref s2 sl d
for convenience we also define a subjunctive form of expectation to reason about expectations that would arise as a result of future actions e.g. plan adoption or that must be considered when evaluating a potential repair
we treat the points of syntactic encoding of noun phrases as forward references that are temporarily maintained in a symbol table for later binding
traum and hinkelman suggest that such violations should be used to trigger a repair but admit that except when a repair has been requested explicitly the model itself says nothing about when a repair should be uttered p
w is the number of occurrences of the word like x n is the number of nearest neighbors to include in the calculation and depends on the overall frequency of the word in the text
they show that word rates vary from genre to genre topic to topic author to author document to document section to sec tion paragraph to paragraph
NUM NUM about binary and n ary n NUM
but for the most frequently occurring words the number of nearest neighbors is ten figure NUM shows the main features of the performance of this significance assignment algorithm when tested on a sample text
although the antecedent clause for susan does is mary likes him there is a sloppy reading in which bill told a boy that susan likes bill
for instance in the structure above NUM denotes third person s denotes singular number nora and acc denote nominative and accusative case respectively
however whether a clause can be extraposed is independent of its adjunct complement status within the np
the same also holds for other kinds of extraposable constituents such as vps and pps
the value of the dom attribute thus consists of a list of domain objects
therefore there is no longer a need for the unioned feature for extraposition
postnominal genitives can not be extraposed out of nps despite their final occurrence
j up figure NUM extraposition via partial compaction
if the np combines with a verbal head it may be partially compacted
the vertical bars signify the actual article boundaries
more than one such test can be appended to a rule
with the shorter trec NUM topics indicates that other types of noise control may be needed for short topics
every sense of words in articles for extracting key paragraphs is automatically disambiguated in advance
this shows that our context dependency model is applicable for different size of the samples
a similar measure a head i.e. the precision equals recall reported as NUM NUM
therefore there is a set of rules for each function
the wh clause itself may function as a subject object etc
the toy grammar containing NUM rules is presented in figure NUM
safecard services inc and jostens inc show title names
zij in NUM is the frequency of word i in the domain j
the selected domain names and the number of articles are shown in table NUM
therefore the deviation value of o in the paragraph is small
wall street journal consists of many articles and each article has a title name
wall street journal although the test set was small NUM articles
the system is taking a long time to respond
NUM please remember to start end utterances with verbie over
NUM please restrict your utterances to one sentence
NUM please focus on interaction with the computer
these utterances may have any of several meanings
the idea for each of these is similar
the voice output system will not be discussed here
in the example this is achieved by instantiation with different atoms but an inequality constraint prolog i s dif would serve the same purpose
it is very likely that the most efficient commercial prolog systems which provide a basis for the implementation of nlp systems will conform to the proposed iso standard
for instance the cooccurfence heuristics have been applied quite indiscriminately even in low frequency conditions
in dgile window size NUM proved the most suitable whereas in lppl whole definitions were used
as there is only one hypernym sense candidate the hyponym sense is attached to it
this link allows us to get two possible semantic fields for vin noun food
the order in which the heuristics are applied has no relevance at
from left to right association ratio and number of occurrences
unlike prolog the concrete syntax of profit allows to write down cyclic terms by making use of conjunction x x
the member relation can be defined with the following clauses which correspond very closely to the natural language statement of the member relation given as comments
cyclic terms constitute no longer a theoretical or practical problem in logic programming and almost all modern prolog implementations can perform their unification although they ca n t print them out
this makes it possible to provide a notation in which the sort name can be omitted since it can be inferred from the use of a feature that is appropriate for that sort
profit provides a mechanism to search for paths to features automatically provided that the sortal restrictions for the feature values are strong enough to ensure that there is a unique minimal path
profit is not a grammar formalism but rather aims to extend current and future formalisms and processing models in the logic grammar tradition with the expressive power of sorted feature terms
this notion of appropriateness is desirable for structuring linguistic knowledge as it prevents the ad hoc introduction of features and requires a careful design of the sort and feature hierarchy
NUM a program file a prolog program that con null tains the clauses with all profit terms compiled into their pro og term representation
variants of the composition rule were proposed in order to deal with non peripheral extraction bar hillel who used a slightly problematic double slash notation for functions of functions
thanks are due to the members of both the itahashi laboratory at the university of tsukuba and the nakagawa laboratory at the toyohashi university of technology for their help and criticism at various stages of this research
this relates to there being no spurious ambiguity each choice of transition has semantic consequences each choice affects whether a particular part of the semantics is to be modified or not
this is slightly different from the standard notion of soundness and completeness of a parser where the parser accepts the same strings as the grammar and assigns them the same syntax trees
13this result should however be treated with some caution in this implementation there was no attempt to perform any packing of different possible transitions and the algorithm has exponential complexity
in processing a sentence using a lexicalised formalism we do not have to look at the grammar as a whole but only at the grammatical information indexed by each of the words
constituency so that an initial fragment of a sentence such as john likes can be treated as a constituent and hence be assigned a type and a semantics
here this will be made particularly explicit with the parser described in terms of just two rules which take a state a new word and create a new state NUM
although individual examples might be possible to rule out using appropriate features it is difficult to see how to do this in general whilst retaining a calculus suitable for incremental interpretation
NUM NUM decision trees c transformation lists
we are currently experimenting with this idea
below is a transformation list for performing this classification
he will not race verb the car
discourse particles and routine formulas in spoken language translation
approaches of this type aim to improve the statis tical significance of probability estimations taclde the data sparseness problems and reduce the number of the model parameters
the main difference in richness is that each node is labeled twice
this error rate is much lower than the one we get using the hidden markov model NUM NUM
the process of combining the three steps described above eventually leads to more errors than running the constraint based tagger alone
the accuracy of the statistical method is reasonably good comparable to taggers for english
our training corpus was rather small because the training had to be repeated frequently
the tagger seems to prefer the noun reading between a singular noun and a preposition
this text also seems to be generally more difficult to parse than the first one
for a human or a constraint based tagger this is an easy task for a statistical tagger it is not
each transducer may remove or in principle it may also change one or more readings of the words
when it seemed that the results could not be further improved we tested the tagger on a new corpus
two anonymous reviewers provided very useful comments we regret not being able to do justice to all their suggestions
rule or during the rewrite phase in a greedy algorithm this initial graph is updated and tested for connectivity
n is in order to delay their combination with other constituents until rood tiers e.g.
l al le NUM effect of pruning times in secs
assumption NUM all le ical signs must be connecled to each other
in one ew ry inactive edge constructed was added to the chart
as an example the outer domain of np as derived from the above grammar is
the technique relies on a connectivity constraint imposed on the semantic relationships expressed in the input bag
secondly a new node corresponding to tit new wns is added to the graph
recognition of alternative ways of identifying an entit y constitutes a large portion of the coreference task and another critical portion of the template element task an d has been shown to represent only a modest challenge when the referents are names or pronouns
human performance was measured in terms of variability between the outputs produced b y the two nrad and saic evaluators for NUM of the articles in the test set the same NUM articles that were used fo r ne and co testing
leaving aside the fact that descriptors are common noun phrases which makes them less obvious candidate s for extraction than proper noun phrases would be what reasons can we find to account for the relatively lo w performance on the org descriptor slot
one indication of immaturity of the task definition as well as an indication of the amount of genuine textual ambiguity is the fact that over te n percent of the linkages in the answer key were marked as optional
this slot has a limited number of fill options and the right answer is almost alway s either in or out depending on whether the person involved is assuming a post in or vacating a post out
whereas the text filter row in the score report shows the system s ability to do text filtering documen t detection the all objects row and the individual slot rows show the system s ability to do information extraction
ne results on walkthrough article in the answer key for the walkthrough article see appendix a to this proceedings there are NUM enamex tags including a few optional ones six timex tags and six numex tags
despite this flexibility in th e expected contents of the response the systems nonetheless had to implicitly recognize the full np since to b e considered coreferential the head and its modifiers all had to be consistent with another markable
there are cases of organization names misidentified as person names there is a case of a location name misidentified as an organization name and there are cases of nonrelevant entity type s publications products indefinite references etc
the general pattern is for systems to have done better on the text slot than on the type slot for enamex tags and for systems t o have done better on the type slot than on the text slot for numex and timex tags
interval indicates the frequency of status information
no special operations are provided in the current architecture for manipulating constituents
type string spans sequence of span
the processes of derivation proceed synchronously in the two devices by applying the paired grammar rules only to linked nonterminals introduced previously in the derivation
other levels of annotation will be optional
the regular expression operators used in the paper are zero or more kleene star one or more kleene plus not complement contains ignore i or union t and intersection minus relative complement x crossproduct o composition simple replace
such decompositions can be represented by annotations on nested sets of spans
a simple illustration would be the decomposition of a sentence into tokens
to d a tli tv aa l rl although the input string dannvaan contains many other instances of the noun phrase pattern n an nn etc the left to right and longest match constraints pick out just the two maximal ones
in the past this has been attempted with a generalpurpose thesaurus or with a keyword list or topic navigation outline
since all the information is gathered from the text collection at hand the term relations are relevant to the text
the text retrieval conference trec and the message understanding conferences muc evaluated and baselined the technology developments
there is a relatively small number of traditional chinese surnames but given names are essentially unrestricted combinations of two character sequences
this paper describes program and technical issues identified dunng the joint government contractor effort and shares lessons learned in these two areas
the deployments were called demonstration systems because their success in daily use would demonstrate the capabilities of the technologies to endusers
for each query we had a chinese language expert examine and judge the ten documents ranked most highly by chinese inquery
our experience with both japanese and chinese has shown that character based indexing is the most flexible approach to take for chinese
although we have developed a chinese and foreign name recognizer it was not used in the segmentation for this experiment
as the precision figures for the thirty queries in table NUM show even the unsegmented character based queries give respectable results
like the other lexicalized frameworks the dependency approach does not produce spurious grammars and this facility is of a practical interest especially in writing realistic grammars
no additional classification of vowel tokens is needed in the following cases i vowel tokens not in the iuu set ii vowel tokens that appear between any consonant sequences iii stressed vowel tokens in the i u u set that have as a left neighbor a consonant sequence with an r suffix iv vowel tokens that simultaneously have stress and diaeresis marks
consequently woz studies can provide an indication of the types of adaptations that humans will make in human computer interaction
the previous goal is converted to user learn that the light is on when the switch is up
in addition all candidates having as first or second token a c always split e.g. NUM c o ilos immaterial 7rpo c rc6oecrt1 pro ip6oesi prerequistic aaopo c aaovpv la0roi alurvia glass smuggling
the NUM missing wires were selected from the NUM missing wires used during the first five problems of the session
obviously ppfo fk ppvo v and pvo v NUM d pfo fk according to NUM the elements of their set difference pvov v pfofl fk are all impermissible
the actual hyphenation phase follows where the hyphenator traverses the token sequence identifies all ordered sequences of type a ivowel token i iconsonant token i ivowel token i and b ivowel token i ivowel token i and applies the corresponding hyphenation rules
for all dialogues initial interrater agreement on both speaker and global perspective of the current subdialogue was NUM NUM
of the NUM dialogues not completed NUM were terminated prematurely due to excessive time being spent on the dialogue
it is working and the basic nature of repairs but will need some assistance with diagnosing specific problems
this decision can be made in the context of the target language utterance
examples by the way anyway davon abgesehen wie auch immer
NUM as section NUM indicated there are a large number of lexical and grammatical forms in which such procedural relations are typically expressed each used in a particular functional context
rather the inquiries are answered manually allowing us to focus on determining the appropriate set of inquiries and the precise lexical and grammatical consequences of the responses of these inquiries
this input is implemented as a set of responses to the inquiries made by the imagene system network pursuant to determining the appropriate path to be taken through the imagene system network
inserting nodes into the text structure iterative insert insert copy imagene starts with an empty text structure and uses these statements to insert action nodes as appropriate
the concerns of the imagene project are similar except that both the text type instructional text and the linguistic phenomenon expressing rhetorical relations are much more focused
the imagene project can be seen as an extension of their work computational linguistics volume NUM number NUM that employs such a study to help manage diversity of forms of expression
this section will now make some general observations concerning rst present an example rst analysis of the remove phone text and conclude with definitions of the relations used in the study
imagene which produces five linkers and nine forms produces a match for NUM of the precondition expressions in the training set and NUM in the testing set
this paper presents the results of an empirical investigation of temporal reference resolution in scheduling dialogs
daiwa as an alias for daiwabank
first we have encountered what we call chicken and egg problems
nametag has several unique features beside being able to handle multiple languages
for met we used nametag in its japanese and spanish configurations
the model is geared toward allowing the most recent temporal unit to be an appropriate antecedent
the second big challenge is dealing with japanese aliases which are more complex than english aliases
we assess the challenges the data present to our model when only this task is attempted
finally nametag provides a gui based multilingual development environment which facilitates rapid development of patterns
this probability is equal to the number of occurrences of a subtree t divided by the total number of occurrences of subtrees t with the same root node label as t
besides that we observe higher accuracy and higher coverage due to a new method of organizing the information in the tree bank before it is used for building the actual parser
it may be noted that the rather oblique description of the semantics of the higher nodes in the tree would easily lead to mistakes if annotation would be carried out completely manually
for every meaningful non lexical node a for null mula schema is specified which indicates how its meaning representation may be put together out of the formulas assigned to its daughter nodes
an interpretation of a string is a formula which is provably equivalent to the semantic annotation of the top node of a parse of this string
these articles were retrieved using the keyword press conference
correspondingly when the composition operation substitutes a subtree at this node this unification variable is unified with the semantic formula on the substituting tree
the probability of a parse is the probability that any of its derivations occurs this is the sum of the probabilities of all its derivations
consequently the spanish system needed to perform deeper analysis of the texts to achieve comparable results
semantic determinacy is very attractive from a computational point of view if our processed tree bank has semantic determinacy we do not need to involve the semantic rules in the parsing process
apparently for this domain fragments of depth NUM are too large and deteriorate probability estimations NUM the results also confirm our earlier findings that semantic parsing is robust
hindle and rooths NUM show evident limitations in coverage and efficacy to deal with complex forms
in table NUM the values in the matrix cells are based on comparisons between the dialogue and scenario key avms
determining correlations requires a suite of metrics that are widely used and testing whether correlations hold across multiple dialogue applications
in this case on the basis of the model in figure NUM us is treated as the predicted factor
intuitively cost measures should be calculated on the basis of any user or agent dialogue behaviors that should be minimized
and the combination of n and rep account for NUM of the variance in us the external validation criterion
a second limitation is that various metrics may be highly correlated with one another and provide redundant information on performance
section NUM NUM describes paradise s task representation which is needed to calculate the task based success measure described in section NUM NUM
figure NUM proof tree as discourse model
the efficiency measures arise from the list of objective performance measures used in previous work as described above
kiyono kiy94b kiy94a combined symbolic and statistical approaches to extract useful grammar rules from a partially bracketed corpus
in our experiments the category of unknown proper nouns had a larger share NUM than we expect in real life because all the capitalized words with frequency less than NUM were taken out of the lexicon
basis probability for complete link outside probability is as follow
however the definition of p2 NUM nl v n2 is somewhat different it makes use of both the subject and object relations implicit in the tuple
in the current setting this approach involves learning the classes of nouns occurring unambiguously as subject object of a verb in sample text and using the classes thus obtained to disambiguate ambiguous constructs
a generator like this could be part of an electronic shopping system where the system provides information and sales talk
if ni is masculine and the nc headed by ni is unambiguously accusative then nl v n2 g j is a training tuple agreement rule
the result is a semantically annotated corpus where lexical phenomena can be studied with a reduced ambiguity
further both nps agree in number with the verb and since in german any major constituent may be fronted in a verb second clause both nps may be the subject object of the verb
for the remaining ambiguous words the local disambiguation model is applied by NUM
NUM eine hohe inflationsrate erwartet der 0konom
for the intrinsic difficulty of deciding the proper domain classes for verbs we designed two tests
there are many other mps dealing with these verbs since there is a great deal more to be said about them but we do not need this extra detail here and hence we will omit it
NUM der tennisspieler trainiert das ganze jahr
NUM notice that there is always a functional dependency of individuals denoted by r upon individuals denoted by f
the accuracy for p2 and ps exceeds NUM
the collocator or multi word term extraction module finds in the corpus significant co occurrence of lexical items words and phrases which constitute terminology
NUM eine altersgrenze nennt das gesetz nicht
formula NUM similarly expresses the restriction that a dotted rule of the form z may be followed only by nothing or by a dotted rule that is not of the form NUM
where t is the set of terminal symbols v is the set of nonterminals mx is the number of productions for nonterminal x and nx m is the number of symbols on the right hand side of the ruth production for x
finally combine the results from each subgrammar by starting with the approximation for the start symbol s and substituting the approximations from the other subgrammars in an order consistent with the partial ordering that is induced by NUM on the subgrammars
the new algorithm does not share the problem of pereira and wright s algorithm that certain right linear grammars give an intermediate automaton of exponential size and it was possible to calculate a useful approximation fairly rapidly in the case of the NUM rule grammar in the previous section
atc b iff b appears on the right hand side of a production for a then the relation NUM a NUM NUM the reflexive transitive closure of NUM intersected with its inverse is an equivalence relation
this is because these restrictions apply locally the state that the automaton is in after reading a dotted rule is a function of that dotted rule when restrictions NUM NUM are applied the final automaton may have size exponential in the size of the input grammar
for text NUM three and one speaker completely agree with trp and tr3 respectively
we selected five texts generated by our system for the test
what we have to do for each generation system is simply to insert the corresponding generation rule
the second constraint determines whether an anaphor occurs in a position violating syntactic constraints on zero anaphors
we have developed a new representation that neatly captures the domain characteristics and in our experience greatly improves the coverage and accuracy of our bracketer
the excluded category function the excluded category function is denoted as a b that means a constituent labeled a which moreover can not be labeled as b
there are two usual routes either NUM keep the context free basis but introduce finergrained categories or NUM move to context sensitive grammars
a strategy we are pursuing is the use of automatic methods to aid in the acquisition of such resources NUM NUM NUM
at the same time however we will show that the natural format of the rules has greatly facilitated the writing of robust large grammars
the number and type of context conditions used in the grammar and the kind of nonterminal functions will greatly affect the efficiency of parsing
but because can be labeled as either vn or vnn it does not match vn vnn and therefore the rule can not be applied to the ditransitive phrase
we employ probabilistic grammars so that it is possible to choose the viterbi most probable parse but probabilities alone do not compensate sufficiently for the inadequacy of structural constraints
because we do n t give a single parse tree if there is for a sentence at the current stage we uniformly weight the precision over all the parse trees for the sentence
again the average matching rates of tr3 are sightly lower than tr2 for these two texts
this offers an obvious advantage for mt results
where appropriate easyenglish makes suggestions for rephrasings
totally there are currently about forty checks
okay if subject of signing on is the user as a baboon who grew up wild in the jungle i realized that wiki had special nutritional needs
on the other hand in many applications it may not be necessary for writers to restrict themselves to a very limited subset of english in order to write easily understandable and translatable documents
this is informal evidence that our goal of easing the task of translation has been accomplished however we still need to make formal studies to be able to quantify the exact savings
easyenglish is based on a full parse by english slot grammar this makes it possible to produce a higher degree of accuracy in error messages as well as handle a large variety of texts
is the subject of s olen 2he groveton police f in contrast to this group of syntactic problems a check for subject verb agreement is much harder to implement reliably
we consider a random process that produces an output value y a member of a finite set y
a weighted co occurrence betweerf morphemes or lexemes can be viewed as an association between these itemsi so the set of co occurrences which co oc discovers can be viewed as an associative or semantic network
many heuristics look for particular semantic relations linking the two input words to a common word or synset e.g. a church and a home are both buildings
the tag np is uninformative about its case and therefore the tagger has to distinguish sb subject and functions depending on the category of the mother node
if the predicate is a noun or the referent refers to an event we assign the tag thing
there are three possible results of the analysis a the analysis is successful and no syntactic inconsistencies were found at this stage of processing it is too early to use the term syntactic error because in our terminology the term error is reserved for something what is being announced to the user after the evaluation in this case the sentence is considered to be correct and no message is issued
typical examples are bad choices of determiners or prepositions
in the mcca dictionary of i NUM NUM words s the average number of words in a category is NUM with a range from i to about NUM
the examples given then may be related to the phenomenon of synecdoche according to the oed synecdoche is a rhetorical figure by which a more comprehensive term is used for a less comprehensive term and vice versa a whole for a part or a part for a whole genus for species or species for genus
if the mcca categories had richer definitions based on additional lexical semantic information the analysis could be performed based on less subjective and more rigorously defined principles
NUM estimate the parameters of a maximumlikelihood word translation model
in terms of the relationship between the size of training corpus and domain dependency we will compare the performance of the grammar acquired from NUM samples of the same domain we will call it baseline grammar and that of the other grammars
table NUM parts of speech sorted by mean semantic entropy
null to what they mean by the concept to what may happen next NUM NUM NUM NUM whpp of sbar of what it is all about of what he had to show his country
NUM repeat from step NUM until the lexicon converges
semantic entropy is a measure of semantic mbiguity and uninformativehess
speakera1 sym okay uh e s i prp think vbp the dt first jj thing nn they prp said vbd n s i prp have vbp written vbn this dt down rp e s lcb c so rb rcb it prp would md n s is vbz it prp p xx do vbp you prp think vb it prp s bes possible jj to to have vb honesty nn in in government nn or cc an dt honest jj government nn NUM
though the results for the linguistically segmented test set NUM are significantly better than the corresponding matched case for the acoustic segmentations NUM we can not conclusively state that this is due to better segmentation since we have not controlled for the length of the different segments
lcb c and rcb he s pretty okay ex NUM b lcb c and then rcb i painted lcb f uh rcb about eight different lcb f uh rcb colors example NUM is of so as a coordinating conjunction
first in conversational speech where there is a less clear notion of sentence than in written text does segmenting the text into linguistically or semantically based units contribute to a better language model than merely segmenting based on broad acoustic information such as pauses
the rule of the thumb to be followed is split sentences whenever possible except when the two sentences if split are grammatically incorrect for example the second sentence in the spilt does not have a subject since it is in the earlier sentence
the differences between the two forms of segmentations can be observed with the example given below1 acoustic segmentations i m not sure how many active volcanoes there are now and and what the amount of material that they do s uh s put into the atmosphere s
in other cases the other participant in the conversation interrupts the speaker and the speaker never finishes the sentence in contrast with cases such as example NUM above where the first speaker finishes the sentence in the next turn after or during the interruption
there are some other terms such as so and actually which can also serve as discourse markers as in example NUM however so can also be a coordinating conjunction or a subordinating conjunction as discussed in ss2 NUM NUM
table NUM shows the breakdown of the data into the four divisions before the pivot before after the pivot after complete sentences with no pivot npc incomplete sentences with no pivot npi
first mean semantic entropy was compared across parts of speech
note that this is not exactly a conditional probability because a single word occurrence can belong to more than one context window
figure NUM an example of syncronous points
noun NUM verb NUM adjective NUM
the extended algorithm calculates the probability of the
the measurement method is well defined for all words including function words and even for punctuation
the workbench we are aiming at integrates computational tools and a user interface to support phases of data extraction data analysis and hypotheses refinement
coverage the algorithm of section NUM for any set c i does not allow a full coverage of the nouns in the domain
significantly our method selects a limited number of categories NUM NUM depending upon the learning corpus and the model parameters out of the initial NUM NUM leafsynsets of wordnet NUM
another drawback of these methods is that since clusters have only a numeric description they are often hard to evaluate on a linguistic ground
else if p s ub then lcb let s be the set of direct descendents of s new cat s rcb
in this range the precision is around NUM NUM and the reduction of ambiguity is around NUM which are both valuable results
the best set is modelled as the linear function of four performance factors generality coverage of the domain average ambiguity and discrimination power
gories the algorithm of section NUM creates alternative sets of balanced and increasingly general categories c i we now need a scoring function to evaluate these alternatives
semantic tagging another adopted solution is to gener null alise the observed word patterns by grouping patterns in which words have the same semantic tag
labels vl to v4 in each matrix represent the possible values of depart city shown in table NUM v5 to v8 are for arrivalcity etc columns represent the key specifying which information values the agent and user were supposed to communicate to one another given a particular scenario
in that matter edit distances could help a lot close means at a distance not too large and modifications are edit operations
NUM l rototype upper left corner sentences obtained by approximate matching and x sentence oi tained by analogy and retrieved from the tree bank
arabic arsala mursilun aslama x x muslimun it also accounts for some not all examples of sound changes like umlaut in german s
the tool we have built for the edition of text with trees allows approximate matching on trees and generation is performed using the same functions as for analysis
null ret all in document retrieval recall is delined as the ratio of the number of relevant documents retrieved over the total mmlber of relewmt docu inents ill the data base
precision again in document retriewfl precision is defined as the ratio of the nmnt er of rele null rant documents retrieved over the total number of documents retrieved
while speakers often repeat an interlocutor s utterance to confirm it we do not use a repeat to confirm ca since it is apparently signaled by no cue patterns and thus could only be recognized by noting inter utterance repetition
alternatively 7i will have s i ue to space limitations in the following definitions we are forced to be somewhat imprecise when we identify a node in a derived d tree with the node in the elementary d trees elementary nodes from which it was derived
ture into the claims d tree by substituting the s node of seems at the s complement node of claims and by inserting the object of adores which has not yet been used in the derivation in the d edge of the claims d tree above its s node
the analysis on the right hand side of figure NUM shows the two branches in different patterns
once me roy a is seen the continuation can only be hour so the initial sst before seeing this category label in the input has already produced the whole output including hour
the reason for this paradox is perceptive di erences that between the designers of the muc NUM domain specific hierarchy we adopted and the wordnet hierarchy and that between the an orator of the answer corpus and the wordnet designers
the only time expression on the focus stack at that point would be next week
firstly the domain specific hierarchy is mapped onto the semantic network of wordnet by manually as zni g corresponding wordnet node s to the classes in the do speci c hierarchy
huwever as we are unable to report comparative tests with k ore zdeg we adapted cwo other supervised algorithm both successfully applied to general word sense di mhiguation to the task of semantic class disambiguation
allows to recognize pns in a more satisfactory way
if a ena y is in the sic associated with a d edge between 7z and r NUM in an elementary d tree a then can not appear properly within the path that appears from t t to t NUM in the derived each node of elementary d trees has an associated sister adjunction constraint sac
since higher level classes such as the level NUM human class encompasses at wider range of words it is evident that the thresholds for higher level classes r n ot be stricter than that of lower level classes
as with k more the tr g set contains features of all the words in the training sentences and the algorithms are to pick one s tic class for each word in the testing set
jang ga nei neun can be analyzed in NUM ways i.e.
yuhaing ga leul buleu go isseul ddai yengyang ga ebs neun bbangbuseulegi juwi ei
we presented a new computational treatment of hpsg lexical rules by describing a compiler that translates a set of lexical rules as specifed by a linguist into definite relations which are used to constrain lexical entries
the research reported here was supported by teilprojekt b4 from constraints to rules efficient compilation of hpsg grammars of sfb NUM sprachtheoretische grundlagen f ir die computerlinguistik of the deutsche forschungsgemeinschaft
the matching rates as shown in table NUM increase from NUM to NUM for tr2 which shows that the constraint on 4a topic chain is a situation where a referent is referred to in the first clause and then several more clauses follow talking about the same referent namely the toi c
if a weaker form of a correct speech act was recognized it was counted as acceptable
italian mi potreste dare le chiavi della stanza per favore to these schema specifications in order to express optional rules permutation of phrases concordance of gender number and case etc
the stop of a sentence a comma within a sentence indicates a temporary stop
note that the value of the in feature is of type word and thus also has to satisfy either a base lexical entry or an out specification of a lexical rule
if all of these referring expression components are embedded in the same chinese natural language generation system as in fig NUM for example then given an input to the system anaphors in the resulting texts can be characterised by the rules used in the referring expression component and their implementation
null in each category the number of speech acts determined based on plan inference is noted
however this decrease of average matching rate does not deny the effectiveness of the salience constraint in tr3
for the texts having complicated discourse segment structures tr2 is slightly better than tr3 on average matching rates
these anaphors are all zeroed according to the conditions of locality and syntactic constraints in the three test rules
tr3 obtains NUM matching rate on average which is NUM lower than its predecessor tr2
NUM contains the item v NUM NUM obtained by the scanner that advances on the verb saw
the actions of the constructed plans form the response of the system in a complete natural language system they would be converted to a surface utterance
the length of a dependency rule cat c0 is the length of
c ten minutes later kluivert scored for the second time
the initialization step consists in setting all entries of the table to the empty set
a manual survey showed that once hesitation expressions axe filtered from them some NUM of the pause units studied can be parsed using standard japanese grammars a variety of special problems appear in the remaining NUM
the action can either change the label of the satisfyin g phrase grow its boundaries or create new phrases
nevertheless we felt that the maturity of our st processing was sufficiently questionable to preclude participating in the official evaluation
as a limited inference system it allows domain specific and general constraints to be instantiate d through carefully controlled forward chaining
we had hoped to include more machine learned phraser rules and as the rule learner matures we almost certainly will
in particular we wanted to explain the slot by slot discrepancies we had noted between our training and test performance cf
every such pair of same sort individuals is compared t o determine whether one is a derivative form of the other
the first class of inferenc e rules enforce so called terminological reasoning local inference that composes the meaning of words
one of the clearest such uses is in enforcing the semantics of coreference either definite reference or appositional coreference
the final role of the alembic inference component is to derive new facts through the application o f carefully controlled forward inference
take for example the following phrase from the walkthrough message which we show here a s parsed by the phraser
the stranded auxiliary in the second clause henceforth the target clause marks a vestigial verb phrase vp a meaning for which is to be recovered from another clause henceforth the source clause in this case the first clause
to simplify it we only try to outline the semantic space by locating the mono sense words in the space rather than build it completely by spotting all word senses in the space
simr opens up several new avenues of research
table NUM simr s error distribution on the
it is not fazed by word order differences
bitext maps are NUM to NUM functions in bitext spaces
simulated annealing requires an objective function to optimize
the simr implementation for spanish english uses only cognates
dog dog val s s val dcf dog root dog noun suit s def seq dog root noun surf dog s dcf dog plur dog s the final rule of figure NUM deals with datr s evaluable path construct
compared with the distance defined in NUM this distance is to measure the similarity between definitions while the distance in NUM is to measure the similarity between contexts
displaced items are stored in main memory
our investigations are based on the trec NUM chinese collection of NUM NUM xinhua and NUM NUM people s daily news articles totaling about NUM mb
an entry in our lexicon list can serve the purpose of a segmentation marker or in addition for detection of stopwords
information retrieval ir deals with the problem of selecting relevant documents for a user need that is expressed in free text
in bigram representation of text no lexicon is used and many meaningless bigrams as well as many that are true stopwords are included
in these cases we rely on japan or u s being on the lexicon and identified first before applying this rule
a run through the collection shows that the number of times tag NUM and rule NUM were exercised are about NUM NUM m and NUM NUM m
usually one needs as large a dictionary as possible so that many segmentation patterns are available for the system to select the correct one
in our system stopwords can be determined in three ways based on lexicon rule or frequency threshold statistical
however some chinese names do use double same characters ex NUM and we would stop them wrong
the first source of expectation is the domain processor described below
it is less likely that it denotes comprehension of the request
our system does not account for this effect at this time
the only implemented variation in behavior concerns the treatment of silence
in the following example the system is in directive mode
would you please turn the switch up
department of mathematics east carolina university greenville n c
domain processor general reasoning c enera oomph kno kx
although the rules abstract away from particular impleinentational details such as order of evaluation they can be rea ily understood in computational terms and may prove useful as a guide to the construction of practical datr interpreters
figure NUM the zero level model of the main subdialog processing algorithm
the third step in lexical analysis is the insertion of special marker tokens to indicate capitalize d words
it also includes finding multi token lexicon entries such as new york and coca cola
the processing specific to st diverged after all the phrase level reductions for ne and te had been performed
in each stage all the patterns appropriate to that stage are tried on each sentence in turn
in retrospect including indefinite references that are not appositives appears to have been the wrong thing to do
then there is the trivial extraction step which turns the organization and person mtokens int o expectations
the former provides a compositional way of putting possibly embedded quantifiers to the scope taking positions and the latter utilizes a syntactic movement operation at the level of semantics for quantifier placement
notice that the conjunction forces subject np to be first composed with the verb so that subject np must be type raised and be combined with the semantics of the transitive verb
a shows a derivation for a reading in which object np takes wide scope and b shows a derivation for a reading in which subject np takes wide scope
extraction an extraction component uses the results of a pattern match to generate an expectation and fill its
for example wide scope reading of a woman in a below is accounted for by quantifying in with a meaning postulate patterned after one for b
at the end of next section we show how these finer distinctions are made under the ccg framework see discussion of figure NUM
NUM these lexical entries are just two instances of a general schema for type raised categories of quantifiers shown below where t is an arbitrary category
suppose that in ps the semantic form of a quantified np is a syntactic argument of the semantic form of a verb or a preposition
a reasonable heuristic for predicting how the viterbi parse will change is to replace adjacent x s that expand to atazk and a zo ty respectively with a single x that expands to b as displayed in figure
having independently analyzed the two sub corpora of NUM dialogues each the analysers discussed each of the NUM claimed guideline violations and sought to reach consensus on as many classifications by guideline as possible
we define the distance between an activated cluster in the semantic space and the sense of a word as NUM again in terms of the cosine of the angle between their definition vectors
announced a major management shake up
overview of results of the muc NUM evaluation
figure NUM management succession template structure
he will be succeeded by mr
found in the body of the text
or can only referrin g expressions corefer
nonetheless performance is much lower on this slot than on others
depending on the thematic structure it can be translated as next if the date referred to is immediately after the speaking time or following in the other cases
in the upper left corner we see the structures of the dialogue sequence memory where the middle right row represents turns and the left and right rows represent utterances as segmented by different analysis components
using the verbmobil corpus as empirical basis for training and test purposes significantly improved the functionality and robustness of our module and allowed for focusing our efforts on real problems
in the example used in this paper we are processing a well formed dialogue so the turn structure can be linked into a structure spanning over the whole dialogue
although prediction quality gets worse if a sequence of dialogue acts has never been seen the interpola null tion approach to compute the predictions still delivers useful data
the former module that uses mainly knowledge based methods to determine the dialogue act of an utterance exploits the predictions to narrow down the number of possible acts to consider
it splits up the input into two utterances guten tag f au klein wit m ssen die mitarbeiterbesprechung and assigns the dialogue acts greet and init date
in our domain in addition to the dialogue act the most important propositional information are the dates as proposed rejected and finally accepted by the users of verbmobil
compiling labels according to interpretations in groupoids provides a general method for calculi with various structural properties and also for multimodal hybrid formulations
the problem is that c and fl are not deterministically given by NUM at the compile time of unfolding
a rcb he lcb o NUM rcb and a binary infix formula constructor
we solve a higher order goal first on the agenda by adding its precondition to the database and trying to prove its postcondition
for the non associative calculus we drop the condition of associativity and interpret in arbitrary groupoids intuitively trees under adjunctionl
so for l the relational compilation allows partitioning by the binary rules to be discovered by simple constraint propagation rather than by the generate and test strategy of normalised sequent proof
lifting is derivable in nl as follows it is also derivable in l indeed all nl derivations are converted to l derivations by simply erasing the brackets
a sequent comprises a succedent type a and an antecedent configuration f which is a list of one or more types again we write f a
we show how categorial deduction can be implemented in higher order linear logic programming thereby realizing parsing as deduction for the associative and non associative lambek calculi
we aim to show here how such unfolding allows compilation into programs executable by a version of sld resolution implementing categorial deduction in dynamic linear clauses
in other words how do we deal with sub and supertypes
this section presents action schemas for referring expressions
figure NUM modifiers schema for terminating the recursion
our terminology for planning follows the general literature
NUM NUM understanding no on the television
agents adopt goals to further the collaborative activity
otherwise the speaker must choose the candidate
1deg the first step involves choosing a candidate
this work was funded in part by nsf grant iri NUM
below we link a suffix tree to more than one suffixtree
we can now present the algorithm for the construction of suffix tree alignments
consider f lcb c1 ct rcb as an alphabet
a multi set of aligned pairs is called an aligned corpus
this extends to an aligned corpus in the obvious way
the resulting data structure is called here suffix tree aligmnent
we briefly introduce here the basic assumptions of the approach
we now specify a method to compute suffix tree alignments
we can improve on this by using suffix tree alignments
the combination of sparse data and too few lexical heads renders backed off estimation ineffective
for any cluster clu in the space let cvau be its context vector we also define its distance from w based on the cosine of the angle between their context vectors as NUM
NUM penney decided to extend its involvement with the service for at least five years
NUM define recognize words member s words NUM memoization and left recursion as noted above the scheme functions defined in this way behave as top down backtracking recognizers
computational linguistics volume NUM number NUM 11c define s memo vacuous seq np vp as an aside it is interesting to note that memoization can be applied selectively in this approach
specifically i show how to formulate top down parsers in a continuation passing style which incrementally enumerates the right string positions of a category rather than returning a set of such positions as a single value
moreover the evaluation of the procedure pa corresponding to a category a at string position l corresponds to predicting a at position l and the evaluation of the caller continuations corresponds to the completion steps in chart parsing
in fact it is not clear how memoization could help in these cases given that we require that memo behaves semantically as the identity function i.e. that memo f and f are the same function
the cps memoization described here caches such evaluations in the same way that the chart caches predictions and the termination in the face of left recursive follows from the fact that no procedure pa is ever called with the same arguments twice
the local variable entry is bound to the table entry that corresponds to args the set of caller continuations stored in entry is null iff the memoized function has not been called with this particular set of arguments before
project hookah is a tipster implementation project with the drug enforcement administration to extract information from dea field reports in support of populating a database
for example the hookah user interface relies on the now standard features of window based systems scroll bars buttons and mouse operations
participants as of april NUM contractors include betac prc idi hnc and lockheed martin
prototype will allow users to choose among detection tools do sophisticated searches on particular topic s maintain chosen references to documents in project files view those documents either within the detection tool or through a
once the prototype is complete other detection and extraction tools gui s or alternative tipster compliant document managers could easily be added to the ftm prototype providing an excellent unclassified environment for continuing to evaluate and demonstrate tipster technology
updating the database with the new information in the hookah application interfacing with the naddis database has been a difficult systems engineenng problem
a detectionneed is a type of document and so partakes of all the operations which can be applied to documents
documents are gathered into collections which may have attributes on the collection level as well as on the individual documents
furthermore each annotation has a span which can link the object to the text from which it has been derived
the collection is then fed along with the original query to an updateusingrelevancefeedback operation producing an updated query
the arguments are to be matched against that portion of the document annotated with the annotation of type name
the transformation process is divided into these two stages because a retrieval system may provide specialized tools for modifying the detectionquery
where the value of an attribute is a sequence of items these items are separated by commas
which generates collocations within the paradigm of sentence planning
figure NUM ltag trees with semantic and pragmatic specifications
moreover they do not represent any pragmatic inforrnatign
copy area reference desk interlibrary loan office
one tag chunks cover about NUM
these aspects of tags are crucial for us
descriptions of these places are typically collocations e.g.
these various existing computational approaches have three main deficiencies
each chunk contains one or more parts of speech
development of a partially bracketed corpus with part of speech information only
thus an automatic tag mapping algorithm is provided
thus the first heuristic rule has no effects
it sets up the mapping between different tagging sets
that is it is suitable for tall thin trees
table NUM experimental results for definition NUM and NUM
definition NUM for two parts of speech
figure NUM schematically shows the embedding of hpsg ii descriptions in the definition of a relation
in the hpsg ii architecture any description can be used as antecedent of an implicative constraint
this is p0ssible because the sentence now corresponds to a whole class of implicit definitional sentences each of which is obtained by extending the paths found on the left and right hand sides ill the same way
consider for example the standard hpsg encoding of list structures
normally there will be no extra constraints on ne list
therefore we have to enter this node into the rhs
if they are identical then proceed as in step NUM
the signature introduces the structures the linguist wants to talk about
the type ne list that we saw above is a hiding type
we have presented a compiler that can encode hpsg type definitions as a definite clause program
second a relation is needed to capture the hierarchical organization of constraints
complete sequence is defined as a sequence of null or more adjacent complete lknks of same direction
this is because both are composed of the same sub st and sub st with rnayimum probabilities
note NUM with a disjunction of the verbs stored in the lexicon
a clause skeleton is added to the top level of the fd cf
each of its entries groups the alternative words to express a given concept
note NUM NUM a generation lexicon is indexed by concepts instead of words
NUM to handle constituents the complete unification procedure implemented in fuf is
a complete set of iifference rules for datr is shown in figure NUM the rules for values sequences and evaluable paths require only slight modification as the path extension is simply passed through from premises to consequent
we have not investigated further which pragmatic factors affect the selection of perspective
the lex cset attribute triggers recursion on the immediate descendants of the linguistic head
surge is usable as a portable front end for syntactic processing
most of the works on phrase structure grammar induction however have partially succeeded
even we if acknowledge the mostly phonographical organization of say french orthography we believe that the nmltiple deviations from a strict grapheme phoneme correspondance are best captured in a model which weakens somehow the assumption of a strong dependancy between orthographical and phonological representations
in this case k denotes one of fourteen possible attachment configurations shown earlier
the first strategy implements a depth first search of the analog set each time the topmost element of the stack is searched but not found in the lexicon its derivatives are immediately generated and added to the stack
using this technique collins and brooks achieve an overall accuracy of NUM NUM
for example apposition as a markable phenomenon was restrictively defined to exclude constructs that could rather be analyzed as left modification such as chief executive scott mcnealy which lacks the comma punctuation that would clearly identify executive as the head of an appositive construction
human performance was measured in terms of interannotator variability on only NUM texts in the test set and showed agreement to be approximately NUM when one annotator s templates were treated as the key and the other annotator s templates were treated as the response
there was just one system that posted a higher error score on the body than on the headline the baseline nmsu crl configuration and the difference in scores is largely due to the fact that the system overgenerated to a greater extent on the body than on the headline
as a condition for participation in the evaluation the sites agreed not to seek out and exploit wall street journal articles from that epoch once the training phase of the evaluation had begun i.e. once the scenario for the scenario template task had been disclosed to the participants
commercial systems are available already that include identification of those defined for this muc NUM task and since a number of systems performed very well for muc NUM it is evident that high performance is probably within reach of any development site that devotes enough effort to the task
using the scoring method in which one annotator s draft key serves as the key and the other annotator s draft key serves as the response the overall consistency score was NUM NUM on the f measure with NUM recall and NUM precision
james out dooner in as ceo of mccann erickson as a result of james departing the workforce james is still on the job as ceo dooner is not on the job as ceo yet and his old job was with the same org as his new job
once the scenario had been identified the ranked retrieval method was used and the ranked list was sampled at different points to collect approximately NUM relevant and NUM nonrelevant articles representing a variety of article types feature articles brief notices editorials etc
two taggers developed from work done at cambridge university under the acquilex program assigned NUM and NUM correctly while the commercial prospero parser performed best assigning NUM correctly
NUM the fact that the domain neutral template element evaluation was being conducted led to increased focus on getting the low level information correct which would carry over to the st task since approximately NUM of the expected information in the st test set was contained in the low level objects
for most events however the fill is one of a large handful of possibilities including chairman president chief executive officer ceo chief operating officer chief financial officer etc
the set of possible antecedents tends to be reduced drastically during constraint application
antecedent selection consider anaphors y in the order determined in step 2c
13e pauli revises hisi decision for himi
let n be the number of np nodes in the surface structure representation
verify that the binding principle of x is not violated
llb pauli accepts the decision for himselfi
does not guarantee that the global maximum of plausibility is reached
clientj a story while he shaved him
3b the elienti appreciates that the barber shaves himi
in example NUM NUM the father visited his daughter
regarding the overall system architecture the deep analysis phase as we have described it need not be necessary for each and every utterance if the input allows for a standard transfer based translation e.g. because it does n t contain ambiguous particles that will typically be sufficient
another application which is more difficult in hebrew than in other languages is text to speech systems which can not be implemented in hebrew without first solving the morphological ambiguity since in many cases different analyses of a word imply different pronunciations
building on the assumption that most if not all dialogue design errors can be viewed as problems of non cooperative system behavior det has two closely related aspects to its use
the semantic representations were not only noise free and unambiguous but corresponded directly to the words in the utterance
some of them like j iz are conglomerations that should have been divided
NUM of the words in the dictionary have never been used in a good parse
the general operation of the program should be made clearer by the following two examples
the algorithm described above is extremely simple as was the input fed to it
having successfully parsed the input it adds the new word to the dictionary
where the semantic input is an unordered set of identifiers corresponding to word paradigms
the final dictionary contains NUM words where some entries are different forms of a common stem
we hope to transition to phonemic input produced by a phoneme based speech recognizer in the near future
for example we approximate the greater problem as that of learning from inputs like phon
the right file is opened and searched until a match with the icy eine occurs
relatively little attention has been paid in ir to the differences in a word s part of speech
the research in this section is concerned with a subset of these phrases namely those that are lexical
the results of disambiguation and morpltologieel analysis serve not only as input to dictionary lookup hut also to corpus search
first it is difficult to scale up researchers have generally focused on only two or three words
we also conducted an experiment to determine idiomatic senses were identified by the use of font codes
we found that when a word form appears to be a variant it often is a variant
these parameters include k the number of nearest neighbors to use for determining the class of a test example exemplar weights feature weights etc
NUM NUM error analysis for word identification models
figure NUM a chinese dictionary construction system
thus we write a non terminal as x x where x w t and x is a constituent label
we would also like to try languages where new word formation poses problems such as in german
our first step was to score the plum system against the shogun system as if the shogun system were the key
accordingly we used as test corpus another previously unseen set of NUM NUM texts from vol NUM of the ziff davis corpus which contained texts of the same nature and genre as vol NUM
for example an opp that selects all the sentences in the original text certainly has a very high rm but this extract duplicates the original text and is the last thing we want as a summary
lexicon editor le maintains the dictionary of words lexicon to be recognized by the nlu application
in flatter template structure without the linchpin phenomenon the penalty for a mistake in merging templates would be less severe
this is explained by the fact that NUM NUM of paragraphs in the corpus contain only one sentence and NUM NUM of the paragraphs contain two sentences and the spp is NUM NUM the second last sentence is the first figure NUM vol NUM dhit distribution for the last NUM paragraph positions counting backward
shogun had the better recall score it extracted a higher proportion of the information contained in the key templates
this experiment showed a large improvement since the no spurious objects would be produced only the occasional spurious slot
then for the first NUM sentence positions the top NUM NUM NUM taken according to the opp we counted the number of times a window of text in the extracted sentences matched i.e. exactly equalled a window of text in the abstract
to do all this one requires a much larger document collection than that available to edmundson and baxendale
lightweight techniques are those that rely only on local processing do not involve deep understanding and can be optimized
it is difficult to develop an effective computer based aid to enable children whose speech is hard to understand to participate in social conversations
in the middle of the effort of preparing the test data for the formal evaluation an interannotator variability test was conducted
NUM a every girl admired but most boys detested one of the saxophonists
first we discuss in ss2 how traditional techniques address availability of readings and note some residual problems
water and of surface evaporation incorrect variant palmier d huile palm tree yielding oil n to n initier des bourgeons n to v initiate buds and without variant expansion
the precision and recall of the extraction of term variants are given in table NUM where precision is the ratio of correct variants among the variants extracted and the recall is the ratio of variants retrieved among the collocates
results were obtained through a manual inspection of NUM NUM type NUM variants NUM type NUM variants NUM NUM type NUM collocates and NUM NUM type NUM collocates extracted from the agr corpus and the agrovoc term list
thirdly variants such as de l humiditd et de la vitesse de l air literally of humidity and of the speed of the air indicate that the conjunction can be followed by an optional preposition and an optional determiner
applications to be explored in future research involve the incorporation of the system as part of the indexing module of an ir system to be able to accurately measure improvements in system coverage as well as areas of possible degradation
we chose a window size twice as large because french is a romance language with longer syntactic structures due to the absence of compounding and because we want to be sure to observe structures spanning over large textual sequences
the domain processor also supplies situation related expectations which are not directly connected to the observation but which could naturally occur
the other source of expectations is the dialog controller also described below which provides coordination for the complete system
the expectations for the response to NUM are checked but this utterance is not one of them
we will in this paper only treat the generation of nl from fact bases
here we show an example of the latter see figure NUM
observe that there is no time feature in the igf since loxy has an embedded time
before generation input propositions are ordered based on the characteristics of their subjects as
NUM file edit uinst 1mulatorlt can execute events and the inter preter intewrets the specification
the vinst system is a multi modal specification and validation tool specifically for the functionality of telecom services
the used words of the user will be reused for generation together with the loxy formula
type is type of sentence and feature list is a list of feature names describing the sentences
if the input describes some physical state then conclude that the user knows how to observe this physical state
one future suggestion is also to use the results from the nl parsing for the generation
the system may allow rather longer silences when it is in passive mode than when it is in directive mode
figure NUM shows an example of dsu b for one class out of NUM classes constructed using this algorithm with a vocabulary of the NUM NUM most frequently occurring words in the wall street journal corpus
since the atr corpus is still in the process of development the size of the texts we have at hand for this experiment is rather minimal considering the large size of the tag set
now if the words apartment and bus are unknown to the parsing system a part of this work is done when the author was at atr interpreting telecommunications research laboratories kyoto japan
in that case an appropriate translation of b is expected to be derived with an example translation of a if the system has an access to the classes of words
if we apply this method to the above o c2v algorithm straightforwardly however we obtain for each class an extremely unbalanced almost left branching subtree
expectations are specified in gadl goal and action description language form an internal language for representing predicates
a large vocabulary of english words NUM NUM words is clustered bottom up with respect to corpora ranging in size from NUM million to NUM million words using mutual information as an objective function
a statement about another task step which along with s is needed to accomplish some ancestor task step
by asking a value of a specific feature on each event in the set the set can be split into n subsets where n is the number of possible values for the feature
the zero clustering text size again corresponds since a distinctive bit string is assigned to each word the tagger also uses the bit string as an id number for each word in the process
where the rule based tagger described above is able to determine the part ofspeech of individual words based on prior training and contextual rules pre rules can select individual readings of words within the same partof speech
due to the fail soft mechanisin discussed below the structure of the objects which the transfer rules nmst apply to can not he flflly predicted
as mllranslatable tal les formulas illlllflbe rs el and a separate conversion program converts the output into woldperfecl for hial
paqa ans has a highly develot ed mori hological module which l rovi les an almost eomt lele coverage of dmfish infleclional morl hoh gy
the community started the program in NUM with the goal of creating an advanced systeln for automatic translation capable of treating all the otficial working languages of the community
in order for complex transfer to work in all cases rules must be set up not only for correctly parsed input structures but also for tile special fail soft structures
at lhe second level lhe relational level surface syntactic flmclions are alculaled and certain flmclion words sut h as t reposilional markers are inserted
because of the multilinguality the prototype was quite clean in terms of separate modules for analysis transtl r and synthesis of the various languages and language pairs
since the system runs in a praetical environment it must ne ver fail to i roduce an olltput even if il encounlers an unanalysable sentence
NUM extensions to the ccg formalism in addition to the bn generalized composition rules given in ss2 which give ccg power equivalent to tag rules based on the s substitution and t type raising combinators can be linguistically useful
no matter what lexical interpretations f g h k are fed into the leaves a b b c d d e e f both the trees end up with the same derived interpretation namely a model element that can be determined from f g h k by calculating ax y f g h k x y
the notion of scope is relevant because semantic interpretations for ccg constituents can be written as restricted lambda terms in such a way that constituents having distinct terms must have different interpretations in the model for suitable interpretations of the words as in ss4 NUM
nor do they recognize the redundancy in NUM because just as for the example softly knock twice in ss4 NUM it is contingent on a kind of lexical coincidence namely that a type raised subject commutes with a generically type raised object
r NUM denotes the parse tree formed by com null bining subtrees NUM via rule r if r fl NUM then take nf c r gf fl nf NUM which exists by inductive hypothesis unless this is not an nf tree
the paragraph spec null the order of the paragraph clusters controls the global structure of the final textual explanation the order of the views in each paragraph cluster determines the order of sentences in the final text
to investigate the issues and problems of generating natural language explanations from semantically rich large scale knowledge bases we have designed and implemented knight a fully functioning explanation system that automatically constructs multisentential and multiparagraph natural language explanations
functional description skeleton library contains a large number of functional description fd skeletons each of which encodes the associated syntactic semantic and role assignments for interpreting a specific type of message specification
the explain algorithm figure NUM is supplied with a query type e.g. describe process a primary concept e.g. embryo sac formation and a verbosity specification e.g. high
to ensure that the difficulty of the concepts assigned to the writers were the same as those assigned to knight the writers were given the task of explaining exactly the same set of concepts that knight had explained
second even if large scale knowledge bases were more plentiful an explanation generator can not be evaluated unless it is sufficiently robust to produce many explana null an explanation plan for embryo sac formation high verbosity
lester and porter robust explanation generators view the functional realizer uses its knowledge of case mappings syntax and lexical information to construct a functional description which it then passes to the fuf surface generator
for example an in depth analysis at the discourse sentential and lexical levels of all of the texts produced by both the humans and the system may reveal which characteristics of the highly rated texts are desirable
for a given writer we assessed knight s performance relative to that writer we compared the grades awarded to knight and the grades awarded to the writer on explanations generated in response to the same set of questions
for example a discourse knowledge engineer might express the rule the system should communicate the location of a process if and only if the user of the system is familiar with the object where the process occurs
in order to gain a better understanding of the underlying tagging performance of the rule learner and so separate out some of these human factors issues we ran an automated experiment in which different random subsets of sentences were used to train rule sets which were then evaluated on a static test corpus
the separate alembic nlp system consists of c pre processing taggers for dates word and sentence tokenizafion and part of speech assignments and a lisp image that incorporates the rest of alembic the phrase rule interpreter the phrase rule learner and a number of discourse level inference mechanisms described in NUM
these files me parsed with the help of an sgml normalizer NUM during the course of the annotation process the workbench uses a parallel tag file ptf format which separates out the embedded annotations from the source text and organizes user defined sets of annotations within distinct tag files
to place this in the perspective of the human annotator after only about NUM minutes of named entity tagging having annotated some NUM NUM words of text with approximately NUM phrases the phrase rule learner can derive heuristic rules that produce a pre tagging performance rate p r of between NUM and NUM percent
NUM indeed in situations where the quality of the data is particularly important as it is in say a multi system evaluation such as muc it is typical that multiple reviews of the same corpus is performed by various annotators especially given the known ambiguity of any annotation task definition
the earlier we can extract heuristic rules on the basis of manually tagged data the earlier the user can be relieved from some portion of the chore of physically marking up the text the user will need to edit and or add only a fraction of the total phrases in a given document
we anticipate integrating the workbench with other tipster compliant modules and document managers via the exchange of sgmlformatted documents
a goal for our future research is to explore new methods for incorporating end user feedback to the learning procedure
while our dominant focus so far has been on supporting the language research community it is important to remember that new domains for language processing generally and information extraction in particular will have their own domain experts and we want the text annotation aspects of the tool to be quite usable by a wide population
it is extremely difficult to control many of the features that influence the annotation process such as the intrinsic complexity of the topic in a particular document the variation in tag density tags per word that may occur the user s own training effect as the structure and content of documents become more familiar office distractions etc
lk show this consider the test rewrite sequence for l xaml h
the compilation process results in a set of sign lex bindings triples called outer domains
this set indicates that for any nip the only terminal categories not contained in the subtree with root np and with which the np shares a semantic index are vtra and p for instance the first triple arises from the following tree the pruning technique developed here operates on grammars whose analyses result in connected leaves
bag generation is a form of natural language gel er ttion in which the input is bag mso known as a inultiset a set in which rcpe ted elements are significant of lexicm elements and the output is a grammatical sentence or a statistically most probable permutation with respect to some
given the inner domains of each category in the grammar the construction of the outer domains involves the computation of the lixed point of set equations relating the outer domain of a category to the inner domain of its sisters and to the outer domain of its mother in a manner analogous to the eoinputation of follow sets
in such cases brown would modify a different noun with a different index ex a lcb the dog withl NUM the lnvwn2 collar2 rcb a naive implementation of this deduction would attempt to expand the vp depth ill st left to right ill order to accommodate brown in a complete derivation
by identifying signs which are directly connected in e it is possible to determine whether g is connected and consequently whether c can form part of a complete derivation instead of simply comparing the value of index paths it is more restrictive to use outer domains since they give us precisely those elements which are directly connected to a sign and are in its outer domain
it includes a robust dialogue plan recognizing module which uses repair techniques to treat unexpected dialogue steps
for example dialogue act predictions are employed to allow for dynamically adaptable language models in word recognition
note that it is not presented earlier as this might bias the decision about recognition acceptability
the second part of the lexical entry therefore determines how the semr features of the two syntactic participants are to be linked to the semantic arguments of the input semantic relation in our case this is done by the fuf pointers next to notes NUM and NUM in figure NUM
the clause planning process has two components one domain specific maps from the domain relations to a clause structure and one generic maps the clause structure to the appropriate types of syntactic modifiers relative clause prepositional adjunct adjectival premodifier or noun noun modifier
this paper presents the method for statistical dialogue act prediction currently used in the dialogue component of verbmobil
to asses the effectiveness of the plotalign algorithms we conducted a series of experiments
finally we owe the bulk of the system s success to the underlying framework with its emphasis on sequences of simple rules
i sys is the destination of your call frankfurt
several of these can be realized within a single utterance
the components developed for tipster i enabled it to function in two languages japanese and english
the reasons for performing a dialogue act
further we focus on the access knowledge kind
formative evaluations are short empirical design evaluation studies that focus on system improvement not system validation
continuations merely represent the illocutionary aspect of how a dialogue can continue
b usr how much is a call to frankfurt please
a prototypical system that answers for the tesadis telephone rate inquiry system
further system responses must be efficient
for a complete bibliography please see ouj web page http www
it collects the student s predictions and calls the instructional planner to conduct a conversation
students use circsim tutor to learn to solve problems like those taught in their physiology course
ru charn chang is now at baxter laboratories north chicago il
after the predictions are entered the dialogue will unfold on the other side of the screen
text generation the text generator produces sentences from logic forms generated by the planner
this task is simplified by the fact that ten predicates cover most student answers
also in the lexicon are basic lexical functional grammar annotations to be used by the parser
the second variable denoted by lmixn s is the likelihood of the mixture of all possible trees that have a subtree rooted at s on the observed suffixes all observations that reached s
what is to be established in this paper is the notion of critical tokenization itself together with its precise descriptions and well proved properties
lmiz s o l s NUM c ix lmixn us NUM ueu the recursive computation of the mixture likelihood terminates at the leaves lmiz s l s if isl d NUM
in other words any introduction of high level knowledge must at least be effective in resolving some critical ambiguities in tokenization
on the one hand critical tokenization can help greatly in developing tokenization knowledge and heuristics especially those tokenization specific understandings such
since our japanese developer could not actually read most of the japanese material he could only interpret changes to the guidelines in so far as they were incorporated in the training set
further experiments using longer maximal depth and allowing comparisons with existing n gram models trained on the full NUM million word nab corpus will require improved data structures and pruning policies to stay within reasonable memory limits
the location is indicated by the bracketed item subject direct object noun phrase in a pp
after the normalization process highly accurate obvious inferences are made and added to the representation
the two groups of words are merged and used as the selectors of facility
in compound tenses the infinitival form of the modal is usually used instead of its past participle form in example 4a the infinitive wollen substitutes for the participle gewoll
this strategy has been extended to handle long distance scrambling so that arguments are transferred from the clause in which they are attached to an embedded clause in which they receive an interpretation
however when the condition is relaxed its performance gain is much lager than the baseline
although german is a partially free word order language we will assume that it has a fixed base word order which is modified by a set of movement transformations
null among subject control verbs there is a class of verbs called coherent verbs which form a clause union with their infinitival complement by restructuring
in NUM the direct object ihn of the infinitival verb schlagen being in a scrambled position is inserted into the argument table of the raising verb and marked as uninterpreted
furthermore the subject of the infinitival clause ihrt can be attached to a position higher than the subject of the main clause as a result of scrambling
for the hypothesis of haben as an auxiliary die kinder is inserted into the provisional argument table as the subject or the direct object of a forthcoming verb and diesen bericht is inserted as direct object
if both of these conditions are fulfilled the uninterpreted arguments are transferred from the argument table of the main verb to a provisional argument table which is matched with the predicate of the infinitival complement
if it is available the new argument is matched with the argument structures if there is a provisional argument table instead of one argument the matching is effeeted for each argument in turn
as a solution to this problem we propose attaching the structure that contains the verb to the left and extracting all of the heads which are adjoined to the right of the upper verb
simr employs a simple heuristic to select regions of the bitext space to search
learning micro planning rules for preventative expressions
in either case in utterance 2c john seems to be central requiring a shift from utterance 2b while the store becomes central again in utterance 2d requiring yet another shift
the differences can only be explained however by looking beyond the surface form of the utterances in the discourse different types of referring expressions and different syntactic forms make different inference demands on a hearer or reader
our use of similarity measure to relax the correctness criterion provides a possible solution to this problem
analyse the variability in the features used by different children in the setting and eventually by the same children in different settings
for example several interpretations are possible for the noun phrase the vice president of the united states in the utterance NUM the vice president of the united states is also president of the senate
a semantic theory that forces a unique interpretation of utterance NUM will require that a computational theory or system either manage several alternatives simultaneously or provide some mechanism for retracting one choice and trying another later
more importantly when np is a pronoun the principles that determine the c s for which it is the case that np directly realizes c do not derive exclusively from syntactic semantic or pragmatic factors
they used focusing to order candidates as a result the need for search was greatly reduced and the use of inference could be restricted to determining whether a particular candidate was appropriate given the embedding utterance interpretation
the paper examines interactions between local coherence and choice of referring expressions it argues that differences in coherence correspond in part to the inference demands made by different types of referring expressions given a particular attentional state
corresponding to these two levels of coherence are two components of attentional state the local level models changes in attentional state within a discourse segment and the global level models attentional state properties at the intersegmental level
in which he may be interpreted either vf or vl may be followed by either NUM or NUM NUM as ambassador to china he handled many tricky negotiations
this can be seen by observing that both discourses seem equally appropriate and that the backward looking centers of 32b and 33b are respectively the husband and the lover which are realized by their anaphoric elements
the normalization function is used to overcome the problem that the values of ci are not on the same scale as x and that the cost measures ci may also be calculated over widely varying scales e.g.
in the next subsection we define the similarity between two word senses or concepts
from a hypothetical experiment in which eight users were randomly assigned to communicate with agent a and eight users were randomly assigned to communicate with agent b table NUM shows user satisfaction us ratings discussed below number of utterances utt and number of repair utterances rep for each of these users
given the confusion matrices in tables NUM and NUM p e NUM NUM for both agents s for agent a p a NUM NUM and NUM NUM while for agent b p a NUM NUM and a NUM NUM suggesting that agent a is more successful than b in achieving the task goals
as a convention of this type of tagging susing a single confusion matrix for all attributes as in tables NUM and NUM inflates n when there are few cross attribute confusions by making p e smaller
assessment a establish the current behavior diagnosis d establish the cause for the errant behavior repair r establish that the correction for the errant behavior has been made test t establish that the behavior is now correct our informational analysis of this task results in the avm shown in table NUM
like many sentence planners we assume that there is a flexible association between the content input to a sentence planner and the meaning that comes out
figure l b shows an auxiliary tree representing the modifier syntax which could adjoin into the tree for the book to give the syntax book
consider another example that involves an unknown proper name NUM dreamland employed NUM programmers
the first pair indicates the begin and end position of the category the second pair indicates the extreme positions between which the first pair should lie
furthermore it may shift onto the parse stack elements that are similar to the active items or dotted rules of active chart parsers
the result of the parser will be a parse forest a compact representation of all possible parse trees rather than an enumeration of all parse trees
NUM this item indicates that there is a possible derivation of the category defined in result item NUM of the form illustrated in figure NUM
bottom up parsing is far more attractive for lexicalist formalisms as it is driven by the syntactic information associated with lexical elements but certain inadequacies remain
the head relation of two categories h m holds with respect to a grammar iff the grammar contains a rule with left hand side m and head daughter h
for example in the alvey nl tools grammar in only NUM rules out of more than NUM the head of the rule could be gapped
NUM now one might expect that such an underspecified goal will dramatically slow down the head corner parser but this turns out to be false
note that each test set contains three complete dialogs with an average of NUM utterances per dialog
selectorsw jlcelc c ic lcb w rcb
this amloi ation w s lone in i he winter o NUM NUM
a couple of these systems have been commercialized and several are being incorporated into government text processing systems
tctmd annot al ion have been omitted here fin the s tke of readm ilil y
systems did particularly poorly in identifying descriptions the highest scoring system had NUM recall and NUM precision for descriptions
perhaps a micro muc NUM with an even simpler template structure is needed to push the limits of port ability
appendix sample scenario template shown below is a set of templates for the muc NUM scenario template task
however o lds of that hapt ening are slim since word from coke headquarters in atlanta is that
muc NUM introduced several innovations over prior mucs most notably in the range of different tasks for which evaluations were conducted
it was n t clear whether much progress was being made on the underlying technologies which would be needed for hetter understanding
where the denominator is given by eq
all form entries may be negated no stopovers and disjunctive enquiries are indicated by dint of indexing delta on thursday or american on friday
on entering the page for a given utterance the judge first clicks a button that plays an audio file and then fills in an html form describing what they heard
figure NUM shows the state diagram of a transducer that encodes this relation
but the two level formalism is only defined for symbol tosymbol replacements
the left bracket indicates the end of a left context
for example if the ab match should have precedence we write
examples let us illustrate the consequences of these definitions with a few examples
the order in the above table corresponds to the precedence of the operations
here upper and lower are any regular expressions that describe simple regular languages
a regular relation is a mapping from one regular language to another one
we discuss their relationship in a section at the end of the paper
the processing of a more complex structure like
this method is currently in embryonic form but the pilot experiment described here leads us to think that the method shows promise for further development
on the other hand it can also be the case that constructions which would be regarded as unacceptably sloppy in written text pass unnoticed in speech
table NUM text input results translation word error rates wer and sizes of the transducers for different number of training pairs
documentation to enhance the usability and extensibility of tsni p results a three vohnne user guide is under preparation providing clear instructions for the assessment of the methodology test data and tools developed
multillnguallty multilinguality is achieved in the tsnlp test suites by covering the same range of phenomena in english french and german and adopting the same classification for these phenomena in the three languages
np modification diathesis tense aspect modalit sentence types coordination negation
simulated annealing is used to searcti the optimal tag sequence as gibbs distribution provides simulated anneming facility with teliiperatur arid eilefgy ollcept
we can expand the clique flnlction of the model NUM easily by just adding stlficix inforui ttion to the clique function of the ntodel NUM
clique fimction contributes to reduce the comi utation of evahmtion function of entire mrf by clique concept that separates random v triables to the subsets
cases that wn could handle next we considered only the NUM cases of syn hyp mer relations and tested whether wn encoded a semantic relation between them and their manually identified anchors
mi lcb f i has lower error rate lha n that of ilmm when l lie size of training data is slnml
for instance the industry the topic being oil companies and the first half the topic being a concert
also whereas we identified the pair koreans the population the search found a wn relation for nation the population
the np and the dd are in direct hyponymy relation with each other for instance dollar the currency
yve used the probat ility d istribu tion of five huntired error proiie words ill model NUM in omer to reduce the tltllllber 0t paf31ileters
to process dds based on events we could try first to transform verbs into their nominalisations and then looking for a relation between nouns in a semantic net
types of bridging definite descriptions a closer analysis revealed one reason for the poor results anchors and descriptions are often linked by other means than direct lexico semantic relations
by adopting this heuristic we found the correct anchors for NUM dds instead of NUM and reduced the number of false positives from NUM to NUM
we repeated our first experiment using a simpler heuristic considering only the closest anchor found in a five sentence window instead of all possible anchors
the theory presented here addresses only the former case the latter one might be handled by adding an extra default with a stronger priority level
for example table NUM describes how an observed inconsistency of sl performing anew might be a symptom of s2 s misinterpretation of an earlier act by sl
thus our system can be seen as the result of integrating a series of finite state models at different levels acoustic level
there were clearly two different sets of experience and expertise present during these meetings
indeed having lexicons which offer maximal cover on a specific topic is an important benefit in many applications of automatic speech and natural language processing
following the evaluation driven research paradigm has served the tipster text program exceedingly well
typically such errors can be recognized immediately when an expression is not interpretable with respect to the computer s presumedly perfect knowledge of the world
experimental results with the task defined in the project show that this approach reduces the number of examples required for achieving good models
agents know that their utterances will be taken to display their understanding of some culturally determined rules of conversation and the situation prior to the utterance
prototype systems are being developed for france v i have found the following connection
we hope the next version will increase user s acceptancy of automated speech processing systems
the pilot corpus consists of dialogues that concern the exchange of train information only
this presentation follows the temporal order of the different stages in the travel plan
the dialogue manager will proceed with the next chunk if the user has acknowledged the presented information
in NUM of the cases the reactions concern questions for other or related travel plans
in such cases she will interrupt the information presentation by a correcting sequence
for the places of change and the directions the result is roughly fifty fifty
the given new distinctions in the table reflect the order in which the elements occur
although the second version of vios is implemented it is far from perfect
each sentence in the first group is prepared to give a context for a word which has a possibility to become an implicit spelling error and a context for a sequence of words that have word boundary ambiguity
if the character prior to the ed is a consonant except y the previous character is a vowel and the next character is not a vowel add an e to the end of the word
if the character prior to the ing is a consonant except y the previous character is a vowel and the next character is not a vowel add an e to the end of the word
before the release of his last studio album NUM s ten summoner s tales sting commented that he could no longer put his whole heart into his work it left him feeling too vulnerable
since the trec data is very diverse and is classified into fifty classes the mulfinomial distribution method is expected to perform even better than the other methods as it is particularly good at distingui qhing fine detail between classes
h l the pledge of allegiance txt i pledge allegiance to the flag of the united states of america and to the republic for which it stands one nation under god indivisible with liberty and justice for all
walter mccarty scored NUM points and antoine walker had NUM and nine rebounds as kentucky pulled away in the second half to beat upstart san jose state NUM NUM in the first round of the midwest regional in dallas
some extremely short documents were included which were no longer than the header information which was stripped before use the rifle author and source and a note that the article was in viemamese and had not been translated
this produced results which were close to perfect for all of the methods and the multinomial distribution method was less than NUM different than the smart method in clas null sification and only NUM better in routing
future efforts will investigate these modificationsdeg null this test for classification and routing was much simpler than the trec task since the size of the corpus was significantly smaller and less diverse and every document was relevant to a single category
we identify all sentences in the text corpus that contain one of the seed words
as an example the seed word lists used in our experiments are shown below
it is worth noting that these results were obtained in a hp NUM workstation without resorting to any type of specialised hardware or signal processing device
for each category we selected the top NUM words from its ranked list and presented them to a user
if no match for the given quadruple was found the algorithm backed off to a combined frequency count of the occurences of matches on three words only i.e. on the verb noun preposition verb prepositiondescription and noun preposition description
although the algorithm does not provide high enough accuracy from the point of view of word sense disambiguation it is more important to bear in mind that our main goal is the pp attachment ambiguity resolution
a high level relation is agent which relates an animate nominal to a predicate
figure NUM shows how this model is implemented as part of the dictionary wfst
because the induction of the decision tree for the pp attachment is based on a supervised learning from sense tagged examples it was necessary to sense disambiguate the entire training set
two words are similar if their semantic distance is less than NUM NUM and if either their character strings are different or if one of the words has been previously disambiguated
for example the noun bank can take any of the nine meanings defined in wordnet financial institution building ridge container slope etc
in order to determine the position of a word in the semantic hierarchy we have to determine the meaning of the word from the context in which it appears
we will evaluate various specific aspects of the segmentation as well as the overall segmentation performance
for example given a sequence f1g1g2 where f1 is a legal single hanzi family name and
for that application at a minimum one would want to know the phonological word boundaries
space or punctuation delimited a chinese sentence in a illustrating the lack of word boundaries
it may seem surprising to some readers that the interhuman agreement scores reported here are so low
NUM wang li and chang also compare their performance with chang et al s system
first the model assumes independence between the first and second hanzi of a double given name
the second weakness is purely conceptual and probably does not affect the performance of the model
while neither method is consistently better in the german experiments we found that lexicographic orderings performed more poorly than the input based ordering of the input samples for the english experiments s the lexicographic ordering of the original algorithm is not always optimal
this software allows the use of several syntactic extensions spanish pot favor quieren pedirnos un taxi para la habitacidn trescientos diez
in the slt system s current domain of air travel planning atis a simple form containing about NUM questions extracts enough content from most utterances that it can be used as a reliable measure of a subject s understanding
NUM the algorithm interfaces a subprocess that incrementally attempts to build natural language expressions out of the descriptors selected
the second round work is ongoing currently with emphasis on two aspects NUM to promote the algorithm particularly those associated with agents and cache carefully NUM to improve the quality of knowledge base by both enlarging the size of the relevant resources textual corpora unknown word banks etc and refining the lexicon tagged corpus and the rule base
NUM figure NUM shows that theorist abduces that t3 is attributable to a misunderstanding on russ s part in particular to his having incorrectly interpreted one of mother s utterances as a pretelling rather than as an askref
trans r rule relating that sense of serve to one sense of french desservir trule eng fre lex simple serve flyto desservir servecity
these constraints enable the speaker to construct a referring expression that she believes will allow the hearer to identify the referent
these social norms allow participants to add to their common ground by adopting the inferences about an utterance as mutual beliefs
this plan serves to coordinate their activity and so agents will have intentions to keep this plan in their common ground
the text planner thus needs to re express the contents of its kb into the ideational notation used by the sentencerealiser
the rule requires that the agent believe that it is mutually believed that there is an error in the current plan
the rules that we have given are used to update the mental state of the agent and to guide its activity
however this constraint fails since there are two objects that match the description rather than one as required
second our work has built on the research done in modeling clarifications in the planning paradigm and on plan repair
the initial sections do not mention ahab it is here that d k reveals its highest values and here too we find the largest discrepancies between e v n and v n
an important part of our work involves accounting for clarifications of referring expressions by using meta actions that incorporate plan repair techniques
if we eliminate the possibility of parallelism as in NUM john revised his paper and then bill handed it in
NUM the man who gives his paycheck to his wife is wiser than the man who gives it to his mistress
extended parallelism in some cases the elements involved in a sloppy reading may not be contained in the minimal clause containing the ellipsis
the readings derived by our analysis depend on the core relations that hold between the coreferring noun phrases in the antecedent clauses
from the second clause we know there is an elided eventuality e22 of unknown type p the logical subject of which is the teacher t
furthermore the generality of the approach makes it directly applicable to a variety of other types of ellipsis and reference in natural language
for instance in the sentence e13 c13 the first word daxiang elephant is the topic and the second word bizi nose is the subject elephant is the focus of the discourse but it is the subject nose that is very long not elephant
the variables contain two pieces of information an index of the input segment referenced by the variable relative to the current position in the index string and a possibly empty list of phonological feature values to change in the input segment
the extended semantics of locate is therefore locate d where d has the form x y
type v such as dego z yang miss in the lexicon on the basis of which our local grammars are constructed we could obtain a more reliable result as shown in the following table figure NUM
finally given a target word in a particular context some clusters in the dendrogram can be activated by the context then we can make use of the definitions of the target word and the words in the clusters to determine its correct sense in the context
NUM association for computational linguistics computational linguistics volume NUM number NUM table NUM case markers and their possible grammatical functions
since we currently have only english travel data we developed english analysis and generation grammars for english to english translation or paraphrase using the phoenix system
however these appear to be highly dependent on the method used for obtaining the language models and did not seem to form a consistent pattern
preliminary tests on unseen data indicate that the simple classifier correctly identifies sub domains classified according to the first dimension about NUM of the time
now that we do n t try to specify all word senses in the semantic space for a word in a particular context it may be the case that we can not directly spot its correct sense in the space because the space may not contain the sense at all
null introduction spoken language understanding systems have been reasonably successful in limited semantic domains i the limited domains naturally constrain vocabulary and perplexity making speech recognition tractable
therefore it provides excellent prerequisites for producing natural referring expressions in terms of both descriptors selected and structural appearance
could be a time price twelve dollars and fifteen cents or one thousand two hundred and fifteen dollars room number flight number etc
each sentence will be parsed in parallel by a number of sub domain grammars each of which is faster and less ambiguous than a large grammar would be
if however y is a nameholder n locate returns the value field of the pair inf whose name field is held by n
the translation of an utterance is manually evaluated by assigning it a grade or a set of grades based on the number of sdus in the utterance
in addition the noise rate noisetokens total tokens is NUM NUM for the esst training set and NUM NUM for the travel domain training set
the core of the project is a joint government contractor committee whose goal is to specify an architecture for tipster ii
this minimization issue can be interpreted in different degrees of specificity which also has consequences on the associated computational complexity
note that here we refer to such database searches and not to the string searches as offered by lycos webcrawler excite etc
in ward s method the internal variance of a cluster is the sum of squared distances between each observation in the cluster and the mean observation for that cluster i.e. the average of all the observations in the cluster
the values of these features are defined much like the unrestricted collocations above except that these are restricted to the NUM most frequent content words that occur only one position to the left or right of the ambiguous word
this is undesirable and may point to a need to expand the feature set in order to reduce ties for the em algorithm a high standard deviation means that the algorithm is not settling on any particular maxima
at each step in ward s method a new cluster ckl with the smallest possible internal variance is created by merging the two clusters ck and cl that have the minimum variance between them
in these experiments we used NUM pos features pl1 pl2 pr1 and pr2 to record the pos of the words NUM and NUM positions to the left and right of the ambiguous word
one possible explanation for the consistency of results as feature sets varied is that perhaps the features most indicative of word senses are included in all the sets due to the selection methods and the commonality of feature types
for example mcquitty s method was significantly more accurate overall in combination with feature set c while the em algorithm was more accurate with feature set a and the accuracy of ward s method was the least favorable with feature set b
where s a b and c denote specific values of s a b and c respectively and p slb and p s c are defined analogously to p sia
assume that q represents a factor u x v of some string in lx and p represents a factor NUM e f of some string in lr where NUM lul
oleada also uses tuit crl s tipster user interface toolkit and tdm crl s tipster document manager
if NUM includes some occurrence of b then r can not match pa and the positive evidence of r will not exceed iei NUM k contrary to our assumption
we denote by parent p the parent node of implicit node p and by label p q the label of the edge spanning implicit nodes p and q
the next function runs faster than slow scan and can be used whenever we already know that u is an implicit node in the tree u completely matches some path ill the tree
similarly to the case of algorithm NUM this is the highest score achieved in l and other transformations with the same score can be obtained from some of the implicit nodes immediately dominating p and q
at each encountered node of t and at the implicit node of t corresponding to the last successful match create an a link to the paired implicit node of t
intuitively speaking positive negative evidence is a count of how many times we will do well badly respectively when using v on w in trying to get w
headword lists consist primarily of the root form of a word however searches can be performed using morphological variants
stlbtypc lag i uring this process mui i itai e uses the following knowledge bases a the st rgical deed lexicon a lexicon of surgical deed concepts containing about NUM tokens with their collcepl type cc st rgical deed
NUM waarna de jkontale lob wm z jn adherenties wordt vrijgemaakt null after that the frontal lobe has been freed the specifications of van zijn adherenties cc pathology and NUM van mect both the conditions on the filler lbr tim r indirect object of the surgical deed concept and the conditions on the filler of the r post mod of the non surgical deed concept
the set consists of a slot called role for the type of semantic link a slot called arg lbr the pointer to the clement in the sentence which is linked to the surgical deed concept a slot called cc tbr the concept type of the element which is linked and finally a slot called ind for the indication i value of the function of the linked element
the results are shown in the following three table s for named entity template element and scenario template
the systems that make up that group can be considered to have gotte n their different f measures just by chance
note that coreference was not characterized by f or any other unified measure because of the linkages that were being evaluated
after all it is the human interpretation of the task definitions that informs the systems during development
the parameters in the f measure used are such that recall and precision scores are combined with equal weighting
the general method employed to analyze the muc NUM results is the approximate randomization method described in NUM
the statistical program outputs th e significance and confidence levels in a matrix format for the analyst to inspect
the row headings contain the f measures for the systems an d the rows are ordered from highest to lowest f the columns are ordered in the same way as the rows and the header s contain the numerical order of the f values rather than the f value itself because of the size of the table on the page
the depth of a tree is defined as the length of its longest path
it accomplishes this by maintaining a large corpus of analyses of previously occurring utterances
if once occurring subtrees were ignored the maximum parse accuracy decreased to NUM
the existence of unobserved rules is unacceptable from such a competence point of view
so participants rather than just passively hoping that they have understood and have been understood actively listen for trouble and let each other know whether things seem okay
in systems that use a lexical grammar i.e. whore part of the grammatical knowledge is stored outside the non terminals of the grammar proper using subcategorization frames associated with terminals words in ihe lexicon the peril likewise lcb s that this resource becomes bhmted over time with options exercised only in certain settings or when the word is used in a marginal sense
if the speaker is sincere she actually believes the content of what she expresses if the hearer is trusting he might come to believe that she believes it
NUM note that although linguistic intentions often express that an action is intended e.g. questions express an intention that the hearer answer the two conventions are independent
NUM in the figure we have used the symbol intend to name both the intention to achieve a situation in which a property holds and the intention to do action
in particular we shall address the following questions how russ decides after first concluding that t1 was a pretelling that he will respond with an askref
this act signals a misunderstanding because the linguistic intentions associated with it are incompatible with those previously assumed ruling out an explanation that uses the default for intentional acts
the model treats different features in the input such as the mood of a sentence or the presence of a particular lexical item as manifestations of different speech acts
since at most all iei edges might share a common vertex the data structure has to be a multiset which contains iei copies of each vertex
although expressed not p t and expressednot p t represent the same state of affairs the latter expression avoids infinite recursion by theorist
in the small test sample shown this system achieved NUM recall for correct brackets
this created substantial additional ambiguity for the system which had to distinguish prepositions from particles
the par null titioning chunks do appear to be somewhat harder to predict than basenp chunks
voutilainen claims recall rates of NUM NUM or better with precision of NUM or better
various optimizations proved to be crucial to make the tests described feasible
table NUM first ten partitioning chunk rules
the test set in all cases was 50k words
again the possessive marker was viewed as initiating a new n type chunk
some interesting adaptations to the transformation based learning approach are also suggested by this application
eric brill introduced transformation based learning and showed that it can do part of speech tagging with fairly high accuracy
the process of dialog described here is a kind of interactive theorem proving where the guidance down paths can come from either the user s knowledge or system knowledge
the interrupt will cause control to pass to another existing proof tree that was previously frozen or to a new one aimed at the newly presented goal
this may come for example from a new goal suggested by the domain processor or from a statement by the user causing movement to a different subdialog
if the input indicates that the user has not performed some primitive action make the appropriate inference about the user s knowledge about how to perform this action
for example if the machine has no means to manipulate some variable and the user does the only alternative is to make a request of the user
this system maintains an and or goal tree to represent the problem solving space and it engages in dialog in the process of trying to achieve subgoals in the tree
the computer will make suggestions about the subgoal to perform next but it is also willing to change the direction of the dialog according to stated user preferences
when the user is preparing to respond with utterance NUM the system expects the response to pertain to the command put the knob to one zero
the expectation cost e will be small if the meaning of the utterance as generated by the translation grammar precisely matches an expected meaning for the currently active subdialog
some people who spend a lot of time contributing to online forums have reported typing speeds which are considerably higher than this range but they still can not approach normal conversation speeds
it can be easily verified that the representation of resentation of fig NUM is a valid s form
the first is ttmt they neutralize certain details of syntactic structure that de not carry easily between languages
NUM which is different from the previous u form although tile predicate argument rehitions are exactly tile sanie in hoth cases
ad hoc formatters transform the dgraphs into formatting instructions for the targeted output medium
consnlting the list of composition rules we see that the only applicable rnle is c2
by contrast this secondary mechanism of montague o ammar is graduated to a central position
where x i x are hesh identiliers and by renaming each such htbel i resp
x one obtains a flew tree where argunmnl numbers have been replaced by argument haines
dr u7 i want to travel in the evening
the annotated scores are the product of the transition probabilities times NUM between the previous dialogue act the potential insertion and the current dialogue act which are provided by the statistic module
for the task of dialogue act prediction a knowledge source like the network model can not be used since the average number of predictions in any state of the main network is five
therefore to predict the nth dialogue act sn we can use the previously uttered dialogue acts and determine the most probable dialogue act by comput
this contextual information is decomposed into the intentional structure the referential structure and the temporal structure which refers to the dates mentioned in the dialogue
while text and sentence planning may sometimes be combined a realizer is almost always included as a distinct module
NUM we would encourage looking at harder cases for ne evaluation
we have begun making a distinction between lightweight techniques and heavyweight processing
in the st system the discourse processor performs an additional task
figure NUM parser output partial parse found for the example sentences
when matched against the sentence mr
this avoids overfitting to the development data
NUM our te system by design employs no domain specific knowledge
we hope to estimate this similarity by the length of the path through branches NUM and NUM and derive an equation xs x4 sirn wl w2
the first key feature is the use of statistical modeling to guide processing
n e the ne system uses a generic sgml parser to read messages
using our method we built a maximum entropy model for part of speech tagging
there can be many possible criteria when to stop the generalization algorithm
nodes linked with the c relation
this was boiled down to NUM nodes by the constraint removal algorithm
this proved to decrease the number of required iterations by about tenfold
we will have the equation as follows p mr
thus we will retain in the lattice only the predictive atomic features
in this case the reference probability for a feature is computed as
to model this observation we can introduce a complec feature i.e.
now our model will predict the probability for the word mr
the situation is reversed for but which usually connects two adjectives of different orientations
note that for these prediction models dissimilarities are identical for similarly classifted links
NUM a seiner tochter ein mpsrchen erz hlen wird er
so for example in grammar NUM one finds that when the left corner is a maximal projection NUM in all cases this is caused by the x form of the grammar
in this parser empty categories are postulated by the lr parser when building structure and their licensing is immediately checked by the appropriate condition on rule reductions shown in figure NUM
ken top this morning since always that kimono
experimentation with different kinds of algorithms suggests that some amount of compilation of the principles might be necessary to alleviate the problem of inefficiency but that too much compilation slows down the parser again
second in the measure of complexity frank does not count the cost of choosing which elementary tree to unadjoin or unsubstitute or the cost of backtracking if the wrong decision is made
it is desirable for a syntactic analyser to make use of linguistic theories to obtain at least in principle the same empirical coverage as the theory and to capture the same generalizations
m separating x from lexical information yields more compact data structures i propose a parser that uses two compiled tables one that encodes structural information and the other that encodes lexical information
they focus on the resultant state
if c were not checked nlab would not distinguish between a feet and a feet thus it would output ah ah ai ai lcb af af rcb
these claims are supported in the next section where i discuss the properties of an implemented parser which computes simple complex and multiple chain formation as exemplified in figure NUM
in this paper we present a corpus based method that can be used to build semantic lexicons for specific categories
NUM er wird seiner tochter ein m rchen erzpshlen miissen
NUM in the analyses of 10a a trace flmctions as a verbal complement
since the comps list of the head is variable any constituent is a possible complement
this means that variance seems to be a good measure for selecting a set of effective contexts in the clustering process
before any linguistic processing is carried out the word sequence at the top of the n best list is the most preferred one as only recognition preferences shown by position in the list are available
another problem that was pointed out by hinrichs and nakazawa themselves is sentences like NUM
currently the relative mode with a NUM passing rate i.e. NUM of the training tokens pass through the margin is used in our implementation
the decomposed phrase levels and the corresponding syntactic scores for the correct and the top candidate are shown in table NUM a and b respectively
finally a parameter tying scheme is proposed to tie those highly correlated but unreliably estimated parameters together so that the parameters can be better trained in the learning process
then the misclassification distance denoted by d k for selecting the syntactic structure synj k as the final output is defined by the following equation
lex l2 syn l2 this model uses a trigram model in computing lexical scores and the l2 mode of operation in computing syntactic scores
a story tell b well er ihr ein mgrchen erz ahlen m lcb issen wird
p x i m NUM x i is estimated by the following equation p xm i m NUM c xl xm i x NUM
furthermore comparing the results in table NUM and table NUM we find that the performance with the robust learning procedure is much better than that with the smoothing techniques
so other methods have to be tried for languages that use extensively prefixes infixes or suffixes
the inclusion of new words is not difficult because all the information that is required is their frequency
so the np is on the left and on the right of the same rule and a recursion happens
the system may be adapted to the user updating the frequencies in the lexicons and the probabilities of the tables
each entry in the dictionary is composed by a word its frequency and its recency of use
but to minimize the problems related to that approach tee rest of the sentence is treated using the second approach
one possibility is to group the suffixes depending on their syntactic function to make it possible to have an easy automatisation
so the key question is are the word prediction methods that we have previously shown useful for inflected languages
we close with a discussion of our current directions
the next known component is hecke leaving a residual string hohen which has to be analyzed by means of the syllable model
yet due to its listing of more than NUM million customer records it provides an exhaustive coverage of name related phenomena in german
the transition from root to first which is labeled syllmodel is a place holder for a phonetic syllable model
the first set henceforth training data was a subset of the data that were used in building the name analysis grammar
besides being among the ten largest german cities frankfurt and dresden also meet the requirement of a balanced geographical and dialectal coverage
we ran both versions on two lists of street names one selected from the training material and the other from unseen data
two versions of the german tts system were involved in the evaluation experiments differing in the structure of the text analysis component
it then checks whether it can be lexicalized as a modifier by looking ahead voice ms jones is an NUM year old hypertensive diabetic female patient of dr smith undergoing cabg
one important consideration here is the recognition of the ethnic origin of a name and the application of appropriate specific pronunciation rules
this is in part acceptable due to the fact that magic s output is closer to formal speech such as one might find in a radio show as opposed to informal conversation
in order to produce brief speech for time pressured caregivers the system both combines related information into a single sentence and uses abbreviated references in speech when an unambiguous textual reference is also used
magic s content planner then uses a multimedia plan to select and partially order information for the presentation taking into account the caregiver the briefing is intended for nurse or physician
magic s architecture is shown in figure NUM
for example when referring to the devices which have been implanted speech can use the term pacemaker so long as the textual label specifies it as ventricular pacemaker
secondly in contrast to the global analysis reported in the previous section we investigate the structural idiosyncrasies of each domain in the brown corpus
for example susanne tag nnj2 can be mapped to lob tags nns or vbz in the above experiment
the developer may be able to obtain one or more of those modules from the contractor s and to modify them for his purposes with minimal effort
by definition this is also the probability p x i g assigned to x by the grammar g
unification grammar means that grammatical categories incorporate features that can be assigned values so that when grammatical category expressions are matched in the course of parsing or semantic interpretation the information contained in the features is combined and if the feature values are incompatible the match fails
this window contains a microphone icon that indicates the state of commandtalk ready listening or busy an area for the most recent recognized string to be printed and an area for text messages from the system to appear confirmation messages and error messages
the push andhold method generally seems more satisfactory for a number of reasons push and hold leads to faster response because the system does not have to wait to hear whether the user is done speaking click to talk tends to cut off users who pause in the middle of an utterance to figure out what to say next and pushand hold seems natural to military users because it works like a tactical field radio
in our experience the users are willing to use interactive operation to improve translation quality but never to recover from incomprehensible output
when the user find an appropriate word s he only has to push the return key to enter the word into the original application
when the expression denwa wo kakeru is entered the morphological analyzer recognizes it as an idiomatic expression and retrieves information from the idiom dictionary
however interactive support for conventional mt systems does n t seem suitable for these users since they are primarily intended for professional translators
this dictionary based interactive translation approach allows the user to fully utilize syntactic information in the dictionary while maintaining clarity of the result
consequently v n is smaller than e v n
is it possible to understand this qualitatively different pattern in terms of the discourse structure of these novels
yet the correct translation for j ll is indeed the cross harbor tunnel and not the sea bottom tunnel
for each newspaper the available texts were brought together in one large corpus preserving chronological order
it was matched to the cantonese characters and which separately mean vulgar folk name litle ghost and male
most mt systems do not employ a l ow wful lexica list
this reuse pre empts the use of other word tokens among which diagnostic plots for two dutch newspapers
our exl erimental implementa tion of a pa tto rn l ased
tt shows one of the l riva tion
d q eridcnci s tiia n
sel f several lutlterns for a specific typo of ttgroolll 311t
the randomization test proceeded as follows the sequence of words of a text was randomized NUM NUM times
ha re i e n l roposed to account for them
afler this step one pass through each document is all that is required to calculate a context vector for each document
we assume that semantic description of nlis is described by such an rldt
there is no difference between capital letters and small letters in chinese and no difference between singular and plural forms of the same term
we shall also consider the possible uses of our work in general nlp
section NUM contains discussion including related and future work
we do not suppose that the output formula contains pure database predicates
thus the horizontal axis can be viewed as displaying the text time measured in word tokens
if the predicate explicitly specifies that the referent has some attribute e.g.
we found that many of the mistaken translations resulted from insufficient data suggesting that we should use a larger size corpus in our future work
the idea is to find the node vector that is nearest in vector space to the input vector
the information used in the translation process is an ldt
existential equivalences in kldt s logic will not be allowed
figure NUM variations of tile lasslhcatlon results for the editorial articles by the number of domain
the words legislative and council were both matched to c r and similarly we can deduce that legislative council is a compound noun collocation
so for keeping the domains well balanced we combined NUM domains to NUM manually
there fore we can chussit y these articles into the authors specialties autonlalically
this is strongly suggestive of state transitions in finite state models of language parsing etc
after processing the last symbol the parser verifies that NUM NUM s
figure NUM engcg syntax and morphosyntactic level of the dependency grammar
as discussed in section NUM NUM the worst case run time on fully parameterized cnf grammars is dominated by the completion step
because of the collapse of transitive predictions this step can be implemented in a very efficient and straightforward manner
where p s x is induced by the rule probabilities according to definition l a
both of these approaches are restricted to grammars without unbounded ambiguities which can arise from unit or null productions
if the value x NUM is relatively big we consider that the kanji i is distributed unevenly
for example the editorial article qbo many katakana words is classified into three domains
the reasons why the result is far worse than the results of the other are NUM
figure NUM variations of the classification results for tensei jingo by the number of domain
section NUM provides references to other work on centering algorithms
i rolll llow oli these kanji characters are called the domain specific kanji characlcrs
v is a fragment of u usually but not necessarily connex the scope of the ambiguity
as the usual notion of ambiguity is too vague for our purpose it is necessary to refine it
for lack of space we can present only a few of the interesting examples from the same dialogue
the first case often happens in the case of anaphoras ref or in the case where some information has not been exactly computed e.g. taskdomain decade of month but is necessary for translating in at least one of tile target languages
further extralinguistic and sure disambiguation may be performed NUM by an expert system if the task is constrained enough NUM by the users author or speakers through interactive disambiguation and NUM by a human expert translator or interpreter accessible through the network
a paragraph can be segmented in at least two different ways into utterances or an utterance can be analyzed in at least two different ways whereby the analysis is performed in view of translation into one or several l ngugges inthe context o i a certifin generic task
tense lcb pres past books lex book n cat noun there would be NUM proper representations one with tense pres and the other with tense past
however as soon as they are taken out of context they look again as artificial as linguist s examples although many studies on ambiguities have been published the specific goal of studying ambiguities in the context of interactive disambiguation in text and speech translation has led us to explore new ground and to propose the concept of ambiguity labeling
in table NUM NUM NUM indicates the extraction ratio used
if the rule is correct in the majority of times it was applied it is obviously a good rule
from a pre tagged training corpus it constructs the suffix tree where every suffix is associated with its information measure
this system divides a string into three regions and from training examples infers their correspondence to underlying morphological features
however NUM to NUM of word tokens are usually missing in the lexicon when tagging real world texts
unlike other approaches we do n t require the corpus to be pre annotated but use it in its raw form
the longer the length the smaller the sample which wilt be considered representative enough for a confident rule estimation
this process is applied until no merges can be done to the rules which have scored below the threshold
thus we are mostly interested in how the advantage of one rule set over another will affect the tagging performance
for instance by applying this rule to the word undeveloped we first segment the prefix un and if the remaining part developed is found in the lexicon as vbd vbn we conclude that the word undeveloped is an adjective jj
however sometimes sub optimal languages were initially selected and occasionally these persisted despite the later appearance of a more optimal language but with few speakers
figure NUM displays the trees involved
figure NUM elementary trees and attached semantic
section NUM NUM 8joker trees are similar to elementary tree
figure NUM word scores across n best ranks for the
table NUM results on filtering and subsequent repairing strategy
operationally an annotator starts by generating a small initial corpus and then invokes the learner to derive a set of pre tagging rules
singular value decomposition and the translation matrix
but evaluation rests on an oracle and for text processing that oracle is the training and test corpora for a particular task
this enlarged corpus was then used to derive a new rule set to be applied to the next group of documents and so on
a tool that generates phrase based kwlc keyword in context reports to help the user identify common patterns in the markup
historically tailoring language processing systems to specific domains and languages for which they were not originally built has required a great deal of effort
boundaries are not really difficult to modify but the time required is approximately the same as inserting a tag from scratch
of course there are two important advantages that a human expert might have over the machine algorithm linguistic intuition and world knowledge
can not be generated faster than this and that these data may indeed be helpful in the learning procedure of some other systems
the current set of utilities includes a string matching mechanism that can automatically replicate new markup to identical instances elsewhere in the document
it is provided for readers who are skeptical about our use of transcribed word boundaries
coders were also able to agree on the subclass of question k NUM
that the speaker has some reason to believe but is not entirely sure about
g right em go to your right towards the carpenter s house
f left of the bottom or left of the top of the chestnut tree
example NUM g towards the chapel and then you ve f towards what
often refusal takes the form of ignoring the initiation and simply initiating some other move
example NUM g do you have the west lake down to your left
neither of the coders used instruct ready or check moves for this dialogue
since game beginnings are rare compared to word boundaries pairwise percent agreement is used
the initial results from our prototype are very promising but extension of system coverage and subsequent large scale evaluation is needed
the central difficulty is finding a representative sample of genuine errors by native speakers in context with the correct version of the text attached
with respect to the editor that pet is attached to this could correspond to a log of errors already encountered in the file being edited
the recursive analogical transfer module matches the input shallow syntactic tree against the source language portions of example shallow syntactic trees
but if a program is to catch such errors very soon after they are entered it will have to operate with less than the complete sentence
suggestions have been made to look for low frequency words in corpora and news mail archives and to the longmans learner corpus not native speakers
on the right hand end can start rule possibilities of words can be considered using the prediction facility already built into the parsing process
partial parsing needs to be adapted to support the idea of the pet purview partial parsing that accepts any string likely to constitute part of a sentence
thanks to all who offered advice on finding data and to doug mcllroy sue blackwell and neil rowe for sending me their misspelling lists
corrections for the re could include there or the red but the present system will not generate the former possibility
tag transitions are checked against an occurrence matrix of the tagged lob corpus using positional binary trigrams similar to those used in the spelling checks mentioned above
the source language input expression is matched against the source language portion of each example pair and the best matching example
in this framework the most important step consists of robustly matching the recognized input expression with the stored examples
an experimental configuration of the sra system produced the same output as the baseline configuration and has been disregarded in the tallies thus the total number of systems tallied is eleven NUM miscategorizations of entities as person
this yields a more efficient search procedure during the matching process while only assuming non controversial notions of syntactic constituency
NUM p iie p lcb distortl distortz rcb e
after an initial distribution is estimated these probabilities can be adjusted to solve translation problems due to idiosyncratic exrmples
at the moment in parsing sentences we are using a temporal expressions recognizer a noun phrase recognizer a simple verb phrase recognizer and a simple anaphoric binder
all these resources are to be put together into a knowledge acquisition workbench kawb which is under development at ltg of the university of edinburgh
the simplest form of linguistic description of the content of a machine readable document is in the form of a sequence or a set of words
however general and full scale parsing is not required for many tasks of knowledge acquisition but rather a robust identification of certain text segments is needed
this method was fairly successfully used in our experiment however a large scale evaluation of sense discrimination for constituent words is still needed to be done
the lexico semantic generalizer is a tool which extracts general lexico semantic patterns in an empirical corpus sensitive manner analogous to that used to automatically extract word class dendrograms
the system recognizes which structure should be used and presents it to the knowledge engineer with optional explanations or a question guided strategy for filling it up
the workbench outlined in this paper encompasses a number of tools which facilitate different stages of knowledge extraction analysis and refinement based on corpus processing paradigm
recognition of alternative ways of identifying an entity constitutes a large portion of the coreference task and another critical portion of the template element task and has been shown to represent only a modest challenge when the referents are names or pronouns
we however will not present any technical details and suggestions on an actual implementation because the workbench should be able to incorporate different implementations
for each source a converter which transforms source information into sgml marked data which then can be used in the workbench should be written
NUM a john has been acting quite odd
our algorithm learned NUM transformations from the NUM sentence training set
for example a user might be able to ask what is meant by organization in the second paragraph of the document
we have studied three aspects of robustness in such a system accent differences mixed language input and the use of common feature sets for hmm based speech recognizers for english and cantonese
there is a genuine difference in analysis
the recognition results are shown in figure NUM
the final unique task the tokenizer addresses is hyphenated word splitting
precision figures for the method were collected as follows
for example canada and tuesday are women s names
this evidence is weighted separately from the wordnet look up results
it is often used anaphorically in wall street journal text
we were disappointed by the performance of the pronoun resolution component
the words in either string are on a stop word list
this is a labor intensive task
for this reason semantic filtering is required to raise precision
in one of the tokenizations hyphenated words are left unaltered
one problem in a multilingual system is accent variability
it uses a lexicon with just over NUM NUM entries
however derivational morphology is a good cue in the following ways
many of the important sentences are long and some cause our parse decoding stage some problems in that they time out
attribute possible values information flow depart city dc milano roma tofino trento to agent arrival city ac milano roma tonno trento to agent depart range dr morning evening to agent depart time dt 6am NUM am NUM pm NUM pm to user table NUM attribute value matrix simplified train timetable domain a NUM hello this is train enquiry service
file NUM and the whole structure of the hierarchy in wordnet for each of the concepts
this heuristic trusts that related concepts will be expressed using the same content words
they form a bidirectional pattern where the reading as a preposition is confused with the reading as a subordinating conjunction
kehler centering for pronoun interpretation terry assuming he refers to terry the occurrence of him later in the sentence in 6e2 and similarly tony in 6e3 causes the cb to be tony thus changing the bindings that constitute the various transition possibilities and in this case the predicted preferred referents
the problem lies more generally in their proposal to utilize rule NUM along with the definition of cb un l to interpret pronouns any algorithm incorporating this proposal will have to process an entire sentence before determining the preferred referents of pronouns no reordering of processing within the bfp algorithm can alter this fact
there is a well known contrast between passages that are coherent by virtue of being a narration as is the case for sentence 7c and follow on 7d versus those coherent by virtue of parallelism as is the case for sentence 7c and follow on 7d
this property results from the fact that determining the transition type between a pair of utterances un and un l requires the identification of cb un l and a noun phrase pronominal or not can occur at any point in the utterance that will alter the assignment of cb un l
given these definitions their algorithm as described in wic is defined as follows the pronominal referents that get assigned are those which yield the most preferred relation in rule NUM assuming rule NUM and other coreference constraints gender number syntactic semantic type of predicate arguments are not violated
the reason for this difference is attributable solely to the fact that the pronoun him occurs in 6e2 because there are two non coreferring pronouns in 6e2 one must refer to tony and because tony is cp u6d by definition tony is cb u6e2 instead of terry
this is what occurs in the analysis of passage NUM whereas the cb of sentence 6el is these sentences in that any garden path in sentence 6e3 may be resolved earlier than in 6el and 6e2 specifically at the point at which tony is reached
the key operation in NUM is to find bestpaths a NUM c where NUM is an unweighted factored automaton and c is an ordinary weighted fsa a constraint
in either regard it is unclear why the inclusion of the phrases with him in variant 6e2 and with tony in variant 6e3 should lead to such varied predictions for the subject pronoun
in fact the only aspects of un and un NUM utilized by the bfp algorithm are the identities of cb u cp un cb un l and cp u i as well as the types of expressions used to refer to them
without knowing what classes of constraints may appear in grammars we can say only so much about the properties of the system or about algorithms for generation comprehension and learning
but neither it nor any other trick can help for all grammars for in the worst case the otp generation problem is np hard on the number of tiers used by the grammar
to reduce the size of these automata it is convenient to label arcs not with individual elements of el which is huge but with subsets of e denoted by predicates
the preliminary results are extremely encouraging
in geometric terms the difference is a distance
figure NUM symmetric approach system generation
it consists of the evaluation of a teachability predicate for the antecedent on which we will concentrate here and of the evaluation of the predicate lsanaphorfor which contains the linguistic and conceptual constraints imposed on a pro nominal anaphor viz
our study was based on the analysis of twelve texts from the information technology domain it of one text from a ger null man news magazine spiegel NUM and of two literary texts NUM lit
since the method for computing levels of discourse segments depends heavily on different kinds of anaphoric expressions pro nominal anaphors and textual ellipses are marked by italics and the pro nominal anaphors are underlined in addition
die software seite wurde im handbuch dagegen stiefmsttedich behandelt the software part was in the manual however like a stepmother treated bis auf eine karge seite mit einem inhaltsverzeichnis zum hp modus sucht man vergebens weitere informationen
the anaphor hl NUM does not co specify the cp of the utterance which represents the end of the hierarchically preceding discourse segment ut but it co specifies an element of the c NUM ut
segment which ended with u4 is now continued up to u6 at level NUM as a consequence the centering data of u5 are excluded from further consideration as far as the co specification by any subsequent anaphoric expression is concerned
the phrase das diinne handbiichlein the thin leaflet in u5 does not co specify the c v NUM u4 but co specifies an element of the c NUM u4 instead viz
centered segmentation has also the additional advantage of restricting the search space of anaphoric antecedents to those discourse entities actually referred to in the discourse while the cache model allows unrestricted retrieval in the main or long term memory
however useful this strategy might be we see the danger that such a surface level description may actually hide structural regularities at deeper levels of investigation illustrated by access mechanisms for centering data at different levels of discourse segmentation
the tal recognizer given in this paper was implemented in scheme on a sparc station NUM NUM
it is easy to see that conditions for parts a and b are met for this gap
NUM compute nodes rl p z rp
a k can be thought of as a minimal node in this sense the
e b is no longer quite true because of the presence
e check for adjunctions involving nodes realized from steps a and b
all internal nodes of elementary trees are labeled with nonterminal symbols
the algorithm we present here can he modified to include constraints
steps la lb and 4a can be computed in o nem p
quite recently an o n NUM m n algorithm has been proposed
consider the french e lcb luivalent of in NUM
in particular in both the experiments subjects reaction to recognition errors was characterized by an alteration in the way of speaking
in addition a number of errors identifying entity names were made some of those errors als o showed up as errors on the template element task and are described in a later section of this paper
as an aut omatic and easy to use measure of the translation errors the levenshtein distance between the automatic translation and the reference translation was calculated
in more general cases and applications there will ahvays be sentence pairs with word alignments for which the monotony constraint is lot satisfied
the transformations are very much dependent on the language pair and the specific translation task and are therefore discussed in the context of the task description
thus the approach amounts to a first order hidden markov model hmm as they are used successfully in speech recognition for the time alignment problem
under the assumption that the alignment is monotone with respect to the word order in both languages an efficient search strategy for translation can be formulated
among all possible target strings we will choose the one with the highest probability which is given by bayes decision rule brown et al
the purpose of the text transformations is to make the two languages resenable each other as closely as possible with respect to sentence length and word order
a distinguishing description is completed c9 c11 NUM NUM the exit in case of full success
the results that have been presented here are based on uniform treatment for each confusion set
since the word principle is listed in the right half of the table it must
the goal is to treat the different morphological variants of a word as the same entity
bigram creation is performed for the words that were not removed in the context reduction step
training the system consists of processing the training sentences and constructing an lsa space from them
in other words each training sentence is used as a column in the lsa matrix
the remaining NUM of the corpus was used to test how well the system performed
the next two columns show the training and testing corpus sentence counts for each confusion set
multiplying t s and d together perfectly reproduces the original representation of the text collection
the perpendicular distance is a more robust average
in this section we first describe the empirical and methodological framework in which our evaluation experiments were embedded and then turn to a discussion of evaluation results and the conclusions we draw from the data
in this paper concepts are superordinate terms which contain one or more subordinate metonymic terms
the second relation depicted in table NUM isbou u denotes preference relations dealing exclusively with multiple occurrences of resolved anaphora i.e. bound elements in the preceding utterance
so the maximum number of responses for this item over the year would be approximately NUM NUM
we reran the scoring program using the augmented lexicon on the same set of data
the ones contained in cl u i and cy u bound discourse elements are thematic with the theme rheme hierarchy corresponding to the ranking in the cls
the items which we work with are either experimental or have been administered as paper and pencil exams
the response sets typically range from NUM NUM responses which we have to use for training and testing
second we have gathered some evidence still far from being conclusive that the functional constraints on centering seem to incorporate the structural constraints for english and the modified structural constraints for japanese
based on empirical evidence from a free word order language german we propose a fundamental revision of the principles guiding the ordering of discourse entities in the forward looking centers within the centering model
relevant synonyms of the metonyms can be added to expand the lexicon using dictionary and thesaurus sources
it is crucial that a domain specific lexicon is created to represent the concepts in the response set
in the interviews collected in the second experimented the subjects that made errors expressed the fatigue in experimenting repetitive recognition errors
educators of the deaf and other people working with deaf individuals report that such checkers geared toward the errors of hearing writers frustrate deaf students
even some of these underlying rule patterns however were questionable since their incidence is very low maybe once in the whole corpus or their form is so linguistically strange so as to call into doubt their correctness possibly idiosyncratic misparses as in NUM
y NUM w w w NUM a theoretical approach the theoretical starting point is that punctuation seems to occur at a phrasal level i.e. it comes immediately before or after a phrasal level lexical item e.g. a noun phrase
since the results of the two studies do not seem incompatible it should prove possible to combine them and it will be interesting to see if the results from using the combined approaches differ at all from the results of using the approaches individually
the theoretical approach not only seems to confirm the reality of the generalised punctuation rules derived observationally since they all seem to have an adjunctive nature but it also gives us a framework with which those generalised rules could be included in proper linguistically based grammars
our findings also reveal that many of the error classes perhaps as many as NUM of those found in our initial sample analysis could be attributed to language transfer from asl to english if language transfer is defined in the way that we sugges0
if a reference created by the simulated computer is the same as the one in the real text then it belongs to the matched type
a second area of research which may also shed some light on the acquisition process encompasses research in language assessment and educational grade expectations e.g. ber88 lee74 cry82
in acquiring english as a second language there is considerable linguistic evidence that the acquisition order of english features is relatively consistent and fixed regardless of the first language ling89 db74 bmk74
the student s writing is fairly good but still contains many errors involving appropriate verb morphology it s very hard for me to tell you what i am think about xxx because NUM notice that the errors look very similar to each other
the over and under generated types are counted as the numbers of nonzero and zero anaphora associated with zero and nonzero leaf nodes in the tree
now the response generator will take this information along with data from the user model only a portion of which is described in this paper and decide which errors to correct in detail and how each should be corrected
so for example the model reflects the fact that the ing progressive form of verbs is generally acquired before the s plural form of nouns which is generally acquired before the s form of possessives etc
while our belief is that for specific individuals this ordering may be influenced by some factors such as the instructional situation or significant transfer from l1 these basic findings should play an important role in a model of second language acquisition
in deciding how to say it the system can attempt to use the constructions that are currently being leamed as well as those that have been mastered and so provide the student with correct exemplars of the second language
initially we consider simply the decision of whether a generated anaphor should be a zero pronoun z or some nonzero phrase nz
conversely the undergenerated cases of zero anaphora for instance are the sum of zero anaphora associated with the leaf nodes labeled with nonzeros
for each of the candidate strings of the list
an important idea in the theory is the effect of the linguistic expressions in utterances constituting the discourse and the discourse segment structure on each other
it is embedded to the c value approach for automatic term recognition atr in the form of weights constructed from statistical characteristics of the context words of the candidate string
in the final rule the implementation of the test of the beginning of a discourse segment is not quite as straightforward as the other constraints
step NUM since c value is a measure for extracting terms the top of the previously constructed list presents the higher density on terms among any other part of the list
the candidate terms will be now re ranked according to
we take the top ranked candidate strings and from the initial corpus we extract their context which currently are the adjectives nouns and verbs that surround the candidate term
tr2 s text differs from tr3 s in the three topic shifts tr2 generates zero anaphora for these shifts while tr3 generates full descriptions
this gives a brief overview of our sd system
among the groups of initial and subsequent references we focus on the one indexed j lafengzheng de xian the string pulling the kite
the dialogue ends when the quit state is reached
in addition to the general requirement of
this is desirable since it allows a system to take into account only those syntactic constraints on lexical choice that are relevant
time in verb manner as adverb NUM stock indexes surged at the start of the trading day
an example of news article classified in bop balance of payments and trade is shown in figure NUM
for instance if similarities are normalized to the NUM NUM interval eleven levels of prob
this approach integrates the use of a lexical database and a training collection in a vector space model for tc
some of the correspondence words include additional information which describes the constraints where the correspondence words are u d
the lexical chooser must first decide which relation to map to the main clause and which one to embed as a modifier
NUM the only information this mapping encodes is that one option to lexicalize the domain relation class asslgnt in english is a possessive clause
in order to continue the lexicalization of the arguments the lexical chooser must specify which constituents in this fd require further processing
when unifying an input fd with such a disjunction the unifier nondeterministically selects one branch that is compatible with the input
in fuf tags like NUM are encoded with the path notation such as lcb verb number rcb
this mapping is illustrated in the bottom half of figure NUM for the example sentence NUM ai has programming assignments
suppose a sentence contains a surgical deed concept but the system is not able to make a semantic link between the surgical deed concept and another concept in the surgical deedclause
4b de catheter wordt in de wend geplaatst there is only one set of slots expressing the link between two non surgi cal deed concepts in a sentence ex
if the st rgical deed concept is a noun or an infinitive form of the verb the r direct object is marked by thc preposition wm i valuc l van
the system is preferential in that it has defined based upon corpus observation and sublanguage modelling a priority ranking hierarchy of concepts when occurring in combination to lbrm one complex concept
this system is still work in progress and more work remains
in the above example the preposition met with is an indicator for means and it is the absence of a preposition which points to the direct object
ex NUM enkele j agmenten discus ni cc anatomy worden weggenomen cc surgical deed cs r cnlovc
the input of tile linking module is the sentence segmented in nps i ps and verbs wilh if relevant their concept type assigned by the concept type assigmncllt module
v the guessing module v NUM introduction the guessing module of the multitale system deals with the semi automatic augmentation of the concept lexicons lexicons of surgical deeds and non sm gical deeds
they can also be encoded as single feature structures
the analysis of agentive modification is also more complex
the modifiers glass and silicon denote materials
qualia structure and the compositional interpretation of compounds
it alternates between a processand a result interpretation
the corresponding forms in italian utilize the preposition di
these lexical entries are encoded using typed feature structures
these schemata are essentially phrase structure rules
these are addressed in the next section
the translations are as the deep processing line of verbmobil provides them
accuracy is consistent across these corpora and tag sets
nametag tm japanese and spanish systems as used for met
keep in mind that no specific affixes are prespecified
the same engine is used for different languages using language specific plug ins such as tokenizers patterns lexical data alias generators morphological analyzers and segmenters
the average ambiguity of verbs among these categories is NUM NUM for our sample in the rsd
availability of source information to support any tagging activity is problematic general purpose sources e.g.
in table NUM and NUM the kernel words for both noun and verb classes are reported
the third fixes might md not reply nn vb
semantic information greatly improve the precision of a verb syntactic classification
in this paper a framework to bootstrapping lexical acqusition in a given domain has been outlined
be a potential source document taken form our rsd domain
derivation of ordered dependency trees proceeds recursively by generating the dependent relations for a node according to the word and acceptor at that node and then generating the trees dominated by these relation edges
in NUM NUM of cases the tagging system selected one tag
character z appears in the word
the right part of the figure represents the chart
the probability of each object is defined as follows
figure NUM abstract dependency tree of a sentence
consider the part of speech tagging example above
the larger entries are inscribed into the bold box
is used in the given tree and NUM otherwise
resolvent the next f after the dialog date augmented with the fillers of the fields in tueurrent at or below the level of time of day
the formalism does not distinguish between various types of ambiguity nor are ambiguity class specific rule sets needed
it also facilitates information extraction since some of the information in the extraction templates is in the form of literal tex t strings which some systems have in the past had difficulty reproducing in their output
as well candidates that have as a first part only a nonstressed always split
it concerns the natural semantics of excessive diphthongs the avoidance of hiatus in the spoken language
we will first illustrate the diltereltce between file original rormuh e iul t the oues we used lind theft introduce the word bits co st ruction
implementation issues are discussed as well as the problem of words written in uppercase letters
the beginning or end of the input word is indicated by the symbol o
double vowel blends are phonetically equivalent to vowels and their orthographic representation comprises two vowel characters
the second type of questions word bits questions are on clusters and word bits such as what is the 29th bit of the previous word s word bits
tokenization might be ambiguous in that it might generate alternative token lists for specific vowel sequences
let us first formally specify the impermissible hyphen points in the particular sequences of v1 v4 rules
that diphthong is not excessive and should always be split rule f12 table NUM
thus uppercase words are transformed to lowercase hyphenated and transformed back to uppercase forms
the limitations of ie systems the properties of typical ie systems such as fastus also make this task challenging
the detection of complex terms assumes a crucial role in improving robust parsing and pos tagging for lexical acquisition thus supporting a more precise induction of lexical properties e.g.
these two modes depend on the fact that verbmobil is only translating on demand i.e. when the user s knowledge of english is not sufficient to participate in a dialogue
as mentioned in the introduction it is not only important to extract the dialogue act of the current utterance but also to predict possible follow up dialogue acts
the mechanism proposed here contributes to the robustness of the whole verbmobil system insofar as it is able to recognize cases where dialogue act attribution has delivered incorrect or insufficient results
looking at the sample dialogues we then checked which of the proposed dialogue acts could actually occur together in one utterance thereby gaining a list of admissible dialogue act combinations
in figure NUM the plan recognizer issues a warning after processing the deliberate dialogue act because this act was inserted by means of a repair operator into the dialogue structure
in addition to the dialogue acts in the main dialogue network there are five dialogue acts which we call deviations that can occur at any point of the dialogue
the latter dialogue act can occur at any point of the dialogue it refers to utterances which do not contribute to the negotiation as such and which can be best seen as thinking aloud
the trace of the dialogue component is given in figure NUM starting with pro null in this example the case for statistical repair occurs when a reject does not as expected follow a suggest
nominal forms are in fact lexicalization of domain concepts proper nouns acronyms as well as technical concepts are mostly represented as nominal phrases of different length and complexity
by first order unification in this paper
in this framework a term is more than a token or word to be searched for as it stands in a more subtle relation with a piece of information in a specific knowledge domain
figure NUM declarations for aprolog representation of
it also permits universal quantification and implication in the goals of clauses
where n is a meta level function of type ta tat
raise tn abe p app p tm
the original implementation of this system was in fact done in this manner
figure NUM implementation of the ccg category sys tem
for the dialogue module there are two major points of insecurity during operation
lcb uiilciale visit6 rcb lcb aereoporto di fiumicino visit6 di fiumicino rcb lcb ufficime della guardia rcb lcb visit6 aeroporto rcb
for example our method could not find an association between house and walls because house was not entered as a hyponym of building but of housing and housing does NUM our previous experiment found correct relations for NUM dds from which only NUM were in the syn hyp mer class
ill order to accomplish the task further reference information has been used two standard domain specific thesaura have been used for comparing the result of the terminology extraction in the environmental domain enea corpus
a deictic expression is resolved into a time interpreted with respect to the dialog date e.g. tomorrow last week
the decision in step NUM is again sensible to a principled way a language expresses concept specifications but needs also to be specific to the given knowledge domain i.e. to the underlying sublanguage
the most popular approach to dealing with segmentation ambiguities is the maximum matching method possibly augmented with further heuristics
in b is a plausible segmentation for this sentence in c is an implausible segmentation
now for this application one might be tempted to simply bypass the segmentation problem and pronounce the text character by character
it is important to bear in mind though that this is not an inherent limitation of the model
it is based on the traditional character set rather than the simplified character set used in singapore and mainland china
NUM in chinese numerals and demonstratives can not modify nouns directly and must be accompanied by a classifier
the use of the good turing equation presumes suitable estimates of the unknown expectations it requires
other good classes include jade and gold other bad classes are death and rat
an ilt once it has been augmented by our system with temporal and speech act information is called an augmented ilt an ailt
recall rtterrnsf ldterrns rtterrns for example within the section related to the head smaltimento we have NUM rt terms of which NUM is in cnr and aib respectively and NUM are in td
NUM out of bounds this state is reached when the system realizes that the user either wants to access information that the system is not equipped to handle or access legitimate information in ways the system is not designed to handle
the processing rules by their specificity eliminate the need for many of the heuristics
this addition terminates the activation of knowref m whoisgoing from the first turn
typically an utterance will be interpreted according to the expectation that matches it most closely
the strongest level is reserved for attitudes about beliefs and suppositions
in particular both interpretation and repair are treated as explanation problems modeled as abduction
suppositions are terms that name propositions that agents believe or express
a set a i of potential assumptions about misunderstandings and metaplanning decisions
nothing in his beliefs rules out abducing explanations from either the askif or the askref interpretation
we also assume that russ believes that he knows whether or not he knows
third it integrates the generation of sentences and the generation of texts and hypertexts in a simple seamless way
for each component of the extracted temporal structure counts were maintained for the number of correct and incorrect cases of the system versus the tagged file
by consulting the subcategorization information the parser can eliminate the second option as incorrect
grammar NUM differs minimally from grammar NUM because each head is instantiated by category
uous frequencies of x y respective135 within a simple noun phrase i.e. the frequency of patterns x y and patterns x y respectively
to establish a preference metric we use two statistics NUM the frequency of the pair in the corpus f w1 w2 and NUM the number of the times that the pair is locally dominant in any np in which the pair occurs
NUM a john seems ip t to like bill b
it is also shown that this method of building long distance dependencies can be computed incrementally
for n NUM i.e. algorithm nlab the inequality is never satisfied
moreover a parser that makes direct use of a linguistic theory is more explanatory
in particular we explored an extension of the phrase based indexing in the clarit tm system deg using a hybrid approach to the extraction of meaningful continuous or discontinuous subcompounds from complex noun phrases exploiting both corpusstatistics and linguistic heuristics
for example if only the individual words junior and college are used for indexing both junior college and college junior will match a query with the phrase junior college equally well
in fact the set of small compounds extracted from a noun phrase can be regarded as a weak representation of the meaning of the noun phrase since each meaningful small compound captures a part of the meaning of the noun phrase
while the clarit system does index at the level of phrases and subphrases it does not currently index on lexical atoms or on the small compounds that can be derived from complex nps in particular reflecting cross simplex np dependency relations
while the large amount of unrestricted text makes nlp more difficult for ir the fact that a deep and complete understanding of the text may not be necessary for ir makes nlp for ir relatively easier than other nlp tasks such as machine translation
in particular we substituted the pes for the default nlp module in the clarit system and then indexed a large corpus using the terms nominated by the pes essentially the extracted small compounds and single words but not words within a lexical atom
note that the ll compilation does not maintain the paired rankings of actions and rules
the pes which was not optimized for processing required approximately NUM NUM hours per 20megabyte subset of ap89 on a NUM mhz dec alpha processor NUM most processing time more than NUM of every NUM NUM hours was spent on simplex np parsing
for examt le the first line
lit the pa rser rllles too
NUM prefer tile shortest deriwl tion
a nd l restore NUM NUM tpsg polla rd
qua NUM ity through us r intera ction
we use pentium NUM 133mhz 32m memory to calculate
it is argued that an efficient and faithful parser can be built by taking advantage of the way in which principles are stated
the advantage of this treatment is that common properties of language here certain classes of verbs are expressed by common principles
in order to explore the validity of the proposed hypothesis about the modularity of the parser an analyzer for english was developed
in accounting for the growth rate in the space of hypothesis of these modified algorithms two factors must be taken into consideration
the classic example is the use of natural classes of distinctive features in phonology in order to compact several rules into one
for the kind of input lengths that are relevant for natural language the size of the grammar easily becomes the predominant factor
moreover as we saw in th e lr tables projections to the same level have the same pattern of conflicts
NUM the icmh is not sufficient to predict a specific parsing architecture but rather it loosely dictates the organization of the parser
hinri hs and nakazawa introduced the concept of argument attraction into the hpsg framework
this is possible because two different levels of representation for combinatorial and order information are used
NUM a seiner tochter erziihlen wird er das m irchen
it is therefore possible to avoid multiple structures in the mittelfeld
reape assumes word order domains as an additional level of representation
in such a domain all daughters of a head occur
another basic assumption of reape is that constituents may be discontinuous
the comps list of the extracted element therefore is specified
we do the test on chinese in the same way
the pattern matching phases of fastus may intermittently misanalyze phrases that serve as antecedents for subsequent referring expressions
realpro is licensed free of charge to qualified academic institutions and is licensed for a fee to commercial sites
what has happened here is that the learning method has overgeneralized a rule predicting kje after the velar nasal because the data do not contain enough information to correctly handle the notoriously difficult opposition between words like leerling pupil takes etje and koning king takes kje
n is the number of words in the training corpus
purthermore the error rate on pje is doubled when onset information is left out from the corpus
this decision tree should be read as follows first check the coda of the last syllable
this decision tree has tests feature names as nodes and feature values as branches between nodes
a decision tree constructed on the basis of examples is used after training to assign a class to patterns
categories t roposed in phonology are inspired by articulatory acoustic or tmreeptual phonetic ditferences between speech sounds
this situation has led to the t roposa l of many dillhrenc phonoh gieal category systems
the default class is tjc which is the allomorph chosen when none of the other rules apply
speech sounds behmg to different categories i.e. are defined by ditferent e tures
figure NUM shows the difference of the three methods
few works have examined unsupervised word segmentation in japanese
character unigram probabilities can be estimated from unsegmented texts
we applied the viterbi re estimation procedure three times
the results are also diagramed in figure NUM
we set fl NUM NUM throughout this experiment
NUM NUM classification of the effects of be estimation
all of these provided performance comparable to or better than previous attempts
we then incrementally increased the amount of training data and repeated the experiment
machine learning to acquire the rules rather than expensive manual knowledge engineering
it differs from other common corpus based methods in several ways
NUM since english language resources e.g.
a trainable rule based algorithm for word segmentation
table NUM english training set sizes
we discuss results of english experiments with different amounts of training data
initial score of test data NUM sentences was NUM NUM
we divided the hand segmented data randomly into training and test sets
stevenson s model comes the closest in design to the current principle based message passing model in that it uses distributed message passing as the basic underlying mechanism and it encodes gb principles directly i.e. there are precise correspondences between functional components and linguistic principles
uy english grammar network korean grammar network n head domina e adjunct dominance complement dominance NUM m specializati specifier dominance barrier figure NUM network representation of english and korean grammar
we argue that the efficiency of the system is not simply a side effect of using an efficient programming language i.e. c but that the algorithm is inherently efficient independent of the programming language used for the implementation
to make the parsing time vs sentence length distribution of these three parsers more comparable we normalized the curves the parsing time of each of the cfg parses was multiplied by a constant so that they would have the same average time as principar
however we claim that the efficiency of the system is not purely a result of using an efficient programming language c this has been achieved by running experiments that compare the performance of the parser with two alternative cfg parsers
the grammar for each language is encoded as a network of nodes that represent grammatical categories e.g. np nbar n or subcategories such as v np i.e. a transitive verb that takes an np as complement
the total size of it is NUM million bytes
an item is a triplet that represents an x structure surface string attributevalues sourc where surface string is an integer interval i j denoting the i th to j th word in the input sentence attribute values specifies syntactic features of the root node fl and source messages is a set of messages that represent immediate constituents of fl and from which this item is combined
by a computation of a pda we mean a sequence qi v t NUM wl h t 6n wn n NUM a pda is called deterministic if for all possible configurations at most one transition is applicable
a context free grammar cfg is a NUM tuple g s n p s where s and n are two finite disjoint sets of terminal and nonterminal symbols respectively s e n is the start symbol and p is a finite set of rules
for technicm reasons we sometimes use the augmented grammar associated with g defined as g t st n t pt st where st t and NUM are fresh symbols s t su lcb t l n t nu s t and pt p u s t s rcb
informally we have NUM uw NUM w if configuration NUM w can be reached from NUM uw without the bottom most part NUM of the intermediate stacks being affected by any of the transitions furthermore at least one element is pushed on top of NUM
we define the binary relation t on configurations as the least relation satisfying NUM w NUM w if there is a transition NUM NUM and NUM aw t NUM w if there is a transition NUM a NUM
observe that these steps involve the new stack symbols a a NUM ili that are distinguishable from possible stack symbols lcb a a NUM rcb lr we now turn to the second above mentioned problem regarding the size of set 7dgr
for any set q c i2lt closurel q is the smallest collection of sets such that i q c elosure q and ii aft e closure q and a NUM pt together imply NUM closure q
one may notice that the critical content of this task is the computation of the similarity between case fillers nouns in equation NUM
such applications of the system have a clear relationship to the communicational goals of mutual enjoyment and enhancement of the perceived status of the speaker
it may be that work currently underway in the field of natural language processing can be of assistance in suggesting ways to accomplish this task
based on analysis by the qjp parser we removed sentences with missing verb complements in most cases due to ellipsis or zero aaaphora
finally we have employed both wordnet and reuters to get a betterrepresentation of undertramed
however learning is conducted using both languages simultaneously thus removing any donor language biases
explanations of the approach will be given from the frame of reference of two simultaneous languages
however figure NUM can be viewed as a one step approximation to the optimal solution
word stem i andj context vectors figure NUM learning law cost function subject to the constraints
the unified hash table provides the mechanism to translate a stem into an associated context vector
the symmetric learning approach exploits the learned relationships without the need to translate the foreign text
attributes of the symmetric learning approach are as follows NUM
the block diagram for this process is shown in figure NUM
the context vector for attack is moved in the direction of its neighbors
figure NUM shows the context window for the stemmed text centered on the tie word attack
in each case we use NUM sentences for training with NUM of these sentences held out for smoothing
the smoothing parameters i c are trained through the forward backward algorithm on held out data
for n gram models we tried n NUM NUM for each domain
we maintain a single hypothesis grammar which is initialized to a small trivial grammar
notice that this grammar models a sentence as a sequence of independently generated nonterminal symbols
the ideal grammar denotes the grammar used to generate the training and test data
for example 3this is not to be confused with the use of the term triggers in dynamic language modeling
consider the task of calculating the objective function p oig p g for some grammar g calculating
this material is based on work supported by the national science foundation under grant number iri NUM to stuart m shieber
parameters are set to reflect the frequency of the corresponding rule in the parsed corpus
the structure of the database and the strategies for its implementation have been chosen out of pragmatic considerations
would n t it have been possible to re use these context models for our purposes
the slots some of which have a simple internal structure of their own identify elements of the job ad
the analysis technique that we have chosen to implement falls into the relatively new paradigm of analogy or example based processing
as soon as a sentence must be produced several times with only slight alterations a template based approach is more appropriate
we have focused on domain specific terms and classifications not covering generic language issues nor providing a general lexicon and thesaurus
the sets of edges ai form a partition of the edges of s
using such extensions discourse representation theory can be mirrored in the ist formalism
we also present experimental results comparing the performance of different cost assignment methods
experimental results are reported comparing methods for assigning cost functions to these models
these actions together with associated probabilistic model parameters are as follows
process states are distinct from but may include head automaton states
we have experimented with a number of model types including the following
for other purposes the probability of strings may be of more interest
the head automata model and transfer model were originally conceived as probabilistic models
operator characteristics can be altered as shown
we do not know whether the same holds for the brill tagger and the brill and xerox guessers since we took them pretrained
precision seems to be slightly less important since the disambiguator should be able to handle additional noise but obviously not in large amounts
statustype is the type of report requested
usually the threshold is set in the range of NUM NUM points and the rule sets are reduced down to a few hundred entries
since they are more accurate than ending guessing rules they were applied first and improved the precision of the guesses by about NUM
a more appealing approach is automatic acquisition of such rules from available lexical resources since it is usually less labor intensive and less error prone
from a training corpus it constructs a suffix tree where every suffix is associated with its information measure to emit a particular pos tag
this is quite different from the output of the original brill s guesser which provides only one pos tag for an unknown word
NUM here we want to clarify that we evaluated the overall results of the brill tagger rather than just its unknown word tagging component
we noticed a certain inconsistency in the markup of proper nouns nnp in the brown corpus supplied with the penn treebank
in order to keep him from missing tagging errors the grammatical function tagger is equipped with a function measuring the reliability of its output
haifa NUM israel francez cs technion ac
the past tense of the verb adds the condition t n
splitting the reference time temporal anaphora and quantification in drt
he pulled the blind down and went back to bed
trying to use the event times would give the wrong analysis
different concepts have been used in the literature as primitives
we will henceforth informally refer to this problem as partee s quantification problem
still the sentence needs to be interpreted relative to a reference time
NUM mary wrote the letter when bill left
the rpt is reset during the processing of the discourse
NUM NUM object classes NUM NUM NUM detection needs and queries
the architecture helps system support officers perform their work more effectively in several ways
documents are one of the links between the outside world and the tipster environment
the modular nature of the architecture supports review packages that have well defined characteristics
the system support officer is mainly concerned with the life cycle support of the application
modules that comply with the tipster icd specifications may be sharable by other applications
there are four components detection extraction annotation and document management
modules are loosely coupled communicating by means of shared data and control messages
each instance of a vector used in the derivation is represented as a single node which we label with that vector s lexeme
sports water sports winter sports military hospital
in this paper we differentiate between such ia tasks and the more complicated problem solving tasks where multiple sub problems are concurrently active each with different constraints on them and the final solution consists of identifying and meeting the user s goals while satisfying these multiple constraints
if the lower language consists of a single string then the relation encoded by the transducer is in berstel s terms a rational function and the network is an unambigous transducer even though it may contain states with outgoing transitions to two or more destinations for the same input symbol
all the work is based on some similarity metrics
an advantage of using word frequency lists is that there is so much data two corpora can be compared in respect of thousands of data points e.g. words
the aim is to fill the unfilled roles in the css due to anaphora or unattached pps
the order in which the two modules are called is based on efficiency deduced from statistical data performed on cobalt corpuses
however none of these methods has considered the way of dealing both phenomena in the same concrete system
the proposed procedure is based on successive calls to the anaphora module and to the pp attachment module
notice that even if each module is called several times there is no redundancy in the processing
NUM repeat NUM and NUM until all vps and anaphors are treated
this work was accomplished in the context of cobalt project lre NUM NUM dealing with financial news
both anaphora resolution and prepositional phrase pp attachment are the most frequent ambiguities in natural language processing
two of the main principles of the algorithm are a the algorithm is applied on the text sentence by sentence i.e. the ambiguities of the previous sentences have already been considered resolved or not
several methods have been proposed to deal with each phenomenon separately however none of proposed systems has considered the way of dealing both phenomena we tackle this issue here proposing an algorithm to co ordinate the treatment of these two problems efficiently i.e. the aim is also to exploit at each step all the results that each component can provide
this gives you the semantic structuring of a particular set of wms according to another wordnet as compared to the source wordnet
moreover the discourse model as we have seen contains semantic information about the sentence
on the other hand engcg does not spell out part of speech ambiguity in the description of ing and nonfinite ed forms noun adjective homographs when the core meanings of the adjective and noun readings are similar nor abbreviations vs proper vs common nouns
previous approaches such as our own identifinder system described earlier in this paper and evaluated in muc NUM have used manually constructed finite state patterns
NUM the differences were jointly examined by the judges in order to see whether they were due to i inattention ii incomplete specification of the grammatical representation or iii an undecidable analysis
this procedure was successively applied to the three texts to see how much previous updates of the grammar definition manual decreased the need for further updates and how much the interjudge agreement might increase even after the first mechanical comparison cf
NUM dn represents determiners an represents premodifying adjectives sub j represents subjects fauxv represents finite auxiliaries and fmainv represents nonfinite m in verbs
using the same language model and nothing specific to spanish other than spanish training examples we are achieving scores even higher than in english
to compare the engcg morphological description with another well known tag set the brown corpus tag set engcg is more distinctive in that the part of speech distinction is spelled out in the description of determiner pronoun preposition conjunction and determiner adverb pronoun homographs as well as uninflected verb forms which are represented as ambiguous due to the subjunctive imperative infinitive and present tense readings
she had to ask because some of the six year olds from other schools who attend v inf v pres her classes know the names of as prep ad a many hard drugs as she does
is it possible to specify a grammatical representation descriptors and their application guidelines to such a degree that it can be consistently applied by different grammarians e.g. for producing a benchmark corpus for p arser evaluation
finally a disambiguated sample analysis of the above sample sentence syntactic tags are flanked with the sign NUM morphological tags and the base form are given to the left of the syntactic tags
our default assumption was that a difference in form is associated with a difference in meaning unless we could establish that the different word forms were related
the second problem is that a document can be relevant even though it does not use the same words as those that are provided in the query
dictionaries often make very fine distinctions between word meanings and it is n t clear whether these distinctions are important in the context of a particular application
for example the porter stemmer will reduce department to depart but this has no effect in the context of the phrase justice department
database data base occurred in about a NUM NUM distribution and the queries in which they occurred were significantly improved when the related form was included
this work will give us a better idea of how language processing can provide further improvements in ir and a better understanding of language in general
the absence of a lexicon causes the porter stemmer to make errors by grouping morphological false friends e.g. author authority or police policy
the literature mentions examples such as blind venetians vs venetian blinds or science library vs library science but these are primarily just cute examples
we can extend our classification to genres not previously encountered
furthermore an important feature of dialogue that is difficult to simulate via the woz paradigm is that of initiative
here again the appropriate baseline could be determined two ways
for binary decisions the application of lr was straightforward
within this stratified framework texts were chosen by a pseudo random number generator
the genre facet has the values reportage ed itorial scitech legal
means significantly better than baseline at p NUM
thus the number NUM for narrative under method lr surf
it would be a bad strategy to systematically request a repetition from the user as users are known to vary their pronunciation during subsequent attempts volume pitch rate as they would do when a human dialogue partner made a speech recognition error which has the undesired side effect of deteriorating speech recognition results
those dimensions are then characterized in terms such as informative vs
these strings are a proper subset of vlcl c2c c3 v2 and they do not contain consonants c2c c NUM thus c1 c2 is degenerated to cl while cl c c by definition and hence vlcl c2c c3 v2 are always hyphenated as vl c1 c2c c3 v2
this is not sufficient because vowel sequences that are not next to consonants may split as in iio rom w vov papa i o in nou noussia hyphenator for modern greek the orthographic representation of the various word substrings
consequently the experimenter told the subject due to misrecognition your words came out as faster
thirdly a tentative effort was made to incorporate semantic information about the noun phrase into the prediction algorithm
in the results to be presented the current subdialogue is based on global rather than speaker perspective
low heterogeneity scores will typically relate to corpora of a single language variety so here similarity scores may be interpreted as a measure of the distance between the two varieties
if both are events then the times are temporally close with the exact relation undetermined
the system determines potential antecedents for ellipsis by applying syntactic constraints and these antecedents are ranked by combining structural and discourse preference factors such as recency clausal relations and parallelism
used to guide ellipsis resolution is to our knowledge a new one NUM our current results involving parallelism provide support for this claim
to evaluate our identification criteria we performed a manual search for vpe occurrences in a sample of files constituting about NUM NUM of the treebank
NUM in this sample we found that the recall was NUM and the precision was NUM as depicted in table NUM
the contribution of clause rel is not evident individually if it is the only factor activated together with recency only performance in the complete corpus actually declines from NUM NUM to NUM NUM
a vp must be ruled out if the vpe is within a nonquantificational argument when a vpe occurs in an adjunct position the containing vp is a permissible antecedent
the vp convulsed mr gorboduc is penalized by the standard penalty value because it is not within quotations while the vpe is within quotations
to test the performance of the system we first obtained a coded file which indicates a human coder s preferred antecedent for each example
our work will aim to confirm the extent to which the potential strengths of an object oriented paradigm system extensibility component reuse etc can be realized in a natural language dialogue system and the extent to which a functionally rich suite of collaborating and inheriting objects can support purposeful humancomputer conversations that are adaptable in structure and wide ranging in subject matter and skillsets
NUM a pitch accent on a direct object like buch in NUM can serve to mark a number of constituents as focused NUM NUM the focus dature is usually assumed to proje ct
when all the cues proposed in the previous subsections are all applied to the baseline model the final experimental results are listed in table NUM
first as prevost himself notes it is very difficult to define exactly which items count as being of the same type
in NUM these would be the player and the minute fields of structures c and d shown in figure NUM
the former case is only allowed when another more global relation eq near synonym has been used see above
the paper describes some preliminary results of making this transfer for the janus system and some modifications that may be required
fully acceptable except that style is not completely natural
seligman observes that accepting the importance of these issues suggests a particular architecture for an experimental slt system which differs from systems described in other contributions in significant ways
suppose that a university has hired a consulting company to build an information system for its administration
however he is unfamiliar with the crow s foot notation used in figure NUM
the evaluation of modex is based on anecdotal user feedback obtained during iterative prototyping
modex output integrates tables text generated automatically and text entered freely by the user
the first version of modex for adm was supported by usaf rome laboratory under contract f30602 NUM c0015
general enhancements to the linguistic machinery were supported by sbir f30602 NUM c NUM awarded by usaf rome laboratory
these human authored texts are used by some of the predefined text functions to generate the descriptions
he points out the error to the analyst who can change the model
the statistics we used do not produce good results when the frequencies are low
anmogy sec l s to h lve tiever b en lheorise ill a ltionolingual frmnework maldng its hilingum al l li vtion questionable
if no suitable chains are found the search rectangle is proportionally expanded and the generation recognition cycle 2since distances in the bitext space are measured in characters the position of a token is defined as the mean position of its characters
church s solution was to look at the smallest of text units characters and to use digital signal processing techniques to grapple with the much larger number of text units that might match between the two halves of a bitext
for each point p x y lct x be the number of points in column x within the search rectangle and let y be the number of points in row y within the search rectangle
if the order of words in a certain text passage is radically altered during translation simr will simply ignore the words that move too much and construct chains out of those that remain more stationary
although it is not possible to compare simr s performance on these language pairs to the performance of other algorithms table NUM shows that the performance on other language pairs is no worse than performance on french english
null when lexical cognates are not being used the axis generator only needs to identify punctuation numbers and those character strings in the text which also appear on the relevant side of the translation lexicon NUM it would be pointless to plot other words on the axes because the matching predicate could never match them anyway
for example if a token at position p on the x axis and a token at position q on the y axis are translations of each other then the coordinate p q in the bitext space is a tpc NUM tpcs also exist at corresponding boundaries of text units such as sentences paragraphs and chapters
if ni is masculine and the nc headed by ni is unambiguously nominative NUM then nx v n2 g i is a training tuple case accusative rule
b put e x y z hold e2 to t n in words the conditions in NUM require the object denoted by the definite description to be linked by some bridging relation b possibly identity cf
allowing an input where the copula verb be is omitted in the grammar causes the past tense form of a verb to be interpreted either as the main verb with the appropriate form of be omitted as in NUM a or as a reduced relative clause modifying the preceding noun as in NUM b
to produce an adequate translation output from the input containing parts of speech there has to be a mechanism by which parts of speech are used for parsing purposes and the corresponding lexical items are used for the semantic frame representation
an input to the parser driven by a grammar which utilizes both syntactic and lexicalized semantic rules consists of words to be covered by lexicalized semantic rules and parts of speech to be covered by syntactic rules
total no of sentences i iii i no of parsed sentences i NUM ili NUM NUM no of misparsed sentences NUM NUM NUM i rate of misparse i.e.
these high rates of tagging accuracy are largely due to two factors NUM combination of domain specific contextual rules obtained by training the muc ii corpus with general contextual rules obtained by training the wsj corpus and NUM combination of the muc ii lexicon with the lexicon for the wsj corpus
the reason why we do not give the statistics of the parsing failure due to unknown words for the syntactic and the mixed grammar is because the part of speech tagging process which will be discussed in detail in section NUM has the effect of handling unknown words and therefore the problem does not arise
one way of reducing the ambiguity at an early stage of processing without relying on a semantic module is to incorporate domain semantic knowledge into the grammar as follows lexicalize grammar rules to delimit the lexical items which typically occur in phrases with omission introduce semantic categories to capture the co occurrence restrictions of lexical items
after the system was developed on all the training data of the muc ii corpus NUM sentences NUM words sentence average the system was evaluated on the heldout test set of NUM sentences hereafter test set
to accommodate sentences like NUM a b the grammar needs to allow all instances of noun phrases np hereafter to be ambiguous between an np and a prepositional phrase pp hereafter where the preposition is omitted
NUM a susan gave betsy a pet hamster
c betsy told her that she really liked the gift
computational linguistics volume NUM number NUM NUM b
NUM a susan gave betsy a pet hamster
the acquisition of additional partitioning collocations from co occurrence with previously identified ones is illustrated in the lower portion of figure NUM step 3c optionally the one sense per discourse constraint is then used both to filter and augment this addition
they should plan ahead to minimize the number of shifts
thus we can establish a preference for subject position
centering NUM a susan is a fine friend
he can not find anyone to take over his responsibilities
NUM initial decision list for plant abbreviated multiple times in the list in different collocations1 relationships including left adjacent right adjacent co occurrence at other positions in a k word window and various other syntactic associations
the algorithm is especially well suited for utilizing a large set of highly non independent evidence such as found here
sense a seeds plus newly added examples will tend to grow while the residual will tend to shrink
the details of this process are discussed in section NUM in brief if several instances of the polysemous word in a discourse have already been assigned sense a this sense tag may be extended to all examples in the discourse conditional on the relative numbers and the probabilities associated with the tagged examples
for instance doctor which has both medical and academic meanings co occurs with nurse within a medical topic and co occurs with professor within an academic topic
there exists a node v t e g input graph whose distance from e is NUM and it is connected to v with a branch
they appear in clusters at threshold NUM NUM as follows accountant audit bracket deduction filer income offset tax taxpayer convert conversion debenture debt holder out stand
if cancer is also connected with these three words both cancer and pneumonia the different subtopical words within a medical topic are included in a cluster
in this case when several biconnected components are connected in a ring articulation nodes could not be detected figure NUM
regarding words as nodes and co occurring re null latious as branches a graph can be constructed from a given corpus
for example when minimum distance of a b d is NUM and that of a c d is NUM then the anchor distance is NUM
we obtain clusters by accumulating adjacent nodes so that every branch has anchor branches and the resulting clusters include no duplicate branch
step NUM examine every pair of subgraphs a b andifa includes b then drop b the remaining subgraphs are defined as clusters
here the branch j v is the anchor branch so that e t is hindered to be the duplicate branch in the resulting cluster
therefore if in addition x entails t then x and t are equivalent logically
h was nuinber sevelt ill tit ra c
h ie NUM the results of the experiments
the value of table NUM shows the semantic deviation vmue of two nollns NUM
table NUM shows sample of the results of nouns with their semantic deviation values
this text was detected to have NUM unknown words
NUM NUM rule extraction phase NUM NUM NUM extraction of morphological rules
the lower confidence limit 7r l is calculated as
in figure NUM they are clustered with high similarity wflue NUM NUM
as a result these tol ics are clustered with low similarity wdue
in doing so we showed how a variety of examples that have been problematic for previous approaches are accounted for in a natural and straightforward fashion
transducers that are reduced and that are deterministic in the sense of finite state automata
line NUM actually builds the transition between NUM and e NUM labeled a a
for instance a is a label that verifies the conditions of line NUM
refers to the set of states s and is marked by the type type
where m is the number of categories andpj is the proportion of objects assigned to category j
for nominal data this statistic not only measures agreement but also factors out chance agreement
as we have seen in the previous section this method is not efficient
transductions that can be computed by some deterministic finite state transducer are called subsequential functions
these are the examples on which the two authors agreed on their coding of all the features
there are suggestions in the literature that allow us to draw general conclusions without these further computations
the transduction ty that generates the set of y decompositions is defined by ty idx
from NUM million dm the society expects this year in southeast asia a turnover of NUM million dm nominal and verbal constituents display person and number information nominal constituents also display case information
the errors belong mainly to three classes some errors appear predominantly with the statistical tagger and almost never with the constraint based tagger
if the main verb of sentence n has the same meaning as or a meaning included in that of sentence n NUM in the sense of hyponymy then it belongs to the topic
in the prototypical case when the intonation center occupies the rightmost position and other conditions discussed in section NUM are met so directly determines the underlying word order in the focus part of the sentence
if negation or another focalizer such as only even also is present then primarily its scope or its focus is constituted just by the focus of the sentence
many though not all such marked cases are accounted for by the parser described in section NUM the output language of this parser has been illustrated by examples NUM NUM
rule NUM also applies but otherwise only certain important regularities can be stated here on the basis of word order and grammatical values especially a definite noun group is often cb and an indefinite one regularly is nb
even in english there are instances of free word order i.e. of surface word order determined directly by tfa as in NUM NUM and NUM
NUM neighbor indef act give pret boy indef addr book indef obj NUM a painter arrived at a french village on a nice september day
since a linktr q dominates p we must have h2 u x v NUM
the topic focus articulation tfa is both expressed by grammatical means word order morphemes or their clitic versus strong shapes syntactic constructions position of the sentence stress or intonation center and semantically relevant
this implies that the main verb is always more dynamic than all its cb complementations and less dynamic than the nb ones i.e. in the scale of cd the verb stands immediately after or before the boundary between topic and focus
due to ongoing application efforts with tight deadlines the limited availability of experienced muccaneers an d prior investment in software to find names we put in less effort than on any of the mucs NUM NUM and NUM which we had previously participated in
NUM check whether g s associated functions are satisfied by c a if g has the form a b or b check all the entries in the chart that span the same range as c returning NUM if any have category b
have eaten the food while an example of the ditransitive case is NUM a j t b b t fpsu sbng rdn le c food give somebody null d give food to somebody the former phrase can be correctly parsed by the monotransitive rule
to check whether an entry matches the left corner of a rule or whether an edge can be extended by an entry we need to check not only that the category of the constituent is matched but also that the attached function if any is satisfied
based on definitions NUM the probabilistic chunker is presented as follows
the monotransitive phrase can still be parsed by this new rule since can not have the part of speech vnn NUM np y vn vp j
clearly the algorithm will be more time consuming than for cfgs because the match procedure will need to check not only the categories of the constituents but also their associated functions and this check will not tak constant time as for cfgs
we use multiple levels of semantic lexicons first a generic application independent lexicon with very shallow semantic information then th e te lexicon which provides more detailed entity related semantic information and finally the st level lexicon whic h provides detailed succession related entries
furthermore the official test was not even reflective of plum s performance since a set of rule variations that was known to improve performance was saved at the st level rather than at the te level whic h NUM
at that point we decided that the limited resources we had for muc should be devoted exclusively to improve scores on the application tasks ne te and st rather than trying to integrate the spatter parser for the application evaluations
NUM our next steps are to improve identifinder s prediction of aliases once a name has been seen and to add rules fo r low frequency cases e g improving performance on names that are quite unlike western european names
looking at figure NUM one can see that the precision of random sampling was surpassed by our training utility sampling method
ccd c expresses the weight factor of the case c contribution to the current verb sense disambiguation
sl x and s2 x are the highest and second highest scores for x respectively
in figure NUM when the x axis is zero the system has used only the seeds given by ipal
however such an approach implies a significant overhead for the manual training of each example prior to the generalization
in other words sentences in t have been selected as samples and are hence stored in the database
we ve also shown that for the cost of experimentation with different parameter combinations lsa s performance can be tuned for individual confusion sets
our method based on the utility maximization principle decides on which examples should be included in the database
however it occurs in only NUM of the test sentences and thus the baseline predictor scores only NUM for this confusion set
consequently we believe that lsa is a competitive alternative to a bayesian classifier for making predictions among words of the same part of speech
we also assessed the challenges presented by the data to a method that does not recognize discourse structure based on an extensively annotated corpus and our experience developing a fully automatic system
the individual cell values are based on some function of the term s frequency in the corresponding document and its frequency in the whole collection
larger context sizes up to the size of the entire sentence produced results which were not significantly different from the results reported here
during the verb sense disambiguation process the system discards first those candidates whose case frame does not fit the input
correspondingly using fewer terms in the initial matrix reduces the average running time and storage space requirements by NUM and NUM respectively
ence of mr in the following sentence discounts the probability of an article boundary by NUM NUM a factor of roughly NUM
it is instructive to compare the values of p with precision and recall for these default algorithms in order to obtain some intuition for the new error metric
this technique uses a simplified notion of lexical cohesion depending exclusively on word repetition to find tight regions of topic similarity
in contrast to hearst s focus on strict repetition kozima uses a semantic network to provide knowledge about related word pairs
this is the improvement to the model that would result from adding the feature g and adjusting its weight to the best value
for the segmentation task they might be used to gauge how frequently boundaries actually occur when they are hypothesized and vice versa
gradually as the cache fills with words drawn from the current article the long range model gains steam and r improves
for the wsj experiments which we describe first a total of NUM NUM candidate features were available to the induction program
is there a match between the spectrum of the current image and an image near the last segment boundary
a h w log pexp wlh kptri w i w 2w NUM j
the algorithm uses dynamic programming to build up in a bottom up fashion the scores for matching each node in t against each node of 2gl
the function matchi a v v is a measure of how well the nodes v and v align and is computed as follows
the kh a which makes it possible to align s m tenets quickly is that we place restrictions on the ways in which we align the parse trees
this prevents natural conversation not simply because of the time which is taken but because the delays completely disrupt the usual processes of isome typists can copy text much faster than this but constructing text takes more time even with informal text such as email
we can reduce tile computation time of the max term in NUM if we do not consider all of the o d pairings of the children of v and v
one possible way of analyzing this would be to employ a straightforward pattern matching approach searching for trigger phrases such as em ployer name is seeking job title with special processors for analyzing the slot filler portions of the text
table NUM shows an extract from the first data set
table NUM example of the data used for np clustering
the same filtering method NUM and clustering algorithm are applied in both cases
he puts some concepts in it by either validating a candidate term e.g.
planning in the term regional network planning and expansion e.g.
it is not yet known how often the most likely interpretation and the interpretation of the most likely combination of semantically enriched subtrees do actually coincide
for example in NUM the sequence v np is first investigated as a potential analysis of vp and then the sequence v s is investigated
in the parsing context here optimal performance would probably be obtained by encoding string positions with integers allowing memo table lookup to be a single array reference
we can then classify the anaphora corresponding to the decision tree of the rule as in figure NUM
restarts are considered to have the following form in shfiberg s work and elsewhere
the result shows that rule NUM is helpful for the decision as to whether to use a zero anaphor
the basic idea here is to investigate the positions of the antecedent and the anaphor in their respective clauses
in the following we divided the position of anaphora in their respective utterances into topic and nontopic cases
types and occurrence of antecedent anaphor pairs in the subset of test data corresponding to zero leaf of rule NUM
note in example NUM it is not always clear how much should appear in the repair
this rate looks quite promising however it does not truly reflect the use of different nominal forms
the conditions of minor discontinuity were not clearly stated and individual judgements on this are likely to vary
however this decrease in average matching rate does not negate the effectiveness of the salience constraint in tr3
we need to replace the existing partial parser with a better parser to improve the overall system accuracy
the second step m the summarization process is that of concept interpretation in this step a collection of extracted concepts are fused into their one or more higher level unifying concept s concept fusion can be as simple as part whole construction for example when wheel chain pedal saddle hght frame
this breakdown is motivated as follows NUM identification select or filter the input to determine the most important central topics for generahty we assume that a text can have many sub toplcs and that the topic extraction process can be parametertzed to include more or fewer of them to produce longer or shorter summaries
most ia tasks have only one discourse purpose and that is to get some information from the system
it is our belief that it is possible to develop an almost pure system for ia tasks
finalization routines are then invoked on the near final template to fill the org type slot and to normalize the geographical fills of the org locale and org country slots
except for the org descriptor slot the fil l rules line up more readily with semantic notions than with syntactic considerations e g maximal projections
our part of speech tagger is closest among the components of our muc NUM system to brill s original work o n rule sequences s NUM NUM
the parasenter is also intended to filter lines in the text body that begin with r but see our error analysis below
finally the template generation module forms the final te and s t output by a roughly one to one mapping from facts in the inferential database to templates
in particular we had failed to merge short name forms that appeared i n headlines with the longer forms that appeared in the body of the message
entries marked with daggers t correspond to knowledg e gaps e.g. missing or incorrect rules the other entries are coding or design problems
the overall score is remarkably close to our performance on th e dry run test set which served as our principal source of data for ne training and self evaluation
for example an early rule in the lexical rul e sequence retags unknown words ending in ly with the NUM tag adverb
we knew we would need an alternative to traditional linguistic grammars even to the somewhat non traditional categoria l pseudo parser we had in place at the time
a final guideline which is not likely to be violated in the transcriptions is sg i on user commitments
lindop and tsujii NUM and dorr NUM including manyto many word mapping argument switching and head switching
a second reason why software engineering tool or method development is difficult and time consuming is the problem of objectivity
inform the dialogue partners of important non normal characteristics which they should take into account in order to behave cooperatively in dialogue
the earlier incarnation had used a corpus of considerably less than NUM megabytes of text compared to the NUM megabytes used for the results described herein
each of these produces a set of candidate translations for various segments of the input which are then combined into a chart figure NUM
in the case of one of the primary errors recency commits a self correcting error without this luck the remainder of the dialog would have represented additional cumulative error
first the phrase is checked to make sure it has n t already been recognized and linked by the ne system
this part of the system is entirely dependent on the domain and can b e customized at will by the developer
the te system then uses the current name to find all references to the current and old o r future names
later these links will allow for the replacing of noun phrases with for example normalized organization template elements
by testing that the variation is part of a hyphenated name we could then allow the variation to be valid
louella experienced two bugs during the evaluation which caused at least one documen t not to be scored in each task
figures NUM and NUM illustrate f mea null sure rankings in the descriptor and locale country slots respectively
the discrepancy in the score for the person object is due to the incorrect string fill for the name of alan gottesman
the value of the dice coefficient between the word and the source collocation w is at least ta where t is an empirically chosen threshold and NUM the word appears in the target language opposite the source collocation at least tf times where tf is another empirically chosen threshold
to our surprise we found that the filtering process may even increase the quality of the proposed translation
for each member x of p champollion computes the dice coefficient between the source language collocation w and x
however if only very few translations are missed in practice the algorithm is indeed a good choice
this phenomenon occurs even if we are allowed to vary the dice thresholds at each stage of the algorithm
this speeds up the system when opea ating in tnmsl tion memory mode as would be the case in a system used to translate revisions of previous texts
the ineifieiency of parsing in hpsg is due to the fact that what kind of constituents phrasal signs would become is invisible until the whole sequence of applications of rule schemata is completed
a qua si sign n can not rel resent a parse tree whose height is inore than n while a sign can express a parse tree with any height
the intuition behind this definition is ps l lays the role of a non termimd in cfg though it is actually a quasi sign o
note that in has NUM unification is replaced with nnifiability checking which is more efficient than unification in terlns of space an l time
the report of the board of auditors to the general assembly which incorporates the observations of the executive director of unicef on the comments and recommendations of the board of
during the generation of the transition arc since the first argument of the query is it is frozen
then the signs are unified with the head dtr value and the non head dtr value of the feature structure of the schema fs r
before giving the definition of las we detine the notion of a quasi sign which is part of a sign and constitutes l as
during the course of the four trec conferences we have built a prototype ir system designed around a statistical full text indexing and search backbone provided by the nist s prise engine
the simplest word based representations of content while relatively better understood are usually inadequate since single words are rarely specific enough for accurate discrimination and their grouping is often accidental
considering fig NUM again a precedence restriction for likes to precede its object has no effect since the two are in different domains
a typical full text information retrieval ir task is to select documents from a database in response to a user s query and rank these documents according to relevance
summary statistics for routing runs are shown in tables NUM and NUM in general we can note substantial improvement in performance when phrasal terms are used especially in ad hoc runs
indeed our unofficial manual runs performed after trec NUM conference show superior results in these categories topping by a large margin the best manual scores by any system in the official evaluation
it should be noted that the most significant gain in performance seems to have occurred in precision near the top of the ranking at NUM NUM NUM and NUM documents
the same can be said about language and english unless language is in fact a part of the compound term programming language in which case the association language fortran is appropriate
this is certainly a serious problem since we now attach more weight to concept matching than isolated word matching and missing a concept can reflect more dramatically on system s recall
while many problems remain to be resolved including the question of adequacy of term based representation of document content we attempted to demonstrate that the architecture described here is nonetheless viable
this design is a careful compromise between purely statistical non linguistic approaches and those requiring rather accomplished and expensive semantic analysis of data often referred to as conceptual retrieval
the first notion is a more structural term the second notion a more process oriented term
furthermore we show how to parse idiomatic sentences and how to process the proposed seinantic representation
NUM was fiir cinch ikiren h at tom kim a ufqcb lmden
this similarity measure leads to a value of zero for identical matrices and to a value of NUM NUM in the case that a non zero entry in one of the NUM NUM matrices always corresponds to a zero value in the other
with criteria like the corpus frequency of a word its specificity for a given domain and the salience of its co occurrence patterns it should be possible to make a selection of corresponding vocabularies in the two languages
the following examples show the feature structures of schicflcn and bock of ore running example
this referent can serve as an anchor for an i ossible adjectival modifier as unglanblich
each of the curves increases monotonically with formula NUM having the steepest i e best discriminating characteristic
the common occurrence of two words was defined as both words being separated by at most NUM other words
in general word order in the lines and columns of a co occurrence matrix is independent of each other but for the purpose of this paper can always be assumed to be equal without loss of generality
x the logarithm has been removed from the mutual information measure since it is not defined for zero cooccurrences
in this experiment for an equivalent english and german vocabulary two co occurrence matrices were computed and then compared
NUM he chart edges arc marked as usual with cate gory symhols
it is assumed that there is a correlation between the co occurrences of words which are translations of each other
this study suggests that the identification of word translations should also be possible with non paxmlel and even unrelated texts
the docuverse system is based on two technologies context vectors and the som
these vectors are constrained to be unit vectors in the high dimensional vector space
the numbers that are significantly better than chance at p NUM
stemming is the process of representing similar word forms as the base form of the words i.e.
afler preprocessing context vectors for the remaining word stems are learned and stored into a database
it is important to note that it is not necessary that each node have a different theme
figure 4b shows a second dialog window that is used to display the labels for the regions
as information visualization technology evolves and matures so too will tools like docuverse
fortunately the nature of these algorithms is well suited for parallel processing architectures
each node is uniquely identified by it s i j position
section NUM presents an overview of both context vectors and the self organizing map
we used this feature space llot only for i he text representation but also for the docunient classification
we think the reason why we achieved the good result in the classification of the editorial articles and scientific american in japanese is that many technical terms are used in there and it is likely that the kanji characters which represent the technical terms are domain specific kanji characters in that domain
if the size of each training sample is different the ranking of domain specific kanji characters is not equal to tile ranking of tile value x NUM file second is that we can not recognize which domains are represented by the extracted kanji characters using only the value x of equation NUM
the specialties used i the encyck rcb l edia are wide but they a re not well balanced i moreover some doniains of the authors specialties contain only few ifor exaniple the specialty of yuriko takeuchi is anglo american literature oil the other hand that of koichi anlano is science fiction
each kanji character has its meaning and japanese words nouns verbs adjectives and so on usually contain one or more kanji characters which represent the meaning of the words to some extent
not only the value x of equation NUM but the value x d of equation NUM become big when the kanji i appears more frequently in the domain j than in the other
on the other hand in the statistical approach a human exl ert classifies a sample set of documents into predefined domains and the computer learns from these samples how to classify documents into these domains
therefore it is important to consider that NUM specialties in the encyclopedia which represent almost a half of the specialties are used as the subjects of the domain in the nippon decimal classification ndc
conventional way to develop document classification systems can be divided into the following two groups NUM semantic approach NUM statistical approach in the semantic approach document classification is based on words and keywords of a thesaurus
and it is correct that the system classified chapter NUM and chapter NUM into the linguistics and psychology respectively because human language is described in chapter NUM and human psychological aspect is described in chapter NUM
thus prompts should be short and to the point and violations of this principle should serve a purpose
suppose for example that the n best list of sr results contains radio as the first candidate
we have shown that current dg theorizing exhibits a feature not contained in previous formal studies of dg namely the independent specification of dominance and precedence constraints
plain recog null of such corrections the dm can perform an update on the original list of annotated inputs
if parsing one candidate results in a nonsingleton set of interpretations it is ambiguous
the algorithm has a certain resolving power
atelic verbs on the other hand
to determine the best k for disambiguating a word on a particular training set we run NUM fold cross validation using pebls NUM times each time with k NUM NUM NUM NUM NUM NUM NUM NUM
precision but also in coverage better captured by recall
we run this test on the environmental domain enea corpus
a dedicated subsystem has been developed to support manual validation of single terms
null the distributional property needed for the select step is the term specificity
h or h mn t is represented as a single event x
the specific nature of the corpus is well reproduced by the data
from the enea corpus we derived a dictionary of about NUM words
in general the system can not decide whether the first candidate of the list is NUM the right candidate as it will be in most cases NUM an error due to confusion within the sr unit or NUM an error due to the user e.g. because a phrase was uttered outside the currently active vocabulary
whenever an error occurs the error handling part of commandment iv is obeyed as well no blame is assigned the focus is on recovering the error and there is an undo option abort
table NUM word accuracy and sentence accuracy based on acoustic score only acoustic using the best
essentially the user will have the same options as for the keyboard based communication figure NUM except that s he now will have the additional opportunity of clarifying his challenge user no i do n t want to go to bistro le pot de fer but to bistro le pot de terre
collection of scientific abstracts on the environment made of about NUM NUM words
a section is the set of terms that share the same term head
experimental evaluations of the first prototype will be the input to design and development of the second prototype which also aims at broadening the range of possible user s input by allowing spontaneously spoken database queries for the navigation task
in particular the discounted weight of the skip k prediction was given by
because the dialogs are centrally concerned with negotiating an interval of time in which to hold a meeting our representations are geared toward such intervals
NUM NUM relationship to segmentation in hierarchical discourse models
the conditions that are introduced below the clotted line exemplify possible resolutions
a und dann auf die zweite
we have nothing sf eciiic to say about this here
output from the sealing provides rotation maps at each dimension projected onto NUM dimensional space
each maximal merging is then merged with the normalized input ilt resulting in a set of ailts
also note that the values in the missing column are higher than those in the extra column
this reflects the conservative coding convention mentioned in section NUM for filling in unspecified end points
given this state of affairs what is the best practical support that can be given to advance the field
a central function of the ggi is to provide a graphical launchpad for the various le subsystems available in gate
the paths through the graph indicate the dependencies amongst the various modules making up this subsystem
tile creole apis may also be used for programming new objects
all communication between the components of an le system goes through gdm which insulates these components from direct contact with each other and provides them with a uniform api for manipulating the data they produce and consume
the gdm provides a central repository or server that stores all information an le system generates about the texts it processes
but the pressure towards theoretical diversity means that there is no point attempting to gain agreement in the short term on what set of component technologies should be developed or on the informational content or syntax of representations that these components should require or produce
uk for a variety of reasons nlp has recently spawned a related engineering discipline called language engineering le whose orientation is towards the application of nlp techniques to solving large scale real world language processing problems in a robust and predictable way
the electrlc cadaver computerized anatomy lcb assorts and digits
the intergration of existing morphologica i proeessing tools has led to a powerful cai i tool
thus the user can also view the subtopic structure within the document itself
synset named by its first synset element or occasionally a term i.e. synset element linked to its concept by the designation relation which is shown by its inverse from the concept side and is thus labeled term
there is a moderate or high level of agreement among annotators in all cases except the ending time of day a weakness we are investigating
although we ll report on this seperately it indicated user interest in the uear fllture we re planning to index the corpora on basis of lexemes l al er we wish to extend tim software with for example a teaching rod diagnosting module so that the tool matures to real all software
following a review of relevant work in the area of natural language generation this paper will discuss how these four steps have been applied to the generation of rhetorical relations in instructional text
we would also like to test these morphological recognizers on other
follow steps in the illustration for desk installation
first we construct g from g as in the previous section
our system needs to know which pieces of information are about the same time but does not need to know about the additional relationships
the analyst data setup process csci collects and
in particular there has arisen a distinct paradigm of processing on the basis of pre analyzed data which has taken the name data oriented parsing
document details review displays the classifica
figure NUM NUM canis external interface design
the analyst data setup process bridges the gap he
canis runs on the customer specified hardware and software plaffozms
canis performs all processing and stores the results internally
the analyst data setup process csci validates file
the analyst data setup process csci links named
the full cogenthelp component architecture and dependencies reflects the particular requirements of this group and are as follows
note that the list of widgets in the dynamic toc on the left side of the page is arranged according to this traversal consequently stepping through the contents using the toc or the next button for this window will lead from widget to widget and cluster to cluster in a sensible fashion
this calculation is difficult because the agent does not have direct access to its collaborator s knowledge
the quality of a collaborator s negotiation reflects the quality of its underlying knowledge
currently two spoken dialogue human computer systems are being developed using the underlying algorithms described in this paper
the final test of course must be in the implementation of a human computer dialogue system
of an on line help system up to date automatically since both the application gui and the documentation can be evolved in sync
extensive testing remains to be done to determine the actual gains in efficiency due to various mechanisms
finally in section NUM we conclude by discussing the outlook for cogenthelp s use and further development
these techniques fall into two categories those pertaining to knowledge representation and those pertaining to text planning
continuous mode 2this distribution is normalized to insure that all the knowledge is distributed between each agent
the participants must continuously focus all concerns on the goals of the task and avoid extraneous paths
for example the field of human anatomy and physiology encompasses a body of knowledge so immense that many years of study are required to assimilate only one of its subfields such as immunology
each view is a coherent subgraph of the knowledge base describing the structure and function of objects the change made to objects by processes and the temporal attributes and temporal decompositions of processes
because the computational linguistics volume NUM number NUM planner can reason about the types of problems it can properly attend to them by excising the offending content from the explanation it is constructing
by synthesizing a broad range of research in natural language generation knight provides a start to finish solution to the problem of automatically constructing expository explanations from semantically rich large scale knowledge bases
however this project is a significant achievement in terms of evaluation scale because of the sheer number of texts it produced pauline generated more than NUM different paragraphs on the same subject
computational linguistics volume NUM number NUM node of the explain process edp has four children process overview output actor fates temporal info and process details each of which is a topic node
viewed as energy transduction it would be described in terms of input energy forms and output energy forms during photosynthesis a chloroplast converts light energy to chemical bond energy
in addition to these top level accessors the library also provides a collection of some NUM utility accessors that extract particular aspects of views previously constructed by the system
cases 4bii c depend on the simulation of the previous section
since the input expression and therefore p i
case 4dii only appears in sublemmas as the result category is gtrc
we also consider the knowledge sources to be natural
this dominance link will act as a constraint on derivations if p is used in a derivation then p must be used subsequently in the subderivation that starts with the occurrence of a introduced by p
for every application of a rule set to a word we computed the precision and recall and then using the total number of guessed words we computed the coverage
an entry in the database is a pair
different systems have different definitions of local contexts
there may in fact be at least NUM more senses and entries for the adjective but we will come back to them shortly in section NUM NUM
a true relative adjective can not indeed be used predicatively and or comparatively but practically it is hard to come up with an example which is guaranteed against that
the word line has NUM senses
NUM lr el e NUM typically each e NUM is produced manually that is by a qualified human on the basis of all the
abandoned can of course be a participle as well but we have discovered no reason whatsoever to treat participles differently from deverbal adjectives
our microtheory associates the meaning of a typical truly scalar adjective with a region on a scale which is defined as the range of an ontological property
the formula of transition from the noun entry to that of the adjective is simple and transparent and it remains constant for this type of adjective
each numerical scale can be measured in actual measuring units such as linear size in feet yards or millimeters or time in seconds
the probability distributions for adding and deleting words and features can also be estimated from corpora
the slot is part of the common ground the value is new information
we are currently still using the original hodyne function because it works well in practice
then the weights are fixed and the trained net is ready to classify unseen sentences
it was found that triples alone gave as good results as pairs and triples together
if NUM NUM for strengthening weights and NUM for weakening them then
prohibition tables the grammatic framework alone does not reduce the number of candidate strings sufficiently for the subject detection stage
these should be distinguished from possibly correct parses that are not in the training data
the input to the net is derived from the candid null ate strings the sequences of tags and hypertags
the correct tag is almost always included in the set allocated but more tags than necessary are often proposed
now in the natural language domain it is desirable to get information from infrequent as well as common events
the weights on the connections between input and output nodes are adjusted until a required level of performance is reached
next we describe a heuristic which achieves a time bound quadratic in the size of the tree
more information on the tree bank can be found on http grid
the main problem of vowel splitting is that the grammar indicates the cases where splitting is not allowed and the splitting of a large number of these cases is ambiguous
the process flow of our text translation system is given in figure NUM
overall we present an algorithm a variation of littlestone s winnow which performs significantly better than any other algorithm tested on this task using a similar feature set
this is a tentative solution which requires further research
however because of the assumed transitivity of troponymy the database stores only direct troponym links NUM exceptions see above subsection NUM NUM and taking this into account the rule should be formulated more exphcitly if a verb concept v1 is a direct or indtrect troponym of v2 then v1 entails v2
example NUM can be used as illustration
initially the word feature was not in the model instead the system relied on a third level back off part of speech tag which in turn was computed by our stochastic part of speech tagger
with increased training data it would be possible to use even more detailed models that require more data and could achieve significantly improved overall system performance with those more detailed models
third there are some languages in which the orthography had a close fit with the phonemic system
however when we compare different subsytems of any two languages it quickly becomes clear that subsystems
on the huggins corpus without the use of the exceptions dictionary our rule set scored NUM NUM of words
no matter what smoothing technique is used one must remember that smoothing is the art of estimating the probability of that which is unknown i.e. not seen in training
our test set of english data for reporting results is that of the muc NUM test set a collection of NUM wsj documents we used a different test set during development
NUM is rewritten as cent vingt trois by a set of rules checking the left and right context for each digit
the work reported here was supported in part by the defense advanced research projects agency a technical agent for part of the work was fort huachucha under contract number dabt63 NUM c0062
originally we had a very small number of features indicating whether the word was a number the first word of a sentence all uppercase inital capitalized or lower case
while our initial results have been quite favorable there is still much that can be done potentially to improve performance and completely close the gap between learned and rule based name finding systems
w end if pr nc no l w l NUM nc start of sentence w i last observed word otherwise NUM NUM
g uses all the nonterminal symbols of g and g and some compound nonterminals of the form a b a and b nonterminals of g and g respectively
if the correct class code is contained in these assigned codes the test case is considered to be assigned the correct code
section NUM briefly described the organization of an ltag in families of trees
we conducted experiments using the japanese bunruigoihy5 thesaurus and about NUM NUM co occurrence pairs of verbs and nouns related by the wo postposition
in other words the proposed method identifies appropriate word classes of the thesaurus for a new word which is not included in the thesaurus
this paper follows this line of research and proposes a method to extend an existing thesaurus by classifying new words in terms of that thesaurus
he used several factors such as similarity between a target word and words in each classes class levels and so forth
one possible explanation for this contradiction may be that the basis of the classification for bgh and our probabilistic model is very different
we identified class codes at the fifth level of bgh while uramoto searched for a set of class codes at various levels
the performance of his method was reported to be from NUM to NUM in accuracy which seems better than ours
third the coders could disagree on which relation a cue was associated with
the ure NUM categorization of core contributor relations will not be assessed here
a contributor is analyzed for both its intentional and informational relations to its core
each constituent may itself be a segment with its own core contributor structure
for each cue we determine the best description of its distribution in the corpus
first the rda coding task is more complex than identifying locations of segment boundaries
in these cases we took the core to be the presupposition to the question
it provides a system for analyzing discourse and formulating hypotheses about cue selection and placement
tradmonally the document summansatlon task has been tackled rather as a natural language processing problem with an
intentional relations describe how a contributor may affect the heater s adoption of the core
to illustrate the application of rda consider the partial tutor explanation in figure i t
in spite of this deficiency the hubert labbe curve appears to be an optimal smoother and this suggests that the value obtained for the coefficient of vocabulary partition p is a fairly reliable estimate of the extent to which a text is characterized by lexical specialization
furthermore the role of probabilities for this application goes beyond selecting the correct coreference relationships the probability assigned to an alternative will be central in determining how the downstream system will weigh it against information from other sources during data fusion
it comprises a principled and hierarchical representation of lexico syntactic structures
due to intra textual and inter textual cohesion the v n v n NUM types that have already been observed have a slightly higher probability of appearing than expected under chance conditions and consequently the unseen types have a lower probability
reference nodes actually represent the observed configuration space w
in this case a window size of around six is more effective
in order to improve accuracy language dependent methods could be considered
in this manner an initial segmentation can be obtained that is more informed than a simple character as word approach
taking three neighbouring points on the graph p1 p2 p3
computational linguistics volume NUM number NUM figure NUM revealed that e v n v n is largest around text slices NUM to NUM but becomes negative for roughly the last third of the novel
we found that using hyponyms of hypernyms going up one level in abstraction and then one level back down gave much better recall than plain synonymy although the precision is lower
the syntactic filter reduces the number of assignments under consideration to NUM NUM NUM NUM of the number of potential assignments while preserving NUM of the assignments we know to be correct
each of these verbs was assigned up to NUM possible semantic classes ranked by the degree of likelihood that the verb belongs to that class giving a total of NUM NUM ranked assignments
by wrong assignments we mean cases in which the system assigns a verb to a given levin class when that verb does not appear in that class in levin s book
since the filter is based on synonyms of levin verbs in some cases a synonym of a verb from some other class will appear in the set that does not belong there
we call this extended list a semantic field
table NUM increasing precision with the semantic filter
recall that there are NUM verbs all together
we call these the known verbs
precision has increased from NUM NUM to NUM NUM
since the wordnet senses are ordered according to their frequency in semcor choosing the first sense is roughly the same as choosing the sense with highest prior probability except that we are not using all the files in semcor
in this latter case the parser makes a provisional interpretation which is checked when the argument structure of the predicate is available
we can define a set of binary features that relate a possible value of a characteristic of x with a possible outcome y i.e. whether the two templates corefer y NUM or not y NUM
in the following section key problems in thai morphological analysis are described
finally we present the conclusion result of he experiment
some standard grammar checkers insist on supplying an active rephrasing even in this case and they do that by introducing a fake subject t they or he
some of them contains more than monosyllabic words as parts of its component
this research was supported by the natural sciences and engineering research council of canada
moreover this tcelmique is fairly good at the implicit error correction
a statistical approach to thai morphological analyzer
this tag ambiguity can cause a large set of tagged word combinations
this difference increases significantly in the lexicon coverage NUM NUM percent for the english and NUM NUM percent for the french lexicon
in particular the decoding algorithms enforce models which do not exploit all linguistic knowledge mainly due to computational complexity
one of the characteristics of our approach is that every sense of word in artmes is automatically disambiguated using dictionary definitions
the approach proposed in this paper focuses on these l roblems i.e. NUM olysemy and a phrasal lexicon
toward or away from is the telicity indicator in the next three the uninstantiated constant in the rightmost leaf node argument and in the last case the special instantiated constant exist
the results of these methods when al plied to articles cbussification task seem to show its etfectiveness
one of major approaches in automatic clustering of articles is based on statistical information of words in rticles
the frequency of ewwy disambiguated noun in new articles is lower than that of every polysemous noun in oriqinal articles
for exalnple the frequency of nulnber5 in table NUM is lower than that of number t
in order to cope with these prol lems we linked nouns in new articles with their semantically similar nouns
where wl is the element of a new arti le and corresponds to the weight of the noun wl
for the selected chtsters if there is a noun which belongs to several clusters these clusters are grouped together
the greater the wtlue of sim ai aj is the ntore xinfilar these two articles are
practically this is either a positive contribution if wj is well aligned or a negative contribution if wj is badly aligned
produces person phrases around any word immediately to the fight of a title and or honorific and then NUM grows the extent of the phrase to the fight one lexeme if that word i s a proper noun
these can be identified as carrying out four different types of task
an implementation of quinlan s id3 algorithm was used NUM NUM
this takes the lists of names found so far and truncates them
the system consists of suite of c and lex programs
weiss s m and kulikowski c a morgan kaufmann NUM
this avoids recognition o f organizations such as leaves bank
we intend to rebuild the basic system
for ftp address and access password
ideally at this stage the tagging would be done
performance a high precision was expected from this system
however one might attribute this to repetition of the use of it and so we have avoided the repeated use of a pronoun
the first choice list is a subset of the second choice list
the lexicon ks consulted for each word in the sentence
these assumptions will not always hold while parsing many corpora
closed class words are words with a closed class part of speech
the problem is not merely a change from a pronoun back to a proper name as this happens to the same extent in all four variants
in the second utterance of these sequences susan is realized by a pronoun in subject position she is the cb of this utterance
based on the above we can complete both optimization criteria of the hmm formulation given in NUM NUM NUM and NUM NUM NUM
for example the utterance in NUM is perfectly fine after NUM but yields an incoherent sequence after NUM
syntactic knowledge is used implicitly in this research by the parser
however if we consider different subsequent utterances it becomes clear that susan and betsy do not have an equivalent status in the second utterance
a sb is the difference in optimal code lengths when symbols at node sb are compressed by using probability distribution p is at node s and p lsb at node sb
by using the hierarchical tag context tree the constituents of a series of tag models gradually change from broad coverage tags e.g. noun to specific exceptional words that can not be captured by general tags in other words the method incorporates not only frequent connections but also infrequent ones that are often considered to be exceptional
a hierarchical tag context tree offers a slight improvement but initialize weight j NUM for all examples j input sequence of n examples pl dl wl
c t i d for examples correctly predicted by ht update the weights vector to be weight i weight i flt NUM
because the kl divergence defines a distance measure between probability distributions p sb and p is there is the following trade off between the two terms of equation NUM
if no more general version of the result exists then the result is added to the table
the first objection may be that this is a task that can hardly be expected from a lexical lookup procedure
the head corner table contains NUM pairs the lexical head corner table contains NUM pairs the gap head corner table contains NUM pairs
the left corner table contains NUM pairs the lexical left corner table contains NUM pairs the gap left corner table contains NUM pairs
xp sem np sem xp sem s sem
the alembic text processing system applies eric brill s notion of ru e sequences NUM NUM at almost every one of its processing stages from part of speech tagging to phrase tagging and even to some portions of semantic interpretation and inference
the implementation allows for the possibility that state names are not instantiated as required by the treatment of gaps
the parser is modified in such a way that it finds all derivations of the start symbol anywhere in the input
the root node is special too it has both an associated rule name and a reference to a result item
the predicate connection NUM is true if there is a path in the word graph from the first argument to the second argument
in fact every rule composed by the learning procedure is completely inspectable by the user and so some users may want to modify individual machine derived rules perhaps to expand their generality beyond the particular data available in the emerging corpus
this feature should make modex more easely portable among different types of users
in computation if n number of ancestors and r number of siblings of fi m have been consulted the case score function is approximated as
compared with the baseline system the error reduction rate is NUM NUM for case and NUM NUM for sense discrimination and NUM NUM for parsing accuracy
in this paper an integrated score function is proposed to resolve the ambiguity of deepstructure which includes the cases of constituents and the senses of words
motivated by the above concerns an integrated score function which encodes lexical syntactic and semantic information in a uniform formulation is proposed in this paper
furthermore to reduce the estimation error resulting from the mle good tudng s smoothing method is applied significant improvement is obtained with this parameter smoothing method
finally a robust discriminative learning algorithm is derived in this paper to minimize the testing set error and very promising results are obtained with this algorithm
excluding the effect of syntactic ambiguity we checked out the errors of the semantic interpreter and found that NUM NUM of normal form errors occur in identifying case
this integrated score function incorporates the various knowledge sources including parts of speech syntax and semantics in a uniform formulation to resolve the ambiguities at the various levels
based on the integrated score function the lexical score function the syntactic score function the case score function and the sense score function are derived accordingly
similarly the nf1 structure is also decomposed into another set of production rules each of which corresponds to a normal form one nf1 subtree
the parsing reveals through linguistic anomalies errors that would n t be spotted efficiently by acous null tic criteria
following the example discussed in section NUM the functionality of the rule vbn vbd prevtag np is represented by the transducer shown on the left of each contextual rule is defined locally that is the transformation it describes must be applied at each position of the input sequence
NUM construction of the finite state tagger we show how the function represented by each contextual rule can be represented as a nondeterministic finite state transducer and how the sequential application of each contextual rule also corresponds to a nondeterministic finite state transducer being the result of the composition of each individual transducer
for example when the rules in figure NUM are applied to sentence NUM the first rule results emmanuel roche and yves schabes deterministic part of speech tagging in a change NUM that is undone by the second rule as shown in NUM
referring to the example of section NUM the local extension of the transducer for the rule vbn vbd prevtag np is shown to the right of figure NUM similarly the transducer for the contextual rule vbd vbn nexttag by and its local extension are shown in figure NUM
although the speed of current part of speech taggers is acceptable for interactive systems where a sentence at a time is being processed it is not adequate for applications where large bodies of text need to be tagged such as in information retrieval indexing applications and grammar checking systems
in this paper we present a finite state tagger inspired by the rule based tagger that operates in optimal time in the sense that the time to assign tags to a sentence corresponds to the time required to follow a single path in a deterministic finite state machine
mr enamex type quot person quot james enamex who has a reputation as a n extraordinarily tough taskmaster nays that because he had a grea t time is advertising he does n t want to talk about th e disappointments
semantically this data structure represents this subset o f individuals of the type woman for which the attribute health has the value sick and the function happy has the value very unhappy
the module contains functions for deriving the disjunctive normal form of a complex boolean expression independently of its algebra as well as functions for creating the representatio n of the element that this complex boolean expression stands for
por m namex type person dooner enamex it mesas maintaining his running and enercise schedule a r the agency it mean sdeveloping mo global campaigns that nonetheless reflect loca l cultures
why and what for muc NUM muc6 tasks not inconsistent with our goal s we view the muc6 tasks as not inconsistent with our goal of providing further experimental evidence i n support of our research hypothesis
this correct interpretation is automatically preferred by the system because of the context created by the immediately following sentence he points to several campaigns with pride including the taster s choice commercials that are like a running soap opera
the changes to these old modules include augmenting the parser to produce the uno representation of sentences enhancing the morphological analyzer to handle prefixes and supplying the reader with structures for storing the information gained at various stages of processing
updating knowledge base and automated inferencing is done by the same semantically clea n computational mechanism of performing boolean operations on the representation of natural language inpu t and the representation of previously obtained information stored in the knowledge base
for those things that we were able to mark our own very unofficial estimate is that we performed in th e high nineties in both recall and precision as can be seen in the enclosed sample article
in the greek text the error rate reaches approximately NUM percent when NUM NUM word text is used to define the parameters of the hmm
taggers based on mlm require the training process to store each tag assigned to every lexicon entry and to define the unknown word tagset
the probability p t i less probable word and the tags probability p t are measured in the training text
probability distributions is minimized for NUM NUM words training text for the set of main grammatical classes and for NUM NUM words for the extended set
nevertheless if a large pos set is specified the number of rules increases significantly and rule definition becomes highly costly and cumbersome
the last part was tagged each time after the model parameters were updated giving results of the tagger performance on open testing text
in contrast to the extended tagset experiments where a greater size training text for the german greek and spanish languages is required
in the majority of the experiments the tagger error rate decreases when new text updates the dermatas and kokkinakis stochastic tagging model parameters
the influence of the application tagset on the tagger performance was measured by testing the two totally different tagsets described in section NUM NUM
the taggers based on the hmm reduce the prediction error almost to half in comparison to the same order taggers based on mlm
the goal of these definitions is to give enough information to study relationships between game structure and other aspects of dialogue while keeping those relationships simple enough to code
for a fixed chain size of NUM there are NUM NUM NUM NUM contiguous subsequences in this region of NUM points
it s difficult to measure exactly how much noise is generated by frequent tokens and of course the proportion is different for every bitext
similarly any non monotonic segment of the tbm will occupy the intersection of a vertical gap and a horizontal gap in the monotonic first pass map
it would also be interesting to experiment with simr and gsa on language pairs that are not as closely related as english and french
each set of parameter values was scored according to the root mean squared error between the resulting bitext map and the reference set of tpcs
when language l1 borrows a word from language l2 the word is usually written in l1 similarly to the way it sounds in l2
for english and french such a matching predicate can generate enough points in the bitext space to obviate the need for a translation lexicon
rectangle the search alternates between a generation phase and a recognition phase which are described in more detail in sections NUM NUM and NUM NUM
this compares with out of vocabulary rates for esst that have ranged between NUM to NUM
pairwise percent agreement is the best measure to use in assessing segmentation tasks when there is no reasonable independent definition of units to use as the basis of kappa
in such cases there are three reasons to favor gsa over other options for alignment one it is simply more accurate
although gsa sometimes backs off to a quadratic time alignment algorithm in practice its running time is linear in the number of input sentences
n NUM k NUM distinguishing between different types of moves that all contribute new unelicited information instruct explain and clarify
this violates our assumptions in that although w8 and wt might not correlate closely with the same set of seed words the matching score would be nevertheless high
to avoid problems of polysemy and nonstandardization in dictionary entries we choose a more reliable less ambiguous subset of dictionary entries as the seed word list
we illustrate the possible correlations using the word debentures in the two different parts of wsj figure NUM shows segments from both texts containing the word demntures
it is so far questionable whether feature vectors of lower dimensions are discriminating enough for extracting bilingual lex cal pairs from non parallel corpora with a large number of candidates
furthermore using such an english english test set the output can be evaluated automatically a translated pair is considered correct if they are identical english words
where the standard is the coding of the scheme s expert developer the test simply shows how well the coding instructions fit the developer s intention
consequently we use the paragraph as the segment size for our experiment on wall street journal nikkei corpus since all the seed words are mid frequency content words
for future work the quality of seed words can be improved by using a training algorithm to select seed words according to their discriminative power
we then calculated the accuracy by counting the number of words whose top one candidate is identical to itself obtaining a precision of NUM
all of the coders interacted verbally with the coding developers making it harder to say what they agree upon than if they had worked solely from written instructions
we store timeline expressions like NUM as deterministic fsas
proof of np hardness by polytime reduction from hamilton path
in particular it is more constrained than theories using generalized alignment
there are nonetheless three reasons why the above result is important
how to invert the inputoutput mapping in NUM
how to implement the inputoutput mapping in NUM
the underlining is a notational convention to denote input material
given the kind of evidence available to child language learners
iz NUM p log x2 zexyey
an advantage of our approach is the use of a core module that is independent from any application
to identify the best alignment the algorithm must assign a penalty cost to every skip or match
linguistic research is a bootstrapping process in which data leads to analysis and analysis leads to more and better interpreted data
the aligner s only job is to line up words to maximize phonetic similarity
with english and german it did almost as well tables NUM and NUM
in some cases it was just plain wrong e.g. aligning tooth with the tis ending of dentis
it can serve as a front end to computer implementations of the comparative method
we know that these words correspond segment by segment NUM but the aligner does not
without the no alternating skip rule the number would be about NUM NUM
we participated in all four muc NUM evaluations in order to obtain as much feedback about our variou s components as possible
in spite of these difficulties we are quite pleased with the portability of our trainable system components
the s t training texts provided only about NUM instances each for status in and status out which was insufficien t training
however resolve s version adds an extr a constraint the previous phrase must be found in the subject buffer
to get a feel for the resolve d tree we present a piece of the tree starting at the root node
james chief executive officer and chairman are al l descriptions of the same person entity
each of these components utilizes machine learning techniques i n order to customize crucial extraction capabilities based on representative training texts
the creation of a cn dictionary is now fully automated and accomplished by crystal an inductiv e dictionary construction system
as long as the system i s handling noun verb ambiguities consistently we do not suffer substantially from these tagging errors
each status o r status evidence which has been identified in the text is paired with each person to form an instance
the parameters a1 an are lagrange multipliers that impose the constraints corresponding to the chosen features fl fn the term z x normalizes the probabilities by summing over all possible outcomes y
they are subgraphs not included in any other subgraphs
the algorithm has been presented abstractly from the actual constraint system and can be 2dapted to any constraint based grammar formalism
from this follows that poly hypernymy has to be treated with care multiple superconcepts to be implicitly combined by or disjunction and not by and conjunction invalidate that every troponym v NUM of a more general verb v2 also entails v2 see below figure NUM and figure NUM
any link that is still disabled is activated and initialised to NUM so that tuples which have not occurred in the training corpus make no contribution to the classification task
NUM and the vectors represent the motion of the parameters from one iteration to the next when a p cl pc NUM
since this grammar makes the head a available to predict b c and d without multiple expansions rules for ap it is impossible to get this
in languages like german where case is overtly manifested in affix and determiner choice the noun ice clearly receives case from the preposition rather than the verb
here ed and by occur together not because they are part of a common word but because engfish syntax and semantics places these two morphemes sideby side
this suggests that the inside outside algorithm is likely to be highly sensitive to the form of grammar and how many different analyses it permits of a sentence
we have also argued that the standard context free grammar estimation procedure the inside outside algorithm is essentially incapable of finding an optimal grammar without bracketing help
note that it is often possible to issue a database query even if this information is not known and that is why this state belongs to the set of possible states after a query has been made
however the test set performance with this hybrid approach is slightly but not significantly better than the turing s formula robust learning approach
as for the attachment problems we found that the system appears to have a preference for local attachment which is not always inappropriate
simplicity we have made a strong effort from the beginning to assemble an architecture from a small number of simple yet powerful objects and where necessary to rethink parts of the design rather than simply adding stuff on
by including representatives of the contractors who would have to use the architecture during the rest of phase ii the government sought to insure that all the essential needs of various detection and extraction applications would be addressed
fortunately a series of planning workshops for phase ii held in the spring of NUM had identified some basic concepts for an architecture and provided a strong base of ideas from which to start
the details of the interface have as yet been fully spelled out only for c based on the implementations which are planned we need to add explicit specifications for c corba and common lisp
in particular we have been looking recently at the problems of retrieval to insure that the operations associated with creating a new collection of documents for the results of a retrieval operation can be performed quickly
to meet this challenge the government assembled a group of representatives of the contractors who would be conducting research and development under tipster phase ii and told us we had six months to create an architecture
nonetheless it was a sobering experience in the fall of NUM when for the first time a contractor who had not been part of the cawg and hence was not privy to the oral tradition of the cawg prepared a design for a tipster compliant application and interpreted the design document in some unexpected ways
all of the information which was learned about a document in the course of its analysis header zones paragraph and sentence boundaries person and organization names in the text relational information about selected types of events comments by human analysts etc would be stored as annotations which would be kept with the document
interactor it is responsible for converting the interaction template generated by the dialogue manager into english sentences that can be printed and or spoken using a text to speech system to the user to provide feedback
because the expressiveness of itgs naturally constrains the space of possible matchings in a highly appropriate fashion the possibility arises that the information supplied by a word translation lexicon alone may be adequately discriminating to match constituents without language specific monolingual grammars for the source and target languages simply by bringing the itg constraints to bear in tandem with lexical matching
o s s t s t v u v NUM u u s u o s t u o the estimation procedure for adjusting the model parameter set ff is defined in terms of the inside and outside probabilities
either or both x and y may take the special value e denoting an empty string allowing a symbol of either language to have no counterpart in the other language by being matched to an empty string
consider each nonterminal symbol to stand for a pair of matched strings so that for example a1 a2 denotes the string pair generated by a the operator performs the usual pairwise concatenation so that ab yields the string pair c1 c2 where c1 aib and c a2b2
however this hybrid approach is subject to the same incompatibility and ambiguity problems that arise for pure parse parse match procedures thus the proposed coarse bilingual grammar approach is superior for the same reasons given above
the formalism also differs from standard context free grammars in that the concatenation operation which is implicit in any production rule s right hand side is replaced with two kinds of concatenation with either straight or inverted orientation
an unavoidable consequence of using more structured complex grammars coarse though they may be is that the bilingual matching process becomes more sensitive to the syntactic production probabilities than under the earlier generic bracketing grammar approaches
the first column of table NUM shows eleven levels of recall while the second and third columns show the precision scores for baseline and proximity based name searching respectively for the corresponding level of recall
figure NUM depicts what we call the corpus integral
information retrieval differs from both of these types of applications because it has neither the structure provided by a database record nor the linguistic depth or domain knowledge representation of the natural language understanding system
the terms in this equation have a simple interpretation
naturally the mutative segment of such rules is always set to an empty string
in other cases a secondary placement of the intonation center is used as in NUM
the latter examples usually belong to the topic whereas the former ones typically occur in the focus
an algorithm for the analysis of english sentences has been implemented and is discussed and illustrated on several examples
we therefore work with the distinction of contextually bound cb and non bound nb lexical occurrences
however an algorithm is included that determines the topic focus structure of the input sentences on their nonmarginal readings
with this notation every dependent item is included in its pair of parentheses labeled by the corresponding syntactic symbol
the word order of natural languages is determined not only by so but also by other factors
so is one of the factors relevant for word order and for the placement of the intonation center
quantitative evaluations of the correlations include the use of statistical measures and information retrieval metrics
as noted below more detailed discussion of the statistic we use is presented elsewhere
we then develop a second set using two methods error analysis and machine learning
null in sum relatively few studies correlate linguistic devices with empirically justified discourse segmentations
for an utterance cj requires that all boundaries occurring prior to cj have been assigned
in the notation presented here the morphological categories are handled so that only their marked values are indicated
as discussed above a variety of criteria for identifying discourse units have been proposed
hnc is in the process of exploring new ways to visualize this powerful information representation technology
in the evaluation we measured the word overlap of sentences contained in the abstracts with sentence s extracted from a text according to the opp
our hit function h measures the similarity between topic keyword ti and a window wij that moves across each sentence pm sn of the text
the performance of variable length windows compared with windows of size NUM should have a difference less than the amount shown in the segments of window size NUM
if a sentence in an abstract matched more than one sentence extracted by the op only the first match was tallied
furthermore he did not trying out other possible combinations such as the second and third paragraphs or the second last paragraph
we acquired the h scores for all sentences in t and repeated the whole process for the each text in the corpus
the contribution of coverage score c solely from m word match between e and a can be computed as follows
we describe the methodology used to build the slt system itself particularly in the areas of customization section NUM robustness section NUM and multilinguality section NUM
the syntactic analysis has been done in a normal pc with the linux operating system
after that the pruning operation disambiguates finite verbs and rule NUM will apply
the wh clause as subject rule looks for a finite verb to the right
NUM the infinitive marker is indexed to the infinitive by the link named infmaxk
we first describe the older constraint grammar parser where many of the ideas come from
the parser creates links between words and names the links according to their syntactic functions
box NUM fin NUM university of helsinki finland lcb pas i tapanainen timo
trying out some very heuristic methods to assign heads would raise recall but lower precision
these dependencies are usually resolved more reliably than say appositions prepositional attachments etc
the dg success rate is similar or maybe even slightly better than in engcg
NUM a erst in miinchen war er gestern
there are refinements of this criterion that we tnust omit here
NUM NUM the t nqmral location crlte ri m
lhe next day al NUM the r reading seems acceptable
b nicht zuvor auf die erste zweite oder dlqtte
NUM i etra war lcb ibc rrascht
i.e. a constitutive element of the r reading can not h constructed in this ease
peter only pointed to the fourth lucky number so far
this means that against the background of the presupposition this information is not new
we stress that what we have said relates to temporal adjuncts in tile scope of erst
the test bitexts in the other two language pairs were created when simr was being ported to those languages
wh arguments can be treated as similar to other arguments i.e. as lambda abstracted in the semantics
12the parser accepts the same strings as the grammar and assigns them the same semantic values
here a sentence was expected but what was encountered was a noun phrase john
it therefore becomes crucial to either perform some kind of ambiguity packing or language tuning
it is therefore worth giving some brief indications of how it fits in with these developments
q thinks mary p john p ar
the paper describes a parser for categorial grammar which provides fully word by word incremental interpretation
the headed list distinguishes between the two cases with only the first having an np on its
the second of these is perhaps the most interesting and is given in figure NUM
mediumhigh level categories those between NUM NUM and NUM NUM maximum words range between NUM NUM members for each figure NUM plots the values of g co dp and NUM a for the different sets of categories generated by the algorithm of section NUM alternative sets of categories are identified by their upperbound NUM the figure shows that dp ci has a regular decreasing behavior while NUM a ci is less regular
figure i illustrates a possible sysnsets hierarchical in which for ci cil ci2 rcb being dm cil NUM NUM NUM as defined g c i is a linear function low values for low generality high value for high generality whilst our goal is to mediate at best between overspecificity and overgenerality
the last occurrence must be rejected because starch is the argument of the head noun properties whereas flour is the argument of the head noun properties in the original term
figure NUM shows four very frequent and very ambiguous words in the domain bank business market and stock with attached list of synsets as generated by wordnet ordered from left to right by the increasing level of generality leaf sysnset leftmost
the obtained plot is the reference against which we apply a standard linear interpolation method to estimate the values of the model parameters cc x and NUM that minimize the difference between the values of the two functions for each c i
even context based sense disambiguation becomes a prohibitive task on a wide scale basis because when words in the context of an ambiguous word are replaced by NUM manually assigning semantic tags if of course rather time consuming however on line thesaura are not available in many languages like italian
while the reference function has a peak on the class set cj with ub NUM NUM and the score function assigns the maximum value to the class set c k with ub NUM NUM the performance of the sets in the range j k is very similar
the agr corpus has been indexed with the agrovoc thesaurus in two different ways NUM simple indexing extraction of occurrences of multi word terms without considering variation
on the other hand high level tags may be overgeneral and the acquired lexical rules while usually perform well in the task of selecting the correct word associations for example in pp disambiguation or sense interpretation are less capable of filtering out the noise
we measure the discrimination power dp c i as the ratio nc ci npc ci nc ci where nc ci is the number of words that reach at least one category of c i and npc ci is the number of words that have at least two leaves synsets that reach the same category cij of c i
in its modified version the algorithm is as follows2 let s be a set of wordnet synsets s w the set of different words nouns in the corpus p s the number of words in w that are instances of s weighted by their frequency lib and lb the upper and lower bound for p s n h and k constant values
in table NUM we give the numbers of centering transitions between the utterances in the three test sets
by taking into account such cues during dialogue interactions the system is better able to determine the task and dialogue initiative holders for each turn and to tailor its response to user utterances accordingly
however a token type that is relatively frequent overall can be rare in some parts of the text
this indicates that wsd is a challenging task and much improvement is still needed
this is due to the fact that a retain transition ideally predicts a smooth shift in the following utterance
it does not rely on pre segmented input and is portable to any pair of languages with a minimal effort
the search is data driven so only a very small percentage of possible transformations really need be examined
the tenth transformation is for the token s which is a separate token in the penn treebank
below we list two lexicalized transformations that were learned training once again on the wall street journal
adding lexicalized transformations resulted in a NUM NUM decrease in the error rate see table NUM
with many of the current corpus based approaches to natural language processing this is a nearly impossible task
NUM one can also estimate these probabilities without a manually tagged corpus using a hidden markov model
below are the lexical entries for half in a markov model tagger extracted from the same corpus
all possible transformations have been tried the transformation that resulted in the greatest error reduction is chosen
in this way one might hope to illuminate the empirical consequences of these distinctions should any in fact exist
finally by providing a uniform representation for a variety of linguistic theories it offers a framework for comparing their consequences
one finds a strong contrast on the other hand in the way in which gb and gpsg encode language universals
the key element of these accounts from our point of view is that the antecedent of a trace must be the closest antecedent governor of the appropriate type
the second order quantification allows us to reason directly in terms of the sequence of nodes extending from the privileged node to the local tree that actually licenses the privilege
the regular languages for instance can be characterized by finite state string automata these languages can be processed using a fixed amount of memory
consequently these principles can not override fsds by themselves rather every violation of a default must be licensed by an inherited feature somewhere in the tree
also when compared to other languages turkish relies more on overt case markings which mark the role of the argument in a sentence
this section introduces two data structures that are basic to the development of the algorithms presented in this paper
in this paper an english to turkish mt system using the structural transfer approach with a limited amount of semantic analysis has been described
in the example execution is the head noun of the english phrase whereas tesebbus attempt becomes the head noun in the target phrase
we base these observations on an evaluation approach which considers transition pairs in terms of the inference load specific pairs imply
each event object captures the changes occurring within a company with respect to one management post
for all completion chains it is true that the start indices of the states are monotonically increasing kl k2 a state can only complete an expansion that started at the same or a previous position
in contrast a transfer based representation can be shallower cat the level of linguistic predicates while still abstracting far enough away from surface form to make most of the transfer rules simple atomic substitutions
focusing algorithms prefer the discourse element already in focus for anaphora resolution thus considering context boundedness too
lemma NUM if g is a proper consistent scfg without useless nonterminals then the powers p of the left corner relation and p of the unit production relation converge to zero as h oo
output actor fates discusses how the products of a process are used by other processes
explanation design packages emerged from an effort to accelerate the representation of discourse knowledge without sacrificing expressiveness
edps give discourse knowledge engineers an appropriate set of abstractions for specifying the content and organization of explanations
in particular it was very difficult to maintain and extend discourse knowledge expressed directly in code
although gauging the performance of explanation systems is inherently difficult five evaluation criteria can be applied
coherence a global assessment of the overall quality of the explanations generated by a system
the organizational aspect of discourse knowledge plays a particularly important role in the construction of extended explanations
a probabilistic parsing model using the acquired grammar was described and its potential was et m lcb ned
rule NUM means that only the conclusions of closed subordinated subproofs still remain in the reader s focus of attention
for reasons discussed below augmented versions of the naive and the canonical approaches will also be considered
due to this a criterion is needed for determining whether this merging process should be continued or terminated
things are a bit trickier if l g is too large to enumerate
q x NUM 2po x i ii fl x
we defined the initial distribution in terms of weights attached to the rules of g
fortunately there is an alternative random sampling method we can use metropolis hastings sampling
in summary we can not simply transplant cf methods to the av grammar case
the idea is to reject y with a probability corresponding to its degree of overrepresentation
since its label is a terminal category it does not need to be expanded and we are done
unlike in the context free case the four dags in figure NUM constitute the entirety of l g2
the expectation of each rule frequencyy is a sum of terms x fi x
what if we wish to adopt a grammar that imposes the constraint that both a s rewrite the same way
this result is evaluated by comparing the system s result with nontermlnal symbols given in the wsj corpus
this will enable the natural language generation community to begin making inroads into producing discourse in the same manner that corpus based techniques have aided discourse understanding efforts
for instance the semantic class human is mapped onto its sense i node in wordnet the uhuman l concept node
consider a domainspecit c hierarchy with just NUM classes vehicle aircraft and car as shown in figure NUM a
most of the texts also contained information about other aspects of botany such as experimental methods and historical developments these were omitted from the analysis
if the distance between the concept node and a semantic class node is below some threshold the semantic class node becomes a candidate class node
the word sense disambiguation module hence effectively pinpoints a partic l r node in wordnet that corresponds to the current sense of the word
two human judges are presented with a set of NUM sentences randomly selected from the 1023example test corpus each with a noun to be disambiguated
null a cross category projection creates a new constituent with the same start and end vertex in the chart as the subconstituent from which it is projected
in raising constructions the subject of the infinitive is a trace coindexed with the subject of the higher clause example 7c
if the infinitival clause is a complement of a control verb the empty subject must be eoindexed with the controlling argument lexically specified
from the statistical point of view a word is a string with a fixed pattern that is used repeatedly meaning that it shouht occur with a higher frequency than a string that is not a word
of our parser for german which is able to handle the difficulties arising from word order variations focusing on the treatment of infinitival constructions NUM
define that a i is the number of clusters in the string a n a is the number of occurrences of the string ud n a l is the number of occurrences of the string a with one additional cluster added
then the methodology of data preparw tion and open compound extraction is explained finally we discuss the result of an experiment on both large and small test corpora to investigate the effectiveness of our method
NUM nachdem ihn die polizei hatte fliehen sehen after him the pofiee had escape see after the police had seen him escape
according to condition NUM the string a un in table NUM is considered an open compound because the difference of betweml n a and n a l is as high as NUM
as this first constituent is morphologically ambiguous between nominative and accusative it can bc interpreted a priori as a subject or as a direct object
the parsing strategy is data triggered mainly bottom up proceeds from left to right and treats alternatives in parallel by using a chart cf
can we not replace it with a single statement in the verb node
evans and gazdar lexical knowledge representation process repeats until a value is returned
local inheritance uses definitional inheritance statements to distribute simple values and global descriptors
our replacement transducers in general are not unambiguous because we allow lower to be any regular language
because the placement of and is strictly controlled they do not occur anywhere else
thus we allow replacement expressions of the form upper prefix suffix
this problem is complicated by the fact that the list of multiword tokens may contain overlapping expressions
the definition of directed parallel replacement requires no additions to the techniques already presented
figure NUM shows the component relations and how they are composed with the input
given n and x the best parameter value may be estimated by one of several estimation methods
the more the committee members agree on the classification of the example the greater our certainty in its classification
we also show that sample selection yields a significant reduction in the size of the model used by the tagger
the score function is typically the joint or conditional probability of the sentence and the tag sequence NUM
this allows us to train on smaller examples focusing training more on the truly informative parts of the corpus
in such contexts committee members might be generated by randomly varying some of the decisions made in the learning algorithm
by appropriate classification we mean the classification given by a perfectly trained model that is one with accurate parameter values
property NUM is addressed by randomly drawing the parameter values for committee members from the posterior distribution given the current statistics
for simplicity we describe here the approximation of p i ails for the unsmoothed estimator NUM
there are a number of reasons for this there are genuine ambiguities
the transformations we investigate make use of classes of symbols in order to generalize regularities in rule applications
that is we actually run the algorithm multiple times to termination first changing thresholds by a factor of NUM
it merely states that a word is translated in a way that is understandable in some context
note that our judgement does not say that a word is translated correctly in a given context
we found this improved prediction rates by an additional NUM NUM at a menu size of NUM
after all translations had been tagged the tags were checked for consistency and automatically summed up
first it is time consuming to find out if a word is translated correctly within running text
comparing the systems averages we can observe that personal translator scores highest for all frequency classes
in the low frequency class NUM adjectives NUM nouns and NUM verbs got a translation
for langenscheidts t1 we tested the word compiler which is marked with data processing and computer software
as another side effect we used the lexicon evaluation to check for agreement within the noun phrase
but in particular if noun compounds are segmented and the translation is synthesized this operation sometimes fails
e and e are the set of all strings and all non null strings over e respectively
but most of the errors come from foreign words such as accelerando adagio allegro artefact posteriori mea culpa beluga placebo torero baby girl shirt blue jeans base ball steward business building copyright bonsay
table NUM performance of manual prob and bi
the collected data was split into training and test sets NUM of total data
some of these examples can become quite complex NUM is retranscribed or phoneticized as fifty dollars NUM NUM as fifty dollars and sixty cents NUM million as fifty million dollars NUM NUM million as fifty point two million dollars and so on
in particular each segmentation of a word is evahtated as follows
wenca t lo with atom atom lnst NUM u mi ntal
arbitrarily long compound nouns are possible and not rare in real texts
the verification of the method through experiments is described in section NUM
tile score of the imrticulm deceit position
a laborious work for the manual construction of nominal dictionaries is not needed
morphological analysis will return morphologically valid component words constituting a given compound word
the best sequence of these segmentations for a sentence can be obtained
is possible to approach this requirement see section NUM NUM
we now assume a first order dependence on the alignments aj only
so far there has been no basic restriction of the approach
r send up my suitcases to my room please
o sfibanme las maletas a mi habitacidn pot favor
above mentioned techniques take into account various semantic factors depending on specific domains on question in recovering extragrammatical sentences
so we extend the original algorithm in order to handle the errors of nonterminal symbols as well
the various attempts at rule formulation were related to differences in the phonemic inventory the number of rules the type and format of rules and even the direction of parse of the rules whether they were scanned from left to right or from right to left
for any input including grammatical and extragrammatical sentences this algorithm can generate the resultant parse tree
the same jealousy can breed confusion however in the absence of any authorization bill this year
scan checks various correspondences of input token t i against terminal symbols in rhs of rules
null a n er ion is the cost of an insertion error for a terminal symbol
so parsing systems are likely to have extragrammatical sentences which can not be analyzed by the systems
table NUM is the results of the robust parser on atis which we did not refer to before
for example a they re active generally at night or on damp cloudy days
the robust pa ser using the extended least errors recognition algorithm overgenerates many error hypothesis edges during parsing process
everybody is an expert in reading his or her own language and the average educated individual does not hesitate in front of a word like monsieur or second in french or hiccough or edinburgh in english even though the pronunciation may be quite different from the spelling
plain i.e. nonhonorific case market s and honorific case markers corresponding to them are as shown in NUM
thus within a sign based approach it is possible to compute relative social status on the basis of the collected relations of social status
feature structures are converted into prolog facts since the reasoning component comprised of inference rules accepts prolog facts not feature structures
all pieces of information that are necessary for the computation of social status are stored in the value of the feature s status
since a series of sentences forms a dialogue the feature structure of a dialoguc is its shown in NUM
for example the query for parsing the sentence in NUM should have the format illustrated in NUM
their approach however is incomplete and can not be applied to the computation of social status for the following reasons
third the honorific infix si appearing in a verb indicates that the referent of a subject np is respected by speaker
finally when the inference rule in NUM is applied to NUM and NUM the result is as shown in NUM
when the inference rule in NUM is applied to 33c and 33d the results are NUM and NUM
we define the sorted list of branches for a goal g that an agent knows b b where for each be p b is the likelihood that branch b will result in success where p b p b vi j
p b NUM fi NUM f i wi NUM k NUM i i xi where b is a branch out of a list of k branches and f i NUM if the agent knows branch b satisfies factor f and f i xi NUM qa otherwise
if an agent a knows q qn a percentage of the knowledge concerning factors fl f respectively and assuming independence of factors using bayes rule an agent can calculate the success likelihood of each u how do i fix i this circuit
suppose the agent has NUM a user model that states that the collaborator knows percentages q lcb q q about factors fl f2 fm respectively and NUM a model of the domain which states the approximate number of branches n
is the probability that the branch satisfies factor fi but the agent does not know this fact
in section NUM we will present the methodology and results of using these schemes in a simulated dialogue environment
random in random mode one agent is given initiative at random in the event of a conflict
using an appropriate implementation for finite state transducers see section NUM the resulting part of speech tagger operates in linear time independently of the number of rules and the length of the context
in this paper we design a tagger that requires n steps to tag a sentence of length n independently of the number of rules and the length of the context they require
in summary brill s algorithm for implementing the contextual tagger may require rkn elementary steps to tag an input of n words with r contextual rules requiring at most k tokens of context
line NUM adds the current identity state to the set of final states and a transition to the initial state for all letters that do not appear on any outgoing arc from this state
first we extend the definition of the transition function d the emission function NUM the deterministic transition function and the deterministic emission function on words in the classical way
computational linguistics volume NUM number NUM the following lemma states a common property of the state s which will be used in the complexity analysis of the algorithm
whilst features such as over informativeness are either present or not others are finer grained the interaction strategy can be system orientated user orientated or a combination of both
null proposition if a transducer t represents a subsequential function f then the algorithm determinizetransducer described in the previous section applied on t computes a subsequential transducer representing the same function
the second situation is ii NUM
this form of anaphora includes expressions such as next week and the next entry which can only be resolved in relation to a previous expression
at the time of writing we have identified NUM occurrences of cue phrases that exhibit discourse usages and associated with each of them procedures that instruct a shallow analyzer how the surrounding text should be broken into textual units
even if one ignores some computational bonuses that can be easily exploited by a japanese discourse analyzer such as co reference and topic identification there are still some key differences between sumita s work and ours
the database was then examined semiautomatically with the purpose of deriving procedures that a shallow analyzer could use to identify discourse usages of cue phrases break sentences into clauses and hypothesize rhetorical relations between textual units
the spearman correlation coefficients with respect to the importance of textual units between the discourse trees built by our program and those built by each analyst were NUM NUM p NUM NUM and NUM NUM p NUM NUM
for example if an and was used in a sentence and if the judges agreed that a clause boundary existed just before the and we assigned that and a discourse usage
as a consequence we assume that one can bootstrap the full syntactic semantic and pragmatic analysis of the clauses that make up a text and still end up with a reliable discourse structure for that text
it is true that in this way the discourse structures that we build lose some potential finer granularity but fortunately from a rhetorical analysis perspective the loss has insignificant global repercussions the vast majority of the relations that we miss due to recall failures of and are joint and sequence relations that hold between adjacent clauses
NUM yet even on the summer pole lcb where the sun remains in the sky all day long rcb temperatures never warm enough to melt frozen water deg since parenthetical information is related only to the elementary unit that it belongs to we do not assign it an elementary textual unit status
mm exl tienc l eclairs NUM smallj ountof athetthvolve i melelnofthia sumn rpole p teml raml n et degtbit p and sl m frigid weather dagr fahzenheit cdegnmut NUM i t a osphcaficblanket oonthlion3
the rhetorical parser presented in this paper uses only the structural constraints that were enumerated in section NUM co relational constraints focus theme anaphoric links and other syntactic semantic and pragmatic factors do not yet play a role in our system but we nevertheless expect them to reduce the number of valid discourse trees that can be associated with a text
inter tagger agreement decreased significantly p NUM NUM
in other cases a usage can be assigned to several senses that have been accorded polyseme status on the basis of previously encountered usages but may overlap with respect to other usages
of the remaining NUM polysemous words NUM were nouns NUM were verbs NUM were adjectives and NUM were adverbs a distribution similar to that found in standard prose texts
when encountering a sense that appears to match the usage taggers do not know whether another sense which they have not yet read will present a still more subtle meaning difference
besides having to weigh and compare more options the taggers needed to adjust their own ideas of the polysemous words meanings to the particular way these are split up and represented in wordnet
in the random order condition no bias towards the first sense existed so the strategy of choosing the first sense or an appropriate sense near the top of the list was not available
we first report the percentage of overlap between taggers and experts choices in terms of the three main variables pos degree of polysemy and the order of senses in wordnet
similarly we found that in the random order condition inter tagger agreement was higher for all pos when the agreed upon sense was the first in the dictionary NUM NUM vs NUM NUM
as the grammar assumes quite complex lexical signs inheritance is absolutely essential for organizing the lexicon succinctly
because the toplevel cluster partitions based purely on distributional information do not necessarily align with standard sense distinctions he generated up to NUM sense clusters and manually assigned each to a fixed sense label based on the hand inspection of NUM NUM sentences per cluster
although these earlier approaches have used often sophisticated measures of overlap with dictionary definitions they have not realized the potential for combining the relatively limited seed information in such definitions with the nearly unlimited co occurrence information extractable from text corpora
he arrived just as the store was closing for the day
we think this is an advantage compared to the use of classes which are more tightly linked to real world knowledge
3part of the study is also concerned with french english translation
we have n t yet connected the system to a working muc component NUM the user model has n t been implemented yet
we present a system that incorporates agent based technology and natural language generation to address the problem of natural language summarization of live sources of data
in our system the role of data collectors is performed by the muc systems and the facilitators connected to the world book
a problem that we have n t addressed is related to the clustering of articles according to their relevance to a specific event
this represents the database containing the ontologies including geographical locations weapons and incident types available from the muc conference
this confirms a weaker hypothesis which is nonetheless related to the initial one namely that nondeterminism does not vary in an inverse function to content of information
after an arbitrary number of trials two in the example the dialogue manager surrenders
unfortunately i have got difficulties in understanding you due to the music in the background
sie sind wegen der musik im hintergrund leider schwer zu verstehen
i would like to travel to hamburg at two o clock
one approach for classifying acoustic conditions into different categories e.g.
research is also needed in the realm of dialogue management
wpsre es msglich dat sie nochmals anrufen wenn sie die musik abgestellt haben
unlike in the scenario above however little can be done to change the environment
the quality of responses to users equally well can profit from information about the acoustic environment
an incoming signal could then be assigned to the category whose hmm gives the best match
in fact looking at analogy fi om the previous point of view is misleading because intentionally or not we think of nmnbers which enforces too many constraints
dialogue participants are engaged in a cooperative task whereby a model of the joint purpose is constructed
moreover any contribution is trivially unrelated to the previous topic since no previous topic exists
this contributes to the generality of the model by spelling out specific requirements of different communicative situations
the joint purpose describes coinnmnieative intentions in a context where no speaker obligations or considerations hold
the analysis of the first user contribution u1 is given in fig NUM
the constants n u c h identify instanti ated concepts
figure NUM possible joint purposes if the contextual factors are assigned binary values
NUM the agent has unfulfilled goals but no initiative adopt the partner s goal
the departure point is in general communicative principles which constrain cooperative and coherent communication
NUM ok ttere are the car hire companies in bolton
efficiency and accuracy results are presented
we also discuss further automation steps
this procedure was repeated NUM times with different partitions
for efficiency an extended viterbi algorithm is used
all unused states transitions and outputs are omitted
NUM separating unreliable decisions from those considered almost reliable
comments can be added to structures
the second level of automation cf
in this case NUM verbs were chosen at random to use in constructing the filter
this illustrates the difficulty of using an approach that does not account for multiple word senses
finally we show the results of our classification of unknown verbs and we evaluate these results
clearly the semantic filter would behave better if we used word senses in creating the fields
we examined several relations based on combinations of synonymy and hyponymy
acquisition of semantic lexicons using word sense disambiguation to improve precision
we then show the results of our classification on unknown words and we evaluate these results
the average size of levin s semantic classes is NUM verbs
NUM of these NUM NUM are correct a twelve fold improvement over the unfiltered assignments
we first describe the ldoce verb classification resulting from a purely syntactic approach to deriving semantic classes
in the example the first equation stands for a conservation in meaning mathematics as opposed to physics and a change in categories
by consequence if the analysis of a first sentence is known the analysis of the second one could be obtained by performing slight modifications it it
a similar analysis assuming a clever handling which prevents individual interdependency checks from being done more then once reveals that the complexity of step NUM is o n NUM too
in its effect this prefl rence rule al proximat s the often suggested heuristic of ke eping rather then shifting ret rential focus of
whereas for nps with possessive markers of example NUM the matter tends to be clear a common source of difficulties emerges from adjectivally used participles and from deverbative nps
c if y is a type b NUM ronoun antecedent candidate x is intr scntential and according to surfa e order x follows y i.e.
special techniques are employed in representing local np domains which are introduced by deverbative nps and nps with possessive markers saxonian genitive genitivus possessivus possessive pronoun or certain attributive pps e.g.
an anal hor resolution algorithm is presented which relies on a combination of strategies for narrowing down and selecting ti om antecedent sets fl r reflexive pronouns nonreflexive pronom s and common NUM
in cases in whi h semantic ase is not available however promoting syntactic as t arallelism serves as a good at proximal on
the case role inertia criterion which proved to e very useful in practice is explainal h by the following examt le NUM peter visited his brvther
the former determines the global ordering of the claim text while the latter deals with local text coherence
in our system the last word in a string is practically always the syntactic head of the phrase
zone NUM lists the verb s frequency rank in the list of all the verbs belonging to its semantic class
it is at the realization stage that we reintroduce the actual strings and determine their required inflectional forms
once a predicate is selected the system proceeds requesting information about the values of the case roles of this predicate
this essentially amounts to a mixed depth analysis in the translation process an important question that we can not discuss further here
particles that are mere fillers can be removed entirely from the translation and similarly those particles that are used to smooth the intonation contour in german
we use the draft as the input to the process of stylistic and rhetorical text planning and realization
we built a representation of this schema based on our study of a training corpus of u s patents
however we do not use this list of simple sentences in our generation or revision
NUM NUM use word classification resul in statistical language modefing
both possibilities present increased opportunities for systems to undergenerate or overgenerate
in the NUM other strings we should not expect occurrences of pns in order to recognize an 1nga first dictionaries of all common nouns i.e.
there are many more ways to paraphrase something than to translate it literally and translators usually strive for variety in order to improve readability
sentences contain literal semantic contents each one being under the responsibility of an utterer
within ducrot s framework we use his theory of polyphony topoi and modifiers
certain utterance structures contain linguistic clues that constrain their interpretation on an argumentative basis
the signification of tss is assumed to be computed from the lexicon by a compositional process
the latter is a functional of the entire pdf of source words whereas the former is a function of the particular source word s
it drives the top down process for generating data corresponding to odd contexts
such words include very little or a little
we have discarded the utterance sentence level of polyphony in order to simplify the presentation
it also further selects in the sets of topoi those connected to the situation
we used NUM semantic features and the creation of this semantic tagging dictionary took NUM hours
sometimes these variou s sources provide consistent interpretations but they often disagree with one another
james paired with the status out from the x is stepping down concept node
if both are found in the same noun phrase which is the case for mr
this graph shows the average recall and precision for NUM random partitionings at each training size
crystal learned cns named status in and status out to identify persons involved in a change of status
so it is fair to expect that te will operate as an upper bound for st
it is interesting to compare our te scores with our ne scores for organizations and people
this introduces an interference effect into the precision scores for each of the three enamex types
with only NUM st trainin g documents this year our dictionary was missing important verb constructions
type i pn postposition e this type of noun phrases is without any particular characteristics inherent to proper names ply
in order to simplify the parser and speed it up three important points to bear in mind when cousidering the morphological processing are neat segmentation of characters into words part of speech tagging selection and implicit spelling error detection
thus the more information represented in a base lexical entry the greater the space saving achieved by the covariation encoding
as a result it is necessary to execute the call to q l immediately when the lexical entry is used during processing
the search tree that would have resulted from pursuing these possibilities at the beginning of processing does not have to be explored
the pruning is done by performing the lexical rule applications corresponding to the transitions in the automaton representing global lexical rule interaction
two problems remain first because of the procedural interpretation of lexical rules duplicate lexical entries can possibly be derived
this way the disjunctive possibilities arising from lexical rule application are encoded as systematic covariation in the specification of lexical entries
while this provides a front end to include lexical rules in the grammars it has the disadvantage that the generalizations captured by lexical rules are not used for computation
we suggest that the mcca categories and wordnet synsets represent two such systems of domains each reflecting particular perspectives
as described above mcca produces a set of c seores and e scores for each text
these are the results an investigator uses in reporting on the content analysis using mcca
we describe novel perspectives on how this information can be used in various nlp tasks
in any event we have seen that mcca categories are consistent with wordnet synsets
the analyst examines the graphical output to label points with the dominant mcca categories
each verb found in the corpora is submitted to the morpho semantic generator which produces all its morphological derivations and based on a detailed set of tested heuristics attaches to each form an appropriate semantic lr label for instance the nominal form comprador will be among the ones generated from the verb comprar and the semantic lr agent of is attached to it
the answer seems to be that because hesitations so often coincide with pause boundaries the segments they mark out are nearly the same as the segments marked by pauses alone
as shown above the word in c czc c4cs pattern has two ambiguous forras
consequently word filtering will consists of two processes a filtering process used to eliminate unuseful ragged word combinations and a scanning process used to detect and correct an implicit spelling error by generating a new set of words according to the cause of errors and selecting the one that maximizes the probabilities of word cluster
iqll n j deg which which at place l NUM ch NUM el i di dish i NUM lags NUM tags relpron i which
two constants that need to be mentioned are system and user
our work follows the plan based approach to language generation and understanding
errors are attributed to the action that contains the failed constraint
it also sanctions the adoption of beliefs about the current plan
actions are the primitive actions that are added to the plan
plan derivation pl for the weird creature
peter a heeman and graeme hirst collaborating on referring expressions necessary
verb noun collocations see example NUM below
in this paper a method for the automatic extraction of terminological possibly complex units of information from corpora is presented
tests against a domain specific user oriented dictionary have been carried out in comparison with large scale thesaura in the domain
as can be seen our approach consists of two tiers
this final expression is contributed to the participants common ground
to evaluate the result of the experiment we examined the meaning of leiru which is one of the most fundamental aspectual markers in japanese and obtained the correct recognition score of NUM for the NUM sentences
however accuracy can be improved by also exploiting the fact that all occurrences of a word in the discourse are likely to exhibit the same sense
she trained her fully supervised algorithm on hand labelled sentences applied the result to new data and added the most confidently tagged examples to the training set
indeed one of the strengths of this work is that it is sensitive to a wider range of language detail than typically captured in statistical sense disambiguation algorithms
figure NUM shows an example of a name recognition pattern that identifies a person s name
for each sense this procedure is robust and selfcorrecting and exhibits many strengths of supervised approaches including sensitivity to word order information lost in earlier unsupervised algorithms
take those members in the residual that are tagged as sense a or sense b with probability above a certain threshold and add those examples to the growing seed sets
erie achieved a high processing accuracy in the japanese met task
ntt data description of the erie system used for muc NUM
words to which the sub category is added are listed on the right side of the pattern
newly segmented words and their parts of speech are defined in the right side of the pattern
then the dictionary pattern is used to add a sub category to the word
the pattern matching conditions of the matched word can be described in parenthesis
iif their classification begins to waver because new examples have discredited the crucial collocate they are returned to the residual and may later be classified differently
by not combining probabilities this decision list approach avoids the problematic complex modeling of statistical dependencies 3it is interesting to speculate on the reasons for this phenomenon
finally we subsert this derived struc9we enforce island effects for wh movement by using a extract feature on substitution nodes
unfortunately as seen in the examples given in section NUM NUM there are cases where satisfactory analyses can not be obtained with adjunction
we first subsert the to adore d tree into the seems tree as above by substituting the anchor component at the substitution node of seems
r ambow was supported by the north atlantic treaty organization under a grant awarded in NUM while at talana universitd paris NUM
the search rectangle is anchored at the top right corner of the previously found chain
this raises the possibility that in grammars that express certain linguistic principles substitutability is not needed for ruling out derivations of this nature
becanse of lexicalization the size of these multi sets is polynomially bounded from which the polynomial time and space complexity of the algorithm follows
however bitexts are of little use without an automatic method for constructing bitext maps
a d edge is removed by merging the nodes at either end of the edge as long as they are labeled by the same symbol
a second problem discussed in section NUM NUM has to do with the inability of tag to provide analyses for certain syntactic phenomena
let us first consider morphological issues
simr encapsulates its language specific heuristics so that it can be ported to any language pair with a minimal effort
of these expressions to those related to their grammaticalform
not being in a controlling position within a speech processing system but tracking a mediated dialogue calls for an architecture where different approaches to dialogue processing cooperate
the dialogue component within verbmobil has four mqor tasks NUM to support speech recognition and linguis null tic analysis when processing the speech signal
it makes sense to first convert all person template elements into potential in and out objects
section NUM NUM formally defines this notion and gives an algorithm for computing the local extension
it adds the incoming speech acts to the intentional structure keeps track of the dates being negotiated stores the various linguistic realizations of objects e.g.
planning proceeds in a top down fashion i.e. high level goals are decomposed into subgoals each of which has to be achieved individually in order to be fulfilled
for this purpose specialized repair operators have been implemented which determine both the type of error occurred and the most likely and plausible way to continue the dialogue
in speech recognition language models are commonly used to reduce the search space when determining a word that can match a given part of the indeg put
the overall prediction rates for the whole dialogue are NUM NUM NUM NUM and NUM NUM for one two and three predictions respectively
for example the rules in figure NUM might be found in a contextual tagger
when both dialogue participants speak english and no automatic translation is necessary verbmobil is passive i.e. no syntactic or semantic analyses are performed
in comparison with the fourth prediction the first three predictions have a very similar ranking so that the failure can only be considered a near miss
consider the implications of this for information retrieval
in other words any transformation based system can be turned into a deterministic finite state transducer
NUM by design the finite state tagger produces the same output as the rule based tagger
as shown in table NUM performance on the ne task overall was over NUM on the f measure for half of the systems tested which includes systems from seven different sites
te performance of all systems on the walkthrough article was not as good as performance on the test set as a whole but the difference is small for about half the systems
the subcategory error scores were NUM on organization NUM on person and NUM on location NUM on date and NUM on money and percent
even the simplest of the tasks named entity occasionally requires in depth processing e.g. to determine whether NUM pounds is an expression of weight or of monetary value
NUM many of the veteran participating sites had gotten to the point in their ongoing development where they had fast and efficient methods for updating their systems and monitoring their progress
the resulting decision trees tended not to include all three features
but the problems are certainly tractable none of the fifteen te entities in the key ten organization entities and five person entities was miscategofized by all of the systems
the task definition is now under review by a discourse working group formed in NUM with representatives from both inside and outside the muc commuity including representatives from the spoken language community
at the far end of the spectrum are bare common nouns such as the prenominal company in the example whose status as a referring expression may be questionable
sra ran an experiment on an upper case version of the test set that showed NUM recall and NUM precision overall with identification of organization names presenting the greatest problem
the results show that for naive back off and ml the addition of more possibly irrelevant features quickly becomes detrimental decrease from NUM NUM to NUM NUM even if these added features do make a generalisation performance increase possible witness the increase with ibi ig from NUM NUM to NUM NUM
as shown in line NUM labeled final coding
this rule introduces a passive form of v as complement to be and sends up the default agent meaning
in all implementations of lfg parsers that i am aware of such checks are built into the parsing algorithm
NUM threading and linear precedence threading can also be used as an efficient way of encoding linear precedence constraints
first we encode the categories in question with the lp constraints in terms of what can precede and follow them
thus the position in the tuple for a b must have a b in that position in the out value
in NUM countries mccann is tanked in the top three in NUM countries it is in the top NUM
p NUM mccann has initiated a new so called global collaborative system composed of world wide account directors paired with creative partnen
for mr enamex type person dooner enamex it means maintaining hi s running and exercise schedule and for the agency it mein
i would be less than honest to say i m n o dis sated not to be able to claim creative leadership to
but m enamex type person dooner enamex hasa big challenge that will be his top priority
the learning module consists of functions mixing statistics and inductive learning techniques and i s used for corpus analysis and definite anaphora based knowledge acquisition
each grammar rule is supplied with the name of a function translating the recognized expressions of natura l language into the uno representation
we have shown that computing logical context independent and non monotonic context dependent inferences fo r temporal and non temporal objects is almost exactly analogous
our system offers a highly efficient boolean meet operation which is mathematically guaranteed to combine information in the most general way
work done during june october NUM NUM below we elaborate on the work done between the end of june and beginning of october NUM
as the s ml rity of any two labels is estimated based on local contextual information which is defined by a set of category pairs of left aad right words there is an interesting question of which contexts are useful for calculation of s ml rity
the number of sentences which the discourse processor was able to assign a speech act based on plan inference increases from NUM NUM with standard tst to NUM NUM with extended tst
as a result we are left with the question of how to account for shifts in focus which seem to occur within the deliberation segment as evidenced by the types of pronominal references which occur within it
if the reply sentence NUM in this example contains an abbreviated or anaphoric expression referring to the date and time in question and if the chain of inference attaches to the wrong place on the plan tree as in this case the normal procedure for augmenting the shortened referring expression from context could not take place correctly as the attachment is made
in summary figure NUM makes clear with the extended version of tst the number of speech acts identified correctly increases from NUM NUM to NUM NUM and the number of sentences which the discourse processor was able to assign a speech act based on plan inference increases from NUM NUM to NUM NUM
in order to have a focus stack which can branch out like a graph structured stack in this framework we have extended lambert s plan operator formalism to include annotations on the actions in the body of decomposition plan operators which indicate whether that action should appear NUM or NUM times NUM or more times NUM or more times or exactly NUM time
it should be noted that although the correct speech act can be identified without plan inference in many cases it is far better to recognize the speech act by first recognizing the role the sentence plays in the dialogue with the discourse processor since this makes it possible for further processing to take place such as ellipsis and anaphora resolution
the first experiment involves an evaluation of performance of our proposed grammar learning method shown in the section NUM in this prp imi ary experiment only rules which have lexical categories as their right hand side are considered and the acquired nontermlnal labels are compared with those assigned in the wsj corpus
parse tzee NUM dt the nn tx yn vb dropp ed i pi p his nn wallet rb somewhere i
for tance ci j n c2 dt nn and cs p rp n n in figure NUM due to the reason of computation time and space we use the rule tokens which appear more than NUM times in the corpus
lcb aberdeen john day lynette palmer parann mbv mitre org
examples include oak maple birch cedar and many many others
although the difference between the underlying word frequency estimation methods is small the longest match string frequency method generally performs best
since it is inconvenient to use both recall and precision all the we also use the f measure to indicate the overall performance
re estimation helps in adjusting word frequencies and removing inappropriate word hypotheses although it has little impact on word segmentation accuracy if the word unigram model is used
it shows two pairs of distributions word length of all words NUM NUM and that of words appearing only once NUM NUM
kanfi which means chinese character is used for both chinese origin words and japanese words semantically equivalent to chinese characters
although d200 consists of only NUM words it covers NUM NUM oov rate NUM NUM of the test sentences
from the remaining of NUM thousand sentences we randomly selected NUM test sentences to evaluate the accuracy of the word segmenters
when the word segmeuter is trained on NUM NUM m character texts and NUM initial words its word segmentation accuracy is NUM NUM recall and NUM NUM precision
we approx4mate the spelling probability given word length p cl ca k by the product of character unigram probabilities regardless of word length
this power i is like a invisible hand
b zuocheng yige yuanwan i he yige fangwanj
although the increase in the overall matched rate was not significant NUM NUM NUM of the pronouns in the test data however were matched by using the new rule
NUM note that we only deal with third person pronouns in chinese thus in the table and the following pronominal anaphora or pronouns refer to third person cases
within the traversal when a reference is met if it is a subsequent one then the program consults rule NUM to obtain a form zero pronominal or nominal
on accepting an input goal from the user the system invokes the text planner according to the operators in the plan library to build a hierarchical discourse structure that satisfies the input goal
each anaphor position in a generated text was left empty and all candidate forms of the anaphor including zero pronominal and full and reduced descriptions were put under the empty space
each system is assumed to have the same system components as described in section NUM NUM except that the referring expression component of each system is equipped with a different anaphor generation rule
both tr2 and tr3 perform better than tr1
results of kenm e on s m t ic class dl higuation can not yet be reportecl the features used for both supervised algorithms are the local collocations of the surrounding NUM words zz
using context however no information about the word that is to be categorized was used
it is by no means generally accepted that such a classification is linguistically adequate
precision is the number of correct tokens divided by the sum of correct and incorrect tokens
in this light it is not surprising that the word type method does better on cardinals
we formed such concatenated vectors for all NUM NUM words surface forms in the brown corpus
svd can be used to approximate the row and column vectors of c in a low dimensional space
we are planning to explore soft classification algorithms that can account for these phenomena
so is a diagonal k by k matrix that contains the singular values of c in descending order
still a lexicon is needed that specifies the possible parts of speech for every word
our generator uses a notion of approximate matching and can happen to con null vey more or less information than is originally specified in its semantic input
w are in a position to express subparts of the input semantics as different syntactic categories as appropriate for the current generation goxl e.g. vps and nominalisations
if the mapping rule s semantic additions are merely in lowersem then information can not flow from lowersem to the mapping rule area NUM in figure NUM
degterminal mapping rules are mapping rules which have no internal generation goals and in which all terminal nodes of the syntactic structure are labeled with terminal symbols lexemes
the syntactic coverage of protector includes intransitive transitive and ditransitive verbs topicalisation verb particles passive sentential complements control constructions relative clauses nominalisations and a variety of idioms
the use of a syntactic theory d tree grammars allows for the production of linguistically motivated syntactic structures which will pay off in terms of better coverage of the language and overall maintainability of the generator
the choice of mapping rules is influenced by the following criteria connectivity the semantics of the mapping rule has to match cover part of the covered semantics and part of the remaining semantics
generation starts by first trying to find a mapping rule whose semantic structure matches s part of the initial graph and whose syntactic structure is compatible with the goal syntax the syntactic part of partial
its internal generation goals are to realize the instantiation of action which is movement as a verb and similarly person fred f as a noun phrase
the generator assumes it is given as input an input semantics inputsem and boundary constraints for the semantics of the generated sentence builtsem which in general is different from inputsemh
with family relation fr type iv
type i noun position type ii
accurate analysis of the string in figure NUM NUM NUM
and of course all fields are null for utterances that do not contain temporal information
all nonmonotonic reasoning remains packed into the definition of update t a f where one needs pragmatic reasoning anyway for inferring rhetorical relations
still it is no straightforward task to employ that kind of software for new applications
siliec rliilcdonal ea l egories col l si ond
an example of temporal reference resolution is that NUM refers to NUM 4pm thursday NUM august
a cgi is a standalone script or program invoked by a web server to provide services beyond those included in its suite
save folders allow the user to collect articles from both dissemination results and query results for an open ended amount of time
prides provides a robust easy to user retrospective search capability against a corpus of fbis articles accumulated since may NUM
fbis collects translates and disseminates selected foreign media content including newspaper and magazine articles and television and radio broadcasts
each layer has a unique set of responsibilities and communicates only with its adjacent layer s via a well def med api
pa interfaces with the tipster data access tda layer to store index search and retrieve prides data via api calls
the prides user interface layer pui is responsible for creating and managing the screen displays that comprise the prides user interface
after installation at the customer s site and an acceptance test period prides will begin serving production users in july NUM
w w is the number of different positions at which factors u and u are aligned within i and ending at position j hence NUM NUM denotes ac
a web server package augmented by a set of prides specific common gateway interfaces cgis communicates with the client via hypertext transport protocol http
prides applies a portion of the tipster detection architecture and several tipster components to the problem of timely dissemination of foreign broadcast information service fbis articles
we let e NUM be the sum of the counts of the dominated nodes in f p and let e be the value retrieved from the a link to t if any
we apply this occurrence test to both the right and left hand sides of all strings to ensure the accurate detection of both boundaries of the string
since lexical rules are expressed in the theory just like any other part of the theory they are represented in the same way as unary immediate dominance schemata
observing tile same string a in table NUM the difference between n a and n a l is only NUM
furthermore our method has ensured the extraction of new words from the text file of the language that has no explicit word boundary such as thai
one of the advantages of our method is that there is an inherent trade off between the quantity and the quality of the extracted strings
a major concern in corpus based approaches is that the applicability of the acquired knowledge may be limited by some feature of the corpus in particular the notion of text domain
the order of the performance is generally the following the same domain best the same class all dommns the other class and the other domain worst
the difficulty of the glass box method is the lack of a clear division between components in generation systems
it is intuitively conceivable that there are syntactic differences between telegraphic messages and press report or between weather forecast sentences and romance and love story
the experiments we undertook to assess the performance of these algorithms are the topic of section NUM quantitative experimental results are also summarized
they recommend using a bilingual corpus to train the parameters of translation probability pr s i t in the translation model
it is possible that a string with an invalid starting pattern will be extracted because a string too long in character length has been extracted previously
such mandarin function words are often quite ambiguous in part of speech as well as in word sense leading to numerous alignment errors
as a result of facts such as these many linguists contend that mandarin is a language in transition from svo to sov
we have presented a dialogue management architecture that is mixed initiative self organizing and has a two layered state set whose upper layer is portable to other applications
computationally lexical rules have mainly been dealt with in two ways on the one hand lexical rules are used to expand out the full lexicon at compile time
therefore after lifting the common information into the extended lexical entry the out argument in many cases contains enough information to permit a postponed execution of the interaction predicate
given a lexical entry as in figure NUM we can discard all frame clauses that presuppose tl as the value of c as discussed in the previous section
however identical automata are obtained for certain groups of lexical entries and as shown in the next section each automaton is translated into definite relations only once
we tackle these problems by means of word class specialization i.e. we prune the automaton with respect to the propagation of specifications belonging to the base lexical entries
since this information is local to any given structure the interpreter does not need to know about it and the control information is interpreted as compiler directives
finally a relation is defined to collect all constraints on a type atyp this ri NUM a
the question we are concerned with in the following is how a hpsg ii theory can be modelled in such a setup
this implies that every object in the denotation of a non minimal type is also described by at least one of its subtypes
even though the simple picture with its tripartite definition for each type yields perspicuous code it falls short in several respects
see also NUM NUM in the corpus example NUM
e.g. if the type hierarchy contains a type sign with subtypes word and phrase the user may specify that word should always be tried before phrase
this is necessary since although we avoid redundancies as shown in the last example there are still cases where the same node gets checked more than once
formally given a sentence s and a tree t the model estimates the conditional probability p t s
transliteration is not trivial to automate but we will be concerned with an even more challenging problem going from katakana back to english i.e. back transliteration automating back transliteration has great practical importance in japanese english machine translation
we have approximated such a model by removing high frequency words like has an are am were their and does plus unlikely words corresponding to japanese sound bites like coup and oh
only two pairs failed to align when we wished they had both involved turning english y uw into japanese u as in y uw k ah l ey l iy u kurere
this gives a total of NUM sounds including NUM vowel sounds e.g. aa ae uw NUM consonant sounds e.g. k 1tlt it plus our special symbol pause
next comes the p jlk model which produces a NUM state NUM arc wfsa whose highest scoring sequence is mas ut aazut o o ch im ent o next comes p elj yielding a NUM state NUM arc wfsa whose best sequence is
finally we recognize or reclassify names on the basis of their immediate context
v en and adv refer to words that are past participles and adverbs respectively
but there are some domain dependent specializations of the rules where special semantics applies
null the metarule for passives as in talks were resumed is
this consideration led us to implement what can be called compile time transformations
the pronoun they can be identified with either a plural noun group or an organization
if the topic concerns computer companies references to ibm will increase the score
from these features we can define constraints on the probabilistic model that is learned in which we assume that the expected value of the feature with respect to the distribution of the training data pd holds with respect to the general model pro
thus excessive or insufficient classification may be encountered
up to now three different types of agent systems have been hooked up to the nl server
this argument can be illustrated as in figure NUM in which the symbols NUM and NUM denote example case fillers of different case frames and an input sentence includes two case fillers denoted by x and y
figure NUM the cosma architecture a client connected to a server instance may issue requests to receive a
based on the information delivered by the morphological analysis of the identified fragment patterns the system performs a constituent analysis
temporal anaphora include expressions such as on monday tomorrow next month whose interpretation depends on the discourse context
we described cosma a nl server system for existing machine agents in the domain of appointment scheduling
thus multiple dialogues are processed in parallel just by running each dialogue in a separate virtual system
the underlying idea of the algorithm is really very simple
if the resulting il expression satisfies the constraints on well formedness it is shipped to the pasha ii client
after an overview of generation in cosma section NUM we discuss component interaction in section NUM
consider again the case of toru in figure NUM since the semantic range of nouns collocating with the verb in the nominative does not seem to have a strong delinearization in a semantic sense in figure NUM the nominative of each verb sense displays the same general concept i.e.
each of the sentences in the training test data used in our experiment contained one or several complement s followed by one of the ten verbs enumerated in table NUM in table NUM the column of english gloss describes typical english translations of the japanese verbs
similarly in our example based verb sense disambiguation system we introduce the notion of interpretation certainty of examples based on the following applicability restrictions NUM the highest interpretation score is sufficiently large NUM the highest interpretation score is significantly larger than the second highest score
in other words it is a measure of how many irrelevant error reports the user will be bothered with
NUM we assume that the parser determines the appropriate discourse entities in these actions entity1 is the discourse entity for the object being referred to and entity3 is another discourse entity
theretbre the optimal position i for each position j can be determined in lependently of the neighbouring positions
we will is this model as reference tbr the iimm based alignments to lie NUM resented later
each word of the german sentence is assigned to a word of the english sentence
fig NUM illustrates this effect for the language pair german NUM nglish
we describe the details of the model and test the model on several bilingual corpora
in this paper we address the problem of word alignments for a bilingual corpus
align inch rather than the set of all alignm nts
finally we present some experimental results and compare our model with the conventional model
these dep ndencies are captured in the lbrm of a rnixtnre distritmtion
for example in figure NUM yamaichi id NUM and sony prudential id NUM referring back to yamaichi shouken id NUM yamaichi securities and sony prudential seimeihoken id NUM sony prudential life insurance respectively are name anaphora
i moreover an anaphora resolution system within an nlp system for real applications must handle degraded or missing input no nlp system has complete lexicons grammars or semantic knowledge and outputs perfect results and different anaphoric phenomena in different do null mains languages and applications
in table NUM shows the extraction ratio NUM NUM and para shows the number of paragraphs corresponding to each percentage
formulae NUM and NUM are applied to NUM articles which are the results of wsd and linking methods and as a result we have obtained NUM NUM keywords in all
comparing the difference ratio of our method and not wsd to that of not wsd and method a the former was NUM NUM and the latter was NUM NUM
paragraph NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM lo NUM NUM NUM
NUM the error of wsd when the extracted ratio was NUM there was one article out of four articles which could not be extracted correctly because of the error of wsd
our method not wsd and metbod h shows the results using our method the method which wsd and linking are not applied and method a respectively
in formula NUM k is the number of contexts in paragraph and mi is the mean value of the total frequency of word i in paragraph which consists of k contexts
in formulae NUM and NUM xp xa and xd are the deviation value of a set of paragraph article and domain respectively
the api provides functions getnextitem and printitem to read and write the next complete sgml element
in fact sgml hytime allows much more flexible addressing not just character offsets
the intention is presumably to find a particular sub class of noun phrases
the cqp model cqp treats corpora as sequences of attribute value bundles
a muc NUM compatible information extraction system vie has been built using the gate architecture
it is unlikely that lt nsl will prove the rate limiting step in sequential corpus processing
at a minimum links are able to target single elements or sequences of contiguous elements
we carried out an experiment to examine the meaning of teiru automatically by means of the classifications of verbs and adverbs obtained in the previous experiment
we would like to answer these points with reference to the latest version of our software
the ggi graphical tool shell lets one build store and recover complex processing specifications
finally test pseudo words were created from pairs of verbs with similar frequencies so as to control for word frequency in the decision task
further action must be taken to identify the nature of such relations before this kind of ambiguity can be successfully resolved
in order to test the performance of each heuristic and their combination we selected two test sets at random one per dictionary NUM noun senses for dgile and NUM noun senses for lppl which give confidence rates of NUM and NUM respectively
as the results for lppl show small dictionaries with short definitions can not profit from raw corpus techniques heuristics NUM NUM and consequently the improvement of precision over the random baseline or first sense heuristic is lower than in dgile
thus when constructing complete disambiguated taxonomies the correct dictionary sense of the genus term must be selected in each dictionary for other kind of definition patterns not based on genus a genus like term was added after studying those patterns
word wl is simply pml w2 wl c wl w2 NUM c i
the method by mixing different levels of trees incorporates not only frequent connections but also infrequent ones that are often considered to be collocational without over fitting the data
by documenting such border line cases we hope to achieve consensus about the ways in which they should be treated and the severity of the incompatibility
furthermore the lexical aspect of verbs is closely related with their deep complement structures which may not be directly reflected on the surface argument structures
this section gives an explanation of grammar acquisition based on clustering analysis
other keywords such as rood and race trac are related functionally to bank through the partofrelation
intuitively in both cases the extra element has to be bound further up the tree after it has found intervening material which identifies it as extraposed
net the contrast between NUM and NUM shows that extraposition inside a vp is possible only if the vp is fronted
stz trilogy NUM abet es wurde 5ffentlich aufmerksam but it was publicly attention gemacht auf eine prek ire situation
in case b the extra element originates directly from a lexical head and would be indistinguishable from a non extraposed complement or adjunct if bound immediately
1extraposition data was acquired from the following corpora upenn treebank up london lund corpus ll stuttgart newspaper corpus stz
the constraint of frozenness to further extraction which states that no dislocation is possible out of an extraposed phrase is widely accepted in the literature
NUM in der nacht hatte es tote gegeben in in the night had there victims been in moskau
e the upward boundedness of extraposition can be captured by stating that a sentence has to be inheriextra lcb rcb
at least the same accuracy as spatter was acquired for this parser
although a large number of bottom up hypotheses regarding the position of an empty element can be eliminated by providing the parser with the aforementioned information the number of wrong hypotheses is still significant
we show that by requiring certain prosodic properties from those positions in the input where the presence of an empty category has to be hypothesized a derivation can be accomplished more efficiently
we obtained the above results without using the etd speech data to train the acoustic models
note that such a setting where a position in the input is annotated with scores representing the respective boundary probabilities is much more robust w r t unclear classification results than a pure binary boundary vs nonboundary
the introduction of empty arcs is then not only conditioned by the syntactic constraints mentioned before but additionally by certain requirements on the prosodic structure of the input
however the question to be answered was can we devise an automatic procedure to identify the syntactic bound null aries with at least about the same reliability as the prosodic ones
as a consequence of c the parser has a fully specified verb form although with empty phonology at hand rather than having to cope with the underspecified structure in NUM
since the structural position of an empty verbal head is identical to the structural position of an overt finite verb in a verb final clause the invariance does not come as a surprise
what table NUM shows then is that syntactic NUM boundaries can be classified using only prosodic information yielding recognition rates comparable to those for the recognition of perceptually identified b3 boundaries
but the automatic triggering ordering and bounding of the lexical rules is not discussed NUM
a dominance link can be further specified as a path of length superior or equal to zero
a few solutions have been proposed for the problems described above
so to represent hierarchically this set we do not think that nonmonotonicity is linguistically justified
it comprises a principled and hierarchical representation of lexico syntactic structures
these crossings are precisely the major source of redundancy in ltags
a partial description is a set of constraints that characterizes a set of trees
eachtime the algorithm reads an example it first reaches current leaf node s by following the past sequence computes a sb and then selects the optimal b
we show that some families of transformations can be efficiently learned exploiting the data structures of section NUM we also consider more general kinds of transformations and show that for this class the learning problem is np hard
the form hajimeru begin can follow the verbs which have process bp and takes up the start time of the process
we use jackendoff s notion of field which carries loc ational semantic primitives into non spatial domains such as poss essional temp oral ident ificational
a number of works in the linguistics literature have proposed lexical semantic templates for representing the aspectual properties of verbs dowry NUM hovav and levin NUM levin and rappaport hovav
figure NUM geig evaluation metrics for parser against
however we are experimenting with alternative approaches
the results were evaluated against a merged entry for these verbs from the anlt and comlex syntax dictionaries and also against a manual analysis of the corpus data for seven of the verbs
others are syntactic tests involving diathesis alternation possibilities e.g.
figure NUM raw results for test of NUM verbs
instances of passive constructions are recognized and treated specially
we borrowed the idea of re sampling to detect exceptional connections and first proved that such a re sampling method is also effective for a practical application using a large amount of data
parsers or grammars being the same
NUM NUM the extractor classifier and evaluator
figure NUM precision vs degree of generalization
duke s trainable information and meaning extraction system
the partial parser produces a sequence of nonoverlapping phrases as output
NUM NUM the tokenizer the preprocessor and the partial parser
the preprocessor tries to identify some important entities like names of companies proper names etc
lwordnet is an on line lexical reference system developed by george miller at princeton university
wordnet role generalization routines templates answer queries or generate abstracts
figure NUM shows the different degrees of generalization of the concept ibm cor
then we refine this notion in order to filter out genuine variants and to reject spurious ones
if the parse fails parsing is restarted with the constant lowered
this seems to be due to the failure to detect the expression of repetition therefore we chose the category determined in the step NUM NUM
a perusal of the state transitions associated with individual words in la reveals an obvious relationship to the types of categorial grammar
this does not present a problem however as in dop it is information in the parsed corpus which determines the structures that are possible
karttunen NUM in which all rules are encoded in the lexicon there being no phrase structure rules which do not introduce lexical items
coverage the criterion that remains to be satisfied is that of width of coverage can the formalism cope with the many peripheral structures found in real written and spoken texts
although far from exhausting the possible methods for smoothing the following three are those used in the implementation described at the end of the paper
chased s np dog np cat barked s np dog
to make this concrete there are five tokens of the word dog in the examples thus far and so dog will have the transition probability distribution
in the finite state grammar each word is associated with a transition between two categories in the tree above a with the transition a NUM b and so on
in addition to satisfying our first criterion a finite state grammar also fulfills the requirement that the formalism be radically lexicalist as by definition every rule introduces a lexical item
repeating an utterance or requesting or responding to a need for clarification and use of discourse connectors for topic shift or to continue the conversation after repair and to signal turntaking
the second pass requires more changes
finally pictalk users are likely to have difficulty deciding how to make the conversation flow when the utterance they would like to use is not available
pre loaded utterances currently have to be constructed by someone other than the pictalk user each of these utterances needs to be considered very carefully for several reasons
even young children use verbal and non verbal means to accomplish these activities as well as changes in prosody and variations in politeness depending on the partner
while later work has suggested that the level of egocentrism which is in general exhibited by young children may not be as great as suggested by piaget e.g.
step NUM for each verb in verbs step NUM NUM narrow down the candidates by means of the array forms on the basis of possible categories shown in table NUM
however this is not the case here since r5 and r6 have different rule features
when the operator is mentioned without reference to arguments it appears on its own e.g.
this enables separate and parm lel
pressions formed during the planning process
showever each module is an independent black box
a NUM x NUM design mode x subdialogue phase was used the introduction phase was omitted
table NUM shows the average and relative number of utterances spoken per dialogue in each of the main task subdialogues
there is evidence that users are willing to modify their behavior as they gain expertise provided the computer allows it
these results indicate differences in both dialogue structure and user behavior as a function of the computer s level of initiative
table NUM presents the mean difference in the average number of utterances between control shifts for each of the balanced problems
NUM the user of a serious misrecognition leaves the responsibility with the user to try to correct the computer s misunderstanding
contrast this with the following c the led is supposed to be displaying an alternately flashing one and seven
tic control only NUM NUM of the time in declarative mode this is much more often than in directive mode
the transition percentages that are most surprising are the diagnosis to assessment transitions in both modes and the test to repair transitions in directive mode
consider the following dialogue excerpt c the led is supposed to be displaying an alternately flashing one and seven
in order to handle the tagging ambiguity problem
NUM an overview of a computational morphological processing for thai a computalional model consists of word segmenting spelling checking and word filtering processes is proposed to handle the morphological problems mentioned earlier
tag ambiguity found in NUM words corpus
however only one word chain is correct
additionally most of thai words are multisyllabic words
all of these sentences have already segmented and tagged
NUM NUM markov model as a statistical model of filtering process
it can be reduced by using multiple models that have been previously trained in different text styles
in the german spanish and italian texts the same ambiguity is measured
in table NUM the type and the size of these corpora is shown
by calculating the conditional probability of the unknown word tags using bayes rule
after the logarithmic and the fixed point transformation equations NUM and NUM become null
for the extended set of grammatical categories three types of corpora can be distinguished a
thus they are preferred when a new language or a new tagset is used
therefore the model parameters are adapted each time an open testing text is being tagged
as a single tree estimator number of mixture NUM the hierarchical tag context tree attained NUM NUM accuracy while bi gram yielded NUM NUM
over the course of two porting efforts i have develol ed and refined tools and methods that allow a bilingual annotator to construct the required tbms very efficiently from a raw bitext
the word model is a set of probabilities that a word occurs with a tag part of speech when given the preceding words and their tags in a sentence
the hierarchical tag context trees produced by the mistake driven mixture method greatly improved the accuracy and over fitting data was not serious
to well reflect the data distribution we represent each tag model as a hierarchical tag i.e. ntt NUM proper noun noun context tree
this shows that our corpus is difficult to tag because the corpus contains various genres of texts from obituaries to poetry
example data are then tagged by the tree and the weights of correctly handled examples are reduced by equation NUM
not a gret deal this conclusion agrees with schiitze and singer s experiments that used a context tree of usual part of speech
in this experiment only post positional particles and auxiliaries were word level elements of basic tags and all other elements were subdivision level
divay and vitale grapheme phoneme translation NUM NUM morphology
NUM NUM elision from phonemes to phonemes
the input string is not modified
here the order of the rules is irrelevant
some of these classes contain NUM or more elements
both input and output are graphemes
let us consider the following rules
its diagonal remains parallel to the main diagonal
second since the korean honorification system consists of subject honorification object honorification and addressee honorification these types of honorification should be considered simultaneously when we look at a sentence
NUM indsp inds finally when an honorific verbal ending is used the social status of speaker is different from that of addressee as illustrated in NUM
to compute relative social status of the individuals involved in a dialogue the dialogue should be parsed and contextual information about social status must be available at dialogue level
NUM k p l s so the relative order of social status shown in NUM is derived fi om the dialogue in NUM
relative social status of the individuals involved in a dialogue can be inferred by collecting and computing the relations of social status collected at sentence level
ll contextual indices inheritance principle the conx t c indices value of a given phrase is token identical to that of any of its daughters
speaker mansoo addressee chulho NUM ree mansoo chulho soonchul i minyoung ul maxma ss e
although the indexes of speaker and addressee are variables in the entry of lexicons these variables are instantiated to speaker and addressee specified in the input string when a sentence is parsed
aggregate markov models can be viewed as approximating the full bigram transition matrix by a matrix of lower rank
the main points are as follows first the problem with previous works is that they can not incorporate sentence external individuals such as speaker and addressee in honorification phenomenon because just a sentence itself is considered
the map can also be annotated with line features such as barbed wire and fortified lines and area features such as minefields and landing zones
speech or gesture input is marked as complete if it provides a full command specification and therefore does not need to be integrated with another mode
for this family of applications at least it appears to be the case that as part of a multimodal architecture current speech recognition technology is sufficiently robust to support easy to use interfaces
unification is an operation that determines the consistency of two pieces of partial information and if they are consistent combines them into a single result
the bridge agent accepts commands in the form of typed feature structures and translates them into commands for whichever applications the system is providing an interface to
the probability of each multimodal interpretation in the resulting set licensed by unification is determined by multiplying the probabilities assigned to the speech and gesture interpretations
color blue coordlist NUM NUM NUM NUM figure NUM unimodal fortified line feature structure
mortar tank deletion mechanized platoon company given the potential for error the gesture recognizer issues not just a single interpretation but a series of potential interpretations ranked with respect to probability
in these systems integration of gesture is triggered by the appearance of expressions in the speech stream whose reference needs to be resolved such as definite and deictic noun phrases e.g.
then an overall clustering score is computed
the best scoring set of parameter values was used to evaluate simr
the corresponding schema of such a proof tree i is shown in figure NUM where
therefore gsa treats all empty blocks just like aligned blocks
without the translation lexicon the largest re aligned block was 7x7
there is no guarantee that this will never happen with simr
the aligned blocks in figure NUM are outlined with solid lines
there are many possible enhancements to the algorithm outlined above
simr ignores points whose ambiguity level exceeds the maxpal threshold
gsa works almost as well without such hard constraints
simr produced bitext maps for NUM megabytes of the canadian hansards
now we are ready to modify the restrict relation
the second component in eq NUM simply becomes
compiling regular formalisms with rule features into finite state automata
the overall lexicon can be expressed by NUM
this is because lr bos eos for any sentence is always composed of null sr bos bos and st NUM eos
complete sequence outside probabilities asr this is the probability of producing word sequence wl i NUM and wj l n while words i through j construct a complete sequence
a set of dependency links constructed for word sequence wid is defined as complete link ff the set satisfies following conditions the set has exclusively wi wj or wi wj
deg does a mismatch in training testing segmentation hurt language model performance perplexity and word error rate
in each comparison we noted down the number of matches between the computer generated text and the human result
in particular when restrictions NUM NUM were applied only for the s and vp rules the calculations could be completed relatively quickly as the largest intermediate automaton had only NUM states
we plan to run some controlled perplexity and recognition experiments in the future to use this information in our recognition system
e however thin the string pulling the kite q is f it j all has weight
t NUM t i NUM NUM process result verbs c c t j i NUM j s NUM e NUM non gradual procese verbs NUM t j i NUM NUM gradual process verbs
tateru build nobasu lengthen rnatomeru put together narabu form a line tutumu wrap majiwaru associate tiru fall torikakomu surround nomu drink hakobu carry tanosimu enjoy kansatusuru observe furueru shake hibiku ring tobimawaru fly about taberu eat sugosu spend
in practice it turned out that adopting the simpler transducer models did not invoive sacrificing accuracy at least for our limited domain application
so when flying a kite p the string fluttering in the sky j forms a curved arc
however experimentation reported in alshawi and buchsbaum NUM suggests that improved translation accuracy can be achieved by adopting cost functions other than log probability
in this section we briefly describe the implementation of the rules in our chinese natural language generation system
in step NUM we categorize the differences between the results as matched overgenerated and under generated types
lcb f um rcb it just seems kind of funny that this is a topic of discussion
NUM NUM elizabeth made many efforts to contact her lawyer
what is it that differentiates their mass from their count uses
the initial reference is a bare noun and the subsequent reference is the same as the initial reference
some footwear in this store sells for under thirty dollars
thus the overall matched rate becomes NUM if we take different descriptions of nominal anaphora into account
NUM NUM some bmw was involved in the traffic accident
each jenner at the wedding had a sarcastic remark to make
intuitively we expect this problem to be alleviated if a higher threshold value is used for the final admittance of a translation but a lower threshold is used internally when the subparts of the translation are considered
the dice coefficient satisfies the above requirement of asymmetry adding NUM NUM matches does not change any of the absolute frequencies fxy fx and fy and so does not affect dice x y
even when the word groups have been observed relatively few times together or separately seeing additional sentences containing none of the groups of words we are interested in should not affect our estimate of their similarity
the fourth column gives the frequency of each french word pair in the french counterpart of the same corpus and the fifth column gives the frequency of appearance of today and each french word pair in matched sentences
this is not a problem for our application as champollion applies absolute frequency thresholds to avoid considering very rare words and word groups but it indicates another potential problem with the use of si to measure similarity
applying the dice measure or any other statistical similarity measure to very sparse data can produce misleading results so we use tf as a guide for the applicability of our method to low frequency words
vc p v dan p i c v i v dan figure NUM finite state approximations for the grammar in figure NUM calculated with the finite state calculus
but given the many possible groups of words that can appear in each sentence the fact that neither of two groups of words appears in a pair of aligned sentences does not offer any information about their similarity
viewing text structure as a tree p a and p b are both sons of they are merged together by grouping the arguments a and b under another operator
rule c NUM derivation chain NUM derive r1 m1 c1 derive r2 ivi2 c2 derive chain r1 m1 c1 derive r2 c1 m2 c2
in this paper we first motivate the needs for paraphrasing and aggregation for the generation of argumentative texts in particular of mathematical proofs and then describe how our microplanning operations can be formulated in terms of meteer s text structure
t in q t concepts f t denotes the upper model concepts the argument t of f may take concepts p denotes the upper model concept p may result in
based on an analysis of proofs in mathematical textbooks there are mainly two types of goals conveying derivation step in terms of rhetorical relations pcas in this category represent a variation of the rhetorical relation derive NUM
to achieve the second verbalization of equation NUM in the introduction however we have to combine set f and subset f g to form an embedded structure subset set f g
on account of this panaget split the type restrictions into two orthogonal dimensions the ideational dimension in terms of the upper model NUM and the hierarchy of textual semantic categories based on an analysis of french and of english
f is a subset of g although the following is much more natural the set f is a subset of g therefore we came to the conclusion that an intermediate level of representation is necessary that allows flexible combinations of linguistic resources
in the next two sections we concentrate on two major tasks of the text structure generator to choose compatible paraphrases of application program concepts and to improve the textual structure by applying agture generator chooses among paraphrases and avoids building inexpressible text structures via type checking
whereas the application of the grouping rule for independent derive pms provides derive element a f a element b f a subset f g e element a g a element b g after that the predicate grouping rule a NUM is applied to the arguments of assume which are grouped to
otp s computational complexity for generation is nonetheless high np complete on the size of the grammar
for example giwm the series walk walked and look how can we oil t tl fourth term looked
figure NUM illustrates this the prototype is in the upper left corner the two sentences on its right and under it have been obtained by approximate matching
in the experiment the precision is NUM NUM which means thai in almost half of the cases one of the structures delivered is the right one
now if at least two different sentences are retrieved by approximate matching a fourth one can be built by analogy
null wc also recall that edit distances and edit operations are not contined to strings they extend in a natural way to forests and hence to trees
mi tive singular oralorem and honorcm a ccu smivc singula r
for a new sentence called the prototype our goal is to build its analysis i.e. a corresponding tree
on each side of the equal sign something is conserved one dimension and something changes second dimension
work is underway to improve the handling of structural ambiguity possibly by passing a graph structure to subsequent analysis
due to the complexity o f the grammar this forest is frequently very large implying many possible parses
the worst category is enamez where the person slot scores only NUM recall
co reference links between entities and events are made and a set of new elements is added to the global context
evaluation wise we have a set of measures with which to evaluate at least some aspects o f our progress
it has meaning in that context and it is so interpreted
note that the final foot if any will always violate this constraint
every token contains its original string so we can still recover it for use in filling slots
NUM NUM traversal and linearization of the trees
table NUM lexical categories in the
the templates for the example claim
figure NUM illustrates an input template
the most frequent form is marked
this is done in expectation of an elliptical realization
the system simply helps the inventor express this input
the system has an interactive and an automatic components
numbers marked with colons are case roles ranks
NUM NUM from the conceptual schema to a text plan
several examples of phonologically plausible constraints with monikers and descriptions are given below
for the second assumption we make the assumption that the individual distortion operators only depend on the words and features that they directly involve
NUM figure NUM shows a derivation for a reading among six in which most customers outscopes every dealer which in turn outscopes three cars
we denote the probability that an example expression is appropriate for translating some input as the conditional probability of the example given the input
the interlingua representation in our system was designed to capture meaning at the level of such sdus
since mccann erickson was not recognized as an organization all those occurrences are picked up too
similarly let the source expression e of an example pair consist of ewl ew2 ewp and efb el2 efq
the hybrid approach combines analogical matching and transfer with a rule baaed component that accounts for one of the fundamental properties of language its productiveness
in the other cases NUM they either refer to a crucial step in the information exchange or give a alternative description of a previously introduced element
the shortcomings of the current system have been investigated and a study is made of how dialogues in the ovr domain usually occur between a human operator and a client
c o l eet start and end on syllable boundaries
the information acquired from the database will influence the choice for a certain scenario most since a travel scheme with two changes will result in another presentation than a direct connection
null the dialogue manager will incorporate the lines into separate statements and will send them one by one the text generator awaiting the user s reaction before to decide to go on
considerably less of the caller s reactions are wh questions NUM checks NUM or reconfirmations NUM and very few reactions are corrections
these two operations constitute clause planning similar to text planning at the paragraph level
since in such a case the system may have to use extra linguistic devices to show the user that he is going to continue the presentation of the travel plan
NUM a functional unifier takes as input two fds and produces a new fd if unification succeeds and failure otherwise
this eliminates the class and source information which is added by the collection agent which would bias the word set
simplifying the clam to a single number measure area under the curve gives the following comparison
one of the dice is fair and one is loaded to be more likely to give a NUM outcome
the weight associated with each term in the training set is calculated by the following equation NUM
as it turns out it is just slightly more likely that the fair die was used to generate set NUM
using the multinomiai distribution we may calculate which is the more likely die to have produced each of the outputs
are in the highest so many on the list and not in the highest so many on any other list
null simplifying the charts to a single number f measure average of precision plus recall gives the following comparison
set NUM contained articles from the soviet union about various military affairs including those in other countries
but now we take the second step of calculating the probability that an output is in a class
on the other are they restrictive enough to admit good comprehension and unsupervised learning algorithms
the fourth and final stage of pattern recognition involves the scenario specific patterns
otherwise a search is made through the prior discourse for possible antecedents
this allows the whole table to be constructed in a single run through the corpus
from the controlled vocabulary we manually constructed a list of NUM defining concepts
being a dictionary based method the natural limitation of our approach is the dictionary
we are currently working on ways to disambiguate the words in the dictionary definitions
we then serially compose it with the models in reverse order
NUM n marks the column with the number of tcst samples for each sense
in our approach each word is modelled by its own set of defining concepts
since the training data is not sense tagged the occurrence probability is highly unreliable
error plan node plan has an error at the action labeled node
this predicate is used to encode an agent s belief about an invalidity in a plan
the plan constructor is then called to fill in the details thereby creating the expansion
we however use a single current plan modifying it as clarifications are made
the information that the train boards at gate NUM is represented only in the clarification plan
unlike traum s our work does not differentiate the proposal state from the shared state
modifier relative pred pred pred is a predicate that describes the relationship between two objects
below we discuss the rules for updating the mental state after a contribution is made
the second set of rules that we give concern how the agents update the collaborative state
bel system error p1 p22 NUM NUM
in text it normally requires an explicit coreferring antecedent
finally we have to show l and z are similar
this is shown in the table below with the most frequent configuration shown in parentheses
NUM john revised his paper before the teacher did
vp ellipsis is resolved as a side effect of this unification
detailed comparisons with our approach are given with the examples below
figure NUM below shows a screen shot of the debugger
figure NUM x bar theory head feature principle and sub cat principle
the debugger has turned out to be an indispensable tool for grammar development
the solution in this case is the avm in fig NUM
the system thus correctly infers that only bar level zero is possible
2ale has a restricted form of universal constraints see the comparison section
figure NUM splitting up the wf phrase relation to accom
suppose the grammar writer writes a principle c NUM c
this is both inelegant and barring a clever indexing scheme inefficient
now note that the difference between l i j l m and l l m only comes fronl k NUM the terms which are affected by mergiug the pair c i c j
we then inerge wo cla sses if die merging of lcb hem induces i immn ami reduction arllong all pmrs of classes ttll we rei e d the nlergitlg NUM e f until lcb tie numl er of the lasses is reduced to the pre leliiied nunit er c
let lcb c i c NUM c c rcb e the set of the classes obtained at step l l or each i NUM i c do the following
figure NUM sanlple sub ree for one lass region constraint mentioned in NUM NUM NUM outer clustering replace all words in the text with their class token NUM and execute binary merg ing without l lle merging region constraint until all
the reason is that after classes in the merging region are grown to a certain size it is much less expensive in terms of am1 to merge a singleton class with lower frequency into a higher frequency class than merging two higher frequency classes with substantim sizes
in the transformation based unknown word tagger the initial state annotator naively assumes the most likely tag for an unknown word is proper noun if the word is capitalized and common noun otherwise
we have obtained comparable performance on unknown words while capturing the information in a much more concise and perspicuous manner and without prespecifying any information specific to english or to a specific corpus
canis also extracts and stores relationship information such as family relations employment and affiliations
this portion of the stochastic model has over NUM NUM parameters with NUM possible unique emit probabilities as opposed to a small number of simple rules that are learned and used in the rule based approach
since as is most frequently tagged as a preposition in the training corpus the initial state tagger will mistag the phrase as tall as as as in tall jj as in the first lexicalized transformation corrects this mistagging
although corpus based approaches have been successful in many different areas of natural language processing it is often the case that these methods capture the linguistic information they are modelling indirectly in large opaque tables of statistics
the transformation based learner achieved better performance despite the fact that contextual information was captured in a small number of simple nonstochastic rules as opposed to NUM NUM contextual probabilities that were learned by the stochastic tagger
preceding following word is w and the preceding following tag is the current word is w the preceding following word is w2 and the preceding following tag is t
therefore verbmobil and especially its dialogue component has to follow the dialogue in any direction
as described above the dialogue sequence memory serves as the central repository for this information
we discuss some general problems implied by such inter mode transcription
the information about the dates is split in a specialization hierarchy
a wide range of operation has been defined on this structure
a turn is defined as one contribution of a dialogue participant
major consumers of the predictions are the semantic evaluation module and the shallow translation module
recognize similar words in the input that will be most likely exchanged by the speech recognizer
in this paper we presented different aspects of the dialogue module while processing one example dialog
this phase information contributes to the correct transfer of an utterance
we suspect there are other basic objects that would be broadly applicable as te seems to be and that there may be many generically useful event types and relationships that can be defined
in the hope that our results could be applied to combining any two template generating systems we ignored the methods and mechanisms employed by the two extraction programs and used only their output
it was often necessary to move from one tool to another to complete an operation e.g. using the semantic model editor during word definition to pick out the right meaning for a word
surface and lexical are both strings of the form lcontext i target i rcontext meaning that the surface and lexical targets may correspond if the left and right contexts and the features specification are satisfied
for every new language and every new class of new information to spot one has to write a new set of rules to cover the new language and to cover the new class of information
always to make lexical targets exactly one character long because by definition an obligatory rule can not block the application of another rule if their lexicm targets axe of different lengths
diane creel recently promoted to chief executive officer said the return to profitability reflects the success of the company s back tobasics strategy focusing on its core government and commercial hazardous waste consulting businesses
for example masculine adjectives and nouns ending in eau have feminine counterparts ending in elle beau nice becomes belle chameau camel becomes chamelle
in this case nearly mi features are shared between the inflected word and the root as is the logical form for the word shown as adj in the doriv rule
figure NUM debugger trace of derivation of chore
we then took only the frames from this combined result that matched frames in the answer key and we scored these selected frames as if they were the result of some ideal combining system
as to segmentation other groups such as the university of massachusetts have explored probabilistic techniques for segmenting chinese xerox has reported good results on learning to predict sentence boundaries from example data
the table is a standard for the trec evaluation
this expanded query retrieval then provides the final result
other researchers have used lexicons of hundreds of thousands
yet they do not seem to affect retrieval effectiveness
l0 due to bad result of one single query
xianlin zhang and jing yan helped prepare the lexicons
situations where the choice of ordering of the elements and the choice of marker plus expression are not mutually constraining are limited to the purpose discourse relation marked by pour and the sequence discourse relation marked otherwise than by hy
in what follows we look at the syntactic resources that are used in each language to convey the two parts of the two relations and look at tile constraints on tile ordering of tile two parts then at what discourse markers play a role in further ensuring the clarity of the relation intended and finally show how different rhetorical interpretations result from these choices
the english corpus on the other hand while it has a strong showing for purpose around NUM reveals a relatively strong showing around NUM for the relation of result a relation found in only NUM NUM of the portuguese relations and not at all in french
we present a study of the mappings from semantic content to syntactic expression with the aim of isolating the precise locus and role of pragmatic information in the generation process l om a corpus of english french and portuguese instructions for consumer products we demonstrate the range of expressions of two semantic relations gen
overall however there is a more even spread between choices than NUM in order to satisfy ourselves that the linguistic ex amples in the corpus were indeed representative realisations of the two semantic relations described we also perfornmd an experiment requiring naive informa ts to identify linguistic cxamples as cases of one or other relation
in english figure NUM although the imperative is the most popular expression of enablement over NUM of tokens when it expresses the enabled part of the relation it must appear second to place it first would be misleading as it would imply that this action should be performed first
tu continues tout droit and landmark indications e.g.
in particular we found different levels of tolerance of residual ambiguity portuguese has little ambiguity in the mapping from semantic content to syntactic realisation the least ambiguous markers of rhetorical relation fewest available syntactic realisations least overlap in the roles of these realisations for conveying one or the other semantic relation most restricted set of favored rhetorical relations
finally these data suggest that the order of occurrence of the ed and ing components in a sentence does not interact with decisions of choice of expression in general once a syntactic form is made available lbr expressing el or ing components it can be used irrespective of the order of occurrence of that component in the sentence
removes the premodifier tag n from an ambiguous reading if somewhere to the right NUM there is an unambiguous c occurrence of a member of the set sentence boundary symbols or the verb tag v or the subordinating conjunction tag cs and there are no intervening tags for nominal heads nh
the parser reads a sentence at a time and discards those ambiguity forming readings that are disallowed by a constraint
defining a generalized template structure and using template element objects as one layer in the structur e reduced the amount of effort required for participants to move their system from one scenario to another
the annotators problems with on the job were probably more substantive since the heuristics documented in the appendi x were complex and sometimes hard to map onto the expressions found in the news articles
the identification of a name as that of an organization hence instantiation of a n organization object or as a person person object is a named entity identification task
it removes the premodifier tag if all three context conditions are satisfied the word to be disambiguated NUM is not a determiner numeral or adjective the first word to the right NUM is an unambiguous coordinating conjunction and the second word to the right is an unambiguous determiner
this period comprised the evaluation epoch
performance on te overall is as high as NUM on the f measure with performance on organizatio n objects significantly lower 70th percentile than on person objects 90th percentile
three succession events are reported in the walkthrough article
the changes occurred only in performance on identifying organizations
a variety of proper name types were excluded e.g.
select constraints are usually applied before remove constraints we adjust the compatibility values to get a similar effect if the value for select constraints is NUM the value for remove constraints will be lower in absolute value i.e.
where r j is the set of constraints on label j for variable i i.e. the constraints formed by any combination of variable label pairs that includes the pair vi tj
by repeating this pattern elimination for all the rules the number of rule patterns were reduced to just
every knowledge source produces a set of constraints which are used together with constraints from other knowledge sources so no interpolation needed
NUM on china s turmoil it is a very unhappy scene he said
usually if we do n t have other evidence we start with the uniform distribution so all lambdas are set to the same value e.g.
the parameter estimation for the exponential maximum entropy distribution is based on the improved iterative scaling algorithm presented in della pietra et ai
when the system tries to translate these sentences it must be aware of the difference among them
the nodes in bold stand for the nodes decided by the optimized lattice i.e. they can be assigned with some probabilities
this model employed atomic features such as the lexicon information for the words before and after the period their capitalization and spellings
NUM another concern the funds share prices tend to swing more than the broader market
with such models we can answer questions such as what is the probability of generating an entity described as a configuration of atomic features
when we have a set of atomic features t and a training sample of configurations w we can build a feature collocation lattice
NUM otherwise if test fi of the current node does not originate an arc labeled with ffi output default class c associated with n
all nodes contain a test based on one of the features and a class label representing the default class at that node
NUM define future aux continuation pos if and pair
again this is also true for the recognizers just described
compared to hand crafted rule based approaches our approach provides a solution to the knowledge acquisition and reusability bottlenecks and to robustness and coverage problems similar advantages motivated markov model based statistical approaches
the most important improvement is the use of igtree to index and search the case base solving the computational complexity problems a case based approach would run into when using large case bases
in the empirical study the human created texts perhaps provided enough information for the hypothetical computer to decide on an appropriate anaphoric form
the figures in the table show that the speakers do not achieve agreement among themselves for the use of anaphora in this test
basically the main goal of our work is to generate coherent texts by taking advantage of various forms of anaphora in chinese
this experiment shows that for this problem we can use igtree as a time and memory saving approximation of memory based learning ib ig version without loss in generalization accuracy
thus the applicative order reduction of such expressions does not terminate
this method of defining the functions corresponding to categories is quite appealing
the entity the big cat is not a distractor to the black dog because it is of different category cat
finally the recognize predicate can be defined as follows
apparently scheme requires that mutually recursive functional expressions syntactically contain a lambda expression
the figures in table NUM show that full descriptions namely types bare and full are frequently used for nominal anaphora
thus the generalised punctuation rules obtained above could be encoded into a normal syntactic grammar to add punctuation capabilities
the direct internal argument can aspectually measure out the event to which the verb refers
therefor instances of this rule application are covered by the np np s rule
paradise uses an attribute value matrix avm to represent dialogue tasks
table NUM confusion matrix agent a
the evaluator must then however test these performance differences for statistical significance
finally section NUM NUM summarizes the method
the cost factors consist of two types
qualitative measures try to capture aspects of the quality of the dialog
agent a do you want to go from trento to milano
dc ac dr dt which information do you need
this paper describes an approach to extract the aspectual information of japanese verb phrases from a monolingual corpus
however we later found that these blocks of anchor points are not precise enough for our chinese english corpus
for the nouns we are interested in finding the translations for we again look at the position vectors
in section NUM we describe the first four stages in our algorithm cumulating in a primary lexicon
we believe these anchor points are more reliable than those obtained by tracing all the words in the texts
this word reflects a certain cultural context and can not be simply replaced by a word to word translation
the complexity of dtw is nm and the complexity of the matching is o ijnm where i is the number of nouns and proper nouns in the english text j is the number of unique words in the chinese text n is the occurrence count of one english word and m the occurrence count of one chinese word
to match these binary vectors v1 with their counterparts in chinese v2 we use a mutual information score m
compound noun translations carbon could be translated as i and monoxide as
optimal path i il i2 im NUM j where in n l in l n n NUM n NUM NUM with in j we thresholded the bilingual word pairs obtained from above stages in the algorithm and stored the more reliable pairs as our primary bilingual lexicon
therefore the syntactic signature was coml osed from all of the example sentences fi om every lass the verb appeared in
NUM although the effects of the various distinctions were present in the verb based experiment these effects are much clearer in the class based experiments
the use of ldoce as a syntactic filter on tire semantics derived from wordnet is tire key to resolving word sense ambiguity during the acquisition process
in the first experiment we ignored word sense distinctions and considered each verb only once regardless of whether it occurred in multiple classes
when these two groupings overlap we have discovered a mapping from the syntax of the verbs to their semantics via the verb tokens
he second experiment attelnpted to determine a relationship between a semantic class and the syntactic information associated with each class
but it is cleat ly much easier to niap between fiat semantic representations than between either syntact ic trees or deeply nested semantic re presentations an inl erlingua approadt presumes thai a sire gle representation for arbitrat y languages exists or can be developed
this allows the use of logical variables for labels and markers in transfer rules to express coindexation constraints between individual entities such as predicates operators quantifiers and afor tim concrete exaanple at hand the relative scope ha s been fully resolved by using the explicit labds of other conditions
if we would use a hierarchical semantics instead as in the original shake and bake aproach where the negation operator embeds the verb semantics we would have to translate schlecht e passen e into not suit e well e in one rule because there is no coindexation possible to express the correct embedding without the unique labeling of predicates
in operation draws it with a heavy noise level attention to itself which also in the stand by mode is still well audible
NUM kein wander unter dem lnhaltsverzeichnis steht der lapidare hinweis man m6ge sich die seiten dieses kapitels doch bitte yon diskette ausdrucken frechheit
hence block NUM of the algorithm applies to each of the utterances and correspondingly new segments at the levels NUM to NUM are created
we refrain however from establishing a new data type even worse different types of stacks that has to be managed on its own
the main algorithm see table NUM consists of three major logical blocks s and ui denote the current discourse segment level and utterance respectively
it serves as a kind of garbage collector for globally insignificant discourse segments which nevertheless were reasonable from a local perspective for reference resolution purposes
if an antecedent matches the segment which contains ui NUM is ultimately closed since ui opens a parallel segment at the same level of embedding
horizontal lines indicate the beginning of a segment in the algorithm this corresponds to a value assignment to ds s beg
lift only applies to structural configurations in the centering lists in which themes continuously shift at three different consecutive segment levels and associated preferred centers at least cf
the relative scope of two quantifiers is only considered for variables we adopt the convention of naming variables with the first letter of the head noun with which they are associated r representative c company s sample and using the symbol to denote relative scope
saw every r rep r aof r a c com c some s sample s each variable in a pas has a quantifier and a restriction which restricts the values which it may take
the first two items indicate that the system is having difficulty recognizing the function of abbreviations
this pruning produces a decision tree better able to classify data different from the training data
for example dlevde pour un sol literally high for a soil is not a correct variant of dlevage hors sol off soil breeding because dlevde and dlevage are morphologically related to two different senses of the verb dlever dlevde derives from the meaning to raise whereas dlevage derives from to breed
such explicit cues can be recognized by inferring the discourse and or problem solving intentions conveyed by the speaker s utterances
to assess the reliability of our annotations approximately NUM of the dialogues were annotated by two additional coders
with regard to the theme of this conference we are clearly emphasizing representation over algorithms
however the exceptions of the rules still resulted in undesirable effects thus the further improved performance by constant increment with counter
where one of the systems was correct and the other one was wrong we disregard here all cases where both systems were correct as well as the NUM names for which no correct transcription was given by either system
the main loop of the parsing algorithm employs the following schema
for instance the grapheme e in an open stressed syllable is usually pronouned e however in many first names stefan melanie it is pronounced e
the costs actually vary depending upon the number of syllables in the residual string and the number of graphemes in each syllable the string hohen would thus have be decomposed into a root hohe and the fuge n
the next name component that can be found in the grammar is stein we have to return to root by way of an arc that is labeled with a morph boundary and a cost of NUM NUM
for german the accuracy rate for quality band iii names which were transcribed by rule only was NUM in other words the error rate in the same sense as used in this paper was NUM
in addition even for an initially incomplete factor set the corresponding feature space is likely to cause coverage problems neural nets for instance are known to perform rather poorly at predicting unseen feature vectors
it is very hard to distinguish these two cases in general
on the test data set the old system gives the correct solution in NUM of NUM cases NUM NUM compared to NUM names NUM NUM for which the new system gives the correct transcription again all cases were excluded in which both systems performed equally well or poorly
these street name markers are used to construct street names involving persons stephan lochner strafle kennedyallee geographical places tiibinger allee or objects chrysanthemenweg containerbahnho street names with local regional or dialectal peculiarities sb bendieken hjglstieg and finally intransparent street names kriisistrafle damaschkestrafle
we describe a novel parsing strategy we are employing for chinese
this approach allows for estimating good features relatively fast but it does not guarantee that at every single point we add the best feature because when we add a new feature to the model all its parameters can che uge
these checks are carried by calls to q dec NUM an q inc NUM with appropriate input values
thus many systems opt for some variety of context sensitive grammar
another obstacle to systems that rely on brittle features is that many texts are not well formed
one important use of substring linking in chinese is for reduplicative patterns
this article presents an efficient trainable system for sentence boundary disambiguation that circumvents these obstacles
if the only rule in the grammar to handle these examples is
NUM focus saw lcb rl r2 rcb power lcb sl s2 rcb
consistently considering the head of putative compound nouns to be the head of nominal constituents may in some cases lead to awkward results
figure NUM examples of parse output see text
focus sets were discussed briefly above and are made more precise now in the context of dependency function partitions
jacobs and zernik NUM make use of morphology in their case study of lexical acquisition in which they attempt to augment their lexicon using a variety of knowledge sources
evidence indicates that taggers selecting senses from a list ordered by frequency of occurrence where salient core senses are found at the beginning of the entry use a different strategy than taggers working with a randomly ordered list of senses
the reuters NUM collection consists of NUM NUM newswire articles from reuters collected during NUM
r figure i l special credits represented as oxymora webster a figure of speech in which opposite or contradictory ideas or terms are combmed NUM or can a credit which has become a real debt be accepted as an asset entry
to account for how conversants collaborate in dialog however this cooperation is not strong enough
in the rest of the paper we will use the terms constraint feature feature and constraint interchangeably fx wi is the indicator function which indicates whether or not the j th constraint xj is active for the configuration wi
most of the other verbs at the top of the list are light verbs
y pcy i p xk NUM x6x yey xex yey where x y is an empirical probability of a joint configuration w of certain instantiated factor
dmlp lsp mlp processing for the sentence in figure NUM the gui consists of two www pages
the current state of the transducer allows to transform nearly all the parse trees
such redundant atomic features can be classified into three categories a features which are simply not informative b features which are always seen with some other feature cofounded features and c features which are mutually exclusive with some other feature
null NUM after the procedure the patient has been admitted to the intensive care unit
viewpoint of a medical encoder using icd NUM cm and to evaluate the system s responses
before a large scale validation involving a gold standard and various statistical metrics e.g.
figure NUM lsp like parse tree generated by the dmlp transducer for operatieve procedure vijfvoudige
the linguistic data are passed on from the dmlp to the lsp mlp system via syntactic parse trees
the precision ranged from NUM to NUM depending on the label combination
the above presented www application could thus be integrated in such a hypertextum emr system
given only monolingual data log frequency is a relatively good estimator of semantic entropy
there is a sample of entities w lcb w0 wm rcb which are representable as configurations of atomic features NUM from t we define a function w t which maps entities w into t for instance c mr
the iterative scaling algorithm applied for the parameter estimation provides us with a set of as which ensure that the model fits to the reference distribution and does not make spurious assumptions as required by the maximum entropy principle about events beyond the reference events
stochastic pos disaml iguation is implemented in the rank xerox loeolex package
figure NUM user inter faci glossh lcb rug
th senten in which the word occurs
as the index consists of two parts so does the lookup
as the corpus grows the time for incrcinental search likwise grows linearly
l igure NUM similarity among the chirurgical act family
the syclade graphs based on shared contexts can facilitate this process
we compare our results with hindle s scores of similarity
sl udies in l erminology exlra clk n
speech recognition is performed by a verbex NUM running on an ibm pc
this paper presents an analysis of the dialogue structure of actual human computer interactions
an identity of relations analysis however predicts that this reading does not exist
this model was helpful in classifying each utterance into the appropriate subdialogue
misunderstandings due to misrecognition were the cause in NUM of these failures
misunderstandings due to inadequate grammar coverage occurred in NUM of the failures
for the current system how did miscommunication impact on the dialogue structure
the f column represents the finished state i.e. dialogue completion
a maximum of two and one half hours was spent on the first session
they did not have excessive familiarity with ai and natural language processing
to improve speech recognition performance we restrict the vocabulary to NUM words
NUM selection of alternative sets of semantic categories from wordnet the first step of the method is generating alternative sets of wordnet categories
an interpolation method is adopted to estimate the parameters of the model against a reference correctly tagged corpus semcor
the precision measures the ability of each set c i at correctly pruning out some of the senses of w ci
the purpose was rather to provide the linguists with a very refined general purpose linguistically motivated source of taxonomic knowledge
this seems reasonable but in any case we observed that our results do not vary when counting each word only once
in any case figure 4a shows that the sets c have peak performances in the range NUM NUM NUM d00
we also discuss how other missing readings cases are accounted for
the method presented in this paper allows an efficient and simple selection of a fiat set of domain tuned categories that dramatically reduce the initial overambiguity of the thesaurus
the selection seems good according to our linguistic intuition of the domains but the absence of a correctly tagged corpus does not allow a large scale evaluation
grammars based on sol can generate any context free language and more than that
different types of properties typical of such languages as english and german are based on morphological considerations that were not an issue for our system
this inflexibility of lambek calculus is one of the reasons why many researchers study richer systems today
therefore we can state the following theorem NUM the parsing problem for sdi is npcomplete
for practical grammar engineering one can devise the motto avoid accumulation of unbounded dependencies by whatever means
and hence this result increases our understanding of the necessary computational properties of such richer systems
the constructed mapping between pairs of arguments must be preserved and remain one to one
occurrences of overlapping patterns have been eliminated by analytically calculating the intersection of the sets of patterns for all pairs of rules f1 f11
NUM this well known np complete problem is cited in gareyjohnson NUM as follows
lemma NUM completeness let f NUM
when analytical work reached the point where the available data could no longer provide the necessary pronunciation information it was replaced by empirical work
p e22 t t teachert e21 t
the output of our system is a bilingual list of collocations that can be used in a variety of multilingual applications
if a and c are chosen the jjjj reading results
the aligned corpus is used as a reference or database corpus and represents champollion s knowledge of both languages
the following section focuses on the prosodic classification schema section NUM features the results of the current experiments
the strategy that will be advocated in the remainder of this paper employs prosodic information to accomplish this reduction
empty verbal heads can only occur in the right periphery of a phrase i.e. at a phrase boundary
a lexical operation removes the argument from sur ca r and puts it onto si astt
this form can be determined at compile time and stored in the lexicon together with the corresponding verb form
the observations were rated according to the ff llowing scheme identification of possible verb trace positions
a first set of reference labels was based on perceptive evaluation of prosodically marked boundaries by non naive listeners cf
these results were obtained from a corpus of spoken utterances many of which contained several independent phrases and sentences
there is a performance penalty in comparison with using the c apis but for simple cases this is the easiest integration route
modules were written in a variety of programming languages including c c flex perl and prolog
packages written in c or in languages which obey c linkage conventions can be compiled into gate directly as a tcl package
we are currently developing a multilingual summarization system in which we will use the results from champollion
we begin by reviewing examples of the three approaches we sketched above and a system that falls into the fourth category
supplying a generic system to do every le task is clearly impossible and prone to instant obsolescence in a rapidly changing field
NUM tipster can easily support multi level access control via a database s protection mechanisms this is again not straightforward in sgml
input from sgml text and tei conformant output are becoming increasingly necessary for le applications as more and more publishers adopts these standards
however from the above discussion it is clear that certain tools would improve the system s performance
the values for the configuration varies over a range NUM NUM corresponding to the NUM grammatical structures possible for NUM pps shown and exemplified below with their counts in the corpus
the algorithm presented in the previous section for example simply determines the maximally likely attachment event to np or vp based on the supervised training provided by a parsed corpus
performance of learned miss on independent test set of NUM sentences
figure NUM compares the productivity rates using different corpus development utilities
a procedure that generates word lists based on their frequency
the workbench mouse interface is engineered specifically to minimize hand motion
the alembic workbench is our attempt to build such an environment
this code currently runs on sun workstations running sun os NUM NUM NUM
a summarization of the results are presented in figure NUM
figure NUM two measures of corpus annotation productivity using the alembic workbench
this ten percent was chosen based on the size of their keys so as to accurately represen t the complexity of the development set
during training the system is run over both the blind set and the development set of message s overnight several times a week
official performance on the template element task was degraded by two bugs which cause d louella to lose two articles from the set of NUM
official performance on the scenario template task was degraded by two bugs which cause d us to lose two articles from the set of NUM
the corpus annotation is updated by applying the chosen rule and the learning cycle repeats
this is the nature of the auto tagging facility built in to the workbench interface
having established that the distance parameter is not as influential a factor as we hypothesized we exploit the observation that attachment preferences do not significantly change depending on the distance testing on the training data
the one exception again was the energy category which we will discuss in the next section the size of the ranked lists ranged from NUM for the financial category to NUM for the military category so it would be interesting to know how many category members would have been found if we had given the entire lists to our judges
the important generalisation NUM poses a particular problem of objectivity
an example is shown in figure NUM
the guidelines are presented in figure NUM
reliability in essence measures the amount of noise in the data whether or not that will interfere with results depends on where the noise is and the strength of the relationship being measured
in addition we have a led markup for guideline violation
sg2 provide feedback on each piece of information provided by the user
the information requested or is it amplified
the left hand column characterises the aspect of dialogue addressed by each guideline
rej rejects agreed rejections of attributed design error cases
re reclassification agreed reclassification of a design error case
however current work in partof speech tagging has succeeded in showing that it is possible to carve one particular subproblem and solve it by approximation using statistical techniques independently of the other levels of computation
we also wanted to see whether the number of category words leveled off or whether it continued to grow
daft hobbes ihm alas buch zuriickgeben wird
figure NUM lexical entry for verschenken
figure NUM prototypical ineaning description and
the benefits thereof are on both sides
as 1note here the difference between atomic features and constraint features constraint features consist from atomic features but we can have a set of constraints which does not include some or even all atomic features per se but only their combinations
table NUM b shows that when a perceptible silence is detected at the end of an utterance when the speaker utters a prompt or when an outstanding discourse obligation is fulfilled first three rows in table the system correctly predicted the dialogue initiative holder in the vast majority of cases
for example an or operator is used to tag as male words which ar e descendants of either the male node or the kinsman node which subsumes uncle
but in most contexts this sense of end will be wrong and this word should not be considered as the potentia l antecedent for a pronoun such as he
as a result building a new module which requires input from earlier components is as simple as loading the files created by those components and performing the necessary processing
by that time we had developed some of the system s infrastructure and had implemented a simplistic coreference resolution system which resolved proper nouns by means of string matching
we estimate the tota l number of hours spent on the project itself to be roughly NUM distributed among the eight graduate students wh o worked on the project
additionally as with any semantic inheritance hierarchy not all features are alway s passed down from parent to child so that strictly monotonic reasoning is not valid
part of speech tagging several components of the muc coreference system such as the noun phrase detector require part of speech pos tags for all of the words in an article
some such phrases are NUM or NUM cents a share NUM billion marks NUM NUM billion dollars and profits climbed to NUM million dollars
for instance apple is a substring of apple ceo john sculley but they can not be coreferent since john sculley is a person and apple is a corporation
ultimately the majority of the code written explicitly for muc was in perl NUM but some programs wer e also written in c and several different shell languages
first we have removed those wordnet terms not ocurring in thetraining collection
for database searches a set of equivalences can be devised where two or more phonemes or allophones could be considered correct
to our knowledge learning algorithms although promising have not yet reached the level of rule sets developed by humans
on the brown corpus we had a large number of dictionary hits which was not unexpected since the corpus contains many high frequency forms
a more systematic test can be carried out using an electronic dictionary having for each entry grapheme string the corresponding phoneme string
this is especially true for linking words one a the is etc which are then counted many times
preprocessing procedures are also used in cases like NUM which gives cinquante dollars and where you have to permute and NUM
using the formalism of the expert system the expert is in charge of defining a set of rules to simulate his or her expertise
if the contexts are true the rule is applied and the next character to process is e in the input string
these routines contain special rules which contain a number of different options and word mode speak word by word
a phone scanning from right to left marks the positions of the syllables according to consonant clusters vowels and morph boundaries
for routing the score is the probability for each class calculated given the words in the document
the proposed estimation algorithm aims at reducing the redundant inside computations in computing an outside probability
ranking with the probability of getting the outputs table NUM NUM NUM would have given the same ranking
the better this set of distinguishing terms is the better the results will be for routing and classification
a variety of weighting schemes are possible and a common oue is called lnc ltc
this can be calculated with bayes theoreln using the assumption that all classes have equally likely occurrences
so using this multinomial distribution to rank documents is less likely to be adversely affected by varying document lengths
the expression terminal x evaluates to a function that maps a string position i to the singleton set lcb r rcb iff the terminal x spans from i to r and the empty set otherwise
moreover the tokenization funds and has tokenization ambiguity since there exists another possible tokenization fund sand for the same character string
since the theory of partially ordered sets is well established we can use it to enhance our understanding of the mathematical structure of string tokenization
1deg NUM this formalization makes use of impure features of scheme specifically destructive assignment to add an element to the table list which is why this list contains the dummy element head
as depicted in this paper unlike general sentence derivation for complex natural languages the character string generation process can be very simple and straightforward
for the character string s fundsand there is sd fundsand lcb funds and fund sand rcb
in this l sl l the pr l s NUM algoritlun is a sta tisti al apl roach
oral in furth r NUM t rm NUM NUM higher r lcr d omt und words
NUM define memo fn to memoize the recognizer the original definitions of the functions should be replaced with their memoized counterparts e.g. llb should be replaced with 11c
since iilost of tlw l iule spent ill mm lyzing a line o text is ill linding a match among the h xicon ntries a chwcr organization o the lexicon slmcds up the s mching NUM rot ss trclnc lmously
searching for l mg r wol ds i ol sholt w olios s ln at tised iu ma ximum nu tching recalls sl en ling a great ileal of time s arching for ram existent targets
alternative orresponding to the sllt s qilo iicc of hari l t ws fr ill c and NUM together will NUM e evaluated according to the produ t o its constituent word binding for es
in partitular she showed that the positively marked features did not vary telic verbs such as win were always bounded for exainple in contrast the negatively marked features could be changed by other sentence constituents or pragmatic context telic verbs like march could therefore be made telic
furthermore we wish to discover empirically which textual positions are in fact the richest ones for topics and to develop a method by which the optimal positions can be determined automatically and their importance evaluated
in fact using just the strategy of lowering the threshold to reduce the over verification rate to NUM NUM causes the under verification rate to rise to NUM NUM
one approach that may prove helpful with this problem is the use of speech recognition systems that provide alternate hypotheses for the speech signal along with scoring information
however it is shown how underspecification which arises very naturally in a logic programming environment can be used in the head corner parser to allow such robust parsing techniques
the table is defined by a number of clauses for the predicate head link NUM where the first argument is a category for which the second argument is a possible headcorner
this is an interesting result a head corner parser performs at least almost as well as a left corner parser and as some of the experiments indicate often better
NUM each time an arbitrary constituent arg is derived the parser will consider applying this rule and a search for a matching vp constituent will be carried out
the head corner parser maintains a table of partial derivation trees each of which represents a successful path from a lexical head or gap up to a goal category
clearly one must be careful not to remove essential information from the goal in the worst case this may even lead to nontermination of otherwise well behaved programs
the history table is a lexicalized tree substitution grammar in which all nodes except substitution nodes are associated with a rule identifier of the original grammar
it is assumed in such approaches that even if no full parse for the input could be constructed the discovery of other phrases in the input might still be useful
furthermore if this situation arose very often then the first phase would tend to be useless and all work would have to be done during the recovery phase
for example the bunruigoihyo thesaurus contains about NUM NUM noun entries and therefore the number of similarity values for those nouns becomes about NUM NUM x109 ss NUM c2
if the two occurrences of set are instantiated differently this rule will be blocked
we require also that pit is realized as an object t with modifiers
hasten rapidly achieved its extraction performance as illustrated in figure NUM
sra s focus was on experimentation with muc NUM providing a testing environment
hasten is simple to customize and involves the steps listed below
as more examples are encoded hasten s coverage and accuracy improve
in addition peter kim was hired from wpp group s j
a simple adjustment to the similarity metric threshold created thes e configurations
the reference resolver did not attempt to resolve the other two descriptors
it is the collector s responsibility to merge identical or compatible representations
hasten failed to extract a secondary succession event involving kim
define the collector concept specifications including semantic roles and constraints
dcgs are represented using the same notation we used for context free grammars but now of course the category symbols can be first order terms of arbitrary complexity note that without loss of generality we do n t take into account dcgs having exter in fact the standard compilation of dcg into prolog clauses does something similar using variables instead of actual state names
this algorithm could then be used to solve the pcp because a pcp r has a solution if and only if its encoding given above as a fsa and an off line parsable dcg is not empty
however we also show that the termination properties change drastically we show that it is undecidable whether the intersection of a fsa and a dcg is empty even if the dcg is off line parsable
thus it is still possible to have cycles in the fsa but anytime the cycle is used the probability decreases and if too many cycles are encountered the threshold will cut off that derivation
the following approaches towards the undecidability problem can be taken limit the power of the fsa limit the power of the dcg compromise completeness compromise soundness these approaches are discussed now in turn
however following bernard lang we argue that it might be fruitful to take the input more generally as a finite state automaton fsa to model cases in which we are uncertain about the actual input
for example if we assume that each edge in the fsa is associated with a probability it is possible to define a threshold such that each partial result that is derived has a probability higher than the threshold
the first two analysis modules independently assign explicit regularised patr like representations to the time and location phrases they find
wlth without paragraph heuristic varying the clustering strategy etc and the resulting grids are converted into binary strings
scoring has yielded some interesting results as well as suggesting further areas to investigate
the role of the event manager is to propose an event segmentation of the text
perhaps we can make use of this information in assigning an event segmentation to a text
the system is being tested on a corpus of NUM messages average length NUM words
figure NUM shows an example of a binary string corresponding to the grid in the same figure
we present a program for segmenting texts according to the separate events they describe
each message is processed by the system in each of NUM different configurations i.e.
it used the same topics training documents and test documents as the routing task
rel other org slot definition identification of whether the person s new job and old job are with the same organization a related organization or an unrelated organization
perplexity comparision between n gram for word and n gram
suppose some text is communicated over a channel and is encoded using a pst
in another case the whole expression can turn into completely another meaning
minimum instantiation conditions text must directly identify the management post o r indirectly but clearly refer to a top management post
words such a s acting interim and temporary should be excluded from the post slot fill
phraseo lex is a computational lexicon which was specially develot ed for idiomatic knowledge
the underlying unification mechanism is enriched with sequences as well as simple wflue disjunctions
let us discuss these concepts formally
the nodes of the derivation tree are the tree names that are anchored by the appropriate lexical items
the result of combining the elementary trees 1nodes marked for substitution are associated with only the top fs
we now present experimental results from two different sets of experiments performed to show the effectiveness of our approach
if the machine reaches the final state then the test pattern matches one of the stored patterns
if the retrieval fails to yield any generalized parse then the input sentence is parsed using the full parser
so it is the task of the stapler to compute all the alter null nate attachments for modifiers
the stapler uses both the elementary tree assignment information and the dependency information present in the almost parse and speeds up the performance even further by a factor of about NUM with further decrease in coverage by NUM due to the same reason as mentioned in experiment NUM c
each node of an elementary tree is associated with the top and the bottom feature structures fs
thus the use of the finite mixture model can be considered as a stochastic implementation of this process
a larger cost width would result in a larger network lower precision and higher recall table NUM
including the variable credit factor in these models is an effective way to improve precision fig NUM
in all experiments nt ns and nd were fixed at NUM NUM and NUM NUM respectively
three kinds of models were estimated using the untagged training data with the initial parameters set to the equivalent probabilities
figure NUM is an example of the syncronous points for the morpheme network example given above fig NUM
note that the number of parameters of the tag model of the tag hmm is one tenth that of the tag bigram model
this problem is native to speech modeling but in general the modeling of text is free from this problem
stochastic language models are useful for many language processing applications such as speech recognition natural language processing and so on
on the other hand while there may be many different affixes their possibilities for combination within a word are fairly limited so dimension b is quite manageable
NUM and NUM or with fig NUM the credit factor assignment function described in the previous section
after several iterations of reestimation development data tagged by hand is used to evaluate the estimated model
the syntactic and semantic production rules for deriving the feminine singular of a french adjective by suffixation with e are given with some details omitted in figure NUM
computational linguistics volume NUM number NUM
both xwd h and xwd w sw2 are ambiguous words
NUM the noun hqph ngpn encirclement
our grammar incorporates two additional principles
agglutinative languages could be handled ef null flciently by the current mechanism if specifications were provided for the affix combinations that were likely to occur at all often in real texts
integrating a lexical database and a training collection for text categoriza tion
the most widely used resource for tc is the training collection
we have worked with raw data provided in the reuters distribution
so the probabdity thresholding approach seems the most sensible one
as expected wordnet and training both beat the direct approach
we are planning to strengthen wordnet influence to overcome this problem
this aspect has not been addressed in the tipster program or the message understanding conference muc evaluations so there is only limited previous experience to draw on for this aspect of the project
one obvious metric is to measure the change in productivity though it is harder to then determine whether that change results from the use of extraction or simply from a different user interface design
this suggests a implementation strategy of spending early effort on interface design and incrementally improving the quality of the extraction engine over time a strategy we have pursued for hookah
in addition to supporting displaying and editing information the user interface also supports manual browsing of naddis in case a different subject match is required
we would also like to be able to measure the effort equired to correct extraction results which relates more directly to the performance of extraction technology per se
using this information they then attempt to uniquely identify the subject in the naddis database which contains millions of subjects and is widely used throughout the law enforcement community
queries from hookah are translated into commands to this interface and the resulting display screens are parsed by the naddis interface module into normalized data structures
the analyst will then verify the system s naddis match and review the proposed database updates correcting them as required and entering any additional information
its goal is the partial automation of dea operations by moving information extraction technology into the dea fileroom where these reports are currently manually processed
the manual extraction of information from dea NUM documents represents a major expense which is contracted out to more than NUM analysts working in two shifts
for example the defining concept point is used in its place sense idea sense and sharp end sense in different definitions
in a ddition the a t proach provides empirical material lot psycholinguistic investigation since preferences for the choice of certain syntactic constructions linea rizations
the system then performs several stages of pattern matching
the resulting scheme reflects a stratificational notion of language and makes only minimal assumptions about the interrelation of the particu jar representational strata
the subject is itself a sentence in which the copula is NUM does occur and is assigned the tag hd NUM
these approaches provide an adequate explanation for several issues problematic ibr phrase structure grammars clause union extraposition diverse second position phenomena
the typical treebank architecture is as follows structures a context free backboi e is augmented with trace filler representations of non local dependencies
in order to make the annotation process more efficient extra effort has been put into the development of an annotation tool
each experiment measures how our algorithm performs for a given level of precision p for identifying links and a given average number of links k for each word
in summary for each observed word we follow a path from the root of the tree back in the text until a longest context maximal depth is reached
our current learning algorithm is able to handle moderate size corpora but we hope to adapt it to work with very large training corpora 100s of millions of words
in such systems the last stage is a language model usually a trigram model that selects the most likely alternative between the several options passed by the previous stage
note that a maximal depth NUM corresponds to a bag of words model zero order NUM to a bigram model and NUM to a trigram model
identi cation of most representative clusters
for example the text but this was is matched by the node label this which ignores the most recently read word was
our experiments so far suggest that the resulting models are fairly insensitive to the choice of the prior probability a and a prior which favors deep trees performed well
we then trained a model by running the online algorithm on the training set and the resulting model kept fixed was then used to predict the test data
the mixture elements are drawn from some pre specified set t which in our case is typically the set of all psts with maximal depth d for some suitably chosen d
let u c e be a set of words over the finite alphabet e which represents here the set of actual and future words of a natural language
this experiment produced rather appropriate classifications
in the rsd algorithms abs deal with parameters pa
the second problem is the overspecificity of ciaula
there are cases of organization names misidentified as person names there is a case of a location name misidentified as an organization name and there are cases of nonrelevant entity types publications products indefinite references etc misidentified as organizations
the onemonth limitation on development in preparation for muc NUM would be difficult to factor into the computation and even without that additional factor the problem of coming up with a reasonable objective way of measuring relative task difficulty has not been adequately addressed
two useful attributes for the equivalence class as a whole would be one to distinguish individual coreference from type coreference and one to identify the general semantic type of the class organization person location time currency etc
event kim in as vice chairman chief strategy NUM officer world wide of mccann erickson where the vacancy existed for other unknown reasons he is already on the job in the post and his old job was with j walter thompson
test set used for the muc NUM dry run which was based on a scenario concerning labor union contract negotiations there were only about half as many organizations and persons mentioned as there were in the test set used for the formal run
the percentage of personal pronouns is relatively high NUM compared to the test set overall NUM as is the percentage of proper names NUM on this text versus an estimate of NUM overall
although we have explained our clustering method top down up to now we propose our clustering method as bottom up
this is convenient because the condition for anchor and duplicate branches is denoted by local relationships among nodes
a subgraph a including a branch e in the input graph can be extracted as follows step NUM
NUM others one of the words is uncertain or its cluster s context is not estimated
the effectiveness of our method is examined by an experiment using co occurrence graph obtained from a 30m bytes corpus
a general corpus such as newspaper corpus contains many corpus portious each specializing in one topic
so far we did not explain how to detect the duplicate and anchor branches given a graph
a newspaper article is understood more fully if the reader is well versed in the political or other circumstances of its publication
since the message is thrown out m when a text is reduced to a frequency list the heart of the text is jettisoned
in the example of pneumonia the word will be included if it is connected directly with at least one of the words among doctor hospital nurse and cancer and connected indirectly with the others
then if the word begins with a consonant or consonant sequence the number of non ending maximal consonant sequences is n or otherwise n NUM
in extreme cases they will propose two hyphen sets for the same word one being a proper subset of the other but both being acceptable
as already discussed the rules presented in table NUM cover hyphenation of word substrings containing at least one consonant cases of vowel splitting are not covered
cases of more than four consonants may also exist or might appear in new loanwords or most likely occur in artificial words such as tongue twisters
q NUM association for computational linguistics computational linguistics volume NUM number NUM and thus hyphenator dependencies on lists of exceptions must be restricted as much as possible
the substrings of rules c1 c2 and c3 constitute one or more consonants between two vowels or the strings of the expression vlc v2
on the other hand although the second approach does not raise such problems it has the disadvantage of being unable to guarantee complete and accurate hyphenation
in this case the specification of permissible hyphens would be based on whether each sequence of pairwise consecutive tokens is an element of one of these sets
let also ipfo fk and ppfo fk be the sets of impermissible and permissible hyphen points of the token sequence respectively
during the first experiment we designed tagset which contains NUM tags
the second stage of training is learning rules to improve tagging accuracy based on contextual cues
the results of the czech experiments are displayed in table NUM NUM
figures representing the results of all experiments are presented in the following table
the corpus was originally hand tagged including the lemmatization and syntactic tags
therefore we had to modify the method for the initial guess
these experiments differ in the pos tagset
the rest of the tags stayed unchanged
typically an application will be able to use a pre existing index for a collection of content comparable to the documents to be routed
each detectionneed user profile in the detectionneedcollection is translated in two stages first to a detectionquery and then into a routingquery
this is necessary because the rawdata may contain non text data such as graphics or sounds for which character addressing would not be meaningful
as a specialization of collections the architecture includes detectionneedcollections these are required primarily for routing operations which typically involve sets of detectionneeds
the detectionneed is transformed in two stages it is first transformed into a detectionquery and thence into either a retrievalquery or a routingquery
a compliant system is one that can translate any valid detectionneed into its own query language and that documents how each operator is handled
the decision about the representation of a sequence of bytes which constitutes the contents of a document should be hidden from most applications
the primary mission of the tipster common architecture is to provide a vehicle for efficiently delivering this detection and extraction technology to the government agencies
it would also allow for discontinuous linguistic elements such as verb plus particle pairs i gave my gun up
our methods require significantly less for example we trained the smoothed m NUM mixed order model from start to finish in less than NUM cpu hours while using a larger training corpus
t action of the human supphed keywords that we ae oe nere just our work on are included verbatim m the sentences selected ii summarist s positaon module
several features make this algorithm attractive for large vocabulary language modeling it has no tuning parameters converges monotonically in the log likelihood and handles probabilistic constraints in a natural way
the e step of the algorithm is to compute for each word in the training set the posterior probability that it was generated by mk wt k wt
if after m NUM tosses we have not settled on a prediction then as a last resort we make a prediction using mm wt m wt
the most interesting observation from the table is that omitting very low frequency trigrams does not decrease the quality of the mixed order model and may in fact slightly improve it
a final issue that needs to be addressed is scaling that is how the performance of these models depends on the vocabulary size and amount of training data
therefore we use a deep utterance representation of dialogue act and propositional content into which discourse functions are integrated
when processing written language this is difficult enough with speech and the additional uncertainties of word recognition the problems are even harder
this procedure is a start but it can not deal with all the facets of meaning found in discourse particles as outlined above
the decision as to what discourse function to associate with a particle is seldom a strict one not even for the human analyst
repair markers can also be phrasal as in x oder besser gesagt y or in x nein ich wollte sagen y
null in our conceptual representation the discourse particles in their pragmatic reading are represented by labels signifying their discourse function
the fifth term identifies the contexts dj as interior vertices in the tree that are proper suffices of another context in d
the incremental profit al is the incremental benefit alt minus the incremental cost ale
firstly we will encode the text t using the probability model c and an arithmetic code obtaining the following codelength
in such a situation the best context model includes the contexts NUM and NUM along with the empty context c
figure NUM graphs the number of model parameters required to achieve a given test message entropy for each of the three model classes
however there are frequently alternative less left branching derivations of the same logical form
some combinations of p settings result in impossible grammars or ugs
grammatical constraints of order and agreement are captured by only allowing directed application to adjacent matching categories
in the memory limited runs default parameters came to dominate some but not all populations
this algorithm finds the most left branching derivation for a sentence type because reduce is ordered before shift
in exptyp NUM under l0 for example where tags NUM NUM as well as rule NUM are in effect for segmentation only an average precision of NUM NUM and recall of relevants at NUM retrieved of NUM out of NUM are achieved
each entry is tagged as NUM useful total NUM NUM stopword NUM s symbol NUM NUM numeric NUM NUM punctuation NUM and NUM or NUM for the rules below
this is a bit counter intuitive because the bigram method leads to three times as large an indexing feature space compared with our segmentation approximately NUM NUM million vs NUM NUM million and one would expect that there are many random non content matchings between queries and documents that may adversely affect precision
thus we need to construct a series of data sets or graphs which represent different scenarios corresponding to a given combination of values of p and k
in surge possessive clauses expect two participants named possessor and possessed
table NUM shows the test corpus probabilities and the approximated probabilities for five representative ambiguous words from our test groups
at present some approaches to language modeling take advantage of contextual information sent by the dialogue model
various segmentation approaches were then compared with human performance
this smooth guarantees that there are no zeroes estimated
n is the number of words in the corpus
d is the calculating distance considered in the corpus
figure NUM alternatives for kakeru in literal interpre tation
frequency and content of interaction are determined by the user
another example is constraint posed by a verb subcategorization frame to subordinate elements
however this assumption apparently needs modification to be applied to broader phenomena
other possible interactive operations include editing and undoing the translation
then the original area is replaced with resulting english expression
null after confirming all selections the user triggers translation
this process repeats until the system reaches the root node
it appears when the cursor is placed on that word
then g the desired result follows
we will now consider the knowledge structures that enable a participant s behavior and the reasoning algorithms that produce it
the key difference is that try allows that the best interpretation might be contextually inappropriate see figure NUM
a complete account must take into consideration possible entailments among expressed propositions however no such account yet exists
we assume that such forms can be identified by the parser for example treating all declarative sentences as surface informs
they are shown as circles in figure NUM the boxes in the figure are the objects that they relate
in the model participants actual beliefs and goals are distinguished from those that they express through their utterances
interpretation corresponds to the following problem in theorist explain utter sl s2 u ts
assumptions about expectations i.e. expectedreply acceptance makethird turnrepair and makefourthturnrepair are given as weak defaults
mcroy and hirst the repair of speech act misunderstandings agents rely on both structural and intentional information in the discourse
to prevent misconceptions from triggering a misunderstanding agents can check for evidence of misconception and try to resolve apparent errors
the mapping from the content to the linguistic resources now happens in a two staged way
finally the option of encoding grammatical constraints as either implicational constraints or relations opens the possibility to chose the encoding most naturally suited to the specific problem
in case all grammatical phrases are constrained by a term c and some relation p we can define the relation wf phrase shown in fig NUM
this is the appropriate thing to do in those cases where many of the generated disjuncts are inconsistent and the resulting disjunction thus turns out to be small
debugging having addressed the key issues behind compilation and interpretation we now turn to a practical problem which quickly arises once one tries to implement larger grammars
7to view grammars and computations our system uses a gui which allows the user to interactively view parts of avms compare and search avm8 etc
we also plan to investigate a specialised constraint language for linearisation grammars to be able to optiraise the processing of freer word order languages such as german
we consider determinacy only with respect to head unification a goal is recognized to be determinate if there is at most one clause head that unifies with it
the organization of the data structure as typed feature structures already provides the necessary structure and the grammatical constraints only need to enforce additional constraints on the relevant subsets
if any of these analyses is substantially more frequent than the others choose it as the right analysis
extract from the statistics sm tic field from the edf thesaurus this example gives an overview of the various relations between terms
however this approach can not yet provide for the automatic acquisition of lexical semantics for use in nlp systems because the input required must be hand coded no current artificial intelligence system has the perceptual and cognitive capabilities required to produce the needed semantic representations
it may also build an appropriate base using other affixes e.g. tradition a aize NUM finally all derived forms are assigned the lexical semantic feature change of state and all the bases are assigned the lexical semantic feature ize dependent
it should be noted that the set of fixed correspondences between surface characteristics and lexical semantic information at this point have to be acquired through the analysis of the researcher the issue of how the fixed correspondences can be automatically acquired will not be addressed here
one way to summarize these tables is to calculate a single precision number for all the features in a table i.e. average the number of correct types for each affix sum these averages and then divide this sum by the total number of types
using this statistic it can be said that if a random word is derived its features have a NUM percent chance of being true and if it is a stem of a derived form its features have a NUM percent chance of being true
in addition for un and de the result state of the derived form is the negation of the result state of the base neg of base is rstate e.g. the result of unfastening something is the opposite of the result of fastening it
however these new bases seldom correspond to actual words and thus the results presented here were derived using a morphological analyzer configured to only use bases that are directly in its lexicon or can be constructed from words in its lexicon
the words derived using hess refer to a state of something having the property of the base state of having prop of base e.g. in kim s fierceness at the meeting yesterday was unusual the word fierceness refers to a state of kim being fierce
results of the tez inological extraction during this experiment the terminological extraction ultimately produced NUM NUM new terms that did not belong to the thesaurus
this means as desired that for each choice of an event el of mary s telephoning and reference time rl just after it there is a state of sam s being asleep that surrounds rl
the embedding conditions determine that this reference time be universally quantified over causing an erroneous reading in which for each event el of john s calling for each earlier time rl he lights up a cigarette
for example figure la shows a drs for sentence NUM according to the principles above rl the reference time used for the interpretation of the main clause is placed in the universe of the antecedent box
anaphora hinrichs and partee s use of a notion of reference time provides for a unified treatment of temporal anaphoric relations in discourse which include narrative progression especially in sequences of simple past tense sentences temporal adverbs and temporal adverbial clauses introduced by a temporal connective
a sentence such as ta which is the same as sentence NUM except the whenever is replaced by when and always is added in the main clause would get the same dtls
it is a computer intensive method which approximates the entire sample space in such a way as t o allow us to determine the significance of the differences in f measures between each pair of systems and th e confidence in that significance
especially in named entity where machine performance and human performance are close we would expect to see inherent human differences i n interpreting language during both system and answer key development to be a considerable factor holding th e machines back
to use the table you first determine which system you are interested in and identify its f measure in the left column then look across the row or down the corresponding column to see which systems f measures its f measure is not significantly different from
the general method was applied on the basis of a message by message shuffling of a pair of muc systems responses to rule out differences that could have occurred by chance and to give us a picture o f the similarities of the systems in terms of performance
in the second form productions NUM NUM and NUM exemplified by a c NUM b c rlr2 r there are four non terminals in vn i.e.
remarkable about this fragment is that any positive occurrence of an implication must be o and any negative one must be or
the semidirectional lambek calculus henceforth sdl is a variant of j lambek s original lambek NUM calculus of syntactic types
define make entry cons define entry continuations car define entry results cdr define push continuation
fourth and of particular importance in the current paper one can require that a tig be lexicalized
the order in the sequences of left and right auxiliary trees fixes the order of the strings being combined
derivations and therefore trees can then be retrieved from the chart each in linear time
the indices i j record the portion of the input string that is spanned by the left context
in the case of figure NUM sharing reduces the grammar size ig from NUM to NUM
for the sake of simplicity it was assumed in the discussion above that there are no adjunction constraints
at each step in the learning procedure the evolving tree is branched on the attribute that divides the data items with the highest gain in information
training times for all experiments reported in this article were less than one minute and were obtained on a dec alpha NUM workstation unless otherwise noted
NUM although the regular grammar approach can be successful it requires a large manual effort to compile the individual rules used to recognize the sentence boundaries
in examples NUM NUM the word immediately preceding and the word immediately following a punctuation mark provide important information about its role in the sentence
error rates of NUM NUM are reported for this method tested on over NUM NUM scientific abstracts with lower bounds ranging from NUM NUM to NUM NUM
he had thought using this approach a representation of an individual word s position in a context must be made for every word in the language
the lexical lookup stage of the satz system finds a word in the lexicon if it is present and returns the possible parts of speech
as the lack of actual frequency data in the lexicon made construction of a probabilistic descriptor array impossible we performed all german experiments using binary vectors
NUM testing on a sample of NUM NUM separate items from the same corpus resulted in error rates less than NUM NUM as summarized in table NUM
repeating the testing with a smaller lexicon containing less than NUM NUM words still produced an error rate lower than NUM with a slightly higher training time
NUM fundamental contextual processing is performed
and NUM out of NUM were not acceptable by the grammar
NUM out of NUM were acceptable by the grammar of the recognizer
we assume that the interjections and restarts occur at the phrase boundaries
in section NUM we show the results of the evaluation experiments
the steps in the following process are carried out one by one
if there are n t any applicable heuristics go to process NUM
the score for frame is the number of input words it accounts for
b syntax and semantics analysis for sentence including omission of post positions
2j f NUM NUM mrf model NUM basic model
framework in catalan tl rules depend on word formation processes
the rest of the entries are prepositions conjunctions etc
the system covers nominal and verbal inflection fully
figure NUM internal structure of catmorf
NUM the original mrd contains NUM entries
a few nominal derivation processes are also covered
the system is currently being used in the analysis of catalan newspapers
the rest of the entries around NUM have been added automatically
catmorf multi two level steps for catalan morphology
thus a rule in catmorf may make use of the following data structures the surface left and right morphographemic contexts the lexical left and right morphographemic contexts the morphological left and right contexts and the application context i e a feature structure which keeps trace of the application of rules and which must unify with the application fs associated to every morph found in the lexicon
in most cases the first candidate will be the right one see discussion in section NUM
this model will closely fit the reference distribution of the optimized feature lattice but usually it will be too specific and might poorly predict the unseen cases
we postulate that this technique may be best achieved by providing positive examples from the student s own work
in a first set of experiments we compared our igtree implementation of memory based learning to more traditional implementations of the approach
in that case a category can be guessed only on the basis of the form or the context of the word
the original tag set consisting of NUM morphosyntactic tags was expanded this way to NUM possibly ambiguous tags
an interesting property of memory based learning is that case representations can be easily extended with different sources of information if available e.g.
in practice for our part of speech tagging experiments igtree retrieval is NUM to NUM times faster than normal memory based retrieval and uses over NUM less memory
in this distance metric all features describing an example are interpreted as being equally important in solving the classification problem but this is not necessarily the case
since during constraint deselection at every point we have a fully fit maximum entropy model we rank the features on the basis of their weights in the model
the clique filnction of the model NUM is ttt de as follows
the cognitive elements used to store signs in shortterm memory are distinctively different from those used with a spoken written language
it does not however model the interaction between different knowledge sources NUM and only provides the best weights for the them under the assumption of independence
NUM few matches if the database query results in a few matches then the dialogue enters this state
each lexical expression is a triple lexical expressions with one symbol assume e on the remaining positions
first it must have the ability to analyze the input texts and determine what errors have occurred
the system rarely produces the less obvious interpretations
table NUM performance evaluation of terminology driven
state of the workspace at cycle NUM
this heuristic selects the optimal choice the vast majority of the time however under this constraint the moves described earlier in this section can not yield arbitrary context free languages
for smoothing we combine the expansion distribution of each symbol with a uniform distribution that is we take the smoothed parameter ps a a to be
the first corpus enea is a
while we partially attribute this difference to using a bayesian instead of maximum likelihood objective function we believe that part of this difference results from a more effective search strategy
notice that our algorithm produces a significantly more compact model than the n gram model while running significantly faster than the inside outside algorithm even though we use an inside outside post pass
qbny broke the wall with the cup
the total nmnber of codes is NUM
after reviewing some details about the overall dialogue processing model and its implementation in section NUM and a review of the experimental environment in section NUM the remainder of the paper focuses on the results of this analysis a review of some related analyses and some concluding remarks about the usefulness of the analysis and the role of experimental natural language dialogue systems in modeling human computer dialogue
there are NUM positive examples and NUM negative examples
fable NUM cla ss based l lcb esnlts
see dorr and voss to appear
the six variations of the experiment are as follows
only NUM mappings have complete overlaps
the general performance results obtained during our testing of the circuit fix it shop section NUM NUM lend support to their claim as NUM of our attempted dialogues in directive mode were completed successfully compared to NUM in declarative mode and experimenter interaction of any kind occurred only once every NUM user utterances in directive mode but once every NUM user utterances in declarative mode
we ran four experiments training a grammar with and without bracketing and with and without use of features
depending on whether or not they can be found there a case representation is constructed for them and they are retrieved from either the known words case base or the unknown words case base
our algorithm and its implementation show that it is not only possible in theory but also feasible in practice to construct packed semantical representations directly from parse forests for sentence that exhibit massive syntactic ambiguity
an hybrid symbolic and probabilistic approach to terminology extraction has been defined
table NUM lists the distribution of the number of the sense clusters activated by these collocation words
we now introduce an algorithm for parsing with stochastic itgs that computes an optimal parse given a sentence pair using dynamic programming
this simple strategem is effective because the majority of unmatched singletons are function words that lack counterparts in the other language
the only two matchings that can not be generated are very distorted transpositions that we might call inside out matchings
the problem is that singletons have no discriminative power between alternative bracket matchings they only contribute to the ambiguity
the tree structure reflects the assumption that crossings should not be penalized as long as they are consistent with constituent structure
NUM the NUM permitted matchings are representative of real transpositions in word order between the english chinese sentences in our data
following the standard convention we use a and b to denote probabilities for syntactic and lexical rules respectively
in the worst case both sentences might have perfectly aligned words lending no discriminative leverage whatsoever to the bracketer
we are developing an iterative training method based on expectation maximization for estimating the probabilities from parallel training corpora
the french coordination with el serves throughout the paper as an example
NUM a de tristes enfants de la mort de leur lnere b
their semantic polymorphism is then explained without having to list the different senses
the gl approach is then described and a gl analysis of the data
recently work in computational semantics and lexical semantics has made an interesting shift
the adjective can be headed either on the state or the event it denotes
je suis ing6nieux aux hecs i m clever at playing chess c
in this article we extended gl to the treatment of french mental state adjectives
the event structure eventsttl indicates that mental adjectives have a complex event structure
the complement is identified as a subtype of the experiencing event or the intellectual act
la jean danse la valse et pierre le tango
NUM je sais son gge et qu elle est venue ici
these an then serve as the example base for a form of examph based mt of
the precision for each aligned pair of sentences is computed according to the formula ltc s ultsct
the timings include all phases of the nlp component including lexical lookup syntactic and semantic analysis robustness and the compilation of semantic representations into updates
however we not only use inheritance at the level of the lexicon which is a well known approach to computational lexica but have also structured the rule component using inheritance
in data oriented language processing an annotated language corpus is used as a stochastic grammar
thus even though the grammar contains a relatively large number of rules compared to lexicalist frameworks such as hpsg and cg the redundancy in these rules is minimal
a node n is marked as seen when a triple has been encountered at n that must be optimal with respect to all paths leading to n from the start node
for this reason we present in the second table the proportion of word graphs that can be treated by the nlp component within a given amount of cpu time in milliseconds
for example the utterance no i do n t want to travel to leiden but to abcoude yields the following update us erwant s tray el
for ovis the various objects are related to concepts in the train travel domain
the virtual system architecture allows for efficient parallel dialogue processing
a faithful implementation is particularly difficult in the gb framework as gb principles are informally expressed as english statements and can take a variety of forms
add adjuncts i a t a add adjunc s l t NUM
quit if the system detects that the user wants to terminate the current dialogue then the dialogne enters this state
consequently rules were not only construction specific but also language specific french italian and spanish for instance have no dative shift
all utterances of length greater than one in this selection are used as testing material
NUM completed clauses can contain arbitrary negative literals rather than just equality constraints as in earley deduction
table NUM n best hypothesis for the sentence do you
table NUM n best hypothesis for the sentence can you
the modules of the system articulate in a complementary way
the robust parsing strategy applies syntactic and semantic well formedness constraints
it disfavors especially the recidivist occurrences of a word candidate
several collaboration modalities between asr and nlp have been investigated
table NUM shows the processing performances for each parsing pass
the second factor of the re evaluation is the amplitude cf
figure NUM analysis of whom is this chair chosen by
for example it might be possible for the pictalk system to recognize that its user is searching for something to say when con secutive menu presses are made or even to recognize that a topic shift is being initiated when the utterance just selected differs greatly in semantic content from the previous utterance
if it then receives some clues about background noise e.g. a radio it might initiate a request to the user to establish a better acoustic environment
thus with information on the acoustic conditions environment the dialogue in figure NUM could become the one outlined in fi null ich mschte gerne um zwei uhr nach hamburg fahren
table NUM trained translation probabilities using poisson fertility table
exploits representations of unknown words and information about phonetically similar words to generate clarification dialogues along the lines of figure NUM cf
an incoming signal could then be classified as belonging to one of the categories depending on which keywords appear most frequently
only recently in the background of the darpa evaluations for speech recognition systems the importance of the type of noise tracking that is needed has been realized
traditionally the most important input to the dialogue management component of a natural language system are semantic representations e.g. formulae of first order predicate calculus
in what follows we first explicate how information from the accoustic level e.g. the presence of certain kinds of background noise from a radio enables better system performance
two analysers each violation being characterised in addition by its unique utterance identifier its status descriptor and a brief description figure NUM
in fact it is not much different to use the guidelines for early evaluation during woz and using the guidelines for diagnostic evaluation of an implemented system
time NUM coughs NUM coughs u id quot s NUM NUM NUM quot i NUM flight two eight six from san francisco arrives at
the tabled arrival time is probably always the same for a given flight number but there may be days on which there is no flight with a given number
a single annotation once accepted will lead to a different and improved de guide no of agreed no of line violations types i sorted by guideline violated
figure NUM shows the nature of the types of guideline violation referred to in figure NUM as well as the types that were undecidable disagreed upon or rejected
still we do n t know how many new guideline violations a third expert might find and whether we would see rapid convergence towards zero new guideline violations
the resulting NUM guidelines were grouped under seven different aspects of dialogue such as informativeness and partner asymmetry and split into generic guidelines and specific guidelines
another point about the corpus worth mentioning is that the simulated system understands the user amazingly well and in many respects behaves just like a human travel agent
null deb debatable the annotators disagreed on a higher null level issue involved in whether to classify a system utterance as a design error case
after every user utterance the dm checks to see if the dialogue is in one of the upper layer dialogue states
for example identifying personal and company names is facilitated by the fact that personal name s often start with a title such as ms
1degthe various cooccurrence scores retrieve sets of collocations which are sharply different fi om the contexts shown by syclade connected components
it also takes care of entities like simpl e dates phone numbers and other regular quasi lexical units
the facts extraction engine rules parser and cascaded non deterministic state machines interpreter the rules set evidence combiner basic document scanner and lexicon interfac e system interface the facts extraction engine uses cascaded non deterministic state machines cascaded ndfsm to parse the text and to look for patterns
therefore in the grammar n country or country n are equivalent notations
then 9kis applied to the next segment of the output of his used as the input for another ndfsm
thus the name type defmition is performed based on several kinds of evidence name s form during step NUM name s context during step NUM similarity with already defined name step NUM database lookup step NUM the history and the future NUM NUM
this include s matching the variants of names resolving conflicts between contradicting evidenc e checking the names against the known names database the document scanner parses the input documents in their native format fields sgml tags etc breaks a document into paragraphs and does the lexicon look up
NUM punctuation NUM interpublic group s mccann erickson and wpp group s j walter thompson were treated as single entities this is strange because we have rules to split them and the evidence combiner mus t have picked the second parts of these names because they occur elsewhere
however odds of that happening are slim since word from coke headquarters in enamex type location atlanta enamex is that enamex type organization caa enamex and other ad agencies such as enamex type person quot fallon mcelligott enamex will months says now that he has reinvented himself he wants to do the same for the agency
NUM the pattern matchers several stages of ndfsm detect certain interesting types of noun groups using patterns abc s president x and look for other patterns relevant to the vanf task for each interesting pattern an attached function is executed to pass on a candidate name to the evidenc e combiner
one thousand of the documents were then used as a training set and the othe r the overall effort on vanf was about NUM months of one developer s time developing the engine the lexicons and the rules NUM person months of the content specialists effort and about NUM person months o f project management effort
another is the tendency of enumerating almost blindly every heuristic and trick possible in ambiguity resolution
where the coders managed to agree on the beginning of a game i.e. for the most orderly parts of the dialogues they also tended to agree on what type of game it was instruct explain query w query yn align or check k NUM
describing a typical size may improve agreement but might also weaken the influence of the real segmentation criteria
they were then given the four complete dialogues and maps to take away and code in their own time
the transcripts did not name the moves or indicate why the potential transaction boundaries were placed where they were
one coder never used the check move when that coder was removed from the pool k NUM
on the task of assigning television characters to categories with complicated dutch names that did not resemble english words
the second is reproducibility or intercoder variance which requires different coders to code in the same way
dialogue work like the rest of linguistics has traditionally used isolated examples either constructed or real
the ends of transactions are not explicitly coded because computational linguistics volume NUM number NUM generally speaking transactions do not appear to nest for instance if a transaction is interrupted to review a previous route segment participants by and large restart the goal of the interrupted transaction afterwards
which ileverllie le s sha r the iiiosl siguifica nl
since a personal pronoun typically requires an antecedent the presence of he among the first words is a sign that the current position is not near an article boundary and this feature therefore has a discounting factor of NUM NUM
for some applications it may be sufficient for the system to return only that sentence but in general we desire that it return as many sentences directly related to the target sentence as possible without returning too many unrelated sentences
this observed behavior is consistent with our earlier intuition the cache of the long range model is destructive early in a document when the new content words bear little in common with the content words from the previous article
for example an adaptive model might for some period of time after seeing a word like homerun boost the probabilities of the words lcb homerun pitcher fielder er ror batter triple out rcb
the few very high points to the left of a segment boundary are primarily a consequence of the word cnn which is a trigger word and often appears at the beginning and end of a broadcast news segment
the figure plots the average value of the logarithm of the ratio of the adaptive language model to the static trigram model as a function of relative position in the segment with position zero indicating the beginning of a segment
the function d is a distance probability distribution over the set of possible distances between sentences chosen randomly from the corpus and will in general depend on certain parameters such as the average spacing between sentences
it formalizes in a probabilistic manner the effect of document co occurrence on goodness in which it is deemed desirable for related units of information to appear in the same document and unrelated units to appear in separate documents
unlike many other machine learning methods feature induction for exponential models is quite robust to overfitting since the features act in concert to assign probability to events rather than splitting the event space and assigning probability using relative counts
this predicate simply consists of a number of unit clauses indicating all goals that have been searched completely
the advantage of this technique is that we use prolog s first argument indexing for such tables
such a maximal projection may be represented by a term xp sere
it is defined by unit clauses that each represent an instantiated goal i.e. a solution
in general the parse predicate is thus provided with a category and two pairs of indices
observe that each parse goal in the left corner parser is provided with a category and a left most position
lexical analysis NUM o np np lexical analysis i i np np
firstly for constraint based grammars of the type assumed here the number of possible nonterminals is infinite
this is obvious because an acyclic finite state automaton defines a finite number of strings
we now define a translation r from f structures to udrss
in addition schiitze performs his classifications by treating documents as a large unordered bag of words
the rows correspond to various translation models
the resulting polynomial time expressions are
table NUM trained poisson and general fertility
our decoder is a simple pattern matcher
similarly if the formal language were
already these models represent significant progress
then a polynomial time training algorithm exists
this allows comparison between an unannotated corpus and a partially annotated one
substituting equation NUM into equation NUM yields
the formation rules pivot on the semantic form pred values
our translation process yields a lexicalized feature based tag vsj88 in which feature structures are associated with nodes in the frontier of trees and two feature structures top and bottom with nodes in the interior
as observed above adjoining this tree will preserve these values across any domination links where it might be adjoined and if the values stated there are reducible then they will be reduced in the other tree
computer disk drive keyword in context plant life from the plant life
however note that the slash value is unspecified at the root of the trees t2 and t3
scription and project phrases by using the schemata to reduce the selection information specified by the lexical type
most of the relevant hpsg rule schemata and lexical entries necessary to derive this sentence were already given above
for the noun phrases what kim and sandy and the preposition to no special assumptions are made
thus once we have produced a tree we examine the root and the nodes in its frontier
for the headadjunct schema the adjunct dtr is the sd because it selects the head dtr by its nod feature
while auxiliary trees allow arguments selected at the root to be realized elsewhere it is never the case for initial trees that an argument selected at the root can be realized elsewhere because by our definition of initial trees the selection of arguments is not passed on to a node in the frontier
for example changing a bigram tagger into a trigram tagger requires only adding questions about the additional nodes
li l io mry is absolut ly essential
in a first experiment to determine consistency we asked each of the three te m members to
among test data sentences NUM have more than one correct exact syntactic matches and NUM have NUM NUM
r esults for parsing from raw text are given for both the exact match and exact syntactic match criteria described in NUM NUM
the result was a NUM expected rate of disagreement among the team members on this task
the results in table NUM reflect tagging accuracy as well as the pefformaace of the parser models per se
then wc describe iu detail the main compolmnts modnles that are part of the implemented prototype
leas noted in NUM NUM fn NUM our experience indicates that we can expect a roughly NUM o improvement in this score when we compare performance against golden standard test data in which all correct answers are indicated this would bring our tagging accuracy into the NUM percent area
the tagger currently produces an exact match NUM of the time for the NUM NUM word test set comparing against a single tag sequence for each sentence le we present parsing results both for text which starts out correctly tagged table NUM NUM and for raw text table NUM
research on pos tagging has proved it to be a good method to disambiguate a sentence
miglfl not he enough to get the right entry in he dictionary
lexical ambiguity can be divided into homonymy and polysemy depending on whether or not the meanings are related
from a state qi these actions are as follows null selection of a pair of dependent words w and v and transducer m given head words w and v and source and target dependency relations rl and r s w w e v1 v v t e vs
we found that out of NUM incorrect word pairs only NUM were grouped by the porter stemmer
if we have to exhaustively match all nouns and proper nouns against all chinese words the matching will be very expensive since it involves computing all possible paths between two vectors and then backtracking to find the optimal path and doing this for all english chinese word pairs in the texts
these latter have specific implications for what concerns artificial pollution in the environment
table NUM reports also the distribution of the term in a set of NUM documents
totale p ubblico oubblic o estero netto finanziario cornm figure NUM user interface for terminology validation
the method has been widely applied to different corpora and it demonstrated to be easily portable without any heavy customization
we conducted a systematic analysis of correct parsing results by contrasting a parser with and without access to domain terminology
complex nominals and iii the distributional properties of entries in i and ii
the model described in the previous section has been used to implement a system for terminology derivation from a corpus
slope constraint j i NUM n NUM window size constraint i NUM t iprevious continuity constraint j jpreviou offset constraini j jprevious NUM
it attempts to derive maximal leverage from these properties by modeling a rich diversity of collocational relationships
since the positional difference vector representation relies on the fact that words which are similar in meaning appear fairly consistently in a parallel text this representation is best for nouns or proper nouns because these are the kind of words which have consistent translations over the entire text
o unlike moffat our estimate NUM does not use escape probabilities or any other form of context blending
it might be thought that a more general definition would allow for multiple backward looking centers as well as multiple forward looking centers
in this paper we investigate linguistic and attentional state factors that contribute to coherence among utterances within a discourse segment
in section NUM we discuss several factors that affect centering constraints and govern the centering rules given in section NUM
in this paper we generalize and clarify certain of sidner s results but adopt the centering terminology
their notions of forward looking and backward looking centers correspond approximately to sidner s potential foci and discourse focus
a draft manuscript describing this elaborated centering framework and presenting some initial theoretical claims has been in wide circulation since NUM
whereas discourse NUM is clearly about john discourse NUM has no single clear center of attention
thus for a semantic theory to support centering it must provide an adequate basis for computing the realization relation
in such cases additional criteria are needed for deciding which single entity is the cb un l
rule NUM depends only on the ordering of elements of cf and not on the notions of retaining and continuation
for example even local clusters of the word the only receive relatively low significance scores simply because the word has a high frequency throughout the document
in contrast an extension model maps every history to a sel of contexts one for each symbol in the alphabet
conversation the need for responses generally to be appropriate and fast rather than ideal and slow has already been discussed
the second muc also worked out the details of the primary evaluation measures recall and precision
the text we are striving to have a strong renewed creative partnership with coca cola mr
figure NUM a sample message and associated filled template from muc NUM terrorist domain
both muc NUM and muc NUM involved sanitized forms of military messages about nava l sightings and engagements
for coreference it proved remarkably difficult to formulate guidelines which were reasonably precise and consistent NUM
round NUM resolution of semeval the committee had proposed a very ambitious program of evaluations
of the sites which were involved in the annotation process ten participated in the dry run
a major role of the annotation process was to identify and resolve problems with the task specifications
the monthly output will b e later raised to NUM NUM units bridgeston sports officials said
results of the dry run were reported at the tipster phase ii NUM month meeting in may NUM
for example one of our extension models saves NUM NUM bits char over the trigram while using less than NUM NUM as many parameters
since wordnet orders its senses such that sense NUM is the most frequent sense one possibility is to always pick sense NUM as the best sense assignment
our results indicate that although pebls with k NUM gives lower accuracy compared with naive bayes pebls with k NUM performs as well as naive bayes
several different approaches had been taken to assisting an aac user to handle the central part of a conversation
in m fold cross validation a training data set is partitioned into m approximately equal sized blocks and the learning algorithm is run m times
the next requirement is for design features that model those aspects of natural conversation that are particularly content dependent i.e.
in other words simply increasing the number of parameters in the model does not necessarily increase predictive power of the model
in our present study we used NUM fold cross validation to automatically determine the best k number of nearest neighbors to use from the training data
were we to replace the warning with other sorts of warnings the expression would also change according to the learned micro planning network
the reader may be told for example do not enter or take care not to push too hard
as mentioned above this integration was done by manually specifying the desired input conditions for the sub network when the micro planning rules are built
see rule a2 let f be the most specific field in tug trent
it is used in two situations when a is totally accidental or the agent may not take into account a crucial feature of a
those for the anaphoric relations involve various applicability conditions on the current utterance and a potential antecedent
this assumes however the existence of a core set of micro plans which perform the procedural categorisation properly these were built by hand
this tool allows the authors to specify both the propositional representations of the actions to be included and the procedural relationships between those propositions
when the technical author generates from the root goal node in figure NUM for example the following texts are produced english
note that the french version employs oviter avoid rather than the less common prendre soin de ne pas take care not
four scorers and seven years ago
NUM computer add a wire between the corn hole on the voltmeter and connector NUM
the format is sgml tagged texts
however careful account has been taken of the way prolog operates its indexing in particular in order to ensure that the asymptotic complexity is as good as that of the best published algorithms with the result that for large problems the prolog implementation outperforms some of the publicly available implementations in c
however there does not seem to be a simple correspondence between conditions NUM NUM and the unfolding used by pereira and wright or rood even some simple grammars such as s a s a b s b i e are approximated differently by NUM NUM than by pereira and wright s and rood s methods
network have one morpheme on each synchronous point
the goals of this paper are as follows
all morphemes on the morpheme network are numbered
the algorithm formulation is as follows initial
figure NUM an example of the network trellis
otherwise the pointers are considered unmatched
i will show that model estimations excluding the credit factors can not overcome the noise problem in section NUM credit factors play a very important role by supressing noise in the training data
where on l is the set of numbers of the left most morphemes in the morpheme network and on b is the set of numbers of the right most morphemes
the scaling technique was used with all estimations
the scaled forward backward probabilities have the following property
by heavyweight processing we mean procedures that depend on global evidence and involve deeper understanding
the lexical pattern matcher is the final step in the processing done by the ne system
the translational inconsistency of words can be computed following the principles of information theory z
h tis p s h tls
the whole sentence was parsed as a single fragment by fpp
neither a complete grammatical analysis nor complete semantic interpretation is required
we believe this offers the opportunity to agai n try heavyweight techniques to attempt deeper understanding
the template generator uses a combination of data driven and expectation driven strategies
in wall street journal ne i s substantially simplified by accurate usage of mixed case
it felt as though the amount of domain specific overhead was lowered compared to previous mucs
entities correspond to the people places things and time intervals of the domain
the semantic pattern matching omponent employs the same core engine as the lexical pattern matcher
in the case of figure NUM s3 is discarded because the case frame of v 8a does not subcategorize for the case cl
first since in equation NUM the system calculates the similarity between x and each example in x computation of tuf x s becomes time consuming
we compared the two sampling methods by evaluating the relation between various numbers of examples in training and the performance of the system on another corpus
in the next step the system computes the score of the remaining candidates and chooses as the most plausible interpretation the one with the highest score
in table NUM we show our measure of similarity based on the length of the path between two terms as proposed by kurohashi et al NUM
a preliminary experiment on ten japanese verbs showed that the system needed on average about one hundred examples for each verb in order to achieve NUM of accuracy in disambiguating verb senses
one can also see that the size of the database can be reduced without degrading the system s precision and as such it can solve the third problem mentioned in section NUM
let the input be lcb ncl mcl nc2 mc2 nc3 mc3 v rcb where nei denotes the case filler for the case ci and mci denotes the case marker for ci
to the degree that time permits the same openness has been evident during each muc met and trec
clearly tipster has been a major driving force behind these improvements within both the information retrieval and information extraction r d communities
however in figure NUM b the degree of certainty for the interpretation of any x which is located inside the intersection of the two semantic vicinities can not be great
however given other existing thesauri like the edr electronic dictionary NUM or wordnet NUM these two situations should be strictly differentiated
to make matters worse each of these requirements took on many different forms when we took into account specific applications
in phase i the research goal was to significantly push the state of the art in both fields using multiple different technical approaches
during phase i all tipster participants were formally evaluated shortly before the NUM NUM and NUM month workshops
what we brought to the table were strong credentials and experience in artificial intelligence natural language processing and computational linguistics
NUM the expected utterances provide strong guidance for the speech recognition system so that error correction can be enhanced
boycott for example is a better keyword than somewhat because it bunches up into a relatively small set of documents
unfortunately the prediction errors were d relatively large for the most important keywords words with moderate frequencies such as germans
if you want to predict next year s idf it is better to use this year s estimate than a ten yearold estimate
the correlations can not be attributed to variations in frequency in NUM since all NUM words have almost the same NUM frequency
we have found thatfand idf are particularly easy to work with and are more robust than some others such as NUM
we were concerned that the crucial deviations from poisson behavior might not hold up if we looked at another corpus of similar material
three measures of closeness are presented in table NUM idf variance o2 and entropy h
why are the deviations from poisson more salient for interesting words like boycott than for boring words like somewhat
in fact somewhat was found in NUM documents only a little less than what would have been expected by chance
the qualitative results were that segments varied in size from NUM to NUM phrases in length avg NUM NUM and the rate at which subjects assigned boundaries ranged from NUM NUM to NUM NUM
to get an intuitive summary of overall performance we also sum the deviation of the observed value from the ideal value for each metric NUM recall NUM precision fallout error
subjects were given transcripts asked to place a new segment boundary between lines prosodic phrases NUM wherever the speaker had a new communicative goal and to briefly describe the completed segment
cuel is true na not applicable otherwise a cue2 is assigned true if cue is true and the second lexical item is also a cue word
morphological realization has been implemented using an external morpholggical analysis generation component which performs concrete morpheme selection and handles morphographemic processes
genkit provides writing a context free backbone grammar along with feature structure constraints on the non terminals
any change to a iw will also change the expansion factor NUM w in that context which may in turn change the conditional probabilities e e w lw c of symbols not available in that context
obviously there are certain constraints on constituent order especially inside noun and postpositional phrases
the definiteness of the direct object adds a minor twist to the default order
we would like to thank carnegie mellon university center for machine translation for providing us the genkit environment
she has demonstrated the capabilities of her system as a component of a prototype database query system
introduction natural language generation is the operation of producing natural language sentences using specified communicative goals
if the direct object is an indefinite noun phrase it has to be immediately preverbal
currently we calculate w as min q w l iq w so that highly variable contexts receive more flattening but no novel symbol in w receives more than unity weight
we will write n x a for an assumption such as that just shown and x y z m n for a combination of subproofs x and y to give result formula z by inference m n
for example if the order of arrival of the formulae in NUM were i iv ii iii recall that i iv originate from the same initial formula and so must arrive together then the proof NUM would be an incremental analysis
xo yo z yo w xo wo z compilation of the higher order assumption would yield xo y plus z of which the first formula can compose with the second assumption yo w to give xo w thereby achieving some semantically contentful combination of their associated meanings which would not be allowed by composition over the original formulae
recent work has seen the emergence of a common framework for parsing categorial grammar cg formalisms that fall within the type logical tradition such as the lambek calculus and related systems whereby some method of linear logic theorem proving is used in combination with a system of labeling that ensures only deductions appropriate to the relevant grammatical logic are allowed
thus rule NUM can be used
the last four cases deal with empty categories
NUM a maryi was loved ti b
however adding information does not reduce nondeterminism
i then turn to the computation of long distance dependencies
the input to the algorithm is an unannotated sentence
this theory is called functional determination of empty categories
second empty nodes never start a new chain
no such precise analysis is available for principle based algorithms
who did you think that john seemed to like
since each verification of the input is o nk and there are o k nodes at each of o n states to attempt to prune one iteration through the set of states attempting pruning at each state is therefore o n2k2
memory limitations during learning are creating the bulk of the preference for a default learner though there appears to be an additive effect
baddeley NUM and this may assist learning by ensuring that trigger sentences gradually increase in complexity through the acquisition period e.g.
the recursion in NUM says that each symbol should be predicted in its longest candidate context while the expansion factor NUM h says that longer contexts in the model should be trusted more than shorter contexts when combining the predictions from different contexts
since not all aspects of these actual languages are represented in the grammars conclusions about actual languages must be made with care
languages within families further specify the order of modifiers and specifiers in phrases the order of adpositions and further phrasal level ordering parameters
the number of possible permutation operations is finite and bounded by the maximum number of arguments of any functor category in the grammar
an interaction cycle consists of a prespecified number of individual random interactions between lagts with generating and parsing agents also selected randomly
lagts learn during a critical period from ages NUM NUM and reproduce from NUM NUM parsing and or generating any language learned throughout their life
organisational structure most of the utterances available to the pictalk user are organised in a shallow menu structure
church NUM the part of speech of several percentage points of words in running text is impossible to agree on by different judges even after negotiations
what we are less specific about here is the exact formal properties that make a representation easy to specify this topic remains open for future investigation
the vast majority of the initial differences were due to inattention and the remaining few to incomplete specification of the morphological representation
in addition to general descriptive principles only a few dozen construction specific entries seem necessary for reaching a high coverage of running text
the verbal prefixes un de and re cue aspectual information for their base and derived forms
in short in this paper we give empirical evidence for the possibility of specifying a grammatical representation in enough detail to make it almost consistently applicable
two linguists trained to apply a tag set to running text according to application guidelines a style sheet are to analyze a given data individually
the morpholexical component in engcg employs NUM morphological tags for part of speech inflection derivation and certain syntactic properties e.g.
null supposing defining these lower levels of grammatical representation is so problematic the more distinctive levels should be even more difficult
our training set of NUM narratives provides NUM examples of potential boundary sites
figure NUM shows the cumulative average coverage scores of the top ten sentence positions of the training set following the opp figure NUM indicates that NUM of sentences in a shared with the title sentence at least one word NUM two words NUM three words NUM four words and NUM five words
for met headlines marked by slug or hl were processed after the main body of text marked by txt
we have participated in the multilingual entity task met for japanese and spanish using sra s multilingual text indexing software called nametag tm
the nametag japanese and spanish systems were customized to accommodate the met specific requirements and were able to achieve high performance in both recall and precision
NUM a recognition algorithm or module must apply the probabilistic learned model to new sentences to provide their annotations
for japanese one of us taught himself to read kanji at a fifth grade level and developed a name tagging sequence through repeated scrutiny of rough boundaries transformati f rnerge boundariesj lone japanese met developer had only passing understanding of the texts he was reading
and an optional suffix title such as director
these two entries also are coded as being names and capitalized
other elements of the dx system are similarly quite new
it did not magically all come together at the last moment
certainly that would appear to offer the highest scores since even the domain independent tasks were evaluated only on domain dependent data
each text is accompanied by both a set of three to eight topic keywords and an abstract of approx
note that human performance is basically the same for both sets of narratives
alternatively a graphical parse tree notation is shown in figure NUM where the level of bracketing is indicated by a horizontal line
for example the verb phrase joined the board as a nonexecutive director would give the quintuple NUM joined board as director the elements of this quintuple will from here on be referred to as the random variables a v n1 p and n2
we call these expressions endexpr expressions
an example of a task oriented dialogue in japanese
NUM NUM calculating the plausibility of local cohesion
j where cohesion endexpr is a function giving the
a surprising difference fi om language modeling is that a cut off frequency of NUM is found to be optimum at all stages
an iterative unsupervised method was thell used to decide between noun and verb attachment for each triple
the backed off estimate scores appreciably better than other methods which have been tested on the wall street journal corpus
crucially they ignore low count events in training data by imposing a frequency cut off somewhere between NUM and NUM
this effectively means however low a count is still use it rather than backing off a level
analysis of the formalism s expressiveness suggests that it is particularly well suited to modeling ordering shifts between languages balancing needed flexibility against complexity constraints
a grammar for this purpose must be robust since it must still identify constituents for the subsequent matching process even for unanticipated or ill formed input sentences
for users to take the initiative in the task domain they must have some expertise in the domain
not all subjects completed the same number of dialogues for problems NUM through NUM in the two experimental sessions
wag also has been designed to support this planner realiser separation if need be
but there are various ways to encode this information to present our message
negotiatory vs salutory negotiatory speech acts contribute towards the construction of an ideational proposition
each of these has been the platform supporting various text planners often experimental
wag thus uses the same formalism for representing ideational intelctional and lexico grammatical information
NUM NUM the root of this ontology is shown in figure NUM
wag s treatment of theme needs to be extended to handle the full range of thematic phenomena
the information status of ideational entities affects the way in which those items can be referred to
the ideational specification is provided as a role of the speech act the proposition role
it needs to map from the language of its kb to the language of the sentence specification
typical execution of zmodsubdialog involves opening a proof tree and proceeding with a computation until an interrupt or clarification subdialog occurs
a naive spatial sorting was likewise considered inadequate as this would inevitably separate elements of a functional group appearing in a certain area of the window
note that the number of output words is equal to the number of nodes in the ssynts because it is a dependency tree and furthermore the number of nodes in the ssynts is greater than or equal to the number of nodes in the dsynts
we have presented two methods for developing segmentation hypotheses using multiple linguistic features
NUM NUM configuration management in a tipster application life cycle
mean win mood pr p study cooy note that realpro does not perform the task of lexical choice the input to realpro must specify all meaning bearing lexemes including features for free pronominalization
using the dsynt grammar and the lexicon it inserts function words such as auxiliaries and governed prepositions and produces a second dependency tree the surface syntactic structure or ssynts with more specialized arc labels
the following ascii based specification corresponds to the dsynts of sentence NUM in this definition parentheses NUM are used to specify the scope of dependency while square brackets are used to specify features associated with a lexeme
lexeme see category verb morphology mood past part seen inv tense past saw inv
this has the advantage that the sentence planner can be unabashedly domain specific which is necessary in today s applications since a broad coverage implementation of a domain independent theory of conceptual representations and their mapping to linguistic representations is still far from being realistic
the row labeled sec represents average execution time over several test runs for the sentence of the given input length in seconds on a pc with a 150mhz pentium processor and NUM megs of ram
null the dsynts is a syntactic representation mean null ing that the arcs of the tree are labeled with syntactic relations such as subject represented in dsyntss as i rather than conceptual or semantic relations such as agent
however in fact it is dependent on two variables the maximal size of grammar rules in the grammar or n whichever is greater and the branching factor maximum number of daughter nodes for a node of the input representation
table NUM texts for tagging experiments
questiona and these are compiled by an expert grmlmmrian
figure NUM summation regions for l s
make a dendrogram out of this process
table NUM shows the sizes of texts used for the experiment
the probability mass of the unseen types is generally large enough to significantly bias population probabilities estimated from sample relative frequencies
the expected overall number of different types in the sample irrespective of their frequency follows immediately
surprisingly the unadjusted estimates overestimate the population values to roughly the same extent that the adjusted estimates lead to underestimation
tokens of types that have not been observed among the preceding tokens and leads to a decrease in type richness
our alignment program employs dynamic programming i algorithms which are described in detail in later sect ions
the five most frequent underdispersed function words are you ye such her and any
here the use of the heuristically adjusted estimates proposed in section NUM NUM may prove to be useful
table NUM part of the concordance for air in chinese
so the translation of a single chinese character is ill defined
that is they assume that some definitions already exist and use those definitions to create new ones
this is by no means a complete list of english function words
meanwhile much larger non parallel corpora are needed for compilation of bilingual lexicons
however the english text did not under go a collocation extraction process
they are used mostly in similar contexts as shown in the concordances
the usage of idioms in chinese is significantly more frequent than in english
values indicating that air has much more productive context than n
people to enjoy fresh is it possible for room houses and institutions
the initial results are revealing as shown by the histograms in figure NUM
grosz and sidner s theory of discourse structure on the other hand does address these problems
deixis and anaphora a problem for all three of the referent resolution models is the resolution of cataphors
i using this procedure every document is classified into the closest domain
table NUM shows top NUM domain specific kanji characters of the NUM domains
in conclusion our apljroach is not effective in classifying these articles
the maximum recognition scores for tensei jingo is NUM
we aligned these domains to the NUM domains described in section NUM NUM
chapter and section these semantic objects may be classified in our approach
grosz and sidner s model presupposes several sorts of information at moments when edward s interpreter does not have these available
find out who live in nijmegen followed by find out whether all women living in nijmegen work at the nici
figure NUM evolution of translation wer with the size of the training set spanish to english text corpus
syntactic tags are added to the morphological analysis with a simple lookup module
we describe the use of energy function optimisation in very shallow syntactic parsing
precision is the percentage of tags proposed by the system that are correct
the select constraint will win as if it had been applied before
ah represents adverbs that function as head of an adverbial phrase
NUM compute the support value for each label of each variable
in these cases all the equal alternatives were marked as correct
NUM NUM y NUM e n
maximising global consistency is defined as maximizing j p
in these cases no reading was marked as correct
taken treffen kwetsen NUM NUM 70arts len NUM NUM un liwe sur l armoire bij een boek op nun NUM NUM qn
assuming this interpretation mother can then demonstrate acceptance using an inform notknowref
in conversation grounding acts that violate the grammar are not recognized
our colleagues heeman and edmonds have looked at the repair of non understanding
one important convention is the adjacency pair
NUM NUM characterizing interpretation production and repair
examples of different types of coherence strategies
misconceptions are addressed by second turn repairs which are not considered here
it can generate either a sql query for a relational database or a cgi script query for querying a web site
in other words the objective is to develop a pure portable usable robust extensible system
for example the american airlines web site provides three different web pages to support queries about flight arrival departure information
for example a system could utilize word frequency and a word cooccurrence matrix in order to perform information retrieval
confirm value asks the user to con null firm some field values provided by the user
existing approaches to designing dialogue managers can be broadly classified into three types graphbased frame based and plan based
the user does not need to know anything about the structure of the database or the architecture of the system
with the integrated score function different knowledge sources including part of speech syntax and semantics are integrated in a uniform formulation
for example activitd thermodynamique de l eau thermodynamic activity of water is a substitution variant of activitg de l eau activity of water if activitd thermodynamique thermodynamic activity is a term otherwise it is a modification
the system can be used for querying a relational database using sql or invoking a cgi NUM script on the web
dialogue manager it evaluates the knowledge extracted by the pragmatics component to determine the current state of the dialogue
the mrd filter increased this ratio to NUM and NUM respectively
fj character position in bitoxt half a
the recognized patterns may contain non monotonic sequences of points of correspondence to account for
scalability sable has been used successfully on input bitexts larger than 130mb
thus perfect recall implies at least one entry containing each word in the corpus
at both recall levels the extra hansards based filter had a detrimental effect on precision
for the same reason bitext maps allow a more general definition of token cooccurrence
NUM entries remained on or above the 2nd likelihood plateau including NUM on the 3rd likelihood plateau or higher
we also explicitly annotated candidates that might be useful in constructing a translation lexicon but possibly require further elaboration
variations can be classified into three major categories syntactic type NUM the content words of the original term are found in the variant but the syntactic structure of the term is modified e.g.
this mode of grammar conception has led us to the following decisions reject linguistic phenomena which could not be accounted for by regular expressions such as sentential complements of nouns reject noisy and inaccurate variations such as long distance dependencies specifically within a verb phrase focus on productive and safe variations which are felicitously represented in our framework
on the average there are NUM NUM alternative parse trees per sentence for the training set and NUM NUM for the testing set
in addition the rules were constructed on the basis of a rather small set of example sentences
the biggest group NUM errors contains errors that could have been resolved correctly but were not
we found that a very limited set of word forms covers a large part of the total ambiguity
for the most frequent ambiguous word forms one may safely define principled contextual restrictions to resolve ambiguities
the rules are based on a short list of extremely common words fewer than NUM words
figure NUM the result in a difficult test sample with many lexicon mismatches to do so correctly in some sentences
le rain part t cinq heures the rain leaves a NUM o clock
it is based on the general assumption that neologisms and uncommon words tend to follow regular inflectional patterns
the second states that a singular noun is likely to be followed by a singular third person verb
all tests were run on a silicon graphics iris indigo NUM NUM mhz ip20 processor NUM mbytes ram running irix NUM NUM figure NUM shows the relative performance of the two algorithms for the left context apparently the performance of both algorithms is roughly linear in the length of the left context but kk has a worse constant due to the larger number of operations involved
the output of catmorf in the form of a set of sgml tags is returned to the master program which assigns a dummy tag to the still unrecognised words and passes over the tagged text to the third module
note that some of the facilities in segmorf were not available in the alep formalism the specification of the morphotactical context the possibility of mapping single characters onto multiple ones and the ability to cross morpheme boundaries
our system models morphotactics in a dcg like wg and morphographemics in the main characteristics of the formalism is that it allows the linguist to express both the morphographemic and morphotacticai contexts thus constraining the application of tlrs
morphemes prefixes noun stems verbal stems etc do not direct to continuation classes or sublexicons instead word formation processes according to the wg select their appropiate sublexicons
it operates on texts structurally marked with sgml tags and attaches sgml tags to every word of the text
NUM NUM lexical rules as definite relations and the automatic specification of frames
the translation of the lexical rule into a predicate is trivial
the possible causes of the error were summarised the following two points in table NUM each value of paragraph article and domain shows each x NUM value
exchangel offer4 notes shares stocks amount4 tradingl stockl cents according to table NUM crystal and oil in paragraph NUM are disambiguated incorrectly and were replaced by crystal4 and oi14 respectively while crystal should have been replaced by crystal2 and oi1 with oi13
to the results of wsd and linking methods we then applied a term weighting method to extract keywords
in figure NUM let o be a keyword in the article general signal corp
in formula NUM xlj is the frequency of word i in the context j of paragraph
the numbers show the paragraph number and the underlined words are keywords which are extracted in our method
the assignment of focusing scores reflects the assumption thai the ntost coherent move in a dimogue is to continue the most salient focused actions namely the ones on the rightfl ost frontier of the plan tree
in the original discourse processor all of the constraints on plan operators which we all elimination constraints were used solely or the purpose of binding w riables and eliminating certain inference possibilities
type o1 ambiguity l est hat dh d with contextd ase i approactl is the day vs hour md iguity exenq lified by tim phrase dos a cua v
if the temporal information represented in an ii t is in conflict with the dialogue record date e.g. scheduling a time before the record date or with the temporal constraints already in the calendar e.g. propose a time that is ah eady rqiected a penalty score is assigned to that inference chain otherwise a default value i.e.
our results show that the discourse processor is indeed making nsefld predictions for disambiguation when we abstract away the problem of cumulative error we can achieve an improvement of NUM with the genetic programming approach and of NUM NUM with the neural net approach over the parser s non context based statistical disambiguatiou technique
it returns NUM if the inference chain for the associated NUM NUM attaches t o the rightmost fl outier of the plan tree NUM if it either attaches to the tree but trot to tit right frontier or does n t attach to the tree
the latter quantification is restricted by the set of contextually salient values c
lain the example below simple predicate argument semantics is used for illustration
as noted above entaihnent by context introduces additional solid line arrows
c karl hat ein bucx gelesen f
so deaccenting is no longer a special case for the theory
the nodes are now labeled by pairs
underspecification is expressed in a graphical way
then for each ambiguity the discourse processor combines these three kinds of context based scores with the non context based scores l roduced by other modules of the system to make tire final choice and returns the chosen iui
however such descriptions are of no utility and it would be desirable to find a mechanical way of eliminating them
mor root be NUM orthographically the form does could simply be treated as regular from do s
in this case we can simply omit the node come mor root come mor past participle mor root
as we saw above we can think of local inheritance in terms of following descriptors starting from the query
when we consider quoted node path pairs it turns out that this is the only property that makes them useful
the statements must be of a formally distinct type to prevent local inheritance descriptors from distributing them still further
in particular global descriptors that alter the global context in one nested definition have no effect on any others
for example the sentence verb mor present tense mor root mor form mor quot syn form
for the formalism to be coherent it must provide a way of avoiding or resolving any conflict that might arise
thus although the local inheritance makes the description become syntactically nonfunctional the specification of values or global descriptors remains functional
however in the tipster phase iii program a corba version of the tipster architecture will be developed to support distributed processing
it does not however provide a solution for translating linguistic structures e.g. mapping a dependency tree to a constituent structure
the gate system already uses a previous version written in c of a tipster document manager developed at crl
communication between the coordinator and the components can be asynchronous and the coordinator needs then to serialize the actions of each component
the tipster document architecture makes no assumption about the solution used either at the process level or at the linguistic level
a simple and small api which can be easily learned and does not make any presupposition about the type of application
a client wishing to invoke methods on those remote objects creates stub object references and accesses the orb to resolve them with the implementation references on the server side
figure NUM gives an overview of the corelli document architecture an nlp component accesses a document service provided by a document server using the corelli document architecture api
also of interest are some ergonomic issues such as tractability understandability and ease of use of the architecture the programmer being the user in this case
the document management service module provides methods to access and manipulate the components of objects e.g. attributes annotations and content of a document object
after that we obtain the frequencies of words in each cluster as shown in tab NUM
lexico semantic pattern matching for field values ensures that misrecognition of a part of the utterance will still extract useful information from the correctly recognized part
suppose that we want to parse sentences using a statistical parser and that sentences a and b appeared in the training and test data respectively
panl 3bmt is one of the translation engines used by pangloss
the seven fold increase in corpus size produces a proportional increase in matches
spanish rail milliones corresponding to english billions
however current source determined analyses predict four readings also including the two in which james likes one of ivan s parents and one of his own parents
example based machine translation in the pangloss system
these associations are used in performing subsentential alignment
the probability given an initial state q that automaton m will a generate a pair of sequences i.e.
consider example NUM but with exaggerated accent on the second pronoun and simultaneous pointing to say kris
to experiment with different models we implemented a general mechanism for associating costs to solutions of a search process
overall search control is as follows NUM determine the set of decomposition nodes
NUM if q is empty return the lowest cost solution found and stop
in our approach target word order is handled exclusively by the target monolingual model
computational linguistics volume NUM number NUM way that the prepositional phrase at bob s party in sentence 10b does not
the model is intended to combine the lexical sensitivity of n gram models jelinek et al
example NUM demonstrates that pronouns within copied vps are not as free to seek extrasentential referents as their unelided vp counterparts
so whenever possible this dialogue state identifies what piece of information would be most informative at that point in time and asks the user to specify its value
we then identified each instance in these samples in which a target adjective modified a projected indicator and tested the agreement of the target s sense with that which the noun was projected to indicate
while we found a good semantic matching of adjective senses with the indicators that were recovered from the co occurrence sentences the indicator selection depended on an arbitrarily selected NUM level of statistical significance
the approach presupposes that the natural language processing system within which it is applied includes a reliable wide coverage parser to determine the noun phrase modified by an adjective and the head of that noun phrase
in the NUM sentence sample all of the NUM aged instances of old that were not covered by the indicator nouns refer to members of these categories NUM of them to human beings
subcorpora were extracted for each of the five target adjectives consisting of sentences in which the target was disambiguated by its co occurrence with an antonym as modifiers of the same noun section NUM NUM
four sentences contain instances in which old and new both modify house or houses cast iron balustrades became the fashion to be sought out when old houses were pulled down and removed to new houses
accordingly it is the time period attribute that would appear to be involved in this case an attribute of activities that constitute the typical use of texts not an attribute of texts themselves
when types are construed as concrete as when referring to roles such uses are specific to not new senses of old and to not wrong senses of right
although the sense clues discussed so far can be readily implemented as a disambiguation procedure about a quarter of all instances of the adjectives under study were not covered by the rules presented in table NUM
katz principled disambiguation shell palate tissue are sense specific for not soft being specific cases of concrete and subject to the reservations on concrete indicator nouns noted above
note that the graph density is largely a function of corpus size and thus can be increased by adding more data
the extracted sentences have been hand tagged with senses defined in the longman dictionary of contemporary english ldoce
in total eight different decomposable models were selected via a model search for each of the NUM words
the accuracy NUM of each of these classifiers for each of the NUM words is shown in figure NUM
next the algorithm for labeling ldoce senses is described
we had a lot of common interests
no results were reported for closely related senses within a part ofspeech
the author reported that the success rate was over NUM
words in lloce are organized mainly according to subject matter
this work also underscores the effectiveness oflexical rules for coarse wsd
texttiling is used to partition each document in advance into a set of multi paragraph subtopical segments
perhaps a more appropriate use of motivated segment information is in the display of information to the user
in this context passage refers to any segment of text isolated from the full text
this level of analysis is important for many discourse processing tasks such as anaphor resolution and dialogue generation
research in hypertext and text display has produced hypotheses about how textual information should be displayed to users
at points where all of these change in a maximal way an episode boundary is strongly present
in contrast texttiling has the goal of identifying major subtopic boundaries attempting only a linear segmentation
in the experiment the text is divided using premarked sectional information and one section is placed in each window
the second uses a heuristic in which small numbers of paragraphs are grouped together until they exceed a size threshold
most work in discourse processing both theoretical and computational has focused on analysis of interclausal or intersentential phenomena
this object points down to one or more succession event objects if the document meets the event relevance criteria given in the task documentation
the inner indices constitute a check on kl and the substring adjacency constitutes a check on k2
as per the discussion above even if such parsers exist they would in all likelihood not be very practical
what would performance be on data where case provided no reliable clues and for languages where case does n t distinguish names
however we avoid epsilon productions in order to facilitate the conversion to chomsky normal form discussed later
many of the selectors in tables NUM and table NUM have artifact senses such as post product system unit memory device machine plant model program etc
assumption NUM the differences between a and b is measured by i describe a b i common a b where describe a b is a proposition that describes what a and b are
a compromise between these two is sim sans er skew NUM NUM where NUM NUM is the average similarity of NUM NUM randomly generated pairs w w in which w and w belong to the same roget s category
the most relaxed interpretation is sim s nsw skey NUM which is true if 8answer and 8key are the descendents of the same top level concepts in wordnet e.g. entity group location etc
finally thanks to giorgio satta who mailed me a preprint of his bmm tag paper several years ago
we use instead an information theoretic definition of similarity that can be derived from the following assumptions assumption NUM the commonality between a and b is measured by i common a b where common a b is a proposition that states the commonalities between a and b i s is the amount of information contained in the proposition s
the elements of the background set are the minimal aggregates in the set of all aggregates while the aggregate formed from all of the background set s elements is the unique maximal aggregate that is the greatest aggregate or unit aggregate
the noun machinery is a mass noun hence it has the feature ct so its denotation is the set whose sole element is the greatest aggregate of machinery formed from the universe of discourse
to begin with any noun with the feature ct must be assigned exactly one of the features pl and any noun with the feature ct must be assigned the feature pl
the set of desks in a room can form an aggregate whose atomic constituents are precisely the desks in the room the aggregate in question here is a scattered object ep indonesia is a scattered object
quantified mass noun phrases also range over elements in the aggregation formed from the denotation of the noun phrase s mass noun which is the greatest aggregate in the domain of discourse of which the mass noun is true
thus for example police which is a count noun has the feature pl specified in its lexical entry NUM plural mass nouns all have the feature pl specified in their lexical entries
sit must be stressed here that the notion of part here is not the mereological notion of part which is a transitive asymmetric relation but the natltral language notion of part which is not in general transitive
however a word is therefore modeled by the average behavior of many words which may cause the given word s idiosyncrasies to be ignored
p w2 p w2lwl 7p w2 NUM NUM psim w2lwl
d wx lw p w2lwl log p wu wx p w21wl NUM
we are therefore able to ignore constant factors and so we neither normalize the similarity measures nor calculate the denominator in equation NUM
we have described and compared the performance of four such models against two classical estimation methods the mle method and katz s back off scheme on a pseudo word disambiguation task
the methods going from left to right are rand pc l and a the performances shown are for settings offl that were optimal for the corresponding training set
instead estimates for the most similar words to a word w are combined the evidence provided by word w is weighted by a function of its similarity to w
similarity based models assume that if word w is similar to word wl then w can yield information about the probability of unseen word pairs involving wl
but the tagger is trained on data that contain incomplete sentences and therefore sometimes erroneously assumes an incomplete s instead of a vp
user feedback is handled in one of two ways changes in the graphical display in response to commands and verbal answers output through the speech synthesizer in response to questions
it is especially useful for controlling things that do not have a physical presence in the ve such as object scale display characteristics and time
language however is ideally suited to abstract manipulations it is also the most natural form of communication for humans and does not require the use of one s hands or eyes
nlu provides a way to control abstract features such as time without the need for manual controls and enables the user to access information in the underlying knowledge base s by asking questions
it also supports relative clauses all the ships that have missiles on board and elliptical follow ups involving substitution noun phrases how about the titanic
wh ere t c no ucriterial figure NUM a morpho syntactic augmel tation
controller s and goal s factor noncriterial means that no particular observable behavior is required for an argument to play these particular roles
the argument of walk is a source and a controller and it undergoes a monotonic development with respect to some one dimensional path
in a nlu system a given sentence may have different meanings depending on the context so a logical analysis of the utterance is required to determine the appropriate interpretation
the compositional rule from figure NUM e combines these two structures and produces the lexical entry shown in figure NUM
argument NUM is also given the monotonic role which means that it undergoes some monotonic change in the course of painting
if the whole lexical entry is to be addressed by the rule the composition part is omitted in the rule specification
similarly if the if y part is not present it means that there is no requirement for using the rule
next consider the definite clause specification of a fsa
we implemented a variation on a steepest descent search technique
template element te extract basic information related to organization and person entities drawing evidence from anywhere in the text
note that this algorithm is fairly domain inde pendent
there are several subtleties when thresholds are set very tightly
the algorithm works analogously to the viterbi algorithm for hmms
using this expression we can threshold each node quickly
grammars such as these can best be parsed bottom up
we use the simple dynamic programming algorithm in figure NUM
we show that multiple pass parsing techniques can yield large speedups
in the second technique there is a constant probability threshold
table NUM NUM shows a graphic illustration of the back off scheme the weight for each back off model is computed onthe fly using the following formula if computing pr xiy assign weight of to the direct computation using one of the formulae of ss3 NUM NUM and a weight of NUM t to the back off model where i
in order to overcome the limitations of a small amount of training data particularly in spanish we hold out NUM of our data to train the unknown word model the vocabulary is built up on the first NUM save these counts in training data file then hold out the other NUM and concatentate these bigram counts with the first unknown word training file
this is a rather simple method of smoothing which tends to work well when there are only three or four levels of back off this method also overcomes the problem when a back off model has roughly the same amount of training as the current model via the first factor of equation NUM NUM which essentially ignores the back off model and puts all the weight on the primary model in such an equi trained situation
given the incredibly difficult nature of many nlp tasks this example of a learned stochastic approach to name finding lends credence to the argument that the nlp community ought to push these approaches to find the limit of phenomena that may be captured by probabilistic finite state methods
the following are examples a small subset of ordered rules of the final allophonic pass n NUM lcb k g rcb pancake previously transcribed p enkeik becomes p eokeik
this probability is calculated based on co occurrences of nouns and verbs
figm e NUM i ia og act network with dialog plausi1 ilil y vectors a s input input to t his network is tile current word represented by its dialog plausibility vector
this research was funded by the german federal ministry for fteseareh and echnology bmbf under grant 01iv101a0 and by the german l lcb esearch association di g under contract i i g iia NUM NUM NUM
tables NUM to NUM show the results of each approach
they are handcrafted machine readable and have fairly broad coverage
the vowel nucleus of the first syllable is 0e the stressed syllable va is manifested by ei and vowel nucleus of the unstressed syllable gra in this case undergoes automatic vowel reduction and is realized as o
the paucity of literature in grapheme to phoneme translation is partially due to the fact that the field of linguistics and in particular descriptive linguistics has traditionally shied away from the writing system except as a study in its own right since the phonological system was considered of primary importance
in the middle of a word elision is done for words such as tellement t lm so much but not for justement 3yst3ma precisely which is additional support for the three consonant cluster ccc constraint
the same spelling can produce different phonemic forms ills fis son vs fil thread pr6sident prezida president vs prezid they preside etc
if the last syllable is a consonant cluster ending in e and the next word begins with a consonant a short NUM is heard as in les ch vres de NUM ysvr3d3 the goats of otherwise more than two consonants would be in the same consonant cluster and this presents articulatory difficulty in french and violates the constraints on syllable structure
on i he other haud the word is is not i articul u ly significant for certain dialog acts and therefore has a plausibility vector with relatively evenly distributed v ltes
after conversion we have for walked the following phoneme string w ol k d lcb d rcb t cons voice applies for d which is preceded by and by an unvoiced consonant
after conversion we have for cats the following phoneme string k a t z lcb z rcb s cons voice NUM lcb z rcb and lcb d rcb are abstract base forms that are replaced by appropriate phones
generally the national tsing hua university department of electrical engineering hsinchu taiwan NUM r o c t email kysu bdc com tw
in this approach some less reliably estimated but highly correlated parameters are tied together and then trained through the robust learning procedure
however minimizing the error rate in the training set can not guarantee that the error rate in the test set is also minimized
a better initial estimate of the parameters makes the robust learning procedure achieve better performance when many local optima exist in the parameter space
to improve performance a discrimination and robustness oriented method is adopted to directly pursue the correct ranking orders of possible alternative syntactic structures
in this paper we start with a baseline system based on this scoring function and then proceed with different proposed enhancement methods
in contrast a statistical approach provides an objective measuring function to evaluate all possible alternative structures in terms of a set of parameters
the selection factor for an input sentence is defined as the least proportion of all possible alternative structures that includes the selected syntactic structure
note that it is the ranking of competitors instead of the likelihood value that directly affects the performance of a disambiguation task
this will cause the system to invoke inform ckscourse actions to generate the following utterances NUM s dr smith is not going on sabbatical next year
the proposed modifications will again be evaluated by a and if conflicts arise she may propose modifications to b s previously proposed modifications resulting in a recursive process
bel NUM NUM if supports beli bel is accepted but beli is not select focus modlficatioa bel
thus the algorithm first considers whether or not attacking the user s support for bel is sufficient to convince him of bel step NUM NUM
for each piece of evidence t t could be used to directly support bel the system first predicts whether the user will accept the evidence without justification
the fact that the input data structures of the system are organized in such a way that identical data types express semantically parallel information allows us to make use of the world or domain knowledge incorporated in the design of these data structures without having to separately encode this knowledge
figure NUM shows these structures a and b which are both of the type goalevent a record with fields specifying the team for which a goal was scored the player who scored the time and the kind of goal normal own goal or penalty
the approach sketched above will also give the desired result for example NUM sentence NUM c will not be regarded as contrastive with NUM b since NUM c expresses a goal event but NUM b does not
discourse topic there are some cases of dds which are anchored to an implicit discourse topic rather than to some specific np or vp
names definite descriptions may be anchored to proper names as in mrs park the housewife and pinkerton s inc the company
it was supported by the national science foundation library of congress and department of commerce raider cooperative agreement number eec NUM
they are not based as in a terminological logic system on an understanding of the individual concept definitions
the org country slot is a special case in a way since it is required to be filled when the org locale slot is filled
the original and hence uncorrected wordnet NUM NUM data is shown as a graph where a node may represent a concept i.e.
in general the cache consists of content words s which promote the probability of their mate t and correspondingly demote the probability of other words
construction industry industry group heavy construction
in this setting the features might include questions such as does the phrase coming up appear in the last utterance of the decoded speech
in the models that we built feature fi is an indicator function testing for the occurrence of a trigger pair si tl
most of the features in figure NUM make a good deal active range of the feature in words or sentences relative to the current word
the figure plots the average value of r as a function of relative position in the segment with position zero indicating the beginning of a segment
we expect that significantly better results can be obtained by simply training on much more data and by allowing a more sophisticated set of features
in these figures the reference segmentation is shown below the horizontal line as a vertical line at the position between sentences where the article boundary occurred
figure NUM syntactic representation for NUM
performance on the post slot was not quite as good the lowest error was NUM median of NUM
figure NUM semantic representation for 2b
i i distance zone i alce ource takeo de tinatpson i i source destination i total cost i i
the linear interpolation combines such knowledge sources simply by weighting them as pcornbined z aipi for k knowledge sources
this heuristic should not be taken too far however in light of the imperfection of today s tagging technology
the different ways to remap different tag sets into a more general common tag set represent a number of design decisions
in particular we carefull y studied the fastus system of hobbs et al
for each k the kth percent correct can be evaluated instead of precision by switching lines NUM and NUM
we also experienced first hand some of the shortcomings of the partial parsing semantic pattern approach
we wish to thank bbn systems and technologies for providing us with this tagger
the structure of this paper is as follows
left transition write a symbol rl onto the right end of l1 write symbol r2 to position a in the target sequences and enter state qi l
when complete derivations are not possible our experimental system searches for a span of the input string or lattice with the fewest fragments or the lowest cost such span if there are several rcb
in an experiment comparing the efficiency of head transduction to our earlier transfer approach the average time for translating transcribed utterances from the atis corpus was NUM NUM seconds for transfer and NUM NUM for head transduction
table NUM shows a simple grammar and a trace of earley parser operation on a sample sentence
figure NUM polysemy of nouns in brown
first the problem of multiple reference
figure NUM lexical entry for evidence
an important corollary to this investigation is that it is possible to refine the lexicon because variable meaning may in many cases be attributed to lexical aspect variation predictable by composition rules
figure NUM representation for type animalofood
sum of inner probabilities over all complete states with lhs x and start index k
adaptable semantic lexicon with systematic pol
see section NUM NUM for more on the syntax semantics interface
as a result w sele te l clusters on ondition that the threshold value for similarity wax NUM NUM
in a simibtr way i bmt which in uned in flood sense is linked with environmeltt forest
as shown in table NUM there are NUM sets which could be clustered correctly in link while NUM sets ill freq
dis is con erned with disambiguationd ased experim mt i.e. the lustering algorithm is applied to new artmes
se l nlllil wordl luol d NUM iuol d3 word4 word5 semanticmly similar nouns bank3 banks3 emiada3 emmda4
the widf is reported to have a marked ulwmtage over the idf invers document frequency for the text categoris ltion tank
given the vector rel resentations of articles as in formula NUM a similarity between ai and aj are cajculated using formula NUM
every article is characterised by a vector each dimension of which is associated with a specific word in articles and every coordinate of the artme is represented by tern weighting
n this case the dictionary lookup module will fetch the wrong entry namely of avoir
the proposed model of tb ai morphological analysis consists of three steps sentence segmenting spell checking and word ill ring
b b i thought he played some important role in the house
historically he is the president s key man in negotiations with congress
a singular definite noun phrase may contribute a number of different interpretations to cf
as we discuss later taken as spoken now they illustrate new points
the violation of rule NUM leads to the incoherence of the sequence
the first case concerns realization of the cb by a nonpronominal expression
a complete discussion of these issues is beyond the scope of this paper
he does n t realize that it is an invention that changed the world
it uses only linguistic distributional rules yet reaches an accuracy clearly better than any competing system
they are principles that must be elicited from the study of discourse itself
discourse NUM is intuitively more coherent than discourse NUM
is there something in parts of speech that makes them less accessible to the rule based linguistic approach
the sixth in a series of message understanding conferences which are designed to promote and evaluate research in information extraction was held last fall
language understanding technology might develop in ways very different from those imagined by the committee and these internal evaluation s might turn out to be irrelevant distractions
for sense tagging the annotators found that in some cases wordnet made very fine distinctions and that making these distinctions consistently in tagging was very difficult
overall the evaluation met many though not all of th e goals which had been set by the initial planning conference in december of NUM
to address these goals the meeting formulated an ambitious menu of tasks for muc NUM with the idea that individual participants could choose a subset of these tasks
the nearly half the sites had recall and precision ove r NUM the highest scoring system had a recall of NUM and a precision of NUM
the in and out object contains references to the objects for the person and for the organi zation from which the person came if he she is starting a new job
it was n t clear whether much progress was being made on the underlying technologies which would be neede d for better understanding
muc NUM introduced several innovation s over prior mucs most notably in the range of different tasks for which evaluations were conducted
finally after all rank ordered pairs have been considered the mapper forces pairings or connections o n the remaining unmapped objects
the pre selection obtained this way can be based on salience eventually combined with some measure of computational effort
the notation n p is used to signify the result of adding the constraint p to the network n
we first illustrate the basic structure of the procedure from some sort of a bird s eyes view
the requirements on the descriptor providing component should widely be unconstrained allowing for incremental and goal driven processing
motivated by the resulting deficits we develop a new algorithm that does not rely on these assumptions
examining these structures is much less expensive than a global anticipation feedback loop but it requires specialized grammatical knowledge
local r and global referents gr and variables v and gv associated with them
the second part entails a call to an external descriptor selection component NUM
for example given the alphabet g lcb a b c d rcb and the dictionary d lcb a abc bcd rcb there is td abcd lcb a bcd rcb
the scorer for scenario template was much the same as in the past except for improvements in mappin g and performance speed and memory usage due to the translation into c
for the cases of redundancy mentioned here the premises apparently were correct
gigabytes of japanese text are readily available from newspapers patents html documents etc
both of these suggest that we need a word segmenter to build a more sophisticated word segmenter
in general when a component c i of some d tree a is inserted into a d edge between nodes NUM and r NUM two new d edges are created the first of which relates r t and the root node of a i and the second of which relates the frontier
inferable or computable relations on top of real or virtual relations
it covers semitic errors relating to vocalisation diacritics phonetic syncopation and morphographemic idiosyncrasies
r9 allows the optional deletion of a short vowel what is the cause of spreading
problems resulting from phonetic syncopation can be treated as accidental omission of a consonant e.g.
NUM most editors allow the user to enter such diacritics above and below letters
if the rules were to be compiled into automata a genuine symbol e.g.
this paper aims at presenting a morphographemic model which can cope with both issues
notice that llc ensures that the right boundary rule is invoked at the right time
descriptor one of the largest world wide agencies missing location patterns t coke s headquarters in atlanta NUM mis org
these observation s are summarized in table NUM once again single daggers t mark errors attributable to knowledge gaps
on the person name and person alias slots we respectively found a NUM point drop in precision and an NUM point drop in recall
these rules yield six fewer points o f p r than the hand coded enamex rules still an impressive result for machine learned rules
the fact that multiple rules are needed t o distribute adjectives over coordinated noun phrases is one of the drawbacks of semantic grammars
one approach to contextualizing the succession clause in this text would require first resolving th e pronominal subject he to mr
once again this performance is encouragingly close to alembic s performance on our final self evaluatio n using the formal training data set
alias mccann all treated as person john dooner treated as two persons NUM spu pets NUM mis pers
note that in the formal evaluation we failed to find a more correct job out phrase which should have included mccann
what about using a rule solely based on context
figure NUM summary of main expectations for major goals
an over verification occurs when a correct meaning is verified
the knob and add one zero NUM
a question about how to do the action
an acknowledgment that the action has been completed
obtaining a higher accuracy requires reducing the under verification rate
i c this is the circuit fix it shop
notable features of such verification subdialogs include the following
two important definitions concerning selective verification are the following
the final component of our contribution is a model selection algorithm for the extension model class
let us now consider the incremental benefit of adding the extensions e to a given context w
thus the incremental benefit of adding the extensions e to the context w may be calculated as
extend w determines all profitable extensions of the candidate context w if any exist
the use of proprietary training data means that these results are not independently repeatable
these constraints suffice to ensure that the model c defines a probability function
these deductive steps leading to a require some assumptions about language that constituent structure and category labels introduce specific constraints on sentence buildi ng operations and that the range of hypothetical grammars is small our enumeration a h was over grammars of binary rules where the category of a phrase is tied to the category of one of its constituents its head
therefore on the second iteration these three rules will have higher probabilities than the others and will cause parses j and k to be favored over i and l with k favored over j because i a b i a c i b c i a c
let us look again at a reproduced below and center discussion on an extended stochastic context free grammar model in which a binary context free rule z a b with terminal parts of speech on the right hand side first generates a word a chosen from a distribution pa a then generates a word b from a distribution p b
however often the initial optimal svo v2 itself was lost before enough lagts evolved capable of learning this language
moreover a svo bioprogram learner is only likely to evolve if the environment contains a dominant svo language
figure NUM fitness functions eters which either were unset or had default settings at birth
pocock and atwell NUM investigate statistical grammars extracted from spoken english corpus sec and apply these grammars to find the grammatically optimal path through a word lattice
chen and chen NUM propose a probabilistic chunker to decide the implicit boundaries of constituents and utilize the linguistic knowledge to extract the noun phrases by a finite state mechanism
for example susanne corpus tags genitive case noun as john np s gg but lob corpus tags it as john s pn
for evaluating the performance a criterion NUM i.e. the content of each chunk should be dominated by one non terminal node in susanne parse field is adopted
the performance evaluation model compares the chunked result c with the corresponding syntactic structure t accordmg to this criterion the experimental results for definitions NUM and NUM are shown in table NUM as follows
when all the words extracted from susanne corpus for a susanne tag can not be found in lob corpus the susanne tag is mapped to no match
NUM a probabilistic chunker gale and church NUM propose d NUM a x2 1ike statistic to measure the association between two words
the NUM distribution for the first seven words figure NUM shows that there are four local minimal positions i.e. positions NUM NUM NUM and NUM
cell d counts the number of sentences that does not contain both w NUM and w NUM that is if n is the total number of sentences d n a b c
the quality of the tagging is not particularly uniform but no attempt has been made to improve this
a third possible use is in the discovery of topic transitions we can hypothesize that a span within a dialogue where few co occurrence predictions are fulfilled is a topic boundary
this is one of the changes to be taken into account for the design of a predictor for this type of languages
the result of the detailed exam ration of the zero pronouns whose antecedents can not be identified correctly using this method NUM instances are summarized in table NUM
the most common cause of error is that the japanese sentence was translated freely and there is no corresponding antecedent within the english translation NUM out of NUM instances
this is particularly so with a bilingual corpus of japanese and engl sh whose language farn lles are so different and in which the distribution of zero pronouns is also very different
but this method can be applied to various kinds of language pairs such as italian and english and the effectiveness of the identification depends on how different the two languages are
in the future the effectiveness of proposed method for aligned sentence pairs with zero pronouns with intersentential antecedents and for very large corpora of aligned sentence pairs will be ex mlned
i would like to thank fun ichi tsnjii for helpful discussion of many of the ideas and proposals presented here during my stay at umist from september NUM to september NUM
in particular the identification of unatignable for zero pronouns with deictic referents which are not expressed in english translation is NUM accurate in both tests
but t zese methods use monolingual corpora and they find it difficult to extract resolution rules of zero pronouns whose referents are normally unexpressed in japanese
we identify two prototypical positions and give results for both
data and decreasing the size of their test rlous
we corrected punctuation mistakes and erroneous sentence boundaries in the training data
several additional criteria were used to filter out unsuitable sentence pairs
note that inversion is permitted at any level of rule expansion
argmax as to allow arg to specify the index of interest
in general this improves precision since wide scope brackets are less constraining
but do not actually build such a system
table NUM our best pertbrmance on two corpora
they obtained similar results using the decision tree
for exampie a useful feature might be
table NUM performance on the sa me two corpora using
it is sometimes useful to explicitly designate one of the two possible orientations when writing productions
that is not a sentence boundary
generated by scanning the training data with question templates
in the present version these rules are assumed to be built in to the writesgml operation but in later versions it may be desirable to provide these rules explicitly as a stylesheet
table NUM ranking of the syntactically correct
the initial queries for this test were the queries from the high frequency lookup strategy discussed above
lexical transfer the first method was to perform term by term translation with the collins english spanish bilingual dictionary
optimization proceeded for NUM generations resulting in a wide range of changes to each query
previously we have used a lexicon to generate initial queries NUM
we chose to translate the queries since they were very short
this justified an examination of the comparative merits of both approaches
individual terms in the english query were reduced to their morphological roots and lookup was performed
figure NUM shows the completed search for spanish documents on infoseek
the translation of an unordered set of terms is therefore approximately the translation of the terms themselves
this method is at heart a numerical approach to derive a translation matrix from parallel texts
crossing constraints prohibit arrangements where the matchings between subtrees cross each another unless the subtrees immediate parent constituents are also matched to each other
fanout constraints limit the number of direct sub constituents of any single constituent i.e. the number of subtrees whose matchings may cross at any level
is alternatively we can show the common structure of the two sentences more compactly using bracket notation with the aid of the operator
there are also lexical productions of the form a x y where x and y am symbols of languages l1 and l2 respectively
for this purpose we have devised an em based algorithm a bilingual generalization of the inside outside method that iteratively improves the likelihood of the training corpus
to this end we present an em expectation maximization algorithm for iteratively improving the syntactic production parameters of a sitg according to a likelihood criterion
it therefore becomes desirable to find means to tune the syntactic production probabilities automatically so as to be optimal with respect to some training data set
an itg is a context free grammar that generates output on two separate streams together with a matching that associates the corresponding tokens and constituents of each stream
sitgs are a generalization of context free grammars that have several desirable properties for parallel corpus analysis a brief summary of these properties is given in section NUM
f usr yes to frankfurt please
this accounted for another approximate NUM of the errors
commercial on line translation lexicons could also be employed if available
for a word w with k analyses a1 ak the morpho lexical probability of ai is the estimate of the conditional probability p ai w from the given corpus i.e. note that pi is the probability that ai is the right analysis of w independently of the context in which w appears
the huge task of developing concepts of word meanings is one that human beings readily achieve we are all generally aware of the similarities and differences between the meanings of words despite the fact that in many cases these meanings are not amenable to rigourous definition
the methods outlined above were used to cluster words appearing in the lund corpus NUM NUM words a corpus created from issues of the wall street journal NUM NUM million words and a corpus created from the works of anthony trollope NUM NUM million words
likewise going has a higher entropy than go in table NUM even though it is less than one fifth as frequent because going can be used as a near future tense marker whereas go has no such function
state NUM is the initial state
verbs like get make come take put stand and give are often used as syntactic filler while most of the semantic content of the phrase is conveyed by their argument
a mechanism goes through the parse tre e in depth first post order traversal applying semantic rules mainly on the basis of the syntactic phrase typ e of the current tree node
very high on the list are various forms of the functional auxiliaries be have and be going to as well as the modals may might and shall
we would expect the different senses to have different translations in other languages and we would expect several of these senses to occur in any sufficiently large bitext resulting in a high estimate of semantic entropy for run NUM NUM in the hansards
for instance in sentence NUM the first word in the expression hand in hand is considered the object of the verb leading to the training tuple architekten arbeiten hand NUM architect work hand
a lower bound for the accuracy of the decision algorithm can be defined by considering the first noun in every test tuple to be the subject of the verb by far the most common construct yielding for these NUM tuples an accuracy of NUM NUM
since the nominal constituent nc bill is ambiguous with respect to case and possibly accusative the erroneous tupie wagen gehsren bill NUM car belong bill is produced for this sentence
of these NUM contained errors based on the judgements of a single judge given the original sentence NUM the results produced by the system for the remaining NUM tuples were compared to the judgements of a single judge given the original text
sentence NUM was the source for the test tuple ausstellung zeigen spektrum exhibition show 2the higher error rate for test tuples is due to the soft constraints used for words unknown to the morphology
during the recursion phase computation of these entries is skipped
training data consists of tuples nl v n2 x where v is a verb nl and n2 are nouns and x e lcb NUM NUM rcb indicates whether nl is the subject of the verb
however its capitalized form may also be a noun leading in this case to the erroneous training tuple morgen trainieren tennisspieler o since no der tennisspieler is unambiguously nominative
it is implemented using a simple grammar of low ambiguity and a parser which attempts to find the largest non overlapping sequences which match the grammar working fro m left to right
a parsing algorithm for this case can be implemented very efficiently
expects because the economist expects a high inflation rate note that the heuristic rule does not apply to verb final clauses introduced by a relative or interrogative item such as in NUM NUM die rate die die okonomin erwartet
in NUM both nps are ambiguous with respect to case however the nominal phrase np die 0konomen with a plural head noun is the only one to agree in number with the verb identifying it as its subject
the results indicate significance arises when at least three subjects agree on a boundary
we do not however ask coders to identify hierarchical relations among segments
NUM here we use the NUM narratives previously used for testing as training data
on average percent agreement is highest on nonboundaries NUM max
tential boundary sites coded with respect to a wide variety of linguistic features
conversely the superior fallout of condition NUM and superior error rate of condition NUM are significant
on the one hand performance was consistently improved by enriching the linguistic input
although the labelers had high levels of agreement the segmentations were fairly trivial
other notions of segment have also been used in evaluating naive or trained coders
since cd theblueprint lcb the blueprint rcb the character string theblueprint does not have critical ambiguity in tokenization
given a character string and a dictionary it is always possible to answer deterministically whether or not a string is ambiguous in tokenization
in this section we have described sentence tokenization ambiguity from three different angles character strings tokenizations and individual string positions
other factors may cause one concept to be preferred over others such as the amount of knowledge the system has about a given concept or the concept s frequency of use
the resulting database contains NUM NUM local contexts with a total of NUM NUM NUM words in them table NUM is counted as one local context with NUM words in it
moreover careful checking showed that the missed st tokenization is not in any of the eight tokenization solutions covered by the asm model
so doing would also avoid counting matches on window size NUM into matches of larger window sizes
slowly and the recall score increases more rapidly as we choose more sentences according to the opp
but the results gained from what is after all a fairly simple technique are rather astounding nonetheless
where p fqi ci is the probability of that an object belongs to all the maximally specific super classes cis of both c and c
this method can be used in applications such as information retrieval routing and text summarization
this paper addresses the problem of identifying likely topics of texts by their position in the text
following the same steps as before we therefore derived a new opp on the test corpus
we had a choice between the topic keywords and the abstracts accompanying each text in the corpus
tmrs are realized in a frame based language where frame names typically refer to instances of ontological concepts and slots are usually filled with values of properties of those concepts
since ppt NUM NUM the first and last NUM positions fully cover the majority of texts
thus a fairly small number of sentences provides NUM NUM of the keyword topics
the results of the experiment are given in tables NUM NUM and NUM
null we use three words duty interest and line as examples to provide a rough idea about what sirn s nswer skew NUM NUM means
it is however still unclear whether this heavily lexicalized method can account for all sentence structures actually found in corpora especially due to the proliferation of non argumental complements in corpus analysis
we then use the segmentation tags and some additional information including typography to mark subjects which in turn determine to what extent vcs verb chunks can be expanded
as one can notice from the example above segmentation is very cautious and structural ambiguity inherent to modifier attachment even postnominal adjectives verb arguments and coordination is not resolved at this stage
potential subjects are marked first an np is a potential subject if and only if it satisfies some typographical conditions it should not be separated from the verb with only one comma etc
because primary segmentation is cautious verb segments end right after a verb in order to avoid arbitrary attachment of argument or adjunct segments nps pps and aps on the right of a verb
an additional feature of the incremental parser derives from its modular architecture one may handle underspecified elements in a tractable fashion by adding optional transducers to the sequence
however experiments have shown that in some kinds of texts mainly in technical manuals written in a controlled language it is worth applying the nearest attachment principle
the new approach proposed in this paper aims at merging the constructive and the reductionist approaches so as to maintain the coverage and granularity of the constraint based approach at a much lower computational cost
however not all these words are certain vc boundaries et could be an np coordinator while que tagged as conjque by the hmm tagger could be used in comparatives e.g.
if a construction is not recognized at some point of the sequence because the constraints are too strong it can still be recognized at a later stage using other linguistic statements and different background information
the goal of an ir system is essentially to classify documents as relevant or irrelevant vis a vis a query
the selection of appropriate indexing terms is critical to the improvement of both precision and recall in an ir task
for our proposes we have based the semantic distance calculation on a combination of the path distance between two nodes and their depth
we tested the phrase extraction system pes by using it to index documents in an actual retrieval task
cky where 5rg w is the table of substring derivations and earley type parsers where g is the chart
as a second data set we used the entire reuters21578 data with the lewis split
we then consider the frequency of a word in a cluster
we did not conduct stemming or use stop words s
NUM for cos we conducted classification in a similar way
NUM shows the logarithms of the resulting likelihood values
we propose a new method of classifying documents into categories
we shall begin with the formal representation of the grammar rule subword patterns
this step completes the detachment of generative grammar from its procedural roots
both the ecpo principle and the metarules can be understood in this way
the exceptions to the defaults are fully determined when the grammar is written
privset x is true of the smallest such set
these principles are simply properties of trees
it is only from the model theoretic perspective that the question even arises
in the second example we sketch a definition of chains in gb
we draw examples from the realms of gpsg and gb
null but the accompanying loss of language theoretic complexity results is unfortunate
we proposed a new probabilistic model for noun phrase parsing and developed a fast noun phrase parser that can handle relatively large amounts of text efficiently
avg prec means average precision and is the average of all the precision values computed after each new relevant document is retrieved
syntactic phrases i.e. phrases with certain syntactic relations are almost always more specific than single words and thus are intuitively attractive for indexing
this paper proposes a new probabilistic model for noun phrase parsing and reports on the application of such a parsing technique to enhance document indexing
the effectiveness of enhancing document indexing with the syntactic phrases provided by the noun phrase parser was evaluated on the wall street journal database in tipster disk2 using NUM trec NUM ad hoc topics
summing over all the possible structures for any noun phrase is computed by enumerating all the possible structures with an equal length as the noun phrase
since noun phrases with more than two words are structurally ambiguous if we only observe the noun phrase then the actual structure that generates the noun phrase is hidden
information retrievat will be grouped first if information retrievaf has a stronger association than retrieval technique otherwise retrieval technique will be grouped first
we therefore take a position which is intermediate between the two extremes outlined above
for example canoe is less frequent as a verb than as a noun
each applicable schema corresponds to a different sense so cotton bag is ambiguous rather than vague
the authors would like to thank ted briscoe and three anonymous reviewers for comments on previous drafts
a similar case can be made for the interpretation of morphologically derived forms and words in extended usages
in general pragmatic reasoning is computationally expensive even in very restricted domains
linen chest ice cream container figure NUM fragment of hierarchy of noun noun compound schemata
we treat these as a subtype of noun noun compound with the possessive analyzed as a case marker
second our probabilities encode the frequency of word senses associated with word forms
based on their understanding of the sentence each noun is assigned a specific semantic cla of the dom n specific hierarchy
the conceptual distance link probability and de scend mt coverage metrics all require traversal of NUM NUM from one node to another
we will be constructing a string of length NUM we choose NUM slightly larger than n in order to avoid having epsilon productions in our grammar
the semantic class disambi ion problem however is essentially to identify membership of the chosen concept node in the semantic class nodes
these concepts may be explicitly expressed in a pre defmed taxonomy of classes or implicitly derived through the clustering of sen ticany related words
we ran our two mplementstions of word sense disambiguation algorithms the information content algorithm and the conceptual density method on our domain specific rest set
the nouns extracted are the head nouns within noun phrases which are recognized by wordnet including proper nouns such as united states
thus verb final languages such as korean can be modeled by using this direction feature in verbal categories e.g.
the grammar presented in the last section determines the predicate argument structure of a sentence regardless of word order
the expected recognition rate of the concatenation model is the product of the accuracies of the pure language model
thus multiset ccg allows certain pragmatic distinctions to influence the syntactic construction of the sentence using a lexicalized compositional method
for wh questions the information that is retrieved from the database to answer the question becomes the focus of the answer
in fixed word order languages such as english these are indicated largely through intonation and stress rather than word order
in this section i add the ordering component of the grammar where the information structure of a sentence is determined
however it is associated with a propositional interpretation that does express the hierarchical ranking of the arguments
through the use of the composition rules multiset ccgs can handle the free word order of sentential adjuncts
in other domains finding the topic and focus of sentences according to the context may be more complicated
it is obviously impossible for any finite state b o figure NUM a simple parallel replacement of the two auxiliary brackets that mark the selected regions
by composing two or more marking transducers we can also construct a single transducer that builds nested syntactic structures up to any desired depth
for example it maps damlvaan into dann v aan as shown in figure NUM
in the transducer diagrams figures NUM NUM etc the nonfinal states are represented by single circles final states by double circles
note that it must introduce an end 0f token mark after a sequence of letters just in case the word is not part of some longer multiword token
because aba is in the upper language there is a longer and therefore preferred a b a alternative at the same starting location figure NUM
clearly the success of mixed order models depends on the ability to gauge the predictive value of each word relative to earlier words in the same sentence
these projects go beyond the definition of interchange formats to define a neutral linguistic representation in which all lexical knowledge is encoded and from which by means of specialized compilers application specific dictionaries can be extracted
following the corba model the architecture is structured as a set of services with well defined interfaces a document management service dms provides functions for manipulating collections documents annotations and attributes
for example to create a new document the client program creates it through the life cycle service bind a name to it using the naming service and add attributes and annotations to it through the document management service
we believe that it is not simply a new fashion but that it is indicative of the growing maturation of the field as also suggested by an emphasis on building large scale systems away from toy research systems
communication layer to support integration and communication at the process level the current version of the corelli architecture provides component inter communication via the corelli plug n play architecture see below and the java door orb
to be integrated a component needs to support synchronous or asynchronous versions of one or several of four basic operations execute query convert and exchange in addition to standard initialization ad termination operations
the following are properties which are required for our system the system should output as many correct answers as possible the system should output correct answers with great interpretation certainty the system should output incorrect answers with diminished interpretation certainty
in this case the lowered node and its replacement will be of the same syntactic category like the root and foot node of a tag auxiliary tree
these resources constitute the basic raw materials for building nlp software but not all of these resources can be readily used they might be available in formats that require extensive pre processing to transform them into resources that are tractable by nlp software
a dynamic plug n play architecture enabling easier integration of components written in different programming languages c c lisp java etc where components are wrapped as tools supporting a common interface
figure NUM cases and types of dialogue design errors
the results of their on and off line experiments show clearly that the low attachment corresponding to NUM is easiest but the middle attachment corresponding to NUM is most difficult
each sub corpus was analyzed by the two analysers
i am sure this is what he means
finally we make conclusion with future direction
deletion and mutation errors of terminal symbols
heuristics NUM error types the analysis on NUM NUM sentences of the penn treebank corpus ws i shows that there are NUM sentences with phrase deletions and NUM sentences with phrase insertions
in phrase insertion error hypothesis of figure NUM the original sentence is other countries including west germany m y hgve where the inserted phrase vp is surrounded by commas
the experiment says that our robust parser with heuristics can recover perfectly about NUM sentences out of NUM sentences which axe just failed in normal parsing as the percentage of no crossing sentences is about NUM NUM
an alignment method for noisy parallel corpora based on image processing techniques
a novel and its translation was chosen as the test data
for simplicity we have selected mutual information to estimate ltp
she was tall maybe five ten and a half but she did n t stoop
only NUM of english text and NUM of chinese text have a connection counterpart
maybe i lcb it clouded over more she might take off her dark glasses
so we did n t implement the phrase mutation error hypothesis
completer handles substitution of final states k
p a is the proportion of times that the avms for the actual set of dialogues agree with the avms for the scenario keys and p e is the proportion of times that the avms for the dialogues and the keys are expected to agree by chance
we have described the system architecture section NUM the source language text section NUM and the system evaluation results lcb section NUM
the rewrite rules are intended to capture surt aee phonological constraints and contractions in particular the conditions under which a single morpheme has different phonological realizations
this approach might also be effective as a backoff mechanism when the system fails to parse a sentence containing only known words
this module plays an important role in korean due to m merous instances of phonologically conditioned allonmrphs in the language
it is driven by three submodules a lexicon a set of message templates and a set of rewrite rules
highly telegraphic with many im stances of sentence f agme ts asillustrb ted in NUM
a set of message templates used to produce the korean translation from the semantic frame in figure NUM is given in table NUM
in a hlition we are in the process of porting the system to a pen iron laptop running on linux
performs between NUM NUM better than singleselection
who is the murderer of lord dunsmore
cholmes is challenging watson s investigative choice
at present the treebank comprises NUM sentences each annotated independently by two annotators
when a and b are identical knowning their commonalities means knowing what they are i.e. i comrnon NUM
therefore senses NUM and NUM of facility received much more support NUM NUM and NUM NUM respectively than other senses
this number also includes proper nouns that do not contain simple markers e.g. mr inc to indicate its category
it can be seen from table NUM that our algorithm performed slightly worse than the baseline when the strictest correctness criterion is used
they proposed a set of heuristic rules that are based on the idea that objects of the same or similar verbs are similar
the reduced senses are senses NUM NUM NUM and NUM something that is communicated between people or groups
we then filtered out lc word pairs with a likelihood ratio lower than NUM an arbitrary threshold
the difference between the syntactic likelihood values of the two interpretations is solely determined by
in place of a length probability model we used pcfg for calculating syntactic preference
figure NUM shows the top NUM accuracies of the stochastic approach and the deterministic approach
we ranked the interpretations of each of the NUM test sentences using only syntactic likelihood
figure NUM plots the estimated length probabilities versus the lengths for two cfg rules
we used sax to analyze the sentences and selected the correct syntactic trees by hand
further note that the preferential order can not be determined or can only be determined
the realization of lexical preference in terms of selectional restrictions has some disadvantages however
in this section we describe our probabilistic disambiguation method based on rap and alpp
we refer to the number of words in a given sequence as distance
these errors however are peripheral to the underlying methods applied in this study
sample lexical entries woum be digested only once leaving NUM pieces
in NUM fragment and move are the higher level lexical concepts
one hundred training essays were used to build an example based lexicon and concept granunars
the associated metonymsfor fragment and move are in adjacent lists illustrated in NUM
conceptual analysis in essays is essential to provide a classification based on the essay content
in addition computergenerated information about essay content can be used to produce diagnostic feedback
automated rule generation is significantly faster and more accurate than writing the rules by hand
we estimate that it would have taken two people about two weeks of full time work to manually create the rules
each csr represents a sentence according to conceptual content and phra l constituent structure
another use of dialogue act processing in verb null mobil is the prediction of follow up dialogue acts to narrow down the search space on the analysis side
figure NUM correspondences between italian and english synsets for the verb scrsrere write
the grammar is written adopting a hpsg like style and each rule is regarded as typed feature structure tfs
as far as nouns are concerned a lexlcal entry includes all the senses found in italian wordnet
the exploited idea was to rebuild the wordnet hierarchy in clos the object oriented part of common lisp
lemmas about NUM NUM for version NUM NUM are organized in synonyms classes about NUM NUM synsets
as for figurative uses they can also be coupled with wordnet provided that an appropriate synset do exist
in light of the concrete use of the italian wordnet we propose the integration of selectional restrictions into the verbal taxonomy
so we build a small number of lexical entries by means of which we composed the sentences of the experiment
as an example figure NUM illustrates the senses for the italian verb scrivere write found in italian wordnet
however for developers of a multilingual system at one single site it would be more efficient if the speech interfaces for the different languages shared a common engine with one set of features one set of parameters one recognition algorithm and one system architecture but differed in the parameter values used
as first option the plan recognizer tries to repair this state using statistical information finding a dialogue act which is able to connect init and reject NUM
g z n wl w2 lnp w21wl NUM wlw2 at each iteration
recently we further augment our l0 to a larger initial lexicon l1 with NUM NUM entries
first of all a lot of attention is paid to optimizing the speech recognition unit off line e.g. by noise reduction
the literature on designing good user interfaces involving natural language dialogue in general and speech in particular is abundant with useful guidelines for actual development
if an error does occur let the system take the blame e.g. system i did n t understand your utterance
for example it might turn out that users do not like to proceed with the discourse but would prefer explicit validation of each input
the first is evaluation the second concerns the generalizability of the methods described in this paper the third the applicability of the NUM commandments
NUM the result of this process is a list candidates the first element of which consists of the task representation of the first disambiguated sr result
in fact obeying all guidelines subsumed by the NUM commandments is effectively impossible since as the reader will have noticed they contain some inconsistencies
last but not least the necessity of a design phase should not be underestimated and this is where commandments i to vi are useful
abeill notes that there are occasions where it is necessary to replace an elementary tree by a derived tree for example in hopefully john will work becomes on esp re que jean travaillera hopefully an elementary tree matches on esp re que derived
the metarules express a procedural description of the process of checking the applicability of a given metarule to a particular pair of input items a and b a stands to the left from b i n the input
when we look more closely at the resulting syntactic representation of the previous variants of the input sentence we may notice that the word in en3 ra engineer gen
let us take the sentence kds nep edpokhidfi spoluprfici se stranou pana sladka a neni pravdou e ptedseda k est ansk37ch demokratfi pan benda v telefonick6m rozhovoru s petrem pithartem prosazoval ing
in such a case it is necessary to apply some additional constraints in the grammar for example the restriction on the order of subcategorization an item to the left of a verb should be processed first
this sentence is ambiguous it is either correct and nonprojective meaning woman watered charles flowers or incorrect disagreement in number between karlovy and ena and projective
the processing of an input sentence is divided into three phases a positive projective this phase is in fact a standard parser it checks if it is possible to represent a given input sentence by means of a projective syntactic tree not containing any negative symbol these symbols represent the application of a grammar rule with relaxed constraints or an error anticipating rule
the claim that the first two words are unambiguous is supported by the fact that the form of the word p in mister is different in czech in case the word is independent and in case it is used as a title p na vs pana gen acc
the most polysemous verb in our databases run is identified as having NUM senses
this is supported by our initial experiments but is an issue we will continue to investigate
the semantic link based method on the other hand can eliminate some senses from this tag
the first step of the method is to identify the subcategorization pattern for a specific verb token
about half the verbs have more than one sense and NUM have more than two
when we make this change we get more than NUM shorter processing time namely NUM NUM s also the number of resulting structures is a half of the original number NUM and only NUM items are derived
during the development of this application we had to solve a number of problems concerning the theoretical background to develop a formalism allowing efficient implementation and of course to create a grammar and define the structure of the lexical data
to demonstrate this robustness we removed all abbreviations from the lexicon after reducing it in size to NUM NUM words
this sum is then passed through a squashing function to produce a node output between NUM and NUM
her method incorrectly classified less than NUM of the sentence boundaries when tested on NUM NUM periods from the corpus
the program handles numbers with embedded decimal points and commas and makes use of an abbreviation list with NUM entries
in section NUM NUM we give the results of a comparative study of system performance with both probabilistic and binary part of speech vectors
running the induction algorithm on upper case only and lower case only texts both produced the same decision tree shown in figure NUM
the decision tree created from the small training set of NUM items resulted in an error rate of NUM NUM
when this information is not available the system is nevertheless able to adapt and produce a low error rate
the morphological analysis makes it possible to identify words not otherwise present in the extensive word lists used to identify abbreviations
continuing the example the context becomes and for simplicity suppressing the parts of speech with value NUM NUM
ensure the feasibility of what is required of them
further investigation on this point is needed
the reason is that quite often the immediately preceding word has less predictive value than earlier words in the same sentence
therefore postulating a direct correspondence between the parser and theories of grammar is methodologically the strongest position and is usually assumed as a starting point of investigation
the target texts of this method are japanese newspaper articles
the most important sentences are then extracted as an abstract
by default the dialogue manager pursues a sequence of dialogue intentions that is typical of the majority of dialogue domains the system greets the user determines the nature of the user s enquiry gathers the data necessary for the successful answering of the enquiry handles any database transactions associated with the enquiry checks if the user has any further enquiries and concludes the dialogue
a key aim of our work will be to ascertain if our suite of objects which in combination encompass dialogue skills from the generic to the highly specialised can be built into co operative mechanisms in real time to simulate realistically the richness robustness and adaptability of natural human dialogue
the challenge here arises from the combinatorially large number of possibilities only a fraction of which can ever be observed
the dialogue manager is responsible for the overall control of interaction between the system and the user and between the main system subcomponents which in broad terms include corns facilities generate speech facilities the enquiry processing objects and the system database
a dialogue model records individual concepts as they occur notes the extent to which concepts have been confirmed populates request templates and fulfils a remembering and reminding role as the system attempts to gather coherent information from an imperfect speech recognition component
for each sentence calculate the importance
a tester selects important sentences that should be included in an abstract
we conducted an experiment to check the validity of the proposed method
in the other editorials the estrangement values arc comparable
this feature gives NUM point for present and NUM for past
likewise let p w21c denote the probability that words in class c are followed by the word w2
we simply want the score of the best sequence covering the nodes to the left of n f nstart times the score of the node itself times the score of the best sequence of nodes from ns art nt ngth to the end which is just b n u rt nt ngth
read the table in the following way line NUM shows the second most frequent error
table NUM effect of lexicon based and rule based stopwords on long query retrieval using l0 l01
for more complex matchings the making and pairing of derived trees becomes combinatorially large
in this case both the description week and inspection would be assigned their most frequent senses i.e. the first senses of wordnet
the use of intermediate models is found to reduce the perplexity of unseen word combinations by over NUM
thus it is not possible to use the same reference set to evaluate a system that may choose to give a summary as a response in one case ask a disambiguating question in another or respond with a set of database values in another
this involves a fairly straightforward representation as the focus is on paraphrases which simplify sentences by breaking them apart
as described above the syntactic filter discovers NUM of the NUM assignments of ldoce verbs found in levin s semantic classes
there were NUM wrong assignments and NUM right ones giving a precision rate of NUM NUM and recall rate of NUM NUM
prototype verb matched an average of NUM of the levin verbs while having an average size of NUM verbs
given that most large scale nlp applications require lexicons of NUM NUM NUM words automation of the acquisition process has become a necessity
we demonstrate that it is possible to use these filters to build broad coverage lexicons with minimal effort at a depth of knowledge that lies at the syntax semantics interface
setting aside the polysemy problem we see that this semantic filter is very useful for reducing the number of incorrect assignments
the theory a clause conveys a property or eventuality or describes a situation or expresses a proposition
since there are NUM verbs in ldoce and there are NUM semantic classes in levin there are NUM NUM NUM potential assignments of verbs to these semantic classes
recall that to assess this behavior we excluded randomly selected levin verbs from the semantic filter and saw how the filter behaved on these verbs
consider the rows that show the behavior of the experiment which uses NUM of levin s verbs and tries to guess the remaining verbs using synonymy
and we can bail out of proving similarity by proving or assuming coreference between the two entities
non identity approaches are supported by examples such as NUM which has reading NUM
if the first ellipsis is resolved to the sloppy reading then only the jtbt reading is possible
one weakness of this approach is that it appears overly restrictive in the syntactic similarity that it requires
a constraint on the arguments of corefis that el and e2 be properties of x and y respectively
in this paper we give an account of resolution rooted in a general computational theory of parallelism
for instance if the dialogues that result in a computer computer conversation are incoherent to a human observer this suggests that the dialogue mechanisms employed may be inappropriate for a human computer system
we find the results reported in this paper encouraging
therefore a new and more compact notation is proposed to overcome these two disadvantages
in general a small fluctuation is preferred to s larger one because when de is large the current merging process introduces a large amount of information fluctuation and its reliability becomes low
NUM NUM mr casey succeeds m james barrett NUM as president of genetic therapy but the mainstream civil rights leadership generally avoided the rhetoric of law and order regarding it
another feature of using stags for paraphrasing is that the links are not necessarily one to one
another claim of this paper is that statistics from a large bracketed corpus without nonterminal labels combined with clustering techniques can help us construct a probabilistic grammar which produces an accurate natural language statistical parser
these must now be integrated with the entities and events from the prior discourse prior sentences in the article
let z be the given input sentence t zv be the set of parse trees of zv t be a parse tree in t zv e t be the set of verb noun collocations contained in t
then we compare the products c t in the equation NUM of the conditional probabilities of the constituent verb noun collocations between the correct and the erroneous pairs and calculate the rate of selecting the correct pair
a subcategorization frame s can be divided into two parts one is the verbal part s contai lug the verb v while the other is the nominal part sp containing all the pairs of case markers p and sense restriction c of case marked nouns
let w be the given input sentence t w be the set of parse trees of w t be a parse tree in t w e t be the set of verb noun collocations contained in t
suppose that only the two cases ga nom and uwo acc are dependent on each other and the de at case is independent of those two cases as in the formula NUM
one of the most promising results of grammar inference based on grammar based approaches is the inside outside algorithm proposed by laxi lazg0 to construct the gr mmax from unbracketed corpus
for each subcategorization frame s which has only one case a binary valued feature function fs v is defined to be true if and only if the given verb noun collocation e has the same case and is also subsumed by s
along with the estimated conditional probabilities ps e s i v and the basic model above we consider a heuristics concerning covering of the cases of verb noun collocations as below and evaluate their effectiveness in the experiments of the next section
a word in the sentence can play the role of the head in several dependency relations i.e. it can have several modifiers but each word can play the role of the modifier exactly once
the recognizer is an improved earley type algorithm whose performances are comparable to the best recognizers for the context free grammars the formalism which is equivalent to the dependency formalism described in this paper
b NUM hello this is train enquiry service
given a grammar g s c w l t a state of the transition graph for a category cat c is a set of dotted slxings of the ibrm
NUM recognition with a dependency grammar he recognizer is an improved earley type algorithm where the predictive component has been compiled in a set of parse tables
the modifier symbols yj can take the form yj as usual this means that an indefinite number of yj s zero or more may appear in an application of the rule NUM
a state that contains the dotted string is called final a final state signals that the recognition of one or more dependency rules has been completed
when the morpheme is given the same status as the lexeme in terms of its lexical syntactic and semantic contribution the distinction between the process models of morphotactics and syntax disappears
as a computational framework rather than treating morphology syntax and semantics in a cascaded manner we propose an integrated model to capture the high level of interaction between the three domains
in order to perform morphological and syntactic compositions in a unified framework the slash operators of categorial grammar must be enriched with the knowledge about the type of process and the type of morpheme
semantic composition is also affected by the interplay of morphology and syntax for instance the change in the scope of modifiers and genitive suffixes or valency and thematic role change in causatives
as indicated by turkish data in sections NUM and NUM fi may in fact have a domain larger than but compatible with di
we describe a computational framework for a grammar architecture in which different linguistic domains such as morphology syntax and semantics are treated not as separate components but compositional domains
for instance causative suffixes change the valence of the verb mad the reciprocal suffix subcategorize the verb for a noun phrase marked with the comitative case
turkish is a language in which grammatical functions can be marked morphologically e.g. case or syntactically e.g. indirect objects
in this paper we provide a natural framework for dealing with interactions and ensuring contextually appropriate output in a single pass
a computational framework for composition in multiple linguistic domains
figure NUM sample ltag trees a np b noun noun compound c topicalized transitive
finally a certain set of label groups is
thus a topicalized have tree appropriately instantiated as shown in figure NUM is added to the description
i NUM statistical parsing model i i i
this approach captures naturally and elegantly the interaction between pragmatic and syntactic constraints on descriptions in a sentence and the inferential interactions between multiple descriptions in a sentence
for example mapping between active and passive voice versions of a sentence is represented by the tree in figure NUM
other constructions that describe one entity in terms of another such as complex nps relative clauses and semantic collocations are also handled this way by spud
the goal is the translation of a text given in some language f into a target language e for convenience we choose for the following exposition as language pair french and english i.e. we are given a french string f fx fj fj which is to be translated into an english string e el ei cl
a key issne in modeling the string translation probability pr j le i is the question of how we define the correspondence between the words of the english sentence and the words of the french sentence
in training this criterion amounts to a sequence of iterations each of which consists of two steps posilion alignmcnl riven the model parameters delerlniim the mosl likely position align lient
for mixture alignment model with nonunilbrm alignment probabilities subsequently referred to as ibm2 model there tre to many alignrnent parameters pill j i to be estimated for smml col pora
it is possible that in the process of the interaction the user has both found the switch and reported its position and that both find swl and report position swl up appear in the database
it connects nodes such as the main verb of each tree and indicates that particular attributes are held in common
the average successful dialogue duration is about NUM minutes in most of the dialogues all the parameters were acquired and confirmed during the first minute of user system interaction
a subdialog is opened leading to another then another then a jump to a previously opened subdialog and so forth in an unpredictable order until the necessary subgoals have been solved for an overall success
thus it is important that parsers have the ability to cope with new words
in addition these closed class words are not generally used as other parts of speech
the irregular verbs are enumerated by quirk et al NUM
the enumeration of irregular verbs allows the recognition of unknown verb forms to be rule based
for similar reasons irregular noun plurals are included in the set of closed class words
the second run assigns parts of speech using the post mortem approach described in section NUM NUM
each time through the corpus all the dosed class words are loaded into the lexicon
there is one caveat concerning the use of only affix information in a morphological recognizer
they also briefly address the effect of unknown words in parsing with their part of speech tagger
the reverse constraint on the lexical target does not apply
we have encountered several types of zeroing in the corpus which occur with verbs which we would normally consider transitive or verbs which can be intransitive only under special circumstances
and let us suppose that we want to exclude some particular combinations of these
object type relation type example object NUM object NUM character affinity relation character affix relation word classifier relation cl f snake word reflexive adjective relation lj they NUM self word structure relation i NUM struc j
the character yudn combines with the character preceding it li zhi to form the bisyllabic word zhiyudn worker and the two characters NUM gong and zud form a word
in terms of processing speed the mutual information approach took an average of NUM NUM ms to process one character our approach took NUM NUM s NUM the extra time in our approach is spent in parsing sentences
in NUM the two characters in the fragment nt shif n can either function as two autonomous words q shi ten and f n mark or they can combine together to function as a bisyllabic word ff shif n very
b nr n self is a reflexive adjective relation the connection between the word objects sh ng give birth and j le asp is an aspectual relation and the two arcs connecting the character objects hdi and zi are affix and affinity relations
in fact when he is asked his opinion of the new batch of coke eds from caa mr
we are striving to have a strong renewed creative partnership with coca cola mr
this uno operation provides an alternative to ad hoc mergin g employed by many other systems
in addition enamex type person peter kim enamex was hired from wpp group s j
the knowledge representation module implements the theory behind the uno model of natural language
the uno natural language processin g system as used for muc NUM ph
however we did not expect a failure of the function printing out the markings
incidentaly the number of articles coincides with the number of the muc NUM development data
needless to say limited by the subset of english covered by the model
and company names often end with a corporate extension suc h as ltd
i extend my previous atialysis in several wws for exami le i refine the notion of continue altd discuss the centering functions of full nps
i lcb oii hi silift cb mary cf lucy mary i1 2c i
while cb computation does not appear to be affected by a possessive that behaves like a i ronoun the cf ranking ueeds to be modified
table NUM illust r ttes the disl ribul ion of referring expressions with respect i o eenl ering lransil ions
weak and strong pronouns are often in complementary distribution as strong pronouns have to be used in prepositional phrases e.g. per lui for him
additionally the keyword spotter is provided with words expected in the next utterance
translations are produced on demand so that only parts of the dialogue are processed
ggi ggi is a graphical launchpad for le subsystems and provides various facilities for testing and viewing results and interactively assembling le components into different system configurations
tools in a multext system communicate via interfaces specified as sgml document type definitions dtds essentially tag set descriptions
is null sthe grammar is taken from the otp stress typology proposed by eisner in press
NUM shows the constraints p and q b and p NUM q
the project has defined an architecture centred on a model of the data passed between the various phases of processing implemented by the tools
this reflects an increased focus on viable applications of language technology promoting a view of the software infrastructure as central to the development process
the initial release is delivered with a creole set comprising a complete muc compatible ie system called vie a vanilla ie system
in either case the object provides a standardised api to the underlying resources with access via ggi and i o via gdm
in order to create a useful system however the system implementor must work closely with future customers to identify a problem while at the same time bearing in mind that uncertainties in the technology extension process can complicate finding a match between an application problem and the technical capabilities
as a result underlying software such as operating systems programming languages text editors and user interfaces require substantial effort for each new language the associated costs to obtain them install them learn them and work around their limitations are not going down
integration of the results of both projects would seem to be the best of both worlds and we hope to achieve this in gate
tipster in eu projects the sheffield nlp group is moving all its research and development work to gate and therefore to the tipster architecture
we also evaluate the computational efficiency of the different variants and the number of unlabeled examples they consume
if the left context is very large a new class can be created and used as left context
the second function is the pure back off function if the more specific terms have zero frequency the probabilities of the more general terms are used instead
and NUM where a x y is the distance between pat terns x and y represented by n features wi is a
contrary to naive back off and ib1 memory based learning with feature weighting ml ig manages to integrate diverse information sources by differentially assigning relevance to the different features
in the next section we will argue that a formal operationalization of similarity between events as provided by mbl can be used for this purpose
the most basic metric for patterns with symbolic features is the overlap metric given in equations NUM weight for feature i and NUM is the distance per feature
this is a very convenient methodology if theory does not constrain the choice enough beforehand or if we wish to measure the importance of various information sources experimentally
blocks of rules should indicate if the scan is to be done from left to right or from right to left
figure NUM an analysis of nearest neighbor sets into buckets from left to right and schemata stacked
this identifies the preposition as the most important feature its weight is higher than the sum of the other three weights
rules can check the left and right contexts of the input string and the left context of the output string
we turn the string into a chained NUM state ll arc wfsa and compose it with the p k o model
in general they converge to a local
these basic input and output units or elements are expressed as ei where i is some number
learnability measures how easily subjects could learn to communicate with the machine
a model is represented as a collection of facts like the following rep rl
that connects each english sound with one or more japanese sounds such that all japanese sounds are covered and no lines cross
the main reason for this amount of ambiguity is the standard writing system used in modern hebrew unpointed script
1deg to see the reason for this consider the word xwd vd nn and its two analyses
NUM in our test sample of NUM words the probabilities were significantly affected by this phenomenon in only three cases
we have shown that the use of contextual information in different system modules may reduce the recognition errors and increase the usability of telephone human computer dialogue
the concept of a focus set will be made more precise below where dependency functions are discussed
i te calls such deliberate speaker s messages conversational implicatures
level of generality as are the gricean maxims marked with an
provide feedback on each piece of information provided b c the user
we therefore propose to introduce a new generic principle which mirrors gp11 gpi2
three groups of principles reveal aspecls of cooperative dialogue left unaddressed hy the maxims
NUM is expressed at the level of generality of h icc s theory
NUM is a generalised version of gp6 non obscurity and gp7 non ambiguity
take into account possible and possibly emmeous user inferences by analogy from related task domains
we needed to optimist system dialogue cooperativity in order to prevent situations such as those described above
NUM NUM measuring the utility of domain independent information
NUM NUM a reduction in the training regimen
proof terms have been used in categorial work for handling the natural language semantic consequences of type combinations
subterms p r q may be rewritten to q r p etc
correctness measures whether or not there was successful completion of the task
the above discussion suggests how the systems l and lp might be interrelated in a logic where they coexist
the additional anaphoric types included dpro reflexive and timei cf
the confidence factor parameter NUM NUM is used in pruning decision trees
we used confidence factors of NUM NUM NUM and NUM
the anaphoric types used to tag this corpus are shown in table NUM
this causes the system to attempt more anaphor resolutions albeit with lower precision
changing the three parameters in the mlrs caused changes in anaphora resolution performance
domain specific features from those templates are employed for the learning
however such information is not available to the mlrs when resolving z NUM
those which are consistent are then generated and those which are inconsistent are rejected
by generation here we mean determining the lowest cost linear surface ordering for the dependents of each word in an unordered dependency structure resulting from the transfer mapping described in section NUM in general the output of transfer is a dependency graph and the task of the generator involves a search for a backbone dependency tree for the graph if necessary by adding dependency edges to join up unconnected components of the graph
if the lattice contains two phrases abutting at position k in the string wl tl i k ml ql cl w2 t2 k j ra2 q2 c2 and the parameter table contains the following finite costs parameters a left v transition of m2 a lexical parameter for wl and an r dependency parameter
control follows a standard non deterministic search paradigm NUM initialize q to contain a single configuration c r0 c c c NUM i0 with the input subtree r0 and the set of nodes i0 in r0
accuracy within NUM w i NUM or NUM points w i NUM shows the amount of agreement between the computer scores and human raters scores within NUM or NUM points of human rater scores respectively
essays receiving a total of at least NUM points are classified as excellent essays with NUM points or less are classified as poor and essays with NUM NUM points are classified as not excellent
in english for instance it is conventional to express certain invitations using the patterns lets or shall we
the solution obtained by the forward backward algorithm can not be logarithmically transformed because of the presence of summations
for applications like speech translation which must be carried out in nearly real time it seems wise to exploit shallow analysis as far as possible
cogenthelp operates in two modes a static mode which does not make use of run time information and a dynamic mode which uses widget run time status information to produce more specialized contextually appropriate messages
in this framework text planning rules the exemplars so called because they are meant to capture an exemplary way of achieving a communicative goal in a given communicative context are objects which cooperate to efficiently produce the desired texts
when the knowledge coming from the discourse is incoherent with the knowledge base there is world change
we then have to define what are the types of negations involved in the type rel resentation
in this paper we describe cogenthelp highlighting the usefulness of certain natural language generation nlg techniques in supporting software engineering se goals for help authoring tools principally quality and evolvability of help texts
the use of domain structure driven text planning is central to supporting the software engineering goals identified in section NUM rather obviously generating by rule helps to achieve consistency completeness and fidelity eliminating much mind numbing drudgery along the way
in the particular topic shown the user has reached the second button view k factors of the group of four buttons beneath the operators list boxes as can be seen from the highlighting in the thumbnail sketch applet cf
from this it resalts that the only coherence for an extensional object is internal it can not have contradictory sub objects
next we determined that since no inference is required beyond checking for equality these propositions and properties can be conflated with their linguistic realizations i.e. the indexed human authored help snippets cogenthelp takes as input
in developing the exemplars for cogenthelp we have made use of three nlg techniques structuring texts by traversing domain relations automatically grouping related information and using revisions to simplify the handling of constraint interactions
finally cogenthelp displays hypertext in netscape navigator using http to mediate access to the dynamically generated texts since netscape navigator remains the most widely used cross platform browser we have yet to investigate using other browsers
mereology achou a and rouault NUM
this functional classification should yield a set of language specific speech act labels which can help to put speech act analysis for speech translation on a firmer foundation
first the restriction is informal and as such provides no good basis for a mathematical and computational evaluation
the second wfst maps japanese sounds onto katakana symbols
these are the symbol mapping probabilities shown in figure NUM
unfortunately the correct answer here is ice cream
these techniques rely on probabilities and bayes rule
robust against errors introduced by optical character recognition
we will look at japanese english transliteration in this paper
exactly the two distributions we have modeled
still the machine s performance is impressive
once scan is done completer substitutes all final states of s i into all other analyses which can use them as components
therefore it is important to recover extragrammatical sentences using syntactic factors only which are independent of any particular system and any particular domain
our example is an encodin of a definite relation in a type constraint setup2 append c appends an arbitrary list onto a list of constants
figure NUM a sample session step NUM q he version space method is applie i
the trees obtained by including trib pos i tert struc infor struc at the same time are in general more complex and not significantly better than other trees obtained by including only one of these three features
the same split occurs in some of the best trees induced on core1 with the same outcome i.e. convince directly correlates with the occurrence of a cue whereas for enable other features must be taken into account
syn tactic rel atiou captures whether the core and contributor are independent units segments or sentences whether they are coordinated clauses or which of the two is subordinate to the other
therefore the function f must have the following property vz z z NUM
thus we ran an experiment on the NUM cued relations from core to investigate which factors affect placing the cue on the contributor in first position or on the core in second see table NUM
the exact definition of what the common part or the remainder shall be naturally depends on the actual constraint system chosen
in other words they have the strength of association of word in a chain more than la and ld have
thus the main goal of word filtering is to reduce the combination of unuseful tagged words and to identify implicit spelling error
in the section NUM the concept of how to use the statistical information to handle word boundary ambiguity tagging ambiguity and implicit spelling error will be explained
we can also identify some specific motivations and applications
from results of the experiments on small corpus about NUM NUM sentences word filter can criminate alternative word sequences and can correct the implicit error quite well
since a word may be involved in several dependency relationships each occurrence of a word may have multiple local contexts
thai morphological n lysis must face these three problems which cause many possible alternative or and the erroneous chains of words
our holy grail like that of many groups is to eventually get the computational linguist out of the loop in adapting an information extraction system for a new scenario
in addition cpmc main databases provide information from the online patient record e.g. medical history
the context of multimedia briefings for access to healthcare data places new demands on the language generation process
in actual output sentences are coordinated with the corresponding part of the graphical illustration using highlighting and other graphical actions
aggregation using semantic operators is enabled through access to the underlying domain hierarchy while aggregation using linguistic operators e.g. hypotactic operators which add information using modifiers such as adjectives and paratactic operators which create for example conjunctions is enabled through lookahead to the lexicon used during realization
using this assumption but by introducing a new measure of word significance we have been able to build a robust and reliable algorithm which exhibits improved accuracy without sacrificing language independency
this depth is calculated simply by taking the average of the heights of the peak relative to the height of the minimum on either side of the minimum
for example the cluster of three occurrences of software a content word at the end of the document have high significance scores
another interesting line of research would be to use the information from stage two of the algorithm to discover the significant words of a section and thereby attach a label to it
we would ideally like firstly to reduce the effect of noisy non content words on the algorithm s performance and secondly to pay more attention to words with a high semantic content
the important result shown by the graph is that content words real names such as mcnealy receive higher significance values than function words the
firstly elevated significance scores are associated with local clusters of a word
the algorithm then moves to the next sentence break and repeats the process
number of iterations of the em algorithm
detecting subject boundaries within text a language independent statistical approach
the plugging function for this case is as fol lows NUM
but natural language is no doubt more structured than this
this is performed simultaneously on every point on the graph
these numbers provide the input for stage four smoothing
the search may thus be relaxed to similar sentences
in order to improve the efficiency of the algodt1 some matching restriction schemes are needed which include NUM to label the matched constituents with reasonable syntactic tags NUM to set the matching restriction regions NUM to discard unnecess try matching operations according to local preference information
b argmax p b s argmaxp s b p b NUM assume the effects of word information and pos information are independent we get p b p wl b p ti b NUM
this paper describes a statistics based chinese parser which parses the chinese sentences with correct segmentation and pos tagging information through the following processing stages NUM to predict constituent boundaries NUM to match open and close brackets and produce syntactic trees NUM to disambiguate and choose the best parse tree
p b c is the fight combination probability for constituent b and p a b is its left combination probabilit which can be easily computed using the constituent preference data NUM described in sc ction NUM set NUM as the difference
let s w t be the input sentence for syntactic analyzing where w wl w NUM w n is the word sequence in the sentence and t tl t2 t n is the corresponding pos tag sequence i.e. t i is the pos tag ofwi
local preference matching consider such a parsing state after the simple matching operation sm ij ti NUM mc ij tj l starting from it there are two possible expanded matching operations em i ij or em ij i
therefore we have the noun phrases with constituent smscture v n in chinese treebank
figure NUM text created by a random walk over a pst trained on the brown corpus
moreover many heuristic rules to find ungrammatical constituents can also be summarized according to constituent combination principles
porting simr to korean english would not have been possible without young suk lee of mit s lincoln laboratories who provided the seed translation lexicon and aligned all the training and test bitexts
we treat extraposition as a nonlocal dependency and introduce a new nonlocal feature extra to establish the connection between an extraposed element and its antecedent
the particular combination of heuristics described in section NUM can certainly be improved on but research into better bitext mapping algorithms is likely to be most fruitfull at the word level
if the word is unknown the system reconsiders analysis from the point where it broke down with 1in particular there axe many hci issues associated with such a system which are beyond the scope of this paper
work described in this paper started from an idea of an error processor that would sit on top of an editor detecting correcting errors just after entry while the user continued with further text relieved from tedious backtracking
most previous corpus based algorithms disambiguate a word with a classifier trained from previous usages of the same word
mographic sense distinction is needed say for some nlp applications
simr can employ a small hand constructed translation lexicon to map bitexts in any pair of languages even when the cognate heuristic is not applicable and sentences can not be found
in table NUM we demonstrate one such case where the first alternative is the correct one
table NUM distribution of task and dialogue initiatives
they have developed a system that uses text planning and user modelling techniques to generate natural language descriptions of migraine its symptoms triggering factors and prescriptions
the effort may be lessened however by the realization that it is acceptable for the tokenization program to overgenerate just as it is acceptable for the matching predicate
a closer look at the exact information transfer in an ovr dialogue reveals even more about the exact information structure of the individual utterances of the information service
in this way the operator is able to communicate the information as clear as possible and the caller can relate the new information to information already known
it shows which scenario should be used given certain information elements gathered during the query phase and the information elements brought up by the database query
if the user reacts by a wh question a check or a reconfirmation the appropriate response will be given before it will continue the presentation
in the information phase the information service communicates the travel plan to the caller and the caller tries to get the plan as straight as possible
we see that NUM of the utterances contain NUM information elements that NUM contains only one element and NUM contains NUM elements
this stepwise presentation and acceptance of the travel plan is one of the most important characteristics of the information phase of a naturally occurring ovr dialogue
they give the following proof on page NUM
however extracted collocations were used only to determine realization of an input concept
large scale knowledge bases whose representations are semantically rich are particularly intriguing
each of the arcs is a relation in the knowledge base
it is below the hypocotyl and is surrounded by the rhizosphere
NUM spores are produced from the spore mother cell during sporogenesis
the first three aspects of expressiveness are concerned with content determination
in addition to coherence robustness is an important design criterion
while this evaluation technique is important it is not sufficient
for example section NUM shows other explanations generated by knight
first we attempted to control for the length of explanations
NUM generation of explanations by one panel of domain experts
some of the tested translation systems even mark an unknown word in the target sentence with special symbols
this figure is obtained by taking the unknown words as negative counts and all others as positive counts
the indefinite article for a noun sentence was manually adjusted to eine for female gender nouns
the modal can was used in noun sentences to avoid number agreement problems for plural only words like people
this is due to the fact that systran does not give many wrong form wf translations
when selecting the word lists for our lexicon evaluation we concentrated on adjectives nouns and verbs
personal translator again ranks among the systems with the widest coverage while german assistant shows the smallest coverage
traces of a derivational process based on prefixes have been found for langenscheidts t1 and for personal translator
out of these tags only u can be inserted automatically when the target sentence word is identical with the source word
every new judgement will be added to the translation list for comparison with the next system s translations
c b john referent he c mike was studying for his driver s test
although several cross linguistic studies have investigated rule NUM see section NUM there are as yet no psycholinguistic results empirically validating it
this placement must be more sophisticated than simply looking at errors since some learners will avoid structures they do not know perfectly well in order to prevent error
this research outlines sets of syntactic constructions language features that students are generally expected to master by a certain point in their study of the language
a much more natural sequence results if tony is used as the sequence 4a 4e illustrates
to describe these types we need to introduce two new relations realizes and directly realizes that relate centers to linguistic expressions
however this leaves open questions of the independence of syntactic role and pronominalization and the predominance of either for controlling centering
centering theory and the centering framework rely on a certain picture of the ways in which utterances function to convey information about the world
there is now a large situation semantics literature that contains many extensions and refinements of the theory to which we refer the interested reader
computational linguistics volume NUM number NUM utterance 20b establishes john both as the c b and as the most highly ranked cf
however the lack of complete information at the time of processing an utterance means that a unique interpretation can not be definitely determined
figure NUM knowledge bases for the example
if the address or number exists then all information is processed as a reference to the existing records
the codings were devised for use with the hcrc map task corpus
showing considerable variation from subject to subject
figure NUM sample entries from the lexicon
finally in section NUM we compare our system with related approaches
the challenge lies in integrating constraints from syntax semantics and pragmatics
NUM NUM joe saw that jackie was biting molly
the applicability conditions of constructions can freely make reference to this information
big will then get something like a NUM NUM value on the size scale NUM
the purpose and result of the mikrokosmos analysis process is a rendering of the source language text into an interlingua text
for ease of implementation we used a complete closed lexicon which contains all the words in the corpus
we thus use unambiguous words those with only one possible part of speech as example boundaries in bigram tagging
an informative example is therefore one whose contribution to the statistics leads to a significantly useful improvement of model parameter estimates
the ability of committee based selection to focus on the more informative parts of the training corpus is analyzed in figure NUM
the two member version is simpler to implement has no parameters to tune and is computationally more efficient
most importantly for this paper the crucial taxonomic criterion for each adjective is its anchoring in the underlying ontology
the general topic of this paper is the information about adjectival meaning which should be included in a computational lexicon
the work was based on the set of over NUM NUM english and about NUM NUM spanish adjectives obtained from taskoriented corpora
each lr takes a ready entry el and creates another entry e2 out of it automatically
the most statistically and conceptually significant complications deviations and exceptions from the common case are briefly sketched next
the modality in the entries above corresponds of course to can in the informal formula above
in the sem struc zone instead of variables which are bound to syntactic elements the meanings of the elements re
equally crucial is the syntactic semantic mapping between the syntactic structure syn struc and sem struc zones with the help of special variables
the more unreliable the segmentation the more data must be omitted
speeding up the performance it is often the case that nondeterministic parsers the author of the grammar has to prevent an unnecessary multiplication of results by means of tricks which are not supported by the linguistic theory let us take for example the problem of subject predicate object construction
also some singular count nouns such as committee may be the antecedents of plural pronouns
for this reason we had to abandon even feature structures as the form of the representation of lexical data
the user may get several types of messages about the correctness of the text a the macro changes the color of words in the text according to the type of the detected error the unknown words are marked blue the pairs of words involved in a syntactic error are marked red
there are also questions of more general interest
oo oo pr3edseda pan funkce dejmala rozhouor ithartem telefonickebbtrem ministra demokratu
if we take into account the results of the previous examples we should not be surprised by the results
the boxed nodes indicate actual schemata other nodes are included for convenience in expressing generalisations
we intended to create a dll library with the standard grammar checking interface required by a particular text editor
if a character string could be tokenized in multiple ways it would be ambiguous in tokenization
it is believed that critical tokenization provides a precise mathematical description of the principle of maximum tokenization
the most ambiguous is the corpus of the greek language because of the great number of grammatical tags NUM and the strong presence of unknown words in the open testing text
nevertheless these methods fail if only a small training text is available because of the huge number of events not occurring in this text such as pairs of tags and word endings
to address the above problem we have approximated the conditional probabilities of the unknown word tags by the conditional probabilities of the less probable word tags i.e. tags of the words occurring only once
consequently the bulk of the work has been in developing representing and processing grammar
chi square test for the distribution of the grammatical tags of the unknown words and the less probable words in the english text for the extended tagset of grammatical classes and various training text sizes
this type of error appears when the testing text has a style unknown to the model i.e. a style used in the open testing text not included in the training text
the tagger performance has been measured in extensive experiments carried out on corpora of seven languages english dutch german french greek italian and spanish annotated according to detailed grammatical categories
the most ambiguous texts are the french italian and english in the tagset of main grammatical classes and the german greek italian and french in the extended set of grammatical categories
in figures NUM and NUM the results of chi square tests that measure the difference between the probability distribution of the tags of the less probable words and that of the unknown words are shown
a related objection to wsd research is that the sense distinction made by a good desk top dictionary like woi dnet is simply too refined to the point that two humans can not genuinely agree on the most
table NUM lists the average number of senses per polysemous word in the brown corpus for the top NUM top NUM and the bottom NUM bottom NUM of word occurrences where the words are again ordered from the most frequently occurring word to the least frequently occurring word
this NUM of all noun occurrences include all nouns in the brown corpus that are monosemous about NUM NUM and all rare nouns in the brown corpus that do not appear in word np t and hence have no valid sense definition about NUM NUM i.e. the remaining NUM noun occurrences are all polysemous
this brings us to the question how much data do we need to achieve wide coverage high accuracy of speech making up the top NUM NUM of word occurrences in the brown corpus
assuming human sense tagging throughput at NUM words or NUM NUM word occurrences per man year which is the approximate human tagging throughput of my completed sense tagging mini project such a corpus will require about NUM man years to construct
lexas was given training examples in multiple s of i00 starting with i00 NUM NUM training examples up to the maximum number of training examples in a multiple of NUM available in the corpus
we can also see a difference in the distribution coverage of the unknown words using different taggers
thus we need to adjust the estimation error in accordance with the length of the affix or ending
suppose that the guesser categorized it as developed jj nn rb vbd vbz
our main interest is in how the advantage of one rule set over another will affect the tagging performance
different lexicon entries can share the same pos class but they can not share the same surface lexical form
such rules guess the pos class for a word on the basis of its ending or leading segments alone
we adopted NUM confidence for which t NUM o NUM o NUM to o5
the v1 operator will extract the rules with the alterations in the last letter of the main word
according to the definition of unit element 1v e u therefore there is an x x e u now suppose that u is such an x
node NUM and NUM are in the current space u6 and was activated only one or two sentence ago they are therefore omitted
pro verb navigates such relatively small parts of a proof and chooses the next conclusion to be presented under the guidance of a local focus mechanism
to assign capitalized unknown words the category proper noun seems a good heuristic but may not always work
we therefore decided to collect ending guessing rules separately for capitalized words hyphenated words and all other words
it is interesting to note that elementary attentional space can not contain pcas that are produced by consecutive planning operators in a pure hierarchical planning framework
cardinality NUM may also be caused by an error of a type we try to describe in the next subsection
another point we had to struggle with was wordnet s treatment of disjunctive hypernyms especially when they are lexical gaps
is the assignment of term drill as a synonym of electric drdl avoidable redundancy leading to avoidable homography or not
in addition because paradise s success measure normalizes for task complexity it provides a basis for comparing agents performing different tasks
person names all types company organization names locations dates phone numbers license numbers identification numbers example social security gender country of birth date of birth occupation subject line file numbers cable numbers the following associations will be extracted by the canis prototype from the cables family employment affiliations
when building large scale lexical semantic resources subsequent or better simultaneous validation of content is essential
the numbers of hyponyms from lipid down to glycertde are NUM NUM NUM and NUM respectively
dc dr b4 a train leaves at NUM p m dt key for dialogues NUM and NUM dialogue or subdialogue
let agent a s repair dialogue strategy for subdialogues repairing depart city be ra and agent b s repair strategy for depart city be rb
np pp pval for NUM sets were developed and distributed and lantern slide teaching sets on 2j pathology subjects were added to the loan library of the medical illustration service
to obtain the correct linking tile argument structure of the verbal base must be available
note that this treatment does not as yet include a fine grained represention of tense and aspect
dl may contain only one partial dependency tree the extracted phrase d2 contains the rest of the sentence
l we will write singleton domains as while other domains are represented by
our own approach presented below improves on these proposals because it allows the lexicalized and declarative formulation of precedence constraints
as we have argued above such a formulation can not capture semantically motivated dependencies
lcb subject self self object rcb
the rule can be viewed as an ordered tree of depth one with node labels
this formalization is restricted to projective trees with a completely specified order of sister nodes
NUM NUM of the sentences are compared with the corresponding second annotation and are cleaned NUM NUM are currently cleaned
we separate the elements of this data structure into the maximal vertex cover set and its complement set
the af p completeness of the dg recognition problem follows directly from lemmata NUM and NUM
NUM the user determines phrase boundaries and syntactic categories s np vp
a grab the tanker pick up oranges go to elmira make them into orange juice
our muc NUM system figure NUM consists of NUM major components applied in sequence lexical analysis reduction extraction merging postprocessing
during reduction our system actually splits a person s name across slots called given name family name and suffix name so that the expectations for say harry l
these results show that our semantic pattern based approach to entity detection and templating is a very good one and one which can be brought to bear o n a new application quickly
we build up the semantic structure of an initial reference by taking all the elements in the property list along with the substance of the entity corresponding to the head noun in the surface noun phrase
these tradeoffs led us to constrain the source positions of transitions to just two specifically the simple left and right source positions mentioned in the description of transitions in section NUM NUM
the transfer module is free to attempt structural transfer in order to produce the best possible first guess
the problem is justifying the main subordinate distinction in every language that we might wish to translate into german
their best result combined only two of the query types one a p norm and one a vector space
as a second point it should be noted that these adhoc results represent significant improvements over trec NUM
figure NUM paradise s structure of objectives for spoken dialogue performance
this section is designed to provide a mini knowledgebase about a topic such as a real searcher might possess
in effect an arbitrary permutation of signs is input to a shift reduce parser which tests them for grammatical well formedness
in the diagrams we use a cross to represent ill formed nodes and a black circle to represent undetermined ones
this distinction can be justified monolingually for the other languages that we treat english french and japanese
null the ancestors of the new well formed node will be at least as well formed as they were prior to the movement
we give some definitions state some key assumptions about suitable tncbs for generation and then describe the algorithm itself
we present a polynomial time algorithm for lexicalist mt generation provided that sufficient information can be transferred to ensure more determinism
in the left hand figure we assume we wish to move the maximal tncb NUM next to the maximal tncb NUM
the third author is a researcher of ici institutul de cercetari in informatica bucarest romania the work reported in this paper was implemented while she was visiting cselt
it is possible to modify our joint purpose algorithm with information about rhetorical relations so as to check expectations in regard to argmnentation or to include rhetorical knowledge in the obligations used when reasoning about multisentential contributions but as our primary goal has been to specify communicative principles and use them in the formalisation of the cooperative and rational nature of dialogues this kind of extension is left for future
in the following we briefly explain the various functions
our departure point is in general conmmnicative principles which constrain cooperative and coherent communication and radical steps are taken in two respects the dialogue grammar is abandoned as an infeasible way to describe dialogues and also speech act recognition is abandoned as a redundant labeling of intention configurations
we mso use contextual knowledge extensively and connect intention based approaches to practical dialogue management rationality and cooperation are not only tied to the agent s beliefs and intentions of the desidered next state of the world but also to the wider social context in which the communication takes place
the former sees dialogues as products and compiles participants beliefs and intentions into a predefined dialogue structure whereas the latter focusses on the participants goals and hides the structure in the relations between acts which contain appropriately chosen sets of beliefs and intentions as their preconditions and effects
for a given pair of vectors v1 v2 we attempt to discover which point in v1 corresponds to which point in v2
moreover with an improved acoustic model trained on a domain dependent training set the error reduction is even greater over NUM for both wa and su
in this section we describe our algorithm which assigns probabilities to hand written optional phonological rules like flapping
we now plan to augment our probability estimation to use the pronunciations from this new hmm induction based generalization step
the derivation probability is computed simply by multiplying together the probability of each of the applications or non applications of the rule
consider a sentence whose first two words are of the and assume the simplified lexicon in figure NUM
while our procedure is not guaranteed to terminate in practice the phonological rules we apply have a finite recursive depth
this was expanded to include syllabics stop closures and reduced vowels alveolar flap and voiced h
in each experiment the test set contains abou the tenth of the available data
the paradigmatic cascades model crucially relies upon the existence of numerous paradigmatic relationships in lexical databases
all these models have been tested nsing exa c ly the sanle evmual ion
we present and experimentally evaluate a new model of pronunciation by analogy the paradigmatic cascades model
most alternations substituting one initial consonant are very productive in english like in many other languages
if several derivatives are simultaneously found the search procedure halts and returns more than one analog
various pruning procedures have also been implemented in order to control the exponential growth of the stack
suff x y denotes their longest common prefix resp
morphologically related pairs provide us with numerous examples of orthographical proportions as in
the domain of an alternation f will be denoted by dora f
the resulting probability distribution is then examined and we rederive the recurrence equation from it
evaluating interactive dialogue systems extending component evaluation to integrated system evaluation
it is to avoid this anomaly that the following constraint on the acceptability of dependency function partitions in this context
for a monotone decreasing quantifier the check depends on whether it is in wide scope position or narrow scope position
for example hobbs and shieber s algorithm produces the scopings and sentence 8a has the following one
after the choose scoping step in the algorithm quantifiers can be proposed which are preferred in the given scoping position
the algorithm performs a great deal of search with three levels of non determinism corresponding to
the check for monotone decreasing quantifiers in wide scope position is a little bit trickier
the scoping framework which underlies the generation algorithm recognizes fewer scopings than NUM
these proposed quantifiers are then checked first by q inc NUM and q dec NUM
a parallel but different constraint is applied whenever r is in narrow scope position
all other r s outside this set must certainly not have seen a sample
case dependencies and noun class generalization axe represented as features in the maximum entropy approach
initial references nominal anaphora bare zuqiu football zuqiu football full tie tong iron barrel tie tong iron barrel reduced tie tong iron barrel tong barrel new shui water yuan wan zhong de shui water in the round bowl other qian money neixie chaopiao those notes types and examples of nominal anaphora
james NUM universal james NUM rank u subject of
to get our goal i.e. developing a system with approximately NUM segmentation precision and NUM tagging precision for any running chinese texts in any cases quite a lot of work is still waiting there to be done
we shall call the forms g qi via metastructure or m structure
since bangla has no subject verb agreement based on number the num feature has been omitted
functional heads obtain their contents via movement of elements from positions lower in the tree
this second phrase marker is itself built up in other applications of gt and or move a
metavariable let n be a new nameholder for the metavariable
its main characteristic is that it does not work from left to right but instead works bidirectionally
suppose that the parser should consider agrs as the head corner of agrsp which accords with x theory
elements move from the lexical domain vp to the functional domain e.g.
we have patterns for recognizing the internal structure of names as in a c nielsen co we have a list of common names many of which could not otherwise be recognized such as ibm and toys r us
this module was implemented early in NUM in order to participate in the coreference evaluation in muc6 but it was done in a way that was completely in accord with normal fastus processing and the results of coreference resolution are used by subsequent phases
event ng obj NUM vg active head NUM p prep ng pobj NUM event adj rcb semantics
event ng obj NUM vg passive head NUM lcb p prep ng pobj NUM event adj rcb semantics
for the sample text merging the four events found by the clause level event recognizer results in the two following transitions both with the same end state the first person centered and the second p0sition centered this result is then mapped into the desired template which may be different since in general its structure will be determined by retrieval requirements rather than how the information is typically expressed in texts
to handle this there are patterns in the complex phrase recognizer that recognize a conjunction of the subject and the prepositional argument when the verb is designated symmetrical ng subj and ng pobj this is then given a special attribute symconj and in the clause level event recognition phase complex noun groups with this property are sought as subjects for symmetric verbs
in our sample text this phase results in the following labeling a c nielsen co co said george garrickper NUM years old president of servicesco operation will become president and chief operating officer of nielsen marketing research usaco a unit of dun bradstreet corp co
for the sample text the following four event structures are constructed corresponding to the four patterns above NUM once individual clause level patterns have been recognized the event structures that are built up are merged with other event structures from the same and previous sentences
transitions are shown as arcs between states the label on an arc specifies the relation symbol source position and target position respectively
in another example the attributes for a document may be anything the developer chooses with no constraints on definition or scope
conduct engineering review boards erbs track and evaluate architecture requests for changes rfcs exercise configuration control of
now the user utters call bill
this technique provides a simple algorithm for learning a sequence of rules that can be applied to various nlp tasks
vii thou shalt cogitate before thou com
in particular we use a dynamic programming tabular algorithm to find the minimal cost transduction of a word string or word lattice from a speech recognizer
by the time the user starts to point a second time the analysis of the previous multimodal referring expression has been completed and the context effect of the second pointing gesture is used to solve the corresponding referring expression
creating associative cfs for all of these associate individual instances is computationally expensive especially since most of them would have been created without being of any use only seldom are there several bridges to cross simultaneously
the perceptual cfs are as follows visible referent cf selected referent cf and indicated referent ce visible referent cfs cause referents that are visible to have a higher sv than referents that are not visible
in pars pro toto pointing an object is selected by pointing to a pixel that is within the object s selection area which encloses the area covered by its icon and subsequently pressing the select object mouse button
in the case of dit boek pointing to a report or screen location was not intended and thus the dialogue manager decides that the indicated cfs and selected cfs update of the report and screen location were invalid
to show the variety in use of referring expressions we present under a the sentences with the largest amount of deictic and anaphoric expressions keyed in by the subjects and under b the least amount
the subjects were not informed which words and syntactic and semantic constructs could be handled by the system and which could not but they all knew from their previous encounters that the system was not an unrestricted nl interface
in simple head transducers the target positions can be restricted in a similar way to the source positions i.e. the right end of l2 or the left end of r2
the test corpus chosen to validate our automatic lexicon enhancement method was composed with articles of the newspaper le monde diplomatique from NUM until NUM
domain dependence and the differences in word behavior for example the differences in behavior between two verbs with the same subcategorization were due to the costs applied when running the automata
the new goal leads to vocalization in the same manner described above
lowest cost translations of such fragments will already have been produced by the transduction algorithm so an approximate translation of the utterance can be formed by concatenating the fragments in temporal order
after three repetitions caused by subject s refusal to cooperate
NUM please keep your tone volume rhythm similar to the way you trained
this section describes the design of the tests and the results obtained
these sentinel interactions help to keep the user and machine in synchronization
a low value was assigned for all expectations at the current subdialog
their subsystems are wires switches transistors and so forth
the choice is partially dependent on the current level of initiative
this is the overall boss of the dialog processing system
the use of the part of speech tagger was both a strength and a weakness in chinese
data annotated with the correct answers was provided by the government in its training materials
probabilistic learned approaches can be developed in a short amount of time
the gap between manually constructed systems using patterns and learned systems is shrinking dramatically
the tests of locality syntactic constraints and salience are straightforward to implement because the system has complete knowledge of the discourse to be generated yeh and mellish an empirical study on anaphora and its syntactic structure
we learned the following lessons high performances are possible using one approach across several languages
this creates a challenge in getting started since several of the patterns look for distributed categories
there are several pleasant surprises corresponding to strengths in the learned system as applied to spanish
therefore changes to the model could be quickly tested
another strength and challenge in chinese is the fact that several of the categories are interrelated
conversely a unioned marking in reape s and nerbonne s system effects the insertion of a single domain object corresponding to the constituent thus specified
each time two categories are combined a new domain is formed from the domains of the daughters of that node given as a list value for the feature dom
thus in figure NUM both elements of the vp NUM domain become part of the higher clausal NUM domain
intuitively partial compaction allows designated domain objects to be liberated into a higher domain while the remaining elements of the source domain are compacted into a single domain object
at the heart of our proposal is a new kind of domain formation which affords analyses of extraposition constructions that are linguistically more adequate than those previously suggested in the literature
as a result we can dispense with the unioned feature altogether and instead derive linearization conditions from general principles of syntactic combination that are not subject to lexical variation
the representation in figure NUM involves a number of order domains along the head projection of the clause NUM NUM
adjuncts and complements on the other hand follow the nominal head by virtue of their t extra specification which also renders them extraposable
temporal l NUM relative l NUM de NUM NUM locallve l NUM n tempond l NUM mlative l NUM d NUM NUM relanve
the virtual server architecture is a basis for the flexible use of heterogeneous nlp systems in real world applications including and going beyond cosma
it is based on the con null cept of cooperating object oriented managers with the ability to define one to many relationships between components and c cms
the results of database queries provided valuable insights into the range of linguistic phenomena the parsing system must cope with in the domain at hand
calendar systems they not only guarantee a maximum privacy of calendar information but also offer their services to members or employees in external organizations
the local planning layer consists of a constraint planner which reasons about time slots in the agent s i.e. its owner s calendar
if the search fails again the expression is interpreted deictically and resolved w r t to the time the message was sent
NUM NUM rules for updating the collaborative state
these words may appear in many different contexts but in texts about energy topics these words are likely to be relevant and probably should be defined in the dictionary therefore we expect that a user would likely keep some of these words in the semantic lexicon but would probably be very selective
for example the word horse can refer to an animal a piece of gymnastics equipment or it can mean to fool around e.g. do n t horse around
21t is possible that a word may be near the top of the ranked list during one iteration and subsequently become a seed word but become buried at the bottom of the ranked list during later iterations
s attrib rel entityl entity2 xx
in addition to unsorted prolog terms profit allows sorted feature terms for which the sorts and features must be declared in advance
in the example the domain must only be specified for the value NUM which could otherwise be confused with the integer NUM
phrasal headed non headed decl int rel intro daughters
there are two key ingredients for building an nlp system a linguistic description a processing model parser generator etc
on the basis of the declarations sorted feature terms can be used in definite clauses in addition to and in combination with prolog terms
a profit program consists of declarations for sorts declarations for features declarations for templates declarations for finite domains definite clauses
and arto anttila eds constraint grammar a language independent system for parsing
the NUM most frequent ambiguous word forms NUM account for NUM of all ambiguity
the verb pense is ambiguous NUM in the first person or in the third person
it is usually easy to determine the person just by checking the personal pronoun nearby
the easiest way to improve the constraint based tagger is to concentrate on the final class
we can take an easy sentence to demonstrate this je ne le pense pas
the principled rules do not require any tagged corpus and should be thus corpus independent
they are relatively infrequent thus the global accuracy of the constraint based tagger remains higher
two thirds of the ambiguity are due to the NUM most frequent ambiguous words NUM
this is in particular the case for clitic determiner ambiguities attached to words like le or la
the simplest way to achieve the desired result is to have multiple entries for the preposition one for each sense
alshawi NUM the european community s alep advanced linguistic engineering platform system alshawi et al NUM
notice that for the original boolean expressions we may not be able to fill in all the extra argument places
the sharp eyed reader will see various other list and term representations of things that are logically bitstrings in what follows
we enrich the grammatical notation with a which can appear as a suffix on a daughter category in a rule
we have to extend our various selectors and the lists they appear in to accommodate this fourth position
this approach to partial ordering can be implemented by requiring the grammarian to make linear precedence declarations encoding the partial orderings
german dcr manager arbeitet den vortrag
manning evaluates his system by computing precision and recall scores with the oald dictionary as golden standard
the rough structure of the sentence is computed a process known as partial parsing
it should be pointed out that these NUM sentences contain an average of NUM NUM verbs
an approximate model with fewer dimensions can be constructed by ignoring these small components
1colours axe indicated by subscripts labeling term occurrences whenever colors axe irrelevant we simply omit them
in contrast the representation for NUM is ex pf we of
the resulting matrix is very sparse however the density for the verb category space is only NUM NUM percent
context digests are formed by combining the NUM fixed windows each consisting of co occurrence counts with NUM NUM possible neighbors
NUM both positive and negative evidence the absence of a feature for a particular verb should be considered
the first condition ensures that the color erasure of a c substitution is a well defined classical substitution of the simply typed a calculus
now we can directly eliminate the variable r pf since there are no other variants
given some standard assumptions about the semantics of 3as in dsp the identification of parallel elements is taken as given
in section NUM NUM we have seen that hocu allowed for a simple theoretical rendering of dsp s primary occurrence restriction
and not azw ia as one is tempted to believe since it has to be pf monochrome
in the colored a calculus the space of semantic solutions is further constrained by requiring the solutions to be g substitutions
if no monitor object is provided no monitoring or interruption of the retrievedocuments operation is possible
NUM part of speech labels using the penn treebank set as a standard for english
nextdocument collection document or nil returns the next document within a collection
the raw document may contain several types of information including text tables and graphics
if these particular annotation type names are used they must be used for the purpose designated
looking at their close context it is often impossible to handle the situation with some smart tagging restriction or other device
the morphological errors in the material are not disturbingly many considering the fact that all swedish content words have such features
figure NUM elementary structures in nlsynchtag
the automatic tagger suggests adverb where there should have been an adjective while human annotators sometimes call an adverb an adjective
the error analysis on which this study is based was carried out on material from the stockholm ume corpus of modem written swedish
relevance feedback begins with an initial retrievalquery which is used to retrieve a set of documents
constraint grammar in the rare cases where two analyses were regarded as equally legitimate both could be marked
of course this is all speculative but it indicates that the dm methods to deal with sr results presented above can be used for the second prototype as well
among the domain independent speech acts we use acts as e.g.
it can also handle recursively embedded clarification dialogues
finally results from our implemented system are presented
we conclude with an outline of future extensions
therefore different descriptive units have to be chosen
to get an impression of the flmctionality of the dialogue module we will show the processing of three sentences which are part of an example dialogue which has a total length of NUM turns
NUM NUM the statistical layer statistical modeling and prediction
an important task of this layer is to signal to the planner when an inconsistency has occurred i.e. when a speech act is not within the standard model so that it can activate repair techniques
deo04 oh ja gut nach meinem terminkalender pause wie w ars im oktober vorschlag vmo05 just lookin at my diary i would suggest october
the resulting duplication of effort among test uito
tonsequenl ly the random variable NUM is mrf for neighborhood systenl n i its NUM satisties the positivity and the locality
the t llll l ature NUM m is started in high vahle and lower to zero as tile above process is doing
for given x the quantity x is capable of assuming the discrete wdues xi i NUM NUM n
the weighting parameter a in tile clique unction NUM call be estiinated frolil training data by mig principle ayiles57
information sourccs for tagging aro combiimd hy m f NUM rincil le which is sod i m i lcb f as theoretical background
we are not given the corresponding probabilities pi all we know is tire expectation value of the function f x r
i1mm based l agging modd times unigram t igl atll a lld t rigt altt in ortn d iott
many researchers haw tried to solve the problem by hidden marker model hmm which is well known as one of the statistical models
in that case the dialogue module initiates clarification subdialogues
NUM however it is possible to undermine sensitivity to aspects of resource structure by inclusion of structural rules which act to modify the structure of the antecedent configuration
however a type s knp corresponds to a sentence missing np at some position and so 6the original structural modalities are linear logic s exponentials
adding p to nl gives nlp a system whose implicit notion of linguistic structure is binary the systems of algebraic semantics that are provided for such logics
for example a unary operator k allowing controlled permutation might have the following rules where kf indicates a configuration in which all types are of the form kx
on the other hand where the freedom of associative combination is required it is still available given that we have e.g. xtgy v x y
this method can not be used for the hybrid approach because for any theorem there exist other theorems for combining the same antecedent types under any possible ordering thereof
nl is a system whose implicit notion of linguistic structure is binary branching tree like objects and this rigidity of structure is reflected in the type combinations that the system allows
figure NUM alternative metric weights experiment extraction by hand
in the latter case we must preserve the previously constructed mapping between john j on which xl is dependent and the teacher t thus x2 is similar to xl if taken to be coreferential with t giving us the sloppy reading
table NUM template element processing statistics
input he will be succeeded by mr
the dashed lines in figure NUM indicate some of the information sources that the inquiry implementations will access i.e. the process structure the penman lexicon and grammar and the evolving text structure
currently however it is simply used by the final component of the architecture the sentence builder to specify the appropriate lexical items and case structures for the action input to the text level inquiries
the collector maintains separate semantic representations for incompatible information
for our experiments we obtained corpora which had been manually segmented by native or nearnative speakers of chinese and thai
the input to imagene is the set of features of the functional context that affect the form of expression of the plan called the text level inquiries in figure NUM
he will be succeeded by mr dooner NUM
here are the various forms as generated by imagene according to the distinctions discussed in the previous section to end a call hold down the flash button for two seconds then release it
a keith vander linden and james h martin expressing rhetorical relations study of referring expressions similar to our work on expressing rhetorical relations would allow the development of a more principled solution to this problem
note also that the prl structure in the various slots for each node specifies the penman lexical entries for most of the lexical choice issues thus allowing imagene to concentrate on expressing procedural relations
it also includes semantic information such as the agent of a particular action e.g. the reader or the device and the semantic type of the predication e.g. material or relational process
natural language provides an extensive set of lexical and grammatical forms for expressing concepts a set from which writers choose the particular form that they feel will produce the most effective expression given the communicative context
this algorithm constructs a decision tree which decides whether an element of a collection satisfies a property or not
d advanced medical technology has made it possible to save more lives
for example the substring ovps in the word flc 6ovevoc ve6ufnos bedouin is separated as ov and e and not as o and vl NUM double vowel blends are included in this excerpt
proof every syllable has at least one vowel thus a word can not have syllables exceeding the number of its vowels and it can not have fewer syllables than the number of non ending maximal consonant sequences
the degree of complete hyphenated words of newspaper texts was manually calculated to be over NUM as expected because the frequency of occurrence of the remaining ambiguous vowel sequences in words of real texts is relatively low
the author is grateful to m stamison atmatzidi for her long hours of proofreading to the three cl reviewers for their valuable suggestions and comments and to the greek newspaper to vima for the availability of the text corpus
however ancient greek words and borrowed foreign words that are frequently used in both written and spoken forms contain additional sequences and as has already been mentioned their hyphenation is governed by the same rules
for the case of a nonfinal consonant sequence following v2 the point implied by theorem NUM may in certain contexts indicate the same point as the assumption but in other contexts it may not
it is important to note that the ultimate goal is to specify the permissible hyphen points in any vowel sequence and not only in the particular substrings of sequences mentioned in v1 v2 v3 and v4
if it is at the beginning of the word according to lemma NUM a the hyphen can not be inserted after the consonant s and hence the assumption of a hyphen before vl is false
the normalization function is used to overcome the problem that the values of ci are not on the same scale as and that the cost measures ci may also be calculated over widely varying scales e.g.
given a set of dialogues for which user satisfaction us and the set of ci have been collected experimentally the weights c and wi can be solved for using multiple linear regression
when the prior distribution of the categories is unknown p e the expected chance agreement between the data and the key can be estimated from the distribution of the values in the keys
success at the task for a whole dialogue or subdialogue is measured by how well the agent and user achieve the information requirements of the task by the end of the dialogue or subdialogue
danieli and gerbino found that agent a had a higher transaction success rate and produced less inappropriate and repair utterances than agent b in addition they found that agent a s dialogue strategy produced dialogues that were approximately twice as long as agent b s but they could not determine whether agent a s higher transaction success or agent b s efficiency was more critical to performance
for example an ambiguous human name in isolation may be recognized if it is followed closely by a title
thanks to james allen jennifer chu carroll morena danieli wieland eckert giuseppe di fabbrizio don hindle julia hirschberg shri narayanan jay wilpon and steve whittaker for helpful discussion on this work
primacy of partial information the information needed to compute a complete unique interpretation for an utterance may not be available until subsequent utterances are produced
although people may agree in the case of simple phonological rewrite rules what the outcome of a deterministic rewrite operation should be it is not clear that this is the case for replacement expressions that involve arbitrary regular languages
we do not handle the generation of long distance pronouns which were rare in our texts
if all this lhs succeeds then b and c are to be permuted d is to be deleted an d f is to be added inserted between c and b with neither a nor e being further involved
there are many trains in the evening
in section NUM i estimate the amount of human sense tagged corpus and the manual annotation effort needed to build a broad coverage high accuracy wsd program
morphological variants in these sentences were stemmed to a canonical form
table NUM entries for french premier in NUM best lexicons
taken together the filters produce models which approach human performance
the ndp members also mentioned general motors in this context
so the lcsr for these two words is NUM NUM
there are many possible notions of what a cognate is
the pos and cognate filters reduce noise better together than separately
token based evaluation scores would be misleadingly inflated with very little variation
however each filter improves a large number of different entries
the pos filter only degrades precision for large training corpora
still there remains the question of whether a system is able to pick the most appropriate reading in a given context which brings us to the second dimension
that means that a subject area if chosen is used as disambiguator but if translating without a subject area the system has access to the complete lexicon
furthermore it is questionable if a running text of NUM words says much about lexicon size since most of this figure is usually taken up by frequent closed class words
wd wrong determiner nouns only the source word was correctly translated but comes with an incorrect determiner wristband die handgelenkband instead of das handgelenkband
as part of the linguistic evaluation we wanted to determine the lexical coverage of the mt systems since only some of the systems provide figures on lexicon size in the documentation
but the results of this study can not be used for comparing the sizes of lexicons since the number of error tokens is given rather than the number of error types
if source word and target word are identical we can not determine if the word in the target sentence comes from the lexicon or is simply inserted because it is unknown
we expected that words with high frequency should all be included in the lexicon whereas words with medium and low frequency should give us a comparative measure of lexicon size
translating from english to german the mt system has to get the gender of the german noun from the lexicon since it can not be derived from the english source
such obviously misplaced words were eliminated from the list which was refilled with subsequent items in order to contain exactly NUM words in each frequency class of each word
the expert also identified the set of relevant documents from the fed collection associated with each query
finally the final tag model is constructed by mixing t trees according to equation NUM
table NUM shows the frequency counts and normalized idf for the concepts in the quely cases involving jailhouse
information on test text size was unavailable for this system
keystroke savings are here presented for the various prediction systems
it would therefore be useful to expand the semantic classification system
name searching in the general case includes both name recognition and name matching
null recently two strictly quantitative comparative studies without subjects were also performed
a baseline based on the current version of profet has already been established
relevant examples are inflectional paradigm size and word order flexibility
differences in morphosyntactic typology should logically also influence keystroke savings
a name typically is one field of a record corresponding to the named entity
for subject g the only difference was decreased writing speed
a large percentage of the words belonged to all four categories
however practical systems require more accuracy because pos tagging is an inevitable pre processing step for all practical systems
the NUM queries shown in the appendix were run against the fed
they then try to analyze data by using the rules and extract exceptions that the rules can not handle
a graph can be plotted with lexical correspondence along the y axis and sentence number along the xaxis
notice the additional benefit we get from basing the suggestion on a parse the correct subject verb agreement can be inferred for use in the suggestions
this speeds up the task of translation considerably since their terminologists can decide on the proper translations before the translators actually start the translation process
lexical problems on the other hand are not very much affected by bad parses and can be spotted with a high degree of reliability
the recall figures for grammatik and amipro may be artificially high since we may not have been able to identify all the problems that these grammar checkers intend to address
this is caused not only by too narrow coverage of the parser but also by the ill formed input that a standard gram3we have conflated the notions of grammar errors and style tveaknesses
for exampie if a present participial clause is attached to the object of a verb there will also be the possibility that the participial clause actually should modify the subject instead
it is our claim that the types of checks easyenglish performs are vastly more relevant for ensuring high document quality than a majority of the checks in the above mentioned grammar checkers e.g.
for the study we used a variety of text types including technical documents a manager s speech and an online job advertisement written by a non native english speaker
the figures for the medium and low frequency classes require a closer look
table NUM inehtdes two ohunns t hal do n t refer t entering transitions
the inference rules provide a clear picture of the way in which datr works and should lead to a better understanding of the mathematical and computational properties of the language
contexts are denoted t y c the evaluation relation is now taken to be a mapping from elements of cent x esc to atom
i osition rather than the grammal ieal sut jet t thai shouhl be r mked higher
difference in he usages f he mr referrillg expressions regards cli nt est
the sentence noun plur root surf states that the plural is obtained by attaching the plural suffix to the root
the value of dog plur is specified indirectly by a sequence of descriptors dog root noun suit
more generally the global context is used to fill in the missing node t ath when a quoted path node is encountered
this means hat when a global descriptor is encountered any path extension present is treated globally rather than locally
the rules eaptme the essential details of the t roeess of evaluating datr expressions and for this reason silouhl prove of use to the language imt lementer
it demonstrates that dog sing evaluates to dog given the datro theory aim when the initial global context is taken to be dog sing
on the other hand information about the formation of singular and plural forms of dog must still be located at the dog node even though the processes involved are entirely regular
informally speaking it is shown that the multiplicative update algorithms have advantages in high dimensional problems i.e. when the number of features is large and when the target weight vector is sparse i.e. contain many weights that are close to NUM
the distinction between semantic and syntactic information is compiled into the grammar rules on the basis of a user declaration
thus what is involved in identifying a topic is a task of locating an instance of a title term
one might ask how we might locate an instance of topic without any information on the title or keywords
in case that the length of a text is smaller than that of the segment the whole text is used
information is based on the news articles in the test set we used in the experiments later
for each of the NUM word segments contained in the text we measured its similarity to the title
this language will be the source language sl
it is important to note that when an utterance was misunderstood the experimenter did not tell the subject what to do but merely described what happened
overall there was a difference between speaker and global perspective in NUM NUM of the declarative mode utterances and in NUM NUM of the directive mode utterances
while user control means the user s goals have priority it does not necessarily mean the user will initiate every transition from one subdialogue to the next
in this paper we have reviewed an integrated approach to dialogue processing that allows a system to support variable initiative behavior in its interactions with human users
while there is ample analysis of dialogue structure based on human human and simulated human computer dialogue there is very little information on the structure of actual human computer dialogue
overall while the computer was operating in directive mode the user attempted to correct only NUM of the misunderstandings for which the user received notification
computer misinterpretation of the user s utterances due to misrecognition of words can cause confusion between the user and computer and ultimately failure of the dialogue
for the most part the percentages are consistent with the model especially in the early phase transitions and in the transitions out of the test subdialogue
in contrast only NUM of the declarative mode dialogues had no unusual transitions again demonstrating how users felt free to skip steps without discussion
as an example figure NUM shows the output of the parser for the sentence la regzna scrtsse ma leltera a gzovanng the queen wrote a letter to john
many extra subject boundaries were found in the long article starting at sentence NUM
the italian version of wordnet is based on the assumption that a large part of the conceptual relations defined for english about NUM NUM isa relations and NUM NUM part of relations can be shared with italian
in both cases weights of inactive features maintain the same value
a mistake driven algorithm updates its hypothesis only when a mistake is made
the algorithms are used to learn a classifier fc for each category c
it evaluates the score of the document by computing the dot product
first there are some important technical differences between the algorithms used
the figures reported are the average results on the two test sets
il yael karov dept of appl
partly supported by a grant no
the confidence of a boundary is calculated from the depth of the local minimum
next set b is generated by taking all the words in the following fifteen sentences
in the time that has passed since muc NUM we have been exploring trainable information extraction technologie s in a number of different application areas NUM NUM
if the cn definitions had been operating at a finer level of granularity we might have been able to acquire usefu l extraction rules for noun phrase analysis
the appropriate combination of synsets for an argumental position has to be both enough general to preserve all the human readings and enough restricted for discriminating among different senses of both verb and noun
this tree was created by an inductive algorithm in response to a collection of representative np pairs extracted from available training data
we know from our ne evaluation that our string specialists sometimes labeled an organization as a person or location by mistake
crystal requires no such human review and creates cn dictionaries on the basis of machine learnin g techniques NUM
we began with our domain independent core tags lexicon which includes prepositions determiners and a number of common verbs
to understand how resolve handled our focus sentence we have to examine portions of the tree further fro m the root
wrap up is most effective when its training has given it the experience i t needs to deal with various kinds of incongruencies
as other trainable text processing technologies emerge and develop independent from muc NUM it may b e impossible to create an annotation system that is equally accommodating to all
it appears that the following decision points were important for this sentence tree NUM another portion of the coreference tre e
for the current experiment the grammar coverage is limited to very simple verbal sentences formed by a subject a main verb together with its internal arguments and possibly an adjunct phrase
a query yn asks the partner any question that takes a yes or no answer and does not count as a check or an align
note that it is possible for several transactions even of the same type to have the same starting point on the route
if the transferer needs computational linguistics volume NUM number NUM more evidence of success then alignment can be achieved in two ways
some participants ask for this kind of confirmation immediately after issuing an instruction probably to force more explicit responses to what they say
example NUM f no i ve got a i ve got a trout farm over to the right underneath indian country here
this includes questions that ask the partner to choose one alternative from a set as long as the set is not yes and no
in the move coding a set of initiating moves are differentiated all of which signal some kind of purpose in the dialogue
these cases were sufficiently rare in the corpora used to develop the coding scheme that it was impractical to include a category for them
the question of where a game ends is related to the embedding subcode since games end after other games that are embedded within them
finally the transaction coding divides the entire dialogue into subdialogues corresponding to major steps in the participants plan for completing the task
for instance it would not be necessary to distinguish between house windows and windows on a computer screen if open window were input to the request template considered above because the generated utterance would be the same in either case
assuming a letter can be huffman encoded in NUM bits and choosing from a menu of NUM items is NUM bits we have an entropy figure of NUM NUM bits per character close to brown et al s result
for example kitchen window occurs only once in the approximately one million word lob corpus and does not occur at all in the similarly sized portion of the wall street journal distributed as part of the penn treebank
NUM we expect to be able to achieve slightly better results by further expanding the tagset by using semantic tags of the sort discussed in ss3 to improve the prediction rates for nouns and perhaps verbs
this problem can potentially be alleviated in two ways by improving the design of the physical interface keyboard head stick head pointer eye tracker etc or by minimizing the input that is required for a given output
as a first step we intend to see whether these categories can be used to improve the prediction rate for free text entry by using the semantic classes to expand the tagset described in ss2 NUM
the choices are usually displayed as some sort of menu if the user selects one of the items that word is output but if no appropriate choice is present the user continues entering letters
the techniques described are all ones which can be used on the fly although for efficiency it might be desirable todo morphological analysis and n gram frequency at times when the system is not being actively used e.g.
the longest match string frequency method sf and lsf outperformed the greedy longest match method lm by about NUM NUM when the initial word list size was under 20k from d5 to d200
the integration with the computational lexicon ilex is under development it will make the access to other levels of lexical information such as morphological classes syntactic categories and sub categorization frames available
this significance score can then be taken into account when analyzing the text for subject boundaries
an explanation may be that the exact match metric is very sensitive to annotation errors
most human errors pertained to definite descriptions and bare nominals not to names and pronouns
in this case NUM trials are made and the average value is employed
as our further work there are still many possibilities for improvement which are encouraging
finally a change in administration of the muc evaluations is occurring that will bring fresh ideas
some experiments were done to support this assumption and their results are shown in the next section
an entire appendix to the scenario definition is devoted to heuristics for filling the on the job slot
especially pp shows that almost all same labels in the wsj are assigned in same groups
figure NUM the graphical representation of the parse structures of a big man slipped
to do this the grammar acquisition algorithm operates in five steps as follows
in the experhnents a peak with de NUM NUM is applied
we do not try to characterize them as an axiomatic class but attach to them a formal definition to determine explicitly the object of our study
in addition with proper names they can also occur in assertive sentences like kim yenggara i wa ssda pn kixn sir postp come past sir
remember that our goal is not to discuss the nature of this noun class but to complete the lexicon of korean nouns in nlp systems
this type of noun phrase is similar to the preceding one what we call incomplete nouns in is also used for social appellation
NUM building local grammars of pns let us summarize the formal definition of the five contexts where a proper name pn can occur
successful interpretation of three sentences from the walkthrough article is necessary for high performance on these events
we should perhaps consider a continuum of the noun system a thesaurus of nouns constituted of the most generic nouns to the most specific nouns which we call proper nouns
NUM difference between ft and fr what we call nouns of family relation fr can not appear with a proper name when they are used in the vocative case
however they are different from the preceding ones by the fact that they do not have syntactic autonomy and therefore they never can appear alone in any positions of a sentence
then one should either attach to them a vocative suffix such as d him or adjoin them to proper names gyosu nira kim minu gyosu
figure NUM frequent tokens cause false points of cor
simr s rms error on this bitext was NUM NUM characters
translators create a bitext each time they translate a text
the slope of the main diagonal is the bitext slope
bitexts are the raw material from which translation models are built
the filter is based on the maximum point ambiguity level parameter
it is significantly more accurate than other algorithms in the literature
therefore sentence mapping algorithms need not worry about crossing correspondences
especially unlike other classification methods this method takes the context sensitive information which most classification methods do not consider into account and make it reflect the properties of natural language more clearly
it should be noted that p iw must be normalized both for the left right and the right left results
this mapping should be done with the matching predicate in mind
sentences are often difficult to detect especially where punctuation is missing due to ocr errors
for the classes the binary tree which represent the word relation direction from the right to the left produce pw lw is calculated likewise
that is to say if we distribute the words to the wrong classes from global sense we will not be able to any longer move it back
consequently in the time span of less that NUM years the phonological and phonetic systems of the language have not had time to change to any significant extent
both languages have evolved from different origins and are the results of the historical influence of other languages from which words have been borrowed and assimilated sometimes only partially
affixes do not alter the pronunciation of the root compare for instance photo photograph and photography in english with photo photographe and photographie in french
in the right to left scan the right context of the output buffer would be usable instead of the left context if the scan is done from left to right
similarly o NUM and l syllabic NUM can also be considered equivalents
er at the end of words is pronounced el as in chanter danser but er in super joker fer or hier
computational linguistics volume NUM number NUM applies for z which is preceded by and an unvoiced consonant t
in proper names like lesage desprds bourgneuf montrouge lesventes it is important to recognize the morphemes le des bourg mont to correctly transcribe
table NUM shows how many entities we retrieve at this stage and of them how many pass the semantic filtering test
research related to ours falls into two main categories extraction of information from input text and construction of knowledge sources for generation
the purpose for and the scenario in which description extraction is done is quite different but the techniques are very similar
in our work the string that is extracted may be merged or regenerated as part of a larger textual summary
the grammar matches arbitrary noun phrases in each of these two cases to the extent that the pos part of speech tagger provides a correct tagging
at this stage the system generates functional descriptions fd but they are not being used in a summarization system yet
in the first one we have to pick one single description from the database which best fits the summary that we are generating
all these sources share a common characteristic in that they are all updated in real time and all contain information about current events
when running on NUM NUM words from the same data set alignment variable notation and building the initial tree took NUM minute NUM seconds the state merging took NUM minutes NUM seconds inducing decision trees took NUM seconds and pruning decision trees took NUM hours NUM minutes and NUM seconds
to assign the phrase label automatically we run all models in parallel
we represent phonological rules as finite state transducers that accept underlying forms as input and generate surface forms as output
although in fact the final transducers induced by our new method do tend to be onward
this operation is only allowed under certain conditions that guarantee that the transductions accepted by the machine are preserved
we will use decision trees to decide what actions and outputs a transducer should produce given certain phonological inputs
furthermore we have shown that some of the remaining errors in our augmented model are due to implicit biases in the traditional spe style rewrite system that are not similarly represented in the transducer formalism suggesting that while transducers may be formally equivalent to rewrite rules they may not have identical evaluation procedures
NUM our goal was to learn such a composed transducer directly from the original underlying and ultimate surface forms
a decision tree takes a set of properties that describe an object and outputs a decision about that object
for each possible value aw of the selected attribute a construct recursively a subtree s w calling the same algorithm on a set of quadruples for which a belongs to the same wordnet class as a w
similarly the noun facility in q6 is disambiguated whereas the descriptions in both qi and q5 can not be successfully disarnbiguated because only a very small set of quadruples was used in this example
this is particularly valuable for work on languages for which online knowledge resources are relatively scarce compared with english
we now describe how a dynzmic programming parser can compute an optimal bxackcting given a sentence pair and a stochastic btg
it suffices to show that the production a a call be removed without any loss to the generated language i.e. tha the remaining productions in t can still derive any string pair derivable by t removing a production can not increase the set of derivable string pairs
one position uses a linguistic evaluation criterion where accuracy is measured against some theoretic notion of constituent structure
table NUM comparison of error distributions for simr and char align in characters
cognates are words with a common etymology and a similar meaning in different languages
the development bitext used in the simulated annealing parameter optimization contained over NUM words
simr was evaluated on the easy and hard hansard bitexts
alas the points of correspondence postulated by simr are neither complete nor noisefree
figure NUM illustrates how sentence boundaries form a grid over the bitext space NUM
fourth it accepts non monotonic segments to account for inversions and word order differences
it is highly likely that at alignment that is missing from the test alignment
it consists of correspondence points that line up in rows or columns associated with frequent token types
the filter is based on another threshold parameter the maximum point ambiguity level maxpal
if si is the ith occurrence of lsd in s and sj is the matching rsd of si then the extent of subtree i of s is the sequence si sj
hypotheses will be checked with the data using a more refined statistically motivated notion of productivity
to see this suppose that there are two distinct subtrees t t r such that yield t yield t t or i j
methods have been developed to translate engfish queries into spanish see research papers crl has developed systems for recognizing proper names in english spanish japanese and chinese texts see multilingual named entity task
each meaning is followed by the synset as a list of variants with a sense number and on the next lines by the ili records to which it is linked if any
then we would be led back to the check of subsection NUM NUM may antosems share hyponyms
therefore smuggle is a troponym of a concept export or import or yet transport
here the star operator designates the transitive closures of the relations it is applied to
because of the transitivity of the generic hierarchy relation this link is redundantly stored in wordnet NUM NUM
the evaluations were performed on the test sets with respect to the final distribution generated for the coreference sets with the result being measured in terms of the average cross entropy between the model and the test data
to accomplish this the fusion system must know the reliability of the information received from each source in this way unreliable information from one source can be disregarded in favor of highly reliable information from another
the author thanks john bear joshua goodman and two anonymous reviewers for helpful comments and criticisms and the sri message handler project team for their contributions to the system in which this work is embedded
these two features model the cases in which template t contains information not contained in template s reflecting the fact that expressions referring to the same entity usually do not become more specific as the discourse proceeds
for instance if templates b and c in our example had been compatible with a and c remaining incompatible then the approximation above would assign positive probability mass to the coreference configuration a b c d because the zero probability of a coreferring with c would not come into play
determine constant markup transformations having identified aligned subtrees the labels of a pair of trees may be recorded and the results for the pair of corpora analyzed to determine consistent differences in markup
given that a goal of these experiments is to see how well the strategies would perform with a fairly crude easily computable and portable set of characteristics of con1degthe merging decision approach did not do any better than the greedy approach in terms of raw accuracy and in fact did somewhat worse in the third test
since the expression the fast copier now refers uniquely both to c42 and to copy action the referring expression is adequate
we make the following assumptions which appear reasonable for markup schemes with which we are familiar the content of each text consists of a sequence of terminal elements
given a coreference set of templates possibly coupled with a list of template pairs known a priori not to corefer the task is to assign a probability distribution over the possible coreference configurations for that set
in our example above mab assigns probability mass to two such am the set containing configurations NUM NUM and NUM and the set containing configurations NUM NUM NUM and NUM
this action takes as a parameter the plan that is being refashioned and a set of surface speech actions that the speaker wants to incorporate into the referring expression plan
in the second case the referring expression is underconstrained and so the evaluation would have failed on the constraint that specifies the termination of the addition of modifiers
NUM NUM a NUM okay and the next one is the person that looks like they re carrying something and it s sticking out to the left
in the case where the plan is invalid we need to partially evaluate the plan in order to determine which action contains a constraint that can not be satisfied
the subset constraint in the modifier action is then evaluated which does not eliminate either of the candidates since the system finds both of them weird
we do not reason about how the error affects the satisfiability of the goal of the plan nor use the error to revise the beliefs of the hearer
since this instance of replace plan is the only valid derivation corresponding to the surface speech actions observed the system takes it as the plan behind the user s utterance
the second set of priors deals with novel events words observed for the first time by assuming a scalable probability of observing a new word at each node
bmb system user error p NUM p56 NUM with this belief the system will have the context that it needs to understand the user s refashioning plan
for example in the dialogue presented in figure NUM how would it be possible to account for the differences in interpretation of monday and tuesday in NUM with monday and tuesday in NUM
we will also present a comparison of the performance of two versions of our discourse processor one based on strict tst and one with our extended version of tst demonstrating that our extension of tst yields an improvement in performance on spontaneous scheduling dialogues
furthermore since the focus space for ds NUM is popped off when the focus space for ds NUM is pushed on wednesday is nowhere on the focus stack when the other day from sentence NUM must be resolved
assuming that potential intentions form the basis for discourse segment purposes just as intentions do we present two alternative analyses for an example dialogue in figure NUM the one on the left is the one which would be obtained if attentional state were modeled as a stack
for example in figure NUM after sentence NUM not only is the second rightmost suggestion in focus along with its corresponding inference chain but both suggestions are in focus with the rightmost one being slightly more accessible than the previous one
we are encouraged by the promising results presented in figure NUM indicating both that it is possible to successfully process a good measure of spontaneous dialogues in a restricted domain with current technology NUM and that our extension of tst yields an improvement in performance
we have chosen to pattern our discourse processor after lambert s recent work because of its relatively broad coverage in comparison with other computational discourse models and because of the way it represents relationships between sentences making it possible to recognize actions expressed over multiple sentences
clearly this pcp does not have a solution
because it is possible to maintain more than one top element it is possible to separate multiple threads in discourse by allowing the stack to branch out keeping one branch for each thread with the one most recently referred to more strongly in focus than the others
a practical variation can be conceived as follows
figure NUM instance of a pcp problem
NUM finally the fsa consists of a single state q which is both the start state and the final state and a single transition q z q
this definition will be extended shortly
the rather high tagger expert agreement indicated that the novice taggers found the annotation task feasible
for instance in our example in section NUM template a is properly subsumed by template b and a b and c are all properly subsumed by d since in each case the latter template is more general than the former
the solution is identical to the one that results when the probabilities of all the relevant pairwise relations indicating either coreference or not are multiplied normalized by the amount of probability mass assigned to coreference configurations that are impossible because coreference is transitive
for instance the auxiliary dizut is related to the subject of the first person singular the object complement in the singular and the indirect complement of the second person singular
furthermore the text generation schemes are rather regular
this is the case for the pairwise probabilities in our example which can be seen most easily by considering only templates a c and d the probability of a and d coreferring is NUM NUM and of c and d coreferring is NUM NUM
both these options are illustrated in the above screen dump
for example in the example for anaphoric relation NUM the second utterance as well as the first is a possible antecedent of the third
as others before we have selected the 135topics for our experiments
we have implemented a preliminary version of spud and realized the examples discussed in section NUM our future work includes refining this implementation and enriching its linguistic knowledge
however since many other parts of the library are also areas the current description does not rule out all possible distractors and spud further elaborates the description
hence spud offers a natural framework for dealing with the interactions between syntax semantics and pragmatics which characterize the sentence planning problem and ensuring contextually appropriate output
the probabilistic chunker partitions p into c i.e. a sequence of chunks
however the pei formance evaluation of the chunker is a sticky work
third by recognizing collocations only when transducing underlying semantic representations researchers limit the extent to which knowledge of collocations can be exploited in generating flu null ent text
instead of using the above definition a modified version is shown as follows
table NUM lob tags extracted from lob corpus for each word in table NUM
for a probabilistic chunker the generalized contingency table is defined as follows
by our investigation we found that words are good clues to relate these two tagging sets
the experimental results show the probabilistic chunker has more than NUM correct rate in outside test
besides some tags can not be located at the end of the chunks
such an algorithm facilitates the development of a large scale tagged corpus from different sources
this paper shows how constraints can be propagated in a memoizing parser such as a chart parser in much the same way that variable bindings are providing a general treatment of constraint coroutining in memoization
a completed i.e. atomic clause p t with an instantiated argument t abbreviates the non atomic clause p x x t where the equality constraint makes the instantiation specific
definition NUM a control rule is a function from clauses g b to one of program solution or table c for some goal c c b a selection rule is a function from clauses g b where b contains at least one relational atom to relational atoms a where a appears in b
otherwise if the clause contains any non delay literals then the control rule 3in order to handle the more general form of abstraction discussed in footnote NUM which may be useful with typed feature structure constraints replace b with a b in this step where a b is the result of applying the abstraction operation to b
completeness follows from the fact that lemma table proofs can be unfolded into standard sld search trees this unfolding is wellfounded because the first step of every table initiated subcomputation is required to be a program resolution so completeness follows from hshfeld and smolka s completeness theorem for sld resolution in clp
in order to simplify the presentation of the proof procedure interpreter below we write clauses adds adjuncts to verbs d is a lexical division rule which enables a control or raising verb to combine with arguments of higher arity and d is a unary modal operator which diacritically marks infinitival verbs
both of these are classified as delay literals so item NUM is tagged solution and both are inherited when item NUM resolves with the table tagged items NUM and NUM producing items NUM corresponding to a right application analysis with lijkt te as functor and item NUM corresponding to a left application analysis with ont
null definition NUM an item is a pair t c where c is a clause and t is a tag i.e. one of program solution or table b for some goal b a lemma table for a goal g is a pair g la where la is a finite list of items
because program steps do not require memoization and given the constraints on the control rule just mentioned the list lg associated with a lemma table g lg will only contain items of the form t g b where t is either solution or table c for some goal c c b
we apply a new statistical approach to representing the context of a word through lexical co occurrence networks
table NUM shows the results for all seven sets of synonyms under different versions of the program
we replaced each occurrence by a gap that the program then had to fill
this justifies our assumption that we need size refers to the size of the sample collection
all differences from baseline are significant at the NUM level according to pearson s x NUM test unless indicated
we are planning to extend the model to account for more structure in the narrow window of context
this work is financially supported by the natural sciences and engineering council of canada
perhaps our results actually show the level of typical usage in the newspaper
there are two reasons why the approach does n t do as well as an automatic approach ought to
second lexical ambiguity is a major problem affecting both evaluation and the construction of the co occurrence network
multiword categories imply the use of multiwordterms
this mapping of conceptual structure to linguistic structure is carried out first in the lexical chooser
similarly any goals of the speaker must be provided as input to the lexical chooser
the resulting lexicalized thematic tree is then unified with surge which produces the final sentence
then the lexical chooser selects the actual words that are used to realize each role
note NUM thus indicating that the main verb maps to the semantic relation class assignt
at this point the top level unification of the input with the lexical chooser is completed
NUM association for computational linguistics computational linguistics volume NUM number NUM domains
it consists of the input semr attribute enriched by a syntactic structure of category clause cf
the lexical entry for an argument bearing word usually a verb includes a lex cset declaration
selecting the features the grammar needs in order to select a determiner if any
we measure the performance of the different models in terms of recall and precision
the input of our parser is morphologically analyzed and disambiguated text enriched with alternative syntactic tags e.g.
how compatible it is with the current weights for the labels of the other variables
the compatibility value is the mutual information computed from the probabilities estimated from a training corpus
for instance the following constraints have been statistically extracted from bi trigram occurrences in the training corpus
so we will make the compatibility values six times stronger that is h60
modifying those values means changing the relative weights of the linguistic and statistical parts of the model
objectives the project objectives from the memorandum of agreement are to prototype and demonstrate new technology which might be incorporated into production systems at ndic
expand the prototype to include conversion of hard copy documents or the simulation of them including degraded ocr data
develop or acquire a minimal user interface and other interfaces which are necessary to demonstrate integrated use of detection and extraction tools and any other analysis tools to be demonstrated
explore the implications of the prototype for an end to end architecture i.e. expanding the tipster architecture to include end to end text processing e.g. ocr machine translation and database loading
common viewer copy and paste from full text information into word processing documents while retaining the source information and extract particular fields and relationships from project files or any other tipster collection
project to build a prototype which retrieves and extracts against free unstructured text and to evaluate the prototype s usefulness for the national drug intelligence center ndic
the software environment includes the digital osf operating environment on the server windows nt on the workstations exceed an x windows emulator for windows nt oracle and ms word
a word is syntactically ambiguous if it has more than one syntactic tags e.g.
the analyst begins by preparing an extractionneed
integers are used as the annotation ids
ehl v m p v f NUM hubert and labbe s model contains one free parameter the coefficient of vocabulary partition p an estimate of the proportion of specialized words in the vocabulary
a number of classes will have attributes
the body would be annotated with a body annotation
the operators are listed below in alphabetic order
typically used with vector space or probabilistic systems
the relevance feedback is provided through the relevant docs argument
type declarations are organized into packages
adds an annotation to a document
since we are left with variation that is probably to be attributed to the particularities of the individual randomization orders we may conclude that at the global level of the text as an unordered aggregate of sentences the randomness assumption remains reasonable
the expected number of different types e v m for m n conditional on the frequency spectrum lcb v n f rcb f NUM NUM NUM can be estimated by
the known similarity corpora evaluation methodology presented in section NUM will be applicable to the issue of assessing how well cross entropy captures pre theoretical notions of corpus similarity and homogeneity
it is clear that the british national corpus is less homogeneous than a corpus of software manuals but it is not clear how to measure the difference
to avoid these problems i will now sketch a somewhat more fine grained approach to understanding why v n and its expectation diverge adopting hubert and labbe s central insight that lexical specialization can be modeled in terms of local concentration
when a text or corpus is represented as a frequency list much information is lost but the tradeoff is an object that is susceptible to statistical processing
for current purposes we can happily pool the data referring only to individual words when we seek further insight into why we get the results we do
NUM the rule that any string of alphanumerics surrounded by whitespace or punctuation is a word may have its shortcomings but it makes word counting very reliable
he parses corpora of various text genres identifies the subtrees of depth NUM in each corpus and counts the number of occurrences of each subtree
this model relies on the surface lexical features of words such as word capitalization c class of the character consonant c vowel v punctuation p or digit d in the four last positions of the word and the length NUM NUM NUM NUM NUM NUM of the word
we need not consider factors that will not affect the choice of paths in c above
in the rest of the paper we first introduce the feature collocation lattice as a graphical way to represent complex models then we introduce a feature selection process using the collocation lattice and finally we present some details of application of our method to the tasks of sentence boundary disarnbiguation part of speech tagging and automatic document abstracting via sentence extraction
this account of parallelism is semantic in the sense that it depends on the content of the discourse rather than directly on its form
in fact the nodes in the lattice NUM can have dual interpretation on one hand they can act as mapped configurations from the extended configuration space w and on the other hand they can act as features from the constraint space x c is a transitive antisymmetric relation over NUM x NUM a partial ordering
all other nodes on figure NUM are hidden nodes that were observed only as parts of the higher level nodes NUM hidden nodes dr NUM mr NUM mr g and dr NUM directly support only one node each and thus do not provide any gener j ations
case a stands for adding the atomic feature a to the empty lattice case b stands for adding the atomic feature b to the lattice with the atomic feature a and case c stands for adding the atomic feature c to the lattice with the atomic features a and b and their collocations
old style derivational analyses have all but vanished from the linguistics conferences
where fxk wd ensures that only those configurations wd which include the constrained feature xk contribute to the mass probability and ex fx wd is simply the number of all the features from the model s feature space a which are active for the i th contributing configuration wi
from this we can infer y x but not e2 el and the coreferentiality can not be washed out in substitution
simultaneously the pair f n is entered as a new entry of the symbol table
squibs and discussions a delayed syntactic encoding based lfg parsing strategy for an indian language bangla probal sengupta indian statistical institute
locate ing a construct like f n where both f and n are already defined placeholder and nameholder respectively returns a pointer to the value part of the pair in the f structure pointed at by f whose name is pointed at by n
final solution for bangla sentence a pni a ma ke ekt a bai dilen
if fn is the f structure of an np of a sentence s with f structure fs and the case marker on the head noun of the np is c the semantics of schemata NUM annotating the np is fs ctog c fn where denotes unification
a symbol table entry f n satisfies an m structure schemata g qi vi projected by the verb v of a sentence s iff is the f structure of s and the structure f n where n is treated as an atom contains the pair qi vi
the trick is to delay the evaluation of encoding schema of constituent nps till an appropriate moment while maintaining a persistent data structure such as a symbol table to keep track of the points of forward reference at which actual function names get instantiated and their local environments the internal f structure of the constituent nps
since ctog c is a disjunction in a parser implementation it effectively multiplies out to ictog c l nondeterministic choices for the functional role played by the fn in fs
much of the explosion in this case is introduced at the start and can be avoided
in NUM we intersect it with repns which constrains only the output tiers
another assignment method is to determine the most frequently occurring sense in the training examples and to assign this sense to all test examples
using corpora to develop limited domain speech translation systems proc translating and the computer NUM also sri cambridge
for concreteness let us suppose that c encodes f x a two state constraint
it usually has far fewer states than NUM NUM would if the intersection were carried out
a failure analysis indicated that this was due to the need to assign partial credit to individual words of a phrase
we also found that most of the instances of open closed compounds e.g. database data base were related
we analyzed the distribution of the connections and this is given in table NUM n NUM
to the best of our knowledge the differences have never been examined for distinguishing meanings within the context of ir
of the NUM unrelated word pairs only NUM were grouped by the porter stemmer because of the consonant vowel constraints
we also assumed that a difference in part of speech would correspond to a difference in meaning
when we ran the experiments we found that performance decreased compared with the baseline
the paper will conclude with a summary of what has been accomplished and what work remains for the future
k j r s NUM where ik e terminal layer i ij e nonterminal layer i u last layer j v e bin layer j and layer i layer v
where x e bout layer i y e bin layer i f first layer i e last layer i layer i layer and layer x layer y
this implies that the weight set i is not such a bad estimate for editorials
nonterminal and its corresponding pop transitions are defined to be NUM when a NUM and b n
the goal of our experiments is to see how much saving the new estimation algorithm achieves in computational cost
the complexity is too much for current workstations when either n or g becomes bigger than a few 10s
stack operations are associated with transitions transitions are classified into three types according to the stack operation
useless advancements into high layers that do not lead to the successful completion of a given sentence can be avoided by making sure that ix y generates wa b and the category of current layer c is defined which can be checked by consulting the chart items for state x
louella uses a non deterministic lexico semantic finite state pattern matcher
where u e bout layer i j c bin layer i v first layer i layer u layer j layer v layer i and uv is a nonterminal transition
definition NUM the outside probability denoted by po i j s t is the probability that partial sequences wl s l and wt l n are generated provided that the partial sequence ws t is generated by i j given a model
if america can keep keep up
furthermore pebls with NUM fold cross validation to select the best k yields results slightly better than the naive bayes algorithm
this search uses several tactics to find the correct referent
if the distance between two examples is small then the two examples are similar
future work will explore the addition of these other features to further improve disambiguation accuracy
the average error rate of the m runs is a good estimate of the error rate of the induced classifier
the following are the verbs that were tagged
louella s ne system uses a variety of matching methods
importantly for NUM of these NUM words the best k found by pebls are at least NUM and above
the first three stages are used in the ne task
NUM plausibility scoring and sorting a for each surviving pair y a of anaphor and antecedent candidate deterinine the munerical plausibility score v y xj which ranks xj relatively to y based on case role inertia recency cataphor penalty and subject prefe rence
the construction of this representation involves disambiguation decisions relative clause attachment prepositional phrase attachment and uncertainty of syntactic flmction which due to their structure determining effects may interfere with the antecedent options of anaphor resolution cf
the original statement of binding theory forms part of gb theory in which a broader set NUM in tera ting l rin iph s is f rmulated
the kontext anaphor resolution algorithm as shown in figure NUM consists of three phases constraint application preference criteria application and plausibility sorting and antecedent selection including reverification of constraints which may be involved in decision interdependencies
statistical sense disambiguation with relatively small corpora using dictionary definitions
NUM NUM using the conceptual co occurrence data for sense disambiguation
the entry of the noun sentence and its corresponding conceptual expansion NUM manually constructed semantic frames could be more useful computationally but building semantic frames for a huge lexicon is an extremely expensive exercise
statistical disambiguation systems which rely on semantic coherence will generally perform better on technical writing which encyclopedia entry can be regarded as one kind of than on most other kinds of text
NUM analysis of the test samples which our system fails to correctly disambiguate also shows that increasing the window size will benefit the disambiguation process only in a very small proportion of these samples
we have shown that using definition based conceptual co occurrence data collected from a relatively small corpus our sense disambiguation system has achieved accuracy comparable to human performance given the same amount of contextual information
to strengthen the signals words which have the same semantic root are combined as one element in the list e.g. habit and habitual are combined as lcb habit habitual rcb
figure NUM the algorithm for collecting conceptual co occurrence data
the use of up as a particle takes away its literal physical meaning and attaches it semantically to keep making an idiom definition necessary
the auxiliary and intermediateuses of have together represent well over half of the occurrences so breaking these out as separate categories enables the preprocessor to assist the tagger greatly
moreover tuples that contain prepositions are the most informative
NUM would the particle be mistaken for a preposition beginning a prepositional phrase and thereby changing the meaning of the sentence if viewed as separate from the main verb
papers backe group inc agreed to acquire atlantic publications inc which has have verb NUM NUM community papers and annual sales of NUM million
when there is an appropriate wordnet entry for the idiom as a whole we store that entry with the first word of the idiom but the entry could be stored with both
we show clearly how the semantic structure is declaratively related to linguistically motivated syntactic representation
in fig NUM getsuyoubi amounts to this part
in section NUM tile formalism of lud is introduced
every discourse relation is situated above any other scope taking element
thus lud allows for only one discourse relation in each sentence
next question which kind of relative scope holds among discourse relations
the labels NUM and NUM are presuppositions of NUM and NUM
there are two main exceptions to this characterization of lud
a system for the automatic production of controlled index terms is presented using linguistically motivated techniques
the extraction of terms and their variants from corpora is performed by a unification based parser
in japanese sentences discourse relations occur in various grammatical positions
lexical entries which realize discourse relations occur in various grammatical positions
recall is weaker than precision because only NUM NUM of the possible variants are retrieved
errors generally correspond to a semantic discrepancy between a word and its morphologically derived form
null our empirical selection of valid metarules is guided by linguistic considerations and corpus observations
examples of metarules of type NUM and type NUM variations are given in table NUM
NUM repeat i and NUM until c classes remain
an event is a set of feature value pairs or question answer pairs
d he went to the house by the sea
table NUM shows the sizes of texts used for the experiment
the obtained probability distributions are then smoothed using the held out data
b he went to the apartment by bus
the mutual information clustering method employs a bottum up merging procedure
figure NUM comparison of wordbits with lingquest wordbits questions are used in this experiment
introduction of linguistic questions is shown to significantly reduce the error rates for the wsj corpus
in addition to annotating from scratch rules can be learned to improve the performance of a mature annotation system by using the mature system as the initial state annotator
a decision tree is trained on a set of preclassified entities and outputs a set of questions that can be asked about an entity to determine its proper classification
as an example of how rules can correct errors generated by prior rules note that applying the first transformation will result in the mistagging of the word actress
if the most likely tag for unknown words can be assigned with high accuracy then the contextual rules can be used to improve accuracy as described above
NUM in the penn treebank n t is treated as a separate token so do n t becomes do vbp n t rb
there are certain circumstances where one is willing to relax the one tag per word requirement in order to increase the probability that the correct tag will be assigned to each word
first we will describe a nonlexicalized version of the tagger where transformation templates do not make reference to specific words
the transformation templates we add are change tag a to tag b when the preceding following word is w
if x is false the transformations from l2 will be applied because s is the initial state label for l2
we have also recently begun exploring the use of this technique for letter to sound generation and for building pronunciation networks for speech recognition
we suspect that the single semantic grammar approach which we have been following for the scheduling domain will not be feasible for the travel domain
the best alignment is the one with the lowest total penalty
we plan to achieve this task by running a comprehensive grammar over a corpus in which each sentence is tagged with its corresponding sub domain and correct parse
this paper presents an algorithm for finding probably correct alignments on the basis of phonetic similarity
most of the time semantic knowledge is defined manually for the target application but several techniques have been developed for generating semantic knowledge automatically
therefore the number of possible segmentation patterns is extremely large
to classify the character n grams we need to use some discriminative features for the classifier
two different system configurations had been developed to construct the dictionary
the following figure shows the block diagram of such a system
in seed size does provide significant improvement on precision and recall
the definition of mutual information for a bigram is defined as
in other words the score vector for the classifier is
figure NUM block diagram for a viterbi pos tag training system
this process is repeated until a stopping criterion is met
the basic model is based on a viterbi reestimation technique
the system is fully implemented and has been used as a workbench to develop and test large hpsg grammars
conclusion and future research we have presented an architecture that integrates relational and implicational constraints over typed feature logic
finally clause NUM defines lexical entries for the proper names arthur and tintagel
clause NUM of wfs combines a verbal projection with its subject and clause NUM with its complements
delay wfs argl list delay phrase subcat list delay deterministic sign
the negation of the complex antecedent is added to the consequent which can result in highly disjunctive specifications
the reason is that our compiler transforms an implication with complex antecedents to an implication with a type antecedent
then we apply recursive formula of ncs shown in equations NUM and NUM to identifying the topic set for test texts
the average frequencies of assumed topics and computed topics are close and both of them are larger than average frequency of candidates
the less frequent word has a higher idf value so that the strength snv and snn of one occurrence may be larger
here nc means cited words and nnu nnus denotes abbreviated plural unit of measurement unmarked for number
rows NUM NUM and NUM list the frequencies of candidates assumed topics and computed topic
therefore the cardinal number of problems c problems equals to NUM and c held is NUM
denotes correct number denotes error number and denotes undecidable number in topic identification
an editor who was not the author made what he could of the chaos by placing the fragments or sheets or pages in order
use the utterance features to predict which utterances will be useful in the chosen setting and to help individual children to select utterances from the corpus to include in pictalk for use in the setting
we measure the accuracy of the tagger by comparing text tagged by the trained tagger to the gold standard manually annotated corpus
so if in the optimized feature lattice there is just one feature a then when we add the atomic feature b we also have to add the collocation a b if it exists in the empirical lattice
initially each word in the training set is tagged with all tags allowed for that word as indicated in the dictionary
NUM example of complex linguistic phenomena processing the sentence to process is should v i y correct fadj v the d t apcr fnoun v and c intra inter address fnoun v hiln y tit process of this interrogative sentence will begin by the
in tile processing of a very large corpus the problem is to find an apt roach allowing tile best interaction between different knowledge levels morphological syntactic semantic in order to reduce the generation of tile ambiguities that occur within any general system of sequential analysis
at present the society in the talisman application is represented by the following linguistic agents pret for preprocessing morph for morphological analysis segm for splitting into clauses maegaard spang hanssen NUM synt for syntactic analysis transf for transformations of utterances interrogatives imperatives etc in declarative clauses coord for coordinations nega for negations and ellip for ellipses
when one works with a general language and not a sublanguage there are different cases of ambiguities at difterent classical levels and more particularly when one works on coml lex language t henomena analysis coordination ellipsis negation it is ditfic ult
if he receiver n a nswer i will send an answer assert otherwise an answer noopinion i e if the agent can not answer or does not understand the question
when the computer operated in declarative mode yielding the initiative to human users who could then take advantage of their acquired expertise null the dialogues were completed faster NUM NUM minutes versus NUM NUM minutes
the individual main effects showed very strong statistical significance under both forms of analysis while the interaction effect of mode and subdialogue phase also appears to be statistically significant but not quite as strongly as the main effects individually
by direct testing of a computer system that implements our proposed model of variable initiative dialogue we could more rigorously control the system performance and more easily run repeated tests with subjects and allow them to gain task expertise
the two dialogues of figure NUM obtained from actual usage of the implemented system illustrate differences between the two modes in computational linguistics volume NUM number NUM which the system was experimentally evaluated directive and declarative
not counting the introduction which had to be initiated by the computer only NUM of all subdialogues in directive mode were initiated by the user while NUM of the subdialogues in declarative mode were user initiated
the goal is to show that even when p is low given enough data i.e. high k we can achieve high performance for the grouping
the limitations of real time continuous speech recognition at the time of the experiments had an impact on the nature of the spoken human computer interaction that was observed in comparison to what might be expected in a spoken human human interaction
consequently we would expect the following differences between modes for users who are able to take the initiative introduction subdialogue the number of utterances will change little since problem introduction seems independent of initiative
these results are somewhat more optimistic than those obtained with real data section NUM a difference which is probably due to the uniform distributional assumptions in the simulation
by restricting the labeling decisions to words with high values of this measure we can also increase the precision of our system at the cost of sacrificing some coverage
we observe figure NUM that even for relatively low t9 our ability to correctly classify the nodes approaches very high levels with a modest number of links
a log linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations achieving NUM accuracy in this task when each conjunction is considered independently
we show in table NUM the results for several representative categories and summarize all results below null our conjunction hypothesis is validated overall and for almost all individual cases
this value would be paired with the value NUM NUM NUM also for problem NUM in the assessment phase but for subjects who operated in directive mode in session NUM and declarative mode in session NUM
it is hoped that these results will encourage other researchers to construct experimental nl dialogue systems test these systems and then analyze and report the results so that a more comprehensive view of human computer dialogue structure can be obtained
graphics and text have to be well integrated in order to achieve their full potential
if its probability is close to the winner closeness is defined by a threshold on the quotient the assignment is regarded as unreliable and the annotator is asked to confirm the assignment
accordingly we allow either the third or the fourth boolean condition to be satisfied
document creation and document management system
in proverb our local focus is the last derived step while focal centers are semantic objects mentioned in the local focus
we choose a reference distribution to fit our model to so we can associate every feature from the model s constraint space with a corresponding reference probability NUM xo is xn
semantic relations which are induced by lexical relations when asking for possible checks not yet performed for wordnet s term term relations lexical relations we became aware of their interference with the concept concept relations semantic relations
erl behauptete cl proi sic gesehen zu haben
non formal attachments concern thematic complements and are licensed by subcategorization and theta properties
for each of these argument tables it is checked whether its arguments obey the ordering constraints
let us illustrate how the analysis proceeds on the basis of the sentence in NUM
this work has been supported in part by a grant from the fnrs grant no NUM NUM NUM
scrambhng is a process that modifies the order of clause internal arguments and adjuncts under some constraints cf
when the parser reads versucht it interprets the dp unambiguously as the subject
she seemed him seen to have she seemed to have seen him
german displays two types of infinitives infinitives introduced by the conjunction zn and infinitives without zu
for the pronoun two analyses are taken into account in parallel
NUM v ller41 fngure NUM an interesting vmlation oftransitiwty however if n ary antonymy is admitted the law of transitivity is at stake
as ambassador to china he handled many tricky negotiations so he is well prepared for this job
the actual situation is more complicated even if we ignore for the moment quantifiers and other syntactic complexities cf
they differ not in content or what is said but in expression or how it is said
this difference may be seen to arise from different degrees of continuity in what the discourse is about
the backward looking center of utterance un l connects with one of the forward looking centers of utterance un
NUM the following variations of a discourse sequence illustrate this problem and provide additional evidence for our conjecture
NUM for the sake of this argument assume that children like bigger things more than smaller things
interactive spoken dialogue systems are based on many component technologies speech recognition text tospeech natural language understanding natural language generation and database query languages
it presents a framework and initial theory of centering intended to model the local component of attentional state
d tommy likes it better than the bear too but only because the silly thing is bigger
so the question mark the exclamation mark and the period have almost no semantic entropy
figure NUM four of NUM noun concepts fulfilhng nezther weak nor strong commutatwity of antosemy and hypernymy the four concepts are honorableness dtshonorableness and fidehty NUM mfidehty
like passages NUM and NUM passages NUM and NUM express the same propositional content yet they are not equally coherent
to articulate some of these constraints they define several fundamental centering concepts and propose rules based on them that should be followed by a speaker in producing coherent discourse
an entirely knowledge based algorithm would not reproduce an addressee s immediate tendency to interpret a pronoun as cospecifying the backward center even when this results in an implausible interpretation
sentence 3e causes the hearer to be misled whereas common sense considerations indicate that the intended referent for he is tony hearers tend to initially assign terry as its referent
to be clear this is not an issue regarding the efficiency nor the cognitive reality of bfp s particular algorithm in fact neither bfp nor wic make any claims to these effects
the third sentence in this passage is quite odd presumably because the more central element john is not referred to with a pronoun whereas the less central element mike is
this strategy NUM a case in which rule NUM does make a prediction is given in example i assigning sam as the referent of he causes a violation whereas assigning john does not
b bob dole began by bashing bill clinton
he criticized him on his opposition to tobacco
arguably mark johnson memoization in top down parsing a table is a headed association list NUM which is extended as needed by table ref NUM
during testing a test example is compared against all the training examples
for example in translating japanese text into english a single kanji chinese character standing for england can be also a first name of a japanese personal name which should be translated to hide and not england
the user has the choice of selecting the sources e g washington post nikkei newspaper web pages languages e.g. english japanese or both and specific date ranges of documents to constrain queries
cony wed with t american i aident clinton at whitelumse conferred c0ncermng middle eutetn peace negotiation md the torist meamre which are aagnant with strong sroup neva item adminirr atio n start of h reel
for our current application the system indexes names of people entities and locations and scientific and technical s zt terms in both english and japanese texts and allows the user to query and browse the database in english
thh entirely that dete minatio n wu shown is lint it d not e ape tsome di conthm ance and tagnadon that attendant up0a hraeli nistration allefntlion it did did
as the indices are names and terms which may consist of multiple words e g bill clinton personal computer the query terms are delimited in separate boxes in the form making sure no ambiguity occurs in both translation and retrieval
as transliterations of the same english names may differ multiple katakana translations may be generated for single english names3 the remaining terms are currently translated using the english japanese translation lexicons and we are expanding the lexicons by utilizing on line resources and corpora and a translation aiding tool
if the complete llnk has wi wj it is rightward and if the complete link has wi wj then it is leftward
first goals of the form identify x as cat instruct the algorithm to construct a description of entity x using the syntactic category cat
according to common tagging principles this would be a disadvantage b there would be fewer obserw tions of each of the alternative tags as the competing unambiguous tags both would lose some of their instances to their common underspecified alternative
in order to analyze the above issues we first obtained our baseline training and testing data
then the second step identifies which of the associated trees are applicable by testing their pragmatic conditions against the current representation of discourse
for example consider the lexical item fast it constrains the typical rate of some action performed by or with the entity it describes
we must specify not only the salience of different states for the same copier but also the salience of corresponding states for different copiers
these low level t emptates were named telnplate elements
walter thompson last septenfl er as vice chairman chief strategy officer worhlwide
by error type is here meant a classification of tag pairs with an erroneous tag followed by the correct tag e.g. an error can be of the type preposition suggested where it should have been an adverb
one can observe an increasing convergence of methods tbr information extraction
the top scoring system had NUM recall NUM precision
in l e emlmr f NUM
wouht yiehl an organization telnplate elenmnt with live of these six slots filled
figure NUM shows a sample sentence with named entity annotations
post vice chairman chief strategy officer world wide
however sometimes the glosses are faulty and the link structure is correct and sometimes the contrary is true thus they could hold each other in check as long as they are independent of each other
this sensitivity to close context probably explains why the high number of tags does not influence performance when it comes to picking an alternative but it does not explain why training is so little affected by the high number of different observed situations
ex NUM b i painted about eight different colors you know
since syntactic annotation of corpora is timeconsuming a partially automated annotation tool has been developed in order to increase efficiency
terms are represented in terminologyframework as unique objects having the synset element word phrase as their possibly homographic name and a system generated and maintained homograph counter number which is stored separated from the name string as another term attribute
if the difference exceeds the maximum angle deviation threshold the chain is rejected
the properties of tpcs listed above provide two ways to constrain the search
as illustrated in figure NUM even short sequences of tpcs form characteristic patterns
if this distance exceeds the maximum point dispersal threshold the chain is rejected
the smooth injective map recognizer simr is a new bitext mapping algorithm
simr employs a simple heuristic to select regions of the bitext space to search
second each bitext defines a rectangular bitext space such as figure NUM
yet even the best of these methods can err by several typeset pages
simr s localized search strategy provides the perfect vehicle for a localized noise filter
customizability is attained by allowing the user to specify in a user profile which checks should be applied as well as which user dictionaries should be used
the first vocabulary check looks for restricted words i.e. words that the writer either should never use or that the writer should only use as certain parts of speech
a parse tree in g is an ordered tree representing a derivation in g and encoding at each node the production p used to start the corresponding subderivation and the multiset of productions f used in that subderivation
we have addressed this issue on four fronts high precision generality of the problem types easyenglish is able to identify customizability and user friendly interfaces
when a passive construction is encountered an active transformation provides the desired suggested rephrasing provided the logical subject is available
user dictionaries may be built with the help of the separate terms module eterms which is run independently of easyenglish
the precision recall figures were easyenglish NUM NUM NUM NUM grammatik NUM NUM NUM NUM amipro NUM NUM NUM NUM
error statistics are co stantly updated as the user corrects mistakes so that once a mistake is corrected the user will not be bothered with it again
however we claim that a document that has been easyenglished is also easier to understand for native speakers as well as non native speakers of english
most grammar checkers seem to have a problem with precision NUM and this evidently stems from the inability of the system to make sense of the input
the dysfluency markings are needed since the pivot point is restricted from being inside of a restart
it turns out that in a linguisticallysound machine translation system the surface structure representations specify all the lexical morphological and syntactic information that a speech synthesis system needs
we conducted experiments with three sources of evidence for making these distinctions morphology part of speech and phrases
as opposed to hindle s lists of similar words which are centered on pivot words whose neighbors are all on the same level in syclade graphs a word is represented by its role in a whole syntactic and conceptual network
where f nipn2 is the fi equency of noun n1 occurring with n2 in a noun preposition pattern f n1 is the frequency of ni as head of any n1pn sequence and f n2 the frequency of n2 in modifier argument position of auy n pn2 sequence and k is the count of nxpn v elementary trees in the corpus
in addition the results are clear and more easily interpretable than those given by a statistical method because the reader does not have to supply the explanation as to why and how the words are similar
we showed that for a state of the art application public transport information system grammatical analysis can be applied efficiently and effectively
as a result ignoring these few cases does not seem to result in a degradation of practical system performance
this would simplify the task of maintaining the data in the repository
null an important restriction imposed by the grammar parser interface is that rules must specify the category of their mothers and daughters
the average processing time for indexing and instantiation of a sentence level template determined through parsing of an input mrs is approximately one second
however our preliminary investigation indicates that among the various learning parameters of pebls the number k of nearest neighbors used has a considerable impact on the accuracy of the induced exemplar based classifier
NUM process result verbs are those that express a complex situation consisting of a process which culminates in a new state
worm plots of debentures and administration are shown in figures NUM and NUM respectively
it is easy to show that when g k is a positive instance of the source problem then the corresponding instance of ts is satisfied by at least one transformation
NUM NUM tile test data as well as tile tsnlp technology were validated
phenomenon id NUM t02 author issco elate jan NUM
though not reported below we also confirmed that the results did not vary significantly for different randomly drawn test sets of the same size
two tools t raditiona NUM ly
we can then compute sets r p for all internal nodes p of tr using an amount of time rcb p ir p t o nn
aggregate and mixed order markov models for statistical language processing lawrence saul and fernando pereira lcb isaul pereira rcb c research att
fignre NUM giw s an overview of the test material awfilable
these consisted of some extra patterns for phrasm verbs with complex complementation and with flexible ordering of the preposition particle some for non passivizable patterns with a surface direct object and some for rarer combinations of governed preposition and complementizer combinations
next we collected all words in the test corpus tagged as possibly being verbs giving a total of NUM distinct lemmas and retrieved all citations of them in the lob corpus plus susanne with the NUM test sentences excluded
the latter analysis is necessary because precision and recall measures against the merged entry will still tend to yield inaccurate results as the system can not acquire classes not exemplified in the data and may acquire classes incorrectly absent from the dictionaries
the extractor returns the predicate the vsubcat value and just the heads of the complements except in the case of pps where it returns the psubcat value the preposition head and the heads of the pp s complements
cally significant at the NUM level paired t test NUM NUM NUM dr p NUM NUM although if the pattern of differences were maintained over a larger test set of NUM sentences it would be significant
the frequency distribution of the classes is highly skewed for example for believe there are NUM instances of the most common class in the corpus data but only NUM instances in total of the least common four classes
on the other hand there are NUM false negatives supported by an estimated mean of NUM NUM examples which should ide null ally have been accepted by the filter and NUM false positives which should have been rejected
the subcategorization classes recognized by the classifier were obtained by manually merging the classes exemplified in the comlex syntax and anlt dictionaries and adding around NUM classes found by manual inspection of unclassifiable patterns for corpus examples during development of the system
the goal of this paper is to investigate models that are intermediate in both size and accuracy between different order n gram models
thus p m n p v i is the probability that m or more occurrences of patterns for i will occur with a verb which is not a member of i given n occurrences of that verb
a more robust tipster application requiring less maintenance should result
to an m NUM mixed order model smoothed by a m NUM mixed order model smoothed by a ml bigram model etc
we are also quite encouraged by the success of some of the lastpass stray pickup rules based on semantic contexts and other heuristics
the needed information seems to be primarily available withi n the data class phrase itself in the form of obligatory and optional elements
the third type contribute the most in supporting the pattern based identification principle of the system as facilitated b y the fourth and fifth types
NUM adjust a set of dxl rules which adjusted the start stop positions of the target s to include or exclude punctuation according to muc NUM rules
in this way the toke n chars value can be looked up in the knowledge bank without worrying about punctuation of any kind
that is it appears that having a good sentence parse would only infrequently be of value in determining the data classes embedded in the sentences
the next line on figure NUM shows what the results would be were the knowledge bank looked up for instances of various kinds of titles
the knowledge bank is again a major source of information but this time primarily for signal words which usually preface o r terminate data class patterns
this three fold approach can be argued as meeting most of the performance goals and certainl y fits the finding of data classes being pattern based
in figure NUM the lhs has four elements an optional pattern followed by an obligatory one followed by two optional patterns
in the same way words are strings of characters sentences are strings of words
our definition of analogy fortunately captures linguistic cases where prefixes or suffixes are involved
a degenerated c se of analogy is when two of tile three terms are equal
the overall process is similar to the one for analysis but in the opposite direction
tobe able to solve analogies it is necessary to give a meaning to such a notation
supl ose we get the following analogy to solve mathematics mathematical physics x
the lamp turns on the green signal is on x the signal is off figure
for example we can identify nodes which are superordinate to a group of senses which should be given the same code such as room sense NUM for the category space loc
we believe that the training available to crystal and wrap up was too sparse to enable intelligent inferences about succession events
with almost no training i t got a fourth of the org names relying heavily on the scanner
the surrounding concepts are very often expressed by ambiguous words and a correct sense for these words also has to be determined
finally we found that conflating inflectional variants harmed the performance of about a third of the queries
in case of a bigger training set most of the quadruples get disambiguated however wlth increasing sdt the disambiguation quality decreases
another advantage of this disambiguation mechanism is that the proper nouns which usually refer to people or companies can be also disambiguated
in order to conduct a fair comparison however we used the same testing set as the methods shown in the above table
the disambiguation of unseen testing examples is done by the same algorithm which is modified to exclude the sdt iteration cycles
this is a difficult and important problem because the semantic knowledge needed to solve the problem is very difficult to model and the ambiguity can lead to a very large number of interpretations for sentences
s s s s a s s NUM note that this approach represents a type of implicit parallelism
given a set of dialogues for which user satisfaction us and the set of ci have been collected experimentally the weights and wi can be solved for using multiple linear regression
the paradise model posits that performance can be correlated with a meaningful external criterion such as usability and thus that the overall goal of a spoken dialogue agent is to maximize an objective related to usability
for example to calculate for agent a over the subdialogues that repair depart city p a and p e are computed using only the subpart of table NUM concerned with depart city
an agent s responses to a query are compared with a predefined key of minimum and maximum reference answers performance is the proportion of responses that match the key
in addition to agent factors such as dialogue strategy task factors such as database size and environmental factors such as background noise may also be relevant predictors of performance
since performance can be measured over any subtask and since dialogue strategies can range over subdialogues or the whole dialogue we can associate performance with individual dialogue strategies
since the goal of this paper is to explain and illustrate the application of the paradise framework for expository purposes the paper uses simplified domains with hypothetical data throughout
NUM in table NUM these attribute value pairs are annotated with the direction of information flow to represent who acquires the information although this information is not used for evaluation
head corner parsing has also been considered elsewhere
memorization is implemented by two different tables
there are two objections to this approach
most importantly it uses selective memorization with goal weakening and packing
these results need not be used anymore
the same applies with even more force for top down information on categories
this path is then sent to the parser and the robustness component
certain approaches towards robust parsing use the partial results of the parser
in actual fact we have experienced an increase of efficiency using underspecification
comparison of the results based on z scores see baayen to appear and the results based on the randomization test however reveal only minor differences that leave the main patterns in the data unaffected
the threshold used here is that the frequency of the word in a given text slice should be at least equal to the mean frequency of the word calculated for the text slices in which the word appears
if the misfit disappears NUM m halle in defense of the number two in studies presented to j whatmough the hague NUM quoted in herdan NUM page NUM
NUM NUM even though the value of the p parameter NUM NUM leads to a fit that is much improved with respect to the unadjusted growth curve x NUM NUM NUM
to judge from table NUM the good turing estimate met NUM m is an approximate lower bound and the unadjusted estimate ms NUM m a strict upper bound for mp NUM
the number of different word types encountered after reading n tokens the vocabulary size v n is a function of n analytical expressions for v n based on the urn model are available
computational linguistics volume NUM number NUM as follows see the appendix for further details m NUM p v NUM p v n f NUM
third previous plan based linguistic research has concentrated on either construction or understanding of utterances but not both
conditional on a given frequency spectrum lcb v n f f NUM NUM rcb the vocabulary size e v m for sample size m n equals
these adjusted estimates however appear to overshoot their mark for continuous text in that they underestimate the population relative frequencies to roughly the same extent that the unadjusted probabilities lead to overestimation especially for the lowest frequencies
for case i this can be done by discretizing the ig weights into bins so that minor differences will lose their significance in effect merging some schemata back into buckets
furthermore according to these results the anaphora resolution in english only affects the accuracy of antecedent identification for the zero pronouns with intrasentential antecedents NUM NUM with anaphora resolution NUM NUM without anaphora resolution
we can run out of new arguments in properties
the third pair of arguments are both downward directions
table NUM phenomena giving rise to sloppy interpretations
john revised his paper and bill too
we can do this by assuming they are coreferential
our approach derives exactly the correct five readings
john revised his paper before bill did it
for any word we first annotate its neighboring words within certain distances in the corpus with all of their semantic codes in a thesaurus respectively then make use of such codes and their salience with respect to the word to formalize its contexts
to avoid confusion we refer to this basic unit throughout as a temporal unit tu
on the contrary if it is too sparse there may be no clusters activated by a context even if there is any it may be the case that the senses in the clusters are not similar with the correct sense of the target word
in the chinese thesaurus the words are divided into NUM major classes NUM medium classes and NUM minor classes respectively and each class is given a semantic code we select the semantic codes for the minor classes to formalize the contexts of the words
however the important thing to note is that there is a larg e amount of overlap otherwise
remember that for input sentences without unknown words dop2 is identical to dop1
he has he says fond memories of working with coke executives
figure NUM contains a block diagram of the system under development
writing such a program may constitute a significant part of the porting effort if no such program is available in advance
thus one parse tree can be generated by many derivations involving different corpus subtrees
i e discovered tpc NUM next o o undiscovered tpc tpc j previous chain r strategy
the second question mentioned in the introduction concerns the problem of parsing word stnngs
our learning program has two basic modules iam version space learner which performs the olenmn
given a word w in some context we consider the context as consisting of n words to the left of the word i.e. w w
datal ase to lie resl eete l by any NUM further generation process
we conclude by mentioning some limitations of the system suggesting future directions for investigation
he reports NUM NUM bracketing accuracy and NUM sentence accuracy on the atis
the research reported in this paper was partly supported by contract NUM NUM NUM
every syntactic structure has a unique hea d
this is a partial classification of events i.e. some events may be neither so yes nor so no
in a given event s an individual a classifies events according to what s he sees and knows
but then the event e required to classify joe s visual state must be a proper class
in all three types of applications what is ultimately of interest is not that two names match whether exactly or approximately as character strings but that the entities to which they refer are identical
to measure the gain in retrieval performance that might be achieved using name searching a set of NUM queries conlaining personal names was developed by a domain expert and run against west s fed test collection
the speaker connection function c or anchor grounds the individuals relations and locations mentioned in q to actual entities participating in the discourse situation d is thus a binary relation relating the utterance triple to the described situation ic
software which relied much more on an exhaustive lexicon of names and variants might do better but could not deal with names which were not contained in its lexicon
for the NUM queries with personal names see section NUM NUM run against the fed collection proximity based name searching led to significant improvement over the baseline win searching
with standard entries for verbs as in NUM logical forms such as NUM and NUM are po ible
a shows a derivation for a reading in which the modifying np takes wide scope and b shows the other case
this result is in principle available to other paradigms that invoke operations like qr at lf or type lifting which are essentially equivalent to abstraction
there is a question of how the non standard surface structures of ccg are compatible with well known conditions on binding and control including crossover
a bel john uill uin r c bel john 3r repub r
NUM NUM for a function with n arguments there are n ways of successively providing all the arguments to the function
NUM for NUM a has two NUM readings as viaited has two arguments
NUM is an abstraction for four NUM x2 readings as both of and maw have two arguments each
then the maximal cliques of the graph i.e. the maximal complete subgraphs correspond to the maximal mergings
when a participant notices a discrepancy between her own interpretation and the one displayed by the other participant she can choose to initiate a repair or to let it pass
these conditions are typically stated on standard syntactic dominance relations but these relations are no longer uniquely derivable once ccg allows non standard surface structures
this paper will show that these approaches allow unavailable readings and thereby miss an important generalization concerning the readings that actually are available
in the wsj example the resulting lexicon contains NUM word types NUM NUM of which are ambiguous
the second goal is to facilitate evolution which includes facilitating fidelity the help author should be assisted in producing complete and up to date descriptions of gui components reuse wherever possible the help author should not have to write the same text twice
if however a modifier is placed in a domain of some transitive head as beans in fig NUM discontinuities occur
for generation of an organization object the text must provide either the name full or part or a descriptor of the organization
human performance was measured by comparing the NUM draft answer keys produced by the annotator at nrad with those produced by the annotator at saic
the author is turning over government leadership of the muc work to elaine marsh at the naval research laboratory i n washington d c
we leave it as an open problem whether rules of the form in NUM can be learned in linear time
these receive reading r and are governed by s in valencies r j NUM k NUM iei
we also say that implicit node q immediately dominates node p if q splits the arc between parent p and p
this can be proved by induction on w i i using the definition of move link down and of s link
a similar argument works for visits to nodes of t by fast scan which are charged to symbols of u
the selection was again done blindly with later checks to ensure that the set was fairly representative in terms of article length and type
the articles used in the evaluation were drawn from a corpus of approximately NUM NUM articles spanning the period o f january NUM through june NUM
each turn divides into utterances that sometimes resemble clauses as defined in a traditional grammar
throughout this paper we will refer to the example dialogue partly shown in figure NUM
for the utterances we store e.g. the dialogue act dialogue phase and predictions
the dialogue then proceeds into the negotiation phase where the actual negotiation takes place
we thank our students ralf engel michael kipp martin klesen and panla sevastre for their valuable contributions
although we use one of the largest annotated corpora available for purposes like training we still need more data
the shallow translation module integrates the predictions within a bayesian classifier to compute dialogue acts directly from the word string
for example if both locutors propose different dates an implicit rejection of the former date can be assumed
the system proposes the speaker a more plausible date and waits for an acceptance or rejection of the proposal
when designing a component for such a scenario we have chosen not to use one big constrained processing tool
where qi and qj are the quantities of codelet types ci and cj respectively ui t and uj t are the urgencies of codelet types ci and cj at temperature t respectively and n is the total number of codelet types
while brackets must be correctly paired in order to derive a chunk structure it is easy to define a mapping that can produce a valid chunk structure from any sequence of chunk tags the few hard cases that arise can be handled completely locally
in this work we have found it convenient to do so by encoding the chunking using an additional set of tags so that each word carries both a part of speech tag and also a chunk tag from which the chunk structure can be derived
we believe that the work reported here is the first study which has attempted to find np chunks subject only to the limitation that the structures recognized do not include recursively embedded nps and which has measured performance by automatic comparison with a preparsed corpus
abney s other motivation for chunking is procedural based on the hypothesis that the identification of chunks can be done fairly dependably by finite state methods postponing the decisions that require higher level analysis to a parsing phase that chooses how to combine the chunks
in rule NUM sites currently tagged n but which fall at the beginning of a sentence have their tags switched to bn the dummy tag z and word zzz indicate that the locations one to the left are beyond the sentence boundaries
the automatic derivation of training and testing data from the treebank analyses allowed for fully automatic scoring though the scores are naturally subject to any remaining systematic errors in the data derivation process as well as to bona fide parsing errors in the treebank source
this is done by adding some fraction of the changes made in each pass to the positive scores of the disabled rules and reenabling rules whose adjusted positive scores came within a threshold of the net score of the successful rule on some pass
because this index involves only the stable word identity and part of speech tag values it does not require updating thus it can be stored more compactly and it is also not necessary to maintain back pointers from corpus locations to the applicable rules
the handling of conjunction again follows the treebank parse with nominal conjuncts parsed in the treebank as a single np forming a single n chunk while those parsed as conjoined nps become separate chunks with any coordinating conjunctions attached like prepositions to the following n chunk
rule NUM marks conjunctions with part of speech tag cc as i if they follow an i and precede a noun since such conjunctions are more likely to be embedded in a single basenp than to separate two basenps and rules NUM and NUM do the same
this paola merlo modularity and information content classes it can then be hypothesized that the amount of compilation or conversely the modularity of the parser is captured by the notion of ic classes as follows in other words a parser that takes advantage of the structure of linguistic principles will maintain a modular design based on the five classes in NUM
when mapping apos to umos the microplanner must choose among available alternatives
figure NUM a fragment of the hierarchy of textual semantic categories in proverb
i igure NUM inhoducing scope hy ordering the nodes
such a provisory list is given be ow e
a categorization of the aggregation rules is given in fig NUM
pcas are the primitive actions planned during macroplanning to achieve communicative goals
figure NUM shows a fragment of the upper model in pro verb
the concepts axe organized in a hierarchy based on their textual realization
pcas annotated with these decisions annotated are called preverbal messages pms
there are two more chaining rules for the logical connectors implication and equivalence
without aggregation the system produces let f be a set
set f a g a subset f g
the number of the resulting classes can not be controlled accurately
computationally it renders the use of linguistic theories impractical and empirically it clashes with the d6partement de linguistique g6n6rale universit6 de gen6ve NUM rue de candolle NUM gen6ve switzerland NUM association for computational linguistics computational linguistics volume NUM number NUM observation that humans make use of their knowledge of language very effectively
some classes results and perplexity of word class based language model are presented
a similar proof is applicable to the other direction
std where the lexicon does not involve gtrcs
more precisely recall that the relevant information is a whether a node is lexical or not b whether it has a NUM role or not c whether it has case or not d whether it is a sister of c hence in an a position or not if not it counts as an a position
in order to statically simulate 5b by a ccg std we add s bka to the value of f c in the lexicon of g
the specific use of the representations is somewhat irrelevent to our immediate discussion though various interpretaions will be discussed throughout the paper
the bounded argument condition ensures that every argument category is bounded as follows NUM b x should not apply to the pair const gtrc
an extension of the formalism is also being studied to include lexica type raising of the form t t c ld id for english prepositions articles and japanese particles
but then the parser might reach the end of the input or at least the end of the relevant elementary tree i.e. the main predicate argument structure before realizing either that it pursued an incorrect analysis in the case of ambiguous input or that the input is ill formed
there are two potential problems with this simple augmentation
the research was supported in part by nsf grant nos
we separated our sets of adjectives a containing NUM NUM adjectives and conjunction and morphology based links l containing NUM NUM links into training and testing groups by selecting for several values of the parameter a the maximal subset of a an which includes an adjective z if and only if there exist at least a links from l between x and other elements of an
i do not consider appositions and incidentals as challenging for the general claim incidentals are clearly outside of an x structure assigned to the sentence while appositions are internal to the np thus when the verb is reached the phrase sitting on the stack is indeed the np subject which can therefore receive case
in this paper we show a new method for chinese words classification
selfmisunderstandings are those in which a hearer finds that a speaker s current utterance is inconsistent with something that that speaker said earlier and decides that his own interpretation of the earlier utterance must be incorrect
by their choice of repairing or accepting a displayed interpretation speakers in effect negotiate the meaning of utterances NUM repairs can take many forms depending on how and when a misunderstanding becomes apparent
NUM for present purposes we also assume that the complete model is accessible to the hearer one could better simulate the limitations of working memory by limiting access to only the most recent utterances
fact lexpectation do s1 al p do s2 a2 a believe s1 p wouldexpect st al a2
once a discourse level goal is selected a set of can null computational linguistics volume NUM number NUM didate plans is identified and allen style heuristics are applied to choose one of them
by contrast our approach begins with an expectation using it to premise both the analysis of utterance meaning and any inference computational linguistics volume NUM number NUM about an agent s goals
all these problems suggest a different approach namely to select a small number of optimally informative examples from a given corpora
compared to experiments with random example selection our method reduced the overhead without the degeneration of the performance of the system
motivated by these properties we formulated a new performance estimation measure pm as shown in equation NUM
as with most thesauri the length of the path between two terms in bunruigoihyo is expected to reflect their relative similarity
lcb x rcb u lcb y rcb lcb x rcb if sim x y NUM NUM
with respect to problem NUM a possible solution would be the generalization of redundant examples NUM NUM
this shortcoming is precisely what our approach allows to avoid reducing both the overhead as well as the size of the database
to illustrate the overall algorithm we will consider an abstract specification of both input and the datatbase see figure NUM
NUM given human resource limitations it is not reasonable to manually analyze large corpora as they can provide virtually infinite input
NUM perspective is not in opposition to the current proposal as the specialization of the parser in different tasks is likely to be an adaptive reaction to the different types of inputs
in verb final languages german for example the subject of the sentence in embedded clauses is not string adjacent to the head of the sentence as it is in english
all linguists strive to develop theories that rest on general abstract principles which interact in complex ways so that many empirical facts fall out from a few principles
once the potential chain links have been labeled a second algorithm looks for a chain that can accept a node with that label
m wh chains also called a chains and np movement chains also called a chains the empty categories that occur in these chains have different properties
NUM three grammars were constructed constituting pairwise as close an approximation as possible to minimal pairs with respect to ic classes
null we think that the extraction of aspectual information must be based on principles that are wellgrounded in linguistic theory
in a later version where rules were compiled into an lr NUM table structure building constituted NUM of the total parsing time
the duration described by verbs is twofold an ongoing process and a consequent state
the empty category principle an empty category x is licensed if the NUM following conditions are satisfied x is in the domain of a head h
nevertheless different theory specific representations shml be recoverable from the annotation cf
a structure for NUM is shown in fig NUM
this requires a selection of the left branch in node NUM ql which means that hydraulic f gets expressed in node NUM
the logical encoding of the boolean conditions may seem complex and indeed simpler solutions have been proposed to encode the semantic coverage in kay s algorithm for instance
an annotation scheme for free word order languages
this propeity is the basis for the efficiency gained by using charts as it allows a compact representation in which a polynomial number of edges can potentially encode exponentially many derivations
null the new generation method we propose in this paper is different from kay s mainly in the criteria for indexing phrases and the mechanism used for determining the semantic coverage
it may also enable different kinds of interactions between the translation system and the human expert who operates it tbr instance disambiguation by a monolingual in the target language
this forces a traversal of nodes NUM and NUM which amounts to generating from filter f oil o hydraulic o
another source of complication comes from the fact that the generation chart encodes multiple paraphrases and we need to guarantee that a piece of semantics will not be expressed more than once
to determine whether all the semantic facts are expressed the boolean conditions from all the slots in the array of the top node are conjoined and the result is checked for satisfiability
these two examples demonstrate how we manipulate the boolean conditions of the semantic coverage arrays to allow generation from a disjunctive input and still gain the benefits of the chart generation algorithm
a typical example is shown as follows
where do is a small positive constant
measures accuracy rate and selection power
national science council under nsc NUM NUM e NUM NUM project
NUM NUM robust learning on the smoothed parameters
this procedure is summarized as follows
these functions and interfaces would constitute an architecture the tipster architecture
the performance of the learned decision trees averaged over the NUM test narratives is shown in table NUM
and he does n t really notice oi figure NUM inferential link due to implicit argument
fig NUM mis classification of non boundaries were reduced by changing the coding features pertaining to clauses and nps
however two factors prevent this performance from being closer to ideal e.g. recall and precision of NUM
the ratios of test to training data measured in narratives prosodic phrases and clauses respectively are NUM NUM
we used three distinct algorithms based on the distribution of referential noun phrases cue words and pauses respectively
unfortunately this makes the architecture harder to understand and harder to implement
the boxes in the figure show the subjects responses at each potential boundary site and the resulting boundary classification
it thus will allow the verification of architecture compliance to be partially automated
consnlting the list of composition rules we find that the only applicable rule is c5
no one was sure quite what the final document would look like
the disjunctions in the fug make unification nondeterministic
NUM fuf is currently implemented in common lisp
the output of this stage is a hierarchical structure where heads correspond to linguistic constituents of a given category clause or np but where the lexical heads are not yet selected
perspective class assignt focus ai NUM six programming assignments are required in ai perspective class assignt focus assignt set NUM alternative outputs for the input of figure NUM
NUM phrase planning in lexical choice when the input semantic network contains more than one relation the lexical chooser must decide how to articulate the different predicates into a coherent linguistic structure
because it is based on functional unification surge is driven by both the structure of the grammar and that computational linguistics volume NUM number NUM of the input working in tandem
if only conceptual constraints are accounted for lexical choice may be done early on for example during content planning by associating concepts with the words or phrases that can realize them
NUM polgu re NUM mccoy and cheng NUM carcagno and iordanskaja NUM which must keep track of how focus shifts as it plans the discourse or text
the purpose of this paper is to suggest a new approach to deal with the above mentioned problem
when dealing with automatic disambiguation of a text it is sometimes useful to reduce its ambiguity level
NUM wall street indexes opened strongly
similarly the mapping need not be one to one
this new proposed link would be a summary link indicating the synchronisation of an entire subtree more precisely each subnode of the node with the summary link is mapped to the corresponding node in the paired tree in a synchronous depth first traversal of the subtree
provide clear and comprehensible communication of what the system can and can not do
note that only the closed class lexicon is consulted during this attempt to parse
however they can be used to tune a general purpose taxonomy to a specific domain by reducing sense ambiguity and identifying new domain specific senses
if we relax these constraints we obtain larger clusters and fewer singletons but verbs in a cluster are less semantically close to each other
worst clusters as far as the overlap score is concerned are those in which there are very high level and ambiguous verbs like make
for example the predicted thematic structure for the cluster NUM NUM shows that the verb to deal has a more specific use than in general language
given our experience and results we are inclined to take the second position but we are indeed sensitive to the theoretical motivations of the first
to investigate the commonalities between ciaula and wordnet we decided to automatically select the best wordnet concept as a label to assign to each acquired ciaula cluster
for example the classes NUM NUM NUM NUM NUM NUM NUM NUM NUM are all labeled create make a very general synset in wordnet
as the order of the models increases from NUM ie unigram to NUM we naturally expect the test message entropy to approach a lower bound which is itself bounded below by the true source entropy
but the higher the node the deeper the meaning behind a hyperonim the harder is the human task of evaluating the appropriateness of a classification
like previous approaches for modeling task oriented dialogues we assume that a dialogue can be described by means of a limited but open set of dialogue acts see e.g.
that helps control the large number of processes that make up the commandtalk system
it makes it easy to assign processes to machines distributed over a network
the ptt agent issues messages to the sr agent to start and stop listening
we therefore instantiate the rules in a more careful way that avoids unnecessarily instantiating features and prunes out useless rules
with click to talk the system listens for speech until a sufficiently long pause is detected
for each recursive nonterminal a we divide the rules defining a into right recursive left recursive and nonrecursive subsets
generally the system treats these cases by producing all possibilities in the order of priority and allowing the user to choose one
however there is a large obstacle against global communication over networks namely the language barrier especially for non english speaking people
also the model prefers short sentences to long ones with the same semantic content which favors conciseness but sometimes selects bad n grams to avoid a longer but clearer rendition
the resulting conditional probabilities are converted to log likelihoods for reasons of numerical accuracy and used to estimate the overall probability p s of any english sentence s according to a markov assumption i.e.
while true irregular forms e.g. child children must be kept in a small exception table the problem of multiple regular patterns usually increases the size of this table dramatically
but although such collocational information can be extracted automatically it has to be manually reformulated into the generator s representational framework before it can be used as an additional constraint during pure knowledge based generation
phrases such as three straight we consider lexical choice as a general problem for both open and closed class words not limiting it to the former only as is sometimes done in the generation literature
however while some lexical decisions can affect future or past lexical decisions others are purely local in the sense that they do not affect the lexicmization of other semantic roles
log p s z log p w wi l for bigrams i log p s z log p wilwi z wi NUM for trigrams i
one direction we intend to pursue is the rescoring of the top n generated sentences by more expensive and extensive methods incorporating for example stylistic features or explicit knowledge of flexible collocations
the grammar is organized around semantic feature patterns rather than english syntax rather than having one s np vp rule with many semantic triggers we have one agent patient rule with many english renderings
where pi ri denote atomic formulas q denotes a literal f denotes a formula and v denotes universal closure
aet provides a formalism for describing how a formula consisting of lexical predicates can be tranlsated into formula consisting of database predicates
in our approach we propose a restricted version of ldts rldts that can be normalized and in normalized form used to construct selectional restrictions
our basic idea is to preproeess the semantic information of kldt to create patterns of possible conjunctive contexts for each lexical predicate
are logical consequences of the theory f we shall refer to the last condition as soundness of the nce t
part of the conjunctive context associated with formula take e x y in ftlag is a formula NUM
consider for example the preposition on as used in the phrases on the table or on monday
so we can claim that fab and fzin9 are equivalent in the theory f under an assumption that crept710 and crept720 are courses
first we introduce the term nontrivial normal conditional equivalence with respect to an rldt t n nce t
also suppose that the ldt declares as an assumption aeourse x which can be read as x denotes a course
we have focused on the distinction between homonymy and polysemy unrelated vs related meanings
finally there are questions about how word senses should be used in a retrieval system
hypothesis NUM even a small domain specific collection of documents exhibits a significant degree of lexical ambiguity
there are many issues involved in determining how word senses should be used in information retrieval
in a collection of legal documents which had an average length of more than NUM words
we found that there were three reasons why the porter stemmer improves performance despite such groupings
we found that there was a significant improvement over the baseline performance from grouping morphological variants
we empirically found that the word forms in these groups are almost always related in meaning
we also identified the morphological false friends for the NUM most frequent suffixes
this holds especially in the verbmobil context with its distributed gratnmat development
we use interlingua representations for time and date expressions in the verbmobil domain
pragmatic morpho syntactic mid prosodic information have been left out here
this rule shows how structural divergences can easily be handled within this approach
a variation on example NUM is given in NUM
in addition it also depends on the cardinality and complexity of conditions
the deelaxativc trtmsfer correspondences m e compiled into an executable prolog program
rule forlnalisin which provides an iinplementation platform for a selnantic based transfer approach
all sets are written as prolog lists and optional conditions caa be omitted
sleonds and tlconds are optional sets of sl and tl conditions respectively
the form list generated by the morpho semantic generator is checked against three mrds collins spanish english simon and schuster spanish english and larousse spanish and the forms found in them are submitted to the acquisition process
subjects or objects and to the objects of their prepositions
plum figure NUM employs both lightweight and heavyweight techniques
full parsers semantic inference and co rreference algorithms are examples
our ne and te systems employ no domain specific knowledge by design
we are looking forward to trying it on information extraction problems
a small part of the problem is the lack of capitalization upper case only
systems must rely far less on punctuation and on mixed case than the configurations in muc NUM
these are divided into three categories user interface system speed and missing features
nevertheless we did not achieve a breakthrough in overall f score i.e. in err
finally it is also clear that each lr comes at a certain human labor and computational expense and if the applicability or payload of a rule is limited its use may not be worth the extra effort
in this paper we have discussed several aspects of the discovery representation and implementation of lrs where we believe they count namely in the actual process of developing a realistic size real life nlp system
are simply ene ded a s cha racter strings ra ther than as complex feature structures
se quence q for s using the source cfg skeletons of t a nd every head constra int
sore of the critica NUM issues involv d it th design of such a
b f there is no such pa ired deriva tion
moreover defining a new stag rule is not a s easy for the users as just adding an entry into a
forms so tha t these pa tterns axe a pplica ble to finite a s well a s infinite forms
rather than developing a method to search blindly through the space of possibilities we first provide an initial evaluation of three linguistic devices whose distribution or surface form has frequently been hypothesized to be conditioned by segmental structure referential noun phrases cue words and pauses
ure NUM which describe how three boys come to the aid of another boy who fell off of a bike are more closely related to one another than to those in the intervening segment y which describe the paddleball toy owned by one of the three boys
because machine learning makes it convenient to induce decision trees under various conditions we have performed numerous experiments varying the number of features used the definitions used for classifying a potential boundary site as boundary or nonboundary and the options available for running the c4 NUM program
the authors wish to thank j catlett w chafe k church w cohen j dubois b gale v hatzivassiloglou m hearst j hirschberg d lewis k mckeown and e siegel for helpful comments references and resources
boundary NUM NUM NUM NUM a nd NUM NUM the one thing that struck me about the NUM three little boys that were there is that one had ay uh NUM
only NUM of the NUM possible boundary sites are classified as boundary for both i NUM and i NUM are n NUM potential boundary sites between each pair of prosodic phrases pi and pi l i from NUM to n NUM
nevertheless some regular scopal relations may be found among discourse relations and again in general among scope taking elements
for this case the current treatment of lud implies that the widest scope should be assigned to any discourse relation
possible linear orders can simply be read off the variants a yield term under restructuring e.g.
fig NUM is a graphical representation of the loq constraints of NUM
semantically this is one of the main reasons that underspecification should be introduced rigorously
secondly drss for discourse relations are assumed to always instantiate into the top hole
plug into NUM h5 plug into NUM h2 confinement of resolution possibilities depends on various factors
pressed by the auxiliary noda in the modality position of the verbal complex of the conclusion part of the sentence
if you mean the afternoon yamada will be here figure NUM discourse relations with and without anaphoric force
in the same vein the scope of noda supercedes that of a conditional discourse relation nara in fig NUM
secondly it does not concern multi functions of one discourse relation element but multiple occurrences of various discourse relation elements
ill this paper we have shown that it is possible to implement a system for generating text abstracts which purely operates with word frequency statistics without using either domain specific knowledge or text sort specific heuristics
however since we are constrained by incrementality we will have to make an attachment decision for the pp as soon as the preposition with is encountered and it will be attached in the preferred reading as a sister of the verb
this means that assuming we decide initially to attach low but number agreement on was subsequently forces high attachment as in NUM then a conscious garden path effect will be predicted as lowering can not derive the reanalysis
this involves reconstructing all the clausal structure dominating the lowering site including asserting empty argument positions with reference to the verb s case frame and attempting to attach the result as a relative clause to the head noun
if a transitive verb is found in the input then the parser consults the verb s argument structure and creates a new right attachment site for an np asserting also that this new np is dominated by vp and preceded by v
however the appearance of the head noun seito student means that at least part of the preceding clause must be reinterpreted as a relative clause including a gap note that there is no overt relative pronoun in japanese
it is familiar from the psycholinguistic literature that there is a preference for attaching the with phrase as an instrumental argument of the verb as in NUM on the reading where the telescope is the instrument of seeing
mazuka and itoh in press note that examples where both subject and object must be expelled from the relative clause as would be the first choice in a bottom up search often cause a conscious garden path effect
in the case of example NUM at the point where the truth has been processed the parser must find an accessible node which matches the category of the left attachment site of hurts i.e. an np
if this is possible then we would expect examples like NUM to be unconscious garden paths and this does indeed seem to be reflected in the intuitive data see mazuka and itoh in press
the final result is the tncb in figure NUM whose orthography is the big brown dog barked
the asymptotic complexity of igtree i e in the worst case is extremely favorable
the use of igtree has as additional advantage that optimal context size for disambiguation is dynamically computed
the construction of a pos tagger for a specific corpus is achieved in the following way
during the construction of igtree decision trees cases are stored as paths of connected nodes
k nn is a non parametric technique it assumes no fixed type of distribution of the data
the feature weighting method takes care of the optimal fusing of different sources of information e.g.
with increasingly larger data sets the performance becomes more stable witness the error ranges
for the known words case base paths in the tree represent variable size context widths
context information is added to the case representation in a similar way as with known words
the first element of the pair a context c is an equivalence class of states before transitions
the final set contained NUM NUM adjectives NUM positive and NUM negative terms
with increasing amounts of machine readable information being available one of the major problems for users is to find those texts that are most relevant to their interests and needs in as short an amount of time as possible
thus the overall result can be much more accurate than the individual indicators
kaw architecture is displayed in figure NUM
we evaluated the three prediction models discussed above with and without the secondary source of morphology relations
it matches in the text patterns which represent hypotheses of the knowledge engineer groups together and generalizes cases which have been discovered and presents them to the knowledge engineer for a final decision
semantic categories for many adjectieval modifiers extracted at the word clustering phase are too general if any but collected collocations and external lexical sources as for example wordnet can be used
sometimes there is an indirect link between adjectives
many terms include other terms as their components
the parser supplies information to a case attachement module
the tagger assigns categorial features to words
NUM a clustering algorithm separates the adjectives into two subsets of different orientation
the result is a graph with hypothesized same or different orientation links between adjectives
put in terms of the traditional parsing issues in natural language understanding semantic associations coded as dependency parameters are applied at each parsing step allowing semantically suboptimal analyses to be eliminated so the analysis with the best semantic score can be identified without scoring an exponential number of syntactic parses
NUM y extraction of regularities rein xamlflcs without any a priori knowledge ahout the domain
unsupervised learning is necessarily more limited t hm supervised learning the only information it has to construct categories is the similarity between inputs
we describe a case study in tit application of symbolic machinc learning techniques for the discow ry of linguistic rules and categories
this is the feature that reduces the information entropy of the training sub set on average most when its value would be known
a second focus of this paper is the interaction between supervised and unsulmrvised machine learning me thods in linguistic discovery in supervised learning the
NUM experiments li or ea h of l he NUM nouns we coll cted th following information was kept
some examples are given below l he word itself and its gloss are provided for convenience and were not used in the exllerimenl s
2lp and 3lp are NUM or NUM layer perceptrons using our surface cues
finally the coda nasa liquid or not helps us distinguish between etje and pje for those cases where the nucleus is short
this baseline would t e an accura y of a l out NUM for this prol h m
ken wa ima tonarino heya de kimono wo ki te i ru
a corpus only provides positive evidence
sets l closed under associative binary operations intuitively strings under concatenation
we shall motivate compilation into linear clauses directly from simple algebraic models for the calculi
we aim to indicate here how higher order logic programruing can provide for such a need
however the term labelled implementation as it has been given also fails for right products
multimodal groupoid compilation for ilnplications is immediate NUM this is entirely general
ken wa ano kimono wo san nen maeni ki te i ru
however once a text has been segmented into text and non text portions and the text portion into segments involving different character codes it should be possible to provide operations at the character level i.e. operations which are sensitive to the different sizes of characters in different codes
therefore the resultative or experiential readings are preferred
that is the constituents have a governingdependent relation
this contrasts with the type of slt which is the theme of this first session and indeed more predominantly influences slt research so far namely dialogue translation
because the principles of well formedness implemented are general and capture mainly the extended domain of locality of ltag the generator we have presented can very well be used to generate a grammar with different underlying linguistic choices for instance the gb perspective used in the english grammar cited
the conjunction of the inherited partial descriptions leads to the following description the nodes with same constants have unified s s and the constants with same function meta feature have also unified subject argl and quest arg0 cf
these are first the trees where a redistribution of the syntactic function of the arguments has occurred for instance the passive trees or middle for french or dative shift for english leading to an actual subcategorization different from the canonical one
intuitively a tokenization has hidden ambiquity in tokenization if some words in it can be further decomposed into word strings such as blueprint to blue print
on the other hand a critical tokenization would fully realize the principle of maximum tokenization since it has already attained an extreme form and can not be simplified or compressed further
based on this understanding it is now apparent why forward maximum tokenization backward maximum tokenization and shortest tokenization are all special cases of critical tokenization but not vice versa
moreover without such a complete dictionary it would not be possible to avoid ill formedness in sentence tokenization nor to make the generation tokenization system for character and words closed and complete
based on that we will establish the notion of critical tokenization and prove that every tokenization is a subtokenization of a critical tokenization but no critical tokenization has true supertokenization
so we can replace verb syn type in the example above with just verb
in this way word1 and word2 can both inherit their general verbal properties from verb
our focus on a single lexeme has meant that one class of redundancy has remained hidden
such compound sentences correspond to a number of individual and entirely independent datr statements
i o v e i n g NUM o v i n g i o v e a b NUM e NUM o v a b NUM e
a datr description consists of a sequence of sentences corresponding semantically to a set of statements
this specifies inheritance of mot root from the query node which in this case is wordl
here we have added a definition for mor form that contains the quoted path mor root
these pa tterns ca n NUM e successfully conw rted into the forma NUM representations defined a bow
notice that in this last example the final statement was extensional not definitional
each descriptor in a definitional sequence or evaluable path is evaluated from the same global state
and of course it is now possible to swap in modules such as a different parser with significantly less effort than would have been the case before
in our view alep despite claiming to use a theory neutral formalism an hpsg like formalism is still too committed to a particular approach to linguistic analysis and representation
the next two subsections discuss systems that have adopted these two approaches respectively then we compare the two and indicate why we have chosen a hybrid approached based mainly on the second
the high level tasks which lasie performed include the four muc NUM tasks carried out on wall street journal articles named entity recognition coreference resolution and two template filling tasks
the multext work NUM has led to the development of an architecture based 2note that other partners in the project adopted a different architectural solution see http www
this representation will presumably be stored in intermediate files which implies an overhead from the i o involved in continually reading and writing all the data associated with a document to file
alep does not aim for complete genericity or it would need also to supply algorithms for baum welch estimation fast regular expression matching etc
the developer is required to produce some c or tcl code that uses the gdm tipster api to get information from the database and write back results
tipster is minimal in this respect as there is no inherent need to duplicate the source text and all its markup during the nromalisation process
we believe the above comparison demonstrates that there are significant advantages to the tipster model and it is this model that we have chosen for gate
table NUM n best hypothesis for the sentence whom
this gives the tree in figure NUM representing the sentence NUM the syntax book we have
we assume that information of these four kinds is available in a model of the current discourse state
this allows us to specify the pragmatic constraints associated with the tree type once regardless of which verb selects it
lex l1 syn l2 this model uses a bigram model in computing lexical scores and the l2 mode of operation in computing syntactic scores
our approach is to extend such np planning procedures to apply to sentences using tag syntax and a rich semantics
it performs this task in two steps to take advantage of the regular associations between words and trees in the lexicon
note that spud would use it if either the verb or the discourse context ruled out all distractors
a goal is to identify objectives appropriate for future research and development
in each iteration our algorithm must determine the appropriate elementary tree to incorporate into the current description
the entry may specify additional goals because it describes one entity in terms of a new one
for instance if a determiner is derived there is no need to invoke the rule as there are simply no vp s selecting a determiner as subject
in languages such as c partial evaluation does not seem to be possible because the low levelness of the language makes it impossible to recognize the concepts that are required
finally trl specifies the grammatical form of each action expression using three features that may be attached to expressible nodes in the structure
we want to exploit the fact that the primary data structures of constraint based grammars and the corresponding information combining operation can be modeled by prolog s first order terms and unification
for this reason i will argue for an implementation of the head corner parser in computational linguistics volume NUM number NUM which only large chunks of computation are memorized
second the syntactic category and morphological properties of the mother node are in the default case identical to the category and morphological properties of the head daughter
first of all the head of a rule determines to a large extent what other daughters may or must be present as the head selects the other daughters
as a by product of this view of language penman contains a well developed implementation of the system network the systemic formalism for representing grammar
keith vander linden and james h martin expressing rhetorical relations purpose expressions in our corpus is split fairly evenly between initial and final purpose expressions
if we have two tables then we can also immediately stop working on branches in the search space for which it has already been shown that there is no solution
to distinguish the two uses we use the relation lex head link which is a subset of the head link relation in which the head category is a possible lexical category
the sentence builder then uses a straightforward recursive descent algorithm to produce an spl command for each of the sentences in the trl structure
15c when the phone is installed and the battery is harged move the off stby talk switch to the stby position
computational linguistics volume NUM number NUM simple sequential actions do not fit into the categories discussed above and are marked as imperative commands
NUM these tests were performed without the penman realization component engaged comparing the trl output of the system network with the corpus text
the simplest one is just to set a predefmed threshold on the deviation d ii p of the generalized model from the reference distribution
geschichten aus der produktion NUM berlin rotbuch verlag pp
this claim is backed up by an empirical evaluation of functional centering
this claim will be validated by a substantial body of empirical data cf
we simply ranked the elements of c according to their text position
table NUM contains a detailed synopsis of cheap and expensive transition pairs
i he 316lt is with a nimh accumulator equipped
we now turn to the distribution of transition types for the different approaches
instead we postulate that some centering transition pairs are preferred over others
the updates for aggregate markov models are
recognizing abbreviations gives important evidence as to the location of sentence boundaries and reducing the number of abbreviations in the lexicon naturally reduces the accuracy of the system
coupled with this there have been several improvements which are described next
the case m NUM corresponds to a
in conclusion let us mention some open problems for further research
the representation in figure NUM combines the generalized parse with the pos sequence regular expression that it is indexed by
with this change in notation the two kleene star regular expressions in NUM can be merged into one and the resulting representation is NUM
also the feature values in the feature structures of each node of every elementary tree are instantiated by the parsing process
the elementary trees that they anchor and NUM the substitution and adjunction links to the trees they substitute or adjoin into
to show the effectiveness of our approach we have also discussed the performance of ebl on different corpora and different architectures
table NUM performance comparison of xtag with and without ebl component experiment NUM d the setup for this experiment
experiment NUM a the performance of xtag on the NUM sentences is shown in the first row of table NUM
the performance was measured on the same set of NUM sentences that was used as test data in experiment l a
table NUM evaluation of the nlp component with respect to word accuracy sentence accuracy and concept
for word graphs the average cpu times are actually quite misleading because cpu times vary enormously for different word graphs
for determining concept accuracy we have used a semantically annotated corpus of NUM user responses
the input to the nlp module consists of word graphs produced by the speech recognizer
at an early stage the word graph is optimized to eliminate the epsilon transitions
a5 o1 NUM NUM l niladdp ja NUM NUM pbl ael b c
the results of these experiments provide a measure of repetitiveness of patterns as described in this paper at the sentence level in each of these corpora
if there is only one way to derive a given tree in g the mappings between derivations in g and g are one to one and there is therefore only one way to derive a given tree in g lemma NUM let g e nt i a s be a tig
there also are some cases in which the verb depends on the gender of the absolutive ergative or dative cases
theorem NUM if g g nt p s is a finitely ambiguous cfg that does not generate the empty string then there is an ltig g g nt i a s generating the same language and tree set as g with each tree derivable in only one way
to this extent results from mcca tagging would be similar to those of hearst schtttze
the impossibility to store all the combinations of a word make these methods not very suitable for inflected languages NUM
this implies that some restrictions present in the translation and the output language which could enhance the acoustic search are not taken into account
createdocument parent collection externalld string rawdata bytesequence annotations annotationset attributes sequence of attribute document creates a new document within the collection parent and assigns the document a new unique id copybaredocument newparent collection document document makes a copy of document including only its internal id externalld and rawdata and places the copy in collection newparent
this syllable model reflects the phonotactics and the segmental structure of syllables in german or rather their correlates on the orthographic surface
let t c i be the set of every elementary initial tree t such that the root of t and the leftmost nonempty frontier node of t are both labeled x suppose that every node labeled x where adjunction can occur is the root of an initial tree in i suppose also that there is no tree in a whose root is labeled x
x has property age NUM years old
the approach proposed here is similar to that of dorr s dorr NUM dorr
we distinguish an np kernel consisting of determiners a djective phrases and nouns
step NUM in this step we modify the grammar of step NUM so that every initial tree t e i satisfies the following property fl let the label of the root of t be ai
ii agent orlented adjectives intelligent ingdnieuz clever habile skilful adroit dextrous
this representation and the way to project it at the syntax level will be the focus of the following section
i e considers then that a feeling can have a external n tanif e sl at ion
une destruction furieuse a furious destruction null NUM a un homme ingdnieux a clever man b
state example 3e or its cause examples 3a b
the manifestation sense is impossible as sapin versus book has no intellectual act in its qualia
each of these types can be projected independently if no other constraints apply see NUM NUM
we showed how gl can adequately account for the following a avoiding the multiplication of entries
there are indeed two ways of referring to a quale role direct saturation of a quale role
we measure the speed of training by the number of training epochs required to complete training where an epoch is a single pass through all the training data
data drivenness the scheme must provide representational means for all phenomena occurring in texts
lastly each word is allocate d a textref
simple meaning based translation currently italian to english
the semnet is a NUM NUM node directed hyper graph
figure NUM shows the semantics created for mr
full parsing can force poor local decisions
in the case of hunting rifle the telic of rifle which is fire provides the agentive within the telic of the compound
participants in these predicates other than the knife itself are listed as default arguments d ar i d arc2 and d arg3 in
we would also like to point out that we do not expect to develop an analysis which will handle all and every compound form
it is reasonable to assume however that this corpus provided sufficient coverage of lexical and morphological subcomponents of city names
in italian for telic modification the preposition is da when the modifier describes an activity and di when the modifier describes a result
for example a form like bone knife could be interpreted either as a knife used for cutting bone or a knife made of bone
the indeterminacy with respect to which argument in the telic is coindexed with the modifier in schema NUM is a shorthand representation
in this paper we have shown how the theory of qualia structure within the generative lexicon enables a compositional treatment of compounds
the implementation of the first phase as described in the following paragraphs is completed
two references are needed to form a chain
it is important to note however that not all italian complex nominals involving post modification can be translated as noun noun compounds in english
translation of complex nominals from italian to english will be more straightforward since there is a loss of information rather than a gain
menus are supported as a usefld way of getting help on commands and labels
therefore information about the surface form was discarded
class methods based on taxonomic information may provide more comprehensive information for a larger number of lexical acquisition tasks
experimentation has been carried out over set of NUM NUM disambiguated contexts of about NUM verbs randomly extracted fzom rsd
lexical acquisition la processes strongly rely on basic assumptions embodied by the source information and training examples
bracketed corpora are core components of an underlying grammatical knowledge to which resuits of different inductive methods equivalently refer
such equivalence is no longer valid for semantic tagging when corpora as well as underlying domains change
a naive semantic type system allows a number of lexical phenomena to be captured with a minimal human intervention
further induction would allow to assign thematic descriptions to arguments in order to extend NUM in
for example for the verb require we extract the following syntactic collocations from the source document
as ah eady mentioned these more or iess undecidable bidirectional patterns have been observed and discussed by others working with tile tagging of large corpora and they have seemingly independently of each other come up with similar suggestions
head dependent relations represent specific relations such as modiflee modifier predicate argument etc
a complete sequence is composed of sub null complete sequence and sub complete link
it is determined by the direction of the outermost dependency relation
because gate places no constraints on the linguistic formalisms or im formation content used by creole objects or for that matter the programming language they are iinplemented in the latter problem must be solved by dedicated translation functions e.g.
what does a research group do which either does not have the resources to tmiht such a large system or even if it did would not want to spend effort on areas of language processing outside of its particular specialism
creole should expmld quite rapidly during NUM NUM to cover a wide range of le i lcb d components but for the rest of this section we will use ie as an example of the intended operation of gate
this solution is to adopt a common model for expressing information about text and a common storage mechanism for managing that information thereby cutting out signif leaned parts of the integration overheads that often block algorithmic reuse
one of the ohjectives of the project is to rouse ex isting resources for those subtasks for which at propriate resources exist l or the german tactical generator NUM an irnph mentation of an hps style gramrm r of erman used for parsing and genre ation but on a different software platform was available inhouse
the sax and sgx systems use an efficient hart implementation and their concurrent processing algorithms give turther motivation for eliminating enlpty categories and reducing nondeterminism
the first author would like to thank mr ilitoshi suzuki sharp cort oration and prof aun ichi tsujii umist for making this work possible
we then show how delayed lexical choice can be used in parsing so that some types of ill formed inputs can be parsed but well formed outputs are generated using the same shared linguistic information
we describe a bidirectional framework for natural language parsing and generation using a typed feature formalism and an hpsg based grammar with a parser and generator derived from parallel processing algorithms
reversible delayed lexical choice in a bidirectional framework
because of the relative informality of these evaluation arrangelnents and as the range of evaluation facilities in gate expands beyond the four ie task of tile current muc we should also be able to offset the tendency of evaluation progralnnms to lamt en imlovation
because of corpus availability we can not make single domain grammars of large size training corpus as text
in the romance and love story domain the grammar acquired from the same domain made the solo best performance
here we can find a relationship between these results and the cross entropy observations
for the remaining candidates identification of particular categories of substrings where a general exclusion rule may apply was attempted
we use the domains of press reportage and romance and love story in this intensive experiment
the first is the individual experiment where texts from NUM domains are parsed with NUM different types of grammars
first we investigate the syntactic structure of each domain of the brown corpus and compare these for different domains
except for the case of n with the non fiction grammar these observations explains the result of parsing very nicely
both interpretations specify one common permissible hyphen point which is therefore non ambiguous
after most of an utterance has been seen
for example the grammar of all domains is created using corpus of NUM samples each from the NUM domains
each grammar is acquired from roughly the same size NUM samples except l with NUM samples of corpus
therefore the impermissible hyphen point is located between the two parts in all combinations
it should perhaps be stated that the system itself may not have the capacity to be generalized to other languages
as we shall see in the following section vowel splitting is not always permissible
loanword hyphenation is governed by the same grammar rules as the rest of the language
the words are then classified into so called ambiguity classes according to which set of readings they have been assigned
the automatic tagger sometimes mistakes singular nouns of that declension without modifiers for plurals but never the other way round
the entire corpus of NUM million words has passed through this stage of manual disambiguation and annotation which makes it an important standard that can be used as a tool e.g. when training probabilistic taggers
before concluding with a close analysis of the romanian text we should note that in both the brown corpus and the email corpus clausal adverbial on the other hand occurs more frequently without an expectation raising on the one hand than it does with one
null in the left to right step we enclose in angle brackets all the substrings starting at a location marked in effect no starting location for a replacement can be skipped over except in the context of another replacement starting further left in the input string
they should have what i call a mirror character in that the interchange goes in both directions and they should concern clearly distinct pairs of tags even when a word has several other tags as well
it is still an open question whether the more clearcut distinctions introduced by the underspecified tags compensate 1or the accompanying disadvantages but at least they have the intellectually pleasing property of showing where there are truly ambiguous situations in language
control over test data what makes test suites valuable in comparison to corpora is that they can focus on specific linguistic phenomena and that each phenomenon can be presented both in isolation and controlled combinations in which as many linguistic parameters as possible are being kept under control
tsni i has already achieved a substantially broader and deeper overage than previous general purpose test suites the still very popular hewlett paekard tes suite for instance has a overage of NUM test items for english only
the presupposition of very si ecific assumi tions of a particular theory of grammar or of a language and rather try to capture those distinctions that seem to be relevant across the set of tsni t core phenomena
lit analysis capabilil lcb es of the sysl c3n at0 inl null it d NUM of the tsni p os items were not flflly analyzed
as part of the ustomization t ro ess users of the tsni p est suil es are eli ouraged to extend this part of the test suite database and a ld whatever formal or informal infor eeific requirements
ratings of fl e luency or relewm e or a particular onlailt i8 factore fl om the remainder of the data into user NUM application profile
additionally tsct allows reusing previously constructed and annotated data as quite often when constructing a series of test items it can be easier to duplicate and adapt a sintilar item rather than t roduce annotations froul s ratch
negative test data permits testing for overgeneration as well as for coverage ill formed items are derived from well formed ones by systematic variation of the parameters through the application of one or more of four operations namely replacement e.g.
let us denote the descendants of nonterminal x by nonterminal can descend from at most one first pass nonterminal in each cell
hwa personal communication using a model similar to pcfgs stochastic lexicalized tree insertion grammars also was not able to obtain a speedup using this technique
recall that in beam thresholding we compare nodes n k and n k covering the same span
this introduces problems since in many pcfgs almost any combination of nonterminals is possible perhaps with some low probability
as mentioned earlier the problem with beam thresholding is that it can only threshold out the worst nodes of a cell
in section NUM NUM we will show experiments comparing insideprobability beam thresholding to beam thresholding using the inside probability times the prior
if we get a change that makes both time and entropy better then we make that change regardless of the ratio
in fact it must be part of a sequence of nodes stretching from the start of the string to the end
under these circumstances no non zero probability parse will be thresholded out but many zero probability parses may be removed from consideration
there is no reason that we can not use beam thresholding global thresholding and multiple pass parsing all at the same time
additional advantages specific to a memory based approach include i the relatively small tagged corpus size sufficient for training ii incremental learning iii explanation capabilities iv flexible integration of information in case representations v its non parametric nature vi reasonably good results on unknown words without morphological analysis and vii fast learning and tagging
to support bottom up parsing of noisy material containing gaps and fragments longer range predictions are needed as well
it addressed the user with the request of confirmation of t3 s
those may be so either because of recognition errors or because of user s misconceptions
moreover several city names contained in the dictionary of the system could be easily confused
the dialogue system initially interprets user s correction with respect to its current set of expectations
for example let us consider the excerpt from the dialogos corpus shown in figure NUM
by analizing the dialogos corpus we identified some topics that require further work
the task oriented semantic frames the input of the dialogue module have been put between angles
some of these errors have important consequences in reducing the naturalness of human machine dialogues
however the recognition of spontaneous speech in telephone environment is below that rate
communicative acts are defined in terms of monolingual conventions for expressing certain communicative goals using certain cue patterns
instead we restrict our attention to communicative goals which can be expressed using conventional linguistic cue patterns
in this paper we have argued that chinese word segmentation can be modeled effectively using weighted finite state transducers
second comparisons of different methods are not meaningful unless one can evaluate them on the same corpus
note also that the costs currently used in the system are actually string costs rather than word costs
in section NUM we discuss other issues relating to how higher order language models could be incorporated into the model
costs for unseen bigrams in such a scheme would typically be modeled with a special backoff state
NUM it is fairly standard to report precision and recall scores in the mid to high NUM range
the initial stage of text analysis for any nlp task usually involves the tokenization of the input into words
as we shall argue the semantic class affiliation of a hanzi constitutes useful information in predicting its properties
mutual information was shown to be useful in the segmentation task given that one does not have a dictionary
however the characterization given in the main body of the text is correct sufficiently often to be useful
this separation of general linguistic domain and transfer knowledge improves portability and scalability of the system
furthermore a speech translation module also has to handle the errors introduced by the speech recognition component
first we can use bayes law to obtain a reexpression of the conditional probability that needs to be maximized
the two distortion operators alter word and alterfeature perform the function of matching semantically similar words or feature values
a thesaurus is a semantic ia a hierarchy whose nodes are semantic categories and whose leaves are words
the proposal is to focus on discovery and exploitation of these conventionally expressible speech acts or communicative acts
for just these goals relatively fixed expressive patterns are learned by speakers when they learn the language
ears are too big prt he rejects the princess criticism that his ears are too big
accordingly the model must cope with the fact that training data is much more likely to contain errors
in this example morpho syntactical information is not sufficient to determine that the nominal phrase np die c konomin the economist is the subject of the verb and np eine hohe inflationsrate a high inflation rate its object
since the nc preceding the verb is unambiguously nominative and the one following the verb possibly accusative the training tuple tennisspieler trainieren jahr NUM ten null nis player train year is produced for this sentence although the second nc is not an object of the verb
it is based on shallow parsing techniques employed to collect training and test data from un ambiguous examples in a text corpus and the back off model to determine which np in a morpho syntactically ambiguous construct is the subject object of the verb based on the evidence provided by the collected training data
let ps be the set of lemmata occurring in the training triples obtained from a sample text and let c nl v n2 x denote the frequency count obtained for the training tuple nl v n2 x x e lcb NUM NUM rcb
when the verb v in a test tuple nl v n2 does not occur in any training tuple the default po llnl v n2 NUM NUM is used it reflects the fact that constructs in which the first noun is the subject of the verb are more common
in the estimate pbo wnlw i only one relation the precedence relation is relevant to the problem in the current setting one would like to make use of two implicit relations in the training tuplc subject and object in order to produce an estimate for p l nl v n2
however i have touched upon a number of research areas which seem to me of particular interest
customizing extraction systems to a given application domain is a difficult process
recognition and mt analysis in a certain sense speech recognition and analysis for mt are comparable problems
lexical level spanish phonetics allows the representation of each word as a sequence of phones that can be derived from standard rules
NUM a period for example can denote a decimal point an abbreviation the end of a sentence or even an abbreviation at the end of a sentence
in other words all columns of the ll table are identical with the exception of cell x0 wp in grammar NUM
for the purpose of the following discussion it is only necessary to recall that a chain can only contain one thematic position and one position that receives case
in fact if independent modules are separable modules there is little reason to think that gb is modular as it corresponds to a highly connected graph
the amount of nondeterminism is measured as the average number of conflicts the ratio between the number of actions and the number of entries in a table
thus each principle can generate a set the encoding of which would require a much larger number of bits than the bits needed to encode the principle itself
if one looks at several of the principles of the grammar that are involved in building structure and annotating the phrase marker one finds the same internal organization
first each principle of linguistic theory has a canonical form and second primitives of linguistic theories can be partitioned into classes based on their content
i who i did you meet t i oi without greeting ti computational linguistics volume NUM number NUM such as the head of a wh chain
if not all the chains satisfy the well formedness constraints the parser can attempt to intersect or compose two or more chains in order to satisfy the well formedness conditions
without a complex control strategy late evaluation it is not possible to implement an infinite lexicon
the fronted partial phrase is the filler for a nonlocal dependency which is introduced by their pvp topicalization lexical rule
however an appropriate sign is inserted into the domain of its head when the nonlocal dependency is bound
therefore the infor ination about the nonlocal dependency is present and can be used to license the extracted element
b er wird sciner toehter ein m irchen erz ihlen miissen
adjuncts and complements are inserted into the domain of their head so that word order facts are accounted for
firstly it is not possible to account for cases where a modifier in the mittelfeld modifies the fronted verbal projection without assuming an infinite lexicon because the only way for a modifier to stay in the mittelfeld while the modified constituent is fronted is that the modifier is contained in the slasit set of the fronted constituent
as slasii elements are signs the lexical rule can refer to the slash set of a slaslt element and it is thus possible to establish a relation between the comps list of the auxiliary and the slash set of the fronted verbal 7due to space limitations i can not give a detailed discussion of their approach here
4in fact the muc NUM structure wa s much nor complex because there were separate temt lates for products time activities of organizations etc the representatives of the resear h community were jim cowie ls alph
this paper looks briefly at the history of these conferences and then examines the considerations which led to the structure of muc NUM rcb the message understanding conferences were initiated by nosc to assess and to foster research on the automated analysis of military messages containing textual information
in and out in and out NUM i33 NUM vacancy reason oth unk in and out NUM NUM io person person NUM NUM new status in on the job yes other org organization NUM NUM rel other org outside org organization NUM i org name mccann org type company organization NUM NUM org name j
although it is difficult to meaningfully compare results on different scenarios the scores obtained by most systems after a few weeks NUM to NUM recall NUM to NUM precision were comparable to the best scores obtained in prior mucs
as the specification finally deveh ped tit reinplate element for orgalfizations had six slots for the inaximal organization nalne any aliases the type a descriptive noun phrase the locale inost specific location and country
since resolution of equations is time consuming we tentatively generalized NUM NUM nouns into NUM semantic classes represented by the first NUM digits of the semantic code given in the bunruigoihyo thesaurus reducing the total number of equations to NUM NUM
in a similar manner the figures for lexicon size in mt systems a lexicon of more than NUM NUM
scores on test material in preliminary testing have ranged between NUM and NUM
in assessing the quality of a referent resolution model it is however also necessary to analyze the internal affairs of the model and determine the inherent limitations that follow from its design
NUM the programming language commonorbit used in edward provides pointers back from the object to the cfs that have the object in its scope which compares to alshawi s notion of marking
an indicated referent cf has an initial significance weight of NUM to make sure that the referent in its scope computational linguistics volume NUM number NUM table NUM the four types of referring expressions
the same model and knowledge base are used by edward s generation component to decide the form e.g. he the writer a man the the main components of edward
the user can interact with edward by manipulating the graphical representation of the file system a directed graph by menus by written natural or formal language or by combinations of these
it would be interesting to explore how the insights of grosz and sidner with respect to discourse coherence can be used to elaborate edward s context model to render it able to deal with subdialogues
however assisting in the adaptation of an existing algorithm to different segmentation schemes as discussed in section NUM would most likely be performed with an already accurate fullydeveloped algorithm
roughly NUM of the data was used to train the segmentation algorithm and NUM was used as a blind test set to score the rules learned from the training data
since our transformations consider only a single character of context the learning algorithm was unable to patch the smaller segments back together to produce the desired output economic situation
in this paper we will report all scores as a balanced f measure precision and recall weighted equally with NUM such that
the first extraction problem that we are tackling is learning to identify entities
the results summarized in table NUM clearly indicate not surprisingly that more training sentences produce both a longer rule sequence and a larger error reduction in the test data
if no match is found in the word list the greedy algorithm simply skips that character and begins the search starting at the next character
figure NUM shows the architecture of the tagger generator a tagger is produced by extracting a lexicon and two case bases from the tagged example corpus
the size of the resulting word lists and their out of vocabulary rate oov rate in the test sentences are shown in the second and third colnmn of table NUM
in addition to the problem of multiple correct segmentations of the same texts the comparison of algorithms is difficult because of the lack of a single metric for reporting scores
the initial segmentation was performed using the maximum matching algorithm with a lexicon of NUM thai words from the word separation filter in ctte a thai language latex package
since the maximization is carried out with fixed character sequence c the word segmenter only has to maximize the probability of the word sequence p w
the information gain feature relevance ordering technique achieves a delicate relevance weighting of different information sources when they are fused in a single case representation
in our tagging application this means that only context feature values that actually contribute to disambiguation are used in the construction of the tree
punctuation marks which are ignored in abney s chunk grammar but which the treebank data treats as normal lexical items with their own part of speech tags are unambiguously assigned the chunk tag p items tagged p are allowed to appear within n or v chunks they are irrelevant as far as chunk boundaries are concerned but they are still available to be matched against as elements of the left hand sides of rules
in the preliminary scan of the corpus for each learning pass it is these templates that are applied to each location whose current tag is not correct generating a candidate rule that would apply at least at that one location matching those factors and correcting the chunk tag assignment
reasonable suggestions for baseline heuristics after a text has been tagged for part of speech might include assigning to each word the chunk tag that it carried most frequently in the training set or assigning each part of speech tag the chunk tag that was most frequently associated with that part of speech tag in the training
as shown in fig NUM transformation based learning starts with a supervised training corpus that specifies the correct values for some linguistic feature of interest a baseline heuristic for predicting initial values for that feature and a set of rule templates that determine a space of possible transformational rules
in the first of the basenp rules adjectives with part of speech tag j j that are currently tagged i but that are followed by words tagged NUM have their tags changed to NUM in rule NUM determiners that are preceded by two words both tagged i have their own tag changed to b marking the beginning of a basenp that happens to directly follow another
however even though the confusion matrix does not usefully subdivide the space of possible rules when the tag set is this small it is still possible to apply a similar optimization by sorting the entire list of candidate rules on the basis of their positive scores and then processing the candidate rules which means determining their negative scores and thus their net scores in order of decreasing positive scores
for object oriented templates used in muc NUM the output script must recursively traverse the collecto r representations and apply conversion routines for each sub template
the generator actually produces a template data structure which can be easily printed but also fed directly to hasten s scoring program
the template element task required NUM organization egraphs in order to extract the locations nationalities local descriptors and unnamed organizations
hasten computes the similarity between an annotate d example and the subsequent text and uses that computation to decide how to analyze it
hasten and nametag performed very well on the selected walkthrough document achieving a recall precision of NUM NUM for the base and no ref configurations
the allcaps configuration achieve s NUM NUM due to additional erroneous names big hollywood talent agency james places
hasten did not possess an egraph to match coke headquarters in atlanta thus causing a missing locale and country fill
its limits are determined by what is analyzed f su uctm es
variables introduced by error rules into the surface string are then instantiated by associating surface with lexical and matching lexical strings to the lexicon tree s
idiosyncrasies the application of a morphographemic rule may have constraints as on which lexical morphemes it may or may not apply
for example samaap heaven lcb iyy relative adjective surfaces as samaawiyy where p w in the given context
the rules are accepted rejected by checking that the lexical string s can extend along the lexical tree s from the current position s
morphemes are represented in braces lcb rcb surface phonological forms in solidi and orthographic strings in acute brackets
NUM mothers of reading these are consonantal letters which play the role of long vowels sad are represented in the pattern morpheme by vv e.g.
the substituted surface may be in the form of a variable which is then ground by the normal analysis sequence of lexical matching over the lexicon tree
moving horizontally across the table one notices a change in vowel melody active lcb a rcb passive lcb ui rcb everything else remains invariant
if this error rule succeeds an expectation of further shifted vowels is set up but no other error rule is allowed in the subsequent partitions
vowel and diacritic shifts semitic lan null guages employ a large number of diacritics to represent enter alia short vowels doubled letters and nunation
then name recognition techniques much like those of information extraction could be used to find candidate matching names in free text and name matching techniques much like those of database applications could be used to determine whether names identified in query and text matched
it was first applied to linguistic description by adjukiewicz and bar hillel in the 1950s
given a selected pair ub lb it may well be the case that several words are not assigned to any category because when branching from an overpopulated category to its descendants some of the descendants may be underpopulated
each node has as its primary constituents a set of specifications and a set of flags representing its state
u is the sum of the edit costs required to traverse the graph path and match a grammatical input
without hash table unification based upon the tie word list all four words would have unique and independent context vectors
because all the text is used during training second order relationships will be formed between non tie words in different languages
commandment iv stresses the importance of error prevention iv a and error handling iv b
finally as innovative text visualization techniques are found multilingual text processing will surely enhance the value of such technology
this is not so in the previous approach where the user is limited to using only tie words as query terms
document context vectors are derived as the inverse document frequency weighted sum of the context vectors associated with words in the document
note that the matchplus approach does not use any external dictionaries thesauri or knowledge bases to determine word vector relationships
we can then use this information when building the disjunction e u to factor out the constraints introduced by nodes in must occur v i.e. we build the factor av em ust occur v z u and a remainder constraint e ui for each disjunct
for two satisfiable constraints c and generalise c b yields the triple c NUM such that contains the common part of c and NUM and c represents the remainder NUM and likewise NUM represents NUM
NUM for any node v we have the equivalence v sem u a d v where d u shall denote the constraint obtained from d v when recursively replacing names by the constraints they are bound to in env
the process of semantics construction shall be a completely monotonous process of gathering constraints that never leads to failure
second we assume that the set of syntax trees can be compactly represented as a parse forest cf
due to combinatorial explosion the naive method of building semantics for the different syntactic readings independently is prohibitive
we allow to our disposition a second set of variables called names and two special forms of constraints NUM def name constraint name definition NUM name name use with the requirements that a name may only be used if it is defined and that its definition is unique
we explore the fact that the parts of the e constraints in a disjunction that stem from nodes shared by all disjuncts must be identical and hence can be factored out more precisely we can compute for every node v the set must occur v of nodes transitively dominated by v that must occur in a tree of the forest whenever u occurs
example an example for such a generalisation operation for prolog s constraint system equations over first order terms is the so called anti unify operation the dual of unification that some prolog implementations provide as a library predicate s two terms t1 and t2 anti unify to t iff t is the unique most specific term that subsumes both t1 and t2
in this section we describe how the basic model is extended to treat phenomena that violates this assumption
but the user can also validate it either explicitly by saying yes or ok or implicitly by keeping silent those who do n t speak agree or by proceeding with the discourse via the utterance of a new command e.g. play kiss fm
nodes that are small or not even visible such as those in the upper left comer of the map represent themes that occur with a much lower ffi equency and volume relatively speaking
the measure of accuracy rate of parse tree selection has been widely used in the literature
robust learning smoothing and parameter tying design corporation bdc were collected
later we will show how to improve the baseline model with the proposed enhancement mechanisms
the above mentioned robust learning procedure starts with the parameters obtained by the maximum likelihood estimation method
the investigation for the smoothing robust learning hybrid approach is presented next
in addition these expectations would include responses that can be interpreted as state descriptions of the relevant property
to explore the areas for further improving the system the remaining errors have been examined
thus this approach makes it possible to tune all the parameters through the learning process
since there are NUM words including punctuation the total number of strings would be NUM NUM NUM
a test set of NUM NUM sentences extracted from technical manuals is used for evaluation
accordingly we use a hybrid approach combining the robust learning procedure with the smoothing method
since the sentence is a default pausing node subcategorized sentence usually is already fixed as a finite form before the constraint is applied
since a relatively short visual message might be missed vision is employed for driving essential information should at least see NUM be conveyed via sound NUM since speech is transient information which the user may need to consult more than once should be available via vision
evaluations indicate that by accounting for term variation using corpus tagging morphological derivation and transformation based rules NUM NUM more can be identified than with a traditional indexer which can not account for variation
in addition inflection are re calculated in every translation even if the translation of that word has been already fixed by a former translation
this property guarantees that two overlapping translation units can always be combined in our stepwise bottom up translation method
to move on to the concerns regarding what is counted in exploring word frequency lists we are also investigating a hypothesis
one possible solution would be to provide more disambiguation information possibly a sequence of dialogues to help the user to make decision
by choosing the third alternative it is translated to an auxiliary can showing capability
then the user can browse all the articles relevant to their profile
both inroute and inquery return identical scores for a given document query pair
develop algorithms for incremental relevance feedback to replace the existing batch oriented feedback
the article is displayed in its entirety
each headline is linked to its article
search results are stored in hit folders
this threelayered architecture offers plug and play design
volunteer pilot users will be selected from among the fbis analysts and consumers
the hard copy format is timely but difficult to work with
ie the above inequality is strictly less than or greater than
the cut off frequencies must then be chosen
else backing off continues in the same way
this section gives results which justify this
the verb and preposition fields were converted entirely to lower case
the architecture will promote easy insertion of new technology into existing environments because it defines a common set of external interfaces
once the requirements are established the cotr must be able to evaluate the technical and cost alternatives for meeting them
with interfaces clearly defined it will be easier for the researcher to insert measurement tools around new and supporting components
the tipster architecture will support the r d researcher with many of the same capabilities as are provided for the application developer
the architecture has been designed to meet a large number of text handling requirements for cia dia and nsa
finally the architecture will allow systems to be upgraded in a modular fashion as new text handling technology becomes available
like the end user the cotr program manager must be able to specify requirements but at a more technical level
persistent knowledge is knowledge which is retained i.e. stored from one run to the next of an application
it also provides a specification of the way these different types of information should be marked as input into the architecture
the higher this percentage the more susceptible seems the possibility of specifying a workable grammatical representation
in other words the controversy regarding the specifiability of a grammatical representation is a fundamental issue
input using mainly structural but in some structurally unresolvable cases also higher level information
we argue that a consistently applicable representation for morphology and also shallow syntax can be specified
a practically NUM interjudge agreement can be reached at the level of morphological incl
sometimes however there was a need to dis null cuss the descriptive policies
NUM a shallow dependency oriented functional syntax can be defined very much like a morphological representation
each morphological analysis usually consists of several tags and many words get several analyses as alternatives
NUM the value added information is the kind a n we want ourselves
after negotiations the judges agreed about the correct analysis or analyses in all cases
so far much r la v work about word classification has been done
for each step of derivation the premises derived in the previous context and the inference method such as the application of a particular theorem or definition must be made clear
figure NUM alternatives window for rareru
the final section is the conclusion
steps of interactive translation can be summarized as below
the second line is highlighted as the current selection
instead only a privileged program called the coordinator can read from it and write to it
though the form in which the vector is written may give an illusion of representing order no sequential order is maintained
for instance superlative adjectives can act as nouns so they are initially given the NUM tags noun or adjective
NUM this parser has been trained to find the syntactic subject head that agrees in number with the main verb
however this approach does not fully capture the sense in which inhibitory factors play a negative and not just a neutral role
NUM the word waters could be a 3rd person singular present verb or a plural noun
for the first processing stage we need to place the subject markers and as a further task disambiguate tags
sentences are put through the preprocesser one at a time and the candidate strings which are generated are then presented to the network
with NUM tags NUM hypertags and a start symbol the upper bound on the number of input nodes is NUM NUM
for the prototype in which users can process their own text the net was trained on the whole corpus slightly augmented
for the results given below the networks were trained on part of the corpus and tested on another part of the corpus
this is partly due to the relative scarcity of nattached l organizational noun phrases
the responses scored were produced with the original system configuration which uses the ranked selection system
our process stores each newly recognized named entity along with its computed variations and acronyms
this heuristic turned out to be detrimental to performance it suppressed the descriptor scores substantially
approximately ten missed aliases were due to the fact that the names themselves were not recotmited
the following is an example of a confusing name changing scenario which the automatic system missed
we believe that this success is due to our method of collecting related information during name recognition
unusual firstname seven person aliases were missed because the system did not know the firstname e.g.
coreference is typically defined to mean the identification of noun phrases that refer to the same object
confusien can quickly ensue when trying to link an alias with the correct entity in this case
where s is the set of all the possible modification structures c npi is the count of the noun phrase npi in the corpus and pc npi sj gives the probability of deriving the noun phrase npi using the modification structure sj
the example NUM below shows that the resolution of the anaphoric pronoun that must be performed first and that the pp starting with of be attached later
the anaphora module is called again to resolve the anaphoric pronoun its which is possible in this example since the previous pps have been attached and there is no anaphors before
left unresolved apply the anaphora module again
when a sequence contains more than two such pps i.e. with anaphors as objects the length of a cycle is more than NUM
the pp attachment procedure is then called to determine the attachment of since and at while the object of the in pp comprises an anaphoric pronoun its case c
a theory r contains horn clauses v p a
the sizes in the horizontal axis refer to the first three columns in table NUM a
each one was divided in training and test sets with NUM NUM and NUM NUM pairs respectively
the category expansion step is a bit more complex than just substituting each category labeled arc by the corresponding csst
after characterizing these expectations and their distribution in text we show how an approach that makes use of substitution as well as adjoining on a suitably defined right frontier can be used to both process expectations and constrain discouse processing in general
in contrast we sometimes call the rf of a tree without substitution sites the outer right frontier or outer rf figure NUM d illustrates adjoining on the inner rf of a a tree with a substitution site labeled h
the much smaller email corpus contained six examples of clausal on the one hand with the target contrast cued by on the other hand on the other or at the other extreme
the adverbial on the one hand is used to pose a contrast either phrasally both plans also prohibited common directors officers or employees between du pont christiana and delaware on the one hand and general motors on the other
this would mean that later substitution at either of them would lead to a violation of the principle of sequentiality since the newly we currently have no linguistic evidence for the structure labeled in figure ld but are open to its possibility
here we are focussed on discourse at the level of individual monologue or turn within a larger discourse what we show is that discourse manifests certain forward looking patterns that have similar constraints to those of sentence level syntax and can be handled by similar means
the contrast introduced by on the other hand in sentence NUM b leads to the auxiliary tree shown in figure 4b ii where t stands for the elementary tree corresponding to the interpretation of suppose
the level scitech denominates scientific or technical writings and legal characterizes various types of writings about law and government administration
all means that when all texts were subjected to the narrative test NUM of them were classified correctly
for our experiments we analyzed the texts in terms of three categorical facets brow narra tive and genre
brow characterizes a text in terms of the presumptions made with respect to the required intellectual background of the target audience
this section discusses generic cues the observable properties of a text that are associated with facets
we will replace these cues with two new classes of cues that are easily computable character level cues and deviation cues
trend correctly but not necessarily significantly for scitech and nonfiction but perform less well for editorial and legal texts
the main remaining technical challenge is to find an effective strategy for variable selection in order to avoid overfitting during training
in this discussion section we want to address three issues
the dm can put up this first candidate for validation
this yields undesirable effects in case of noisy input like the one obtained by ocr or speech recognition
no matter which approach one takes however each of the numbers in the table is significant at p NUM
table NUM by contrast shows which classifications are assigned for texts that actually belong to a specific known level
an appropriate domain expert is confirmed through the elaboration of an appropriate dialogue model
the domain expert is used to instantiate and evolve a related dialogue model
a domain spotter supports the ability to move between domains and between individual skillsets
an object oriented development paradigm offers valuable insights into how these challenges might be addressed
the dialogue manager is responsible for select null ing the current dialogue intention of which there are several subclasses
find enquiry type the find enquiry type class a subclass of dialogue intention allows the dialogue manager both to prompt the user into specifying the nature of his her inquiry and to interpret the nature of a user s utterance when it receives an indication that the user has spoken unprompted
identifying and exploiting areas of commonality and specialisation between different processing domains promises rich rewards
again these skills and expertise subclasses provide the dialogue intention subclass with the necessary heuristics to instantiate a dialogue model
however most dialogue intentions make use of the skill and domain expert classes whose heuristics permit rather more specialised enquiries involving either generic but complex skillsets working with colors or gathering address information for example or specialised application domains organising travel itineraries or booking theater tickets for example
if this does indeed prove to be the case our dialogue model will have attained its core communicative goal more than this its object oriented architecture will facilitate the work of the software engineer by providing a set of discrete components that can be easily reused modified or extended in new dialogue systems
where distancez and distancer are functions of the surface string from the head word to the edge of the constituent see figure NUM
the bulk of the time spent in knowledge engineering was spent developing the patterns for all th e reduction and extraction stages
an inference algorithm known as ostia onward sub21n this paper the term function refers to partial functions
it is a moot point whether ready moves should form a distinct move class or should be treated as discourse markers attached to the subsequent moves but the distinction is not a critical one since either interpretation can be placed on the coding
of these kinds of evidence only the last three count as acknowledge moves in this coding scheme the first kind leaves no trace in a dialogue transcript to be coded and the second involves making some other more substantial dialogue move
if the information is substantial enough then the utterance is coded as a reply followed by an explain but in many cases the actual change in meaning is so small that coders are reluctant to mark the addition as truly informative
finally although the other coding schemes appear to have been devised primarily with one purpose in mind this coding scheme is intended to represent dialogue structure generically so that it can be used in conjunction with codings of many other dialogue phenomena
that is the measure considers each place where any coder marked a boundary and averages computational linguistics volume NUM number NUM the ratio of the number of pairs of coders who agreed about that location over the total number of coder pairs
the move coding divides the dialogue up into segments corresponding to the different discourse goals of the participants and classifies the segments into NUM of NUM different categories some of which initiate a discourse expectation and some of which respond to an existing expectation
second some coders marked a reply while others split the reply into a reply plus some sort of move conveying further information not strictly elicited by the opening question i.e. an explain clarify or instruct
as for the other replies whether the answer is coded as a reply y or a reply n depends on the surface form of the answer even though in this case yes and no can mean the same thing
the coding system has two components NUM how route givers divide conveying the route into subtasks and what parts of the dialogue serve each of the subtasks and NUM what actions the route follower takes and when
for instance in the np le commutateur the switch le should be linked to commutateur the head which in turn should be linked to the verb tourne and not to the verb retourne because the two words are not in the same segment
a l heure vendredi soir off les troupes sovidtiques s appr taient pdndtrer dans bakou la minuscule rdpublique autonome du nakhitchevan territoire az6vi enclav en armdnie d la fronti re de l iran proclamait unilatdralement son ind endance par ddcision de son propre soviet supreme
one may also use heuristics which go beyond the cautious statements of the core grammar to get back to the example of french subjects heuristics can identify any underspecified np as the subject of a finite verb if the slot is available at the end of the sequence
no feedback on arrival departure day on ba and or on route missing ambiguous feedback on time u has phone no
to achieve this goal we employ several approximations
the parameters of these rules are initimized randomly
this was no surprise as meta comrnunication had not been simulated and thus was mostly absent in the woz corpus
as in prediction forward and inner probabilities are multiplied by the corresponding e expansion probabilities
given these added complications one might consider simply eliminating all c productions in a preprocessing step
each model was tested using only those sentences in the test set that were not seen in training
to prove the existence of rl and ru it is sufficient to show that the corresponding geometric series converge
there were a total of NUM phones including stressed and unstressed vowels plus two types of silence
for organizations th e process involves several steps
in contrast to romper s views which are domain specific that is confined to the domain of financial securities suthers and knight s views are domain independent
as a result it is considerably more difficult for the system to respond to follow up questions reason about paragraph structure perform goal based content determination and produce discourse cues
NUM the degree to which the experiments controlled for specific factors e.g. the effect of example positioning example types example complexity and example order is remarkable
if a kb accessing system could dynamically construct views the discourse knowledge engineer would be freed from the task of anticipating all queries and rhetorical situations and precompiling semantic units for each situation
he also preferred explanations produced by a version of the explain process edp in which the information that had previously been associated with a process significance topic was associated with the temporal attributes topic
our final step was to generalize the most commonly occurring patterns into abstractions that covered as many aspects of the passages as possible which we then encoded in two explanation design packages
pro type material lex form patti agent semantics partic agent semantics
table NUM disambiguation results at the sentence level using rules learned from c2000
NUM we then start going over the corpus token by token generating contexts as we go
it is conceivable that such a functionality can be used in almost any language
the rows labeled base give the initial state of the text to be tagged
to this end we passed the NUM explanations through a length filter explanations that consisted of at least NUM sentences were retained shorter explanations were disposed of
a probable cause of this result lies in the domain in biology process explanations are often more complex than object explanations therefore making process explanations more challenging to generate
automatic morphological disambiguation is a very crucial component in higher level analysis of natural language text corpora
oze contract or treaty is determined using the most probable combination of words in an english monolingual corpus
it may be corrected if the majority of the seeds forms a coherent collocation space
figure NUM sample intermediate state following steps 3b and 3c step NUM
thus the noise introduced by a few irrelevant or misleading seed words is not fatal
note that the original seed words are no longer at the top of the list
this is particularly true for content words which exhibit a bursty distribution
below is an abbreviated example of the decision list trained on the plant seed data
yet in the syntactic lexicon a particular lemma may select a family only partially
this information spread into the hierarchy is used for tree generation following technical principles of wellformedness
in these objects nodes are referred to by constants
this defines the following actual subcategorization arg0 par object argl subject
we have presented a hierarchical and principle based representation of syntactic information
they argue the advantage of using ah eady existing software
a few solutions have been proposed for the problems described above
the set of tree schemata represents syntactic phenomena that are all productive enough to allow monotonicity
further we have chosen monotonic inheritance especially as far as syntactic descriptions are concerned
iu table NUM bbk shows the topic of the article which is tagging in the wsj i.e. buybacks
the first stage for linking nouns with their semanticmly sinfilar nouns is to calculate mu between noun pmr x and y in new articles
we call inlmt and utput in table NUM mt rriginal artme and a new artme respectively
the training tort us we have used ix the NUM NUM NUM wsj ill acl dci cd i lcb om whi h
nltnl iiipalls the nunlber for each article i.e. we selected NUM i sets for each article
we have not fully completed these experiments however and here we only NUM stop words refers to a predetermined list of words containing those which are considered not useful for document classification such as articles and prepositions
tagging accuracy on unknown words using the cascading guesser was detected at NUM NUM when tagging with the full fledged lexicon and NUM NUM when tagging with the closed class and short word lexicon
if the rule is applicable to the word we perform look up in the lexicon for this word and then compare the result of the guess with the information listed in the lexicon
we gathered about three thousand words from the lexicon developed for the wall street journal corpus NUM and collected frequencies of these words in this corpus
even if one rule has a high estimate but that estimate was obtained over a small sample another rule with a lower estimate but over a large sample might be valued higher
of course not all acquired rules are equally good as plausible guesses about word classes some rules are more accurate in their guessings and some rules are more frequent in their application
there are two important questions which arise at the rule acquisition stage how to choose the scoring threshold NUM and what is the performance of the rule sets produced with different thresholds
these texts were not seen at the training phase which means that neither the taggers nor the guessers had been trained on these texts and they naturally had words unknown to the lexicon
since guessing rules are meant to capture generm language regularities the lexicon should be as general as possible list all possible poss for a word and as large as possible
NUM than the grammar which utilizes both syntactic and semantic categories i.e.
filters are needed to transform data back and forth between the central data structure managed by the coordinator a lattice would be appropriate and the data processed by each component
to support persistency in the file based version an application object needs to implement the read persistent and write persistent interfaces this is provided transparently by the persistent versions
the results are compared with respect to the parsing coverage and the misparse rate
for example some morphological analyzers load their dictionary in the process memory and on small documents simply starting the process could take more time than actual execution
NUM more specifically the current gate document manager will be replaced with the corelli document manager and the plug n play layer will be added to support distributed processing
a central coordinator can incorporate knowl null edge about each component but the component themselves do n t have any knowledge about each other or even about the coordinator
the document management service the life cycle service and the naming service are included in the three versions of the architecture which implement increasingly sophisticated support of database functionalities
the basic file based version of the architecture uses the local file system to store persistent data collections attributes and annotations the contents of a document can however be located anywhere on the internet
although integrated development environments address some of the problems they do not give a complete solution since one still has to develop rules and lexical entries using these systems
such instances of ambiguity are usually resolved on the basis of the semantic information
an example of a misparse due to preposition omission is given in figure NUM
because of the similarity of the methods used we do not provide further details about this module
we also show how the lexicon used by the tagger can be optimally encoded using a finite state machine
we thank eric brill for providing us with the code of his tagger and for many useful discussions
the function mdy is total because the function dec always returns an output that is a y decomposition of w
in addition if several decompositions are possible the one that occurs first is the one chosen
in fact consider the input string daaaad it can be decomposed either into d aaa
it is not true that in general the local extension of a subsequential function is subsequential
contrary to what is needed for defining proper r this may wiry with the intended application
the methods used in the construction of the finite state tagger described in the previous sections were described informally
the tagger we constructed has an accuracy identical s to brill s tagger and comparable to statistical based methods
optional agent standard transitive verb word like verb n voice transverb n voice
computational linguistics volume NUM number NUM and translated to n well adverb g1 gi
it also suppom effective and efficient dialogues by identifying the focus of modification based on its predicted success in resolving the conflict about the top level belief and by using heuristics motivated by research in social psychology to select a set of evidence to justify the proposed modification of beliefs
if the user is predicted not to accept a piece of evidence evidi the system will augment the evidence to be presented to the user by posting evidi as a mutual belief to be achieved and selecting propositions that could serve as justification for it
indirect object NUM a word about word order following hellwig s dug formalism our prolog implementation does not code word order directly in the rules
n peter noun n n peter noun n
it does so by gathering in cand set evidence proposed by the user as direct support for bel but which was not accepted by the system and which the system predicts it can successfully refute i.e. beli focus is not nil
n book noun n n det n book noun n
rather than providing an alternative rule for every possible combination of dependents it is more convenient to declare a dependent optional meaning that a sentence is correct independent of its presence
when select focus modification is applied to teaches smith al the algorithm will first be recursively invoked on on sabbatical smith next year to determine the focus for modifying the child belief step NUM NUM in figure NUM
thus when facing a conflict a collaborative agent should not automatically reject a belief with which she does not agree instead she should evaluate the belief and the evidence provided to her and adopt the belief if the evidence is convincing
when an agent proposes a new belief and gives optional supporting evidence for it this set of proposed beliefs is represented as a belief tree where the belief represented by a child node is intended to support that represented by its parent
while extracting the pattern matcher is allowed to overlap patterns because it is not changing the text found it is merely extracting information of interest and sending it to the text organizer
i an ambiguity occurrence or simply ambiguity a of multiplicity n n NUM relative to a representation system r may be formally defined as a u v p1 p2 pm pl p2 pn where m n and u is a complete utterance called the context of the ambiguity
approximately NUM minutes before the discharge starts the computer for NUM seconds to beep d NUM minuten bevor er sich ausschaltet f angt die low battery led an zu blinken
lea preserving alignments are possible which map any two of the leaves
the ranking imposed on the elements of the g i reflects the assumption that the most highly ranked element of g i tin the preferred center cp un is the most preferred antecedent of an anaphoric or elliptical expression in un l while the remaining elements are partially ordered according to decreasing preference for establishing referential links
the theme rheme hierarchy of in is represented by ci u which in our approach is partly determined by the c un i the rhematic elements of un are the ones not contained in c u i unbound discourse dements they express the new information in un
again we abandoned all extra constraints set up in these studies e.g. the zero topic assignment zta rule and the special role of empathy 7in essence the very specific problem addressed by that example seems to be that friedman has not been previously introduced in the local discourse segment and is only accessible via the global focus
only NUM errors of the functional approach are directly caused by an inappropriate ordering of the c i while the naive approach leads to NUM errors and the canonical to NUM when the antecedent of an elliptical expression is ranked above the elliptical expression itself the error rate of these two augmented approaches increases to NUM and NUM respectively
the test set for our evaluation experiment consisted of three different text sorts NUM product reviews from the information technology it domain one of the two main corpora at our lab one article from the german news magazine der spiegel and the first two chapters of a short story by the german writer heiner miiller NUM
hence the proposal we make seems more general than the ones currently under discussion in that given a functional framework fixed and free word order languages can be accounted for by the same ordering principles
grammatical role constraints can indeed be rephrased by functional ones which is simply due to the fact that grammatical roles and the information structure patterns as we define them coincide in these kinds of languages
this performance shows the system s adapt ability
instead a matcher matches new input against a database of already correctly analyzed models and interprets the new input on the basis of a best match possibly out of several candidates robustness is inherent in the system since failure to analyze is relative
furthermore we can extend the knowledge of the system simply by adding more examples if they contain new structures the knowledge base is extended if they mirror existing examples the system still benefits since the evidence for one interpretation or another is thereby strengthened
so in the first case we could choose an open linguistic filter e.g. one that accepts prepositions while in the second a closed one e.g. one that only accepts nouns
where a is the examined n gram c value a calculated from step NUM wei a the calculated from step NUM sum of the context weights for a n the size of the corpus in terms of number of words
this was remedied post evaluation by allowing the system to collect all organizations and to choose a n organization during postprocessing to act as a default succession org for all organization less events
a variation to improve the results that involves human interaction is the following the candidate terms involved for the extraction of context are firstly manually evaluated and only the real terms will proceed to the extraction of the context and assignment of weights as previously
many thanks to jason eisner martha palmer joseph rosenzweig and three anonymous reviewera for helpful comments on earlier versions of this paper
this paper presented an information theoretic method for measuring the semantic entropy of any word in text using translational distributions estimated from parallel text corpora
specifically we first use the lexical likelihood value calculated as the geometric mean of the three word probabilities of an interpretation and when the lexical likelihood values of obtained interpretations are equal including the case in which all of them are NUM we use the lexical likelihood value calculated as the geometric mean of the two word probabilities of an interpretation
NUM chang et al NUM collins and brooks NUM fujisaki NUM hindle and rooth NUM hindle and rooth NUM jelinek et al NUM magerman and marcus NUM magerman NUM ratnaparkhi et al NUM resnik NUM su and chang NUM
furthermore we assume that the attachments in the syntactic tree of an interpretation are mutually independent and we define the product or the sum depending on the preference function of the syntactic preference values of the attachments in the syntactic tree of the interpretation as the syntactic preference of the interpretation
for the phrase shown in figure NUM a there are two interpretations rap hand side of a cfg rule and n the maximum value of lengths of a category on the left hand side of the rule i k NUM k NUM NUM k NUM
the value assumed by a case slot of a case frame of a verb can be viewed as being generated according a conditional probability distribution where random variable v takes on a value of a set of verbs n a value of a set of nouns and s a value of a set of slot names
let us consider another example illustrating how the operation of the length probability model indicates the functioning of alpp
in pcfg a cfg rule having the form of a NUM is associated with a conditional probability p ia and the likelihood of a syntactic tree is defined as the product of the conditional probabilities of the rules which are applied in the derivation of that tree
we further checked the disambiguation decisions made by syn when lex3 and lex2 fail to work and found that all of the prepositional phrases in these sentences were attached to nearby phrases by syn indicating that using syntactic likelihood can help to achieve a functioning of rap
in an attempt to overcome this we adopt an empirical approach to obtaining rules based on observations of real texts
the results of the comparison show that the rules are fairly effective in dealing with the generation of anaphora in chinese
on the other hand the percentage of inanimate anaphora being encoded in nominal forms is higher than that of pronouns
then in the last sentence it repeats the same patterns as in the second sentence
for reasons that will be elaborated in section NUM our problem is most acute in hebrew and some other languages e.g. arabic though ambiguity problems of a similar nature occur in other languages
when flying a kite in the sky b the string pulling the kite ij ca n t be pulled straight
one rule of thumb that can be followed in case of marking restarts without repairs is that they are always at the beginning or in the middle of the sentence the sentence continues after the restart and the restart usually comprises one to three function words
the notion of linguistic segmentation is important in language modeling because it provides information that is used in many higher order language models for example the given new model described in the next section phrase structure language models or sentence level mixture models
ex3 b lcb pwell rcb lcb fuh rcb wejustmovedrecently laughter lcb c so rcb now we re in the lcb f uh rcb dallas area ex NUM a he s pretty good
tr1 and tr2 produce the same output and hence they obtain the same matching rate NUM
the method we are about to present saves us the laborious effort of tagging a large corpus and enables us to find a good approximation to the morpho lexical probabilities by learning about them from an untagged corpus
second is the sentence itself a good unit to be modeling or should we look at smaller units for example dividing a sentence into a given and new portion and segmenting out acknowledgments and replies
the mismatch between ptest and papp in this case is due to the fact that hmwnh is a misleading word an ambiguous word one analysis of which h present form of mnh r numbered is a frequent idiom in hebrew which numbers
the final step which we are currently in the process of is to find a way to integrate these models and use them within the speech recognition system to see if these more focused models can actually improve recognition performance
in sentence NUM is is the main verb however the algorithm prefers to find a strong verb so it keeps going until it finds know which is actually part of a relative clause
in some cases it is possible that two words together constitute a conjunction for example and then as in example NUM most of the conjunctions that appear between two full clauses are marked as coordinating conjunctions
there are a number of reasons for this including NUM cultural resistance to reuse e.g.
gdm provides a central repository or server that stores all the information an le system generates about the texts it processes
and as creole xpands more and more modules and datahases will be available at low cost
the interface might reuse code from ggi or might be developed from scratch
the parseval tools reconfigured and reevaluated a kind of edit compile test cycle for le components
gate is an architecture in the sense that it provides a common infrastructure for building language engineering le systems
we exploit object orientation for reasons of modularity coupling and cohesion fluency of modelling and ease of reuse see e.g.
the recent muc competition the 6th detlned four ie tasks to be carried out on wall street journal articles
the le user has the possibility to upgrade by swapping parts of the creole set if better technology becomes available elsewhere
first we will try to answer the question of what percentage of the test words was translated at all correctly or incorrectly
automauc text summarization systems lend themselves to many tasks an mformauve summary may be used as the basis for execuuve decisions an mdlcauve summary may be used as an mmal ln cator of relevance prior to reviewing the full
their notion of obligatory rewrite rule incorporates a directionality constraint
for example because ball is assigned to both kl and k2 we distribute its total frequency among the two clusters in proportion to the frequency with which ball appears in cl and c2 respectively
the first column of figure NUM shows that the same cue may occur within a single explanation as long as there is no embedding between the two relations being cued
null in order to keep track of where a constituent can be attached in the structure a list of active nodes specifics the potential attachment sites this list is systematically updated
according to elhadad and mckeown polyphony is a kind of given new distinction and thus the ordering difference between the two cues reduces to the well known tendency for given to precede new
we report two results based on this analysis a comparison of the distribution of sn ce and because in our corpus and the impact of embeddedness on cue selection
the idea that discourse is hierarchically structured by palrwise relations in which one relatum the nucleus is more central to the speaker s purpose is due to mann and thompson
the idea is that since is used when a relatum has its informational source with the hearer e.g. by being previously said or otherwise conveyed by the hearer
for example recall that the relation between c NUM and c NUM in figure NUM was expressed as part is moved frequently and thus it is more susceptible to damage
during this session the system replays the student s solution step by step pointing out good aspects of the solution as well as ways in which the solution could be improved
the rda analysis of the example in figure NUM is shown schematically in figure NUM as a convention the core appears as the mother of all the relations it participates in
intuitively we expect that when a relation is embedded in another relation already marked by because a speaker will select an alternative to because to mark the embedded relation
require subj coor ob obj obl NUM
if we could assign ball to both kt and k2 the likelihood value for classifying a document containing that word to cl or c2 would become larger and that for classifying it into c3 would become smaller
mrds and static lexical knowledge bases may be too generic and worsen the induction quality while specific domain sources are usually absent
three word classes in question adj n v we tested words with high medium and low absolute frequency
while this paper has specifically addressed english chinese corpora the linguistic issues motivating the algorithms seem to be quite general and are to a large extent language independent which means that the algorithm presented here should be adaptable to other language pairs
figure NUM is an example of a directed parallel replacement
all these NUM verbs where ambiguous with an average of NUM NUM semantic classes per verb persisting ambiguity even after the semantic tuning phase
smaller training data set can be used and also unknown collocates are deal with if they are able to trigger the proper semantic generalizations
consequently we also write a a as just a
ob for the direct objets basic instances can be generalized into selectional rules a typical structure induced from the reported instances is thus
class based models can be derived according to the tags appropriate in the corpus and used to derive lexical information according to generalized collocations
this seems to auger well for the ability to apply alembic to different application tasks
figure NUM shows abstract rightward complete llnk for wi j rightward complete sequence for wi m and leftward complete sequence for wrn ld
we indicate this by turning s j and k into deterministic functions on the english constituents writing sst jst and kst to denote the split point and the subtree labels for any constituent es t
once the initial phrasing has taken place the phraser proceeds with phrase identification proper
represents any symbols that are not explicitly present in the network
the development time required to adapt satz to french was two days
these results are discussed in section NUM NUM
fertility models for statistical natural language understanding
preposition NUM NUM article NUM NUM
palmer and hearst multilingual sentence boundary table NUM
it adapts easily to new languages
three characteristic properties of this domain are a very high dimensionality b both the learned concepts and the instances reside very sparsely in the feature space and c a high variation in the number of active features in an instance
we used a NUM NUM word lexicon
lower bound of the wsj corpus
this process is described in more detail in NUM
a unit can be associated with every type e.g. percentage
null the writer s goals have a major role in the generation process
in this graph the small values are not readable because of the scale
these schemas are used for text as well as graphics
however there are important differences between these NUM graphs
this approach is more similar to neural nets than mackinlay s graphical language
for our example in table NUM the specific mutual information is si x y log NUM NUM log NUM NUM NUM x0 NUM NUM bits for the original variables but si x y log NUM NUM log NUM NUM NUM NUM NUM NUM x0 NUM bits for the transformed variables
the first database corpus db1 consists of NUM months of hansards aligned data taken from NUM NUM megabytes NUM NUM million words and the second database corpus db2 consists of all of the NUM and NUM transcripts of the canadian parliament a total of approximately NUM megabytes and NUM NUM million words
the results are represented by the remaining curves of figure NUM surprisingly we found that with moderate values of c close to NUM this method gives a very low failure rate even for high final threshold values and is preferable to using a constant but lower threshold just to reduce the failure rate
the NUM collocations were randomly selected from a larger set of NUM collocations so that the dice coefficient s performance on them is representative i.e. approximately NUM of them are translated correctly by champollion when the dice measure is used and the correct translation is always included in the final set of candidate translations
next the text segmenter interprets the sgml markers and common punctuation and stores the text in a structure that holds the origina l version of the text as a whole it also stores each section of the text such as paragraph sentence headline dateline etc
similarly a morphological analyzer would allow us to produce richer results since several forms of the same word would be conflated increasing both the expected and the actual frequencies of the co occurrence events this has been found empirically to have a positive effect in overall performance in other problems hatzivassiloglou in press
one of the many differences between enamex type quot person quot robert l james enamex chairman and chief executive officer of enamex type quot organization quot mccann erickson enamex null in the above example louella does not know what mccann erickson is however she does know that people are chairman and chief executive officer of an organization
in the second case the results are reversed specific mutual information is equal to log p x l log p x l and it can be shown that the average mutual information becomes equal to the entropy h x of x or y
NUM if the unifiability check is successful find an edge l m d n d q depd strictly covering el and e2
the other part that is not produced by p unify is not required at phase NUM because it is already computed in a state or dfss in las when the las are generated
the grammar does not have a semantic part
i an example is showll in figure NUM
figure NUM a recursive procedure for tile phase NUM
the procedure sub structure is a re
this table also gives results from supervised training using the annotated corpus without any prior unsupervised training
in each learning iteration the score of a transformation is computed based on the current tagging of the training set
in this experiment we also used a training set of NUM NUM words and a separate test set of NUM NUM words
z y compute freq y freq z incontext z c
rival jj nnp gangs nns have vb vbp turned vbd vbn cities nns into in combat nn vb zones nns
learning alone is that the combined approach allows us to utifize both tagged and untagged text in training
in later learning iterations the training set is transformed as a result of applying previously learned transformations
there are also decomposable idioms where decomposability is based on metaphoriocal knowledge
b indicates the union of drss
several bucks shot tom made t big mistake on the meeting
NUM NUM figuratiw referents of idiom chunks
NUM NUM decomposabh idioms are structured entities
as was mentioned above templates in our system are structured sentences with slots for variable parts
for our application the se mantic formalism is of more interest
with the help of constraint equations these feature structures can be modified
an information state is a representation of objects and the relations between them that complies to the frame structure
we performed a number of experiments using a random division of the tree bank data into test and training set
this bias might be ignored or detected by the higher level modules
the graphics analyzer interprets the pointing gestures produced by the user
they report no deictic gestures after completion of the corresponding np
it kills these cfs and subsequently deselects the unintendedly selected object
the context model obviously still lacks several factors that can influence the salience of a referent
selected referent cfs cause selected referents to be more salient than referents that are merely visible
an individual instance may be more or less salient may gradually become less salient etc
the only relation instances passing the file system domain filter are contain relations and name relations
in shoptalk too the interpretation process is based on the approach of grosz and sidner
in many languages the words used in deictic expressions are also used in anaphoric expressions
the linguistic expressions are keyed in by a user and are possibly accompanied by pointing gestures
syntactic functions within non recursive segments ap np and pp are addressed first because they are easier to tag
the results reported here are obtained by randomly selecting NUM trees from the tree bank
for the final system we attempted to fill all the slots but did not address some of the finer detail s of the task
on e approach which we have implemented in the weeks since muc has been clause level patterns which ar e expanded by metarules
for example a present or past participle may mark the beginning of a noun group he enjoys driving ranges more than any golfer i know
NUM global parsing considerations sometimes led to local error s our system was designed to attempt to generate a full sentence parse if at all possible
several components were direct descendants of earlier modules the dictionary was comlex syntax NUM the lexical analyzer for names etc
if we have a predicate of the form succeeds person NUM person2 the system sees what other information it has about personl or person2
as a result the process of building a global syntactic analysis involves a large and relativel y unconstrained search space and is consequently quite expensive
this global goal sometimes led to incorrect local choices of analyses an analyzer which trusted local decisions could i n many cases have done better
the second pass enables a lexical recovery
empirically we found it beneficial to hold the p e i f parameters fixed for NUM iterations to allow the other parameters to train to reasonable values
future planned evaluations will examine caregiver satisfaction with the spoken medium versus text
these techniques applied on particular domains often use terminological resources that supplement the lexical resources
this feedback might include modifying a very simplified form of a single rule for greater generality by integrating thesauri to construct word list suggestions
textual information is becoming more and more accessible in electronic form
this is the first step in a precise positioning using the standard relationships of a thesaurus
our apwoach covers all property modification in language not only adjective noun combinations
recently there have appeared some first indications of lifts attetlfion sce
victor raskin is gratefid to purdue university for pemfitting him to consult crl nmsu on the mikrokosmos project
the research reported in this paper was suplxjrtcd by contract mda904 NUM c NUM with the u s department of defense
thus occasional visitor 5iii is analyzed as a rhetorical paraphrase of visit occasionally
NUM abuse relalive adjectives are denourinal object related in their meaning
initial research shows that the property non l roperty based dichotomy holds there as well
another specific relation is has as part as in malignant adj3 in the sense of containing cancer cells
this issue has been often discussed on the example of the adjective good cf
i.e. whether a specific meaning c m ire expressed adjectivally in a language
furthermore assume a two dimensional array of nodes that will be trained to represent the n input vectors
the som extends this simple competitive learning process to include updates for the neighbor nodes to the winning node
such a range of lower bound figures suggests the need for a robust approach that can adapt rapidly to different text requirements
the size of the array is configurable by the user but the default is a NUM by NUM array of nodes
the stem vectors learned from the training text are used to compute the context vectors for the user s text
the corpus that was used to generate the integral depicted in figure NUM is a set of over NUM NUM documents
again nodes that are large represent the themes that occur with the highest fi equency and volume in the corpus
once a ranked list of documents has been retrieved the user can select any document from the list to view
the corpus integral is computed by summing the dot products of each document context vector with each node context vector
nodes that normally lose the first competition are given favorable biases and those that normally win are given unfavorable biases
when a user desires a more focused search for specific information it is easily accomplished in a variety of ways
despite this flexibility in the expected contents of the response the systems nonetheless had to implicitly recognize the full np since to be considered coreferential the head and its modifiers all had to be consistent with another markable
from the practical point view this measure addresses a problem of sparseness in limited data
it is challenging to translate names and technical terms across languages with different alphabets and sound inventories
NUM disl clu w l cos cvdu cvw we say clu is activated if disl clu w dl
extractions i.e. discontinuities are merely handled by a mechanism built into the parser
we will refer to dgs allowing non projective analyses as discontinuous dgs
japanese frequently imports vocabulary from other languages primarily but not exclusively from english
the n NUM completeness result also holds for the discontinuous dg presented in section NUM
here are a few observations about backtransliteration null back transliteration is less forgiving than transliteration
furthermore it will not point to the same organization object as the succession org slot if the person s only position at that organization is as a nonrelevant member of that organization s board
former ceo retired ceo most recently ceo if person is currently holding a different post and the new post and old post are mutually exclusive
the relational and low level objects capture information on who s in and who s out wher e the new manager came from and where the old manager is going
this slot will normally have one or two fills but it may have three or more in th e case of a shared post such as co chairman or vice president
then we make use of an heuristic method to determine some nodes in the dendrogram which correspond with sets of similar senses which we call sense clusters
in the case of condition NUM any posts other than the person s most recently held one are not relevant thus accounts of a person s prior work experience are not relevant
template doc nr number content succession event comment comment succession event succession org organization a post position title i no title a
NUM the word post and words that are essentially synonymous with it position title job etc
this demonstrates that one main reason for tagging errors is that the considered contexts of the words contain less meaningful information for determining the correct senses
structured semantic space can be seen as a general model to deal with wsd problems because it does n t concern any language specific knowledge at all
x took the helm of ibm in march
definition this object relates information on the person assuming a post and or the person vacating tha t post with information on his her status vis a vis that post and his her past or future corporate affiliation
NUM NUM sentences are used for training a grammar and NUM sentences are for a test set
since the above results are for the system taking ambiguous semantic representations as input the evaluation does not isolate focus related errors
since tin buch bears a pitch accent the focus linking principle NUM applies introducing an obligatory link to foc h link foe
if a wider context was considered antecedents could possibly be found so it makes sense to end NUM t with such a representation after processing the discourse NUM
at first glance this seems to be incompatible with the idea of underspeeification since the f skeleton that is checked against the context for entailment requires settlement on what the actual f marking is
take the following short sentence with two pitch accents
rathe interpretation is given informally in the following examples
NUM karl hat ein bucii gelesen
however focus marking by prosody is often ambiguous
rule 12b may need some refinement
this will leave k NUM iei unmarked vertices in the input structure
in this section we investigate the result of the comparisons made in the last section
the markers simply and just appear only with generating imperatives
our corpus is composed of naturally occurring instructions in the three languages of study
again the markers of each rhetorical relation are distinct
lationship between rhetorical relation discourse marker and syntax
so that which appears rarely marks only generate1 elcments
french unlike portuguese and english has two forms of imperative
the distribution of expressions in english generation is shown in figure NUM
each of twelve native speakers of chinese was given a number of test sheets to finish
it also showes that ti ti t and ti ti l have the neighborhood relation connected by bigram and that ti ti l ti NUM and ti ti l ti NUM have ttm neighborhood relation connected by trigram
otherwise go to step i where q k is the distribution of the iriodel in k th step alld it corresponds to the posteriori pro rcb ability of the tagging model j
sininlat ed annealing is us0flil in the prol leni which has very hugo search sl ace and it is the approxiniation of map est ifllatioll elll iq NUM t
besides the lack of a clear definition of what constitutes a correct segmentation for a given chinese sentence there is the more general issue that the test corpora used in these evaluations differ from system to system so meaningful comparison between systems is rendered even more difficult
yet some hanzi are far more probable in women s names than they are in men s names and there is a similar list of male oriented hanzi mixing hanzi from these two lists is generally less likely than would be predicted by the independence model
for example in northern mandarin dialects there is a morpheme r that attaches mostly to nouns and which is phonologically incorporated into the syllable to which it attaches thus men2 r door r door is realized as mer2
this fsa i can be segmented into words by composing id i with d to form the wfst shown in figure NUM c then selecting the best path through this wfst to produce the wfst in figure NUM d
this implies therefore that a major factor in the performance of a chinese segmenter is the quality of the base dictionary and this is probably a more important factor from the point of view of performance alone than the particular computational methods used
bal er3 and al are often clear indicators that a sequence of hanzi containing them is foreign even a name like j xia4 mi3 er3 shamir which is a legal chinese personal name retains a foreign flavor because of m
we can NUM recall that precision is defined to be the number of correct hits divided by the total number of items selected and that recall is defined to be the number of correct hits divided by the number of items that should have been selected
we have attempted to account for the preceding results in a language assessment model called slalom steps of language acquisition in a layered organization model NUM the basic idea of slalom is to divide the english language the l2 in our case into a set of feature hierarchies e.g. morphology types of noun phrases and types of relative clauses
again famous place names will most likely be found in the dictionary but less well known names such as l bu4 lang3 shi4 wei2 ke4 brunswick as in the new jersey town name new brunswick will not generally be found
indeed as we shall show in section NUM even human judges differ when presented with the task of segmenting a text into words so a definition of the criteria used to determine that a given segmentation is correct is crucial before one can interpret such measures
a check the bill for room lmmber eight two one
the dictionary has pronunciations for NUM NUM words and we organized a phoneme tree based wfst from it
a high efficiency indicates that our model class provides a good description of the data
figure NUM the relationship between the number of model parameters and test message entropy
the amount of training data is known to be a significant factor in model performance
informally statistical efficiency measures the effectiveness of individual parameters in a given model class
constraint 4a states that every symbol has the empty string as a context
traditionally these conditional probabilities are estimated using string frequencies obtained from a training corpus
all novel events NUM w in the context w are assigned uniform probability
given the logarithmic nature of codelengths a savings of NUM NUM bits char is quite significant
the second term encodes the counts lcb nl n2 am rcb
then the context y will never be used when the history is e xy
from the word sequences we extract only nouns adjectives adverbs verbs and unknown words only in japanese because japanese and english closed words are different and impede text alignment
adding the pp zu der zeit at this time the sentence functions as a background for the event described by the first sentence but this discourse sounds awkward and the continuation with a state in NUM is clearly preferred
a how much does a double room including room service
for example the correct infrequent word ambassador is subdivided into two frequent words use root and node
the third rule deals with the evaluation of sequences of descriptors by breaking them up into shorter sequences
the target string resulting from the lowest cost tree that includes all nodes in the graph is selected as the translation target string
a final state is one for which the stop action cost c c djq m is finite
we first describe the model in terms of the familiar paradigm of a generative statistical model presenting the parameters as conditional probabilities
damerau transformations involving the space character e.g.
NUM sort the decomposition nodes into a list d such that if nl dominates n2 in s then n2 precedes nl in d
transfer search before the transfer search proper the resulting runtime entries together with the source graph are analyzed to determine decomposition nodes
the nodes and arcs of t are composed of the nodes and arcs of the target fragments gi for the entries ei
it is obvious that the longest match string frequency method remedies the problem that the string frequency sf method consistently and inappropriately favors short words
in separate experiments we verified that the judgments of this speaker were near the average of five chinese speakers
jishen he constructed the chinese atis language model and bilingual lexicon and identified many problems with early versions of the transfer component
tense aspect rhetorical relations and temporal expressions affect the value of the rhet reln type that expresses the relationship between two i cvs cue words are lexicmly marked according to what rhetorical relation they specify and this rel ation is passed on to the dcu
however it is important not to ignore tense because other combinations of tense and aspect do show that tense affects which relations are possible e.g. a simple past stative NUM can not have a precede relation with any NUM while a past perfect stative NUM can
temp relns e NUM precedes el e3 just after e2 if we allow any of the four possible temporal relations between events both continuations of sentence NUM would have NUM readings NUM x NUM NUM reading in which the third sentence begins a new thread
its novel features are that it treats tense aspect temporal adverbials and rhetorical relations as mutually constraining it postulates less ambiguity than current temporal structuring algorithms do and it uses semantic closeness and other preference techniques rather than full fledged world knowledge postulates to determine preferences over remaining ambiguities
tense past pres fut aspect simple perf prog perf prog to allow the above mentioned types of information to mutually constrain each other we employ a hierarchy of rhetorical and temporal relations illustrated in figure NUM using the ale system in such a way that clues such as tense and cue words work together to reduce the number of possible temporal structures
using this algorithm we can precisely identify the rhetorical and temporal relations when cue words to rhetorical structure are present as in NUM NUM john fell el because mary pushed him temp relns e NUM precedes el we can also narrow the possibilities when no cue word is present by using constraints based on observations of tense and aspect interactions such as those shown in table NUM
for example if a simple past eventive sentence follows a simple past eventive sentence the second event can be understood to occur just after the first to precede the first or to refer to the same event as the first an elaboration relation but the two events can not overlap these constraints are weaker however than explicit clues such as cue words to rhetorical relations and temporal expressions
the first event in the discourse el john s getting up is interpreted relative to a contextually understood reference time r0
they introduce a reference time which overrides the current reference time and provides an anaphoric antecedent for the tense in the main clause
see section NUM NUM for more details on documents
provide for modular substitution of functionally similar architectural parts
a tipster application is an application which uses tipster technology
its impact on staffing is also likely to be known
user and user group permissions to read write execute on a module level
when the needs change the technology should be able to adapt quickly
to ensure that an application meets these criteria it must be tested
the selection of the computing environment is the responsibility of the application NUM NUM NUM NUM
by joining the emerging authoring support crowd and endeavoring to create new opportunities in automated documentation we hope to contribute to the broader acceptance and visibility of nlg technology in the overall computing community
note that this group description is made possible by the use of a phrasal lexicon which has been designed to allow the author s messages to make sense in a variety of contexts
while the central use of domain structure driven text planning makes it possible to generate text in a relatively straightforward top down fashion as variations are added a one pass top down approach can become cumbersome
at the core of cogenthelp is the text planner
in this paper we describe cogenthelp highlighting the usefulness of certain natural language generation techniques in supporting software engineering goals for help authoring tools principally quality and evolvability of help texts
figure NUM a sample help page
figure NUM cogenthelp in authoring mode
roughly speaking word unigram based word segmenters maximize the product of the word frequencies under the fewest word principle which subsumes the longest match pzinciple
words referring to concrete and imagible entities such as objects and persons may generally be easier to interpret
in this box we see examples of hyponymy and meronymy relations between spanish word meanings and some of the equivalence relations with the ill records
all that the x NUM test can tell us then is that the sample size is too small to reject the null hypothesis
the model has the form g r zi NUM where r is the estimated response probability in our case the probability of a particular facet value xi is the feature vector for text i and q is the weight vector which is estimated from the matrix of feature vectors
the author has l eeal supi orted in part by grants from the austrian chamber of commerce and dade lhlndeswirtsehafl skammer and the austrian ministry of science and research bmwf
looking at the independent binary decisions on a task by task basis surface cues are worse in NUM cases note
genres are considered to be generally reducible to bundles of facets though sometimes with some irreducible atomic residue
the lower performance on the editorial and nonfiction tests stems mostly from misclassifying many nonfiction texts as editorial
the experiments indicate that categorization decisions can be made with reasonable accuracy on the basis of surface cues
such confusion suggests that these genre types are closely related to each other as ill fact they are
nothing hangs in the balance on this definition but it seems to accord reasonably well with ordinary usage
siderable degree the circle of participants remains restricted to users with compatible systems
more precisely only utterances between a human and a machine agent are modeled
in principle each participant and the server could reside on a different site in the internet
cosma allows human and machine agents to participate in appointment scheduling dialogues via e mail
dialogues can be initiated by the human participant or by one of the agent systems
during the dialogue the client may request texts to be analyzed or semantic descriptions to be verbalized
they see the result hen it is entered into their electronic calendars
when given a text the server returns the semantic representation and vice versa
agent systems are thus hooked up to e mail to a calendar manager and to the dialogue server
cooperative agent systems developed in the field of distributed ai are designed to account for the scheduling tasks
the taggers had to examine and consider each sense in the entry which made the task more difficult
consequently all exhibit the same range of strict and sloppy readings
neither pre editing nor post editing can be relied on in a speech translation system
it is an important and difficult process to choose the most plausibile structure
the tirst is pended then inunedaitely the second sentence starts without conjunction
it also provides proper scores to each candidate using a similarity calculation
i chikatetsu wa ichiban chikai eki wa doko desu ka
figure NUM general processing for phrase generation
recall measures how many of the relevant documents have been actually retrieved
wordnet s noun database is organized as an inheritance lattice
our emphasis is on robust and efficient nlp techniques to support large scale applications
after preprocessing the system works in two stages parsing and generation
a semantic parser is a typical example of this method
this has a very close relation with item NUM below
block describes a process taking the input text processing it and replacing it by the end NUM NUM NUM
for instance a z phoneme is added between the two words of les enfants
it is never done if suppressing o would result in three or more consecutive consonants
with the word riding a context sensitive rule in ing would produce the phonemes for ing plus a mark indicating that the syllable is unstressed replace the suffix ing by e in the input string which is then ride and continue the conversion from right to left starting on e
in fast speech the phrase je te le dirai 3otolodir i will tell you is pronounced je t le dirai 33tlodir or j tel dirai 3t31dir eliding one or two NUM
such rules are a necessary and integral part of a text to speech system since a database lookup dictionary search is not sufficient to handle derived forms new words nonce forms proper nouns low frequency technical jargon and the like such forms typically are not included in the database
different approaches were also taken in the size of the dictionary the algorithm used to scan or rescan the dictionary if one was used the methods for determining lexical stress placement the amount of morphological analysis used and the difficulties in the prediction of the correct phonemic form of homographs
if the grammatical category is known where v verb it can be used in the rules NUM ent v ent is eliminated at the end of a word right context is a space if the word is a verb ils chantent
note that this would deal correctly with all the above examples
for all four parts of speech we found more tagger expert matches in the frequency condition than in the random condition
the arguments apply to this example just as well
the procedure r re x evaluates the regular expression re and puts the resulting minimized automaton into a register with the name x
squibs and discussions anaphoric dependencies in ellipsis
they argue that verb meanings are more easily altered because they are less cohesive than those of nouns
the first is to describe the finite state approximation using formulae with regular languages and finite state operations and to evaluate the formulae directly using the finite state calculus
third the issue of portability is of considerable interest
porting to another domain and or task will involve three steps
this effect was also found with the same level of significance for verbs alone in both conditions
our recent survey of a number of dialogue management systems has led us to identify those features and components which occur in many of the systems
applying the restrictions expressed by formulae NUM NUM gives an automaton whose size is at most a small constant multiple of the size of the input grammar
figure NUM the description of have27 after substituting the book
the most difficult dialogues to model are those where the initiative can be taken be either the system or the user at various points in the dialogue
for example the inclusion condition on the original output actor fates topic was true
in choosing between rival dialogue managment systems it is not sensible to try to use a simple metric of accuracy or naturalness applicable across all applications
tasks such as identifying the right inter hngual correspondence when a new synset is added in one language or how to control the balance between the languages are good examples of issues that need to be resolved when this approach is taken
the full list of relations distinguished its characteristics and assignment tests as well as the structures of the different records can be found in the eurowordnet deliverables d005 d006 d007 available at http www let uva nl ewn
when generating back the matching dutch synsets for these hyperonyms it becomes clear that they are all present in this fragment except for comida l fare l which does not yield a corresponding dutch synset
NUM NUM transformation of descriptions into nctional descriptions
figure NUM profile for john major
in this paper we proposed a new method for the measurement of word similarity
NUM NUM construction of knowledge sources for generation
NUM NUM extraction of entity names from old
the shortest descriptions are NUM lexical item in length e.g.
building a generation knowledge source using internet accessible newswire
figure NUM overall architecture of profile
extraction of candidates for proper nouns
the results are reported for a NUM NUM level of confidence
the importance of the NUM back off terms is specified using only f parameters the ig weights where f is the number of features
however the theories differ in several important respects
then similarly to NUM assume coherence subtype and elaboration are used to infer that the cotton bag is one of the bags mentioned in 3a and elaboration holds
furthermore they are not simply due to lexical idiosyncrasies for instance arzttermin doctor appointment is representative of many compounds with human denoting first elements which require a possessive in english
prefer frequent senses is a monotonic rule it does n t increase the load on nonmonotonic reasoning and it does n t introduce extra pragmatic machinery peculiar to the task of disambiguating word senses
the groupings in these tables allow an ordering that is less clean than we would like but that is realistic a t this point in the evaluation methodology research
she put her skirt into the cotton bag
she put her skirt in the cotton bag
the winner of this second competition is determined upon the basis of biased distance to the input vector
when it covers the largest semantic territory the first sense may seem like the safest choice
the salience and the shared mental representation of certain word senses might further account for our third main effect
for example for english the word genes is tagged as a plural noun and morphologically connected to genic genetic genome genotoxic genetically etc
it is useful to visualize the process of seed development graphically
details axe provided in yarowsky to appear
unsupervised training using the additional one sense per discourse constraint frequently exceeds this value
this circumvents many of the problemz associated with non independent evidence sources
that leveraging bilingual lexicons and monolingual language models can overcome the need for aligned bilingual corpora
to move each word from the left branch to the right branch we need to match this word throughout the corpus
the words used in this evaluation were randomly selected from those previously studied in the literature
the probability differentials necessary for such a reclassification were determined empirically in an early pilot study
details on the mts system will be provided in the third section
by characterizing of some NUM cross references in lloce most systematic inter sense relations can be easily identiffed among the labeled senses
furthermore conjunctive uses of punctuation NUM NUM conventionally regarded as being distinct from other more grammatical uses the adjunctive ones can also be made to function via the theoretical principles formed here
rather processing in a head corner parser is bidirectional starting from a head outward island driven
this problem is somewhat reduced because of the use of extremes the use of top down information
the predicate smaller equal is true if the first argument is a smaller or equal integer than the second
in order to be able to run a head corner parser in left corner mode this technique is crucial
in the head corner parser it turns out that the parse NUM predicate is a very good candidate for memorization
in certain methods in robust parsing we are interested in the partial results obtained by the parser
similarly if the list of daughters right of the head is empty then qh qm
computational linguistics volume NUM number NUM the first table is represented by the predicate goal item
concurrent with these explicit components the system must be capable of constructing and updating a user model to be consulted in both the selection of errors to be corrected and the generation of corrective text
the corresponding results are listed in the third column of table NUM which show that parameter smoothing improves the performance significantly
for comparison the performance of the parser without combined with the semantic interpreter is also listed in this table
compared with the baseline system NUM NUM error reduction rate for sense discrimination NUM NUM for case identification and NUM NUM for parsing accuracy are obtained
if m number of left contextual symbols and n number of right contextual symbols are consulted in computation the model is said to operate in the lmrn mode
in many tasks such as natural language understanding and machine translation deep structure information other than word sense is often required
for this reason the best way to test the results of these approaches to punctuation s role in syntax is to incorporate them into otherwise identical grammars and study the coverage of the grammars in parsing and the quality and accuracy of the parses
afterwards the parse trees are analyzed by the semantic interpreter and various interpretations represented by the normal form are generated
conventional approaches to case identification usually need a lot of human efforts to encode ad hoc rules NUM NUM NUM
phonological alternations in stems can be embedded in the rules as well
this predicate is inserted into the set of restrictions for the noun
in the following we briefly describe the lexical rules for them
NUM NUM for a description of these processes
figure NUM lexical rule for the locative case
they can be attached to stems via lexical rules
the type of np is changed to an adjunct
the hierarchy is given in figure NUM
vowel harmony and voicing constraints NUM determine their surface realization during morphological composition
this view also represents a middle ground in the complexity of lexical structures
u proof again since case arm case have no disjuncts in cormnon we know that leas case case x icase and therefore that lease i lease r co st
this pat er is organized as follows section NUM gives an informal introduction to dependent dis cf
i would also like to thank dale gerdemann and guido minnen for helpfltl comments on the ideas presented here
in particular modular fea tuie structures are more eflicient r r unification than non inodulm ones
i lcb epresented as a compact alternative case form the alternatives becomes al NUM a NUM c a d with cases a i a a v al a a v a
mmiy grajmnar l ro ssing sysi ms use high level dose tit lions whic h arc i hcn transform d into lttor cxt lic il ow l hw NUM grmmnars
ib compute the free combination we conjoin them and convert the re suit back into dnf
the perplexity is then NUM s may be interpreted as the average number of bits of information needed to compress each word in the test set given that the language model is providing us with information
in this case the word sleep being referred to is not a dependent of yawn the prolog translation n yawn verb n gi g2 n sleep verb n gi g2
for example we and i you are contained in one class derived by the left right binary tree and other two words you and apple belong to another class derived from right left binary tree
that is if we did n t introduce the probability r lw and p lw we would not merge the classes because there is no transitivity between the class in which word relation is from the left to the right and the class in which word relation is from the right to the left
that is for the classes produced by the binary tree which represent the word relation direction from the left to the right we distribute probability r iw for each word w corresponding every class the probability lw reflect the degree the word w belongs to class
in order not to repeat the same set of rules over and over again a reference operator read goes like is introduced that causes branching to the rule of an analogous word as in n yawn verb n n sleep verb n
for example by factoring out common dependency patterns it is possible to generalize the rules for transitive verbs and allow for exceptions to the rule at the same time standard dependents of transitive verbs in active voice transverb n active word noun n subject word noun
which dependents a particular word takes depends not only on its function within the sentence but also on its meaning like other contemporary linguistic frameworks dg integrates both syntactic and semantic aspects of natural language
accordingly if atomic symbols are replaced by first order terms the following toy dg can be implemented in prolog using the dcg rule format s n verb
note that the head literal of the sleep rule need not be repeated in the body because the respective word is removed from the input sentence before the rule is called in this case in the start rule
the dug parser can be adapted to follow this convention by accepting the symbol self in the rule body as in n sleep noun n n noun n self
while context free grammars and dcgs strongly related to the huge linguistic field of constituency or phrase structure grammars and their descendants have become very popular among logic programmers dependency grammars dgs have long remained a widely unnoticed linguistic alternative
friedrich steimann and christoph brzoska dependency unification grammar for prolog transitive verb with additional word give verb n voice transverb n voice word noun
in this paper we call the number of words contained in a category the length of that category
an alternative approach is to view language as a stochastic phenomenon particularly from the viewpoint of information theory and statistics
considering the fact that individual rules will be applied with different frequency it is desirable to modify the syntactic preference to
in psycholinguistics a number of principles have been proposed which attempt to modelize the human disambiguation process
our experimental results indicate that our method of using a length probability model outperforms that of using pcfg
using our method of estimating smoothing probabilities we can cope with the data sparseness problem
as may be seen in figure l a the distance between m and w is d
when rap is implemented the parser prefers shift to reduce whenever a shiftreduce conflict occurs
we can represent this situation as in figure NUM
this indicates that no stem lookup is required
computational linguistics volume NUM number NUM known word
the former feature is indirectly captured in our approach
for instance the scoring program needed to recognize that sentences such as smaller dna fragments move faster than larger ones and the smaller segments of dna will travel more quickly than the bi er ones contain alternate words with similar meanings in the test question domain
predictive properties of those endings from an untagged corpus
could handle NUM fewer unknown words
as usual we separated the test sample from the training sample
these words were not used in the training of the tagger either
i also gratefully acknowledge rens bod s encouraging comments and useful pointers to related work
to a continuous function n x on NUM oo
we have to be a bit careful with the transition to the continuous case
this distribution was created by approximating our original discrete distribution with a continuous one
thus turing s formula seems to be smoothing the frequency estimates towards a geometric distribution
NUM reestablishes eq NUM to the second power of
this article like others has benefited greatly from comments by khalil sima an
in contrast our work on cogeneration is at a very early stage
classes such as figure ground are intended to capture some types of systematic polysemy
thus the other speaker finds it hard to avoid interrupting the prosthesis user
not only must the system believe that the plan achieves the goal but it must also believe that the user also believes this
first our work has built on previous work in referring expressions especially their incorporation into a model based on the planning paradigm
first we have accounted for the tasks of building a referring expression and identifying its referent by using plan construction and plan inference
first it would give a more complete motivation for the processing rules that we used for how agents interact in a collaborative activity
however none of these approaches is within a collaborative framework in which either agent can contribute to the development of the plan
this is important since if the refashioned plan is invalid only the referring expression should be refashioned not the refashioning itself
if it was successful then the referent has been identified and so a goal to communicate this is input to the plan constructor
if there is exactly one object that the system believes to be mutually believed to be of category then object is instantiated to it
the discourse action reject plan shown in figure NUM is used by the speaker if the referring expression plan overconstrains the choice of referent
thus the alignment abc bde null is produced by skipping a then matching b with b then matching c with d then skipping e
likewise negate is considered a weaker form of reject
are you free on the morning of the eighth
for NUM savings the figure is NUM NUM bits
that s which can then become discourse segment purposes
NUM i could do it wednesday morning too
could be either a suggest or an accept
figure NUM speech acts covered by the system
traditionally machine translation systems have processed sentences in isolation
for example i m free tuesday
well monday and tuesday both mornings are good
if all else fails there is always video conferencing
hereafter we use overall matched to refer to the total number of matched anaphora across all the classes
by taking into account the effect of discourse segment structure we obtained NUM matches in the test data
while recognition technology is improving it is not perfect
this means that we voluntarily removed from the lexicon a set of test words
the use of some kind of similarity measure has also demonstrated its effectiveness to circumvent the problem of data sparseness in the context of statistical language modeling
in our model each domain has its own organization which is represented in the form of systematic paradigmatic set of oppositions and alternations
we have used the following experimental design NUM pairs of disjoint learning set test set are randomly selected from the nettalk database and evaluated
this goal is achieved by exploring the neighborhood of x defined by a in order to find one or several analogous lexica NUM entry ies y
now suppose that this word starts with pl the alternation will derive an analog starting with rl and will assess it with a very high score
the expectation NUM does not decrease as more derivatives are added to the stack consequently it can not be used to define a stopping criterion
an example is the word synergistically for which the breadth first search terminates uncessfully whereas the depth first search manages to retrieve the analog energy
psychological models of reading aloud indeed assume that the pronunciation of an unknown word is not influenced by just one analog but rather by its entire lexical neighborhood
which no analog can be found or complex morphological derivatives for which the search procedure is stopped before the existing analog s can be reached
common errors in these three organization objects included missing the descriptor or locale country or failing to identify the organization s alias with its name
the results should also be qualified by saying that they reflect performance on data that makes accurate usage of upper and lower case distinctions
human performance was measured by comparing the NUM draft answer keys produced by the annotator at nrad with those produced by the annotator at saic
the named entity and coreference tasks entailed standard generalized markup language sgml annotation of texts and were being conducted for the first time
documentation of each of the tasks and summary scores for all systems evaluated can be found in the muc NUM proceedings NUM
the selection was again done blindly with later checks to ensure that the set was fairly representative in terms of article length and type
for generation of a person object the text must provide the name of the person full name or part of a name
for generation of an organization object the text must provide either the name full or part or a descriptor of the organization
the standardized template structure minimizes the amount of idiosyncratic programming required to produce the expected types of objects links and slot fills
much less information about the event would be captured but there would be a much stronger focus on the most essential information elements
we call a symbol pair such as x e an ll singleton and ely an l2 singleton
with btgs to parse means to build matched bracketings for senmnce pairs rather than sentences
let e c be any string pair derivable from a b1
as their probabilities always remain zero the illegal bracketings can never participate in any optimal bracketing
for the remainder of this paper we focus our attention on pure bracketing
the latter two singleton forms permit any word in either sentence to be unmatched
then the following properties hold for the and NUM operators
to be legal a rotation must preserve symbol order on both output streams
in particular crossings that are consistent with the constituent tree structure are not penalized
the best parse of the sentence pair is that with probability NUM t NUM y
in particular the parsing algorithm of the next section operates on itgs in normal form
pro parsing segmentation substantially reduces parsing time increases parse accuracy and reduces ambiguity
there are also differences between etd and esst with respect to parsing and ambiguity
this example also illustrates that collocations are domain dependent often forming part of a sublanguage
these words can be separated by up to four intervening words and thus constitute flexible collocations
the work reported in this paper was done while the author was at columbia university
inability to translate on a word by word basis is due in part to the presence of collocations
this was demonstrated in the previous paragraphs for the cases of small and decreasing relative frequencies
in our case we need a clear cut test to decide when two events are correlated
prendre d6cision prendre mesures and year taken from the aligned hansards
furthermore the four si scores are very similar thus not clearly differentiating the results
for example news agencies such as the associated press and reuters publish in several languages
this may be possible by intersecting the output of champollion on corpora from many different domains
for example if bone knife appears in a medical text bone most probably specifies the object to be cut by the knife while if it shows up in a text concerning prehistoric man bone most probably refers to the constitution of the knife
since complex nominals are so frequently used to coin terms which encapsulate important distinguished concepts within a domain their successful identification and processing is an essential element of determination of the topic of a text and the provide important hooks for information retrieval
compounds such as bullet hole and lemon juice NUM c d in which the modifier relates to the origin or bringing about of the object described by the head noun are treated as modification of the agentive role
the accuracy of this tagger for english is comparable to a stochastic english pos tagger
we had to change the format of wsj to prepare it for our tagging software
we have also run three more experiments with greatly reduced tagset to get another comparison based on similar tagset size
we used the same corpus used in the case of the spos tagger for czech
rbpos requires different input format we thus converted the whole corpus into this format preserving the original contents
the numbers show how many times the tagger assigned an incorrect pos tag to a token in the test file
in the experiments we used only those tags which occurred at least once in the training corpus
clearly if NUM trigrams occur four times or less then the statistics is not reliable
the algorithm computes NUM t NUM v s using the following recurrences
for most grammars we have found performance to be comparable or faster than the normal form parser
in section NUM we will discuss how language specific relations and complex equivalence relations are stored
note that the conditions top and bott0x follow the chain of named link if any to the upper or lower end of a chain of a multiple zero or more links with the same name
for example the rule remove v if NUM c det discards a verb v reading if the preceding word NUM is unambiguously c a determiner det
the relation of the object complement and its head is such that the noun phrase to the left of the object complement is an object qobj that has established a dependency relation object to the verb
the rule holds if the closest finite verb to the left is unambiguously c a finite verb vfin and there is no ditransitive verb or participle subcategorisation sv00 between the verb and the indirect object
basically the barrier can be used to limit the test only to the current clause by using clause boundary markers and stop words or to a constituent by using stop categories instead of the whole sentence
for example when a head of an object phrase c 0b j is found and indexed to a verb the noun phrase to the right if any is probably an object complement c pcompl NUM
the grammar is applied to the input sentence in figure NUM where the tags are almost equivalent to those used by the english constraint grammar and the final result equals figure NUM where only the dependencies between the words and certain tags are printed
this has two effects NUM the valency slot is unique i.e. no more than one subject is linked to a finite verb NUM and NUM we can explicitly state in rules which kind of valency slots we expect to be filled
for space limitations we only show a more detailed box for the spanish module
lexical cohesiveness between two words is calculated in the network by activating the node for one word and observing the activity value at the other word after some number of iterations of spreading activation between nodes
thus surface nasal autosegments are bracketed with as and a while underlying nasal autosegments are bracketed with as and
the selection is a stochastic one and it is a function of the relative urgencies of all existing codelets
through a spreading activation mechanism the system gradually shifts to the construction of words and of relations between words
linguistic structures include high level objects words and chunks and relations between two objects see table NUM
at cycle NUM where the temperature is NUM the temperature regulated urgencies of the three codelet types are the same
the strengths of different structures are derived according to either linguistic knowledge encoded in the lexicon or certain statistical measures
note that efforts towards building different structures are interleaved as many codelets are independent and they run in parallel
this has motivated us to study how word identification and sentence analysis can be integrated
each object in the workspace has a list of descriptions not shown in figure NUM
we will therefore only discuss current practices in word identification leaving sentence analysis aside
therefore efforts toward building different structures are interleaved sometimes cooperating and sometimes competing
where dm cij is the average distance of each cij from the topmosts
we measured a NUM precision in reducing the initial ambiguity and a NUM global reduction of ambiguity
for the most part near real time performance was the best result obtained
therefore eliminating a priori a sense of a word may be inappropriate in the domain
a possible alternative is to manually select a set of high level tags from the thesaurus
evaluation is of course more problematic due to the absence of a tagged reference corpus
in general the effect is a higher recall at the price of a lower precision
the described method only requires a medium range stemmed application corpus and a thesaurus
their synsets there is a multiplication of possible contexts rather than a generalization
automatic selection of class labels from a thesaurus for an effective semantic tagging of corpora
further work is needed to investigate the usefulness of the soms in speech synthesis and how they may be integrated in a hybrid system that uses rule based prosody
thus definitions of principles that necessarily employ free indexation have no direct interpretation in l NUM hardly surprising k p as we expect gb to be capable of expressing noncontext free languages
the goal of the research presented here is to apply unsupervised neural network learning methods to some of the lower level problems in speech synthesis currently performed by rule based systems
maps based on acoustic tube data computed from the lpc coefficients have also been created with much the same kind of results as seen in the formant maps
at present waveform segment concatenation is being used to explore a parametric duration model based on the kind of proximity based notations described here
the essential feature is the abstract linguistic description which must be derived before any attempt is made to calculate parameter values
the where x t is the input vector and mc the best matching reference vector for x t
in conclusion arguments have been presented for the use of nonsymbolic codings as the central stage of a text to speech system
link x y a link z y v a ref link x y v a ref link x y v xdeg link x y v
in order to account for this propagation of features the definition of fsds in gkp s is based on identifying pairs of nodes that co vary wrt the relevant features in all possible extensions of the given tree
if we ignore for the moment the effect of the agreement principles the defaults are roughly the converse of the id rules a non default feature occurs iff it is licensed by an id rule
this suggests that the apparent mismatch between formal language theory and natural languages may well have more to do with the unnaturalness of the traditional diagnostics than a lack of relevance of the underlying structural properties
in both cases the complexity of the set of licensed structures can be limited to be strongly context free iff the number of relationships that must be distinguished in a given context can be bounded
in general terms what is needed in phonetics is a notation that captures information about ratios rather than absolute values as is typically seen in biological systems
this definition works for english because it is possible in english to resolve chains into boundedly many types in such a way that no two chains of the same type ever overlap
this makes a significant difference in capturing these theories model theoretically in the gb case one is defining sets of models in the gpsg case one is defining sets of sets of models
some examples of these components are verbs which change meaning when used with different attributes passive existential or conditional sentences relative clauses idiomatic use of prepositional phrases etc
as input the generator receives a recursively embedded target case frame representation where all the lexica NUM choices have been made and produces the turkish sentence conveying the same meaning
this paper describes the design and implementation of an english turkish machine translation mt system developed as a part of the tu language project supported by a nato science for stability project grant
in the beginning of the transfer phase the exception rules are tested and eventually a checklist containing the problematic components of the input is generated
the transfer module maps the input case fi ame into the target case frame which is then filtered to be transformed into the required input format of the target language generator
possibly more significant than the system s performance is its portability to new domains and languages
y e present two systems for identifying sentence boundaries
it is theretbre easy and inexpensive to retrain this syst em
phe portion of the candidate preceding the potent ial
by increasing the quantity ol NUM ra ining
to our knowledge there have been few papers about identifying sentence boundaries
we would also like to thank the anonymous reviewers for their helpful insights
articles using a lexicon which includes part of speech pos tag information
we present a trainable model for identifying sentence boundaries in raw text
if a sort is not declared as extensional it is intensional
we follow the usage in logic programming and the recent hpsg literature
a complete bnf of all profit terms is given in the appendix
the program file is compiled on the basis of the declaration files
compilation of a profit file generates two kinds of files as output
null features are represented by arguments
graphemic form and logical form are represented as normal prolog terms
note by the way that the features of the focus backgronndcriterion are not characteristic of the r reading
cmj conceive contexts that allow tbr i i a or that me can not
the purpose of templates is to give names to frequently used structures
since templates are expanded at compile time template definitions must not be
all the random fields have the same features and weights differing only in their normalizing constants
with the corpus we have been assuming the null field takes the form in figure NUM
namely it is not possible to have a uniform probability mass distribution over an infinite set
we can quantify the degree of overrepresentation as p y q y
in particular the erf method yields correct weights only for scfgs not for av grammars
set fli 6ifli for all i and solve equation NUM again
we wish to construct a sample xl xn from a different distribution q
i mention this only to point out that it is a different special case
rule NUM instructs us to label this child a yielding c
that is both funds and and fund sand are st tokenizations
in the next few sections we shall further investigate certain important aspects of critical tokenizations
null in short we have presented a complete and precise understanding of ambiguity in sentence tokenizations
rather it only serves as a general guideline as different researchers make different interpretations
consequently no solution proposed in the literature is complete with regards to realizing the principle
the textref system has been difficult to implement due to the complexity of passing this additiona l information through all of the processing stages without introducing errors
ok nounphrase relate s NUM w NUM james NUM ok nounphrase choose s NUM w NUM NUM mr
although some experiments with substantial lists of company and place names were tried these produce d little improvement and were therefore not used in the formal evaluation
this stage is currentl y underused and would provide a measure of robustness for the kind of expressions used in named entity should parsing fail
prototype applications have been built using the core facilities some of them are listed below information extraction production of summary and other templates
the lolita architecture means that if core analysis is faulty there is little that can be done in th e muc task modules to correct it
although a backup strategy for named entity would be relatively simple finding a more general strateg y is difficult and so none has been implemented
therefore any investment as mentioned above will not be a waste in any sense
data oriented models of language processing embody the assumption that human language perception and production works with representations of concrete past language experiences rather than with abstract grammar rules
two biconnected graph across the articulation node was the start point of this paper
such paths will be called maximal projections
we report on two different experiments
perform a linguistic analysis of part of the corpus
lf remove phone by pulling out sharply
examples of actual imagene output will be fully italicized
a high level view of the systems in the network
flash uses proper timing to avoid an accidental hangup
keith vander linden and james h martin expressing rhetorical relations
consider the application of rst to the remove phone text
our corpus is divided into training and testing portions
the words between the concepts thus perceived are ignored
to end call press red hang up button
it is detailed in figure NUM
figure i architecture of the translation approach
however the only preprocessing step was categorization
det ermine the most likely position alignn lent
additional countermeasures will be discussed later
proper names and numbers are replaced by category markers
point fi lcb NUM NUM NUM rcb
the user s text is processed by the error analysis component which is responsible for tagging all errors
in addition a color coded menu appears which names the errors and associates them with the colors from the highlighted display
the template also provides an application independent method for assessing systems according to the features they exhibit
a simpler measure is to count the number of semantically distinct tasks a user can perform
system who would you like to make an appointment with
ecent technological advances are bringing spoken dialogue systems closer to markets to real applications
even those systems which exhibit appalling speech recognition performance can nevertheless lead to successful dialogues
the missing structure can be recovered from the context of the dialogue and normally the previous sentences
expressions relative to the current context often need to be interpreted into an absolute or canonical form
the generic model is abstracted from a number of real application systems targetted at very different domains
in everyday conversation it is possible for either participant to take the initiative at any stage
turning to dialogue management the interaction strategy is important when defining the naturalness of the system
for example an and will not be assigned a discourse usage in most of the cases however when it occurs in conjunction with although i.e. and although it will be assigned such a role
the most important criterion for using a cue phrase in the marker identification procedure is that the cue phrase together with its orthographic neighborhood is used as a discourse marker in at least NUM of the examples that were extracted from the corpus
these lower correlation values were due to the differences in the overall shape of the trees and to the fact that the granularity of the discourse trees built by the program was not as fine as that of the trees built by the analysts
a cue phrase is assigned a regular expression if in the corpus it has a discourse usage in most of its occurrences and if a shallow analyzer can detect it and the boundaries of the textual units that it connects
for example the procedure associated with although will hypothesize that there exists a concession between the clause to which it belongs and the clause s that went before in the same sentence
although there were some differences with respect to the names of the relations that the analysts used the agreement with respect to the status assigned to various units nuclei and satellites and the overall shapes of the trees was significant
we derive the rhetorical structures of texts by means of two new surface form based algorithms one that identifies discourse usages of cue phrases and breaks sentences into clauses and one that produces valid rhetorical structure trees for unrestricted natural language texts
if a domain of its direct head contains the modifier a continuous dependency results
naturalness of dialogue state of the art systems that receive their input by high quality microphone have word accuracy scores above NUM
therefore the ability to handle complex nominals is essential for parsing and generation systems for either english or italian
the analysis of complex nominal constructions presented in this paper has a range of important applications in natural language processing
we argue for a compositional treatment of compound constructions which limits the need for listing of compounds in the lexicon
the composition could then licensed by a more general phrase structure schema which would work with all of the different prepositions
for a given form compound form in english it is possible to determine potential realizations of that form in italian
the phrase structure schemata for english are used in order to determine potential interpretations for a given english compound construction
the basic structure of the schemata licensing the combination of nouns to form noun compounds is as in NUM
in order to account for the availability of compound forms in english we utilize a family of phrase structure schemata
these results were greatly influenced by the quality of the telephone acoustic signal and by the noise environment
for each i NUM y traverse the wordnet hierarchy and locate the set of senses of z si that are connected with some sense of
figure NUM is a bar chart showing for each number of senses from NUM to NUM how many verbs with that number of senses occur
we here adapt a result on the complexity of id lp grammars to the dependency framework
such observations inform the increasing trend towards analysis of homogeneous corpora to identify linguistic constraints for use in systems intended to understand or generate coherent discourse
for example the frame advp pred rs in table i occurs in comlex but does not correspond to any of the more general frames mentioned in wordnet
we also counted how many of the verbs receive a wrong tag i.e. a set of senses that does not include the hand assigued one
in the general fertility model the translation probability with revealed alignment and clumping is
to map the results onto word sense associations and thus explicitly identify the predominant senses we utilize the links between senses provided by wordnet
this group contains NUM verbs all but one of them ambiguous including ask call charge regard say and wish
although the encoding of selection information by sfs in hpsg is somewhat different than that traditionally employed in tag we also adopt the notion that the extremities of the spine in an auxiliary tree share some part but not necessarily all of the selection information
therefore we perform clustering by extracting subgraphs whose branches form transitive co occurrence relations
for example the rule for evaluable paths now becolnes
the rules for quoted descriptors are also much as hefore
however whichever way the sequence is broken up the result i.e.
sequences of descriptors and the vahles they may be used to colnlmte
the required value is then obtained by evaluating a a
datr s local inheritance mechanism provides for a simple kind of data abstraction
value obtained should be the same
a simple datrg theory is shown below
given a few category members we wondered whether it would be possible to collect surrounding contexts and use statistics to identify other words that also belong to the category
a datr hierarchy is defined by means of path value specifications
NUM or else inheritance descriptors NUM
the partially annotated corpus provides an increase in performance of about NUM NUM for most models
and this corresponds to the predicate argunlent rehition tlate peter woman
it is impossible that agents perform this kind of potentially infinite nesting during real dialogue and no clear constraint can be given on how many iterations would be necessary in a real dialogue situation
for example the full understanding of the utterance do you know where thomas is depends upon whether the speaker already knows where thomas is and whether he or she believes the hearer knows
despite such work it appears that the mutual belief hypothesis i.e. that agents compute potentially infinite nestings of belief in comprehension appears to be too strong a hypothesis to be realistic
also mistakes in assuming what was common to both agents in a dialogue occurred but were quickly repaired through the use of corrections and repetitions and other dialogue control acts
viewgen is able to reason about its own and other agent s beliefs using belief ascription and inference techniques the current version of viewgen is implemented in quintus prolog
thus in the bottom up method we need no detect duplicate branches
figure NUM the proportion of words in text against words in title
to avoid this problem we would like to introduce hand crafted thesauri into our framework because the topology such as mammal is a hyper class of human allows for higher levels of sophistication based on human knowledge
second since each sbl reflects the statistics taken from co occurrence data f the whole word set statistics of each word can complement each other and thus the data sparseness problem tends to be minimized
in this figure x NUM and x NUM denote the answers for branch i individually derived from subsets NUM and NUM and x is approximated by the average of xzl and x NUM that is x l x NUM to generalize this notion let x j denote the NUM solution associated with branch i in subset j
table NUM results for using paragraphs
size NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM i b e
fd t is the frequency of an index t in d
then by bayes theorem equation NUM can be transformed into
in this paper we propose a probabilistic approach to the problem
the resultant training set contained some NUM NUM million words excluding stop words
NUM all available slots are filled c13 c14 NUM NUM null this is a new criterion
NUM incorporating the descriptor into the functional description created so far leads to a global conflict c43 c44 s14
after this argumentation we indicate how the acoustic cues for dialogue management components can be generated
then we describe concepts to meet these desiderata and we illustrate their operationalization in a schematic and in a detailed version
it remains to be investigated in exactly which ways the acoustic information can be used
NUM a component that takes care of the expressibility of conceptual descriptors in terms of natural language expressions should be interfaced
i am indebted to all of my colleagues there especicially j6rg lberla and david bijl for providing valuable discussions
in the unlikely case that no further descriptors are available NUM the algorithm terminates without complete success NUM
the other fields e.g. age or social security number can be used to infer that the two names being matched do refer to the same individual
the features selected are inflectional and certain derivational markers and stems for open class of words
NUM the number of rules learned are given the sentence level for each of the test texts
in english for example a word such as make or set can be verb or a noun
the word o is either a personal or a demonstrative pronoun in addition to being a determiner
2glosses are given as linear feature value sequences corresponding to the morphemes which are not shown
NUM we consider a token fully disambiguated if it has only one morphological parse remaining after automatic disambiguation
the two additional conditions g e gqi v g c gq2 qi c q2
additionally tsnlp attempts to control the interaction of phenomena by keeping the test items as small as possible
because resolve was designed without any concern for the task definition of the muc NUM c o task the training annotations we developed for resolve were not deducible from the annotation system used i n muc NUM
eight of these identify specific relationships between entities five of them attempt to filter out spurious entities that do not meet the scenario relevance guidelines and four fill in default values for template element attributes
we would have obtained a higher score report b y trusting the specialists working to tighten the performance of the specialists and focusing on the manually code d routines needed to dissect noun phrases correctly
crystal and wrap up could have trained on NUM texts as easily as NUM and resolve would hav e been in the same boat if it had been able to train from the muc annotated documents directly
here is the te score report that results from this minimal cn dictionary in our system design the st task is equivalent to te plus scenano specific cns for changes in position and status and wrap up
in order to map badger output into muc NUM text annotations a number of decisions must be made abou t important noun phrases including semantic type identification concept attribute assignments and coreferenc e recognition
in previous muc evaluations we used the autoslog system to generate c n dictionaries NUM NUM NUM but autoslog required human interaction for a quality control assessment of proposed c n definitions
not only do we need to locate noun phrases that describ e people and organizations we need to pull those nouns phrases apart in order to separate out names and titles aliases and locales
d welopers obviously le tds t o a waste of time and resor os
the restrictions are stated as binding principles definition NUM binding principles a an anaphor is bound in its binding category
a dcjiniteness requirement has to be fulfilled ruling out antecedents which are not predictable i.e. not a lready introduc ed in the
therefore since the scoring and sorting step NUM does not exceed this limit the overall worst case complexity is o n3
up to now it has been assumed tacitly that at the time of binding constraint application the surface structure representation is available
new words are continually added to the language and people will often use words that a parsing system may not expect
the second way is to attempt to analyze the word at the time of encounter with as little human interaction as possible
the deletion rate for the experimental run shows that some parses are being deleted when there is one or more unknown words
by using these measurements we can determine the precision and recall of the parsing system when parsing sentences with unknown words
these percentages are based on a word count not on a definition count many words have more than one definition
he performs no experiment to assess his method s viability but we will demonstrate that this is not a good approach
however they assume a large dictionary of general roots is available and that the unknown words tend to have specialized meanings
the second choice list contains those parts of speech that are fairly likely to be found in words ending or beginning with that affix
for example an unknown word ending in ly is now assumed to be an adverb adjective and a modifier
it assigns a set of parts of speech to each word again based on the second choice list for that word s affixes
this interpretation makes it possible to determine which kind of word objects by ontological status fully specified may undergo the rule
the first argument of a lexical rule predicate corresponds to the in specification of the lexical rule and the second argument to its out specification
in case the frame relation called by a lexical rule has several defining clauses the generalization of the frame possibilities is used
tm NUM we use indexing of predicate names to be able to indicate later on which lexical rule a frame predicate belongs to
however introducing such type sharing would not actually solve the problem since one also needs to account for additional appropriate features
in previous research we used a concept based approach similar to the one described in this study
with the talk prototype two important new features were introduced a method of dealing with topic shift and the inclusion of another important category of speech act context sensitive comments
we give a nondeterministic algorithm for deciding whether a sl
it is almost identical to most of the responses in the category better intervention crook counseling
therefore we augmented the lexicon with metonyms that could be accessed from the test data
figure NUM processing modules in alembic
more precisely the following facts are added to the inferential database
a separate hl taggeris invoked to zone sentence like constructs in the headline field
beyond these coarse grain similarities the system diverges significantly from earlier incarnations
we have experimented with both automatically learned rul e sequences and hand crafted ones
its repercussions are particularly critical to the subsequent stage of template generation
the names can be abbreviated in context they can be interpreted compositionally but substituting synonyms generally sounds odd
for example since periodicals are easily identified by their date of issue we should make this state salient
he mike cb mike cf lcb mike john rcb shift NUM however it can refer to the house
lexical entries pair a semantic constraint with a family of trees that describe the combinatory possibilities for realizing the semantics
the procedure starts from a set of entities to describe and a set of intentions to achieve in describing them
in the discussion and examples in previous sections the cb and the elements of cf have all been the denotations of various noun phrases in an utterance
there is a marked decrease in acceptability from version NUM to version NUM and for many people version NUM is completely unacceptable
the next example illustrates that this effect is NUM these rules and constraints have also been used by others as the basis for pronoun resolution algorithms based on centering
through utterance d terry has been the center of attention and hence is the most likely referent of he in utterance e
in the definitions in section NUM there is a basic asymmetry between the cf which is a set and the cb which is a singleton
the rule interpreter begins processing a word by setting its current position to the rightmost grapheme
the phoneme string generated by the letter to sound rule interpreter is represented as a double linked list
both the english and french translation systems presented in this paper are based on rewriting rules
in french the word byword conversion is probably simpler due to the absence of stressed syllables
if several pronunciations are possible the software has to produce only one for the synthesizer
some rules must be declared optional and the interpreter modified to take them into account
in i b m or vitamine b b is spelled correctly
in sections NUM NUM and NUM NUM we discuss a way of generalizing over semantic information in the tree bank be ore a dop parser is trained on the material
in some cases a new phoneme is added between two words of a same breath group
the problem mentioned in section NUM NUM is solved most of the time using rules for french
each word in the sentence has a certain number of possible senses
it would be possible to use multiplicative probabilities rather than additive weightings
sense tagging in action combining different tests with additive weightings
he was fired by his boss with enthusiasm
capitalization indicating a particular sense to be unlikely
in fong s view all computations are done on line and the parser reflects the theory as directly as possible
each entry in cide has been subject coded
in other words a tokenization w of a character string s is a shortest tokenization if and only if the word string has the minimum word string length among all possible tokenizations
linear logic deductions f u produce scopings in terms of the order sthe notation a a is in analogy with the lfg a projection and here refers to the set of linear logic meaning constructors associated with NUM
adjective selectional preferences are matched in a similar but more simple way
there are also some special features to cope with more long range effects e.g.
head modifier pairs can be extracted based on the modification relations implied by the structure
the annotators problems with vacancy reason may have had more to do with understanding what the scenario definition was saying than with understanding what the news articles were saying
now in the bottom center of this green region is connector three four
consequently speech recognition can be biased to accept this limited class of utterances
here local expectations failed but a previous expectation from sentence NUM is fulfilled
this leads to the fragmented style that so commonly appears in efficient human communication
because considerable information is exchanged during the dialog the user model changes continuously
computer near the top boundary and in the center is a green region
now in the middle right of this green region is the control knob
our theory also shows how variable initiative is built into the same simple architecture
specifically it is aimed at achieving the following convergence to a goal
we fully expected that we will not be able to successfully complete improving our existing anaphora resolution code and make it work reliably with th e newly created large knowledge bases
below we explain our edits and enclose the answer key for the walkthrough article with the hand writte n markings showing the expressions we recognized as well as our edited markings
for either direction however the semantics axe the same and both directiona i rules call these clauses for the semantic computation
1degthe lf for the determiner has the form of a montagovian generalized quantifier giving rise to one fully scoped logical form for the sentence
however odds of that apposing areslim sin word fro ye now that be has r rated himself he weals to d othe same for the agency
also we have been conducting experiments in non committal processing with controlled resource allocation such as exploiting local interpretations taking advantage of loca l contexts and performing expensive computations such as disambiguation only if needed
NUM implemented quick and dirty pronoun resolutio n while this code appears to perform well it breaks occasionally and needs to be further debugged is a simple and flexible architecture of our nlp system NUM
more distressingly however a complete acquisition process for these adjectives uncovers NUM different combinations of semantic roles for the nouns modified by the ble adjectives involving besides the standard theme or beneficiary roles the agent experiencer location and even the entire event expressed by the verb
our errors included NUM years old probably misinterpreted task definition last year deliberatel y kept and three and a half months only some dashes handled
an whose elements a are terms record like structures consisting of a head a type symbol and a body a list of attribute value pairs attribute value
there are two problems with this assumption a without further stipulation there is a tendency to allow too many readings and b there is considerable confusion as to how many readings should be allowed arising from contamination of the semantics of many nl quantifiers by referentiality
however the implementation issues although more complex are also well understood and it can be expected that future work will bring further improvements
i would like to thank aravind joshi dale miller jong park and mark steedman for valuable discussions and comments on earlier drafts
NUM s and p NUM np s p np s q np readings that involve intercalating quantifiers such as the one where every girl outscopes one sazophonist which in turn outscopes most bogs are correctly excluded
b three f frenchmen f five r russians r visited f r we can always argue by enriching the notation that NUM b represents at least four different readings de null pending on the particular sense of each involved np i.e. group vs individual denoting
the d edge connecting the maximal projection of baasaan to the aux component however has a sic that allows only vp wh top nodes to be inserted
the most distinctive feature of dtg is that unlike tag there is complete uniformity in the way that the two dtg operations relate lexical items subsertion always corresponds to complementation and sister adjunction to modification
NUM small spicy hotdogs he claims mary seems to adore when comparing this derivation structure to the dependency structure in figure NUM the following problems become apparent
furthermore a finite verb may optionally also project to s as in the d tree shown for claims indicating that a wh moved or topicalized element is required
our solution takes advantage of lfg s flexime projection based architecture by implementing a projection which models the hierarchical seleetional requirements of auxiliaries yet does not interfere with the sul categorizatiomfl prot erties of verbs as wouhl he the ease under a raising anal
let to g be the set d of elementary d trees of g mark all of the components of each d tree in to g as being substitutable NUM
as in NUM which illustrates our alternatiw solution functional uneertainty is represented by the kleene star NUM tile annotation on the nps indicates lhat they could fulfill the role of any possible grammatical flmetion gf e.g.
in defining dtg we have attempted to resolve these problems with the use of a single operation that we call subsertion for handling ml complementation and a second operation called sisteradjunction for modification
the substituted node to drift arbitrarily far up the d tree and distribute themselves within domination edges or above the root in any way that is compatible with the domination relationships present in the substituted d tree
we discuss how these models affect the system s decisions of both what to say i.e. what errors to tutor the student about and how to say it i.e. what syntactic constructions to use in the realization of the system s message
we computed all binary vectors of the NUM NUM seed words ws where the th dimension of the vector is NUM ff the seed word occurs in the th paragraph in the text zero otherwise
NUM an algorithm for finding terminology translations from non parallel corpora bilingual lexicon translation algorithms for parallel corpora in general make use of fixed correlations between a pair of bilingual terms reflected in their frequent co occurrences in translated texts to find lexicon translations
NUM a onset a c every syllable starts with a consonant
the unifying theme is that each primitive constraint counts the number of times a candidate gets into some bad local configuration
NUM a NUM NUM clash each cr temporally overlaps no NUM
the use of a partial ordering allows the lexicon and morphology to supply floating tones floating morphemes and templatic morphemes
into lists l0 l1 l2 lk according to the highest numbered tier they mention
NUM otherwise mlk exhausts tier k s ability to me null diate relations among the factors
i believe it is worth study both as a hypothesis about universal grammar and as a formal object
we could add b directly back to to NUM as a new factor but it is large
we have developed a model of how the effects of the first language in our case asl can be accounted for in the analysis phase of our system and are currently developing a model which captures the effects of language acquisition itself
rare syntactic constructions pose a related problem there are not enough instances to justify the creation of a separate cluster
each of the NUM tags is defined by the centroid of the corresponding class the sum of its members
it will assign a value close to NUM NUM if two words share many neighbors and NUM NUM if they share none
null table NUM looks at results for natural contexts i.e. those not containing punctuation marks and rare words
table NUM shows that performance for generalized context vectors is better than for word based context vectors NUM NUM vs NUM NUM
therefore the tag adn was introduced for uses of adjectives nouns and participles as adnominal modifiers
we start by constructing representations of the syntactic behavior of a word with respect to its left and right context
how can a categorization be considered meaningful if the infinitive marker to is not distinguished from the homophonous preposition
the penn treebank parses of the brown corpus were used to determine whether a token functions as an adnominal modifier
first a tokenizer converts the input string for the entire article into a sequence of tokens
the next reduction stages take care of primary then secondary references to organizations
we hope to have the opportunity to continue this work as funding permits
for te each expectation is a trivial one containing one person or organization
for te there is one final reduction stage to take care of organization descriptors and locations
the next reduction stages take care of primary then secondary references to persons
obviously the org alias and org descriptor slots were allowed to have multiple fillers and org name was not
early results showed that the system was performing better than the human analyst s in all aspects
however at each stage a number of rules might be applicable
mapping rules state how the semantics is related to the syntactic representation
it misses fallon mcbride and mccann erickson for reasons already noted
james several occurences coca cola coke etc
for example in hong kong although many people can speak english one encounters a large variety of different accents since in addition to hong kong s large population of cantonese speakers there are also many mandarin speakers and many indian british american and australian hong kong residents
that s can be discourse segment purposes but pot int
figure NUM sample interlingua representation with possible speech acts noted
or do you think sometime friday afternoon you could meet
do you have time between twelve and two on thursday
si ds NUM NUM tuesday afternoon looks good
it will try to attach it to the active path
where it attaches determines which speech act is assigned to the input sentence
weaker and stronger forms very roughly correspond to direct and indirect speech acts
our discourse model is based on an analysis of naturally occurring scheduling dialogues
the b form makes explicit the order of incorporation of dependents into the head line
they confirm that all the training examples collected in our corpus are effectively utilized by lzxas to improve its wsd performance
figure NUM effect of number of training examples on wsd accuracy averaged over NUM words with at least NUM training examples
for the task of analyzing spontancous language we pursue a shallow screening analysis which uses prima ily flat representations like category sequences wherever possible
sue j ker and jason s chang word alignment this paper presents a word alignment algorithm based on classification in existing thesauri
instead for each source word s only a handful of target words strongly associated with s are found and stored
in addition a number of clear cut np components can be defined outside that juxtapositional kernel pre and postnorninal genitives gl gr relative clauses rc clausal and sentential complements oc
for a phrase q with children of type t t and grammatical fimctions g NUM we use the lexical probabilities po giiti and the contextual trigram probabilities
since a precise structural description of non constituent coordination would require a rich inventor of incomplete phrase types we have agreed on a sort of nnderspecified representations the coordinated units are assigned structures in which missing lexical material is not represented at the level of primary links
NUM schade dm kein arzt anwesend ist tier pity that no doctor present is who sich auskennt is competent pity that no competent doctor is here note that the root node does not have a head descendant hd as the sentence is a predicative construction consisting of a subject sb and a predicate pd without a copula
the tbllowing commands are available group words and or phrases to a new phrase ungroup a phrase change the name of a phrase or an edge re attach a node generate the postscript output of a sentence
adv v np NUM np i i v daran e NUM wird ihn anna e e e erkennen dass erweint the fairly short sentence contains three non local dependencies marked by co references between traces and the corresponding nodes
to assess the proposed method s effectiveness we have implemented the algorithms described in section NUM and conducted a series of experiments
the similarities and differences between english and mandarin texts are briefly reviewed since our experiments involve the alignment of english mandarin parallel corpora
as for free word order languages the following features may cause problems local a nd ram local dependencies tbrm a continuum rather than clear cut classes of phenomena null there exists a rich inventory of discontinuous constituency types topicalisation scrambling clause union pied piping extraposition split nps and pps word order variation is sensitive to many factors e.g.
the results would be useful for deciding what strategy should be taken in developing a grammar on a domain dependent nlp application systems
a language in general belongs to one of three basic word order types svo sov and vso
second unlike in english word order in mandarin is not determined solely on grammatical grounds but rather depends on semantics
in section NUM we analyze the experimental results and consider ways in which the proposed algorithms might be extended and improved
NUM a small percentage of connections NUM NUM in our evaluation are incomplete ones and are considered to be correct
the result of our planning algorithm is a schema for each group of compatible goals
postfiraphe tries to find the smallest set of schemas that covers the writer s intentions
if a candidate is rejected the next one on the sorted list is tried
only the recurrent connections fi om the hidden layer to the context layer are NUM NUM copy connectr tls which represent the internal learned context of the
graphe system our prototype the postgraphe system is a compromise between keeping the implementation simple and obtaining satisfactory results
null an important aspect of postgraphe is that it uses no high level reasoning on intentions
thus determining which types of graphs or text best satisfy single goals is not sufficient one also has to
the hum null bet of values sometimes has a lot of influence on the choice of an expression schema
sometimes automatic calculation of keys can give strange results which do not fit with the semantics of the variables
null one of the design goals of 9ostgraphe was to be able to function as a front end to a spreadsheet
do you need additional information about this train
the system uses this information in automatically expanding terms for query expansion and hyperlinking
when the alias button is on query terms are expanded to include their aliases
table NUM test set words part two
finally the system does not assume any particular language combination or target language
the user can formulate a boolean query using the box numbers and boolean operators
they identify names in texts dynamically rather than relying on finite lists of names
for example figure NUM shows a list of people co occurring with the place peru
for example the selection of rebound as object noun would entail preferring to grab over to score as main verb while the selection of point would entail the opposite verb choice since to grab rebounds and to score points are lexical collocations whereas to score rebounds and to grab points are not
similarly in the student advising domain we found course evaluation e.g. how difficult or interesting a course is to be floating whereas the description of the assignments required e.g. how many there are or whether they involve writing essays software or proofs in a course is structural
elhadad mckeown and robin floating constraints in lexical choice as we will show this architecture allows us to convey several aspects of the semantic content using the same word and at the same time allows us to realize the same semantic concept at a variety of syntactic ranks depending on the situation
for example consider the conceptual input to advisor ii s lexical chooser whose graph is outlined at the top tier of figure NUM the domain from which concepts are selected is an expert system rule base which uses gradual rules of inference e.g. the more assignments in a class the harder the class
for example if the main verb to allow is selected then the object must be either a clause allow one to select or a noun group allow the selection NUM semantics the concept itself and how it is taxonomized in the domain influence which word should be used
this is complicated by the fact that constraints on lexical choice come from a wide variety of sources syntax the choice of a particular verb influences the syntactic forms that can be used to realize its arguments which in turn constrains the words used to lexical ze these arguments
following our criteria for generation the structure of the conceptual input will not be committed to any linguistic structure in order to avoid encoding assumptions about realization into the domain application and in order to free the content planner from reasoning about linguistic features when determining what information should be included
once names are recognized in query and database record then name matching algorithms are needed to determine whether the names are the same or that they in fact designate the same individual e.g. two instances of the lexical entity judge smith are the same name but may not designate the same individual
traversal is successful if mrsg has been completely processed and if the end node in the decision tree contains a template
NUM in addition its success led to the design of an even more ambitious demo for the NUM month tipster meeting in may NUM
over the spring and summer of NUM the outlines of an architecture began to come together in the form of an architecture design document
however the government had an ambitious set of goals and we were still a long way from meeting all of them
as we try to make the architecture more comprehensive there will be a natural tendency to just add features and operations
many interfaces however have not yet been defined to the level of detail which will be needed for the government to meet its goals of software reuse and modular upgrading i.e. they are under specified
precision we recognized all along that the rel null atively brief english prose describing the operations in the architecture left some things underspecified
it will also provide a definition of some aspects of the architecture which can complement the prose descriptions in the design document
until now conformance to the tipster architecture has been gauged by a manual comparison of an implementation with the design document
this covers a phrase like could not really have left
instead the transformed patterns are generated when the grammar is compiled
then domain specific patterns are defined that provide particular instantiations of the metarules
adverbials are recognized by matching a sequence of input objects with event adj
NUM we have a moderately well developed module for coreference resolution
we could simply have the bare nominal
fastus is a cascade of finite state transducers
there are three primary methods for this
cars are to be manufactured by gm
the only extension is to keep a list of all the nodes that have already been visited to keep the same computation from being repeated
wordnet s large coverage and frequent utilization has led us touse it for our experiments
for example the expression balance of payments is considered as one term
figure NUM we must assign an unknown number of categories to each document in reuters
our approach makes use of wordnet synonymy information toincrease evidence for bad trained categories
subtrees may be shared by quite different parts of the structure not only by disjuncts of the same disjunction
the excerpts are of different lengths with the exceri ts from the two books being the longest
values for the integrated approach show some general advantage over wordnet and training approaches but results are not decisive
that is if the names to be compared are part of records containing additional fielded information e.g. age or social security number this information can be used as additional evidence in the name matching process
there is neither link crossing nor llnk cyele
we generate a table called incontext of all possible unambiguous contexts which contain a token with an unambiguous projected parse along with a count of how many times this parse occurs unambiguously in exactly the same context in the corpus
it is more work to open up part3 for testing the process of opening drawers and extending cards in part3 may induce problems which did not already exist
it may be possible to resolve this one using subcategorization constraints on the object of the verb kur assuming it is in the very near preceding context but this may be very unlikely as turkish allows arbitrary adjuncts between the object and the verb
each relation node has up to two daughters the cue if any and the contributor in the order they appear in the discourse
discourse structure in terms of both segment structure and levels of embedding affects cue occurrence the most intentional relations also play an important role
this would be particularly useful for information retrieval applications
we would like to thank xerox advanced document systems and lauri karttunen of xerox pare and of rank xerox research centre grenoble for providing us with the two level transducer development software on which the morphological and unknown word recognizer were implemented
the value of a tncb is the sign that is formed from the combination of its children or inconsistent representing the fact that they can not grammatically combine or un determined i.e. it has not yet been established whether the signs combine
note that during deletion we remove a surplus node node NUM in this case and during conjunction or adjunction we introduce a new one node NUM in this case thus maintaining the same number of nodes in the tree
a table is is a table where the first token has the morphological parses where the determiner is attached to the noun and the whole phrase is then taken as a vp although the verbal marker is on the second lexical item
adjunction is only attempted if conjunction fails in fact conjunction is merely a special case of adjunction in which no nodes are disrupted an adjunction which disrupts i nodes is attempted before one which disrupts i NUM nodes
lexical ambiguity arises if some user term matches two entities within the same semantic class
thus it would be possible to get an improved f score by selecting turs if we had a good enough selection heuristic or filter
the reason why we believe such improvement is possible is that given adequate information from the previous stages two target signs can not combine by accident they must do so because the underlying semantics within the signs licenses it
because the measurable improvement in parsing is so great compared to manually constructed parsers it appears to offer a qualitatively better parser
for example statistical techniques may have suggested the importance of hire a verb which many groups did not happen to define
pattern matching has given us very robust very portable technology but has not broken the performance barrier all systems have run up against
our first step was to massage the data from the plum and shogun systems into a form that could be read into a merging program
this initial approach proved unwieldy as we had to simultaneously deal with the problems of aligning output from the two systems and merging that output
we have concluded the following our information extraction engines for the muc NUM named entity task and template element task employ no domain specific information
for example can simple template information e.g. who did what to whom where and when be reliably exlracted
a pioneering effort started under darpa funding though not part of the tipster program is the goal of information extraction from speech
it would be very useful to have spoken dialogue interfaces for such information access tasks
two examples of inter linked domains d and top concepts tc are given above the ill records
the range of numerical and temporal expressions covered by the task was also limited one notable example is the restriction of temporal expressions to exclude relative time expressions such as last week
profit prolog with features inheritance and templates
we assume that the domain element l zsg corresponds to the first and second arguments NUM sg to the second and third arguemnts and so on as illustrated below
each feature has a sortal restriction for its value
of the various values that were tested NUM
neither of these models is generative instead they both estimate NUM t s directly
if the semantic representation is to be read off syntactic structure then the parser must provide a single syntax tree possibly with empty nodes
one approach is to use a grammar with non standard this research was supported by the u k science and engineering research council grant rr30718
this improves parsing performance and more importantly adds useful information to the parser s output
in this paper we first propose a new statistical parsing model which is a generative model of lexicalised context free grammar
in model NUM we extend the parser to make the complement adjunct distinction by adding probabilities over subcategorisation frames for head words
distance is a function of the surface string from the word after h to the last word of r2 inclusive
for example a syntax tree missing a noun phrase such as the following can be given a semantics as a function from entities to truth values i.e.
bar hillel employed a single application rule which corresponds to the following ix NUM l l1 i l1 ln r1
np s pp np np s pp np np s pp np
s pp pp np s np this allows peripheral extraction where the gap is at the start or the end of e.g. a relative clause
both techniques can be thought of as marking the wh arguments as requiring special treatment and therefore do not lead to unwanted effects elsewhere in the grammar
in practical terms a naive interactive parallel prolog implementation on a current workstation fails to be interactive in a real sense after about NUM words NUM
the final section contrasted parsing with lexicalised and rule based grammars and argued that statistical language tuning is particularly suitable for incremental lexicalised parsing strategies
the rule of state prediction is obtained by further generalising to allow the lexical item to have missing arguments and for the expected argument to have missing arguments
these knowledge sources are implementedin a name recognition rules consisting of a pattern and an action and in b lexical resources e.g. part of speech information
for instance tb is an soe whose repeating part only likes mary is deaccented
golf although the intuitions underlying the por are clear two main objections can be raised
under this perspective focus is taken to be the semantic value of a prosodically prominent element
here a primary occurrence is an occurrence that is directly associated with a source parallel element
anaphors and ellipses are represented by free variables whose value is determined by solving higher order equations
f xb xa and that symbols that carry different colors are treated differently
we call a formula m cmonochrome if all symbols in m are bound or tagged with c
in order to get a better understanding of the situation let us reconsider our example using colors
at the first level the target language lexical units are looked up in the lexical database and mon rcb lingually relevant features are calculated on the 2recall theft this only applies to words of the general vocabulary which require disaint rcb iguation during analysis and not to terms basis of the language neulral representation e.g.
minimum number of quest ions and comlmtes the remaining linguislic information from the answers re ceived
llefore lhe lext is submilted to the parser the lext is lagged i e
in this way the number of parse paths that the system has to consider is reduced considerably
current work concentrates on improving the coordination of the rule based part of the systeln and the fail soft component
the module is based on slrueture buihling rules whi b allow for downwards ext ansion
finally the doemnent generation module inserls fll sgml inarkers anti all ilems which have been inarke d
mars can and do choose a subset of the possible rules
so these familiar chains are the only source of spurious ambiguity in
NUM if nf NUM not output of fwd
a fixed ccg grammar need not include every phrase structure rule matching these templates
figure NUM gives an efficient cky style algorithm based on this insight
the proof of theorem NUM completeness is longer and more subtle
a variant algorithm ignores preferableto and constructs one parse forest per reading
any sort of chart parser or non deterministic shift reduce parser will do
NUM appear to share this view of semantic equivalence
the main reasons for this are it should be possible to link equivalent non english meanings e.g.
generally let p be a distribution for which we have a sampler
j for all the features simultaneously not just the weight i for feature i
we can estimate qold f k by means of random sampling
the weight of a dag is the product of weights of rules used in generating it
consider the alternative model m given in figure NUM defining probability distribution q
yields the erf distribution xl x2 x3 x4 q2 x NUM NUM NUM NUM NUM NUM NUM NUM
i expect practicability to be quite sensitive to the choice of grammar the more the grammar s
the collocational semantics then is used to identify the topics from paragraphs and to discuss the topic shift phenomenon among paragraphs
if there is any such keyproblem NUM then it is undoubtedly the problem NUM of the unitylo of the gospelll
ditto tags are those words whose senses in combination differ from the role of the same words in other context
where cs denotes the connective strength and par and pv are parameters for csnn and csnv and pn pv i
in general there are more than NUM candidates in a paragraph it is impossible to select topics at random
the results intensify the viewpoint that the repeated words make persons impressive and these words are likely to be topics
d z y abs c x c y NUM
NUM wo lea ical signs arc connected if they are directly connected furthermore the connectivity rclation is h an silivc
a notational convention used in the i al er is that items such as dogt stand for simplitied lexical signs of the
the nouns that have the stronger connectivities with other nouns and verbs in a discourse could form a preferred topic set
other every inactive edge was tested to see if it led to a disconnected graph if it did then the edge was discarded
in this way only those lexical categories which are directly connected to the sign are taken into account the implication of this will become clearer later
as with most lexicalist generators semantic variables ttttlsl c distinguished in order to disallow tr mslationally incorrect permutations of the target bag
outer domains thus represent elements whi h may lie outside a subtree of category sign in a complete sentential they would be indicated through paths such as
to either ignore the levels of abstraction or to require a switch of the alignments would likely yield a poorer score
the class c to which a sentence is assigned is a sequence of the parts of speech tags for the words in the sentence
it is easy to see that two member sequential selection is a special case of general sequential selection where any disagreement is considered sufficient for selection
for example the maximum likelihood estimate for a x is a giving the model m lcb a rcb lcb rcb
in this case the most probable tag sequence according to equation NUM is given by
third sequential selection which implicitly models the expected utility of an example relative to the example distribution worked in general better than batch selection
the coreference scorer will probably stay in emacslisp while the task definition is evolving in order to facilitate rapid prototyping
we do not discuss the first two options since they are standard ai search techniques
the following fragment shows the result of the discourse structuring module for the sample sentences
thus far no theories of anaphora have been tested on an empirical basis and therefore there is no answer to the best anaphora resolution algorithm
the first NUM adjectives nouns verbs with frequency NUM NUM 4celex also contains entries with frequency NUM but we wanted to assure a minimal degree of commonness by selecting words with frequency NUM
table NUM gives the number of readings for the word order n standing for noun readings v for verbal prep for prepositional and phr for phrasal readings
for telegraph the rate of unknown words dropped by NUM for medium frequency and by NUM for low frequency tbr systran the same rate dropped by NUM for medium frequency and by NUM for low frequency words and for langenscheidts t1 the rate dropped by NUM for medium frequency and by NUM for low frequency
wf wrong form the source word was found in the lexicon but it is translated in an inappropriate form e.g. it was translated as a verb although it must be a noun or at least in an unexpected form e.g. it appears with duplicated parts windscreen wiper windschutzscheibenscheibenwischer
the first number in a triple is the percentage of positive counts in the high frequency class the second number is the percentage of positive counts in the medium frequency class and the third number is the percentage of positive counts in the low frequency class
w wrong translation the source word is incorrectly translated either because of an incorrect segmentation of a compound spot on erkennen auf stelle auf instead of haargenau exakt or seldom because of an incorrect lexicon entry would wiirdelen instead of wiirden
table NUM percentage of correctly translated words without wrong forms o assistant i l ng ti personal i power i systr n i telegraph i wd nouns NUM NUM NUM NUM NUM table NUM number of incorrect gender assignments
in this table we can see even clearer the wide coverage of the personal translator lexicon because the system correctly recognizes around NUM of all low frequency words while all the other systems figure around NUM or less
therefore explicit modeling of the discourse purpose or discourse segment purpose is unnecessary
given that the size of our training corpus is fairly small total NUM words a transformation based tagger is wellsuited to our needs
let us explain what these figures mean taking the german assistant as an example NUM adjectives NUM nouns NUM verbs of the medium frequency class were unknown resulting in NUM adjectives NUM nouns NUM verbs getting a translation
once the slalom model is complete we expect to rely on user modeling techniques to place the user within this model
by applying genetic algorithms we consider that our proposed method can effectively solve problems that example based machine translation would require many translation examples
in addition the user may their work on the grammar and linda suri for the writing sample analysis and development of the error taxonomy
in the previous method of selection process described in section NUM translation rules are evaluated only when they are used in the translation process
the main problem is that many erroneous translation rules are produced and these rules can not be completely removed from the dictionary
we proposed a method of machine translation using inductive learning with genetic algorithms and confirmed the effectiveness of applying genetic algorithms
in the all combinations of words in a translation rule the system determines whether the each combination of words is true or false
third nametag can take advantage of sgml markers to improve performance
however the system based on this method produces many erroneous translation rules that can not be completely removed from the dictionary
therefore we need to improve how to apply genetic algorithms to be able to remove erroneous translation rules from the dictionary
if ico s NUM by definition s has critical ambiguity in tokenization
computational linguistics volume NUM number NUM that is any character string has at most one bt tokenization
both the japanese and spanish systems still have room for higher recall because of shorter development time but also partly because of difficult language specific issues to be solved which we will discuss below
on the other hand it tagged roverto marquevich de san isidro as a full person name though de is a preposition and san isidro is a location name
for clarity the elements of the n tuple are separated by colons e.g. a b c q r s describes the NUM relation lcb amq bmr cms m NUM rcb
table NUM compares plug ins used for different languages tasks
the paper is organised as follows
ellipsis and quantification a substitutional approach
this make the elliptical conjunct equivalent to
the nametag engine is designed for multilingual capabilities
class ambiguity arises if a term may belong to two or more semantic classes
s also to pronouns of laziness
9not all vp ellipses have vp antecedents
the same thresholds for search beams as for the first set of experiments were used
the presence of de in spanish person names has made person name recognition more difficult as de is also a preposition and sometimes caused a spanish version of chicken and egg problem
thus they encompassed various subject domains including business politics sports and arts unlike the wall street journal arti null cles used for muc NUM
first consider the simplest case of calculating efficiency measures over a whole dialogue
having no intentional state our model does not distinguish times being negotiated from other times
as shown in figure NUM performance is also a function of a combination of cost measures
subjective metrics can still be quantitative as when a ratio between two subjective categories is computed
contextual appropriateness ca the coherence of system utterances with respect to dialog context
focuslist is the list of discourse entities from all previous utterances
there are no immediate plans toreplace m enamex type person dooner enamex as president m namex type person james enamex operate d ae chairmaet executive officer and president for a period of time
in 7this tagging can be hand generated or system generated and hand corrected
consider the longer versions of dialogues NUM and NUM in figures NUM and NUM
in the calculation of sim g gv we can ignore the value of p c because it occurs ig u gvl times in both denominator and numerator
the first columns in tables NUM and NUM report the semantic classes for nouns and verbs
furthermore since we allow for multiple hyperonyms it is possible that different hyperonyms may still both be valid
for instance there is vast ambiguity in the seen data
abstraction to acquire enogh evidence of most of the phenomena
this section presents our model of temporal reference in scheduling dialogs
for the current not yet resolved temporal unit each rule is applied
it s a numex type quot money quot NUM numex million campaign with the recognition of a numex type money s200 million numex campaign he ways of the commercials that feature a couple that must hold a record for the length of time dating before kissing
the subproof rooted by l4 leads to f v g while subproofs rooted by l2 and l3 are the two cases proving q by assuming f or g respectively the applicability encodes the two scenarios of case anmysis where we do not go into details
proverb s microplanner cuts the entire text into three paragraphs basically mirroring the larger attentional spaces u3 u5 and u6 in figure NUM since nodes NUM and NUM are omitted in this verbalization node NUM the last sentence is merged into the paragraph for u6
table NUM agreement among coders kappa coefficients by field
the pragmatic module is able to deal with such unconnected pieces of information and will perform better if given such partial parse results
normally the head corner parser will be called as follows for example parse s sem NUM NUM
as the result we obtain three grammar rules np art noun np pron noun and np art el
we now have head link x NUM y b x b
therefore the parser only needs to consider gaps in non left most position by a clause similar to the clause in NUM
on the other hand for grammars in which this technique does not provide any useful top down information no extra costs are introduced either
in the implementation of the head corner parser we use an efficient implementation of the head corner relation by exploiting prolog s first argument indexing
it was not possible to perform all the experiments with this parser due to memory problems during the construction of the lr table
active chart parsers memo everything including sequences of categories inactive chart parsers only memo categories but not sequences of categories
not only do we refrain from asserting so called active items but we also refrain from asserting inactive items for nonmaximal projections of heads
inside probability of lt i j can be computed the same as that of lr i j except for the direction of dependency link between zvi and wj
in order to evaluate the data used to train the model NUM training tuples were examined
complete link inside probabilities jslr NUM inside probability of a complete link is the probability that word sequence wij will be generated when there is a dependency m relation between wi and wj
figure NUM an NUM rule cfg derived from a unification grammar
the three cases are depicted in figure NUM
moreover the alternative computational treatment to expand out the full lexicon at compile time is just as costly and furthermore impossible in case of an infinite lexicon
and the dependency link from wv to wj
in summary it is encouraging that an explanation system could begin to approach the performance of multiple domain experts and surpass that of one expert
for example the lexical rule NUM of figure NUM applies to word objects with tl as their c value and to those having t2 as their c value
a computational treatment expanding out the lexicon can not be used for the increasing number of hpsg analyses that propose lexical rules that would result in an infinite lexicon
sparse data is somewhat less problematic for long range than for short range predictions since it is in general easier to predict what is coming soon than what is coming next
we describe four compilation steps that translate a set of lexical rules as specified by the linguist and their interaction into definite relations to constrain lexical entries
NUM a set of word meanings across languages have complex equivalence relations but they have parallel language internal semantic relations
loose ends at either site of the ill records can be used to detect possible ilir records that have not been considered as translations in one wordnet but have been used in another wordnet
null step NUM NUM if the verb is modified by the adverbs contained in the array adverbs refer to the adverb class label and add NUM to an array modified i k where i is the position of the verb in the list verbs and k is the position of the adverb class label in the table4
they are classified as NUM non gradual process verbs and by modifiers p rcb or gradual change indicators g rcb NUM the endpoint of the process is explicitly set up the verb is modified by end state modifiers e ot quantity regulators q or it takes a goal arsument i.e. ni o case etc
the results of the parsing analysis of these sentences indicates that the constituents of the sentence have a dependency struc null step i pick out the items of which the governing and dependent words are a verb and an adverb from the edr co occurrence dictionary and store them with the frequency in an array called pairs cf
gouisuru arrive at an agreement kireru snap furnikiru launch out nureru become wet turnaru become packed tunagaru make a connection au meet suwaru sit down tatamu fold kureru get dark atehamaru fit
NUM a switch from a shared post to a solely held post or vice versa is to be represented in separate succession event objects i e a shared post is distinct from a solely held post
on the job slot definition identification of the status of io person with respect to post as of the date of the article i.e. information on who the current holder of the post was at the time the news appeared
post must identify the company and must identify either NUM the person who has assumed or will be assuming th e post or NUM the person who has vacated or will be vacating the post
thus management posts in government entities international agencies and miscellaneous organizations such as labor unions are nonrelevant whereas management posts in for profi t and not for profit entities such as corporations universities and charitable organizations are relevant
for example if an article says that an incoming person was an office r at a different organization assume that the person came directly from that organization and fil l other org with a pointer to that organization
however they may point to the same organization object under other circumstances as well such as when a person holds more than one post and is going to give up some portion of them but not all of them
when the outgoing person was is occupying the post on an interim acting basis the vacancy reason refers not to th e acting titleholder s reason for departure but to the previous permanent titleholder s reason for departure
as a general guideline in these cases of indirect description two persons ar e considered to share a post at a particular company if they are associated with exactly the same title an d with no differences in responsibilities
x who therefore must be already on the job if person is identified as having come on the job as of a date in the past up to and including the date of th e article
it is quite common for one person to hold several posts at one company and it is no t uncommon for a person to hold posts in more than one company simultaneously if the companie s are related
as the annotation format permits trees with crossing branches we need a convention for determining the relative position of overlapping sibling phrases in order to assign them a position in a markov model
the entire volume of space in a room forms an aggregate but it has no atomic parts
for example let the background set have exactly three distinct elements a b and c
if transitions between states representing labels with different indices are forced to zero probability together with smoothing applied to other transitions all labels assigned to a phrase get the same index
the relation of being a sub aggregate is a partial ordering on the set of all aggregates formed from the background set
having stated and illustrated the principles governing plural demonstrative noun phrases i turn to those governing plural quantified noun phrases
i look first at conversion of mass nouns to count nouns and then at the conversion of count nouns to mass
its conversion to a count noun requires that its denotation must be such that it has minimal parts or atoms
for sometime lexical semanticists have been aware of words which satisfy both the mass criteria and the count criteria
thus tom as a common noun denotes the set of people who have tom as a proper name
as count nouns they denote those things to which one is loyal and those things to which one owes allegiance
NUM NUM states that any part of something denoted by a mass noun is denoted by the same mass noun
NUM semantic representation of the sentence is checked using contextual knowledge we call it filtering hereafter
first the interpreter assumes that there are no omissions and inversions in the sentence l a
furthermore when the interpreter fails the analysis using the heuristics it assumes that the post position is wrong
post positions assumed to be wrong are ignored and the correct post position is guessed using above heuristics i d
it must be noted that the recognition rate of the speech recognizer is limited by the trade off between the looseness of linguistic constraints and recognition precision and that the recognizer may output a sentence as recognition results which human would never say
this information can be semantic features semantic roles subeategorization patterns syntactic alternations e.g. see don in press and semantic components
the discourse component then applies inference rules that may add more semantic information to the discourse predicate database
ladd NUM is triggered which transforms one metrical tree into another default accent rule if a strong node nl is marked a while its weak sister n2 is not then the strong weak labeling of the sisters is reversed nl is now marked weak and n2 is marked strong
for example a a major phrase is marked a if it is not in focus
ist c p can be read as saying that p is true with respect to c now let c be the context that obtains after the sentence mozart composed k NUM has been generated
for example a paragraph may have place and date of performance as its topic and then only those s templates can be used that are associated with the attributes date and place
for brevity we will not represent syntactic structure but only the terminals of templates composition was were written by composer date slots are to be filled with structured expressions that contain database information
this method can be further improved and integrated with other language models
we have seen how the knowledge state the topic state the context state and the dialogue state together form one large context model which is used and maintained by the dyd system to generate its spoken monologues
the present paper highlights a number of these issues focusing on the language and speech generation components of such systems and discusses their implications for the way in which context has to be modeled in a spoken dialogue system
the system pieces together a model of the whole from the parts of the text it can understand
for each category the first row shows the percentage of branches that occur within this category and the overall accuracy the following rows show the relative percentage and accuracy for different levels of reliability
semantic rules are matched based on general syntactic patterns using wildcards and similar mechanisms to provide robustness
in NUM of these occurrences the n p is assigned the grammatical function oa accusative object manually but of these NUM cases the tagger assigned the function sb subject NUM times
the lexical pattern matcher was developed after muc NUM to deal with grammatical forms such as corporatio n names
however their algorithm deals with complex noun phrases only and although the technical terms identified by their algorithm are generally highly topical the algorithm does not provide the context sensitive information of how topical each incidence of a given meaningful phrase is relative to its direct environment
the um thus constrains the possible ideational structures which can be produced
providing twice the messages marked for ne te and st would have made a big difference
the sentence realiser can thus be used in connection with many different textplanners
however the parsing evaluation was eventually canceled after the dry run for muc NUM held in april may NUM
user consultation can occur at several levels of the translation process
the information given for selbst besucht sabine is its category vp and the daughter categories adverb adv past participle wee and proper noun ne
we prepared spatter for such an evaluation and had achieved quite high scores on blind test material
has marked the words as a constituent and the tool s task is to determine simple sub phrases the ap and pp as well as the new node and edge labels cf
because all features are considered of equal importance we call this the naive back off algorithm
the gb parser french and english have been discussed in cf
this representation now includes the condition incredible z talltalc z tell z y z to represent the idiom in NUM the continuation of our sentence is shown
in the following we will first deal with the idea of decomposability of idioms in section NUM in section NUM we will present our proposal of an adequate representation of the idioms meaning by means of drt
that these oi erations mid mo lili ttions in i NUM are not result of tmns or word l lays but gratnmatieally a nd stylistically unmarked onstructions
ur intuition suggests to parai hrase cinch i3ock schicflen t y e inen hler machen lit make t misl ake a nd jmdm
while in the case of verbs the feature val just contains more information than usual namely the stems of the missing parts of the idiom the feature vpl is used to mark idiomatic information in other syntactic feature structures
an example for the latter group often called compositional or decomposable i idioms is spill the beans NUM by classfying idioms with the terms compositional respectively decomposable the same property is decribed by two different point of views
NUM processing decomposable idioms when parsing decomposable idioms with the parser described in the previous section the following steps are taken while initializing the ehart it is important to control whether potential parts of an idiom are found or not
the h llowing example shows both kind of abstraction with the a NUM rs for the indetinite tieterm net and the noun mistake a feature structures are use t to encode the a i li ss
it is difficult to set an appropriate cutoff value a for a significance test
in accounting for how agents collaborate in making a referring action our work aims to make the following contributions to the field
a referring action is composed of these primitive actions and the speaker utters them in her attempt to refer to an object
computational linguistics volume NUM number NUM we use the terms action schema plan derivation plan construction and plan inference
we adopt the prolog convention that variables begin with an uppercase letter and all predicates and constants begin with a lowercase letter
our technique is to remove the subplan rooted at the action in error and replan with another action schema inserted in its place
otherwise the terminating constraint will be satisfiable and so object will be instantiated to the single object in the candidate set
the conceptual schema tree has invention components as nodes whereas the claim text plan has in its nodes clusters of templates which describe the corresponding invention components
this division of labor makes our system immediately practical because it need not rely on a very large lexicon of terminological terms in the subject area
p6 NUM is mounted the patent sublanguage is a union of a legal sublanguage and a sublanguage of the domain of the invention
the draft is then submitted to an automatic text planner which outputs an hierarchical structure of templates which is ordered according to rhetorical and stylistic requirements
using the set of distinctions by robin our approach is content preserving no extra content is added and performs revisions on a shallow representation
the candidate template case role not with a conceptual schema tree node label but rather with case roles of templates in each cluster in turn
to be precise in the case of compound case role values the match may have occurred with the same component of the case role value
a sample lexicon entry is illustrated in figure NUM NUM to simplify the processing it was decided to consider active and passive forms of verbs as separate dictionary entries
the knowledge elicitation scenario consists in the system requesting the user in english to supply information about the invention its components their properties and relations among them
patent law guidelines impose rather rigid constra ats on the structural composition of the text of the informationally central and legally crucial part of a patent disclosure the claim
however the plan inference process has been augmented so as to embody the criteria for understanding that were outlined in section NUM NUM
a term like smaltimento dei rifiuti garbage collection has the noun smaltimento garbage as its term head
we may prepare a loose transitivity as follows vlj NUM
figure NUM NUM is a graph in which the transitivity does not hold
our request is to extract subgraphs each of which focuses on one topic with no ambiguity
the constraints of the schema specify that the plan being accepted achieves its goal and the decomposition is the surface speech action s accept
furthermore clusters grouped according to topics have many application areas such as automatic document classification
we observed that a graph has no ambiguity if its branches representing co occurrence relations are transitive
an algorithm to extract such graphs are proposed and its uniqueness of the output is discussed
although both tools decompose a graph into tightly connected subgraphs these trials resulted in vain
the following chapter describes the relationship between the transitivity in the graph and the ambiguity resolution
two words are said to co occur when they frequently appear close to each other within texts
in method ti we run inverse document frequency after a terminology driven lemmatization of documents i.e. using complex terms as source lemmas
the performance of the discourse processor was evaluated primarily on its ability to assign the correct speech act to each sentence
together these sentences form a monologue
this difference is of course understandable
therefore the inference chain for sentence NUM can not attach to the inference chain for sentence NUM
figure NUM an information phase of an ovr dialogue
we will take the human human dialogues as an example
during the presentation process the system stops listening
figure NUM a vios presentation of a train connection
do you want me to repeat the connection again
wilt u dat ik de verbinding nog eens herhaal
ovr is a partner in both mais and arise
table NUM gives an example of such a scenario
we recommend aic as the evaluation criterion during model selection due to the following NUM
NUM you will notice that figure NUM indicates that the 6ellipsis and anaphora resolution are areas for future development
one special word does not play the role of the modifier in any relation and it is named the root
we express the dependency relations in terms of rules that are very similar to their constituency counterpart i.e. context free grammars
in generation lexical information can be used as soon as a position that is the beginning of a chain is created
based only on these clues they have to select a single sense of the word in the particular sentence context
agirre and rigau uses the conceptual density of the ancestors of the nouns in wordnet as their metric
as an example consider the grammar gi iv iv n p a d i saw a tall where t1 is the following set of dependency rules t
word sense disambiguation is an active research area in natural language processing with a great number of novel methods proposed
since the collocation is not in word net we mapped it to the concept node government agent l
performed below the most frequent baseline it prompted us to evaluate the indicativeness of surrounding nouns for word sense disambiguation
in order to improve the top down algorithm we introduce the concept of first of a category
rather they are simply related in some m ner to the noun to be disambiguated
to obtain a g uge of human performance on this task we sourced two independent human judgements
the condition of projectivity limits the expressive power of the formalism to be equivalent to the context free power
a lexical head is found that is the head corner of the goal i.e. the type of constituent that is parsed
this sequence of sentences is felicitous under the anaphoric relationships indicated when the target clause pronoun is given even light accent
this measures the cosine of the angle between two context digests which can be viewed as vectors in a sdimensional space
d edges and i edges are not distributed arbitrarily in d trees
paradigmatic relatedness indicates how well two words can be substituted lbr each other i.e. how similar their syntactic behavior is
the tool used for this purpose was a slightly modified version of the las2 module from the svdpackc package berry et al
dcspite the shortcomings of knowlcdgc based systems it seems wrong to throw away all that has been gained imperfect as it is
figure NUM derived tree for NUM
the derived structure is shown in figure NUM
figure NUM derived d tree for 2b
the insertion edge will relate the two not necessarily distinct nodes corresponding to appropriate occurrences of a and a and will be labeled by the pair i n
we intend to examine this issue in future work
we are developing a polynomial time earley style parsing algorithm
NUM induction based on neighborhoods proximity in this reduced space is then used to find for all the context digests a neighborhood of words that are paradigmatically related
the derived tree is shown in figure NUM
human inanimate food organization its lexical type e.g.
the main parsing algorithm is a modified lr parsing algorithm augmented by multi action entries and constraints on reduction
fig NUM mis classification of boundaries often occurred where prosodic and cue features conflicted with np features
tag elementary trees abstract the combinatorial properties of words in a linguistically appealing way figure NUM a shows an initial tree representing the book
clustering proceeds by mapping words in the corpus to their semantic category augmented with part of speech information and clustering in the same way as we did for words except that the context vectors are recorded for the set of frequent semantic paradigms
however it seems to be possible to automate some tasks and facilitate human intervention in many parts using a combination of nlp and statistical techniques for data extraction type oriented patterns for conceptual characterization of this data and an intuitive user interface
the general approach to knowledge acquisition supported by the workbench is a combination of methods used in knowledge engineering informa null developed an anterior myocardial an established inferior myocardial an acute inferior myocardial subsequent episodes of unstable he has experienced unstable infarction from which left and two to the right
duster NUM chronic vs acute duster NUM major extensive significant large old vs minor small limited cluster NUM post vs previous ensuing cluster NUM anterior vs posterior duster NUM inferior vs superior
linguistic entries can be words phrases and linguistic types for example of word np head quot infarction quot a noun phrase with the head word infarction synt type n a noun etc patterns themselves are the basis for induction of conceptual structures
these banks vary in their linguistic coverage some list all possible forms singular plural etc for terms while others just a canonical one and in a conceptual coverage some provide an extensive set of different relations among terms concepts others just a subsumption hierarchical inclusion
for instance there a request can be made to find all cooccurrences of the type disease with the type body component when they are at the same structural group noun phrase or verb phrase and the disease is a head of the group lcb disease
this is strong evidence that the tuned algorithm is a better predictor of segment boundaries than the original np algorithm
verb phrase ellipsis is exemplified by sentence NUM NUM ivan loves his mother and james does too
in order to generate instructions clearly it must be obvious which if either of the two relations is intended at any given point eonflmion of one with the other will lead to inadequte incomplete or even dangerous execution of the t k described
infinitives are only capable of conveying a goal and the en null the relationship with actual temporal ordering of events plays no role in determining ordering in the case of avant de and apr s followed by an infinitive the two possible orderings are eqnally likely
the glb of adult and child is teenager the lub is person
taking the agent row we also have a NUM for the institution column
for convenience we will use many of the notational conventions of prolog
of course you do n t get anything for nothing in this game
on an analysis that treats the construction recursively this is no problem
we represent each narrative in our corpus as a sequence of potential boundary sites which occur between prosodic phrases
in our formalism there are several ways of achieving an equivalent effect
a tag grammar consists of a finite set of elemen tary trees which can be combined by these operations to produce derived trees recognized by the grammar
a notable example might be the lfg concepts of functional completeness and coherence
add to ca the feature specification left no
a similar argument shows that the negative evidence of this transformation is stored in e
given an input text we will compute transformation scores by computing statistics of these strings
in what follows p p are tree nodes and u is a non null string
we have been concerned with learning the best transformations that should be applied at a given step
the authors are indebted to eric brill for technical discussions on topics related to this paper
the parameter d in NUM is called the number of alternations of the transformation
in this case the algorithm runs in time o nn for fixed alphabet
we are not aware of any learning method for transformations of the form in NUM
from those candidate articles the trainin g and test sets were selected blindly with later checks and corrections for imbalances in the relevant nonrelevan t categories and in article types
marginally relevant event objects are marked in the answer key as being optional which means that a system is not penalized if it does not produce such an event object
the task as defined for muc NUM was restricted to noun phrases nps and was intended to be limited t o phenomena that were relatively noncontroversial and easy to describe
the manually filled templates were created with the aid of tabula rasa a software tool develope d for the tipster text program by new mexico state university computing research laboratory
pauline produces an impressive range of expressional forms that are based on a list of pragmatic features of the communicative environment including information about the conversational atmosphere the speaker the hearer the relationship between the two and the interpersonal communicative goals of the speaker
in the case of org descriptor the results of the co evaluation seem to provide further evidence for the relative inadequacy of current techniques for relating entity descriptions with entity names
the article was relatively straightforward for the annotators who prepared the answer key and there were n o substantive differences in the output produced by each of the two annotators
as seen in the third event of the walkthrough article the fill can be an extended title such as vice chairman chief strategy officer world wide
in pursuit of other issues many studies have adopted a temporary solution to the problem of managing diverse forms of expression namely that of choosing a single lexical and grammatical form to express each of the relevant types of information dealt with by the system
in substitution the root of the first tree is identified with a leaf of the second tree called the substitution site l
to keep the annotation of the evaluation data fairly simple the muc NUM planning committee decided not t o design the notation to subcategorize linkages and markables in any way
these commands are combined into clauses by the sentence tools system network using and when the concurrency that could be implied is impossible or inconsequential as in example NUM or then when there is possible unwanted concurrency as in example 14a
the corpus based methodology employed is well suited for this problem providing both a principled means for cataloging the lexical and grammatical forms that are consistently used in instructional text and an environment for testing and confirming hypotheses concerning the contextual issues that co vary with these forms
furthermore there are seven combinations of grammatical form linker and clause combining to choose from the relative frequencies and percentages of which are given in table NUM where the letters in example NUM correspond to the letters in the table
in order to make the task of evaluation more realistic we have also created a method in which instead of textual translations it is the spoken form that is judged
to do that good turing uses an additional notion represented by nr which is defined as the number of types which are instantiated by r tokens in an observed sample nr lcb t i fit r rcb
in our experiments with dop2 we used the same initial division of the atis corpus as in section NUM NUM into a training set of NUM trees and a test set of i00 trees but now the trees were not stripped of their words
the official scores were determined by the c version of each because of the more accurat e mapping
our example dialogue is a very good example for the latter problem
also we can actively pursue other more effective mapping algorithms that require more memor y without very much speed impact
the table shows that there is a considerable increase in parse accuracy from NUM to NUM for sentences with unknown words while the accuracy for sentences with only unknown category words shows a slight increase from NUM to NUM
the scorer then proceeds in rank order to align key objects with response objects to generate the final mappings
figure NUM intentional structure for two turns
the leaves of the tree are the dialogue acts
the configuration file contains scoring options that are allowed to be set by the user
this can give rise to an argument for giving credit since matching data does exist in the overall response
we also see the previously proposed interval from NUM NUM
we then discuss the issues that arise in any attempt to evaluate a speech translator and present the results of such an evaluation carried out on slt for several language pairs
each line corresponds to a prosodic phrase and each space between the lines corresponds to a potential boundary site
the groupoid unification will now be one way in the opposite direction
let us note here the relation to
this is easily solved by restricting id to atomic formulas
the first order case naturally corresponds to prolog
c oaps atom NUM then execution is guided by the following rules
we shall see that this is not necessary and that associative unification can be avoided
if an empty block is not lxl gsa re aligns it using a length based algorithm just like it would re align any other many to many aligned block
the deduction theorem rule for higher order clauses also becomes sensitised to the employment of antecedent contexts
by way of further example consider the following in l with terms and types as indicated
any entries in the dynamic programming table corresponding to illegal subhypotheses i.e. those that would violate the given bracket nesting or word alignment conditions are preassigned negative infinity values during initialization indicating impossibility
NUM bracketing bracketing is another intermediate corpus annotation useful especially when a fullcoverage grammar with which to parse a corpus is unavailable for chinese an even more common situation than with english
if the lengths of the pair of sentences differed by more than a NUM NUM ratio the pair was rejected such a difference usually arises as the result of an earlier error in automatic sentence alignment
for each production of the form a b1 bn we replace the production with the set of rules a b1y1 y1 b2 y2
we then raise several desiderata for the expressiveness of any bilingual language modeling formalism in terms of its constituent matching flexibility and discuss how the characteristics of the inversion transduction formalism are particularly suited to address these criteria
for example the tree initially branches based on the value of the feature before
it is of course difficult to make precise claims as to what characteristics are necessary and or sufficient for such a model since no cognitive studies that are directly pertinent to bilingual constituent alignment are available
the verbs to tag were chosen on the basis of how frequently they occur in the text how wide their range of senses and how distinguishable the senses are from one another
spud first selects the np the area eliminating alternatives like the room the desk the stack because they do not truthfully describe e30
figure NUM mrf t is defined for the neighborhood
error results and adding more inforniation of the words
let us describe me principle briefly
the talk system was developed in order to experiment with a number of ways of achieving conversational goals more easily using an aac system
conjunction of a single relational atom and zero or more equality constraints
the forward and backward application rules are specified as clauses of x NUM
the features before and after depend on the final punctuation of the phrases pi and pi i respectively
the standard definition of resolution extends unproblematically to such clauses
if co resolves with cl on c then the clause
null it is easy to formalize this kind of grammar in pure prolog
the prolog code presented in this paper is available via anonymous ftp from ix cog brown edu
for example a set of alternative acknowledge responses might be uh huh yeah i see yeah yeah yeah uh huh
the necessary condition for a node to be taking as a candidate for being removed from the constraint set is that this node should n t have any constrained nodes above it
unlike there however we do n t believe that models with complex overlapping feature interactions can be estimated directly from their feature distribution and use the iterative scaling algorithm instead
then we run the atomic feature selection algorithm and our atomic feature set in NUM minutes was boiled down to NUM atomic features and the feature collocation lattice to NUM NUM nodes
the iterative scaling algorithm that is used for the parameter estimation is computationally expensive while the feature selection process requires to estimate parameters of the model for many candidate features many times
for instance we can try to use the trigram model first and only when there is no suitable trigram known to the model we back off to the bigram model
note here that the total sum of feature probabilities can be greater than NUM x c xi6 xk i since features can overlap with each other
the configuration frequency of the node b will be the number of times of seeing the node b but not the node a b
so our aim is to remove from the constraints as many top level nodes as possible without loosing the model ftness to the reference distribution of the optimized feature lattice
therefore we do n t have to use the iterative scaling for feature ranking and apply it only for linear model regression possibly un constraining several feature configurations nodes at once
NUM applications of the method we applied the above described method of building maximum entropy models to several tasks sentence boundary disambiguation part of speech tagging and document abstracting via sentence extraction
this kind of generality is unattainable with statistically trained word based models
table NUM provides some examples from the lightship user s guides
e16 she is a star with the theater company
there are on average NUM NUM inversions per example translation pair
there are on average NUM NUM inversions per sentence pair
quantitative results of the closed and open tests are also summarized
under that proposal a rough character by character alignment is first performed
e21 how soon does the medicine take effect
a general description of the materials used in the experiments follows
the tipster architecture needs to preserve all this information in the document but for the present will only process the text information at a subsequent stage other structures with embedded text information such as tables may also be processed
the tipster architecture provides an external character based representation of annotated documents so that such documents can be interchanged among modules possibly as part of different tipster systems on different machines without regard to the internal representation used on particular machines
to delimit these different types of information the tipster architecture will use annotations of type textsegment each subsuming a maximal contiguous sequence of text and possibly other annotations such as graphicssegment which would be ignored in subsequent processing
per name umay b funded these might be encoded as a set of annotations as follows the muc style template for such an event might NUM the templates shown here are loosely based on those for the muc NUM information extraction task
this detectionneed is converted in two stages first to a detectionquery and then to an retrievalquery as shown in the right column the latter step may use information for example on term weights from the documentcollectionindex
getbyexternalld collection externalld string document or nil returns the document in the collection with the given externalld if several documents have the same externalld returns one of them if none have this externalld returns nil
a more complex case would be a full syntactic analysis in which a sentence is decomposed into a noun phrase and a verb phrase a verb phrase into a verb and its complement etc down to the level of individual tokens
note future versions of the architecture will include operations for managing the set of annotators for adding an annotator to the set of annotators for recording the types of annotations produced by an annotator and for searching the set of annotators
each token has a single attribute its part of speech pos using the tag set from the university of pennsylvania tree bank each name also has a single attribute indicating the type of name person company etc
while advancing in the composition of the sentence the system parses it and uses this information to offer the most probable words including both lemma and suffix like the second approach does
but if the subject is in the third person plural the indirect complement in the first person plural and the direct complement is in the plural the needed auxiliary has to be dizkigute
if the tense of the verb changes the verb itself also changes for example the past of the indicative of dizut is nizun and the past of dizkigute is zizkiguten
to this end two l inds of statistical data are used the frequency of apparition of each word and the conditioned probability of each syntactic category to follow every other syntactic category
in spanish the word amigo with the same mean null ing than friend may vary in gender and number giving the words amiga amigos and amigas
in our case this is a simple template language developed locally
only open class words were used during construction of the training set
a straightforward encoding of a solution in the dg formalism introduced in section NUM defines a root word s of class s with k valencies for words of class o o has iwl subclasses denoting the nodes of the graph
firstly doing good translation is a mixture of two tasks semantics getting the meaning right and collocation getting the appearance of the translation right
a sample domain structure is given in fig l with two domains dl and d2 associated with the governing verb know solid and one with the embedded verb likes dashed
since these languages are significantly different we need to develop an algorithm which does not rely on any similarity between the languages and which can be readily extended to other language pairs
table NUM test set words part one
formula NUM states similarly that the closest instance of x m that precedes x m n must be either x m z a recursive application of the same rule or x m n the previous stage in parsing the same rule and there must be such an instance
a baseline based on their current method of writing will be established prior to the introduction of the new prolet version
on the other hand air are domain specific words in the text meaning something we breathe as opposed to of some kind of ambiance or attitude
it is useful to point out some significant differences between chinese and english in order to help explain the output of our experiments chinese texts have no word delimiters
while it is particularly useful for starting agent processes it can also be used to start nonagent processes such as additional modsaf simulators and interfaces commandvu and the leathernet sound server
gemini applies a set of syntactic and semantic grammar rules to a word string using a bottom up parser to generate a logical form a structured representation of the context independent meaning of the string
a push to talk button attached to the serial port of the computer can be pushed down to signal the computer to start listening and released to indicate that the utterance is finished push and hold to talk
for the nonterminals that have no recursive rules we simply collect all the rules with the same left hand side and create a single rule by forming the disjunction of all the right hand sides
often there will be a series of queries to modsaf about the current state of the simulation before the modsaf command or commands that represent the final interpretation of an utterance are produced
the set of atomic categories is defined by considering for each daughter category of each rule all instantiations of just the subset of features on the daughter that are constrained by the rule
an agent posts a message in an interagent communication language icl to the facilitator which dispatches the message to the agents that have registered their ability to handle messages of that type
in general we would like the recognizer to accept all word sequences that can be interpreted and any overgeneration by the recognition grammar increases the likelihood of recognition errors without providing any additional functionality
the idea is to represent the candidate set s not as a large unweighted fsa but rather as a collection a of preferably small unweighted fsas called factors each of which mentions as few tiers as possible
thus a timeline may be represented as a finite collection s of labeled edge brackets equipped with ordering relations and that indicate which brackets precede each other or fall in the same place
furthermore the convention means that a zero width input constituent more precisely a sequence of zero width constituents represented as a single NUM symbol will often act as if it has an interior
i xt understanding and high quality machine translation often necessitate the disambiguation of ambigous structures or lexical elements
ultimately a client server architecture separating language specific from domain specific issues and the linguistic aspects from user interface aspects will be the best architecture for a real life application
before the encoder can start to view the nlp processed pdss the html code of the menu page needs to be updated to include all the path names of the files concerned
this can easily be achieved by activating before each encoding session a c shell script that scans a subdirectory and creates an actualised html file for the menu page
semantically ambiguous words will thus be highlighted more than once which is bad for the precision score more non relevant words are flagged
NUM venal jump graft from the aorta to the first branch of the circumflexus further to the second branch of the circumflexus till the rdp
through the html submit command the options selected by the medical encoder are passed via a form and cgi script to an external c program
the medical doctor supervising the medical registration activities was asked to provide some NUM on NUM NUM NUM your patient has been operated in our cardiovascular surgery unit
we developed three variations to score the text sentences on weights of the concepts in the interesting wavefront
following this trend we have developed a new way to identify topics by counting concepts instead of words
to remedy the problem of information overload a robust and automated text summarizer or information extrator is needed
we then define the ratio t at any concept c as follows null NUM max weight of all the direct children of c sum weight of all the direct children of c NUM is a way to identify the degree of summarization informativeness
each constraint yields a filter that permits only minimal violation of the constraint NUM filteri set lcb r e set ci r is minimal rcb given an underlying phonological input its set of legal surface forms under the grammar typically of size NUM is just NUM filter filter
to evaluate the system s performance we defined three counts NUM hits sentences identified by the algorithm and referenced by the professional s abstract NUM mistakes sentences identified by the algorithm but not referenced by the professional s abstract NUM misses sentences in the professional s abstract not identified by the algorithm
we have not yet been able to compare the performance of our system against ir and commerically available extraction packages but since they do not employ concept counting we feel that our method can make a significant contribution
if we start from the top of a hierarchy and proceed downward along each child branch whenever the branch ratio is greater than or equal to NUM t we will eventually stop with a list of interesting concepts
this means that the timeline can not avoid violating a clash constraint simply by instantiating the part as e
therefore in the representation the negated drss for the pointing opportunities el e3 are part of the main drs
a shortcoming of previous work is that it is unclear to what extent the resulting rules are effective in dealing with the generation of anaphora
every following symbol is mapped to the empty string by means of
topological relations e.g. the file near it refer to topological relations between the referent and the relatum in this example the object referred to by it
june NUM a decision was made to go with coreference
sra corporation kindly provided tools which aided in the annotation process
figure NUM shows a sample sentence with named entity annotations
these ingredient systems are obtained by varying the lambek calculus along two dimensions adding the permutation rule p and or dropping the assumption that the type combinator which forms the sequences the systems talk about is associative n for non associative
we extend the lexical map l to nonempty strings of terminals by setting l wlw2 w l wl x l w x x l w for wlw2 wn e
the relative pronoun which would for instance receive category np np np o s with o being implication in lp NUM i.e. it requires as an argument an s lacking an np somewhere NUM
for instance no special category assignments need to be stipulated to handle a relative clause containing a trace because it is analyzed via hypothetical reasoning like a traceless clause with the trace being the hypothesis to be discharged when combined with the relative pronoun
of taiwan and th e remainder by taga co
thus expectations are one of the primary mechanisms needed for tracking the conversation as it jumps from subdialog to subdialog
the scenario involved changes i n corporate executive management personnel
the scenario involved change s in corporate executive management personnel
the basic concepts of the data model underlying the gdm have been explained in the discussion of the tipster model in section NUM NUM above
section NUM illustrates how gate can be used by describing how we have taken a pre existing information extraction system and embedded it in gate
the process of integrating existing modules into gate creoleising has been automated to a large degree and can be driven from the interface
the points above do not contradict this view but indicate that sgml should not form the central representation format of every text processing system
internet material or cd roms which may be used for bulk storage by organizations with large archiving needs without copying each document
at any point the developer can create a new graph from a subset of available creole modules to perform a task of specific interest
a tipster storage system could write data in sgml for processing by lt nsl tools and convert the sgml results back into native format
alep aims to provide the nlp research and engineering community in europe with an open versatile and general purpose development environment
two features of the current situation are of prime importance they constrain how the field can develop and must be acknowledged and addressed
language is a robust and necessarily redundant communication mechanism
the approximate NUM NUM split between relevant and nonrelevant texts was intentional and is comparable to the richness of the muc NUM tst2 test set and the muc NUM tst4 test set
unsupervised learning algorithms are rarely used in isolation
the second is a means of evaluating candidates
the concatenation operators was used with phonemes as terminals
in addition to miscategorization errors the walkthrough text provides other interesting examples of system errors at the object level and the slot level plus a number of examples of system successes
three classes of examples serve to illustrate this
such patterns can be indistinguishable from desired ones
this algorithm is summarized in figure NUM NUM
the general methodology of language learning bycompression is not new
had fewer user utterances per dialogue NUM NUM versus NUM NUM
defining a generalized template structure and using template element objects as one layer in the structure reduced the amount of effort required for participants to move their system from one scenario to another
for muc NUM text filtering scores were as high as NUM recall with precision in the 80th percentile or NUM precision with recall in the 80th percentile
they can also be defined manually to fit identified cases semantic features for the words want to as uncertain not to confuse the higher levels then during the parsing the semantic features mobilised for the tree operations are relieved from their uncertain status
computational linguistics volume NUM number NUM NUM NUM the impact of miscommunication
the subject pool consisted of six male and two female subjects
the resulting largest entry in each row is noted in boldface
the identification of a name as that of an organization hence instantiation of an organization object or as a person person object is a named entity identification task
communication with the speech recognizer was performed through a telephone handset
the subject was seated facing the desk containing the circuit board
and solaris NUM NUM sun os NUM NUM and greater we have begun porting the system to windows nt windows NUM
for simple sgml documents or documents with no original sgml markup at all no dtd needs to be specified
this would possibly lead to significant improvements in performance on the basic event related elements and to development of good end user tools for incorporating some of the domain specific patterns into a generic extraction system
from every sentence we extract the initial class subsequence ci that ends with the first unambiguous class c eq
in addition to careful interface design and support for user customization a core mechanism for enhancing this process is through pre tagging
since a missing or spurious org locale is likely to incur the same error in org country the error scores for the two slots are understandably similar
the alembic workbench provides a graphical user interface by which texts can be annotated using the mouse and user defined key bindings
the alembic workbench also provides specialized interfaces for supporting more complex linked markup such as that needed for coreference
one way this can be done is by invoking the rule learning subsequent to the application of the hand cxxted pre tagging rules
the learner uses indexing based on the actual data present in the corpus to help it explore the rule space efficiently
we are still in the early stages of evaluating the performance of the alembic workbench along a number of different dimensions
performance on te overall is as high as NUM on the f measure with performance on organization objects significantly lower 70th percentile than on person objects 90th percentile
these results are in line with those from the main trial
n NUM k NUM for move classification
when check and query yn were conflated agreement was k NUM
NUM un homme triste ing nieux furieux a sad clever angry nlan which is in a sad clever angry state when they modify an event or an object they can take either a causative gb or a manifestation sense 9c 10e and 11c
NUM NUM formal y triste el je tbmc p ea je agentivz partir e2de focal ing6nieux elje telic jouer eaje NUM checs
at one end of the spectrum are the proper names and aliases which are inherently definite and whose referent may appear anywhere in the text
in NUM on the other hand there is nothing contributed by the ree per se to how the experiencing is achieved as the noun has no telic nor agentive except for it being a physically manifested object with extension
however that does not mean that the noun must be an event but only that its semantic representation or general knowledge concerning its semantic type should provide an event as shown in the next examples NUM and NUM
both of these confusions can be corrected by clarifying the instructions
in the middle of the spectrum are definite descriptions and pronouns whose choice of referent is constrained by such factors as structural relations and discourse focus
complements are however possible if they make direct reference to the agentive as in 16b c where the complement is the cause of the emotional state or relic roles as in 17b c where it is the manifestation
they can therefore modify nouns of type human objec and eveng NUM and will be ambiguous when they modify a noun of type human as an hmnan can be either in a mental state or the object of an experiencing event 31a b
this algorithm produces a balanced binary tree representation of words in which those words which are close in meaning or syntactic feature come close in position
the tagger employs a set of NUM syntactic tags which is one order of magnitude larger than that of the university of pennsylvania treebank project
by repeating this step and dividing the sets into their subsets we can construct a decision tree whose leaf nodes contain conditional probability distributions of tags
moreover the first approach only provides a means of partitioning the vocabulary and it does n t provide a way of constructing a hierarchical clustering of words
a feature can be any attribute of the context in which the current word word o appears it is conveniently expressed as a question
in order to avoid the explosion of the number of compounds to be handled compounds in a small subclass are bundled and treated as a single compound
one way to improve the quality of word bits is to introduce a reshuffling process just after step NUM mi clustering of the word bits construction process cf
in this control experiment bit strings are assigned in a random way but no two words are assigned the same word bits
even with the o v NUM algorithm however the calculation is not practical for a large vocabulary of order NUM or higher
with finer clusters alone the amount of information on the association of the two words that the system can obtain from the clusters is minimal
the technique of gap threading is by now well known in the unification grammar literature
for this we can turn again to the colmerauer encoding of boolean combinations of values
this paper describes the conversion of a hidden markov model into a sequential transducer that closely approximates the behavior of the stochastic model
NUM we are in the process of demonstrating that it is natural language for acquiring knowledge and no t acquiring knowledge in order to process natural language
there was no significant difference between knight s explanations and the biologists explanations on measures of content organization and correctness nor was there a statistically significant difference in overall quality between knight s explanations and those composed by three of the biologists
this section sets forth two design requirements for a representation of discourse knowledge desclibes the explanation design lester and porter robust explanation generators package formalism which was designed to satisfy these requirements and discusses how edps can be used to encode discourse knowledge
the dictionary module contains functions for creating updating loading and checking consistenc y of the uno dictionary and functions for performing morphological analysis of the input
for example given a query about how a biological process such as embryo sac formation is carried out the explanation planner can apply the explain process edp to construct an explanation plan that houses the content and organization of the explanation
we assigned explanations to judges using an allocation policy that obeyed the following four constraints system human division each judge received explanations that were approximately evenly divided between those that were produced by knight and those that were produced by biologists
while the traditional approach to work on explanation has been to develop a proof of concept system and to demonstrate that it can produce well formed explanations on a few examples developing robust explanation generation techniques and scalable discourse knowledge representations facilitates more extensive empirical studies
an explanation system must be able to select from a knowledge base precisely those facts that enable it to construct a response to a user s question organize this information and translate the formal representational structures found in knowledge bases to natural language
improved the accuracy of identifying unmarked sentential boundaries our pre mug6 system was quite good in correctly identifying sentential boundaries in newspaper articles
this number increases when the five dialogue acts from the subnetwork which can occur everywhere are considered as well
adapted to a zero order hmm which means only to use class probabilities b the algorithm would give an no type approximation
otherwise it gives the student the right answer
the tutoring plans are kept on a stack
what controls hr s nervous system
when students start circsim tutor they see the main user interface screen illustrated in figure NUM the precipitating event a broken pacemaker in this case is shown at the top of the screen
the second and final task addressed by bride of cogniac is the resolution of pronominals and words whic h behave like pronominals such as company
most of the entities which la hack NUM annotates are proper nouns but the date information extracted by the tokenizer i s used here as well
parsing interpretation and and spelling correction the input understanding component of circsim tutor v NUM contains a bottom up chart parser producing first a phrase structure parse and then a lexical functional grammar f structure
a single tutor turn may involve several logic forms such as an acknowledgment an explanation expressed as a declarative statement and a question
our efforts prior to that time were mostly directed towards implementing a parallel file data structure which allowed new components to be adde d quickly with minimal effort
the maximum entropy model was trained using the dry run and training portions of the muc NUM coreferenc e annotated data which included sgml annotated sentence boundaries
to ameliorate these deficiencies and complications the query to wordnet takes the form of a boolean quer y about the ancestors of a given word entry
table NUM shows the performance of our system when simple formatting errors which hurt performance o n two of the NUM test files were corrected
second unlike typical research work participation i n muc lasted a finite amount of time and there were clearly defined goals and success metrics
the purpose of the simple type system is mainly to prevent coreference chains from being created by th e substring matching stage which contain substrings of different types
in most cases the majority voting scheme eliminates errors that are esoteric to a single tagger and should therefore perform better than any single tagger
in examining the outpu t briefly the mistakes made were due to knowledge base failures and bugs more than issues inherent to the pronou n resolution algorithm
they divide the texts into NUM segments
there are NUM highly reliable anchor points
tagging information of one language is used
we obtained a NUM NUM precision
if the prior probability of the wildcard j3 is positive then at each level the recursion splits with one path continuing through the node labeled with the wildcard and the other through the node corresponding to the proper suffix of the observation
for each pst t e t and each observation sequence wl wn t s likelihood or evidence p wl wnlt on that observation sequence is given by n p wl
after predicting the next word the counts are updated simply by increasing by one the count of the word if the word already exists or by inserting a new entry for the new word with initial count set to one
the online mode seems much more suitable for adaptive language modeling over longer test corpora for instance in dictation or translation while the batch algorithm can be used in the traditional manner of n gram models in sentence recognition and analysis
in the case of engli h or french one could delimit formally the category proper nouns by means of the upper case even though this criterion does not correspond entirely to our intuition about proper nouns
in order to handle them in an nlp system given that we do not have yet a dictionary which provides all proper nouns auxiliary methods are required such as syntactic information or local grammars that allow to analyze them
figure NUM illustrates the hybrid data structure the likelihood values lmix s and l s decrease exponentially fast with n potentially causing numerical problems even if log representation is used
figure NUM NUM analyses of NUM NUM jang ga nei neun according to the local grammars we have constructed we get the following result for this string figure NUM pn f name in mr
however use of the local grammars of figure NUM and figure NUM only with these two pts above leaves some nonpns precision is NUM NUM NUM strings of NUM which occurred with these n and pts are pns
the number assigned to the subset o1 then denotes the amount of support the evidence directly provides for the conclusions represented by o1
in order to utilize the dempster shafer theory for modeling initiative we must first identify the cues that provide evidence for initiative shifts
these results indicate the difficulty of the prediction problem in each corpus that the task dialogue initiative distribution row NUM falls to convey
row i in the table shows the number of turns where the expert NUM holds the task dialogue initiative with percentages shown in parentheses
these cues may be explicit requests by the speaker to give up his initiative or implicit cues such as ambiguous proposals
the first cue class explicit cues includes explicit requests by the speaker to give up or take over the initiative
evaluation questions on the other hand are questions in which the speaker intends to assess the quality of a proposed plan
in this experiment k is NUM NUM for the task initiative holder agreement and k is NUM NUM for the dialogue initiative holder agreement
such obligations may have resulted from a prior request by the hearer or from an interruption initiated by the speaker himself
previous work on mixed initiative dialogues focused on tracking and allocating a single thread of control the conversational lead among participants
words like accablant phase c ble vase and trois have different pronunciations depending on the dictionary used
a rule matches if the grapheme string matches the left context pattern matches if present and the right context string matches if present
NUM grapheme to phoneme conversion problems for both english and french in this section we describe the problems encountered when converting from graphemes to phonemes for english and french
for example the prefix in normally does not take NUM stress except under contrastive stress e.g. i said include not preclude
if at the end of these rules NUM stress still has not been placed on a word a set of generic rules applies
the first step done by a block of rules is to normalize the text replacing numbers abbreviations and acronyms by their full text equivalents
it may however still be used to convey both syntactic and semantic information that would then serve as input to a parser for more accurate prosodic rules
the french letter to sound rule set was tested on the NUM NUM unique word le petit robert dictionary and the NUM NUM word le grand robert de la langue francaise dictionary
the automatic discovery of the underlying structure of a language is not easy nor is the developing of a universal rewriting rule formalism for the different languages
moreover if the list of daughters left of the head of that rule is empty then the begin positions are identical i.e. ph pro
similarly in an earley deduction system too much effort may be spent on small portions of computation which are inexpensive to re compute anyway
to make sure that a parser is complete with respect to such partial results it is often assumed that a parser must be applied that works exclusively bottom up
from these experiments it can be concluded that selective memorization with goal weakening as applied to head corner and left corner parsing is substantially more efficient than conventional chart parsing
for the experiments discussed in the final section all goal weakening operators were chosen by hand based on small experiments and inspection of the goal table and item table
an interesting special case of goal weakening is constituted by a goal weakening operator that ignores all feature constraints and hence only leaves the functor for each goal category
in the context of definite clause grammar this distinction is often blurred because it is possible to build up the parse tree as part of the complex nonterminal symbols
the present architecture of the head corner parser embodies the assumption that such cases are rare and that the construction of logical forms is grosso modo compositional
required to t roduce a grammatical sentence the
np nadvp loc he put the stakes
NUM to sit more patiently with what they have bought
we discuss below some of the problems encountered in tagging and their resolution
the problem is to forecast how he will react
the price increased by 5z to end at i00
the supreme court defined if companies may defend themselves
nadvp dir he headed to the store
he headed went the cow down the road
measure where the pattern unit is appropriately defined as a single or multi word reference to a unit of measure
there is also a global test y which has to succeed all this i n the right context of e
computational linguistics volume NUM number NUM table NUM transitions in the bfp algorithm
we then demonstrate some problems with a popular centering based approach with respect to these motivations
informally a comparable strategy is one which applies in the same state and has the same effects
in u3 the user similarly asks a yes no question that addresses a subgoal related to answering c1
in the most pronounced cases the wrong choice will mislead a hearer and force backtracking to a correct interpretation
rule NUM is presented as a constraint on center realization and rule NUM as a constraint on center movement
first the linearity of each chain is judged by measuring the root mean squared distance of the chain s points from the chain s least squares line
the optimal fixed chain size with respect to the rms error metric was NUM when the translation lexicon was used and NUM when it was not
moreover a set of correspondence points supplemented with sentence boundary information expresses sentence correspondence which is a richer representation than sentence alignment
with the translation lexicon the lowest error estimates drop to NUM NUM for the easy bitext and NUM NUM for the hard bitext
systems are measured for their performance on distinguishing relevant from nonrelevant texts vi a the text filtering metric which uses the classic information retrieval definitions of recall and precision see prefac e to appendix b
in addition the propositional content of the speaker s own utterance can be marked as presupposed or given examples ja doch
note also that even the best system on the third event was unable to determine tha t the succession event was occurring at mccann erickson in addition it only partially captured the full title of the post
figure NUM egraph key performance figure NUM shows the results of running the egraph keys for the training data in relation to the other configurations
included a few template slots that forced systems to attempt to make subtle and inferential judgements namely the vacancy reason on the job and rel other org slots
concept specifications define what concepts to extract in what order what semantic roles to fill and determine how th e extraction examples are encoded
in total the egraphs referenced NUM structural element classes e g np and were constrained to form NUM NUM unique structural elements
the official muc NUM test results considered onl y one configuration of weights which created a strong preference for the semantic content especially the anchor label
since the erroneous succession event from sentence NUM does not have a post fill the output script invalidates it and no templat e is generated
note the anomaly in location precision measure for the fast configuration this is a side effect of recognizing less organizations which pre empt the location classification
table NUM named entity processing statistic s table NUM shows the processing time and speed of the four official configurations for the named entity test data
thus the training module can run these derivative egraphs to determine how well they perform and construc t an extraction bias to include best ones
since nametag recognizes more names and phrases than defined for muc NUM such as publications and relative temporal expressions the driver program filtered some extracted entities
this section provides a brief overview of the representations and algorithms that sentence planning using description spud uses to address the properties of collocations discussed above
the algorithm identifies the entries that most contribute to current goals and from these selects the entry with the most specific semantic and pragmatic licensing conditions
the abaca to xaxa path is NUM NUM NUM NUM NUM where the NUM NUM transition is over a c x arc
four ways of using contexts the complete definition of the first version of conditional replacement is the composition of these six relations the composition with the left and right context constraints prior to the replacement means that any instance of upper that is subject to replacement is surrounded by the proper context on the upper side
union intersection and relative complement are considered weaker than concatenation but stronger than crossproduct and composition
in case a given input string matches the replacement relation in two ways two outputs are produced
null two auxiliary symbols and are introduced in NUM and NUM
another complication is the fact just mentioned there are several ways to constrain a replacement by a context
correspondingly a simple automaton may be thought of as representing a language or as a transducer for its identity relation
the transducer consists of states and arcs that indicate a transition from state to state over a given pair of symbols
the initial motivation in their original NUM presentation was to model a left to right deterministic process of rule application
the relation that maps the strings of the upper language to the same strings without any context markers
the almost parse from the ebl lookup is input to the full parser of the xtag system
it attempts to measure the coverage and response times for retrieving a generalized parse from the fst
shown in figure NUM is the derived tree shown in figure NUM a
we propose to do this by generalizing at the phrasal level instead of at the sentence level
replacing the elementary trees with unistantiated feature values is all that is needed to achieve this generalization
this results in a finite state transducer fst representation illustrated by the example below
however if the retrieval succeeds then the generalized parses are input to the stapler
nodes on the frontier of initial trees are marked as substitution sites by a
the results of this experiment are shown in the fourth row of table NUM
the elementary trees that the words anchor and their linear order in the sentence
by following the derivation presented in the previous section it can be verified
things are equally difficult on the input side pre editing too is difficult or impossible yet ill formed input and recognition errors are both likely to be quite common
the transducers derived from the definition in figure NUM have the property that they unambiguously parse the input string into a sequence of substrings that are either copied to the output unchanged or replaced by some other strings
roughly speaking the qlf transfer method is used to translate as much as possible of the input utterance any remaining gaps being filled by application of the glossary based method
the preliminary work we now go on to describe attempts to provide an empirically justifiable answer in terms of the relationship between translation quality and comprehensibility of output speech
phrasal parsing identifies an early flight as a likely noun phrase so that this is for the first time selected for translation in c
translation is performed by using the glossary based method at the early stages of processing before parsing is initiated and by using the qlf transfer method during and after parsing
the proposed word filtering method consists of two steps a filtering process and a scanning process
f i want you to go three inches past that going south in other words just to the level of that i mean not the trout farm
it is generally not possible to tell on the basis of syntax alone whether the author has adhered to this rule
knowing the respective distances between these three sentences on the arrows sentence x can be computed by analogy
tree y is the solution of the analogy and we claim that it is the analysis of the prototype sentence
but as a matter of fact this kind of example does not make much linguistic sense
in fact studying the necessary and suificient conditions under which they are true remains an open problem
a problem arises where are the slight modifications to be performed and what are they
suppose we have a collection of sentences a data base already analyzed in fact a tree bank
in our example how mmty characters need to be hanged to transr rm malhemalical into physics
the third equation means that somehow analogy neutralises changes performed at the same time along the two previous dimensions
it by chance sentence x belongs to the tree bank its analysis is also in the tree bank
approximate matching is retrieval of all sentences at a distance less than a threshold kom a given prototype
james chairman and chief executive officer of mccann erickson and john j dooner jr the agency s president and chief operating officer in the body of the article six systems j walter thompson also extracted with the name of walter thompson organization category is indicated by context peter kim was hired from wpp group s j
we found that the improvement varies depending on the test collection and that collections that were made up of shorter documents were more likely to improve
it is designed to accommodate a wide range of text processing requirements in particular document detection needs information extraction needs or both combined target text in any human language domain or subject matter independence different computing environments different security requirements
for example the portion of a document that is contained in a table will require different processing from the portion of a document that is written in english which will in turn require different processing from the portion of a document that is written in arabic
the purposes of this to define the types of documents and document parts which will be exploitable within the tipster architecture to describe types of setup processing which might be performed on a document to facilitate and improve the output of tipster to illustrate some of the ways in which the tipster architecture might be extended for particular applications in order to exploit a wider range of documents and document parts
items of specific types such as personal names places or organization names for example can be located in the text by appropriate modules and the text locations and data types can be passed to any other component or part of the application for further processing or viewing
frequently at least one can be found in clos e proximity to an organization s name e g as an appositive creative artists agency the big hollywood talent agency
using f measures NUM as an indicator for overall performance the mlrs with the chain parameters turned on and type identification turned off i.e.
for example dou sha is equivalent to the company and ryou koku to the two countries
our anaphora resolution systems reported here have the advantages of domain independence and full text handling without the need for creating an extensive domain knowledge base
with a higher confidence factor less pruning of the tree is performed and thus it tends to overfit the training examples
of course such a tagged corpus is necessary to evaluate system performance quantitatively and is also useful to consult with during algorithm construction
while additional coding would have been required for each of these types in the mdr the mlrs picked them up without additional work
in order to both train and evaluate an anaphora resolution system we have been developing corpora which are tagged with discourse information
then we describe the learning approach chosen and discuss training features and training methods that we employed for our current experiments
analysis of sentence structures to identify grammatical relations such as predicate nominals is needed in order to relate those same pieces of information i n creative artists agency is a big talent agency based in hollywood
obviously there is a strong relationship between document frequency dfw and word frequency fw
dn is the set of profitable contexts of length n while en is the set of profitable extensions of those contexts
not only do we have to estimate the parameters for a model we have to find the right parameters to use
its performance all the more impressive when we consider that no context blending or escaping is performed even for novel events
furthermore we reached to the conclusion that the ambiguity can be explained in terms of intransitivity
unfortunately this constraint is too strict because such a graph is restricted to a complete graph
they measured co occurrence between nouns and verbs and clustered nouns of the same distribution of verbs
if it is encountered as the starting branch in step NUM the maximal subgraph is obtained
if star were not duplicated the three different topics would be merged into a single subgraph
the appearance of potato shows the sign that the merge of two clifferent topic has already begun
this intuitively corresponds to loosen the sharpness of the focus of the topic in the resulting cluster
more thun two clusters the number of words belonging to more than two clusters amounts to NUM
thus the lower the threshold is the more the cluster contains ambiguity
next we observed that articulation nodes are ambiguous so we performed decomposition into biconnected components
during trec NUM to trec NUM each participant was evaluated against both a routing task and an ad hoc retrieval task each consisting of NUM test cases
from the NUM test articles a subset of NUM articles some relevant to the scenario template task others not was selected for use as the test set for the named entity and coreference tasks
a group of several in fact the more the merrier leading edge research institutions who are willing to participate in a cooperative corporate program
the models in table NUM were constructed from all n grams NUM n NUM observed in the training data
here we adopt a similar strategy using the m NUM th mixed order model to smooth the ruth one
approximately two thirds of the aliases were correctly identified
any single contractor who chose to drag his or her feet or not fully and openly participate would have put the completion of the whole effort in serious jeopardy
but it is doubtful that most people would think of words like gunships fighter carrier and ambulances
second a structural member like chamber or partnership can stand alone as a variation for example chamber as alias for chamber of commerce and partnership as alias for new york city partnership
if a noun phrase is found to be especially rich in description it is thought to he too specific to refer to a previous entity and is made into an un named entity
during the f dtering process the system used an additional heuristic to decide whether to apply a content filter to a noun phrase or to make it into an un named entity
this atmosphere has proven to be quite contagious as new government participants have joined the tipster program team and it has clearly rubbed off onto the other tipster participants
of the one third which were missed besides an unfortunate system error which threw away four aliases which the system had found five main groups of error were found
idf can be thought of as the usefulness in bits of a keyword to a keyword retrieval system
because there is sc te uncertainty within the text as to whether the change has already taken place the second entity is given optional names covering both alternatives
the solution to this problem is not only to expand the system s knowledge of human firsmames but also to widen the context which can trigger human name recognition
it can be seen that the two methods provide quite similar performance bigram method ranks NUM of the NUM known relevant documents within the first NUM retrieved for the NUM queries while the short word method has about NUM less at NUM
the first scenario was used as an example of the general design of the st task the second was used for the muc NUM dry ru n evaluation and the third was used for the formal evauation
the d best ranked documents from this retrieval are then regarded as relevant without user judgment and employed as feedback data to train the initial query term weights and to add new terms to the query query expansion
the output is a ranked list of potential category words that a user can review to create a semantic lexicon quickly
the reason for this pronounced effect is that when a query is short like two words project hope and a crucial word hope is removed what is left for retrieval is practically useless
rule NUM xq where q currently has only NUM special characters are stopwords for any x these are tagged NUM and is a complement to rule NUM see ex NUM NUM and counter examples ex NUM NUM
rule d for double any two adjacent similar characters xx are considered stopwords this identifies double same characters that are often used as adjectives or adverbs that do not carry much content see ex NUM NUM below
vs NUM lll can improve precision as for long queries but the accidental removal of a crucial word can lead to a much bigger adverse effect of NUM drop in average precision exptyp NUM vs exptyp NUM
when character p is tagged NUM we also try to identify common words where p is used as a word in the construct yp and these are entered into the lexicon yp may or may not be a stopword
we ran the bootstrapping algorithm for eight iterations adding five new words to the seed word list after each cycle
table NUM correlations of idf log var and h in NUM with log f in other years
for example in her design x theory and the other principles are coroutined
for instance consider the case in which the current token is an intransitive verb
subject c however was too fast a typist to benefit from the program
a system independent description of the contents of the documents that the user would like to retrieve
table NUM speech input results translation word er
moreover the complexity of the task can be controlled
the emission distribution of each state was modeled by a mixture of gaussians
the human judges estimated that it took them approximately NUM NUM minutes on average to judge the NUM words for each category
furthermore we leave implicit when conjunctions of constraints are normalised by the constraint solver
for example the egory membership for example only words like gun rifle and bomb should be labeled as weapons
and for some applications any word that is strongly associated with a category might be useful to include in the semantic lexicon
each sentence is given to our parser which segments the sentence into simple noun phrases verb phrases and prepositional phrases
in experiments with five categories users typically found about NUM words per category in NUM NUM minutes to build a core semantic lexicon
the success of this approach depends on the quality of the ranked list especially the density of category members near the top
the user may scan down the list until a sufficient number of category words is found or as long as time permits
the remaining nouns are sorted by category score and ranked so that the nouns most strongly associated with the category appear at the top
to our knowledge our system is the first one aimed at building semantic lexicons from raw text without using any additional semantic knowledge
facts are taken as true in the domain whereas defaults correspond to the hypotheses of the domain i.e. formulae that can be assumed true when the facts alone are insufficient to explain some observation
but if we also use the phrase junior college for indexing then junior college will match better than college junior even though the latter also will receive some credit as a match at the word level
in either case the response is used as an indication of how the second participant interpreted the first as presumably his response must have some rational explanation the indicated interpretation is called the displayed interpretation
in all three grammars the same ambiguities are repeated for each terminal item
but is limited to misunderstandings that appear as misrecognized speech acts NUM such misunderstandings are especially important to detect because the discourse role attributed to an utterance creates expectations that influence the interpretation of subsequent ones
that is we consider the value p y q y p y q xn r p x q xn p xn q y if r NUM then y is underrepresented relative to x and we accept y with probability one
where we factor z as zaz for zfl the normalization constant in qold hence qnew f qold d ji i ij6fj x now there are two problems with this expression it requires us to compute za which we are not able to do and it requires us to determine weights
saying that a corpus is representative is actually not a comment about the corpus itself but the method by which it was generated a corpus representative of distribution q is one generated by a process that samples from q saying that a process m samples from q is to say that the empirical distributions of corpora generated by m converge to q in the limit
for this example we shall assume that russ believes that he knows who is going to the meeting but also allows that mother s knowledge about the meeting would be more accurate than his own
in this diagram the choice of label on a node z with parent x and preceding word y is dependent on the label of x and y but conditionally independent of the label on any other node
we use the grammar to define the set of configurations f l g but in defining a probability distribution over l g we can choose features of dags however we wish
the empirical probability of xl is NUM NUM so xl s contribution to fl is NUM NUM NUM and its contribution to f3 is NUM NUM NUM
the acceptance function then reduces to rain l r y tr x which is rain l q y q x in our notation
computational linguistics volume NUM number NUM based approach to abduction because it relies on a theorem prover to collect the assumptions that would be needed to prove a given set of observations and to verify their consistency
the figure shows a list of attitudes that each act expresses the lists are assumed to be exhaustive with respect to the theory but not to the various connotations that might be associated with each act
the second test set named wsj6 consists of NUM NUM occurrences of the NUM words appearing in NUM text files of the wsj corpus
for meaningful grammars we expect this size function to be linear
the final interpretation is then determined by a set of rating heuristics such as decrease the rating of a path if it contains an action whose effects are already true at the time the action starts
constraint solving in the dcg case is simply unification of terms
further these expected counts can be calculated by multiplying the sample size n by the probability of the complete data within each marginal distribution given NUM and the observed data within each marginal ym this simplifies to counti sm y p smiym
for the naive bayes model with NUM observable fea null tures a b c and an unobservable classification feature s where NUM lcb p a s p b s p c s p s rcb the e and m steps are NUM e step the expected values of the sufficient statistics are computed as follows
7proof sketch refer to the set of disambiguated qlfs resulting from w o through application of the qlf interpretation clauses as t and to the set of conclusions obtained trough linear logic deduction from the premisses of the r projections of p as a o f
note that in the extreme case when k equals the size of the training set pebls will behave exactly like the most frequent classifier
furl her work recons ructs qlf interl retal ion in terms of linear logic deductioi s NUM alrymple et al NUM and provides a scope constraint mechanisln tot such deductions
the first test set named bc50 consists of NUM NUM occurrences of the NUM words appearing in NUM text files of the brown corpus
we use generalise to factor out the common parts of disjunctions
figure NUM constraint e b of example parse forest
however one should also output a label aop which designates the empty operator that binds for instance the empty variable in a parasitic gap construction and other cases of non overt movement such as relative clauses
globally there appears to be an inverse relation between the size of the grammar measured by the number of rules and the average number of conflicts the larger the grammar the smaller the number of conflicts
the results are shown in table NUM which compares some of the indices of the nondeterminism in a given grammar to its size and table NUM which shows the distribution of actions in each of the grammars
we use the electric news corpus named chinese one hundred kinds of newspapers NUM
although this is by no means definitive evidence it suggests that the algorithm for chain formation and feature assignment that i have presented is not obviously inadequate and that it is applicable to a variety of languages with different properties
i would like to thank those who have helped me in this work michael brent bonnie dorr uli frauenfelder paul gorrell luigi rizzi graham russell eric wehrli amy weinberg and two anonymous reviewers
as can be seen from table NUM grammar NUM is three times larger than grammar NUM and is compiled in a table that has twenty nine times as many entries but the average number of conflicts is not significantly smaller
notice that the different size of the collapsed feature set which is larger for nlab is implicitly taken into account by k as the number of relevant links varies with the size of the collapsed feature sets
also the probabilities the word assign to each class is not global optimal
and excessive or insufficient classification may occur because the class number is fixed artificially
a nd l hls que deux alis which would haare to be covered by a la rge numl er of plain cfg rules
t a tterns about NUM idiomatic a nd colloca tiona NUM pa tterns a n lcb a NUM out NUM NUM lexica l
fl at h e vector has a fixed langth unifica tion of two t eaturc vectors is performed in a consta tlt
a parsing a lgorithm for translation patterns ca n be any of the known cfg parsing algorithms including cky and ea rley a lgorithms
a common measure of lm quality is perplexity pp which can be thought of as a measure of the branching factor i.e. the average size of the set of words between which a speech recogniser must choose when transcribing a single word of the spoken text
moreover it is often the case that texts from the same medium are more similar to each other than texts from the same domain e.g. a journal paper on computing may be more similar to a journal paper on geology than an item from a popular computing magazine because the content features are lost among the much more salient genre features
NUM for each individual text in the bnc do NUM NUM create the wfl for the bnc text NUM NUM create a contingency table from the NUM wfls ignoring function words NUM NUM calculate the number of common words n and the rank correlation r NUM store the filename fltle n and r in results NUM output the results sorted on the value for r
where h is the number of correct transcriptions words in the utterance that are found in the transcription d is the number of deletions words in the utterance that are missing from the transcription s is the number of substitutions words in the utterance that are replaced by an incorrect word in the transcription and i is the number of insertions extra words in the transcription
we handtranslated a selected set of technical terms from the nikkei financial news corpus and looked them up in the wall street journal text
we believe the accuracy would be even higher if we only look at really unambiguous test words such as an entire technical term
in addition to being written in languages across linguistic families by different journalists wsj nikkei also share only a limited amount of common topic
satz is very fast in both training and sentence analysis training is accomplished in less than one minute on a workstation and it can process NUM NUM sentences per minute
the reason for this is that instead of being forced to lexicalize each rule in g at the first position on its right hand side one is free to choose the position that minimizes the total number of elementary trees eventually produced
the path set l generated by this grammar contains a variety of paths including sx from the elementary initial tree sasbsx saa from adjoining the elementary auxiliary tree once on the initial tree and so on
NUM there is thus a tradeoff decreasing the error percentage by adjusting the thresholds also decreases the percentage of cases correctly labeled and increases the percentage of items left ambiguous
further if there is only one way to derive a given tree in g there is no ambiguity in the mapping from derivations in g to g because there is no ambiguity in the mapping of t to trees in g
each use of a tree in t must occur as part of the simultaneous adjunction of one or more auxiliary trees on the root of some initial tree u because there are no other nodes at which this tree can be adjoined
each one is converted into a rule in p as follows the label of the root of t becomes the left hand side of r the labels on the frontier of t with any instances of c omitted become the right hand side of r
let u be the set of every initial tree that can be created by substituting t for one or more frontier nodes in an initial tree u e i that are labeled computational linguistics volume NUM number NUM x and marked for substitution
further if there is only one way to derive a given tree in g there is no ambiguity in the mapping from derivations in g r to g because there is no ambiguity in the mapping of u i to trees in g
NUM if it is mutually believed that one of the conversants believes there is an error with the current plan the other also adopts this belief
c p w21wl z p w21c p clwx NUM c l
the document section results show NUM error on document date and dateline NUM erro r on headline and NUM error on text
here the vocalism indicates the inflection e.g.
in the middle of the spectrum are definite descriptions and pronouns whose choice of referent i s constrained by such factors as structural relations and discourse focus
since a missing or spurious org locale i s likely to incur the same error in org country the error scores for the two slots are understandably similar
the introduction of two new tasks into the muc evaluations and the restructuring of information extractio n into two separate tasks have infused new life into the evaluations
in short the preliminary nature of the task design is reflected in the somewhat unmotivated boundarie s between markables and nonmarkables and in weaknesses in the notation
to its credit however it did recognize that the event was relevant only two systems produced output that is recognizable as pertaining to this event
the operator caters for obligatory rules
key j z referentj in zero form
the lowest error scores were NUM on vacancy reason median of NUM and NUM on on the job median o f NUM
in this article the management succession scenario will be used as the basis fo r discussion the details of that scenario are given in appendix f
j full referent j in full noun phrase
figure NUM shows typical plots of the training and test set perplexities versus the number of iterations of the em algorithm
independent principles are intuitively principles that can be computed independently of each other and therefore whose interactions are all possible
the core is depicted as the mother of all
in addition in order to preserve discourse coherence they require either that the linguistic intentions of suggested actions be compatible with the context or that there be some overt acknowledgement of the discrepancy
because NUM according to the discourse model it is true that active do m pretell m r whoisgoing ts NUM
in particular the issue of what is a linguistically motivated way of deriving a parser from principle based theories of grammar is explored
in t3 instead of telling him who s going as one would expect after a pretelling mother claims that she does not know and therefore could not tell
default NUM acceptance s1 areply ts expected s1 areply ts d shouldtry sl NUM areply ts
in the intentional accounts speakers use their beliefs goals and expectations to decide what to say when they interpret an utterance they identify goals that might account for it
the expression accepts the symbols NUM followed by zero or more occurrences of the following NUM one or more v each followed by a and NUM a feature tuple in followed by p
for example if p of rule NUM above is b b c c the context becomes b b c c r NUM f1 NUM this simplified version of the expression suffices for the moment
the strongest value is reserved for attitudes about the prior context whereas assumptions about expectations are given as weak defaults and assumptions about unexpected actions or interpretations are given as very weak defaults
in the text chunking application the tags being assigned are chunk structure tags while the part of speech tags are a fixed part of the environment like the lexical identities of the words themselves
we performed experiments using two different chunk structure targets one that tried to bracket non recursive basenps and one that partitioned sentences into non overlapping n type and v type chunks loosely following abney s model
while this automatic derivation process introduced a small percentage of errors of its own it was the only practical way both to provide the amount of training data required and to ajlow for fully automatic testing
the first line in each table gives the performance of the baseline system which assigned a basenp or chunk tag to each word on the basis of the pos tag assigned in the prepass
a rough hand categorization of a sample of the errors from a basenp run indicates that many fall into classes that are understandably difficult for any process using only local word and part of speech patterns to resolve
the system did discover some rules that allowed it to fix certain classes of vbg and vbn mistaggings for example rules that retagged vbns as i when they preceded an nn or nns tagged i
for example instead of referring to the word two to the left a rule pattern could refer to the first word in the current chunk or the last word of the previous chunk
the treebank tags the words and and frequently with the part of speech tag cc which the baseline system again predicted would fall most often outside of a basenp NUM
in the rules above x is the shifted vowel
an aggressive strategy for global success is to choose the subgoals judged most likely to lead to success and carry out their associated subdialogs
it recommends a subgoal for completion and will use whatever dialog is necessary to obtain the needed item of knowledge related to the subgoal
the resulting system computational linguistics volume NUM number NUM demonstrated dramatic improvements over performance levels that had been observed without such predictive capabilities
in fact the current subdialog specifies the focus of the interaction the set of all objects and actions that are locally appropriate
examples of the request to turn the switch up at various levels of assertiveness are as follows turn the switch up
our implementation shows that the mechanisms are efficient enough to run in real time and sufficiently well designed to yield successful dialogs with humans
the associated task specific expectations would represent expectations based on this general notion with values for property and object instantiated to the situation values
computational linguistics volume NUM number NUM two examples of querying for the switch position are as follows what is the switch position
here the algorithm selects the first subgoal set knob NUM of r and creates another subdialog by entering zmodsubdialog with this subgoal
an important side effect of matching meanings with expectations is the ability to interpret an utterance whose content does not fully specify its meaning
both systems include edges for automaton state transitions
for lexemes not in the lexicon it is necessary to specify the word class as a feature e.g.
the semantic cmnponent which gets this false positive may reject it and request a second reading and the correct parse will most probably come down the pipeline eventually if the grammar is truly broad coverage but a semantic module is not always well equipped to detect such errors and may have a difficult time enough trying to resolve attachment problems anaphoric references etc even when presented with the right parse
grammar induction can be framed as a search problem and has been framed as such almost without exception in past research NUM
we create n new nonterminal symbols lcb x1 x rcb and create all rules of the form
however the superiority of n gram models in the part of speech domain indicates that to be competitive in modeling naturally occurring data it is necessary to model collocational information accurately
this is a case where word sense disambiguation has allowed us to classify a new word and to enhance levin s verb classification by adding a new class to the word try as well
in the verb based experiment we counted the number of perfect overlaps i.e. index of NUM NUM between the verbs as grouped in the semantic classes and grouped by syntactic signature
by h cnsing on the classes the verbs are implicitly disambiguated the word sense is by definition the sense of the verb as a member of a given class
positive example sentences are denoted by the number NUM in the sentence patterns and negative example sentences are denoted by the number NUM corresponding to sentences marked with a
table l shows an example class the break subclass of the change of state verbs NUM NUM along with example sentences and the derived syntactic signature based on sentence patterns
fhe outline for this class based exl eriment is as follows null NUM automatically extract syntactic information from tile example sentences to ymd the syntactic signatnre for the class
the sentential symbol s expands to a sequence of x s where x expands to every other nonterminal symbol in the grammar
however the optimal grammar under this objective function is one which generates only strings in the training data and no other strings
however to date there has been little success in constructing grammar based language models competitive with n gram models in problems of any magnitude
however static language modeling performance has remained basically unchanged since the advent of n gram language models forty years ago NUM
this result marks the first time a grammar based language model has surpassed n gram modeling in a task of at least moderate size
as yet we have not implemented moves that enable the construction of arbitrary context free grammars this belongs to future work
to constrain the symbols we consider on the right hand side of a new rule we use what we call riggcrs
NUM den kanzlerlmndidaten ermorden the chmlcellor candi late
reduction of b type errors raises precision and lowers fallout and error rate
some examples are listed under the corresponding partial tree this supports the idea of domain dependent grammar because these idiosyncratic structures are useful only in that domain
this is a crucial issue for most application systems since most systems operate within a specific domain and we are generally limited in the corpora available in that domain
the degree of homography or is it polysemy
the first word of the sentence is treated using the first approach seen
we accumulate these partial trees for each domain and compute the distribution of partial trees based on their frequency divided by the total number of partial trees in the domain
in addition we should n t forget that suffixes may be recursively concatenated
the number of different tags actually occurring in texts is mostly around NUM
in addition most of the cases admit the recursive concatenation of suffixes
as shown there are sixty two possible word forms for a single dictionary entry
then the proposals may be offered with the most appropriate morphological characteristics
therefore it would be very interesting to treat the entire sentence
most of them have been developed for english or other non inflected languages
this allows the module to analyze substrings of names that are unaccounted for by the explicitly listed name components see residuals in the previous section in arbitrary locations in a complex name
the training corpus for first names and street names was assembled based on data from the four largest cities in germany berlin hamburg kjln cologne and miinchen munich
names are conventionally categorized into personal names first and surnames geographical names place city and street names and brand names organization company and product names
the corpus consists of NUM domains as shown in appendix a in the rest of the paper we use the letters from the list to represent the domains
the street names were split up into their individual word like components i.e. a street name like konrad adenauer platz created three separate entries konrad adenauer and platz
the path through the graph is as follows the arc between the initial state start and root is labeled with a word boundary lcb rcb and zero cost NUM
on the test data at least one of the two systems was incorrect in NUM out of a total of NUM names NUM NUM an almost identical result as for the training data
an example showing the spoken output for a given patient and a screen shot at a single point in the briefing is shown in figure NUM
in the following sections we first provide an overview of the full magic architecture and then describe the specific language generation issues that we address
this ordering indicates its preference for how spoken references are to be ordered in the output linear speech in accordance with both graphical and presentation constraints
besides these specific applications any kind of well formed text input to a general purpose tts system is extremely likely to contain names and the system has to be well equipped to process these names
from the state first there is a transition back to root either directly or via the state fuge thereby allowing arbitrarily long concatenations of name components
in order to make the generated sentences more comprehensible however we have modified the lexical chooser and syntactic generator to produce pauses at complex constitutions to increase intelligibility of the output
to do this from the remaining propositions it selects a proposition which is related to one of the propositions already selected via its arguments
the architecture will allow maximum efficiency for simultaneous multilingual implementation in more than one site and will offer an empirical view on the problems related to the creation of an inter lingua by aligning the wordnets thus revealing mismatches between equivalent semantic configurations
this component will assist in disambiguating among semantically ambiguous analyses using contextual information modeled via statistical and other methods
the extraction need would include annotation type declarations for the annotations to be produced
this way each partial parse will be represented by exactly one wildcard state in the final chart position
alternatively c x a x is the expected number of times that
apart from such considerations the choice between lr methods and earley parsing is a typical space time tradeoff
an earley parser constructs sets of possible items on the fly by following all possible partial derivations
if c return a parse tree with root labeled x and no children
the probabilities of all rules with the same nonterminal x on the lhs must therefore sum to unity
most probabilistic parsers are based on a generalization of bottom up chart parsing such as the cyk algorithm
has planck made the discovery that light has a particle nature
text that is not enclosed between these tags is handled in a system dependent i.e.
nl the arguments are a natural language description of part of the information need
no other query operator can occur in the nl description of the information need
however in order to accept this move each participant also needs to believe that the hearer also finds the plan acceptable third condition
during this evmua l ion i weni y newswire arl icles seleel ed from the l rcb o articles used in lhe l rior a lcb i a evmu tl iol NUM averaging m rcb ul NUM wor ls ea h were l rcb ro essed md sul se luently ex lmine t
in lcb lexing a NUM m lcb gal rcb yi c lcb lcb rcb rl us requircs al rcb l rcb r xitn tl ely NUM ndmaes on a sun si arcsl tti n i x when all tiles are located on local lisks an l an lcb lher lcb rcb lllilllll eb i lcb lmck lcb he
table NUM result of sentence alignment
the detailed algorithm is as follows
null the construction of the paper is as follows
many methods have been proposed to align bilingual corpora
let us consider text NUM as an example
section NUM describes the entire alignment algorithm in detail
one is a bilingual dictionary of general use
the pis can be directly derived from asm
most of these works assume voluminous aligned corpora
the attributes and annotations of the original document are not copied by this operation
NUM and the observed number of new types for the successive text slices of alice in wonderland
different groups of annotations normally exist in some fixed structural relationships to one another
a heuristic adjusted frequency estimate is proposed that at least for novel sized texts is considerably more accurate
because of the second order learning effects of the context vector approach not only will attack be related to people and personas but people will be related to personas matado groupo etc
operator one or more arguments and an ending field marker e.g.
but for more global textual properties such as vocabulary size a motivated answer is less easy to give
it is an attempt to graphically illustrate through various sized information nodes the entire set of prevalent themes contained in the corpus
in order to eliminate such spurious instances of key words it is useful to set a frequency threshold
summing up the good turing frequency estimates are severely effected by the cohesive use of words in normal text
this one step learning law scales as o nn so that it is faster than the current learning law by a factor of k number of original learning law iterations and is faster than the theoretically derived learning law by a factor of kn n
for f NUM as shown in figure NUM the heuristic estimate remains a reasonable compromise
peopl kill attack rebel group neighbor target neighbor person mat ataq group contr neighbor target neighbor the steps that will occur during learning given the text example shown in figure NUM are as follows the convolutional window location is chosen and the target and neighbor stems are identified
the following three subsections describe how edward deals with the specifics of the four types of referring expressions in turn
as an extreme example if white is only used as white house and blanca is only used as in casablanca the user will be able to query using only white and spanish documents about casa blanca will be retrieved
the results are listed in figure NUM precision is received correct links and re received links j call received correct links the difference between desired links
the usage of the context vector technique is predicated upon the rule that symbols stems that are used in a similar context exhibit proximate co occurrence behavior will have trained vectors that point in similar directions
these referents are mentioned in the sentence but they are less prominent than the subject referents or major referents
after each sentence the existing cfs are updated by calling their decay functions and new cfs are created
specifically the attack ataque tie word pair has been influenced by people kill rebel group personas matado groupo and contra
the result of the learning procedure is a vocabulary of stem context vectors that can be used for a variety of applications including document retrieval NUM routing NUM document clustering and other text processing tasks
this has two advantages very heuristic links would confuse the pruning mechanism and words that would not otherwise have a head may still get one
the salience of a referent is influenced by both linguistic and perceptual context as was described in section NUM NUM
and u h they come over nonboundary and they help him nonboundary NUM and NUM you know nonboundary help him pick up the pears and everything
the learning NUM algorithm performs better on the set defined by t NUM table NUM error analysis table NUM and learning NUM table NUM perform better on the t NUM set
in the target output we classify a potential boundary site as boundary if it was identified as such by at least i of the seven subjects in our empirical study where we use two values of i otherwise it is classified as nonboundary
at t NUM learning NUM performance is comparable to human performance table NUM and learning NUM is slightly better than humans at t NUM both learning conditions are superior to human performance
however segment z begins with certain features that indicate a resumption of the speaker goals associated with segment x such as the use of the phrase well anyway and the repeated mention of the event of picking up the pears
whatever the case initially well formed sentence or not the parsing produces a usable analysis for the higher layers to perform the final interpretation or to trigger a repairing dialogue
recall the large amounts of white space in figure NUM contrasting with the few sharp peaks where many subjects identify the same boundary which suggests that the significance of q owes most to the cases where columns have many l s
the linguistic context allows to enrich and complete the analysis in case of an error either detected during the parsing as a linguistic anomaly or signalled previously from confidence scores
we assume only that the terminal elements of some corpus can 1for left and right
an automobile vocalic onset or not for singular noun
arg mu NUM is mapped onto carrier NUM or carrier NUM
on the other hand message generating systems that provide speech of a natural quality e.g.
in a second step an appropriate intonation contour is calculated see section NUM NUM NUM
an example of an ept is provided by figure NUM
subsequently the mus are mapped into one or more carriers
prosody by general model is only used for those parts of the message where flexibility is needed
product name cardinality but the actual message is not known on beforehand
sentence thank you for your attention value in st NUM reference NUM hz
announcement systems phone banking and voice mail applications often combine fixed pieces of pre recorded speech
of these NUM NUM are aligned with trees in the treebank
therefore when the syntactic function is not rashly disambiguated the correct reading may survive even after illegitimate NUM coordination is handled via the coordinator that collects coordinated subjects in one slot
here k is the number of contexts in paragraph article and domain respectively
in all runs with populations initialized to speak english svo or malagasy vos the preference for default settings was NUM
reset the first most general default or unset parameter in a left to right search of the p set according to the following table input d NUM d0
the output of the implementation on the susanne and penn treebank corpora is discussed
for example an intransitive verb might be treated as a subtype of verb inheriting subject directionality by default from a type gendir for general direction
the pattern of language emergence and extinction followed that of the previous series of runs lower mean wml languages were selected from those that emerged during the run
an architecture is a description of all functional activities to be performed to achieve the desired mission the system lcb components rcb needed to perform the functions and the designation of performance levels of those system lcb components rcb
however p settings with an absolute value principles can not be altered during the life time of an lagt
NUM the preprocessor 1st stage of ndfsm looks for sentence boundaries more complicated number an d date expressions obvious names type determination and other names bracketing
for mr enamex type person dooner enamex it means maintaining his running and exercise schedule and for the agency it means developing more global campaigns that nonetheless reflect local cultures
late NUM we realized that a company like knight ridder information dialog information services a t that time had to get into the business of value added reselling of information
the third one with sri led to development of the current system vanf value adding name finder which is routinely used i n production at knight ridder information
so a grammar definition language was defined w e spent some time discussing and refining it and i implemented an engine for dialog s version of the language
as it was pointed out in NUM an efficient implementation and easy to us e tools allowed us to tune a pretty primitive technology to produce quite useful results
i would be less than honest to say i m not disappointed not to be able to claim creative leadership for coke mr enamex type person dooner enamex says
the expressive power of our formalism is the same as that of the attribute grammars at the same time the efficiency i s close to that of finite state machines
once his modules are implemented he may be able to submit them for consideration as extensions to the tipster icd
at present this allows us to modify rules
most important are the actions which assert facts extracted from the text in the vanf case the facts are in the form nnn is a name of an entity of type x
to determine the value of the descriptor it is first necessary to establish what path is specified by the path descriptor b NUM this involves evaluating the descriptor b NUM and then plugging in the resultant value a to obtain the path a
due to their brittle nature and the limits of speech recognition technology rigorous experimental evaluation of systems required extensive training by subjects before testing began
while not a specific measure of a dialog system an integrated dialog system such as minds can provide information that can reduce the perplexity that the speech recognition component must deal with
fortunately speech recognition capabilities are improving and systems are being constructed that allow individuals to walk up and use them after a brief orientation
this paper proposes some measures for evaluation based on a retrospective look at measures used in the past analyzing their relevance in today s environment
furthermore public access to transcripts and the production of videotapes of subjects in the actual experimental situation should also be part of the evaluation framework
another example is the current system under development at duke university that serves as a tutor for liberal arts students learning the basics of pascal programming
speaker independent continuous speech recognition technology is now available so the amount of time required to enable a person to interact with an snlds is much less
longitudinal studies with subjects in such environments are the only way to gain an idea of a system s success in dealing with such a situation
in environments where one may encounter novices experts or individuals with intermediate expertise the ability to interact in a variety of styles becomes essential
its purpose is to describe the performance of the speech recognition component in terms of how accurately it converts the speech signal into the actual words uttered
number if the incident node is an implicit node then we add between parentheses the relative position w r t
v n systems co require cg n v require cg satellites 0b
it can be used to penalize certain specialized expressions that should be used less frequently
still this complexity is considerable in particular perhaps due to the intended ability of the system of distinguishing between timetabled and actual points in time
however common to all these guideline violations is that they should be remedied in the implemented system if at all possible
i d p l n NUM logp alter ewp
if there is a conflict in the information in these five sources the order of precedence should be interface control document architecture design document architecture requirements document architecture concept this document tipster configuration management plan
this problem concerns how to transfer det to other developers in some packaged form which does not assume person to person tuition
as to the novice expert distinction sg7 this is hardly relevant to sophisticated flight information systems such as the present one
the generic guidelines are expressed at the same level of generality as are the gricean maxims marked with an
since documents as originally received are not likely to conform to this structure it will usually be necessary to convert documents as they exist in the external world into documents that conform to the internal document structure used by the architecture
since the instantiated template of a mrs subpart corresponds to a phrasal sign we also call it a phrasal template
the goals of dialogue engineering include optimisation of the user friendliness of sldss which will ultimately determine their rank among emerging input output technologies
cv consequence violations design error cases that would not have arisen had a more fundamental design error been avoided
methodologically we analyzed each system utterance in isolation as well as in its dialogue context to identify violations of the guidelines
japanese however imposes difficulties of subcategorizing in part because it allows arbitrary ellipses of case elements
thus we created the basic description framework in the verb lexicon as is shown in fig NUM
the basic principle of the lexicon requires one to one correspondences between a subcategorization frame and an s block
all the permutation commands in fig NUM actually can match the original surface case flame of e g
the slot name nom2 represent the typical case with two term adjectival predicatec that require two nominative cases
this figure seems appropriate considering the fact that most of japanese transitive verbs and intransitive verbs take separate word lorms
the analyzer checks if the semantic restriction animate matches the case element x john
they constitutes not argument structure but just adjuncts free elements as is explained in modern linguistics
the value xi resse l t y a global inheritan e descriptor lep nds on more than just the proi ertie s of the nodes sl eeified by lcb he definitional sentences of a theory
in the final step spoken output is generated from the target language expression
exhaustive listing of NUM combinatorial subcategorization frames has contributed much to improve the accuracy the contents in the lexicon
among NUM deep cases we defined for the interlingua which is fewer than those in previous e.g.
moreover this approach allows a very efficient implementation technique as described below
it is also possible to deal with epsilon rules in the head corner parser directly
secondly the use of a linking table may give rise to spurious ambiguities
fortunately the memorization technique discussed in section NUM takes care of this problem
arabic numeral is used for numbers
however the new chart state produced by the completion rules does not depend on the identity of the node p in the second chart element but rather only on whether there is any appropriate chart element from j to
this difference is a significant reason for the practical efficiency of the head corner parser
NUM can the source and target languages share the same recognition engine
thus we use the flexibility in the selection of goals to run constraints whenever their arguments are sufficiently instantiated and delay them otherwise
add adjuncts i ps y a add adjuncts x y
the head h0 is interpreted conjunctively i.e. if each element of b0 is true then so is each element of h0
is classified as a completed clause the add adjuncts NUM constraint in its body is inherited by any clause which uses this lemma
the basic intuition behind our approach is quite natural in a clp setting like the one of hshfeld and smolka which we sketch now
thus we were freed to spend time developing the task specific components of the system an d performing data analysis
the vast majority of these hours were contributed between the end of july and competitio n week in early october
while the effort was still informal mark wasson from lexis nexis became an advisor to th e project
after much convincing the faculty agreed at th e end of july that we could formally participate in muc NUM
variant corporate names may be references which exclude corporate designators use acronym s or omit a company s industry
we hope that muc will continue t o encourage participation from new sites by focusing on sub tasks relevant to information extraction
basal noun phrases are those noun phrases in the lowest level of embedding in the penn treebank s annotations
the knowledge sources are used to determine whether an entity is of type person place corporation or other
these could appear in sentential clauses or in relative clauses such as fred flintstone who is wilma s husband
while not appearing in the final output thes e cases are used to aid in positing other types of coreference
for instance theorem NUM states that from critical tokenization any tokenization can be produced enumerated
their model achieves NUM NUM accuracy
many of the errors occurring under the exact match criterion involve alternatives that are virtually identical in meaning as in the following example NUM stephen vincent benet s john brown s body vp comes immediately to mind pp in this connection as does john steinbeck s the grapes of wrath and carl sandburg s the people yes
often a great deal of information is lost in the smoothing procedure
the following composition rules combine two functions with set valued arguments e.g. two verbal categories a verbal category and an adjunct
to express some constraints that appear in real phonologies it is also necessary to allow a and NUM to be non empty conjunctions and disjunctions of events
NUM finite state generation in otp NUM NUM a simple generation algorithm recall that the generation problem is to find the output set s where NUM a
we have seen two formal results of interest both having to do with generation of surface forms otp s generative power is low finite state optimization
finally a clash constraint cq i a2 NUM is identical to the implication constraint or1 and a and false
k timeline can carry tl e full panoply of phonological and morphological onstituents an vthing that phonological constraints might have to refer to
b the result has the interesting implication that candidate sets can arise that can not be concisely represented with fsas
the central representational notion is that of a constituent timeline an infinitely divisible line along on which constituents are laid out
among the verbs tagged in our corpus only have has an auxiliary use which we tag as follows with the string aux replacing the sense number south korea has have verb aux recorded a trade surplus of NUM million dollars so far this year
where appropriate easyenglish makes suggestions for rephrasings that may be substituted directly into the text by using the editor interface
the formal language words have captured reasonable english words for their most likely translation or headword translation
morning and afternoon train to comparable fertilities and preferentially generate a single clump
however early and morning have fairly undesirable looking second and third choices
maximum likelihood ml estimation leads to zero valued probabilities for unseen n grams
then each clump is filled in according to a translation model as before
interestingly both the at t and bbn systems generate words within a clump according to bigram models
the general model s parameters are bootstrapped from the poisson model and updated by the em algorithm
most notably one formal language word may frequently correspond to whole english phrases
the results are presented in table NUM for arpa s december NUM blind test set
of the pp from the verb
michael strube is supported by a postdoctoral grant from dfg str NUM NUM NUM
the hierarchy of discourse segments we compute realizes certain constraints on the reachability of antecedents
the extension of the search space for antecedents is by no means a trivial enterprise
table NUM thematic progression patterns table NUM visualizes the abstract schemata of tp pat
zwar ed iutert das dsnne handbiichlein die bedienung der hardware anschaulich und gut illustriert
the c v is set to NUM in all utterances of this segment
in order for the disambiguation rules to work properly it is crucial to have a deep analysis of the text
as a result of applying lift the whole sequence is captured in one segment
in the reverse case the same ill record is either linked to synsets which have a near synonym relation among them in which case they can be linked as eq synonym or as eq near synonym of the same ill record or any other complex equivalence relation which parallels the relation between the wms
we show that some simple methods are indeed good indicators for the answer to the problem while other proposed methods fail to perform better than would be attributable to chance
the results of our analysis can be used to substantiate theories which are compatible with the empirical evidence and thus offer insight into the complex linguistic phenomenon of antonymy
mixed order markov models combine bigram models whose predictions are conditioned on different words
these distinctions involve q whether to consider the number of distinct words in this category types or the total fre null quency of these words tokens
we extract markedness values according to the methods described in this paper and use them in subsequent phases of the system that further analyze these groups and determine their scalar structure
for example knowing that hearty is a positive term enables the assignment of the collocation hearty eater to the lexical function entry mags eater hearty NUM
the goal of our work is twofold first we are interested in providing hard quantitative evidence on the performance of markedness tests already proposed in the linguistics literature
the need for an automatic corpus based method for the identification of markedness becomes apparent when we consider the high number of adjectives in unrestricted text and the domain dependence of markedness values
for the m NUM th mixed order model
the work presented in this paper t ries to achieve the improvement of recall without the deterioration of preci
an index term can be either a simple noun or a compound noun composed of more than one simple nouns
the noun dictionary is automatically constructed from the obserw tion on the document set
besides the employment of full morphological analysis is often too expensive and requires costly maintenance
bigram indexes shows better precision than unigrams but can suffer from big index size
compound nouns are those one or more substrings of which are recognized as nouns
since the noun dictionary contains only those in a document set the ambiguity in analyzing words is greatly reduced
o lie liloro the two distributions agree the less l will he
o the proposed m tho l and t igram tttetb o i
ml tttmc c critical eil l s on tile retriewd erforlila tlee
the em algorithm for aggregate markov models is particularly simple
p and p simultaneously scan u and h u respectively using function slow scan
attaching multiple prepositional phrases generalized backed off estimation
this is not only because the need to know a specific programming language is eliminated the greater benefit of using nlu shell is that the deep understanding of natural language processing algorithms and heuristics that went into the creation of the nlu shell is not required for its use
the result that the user browses is a document each of whose indexed terms are hyperlinked to other documents containing the same indexed terms cf figure NUM
there is a rule for each of the relations presented in section NUM
s2 NUM then how does from two thirty to four thirty seem to you
the authors wish to acknowledge valuable discussions with tony hartley xiaorong huang adam kilgarriff cecile paris richard power and donia scott as well as detailed comments from the anonymous reviewers
never imperatives these are characterised by the use of the negative adverb never as in NUM whatever you do never go to vienna if you are on a diet
certainly a number of planning architectures and their accompanying plan libraries have been implemented but while the architectures themselves may be reused in a new domain the library of plans typically can not
awareness this feature captures whether or not the writer believes that the agent is aware that the consequences of are bad aw is used when the agent is aware that a is bad
we noted that because the automatic derivation of useful well defined features for corpus analysis is beyond the current state of the art the painstaking process of corpus analysis must still be performed manually
NUM neither the propositional nor the procedural information discussed so far specify the three features needed by the decision network derived in the previous section i.e. intentionality awareness and safety
intentionality this feature encodes whether or not the writer believes that the agent will consciously adopt the intention of performing a con is used to code situations where the agent intends to perform a
the mere fact that k may have a value k greater than zero is not sufficient to draw any conclusion however as it must be established whether k is significantly different from zero
table NUM words with low and high values of al w
thus an approximation based on rimon and herz s next would be aa bb and an approximation based on conditions NUM NUM would be a b a b
a subgrammar consists of all the productions for nonterminals in one of the equivalence classes of s calculate the approximations for each nonterminal by treating the nonterminals that belong to other equivalence classes as if they were terminals
to suggest possible senses each heuristic draws on semantic relations extracted from a webster s dictionary and the semantic thesaurus wordnet
for a given word all applicable heuristics are tried and those senses that are rejected by all heuristics are discarded
for example vanderwende NUM uses semantic relations extracted from ldoce to interpret nominal compounds noun sequences
wordnet is a large manually constructed semantic network built at princeton university by george miller and his colleagues NUM
to take the context into account researchers have used a variety of statistical weighting and spreading activation models e.g. NUM NUM NUM
the basic unit of wordnet is a set of synonyms called a synset e.g. go travel move
the heuristics that are applied to disambiguate a word depend on its part of speech and on its relationship to neighboring salient words in the text
these approaches can be broadly classified based on the reference from which senses are assigned and on the method used to take the context of occurrence into account
several heuristics look for a particular semantic relation like hypernymy or purpose linking the two input words e.g. return is a hypernym of forehand
this agrees with the current wisdom in the ir community that unless disambiguation is highly accurate it might not improve the retrieval system s performance NUM
given a set of entities to describe and a set of intentions to achieve in describing them a plan is constructed by applying operators that enrich the content of the description until all intentions are satisfied
since more than half of the symbols in the observations may be noise models estimated in this way are not reliable
that is we can regard the hmm as a mixed model of unigram bigram trigram and so on
the standalone ne system uses a different message reader than the te and st systems
the semantic lexicon is separate from the parser s lexicon and has much less coverage
semantic structure the semantic representation for he will be succeeded by mr
in unsegmented languages that have no delimiter between words such as japanese candidates for alignment of tag and word have different segmentation
they pointed out limitation of such methods revealed by their experiments and said that the optimization of likelihood did n t necessarily improve tagging accuracy
the input to the plum system is a file containing one or more messages
credit factor in order to overcome the noise of untagged corpora i introduce credit factors that are assigned to training data
in general the stochastic tagging problem can be formulated as a search problem in the stochastic space of sequences of tags and words
in the estimation of a japanese language model from an untagged corpus the segmentation ambiguity of japanese sentences severely degrades the model reliability
in other words a morpheme network of cost width NUM was equivalent to that extracted from the input sentence with a dictionary only
the results of this comparison are charted in figure NUM
following the most frequent word is the baseline performance data
figure NUM comparison of tribayes vs lsa performance above the baseline metric
tribayes clearly out performs lsa for those words of a different part of speech
table NUM shows the performance of lsa on the contextual spelling correction task
d is a similar representation for the original document vectors
this class of problems has been attacked by many others
the lsa prediction accuracy for this set is NUM
the first author is supported under darpa contract sol baa95 NUM
secondly it allows for a simple efficient treatment of mrs as sets during the retrieval phase of the application phase
currently we examine the decomposition field of a planning operator by hand to determine sentence boundaries and fix this for all applications of the operator
we consider some families of transformations and design efficient algorithms for the associated learning problem that improve existing methods
given an input to a test system anaphora in the resulting texts will be determined by the rule used in the referring expression component of the system
once a response was processed and concept tags were assigned all phrasal and clausal categories were collapsed into a general phrasal category xp for the scoring process as illustrated in NUM below
for instance if an examinee had two responses better trained police and cops are more highly trained the scoring system must identify these two responses as duplicates which should not both count toward the final score
for instance police and better occur frequently but in varying structures such as in the responses police officers were better trained and police receiving better training to avoid getting killed in the line of duty
since the preprocessing of this response data is done by hand the total person time must be considered in relation to how long it would take test developers to hand score a data set in a real world application
5we did not use these NUM test data in the initial study since the set of NUM has not been scored by test developers so we could not measure agreement with regard to human scoring decisions
in the spirit of the layered lexicon the definitions associated with the superordinate concepts are modular and can be changed given for this study metonyms for each concept were chosen from the entire set of single words over the whole training set and specialized NUM word and NUM word terms i.e. domain specific and domain independent idioms which were found in the training data
to illustrate the use of such knowledge bases in the development of scoring systems linguistic information from the response set of an inferencing item in this paper a response refers to an examinees NUM NUM word answer to an item which can be either in the form of a complete sentence or sentence fragment
the so called reviswn tags indicate revisions to earlier versions of the d eument
the overgenerated cases of zero anaphora for instance are the sum of nonzero anaphora associated with the leaf nodes labeled z in the classification trees
our goal in building a scoring system for free responses is to be able to classify individual responses by content as well as to determine when responses have duplicate meaning i.e. one response is the paraphrase of another response
yeh and mellish an empirical study on anaphora the factors affecting the use of pronouns are very complicated thus it is difficult to get computable rules
this paper presents one sequence of rules developed using the above methodology and evaluates the effectiveness of the new linguistic principles taken into account at each point
feedback provided by the interactor can be made more domain friendly by specifying some extra domain specific rules at the top of the template to string rules file since these rules are executed in the order specified
n NUM n NUM immedia x ng violatingconstr aints syntactic nz violatin svnt tic nz constraints
most of the well known systems first select and organize the message contents to be generated and then map the organized results into a sequence of surface sentences
the first was typed by one of the authors in an early draft of this paper that is learners do not seem to acquired exactly one construction at a time a similar mistake is found in one of our writing samples from a student who is learning english as a second language
asl grammar components include sign order morphological modulations of signs and non manual behavior that occurs simultaneously with the manual signs bc80 lid80 pad81 kg83 ling78 bak80
we anticipate that slalom when fully developed will initially outline the typical steps of second language acquisition
this work can be interpreted as specifying groups of features that should be acquired at roughly the same time
thus the resulting model will contain annotated mal rules that capture the errors we expect from second language learners
on the surface the fact that asl and written english occur in different modalities seems problematic as well
this has been called the zone of proximal development zpd by vygotsky vyg86
this representation makes a distinction between dependencies between modifiers and complements
the results are shown in the second row of table NUM
figure NUM system setup for experiment NUM c
elementary trees of ltag are the domains for specifying dependencies
consider the sentence NUM with the derivation tree in figure NUM
but see the discussion of the stapler section NUM
NUM show v me s the d flights n from p boston n to p philadelphia n on p monday n
some novel applications of explanation based learning to parsing lexicalized tree adjoining grammars
the sentence show me the flights from boston to philadelphia
the information used for the extraction of terms can be considered as rather internal i.e. coming from the candidate string itself
the investigation of the context used for the evaluation of the candidate string and the amount of information that various context carries
moreover many of them appear as prefixes of the word to be analyzed and their identification is part of the morphological analysis
moreover there are cases where two or even more analyses share exactly the same morphological attributes and differ only in their lexical entry
these could be formalized as the cps function defined in NUM
since japanese sentences have no lexical segmentation the input has to be both morphologically and syntactically analyzed prior to the sense disambiguation process
our experiment on japanese nouns shows that this framework upheld the inequality of statistics based word similarity with an accuracy of more than NUM
besides this since the similarity is computed based on given co occurrence data word similarity can easily be adjusted according to the domain
hqpm o pn masculine their perimeter hqpn lflpn feminine their perimeter rcb
in the domain knowledge base each entity in addition to the information for the head noun in the surface form is accompanied by a property list that will be realized in the modification part of the surface noun phrase for the initial reference
ba round bowl in fill full aspect water fill the round bowl full of water d ranhou ba yuanwanizhong de shui manman daojin fangwanjli then ba round bowl in gen water slowly fill in square bowl in then slowly pour the water in the round bowl into the square bowl
the verb hcby 2xn masculine singular present tense indicates or votes
a more important reason why the matching rates are lower with speakers than with the hypothetical computer may be that in some circumstances more than one solution may be acceptable and the speakers may not always choose the same one as the computer
since we use a large corpus for this purpose the morpho lexical probabilities we acquire must be considered relative to this specific training corpus
if an entity e in the current clause was referred to in the immediately preceding clause and does not violate any syntactic constraint on zero anaphora then a zero anaphor is used for e otherwise a nonzero anaphor is used
a description of an experiment that serves to evaluate the approximated morpho lexical probabilities calculated using an untagged corpus will be given in section NUM
the l wflue of de tumor meets the required value for the slot ind but none of the concept classes required for the the guessing module then relaxes the conditions set and now considers the syntactic function of a noun phrase or a prepositional phrase expressed by its ivalue as a sufficient indication for a semantic link
NUM om de laterale tumorale expansie te kunnen verwijderen de latcrale tumorale expansie cc r direct object verwijderen cc surgical deed cs remove to be able to remove the tumoral expansion the guessed concept type is suggested for all the nouns and adjectives being the meaningful words in the phrase
ex NUM a non surgical deed frame with slots filled in we have established an order for tim matching of the semantic links giving priority to these links which connect a surgical deed concept with another concept
in case grammar selection restrictions arc specilied lbr cases in ciin those parts of the sentence which are candida e lbr a semantic lank refer to erie of tire clinodel ned medical concepts
de tumor cc verwijderd cc surgical deed cs remove the turnout will be removed suppose that tumor is not present in the lexicon then the system is not able to meet the conditions of the slots of verw deren and can not indicate the r directobject
combination with another medical term r t NUM rizzanin syntax a kramer lexicon NUM maks syntax and semantics w martin overall supervision cc modifier bodyside cc modifierextent cc modifier number terms which modit c other medical terms
4a de catheter wordt in de wond geplaatst the catheter has been installed in the wound a consultation of the surgical deed lexicon for finding the subtype entry ofplaatsen in the surgical deed lexicon e if necessary adaptation of the frame with information from the surgical deed lexicon will take place
these occurrences are translated into priority numbers for the constraints on the cc slots which are registered in the type and surgical deed lexicon the concept type with the highest occurrence in combination with the given surgical deed concept and the given semantic link was marked with the highest priority number namely NUM
NUM a guessing module for suggesting the concept type of tmkn rcb wn w rcb rds he last two modules will bc discussed in scction iv and v i rcb elow
the module works semi automatically the list of unknown words is generated in an automatic way but the user of the system has to decide whether the suggestion is correct or not before adding it to the lexicon
null the eurowordnet database will as much as possible be built from available existing resources and databases with semantic information developed in various projects
the word two before after is w
it is however difficult to analyze and requires keeping track of the rank of each word
it may then be advantageous to prune pst nodes and remove small counts corresponding to rarely used words
consider for example the pst shown in figure NUM where some of the values of are
some examples are the data abs illustrate the problem abs co with accuracy pa the algorithm abs efficiently pa calculates the similarity relations detected by ciaula can not be used tout court as a taxonomy in a nlp system
but it is much more difficult to say whether for example the ciaula class measure propose derive evaluate discover classify describe calculate is appropriately described by the wordnet label communicate transmit thoughts transmit feelings
let vi be the members of a ciaula basic level cluster c s vi the synsets for each vi and h s vi or hi the set of supertypes hyperonims of s vi
on the other hand conceptual or compositional models of similarity are much more difficult to understand and formalize on a systematic basis because of the difficulty of defining a commonly agreed upon set of semantic primitives into which words may be decomposed
let syns c denote the set of all hyperonims of at least one verb v in c i.e. let v syns c be the set of verbs of a given cluster c that are hyponyms of syns
the problem is that the similarity relations suggested by the thematic structures of words NUM in sentences are highly domain dependent and it is difficult though perhaps not impossible to find common invariants across sublanguages when this model of word similarity is adopted
in order to analyze the correspondence divergence between human coded verb classes and data driven clusters we can compare the argument structure proposed for the synsets in wordnet and the intentional description of the classes i.e. the prototypical semantic patterns of ciaula clusters figure NUM
decision trees are built by finding the question whose resulting partition is the purest NUM splitting the training data according to that question and then recursively reapplying this procedure on each resulting subset
there is not much difference in learning performance between the online and batch modes as we will see
in the coding theoretic interpretation of the bayesian framework the assignment of priors to novel events is rather delicate
the result establishes isomorphic subsets of the qlf and lfg formalisms
these choices include choices among synonyms and near synonyms and choices among alternative syntactic realizations of a semantic role
this narrows the scope of the lexicon to a specific domain the approach fails to scale up to unrestricted language
because our grammar is organized around semantic patterns it nicely concentrates all of the material required to build word lattices
for example in our first grammar we did not make any lexical or grammatical case distinctions
default choices have the advantage that they can be carefully chosen to mask knowledge gaps to some extent
then the generator is faced with paradigmatic choices among alternatives that without sufficient information may look equivalent
NUM NUM NUM the new companies will have in mind to establish it at february
wrd w create the smallest lattice a single arc labeled with the word w
any failure inside an alternative right hand side of a rule causes that alternative to fail and be ignored
an alternative to this is to forget i and simply score a and b on the basis of fluency
we have established isomorphic subsets of the qlf and lfg formalism
in section NUM the results of a series of experiments using our method are described
f2 the speech act expressions lie on the parts at the end of the utterance
sions endexpr p set endexpr and l endexpr NUM set endexpr and represent the j
using this cohesion a task oriented dialogue is segmented into several subdialogues according to the topic
the discourse structure in task oriented dialogues has two types of cohesion global cohesion and local cohesion
we present two methods of interpolating the plausibility of local cohesion based on surface information on utterances
we do so in the order of longer fixed expressions with cost criteria values above a certain threshold
smoothing method NUM interpolate the plausibility by using partial fixed expressions in set endexpr
moreover six dialogues were taken from the NUM dialogues for the closed data
successful lagts reproduce at the end of interaction cycles by one point crossover of and optionally single point mutation of their ini tial p settings ensuring neo darwinian rather than lamarckian inheritance
ew lutionary simulations suggest that a lea rner with default initial settings for parameters will emerge provided that learning is memory limited and the environment of linguistic adaptation contains an appropriate language
evolutionary simulation predicts that a learner with default parameters is likely to emerge though this is dependent both on the type of language spoken and the presence of memory limitations during learning and parsing
grmnnmtical acquisition proceeds on the basis of a partial genotypic specifica tion of universal grmnmar ug complemented with a learning procedure elmbling the child to complete this specification appropriately
a new account of parameter setting during grammatical acquisition is presented in terms of generalized categorial grammar embedded in a default inheritance hierarchy providing a natural partial ordering on the setting of parameters
present one with premodifying and the other postmodifying relative clauses both with a relative pronoun at the right boundary of the relative clause are shown below with the differing category highlighted
the value of the simulation is to firstly show that a bioprogram learner could have emerged via adaptation and secondly to clarify experimentally the precise conditions required for its emergence
fitness based reproduction ensures that successful and somewhat compatible p settings are preserved in the population and randomly sampled in the search for better versions of universal grammar including better initial settings of genuine parameters
bouma and van noord NUM and the rule schemata especially composition and weak permutation must be restricted in various parametric ways so that overgeneration is prevented for specific languages
according to our view the premises in figure NUM are not valid exhale NUM and mhale NUM are not troponyms of breathe NUM because breathing needs exhaling and inhaling both troponym links should be removed
alternative definitions of r resolve to surface form i.e. minimize qlf contextual resolution
which is harmful smuggle NUM should be a troponym of a concept export or tmport NUM from this diagnosis we are led to the question what was the general practice in wordnet with respect to multiple direct superconcepts
the treatment of modification in both f structure and qlf is open to some flexibility and variation
the outstanding lesson for this project as a result of the muc is that the dx system will in fact be able to meet its performance objectives in the near future that the three part design basis of look up pattern match and semantically resolve is sound
on a recent test involving over a hundre d test cases per class we logged on average NUM misses NUM correctly identified but incorrectly tagged and NUM false alarms for the four simpler classes of time date percentage and money
there are at least two ways to have the system cross the bridge and resolve inferential anaphors
the fact contents of the data base called the knowledge bank such as names of places persons and organizations were acquired only in the last fe w months and entered using a coding scheme which represents their many syntactic and semantic features
for this reason an additional design requirement for the new pattern language was that it would have the capability to represent very powerfu l sentence level parsing grammars e g context sensitive rules unrestricted look ahead eas y representation of discontinuous constituents
finally the absence of user function libraries to provide very high level scotch guarded functions for easy use in the rules mean t that too often we had to use very low level programming functions or drop into scheme neither a forte of the rule writers
the value of the chars attribute is that string which has all beginning brackets or parentheses or braces removed as well as all ending brackets an d punctuation of all kinds appropriate attributes are set to indicate what was removed
the final step is to identify using semantics based contextual reasoning wherein potential remaining data class targets are fuzzily classified as one of several related data classe s e.g. proper names on the basis of suggestive cues e g capitalization
the first and last tokens have special type values start of expanded token and end of expanded token but they have no start or stop attributes being dimensionless with respect to characters
this should be our focus in trec NUM
note however that the translation inay overspecify the range
the system comes with lkbs for english french is currently under development
full range of punctuation such as commas around descriptive relative clauses
ilen hl NUM NUM NUM sec NUM
we are grateful to r kittredge t
the deep syntactic component takes as input a dsynts
the user may also want to change the defaults
the current grammar does not have any rules with more than three nodes
as mentioned in section NUM realpro is configured by specifying several lkbs
korelsky d mccullough a nasr e reiter and m
realpro can output text formatted as ascii html or rtf
in the case of english output most of the speech synthesis work relies on the deetalk system although linguistic structures help to disambiguate non homophonous homographs read lead record wind etc
the direct underspecified interpretation schema extends to the modification cases discussed above in the obvious fashion
the categories of the maximal projections in the list are then combined and the update for the complete utterance is computed
however substitution nodes will always be linked the difference between a substitution node and an adjunction node is that an adjunction node does not introduce a new structure to the partially derived tree whereas a substitution node always does
as the trace feature is locally set within each flat struc ture two op nodes in figure NUM b are co referenced with the same variable NUM indicating where the object should have been in the canonical sentence
the symbol marks substitution sites and the symbol marks the foot node figure l a shows an initial tree representing the book
in our application this often comes down to categories such as temporal expression and locative phrases
take the example of the word air in the english text
NUM an evaluation of the spoken language translator
however if we divide these articles into semantic objects e.g.
we aligned the specialties to the domains in the ndc
NUM NUM procedure tbr the doemnent classification using kanji characte rs
document classification using domain specific kanji characters extracted by x2 method
in addition to this the ndc has hierarchical domains
NUM a simple word extraction technique generates to many words
dimensional feature space the axes of which are the domain specific kanji characters
nainely the domain vl is rel rese nted
fable NUM shows an example of the hierarchical relationship of the ndc
asking about rooms availability features price
this is an improvement over previous approaches which rely on syntactic or semantic grammars
the system architecture separates general linguistic knowledge domain knowledge and transfer knowledge
in this case we are content to chose one of the minimal cost sets at random
for the first assumption we assume that the individual distortion operators are conditionally independent given the
in effect we stipulate that the operators only affect a strictly local portion of the input
linguistic efficiency refers to the notion of how efficient the system is with regard to these regularities
this is modeled using a number distortion operators echo word ewi
this approach sometimes called analogical casebased or memory based originated with the following insight
it provides exactly the type of lexical semantics needed for many nlp tasks the affixes discussed in the previous section cued nominal semantic class verbal aspectual class antonym relationships between words sentience etc
the suffixes le and ate should really be called verbal endings since they are not suffixes in english i.e. if one strips them off one is seldom left with a word
another argument for such an axiomatization is that many nlp systems utilize a denotational logic for representing semantic information and thus the axioms provide a straightforward interface to the lexicon
temples treasuries and other important civic buildings is an example of this pattern and from it the information that temples and treasuries are types of civic buildings would be cued
a human then determines which pairs are correct where correct means that the axiom defining the feature holds for the instances tokens of the word type
i conclude that categorial information should be factored out of the compiled table and separate data structures should be used
the parser could compile this information and assign case directly without even waiting to see the lexical verb
in one case a covering grammar is compiled which overgenerates and is then filtered by constraints
lexical information is consulted only if it is needed to disambiguate a state containing multiple actions in the lr parser
for my purposes note that if anything i am dealing with the worst case for the parser
in rules i and NUM the heads co and i0 respectively are followed by ip and vp obligatorily
the table stores information about obligatory complementation such as the fact that i0 must be followed by a vp
these three grammars are then compiled by the same program bison into three la lr tables
these figures show that there is no relation between the increased specialization of the grammar and the decrease of nondeterminism
hence ordering information should be codable in or recoverable from the representations
unlike most previous corpus based wsd algorithm where separate classifiers are trained for different words we use the same local context database and a concept hierarchy as the knowledge sources for disambiguating all words
telematics hci and certain other issues such as maintenance of the system deleting old ads user training legality of texts in different countries and the information retrieval aspects of the system will not be discussed in this paper
NUM we will now consider an axiomatization of the model
heeman and hirst NUM hirst et al NUM
each of these nodes is also a vector of dimension n figure NUM depicts this arbitrary array of nodes in this case a NUM by NUM regularly spaced array of NUM nodes
although all the labels are not visible in the dialog the region algorithm found a total of NUM distinct regions for the self organized map of the NUM NUM ap news wire documents
for example an undirected information search commonly referred to as browsing is made even easier when using the automatic region finding and labeling mechanism
to solve the problem of browsing the information space in order to fred information of interest new techniques for data retrieval and presentation must be developed
the conscience mechanism allows nodes that are observed to rarely win the competition to subsequently win more often and it prevents nodes that ffi equently win the competition ffi om subsequently winning too oren
documents are segmented into user defined time intervals typically one hour one day or one week depending upon the nature of the data
as a result inconsistentli holds for these linguistic intentions
there is a linguistic expectation that askref follow pretell
NUM NUM turn NUM russ decides to respond with an askref
how russ decides to produce an informref in t4
specifically context vectors for documents and queries are formed as the normalized weighted sum of the word stem context vectors for the word stems found in the document or query
mcroy and hirst the repair of speech act misunderstandings table NUM
the first utterance in the figure is a plan adoption
otherwise it might be taken as evidence of misunderstanding
NUM a mark that is long relative to its width NUM a linear string of words expressing some idea the similarities between senses of the same word are computed during scoring
for jackendoff noun phrases describe ordinary individuals while pps describe places or paths and vps describe actions and eventualities in terms of a reichenbachian reference point
the sublanguage component performs the following four steps NUM select keywords from previously uttered sentellces null NUM collect silnilar articles flom a large corlms based tm the keywords NUM extract sublanguage words fl om the similar articles NUM compute scores of n best hypotheses based on tile sublanguage wor ls a sublanguage analysis is performed separately for each sentence in an artieh afl er the first sentence
senses NUM and NUM are subclasses of artifact
one if them is the fr luen y of the wor t in the NUM n 1lest senten es and the other is the log of the inverse t robability f the wor l in the large eortms
for one thing there are a lot of word errors unrelated to the article topic for exmnple function word replacement a replaced by the or deletion or insertion of topic unrelated words missing num of error wer over
the sul language score we assign t each word is the logarithm of the ratio of d cunlent h e lueal y in the sublanguage m ticles to the word frequen y of the word in tile large corpus
specifically we require that a word aptiear at least NUM tilnes ill the tilt NUM n besl senten es as ranke l l y sl lcb s s ore NUM o luali y as a keyword for retriewd
since we do not have a much larger set of articles with speech data one possibility is to optimize the systeln in terlns of perplexity using a nnlch larger text corpus for training and apply the optimized parameters to the speech recognition system
sin e it is the h nonlinator f a logarithm it winks to reduce the effect of the general laltgliage model whml may be robed led in the trigranl language mo m score
senses NUM and NUM are kinds of state
accurate than the corresponding decision tree
these corpora contained the following information
table NUM lists the learnability results
the suffix shows allomorphic variation table NUM
diminutive forlnation is a productive morphological rule in dutch
unive rsiteitsplein NUM NUM wimjk l elgium
there is no capitalization to indicate the beginning of a sentence
an e ononfica NUM way of orga nizing
they often make subtle distinctions between word senses
critical diff er0 nce s between h xica lized
if the r is a pa ired leriwl tion
however in the framework of a kl one type system they can be necessary and sufficient relying on a set of undefined concepts axioms and a logic calculus while in a manually built up thesaurus or semantic net of the wordnet type only necessary conditions of correctness can be checked
the notations a re so simple tha t even a
the deriw lion q then produces a transla tion
with the cfg skeletons in q is sa tisfied
i xaml le based mt sa to
in conclusion the lesson from experimentation is that parsing done totally on line is inefficient but that compilation is not always a solution
what is presented in NUM as an illustrative example is in fact a consistent form of organization of the principles
linguistic information can be classified into five different classes NUM a configurations sisterhood c command m command maximal b
in the rest of the paper i first discuss the advantages of storing x information separately from lexical information section NUM
one factor is the number of active chain types namely whether a sentence presents only a chains only a chains or both
clearly checking features and using them for building chains and keeping the hypothesis search space small is beneficial in most cases
when the np that is the antecedent head of chain is found it starts a new chain according to csel
the conceptual development of the 80s in many frameworks consists in having identified the unifying principles of many of these construction specific rules
the word interest has NUM senses
accuracy for these cases is NUM
thus the degree of automation increases with the amount of data available
we present a tool developed for annotating corpora with argument structure representations
the nodes and edges are assigned category and grammatical function labels respectively
any change into the structure of the sentence being annotated is immediately displayed
extra effort has been put into the development of a convenient keyboard interface
the average annotation time per sentence improved by NUM
the tagger rates NUM of all assignments as reliable
we will use f z to denote that the function f is undefined for
note that the system has now settled on the correct english word sequence
the functions fi which depend both on the word history h and the word being predicted are the features each fl is assigned a weight aps
both kinds of testing are the same becanse cumulative error is only an issue for context based approaches
given he same input utteran e
altogether NUM sentences were used for training and NUM for testing
introducing graded constraints has two adwm tages over adding more elimination constraints
phe result is the graded constraint score for that ambiguity
an example of an iui is shown in figure NUM
he resulting set of llts is then sent to the discourse processor
i i i i i i NUM grammar acquisition as clustering process i
a lcb argmax lcb p c lcb laa rcb
we then present our first order hmm approach in lull detail
putting everything together we have the ibllowing llmm based model
it has a variety of annotations including word segmentation pronunciation and part of speech tag
rule vp v rule s np vp NUM rule vp vp np pp NUM NUM rule s adv s NUM
vp as vp ai a21as ai a2 NUM or if the number of daughters is not specified by the rule vp ao vp ao an a1 an NUM
the cost parameter for elc in the normalized distance model is
this model gives an indication of performance based solely on model structure
include the cost of the transition e.g.
of which the third daughter constitutes the head is represented now as headed rule x c b x a e b b a a a d c d e d e
if it is never possible that a gap can be used as the head of a rule then we can omit this new clause for the predict predicate and instead use a new clause for the parse s predicate as follows parse small q q eo e gap small
it might be tempting to use only the second table but in that case it would not be possible to tell the difference between a goal that has already been searched but did not result in a solution fail goal and a goal that has not been searched at all
the cost function for the discriminative model is estimated as
parse left ds hit qo q eo parse h qi q e0 q parse left ds t q0 qi e0 parse right ds rightds q0 q e there are categories rightds from q0 to q s t
table NUM translation performance of different cost assignment methods
associated with lower numbers are applied before rules associated with higher numbers
we therefore investigated the dependence of performance on the chosen significance level
fireplaces in the new house but not in the old one
most of these sentences involve phrasal substitution patterns typical of antonyms generally
we did the work manually to avoid john s justeson and slava m
NUM from the NUM this could have been automated using a parser
it consists of stories and articles from books and general circulation magazines
so man and house are reliably associated with different senses of old
such disambiguation requires only simple rules which can be automated easily
clues other than nouns are required when modified nouns are not useable
note however that the short cut so obvious in this picture will probably be hidden to the eye even in a hierarchical line print out when all hyponyms of hpid od NUM animal oil and glycerlde would be displayed
the largest application we have developed to date has NUM answer classes on the topic of the nl assistant product
here c denotes a syntactic category si denotes a semantic value and w a word
a big man slipped on the ice the boy dropped his wallet somewhere
this work was partially supported by the netherlands organization for scientific research nwo
input is provided from an html form consisting of a number of fields which correspond to job schema object attributes e.g.
data entered for any given object attribute is then encoded in the same format used to encode job ad information
users queries are written as lists of words where each list or term set is meant to correspond to a different component of the query NUM this list of words is then translated into conjunctive normal form
girill also finds that using document boundaries is more useful than ignoring document boundaries as is done in some hypertext systems and that premarked sectional information if available and not too long is an appropriate unit for display
however unlike other researchers who have studied setting time characters and the other thematic factors that chafe mentions i attempt to determine where a relatively large set of active themes changes simultaneously regardless of the type of thematic factor
the system design permits users offering jobs to submit via an e mail feed job ads more or less without restrictions
the goal is to simultaneously and compactly indicate NUM the relative length of the document NUM the frequency of the term sets in the document and NUM the distribution of the term sets with respect to the document and to each other
girill does not make a commitment about exactly how large the desired text unit should be but talks about passages and describes passages in terms of the communicative goals they accomplish e.g. a problem statement an illustrative example an enumerated list
they note the notion of topic is clearly an intuitively satisfactory way of describing the unifying principle which makes one stretch of discourse about something and the next stretch about something else for it is appealed to very frequently in the discourse analysis literature
the system converts these texts as far as possible into schematic representations which are then stored in the jobs database
a transfer model consists of a bilingual lexicon and a transfer parameter table
figure NUM the life of a constituent in the chart
thus the nth entry of the lexicon wn can be represented as w c n where w is the surface lexical form and c is its pos class
there are two important questions that arise at the rule acquisition stage how to choose the scoring threshold os and what the performance of the rule sets produced with different thresholds is
they are all on the same level
since arguably the guessing of proper nouns is easier than is the guessing of other categories we also measured the error rate for the subcategory of capitalized unknown words separately
when we compared the xerox ending guesser with the induced ending guessing rule set ending we saw that its precision was about NUM poorer and most importantly it
such rules account for the regular suffixation as for instance book ed booked suffix i suffix morphological rules with a mutative ending in the last letter
the latter transformation is in fact due to the peculiarities of brill s tagging algorithm and in other approaches is captured at the disambiguation phase of the tagger itself
in our approach we guess the words using their features only and provide several possibilities for a word then at the disambiguation phase the context is used to choose the right tag
the acquired guessing rules employed in our cascading guesser are in fact of a standard nature which in some form or other is present in other word pos guessers
the cascading guesser also helped to improve the accuracy on unknown proper nouns by about NUM in comparison to brill s guesser and about NUM in comparison to xerox s guesser
the remaining two error types are the most interesting ones
this reference time can be a large interval and should contain each of the relevant occurrences of mary s telephoning during which bill was asleep
the mechanisms we discuss are the location time rpt and perf NUM drss will contain temporal markers corresponding to location times and rpts
this approach can be summarized as follows in the processing of a discourse the discourse initial sentence is argued to require some contextually determined reference time
the rpt can be either an event or a time discourse marker already present in the drs recorded as assignment rpt e
the portion of the document is specified by a set of spans
the tense morpheme of the main clause locates the event time with respect to the reference time whereas temporal adverbials are used to locate the reference time
but now the location time of the eventuality in the subordinate clause serves as the antecedent for the location time of the eventuality in the main clause
this would seem to be troublesome for our approach which uses the location time of the event in the main clause and not its reference time
this often indicates a redundancy in the lexicon
the other common error type involving content words concerns adverbs derived from adjectives
here both manual and automatic disambiguation leads to errors but in different directions
all words in the suc are tagged for part of speech and for inflectional features
by contextual knowledge we include that information provided by the surrounding language by the salient facts about the non linguistic context and by the general knowledge about the world that can be presumed to be shared by the participants in the conversation NUM
however this example is neither a case of generic auto relationship nor is it a case of avoidable generic synecdoche the hyponym link between satinwood NUM and satmwood NUM must be replaced by a substance link also available in word net
lexical semantics is primarily a matter of pointing the lexical items toward the appropriate locations in the ontology encyclopedia without requiring any except the most basic formal semantic properties mass count number gender etc as directly referenced by rules of the grammar properties which have semantic correlates but which function independently in the grammar
in this paper we present three types of arguments for a pragmatics based approach to treating the phenomenon of lexical polysemy lexical sense extension and rebut three types of arguments that have been used against pragmatics based approaches
we argue instead for a separation of the two with the grammar specific rules firmly within the grammar and information about semantic classes firmly in the encyclopedia of general world knowledge
in this paper we have taken issue with the concept of lexical rule as a rule of grammar combining syntactic morphological and phonological references on the one hand with reference to specific semantic classes on the other
in section NUM we address the first question how does dop perform on unedited data
the structure of the paper is as follows
these experiments provide a better understanding of word based methods and suggest where natural language processing can provide further improvements in retrieval performance
observe that similarity estimates are used for unseen word pairs only
in what follows we focus on probabilistic similarity based estimation methods
we will only be concerned with similarity between words in v1
the average improvement in using a instead of pc is NUM
the parameter NUM was always set to the optimal value for the corresponding training set
these pairs are undoubtedly somewhat noisy given the errors inherent in the part of speech tagging and pattern matching
then we replace each w2 in the test set with its corresponding pseudo word
we selected the noun verb pairs for the NUM most frequent nouns in the corpus
l0 except that the word hope is changed to be a stopword tag NUM
we briefly describe how we have augmented our algorithm to handle the compilation of weighted rules into weighted finite state transducers
in this way the field can make progress in identifying the relationships among various factors and can move towards more predictive models of spoken dialogue agent performance
2the genera question of the decidability of the halting problem even for one rule semi thue systems is still open
overall accuracy of the tagger is NUM
one can devise an on the fly implementation of the composition algorithm leading to the final transducer representing a rule
1degwe here use the symbol to denote all letters different from b rn n p and n
it is interesting to note that rules governing the splitting of subword patterns exist in languages such as english but their application is usually determined by orthographically inexplicit information such as the existence of a long short or stressed vowel in some position of the pattern
one major source of difficulty in this experiment were misrecognitions by the verbex speech recognizer
check for the absence of one or more wires until a missing wire is identified
the system runs on a sparc NUM workstatiou
figure NUM system structure for multilingual speech to speech translation
most facets are correlated with lexical cues
NUM c add a wire between connector eight four and connector nine nine
fypes of NUM si em evaluations
in section NUM we give our systern evaluation
displayed on a hangul window running on unix
reporting data values from only the successful dialogues maintains consistency with the reported statistical values
lr is logistic regression over our surface cues surf
tokenization in this case takes as input a vowel sequence and returns a sequential list of maximal non overlapping tokens of the types 2v vc and v tokens do not overlap in that every vowel of the sequence is assigned to one and only one token
the centering model assumes a preference order among these transitions e.g. continue ranks above retain and retain ranks above shift
in this paper we focus on newspaper articles
for the evaluation of a centering algorithm on naturally occurring text it is necessary to specify how to deal with complex sentences
hence we hypothesize that functional constraints on centering might constitute a general mechanism for treating free an dd fixed word order languages by the same descriptive mechanism
thus lemma NUM the consonant substrings of lemma NUM a and NUM b are contained in the sets of expressions ocl c2c c3 and c NUM c2c c3 e respectively
our basic revision of the ordering scheme completely abandons grammatical role information and replaces it with entirely functional notions reflecting the information structure of the utterances in the discourse
sbo d distinguishes among different forms of context bound elements viz anaphora possessive pronouns and textual ellipses and their associated preference order
but their approach still relies upon grammatical information for the ordering of the centering list while we use only the functional information structure as the guiding principle
another important aspect of the dialogue structure is the nature of the transitions between subdialogues
paradise supports the use of any of the wide range of cost measures used in previous work and provides a way of combining these measures by normalizing them
h we would carry out this idea in a completely direct way the toy corpus of figure NUM might for instance turn into the toy corpus of figure NUM
columns NUM NUM and NUM denote the total repetition repairs the number of repairs proposed by the simple pattern marcher and the number of correct proposed repairs respectively
one widely acknowledged limitation is that the use of reference answers makes it impossible to compare systems that use different dialog strategies for carrying out the same task
instead adverbial complements and adjuncts that are typical of particular verbs are indicated
the probability of substituting a subtree t on a specific node is the probability of selecting t among all subtrees in the multiset that could be substituted on that node
with dialog plausibility vectors in percent table NUM shows the results for our training and test utterances
certain key words are much more significant r r n certain dialog act than others
the interpretation of utterances is based on syntactic semantic and dialog knowledge for each word
table NUM incremental slot filling ill frame NUM well
there were NUM utterances in the training set att l 21d in the test set
noun verb adjective NUM its most plausible abstract syntactic category e.g.
the remaining errors are partly due to seldomly occurring dialog acts
table NUM incremental slot filling in frame NUM literal
fundamental structural disambiguation could be used to deal with these cases
first this category knowledge is needed for our segmentation heuristics
this will produce a set of possible candidate justification chains and three heuristics will then be applied to select from among them
the root nodes of these belief trees rap level beliefs contribute to problem solving actions and thus affect the domain plan being developed
this paper presents a model for engaging in collaborative negoa ion to resolve conflicts in agents beliefs about domain knowledge
figure NUM shows the belief and discourse levels of the dialogue model that captures utterances NUM and NUM
furthermore by capturing collaborative negotiation in a cycle of propose evaluate modify actions the evaluation and modification processes can be applied re cursively to capture embedded negotiation subdialogues
in this recursive process the algorithm annotates each unaccepted belief or evidential relationship proposed to support bel with its focus of modification beli focus
the belief evaluation process will start with the belief at the leaf node of the proposed belief txee on sabbatical smith next year
sapplicabflity conditions are conditions that must already be satisfied in order for an action to be reasonable to pursue whereas an agent can try to achieve unsatisfied preconditions
for example in the case where correct node is selected as the specialization of modify proposal the system must determine how the parameter node in correct node should be instantiated
the algorithm then hypothesizes that the user has changed his mind about each belief in cand set and predicts how this will affect the user s belief about bel
a natural way to reason about developing a segmentation algorithm is therefore to optimize the likelihood that two such units are correctly labeled as being related or being unrelated
so the organization specialist was penalized twice for those errors
of each syntactic constituent forms a node in tile graph while the directed edges express accessibility relations
what has she before done b sie hat eine weisse f rose fotografiert
participants in the tdt pilot study including james allan rich schwartz jon yamron and especially george doddington provided invaluable feedback on the probabilistic evaluation metric
detection encompasses the technology which does text or message dissemination and text retrieval
we will discuss the discourse i rocessor and how we extended it for the disambiguation task in sectiou NUM
the nineteenth feature asks if the term from appears among the previous five words and if the answer is yes raises the probability of a segment boundary by more than a factor of two
to incorporate triggers into a long range language model we begin by constructing a standard static backoff trigram model ptri w w NUM w NUM as described in NUM NUM
NUM for the wsj experiments we modified the language model relevance statistic by adding a weight to each word position depending only on its trigram history w NUM w NUM
information retrieval and message routing and information extraction functions to text handling applications
a second proposal is to keep the nbest hypotheses and to choose one only after having processed a sequence of inputs
relying on this fact however may limit precision because the repetition of concepts within a document is more subtle than can be recognized by only a bag of words tokenizer and morphological filter
while figure NUM shows that this behavior is very pronounced as a law of large numbers our feature induction results indicate that relevance is also a very good predictor of boundaries for individual events
for example l sstd bien can be either the statement it is good or the question is it good
it has been integrated with two object oriented modeling environments the NUM and with ptech a commercial off the shelf object modeling tool
to document it she configures an output text type whose content and structure is compatible with her company s standard for oo documentation
this generates an html doc null ument such as that shown in figure NUM which allows a user to load edit save a text plan macro structure specification
we claim that the alternate view should be provided by an explanation tool that represents the data in the form of a fluent english text
we therefore conclude that graphics in order to assure maximum communicative efficiency needs to be complemented by an alternate view of the data
both user groups showed semantic error rates between NUM and NUM for the separately scored areas of entities attributes and relations
this feedback showed us that the preferences regarding the content of a description can vary depending on the organization or type of user
the human anthored text can capture information not deducible from the model such as high level descriptions of purpose associated with the classes
modex uses an interactive hypertext interface based on standard html based www technology to allow users to browse through the model
in the second experiment a set of dialogue state dependent models trained on the same training set of the first experiment was used however in this case the training set was encoded according to the different dialogue states as explained above
our goal was to combine the predictions from the context based discourse processing approach with those from the non context based parser approach
in order to measure the improvements in recognition performance obtained using dialogue state dependent language models we compared the differences in the word accuracy wa and sentence understanding su rates obtained using different language models on the same test set
while NUM of the users were able to give them in two turns that is without experiencing recognition errors the remaining NUM took from three to eight turns i.e. these users spent in correction from three to eight turns
however the dialogue discarded the information about the partof day since this conflicted with a parameter value that the user had already confirmed and only the second part of the utterance interpretation was retained that is the departure hour
when users are asked to repeat a city name that was misrecognized by the system some of them modify their way of speaking they repeat the name louder or spelt it or even accept a misrecognized name proposed by the system
the larger discourse context is processed and maintained by a plan based discourse processor which also produces context based predictions for ambiguities
karl has a book read NUM a karl hat in bucii f gelesen
by analyzing the NUM dialogues between dialogos and the experimental subjects we found that the occurrence of users errors could be classified into the two classes described above that is users errors were always condomitant with substitutions or insertions in the best decoded sequence
as soon as it realizes that there is a deviation of the user behavior from the expected behavior it hypothesizes a misunderstanding and it re interprets the current utterance on the basis of the context of the misunderstood utterance thanks to a focus shifting mechanism
if the bag in 4b were interpreted as being made of cotton in line with the statistically most frequent sense of the compound then the discourse becomes incoherent because the definite description can not be accommodated into the discourse context
the f skeleton of an expression is the result of rcplacing f marked elements witl variables working top to bottom
unseen prob mass cmp form number of applicable schemata cmp form i eq cmp form number of applicable schemata cmp form prod csl estimated freq interpretationi with cmp formj unseen prob mass cmp formj x prod csl prod cs
an sdns s is well defined written NUM s if there are no conditions of the form x i.e. there are no um esoh ed anaphoric elements and every constituent is attached with a rhetorical relation
NUM ax r y x a made of y x v contain y x v the predicate made of is to be interpreted as material constituency e.g.
assigning u and b these values allows us to use subtype and elaboration to infer elaboration because skirt is a kind of clothing and the bag in sb is one of the bags in 5a
any attempt at distinguishing these senses would have to rely heavily on selectional preferences for prepositions which are yet to be implemented within the tagging program
noun phrase analysis was very important and coreference resolution played a role but we saw no benefit from crystal s dictionary for te
this data suggests that org was probably finding quite a few of the organizations missed by the scanner but i t needed more training
f casacuberta NUM a castafio NUM a marzal NUM f prat NUM j m vilar NUM NUM depto
finally extensive boolean conditions are imposed on the application of each individual rule
it would be difficult to attempt to induce this information from the treebank alone
in all other respects our work departs from previous research on broad coverage
one category of contextual question asks about characteristics of a sentence as a whole
similarly the first and last words of a sentence can be powerful predictors
prediction in our parser is conditioned partially on questions about feature values of words and non terminal nodes
in the grammar parse rule names and lexical tags are replaced by bundles of feature value pairs
there is nothing in the language itself which restricts the context which can be used in models
this approach decouples the search over tag sequences from the search over parse trees
there is some freedom in the order in which the parsing steps are taken
the sentences in which co occurring antonymous adjectives modify the same noun therefore constitute a subcorpus in which the ambiguous members of the antonym pairs are discriminated relative to their antonym specific senses
however it intersperses the intersections with determinization and minimization operations so that the automata being intersected tend not to be large
this requires only o tiers input states not o 21tiersl
a corrected variant is to put i NUM a and run bestpaths on i NUM c let the pruned result be b
many np complete problems such as graph coloring or bin packing attempt to minimize some global count subject to numerous local restrictions
c finally the grammar is not fixed in all circumstances both linguists and children crucially experiment with different theories
otp argues that such non local arithmetic constraints can generally be eliminated in favor of simpler mechanisms eisner in press
the proof is suppressed here for reasons of space but it relies on a form of the pumping lemma for weighted fsas
an implication constraint has the general form NUM suppose that all the c i are interiors not edges
an interval then the constraint needs only one state to penalize moments when the antecedent holds and the consequent does not
e functional perplexity to a lesser extent the range of tasks that can be performed by a particular dialogue is important
this shows the effe tivelmss of our method
there are more rigorous rules for combining probabilities but it is not clear how much benefit this gives if the original probabilities are only rough estimates anyway
as a result each cluster is added to a sequential number
the sample of the results are shown in tal le NUM
table NUM pairs of nouns with dis vl v2 wflues
furthermore some nouns in articles may be semantically similar with each other
as a r sult we obtahwd NUM clusters in all
the sample results of clustering is shown in table NUM
he was lieu hie of ollr nllllll er
we classified NUM artmes into eight categories e g
the response y is non linearly related to r through the inverse logit function
this paper attempts to meet that need
computational linguistics volume NUM number NUM c
it was closing just as john arrived
interestingly the bulk of the additional error in the machine learned rules is not with the hard organization names but with person names or if lp NUM and locations ar i2 ap NUM
the third utterance requires the vf interpretation
my dog is getting quite obstreperous
realizes is a generalization of directly realizes
mike has annoyed him a lot recently
perhaps had we made use of larger name lists we might have obtained better recall improvements a case in point is the gargantuan dunn bradstree t listing exploited by knight ridder for their named entities tagger NUM
such arguments denote events themselves in this case the event of being a particular number o f years old as opposed to the individuals participating in the events the individual and his or her age
note also that because of lenient template mappings on the part of the scorer a number of errors that might intuitively have been whole organization template errors turned out only to manifest themselves a s organization name errors
if one form appears to be an acronym for the other as in caa and creative artist s agency then the forms should be merged with the acronym designated as an alias
the ranking of the cr s matters
we expect that other such constraints exist
we have exploited this opportunity t o apply both machine learned and hand crafted rules extensively choosing in some instances to run sequence s that were primarily machine learned and in other cases to run sequences that were entirely crafted by hand
in this case such a phrase is found two places to the left on the other side of a comma so a new org phrase is created which spans both the original org phrase and its corpnp neighbor
null as an effect of the mediating scenario our module can not serve as a dialogue controller like in man machine dialogues
this notation could be based on articulatory phonetics where a higher level task such as grapheme phoneme conversion is being performed or on a spectral perceptual measure of similarity for more low level tasks such as duration adjustment
in order to handle many of the very hard problems remaining in speech synthesis there is a need to develop a basic underlying notation or method of deriving a notation that can be parameterized for different speakers
these maps are also capable of being operated on by a neural network in further processing stages opening the way to a different type of phonetics based on a multitude of soft constraints rather than the rigid phoneme and rewrite rule
figure NUM suggests that phoneticians have got it right in that the features do result in a clustering of similar sounds such as stops fricatives and nasals as well as the more obvious separation between vowels and consonants
vectors of addresses would be completely different e.g. the endpoints computational linguistics volume NUM number NUM concatenation procedure on which various enhancements based on the som are being tried which will be more fully described in future reports
NUM conclusion and further work the outline of a conventional sbr system has a series of symbolic stages assuming a modularity of data at each level before the final low level stage synthesis routines calculates the synthesizer parameters
while formants have been made use of as training data as well as acoustic tube data as yet no use has been made of a formant synthesizer for creating the output speech due to the need for handcrafting of values
it would also be important to determine whether the distance measure provided by the diphone maps correlates better with subjective perception of the mismatch between successive diphones than more standard measures of spectral distance such as various distance measures between frames of cepstral coefficients
the features were designed so that any phoneme or syllable may be uniquely specified as a cluster of features without reference to specific units segments such as phones syllables etc any feature may run across unit boundaries
functional perplexity is a measure of the density of the topic changes in a single dialogue and is accordingly difficult to calculate
the tree in figure NUM is used as an example to explain the syntactic score function
when the robust learning algorithm is applied very encouraging result is obtained
the block diagram of the deep structure disambiguation system is illustrated in figure NUM
the derivations of these score function are addressed as follows
the formulation of the scoring mechanism is derived as follows
error of this kind is usually tolerable for most applications
therefore the word sense score function is approximated as follows
the basic derivation of the syntactic score includes the following steps
null the experiment has been carried out on NUM sentences with NUM different lectures and formed by using seven verbs wr te eat smell corrode buy receive assocza e coupled with fifty common nouns and two proper nouns
selectional classes are hierarchical in structure like subject domain codes see section NUM NUM so allowance is made for near matches
research in example based machine translation f i mt has been hampered by the lack of efficient tree alignment algorithms for bilingual corpora
ghis paper lescribes an alignment algorithm for f i mt whose running time is quadratic in tile size of the input parse trees
when we introduce no l zero penalties the alignment procedure prefers matches between nodes dominating similar structures since nodes dominating dissimilar structures receive negative scores
awhen penalties are set to zero and an empty t ilingum dictionary is used the alignment algorithm ills tim scoring matrix with zeros
the validity of this heuristic can be tested by comparing the performance of the procedures using the computation it NUM and in NUM
and one of its features is with satellite a whose rule NUM NUM provides for re entry to fill n
another efficiency inlprovcment will be aehieved by factoring all ambiguity into the parse tree as in matsumoto eta NUM
some dialogue acts describe solely the illocutionary force while other more domain specific ones describe additionally aspects of the propositional content of an utterance
barriers i only one crossing permitted ii more than one crossing permitted
the algorithm has been implemented in c and successfully tested on well known translationally divergent sentences
the notion of too many is specified on a per language basis as we will see shortly
the implementation of case theory in our system is based on the following attribute values ca govern era
efficient parsing for korean and english structurally different languages e.g. head initial vs head final with equally efficient run times
NUM the main constraint of trace theory is the subjacency condition which prohibits movement across too many barriers
our focus is on the automatic construction of the korean and english grammar networks from x parameter settings
however this assumption rules out so called multiple subject constructions which are commonly used in korean
the result is that a head final language such as korean is as efficiently parsed as a head initial language such as english
the lexicon in pr1ncipar on the other hand contains about NUM NUM entries extracted from machine readable dictionaries
thus we can estimate the probability for each configuration NUM i NUM by counting the number of times the four head words were observed in that configuration and dividing it by the total number of times the NUM tuple appeared in the training set
as shown below one can obtain a more efficient left to right parsing algorithm for tig that maintains the valid prefix property and requires o n NUM time in the worst case by combining top down prediction as in earley s algorithm for parsing cfgs an auxiliary tree and its textual representation
the tfa positions of the complementations are indicated by their positions in the underlying word order i.e. in cd those belonging to the focus stand to the right of the head verb and those in the topic stand to the left of it
its lexical scope can be enlarged easily if the added lexical items are accompanied by appropriate grammatical data especially by valency case frames specifying the optional and obligatory arguments actor addressee objective origin and effect with verbs
NUM painter indef act village indef french gener dir arrive pret day indef september gener nice gener time NUM the neighbor met him yesterday
this pronunciation is not probable but it is possible as after such a co text as NUM NUM the contextual boundness of this pronoun in the given position is derived from its indexical character and its associative link with the speaker
let us first reproduce here examples NUM NUM from section NUM with a changed numbering accompanied by the corresponding input strings of our program in which the occurring word forms are complemented already by the lexical data
iv one of the most promising prospects is to join a procedure of the kind described in the present paper with an acoustic analysis of spoken discourse in which the position of the intonation center could be determined as one of the important factors
lexicographers have specifically attached cide grammar cedes which give verb complementation patterns to selectionai preference patterns using a restricted list of about NUM selectional classes for nouns
our notion of topic appears to have much in common with the more recently characterized concept of background or restrictor on the other institute of formal and applied linguistics charles university malostransk6 n m NUM NUM NUM praha NUM czech republic
note that it is far from trivial to capture and then percolate this information up a treebank parse without a grammar demarcation of the determl er phrase in each case is involved along with identification of the type of determiner phrase and other steps
in this example the defined value of a nonterminal is only one word
a word is unlabeled if it can not form deeper structure with at least one other word
one disadvantage of this approach is that some features can become obscure and cumbersome
however it is easy for parsing complexity in such systems to become impractical
NUM set rule e to rule e with
NUM pop the first entry from the agenda call the popped entry c
these are raw corefs that is the y correspond to several pieces of text referring to the same concept
it also enforces referential transparency and allows coding in a lazy style which means code is not executed unless needed
another possibility is the provision of hypertext style links to the relevant parts of the original documents in information extraction o r summarisation tasks
this work included providing some completely new but general functionality to the core as well as improving existing functionality and performance
of the NUM person months spent in preparing the system for muc we estimate that NUM was on th e core system
then the basic morphology function is mapped on to all leave s with additional treatment provided for sentence initial words
the forest is selectively explored from the topmost node using heuristics such as feature consistency and hand assigned likelihoods of certain grammatical constructions
the semantic analysis is compositional in general NUM NUM the meaning of a tree is built from the meanings of its subtrees
reading from the bottom it labels the role from th e left as subject and the right role becomes object
lexical ambiguities and anaphora are resolved using a series of preference heuristics which are first applied to disambiguate the action of the event
under these assumptions the trees of figure NUM are elaborated for semantics as in
the larger the initial word list is the more often a hiragana word happens to coincide with a sequence of other hiragana words because the number of character types in hiragana is small NUM
we think however our results are because we used a word uni am model it is too early to conclude that re estimation is useless for word segmentation as discussed in the next section
the major drawbacks of the current word segmenter is its breakdown of unknown words whose substrings coincide with other words in the dictionary and the erroneous longest match at the sequence of functional words written in hiragana
we first compared the three frequency estimation methods described in the previous section greedy longest match method lm string frequency method sf and longest match string frequency method lsf
this method can be implemented by making two suffix arrays srr and sd for text t and dictionary d by using st we first make list lw of all occurrences of string w in the text
table NUM shows the number of sentences words and characters in the training and test sets NUM based on the frequency in the manually segmented corpus training NUM we made NUM different initial word lists d1 d200 whose frequency threshold were NUM NUM NUM NUM NUM NUM respectively
for example the incorrect initial segmentation take away i infl passive auxv i ball i t not yet is correctly adjusted to i l take away i h infl passive auxv i ft
at each stage of re estimation we measured the word segmentation accuracy on the test sentences not the training texts figure NUM shows the word segmentation accuracy the number of word tol ens in the training texts and the number of word types in the dictionary at each stage of re estimation
it is possible of course to avoid this limitation by having rule features match the feature structures of both lexical entries in such cases
morphemes the pattern lcb cvcvc rcb verbal pattern the above mentioned root and the voealism lcb ae rcb active
rule NUM above are not associated with feature structures though there is nothing stopping the grammar writer from doing so
firstly we suffix each lexical entry in the lexicon with the boundary symbol and it s feature structure
the numbers between the lexical expressions and the surface expression denote the rules in figure NUM which sanction the given lexical surface mappings
following current terminology we call the first j elements surface NUM and the remaining elements lexical
it is implementation dependent as to whether t includes other correspondences which are not explicitly given in rules e.g. a set of additional feasible centers
if an operator 0p takes a number of arguments at ak the arguments are shown as a subscript e.g.
this will succeed if only if f1 of rule NUM and fl of the lexical entry were identical
our aim is to construct a fsa which accepts any lexical entry from the ith sublexicon on its j ith tape
from an abstract point of view to develop the multilingual matrix it is necessary to re map the italian lexical forms with corresponding meanings a4 building the set of synsets for italian making explicit the values for the i intersections ps
for the presentation of the travel plan other information elements become important the departure time the arrival time the places where to change the directions of trains the departure and arrival times at the places of change
these include both computer initiated as well as user initiated strategies
table NUM comparative performance of verification subdialog decision strategies
computer near the left boundary and toward the middle is a green region
with each grammatical utterance is associated a parse cost pc which is the sum of the costs of each insertion deletion and substitution required for the transformation
and still less expectation for a clarification question or comment such as which knob where is the knob or i do not see it
nevertheless a small set of under verifications remains
day dania egedi and robin gambill
it is of course possible that the verification subdialog may not succeed but we have not yet assessed the likelihood of that and thus do not consider this possibility during the evaluation of the various strategies
an excerpt from an actual interaction with the system is given in figure NUM NUM the words in parentheses represent the actual sequence of words that the speech recognizer sent to the dialog system for analysis
NUM a higher k s higher k l
in this paper we refer to the aspectual morphemes which follow verbs as aspectual forms including compound verbs such as hazimevu begin
we see that utterances with one element contain in most of the cases NUM a new element
such adverbs as katute once mukasi in the past and izen be ore determine the temporal structure of the event related with tense
the feature process concerns an ongoing process and distinguishes whether events described by verbs have the duration for which some actions unfold
table NUM categories in the first data set i wheat corn oilseed sugar coffee soybean cocoa rice cotton table NUM the second data set
he identified eleven verbs as purely stative of the NUM distinct verbs occurring at least NUM times in the lob corpus
sidaini by degrees etc which modify gradual process verbs g and focus on the process
salience assigns each entity a position in a partial order that indicates how accessible it is for reference in the current context
however they takes a quotative to case that marks the content of the statement and this measures out the event described by verbs
database conflict a database conflict arises when the constraints specified by the user do not match any item in the database
the objective is not just to quit gracefully but to allow the user to re enter the dialogue at some place
since these design objectives are often conflicting in nature one has to strike a balance between them
for the lexical sequence cher e for example the output is as follows
a polish rule set is also under development and swedish is planned for the near future
english without composition results in a subset language
in all this cgug defines just under NUM grammars
lagts generate and parse sentences compatible with their current p setting
all the remaining p settings were genuine parameters for both learners
the default learner is modelled on bickerton s bioprogram learner
the number of names in each file was then counted to arrive at an overall profile of the data distribution
however we try to define this as a series of steps that reasonably approximates a scientific discovery procedure
the present discussion should be taken in this light i e with the understanding that it was not officially evaluated atmuc NUM
the semantic account of the overall apposition ends up as a has age relation modifying pers NUM the semantic individual for the embedded person phrase
this suggested that the initial description was leaving a significant portion of the data unaccounted for
this step can be thought of as a test of the data distribution hypothesis
null doing this systematically is not the same as measuring errors scientifically
this still leaves NUM NUM of person names unaccounted for
step NUM loop back to step NUM or stop when an acceptably high percentage of the data is accounted for and inconsistencies are resolved re examination of the data revealed that among the NUM NUM syllable non vie residual person names NUM NUM are directly preceded on the left by a title e.g.
the division is only rough as the ne sequence yields some number of te related phrases as a side effect of searching for named entities
hnprovenmnt by our method has een measured on NUM ra ndonfly
the phase NUM NUM arsing produces the sub structures in figure NUM
in our formalism those operations are treated by a definite clause program
the bottom up application of the rule schema r is carried out as follows
thus the concepts of nucleus and satellite are treated here as elements of structure
at any point an entity is either new or old to the hearer and either new or old to the discourse
as a further step even with non parallel corpora it should be possible to locate comparable passages of text
the co occurrence frequencies obtained in this way were used to build up the english matrix
thanks also to graham russell and three anonymous reviewers for valuable comments on the manuscript
word n in the english matrix is then the translation of word n in the german matrix
the dotted lines are the minimum and maximum values of each sample of NUM for formula NUM
common algorithms for sentence and word alignment allow the automatic identification of word translations from paxalhl texts
to the knowledge of the author the english and german corpora contain no parallel passages
table NUM when the word orders of the english and
i thank susan armstrong and manfred wettler for their support of this project
thereby different sizes of the two matrices could be allowed for
however we can now distinguish between different part of speech nonterminals
most sentences in the corpus will have multiple possible parses
figure NUM syntactic productions of a stochastic constituent matching itg
figure NUM sample outputs with a coarse bilingual grammar
it is critical under this approach that the english grammar be reasonably robust
similarly all verbs including auxiliaries are grouped to allow simple tail recursive compounding
moreover even for better studied languages parallel bracketed texts are scarce
figure NUM a problematic sentence pair with a generic bracketing grammar
the behavior of a typical training run is shown in figure NUM
given values for c and wi performance can be calculated for both agents using the equation above
for infinite sets of values actual values found in the experimental data constitute the required finite set
besides alparon several other universities and companies in the netherlands are working to improve vios
this can be seen comparing the improvement over baseline for automatic adhoc runs very short queries for manual runs longer queries and for semi interactive runs yet longer queries
looking back at trec NUM and trec NUM one may observe that these improvements appear to be tied to the length and specificity of the query the longer the query the more improvement from linguistic processes
in order to deal with structure the parser s output needs to be normalized or regularized so that complex terms with the same or closely related meanings would indeed receive matching representations
it may be assumed that these advanced techniques will prove even more effective since they address the problem of representation level limits however the experimental evidence is sparse and necessarily limited to rather small scale tests
on the other hand it has to be noted that the character and the level of difficulty of trec queries has changed quite significantly since the last year evaluation
in addition our trec NUM results with long and detailed queries showed NUM NUM improvement in precision attributed to nlp as compared to NUM NUM in trec NUM
in trec NUM where the query length ranged from NUM to NUM valid terms setting n to NUM or NUM including phrasal concepts typically lead to a precision gain of about NUM
this turned out to be a mistake as we rerun trec NUM experiments after the conference only to find out that our results improved visibly when the locality part of the weighting scheme was restored
for example unlawful activity is added to a query trec topic NUM containing the compound term illegal activity via a synonymy link between illegal and unlawful
a subsequent search process will attempt to match preprocessed user queries against term based representations of documents in each case determining a degree of relevance between the two which depends upon the number and types of matching terms
this can be calculated from confusion matrix m since the columns represent the values in the keys
each time the user utters something the sr unit sends an n best list of results to the dm
this requirex a representation of the task s information requirements in terms of an attribute value matrix avm
in order to do systematic work on automatic genre classification
do not blame the user thus not system what you did was illegal
the factors that can contribute to the performance function include any of the cost metrics used in previous work
in this paper we compare what g s and rst say about intentional structure
in the example in figure NUM none of the intentions satisfaction precedes the others
g s makes no claim about the relative ordering between a core and embedded segments
the two theories address different aspects of ordering without suggesting any points of contention
in section NUM NUM we suggest that rst informational relations provide a version of one of these approaches
should all domain moser and moore discourse structure relations across utterances be analyzed in the informational structure
most likely the core of the segment is found in these unembedded utterances
each of the constituent spans may in turn have a structure of subconstituent spans
an instantiated schema specifies the rst relation s between its constituent spans
rst and g s each makes claims about issues not addressed by the other theory
deictic and anaphoric expressions frequently cause problems for natural language analysis
a time interval has a start value and an end value
figure NUM presents a schematic overview of edward s system architecture
edward is implemented in allegro common lisp and runs on decstations
if however the question were woont koen in nijmegen
both the interpreter and the generator operate in an incremental fashion
examples of topological relations are in at and near
explicit references to the spatial environment are references to spatial relations
for the time being this restriction does not cause problems
in edward all linguistic expressions describing spatial relations are interpreted deictically
NUM NUM on the implementation of the dm our ultimate objective is the development of a good dm module as part of the vodis system and we believe that designing a dialogue manager which obeys the NUM commandments as far as possible is a first indispensable step towards that objective
or karlgren and cutting s structural cues struct
however for the cue class questions when the actual initiative shift differs from the norm i.e. speaker retaining initiative for evaluation questions and hearer taking over initiative for domain questions the system s performance worsens
i would like to thank anette frank hans kamp michael schiehlen and the other members of the ims semantics group for helpfull discussion
grice s maxims although having been conceived for a dilterent purpose nevertheless serve the same objective as do tit p inciples namely that of achieving the dialogue goal as directly and smoothly as possible e.g. by preventing questions of claril cation
the results from the comparison will grice s tllaxillls and from the user test suggest lhat the principles of cooperative spoken human machine dialogue lllay represent a step towards a illole or less complete lid practically applicable set of design gtfidelines for cooperative sij s dialogue
even if an slds is able to conduct a perfectly cooperative dialogue it will need to initiate repair and clarification meta communication whenever it has failed to understand the user for instance because of speech recognition or language understanding failure NUM sp9
together with the new specific principles from the user test spi0 and sp11 section NUM NUM is a specific principle of htmmn machine dialogue which may be subsumed by opl NUM initiate repair or clarification meta communicatkm in case of communication failure
gp1 gpi sp1 gpi sp2 gp2 gp3 gp4 gp5 gp6 gp7 gp7 sp3 gp8 gp9 gp i NUM gpi0 sp4 gpi0 sp5 gp11 gp NUM sp6 gpii sp7 gpi2 gpi2 sp8 gpi3 gp NUM p9 gp13 spi0 gpi3 spll generic or specific principle i
the two sections of the table show that no index has been lost by the ti method all of the NUM indexes have been found
lemmas are detected according to their frequency in the observed language sample as well as to their selectivity i.e. how they partition the set of documents
when applying the recall and precision definition to every sections of the rt dictionary we obtained tile average performance scores reported in table NUM over the three dictionaries
in an incremental fashion nps are first selected as possible candidates for term denotation and then inserted in an incremental terminological dictionary according to their mutual information value
in order to apply the standard definition of mutual information we need to extend it to capture the specific nature of the joint event head modifier h m1
as long as we are interested in automatic terminology derivation we can look at terms as surface canonical forms of possibly structured expressions indicating those contents
for example in a banking application having checked the balance in a savings account the user may now wish to transfer money from checking to savings
whenever few matches are found the most efficient way to consummate the query is to enumerate these matches so the user can the select the one of interest
we have attempted to clearly define a comprehensive set of states to handle various contingencies including out of bounds queries meta queries ambiguities and inconsistencies due to user system errors
in case the user s utterance has missing ambiguous inconsistent or erroneous information the system engages the user in a dialogue to resolve these
we argue here that although plan based systems are very useful for problem solving tasks like the ones described earlier that degree of sophistication is not needed for ia tasks
pragrnatics component this component is responsible for identifying the values of relevant fields that are specified in the utterance based on the partial parse of the utterance
given the grammar we need criteria to decide which surface forms that reflect the typical structure of a potential terms are actual lexicalizations of relevant concepts of the corpus
as a result the simple parser obtains several complex nominals but only as syntactic structures so that it fails in detecting higher order syntactic links i.e.
two additional classifiers were evaluated to serve as benchmarks
aic exhibits only minor deviation from bss fss
for verbs it indicates the tense of the verb
there are NUM possible values for the sense variable
the log likelihood ratio statistic g NUM is defined as
to calculate maximum likelihood estimates of the model parameters
this includes all nodes that are not free for f as well 7we will not elaborate here on the encoding of categories in l NUM k p nor on non finite id schema like the iterating co ordination schema
this is a reasonably natural diagnostic for context freeness in gb and is close to common intuitions of what is difficult about head raising constructions it gives those intuitions theoretical substance and provides a reasonably clear strategy for establishing context freeness
generative grammar and formal language theory share a common origin in a procedural notion of grammars the grammar formalism provides a general mechanism for recognizing or generating languages while the grammar itself specializes that mechanism for a specific language
moreover the fact that language theoretic complexity classes have dual automata theoretic characterizations offered the prospect that such results might provide abstract models of the human language faculty thereby not just identifying these regularities but actually accounting for them
for bar0 d pas which says that bar NUM nodes are by default not marked passive we get
in the light of our interpretation of antecedent government one can understand the role of minimality in l izzi s and manzini s accounts as eliminating ambiguity from the sequence of relations connecting the gap with its filler
in this way generative grammar in concert with formal language theory offers insight into a deep aspect of human cognition syntactic processing on the basis of observable behavior the structural properties of human languages
the immediate benefit is the fact that it is clear that the property of satisfying a set of fsds is a static property of labeled trees and does not depend on the particular strategy employed in checking the tree for compliance
over time the two disciplines have gradually become estranged principally due to a realization that the structural properties of languages that characterize natural languages may well not be those that can be distinguished by existing language theoretic complexity classes
formally denoting sequences of nodes by l we threshold node n if tg relax p l max p l linel now the hard part is determining p l the probability of a node sequence
precision measures how many of the retrieved documents are indeed relevant
NUM we propose here a probabilistic model of noun phrase parsing
dependency model provides a substantial advantage over the adjacency model
naturally the author alone is responsible for all the errors
information retrieval provides a good way to quantitatively although indirectly evaluate various nlp techniques
each run involves an automatic feedback with the top NUM documents returned from the initial retrieval
we only consider noun phrases and the subphrases derived from them
it is thus desirable to test the noun phrase parsing technique in other and larger collections
each chunk has about NUM NUM or about NUM NUM unique raw multiple word noun phrases
NUM this forces us to split the whole noun phrase corpus into small chunks for training
the grammarian can define constants and predicates using regular expressions
there are two main methodologies the linguistic and the data driven
one is a grammar specifically developed for resolution of part of speech ambiguities
an explicit difference is made between finite and nonfinite clauses
currently tagger evaluation is only becoming standardised the evaluation method is accordingly reported in detail
if none of the pattern rules apply a nominal reading is assigned as a default
the linguistic approach is labour intensive skill and effort is needed for writing an exhaustive grammar
ideally part of speech disambiguation should fall out as a side effect of syntactic analysis
the grammatical representation used in the finite state framework is an extension of the engcg syntax
a new tagger of english that uses only linguistic distributional rules is outlined and empirically evaluated
sequential searches evaluate models of increasing fss or decreasing bss levels of complexity where complexity is defined by the number of interactions among the feature variables i.e. the number of edges in the graphical representation of the model
during the training process the user with the help of a graphical user interface takes a few prototypical articles from the domain that the system is being trained on and creates rules patterns for the target information contained in the training articles
by splitting the word abc in abc d e co s into a b c ab c or a bc we can make another three tokenizations in to s a b c d ab c d and a bc d
note that the only difference between this is his book and th is is his book is that the word this in the former is split into two words th and is in the latter
on the other hand if y is a subtokenization of x by definition for any word x in x there exists a substring ys of y such that x g ys
the frequencies of these marginals f f1 fl s si and f f2 f2 f3 f3 s si are sufficient statistics in that they provide enough information
sp ibm corporation ng l company generalized at degree NUM generalize sp NUM lcb business concem rcb generalized at degree NUM generalize sp NUM lcb enterprise rcb generalized at degree NUM generalize sp NUM lcb organization rcb generalized at degree NUM generalize sp NUM lcb group social group rcb
for instance you could say character x has many written forms or the character x in this word can be omitted for any character x NUM NUM even so some researchers might still insist that the character x here is just for temporary use and can not be regarded as a regular word with the many linguistic properties generally associated with words
generalization consists of replacing each concept in a rule by a more generalized concept obtained from wordnet
the in dexed lookup is most satisfactory not only has the absolnte time dropped m order of magnitude but the time appears to be constant when eorpns size is varied between NUM and NUM mb
in figure NUM the outside probability of lr which is inscribed in the bold box is computed by summing the products of the inside probabilities in the boxes under the white headed arrow and the outside probabilities in the boxes under the black headed
the precision and recall curves with respect to the degree of generalization are shown in figure NUM and figure NUM respectively
corpus based stochastic grammar induction has many profitable advantages such as simple acquisition and extension of linguistic knowledges easy treatment of ambiguities by virtue of its innate scoring mechanism and fail soi reaction to ill formed or extra grammatical sentences
the best parse can also be found by the maximum st l eos because the lr bos eos is always composed of sr bos bos and sl l eos
the tokenizer accepts ascii characters as input and produces a stream of tokens words as output
training is done by a user with the help of a graphical user training interface
the explosion in the amount of free text materials on the internet and the use of this information by
so solid line arrows replace the two dashed arrows pointing to bg suggested in NUM by tile longer dashes
the h link relation corresponds to the obligatory solid line arrows in the graphs s link reflects defeasible dashed line arrows
for example let us assume that noun phrases consist of an optional determiner d any number of adjectives a and one or more nouns n
a symbol pair a x may be thought of as the crossproduct of a and x the minimal relation consisting of a the upper symbol and x the lower symbol
stands for any symbol in the known alphabet and its extensions in replacement expressions marks the start left context or the end right context of a string
for example in processing an sgml coded document we may wish to delete all the material that appears or does not appear in a region bounded by certain sgml tags say a and a
although a unique substring is selected for replacement at each point in general the transduction is not unambiguous because lower is not required to be a single string it can be any regular language
NUM even non f marked constituents embedded in an f marked constituent like em buch in NUM have to pass this givenness filter
the one path with dannvaan on the upper side is NUM NUM d NUM a3n4n40 5v00 7a3a3a40 NUM
de pesants filences ce qtte fencyclique dit mr le raeisme fimpire de ee que john i a farge a d sj 6crit pour tigmatiser le r acinne anti noir qui 6vit aux etals uni
the solution to this problem is disambiguation to find the right entry in the dictionary a partof speech post disambiguator is applied be brc morphological analysis in order to obtain the eontextually most plausible morphological analysis
the cascading guesser outperformed the guesser supplied with the xerox tagger by about NUM NUM and the guesser supplied with brill s tagger by about NUM NUM
although in general the performance of the cascading guesser is only NUM worse than the lookup of a general language lexicon there is room for improvement
when using the xerox tagger with its original guesser NUM unknown words were incorrectly tagged and the accuracy on the unknown words was measured at NUM NUM
we can merge two rules which have scored below the threshold and have the same affix or ending and the initial class NUM
the accuracy of the taggers on the set of NUM unknown words when they were made known to the lexicon was detected at NUM NUM for both taggers
the cascading guesser performed better than brill s original guesser by about NUM boosting the performance on the unknown words from NUM NUM 1deg to NUM NUM
rules which have scored lower than the threshold 0s can be merged into more general rules which if scored above the threshold are also included into the final rule sets
note also that since NUM itself is smoothed we will not have zeros in positive NUM or negative NUM NUM outcome probabilities
thus reuse of nlp software components can be defined as an integration problem
lcb zaj ac mc asper nige NUM rcb c crl
a life cycle service provides creation copying moving and deletion of objects
NUM the data layer involves exchange and translation of data structures between components
at the linguistic level components need to share the same interpretation of the data they exchange
a generic nlp architecture needs to address component communication and integration at three distinct levels NUM
the architecture provides a top application object class that can be sub classed to define specific application objects
the file based version of the corelli document processing architecture will be made freely available for research purposes
client side application component api calls on remote object references requested from the orb
the shift from computational linguistics to language engineering is indicative of new trends in nlp
inputs and outputs may include the empty symbol e
NUM the sounds are converted into katakana
we originally thought to build a general letter to sound wfst on the theory that while wrong overgeneralized pronunciations might occasionally be generated japanese transliterators also mispronounce words
our japanese sound inventory includes NUM symbols NUM vowel sounds NUM consonant sounds including doubled consonants like kk and one special symbol pause
a similar compromise must be struck for english h and f also japanese generally uses an alternating consonant vowel structure making it impossible to pronounce lfb without intervening vowels
our NUM NUM entry frequency list draws its words and phrases from the wall street journal corpus an online english name list and an online gazeteer of place names
an english sound sequence like p r ow pause s aa k er might map onto a japanese sound sequence like p u r o pause s a kk a a
simply identifying which arabic words to transliterate is a difficult task in itself and while japanese tends to insert extra vowel sounds arabic is usually written without any short vowels
katakana writing is a syllabary rather than an alphabet there is one symbol for ga i another for gi 4e another for gu p etc
the results were as follows correct e.g. spencer abraham spencer abraham phonetically equivalent but misspelled e.g. richard brian there is room for improvement on both sides
he walked on the opposite side of the street from her using a zoom lens he had already shot a whole roll of film
nevertheless this short segment presents most vividly in the result of textural filter shown in figure NUM b
an additional list of some NUM NUM english person names and chinese translations are used to enhance the coverage of proper nouns in the bitext
he knew his quarry was elusive and self protective there were few candid pictures of her which was what would make these valuable
this paper describes a new program plotalign that produces a word level bitext map for noisy or non literal bitext based on these techniques
we are currently looking at the possibilities of exploiting powerful and well established ip techniques to attack other problems in natural language processing
the filtering based on hough transform contrary to the other two filtering methods prefers connection that is consistent with other connections globally
performing the alignment task as image processing proves to be an effective approach and sheds new light on the bitext correspondence problem
we contend that the bcp when seen in this light bears a striking resemblance to the line detection problem in ip
table NUM the results of the robust parser on wsj
in addition people are prone to mistakes in writing sentences
heuristics NUM fiducial nonterminal people often make mistakes in writing english
the other is a robust parser with the error recovery mechanism proposed herein
it shows that the proposed heuristics is valid in parsing the real sentences
table NUM the results of the robust parser on atis
not even a day does the progress of machine stop
the progress of machine does not stop even a day
the overview of the system is shown in figure NUM
i am thankful to my thesis advisor dr t furugori and the anonymous referees for their suggestions and comments
using the number of occurrences obtained we calculate the mutual information for the concepts in the taxonomic hierarchy
in order to calculate the mutual information for any modifier particle modificant pattern we use the conceptual dictionary cd to build a taxonomic hierarchy of the modifiers which occur the conceptual dictionary is a set of graphs consisting of NUM NUM concepts and a number of taxonomic as well as functional relations between them
then comparing these two taxonomic hierarchies one for the modifiers in the cod one for the modifers in the sentence we look for the concept identifier common to both hierarchies that has the highest mutual information
we have applied our method to NUM sentences taken from a leading newspaper and included with rdg software
the arcs in the following example show modifier modificant relations which can be combined into six different dependency structures
if the pattern is not present backing off we search this information for the modificant only
since all structures have the same number of relations this multiplication reflects the likelyhood of the structure
morphologic analysis is rerun according to the compiled td
it takes as input dependency structures generated by rdg for a sentence finds all of modifier particle modificant relations calculates their mutual information and chooses the structure for which the product of the nmtual information of its relations is the highest
to calculate the mutual information for each relation we obtain form the cod the conceptual identifiers a numerical code for the modifiers that appear with the particle modifica t and the number of their occurrences in the corpus
it simply specifies that for some word formation processes only a subset of tlrs should be considered
because the system is intended for children we need careful control of the cognitive demands made on the system s users
hiya howdy hello and the four other utterances that are available to close a conversation see you later goodbye see ya
such an analysis may also be useful for subsequently predicting and locating which of these pre loaded utterances is likely to be required next during a particular conversation
oops or what or i do n t have anything to say to that and discourse connection to signal a topic shift e.g.
evaluating the performance of explanation systems is a critical and nontrivial problem
lester and porter robust explanation generators table NUM library of knowledge base accessors
response pollen tube growth is a step of angiosperm sexual reproduction
figure NUM explanations produced by k night from the biology knowledge base
second sometimes a domain knowledge engineer installs inappropriate values on legal attributes
given a set of functional descriptions fuf constructs the final text
functional object finds functional view of process object with respect to process
tures encountered in large scale knowledge bases without failing halting execution
organization the extent to which the information is well organized
response embryo sac formation is a kind of female gametophyte formation
in general the adjustment involves either interpolation in which the mle is used in linear combination with an estimator guaranteed to be nonzero for unseen word pairs or discounting in which a reduced mle is used for seen word pairs with the probability mass left over from this reduction used to model unseen pairs
the jaccard measure is defined as the number of attributes shared by two objects divided by the total number of unique attributes shared by both objects the jaccard scores for each corelex class are sorted and the class with the highest score is assigned to the noun
consider the senses that wordnet assigns to door gate and window at the top of the wordnet hierarchy these seven senses can be reduced to two unrelated basic senses window and gate obviously these are similar words something which is not expressed in the wordnet sense assignments
the unseen pairs were further divided into five equal sized parts t1 through NUM which formed the basis for fivefold cross validation in each of five runs one of the ti was used as a performance test set with the other NUM sets combined into one set used for tuning parameters if necessary via a simple grid search
linguistic filters of morphological nature are also applied
when an adjective like old has different senses that are associated with different antonyms like new and young the adjective in these sentences is disambiguated by its antonym
NUM NUM experiment NUM effectiveness of the
via troponymy we did not exemplify what is meant by redundancy caused by inheritance via the generic and meronymic relations for nouns of
these taggers are preferred when tagged texts are available for training and large tagsets and multilingual applications are involved
gakusei student and konpyuutaa computer respectively from the to operate sense of tsukau
the bank has already obtained a permission from the city authority sources say
size is an ontological concept of the scalar physical object a frribute property type with the term scalar used here as it is customarily primarily in the sense of gradable
now none of them is anchored in a scale type property concept however which makes them all technically non scalars but in more practical and customary terms non true scalars
these include methods of reducing unnecessary polysemy gauging the optimal grain size and determining the meaning not the least of which is selecting the ontological concept in which the entry is anchored
the former pattern corresponds to the attributive use of the adjective the noun it modifies is assigned the variable varl and the adjective itself the variable var0 in the modifier position
as exemplified in NUM NUM each case related sub lr operates in a well defined way placing varl in the sem struc zone in the value slot of the appropriate case of the event frame
we have foundlvirtually no lr to be exception free and that reduces of course the degree to which a large lr can be used fully automatically thus raising the cost of their application
in this section we introduce our notion of lexical rules lrs place their origin in the descriptive methodology and heuristics of large scale lexical acquisition and establish some usability parameters for them
NUM the bank has already obtained a permission from the city authority sources say
this work belongs to a family of research efforts called microtheories and aimed at describing the static meaning of all lexical categories in several languages in the framework of the mikrokosmos project on computational semantics
let t d be a complete set of non overlapping text segments comprising a document d
in practice the prediction error differences measured for all languages taggers and tagsets were less than NUM NUM percent
cue prosody is assigned complex if NUM before sentence final contour
distribution of the main grammatical classes of the known and unknown words and the words occurring only once in french text
most of them conzain only very small number of words
both results are achieved by this phase of the grammar both nonprojective constructions and negative symbols are allowed
this statement is even more true with respect to grammar checking of the so called free word order languages
this property of the interpreter is used together with other kinds of pruning techniques in all phases of grammar checking
the first possible solution is to relax the constraints in certain order to apply a hierarchy on constraints
let us present a slightly modified sentence from the previous paragraph karlovy ena zal6vala kv tiny
introduction automatic grammar checking is one of the fields of natural language processing where simple means do not provide satisfactory results
in the present paper we describe the basic ideas behind an implementation of a prototype of a grammar checker for czech
chairman of christian democrats mister benda in telephone discussion with petr pithart enforced ing
the same holds about the union of deletable and nondeletable symbols and also about the union of positive and negative symbols
in case that a particular rule may be applied to items a and b a new item x is created
results confirm intuitive hypothesis on the role of selectional restrictions and show evidences for a wordnet iike organization of lexical senses
versions automatically created are then tested against manually acquired data with the aim of incrementally improve the precision level
word class based language model is more competitive than word based language model
in this section we describe the architecture we used for checking wordnet usability in parsing
this sentence was selected because it produces an high number of lectures NUM among the test suite sentences
note that each of these senses are per se valid arguments since they satisfy the selectional restrictions
a number of steps have been followed to add selectional restrictions to italian wordnet
observe that the syntactic analysis does not take into account the ppattachment case
we started with general selectional restrictions and then we validate them against experimental results
part of this work was done while visiting at harvard university supported by onr grant n0001496 NUM NUM
in other words of the words correctly classifted by one method only about half can also be also classified correctly by the other method
his method has several advantages over ours such as no co occurrence data is required not so much computational overhead is required
as above the initial weight vector is typically set to assign equal weight to all features
experiments using the japanese bunruigoihy5 thesaurus on about NUM NUM co occurrences showed that new words can be classified correctly with a maximum accuracy of more than NUM
our experiments showed that new words can be classified correctly with a maximum accuracy of more than NUM when the category based search strategy was used
table NUM results for the k nn approach 4note that we ignore lower digits and therefore lea means the categories formed by NUM digit class code
we hypothesize that the improved results are due to reduction in the noise introduced by irrelevant features
k nn each noun is considered as a singleton cluster and the probability that a target noun is classified into each of the non target noun clusters is calculated
in case of bad recognition rates speech recognizers already deliver confidence scores the dialogue manager could ask for acoustic clues concerning the recognition conditions
the algorithm is given as input a range of weight value which we call the filtering range
argument NUM the painter possesses the semantic roles source and controller
figure NUM lexical entry for suffix ingn and gener
most lexicons today are constructed within the framework of some syntactic theory
syntactic mapping rules are rules that derive syntactic properties from conceptual structures
the whole theory is now being implemented in the troll lexicon project
structuring the lexicon in inheritance hierarchies opens for more compact lexicon representations
NUM x if y composition s
figure NUM stored entry for minimal sign paint
figure NUM stored entry for minimal sign walk
text documents vary widely in their length and a text classifier needs to tolerate this variation
in the long version of the paper we present a more thorough analysis of this issue
the questions are what attributes distinguish one word from another
however because extraction will be a major component of many systems built using the architecture this section describes how extraction fits into the current architecture
the problem of missing roots becomes important when the texts processed belong to a different area than the one used during the building of the lexicon
figure NUM illustrat ion of alignments for the nlonotone hmm
it would be useful to have dynamic lexicons which evolve in accordance with the corpora processed in order to limit as much as possible the oov words
such an enhancement of lexicons could be automatic if big corpora of specialised texts were available medical reports in an electronic form newspaper available in cd rom etc
the text corpus chosen contained NUM NUM words of which NUM NUM were forced oov words these NUM NUM occurrences represent NUM different forms
it is therefore the consideration of the context that allows us to attribute a reliable probability to the likelihood of an oov proper name belonging to a specific class
the presence of oov words in the corpus can produce errors not only in the form itself but also in its context in the sentence
the lack of static coverage of our general lexicon is NUM NUM NUM NUM for the oov common words and NUM NUM for the oov proper names
at present no method can find the optimal word classification
nonetheless inflected languages can have a huge number of affixes that determine the syntactic function of each word and therefore it is not possible to include every variation of a word in the dictionary
for each leftnodeprev e leftprev for each production instance prod from leftnodeprev of size length for each descendant l of prodlelt for each descendant r of prodright for each descendant p of prodpar n such that p l r from each first pass nonterminal to a set of second pass nonterminals and threshold out those second pass nonterminals that map from low scoring first pass nonterminals
also for this algorithm although not for most experiments our measurement of time was the total number of productions searched rather than cpu time we wanted the greater accuracy of measuring productions
according to folk wisdom the best way to measure the likelihood of a node n k is to use the probability that the nonterminal x generates the span tj tk called the inside probability
i would also like to thank michael collins rebecca hwa lillian lee wheeler ruml and stuart shieber for helpful discussions and comments on earlier drafts and the anonymous reviewers for their extensive comments
we can use information from this simple fast first pass to eliminate most states and then run a more complicated slower second pass that does not examine states that were deemed unlikely by the first pass
for instance if an agenda based system first computes the probability for a production s np vp and then later computes some better probability for the np it must update the probability for the s as well
null a set of NUM manually annotated training sentences step NUM was sufficient for a statistical tagger to reliably assign grammatical functions provided the user determines the elements of a phrase and its category step NUM
the information given for selbst besucm sabine is the sequence of categories adverb adv past participle cases in which the tagger assigned a correct phrase category or would have assigned if a decision is forced
noun phrases do not necessarily have a unique head instead we use the last element in the noun kernel elements of the noun kernel are determiners adjectives and nouns to mark the anchor position
the NUM NUM sentences were used to train the tagger for automation step NUM together with improvements in the user interface this increased the efficiency by another NUM from approximately NUM to NUM minutes NUM words per hour
the thresholds for search beams were set to NUM NUM and NUM NUM i.e. a decision is classified as reliable if there is no alternative with a probability larger than NUM NUM of the best function tag
as this encoding strategy is not well suited to a free word order language like german we have focussed on a less surface oriented level of description most closely related to the lfg f structure and representations used in dependency grammar
if some of these probabilities are close to that of the best sequence the alternatives are regarded as equally suited and the most probable one is not taken to be the sole winner the prediction is marked as unreliable in the output of the tagger
in the error analysis the following sources of misinterpretation could be identified insufficient linguistic information in the nodes e.g. missing case information and insufficient information about the global structure of phrases e.g. missing valency information
lcb quite adj calm adj interest n excitement n shrill n rcb lcb liken fancy v attraction n appeal n interest n rcb lcb lend v loan v interest n investment n share n rcb lcb entertain v amuse v game n hobby n interest n rcb
posd lcb a det share n in prep a det company n business n etc adv rcb keyd lcb share n company n business n rcb keydi NUM wshare n wcompany n wbusiness n NUM l lcb del05 hb037 je114 rcb NUM NUM NUM NUM NUM lcb cc042 co292 jh225 rcb NUM NUM NUM NUM NUM lcb gh243 jd138 jh225 rcb NUM NUM NUM
the 4th and 5th sense are metonymically associated with two star senses star NUM n NUM a NUM or more pointed figure and start l n NUM a heavenly body such as a planet respectively
the labels can be used as a coarser sense division so unnecessarily fine sense distinction can be avoided in word sense disambiguation wsd the algorithm is based primarily on simple word matching between an mrd definition sentence and word lists of an lloce topic
in the present paper we have interpreted f structures as udrss and illustrated with a simple example how the deductive mechanisms of udrt can be exploited in the interpretation
if lq and let are the labels associated with the quantificational structure and the containing clause respectively then the constraint lq let enforces clause boundedness
to translate a udrs ps c merge the structural with the content constraints into the equivalent t e u c
we describe a method for interpreting abstract fiat syntactic representations lfg fstructures as underspecified semantic representations here underspecified discourse representation structures udrss
the language of udrss is based on a set l of labels a set ref of discourse referents and a set rel of relation symbols
in one of them coverage is
zlff daws found that the opp ss text an extremely encouraging result
putting into lexical entries the kind of refined subcategorization information that we often do using features is not possible or at least not possible without expanding the vocabulary of symbols like npl pp2 etc to induce a finer partition among instances of the categories in question
if we assume that they do not which can usually be enforced by defining a new shadow feature that simply duplicates the information where it is needed then an attractive and clean way of implementing this technique is as a conditional constraint on feature values
from this declaration it is easy to compute the array representing the reflexive transitive closure of immediately dominates now it is easy to precompute for each atomic type represented by a row of the array a vector like that for bool comb feature
NUM NUM NUM NUM NUM NUM a b c a b c in building the term representing a particular boolean combination of values what we do is work out for each of these positions whether or not it is excluded by the boolean expression
to encode the information in this lattice in a form where glb and lub can be computed via unification we first make an array representing the reflexive transitive closure of the immediately dominates relation which is pictured in the diagram above by lines
and or np the trick is again in the unification of the value of the feature next on the daughter of rule NUM and the mother of rule NUM this unification extends the number of daughters that rule NUM is looking for
we will assume that such macros are defined in ways suggested by the following examples and that at compile time the arguments if any of the defined macro are unified with the arguments of the instance of it in a rule or lexical item
for example lcb fl x f2 yes f3 lcb f4 l f5 x rcb rcb coreference is indicated by shared variables in the preceding example f l and f5 are constrained to have the same value
by adding a line from exports to person either the slave trade or the brain drain we get a situation where exports and living no longer has a greatest lower bound although this would be a perfectly natural inheritance link to want to add
as the subject matter becomes more and more homogeneous the number of subject breaks the algorithm finds decreases
we describe here an algorithm for detecting subject boundaries within text based on a statistical lexical similarity measure
such a proper noun is treated as a NUM way ambiguous word person organization or location
unnecessarily subtle distinction between word senses is a well known problem for evaluating wsd algorithms with general purpose lexical resources
step b search the local context database and find words that appeared in an identical local context as w
this means that when the algorithm makes mistakes the mistakes tend to be close to the correct answer
in the second case there is a way of breaking up the largest aggregate of foliage into sub aggregates of foliage and the largest aggregate of wiring into sub aggregates of wiring such that each sub aggregate of foliage that is each pile of foliage is touching some sub aggregate of wiring that is some bundle of wiring and each sub aggregate of wiring is being touched by some sub aggregate of foliage
the approach presented offers a technical framework that allows a deep generation process to abstract away from many idiosyncrasies of linguistic knowledge by virtue of meaningful weighting functions
it can be influenced with regard to the element in the conflict set to be processed next and the backtrack point to be processed next
the rules can be annotated by equations that either assert equality of a feature s value at two or more constituents or introduce a feature value at a constituent
tgl rules can and should be written with generation in mind i.e. the goal of reversibility of grammars pursued with many constraint based approaches has been sacrificed
this causes the decision to be nondeterministic unless the reasons for the difference are learned and applied to the case at hand
the criteria are defined in terms of rule names and a criterion is fulfilled if some corresponding rule is successfully applied
if an agreement feature receives a different value during backtracking and it relates to material outside the ego inflectional processes for that material must be computed again
although the level of logical form is considered a good candidate for an interface to surface realization practice shows that notational idosyncrasies can pose severe translation problems
all other entries of the table are updated and the set of additional solutions can be read off straightforwardly from the entry of the backtrack point just processed
NUM finally there is the question of how one can restrict the range of candidate lexical items that have to iv be considered at each point in processing and how candidates can be weighted appropriately
assigning a parse structure to the german sentence NUM involves addressing the fact that it is syntactically ambiguous NUM eine hohe inflationsrate erwartet die konomin
we are aware of no attempt in the literature to determine aspectual information on a similar scale in part we suspect because of the difficulty of assigning features to verbs since they appear in sentences denoting situations of multiple aspectual types
however unless one of the sequences is empty variability is possible in the trees that can be produced
it is a bottom up step that concatenates the boundaries of a fully recognized initial tree with a partially recognized tree
this improvement is very important because ig typically is much larger than n for natural language applications
this was a regrettable oversight on our part which undoubtedly hurt recal l for co te and st given the importance of organization entities throughout
used to differentiate the true meaning from the meanings of the previous NUM sentences the program selected correctly NUM NUM of the time or ranked the true meaning tied for first NUM NUM of the time
the privative feature model on which our lcs composition draws allows us to represent verbal and sentential lexical aspect as monotonic composition of the same type and to identify the contribution of both verbs and other elements
this section deals with the computational problem of learning string transformations from an aligned corpus
in this spirit levin and rappaport hovav to appear demonstrate that limiting composition to aspectually described structures is an important part of an account of how verbal meanings are built up and what semantic and syntactic combinations are possible
similarly from table NUM the average number of senses per polysemous word in the wall street journal corpus for the remaining NUM word occurrences is only NUM NUM or less
i also estimate the amount of human sense tagged corpus and the manual annotation effort needed to build a largescale broad coverage word sense disambiguation program which can significantly outperform the most frequent sense classifier
we can readily collapse the refined senses of wordnet into a smaller set if only a coarse hot i will only focus on common noun in this paper and ignore proper noun
my estimate of the amount of human annotation effort needed can be considered as an upper bound on the manual effort needed to construct the necessary sense tagged corpus to achieve wide coverage wsd
the performance of lexas as indicated in table NUM is significantly better than the most frequent sense classifier for the set of NUM words collected in our corpus
although a number of levin s verb classes were aspectually uniform many required subdivisions by aspectual class most of these divided atelic manner verbs from telic result verbs a fundamental linguistic distinction cf
to justify the investment of manpower and time to gather a large sense tagged corpus it is important to examine the benefits brought about by wsd
finally i suggest that intelligent example selection techniques may significantly reduce the amount of sense tagged corpus needed and offer this research problem as a fruitful area for word sense disambiguation research
that is if we succeed in working on the harder wsd task of resolution into refined senses the same techniques will also work on the simpler task of homograph disambiguation
the topic shifted by authors from paragraph to paragraph is demonstrated through comparison of data shown in this row and row NUM
application phase the application module am basically performs the following steps NUM retrievah for a new mrs mrs we first construct the alphabetically sorted generalized mrs mrsg
the main focus of this paper is tactical generation i.e. the mapping of structures usually representing semantic information eventually decorated with some functional features to strings using a lexicon and a grammar
however he focuses on the compilation of a logic grammar using lr compiling techniques where ebl related methods are used to optimize the compiled lr tables in order to avoid spurious non determinisms during normal generation
in other words the decision tree is now a finite representation of an infinite structure because implicitly each endpoint of an index bears a pointer to the root of the decision tree
in the next step of the training module tm the generalized mrs mrs information of the root node of tempi mrs is used for building up an index in a decision tree
in case of normal processing our ebl method serves as a speed up mechanism for those structures which have sebl based generation of all possible templates of an input mrs is less than NUM seconds
the research underlying this paper was supported by a research grant from the german bundesministerium f jr bildung wissenschaft forschung und technologie bmb f to the dfki project paradime fkz itw NUM
figure NUM a blueprint of the architecture
the text structure is transformed by the sentence planner which can aggregate the syntactic representations cf
in the absence of such restrictions computational linguists have assumed convenient ones
the price of this creative ferment has been a certain lack of rigor
according to the language model mentioned in section NUM we build the ann and anv values for each noun noun pair and noun verb pair
NUM bestpaths s0 NUM c1 we need only consider
this is mildly unfortunate for otp and for the ot approach in general
the dialogue memory consists of three layers of dialog structure NUM an intentional structure representing dialogue phases and speech acts as occurring in the dialogue NUM a thematic structure representing the dates being negotiated and NUM a referential structure keeping track oflexical realizations
these are simple and arguably natural constraints no others are used
it is an important question whether these formalisms are useful in practice
none of these questions is well posed without restrictions on gen and con
a brute force approach fails to terminate if gen produces infinitely many candidates
experiments in section NUM demonstrate that for most applications this is not only not a problem but desirable
the text filtering definition of precision is different from the information extraction definition of precision the latter definition includes an element in the formula that accounts for the number of spurious template fills generated
similar tradeoffs and upper bounds on performance can be seen in the tst2 and tst4 results see score reports in sections NUM and NUM of appendix g in NUM
any participant in a future muc evaluation faces the challenge of providing a named entity identification capability that would score in the 90th percentile on the f measure on a task such as the muc NUM one
the annotators problems with onzi he job were probably more substantive since the heuristics documented in the appendix were complex and sometimes hard to map onto the expressions found in the news articles
task and on walkthrough article to keep the annotation of the evaluation data fairly simple the muc NUM planning committee decided not to design the notation to subcategorize linkages and markables in any way
other sources of excitement are the spinoff efforts that the ne and co tasks have inspired that bring these tasks and their potential applications to the attention of new research groups and new customer groups
identification of certain common types of names which constitutes a large portion of the named entity task and a critical portion of the template element task has proven to be largely a solved problem
scenario template st drawing evidence from anywhere in the text extract prespecified event information and relate the event information to the particular organization and person entities involved in the event
table NUM contains a paraphrased summary of the output that was to be generated for each of these events along with a summary of the output that was actually generated by systems evaluated for muc NUM
we have left out aspects of lambert s model which are too knowledge intensive to get the kind of coverage we need
while this study only explores the structure of negotiation dialogues its results have implications for other types of discourse as well
this study indicates that it is not a structural property of discourse that attentional state is constrained to exhibit stack like behavior
in lambert s model the focus stack is represented implicitly in the rightmost frontier of the plan tree called the active path
with standard tst the inference chain for sentence NUM would no longer be on the active path when sentence NUM is processed
we argue that rather than propose an additional mechanism it is more perspicuous to lift the restriction that pot int
development of our discourse processor was based on a corpus of NUM spontaneous spanish scheduling dialogues containing a total of NUM sentences
for example deliberation over how to accomplish a shared plan can be represented as an expression of multiple pot int
in these cases it randomly picks a speech act from the list of possible speech acts returned from the matching rules
be fully explicit in communicating to users the commitments they have made
we ihen argue that the renlaining principles express additional aspects of cooperativity
we first describe how the principles were developed section NUM
this paper analyses the relationship between our principles and grice s maxims
a total of NUM different subjects were involved in the seven iterations
dialogues were based on written descriptions of reservation tasks scenarios
a major concern during woz was to detect problems of user system interaction
fake partners relevant b ack round knowledge into account
example NUM g so you re at a point that s probably two or three inches away from both the top edge and the left hand side edge
the goal is to find an unsupervised method for tagging that relies on general distributional properties of text properties that are invariant across languages and sublanguages
here we use the raw NUM dimensional context vectors and apply the svd to the NUM NUM by NUM matrix NUM NUM words with two NUM dimensional context vectors each
the arrow between each pair of words in the table indicates the direction of influence or flow of information
similarly the lack of normalisation of mis hampers direct comparison of scores with the three dim s
NUM NUM in effect this means that the context of a word serves to restrict its sense
however most attempts at formalising the intuitive notion of context tend to treat the word and its context symmetrically
the recogniser may further associate a confidence value with each of its word choices to communicate finer resolution in its output
context in order to exploit contextual information in the classification of a token we simply use context vectors of the two words occurring next to the token
one way to do this is to let the right context vector record which classes of left conte t vectors occur to the right of a word
svd addresses the problems of generalization and sparseness because broad and stable generalizations are represented on dimensions with large values which will be retained in the dimensionality reduction
there are hardly any distributional clues for distinguishing vbn and prd since both are mainly used as complements of to be s
the case of the tags vbn and prd past participles and predicative adjectives demonstrates the difficulties of word classes with indistinguishable distributions
that only the immediate neighbors are crucial for categorization is clearly a simplification but as the results presented below show it seems to work surprisingly well
in it s a fun thing to do has properties of both a noun and an adjective superlative funnest possible
since it is possible to tell uncontroversially from the video what the route follower drew and when they drew it reliability has only been tested for the other parts of the transaction coding scheme
where in the past the ultimate goal of mt seemed to be to provide a perfect but cheaper and faster alternative to the human translator there is now a clear shift from the ideal of fully automated high quality translation of unrestricted texts to the more practical problem of overcoming the language barriers we encounter in various situations
in the translation application we search for the highest probability derivation or more generally the nhighest probability derivations
we represent events and contexts by finite sequences of symbols typically words or relation symbols in the translation application
let n elc be the count for choice e c leading to negative solutions
table NUM shows the results of evaluating the performance of these models for translating NUM unrestricted length atis sentences into chinese
taken together the events contexts and cost function constitute a process cost model or simply a model
correct translations and n c be the number of times context c was encountered for these solutions
the model is suitable for incremental application of lexical associations in a dynamic programming search for optimal dependency tree derivations
in section NUM we present reversible mono lingual models consisting of collections of simple automata associated with the heads of phrases
there are three types of action for an automaton m left transitions right transitions and stop actions
model parameters for head automata together with dependency parameters and lexical parameters give a probability distribution for derivations
in particular they suggest that the use of a general purpose linguistic rule component and a transfer architecture in combination with statistical information derived from supervised training on corpora make most of the slt system portable across domains and even languages and the remaining non portable parts of the system are such that they require relatively little expert knowledge
the database contains some further redundancies by inheritance via troponymy
an extension mixture model is an extension model whose lw parameters are estimated by linearly interpolating the empirical probability estimates for all extensions that dominate w with respect to c ie all extensions whose symbol is and whose context is a suffix of w
next we encode the model c in three parts the context dictionary as l d the extensions as l eid and the conditional frequencies c as l e d e
nlp technology included a crl developed spanish morphology tool to enable sophisticated searches of on line resources and used lexical indexing for electronic dictionaries and thesauri
users can also limit the search to only those concordances either containing or missing specified strings in the context to the left or right of the keyword
research in human computer interaction suggests that a user centered task oriented approach is the most appropriate method for developing interfaces that deliver new technology to an existing workforce
because the goal has been to get technology into the hands of users in ways that meet their needs crl has focused on user testing that motivates feature development and system enhancements
figure NUM shows the crl s dictionary tool provides users with an integrated and easily accessed interface to a wide variety of on line fixed reference material
using an implementation of the boyer moore search algorithm specially adapted for wide characters x concord can search at over 1mb per second eliminating the need for pre indexing on many moderate scale corpora
task analysis of their work showed that a large part of their effort consisted of gathering retrieving and analyzing text in context and current authentic texts were extremely useful
a significant portion of crl s research involves work on a variety of natural language processing problems human computer interaction and problems associated with getting technology into the hands of end users
tipster is an arpa sponsored program that seeks to develop methods and tools that support analysts in their efforts to filter process and analyze ever increasing quantities of text based information
the constellation then would not be tractable by automatic update
in those systems the primitivt s of an oh jet l model correspond to the is a of l w 3aleulus of names and t he ingredient unctor o1 the
another interesting example is all students succeed we assert a property about the class student and then specify that the class is studious that is the property is valid for all individuals of the lass
for exami le a li gression a sut p sition an invalid hypothesis may be inehlded as a part of a dis om se and ruled out l y what folh ws
in all these cases we have to be able to infer properties about objects from negative assertions which in tttrtt need to re present the formal properties o different kinds of negations operating on sub objects of an object
many knowledge rc iresentation systems exist lie need for a new one came front the type NUM kuowledge we aim to represent rod fl om the reasonings we try lo imp em nt
in other words that the class is the extensional projection of the type student as in previous example he negation can operate on all the class is not studious or on tire property asserted about NUM students
meat is generally not the negation of the property the cow eats meat amon t other kinds of food but the assertion of 27tc cow cats something and he negation of the choice of meat as food
in light of these facts serious difficulties can be expected arising from the structurm component of the existing formalisms
syntactic categories expresse l by category labels assigned to non terminal nodes and by part of speech tags assigned to terlninals
this assumption underlies a growing number of recent syntactic theories which give up the context free constituent ba ckbone cf
in order to avoid inconsistencies the corpus is annotated in two stages basic annotalion and r efincment
the difference is its word order independence structural units phrases need not be contiguous substrings
the tool should also permit a convenient handling of node and edge hd els
headedness versus non headedness headed and non headed structures are distinguished by the presence or absence of a branch labeled hd
as already mentioned this is done separately for each of the three information areas
because of the intended theory independence of the scheme we annotate only the cornmon rninimum
this time however the concept nodes are activated during sentence processing
creating an annotated corpus is much easier than building a dictionary by hand
but the hand crafted dictionary achieved higher precision at recall levels above NUM NUM
the autoslog ts dictionary performed comparably to the hand crafted dictionary on both test sets
after frequency filtering the autoslog ts dictionary contained NUM NUM unique concept nodes
first we describe text classification results for the muc NUM terrorism domain
however building a concept node dictionary by hand is tedious and time consuming
the subject of the verb is extracted as the victim of the murder
these tasks place different demands on the concept node dictionary
NUM figure NUM shows the steps involved in dictionary construction
in other words a zero pronoun is never linked to another zero pronoun
q NUM cor zd 2e rvpe zpm t quot reff i coref ii NUM
this is done to find out the minimum number of training texts to achieve the optimal performance
performance seems to reach a plateau at about NUM training examples with a f measure of around NUM NUM
we are experimenting with techniques to break ties in confidence values from the tree
and it will develop vcr s figure NUM qzpro example
the tree and the features used could most easily be compared to existing theories
henceforth we call the system the manually designed resolver or mdr
we plan to continue to improve machine learning based system performance by introducing other relevant features
core m i quot coref n NUM quot ttl leff mr u j
those terms which cooccur at least with the NUM and at mostwith the NUM of the categories are taken
there is a whole group of theories which attempt to explain the problems of the fourth gospel by explanations based on assumed textual dislocations
also hearers of spoken language buffer the speech in order to process it together in words and phrases but the buffer for visually observed data has a much quicker decay time than that of auditory or visual data which leads to repetition and redundancy in signed languages that does not occur in the same manner elsewhere
the dm receives messages from two sources the controller the software interface to the berlin system and the sr unit
if the terminal string w can be generated by the grammar rules of the from a wl then the analogous derivation using the rules a w2 will produce the translated output in gadl form
the debug null smith hipp and biermann an architecture for voice dialog systems ging tree has as its root a node representing the whole device to be debugged and other nodes representing all of the subsystems
since the algorithm does unification as a rule is invoked the variable y has been set to NUM entry of zmodsubdialog with this subgoal however finds no trivial resolution for this subgoal
instead of turning control over to a depth first policy theorem proving in this system must allow for abrupt freezing of any proof and transfer of control to any other partially completed subproof or function of the system
the first invoked rule tl circuit test2 v set knob NUM measurevoltage NUM NUM v is thus half satisfied and the goal measurevoltage NUM NUM v is undertaken
there are subroutines for example to do processing at the lexical syntactic and semantic levels to handle referencing problems to manage discourse structure and speech act issues tense and much more
the third possibility is that neither find swl or reportposition swl would exist in the database in which case both could be sent to the controller as missing axioms for possible vocalization
lie can i m c advantage of this gradual beh wior l rcb y tmihting lcb he knowledgc sources incrcmenl mly and using i i mt fop l ra llslaj iolls evell i el ore the kn lcb rcb wledge sources trove i ecn eomplc e lcb i
the inverted concatenation operator permits the extra flexibility needed to accommodate many kinds of word order variation between source and target languages
a stochastic inversion transduction grammar is an itg where a probability is associated with each production subject to the constraint that
l his error had n rcb t i rcb een diseovere lcb i earlier NUM rcb e lcb ause il had n lcb rcb ol vious effect ou i rcb anel3mt s perforlnmt e t clear exa ml rcb le of the sysl enl rcb s graceful lcb h gra lcb ial
NUM the conditional distribution over l v productions is uniformly distributed over the chinese vocabulary
the rise in perplexity afterwards is caused by numerical error on overtrained parameters we terminate training as soon as this
the latter example shows problematic behavior on the example given earlier in figure NUM of sentence pairs without sufficient ordering discrimination
the sitg formalism offers another possibility the generic bracketing grammar can be replaced with a context free backbone designed for english
note that we do not expect the parallel training corpus to be parsed or otherwise syntactically annotated beforehand
the common origin of assumptions i and iv i.e. from xo yo z is recorded by the fact that i s argument is marked with iv s index j
the fourth minor and optional knowledge source is the hmguage specific information provided in the conliguration tile which consists of n list of tokenizations equating words within classes such as w0ekdays a list of words which ntay be elided during alignment such as artmes and a list of words which may be inserted
the item sets are known as the states of the lr parser
about NUM informa null tional relations have been coded for
these features more specifically characterize the current core contributor relation
the corpus consists of NUM clauses comprising NUM segments for a total of NUM relations
finally adjacency does not seem to play any substantial role
our results also provide guidance for those building text generation systems
it seems that certain syntactic structures function as a cue
for example the modified earley parser processes fully bracketed inputs in linear time
and thus NUM is more susceptible to damage than part3
NUM rda is a scheme devised for analyzing tutorial explanations in the domain of electronics troubleshooting
the rda analysis of i is shown schematically in the relations it participates in
the led is supposed to be displaying alternately flashing one and seven
NUM subjects a d NUM NUM and that sound was really prominent
passonneau and litman discourse segmentation the cue phrase features are also obtained by automatic analysis of the transcripts
our results suggest that it is possible to approach human levels of performance given multiple knowledge sources
in general subjects assigned boundaries at quite distinct rates thus agreement among subjects is necessarily imperfect
furthermore note that the machine learning algorithm used the changes to the coding features that resulted from the error analysis
before making concluding remarks on part two of our study we mention a few questions for future work on segmentation
the result is all the more striking given that we used naive coders on a loosely defined task
in section NUM we present our analysis of segmentation data collected from a population of naive subjects
for example the np last week in figure NUM would have the tmp temporal tag and the sbar in sbar because the market is down would have the adv adverbial tag
the probability for any sentence can be estimated as p s tp t s or making a viterbi approximation for efficiency reasons as NUM s
noun phrases are most often extracted from subject position object position or from within pps example NUM the store sbar which trace it might be possible to write rule based patterns which identify traces in a parse tree
it seems reasonable to allow the mismatches only for unknown words and for a restricted set of potential unknown category words
r l r l is defined as stop the stop symbol is added to the vocabulary of nonterminals and the model stops generating right modifiers when it is generated
these models can be extended to be statistical by defining probability distributions at points of non determinism in the derivations thereby assigning a probability NUM s t to each s t pair
levelopedk y hand based on observed pat a muc i database to evaluaee gramnigr coverage uecguse terns in the dgta these rules are
figure NUM NUM is a pictorial overview of our model
figure NUM NUM impact of various training
although we used these techniques for genus disambiguation we expect similar results or even better taken the one sense per discourse property and lexical knowledge acquired from corpora for the wsd problem
this paper tries to proof that using an appropriate method to combine those heuristics we can disambiguate the genus terms with reasonable precision and thus construct complete taxonomies from any conventional dictionary in any language
the pair made of the original word and each of the concepts linked to it was included in a file thus producing a mtd with links between spanish or french words and wordnet concepts
obviously some of this links are not correct as the translation in the bilingual dictionary may not necessarily be understood in its senses as listed in wordnet
the best combination for each dictionary vary whereas the dot product association ratio and window size NUM proved best for dgile the cosine mutual information and whole definitions were preferred for lppl
all the heuristics used are unsupervised in the sense that they do not need hand codding of any kind and the proposed method can be adapted to any dictionary with minimal parameter setting
this heuristic is of limited application lppl lacks semantic tags and less than NUM of the definitions in dgile are marked with one of the NUM different semantic domain tags e.g.
for example if the two types n n and n n are composed to give the type n n then this can be modified by an adjectival modifier of type n n n n
this could be in the nature of fixed restrictions to the rules e.g. for english we might rule out uses of prediction when a noun phrase is encountered and two already exist on the left list
thus the noun very old dilapidated car can get the unacceptable bracketing very old dilapidated car
there is a large body of psycholinguistic evidence which suggests that meaning can be extracted before the end of a sentence and before the end of phrasal constituents e.g.
this is due to the fact that composition can be used to form a function which can then be used as an argument to a function of a function
these transitions would capture the likelihood of a word having a particular part of speech and the probability of a particular transition being performed with that part of speech
when we consider full sentence processing as opposed to incremental processing the use of lexicalised grammars has a major advantage over the use of more standard rule based grammars
consider the following pairing of sentence fragments with their simplest possible now consider taking each type as a description of the state that the parser is in after absorbing the fragment
china zhouq s 1000e cs tsinghua edu cn
it provides useful information for syntactic disambiguation
tbest argmax score mc NUM n
treebanks are the collections of sentences marked with syntactic constituent structure trees
they play an important role in the prediction of constituent boundary locations
table NUM shows the basic statistics of these two parts in the treebank
these superfluous matching operations reduces the parsing efficiency of the basic matching algorithm
arcs marked with x indicate that such matching operations are forbidden
but a continue transition which follows a retain transition implies higher processing costs than a smooth shift transition following a retain transition
the naive bayes classifier achieves a high level of accuracy using a model of low complexity
implications of the proposed semantic tagging for typical lexical acquisition tasks e.g.
NUM identifying the topic of senses the labeling of dictionary definition sentences with a coarse sense distinction such as the set labels in lloce is a special form of the wsd problem
further work must be unde xaken to cope with direct and deictic references so that such def mitions can be appropriately labeled and information on sense shifts can be acquired
it is possible that different training processes may result in slightly different parameter estimations because the corpus is arbitrarily segmented into chunks of only roughly NUM megabytes for training and the chunks actually used in different training processes may vary slightly
for example information retrieval technique may be generated using either the structure xi x2xz or the structure x1x2 x3
cross references ref between sets topics and subjects are also given to show various inter sense relations not captured within the same topic
smoothing is made by dropping a certain number of parameters that have the least probabilities taking out the probabilities of the dropped parameters and evenly distributing these probabilities among all the unseen word pairs as well as those pairs of the dropped parameters
this discussion presumes there is a set of desired patterns to extract from input signals
it correctly predicts that both phrases inherit their sound and syntax from their component words
this and the near negligible cost of writing down word lengths will not be discussed further
for instance a continue transition which follows a continue transition is a sequence which requires the lowest processing costs
the algorithm was given the raw encoding and had to deduce the internal two byte structure
is relatively easy to determine when they are useful and their use is limited
during parsing all that is important about a word is its surface form and codelength
the effect on description length of adding a new word can not be exactly computed
the reason for this lies in the fact that nominal anaphors are far more constrained by conceptual criteria than pronominal anaphors
it is capable of compressing a sequence of identical characters of length n to size o log n
null figure NUM contains a small portion of a lexicon learned from NUM NUM utterances of continuous speech by multiple speakers
the first one the introduction of functional notions of information structure in the centering model is methodological in nature
the context is defined as a pair of words immediately before and after a label bracket
NUM minutes before it itself turns off begins the low battery led to flash
any two labels are considered to be identical when they are distributionally siml r
for instance there is a ref link in figure NUM from topic je to topic de belonging and owning getting and giving
figure NUM the traz sltion of pr pp np np and fm during the merging process
in the implementation a limit can be set to the cardinality of s e 21sl to avoid excessively long processing time
this kind of semantic annotation is what will be used in the construction of the corpora described in section NUM of this paper
using this notation the semantically annotated version of the toy corpus of figure NUM is the toy corpus rendered in figure NUM
null NUM establish a method for deriving the mean null ing representations associated with arbitrary corpus subtrees and with compositions of such subtrees
for the syntactic dimension of language various instantiations of this data oriented processing or dop approach have been worked out e.g.
this work was partially supported by nwo the netherlands organization for scientific research priority programme language and speech technology
let rid be the i th subtree in the derivation d that yields tree t then the probability of t is given by
the semantic attributes are rules that indicate how the meaningrepresentation of the expression dominated by that node is built up out of its parts
from this the effectiveness e of a context c can be defined using variance as follow
the statistical parsing model provides a framework for finding the most likely parse of a sentence based on these conditional probabilities
in this work the local contextual information is defined as categories of the words immediately before and after a label
to make out these clusters we make use of the definitions of the words in the modem chinese dictionary and determine the correct sense of the word in the context by measuring the similarity between their definitions
there are also pcas that convey a partial plan for further presentation and thereby update the reader s global attentional structure
since sthese weights are found to be optimum for all three znetric
sincere thanks are due to all three anonymous reviewers of acl eacl NUM who provided valuable comments and constructive suggestions
NUM NUM noun occurrences of the entire brown corpus and wall street jottrnal corpus
classes can also be mapped onto more than one concept node in wordnet
preliminary ewe ca n t draw any conclusions concerning concede as there are only NUM occurrences of concede out of NUM core contributor relations
there are cases in which one word sense corresponds to multiple subcategorization frames other cases in which one word sense corresponds to one subcategorization frame each and the other cases in which multiple word senses correspond to less number of subcategorization frames
this paper presents an approach which exploits general purpose algori t m
the big boxes represent attentional spaces previously called proof units by the author created during the presentation process
as could be expected were a few points lower
this paper has presented a rule based morphological disambiguation approach which combines a set of hand crafted constraint rules and learns additional rules to choose and delete parses from untagged text in an unsupervised manner
in this way our architecture provides a clear way of factoring out domain dependent presentation knowledge from more general nlg techniques
thus any differences or similarities between words must be detected purely from the statistics of the usage of the words which are in turn determined by the characteristics of the contexts in which they occur
however by focusing on the single intralinguistic source of information provided by the language data alone we may be able to obtain useful insights regarding its influence on our conceptual structure
in other words the evaluation of a native speaker can potentially be used to assess performance each time the system encounters a target word in context and assigns that word to a particular sense class
whilst supervision may enable children to learn the meanings of a limited number of common words it seems extremely unlikely that the greater part of our understanding of word meanings is achieved in this way
whilst such assessments might also be applicable to the analysis of dendrograms word sense disambiguation is of interest since it constitutes the task that continually meets human language users when reading text or listening to speech
such a system has the potential to begin developing clusters from the very first exposure to the linguistic input and the clusters into which the input words are placed evolve continuously during the learning process
thus the neural network approach unlike that described above has the potential to allow separate senses of a word to be distinguished on the basis of their context
the limitations of the method of cluster analysis in assessing the success of such analyses are discussed and ongoing research using an alternative unsupervised neural network approach is described
despite our difficulty in being able to provide clear definitions for such words we have strong intuitions about their usage and can readily categorize them on the basis of similarity in meaning
as figure NUM indicates the portion of the moving window in which the context words are contained may exclude a small number of word positions immediately adjacent to the target word
note that the bars in gentzen s notion figure NUM are replaced by links for clarity
a piece of argumentative text such as the proof of a mathematical theorem conveys a sequence of derivations
the joint probability of an initial class subsequence ci of length r together with an initial tag subsequence can be estimated by
as expected early morning dominantly produces two clumps but can produce either one or three clumps with reasonable probability
in the discussion of fertility models we denote an english sentence by e which consists of i e words
for all fertility models the fundamental parameters are the joint probabilities p e c a f
since the clumping and alignment are hidden to compute the probability that e is generated by f one calculates null
where p ni fi is the fertility probability of generating n i clumps by formal word f
this is remarkable since dop is not trained it reads the rules or subtrees directly from hand parsed sentences in a treebank and calculates the probability of a new tree on the basis of raw subtree frequencies in the corpus
for an established form derived or not these depend straightforwardly on the frequency of a particular sense
however the inflexibility of this mode is a severe drawback in the presence of user correctable miscommunications
this represents in effect a linear combination of the similarity estimate and the back off estimate if NUM NUM then we have exactly katz s back off scheme
formulae for calculating the unseen probability mass and for allocating it differentially according to schema productivity are shown in figure NUM finergrained more accurate productivity estimates can be obtained by considering subsets of the possible inputs this allows for some real world effects e.g. the made of schema is unlikely for liquid physicalartifact compounds
for german we chose the sentence templates NUM es ist adjective
in the paper we propose a black box method for comparing the lexical coverage of mt systems
here we will advocate a probing method for determining the lexical coverage of commercial mt systems
german assistant treats this word as a compound and incorrectly translates it as waffe weniger engl
for instance telegraph systran and langenscheidts t1 score much better for german to english
but medium and low frequency words give a clear indication of the underlying relative lexicon size
our method for determining lexical coverage could be refined by looking at more frequency classes e.g.
the method as introduced in this paper requires extensive manual labor in checking the translation results
words more than NUM NUM verbs need
learning at least part of the dialog knowledge is desirable since it could reduce the knowledge engineering effort
subtype and elaboration encapsulate clues about rhetorical structure given by knowledge of subtype relations among events and objects
note that as a side effect hierarchically lower segments are ultimately closed when a match at higher segment levels succeeds
primarily new learning approaches have been successful for leo ically or syntactically tagged text corpora
note also that we attach the discourse segment index s to center expressions e.g. cb s us
the fifth column indicates which block of the algorithm applies to the current utterance cf the right margin in table NUM
the remaining NUM are mostly difficult ambiguous cases some of which could be resolved if more knowledge could be used
however work establishing an explicit account of how both can be joined in a computational model has not been done so far
two recent studies deal with this topic in order to relate attentional and intentional structures on a larger scale of global discourse coherence
since queries differ much more from each other than all other dialog acts they could not be generalized
automatic stochastic tagging of natural language texts
null NUM performance of the systems
the lexicon size is limited by the available ram
stochastic hypothesis for the unknown words
detailed experimental results are included in appendices a and b
the least ambiguous are the dutch and french texts
these errors generate tag assignments that are not valid
we will describe the learning and generalization results for this dialog component and we will point out contributions and further work
when we turn to the mixture estimator a great difference is seen between hierarchical tag context trees and bi grams
from the result we may say exceptional connections are well captured by hierarchical context trees but not by bi grams
such a database typically consists of words that exhibit unusual stress patterns for languages such as english and of unassimilated or partially assimilated loanwords including place names and personal names that do not fit into the canonical phonological or phonotactic form of the language
the more general b is the more subdivision symbols appear at node sb
the initially constructed basic tag context tree is used to compute a sb s
the experimental results show the proposed method significantly outperforms both hand crafted and conventional statistical methods
a simple but efficient approach would be to compute the average plausibility vector for each utterance which has been tbund
before going into detail of the algorithm we briefly explain the context tree by using a simple binary case
let weight t be a weight vector attached to the tth example x t
NUM which level is appropriate for t i NUM NUM which length is to be considered for tl i NUM and
to construct a tag model that captures exceptional connections we have to consider word level context as well as tag level
for example there are NUM training sentences for the confusion set lcb principal principle rcb and NUM test sentences
the baseline performance given in connection with tribayes corresponds to the partitioning of the brown corpus used to test tribayes
the percentage of correct predictions also represents the frequency of sentences in the test corpus that contain the given word
we ran some experiments in which we built lsa spaces using the whole sentence as well as other context window sizes
the global weight given to each term is an attempt to measure its predictive power in the corpus as a whole
golding and schabes selected NUM confusion sets from a list of commonly confused words plus a few that represent typographical errors
the brown corpus was parsed into individual sentences which are randomly assigned to either a training corpus or a test corpus
while lsa can be used to quickly obtain satisfactory results some tuning of the parameters involved can improve its performance
conversely it updates the context model whenever a phrase has been generated
if the difference exceeds the maximum angle deviation threshold the chain is rejected
the system has three modules generation prosody and speech
if this distance exceeds the maximum point dispersal threshold the chain is rejected
but context models have also come up in other settings
the taggers who worked independently from each other were not aware of having been assigned to one of two groups of participants
the semantic make up of some words makes them more difscult to interpret and hence harder to match to dictionary senses than others
the taggers agreed on a sense choice more often than they agreed with two lexicographers suggesting an effect of experience on sense distinction
these variables are the degree of polysemy the part of speech and the position within the dictionary entry of the words
polysemy and syntactic class membership interact verbs and adjectives have on average more senses than nouns in both conventional dictionaries and in wordnet
both the number of senses and the syntactic class membership of verbs and moditiers may conspire to make these words more difficult to tag
statistically one would therefore expect the first sense to be the one that is chosen as the most appropriate one in most cases
verb and adjective meanings on the other hand are more context dependent particularly on the meanings of the nouns with which they co occur
while it is possible that the expert choice did not always reflect the best match we suspect that novice taggers annotate differently from lexicographers
NUM adjectives headed on the agentive ennuyan boring prdoccupan worrying agrdable nice admirable wonderful effroyable appalling etc
we also show a technique developed for determining the appropriate number of bracket groups based on the concept of entropy analysis
we also propose a method to determine the appropriate number of bracket groups based on the concept of entropy analysis
in the initial stage each group is a singleton set g lcb rcb for all i
here the larger sim g g is the more similar two brackets are
finally we present a set of experimental results and evaluate our methods with a model solution given by humans
designing and refining a natural language grammar is a diibcult and time consuming task and requires a large amount of skilled effort
finally we present a set of experimental results and evaluate the obtained results with a model solution given by humans
for a certain merging step p g ic is identical independently of which groups are merged together
in the grouping process a single nontermina label is assigned to each group of brackets which are similar
at this point we can make another approximation modeling fluency as likelihood
when a polysemous word such as plant occurs multiple times in a discourse tokens that were tagged by the algorithm with low confidence using local collocation information may be overridden by the dominant tag for the discourse
this was a major concern in trec NUM and analysis of this issue is clearly needed
burkowski used queries that were manually built in a special query language called cgcl
in addition to recall precision curves there are NUM single value measures used in trec
new for trec NUM was a histogram for each system showing performance on each topic
the eth results were superior for NUM topics mostly because of better ranking
to compute this average a precision average for each topic is first calculated
in addition to clearly defining the tasks other guidelines are provided in trec
the training for this task is shown in the left hand column of figure NUM
the results showed an average agreement between the two judges of about NUM
table NUM shows the statistics from the merging operations in the four trec conferences
as has clearly been recognized in the three papers which make up this opening session identifying the special pragmatic features of spoken dialogue which distinguish such texts from the type of input that a traditional mt system might deal with is a crucial part of the problem
essentially we have here incorporated rst within the sfm rather than the other way about
in this way we have arrived at a unified framework for generating both dialogues and monologues
the present paper follows on from two earlier papers in particular
the first describes the implementation of the sfm as part of the
we have now generated all of the structure of the move
and the potential for recurslon is in principle infinite
implementing an integration of the systemic flowchart model of dialogue and rhetorical structure theory
we use the same term here because the motivation is similar in both
rule NUM NUM inserts the unit act in both cases
this is true in particular for a family of discriminative cost functions
the models are quantitative in that they assign a real number cost to derivations
this ordering is completely defined by the source and target monolingual models
a comparison of head transducers and transfer for a limited domain translation application
table NUM lexicon and model size comparison
discriminative cost functions including likelihood ratios cf
NUM effectiveness comparison NUM NUM english chinese atis models
in addition to the actions performed by the head transducers
the interpreter was implemented to our dialogue system for spontaneous speech which worked in the task domain of mt fuji sightseeing guidance
however the entries do not represent flaps vowel reductions and other coarticulatory effects
if each pronunciation only had a single derivation this would be computed simply as follows
table NUM baseform phone set used was the arpa
figure NUM male vs female probabilities for phono logical rules
figure NUM shows the fit between the automatic and hand transcribed probabilities
table NUM gives the NUM phonological rules used in these experiments
the input layer consists of NUM frames of input speech data
we think that a hybrid between a rule based and a decision tree approach could prove quite powerful
the nondeterministic mapping produces a tagged equiprobable multiple pronunciation lexicon of NUM NUM pronunciations for NUM NUM words
the rest of this section will discuss each of the aspects of the algorithm in detail
the italian version of wordnet aims at the realization of a multilingual lexical matrix through the addition of a third dimension relative to the language
if a symbol table entry satisfies all m structure schema for a function g by our proposed scheme the nameholder n that points to the entry is bound to the function name g
although inore elaborate comi lel ion schemes are imaginable in luding ones that involve the use of alternate hyi othesos or NUM revisions for morl hologieal repair we have ot ted against these for the time t eing because they necessitate st eeial commands whose benetit in terms of characters saved would t e diilicult to estimate
there are essentially three ways of organizing the process by which a person and a machine cooperate to produce a translation prccdition in which the person s contribution takes the form of a source text analysis and occurs before the mt system is brought to bear postedition in which the translator simply edits the system s output and interactive mt imt which involves a dialog between person and machine
such environments when they incorporate mt at all tend to do so wholesale giving the user control over whether and when an mt component is invoked as well as extensive postediting facilities for modifying its outtmt but not the ability to intervene while it is operating
where sj is the jth source text token so is a null token p tlsj is a word for word translation probability and a jli is a position alignment probability equal to NUM m NUM for inodel NUM
it is based on the observation that there are at least three classes of english forms which most often translate into fk ench either verbatim or via a predictable transformation proper nouns numbers and special atphanuineric codes such as c NUM
the lack of direct human control over the tinal target text modulo postedition is a serious drawback in this case and it is not clear that for a competent translator disambiguat ing a source text is much easier than translating it
to normalize ct for user NUM we determine that is NUM NUM and crc is NUM NUM
this paper explains why functional heads are not treated as head corners by the minirealist head corner parser described here
for example 19a shows the sentence subject NUM used for task NUM with two pronouns and 19b shows subject NUM s sentences with only one pronoun the frequency with which the different types of referring expressions occurred can be found in table NUM
the worst case analysis clearly indicates that computing a is much less expensive than computing b empirically however this is not the case when n is large and ai is small which is usually the case in rewrite rules
a selected referent cf is created when an icon has been selected by the user by moving the mouse to the icon and clicking the left mouse button or when the user has requested the system in natural or formal language to select icons
does koen live in amsterdam the time interval of this question relation viz now is included by the time interval of live in NUM found in the knowledge base and thus the system would respond with ja hij woont er
a cf is defined by a scope which is a collection of individual instances a significance weight represented by an integer and a decay function which indicates by what amount the cf s significance weight is to be decreased at the next update
therefore if one is in need of a referent resolution model for a particular computational linguistics volume NUM number NUM nl interpreter in a setting where subdialogues are rare we think that edward s context model is a good alternative to the complex rule system of grosz and sidner
the proper interpretation of deictic expressions depends on the identity of the speaker s and the audience the time of speech the spatial location of speaker and audience at the time of speech and non linguistic communicative acts like facial expressions and eye hand and body movements
extrinsic use of for example the projective preposition left of i.e. left of an object that is being dragged by the user when looking in the direction of dragging is currently impossible since the user can not drag and write linguistic expressions simultaneously
selection is an action only the user can initiate if the selection is done with a pointing action both a selected referent cf and an indicated referent cf are created e.g. for donald report in figure NUM otherwise only a selected referent cf is created
NUM reasons in the controlling attentional space are structurally close
language support is provided through access to vocabularies of suggested verbs and terminological nominal compounds
generating patent claims from interactive input svetlana sheremetyeva i sergei nirenburg NUM irene nlrenburg
the set of templates can be considered a draft text of the patent claim
if there is more than one candidate the procedure finds the best one
a segment is a substring between any two brackets whether opening or closing
NUM a training corpus of over NUM NUM u s patents was used in this work
the inclusion of a human into the process simplifies the task of the system
the schema for our example invention is illustrated in of the patent claim generator
note that the difficulty of the task is not constrained to syntax and style
a subset of the templates created for our example is given in figure NUM
results indicate that the english spanish matchplus prototype is able to learn reasonable word stem interrelationships for tie words and non tie words thereby demonstrating the suitability of this concept for further development
we will extend the parser in such a way that it will cover more advanced linguistic phenomena like anaphors and wh questions
NUM reasons in the active attentional space are structurally close
i would like to thank don mctavish thomas potter robert amsler mary dee harris some wordnet folks george miller shari landes and randee tengi tony davis and anonymous reviewers for their discussions and
we used wordnet synsets in examining mcca categories to determine their coherence to characterize their relations with wordnet and to understand the si ificance of these relations in the mcca analysis of concepts and themes and in tagging with wordnet synsets
about NUM categories implication if colors object being consist of a relatively small number of words NUM NUM NUM i i NUM respectively taken primarily from syntactically or semantically closed class words subordinating conjunctions relativizers the tops of wordnet colors
the important questions at this point are why there is value in having additional lexical semantic information associated with tagging and why mcca categories and wordnet synsets are insufficienl the answer to these questions beans to emerge by considering the further analysis performed after a text has been classified on the basis of the mcca tagging
rather it unfolds the dimensions one by one starting with NUM examines statistically how stressed the solution is and then adds furthor dim asions until the stress shows signs of reaching an asymptote
they do not account for how the judgment is made how the judgment affects the refashioning or the content of the moves
an essential component of this classification process is the identification of sublc dcens that cut across parts of h along with conc t grammars based on collapsing phrasal and constituent nodes into a generalized xp representation
we suggest that such information is useful not only for the primary purposes of disambiguation in parsing and text classification in content analysis and information retrieval but also for tasks in corpus analysis discourse analysis and automatic text summarization
the third session allowed the subject to attempt up to ten additional problems
among these techniques only word frequency counting can be used robustly across different domains the other techniques rely on stereotypical text structure or the functional structures of specific domains
by setting appropriate cutoff values for such parameters as concept generality and child to parent frequency ratio we control the amount and level of generality of concepts extracted from the text NUM
it also indicates what should be omitted because of existing user knowledge
as the first step in an automated text summarization algorithm this work presents a new method for automatically identifying the central ideas in a text based on a knowledge based concept counting paradigm
this is known as plan recognition in the literature
this architecture exhibits a number of behaviors required for efficient human machine dialog
its actions are to carry out a prolog style proof of the goal
using a part of speech tagger and syntatic parser to distinguish different syntatic categories and relations among concepts we can find appropriate concept types on the interesting wavefront and compose them into summary
session NUM was scheduled for three or four days after the second session
consider figure NUM in case a the parent concept s ratio is NUM NUM and in case b it is NUM NUM by the definition of NUM
the words are then counted and sorted by frequency and the word probability in the class is calculated by dividing the frequency by the number of words in the training set
easily the worst album of what has until now been a remarkably successful career the disc is aptly named the temperature never seems to rise on this turgid effort
null olivier saint jean finished with NUM points and seven rebounds for the spartans NUM NUM who were one of two teams in the ncaa tournament with a losing record
our goal eventually is to define an algorithm for choosing distinmlishing word sets in an optimal way i.e. a way that will maximize the probability of correct classification
the wildcats NUM NUM who are seeking their first national championship since NUM will meet the winner of the wisconsin green bay virginia tech game on saturday at reunion arena
set NUM which has nearly half of the outcomes as NUM is much more likely to have been created with the loaded die than the fair one
the third way to choose distinguishing terms is to select only the words which occur more often in one list than in all other lists combined until enough words have been chosen
idf the weight associated with each term in the training set is the log of the number of classes divided by the number of classes which contain the term
NUM for classification place the new document in the category corresponding to the class or bucket or prc fle to which it is most similar
that is first a potential head of a phrase is located and next the sisters of the head are parsed
so we decided first to concentrate only on simple concatenative cases
this function favors the rules with higher estimates obtained over larger samples
prefer frequent senses is a declarative rule for disambiguating constituents in a discourse context
this is done by the operator x
NUM when applied at each iteration this process reduces the training noise yielding the optimal observed accuracy in column NUM
columns NUM and NUM illustrate the effect of adding the probabilistic one sense per discourse constraint to collocation based models using dictionary entries as training seeds
we adopted NUM confidence for which
the head corner parsing algorithm and the structure building operations of the minimalist program gt and move a have much in common
more impressively it achieves nearly the same performance as the supervised algorithm given identical training contexts NUM NUM vs NUM NUM
tat le NUM shows sami le of the results of our disambiguation nn thod
examples added to the the growing seed sets remain there only as long as the probability of the classification stays above the threshold
NUM one sense per discourse the sense of a tar null get word is highly consistent within any given document
in a small study homograph pairs were observed to co occur roughly NUM times less often than arbitrary word pairs of comparable frequency
it thus uses more discriminating information than available to algorithms treating documents as bags of words ignoring relative position and sequence
in contrast our algorithm models these properties carefully adding considerable discriminating power lost in other relatively impoverished models of language
in approaches of that kind there is nothing for example to block extraposition of prenominal elements
since nothing in the present proposal hinges on this detail we keep with the more common binary features
yet there are no known lp constraints in any language that make reference to these types of information
as a result we can regard total compaction as a special case of the p compaction relation in general
prepositions are prepended to the domain of nps in the same way 6it should be pointed out that we do not make the assumption often made in transformational grammar that cases in which a complement of a verb can only occur extraposed necessitates the existence of an underlying non extraposed structure that is never overtly realized
in this paper we propose a statistical approach for clustering of artmes using on line dictionary definitions
the main obstacle to deployment immediately is that generic systems must this material has been reviewed by the cia
the muc NUM evaluation in november NUM provided a good framework for testing the effectiveness of sri s customization tools
that review neither constitutes cia authentication of information nor implies cia i l endorsement of the author s views
this performance promises that information extraction technology will becomes more widely utilized in realworld environments NUM
improve to the point where they can recognize other entities as specified by users who are not developers or computational linguists
the second key area of research in support of transportability was the implementation of compiletime transformations
this language allows one to define patterns for the fastus system in a convenient way
fastspec is one of the influences on standardizing a community wide pattern specification language
therefore the word filtering also includes the scanning process that detect and correct these errors
a statistical information will be collected as a statistical base to support both filtering process and scanning process
it is also apparent that a few additional heuristics could be used to remove many of the extraneous words
some applications might require strict cat3note that some of these words are not nouns such as boardedand u s made
finally a user must review the ranked list and identify the words that are true category members
after the final iteration we had ranked lists of potential category words for each of the five categories
this bootstrapping mechanism dynamically grows the seed word list so that each iteration produces a larger category context
this actually proves that the e75 rule set fully supercedes the xerox rule set
the algorithm is clearly sensitive to the initial seed words but the degree of sensitivity is unknown
if you do not know what a word means then it should be labeled with a NUM
for example consider the sentence i bought an ak NUM gun and an m NUM rifle
this presupposes the user s acceptance of the judgment plan
we are now ready to illustrate our system in action
for a given vocabulary size a language model with lower perplexity is modeling language more accurately which will generally correlate with lower error rates during speech recognition
NUM the second schema shown in figure NUM embodies the recursion
the evaluation then proceeds through the actions in the rest of the plan
the notation used in the action schemas was given in table NUM above
once the context window has been determined the learning rule of move context vector for target in the direction of the context vector of the neighbors is applied
their model makes two strong claims about how agents collaborate
NUM NUM p NUM the NUM NUM to montreal
there are several major differences between our work and theirs
the end result is given below as NUM
from table i we can find surprisely that the computation time for right left is much shorter than the time for left right
the discourse module implements anaphora resolution functions for identifying referring expression s and computing referents if needed and discourse processing functions for computing certain discors e structures that facilitate maintaining dynamic knowledge bases
the uno nlp system uses such type equations bi directionally for answering questions about the properties of a particular individual and for matching particular properties against the properties of individuals in it s knowledge base
in this section we briefly comment on the most distinct characteristics of our system as requested by th e muc NUM program committee technical details can be found in the references provided earlier
NUM would be less than honest to say i m no t disappointed not to be able to claim creative leadership for coke mr mccann still handles promotions and media buying for coke
this extension demonstrates that important inferences about time can be captured by a general representation and reasoning mechanism inherent in natural language many aspects of which are closely mimicked by the uno model
these results demonstrate that approaches to definite anaphora resolution that rely primarily on the availability of such knowledge a standard approach i n all nlp systems we are aware of are misguided
this metaknowledge facilitates maintaining consistency of the dictionary
on namex type organization mccana enamex account i ca n t believe it s not butter abatter subst e is in NUM countries for example
developing more global campaigns that nonetheless reflect localcultures
the morphological analyzer handles both prefixes eg
a stochastic optimal sequence of tags t to be assigned to the words of a sentence w can be expressed as a function of both lexical p w t and language model p t probabilities using bayes rule
the tagging process can be modeled by an hmm by assuming that each hidden tag state produces a word in the sentence each word wi is uncorrelated with neighboring words and their tags and each tag is probabilistic dependent on the n previous tags only
constituents of vp are moved to higher projections by move a which is a special kind of gt
delayed intersection and aggressive pruning prove to be important
as an example the shallow parse structure for the sentence in NUM is shown in NUM below
this do not mean that the words we and apple belong to one class
we are forced therefore to index the system and feature names with integers to disambiguate
one interesting fact that the classification into error types makes clear is that all the different readings of these words do not get mixed tip at random but in rather strong often mirror like patterns
in the next step a human annotator is to mark for each ambiguous word which of the suggested readings is the correct one and for each unambiguous word whether the suggested reading is correct
there are three sub actions consulting the manual unplugging the device and removing the cover
the penn treebank ltowever even given explicit criteria for assigning pos tags to potentially ambiguous words it is not always possible to assign a unique tag to a word with confidence
voutilainen and j irvinen in their study of inter annotator agreement mention three situations where an nnderdetermined analysis was accepted when the judges disagree about the correct analysis even after negotiations
the output of this step is used as the man version in the man machine comparison or rather the woman version as the majority of the annotators were female students
this is an instance of the by far most common error type in the entire material and is of course directly dependent on the way verbal particles are treated in the underlying linguistic description
thus in the notoriously difficult choice between a past participle and the past tense of a verb if there is insufficient probabilistic evidence to choose between the two claws marks the word as vvn vvd
most evaluations of part of speech tagging compare the utput of an automatic tagger to some established standard define the differences as tagging errors and try to remedy them by e.g. more training of the tagger
this classification shows both which parts of speech are most often involved in errors and which readings of a particular word are most often mixed up with each other and in which direction the errors mostly go
a semantic rule creates a semantic representation of the phrase stored with the syntactic parse
the set of recognized entities is used by the output functions to sgml mark the input
the purpose of evaluation is to assess retrieval effectiveness against some standards of expected performance
finding a term stem for indexing can generate a lot of false relations between words
unfortunately the class of words most difficult to segment correctly are proper nouns such as person names and locations
for organizations the alias is generally formed by selecting a character from each word of the full organization name
we examined the verbmobil corpus of appointment scheduling dialogues for their occurrence and for the necessity to introduce new speech acts NUM
the discourse component creates two primary structures a discourse predicate database and the ddos
ablehnulvg pause i could fit it into my schedule smack the week after from saturday to thursday the thirteenth
the idea is that an input word is processed nondeterministically from left to right
one problem that must be handled is a query in one character set retrieving documents encoded in different character sets
the infinder technology constructs an automatic related term database which attacks the two problems of currency relevance with the same mechanism
a planner the hierarchical planner constructs a description of the dialogue s underlying dialogue and thematic structures making extensive use of contextual knowledge
the fsm can be located in between these two components it works like an efficient parser for the detection of inconsistent dialogue states
additionally our plan operators contain an actions slot where operations which are triggered after a successful fulfillment of the subgoals are specified
NUM a sequential transducer is a deterministic transducer for which all states are final
ultimately this reduced the accuracy of the learned trees to NUM NUM in a cross validation test
the terms at a higher level in the back off sequence are more specific than the lower levels
therefore the final heuristics favors the determiner reading for des
the best way to handle them is to lexicalise these expressions
using the ig weights causes the algorithm to rely on the most specific schema only
a priori it is not clear what the relative importance is of these features
figure NUM ig values for features used in predicting the tag of unknown words
if we let the axe depend on the value of x the number of parameters explodes even faster
we ran the tagger on another text and counted the errors
we spent approximately one man month writing biases and tuning the tagger
some ambiguity is extremely difficult to resolve using the information available
a given ending may of course point to various categories e.g.
overall NUM NUM of the words receive the correct tag
this last step ensures that one gets a fully disambiguated text
NUM rules describing heuristics with various degrees of linguistic generality
NUM non contextual rules for the remaining am null biguities
kx o ky c xoy a step corresponding to selection of one of the permitted orders
a standard method for handling the word order consequences of categorial proofs uses the linear order of formulas in the proven sequent in the obvious way
this section describes a statistical parsing model which takes a sentence as input and produce a phrase structure tree as output in this problem there are two components taken into account a statistical model and parsing process
pose an alternative approach involving coexistence and interrelation of different sublogics that eliminates the need for structural modalities whilst maintaining the descriptive power they provide
this problem does not arise for the hybrid approach which freely allows us to use weaker logics for constructing lexical types that richly encode linguistic information
in the hybrid approach however this treatment is still possible even with non associative lexical types provided coordination is done at the associative level e.g.
represents the substitution of z for v w in a NUM we must next consider the issue of resource structure and its consequences for linguistic derivation
in case locolex is wrong which is possible but quite unlikely the user is free to specify an alternative morphological analysis which is then looed up in the dictionary and for which corpora examples are sought
example in handling extraction a sentence missing np somewhere may be derived as so np as in proof b of figure NUM
a corresponding reordering interderivahility would be allowed if x r y translated to any of kx xy
each of the three sorts of information is displayed in separate windows morphology the results of morphological analysis dictionary the french dutch dictionary entry and examples the examples of the word found in corpora search
finally our experimental results show that the cantonese recognizer has a lower recognition rate on the average than the english recognizer despite a common feature set parameter set and common algorithm
rather than jumping to the conclusion that a different feature set is needed for cantonese we would like to find out what other factors could cause a lower performance of the cantonese recognizer
from this preliminary experiment we discover that although a mired language model offers greater flexibility to the speaker it has a considerably lower performance than that of the concatenation of two pure language models
a french example analysis from figure NUM atteignissent as att eindre subj i pl p NUM finv the semi regular form is recognized as a subjunctive third person plural finite form of the verb atteindre
in fact since many native cantonese speakers do not know the chinese translations of many english terms forcing them to speak in pure cantonese is impractical and unrealistic
although the official languages of hong kong are english spoken cantonese and written mandarin most hong kongers speak a hybrid of english and cantonese
a rather surprising finding is that for mixed language input a straight forward implementation of a mixed language model based speech recognizer performs less well than the concatenation of pure language recognizers
if the recognizer knows a priori which dictionary english or chinese it should search for a particular word it would make less error
the essential design questions vis ps vis the cor null pus were i how large must the corpus be in order to guarantee a high expectation that the most frequent words would be found and ii what sort of access techniques are needed on a corpus of the requisite size given that access must succeed within at most a very few seconds
the final text structure for the remove phone text
return to a seat to place a call
the results of this pilot study were encouraging although the level of student was too high dutch foreign language students have a high level of proficiency so that no differnces in comprehension were noted the glosser users were faster and reported enjoying the experience and interested in using the system further
in all seven sites participated in the muc NUM coreference evaluation
it can also be used to do unranked boolean retrievals
keith vander linden and james h martin expressing rhetorical relations the second is the detailed presentation of a methodology for managing diversity of expression at the textual level in the context of text generation
these three exceptions are handled by the three systems depicted in figure NUM NUM the first exception handled by the scope system concerns the number of actions pertained to by the purpose
this evaluation is called the met multilingual named entity
figure NUM overall recall and precision on the te tas k
it was very small only NUM articles
see preface to appendix b for definitions of the metrics
this test measures the amount of variability between the annotators
text strings that are to be annotated are termed markables
for example if dt nn and prp nn are in the same label group we replace them with a new label such as np in the whole corpus
in an attempt to obtain a robust natural language understanding component we have experimented in ovis with the techniques mentioned in the preceding paragraph
the no names configuration resulted in a significant drop in recall for organization names reflecting the references to household names with little contextual clues such as microsoft
let us compare NUM with NUM
first the aim is to tune an e isting word hierarchy to an application domain rather than selecting the best category for a word occurring in a context
the edr electronic dictionary is composed of five types of dictionaries word bilingual concept co occurrence and technical terminology as well as thb edr the japanese word dictionary contains NUM NUM words and the english word dictionary contains NUM NUM words
rule NUM if an entity e in the current clause was referred to in the immediately preceding clause then a zero anaphor is used for e otherwise a nonzero anaphor is used
to demonstrate the result of using the new decision tree we extended the deftnition of matched overgenerated and undergenerated types used previously for zero and nonzero anaphora to zero pronominal and nominal anaphora
k qishi fengzheng iye you zhongliang NUM yinwei feng m chui zhe fengzheng i m b shi fengzheng i xiang shang sheng n suoyi fengzheng bingbu xiang xia chen
note that a full description is used for the subsequent reference in p that is not at the beginning of a sentence because it is the first mention in the sentence
when it is reintroduced into the fourth sentence it appears in another full noun phrase piao zai kongzhong de xian the string fluttering in the sky which is not reduced
NUM all men in the room endorsed some women
there is a wide range of heterogeneous syntactic functions of cardinals in particular contexts quantificational and adnominal uses bare np s is one of dates and ages jan NUM gave his age as NUM and enumerations
sra developed a generic text understanding prototype solomon which was used for text extraction in several domains and languages including muc NUM muc NUM murasaki and iaa
for example for the disambiguation of work in her work seemed to be important only the fact that seemed expects noun phrases to its left is important the right context vector of seemed does not contribute to disambiguation
rather than having separate entries in its right context vector for seemed would and likes a word like he can now be characterized by a generalized entry for inflected verb form occurs frequently to my right
entry vi counts the number of times that a word from class i occurs to the right of w in the corpus as opposed to the number of times that the word with frequency rank i occurs to the right of w
a local grammar based approach to recognizing of proper names in korean texts
we can represent the left vectors of all words in the corpus as a matrix c with n rows one for each word whose left neighbors are to be represented and k columns one for each of the possible neighbors
for example verbs taking bare in null finitives were classified as adverbs since this is too rare a phenomenon to provide strong distributional evidence we do not dare speak of legislation could help remove
since online text becomes available in ever increasing volumes and an ever increasing number of languages there is a growing need for robust processing techniques that can analyze text without expensive and time consuming adaptation to new domains and genres
similarly the generalization that prepositions and transitive verbs are very similar if not identical in the way they govern noun phrases would be lost if left and right properties of words were lumped together in one representation
nevertheless the test results of condition NUM are much worse than the corresponding training results particularly for precision NUM versus NUM
the original np algorithm assigned boundaries wherever the three values coref infer global pro
the third case is where an np in ci is described as part of an event that results directly from an event mentioned in ci NUM
note that for each iteration of the cross validation the learning process begins from scratch and thus each training and testing set are still disjoint
passonneau to appear examined some of the few claims relating discourse anaphoric noun phrases to global discourse structure in the pear corpus
we plan to continue our experiments by further merging the automated and analytic techniques and evaluating new algorithms on our final test corpus
the first pass includes a first feature of robustness since unreliable words signalled by the filtering as probable substitutions are represented by an automatically generated joker tree
instance l k with l an aligned corpus k a positive integer
the assignment of the features pl conforms to certain restrictions
this can easily be done using suffix trees and by pairing statistics corresponding to the same transformation
however this criterion is belied by two classes of words
b therefore bible score differences displayed in figures NUM and NUM are quite reliable
figure NUM a bitext based lexicon evaluation bible algorithm for precision of n best lexicons
here present 3s is a pseudo affix which has the same syntactic and semantic information attached to it as one sense of the affix t which is used to form some regular third person singulars
so it aimed to minimize crossing partitions as shown in figure NUM
higher order models will be required for a pair of languages like english and japanese
figure NUM shows the relative performance of selected filters when the entire training set of one
the best cascades are up to NUM more precise than the baseline model
translation lexicon quality has traditionally been measured on two axes precision and recall
therefore candidate translation pairs involving different parts of speech should be filtered out
for instance particles are often confused with prepositions and adjectives with past participles
brown et al allowed the training data to override inibrmation gleaned from the mrbd
the overall weight of a feature is the difference between these two weights thus allowing for negative weights
in order to find a thick separator we modify in all three algorithms the update rule used during the training phase as follows rather than using a single threshold we use two separate thresholds NUM and NUM such that NUM NUM NUM
by unifying the results of both methods of the observation we iinally obtain tile word
a string a is a rigid expression if it satisfies the following conditions
in traditional dictionary making lexicographers have had to rely on citations collected by human readers from limited text corpora
we have applied our method to an actual thai text corpora without word segmentation preprocessing
NUM tokenize the text at locations of spaces tabs and newline characters
there is no good evidence to support the itemization of a word in a dictionary
the value is arbitrary but this range has proven sufficient to avoid collecting illegible strings
the cache is a limited capacity almost instantaneously accessible memory store
it is significant that the frequency of occurrence of strings de creases when the window size of observation is extended
thailand japan NUM NUM bytes the results of extraction examined in both large and small file sizes are very satisfactory
in these linguistically oriented studies the focus has been on spelling and morphosyntactic improvement or strategy changes
table NUM keystroke savings with the swedish british english danish and norwegian versions of profet
preliminary quantitative tests of the new prediction system were run with an evaluation program developed at the laboratory
in the results presented here we have used instead a different filtering range our filtering range is centered around the initial value assigned to the weights as specified earlier for each algorithm and is bounded above and below by the values obtained after one promotion or demotion step relative to the initial value
to accommodate the weighting of multiple information sources the strictly frequency based program has been replaced by one based on probabilities
tentative results from the profet evaluation will be presented at the workshop in madrid in july NUM
test results of a first version of the new profet show an increase in keystroke savings compared with the current version
however as previously mentioned there is also a qualitative non quantifiable aspect to writing that has to be evaluated
another new feature is the automatic grammatical classification of user words which is based on n gram statistics
in fact the results exhibited a NUM decrease in savings which seems to have two explanations
the result of a combination is recognized as non normal form if it establishes a dependency that is out of order with respect to that of the final combination of at least one of the two subproofs combined which is an adequate criterion since the subproofs are well ordered
total NUM NUM of repetition repairs occur in the same utterance
NUM NUM the length of the repeated syllable string
the conversion can be formulated as follows
there are four and five speakers in these two conversations respectively
that is a simple pattern matching mechanism can not work perfectly
it reveals that the repair processing has much effect in these experiments
any spoken language systems will not perform well without treating speech repairs
this more sophisticated reader can not only pass the portions of the message on to the rest of the system for processing but can also extract header information e.g. the document number from the message and save that information to become part of a template
in the last two years we have ported part or all of the plum system to several new languages chinese german japanese and spanish and new domains law enforcement name finding heterogeneous newswire sources and labo r negotiations
continuing with the example sentence discussed above a pattern recognized the sequence new york np yach t np club np as an organization the pattern s action substituted the single token new york yacht club np wit h semantics of organization
algorithm the key to modifying the earley algorithm to handle the left and right context conditions is that our rules can be rewritten into a full form which includes all symbols including the contexts plus indices indicating the left and or right context boundaries
if the verb has part of speech vn then it is monotransitive and only one object is needed to form a vp but if the verb is a ditransitive vnn then a second object is needed to form the vp
vn j nom np np np however the latter parse is not linguistically meaningful and is rather an artifact of the overly general noun compounding rule NUM
we simply replace the usual string positions with the names of the states in the fsa
let this importance value be s we then get the following equation for each sentence s a lwi pi iml where a is a constant pi is the number of points assigned to the i th featnre which is normalized to be between NUM to l and wi is the weight assigned to the i th feature
an instance of a problem consists of a particular choice of the parameters of that problem
from a technical point of view it would be better to group all the involved software modules nlp rdbms www on the same platform to optimally exploit the potentialities offered by the combination of the components mentioned
consider the sentence NUM operatieve procedure vijfvoudige coronaire bypass NUM displayed in figure NUM the word procedure is semantically ambiguous because it has two semantic labels h ttchir h txproc
the figure shows the distribution of example case fillers for the respective case frames denoted in a semantic space
in this paper we want to show how the morphological component of an existing nlp system for dutch dutch medical language processor dmlp has been extended in order to produce output that is compatible with the language independent modules of the lsp mlp system linguistic string project medical language processor of the new york university
however it might be the case that in practice people tend to use the correct word NUM of the time
null this undecidability result is usually circumvented by considering subsets of dcgs which can be recognized effectively
if the corpora search module has as input 6crit the selected word and d rire the base form the following examples a o will be found NUM phe corpora text tre collected from different sides on the www
in turn texts produced by the generation system provide a means for evaluation and further refinement of our rules for cue selection and placement
c onsider 7or example the sentence je pense que tu as l as de pique NUM think you ve got the ace of spades according to the morphological analyser tile selected word as has two base forms namely avoir indicating a verb avoir igdp sg p NUM avoir and as indicating a noun as masc invpl noun
pr e lcb is the language model of the target language whereas pr fjle lcb is the string translation model
sent mix en accuxation la pr tendue conception des ancient get mains h base de panth im e d idoutifieation entre dieu et le de tin impersonnel outre di a et la race le feu ple l et at le hommcs au pouvoir bref ridolfitrie d an dicu et d une religion l rement nalionaux NUM
the similarity between two different case fillers is estimated according to the length of the path between them in a thesaurus
figure NUM process flow of text translation
these readings are predicted by our account as john and bill are parallel in the main clauses
then we also have to establish the similarity of their second arguments clause NUM and clause NUM
a break even point is obtained by varying the value of k
figure NUM text structure as determined by the title block similarity
terms with top weights are judged important and representative of document
let us explain a bit more about how the estimating works
the horizontal dimension represents a position at which a segment appears
further any repetitions of words are removed from the title
it is meant as a summary figure of the performance
the procedure was carried out with the tokenizer program juman
we will talk about the assigning part later in the paper
assume that every index t will be assigned to some category
cohen and singer experiment also using the same algorithm with more complex features sparse n grams and show that as expected it yields better results
obviously a comparison in this way only makes sense if the semantic scope of the language internal relations is more or less the same
the problem with the approach described above is that a careful estimation of the threshold value is necessary and tiffs threshold may vary from speaker to speaker or between certain discourse situations
the syntactic constraints build on the facts that a a verb trace will occur always to the right of its licenser and b always lower in the syntax tree
however we found that this phenomenon figure is an average result for two pairs of training and testing sets each containing NUM training documents and NUM test documents
in our implementation the weights w are initialized to NUM d and the weights w are set to NUM d where d is the average number of active features in a document in the collection
in addition using negative weights gives the text classifier more flexibility in capturing truly negative features where the presence of a feature is indicative for the irrelevance of the document to the category
the events are fully co extensive in time there is no time point where one event takes place and the other event does not
for the second experiment we considered only those segments in the input that represent v2 clauses i.e. we assumed that the input has been segmented correctly
within these sentences we ranked all the spaces between words according to the associated NUM probability and determined the rank of tile correct verb trace position
we describe a number of experiments that demonstrate the usefulness of prosodic information for a processing module which parses spoken utterances with a feature based grammar employing empty categories
rather than exploring alternative approaches here we will briefly touch upon the representation of the dependency in terms of lips s featu ral architecture
in lh s however only very few constraints are available for a top down regime since most information is contained in lexical items
correct false miss false error c c reet false miss x
for the recognition of these phrase boundaries we use a statistical approach where acoustic prosodic features are classified which are computed from the speech signal
for every node whose NUM boundary probability exceeds a certain threshold wdue we considered the hypothesis that this node is followed by a verb trace
we make use of a duality between properties having a number of arguments and arguments having a number of properties
in the first option a wm is first translated into the second wordnet box yielding a parallel twin structure of ili records
consequently we introduce the relation corer y e x el to express this coreferentiality
as we have seen above a single wm may be linked to multiple ili records and a single ill record may be linked to multiple wms
i l poueuive l NUM sl0ccifier l ii
in addition to more specific meronymy relations such as membergroup portion substance there is an a specific meronymy relation which is compatible with all the specific subtypes
v wi aln wi 6aln wj wi NUM cr aln wj if al wj NUM 6aln wj wi a aln wj if al wj NUM NUM
the procedure for establishing parallelism is illustrated in figure NUM in which parallel elements are placed on the same line
however to get to grips with the multi linguality of the database we have developed a specific interface to deal with the different matching problems
NUM john realizes that he is a fool but bill does not even though his wife does
details of this cost assignment method are presented in alshawi and buchsbaum NUM
one of our claims has been that as users gain experience and are given the initiative by the system they smith and gordon human computer dialogue will take advantage of that
if the repair process is highly dependent on the type of error e.g. debugging a program even the skilled user may require significant advice from the system
nevertheless in a practical environment we believe the capacity to change initiative during a dialogue is essential for obtaining the most effective interaction between repeat users and a system
the focus of the paper is on a few objective and several subjective performance measures of two interaction strategies similar to the directive and declarative modes described in this paper
the interaction was tape recorded in order to make a transcript that included the actual words used by the subject and the interactions that occurred between the subject and the experimenter
that is for NUM NUM of the utterances there was a disagreement between the coders over either speaker perspective of the current subdialogue global perspective or both
the experimenter made notes about the interaction on the raw data form as well as marked occurrences of subject experimenter interaction according to the category into which the interaction could be classified
nevertheless we believe it represents the first widely reported and analyzed spoken human computer co operative problem solving dialogue and that it is representative of such dialogue for the forseeable future
t summaries are more frequent in advisory dialogues due to the need for both participants to verify that they do share the mutual beliefs needed to develop the necessary plan
however the low coverage of the organization specialist s dictionary left many organization names free to be claime d by the person and location specialists
the reason for this is that the version of the workbench used was not yet able to incorporate date and time annotations generated by a separate pre processing step this date and time tagger performs at an extremely high level of precision for this genre in the high nineties p r
as we and others in the information extraction arena have noticed the quality of text processing heuristics is influenced critically not only by the power of one s linguistic theory but also by the ability to evaluate those theories quickly and reliably
a more subtle interaction is theory creep where the heuristics induced by the machine learning component begin to be adopted by the human annotator due in many cases to the intrinsic ambiguity of defining annotation tasks in the first place
the development of the alembic workbench environment came about as a result of myrre s efforts at refining and modifying our natural language processing system alembic NUM NUM to new tasks the message understanding conferences muc5 and muc6 and the
some of the specific extensions to the user interface that we have already begun building include part of speech tagging and dense markup more generally and full parse syntactic tagging where we believe reliable training data can be obtained much more quickly than heretofore
these category restrictions are subject to crosslinguistic variation as the the aelr for english shows cf
NUM a man came in and a woman went out who knew each other well
our analysis reflects this by avoiding the stipulation of a fixed location for extraposition
extraposed elements with the same antecedent are bound at the same level and lpcs apply
for german we assume that only inheriextra has to be empty for all elements of extra dtrs
to account for the differences between english and german concerning the fronting from extraposed elements cf
NUM whoi did you see a picture in the newspaper of
cal constraint which states that extraposed elements are generally higher than fronted ones or vice versa
this can be achieved straightforwardly by specifying adjuncts as modiloclper non extra
if a sentence contains no projection with a right periphery no extraposition is possible
as above the antecedent pronoun is constrained to be given wide scope over the ellipsis on pain of the index h being undischargeable
they give no principle about the form of the hierarchy or the lexical rules whereas we believe that addressing the practical problem of redundancy should give the opportunity of formalizing the well formedness of elementary trees and of tree families
then for each function defined in the actual subcat exactly one realization of function is picked up in dimension NUM
two nodes in two conjunct descriptions referred to by the same constant are the same node
representation and a generation system we propose a system for the writing and or the updating of an ltag
but for a given predicate we expect the canonical arguments to remain constant through redistribution of functions
this reformulated principle presupposes the definition of an actual subcategorization given the canonical subcategorization of a predicate
this solution not only addresses the problem of redundancy but also gives a more principle based representation of an ltag
then if we have the information that the nodes labeled respectively s and v of figures NUM and NUM are the same the conjunction of the two descriptions is equivalent to the description of figure NUM this example shows the declarativity obtained with partial descriptions that use large dominance links
it decided that john j dooner jr and john dooner were distinct persons the jr
from the resulting pl compute the reflexive transitive closure rl and use it to generate predictions as before
this section summarizes the necessary modifications to process null productions correctly using the previous description as a baseline
forward parser operation described so far some of which are due specifically to the probabilistic aspects of parsing
for example s a s ss q is inconsistent for any choice of q NUM but the left corner relation a single number in this case is well defined for all q NUM namely NUM q i p NUM
even though an earley parser runs with the same linear time and space complexity as an lr parser on grammars of the appropriate lr class the constant factors involved will be much in favor of the lr parser as almost all the work has already been compiled into its transition and action table
the internal module by module architecture of the current alembic is illustrated in fig NUM below
as noted above the unix based portion of the system is primarily responsible for part of speech tagging
flr z g y the first summation is carried out once for each state j kx mz whereas the second summation is applied for each choice of z but only if x az is not itself a unit production i.e. a e
these rules reassign a word s tag on the basis of neighboring words and their tags
to interpret th e apposition the interpreter also adds the following proposition to the database
the propositional notation is more perspicuous to the reader and we have adopted it here
named entity phrases that encode an intermediate stage of ne processing must be suppressed at printout
for example the phrase walter izawleigh jr retired chairman of mobil corp
figure NUM shows the extended version of the lexical entry of figure NUM
for space reasons the synsem feature is abbreviated by its first letter
the definite clause encoding of lexical rules NUM NUM and NUM
figure NUM the follow relation for the four lexical rules of the example
computationally a subsumption test could equally well be used in our compiler
we have to ensure that the value of the feature w is transferred
NUM we illustrate the result of constraint propagation with our example grammar
we thereby eliminate the redundant nondeterminism resulting from multiply defined frame predicates
the covariation approach therefore is particularly attractive for grammars with a large lexicon
these approaches seem to propose a completely different way to capture lexical generalizations
with the rapid explosion of the world wide web it is becoming increasingly possible to easily acquire a wide variety of information such as flight schedules yellow pages used car prices current stock prices entertainment event schedules account balances etc
these advantages look very promising and we will be exploring them in detail in our work on summarization in the near future
each of these concepts is triggered by one or more words which we call triggers in the description
we have implemented retrieval facilities to extract descriptions using the nntp usenet news and http world wide web protocols
similarly we have computed the precision of retrieved NUM descriptions using randomly selected entities from the list retrieved in subsection NUM NUM
for all words in the description we try to find a wordnet hypernym that can restrict the semantics of the description
among these are n grams such as prime minister or egyptian president which were tagged as np by pos
the second case is when we want a description to be retrieved in real time based on a request from either a web user or the generation system
some that merit our attention are the ones that can be accessed remotely through small client programs that do n t require any sophisticated protocols to access the newswire articles
examples of such applications include a system that offers investment advice to a user based on personal preferences and the existing market conditions or an atis like application that assists the user in travel planning including flight reservations car rental hotel accommodations etc
as the basis of the overall mathematical model we have introduced both sentence generation and sentence tokenization operations
we believe that critical tokenization is the only type of tokenization completely fulfilling the principle of maximum tokenization
even if the principle of maximum tokenization is not accepted critical ambiguity in tokenization must nevertheless be resolved
in comparison we have continued within the framework and established the critical tokenization together with its interesting properties
for example among the huge number of possible tokenizations we can first concentrate on the much smaller
with this graph representation it is fairly easy to focus on the syntactically ambiguous points p
computational linguistics volume NUM number NUM rcb iiiiiii i rcb iiiiiis rcb i rcb iiiiiii i iiiiiii
many seemingly important factors such as natural language syntax and semantics do not assume fundamental roles in the process
moreover there is lcb abe d ab cd a bcd rcb td s
in case there is no confusion we may refer to the poset simply as td s
it reflects the strength of relationship between words by comparing their actual co occurrence probability with the probabi i ity that would be expected by chance
how should we group similar words together so that the partition of word spaces is most likely to reflect the linguistic properties of languaqe
more specifically they model senses as probabilistic concepts or clusters c with correspondin cluster membership probabilities p clw for each word w
the latter distance will always be shortest on average
however there is evidence that it is extremely unlikely
there are several papers in the literature about bitext alignment
so for example sentence e corresponds with sentence d
one error is counted for each aligned block in the reference
large non monotonic segments simr has no problem with small non monotonic segments inside chains
in those parts the token type can provide valuable clues to correspondence
this is an open question but there is reason to be optimistic
to my knowledge no other bitext mapping algorithm allows non monotonic map segments
the upper right corner is the terminus and represents the texts ends
i am grateful to matt crocker and lex holt for valuable discussion and comments
for example when a conversation is made between friends an informal verbal ending is used
this paper presents a way to compute relative social status of the individuals involved in korean dialogue
their claim is that there is a syntactic agreement between a subject np and its corresponding verb
l et us consider how relative social status is computed using the dialogue in NUM
as another experiment we e y mine elfectiveness of contexts in the clustering process in order to reduce the computation time and space
the output script invokes utility functions to convert the collector data structure into the template format
this section will provide a brief description of hasten s performance on the selected walkthrough document
a np is described by its terminological context
the closest examples achieve d a similarity value of approximately NUM NUM
depending on task specification special purpose routines may be required
notice that the fourth egraph quarter out performs the first three quarters
sequentially ordered by the document number of the originating text unit
nametag also assigns country codes to the place names which support th e normalized org country slot
nametag took NUM seconds to process these documents as shown in the cpu time column
sra performed the named entity task using its commercial name recognition product called nametag
nametag also can be run in case insensitive mode to handle text in all uppe r case
knowledge acquisition from texts using an automatic clustering method based on noun modifier relationship
dr a9 there is a train leaving at NUM NUM p m dt gerbino NUM a corresponding avm instantiation indicating the task information requirements for the scenario where each attribute is paired with the attribute value obtained via the dialogue
as we have demonstrated here any dialogue strategy can be evaluated so it should be possible to show that a cooperative response or other cooperative strategy actually improves task performance by reducing costs or increasing task success
in some cases it might be desirable to calculate first for identification of attributes and then for values within attributes or to average for each attribute to produce an overall tc for the task
NUM as a first illustrative example consider a simplification of the train timetable domain of dialogues NUM and NUM where the timetable only contains information about rush hour trains between four cities as shown in table NUM
data vl v2 v3 v4 v5 v6 v7 v8 v9 vlo vii v12 v13 vl4 sum key depart city arrival ctry depart range depart time vl v2 v3 v4 v5 v6 v7 v8 v9 vl0 vii v12 v13 v14 NUM NUM NUM
in this section we demonstrate that paradise is applicable to a range of tasks domains and dialogues by presenting avms for two tasks involving more than information access and showing how additional dialogue phenomena can be tagged using avm attributes
to calculate c2 for agent a assume that the average number of repair utterances for agent a s subdialogues that repair depart city is NUM that the mean over all comparable repair subdialogues is NUM and the standard deviation is NUM NUM
figure NUM part of the sort hierarchy for verb prefixes
as a first approximation we use the frequency of occurrence used in determining these preferences rather than the probability for each preference
the conceptual fields have to be completed all along the ka process
but when applications to word chains or syntactic element strings are concerued further restriction of unnecessary elements are anticipated
as shown in this paper applications for japanese character chains still involve output of some amount of fractional stings
this is followed by the proposal of a method that enable to extract interrupted collocations
NUM processing time it took about NUM hours to make spt o NUM
3rd condition substrings should be extracted according to the principle of the longest match
2nd condition substrings can be extracted in the order of frequency of use
NUM all records with no values in the ares field are deleted
almost all lexicon knowledge bases have been created with reliance oll human intuition
in the compilation process such variables are taken as the disjunction of all possible predefined values
a linearized tree for a claim text
knowledge the word lattice will degenerate to a string e.g. the large federal l deficit q fell suppose we are uncertain about definiteness and number
lexico syntactic constraints e.g. tell her hi vs say her hi syntax semantics mappings e.g. the vase broke vs the food ate and selectional restrictions are not always available or accurate
we have extended our notation to allow such constructions but the full solution is to move to a unification based framework in which e structures are replaced by arbitrary feature structures with syn sere and lat fields
once the semantic input to the generator has been transformed to a word lattice a search component identifies the n highest scoring paths from the start to the final state according to our statistical language model
at the same time we take advantage of the strength of the knowledge based approach which guarantees grammatical inputs to the statistical component and reduces the amount of language structure that is to be retrieved from statistics
each individual word in an island specifies a choice point in the search and causes the creation of a state in the lattice all continuations of alternative lexicalizations for this island become paths that leave this state
both models try to hypothesize the segment boundaries while computing the likelihood of the no segmentation test set
the most frequent words in the corpus divide rather sharply across the data sets
gender citizenship locations phone numbers etc extraction errors found by the analyst during their processing of a document are appended to a log within the sql database for review by an engineer for extraction csci package adjustments
after this paper was accepted for publication jeff sisskind devised an implementation based on call cc which does not require continuations to be explicitly passed as arguments to functions
canis automatically extracts entity information builds and updates index records from cables and presents it for review
this kind of memoization is akin to that used in logic programming and yields terminating parsers even in the face of left recursion
the comm process csci sends the collec null tion identifier and document identifier to the extraction process csci
when file maintenance is performed periodically overnight the index records are stored in the corporate database
canis analysts can NUM approve the system generated index records NUM add more information to the system generated index records or NUM ignore the system generated index records and create their own
this fde contains a set of entries that identify the resources that should be loaded the debug flags that should be set the organization of the resources and the sequence of operations that the nltoolset should perform
tween the information that was extracted from each document and the information currently stored in the customer s database
rather than defining the functions f by hand as in NUM higher order functions can be introduced to automate this task
the grammar in figure NUM is an example for which lo l1 NUM the approximation found in section NUM includes strings such as vvccvv which are not accepted by l0 for this grammar
computational linguistics volume NUM number NUM in this example v np and s are assumed to have cps definitions
attention is restricted here to approximations of context free grammars because context free languages are the smallest class of formal language that can realistically be applied to the analysis of natural language
NUM vp v np vp v s then the following scheme definition binds vp to fvp
indeed prolog style definite clause grammars dcgs and formalisms such as patr with feature structures and unification have the power of turing machines to recognize arbitrary recursively enumerable sets
although adequate models of human language for syntactic analysis and semantic interpretation are of at least context free complexity for applications such as speech processing in which speed is important finite state models are often preferred
moreover for epsilon rules where there are no symbols on the right hand side we treat the e as it were a real symbol and consider there to be two corresponding dotted rules e.g.
saw qi r rep r q2 s sample s saw q3 r rep r of r q4 c com c qs s sample s
is given wide scope within restriction if it has narrow scope or nil if there are no other variables within the rest n choose a scoping for x and y construct the dependency function choose a partition choose outer quantifiers qx and qy must be consistent with consts
compt saw lcb rl rcb power lcb sl rcb rl lcb sl rcb definition NUM focus set the focus set for a variable given a particular partition is either o the domain of the partition s focus o the union of the range of the partition s focus depending on the variable of interest
s s restriction is sample s in both cases r s restriction is rep r in the first pas and rep r aof r a c company c in the second since the algorithm generates quantifiers its input pass are not exactly like these
rather than write these two grammars separately which is likely to lead to problems in maintaining consistency it would be preferable to derive the finite state grammar automatically from the unification based analysis grammar
qr representative s of q c company s saw qs sample s qr representative s saw qs sample s of qp product s qr representative s of qc company s saw qs sample s of qp product s
keywords lexical choice realisation quantifiers an algorithm for generating quantifiers quantifiers and their associated scoping phenomena are ubiquitous in english and other natural languages and a great deal of attention has been paid to their treatment in the context of natural language analysis alshawi NUM creaney NUM grosz et al
it can be shown that the approximation l0 obtained by flattening the characteristic machine without unfolding it is as good as the approximation l1 NUM obtained by applying conditions NUM NUM l0 c l1 NUM
r in 12a fmax fmin i lcb rl rcb l NUM r in 12c fmax fmin i lcb r2 r3 r4 rcb i NUM for a variable with narrow scope the focus maximum fmax is equal to the size of the biggest member of the range of the focus of the dependency function
c if the generator happens to introduce more semantic information by choosing a particular expression lowersem is the place where such additions can be checked for consistency
two main approaches have been employed for generation from semantic networks utterance path traversal and incremental 2the tree like semantics imposes some restrictions which the language may not support
unfortunately such dominance relationships between nodes in the semantics often stem from language considerations and are not always preserved across languages
the semantic annotations of the s and vp nodes are instructions about how the graphs concepts of their daughters are to be combined
to this end we have explored aspects of a new semanticindexed chart generation which also allows us to rate intermediate results using syntactic as well as semantic preferences
we have chosen a particular type of a non hierarchical knowledge representation formalism conceptual graphs NUM to represent the input to our generator
suppose number of ways fred limped quickly fred hurried with a limp fred s limping was quick the quickness of fred s limping etc
rule that is more specialised than the goal semantics additional concepts relations further type instantiation etc must be within the lower semantic bound lowersem
as mentioned in section NUM NUM due to the closed world interpretation of type hierarchies we know that every object in the denotation of a non minimal type t also has to obey the constraints on one of the minimal subtypes of t
programming expressing definite clause relations as mentioned in the introduction in most computational systems for the implementation of hpsg theories a grammar is expressed using a relational extension of the description language NUM such as definite clauses or phrase structure rules
if the rule has internal generation goals then these are explored recursively possibly via an agenda we will ignore here the svia the maximal join operation
our approach can be seen as a generalisation of semantic head driven generation NUM we deal with a non hierarchical input and nonconcatenative grammars
the hpsg description language is only used to specify the arguments of the relations in the example noted as d e and f the organization of the descriptions i.e. their use as constraints to narrow down the set of described objects is taken over by the relational level
thus if a type t has a subtype t in common with a defined type d then t is a constrained type by virtue of being a subtype of d and t is a constrained type because it subsumes t
the intended interpretation of this constraint is that every object which is being described by type phrase and by dti s h aded str c also has to be described by the consequent i.e. have its head value shared with that of its head daughter
additionally put every branch between v e g and v into a
we extract gs1 the NUM NUM transitive graph
it is natural that an ambiguous word should be included in different clusters
step NUM repeat step NUM until a c nnot be extended
we set a certain threshold to the values to extract the input graph
co occurrences of no s and verbs are extracted by a morphological analyzer
the clusters hierarchical relationships are shown in figure NUM
trial topic several clusters exist on trial in appendix
let v be word set and r be co occurrence relation
symmetric co occurrence relation does not depend on the occurrence order
at the same time we will restrict the configuration space from all possible configurations in the domain w only to the observed and logically implied configurations w as it is described in section NUM
the maximum entropy model is a model which fits to a set of pre defined constraints and assumes m ximum ignorance about everything which is not subject to its constraints thus assigning such cases with the most uniform distribution
NUM we calculate the normalization constant z using equation NUM and the maximum entropy probabilities p wo p wm for each configuration from the total configuration space w using equation NUM
whereas the trec NUM trec NUM topics were designed to mimic a real user s need and were written by people who are actual users of a retrieval system they were intended to represent long standing information needs for which a user might be willing to create elaborate topics
such collocation lattice will represent in fact the factorial constraint space x for the maximum entropy model and at the same time will contain all seen and logically implied configurations w
we will compute joint distributions separately thus benefitting from the possibility of distributed computations each joint model can be independently computed on a separate processor or machine using multi threading together with remote process calls rpc
let l be an aligned corpus with n aligned pairs over a fixed alphabet e and let n be the length of the longest string in a pair in l we start by considering plain transformations of the form
as a result the scores for this articl e were well below the formal evaluation average and in some cases we have described how the task work s given output from a corrected version of the core system
the original coref scores for this article were NUM recall and NUM precision however with the correction of the bugs mentioned above this score was improved to NUM recall and NUM precision
at the word level a textref signifies a specific occurrence of a word at a certain position in the input and is distinct from the nodes representing the lexical or semantic forms o f its root form
a considerable amount of integration work wa s performed to utilise the brill tagger NUM and to make use of tables of data such as the muc gazetteer an d a large list of common companies
this generator relies heavily on the core analysis and although it performs well given a correct analysis errors in the analysis can produce very strange output and a drastic reduction in the perceived performance of the system
the textref system can also be used to provide convenient debugging information since they allo w developers to relate internal structures produced by the system to the portions of the text from whic h they were derived
the contents of the current context together with the topic of the text the latter is given to the syste m in advance influence the choice of word senses those meanings are preferred which are semantically close r to the meanings present in the context or the topic where semantic closeness is computed on the basis of the distance between nodes in the network
clearly our general purpose deep analysis approach to the tasks did not produce scores that compare well with th e best systems however there are some general reasons why we believe this is the case
referring back to the original tex t before muc lolita did not have a method of referring back to its input the previous orientation was to move from language dependent surface forms to a language independent logical representation
several aspects of the core analysis for walk through article are presented first followed by the performance of the four tasks the n a few comments on how we managed to improve scores on the walk through article
deleteannotations document or annotationsct type string or nil constraint sequence of attribute removes from the document or annotationset all annotations which are of type type and which satisfy constraint
nextannotations document or annotationset position integer annotationsct returns the set of annotations from document or annotationset which have the smallest starting point that is greater than or equal to position
we take this as a clue that stopword removal may not play an important role in chinese ir and lead us to investigate its effect
retrievalqueryfromrelevancejudgements relevant docs collection sequence of documentcollectionlndex detectionneed retrievalquery this operation is similar to update usingrelevancefeedback but creates a new retrievalquery from scratch based on the relevance judgments recorded in relevant docs
copydocument newparent collection document document makes a copy of document including its internal id externalld rawdata attributes and annotations and places the copy in collection newparent
a reference to another annotation is represented in the table as annotation hi for example NUM represents a reference to annotation NUM
ovals represent processes operations boxes with only a top and bottom represent data stores persistent repositories of data fully enclosed boxes represent actors active sources of data
each document is given a unique identity by its ld property which is copied by the copydocument and copybaredocument operations and is also copied when a new collection is created by document retrieval operations
the package name declaration has the form type package identifier an annotation type declaration defines an annotation type it specifies the attributes which such annotations may have and the type of value of each attribute
each span consists in turn of a pair of integers specifying the starting and ending byte positions in the rawdata of the document with the first byte of the document counting as byte NUM
this means that a loop using firstdocument and nextdocument must visit all documents which were in the collection when firstdocument was called if and only if the documents are not deleted before the loop reaches them
since to create a multinc nial distribution all possible outcomes must be accounted for an additional count is kept of all of the words in a document are not members of any of the disfinguishing term sets
the second type is pop transition in which transition is determined by the content of the stack
if this set of terms is too small feedback is generally employed in which the full corpus of documents to be classified and routed is compared to the set prevalent words and phrases from highly ranked retrieved documents are added to the set and the full corpus is run again against the larger set of terms
the music passage has many music related words such as studio album disc and record and the sports passage has many sports related words such as scored beat championship game and rebounds
score og ni x log weighti nl ilk ilk l ill n number of words in document k number of classes ni number of terms from the i th set nk l number of words which do not match any set
the task of a re estimation algorithm is to assign probabilities to transitions and the word symbols defined at each terminal transition
figure NUM shows the average counts of insides used in estimating NUM trees randomly selected from NUM NUM samples
not surprisingly that disc was well crafted but a bit void of feeling unfortunate considering the wondrous synergy of heart and craft on sting s masterwork NUM s nothing like the sun
pop transitions represent the returning of a layer to one of its possibly multiple higher layers
the identified insides however do not cover all the insides needed in computing an outside probability
then we establish an a link a pointer from each node u of t u some factor to the implicit node h u of t if h u exists
the model was complete enough to solve any problem of the circuit that involved missing wires
for the purposes of the experiment failures were introduced by removing one or two wires
our success in using definitions of word senses to overcome the data sparseness problem may also lead to further improvement of sense disambiguation technologies
semantic coherence of text on which both systems rely is generally stronger in technical writing than in most other kinds of text
it is possible that a similar approach based on dictionary definitions will be successful in acquiring knowledge of local constraints from a reasonably sized corpus
however using definitions from existing dictionaries rather than derived sets of similar words allows our method to work on corpora of much smaller sizes
our approach works well even with a small window because it is based on the identification of salient concepts rather than salient words
similarly looking at the meaning of the words one should find that some concepts co occur more often with some concepts than with others
the meaning or underlying concepts of a word are very difficult to capture accurately but dictionary definitions provide a reasonable representation and are readily available
by overcoming the data sparseness problem contextual information from a smaller local context becomes sufficient for disambiguation in a large proportion of cases
here s i denotes one of NUM NUM chinese syllables and c i denotes one of NUM NUM chinese characters
ii the type ii addition repair can be represented as follows v different from the above replacement repairs the repaired segment and the repairing segment in this type do not match any characters
a repair is proposed when a string of syllables repeats within an utterance or between two consecutive utterances
in the wc type a wrongly converted syllable is changed to the correct one by the repair processing
in spontaneous or conversational speech we find that there is a significant unfilled pause silence between a repaired segment and a repairing segment for repetition repairs NUM whereas actual or intended repeated characters syllables usually do not have any unfilled pauses between them
that is a repair is proposed when a string of syllables repeats satisfies the criteria of baseline model unfilled pause and glottal stop and the first syllable of the string does not belong to type i cue patterns
based on type ii cue patterns some additional repairs can be proposed when a string of syllables repeats it does not satisfy the criteria of unfilled pause and glottal stop but the first syllable of the string belongs to type ii cue patterns
for example if the system were asked to debug the circuit with no wires it would systematically discover each missing wire and request that the user install it
it determines the role that the computer plays in the dialog by determining how user inputs relate to already established dialog as well as determining the type of response given
our answer to this problem was to post the experimenter nearby and to allow him or her to give the subject several different standard error messages if they were needed
the central pragmatic issues for the management of expectation are what are the sources of expectation or how is the expectation list created and how is it used
numbers in parentheses are for the first eight problems only the last two problems were repeats of the practice problems from session NUM significant problems with execution time
NUM if the incoming utterance is not in the locally active subdialog the expectations of other active subdialogs provide a means for tracing the movement to those subdialogs
these capabilities include problem solving natural language input and output a user model variable initiative and the use of expectation for speech error correction and plan recognition
we propose a general method for determining agreement between two analyses
numbering subtrees we number the subtrees in each corpus as follows
in other words two subtrees have the same yield
these proposals have to do essentially with formal aspects of markup
occurrences of structural delimiters are taken to be properly nested
alignment can be used to detect disagreements between manual annotators
we omit the superscript indicating which corpus we are dealing with
while the corresponding part of the treebank looks as follows
as we needed to segment chinese and japanese texts into separate tokens we adapted the new juman tagger segmenter for japanese and the nmsu segmenter for chinese
we ll refer to a subtree s number as its index
let subtrees c be the set of yields in c
the two main approaches to multi word term conflation in ir are text simplification and structural similarity
a method for term variant extraction based on morphology and simple co occurrences would be very imprecise
NUM rich indexing simple indexing improved with the extraction of variants of multi word terms
the effectiveness of rich indexing is more than three times better than effectiveness of simple indexing
the distinguishing terms are found by processing a training set of documents which are representative of the class
chemical and physical properties gestion de l eau management of the water comp
the derived forms are expressed as tokens with feature structures NUM
null this paper deals with type NUM and type NUM variations
we also plan to explore analysis of semantic variants through a predicative representation of term semantics
type NUM variations are classified according to their syntactic stucture
the system has also been exploited to help segment and index broadcast video and was used for early experiments on variants of the co reference identification task
the perspective is selected using focus constraints the choice between embedding or subordination is based on simple stylistic criteria
another option to attach a second relation is to add it as a separate clause to avoid deeply embedded structures
a simple selection algorithm that reduces the level of morphological ambiguity using the probabilities obtained from the corpus
intuitively left and right encode what can precede or follow the category they appear on in and out encode what actually does precede or follow and store encodes the information to be passed up the tree
dialogue a temporal expression satisfying the constraints under analysis
intervals in general are represented by their boundaries
the task of the latter is to derive a client oriented semantic representation including the communicative intention and the complete specification of time points needed which is based on context and semantic inferences
that is we do not attempt to fit each domain relation under a general ontology based on linguistic generalizations
this request is mailed to the human partner
starting from the main verb NUM a bidirectional search is performed whose domain is restricted by special clause markers sines output yields information about the utterance relevant for the subsequent semantic analysis
server and agents consider the dialogues as completed
this supports easy modification and exchange of plans
one such instance is shown in figure NUM
with lexical constraints that hold between two or more words it is not critical which word is chosen first
the rules are evaluated in order and each rule is allowed to run to completion only once in the course of processing
the performance of wsd is reaching a high stance although usually only small sets of words with clear sense distinctions are selected for disambiguation e.g.
since only the best tagging pattern for each sentence is used for reestimating the parameters such a training procedure will be referred to as a viterbi training vt procedure in contrast to an em algorithm dempster NUM which considers all possible patterns and their expectations
the two vtw modules in figure NUM are identical and each vtw module goes through NUM training iterations
this shows that the viterbi training procedure does provide a significant improvement in precision while maintaining a reasonable recall
this is required because the extracted dictionary is large and human verification will be both subjective and time consuming
in the simplest topology the viterbi training procedure for words is applied until the word segmentation parameters converge
this process repeats until the tagging results no more change or until a maximum number of iteration is reached
the feature values for the n grams are estimated from the statistics of the n grams in the large unsegmented corpus
intuitively a character n gram is likely to be a word if it appears more frequently than the average
unfortunately we are unable to afford the man power for such an evaluation on the large corpus
in other words the control for the execution of principles is not fine grained enough
tfs did not allow universal principles with complex antecedents but only type constraints
expressing grammatical constraints in such a way is both time consuming and error prone
specifies that the universal principles about signs can only be executed in case they are determinate
the ale type constraints were designed to enhance the typing system and not for recursive computation
the execution of some goal is postponed until the call is more specific than a user specified term
although cuf does not offer universal principles their addition should be relatively simple
we are also performing system level integration testing to validate the data passing through each process within the canis application
one round of reshuffling corresponds to moving each word in the vocabulary from its original lass to another class whenever the movement increases the ami starting from the most frequent word through the least frequent one
the figure shows the error rates with zero two and five rounds of reshufi ing NUM overall high error rates are attributed to the very large tag set and the small training set
to cmculate NUM ltere we use the superscript i j to indicate that ck i ck j w as merged in the previous merging step
ing automatically created word bits and handcrafted linguistic questions figure NUM also shows that reshuming the classes several times just after step i mlclustering of the word bits construction process filrther improves the word bits
time colitplexity or this basic algorithnl is o v NUM when iinph rueui ed sl rmglitforwardly l rcb y storing the resu of all the trim nierges lie previous inerging step however the tinie coniplexity call be reduced to o v NUM s shown t elow
for both wsj texts and atr corpus the tagging error rate dropped by more than NUM when using word bits information extracted from the 5mw text and increasing the clustering text size further decreases the error rate
at each merging step merging of only the lasses in the merging region is considered thus reducing the number of trial merges from o v NUM to o c
it defines recall and precision in a geometric way
actual the number of fills provided in the response
the ordering of identically ranked object pairs is arbitrary
for example singular pieces of textual data also known as slot fills may be combine d with others to comprise a larger cohesive body or object of information
emacslisp suited ou r needs at the time which included rapid prototyping a familiar text interface and a portable lisp available to al l participating and evaluating sites at no cost
however the mapping function was much simplified because the tags were anchored in the text and the text had to b e overlapping in order for the items to be candidates for mapping
the purpose of connections is that without them the slot fills of an unmapped ke y will be scored as missing and the slot fills for an unmapped response will be scored as spurious
however the following lemma shows that any subtree can always be rebalanced at its root if either of its children is a singleton of either language
sentences containing more than one word absent from the translation lexicon were also rejected the bracketing method is not intended to be robust against lexicon inadequacies
in other words the tree structure constraint is strong enough to prevent most false matches but almost never inhibits correct word matches when they exist
since this eliminates all degrees of freedom in the english sentence structure the parse of the chinese sentence must conform with that given for the english
our method based on sitgs operates on the novel principle that lexical correspondences between parallel sentences yields information from which partial bracketings for both sentences can be extracted
in tandem with the concept of bilingual language modeling we propose the concept of bilingual parsing where the input is a sentence pair rather than a sentence
no additional string pairs are generated due to the new productions since each xi is only reachable from xi NUM and x1 is only reachable from a
for example given the constituent matchings depicted as solid lines in figure NUM the dotted line matchings corresponding to potential lexical translations would be ruled illegal
string fill from text or string no title see fill rules
nh represents nominal heads nouns adjectives pronouns numerals ing forms and nonfinite ed forms
definition a general categorization of the reason why the post was is or will be vacant
with this we ensure that two contradictory constraints if there are any do not cancel each other
the following is genuine output of the linguistic cg NUM parser using the NUM syntactic disambiguation rules
the compatibility values assigned to the hand written constraints express the strength of these constraints compared to the statistical ones
they do not however actually contain the realization statement to set the textual order of the purpose expression which would read purpose nucleus later systems in this branch of the decision network execute this statement
they may be linked with a variety of conjunctions or prepositions the issue of linker and may or may not be combined into a single sentence with the expression of their sub actions the issue of clause combining
it should be noted that while the determinations made by the systems are based solely on the results of the corpus analysis conducted in step NUM the following sections will include intuitive motivations for the realizations the systems make
the generated NUM because the execution of the content and rhetorical status selection sub network is interleaved with the execution of the grammatical form selection sub networks this structure alone would never exist at any point in the execution of the network
we view content selection as the process of choosing the appropriate actions from the process plan to express and rhetorical status selection as the process of choosing the appropriate rhetorical relation to be used in expressing each of these actions
NUM this paper focuses on the grammatical form selection portion of the network that is the choice given an action to be expressed and its rhetorical status of the appropriate lexical and grammatical forms of expression
before after or during scoping and without recourse to higher order unification
yet many systems for good practical reasons employ this kind of architecture
we replace both the term and its index by the corresponding term and index from the ellipsis
thus no rules are given for evaluating terms or their indices in isolation
NUM a standard example is NUM john loves his mother and simon does too
in the literature the first reading would not be viewed as a case of strict identity
they are not syntactic operations on qlf expressions they are part of the qlf object language
whichever seeping is given to the antecedent a parallel seeping should be given to the ellipsis
this section illustrates the substitutional treatment of ellipsis through a small number of examples
the elliptical qlf will contain a predicate formed from the antecedent qlf plus substitutions
these include the now standard components of the formal moc evaluations name tagging ne in muc NUM name normalization we and template generation st
null the order of the parsing performance is generally the following the same domain best the same class all domain the other class and the other domain worst
a clumping for a sentence partitions e into a tuple of clumps c the number of clumps in c is denoted by g c and is an integer in the range NUM g e
can be calculated in o e e 2e f time if the maximum clump size is unbounded and in o e e i f if bounded
a statistical nlu system translates a request e as the most likely formal expression according to a probability model p are maxp f e are maxp f e
the reason for this is that these undesirable words are frequently adjacent to the english words early and morning hence the training algorithm includes contributions with two word clumps containing these extraneous words
as a further check the a values for from to and the two special classed words city l and city NUM are near NUM ranging between NUM NUM and NUM NUM
table NUM sample of a centered text segmentation analysis
the centered segmentation algorithm reveals a pretty good performance
cluster NUM is also formed from clusters d e f of threshold NUM NUM
the computation starts at u1 the headline
it monitors the file transfer from the computer
note the sequence of shift transitions
the model nevertheless still has several restrictions
an empirical evaluation of the algorithm is supplied
closed segments are inaccessible for the antecedent search
table NUM lifting to the appropriate discourse segment
analysis of the system s performance in this pilot study however as well as annotator comments in a post study questionnaire confirmed that context is quite important
ultimately we intend to measure this in an objective quantitative way by comparing term usage across corpora however for this study we relied on human judgments
the i category captures cases where the correspondence that has been identified may not apply directly at the single world level but nonetheless does capture potentially useful information
as always there is a tradeoff between recall and precision by default sable will choose a likelihood threshold that is known to produce reasonably high precision
NUM however these are not the real figures of interest here because we are mainly concerned in this study with the acquisition of domain specific translation lexicons
the matching heuristics all work at the word level which is a happy medium between larger text units like sentences and smaller text units like character n grams
in previous experiments on the hansard corpus of canadian parliamentary proceedings sable had uncovered valid general usage entries that were not present in the collins mrd e.g.
table NUM provides a qualitative demonstration of how a lexicon entry gradually improves as more e filters are applied
since the filters only serve to remove certain translation candidates any number of filters can be used in sequence
for example a noun in one language is very unlikely to be translated as a verb in another language
if we plot v1 against v2 we can get a diagonal line with slope j i
bible scores were maximized for lexicons using t he cognate filter when a lcsr cut off of NUM NUM was used
for example bible evaluations were used to find the precise optimum value for the lcsr cut off in the cognate filter
for instance several determiners or prepositions in the french sentence often matched the same word in the english sentence
for the case of french and english each of the presented filters makes a significant improvement over the baseline model
these conclusions could not have been drawn without a uniform framework for filter comparison or without a technique for automatic evaluation
the evaluators knowledge of the language and familiarity with the domain also influenced the results
our algorithm bypasses the sentence alignment step to find a bilingual lexicon of nouns and proper nouns
if the scope were underspecifled explicit subordination constraints would be used in a specim scope slot of the vit
the corresponding semat l ic representations are given in 2a and 2b respectively
then it is left to the language specific grammars to make the right lexical choices
it uses a level of underspecified senmntic representations as input and output of transfer
our transfer equivalences abstract away from morphological and syntactic idiosyncracies of source and target languages
the results presented in this article have been implemented and integrated into the verbmobil system
in addition we use matching on first order terms instead of feature structure unification
after skolemization of the sen antic representation the input to transfer is variable free
section NUM of this paper sketches the semantic representations we have used for transfer
the german comparat ive construct ion lieber sei n lit bc
search strategies in section NUM and model evaluation in section NUM are described next followed by the results of an extensive disambiguation experiment involving NUM ambiguous words in sections NUM and NUM
the system is also used in the taurus multimedia database software from dci data concept informatique to create an index on one field of a structure defined by the user of the database and to retrieve the corresponding information even if it is misspelled
in other words we assume that the cue has no effect on changing the current initiative indices
furthermore a cue may provide stronger support for a shift in dialogue initiative than in task initiative
domain questions are questions in which the speaker intends to obtain or verify a piece of domain knowledge
NUM who is teaching nlp 3a a dr smith is teaching nlp
the resulting dependency functions are however much bigger and consequently the search space is also
the algorithm the overall strategy is to process a pas recursively assigning quantifiers to the most deeply nested structures first
the focus minimum is defined along the same lines as he focus maximum except that the minimum set size is taken
in general this improves precision since widescope brackets are less constraining
denote the nonterminal label on q by f q
we now turn to the expressiveness desiderata for a matching formalism
in effect we are relying on the tendency of syntactic arguments to correlate closely with semantics
second sentences containing nonliteral translations obviously can not be aligned down to the word level
since the realistic pa rt of such errors in czech is the i y dichotomy on homophonic past tense verb endings occurring on plural verbs i ending sta ndrag with l lural masculine animate subjects y ending with plural masculine inanimate and feminine subjects the preprocessing finite state automaton marks all sentences not conta ining
correlations to bear on the task of extracting linguistic information for languages less studied than english
and the purpose of the algorithm is to assign values to the oi s given some suitable model
henceforth all transduction grammars will be assumed to be in normal form
this would reduce errors for known idiosyncratic patterns at the cost of manual rule building
in section NUM we introduce quantifier raising and review two types of synchronization and mention some new formal results
the elementary structures are shown in figure NUM we only give one np the others are similar
it has to do with choosing some particular subset of the model on which to base the description
we can not say at this point that lrs provide any advantages in computation or quality of the deliverables
in their procedure the exact conditional test was used to guide the generation of new models and the test of model predictive power was used to select the final model from among those generated during the search
mm distinguishing the two a decision wa s lm er made to limit ourselv s i i lenli y rela i ions
the tag enamex entity name expression is used for both people and organization names the tag nunex numeric expression is used for currency and i ercentages
flnmtional relations the committee recognized that in seh eting sneh internal measures it was inaking sortie presumi tion regarding the structures and decisions which an analyzer should make in understanding a docllmellt
to meet this goal the con mittce developed the named entity task which t asically involves identifying the names of all the people organizations and geographic locations in a text
the in and out template contains references to the tmnt lates fl r the person and tbr the organizati n from which the person came if he she is starting a new job
not everyone would share these pre sumplions lint participants in the next mu j would be free NUM o enter the infornlation extraction evaluation and skip some or all of these internal ewdua lions
most of the systems participating in muc NUM employed a cascade of finite state pattern recognizers with the earlier pattern sets recognizing entities and the later sets recognizing scenario specific patterns
getting systems which can be custonfized by others is also a tall order given the complexity and variety of knowledge sources needed for a typical muc information extraction task
philosol hers of language have been arguing over reference and coreferencc for centuries so we should not have been surprised that it would t e so hard to prepare a precise and consistent definition
mmol alion l ro e ss was lo i lemify and res lv l r fl h ms
zweigenl a ilni NUM lcb i and is used for NUM ilol
table NUM shows a comparison of accuracy on the test set of NUM cases
figure NUM comparison of three adjustment methods
table NUM comparison across different application environments
the findings reported here have been implemented and tested on the basis of spanish and english businessand finance related corpora
figure NUM shows an excerpt from a feature lattice with the atomic features including word length NUM NUM NUM NUM NUM capitalization c spellings mr dr and others
let pc i and pc be two probability distributions of labels ci and over contexts ct the relative entropy between pc and p c is
the optimized feature space can be seen as a feature lattice defined sindeed any time we see a configuration for the words mr3 and dr it always includes their length NUM and their capitalization feature c together with their spellings
we adjust error prone words NUM y collecting the
another important improvement is that since the smaller model deviates from the previous larger model only in a small number of constraints we use the parameters of the old model as the initial values of the parameters for the iterative scaling of the new one
the convergence region of this parameter for the cumulative of the frequency function as rank approaches infinity is also investigated
without full fledged linguistic analysis some ambiguities will not be resolved p NUM
html files can be generated with hard coded instructions to emphasise fixed combinations of semantic labels
for every feature from the candidate feature set the algorithm prescribes to compute the maximum entropy model using the iterative scaling algorithm described above and select the feature which minimized the kullback leibler divergence or maximized the log likelihood of the model in the largest way
the bulk of these data preparation tasks were concentrated into phase i but additional data preparation efforts to support muc met and trec have continued as needed since the completion of phase i in NUM
no actual html codes were furnished but the semantic labels are noted according to the html style see figure NUM
the lsp subselection module generates a pseudo html file consisting of semantic labels and the terminal elements of the parse trees
the former can take advantage of the language independent developments of the latter while focusing on idiosyncrasies for dutch
this is due to the fact that the selection module takes syntactic relationships into account during the semantic disambiguating phase
in some occasions it would be better to do so as the dmlp lsp tree converter sometimes changes the word order
the dtds can act as a locally defined view gui aspect on common sgml data nlp aspect
this temporary file is directly fed into the browser and displayed as a second www page pds page
in figure NUM a sample run of algorithm NUM is schematically represented
for instance although the words duck ducks the ducks and duekdrink all exist and contain the meaning duck the symbol is only written into the description of duck
our analysis accounts for both examples through a mutually constraining interaction of parallelisms
thus there is an incentive to associate meaning with sound although of course the association pays a price in the description of the lexicon
if a text word can have some associated meaning then writing down that word to account for some portion of text also accounts for some portion of the meaning of that text
for example if viterbi analyses are being used then the new word watermelon will completely take the place of all compositions of water and melon
its redundancies commonly manifest themselves as predictable patterns in speech and text signals and it is largely these patterns that enable text and speech compression
furthermore it is more computationally efficient to add and delete many words simultaneously and this complicates the estimation of the change in description length
if there is mutual information between the meaning and text portions of the input then better compression is achieved if the two streams are compressed simultaneously
null coincidences like scratching her nose do not exclude desired structure since they are further broken down into components that they inherit properties from
this approach does however capture the paradigmatic generalization that is represented by the rule and simplifies lexical acquisition
we soon found ourselves in the situation where tipster phase i program execution and data preparation were occurring simultaneously
coreferential with john and x4 with bill
the experiment and comparison reported above suggests that our more comprehensive subcategorization class extractor is able both to assign classes to individual verbal predicates and also to rank them according to relative frequency with comparable accuracy to extant systems
setting a threshold of less than or equal to NUM NUM yields a NUM or better confidence that a high enough proportion of patterns for i have been observed for the verb to be in class i NUM
the ability to recognize that argument slots of different subcategorization classes for the same predicate share semantic restrictions preferences would assist recognition that the predicate undergoes specific alternations this in turn assisting inferences about control equi and raising e.g.
an initial experiment on a sample of NUM verbs which exhibit multiple complementation patterns demonstrates that the technique achieves accuracy comparable to previous approaches which are all limited to a highly restricted set of subcategorization classes
they report an accuracy rate of NUM NUM errors at classifying NUM classifiable tokens of NUM distinct verbs in running text and suggest that incorrect noun phrase boundary detection accounts for the majority of errors
we randomly selected a test set of NUM in coverage sentences of lengths NUM NUM tokens mean NUM NUM from the susanne treebank retagged with possibly multiple tags per word and measured the baseline accuracy of the unlexicalized parser on the sentences using the now standard parseval geig evaluation metrics of mean crossing brackets per sentence and unlabelled bracket recall and precision e.g.
NUM a lemmatizer is used to replace word tag pairs with lemma tag pairs where a lemma is the morphological base or dictionary headword form appropriate for the word given the pos assignment made by the tagger
the performance of the filter for classes with less than NUM exemplars is around chance and a simple heuristic of accepting all classes with more than NUM exemplars would have produced broadly similar results for these verbs
brent does not report comprehensive results but for one class sentential complement verbs he achieves NUM precision and NUM recall at classifying individual tokens of NUM distinct verbs as exemplars or non exemplars of this class
NUM the theory also contains defaults to capture the persistence of activation persists and the willingness of participants to assume that others have a particular belief or goal credulousb and credulousi respectively
a document is considered as a positive example for all categories with which it is labeled and as a negative example to all others the algorithms are on line and mistake driven
in text categorization given a text document and a collection of potential classes the algorithm decides which classes it belongs to or how strongly it belongs to each class
our results significantly outperform by at least NUM all results which appear in that table and use the same set of features based on single words
algorithms that allow the use of negative features such as balancedwinnow and perceptron tolerate variation in the documents length naturally and thus have a significant advantage in this respect
we note that the standard treatment in ir suggests a solution to this problem that suits batch algorithms algorithms that determine the weight of a feature after seeing all the examples
however it seems plausible that most categories depend only on fairly small subsets of indicative features and not on all the features that occur in documents that belong to this class
in order to rank documents for each category a text categorization system keeps a function fc which when evaluated on d produces a score fc d
in general there is no guarantee that a weight vector of this sort exists even in the training data but a good selection of features make this more likely
in addition we have tested our final version of the classifier on two common partitions of the complete reuters collection and compare the results with those of other works
unary branching in the case of unary branching the inverse of yield will not be a function
secondly the nature of the muc NUM tasks is such that only a small percentage of the marks are available for deep analysis and so suc h an analysis is counter productive unless it achieves an extremely high level of robustness
official bottom line and unofficial total slot scores are louella recognized NUM of the succession events after one person month of development
removal of this heuristic alone with no other pattern modifications increased the recall on the walk through message from 15r 80p to 44r 61p
even though post processing chose the wrong organization for the walk throug h message it still got two extra points for having an organization
for this application the other org portion of the in and out object was filledin here based on the information gathered about that person
when this happens the person element must be re instantiate d as an additional in and out object in order to collect the correct value
this phase of text processing extends from the named entity system through to the template element and includes the scenario template phase
next each word is analyzed morphologically and tagged with its possible parts o f speech which are found in the lexicon
through corporate mergers this subgrou p has become the language exploitation technologies group of the lockheed martin management and data systems division
the first text processing module used is nllex a lexical analyzer development package which handles the character string to word translation and tokenizes the text
the ne system louella s named entity system is a multi pass process which builds upon entities whic h are found in previous passes
in particular in a test suite of around NUM windows provided to us by our trial user group we obtained reasonable results no egregiously bad groupings on all of them without undue sensitivity to the exact weights
too many semantic distinctions make coding difficult
the reliability of a dialogue structure coding scheme
ls the utterance a command statement
allowing tentative conclusions to be drawn
the move coders reached k NUM
our coreference resolution system was built from several components each of which addressed differen t types of coreference
throughout the system description section words and phrases which appear in articles will be displayed i n italics
the heuristic we use is similar to the one used to determine whether a headline word should be downcased
the third non standard component determines whether s or is a genitive marker or part of a company name
the system identifies these instances of it by scanning tagged text and applying partly syntactic and partly lexical tests
for example mr dooner is identified as the subject of to succee d with mr james as the object and the sense of the verb is correctly disambiguated because of the pre defined topic of the article
this database contains names of continents islands island groups countries provinces cities and airports
similarly the wordnet entry socialgroup tends to subsume nouns which can have groups of individuals as their referents
in the absence of scope constraints ldeg for a udrs with n quantificational structures q that is including indefinites this results in n scope readings as required
since the named entity task is based on the output of the full core analysis of a text errors in phase s such as semantic analysis can result in the loss of named entities already clearly identified by previou s phases
figure NUM micro muc scenario template test result s figure NUM shows the performance results for the training and test data
to illustrate the method for estimating a performance function we will use a subset of the data from tables NUM and NUM shown in table NUM
in this way the field can make progress on identifying the relationship between various factors and can move towards more predictive models of spoken dialogue agent performance
without the ability to calculate performance over subdialogues it would be impossible to test the effect of the different presentation strategies independently of the different confirmation strategies
for subdialogue NUM in figure NUM which is about the attribute arrival city and consists of utterances a6 and u6 ct s4 is NUM
the repair utterances in figure NUM are a3 through u6 thus c2 d1 is NUM utterances and c2 NUM is NUM utterances
given the definition of success and costs above and the model in figure NUM performance for any sub dialogue d is defined as follows it
fourth performance can combine both objective and subjective cost measures and specifies how to evaluate the relative contributions of those costs factors to overall performance
the paradise performance measure is a function of both task success and dialogue costs ci and has a number of advantages
fortunately experience suggests that grammars exhibiting hidden head recursion can often be avoided
the basic idea behind memorization is simple do not compute things twice
the previous example now becomes head link s verb
obviously the same compilation technique can be applied for the head corner parser
this is a bottom up parser that records only inactive edges
the combined acoustic score as defined in the word graph
the implementation of memorization uses prolog s internal database to store the tables
the predicates dealing with the result table are defined in NUM
we prefer paths with a smaller number of such projections
we prefer paths with a smaller number of such skips
in our current implementation all modules except discourse structuring are defined uniformly using system networks
we derive the participants ttminatic roles deep cases in accof dance with a set of general rules
thereby it is possible to distinguish between the pure disposal and the disposal thai is accompanied by ownership
furthermore verschenken s presupposition includes the semantic roles delivered by its prototypical meaning description
set profits both from the much larger semantic coverage and from the fine grained lexical analyses which reflect inferential behavior
the construction of lexical entries is one of the crucial and challenging tasks given in the field of computational linguistics
moreover section NUM provides evidence that the four main points stated above are backed up by the joined analyses
however bsfs do not only provide the ground for the derivation of grammatical features
luring mechanisms in tern s of t r supposii ions
the axioms are given in figure NUM o denotes temporal overlapping
a sequence of events can further be emphasized by a marker
the alignments computed for the susanne corpus and corresponding portion of the penn treebank have been presented and discussed
ings NUM o successful phase NUM parsing produ es the parse tree whose form is presented in figure NUM
first the certainty factors of the constituent pailts are summed
essentially a letter to sound rule can be viewed as similar to a phonological rule in classical phonology except that it converts a grapheme string to a phoneme string
matched graphemes are not deleted the word is left intact since consumed graphemes could be part of the righthand context of some future rule
primary stress NUM stress is a requisite for all words except certain words already marked otherwise in the dictionary and noun compounds
the entire sentence can be ambiguous as in les ills sont jolis where ills is pronounced differently depending on the meaning sons or threads
with one buffer the rs string replaces the ls string so the left context of a rule must be written according to the rules previously used
text normalization i.e. replacing numbers abbreviations and acronyms by their full text equivalents and grapheme to phoneme transcription can be achieved using this formalism
in some cases the input string is modified to add a morpheme boundary or to replace the suffix by another suffix to continue the conversion
of the words missed NUM NUM NUM NUM missed by only one segmental phoneme or phone and NUM NUM NUM NUM had incorrect stress placement
x y context free or x y w z context sensitive
english is scanned once from right to left to better take into account the suffixes of the word which in certain cases determine the stressed syllable
figure NUM a and NUM b show that signal to noise ratio snr is greatly improved
by combining filters on all three levels of resolution we gather as much evidence as possible for optimal result
the algorithm s performance discussed herein can definitely be improved by enhancing the various components of the algorithms e.g.
nevertheless plotalign algorithms seem to be robust enough to produce reasonably high precision that can be seen from figure NUM figure NUM a shows that a normalization and thresholding process based on one to one constraints does a good job of filtering out noise figure NUM b shows that convolution based filtering remove more noise according to the assumption of structure preserving constraint
however isolated short segments surrounded by deletions are likely to be missed out figure NUM b shows that filtering based on ht missed out the short line segment appearing near the center of the dotplot shown in figure NUM b
two corpora agree on an analysis when they bracket off the same content
in this section we give a brief example to illustrate the operations of the algorithm
that is if ga c i is evaluated with l bound to the left string position of category a then c r will be evaluated zero or more times with r bound to each of a s right string positions r corresponding to i
the first implementation of the sp is discussed examples illustrate the planning process in action
in the case of database retrieval additional context is provided by the structured nature of the data
it suggests that comparable levels of performance may be achievable for other text types as well
woods occurred in NUM NUM documents and had an idf of NUM NUM
the leading systems achieved very high accuracy for personal name recognition
in news text and queries names occur with much greater frequency see table NUM
the numbers here have been altered slightly for the purposes of exposition
while we used a fixed set of NUM adjectives for our analysis the number of adjectives in unrestricted text is much higher as we noted in section NUM this multitude of adjectives combined with the dependence of semantic markedness on the domain makes the manual identification of markedness values impractical
in classical linear modeling the response variable y is modeled as y btx e where b is a vector of weights x is the vector of the values of the predictor variables and e is an error term which is assumed to be normally distributed with zero mean and constant variance independent of the mean of y
this process gave us a sample of NUM adjectives both frequent and infrequent ones in NUM pairs NUM we separated the pairs on the basis of the how test into those that contain one semantically unmarked and one marked term and those that contain two marked terms e.g. fat lhin removing the latter
solutions to this problem are applicable to the more general task of selecting the positive term from the pair
the most sophisticated complex learning method offers a small but statistically significant improvement over the original tests
under the null hypothesis the number of correct responses follows a binomial distribution with parameter p NUM NUM
only the tests of discourse structure and animacy are difficult and for these we have had to approximate what a more sophisticated system might be able to do
to identify which variables we should keep in the model we use the analysis of deviance method with iterative stepwise refinement of the model by iteratively adding or dropping one term if the reduction increase in the deviance compares of the frequency method dotted line and the smoothed log linear model solid line on the morphologically unrelated adjectives
if word types rather than word frequencies are measured we can select to count homographs words identical in form but with different parts of speech e.g. light as an adjective and light as a verb as distinct types or map all homographs of the same word form to the same word type
word bits for all the words ill the vocabulary introducing wor l bits hito i he ati lcb i ecision tree dos tagger is shown to signific mt ly reduce l he ti gging error rld e
because an unmemoized cps procedure can produce multiple result values its memoized version must store not only these results but also the continuations passed to it by its callers which must receive any additional results produced by the original unmemoized procedure
interpretations of gestures as location features are assigned a general command type which unifies with all of commands taken by the system
reduce union map b a p the expression alt fa fb evaluates to a function that maps a string position NUM to fa l u fb NUM
this leaves a very large number of choices if both sentences are of length i m then thel NUM NUM possible lracjw ngs with fanout NUM none of which is better justitied than any other
less important is location information in fact the use of such information actually results in a slight overall degradation of system performance
the paragraph and clause heuristics also seem to be useful with the omission of the clause heuristic causing a considerable degradation in performance
modular constraint based event recognition the system described here consists of currently three analysis modules and an event manager see figure NUM
her algorithm disambiguates noun sequences by using the dictionary to search for predefined relations between the two nouns e.g. in the sequence bird sanctuary the correct sense of sanctuary is chosen because the dictionary definition indicates that a sanctuary is an area for birds or animals
the references have ranged from detailed custom built lexicons e.g. l NUM to standard resources like dictionaries and thesauri like roget s e.g. NUM NUM NUM
many heuristics look for analogous adjacency patterns either in dictionary definitions or in example sentences e.g. write a mystery is disambiguated by analogy to the example sentence writes poems and essays
the disambiguator has been used in two retrieval programs imengine a program for semantic retrieval of image captions and netserf a program for finding internet information archives NUM NUM
thus any subtree terminal is allowed to mismatch with any word of the input sentence
we believe that such an approach is also reasonable from a cognitive point of view
moreover also known subtrees may have population probabilities that differ from their sample probabilities
if we still want to perform experiments with dop3 we need to limit the mismatches as much as possible
no is equal to the difference between the total number of distinct np subtrees and the number of distinct np subtrees seen
in order to clarify this we show in table NUM NUM the adjusted frequencies for a class of NUM np subtrees
these were obtained by dividing the atis trees at random into NUM training set trees and NUM test set trees
in order to test the usefulness of this feature we performed different experiments constraining the depth of the subtrees
in the evaluation work when the speakers were asked to decide their preferences for anaphors in the machine generated texts they may find less complete information shown in the test texts than what they are used to in creating their own texts and hence it may be difficult for them to make their own decisions
thus unknown words are assigned a lexical category such that their surrounding partial parse has maximal probability
table NUM trigram perplexity measurements on lm95 swbd dev
in the case o f machine learned rules it restricts the size of the search space on each epoch of the learning regimen thus making it tractable
furthermore all interactions are conducted in source language only which means that target knowledge is not a prerequisite for users of itsvox
some checks were performed during download as part of the operational semantics of our relation definitions but most of the checks were simulated a posteriori by database queries
as noted earlier the two categories for the object still make both scope possibilities available as desired
to improve speech recognition performance we restrict the vocabulary to NUM words
since the parser orders the ilts based on a measure of acceptability this choice is likely to have the relevant temporal information
considering the most recent antecedent as often as possible supports robustness in the sense that more of the dialog is considered
for both this and anaphoric relation NUM there are subcases for whether the starting and or ending times are involved
aside from phrasing and inference a relatively small but critical amount of processing is required t o perform the muc NUM named entities and template generation tasks
the better performance on the unseen ambiguous nmsu data over the seen ambiguous nmsu data is due to several reasons
the goal in developing the annotation instructions is that they can be used reliably by non experts after a reasonable amount of training cf
the result of this step is the set of pailts produced by the rules that fired i.e. those that succeeded
the labels of m and ml being the same note that in addition to the nodes m captured from a or b we will also be realizing nodes e
tree adjoining grammars tags are formalisms suitable for natural language processing and have received enormous attention in the past among not only natural language processing researchers but also algorithms designers
these substrings need not be a contiguous part of the input in fact when this tree is used for adjunction then a string is inserted between these two suhstrings
firstly the data structure used i.e. the 2dimensional matrix with the given representation is not sufficient as adjunction does not operate on contiguous strings
following is the main procedure compute nodes which takes as input a sequence rlr2 rp of symbol positions not necessarily contiguous
t p 3t 2p NUM NUM o n2m p NUM o ne pe e m pe
r the right most synchronous point that is associated with the s th morpheme
given a cost width juman outputs the candidates of morpheme sequences pruned by this cost width
smith as an alias after seeing john smith instead of merely predicting smith and john a s possible aliases
languages such as japanese and chines e have no capital letters languages such as german use capitalization for all nouns not just nouns in names
the original algorithm does this by a time synchronous procedure operating on unambiguous observation sequence
NUM given that we urge an even simpler template structure for future mucs one where only two levels are present entities and relationships
the following formulae are straightforward formulations whose observed variables are pairs of words and tags
l the left most synchronous point that is associated with the s th morpheme
however this function does n t differentiate among morphemes whose costs are NUM and NUM
therefore we can estimate model accuracy from the precision at cost width NUM or NUM
a more detailed description of the system components their individual outputs an d their knowledge bases is presented in ayuso et al NUM
the te task takes the entity names found by the ne system and merges multiple references to the same entit y using syntactic and semantic information
after manually tagging the first group we invoked the rule learning procedure
this testing will include human generated responses in the test
circsim tutor v NUM uses an overlay model
compost on on a partial dr s as flmctor and a predicative drs as argument
the arcs in the ini null tial sst corresponding to the different categories were expanded using their cssts
as example we show the lexical entries of our first examplary decomt osablc idiom
the resulting system offers a number of advantages
apart from this rather technical problem two further arguments speak against phrase structure as the structural pivot of the annotation scheme phrase structure models stipulated tbr nonconfigura tionm
as theory independence is one of our objectives the annotation scheme incorporates a number of widely accepted linguistic analyses especially ill the area of verbal adverbial and adjectival syntax
since the combinatorics of syntactic constructions crea tes a demand tbr very large corpora efficiency of annotation is an important criterion tbr the success of the developed methodology a nd tools
a similar convention has been adopted ibr constructions in which scope ambiguities ha ve syntactic effe cts but a one to one correspondence between scope a nd attachment does not seem reasonable cf
in order to reduce their ambiguity potential rather simple flat trees should be employed while more information can be expressed by a rich system of function labels
due to the frequency of discontinuous constituents in non eonfigurational langua ges the filler trace mechanism would be used very often yielding syntactic trees fairly different from the underlying predicate argument structures
finally the structural handling of free word order means stating well formedness constraints on structures involving many trace filler dependencies which ha s proved tedious
we call such additional edges secondary links and represent them as dotted lines see fig NUM showing the structure of NUM
NUM the user only determines the conrponents of a new phrase the program determines its syntactic category and the grammatical functions of its elements
the NUM parameter determines the average NUM among the relevant documents
table NUM showed that words with larger idf tend to have more content
figure NUM the strong deviations from poisson for the word boycott show
this work benefited considerably from extensive discussions with slava katz
the fat tails show up in each of the five years
low frequency words tend to be rich in content and vice versa
but we find that these distributions do not fit the data very well
it is these constituents that form the head phrases of the japanese co occurrence dictionary which describes collocational information in the form of binary relations
the deviations from poisson are more salient for good keywords like
documents are much more than just a bag of words
as can be seen in figure NUM the dialogue acts request comment de liberate and suggest can be inserted to achieve a consistent dialogue
the pircs2 run is a manual query version of the baseline pircs system
also every plan operator can trigger follow up actions h typical action is for example the update of the dialogue memory
in depth processing of an utterance takes place in maximally NUM of the dialogue contributions namely when the owner speaks german only
for our purpose we consider a dialogue s as a sequence of utterances si where each utterance has a corresponding dialogue act si
good examples for the differences in the dialogue structure are the dimogue pairs NUM NUM and NUM NUM
the analysis of the statistical method shows that the prediction algorithm shows satisfactory results when deviations from the main dialogue model are excluded
to prevent this the occurrence of one of these dialogue acts is treated as an unforeseen event which triggers the repair operator
to find out which dialogue acts can be combined we examined the corpus for cases where the repair mechanism proposes an additional reading
the main operation on a drss is the fltnctional 4this is the so called co descriptive apprvach
this section describes a ccg approach to deriving scoped logical forms so that they range over only grammatical readings
also maintains the fill rule factbase a data file containing knowledge describing how to fill fields of output records when they are generated by a trigger in the trigger rule factbase
case frame editor cfe maintains the lexical case frame factbase a data file of how to infer information by analyzing how verbs relate to logical sentence elements e.g.
fill rule editor fre maintains the trigger rule factbase a data file containing the knowledge describing when output records should be created given the existence of semantic concepts
more than one lob tags remain
table NUM shows the sample results
as can be seen the topics are indeed much shorter particularly in
NUM in the implementation generic roles role1 role2 are used to point to the arguments of a process as long as the specific verb is not selected
furthermore since words are selected only once the full syntactic tree is constructed it would be quite difficult if not impossible to account for floating constraints
similarly the muc NUM te properties of organizations and persons configuration of plum uses no information regarding succession of corporate officers and therefore can be used on other domains
in particular chunking parsers which built up small chunks using syntactic criteria and then assembled larger structures only if they were semantically licensed might provide a suitable candidate
this decision is done during phrase planning a process we detail in section NUM as the clause is being constructed the feature cat clause is added cf
thus while we are restricted to selecting a relation as a main clause we are not restricted in how we do the mapping of other input relations to syntactic constituents
we focus on the problem of floating constraints semantic or pragmatic constraints that float appearing at a variety of different syntactic ranks often merged with other semantic constraints
constraints that aid in determining which word is best come from a wide variety of sources including syntax semantics pragmatics the lexicon and the underlying domain
for example if syntactic and lexical constraints are the research focus it may make sense to delay lexical choice until late in the generation process during syntactic realization
the link between the arguments of the relation and its fillers is indicated by path values of NUM and NUM respectively
in the matrix notation used here we use a number in brackets in to both label a value and subsequently represent the path to that value
perspective assignt type focus ai example NUM right network of figure NUM focus alternation with fixed perspective NUM ai requires six programming assignments
notice however that the difference in the two pairwise comparisons confirms that simple categorial information does not perform a filtering action on the structure while lexical co occurrence does
now in all cases growth is exponential in the number of relevant links while the possible gain obtained by not checking features can be at most logarithmic in the number of potential empty categories
the output consists of a tree and a list of two chains the list of a chains and the list of a chains that is chains formed by wh movement and np movement respectively
next section NUM NUM anecodatally examines the complex interactions among the parameters of an extension model
NUM by compilation here and below i mean off line computation of some general property of the grammar for example the off line computation of the interaction of principles using partial evaluation or variable substitution
the lr table contains two actions that match the input one action generates a projection of the input node v without branching while the other action creates an empty object np
grammar NUM differs minimally from grammar NUM because it also NUM the average number of conflicts in the table gives a rough measure of the amount of nondeterminism the parser has to face at each step
NUM NUM ip np lcb case assign if i fin rcb i this rule assigns case correctly only if the attribution is not a function of the subconstituents of i
the numbers in the sentence column refer to the type of construction as exemplified in figure NUM sentence types NUM and NUM are not considered because they contain only trivial chains
thus the growth factor is a function of the number of heads seen up to a certain point in the parse the number of empty categories and their respective order in the input
we also have a binary characteristic for s and t having at least two non nil slot values in common
an exptyp NUM will be explained later
larger lexicons can lead to incremental improvements
while several inductive learning approaches could have been taken for construction of the trainable anaphoric resolution system we found it useful to be able to observe the resulting classifier in the form of a decision tree
for example consider a source with two hidden states
despite the fact that such trees are learning preferences they may not produce sufficient preferences to permit selection of a single best anaphor antecedent combination see the related work section be low
they are processed like documents into queries
in our current machine learning experiments we have taken an approach where we train a decision tree by feeding feature vectors for pairs of an anaphor and its possible antecedent
in these cases the dttool lets the user insert a z marker just before the main predicate of the zero pronoun to indicate the existence of the anaphor
the less common and if and when couhl appear with either ing or NUM NUM
a generator generates a set of possible antecedent hypotheses for each anaphor while a filter eliminates in both training and testing we did not include anaphora which refer to multiple discontinuous antecedents
q he term cnablemeut is commonly used to refer to the procedural relation between preconditions and actions
these results are shown in table NUM
table NUM effect of lexicon based and rule based
the artifact object which was not used for either the dry run or the formal evaluation needs to be reviewed with respect to its general utility since its definition reflects primarily the requirements of the muc NUM microelectronics task domain
given the more varied extraction requirements for the organization object it is not surprising that performance on that portion of the te task was not as good as on the person object NUM as is clear in figure NUM
the same set of articles was used for te as for st therefore the content of the articles is oriented toward the terms and subject matter covered by the st task which concerns changes in corporate management
the template element te task requires extraction of certain general types of information about entities and merging of the information about any given entity before presentation in the form of a template or object
figure NUM overall recall and precision on the co task NUM NUM key to recall and precision scores udurham 36r 44p umanitoba 63r 63p umass 44r 51p nyu 53r 62p upenn 55r 63p usheffield 51r 71p sri 59r 72p
a third slot rel other org required special inferencing on the basis of both linguistics and world knowledge in order to determine the corporate relationship between the organization a manager is leaving and the one the manager is going to
the task places heavy emphasis on recognizing proper noun phrases as in the ne task since all slots except org descriptor and pertitle expect proper names as slot fillers in string or canonical form depending on the slot
whereas the text filter row in the score report shows the system s ability to do text filtering document detection the all objects row and the individual slot rows show the system s ability to do information extraction
also the descriptor is not always close to the name and some discourse processing may be requ ed in order to identify it this is likely to increase the opportunity for systems to miss the information
then there are four possible parses of abc cross entropy rate with source given below lower is better model
this criteria measures the statistical efficiency of a model class according to the mdl framework where we would like each parameter to be as cheap as possible and do as much work as possible
this criteria is widely used in the language modeling community in part because model order is typi null the number of model parameters and the amount of computation required to estimate the model
the first term represents the incremental benefit in bits for evaluating e e in the context w using the more accurate expansion factor NUM w
given a sufficiently rich dictionary of words and a sufficiently large training corpus a model of word sequences is likely to outperform an otherwise equivalent model of character sequences
then we transform that constraint to some internal representation usually a feature structure fs
the lexical analyzer reads the input characters and produces as output a sequence of maximal v 2v and vc tokens as well as tokens of the maximal consonant sequences of the word
more precisely alternative token lists can be generated for sequences where a vowel can be associated to its left or its right neighboring vowel in order to build up a 2v or vc token
it presents additional heuristic rules discovered during an exhaustive search of ambiguous vowel patterns and demonstrates the degree of the resolved ambiguity in terms of the number of vowel sequences that have been disambiguated
establishing lists of exceptions has the same disadvantages as the approach to hyphenating through consulting lists of hyphenated words computer technology institute NUM kolokotroni str NUM NUM patras greece
as the goal of the hyphenator is to identify the permissible hyphen points we interpret v1 v2 v3 and v4 complementarily i.e. in all other cases splitting is allowed
a hyphen is not permitted at the beginning or the end of the word thus the possibility that the substring is located at the beginning or the end of the word is by definition excluded
the approach followed was to first select all sequences of the above sets that were mentioned in various grammar books as examples of diphthongs and to assign them to the category of neversplitting sequences
in regard to vowel sequences set v u 2v u vc has NUM elements and according to lemma NUM complete hyphenation of vowel sequences depends on NUM NUM NUM vowel sequences
existing hyphenators for greek are commercial products and usually work on a minimal basis i.e. finding the hyphen points of consonant sequences and in limited cases hyphens of vowel sequences
a straightforward encoding is achieved by expressing each of these three aspects in a set of relations
the coded objects that comprise the system contribute both recognition rules and processing rules heuristics
converting the first disjunct of append c into a feature structure to start our compilation we get something
unfortunately this performance is achieved by settling on an uninteresting right branching rule set save for sentencefinal punctuation
we can also compute the overlap between the definitions of liable and liability and if they have a significant number of words in common then that is evidence that those meanings are related
NUM the stemmer uses a constraint on the form of the resulting stem based on a sequence of consonants and vowels we found that this constraint is surprisingly effective at separating unrelated variants
these two strategies could potentially be used for phrases as well but phrases are one of the areas where dictionaries are incomplete and other methods are needed for determining when phrases are related
the distinction between homonymy and polysemy is central
homonymy is important because it separates unrelated concepts
the collections were then indexed by the word tagged with the part of speech i.e. instead of indexing book we indexed book noun and book verb
table NUM distribution of zero affix morphology inflected
and this was compared to the previous version
tile set oriented representation allows much simpler operations in transfer for accessing individual entities set membership and for combining the result of individual rules set union
the interlingua rule in NUM identifies the abstract teinl oral location predicates under the condition that the internal argument is more specitlc than the sort time
tile translation is exactly the same but tile german verb passen takes an indirect object mir instead of the adjunct be phrase in NUM
additionally all these special constants can be seen as pointers for adding o1 linking informal ion within ml t between multiple levels of the vit
the rule not only identifies the event marker e but unifies the instances x and y of the relevant thematic roles
a set of grammars based on typological distinctions defined by basic constituent order e.g.
the most closely related theories to that presented here are those of steedman e.g.
languages are represented as a finite subset of sentence types generated by the associated grammar
the algorithm is summarized in figure NUM working memory grows through childhood e.g.
NUM evaluating critical evidence comparing the cache with the stack in this section i wish to examine evidence for the cache model look at further predictions of the model and then discuss evidence relevant to both stack and cache models in order to draw direct comparisons between them
this would mean that the processing of incoming information would be slower until all of the required information is in the cache the ease with which the conversants can return to a previous discussion will then rely on the retrievability of the required information from main memory and this in turn depends on what is stored in main memory and the type of cue provided by the speaker as to what to retrieve
thus one prediction of the cache model is that a natural way to make the anaphoric forms in dialogue b more easily interpretable is to re realize the relevant proposition with an iru as in 8aq my problem is that my daughter is working as well as her uh husband
in fact sidner proposed that return pops might always have this property in her stacked focus constraint since anaphors may co specify the focus or a potential focus an anaphor which is intended to co specify a stacked focus must not be acceptable as co specifying either the focus or potential focus
lavie et a s suggestion is that this problem and the problems that arise from increased ambiguity can both be overcome if the larger domain can be factored into a number of sub domains each of which can be given its own semantic grammar
stronger evidence would be the reaction times to the mention of entities in a closed segment after it is clear that a new segment has been initiated but before the topic of that new segment has initiated a retrieval to and hence displacement from the cache
the specification of the cache replacement policy is left open however replacing items that have not been recently used with the exception of those items that are preferentially retained is a good working assumption as shown by previous work on linear recency
gcg as presented is inadequate as an account of ug or of any individual grammar
they show that using morphological information can increase the accuracy of their tagger on unknown words by a factor of five
languages with close to optimal wml scores typically came to dominate the population quite rapidly
like ne this really is domain independent
however many eu citizens are denied full access to employment opportunities because information may not be readily available and even where it is it may not be available in the right language
the query interface will also keep a record of user profiles so that regular users can repeat a previous search the next time they use the system
symbolic case based reasoning techniques are then applied to quantify the difference between the user s ideal job and jobs held within the database in order to identify those jobs most closely resembling the user s ideal job
tree therefore offers two significant services intelligent search and summarization on the one hand and these independent of the original language of the job ad on the other
many though not all of the slots can be specified as part of the search and all of them can be generated as part of the job summary
there are two criteria for terminological status in our system either of which is sufficient i hierarchical structure and ii standardization
the classification and coding schema of vdab one of the end user partners in the project is used but extensions deriving from other schema could obviously be envisaged
however we improve on the matching efficiency by installing tagging and statistical filters
while highly attractive this seems like a long term research agenda
there are at least three ways one could improve lvcsr for names
the initial prototype system currently implemented can store and retrieve job ads in three languages english flemish and french regardless of which of these three languages the job was originally drafted in
looking for a job as a chef even though individual jobs are coded for specific types of chef pastrycook pizza chef etc and of course in different languages e.g.
in this database language independent data is shared and language specific properties are maintained as well
optional optional edible i opt ional cont ainer i optional instrument optional edible nil i edible obj
using constraints on different dimensions o the inh rmation avmlable
figure NUM structure of c lcb tse fi une lexicon entry
in inost eases there are arguments that are not obligatorily required for resolving a verb sense
sense eat1 verb is ye verb takes no reflexive g no dative obl ob j dir 0b j is optional edible abl 0bl 0bj optional container
for the tirst example tile following constraints a re employed l
figure NUM illustrates the simplified form of the constraint sense mapping of the verb yc eal
in this study type hierarchies and relations are mathematically defined
two additional constituents are a dded via these lexieal rules
however is that it is not clear to what extent the brown corpus classification used in this work is relevant for practical or theoretical purposes
factors are not used for genre classification the values of a text on the various dimensions are often not informative with respect to genre
we hope to show that the usefulness of retrieval tools can be dramatically improved if genre is one of the selection criteria that users can exploit
examples of structural cues are passives nominalizations topicalized sentences and counts of the frequency of syntactic categories e.g. part of speech tags
for the reasons mentioned above we used our own classification system and eliminated texts that did not fall unequivocally into one of our categories
variables are selected by summing the cross entropy error over the three validation sets and eliminating the variable that if eliminated results in the lowest cross entropy error
biber ranks genres along several textual dimensions which are constructed by applying factor analysis to a set of linguistic syntactic and lexical features
and once we have the theoretical prerequisites in place we have to ask whether genre can be reliably identified by means of computationally tractable cues
when one takes a closer look at the performance of the component machines it is clear that some facet levels are detected better than others
note that there is no garden path effect even if the preposition is separated from the disambiguating head noun by a series of adjectives i saw the man with the neat quaint old fashioned moustache telescope
this is true on the abstract level as well since there will be nodes in the description which precede the original low position of the relative clause but are dominated by the subsequent high position of the relative clause
john nom essay acc wrote student acc praised john praised the student who wrote the essay up to the first verb kaiia wrote the string is interpretable as a full clause without a gap meaning john wrote an essay and the incremental parser builds the requisite structure
however once the preposition with has been attached the required n node will no longer be accessible and a conscious garden path effect will be predicted which intuitively does not occur
however if we are to allow our parser to handle such examples we must expand the definition of tree lowering since in order to build a relative clause we have to assert extra material including the empty subject and the new s node which is not justified solely by the lexical requirements of the disambiguating word the head noun seito
schemat ically what we require is illustrated below where NUM is intended to represent the current tree description built up after john knows the truth has been parsed and NUM is intended to represent the subtree description of the new word hurts
yamasita nom friend acc visited company loc saw yamasita saw his friend at the company he visited
having formulated the constraints of gorrell s model in terms of the accessibility of a node for ree lowering we can see that the model can be falsified if we can find a case where the relevant disambiguating information comes at a point in processing where the node which is required to be lowered is no longer accessible
but looking at it from a different perspective as gorrell has noted in press one can see the subject np as remaining in the main clause and the constituent bracketed in NUM tonbun wo kaita wrote an essay as being lowered into the relative clause
with acceptance of an utterance agents perform actions that have been elicited by a discourse partner
by contrast a participant is not aware at least initially when misunderstanding has occurred
computational linguistics volume NUM number NUM and the recognition of misunderstanding but none are computational
NUM the inherent difficulty with this approach is thus knowing when to stop searching for potential meanings
russ believes the conditions of this relation knowsbetterref m r whoisgoing
the system uses an oracle represented by the default pickform to simulate this choice
fact believe r knowif r knowref r whoisgoing
this revision then leads him to reinterpret it as an askref and to provide a new response
thus the hearer must also assume that he and the speaker share the same plan hierarchy
mcroy and hirst the repair of speech act misunderstandings discourse context could accomplish the desired goal
the parts of speech returned by the lookup module are thus mapped into the NUM general categories given in figure NUM and the frequencies for each category are summed
because of their reliance on special language specific word lists they are also not portable to other natural languages without repeating the effort of compiling extensive lists and rewriting rules
the lower error rate and faster training time suggest that the simpler approach of using binary feature inputs to the neural network is better than the frequency based inputs previously used
it is important to note however that in reducing the size of the lexicon as a whole the number of abbreviations remained constant at NUM
trained on NUM items this tree produced NUM errors over the NUM NUM item test set an error rate of NUM NUM for both upper case only and lower case only texts
this result was slightly higher than the lowest error rate NUM NUM obtained with the neural network trained with a similar training set and a NUM NUM word lexicon
on the same wsj corpus used to test satz in section NUM the alembic system alone achieved an error rate of only NUM NUM the best error rate achieved by satz was NUM NUM
this error rate is lower than the best result for the neural network NUM NUM on single case texts despite the small size of the training set used
in the case of prior probabilities each word in the context is represented by the probability that the word occurs as each part of speech with all part of speech probabilities in the vector summing to NUM NUM
a training text of NUM potential sentence boundaries was constructed from the hansards corpus and a cross validation text of NUM potential sentence boundaries and the training time was less than one minute in all cases
lf can be turned into cause z lf for causatives where z is the new argument introduced by the causative affix NUM similar arguments can be made for the semantic contribution of adjunct case markers
a mouse line at the bottom of the text window provides further visual feedback indicating all of the annotations associated with the location under the mouse cursor including document structure markup if available
by integrating other tagging modules including complete nlp systems we hope those systems can be more efficiently customized when the cycle of analysis hypothesis generation and testing is tightened into a well integrated loop
as an benefit the combined efforts of machine and user produce domain specific annotation rules that can be used to annotate similar texts automatically through the alembic nlp system
prior to developing the alembic workbench we were able to use this amount of data in alembic to generate a system performing at NUM NUM p r on unseen test data
NUM future work broadly defined there are two distinct types of users who we imagine will find the workbench useful nlp researchers and information extraction system end users
this cycle is continued until a stopping criterion is re ached which is usually defined as the point where performance improvement falls below a threshold or ceases
usually however even a lay end user is likely to have a number of intuitions about how the un annotated data could be pre tagged to reduce the burden of manual tagging
NUM based on the tagging rates we have measured thus far using the workbench it would take somewhere between NUM NUM to NUM NUM hours to tag these NUM NUM words of data
there are a number of psychological and human factors issues that arise when one considers how the pre annotated data in a mixed initiative system may affect the human editing or post processing
thus the workbench is written to be tipster compliant though it is not itself a document manager as envisioned by that architecture see NUM
detinition of it a lisl of tim inst ulees i he ea tegory
tratlitional linguistic ideas about absolute task indel endent and even NUM mguage in h t endeitt categories
considering all possible trees consistent with the data is computationally intractable so a reliable heuristic test selection method has to be found
in our experinmnt phonological categories are discovered in an unsupervised way as a side effect of the supervised learning of a morphological problem
in this set up the database is partitioned l en time s each with t dil orelll
NUM nlormation about l he onset of the last syllabi is irrelevant in predicting the correct allomorph
to test these hypotheses we performed four experiments training and testing the c4 NUM machine learning algorithm with fore different corpora
as fro as we now this generalization has not been prot osed in this form in the lmblished literature on diminutive formation
they are also proposed to allow an optimally concise or elegant formulation of rules for the description of phonological or mot phological processes
alternative approach is to use a suitable similarity metric to acquire further email like training data from the larger corpus henceforth referred to as the background corpus and then build a new language model from the combined text
however homogeneity is defined here as a measure of unigram distributions whereas perplexity is usually calculated using n grams where n is usually NUM so it is not clear to what extent the two measures would be related
in the case where two corpora are being compared it is possible to calculate the g NUM statistic either for single words using a contingency table or for a vocabulary of n words an n NUM table
one way of evaluating this result is to go through the list and calculate the mean rank of the NUM computergram international texts which are typical of the sort of texts this technique should identify as being similar to the email corpus
the success of this approach depends on the use of a reliable similarity metric even more so than the top down approach since it is now being applied to each of the NUM NUM files in the bnc rather than the NUM domain based collections
these figures suggest that an increase in oov rate of NUM NUM leads to a reduction in correct of NUM NUM or in other words a NUM increase in oov rate produces a reduction in correct of around NUM NUM
bnc the whole of the bnc email the NUM million word email corpus for large samples such as these the rank correlation coefficient has a normal distribution with mean NUM and variance NUM n l where n is the number of common words
a brief inspection of the titles of the documents at the top of the list would indicate that the metric has not produced an improvemenl moreover it transpires that the mean rank of the ci texts is now NUM NUM with std dev NUM NUM
the difference between two rank correlation coefficients will be normally distributed with mean NUM the maximum possible value for the standard deviation is l nl NUM l n2 NUM where nl n2 are the two common vocabulary sizes
in section NUM we deal with the question how dop can be used for parsing word strings that contain unknown words
the initial linguistic extracting produced about NUM NUM expressions
terminological filtering in this experiment terminological filtering is used for each document to produce terms that do not belong to the thesaurus but which nonetheless might be useful to describe the documents
b a p constant as p tile unfolding transformations have the same general form for the positive configuration database and negative succedent agenda occurrences the polarity is used to indicate whether new symbols introduced for quantified variables in the interpretation clauses are metavariables in italics or skolem constants in boldface we shall see examples shortly
the simultaneous compilation separates horizontal structure word order represented by interval segments and horizontal and vertical structure linear and hierarchical organization represented by groupoid terms and uses the efficient segment labeling to compute l validity and then the term labeling both to check the stricter nlvalidity and to calculate the hierarchical structure
the proof is thus NUM NUM NUM co NUM NUM a dt NUM NUM NUM c resj NUM NUM NUM NUM b resi NUM NUM NUM NUM a res in this way associative unification is avoided indeed the only matching is trivial unification between constants and variables
a book from which the references are missing NUM the references are missing NUM r n m s nks ks pp r m s pp we have cornpilation for are missing as in figure NUM yielding NUM
a wfsa is an state transition diagram with weights and symbols on the transitions making some output sequences more likely than others
the conditional entropy is actually a weighted sum of the individual translational entropies
one problem that has been less studied is that of updating these resources in particular of classifying a term extracted from a corpus in a subject field discipline or branch of an existing thesaurus
faced with growing volume and accessibility of electronic textual information information retrieval and in general automatic documentation require updated terminological resources that are ever more voluminous
local density sta 95b this is based on the idea that the closer together the documents are that contain the expression the more likely it is to be a term
first he defines semantic entropy over concepts rather than over words
although not stated explicitly in his thesis this is obviously a finite state model as evidenced from his employment of finite state diagrams for representing both the tokenization dictionary and character strings
for instance the simple strategy of tokenization by memorization alone could easily exhibit critical ambiguity resolution accuracy of no less than NUM which is notably higher than what has been achieved in the literature
thus by the cover relation definition for any substring ys of y there exists substring xs of x such that iysl ixsl and g xs g ys
for chinese sentence tokenization is still an unsolved problem which is in part due to its overall complexity but also due to the lack of a good mathematical description and understanding of the problem
as the number of critical tokenizations is normally considerably less than the total amount of all possible tokenizations this theorem leads us to focus on the study of a few critical guo critical tokenization ones
as critical points are all and only unambiguous token boundaries an identification of all of them would allow for a long character string to be broken down into several short but fully ambiguous critical fragments
finally in sections NUM and NUM after discussing some helpful implications of critical tokenization in effective tokenization disambiguation and in efficient tokenization implementation we suggest areas for future research and draw some conclusions
three indicators are used here frequency this is based on the fact that the more often an expression is found in the corpus the more likely it is to be a term
bankl noun mor root bank sem gloss side of river
also relevant here are the various techniques for reducing lexical disjunction discussed in pulman forthcoming
this statement employs a datr construct the evaluable path which we have not encountered before
the basic descriptive features of datr allow the specification of simple orthogonal networks similar to touretzky s
thus for example the well formed negation of a conditional is not if
NUM node names and atoms are distinct but essentially arbitrary classes of tokens in datr
we anticipate that our system will address the unique needs of the deaf population in other ways as well
table NUM explains the atypically high variance of the semantic entropy of punctuation
in this paper we present a gb parsing system for german and in particular the system s strategy for argument interpretation which copes with the difficulty that word order is relatively free in german and also that arguments can precede their predicate
on the other hand cp3 can be left uninterpreted when the cp2 zu versuchen is attached and interpreted as the sentential complement of versprichi the two uninterpreted arguments das fahrrad and zu reparieren are transferred to the cp2 for interpretation
on the one hand it can be the direct object of the main verb gestern hat sic der professor versucht yesterday the professor has tried them this analysis fails when the infinitival complement can not be attached
the argument structure is only available ff the main verb predicate occurs in c o that is the second position in the clause verb second with the main verb and thus at most one argument precedes the verb
on the one hand the cp3 zu reparieren can be interpreted as sentential complement of the main verb versucht which produces an interpretation of das fahrrad as the long distance scrambled argument of zu reparieren resulting in the grammatical sentence NUM
NUM vt vv vv ti tj h tte besuehenl wollenj the phenomena of ipp and verb raising also occur with f cm verbs as example NUM shows
moreover a strategy of argument transfer is used in eases of long distance scrambling according to which arguments and adjuncts are attached to the domain of the coherent verb ecm verb or raising verb and transferred to the infinitival complement for interpretation
the first modification concerns the matching procedure an argument that may be interpreted with respect to the infinitival complement is left in the argument table of the coherent verb for this reading no matching takes place and this argument is marked as uninterpreted
when the parser reads the verb haben the general clause structure el figure NUM is projected from 7p to cp triggered by the tensed verb which is placed in c o leaving a head trace in v deg and in i deg
we also built a maximum entropy model to deal with unknown abbreviations i.e. the model classifies whether or not an unknown to the lexicon word is an abbreviation
the remainder of this paper describes the design of gate
how to do distributed control in lt nsl is not obvious
NUM tipster can support documents on read only media e.g.
however the interface makes it much easier to use
table NUM shows some of the adjectives ranked by semantic entropy
there are two methods by which this may be done
marking columns in sgml requires a tag for each row of the column
gate aims to support both researchers and developers working on component technologies e.g.
in each case the implementation of creole services is completely transparent to gate
wrappers written in tcl can also be loaded at run time loose coupling
NUM discard lexicon entries representing word pairs that are never linked
the ptt button allows the user to take the initiative interaction is not forced the sys null tem just presents the user with his her options and by pressing the button the user requests attention of the speech recognition unit cf
NUM viewed in light of the parallelism between the main clause subjects the sentence does not involve switching of reference any more than any other sloppy reading of an elliptical clause does
by examining pairs of elided and unelided forms we will show that at a minimum discourse determined analyses must make this accent restriction otherwise sentence pairs that counterexemplify them can be constructed
assuming example NUM has this reading it appears that the source clause makes available the necessary relation to license either the deaccenting or the eliding of the vp in the target
the core phenomenon that we address concerns the space of possible readings of the target clause corresponding to the antecedent of the pronoun his in the source clause which exhibits the following dependency
that is example 2a only has the reading reflected by the indices shown in sentence 2b NUM a ivan loves hisk mother and jamesj does too
NUM NUM arguments on the basis of multiple parallel elements
rooth claims that whereas the unelided form in example 11a even without accent gives rise to a sloppy reading the elided form in example 11b does not
an approach based on k nn methods such as memory based and case based methods is a statistical approach but it uses a different kind of statistics than markov model based approaches
without optimisation it has an asymptotic retrieval complexity of o nf where n is the number of items in memory and f the number of features
feedback from a parser in which the tagger operates semantic types the words themselves lexical representations of words obtained from a different source than the corpus etc
grishman commit tee chair jerry hobbs paul jacobs lea schubert carl weir and ralph weischedel
the government people attending wcre george doddington donna harman boyan onyshkevych john prangc bill schultheis and beth sundheim
we consider the three goals in the three sections below and describe the tasks which were developed to address each goal
the goal for scenario templates mini muc was to demonstrate that effective information extraction systems could be created in a few weeks
the muc6 formal ewflu ttion was mhl in lcb q l emt ex NUM
the mucs are remarkable in part because of the degree to which these evaluations have defined a prograin of research and development
for one thing most sites spent several days just studying the scenario description and annotated corpus in order to understand tile scenario definition before coding began
this paper described early work in progress to try to construct such a theory
suggestions are made for the use of these results and for future work
therefore all the patterns were checked against the original corpus to recover the original sentences
these were then reduced to just NUM underlying rule patterns for the colon semicolon dash comma full stop
the notion of headedness seems to be involved so we can postulate that only non head structures can have punctuation attached
however this is a rather general definition so we need to examine the problem more exactly
this system still does not rule out examples like NUM however so further refinement is necessary
NUM dogs cats fish and mice NUM most or many examples
table NUM shows tagging accuracy depending on the category of the phrase and the level of reliability
i dan melamed of computer and information science melamed unagi cis
we obtained the results given in table NUM
general purpose high performance computer general purpose high performance computer general purpose high performance computer
we are also experimenting with the bigram score
this set is used in the experiments below
updates take the form described in section NUM
a list of maximal projections that do not pair wise overlap and that lie on a single path from the start node to a final node in the word graph represents a reading of the utterance
null nein ich mschte auch nicht nach offenburg sondern nach hamburg
there is however room for the utilization of additional knowledge sources
null sie mschten nach offenburg fahren
figure NUM dialogue utilizing acoustic clues
could you call again after having turned it off
bitte versuchen sie es sp ter noch einmal
i would like to travel to hamburg at two o clock
you would like to go to homburg
es scheint ein kommunikationsproblem zu geben
we describe the problems arising when integrating three preexisting resources fuf a unification based generator an hps ramrnm for errnan and x2morf NUM two level morphology omponent and the adaptat ions necessary to come up with a wide coverage l acticm generator for erlnnn
adaptation of linguistic resources to process null ing requirements by adapting our existing ill s grammar for mman to fur we have shown t hat a de laratively writ ten linguistic l eso ll can i e used in a new l rocessing en vironnmnt with modest effort
defparameter phrasal princ iples tiead feature principle head lcb head dtr head rcb semantics principle concepl lcb head dtr concept rcb args lcb head dtr args rcb index lcb head dtr index rcb slash inheritance priniciple slash lcb head dtr slash rcb
the grammar for german follows the version f iipsg giv n in pollard and sag l NUM rat her si rit l ly
thus the simple grammar in fig l has co i e enriched pattern subj pred has co be a dded at cat st pattern in has NUM o he ad le al cat apt and pattern iv is needed ac cat vp
table NUM number of polysemous words in each part
realizing this procedure requires a declarative specification of three kinds of information first what operators are available and how they may combine second how operators specify the content of a description and third how operators achieve pragmatic effects
finally in section NUM i suggest that intelligent example selection techniques may significantly reduce the amount of sense tagged corpus needed and offer this research problem as a fruitful area for wsd research
in particular np trees include the determiner the determiner does not have a separate tree the head noun and pragmatic conditions that match the determiner with the status of the entity in context as in NUM a
the configuration equency of the node ab will be b e abc when we add the atomic feature c to the optimized lattice figure NUM c we produce a fully saturated lattice identical to the empirical lattice since the node c will collocate with the node a producing ac and will collocate with the node b producing bc
therefore we should use the inside probability as our metric of performance however inside probabilities can become very close to zero so instead we measure entropy the negative logarithm of the inside probability
we also wished to combine the thresholding techniques this is relatively difficult since searching for the optimal thresholding parameters in a multi dimensionai space is potentially very time consuming
the parser fills in a cell in the chart by examining the nonterminals in lower shorter cells and combining these nonterminals according to the rules of the grammar
when collins began using a formalism somewhat closer to ours he needed to change his beam thresholding to take into account the prior so this is not unlikely
in any case if some aggregation of senses into coarser grouping is done in future this can be readily incorporated into my proposed sense tagged corpus which uses the refined sense distinction of woitdnet
the second section is analogous but works backwards computing b i which contains the score of the best sequence covering terminals ti tn
this is acceptable if the algorithm is run only after the first level but running it more often would lead to an overall run time of o n4
thus we instead multiply the inside probability simply by the prior probability of the nonterminal type p x which is an approximation to the outside probability
we tried an experiment in which we ran beam thresholding with a tight threshold and then a loose threshold on all sentences of section NUM of length NUM
before we start to describe any experiments on learning dialog acts we show the distributioll of dialog acts across our tr dning and test sets
different from these approaches in this paper we examine the combination of learning techniques in simple recurrent networks with symbolic segmentation parsing at a dialog act level
this disl ribu lion analysis is iml orl mt br judging tit leat ltiug and generalization behavior
the segmentation parser receives one word at a time and builds up a flat frame structure in an incremental manner see tables NUM and NUM
por instance there are only NUM of the training utterances and NUM NUM of the test utterances which belong to the request comment dialog act
for instance prol ose is highly significanl for the dialog act su qgcsl while in is nol
the plausibility value of a word w in a dialog category chti with the frequency f is computed as describ d in tjtc formula below
noun group verb group prepositional group NUM basic semantic category e.g. animate abstract and NUM
part day morning city ginosa hour eighty tt s train NUM leaves from milano centrale at NUM NUM p m it arrives at roma termini at s a m
in this paper we identify some typologies of recognition errors that can not be recovered during the syntactico semantic analysis but that may be effectively approached at the dialogue level
at present the speech recognizer makes use of a class based bigram model then in order to re score the n best decoded sequences it uses a class based trigram model
the acoustic decoding of allora a word that is used in italian for taking turn was erroneous it was substituted with all una at one o clock
the user modeling of the dialogue module of dialogos is based on the assumption that both the system and the user are active agents that cooperate in order to fulfill the goal of the speech interaction
in order to evaluate the effectiveness of the different approaches to face ambiguity we should experiment the different strategies on the same domain or at least with the same interaction modality phone or microphone
actually the telephone input of the recognizer may greatly differ from the uttered acoustic signal due to the noisy environment of the call and to the quality of the telephone microphone and propagation network
in particular dialogue systems for telephone applications have to rely not only on an adequate model of the human user but they should also implement particular techniques for preventing and recovering communication breakdowns
u NUM universe l u takes the vahie inl or ea t
b his composure was broken by the smile
seh ca iotm restrict ions for a s elelnenl ry arguments
ken which takes the past participle form
the salesman made an attempt to wear steven down
here however the sorts of paraphrases which are used are lexically general splitting off a relative clause as in NUM is not dependent on any lexical attribute of the sentence
for example the mapping of examples 2a and 2b involves the pairing of two derived trees as in figure NUM in this case both trees are derived ones
that is it is not possible to define a mapping between two structures reflecting their common features if the structures are not as is standard in stag entire elementary or derived trees
figure NUM stags miss manquer d
he gained the cheers of the audience
in that case we would like to consider the counter of such a word appropriately
in addition ne style methods are applied at this point to recognize and tag management position titles within the text
finally we are instituting an objective test measure rather than examining the dictionary directly we will compare segmentation and morphemelabeling to textual transcripts of the input speech
we have proposed a new probabilistically motivated error metric for the assessment of segmentation algorithms
the even row shows the results of simply hypothesizing a segment boundary every NUM sentences
is there a sharp change in the audio stream in the next utterance
this section provides a peek at the construction of segmenters for two different domains
this approach leverages the observation that text segments are dense with repeated content words
this paper introduces a new statistical approach to partitioning text automatically into coherent segments
the measure is a probability and therefore a real number between NUM and NUM
of the remaining features are easy to guess from a perusal of figure NUM
as a basis of comparison the figures for several baseline models are given
we refer to section NUM for sample results on how these trivial algorithms score
furthermore there may be unary grammar rules rewriting such an xp into appropriate categories for example
if we assume that prolog s unification includes the occur check then no problem would arise
moreover more specific results that may have been put on the table previously are marked
given the fact that goal may contain variables we should be a bit careful here
on the other hand the use of the internal database brings about a certain overhead
the effect will be that head corners are difficult to predict and hence efficiency will decrease
NUM lexical analysis NUM NUM np np lexical analysis NUM NUM np np
as a result for a given goal and head category table lookup is deterministic
if the lexemes involved are softly knock twice then yes as softly twice knock and twice softly knock arguably denote a common function in the semantic model
computational linguistics volume NUM number NUM used for the left and right daughters of that rule
only if a parse tree is recovered from the parse forest we add the logical form constraints
we use the predicate cstate to represent that an agent is in such a state and this predicate takes as its parameters the agents involved the goal they are trying to achieve and their current plan
both assume that the hearer is observant can derive a coherent plan not necessarily valid and can infer the communicative goal which is expressed by the effect of the top level action in the plan
traum models the grounding process by proposing that utterances move through a number of states pushed by grounding acts which include initiate continue repair request repair acknowledge and request acknowledge
in failing to represent such states their model is unable to represent the intermediate states in which a hearer might have understood how the speaker s utterance contributes to a plan but does n t agree with it
for if it did and if the new referring expression were invalid this would imply that the refashioning plan was also invalid which is contrary to clark and wilkes gibbs s model of the acceptance process
once this limitation is overcome their approach could offer us a route for formalizing the mental states of the collaborating agents in our model and for proving that our acceptance and goal adoption rules follow from such states
it could however be the case that there is no instantiation either because this is not the right derivation or because the plan is based on beliefs not shared by the speaker and the hearer
if the evaluation was n t successful then the goal of communicating the error is given to the plan constructor where the error is simply represented by the node in the derivation that the evaluation failed at
it is defined recursively as follows
assigning grammatical relations with a back off model
if the hearer is able to satisfy the constraints then he will have understood the plan and be able to identify the referent since a term corresponding to it would have been instantiated in the inferred plan
more important either approach is mathematically valid as long as all transitions out of a given state sum to one
in the formulae below o w NUM is a normalizing factor and dr a discount coefficient
second a thorough integration of the referential and intentional description of discourse segments still has to be worked out
in our case the integration means the substitution of the arcs of the usst by the automata describing the input language words followed by the substitution of the arcs in this expanded automata by the corresponding hmms
the performance was evaluated in terms of word error rate wer which is the percentage of output words that has to be inserted deleted and substituted for they to exactly match the corresponding expected translations
the lower perplexity of the output languages derives from a design decision multiple variants of the input sentences were introduced to account for different ways of expressing the same idea but they were given the same translation
the main problems are i how to insert the output of the csst within the output of the initial transducer NUM how to deal with more than one final state in the csst NUM how to deal with cycles in the csst involving its initial state
in addition it is generally assumed that lexical ambiguity does not occur very often in domain specific text
a simple example of the effects of this procedure can be seen on figure NUM the drawing a depicts the initial sst b is a csst for the hours between one and three in o clock and half past forms and the expanded usst is in c
the number of sense mismatches was then computed and the mismatches in the relevant documents were identified
although dictionaries contain a large number of phrasal entries there are many lexical phrases that are missing
the misanalyses have not been studied in detail but some general observations can be made many misanalyses made by the finite state parser were due to engcg misanalyses the domino effect
name matching in the context of information retrieval differs from name matching in either database or natural lang ge understanding contexts
one way would be to use name recognition software to tag all personal names in the document collection and also in queries
a different approach to name searching would be to leave the collection unchanged but to handle name queries differently from other queries
it might be thought that the same query recognition software used to recognize names in text could do the same in queries
table NUM shows that percentage of user natural language queries containing person company and other names to several news databases over periods of several days in NUM
either way strings designated as being names in the query would be matched against strings lagged as names in the text
for example in the query cases involvingjailhouse lawyer joe woods the baseline search treated joe and woods as independent concepts
name searching can be defined as the process of using a name as part of a query in order to retrieve information associated with that name in a database
the second part of the study measures retrieval performance with name searching simulated by probabilistic searching with a proximity operator against a standard test collection with associated relevance judgments
np NUM annual authorizations of NUM mi lion were added for area vocational education programs that meet national defense needs for highly skilled
slept and arriw in he arrived idegexaml les no doln it lcb corpus 11we use intrans habitual to rc fer to generic situslions as we ll e.g.
lntrans ellii sis is the nalne of the lass and what is elided ell p is a subject eontrolh d to infinitive to inf sc a comlex eolnplelnent
the ori us us xl for this t agging lcb resists if brown all i.e. NUM mb wall sia lcb lcb t
furl herinor we have noted that not a ll verbs are equally subject u rcb pardcular types of lcb ontexl ua zeroing of ontplenmnl s
NUM why it s all right is n t it mother NUM her woolly minded parent agreed of course dear she said
these examples then have been tagged as parenthetical and the new comlex feature paren thetical has been given to the verbs which can occur in parentimtical constructions
in sentence NUM they bought something in NUM they would agree on the statement and in NUM he she has got somettfing on
np i0 there is perhaps no value statemen on which people would more universally agree than the statement that intense pain is bad
to supply the missing material we would like to NUM e able to reconstruct a coral l inent tot the above instances of agree
NUM the discrepancy is even greater if it is used in the last utterance clause
if this is not the case then the argument merely flips which variants are more acceptable
speaker intentions may also enter into the determination of which entities are in the cf
they convey some additional information i.e. lead the hearer or reader to draw additional inferences
right now he s the president s key person in negotiations with congress
c a he did but that was before he was the vice president
one role of a semantic theory is to give substance to such a picture
c the vice president of the united states is also president of the senate
NUM NUM these examples were first written in NUM when george bush was vice president
we conjecture that this third choice is the appropriate one for noun phrase interpretation
however it will also be argued that though the distribution of parts of speech can to some extent be described with rules specific to this level of representation a more natural account could be given using rules overtly about the form and function of essentially syntactic categories
in these phrase construction systems it is common for some sort of prediction to be incorporated to ease and speed the task of entering content and for provision to be made for storage of a few frequently used messages
one innovation of muc NUM was the use of a nested structure of objects
would yield an organization template element with five of the six slots filled
as with transactional goals social goals may range from the immediate such as enjoyment of a social interaction or making a favourable impression to longer term goals such as the development of relationships or self esteem
figure NUM shows an excerpt from an article annotated for coreference NUM
for muc NUM through NUM all the text was in upper case
the mucs have helped to define a program of research and development
a company active in trading with taiwan the official s said
figure NUM the participants in muc NUM
a reasonable way of approaching the design issues is to consider what pragmatic features of natural conversation seem to support various goals that the participants may have with a view to modelling such features in the aac system
it can all be gone like that
figure NUM sample named entity annotation
for example to shift from a screen displaying content related to how things had occured in the user s past me how past to a screen containing content related to how things io i variety of aspects of topic discussion speak on a topic occurred in the partner s past you how past the you button would be activated
that is for these NUM words pebls will produce on average lower accuracy than the most frequent classifier
the full corpus contains NUM purpose expressions all but four of which occur in one of the following seven forms the purpose i.e. the satellite span of the rhetorical relation is italicized 3a to end a previous call hold down flash NUM for about two seconds then release it
figure NUM clearly demonstrates this point
the instructions are generated in english and in french
vander linden and di eugenio NUM
we are currently experimenting with a number of possibilities
figure NUM the clementine learning environment
correct the number of matches found between the key and the response fills
table NUM distribution of examples from sample
ne jamais deconnecter la borne de terre
our i rotocols will use tit language communication detined al ove
in some of the individual cases however the results could be higher due to several factors
precision the number of correct divided by the number of actual
the batch mode of the named entity scorer is almost completely translated into c
also even in batch mode the scoring took hours for each site
score mapped object pairs are scored against each other by comparing their slot fills
this is a popular heuristics in japanese word segmentation
the sizable number of options are all listed in the users manual
it will tind i y should v corre ct
it solves the first two problems mentioned in section NUM
the database lists several case filler examples for each case
selective sampling of effective example sentence sets for word sense disambiguation
selective sampling directly addresses the first two problems mentioned above
agents also need to have some idea of the beliefs and intentions that particular actions express so they can make judgments about their appropriateness in the context
figure NUM the semantic ranges of the nominative and accusative with verb toru
we used the same corpus as described in table NUM as training test data
in this section we will discuss several remaining problems
these data preparation tasks in both areas were several orders of magnitude greater than previous efforts
to date four trec s have been held and the fifth is currently in progress
as a result system designs were highly stove piped system portability was virtually non existent
so why has tipster been able to exert such a dramatic impact over these two fields
the tipster government sponsors did not fully appreciate this fact until the data collection efforts were underway
components of the evaluation driven research a clearly defined final objective for the overall r d program
sufficient government funding to cover the cost of all aspects of the evaluation driven research paradigm
NUM since its beginning the tipster text program has held technical workshops at NUM month intervals
the machine used for degthe oz implementation has the advantage that feature structure constraint solving is built in
for our purpose it is sufficient to require the following properties if generalise
for it we need an operation generalise that can be characterised informally as follows
the complexity of this abstract algorithm depends primarily on the actual constraint system and generalisation operation employed
in a way the structure of this constraint directly mirrors the structure of the parse forest
tree readings of such graphs are obtained by replacing any or node by one of its children
in general however use of names is actually necessary to avoid exponentially large constraints
however by writing out the constraint we loose the sharings present in the forest
figure NUM packed udrs conjunctive part left column and disjunctive binding environment
the experiment was a sun ultra NUM 168mhz running sicstus NUM NUM NUM
in addition the number of parameters is significantly reduced with the tying process
the numbers of parameters before and after tying for each language model are tabulated in table NUM
overtuning the training set performance usually causes performance on the test set to deteriorate
where c e stands for the frequency count of the event e in the sample
robust learning smoothing and parameter tying ing techniques are first summarized in the following section
robust learning smoothing and parameter tying table NUM performance with the smoothing robust learning hybrid approach
the superiority in terms of both discrimination and robustness for the hybrid approach is thus clearly demonstrated
NUM a smaller sp value would in principle imply better disambiguation power
r6 sanctions the spreading of the first vowel
the following two level grammar handles the above data
the rule deletes the vowel from the surface
r7 and r8 allow the optional deletion of short
in the multi tape version lexical expressions i.e.
a morphographemic model for error correction in nonconcatenative strings
an asterisk indicates ill formed strings
a common mistake is to choose the wrong pattern
in japanese one conventionally expresses similar goals via the patterns v combining stem mashou or v combining stem masen ka
now the above sentences plus many other sentences may be generated given appropriate database entries
the input that the tree system will accept is partially structured but with much scope for freetext input
for several reasons the approach to generation adopted in the tree system can be termed integrated
in our integrated approach to generation a grammar rule has the format NUM co so ss1 ssn conditions NUM where each ssi has the format ci the format cjsi or the format w1 w
the description of the job ranges depending on the classification from a quite broad one to greatly detailed ones sometimes highlighting differences existing in different countries e.g. according to the eures classification a waiter in some eu states is also required to act as a barman while in others is not
it is also apparent that for many jobs in a location where a different language is spoken sufficient linguistic knowledge at least to read an ad for a job in that region would be one of the prerequisites of the job this is certainly the case for the kind of professional positions often advertised on the internet
suppose a system knows something on which we want it to report suppose it knows that both the cafe citrus and the red herring restaurant want to hire chefs facts which could be captured by the following logical interface to the job database it em e i xl yl
furthermore there is a clear requirement that our analysis technique be quite robust since the input is not controlled in any way our analysis procedure must be able to extract as much information as possible from the text but seamlessly ignore or at least allocate to the appropriate unanalysable input slot the text which it can not interpret
NUM in the rule based approach we would probably have to have a rule which specifies the range of redundant modifiers asuming our schema does not store explicitly the level of language skill specified that fillers for the req slots can be a past participle a predicative adjective or a noun and are optional and so on
c did you mean to say the led is displaying the same thing
the total cost of a parse is a weighted sum of pc and ec
one natural strategy for reducing the impact of miscommunication is selective verification of the user s utterances
in conclusion while useful there appear to be limits to the effectiveness of verification subdialogs
the implemented dialog system assists users in repairing a radio shack NUM in one electronic project kit
in order to understand the strategies used an overview of this environment must first be presented
for each abstract task goal we define a subset of the expectations as the main expectation
table NUM summarizes the results of the four strategies for a fixed verification threshold
in general experimental trials to observe subject reaction to verification subdialogs are needed
marks the foot of an auxiliary tree and l a substitution site
prides provides a world wide web interface suitable for deployment on the intemet or an intelligence community intranet
to fulfill the prides requirements logicon has selected technology products that adhere to the tipster architecture that are consistent with an open design and that can be scaled up to accommodate larger volumes of input and more users
in head transducer models the use of relations corresponds to a type of class based model cf je null we can think of the transducer as simultaneously deriving the source and target sequences through a series of transitions followed by a stop action
our method is characterized by the reliance on the notion of the training utility the degree to which each example is informative for future example selection when used for the training of the system
in accordance with prides requirements the inroute product was modified to convert sgml in fbis articles into tipster annotations
when prides begins operation in july NUM it will provide one of the first production tests of the tipster architecture
prides seeks to provide both timely dissemination customized to a user s particular interests and comprehensive retrospective search support
this list includes a one line summary for each article containing the article headline relevance score and date
pui calls the prides application layer pa to service requests and collects and formats that data for easy use
tda satisfies requests for retrieval of a prides document given either an internal or external document id
pa is also responsible for maintaining and validating access privileges and collecting storing and analyzing mis data
the results of the evaluation effort will provide input to the requirements of the final fbis softcopy dissemination system
others yield equivalent grammars for example different combinations of default settings for types and their subtypes can define an identical category set
however the ordering of the directional types gendir and subjdir with values l r is significant as the latter is a more specific type
the steps of the derivation are
rules i1 NUM NUM NUM are measure strut lcb tic
experimental comparison of the default svo learner and the unset learner suggests that the default learner is more efficient on typologically more common constituent orders
there are other languages in the sov family with less consistent left branching syntax in which specifiers and or modifiers precede phrasal heads some of which are attested
trigger input is defined as primary linguistic data which because of its structure or context of use is determinately unparsable with the correct interpretation e.g.
these strings are sentence types since each is equivalent to a finite set of grammatical sentences formed by selecting a lexical instance of each lexicai category
NUM must be introduced by the rule compiler
examples are provided in section NUM
the simplest operation is prefixation e.g.
the latter caters for obligatory rules
let b be a base i.e.
consider the following setting the double blind experiment
engcg syntax employs NUM dependency oriented functional tags that indicate the surface syntactic roles of nominal heads subject object preposition complement apposition etc and modifiers premodifiers postmodifiers
figure NUM results of a tagging test
linked nonterminals no additional link remains to be transferred to the newly introduced nonterminals
this observation was also added to the morphology manual
the following analysis of the sentence that round table might collapse is a rather extreme example aa list of the engcg tags can be retrieved via e mail by sending an empty mail message to engcginfo ling helsinki fi
part of speech analysis provided that i the grammatical representation is based on structural distinctions and ii the individual descriptive practices of the most frequent problem cases are properly documented
a grammatical representation with a near NUM coverage of running text can be specified with a reasonable effort especially if the representation is based on structural distinctions i.e. it is structurally resolvable
NUM NUM a model based on merging decisions
the ie system has two components
we write to mean that the sequences of descriptors c evaluates to the sequence of atoms a
NUM these characteristics fall into three categories
in the second training set we describe below p2 NUM
dempster s rule is defined as follows
null the first category relates to the contents of the templates themselves
and the remaining NUM alternatives would receive probability NUM NUM NUM
the templates that are created are fairly shallow and may be incomplete
lastly there are the inaccuracies that result from processing real text
so the belief module adds the belief that the system finds the new referring plan to be valid
nevertheless because the computer is the ultimate expert we still expect it to respond with assertions of facts designed to assist the user that take a linguistic form that would be classified as continuing or regaining linguistic control e.g. the power is on when the switch is up from the first excerpt
they even make it possible to load non traditional duties onto a generator such as word sense disambiguation for machine translation
space limitations prevent us from tracing the generation of many long sentences we show instead a few short ones
here is an abridged list of outputs log likelihood scores heuristically corrected for length and rankings establishment in february
note that although the lattice is not much larger than in the previous examples it now encodes many more paths
our first goal was to integrate the symbolic knowledge in the penman system with the statistical knowledge in our language model
NUM possible combinations of the values of seven binary and three ternary features that were unspecified in the semantic input
to achieve fluent output within the knowledge based generation paradigm lexical constraints of this type must be explicitly identified and represented
of course we also miss out on sparkling renditions like they plan to say that they will file for bankruptcy
for a generator to be able to produce sufficiently varied text multiple renditions of the same concept must be accessible
both the above examples indicate the presence of perhaps domain dependent lexical constraints that are not explainable on semantic grounds
we discuss a number of examples of how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks such as segmentation bracketing phrasal alignment and parsing
we thank john lafferty for his helpful suggestions
via the error rate of the ati lcb l ecision tree part of speech tagger which is based on spat i t lcb magerman NUM NUM
lie of bile fulida rlrient j issues concernhlg corpus l ased ni p is t he tmla 8i a rsetless prot len l
in tile test phase the system looks up conditionm probability distributions of tags r r eat l word in the test text and chooses the most probable tag sequences using beam search
since the atr corpus is still in the process of development the size of the texts we have at hand for this experiment is rather ndnimal considering tim large size of the tag set
can be used for machine translation
the second example shown in figure NUM was among the NUM noun pairs which were retrieved by the check for generic synecdoche or generic auto relationship
k jerry lul tom i ccossnunla NUM jerry acc tom nom ehase decl e tom chases jerry from the elementary trees in figure NUM both sentences NUM and NUM can be derived
a g s represents a scrambled subject and at g is used for representing the place where the subject would have been in the canonical sentence
for translating sentence NUM the aa go np pair is used for jerry similar to the pair in figure NUM
figure NUM k e transfer lexicon and derived tree
so the pair a in figure NUM shows that korean has an explicit subject case marker i and the pair shows that korean has an explicit object case marker lul
for example figures NUM a NUM b and NUM d can be used for sentence NUM to derive figure NUM a
to translate sentence NUM we start with the pair NUM in figure NUM and we substitute the pair a on the link from the korean node sp to the english node np
the following is the ranking used for the muc6 system NUM appositive NUM predicate nominative NUM prenominal NUM name modified head noun NUM longest descriptor found by reference null this ranking gives greater confidence to those descriptors associated by context with the default choice the longest descriptor having been associated by reference
the scoring program tries to optimize the scores during mapping but if two objects would score equally the scoring program chooses arbitrarily thus in effect sacrificing a slot as a penalty for coreference failure
content filters the jewelry chain jewelry jewel chain smith jewelers smith jewelers jeweler jewel for example if the organization noun phrase the jewelry chain is identified its content filter would be applied to the list of known company names
two new rules were identified to help the name variation algorithm the last area of improvement person names can be improved on two fronts NUM expanding the knowledge base of accepted first names grouped by ethnic origin and NUM better modeling frequent behaviors in which person names participate
missing name many aliases are found because they are variations of names which have been recognized by their form i.e. they contain a corporate designator co or by their context e.g. ceo of atlas
the mucster group consulting firm since the template element task described here res ctea the descriptor slot to a single phrase our system sought to choose the most reliable of all the phrases which had been linked to an entity
this is a thrifty process because it allows the system to mine the very context which it has used to recognize the entity in the first place thus allowing it to store linked information with the entity discovered
named vs un named organizations became of the possibility that a text may refer to an un named organization by a noun phrase alone it is necessary to recognize all definite and imefmite noun phrases that may refer to an organization
location information our system s success in identifying associated location information was due mainly to our methed of collecting related information during name recognition since NUM of the answer key s location information could be found within appositives prenominals and post modifiers
in this example we would like to associate the following information about american express its name is american express an alias for it is amex its location is peking china and it can be described as the large financial institution
the static mode is useful in the authoring process since it displays all available help information and has simpler architectural requirements
the first two of these make life simpler for the end user while the third makes life simpler for the developer
in terms of the dempster shafer theory new task dialogue bpa s mt new md netu NUM are computed by applying dempster s combination rule to the bpa s representing the current initiative indices and the bpa of each observed cue
the total length of the texts is around NUM so each segment has an average window size of NUM words which is considerably longer than a sentence length thus this is a much rougher alignment than sentence alignment but nonetheless we still get a bilingual lexicon out of it
from a software engineering perspective we set out to achieve three main goals in designing cogenthelp each of which has various aspects
this benefit of generating both code and documentation from a single source NUM has long been recognized both in the nlg community cf
unfortunately since these functional groups are often not explicitly represented in gui resource databases we appeared to be at an impasse
thus when an agent takes over the task initiative she also takes over the dialogue initiative since a proposal of actions can be viewed as an attempt to establish the mutual belief that a set of actions be adopted
another feature illustrated in figure NUM as well as figure NUM owes its inspiration to our trim user group at raytheon
NUM seligman s fourth issue is how far natural pauses can be used in segmenting utterances and how far analysis and translation can proceed on the basis of such segmentation
appendix a of this document discusses various implementation related issues
examples of modules are a parser and a stemmer
an agent is said to have the dialogue initiative if she takes the conversational lead in order to establish mutual beliefs such as mutual beliefs about a piece of domain knowledge or about the validity of a proposal between the agents
clmr branch duplicate brm tch figure NUM graph decomposition
while much knowledge can be gained from woz studies they are not an adequate means of studying all elements of human computer natural language dialogue
their key insight is the observation that the two participants may have different domain plans that can be activated at any point in the dialogue
the primary contribution of this paper is to present an analysis of how the dialogue structure varies according to the computer s level of initiative
in addition two pilot subjects one female and one male were run using the proposed experimental design before the formal experiment began
whenever a person misspoke they could start over by issuing the sentinel word cancel rather than over at the end of their utterance
this required the user to signal the beginning of an utterance by speaking the sentinel word verbie and end the utterance with the word over
automatic logging of the words received from the speech recognizer subject input and the words sent to the dectalk computer output
for example problem NUM of both sessions was a power subcircuit problem while problem NUM of both sessions was an led subcircuit problem
for example the men were killed in bogota by john smith produces the pattern killed by x
in the intermediate stages the computer still initiated most subdialogues but users occasionally felt compelled to cause a change to a different phase
NUM the average speech rate by subjects was NUM NUM sentences per minute and the average task completion time for successful dialogues was NUM NUM minutes
they are set forth in the configuration management plan
but a word is not a minimum semantic unit because a word consists of one or more morphemes
the correct recognition scores for them were NUM NUM and NUM respectively
ciiic kanji i and i is the nuniber of all the extracted kanji characters by the x NUM lnethod
from this we believe that our approach is efficient for broadly classifying various subjects of the documents e.g.
where k is the number of varieties of the kanji characters and NUM is tile number of the domains
for keepiug the domains well balanced we combined the specialties using the hierarchical relationship of the ndc
NUM we combined NUM specialties to NUM code domains of the ndc using its hierarchical relationship
NUM kanji characters which distribute unevenly aniong text domahm are extracted by the x NUM iliethod
this enc yclopedia was written by NUM NUM atlthors and coilrains about NUM NUM artich s
this distinction can be used to develop a small core dictionary of closed class words that can greatly ease the task of processing unknown words in a sentence
each large rectangle next to a title indicates a document and each square within the rectangle represents a texttile in the document
in the field of information retrieval there has recently been a surge of interest in the role of passages in full text
with the length of pre subject extended to NUM words and subject to NUM words an average of NUM are excluded NUM out of NUM
this version of the tilebars interface allows the user to filter the retrieved documents according to which aspects of the query are most important
consider a NUM paragraph science news article called stargazers whose main topic is the existence of life on earth and other planets
this section concentrates on two application areas for which the need for multi paragraph units has been recognized hypertext display and information retrieval
the algorithm is fully implemented and is shown to produce segmentation that corresponds well to human judgments of the subtopic boundaries of NUM texts
min hits indicates the minimum number of times words from a term set must appear in the document in order for it to be displayed
in school we are taught that paragraphs are to be written as coherent self contained units complete with topic sentence and summary sentence
v represents verbs and auxiliaries incl
this produces a value of about NUM NUM
null the texts were first analyzed by a recent version of the morphological analyser and rule based disambiguator engcg then the syntactic ambiguities were added with a simple lookup module
in addition to removing also selecting a reading is possible when all context conditions are satisfied all readings but the one the rule was expressly about are discarded
a represents those adverbs that premodify intensify adjectives including adjectival ing forms and non finite ed forms adverbs and various kinds of quantifiers certain determiners pronouns and numerals
remove c n not NUM det or num or a lc cc 2c det
the collected statistics were bigram and trigram occurrences
here we use bigrams and trigrams as constraints
practice above has three alternative syntactic tags
an algorithm that solves clps is relaxation labelling
the system however knew that discount is only possible on return tickets
throughout the above process we performed additional measures and checks in order to help us prevent spurious or wrong rules
the confirmation is needed to ensure that it was not a system or user error that caused a conflict
depending upon the application more lower layer states can be added to improve the usability robustness of the system
this helps to make classification efficient and accurate
where kt is the cluster corresponding to wt
in this section we list those features of our system that are intended to make it pure
we then estimate the probabilities of words in each cluster obtaining the results in tab NUM NUM let us next consider the estimation of p kj ci
we argue however that the use of hard clustering still has the following two problems NUM hcm can not assign a word c NUM more than one cluster at a time
we identify portability usability robustness and extensibility as the four primary design objectives for such systems
we are implementing this architecture in a mixed initiative system that accesses flight arrival departure information from the world wide web
it can also save space for storing knowledge
hcm however can not do that
in the remainder of this paper we describe the NUM models in section NUM discuss practical issues in section NUM give results in section NUM and give conclusions in section NUM
left right the gap is passed on recursively to one of the left or right modifiers of the head or is discharged as a trace argument to the left right of the head
labeled recall number o correct constituents in proposed parse number of constituents in treebank parse crossing brackets number of constituents which violate constituent boundaries with a constituent in the treebank parse
9we exclude infinitival relative clauses from these figures for example i called a plumber trace to fix the sink where plumber is co indexed with the trace subject of the infinitival
figure NUM the next child ra r3 is generated with probability NUM r3 r3 p h h distancer NUM
the same applies to gg2 superfluous information and gg5 relevance superfluous information may be irrelevant information
only then will det be ready for inclusion among the growing number of dialogue engineering best practice development and evaluation tools
the benefits from using a new tool or method should attach to that tool or method rather than to its originators
sg3 provide same formulation of the same question or address to users everywhere in the system s dialogue turns
we have made the NUM th order markov assumptions
this is the case for instance in elliptic technical reports esp
this has a strong impact on the linguistic character of the work
the c1 ux is set to NUM which is meant as an abbreviation of brother hl NUM
the elementary tree describes the maximal projection of the anchor
NUM NUM for further detail they also propose to use
the implementation of the principles gives a real generative power to the tool
in the syntactic lexicon lemmas select the tree schemata they can anchor
and second the wnerative aspect of these solutions is not developed
figure NUM translating a dashed line into a path of length one
at the development stage generation can also be done following other criterions
lexical and syntactic rules to derive new entries
sentences that have no pivot i.e. that have no verb are put into one of two classes those that are considered complete such as yeah and ok and those that are incomplete that is interrupted by either the speaker or the other conversant i.e.
the surface grammar contains its own generation grammar and uses the same dictionary as the nlparser
at the lower limit assuming the grammar were stochastic one could even use sub phone speech segments as grammar terminals thus subsuming even hmm based phone recognition in the parsing regime
an additional lower layer state called map commands had to be implemented under the success state to allow the user to scroll the displayed map in any direction using spoken commands
if directly attacking bel is also predicted to fail the algorithm considers the effect of attacking both bel and its unaccepted proposed evidence by combining the previous two prediction processes step NUM NUM
to convince the user ofa belief bel our system selects appropriate justification by identifying beliefs that could 7in collaborative dialogues an agent should reject a proposal only ff she has strong evidence against it
this results in several distinctive features of collaborative negotiation NUM a collaborative agent does not insist on winning an argument and may change his beliefs ff another agent presents convincing justification for an opposing belief
otherwise this piece of evidence will be included in the candidate loci tree and the system will continue to search through the evidence in the belief tree proposed as support for the unaccepted belief and or evidential relationship
if the user is predicted to accept bel under this hypothesis the algorithm invokes select min set to select a minimum subset of cand set as the unaccepted beliefs that it would actually pursue and the focus of modification bel focus will be the union of the focus for each of the beliefs in this minimum subset
actually this process continues in an interactive manner the system uses the conceptual fields defined by the ke to compute new conceptual structures these are accepted or rejected by the ke and the exploration of both the terminological network and the documentation continues
admittedly gives the thin leaflet the operation of the hardware a clear description of and well illustrated
unless all of these conditions are met a gap in output occurs for the particular input word
since panebmt is a fairly new implementation there is still much that could be done to enhance it
the position that extends the kth subsequence to the left of the head outwards from the head is numbered NUM k NUM while the position that extends this same subsequence inwards towards the head is labeled NUM k
for instance lexter extracts the complex candidate term built dispatching line and analyses it in built dispatching line the adjective built will appear in the terminological context of dispatching line and not in that of dispatching
before we can look at the actual compilation procedure we need some terminology
iug tr mslat ions for a new langtmgc pair in only a few h lcb rcb urs
architecture NUM NUM lind chunks the engine sequentially looks up each word of tile input in the index
instead it attempts to produce translations of every word sequence in the input sentence which appears in its corpus
panebmt uses a re processed version of the bilingual dictionary used by pangloss s dictionary translation engine figure NUM
the corpus index lists all occurrences of every word and punctuation mark in the source language sentences contained in the corpus
the alignment scoring function is computed fl om the weighted sum of a number of extremely simple test flmctions
very poor alignmeats scores greater than five times the source chunk length have already been omitted from the output
the vmue8 for in lcb lividual engines lcb lo not sum t rcb the o cr ll
specifying that the set of possible partial semantic representations for u is the union of those of u s children
in order to approximate the usefulness of prosodic information to reduce the number of verb trace hypotheses for the parser we examined a corpus of NUM utterances with prosodic amlotations denoting the probability of a syntactic boundary after every given word
by way of illustration for l consider composition given the sequent translation NUM
thus instead of labeling a variety of prosodic phenomena which may be interpreted as boundaries the labeling follows systematically the syntactic phrasing assuming that the prosodic realization of syntactic boundaries exhibits properties that can be learned by a prosodic classification algorithm
NUM feature description of a head trace the dsl of a head is identical to the i sl of the mother i.e. l sb does not behave like a nonlo cat but like a ileal feature
these results are extremely significant statistically and compare favorably with validation studies performed for other tasks e.g. sense disambiguation in the past
in a further experiment an n ary anti unify operation was implemented which improved execution times for the larger sentences e.g. the NUM pp sentence took NUM msec
in this paper we describe how syntactic and prosodic information interact in a translation module for spoken utterances which tries to meet the two often conflicting main objectives the implementation of theoretically sound solutions and efficient processing of tile solutions
other values of c will certainly led to other results
the choice of search strategy when using aic is less critical than when using significance tests
NUM the information criteria aic and bic are more robust to changes in search strategy
familiar examples of decomposable models are naive bayes and n gram models
the sparse nature of our data can be illustrated by interest
however we have a training sample of only NUM NUM instances
the preprocessing steps such as part of speech tagging and removal of stop words are necessary for the algorithm to obtain good results
the extensions we propose have been validated by the empirical analysis of real world expository texts of considerable length
a wsd system based on dictionary senses faces an unnecessary and difficult forced choices most researchers resorted to human intervention to identify and group closely related senses
making those relations explicit will open the door to flexible treatment of lexicon semantic typing and semantic under specification all of which have received ever increasing interest
broad coverage about NUM of the labels are assigned during the second run of the algorithm from the extended candidate set
the results show that on the average the algorithm can assign labels to NUM of the senses with NUM precision
else the relation rsbdeg applies to x and y rcb table NUM information structure relation
refjei04 topde lcb bring back contribution doff equip facility keep yield rcb
after the string is transformed the process is iterated starting at the end of the previously transformed string
the computational linguistics volume NUM number NUM tagger outperforms in speed both brill s tagger and stochastic taggers
as an analysis which meets the first criterion but seemingly fails to meet the second one we take an analysis of the german clause which relies on traces in verbal head positions in the framework of head driven phrase structure grammar llt sg cf
the first step consists of turning each contextual rule found in brill s tagger into a finite state transducer
informally speaking a finite state transducer is a finite state automaton whose transitions are labeled by pairs of symbols
q and n respectively denote the current state and the number of states having been built so far
a finite state transducer could be seen as a finite state automaton where each transition label is a pair
the algorithm is both in asymptotic complexity and in real numbers dramatically faster than an earlier approach that also tries to provide an underspecified semantics for syntactic ambiguities
maximum likelihood estimator kac0 NUM p kilci f ci
we use error rate as our performance metric defined as incorrect choices of ties NUM of where n was the size of the test corpus
first the very notion of sense is not clearly defined for instance dictionaries may provide sense distinctions that are too fine or too coarse for the data at hand
a document in the category of tennis is more likely to discuss the topic of tennis i.e. to use words strongly related to tennis but it may sometimes briefly shift to the topic of soccer i.e. use words strongly related to soccer
pd w2 wx c wl w2 NUM NUM w2lwl o wl pr w2 wl o w
however using smoothed estimates for p w2 wl as well requires a sum over all w2 NUM NUM which is expensive or the large vocabularies under consideration
pc w wl however is greater for those w for which p w wj is large when p w21wj p w2 is
in fact the use of smoothed estimates like those of katz s back off scheme is problematic because those estimates typically do not preserve consistency with respect to marginal estimates and bayes s rule
however their scheme does not assign probabilities
in the contextual spelling correction task we can generate a vector representation for each text passage in which a confusion word appears
the trigram component of the system is used to make decisions for those confusion sets that contain words with different parts of speech
traditional spelling checkers flag misspelled words but they do not typically attempt to identify words that are used incorrectly in a sentence
lsa performs slightly better on average than tribayes for those confusion sets which contain words of the same part of speech
contextual spelling errors are defined as the use of an incorrect though valid word in a particular sentence or context
the selection of particular lexical items in a collection of texts is simply evidence for the underlying ideas or information being presented
consequently we will focus most of our attention on the seven confusion sets containing words of the same part of speech
the vertical bar thus represents the performance above or below the baseline predictor for each system on each confusion set
this local context is a poor predictor of the confusion word and its presence tends to dominate the decision made by lsa
our continuing work is to explore the error rate that occurs in unedited text as a means of assessing the true performance of contextual spelling correction systems
all remaining errors are of course my own
this ceil t e done by noting that both the original ase form aim each of the onfine lcb t as forms are in dnf
e.g. we can write a dcgs that produce abnormal large semantic structures of sizes growing exponentially with sentence length for a single reading
while this operation is general enough to be applied to a wide variety of constraint systems it was originally designed to optimize processing of dependent disjunctions in featm e structure based grammars
all of these algorithms will perform at their best when their dependent disjunctions interact as little as possible but if all of the disjunctions interact then these algorithms may perform redundant computations
because every disjunction in a group of lepen le nt disjunctions nmst have the same nund er of disjuncts some of those disjunets may appear more than once
we call these words w the salient words of a category c we define the typicality tw c of w in c as
the kernel of a category kernel c is the set of salient verbs w with a high scorew c
a source semantic tag system is proposed able to guarantee an explicit semantic description of lexical phenomena with a minimal size in order to minirnize the complexity of the inductive algorithms
as wordnet hyperonimy hierarchy is rather bushy and disomogeneous we considered inappropriate as initial classification the wordnet lot est level synsets
this strongly motivated our approach mainly based on the assumption that the corpus itself rather than dictionary definitions could be used to derive disambiguation hints
rather than training the classifier on all the verbs or noun in the learning corpus we select only a subset of prototypical words for each category
namely the loci of recursion and gapping are at both sides of the head and anything can occur there
then separate hirarchies can be generated for each semantic class in order to have a fully domain driven taxonomic description within general classes e.g.
third since we expect also domain specific senses for a word during the classification phase we do not make any initial hypothesis on the subset of consistent categories of a word
finally we consider globally all the contexts in which a given word is encountered in a corpus and compute a domain specific probability distribution over its expected senses i.e.
ules for morphological analysis and disambiguation dictionary access and indexed corpora search with an output module
a substantial morphological knowledge base is likewise necessary if one is to provide information about the grammatical significance of morphological information
lexeme based search looks not only for further occurrences of the same string but also for inflectional variants of the word
the current implementation of this search uses a lexeme based index for rapid and varied access to the corpus
the only effective means of providing such a knowledge base is through morphological analysis software
morphological analysis lemmatization is robust and accurate and more than up to the task
the results of disambiguation and morphological analysis serve not only as input to dictionary lookup but also to corpus search
analysis programs can provide information about these since most are formed according to very general and regular morphological processes
this approach gained up to around NUM bracketing recall for short sentences NUM NUM words but it sut ered with a large amount ambiguity for long ones NUM NUM where NUM recall is gained
a fourth userinterface and display module controls interaction with the user and formats the information provided
the semantic representations mentioned before are actually not given directly but rather as a constraint on some variable thus allowing for partiality in the structural description
in our grammar the verbal complex is always generated in the standard order
the lexie dly 8t ilie i trglltnenl s
the recursive unification process handles only t he dominance relations of NUM c grannuar
ii i s lees uoc employ phrase strut cure rules
v2 and a generation example erman is commonly regarded as an sov language
we then explain how the similarity between a word and its selectors is maximized
thus for the task of sentence boundary disambiguation we define the lower bound of a text collection as the percentage of possible sentence ending punctuation marks in the text that indeed denote sentence boundaries
the resulting system when tested on NUM megabytes of news and case law text achieved an error rate of NUM NUM at speeds of NUM NUM characters per cpu second on a mainframe computer
NUM NUM false negative due to an abbreviation at the end of a sentence most frequently inc co corp or u s which all occur within sentences as well
it is not always possible to obtain or build a large lexicon so it is important to understand the impact of a smaller lexicon on the training time and error rate of the system
these areas probably represented charts or tables in the source text and would most likely need to be eliminated anyway as it is doubtful any text processing program would be able to productively process them
a separate portion of the corpus was used for testing the system and contained over NUM NUM potential sentence boundaries from the months july october NUM with a baseline system performance of NUM NUM
the following sections discuss related work and the criteria used to evaluate such work describe our system in detail and present the results of applying the system to a variety of texts
in the simplest implementation of this method the grammar rules attempt to find patterns of characters such as periodspace capital letter which usually occur at the end of a sentence
this provides a clear and computationally useful correspondence between linguistic theories and their implementation
merit data set total o riginm data modified data
one of these failures was due to th e quote imbalance in the taskmaster sentence
in addition one can not genermize the grammar without side effects
section NUM NUM and fi unkuown constructions of
we have trained ehe system onml the etairiing datgl the
it is interesting to see that the mlr additionally uses the topicalization feature before testing the semantic class feature
in this section we first discuss how we configured and developed the mlrs and the mdr for testing
one advantage of the mdr is that a tagged training corpus is not required for hand coding the resolution algorithms
mlr NUM illustrates that when anaphoric type identification is turned on the mlr s performance drops sf measure is calculated by
with a lower confidence factor more pruning is performed resulting in a smaller more generalized tree
our anaphora resolution system is modular and can be used for other nlp based applications such as machine translation
for our experiments we have used a discoursetagged corpus which consists of japanese newspaper articles about joint ventures
technique to anaphora resolution in this section we first discuss corpora which we created for training and testing
james the second shows the text sequences that are considered as referring to mr
figure NUM two possible cky inner loops
in contrast we optimize thresholding parameters
previous techniques could not be used or easily adapted to thresholding parameters
for a given query type domain and task a discourse knowledge engineer must be able to represent the discourse knowledge needed by an explanation system for responding to questions of that type in that domain about that task
figure NUM gradient descent multiple threshold
this technique is called beam thresholding
this first pass runs relatively quickly
the analyst data setup process csci processes
as we noted this can be very different for different nonterminals
therefore we must consider more information than just the inside probability
the other one was the timeout value which is a seconds per word limit for parsing
the grammar is composed of NUM NUM phrase structure rules that are expressed in terms of NUM terminal symbols parts of speech and NUM nonterminal symbols
if we excluded those unambiguous sentences there were NUM NUM and NUM NUM alternative syntactic structures per sentence for the training set and the test set respectively
moreover the accuracy rate of NUM NUM for parse tree selection or NUM NUM error reduction rate is obtained by using this novel approach
finally a parameter tying scheme for rare events is proposed so that the unreliably estimated parameters are tied and trained together through the robust learning procedure
alternatively it is also possible to consider each group of highly correlated phrase levels as a joint event for evaluating its probability when enough data is available
NUM association for computational linguistics computational linguistics volume NUM number NUM statistics of parameters are estimated from a training corpus by using well developed statistical theorems
this result demonstrates that a better initial estimate of the parameters gives the robust learning procedure a chance to obtain better results when many local maximal points exist
figure NUM shows an example of a constraint the head feature principle of hpsgii
clearly the mu c tasks are an example of this since they require exact phrases
the purpose of this section is to point to another use of the threading technique which is to implement a rather simple but very useful notion of default a notion that is however completely monotonic
the body of the document is divided into sentences and then into tokens
now encode the feature value into this vector as follows for i i to n i if feature value does not hold of the i th model in the set then unify vector positions i and i NUM
unfortunately for these lattices i have not been able to find a way of encoding generalization as disjunction of bitstrings in such a way that the resulting encoding will interact with the previous encoding of unification as conjunction of bitstrings
to encode these values we build a term with a functor say bv for boolean vector with n i variable arguments where n is the size of the product of the sets from which f takes its values
and in turn the advantage of this is that ordinary first order term unification i.e. of the type almost provided by prolog implementations can be used in processing guaranteeing almost linear performance in category matching
for example the verb send in english can occur in at least the following configurations there is some dialect variation here but please bear with me for the sake of the example john sent a letter
an atomic expression like a holds of a model if it is a member and fails otherwise a here only therefore holds of the two models containing a truth functions of atoms can be interpreted in the obvious way
our results on the forma l test
dooner is starting the jobs which mr
therefore it is important for a system to be able to combine fragments from more than one example expression to cover the input expression
noun group syntax remains explicit as one phase of pattern matching
the nyu system for muc NUM or where s the syntax
recognition base d on context is performed by subsequent stages
one way round this is to change the vp schema so that complements are characterized not just by a variable but by an explicit new category say xcomp with a bool comb value feature on it that can serve to identify categories
then we define a feature that can take as values boolean combinations from this set subcat and write v lcb lex send subcat np np pp np np
to enable them select exactly one operator in either the free operators list box or the working operators list box
to indicate these similarities as well as to save space it makes sense to group these descriptions when possible
the sentences in this corpus were drawn from the combined corpus of the i million word brown corpus and the NUM NUM million word wall street journal wsj corpus
thus a synthesis of user modeling techniques will probably be required for effective and efficient collaboration
landmarks can be either associated with relays or with transfers
a sketch can also function as a route representation by itself
from route descriptions to sketches a model for a text to image translator
NUM at the turnstiles of the rer station you turn left
there is a steep a magnificent downgrade to take
ii y a une magni ique descente prendre
our methodology for evaluating competency involves a probabilistic examination of the search space of the problem domain
cogenthelp then dynamically assembles these pieces into a set of well structured help pages for the end user to browse
a series of simulation runs of NUM cycles were performed with random initialization of NUM lagts p settings for any combination of p setting values with a probability of NUM NUM that a setting would be an absolute principle and NUM NUM a parameter with unbiased allocation for default or unset parameters and for values of all settings
goldberg et al NUM kukich et al NUM or explanations of expert system reasoning cf
if the pair clearly can not be of help in constructing a glossary circle invalid and go on to the next pair
an unstructured index as a linking device is most beneficial with respect to the effort needed for the development maintenance future expansion and reusability of the multilingual database
one problem with such simplification is that the model may generate a set of word modification pairs that do not form a noun phrase although such illegal noun phrases are never observed
for example the domain expert consistently preferred a different global organization than the one encoded in the original explain process edp
figure NUM percent correct by token data filters can improve these scores by more than NUM
black box functionality automatic acquisition of translation lexicons requires only that the user provide the input bitexts and identify the two languages involved
like named entity this task has a simple implementation and depends critically on the core analysis
to overcome this a high recall technique is used when the expected relationships are not present
the categories that are to be used for this slot are defined as follows same org the person s old and new positions are with the same organization
each topic is formatted in the same standard method to allow easier automatic construction of queries
the topics were made much shorter and this change triggered extensive investigations in automatic query expansion
note that this process is neutral in that it does not model ocr or speech input
however the manual systems also suffered major drops in performance see figure NUM
a short summary of the techniques used in these runs follows
each topic also has a concepts section with a list of concepts related to the topic
this change in expansion techniques was mostly due to the major change in the basic algorithm
a NUM NUM if new status in a NUM NUM NUM yes if person is identified as currently holding post examples mr
if the person is newly appointed to the job no means that the person is not yet actually onboard and working in that capacity
if the person is vacating the post no means that the person has already been officially relieved of the duties of that post
if the two posts are at unrelated companies the person is giving up the old post to acquire th e new one
this task is similar to that done by news clipping services or by library profiling systems
this will be a continuing effort with a trec NUM conference scheduled in november of NUM
the article must make some reference to the act of leaving but does not have to indicate the reason fo r departure
null new post created vacancy exists because of creation of a new post at an existing company or a t a new company
it is widely accepted that semanti ttleories should as far as possible be compositional
in NUM NUM on the other hand the re does seem to be a problem
in this way the meanings of words will cooperate to convey more complex inessages than each can carry alone
in NUM we have a conceptually instantaneous vent namely a hiccup
the only assumption that i will make any use of is t hat there are intervals and instants
thus the use of the progressive aspect here commits the speaker to the existence of an end date for the state in a way in which commitment to the existence of tile state does not it is this that gives NUM its feeling of being about a temporary state of atfairs NUM
we can obtain the same kind of interpretation for such sent en es paraphrasing part of the diffcre nce bet ween this construction and the simple past arises from the explicit mention here of the r l lq r l n llr
so unlike NUM where there was one peach and the event type we were considering dealt with eating that one peach here there is nothing driving us to onclude that there is only one peach and hence that the set of events nmst be a single ton
deixis and anaphora figure NUM reference resolution of spatial descriptions a schematic lay out of two directory icons and two file icons
table NUM shows several translated sample sentences taken from the set of sentences the five subjects keyed in to perform the NUM tasks
from a computational and an engineering position one mechanism that handles both deictic and anaphoric expressions in the same way is preferable
it is the dialogue manager s task to keep track of who is talking to whom and to update the knowledge base accordingly
however both the user and edward can point creating indicated referent cfs pointing has a more temporary effect than selection
their purpose is to make references to actions expressed in a sentence possible as in for example do it again
name relations if present are represented by a label underneath the icon of the named object see figure NUM
individual objects in the domain are represented by instances e.g. an individual who is a man might be represented as man NUM
we evaluated performance on each language pair in the manner described in section NUM NUM above taking as input two sets of NUM recorded speech utterances each one for english and one for swedish which had not previously been used for system development
we have two positive effects on the parsing activity
however korean allows the head noun of a relative clause to be construed with the empty category across more than one intervening adjunct node cp as shown in the following
case assignment accusative case is assigned by transitive v nominative case is assigned by i tensed infl ection ii ip predication
these approaches typically generate all possible candidate structures of the sentence that satisfy x theory and then subsequently apply filters in order to eliminate those structures that violate gb principles
there are two types of links in the network subsumption links e.g. v to v np and dominance links e.g. nbar to n
in a biunique case assignment language such as english the setting for nominative case assignment would be i in korean the settings would be i and ii
the attribute values ca and govern are assigned by local constraints to items representing phrases whose heads are case assigners e.g. tensed i and governors e.g. v respectively
we will first present our approach to parameterization of each subtheory of grammar and then describe the automatic construction of grammar networks for english and korean using the parameter settings
however the fundamental goals of the two approaches are different stevenson s objective concerns the modeling of human processing behavior and producing a single parse at the end
this prepares for the next shifted vowel to be treated in exactly the same way as the first
broken plural and the preferance of vocalism pattern for that type of inflection belongs to the root
this work can be interpreted as specifying groups of features that should be acquired at roughly the same time
exampies of pattern morphemes are clvlc2vlc3 rcb m NUM lcb clc2vlnc3v2c4 rcb m q3
recall that a semitic stems consists of a root morpheme and a vocalism morpheme arranged according to a canonical pattern morpheme
negative feedback in this area should be avoided as it may result in an abortion of his attempts to communicate
the modality of asl encourages simultaneous communication of information which is not possible with the completely sequential nature of written english
the decision as to which errors to correct in detail will be most influenced by reasoning on the acquisition model
after describing our current implementation status we motivate the need for a model of second language acquisition
r4 applies to stem morphemes reading three boundary symbols simultaneously this marks the end of a stem
note that the good classes jade gold and grass have lower costs than the bad classes sickness death and rat as desired so the trend observed for the results of this method is in the right direction
morphologically derived words such as r r xue2 shengl meno student plural students which is derived by the affixation of the plural affix meno to the noun xue2 shengl
an analysis of nouns that occur in both the singular and the plural in our database reveals that there is indeed a slight but significant positive correlation r NUM NUM NUM p NUM NUM see figure NUM
this model is easily incorporated into the segmenter by building a wfst restricting the names to the four licit types with costs on the arcs for any particular name summing to an estimate of the cost of that name
NUM the differences in performance between the two systems relate directly to three issues which can be seen as differences in the tuning of the models rather than representing differences in the capabilities of the model per se
the method reported in this paper makes use solely of unigram probabilities and is therefore a zeroeth order model the cost of a particular segmentation is estimated as the sum of the costs of the individual words in the segmentation
if no rule satisfies the background condition the simplest rule ageru e c give e is chosen as default
for reasonable values of k n and n this translated into a time savings of a factor of NUM to NUM
it is called symmetric learning and its attributes as well as the implications of its attributes are discussed in the section below
this vector transformation and subsequent renormalization results in a set of context vectors that represents the relationships between word stems in a near optimal fashion
the matchplus system learns relationships at the stem level and then uses those relationships to construct a context vector representation for sets of symbols
this vector expansion and subsequent renormalization results in a set of context vectors that represents the relationships between words stems in a near optimal fashion
the desired dot product values are determined on the basis of information theoretic statistical relationships between co occurring word stems found in the training corpus
using this approach hash function collisions not withstanding each unique stem results in a unique entry and thus a unique context vector
an example is shown in figure NUM assume that attack and ataque have been chosen as a tie word pair
hnc has also developed an approach to learning stem level relationships across multiple languages and has used this approach to develop a prototype multilingual retrieval system
random unit vectors in high dimensional floating point spaces have a property that is referred to a quasi orthogonality NUM
there may be a potential for nlp to contribute to the enhancement of these and other aspects of the pictalk system
for instance information retrieval technique has two possible structures information retrieval technique and information retrieval technique
more specifically talking about the lexical signs wc call pns ixo77 shows that lhey correspond to the classifiers in languages as tzeltal mandarin chinese or viemamese
cow entity portioning terms have been sorted as fl llows contents hellceforlh cent a bucket of water a basket of wheat a basket of lemons
the logical semantics of pns sem will account for their both pm titive and relational nature by adopting as predicate ugulnent structure that of their const role
e.g. the chinese phrase san ben siiu is translatable by three plane entity book three whiskies wotfld be conslructed with a mensural classifier being the translation paraphrasablc by three unit doses whisky
specifically ltt mp operates simultaneously as both a mensural meaning conventional dose and classal denoting a cerlain type of aggrcgale classifier
NUM introduction and motivation processes of inference of meaning concerning part whole relations can be drawn from a lexicon bearing meronymic links between words ef
boundaries bound the tip of the tongue the su we of the sea the top of a box
the meaning of the former focus on the agentive which is a process of cul or fragmentation el a b solid
the honorific expression is thus adopted prior to the normal one when translation is performed from english to japanese
the difficulty lies however in that the sources of such information are unlimited and not always available
for better translation however more elaborated rules are required
the same goes for the rules NUM and NUM
the terms inside lcb rcb specify background conditions for the rule to be applicable
observe that the background condition of NUM subsumes that of NUM
this was an encouraging finding indicating that using pre stored material could actually enhance the perceived quality of conversational content
in the central part of the conversation the conversa null tionalist is either speaking or listening
problems facing designers of aac systems are highlighted in the hope that insights from nlp may contribute to solutions
the main pragmatic structures he notes are greetings initiations attention getting attention directing conversation repair e.g.
the adaptation of the system is made by updating the frequencies of the lemmas and suffies and the weights of the defined rules
we are investigating how an analysis of children s language can be used in the design of such a device
first the predictor tries to guess the category depending on the most highly weighted rule at that point of the sentence
in developing cogenthelp we have taken a minirealist approach to knowledge representation following a methodology for building text generators developed over several years at cogentex
in a reference oriented document such as an on line help system similar or identical descriptions will often be appropriate for elements which have similar or identical functions
in cogenthelp interactions that are cumbersome to anticipate arise in dealing with the various optional phrase sized messages whose inclusion conditions differ between the static and dynamic mode
after trying out a variety of features what we found to work surprisingly well was a simple weighted combination of proximity alignment and type identity
annotated training corpora are expensive to build both in terms of the time and the expertise required to create them
first we give a brief overview of information extraction and the circus sentence analyzer that we used in these experiments
furthermore the classifications are general in nature so various types of systems can make use of them
a good dictionary for text classification should contain patterns that frequently occur in relevant texts but rarely occur in irrelevant texts
furthermore the autoslog ts dictionary should contain a higher percentage of relevant definitions that the original autoslog dictionary
figure NUM shows a sample of some of the new concept nodes that represent patterns associated with terrorism
in contrast autoslog infers the trigger words and patterns on its own but does not generalize them
using these two threshold values we can reduce the size of the dictionary down to NUM definitions
as noted above if no parts are identified the basic tipster architecture will make the default assumption that all parts of a given document are text parts of one particular user specified language
because the list of tipster applications and their modules will be available the cotr will be able to identify what areas of his application development are new and under tested and thus probably more risky
regarding those places within the design which do not comply the erb will issue a recommendation that the architecture be changed that the design be changed or that the exception be allowed
on the basis of the tacad the configurations control board ccb will determine that a tipster application is conformant or non conformant if it exhibits sufficient overlap with the architecture design document
this would include documents and parts of documents application modules persistent knowledge repository items e.g. lexicon saved queries and profiles b ensuring that access control is properly handled
in order to utilize the features of the architecture to the fullest most applications will also need to provide an additional module which is not covered by the architecture the document setup module
this document describes the goals of the tipster architecture over the long term and would give the developer an indication as to whether or not his modules would even be considered relevant to tipster
even a fairly small database of additional items with individually selected and stored attributes could offer benefits to these users
from the sole24ore corpus NUM terms have been extracted
in most of our experiments on different data sets the choice of using n f d performed best
introducing predictive features could help to simplify the control task for the user
table NUM comparison of runtimes in secs for
the training process the net is presented with training strings whose desired classification has been manually marked
tag disambiguation is part of the parsing task handled by the neural net and its pre processor
the sentence parsed at the first level returns still waters run deep
this paper describes a fully automated hybrid system using neural nets operating within a grammatic framework
a small number are excluded because the system can not handle a co ordinated head
rare events rather than being noise can make a useful contribution to a classification task
in testing mode if a previously unseen tuple appears it makes zero contribution to the result
thus the pair most adjective is taken as a single superlative adjective
figure i the frequency of constituent length for pre subject and subject in NUM sentences
while a lexicon tends to produce translations that are shallow but comprehensive covering all possible senses of a term but limited in the range of synonyms that are produced for each term corpus methods tend to produce translations that are deep but narrow with enormous repetition of domainrelated senses of terminology
overgeneration in the translation process is handled by using the longest terms in character count in mundial
several of the generated queries are given in table NUM figure NUM e diagrams the process
starting with trec NUM spanish corpora and query sets have been available for evaluating text retrieval engines
on average the dictionary based queries produced performance which was about NUM worse than the reference queries
lexicon generated spanish queries the lexical transfer approach produced spanish queries rapidly requiring only a simple database lookup procedure
the fitness judgment for a query was based on comparative retrieval results using a training corpus of only NUM NUM aligned sentences
we applied an evolutionary programming ep NUM approach to modify a population of NUM queries
where acquire uval source and acquire uval destination are aggregated
optimization then proceeds by evaluating the comparative fitnesses of the queries mutating a selected sub population of the queries to produce offspring solutions and re evaluating the queries iteratively until a suitable number of generations have passed
the task therefore merges efforts in machine translation with efforts in text retrieval but the machine translation component may be substantially simplified due to some basic assumptions about the design and implementation of high performance text retrieval systems
our corpus consists of transcripts of spontaneous spoken monologues produced by NUM different speakers
their corpus consisted of written interactions between tutors and students using NUM different tutors
in this paper we describe an initial realization of such a cooperative and efficient mixed initiative dialogue system
in general the machine learning results have slightly greater variation around the average
there we present results of an evaluation of an np generation algorithm under various conditions
the linguistic features used in our two sets of experiments are shown in figure NUM
the output is a classification of each potential boundary site as either boundary or nonboundary
NUM each algorithm was developed prior to any acquaintance with the narratives in our corpus
the reliability evaluation is conservative in part because it uses fewer subjects to derive boundaries
however we also report percent agreement in order to compare results with other studies
we use cochran s q to evaluate the significance of the distributions in the matrices
the first stage repair hypothesis formation is responsible for assembling a set of hypotheses about the meaning of the ungrammatical utterance
other hypotheses are also evolved and tested as the genetic programming system runs such as the alternative example included in figure NUM
that s how life goes sometimes expression of sympathy e.g.
this is unlike the mdp approach where the full amount of flexibility is unnecessarily applied to every part of the analysis
it may however be possible to support the pictalk user by predicting and suggesting suitable utterances during a conversation
empty nodes play a role in adjoining and substitution as explained below and hence in building the derived binary tree that represents the structure of the discourse
consider the tree in figure NUM i which has two such sites and an adjoining operation on the right frontier at node rj or above
in sentence 2b on the other hand suppose the adverbial on the other hand signals the expected contrast item
one is followed by a single sentence elaborating the original supposition also flagged by suppose suppose it was not us that killed these aliens
in the course of developing an incremental approach to the latter we noticed a variety of constructions in discourse that raise expectations about its future structural features
adjoining at any node above rj NUM the left sister of the most deeply embedded substitution site leads to the same problem figure 3iii
this we do by allowing an auxiliary tree to contain substitution sites figure ld which as above can only appear on its right
moving on to example NUM figure 4c i shows the same elementary tree as in figure 4a i corresponding to clause 3a
counting preposed gerunds as raising expectations as well as counting the constructions noted previously NUM instances of expectation raising discourse units were identified NUM NUM
but although we must cover it entirely in order to guide the research we need to try already an explanation of its subject matter
for example the implicit subject in a nonfinite clause premodifying the main clause should be the same as the subject of the main clause
the restricted permutation rule kp allows any formula of the form ax to permute freely i.e.
the final result after flattening sentence NUM is as follows
indeed it includes all of the parsing algorithms mentioned in the introduction and can be thought of as a formalization of lang s informal definition of parsing
theorem NUM the parse to forest translation problem for a lexiealized synchuvg dl can be computed in polynomial time
we are now in a position to prove our relation between time bounds for boolean matrix multiplication and time bounds for cfg parsing
recall that c j is non zero if and only if we can find a non zero aik and a non zero j such that k k
therefore our grammar is of size o m2 since g encodes matrices a and b it is of optimal size
so the total time spent by mp is o max m NUM t m2 t mu3 as was claimed
the nonterminals may not always look like those of an ordinary cfg
intuitively a w i is a c derivation if it is consistent with at least one parse of w
we will reduce bmm to c parsing thus proving that any c parsing algorithm can be used as a boolean matrix multiplication algorithm
at pass one the parser does n t succeed in putting together the elementary trees which span the whole sentence
null in this paper we describe easyenglish a tool that helps writers produce clearer and simpler english by pointing out ambiguity and complexity
chinese word segmentation is therefore proposed as the first step in any chinese information processing systems
second the title contains about NUM of the topic keywords the title plus the two most rewarding sentences provide about NUM and the next five or so add another NUM
a uvg dl is lexicalized iff at least one production in every vector contains a terminal symbol
formally let s cl cn be a character string over an alphabet g and let d be a dictionary over the alphabet
it must be remembered that our evaluations treat the abstract as ideal they rest on the assumption that the central topic s of a text are contained in the abstract made of it
however synchtag can not derive all possible scope orderings because of the locality restriction
moving the window from the beginning of a sentence to the end we computed all the h scores and added them together to get the total score h for the whole sentence
to examine baxendale s first last sentence hypothesis we computed the average dhit scores for the first and the last NUM sentence positions in a paragraph as shown in figure NUM and figure NUM respectively
it also describes a method of deriving an optimal position policy for a collection of texts within a genre as long as a small set of topic keywords is defined with each text
the amount of sharing at least one word goes up to NUM if we choose the top NUM positions according to the opp and NUM if we choose the top NUM positions
it counts the number of sentences in a with at least one hit in e i.e. there exists at least one pair of windows wmia and wemj such that wai wej
figure NUM and figure NUM show precision and recall scores with individual contributions from window sizes NUM to NUM precision p and recall r of variable length windows can be estimated as follows
finally now comparing to the abstracts accompanying the texts we measured the coverage of sentences extracted from the texts according to the policy cumulatively in the position order specified by the policy
in summary we did the following to determine the efficacy of the position method we empirically determined the yield of each sentence position in the corpus measuring against the topic keywords
edmundson assigned pos null itive weights to sentences according to their ordinal position in the text giving most weight to the first sentence in the first paragraph and the last sentence in the last paragraph
one solution to such a problem is to recompute the bigram occurrence statistics after making each round of preferred associations
the running time reduction in this case depends heavily on the domain constraints
given a text t and a list of topics keywords t of t we label each sentence of t with its ordinal paragraph and sentence number p sn
cm estimates the potential of the opp procedure
to filter ill recognized words we design an on line computing of word confidence scores based on the recognizer output hypothesis
it describes the automated training and evaluation of an optimal position policy a method of locating the likely positions of topic bearing sentences based on genre specific regularities of discourse structure
i notice that implementing such a system requires the use of the relation between recall and precision
i let consider results of in the contexts of i.e. pn us some experimental our study
in this section we describe experiments with mbl on a data set of prepositional phrase pp attachment disambiguation cases
the traditional context model includes a selection rule s e d whose only input is the history
the mdl principle states that the best model is the simplest model that provides a compact description of the observed data
more details about the implementation of the workbench are provided in section NUM
hand coded rules can be applied in concert with the machine derived rules mentioned earlier
to optimize the term selection an evaluation of the up to NUM f terms on held out data is still necessary
the adjective noun relation is directly pertinent to semantic attributes of both the adjective and the noun only when there is a deep syntactic relation between them
this indicator is found in statements of the form it be adj infinitival clause where be is a form of the verb to be
a natural way to pursue the necessary revision is in terms of semantic attributes of these nouns rather than in terms of the nouns themselves
our problem is a specific case of the more general problem of finding clues within the context of a word that indicate its sense fairly reliably
every one of these co occurrences had sense concordant antonyms modifying the same noun any other sense is usually semantically incongruous especially in direct phrasal substitutions
entire crates of dishes have been smashed when the trailers cross railroad tracks or other rough spots located between the old and the new house
for noninherently paired entities more complex phrasings such as on the right or rightmost are used instead also on the left or leflmost
more than NUM nouns that are identifiable as indicators of adjective senses reflect a much smaller number of conceptual categories that directly relate to these senses
the observed errors illustrate this underlined nouns are from the projected indicators i have something hard to speak he remarked
the modified noun is not always relevant to the process of disambiguation and even when the noun is relevant it is not always sufficient
only if terms are of a similar specificity the a s truly serve to weight relevance of the interpolation terms
a typical example is when most of the utterance has been correctly recognized and translated but there is a short false start at the beginning which has resulted in a word or two of junk at the start of the translation
as it happens these results are probably underestimating the pre tagging productivity
these are then sent to the media coordinator for negotiating with graphics an ordering that is compatible to both
before going any further it must be stressed that the various versions of the system differ in important ways some language pairs are intrinsically much easier than others and some versions of the system have received far more effort than others
adding a new parameter to the model will decrease the codelength of the data and increase the codelength of the model
the two panel evaluation methodology can be used to empirically evaluate natural language generation work
on average each judge took an hour to evaluate NUM explanations
second the realization system must be extended by adding new functional description skeletons
NUM NUM the realization algorithm treats these groupings as suggestions that may be overridden in extenuating circumstances
because it was the apply edp algorithm that invoked determine content this is a recursive call
NUM the planner first locates the root of the selected edp which is an exposition node
as a final test we compared knight to each of the individual writers
mittal developed and formally evaluated a generator that produced descriptions integrating text and examples
kukich employed a corpus based methodology to judge the coverage of ana s knowledge structures
two principle mechanisms have been developed for generating discourse schemata and top down planners
this lead to the following NUM status descriptors for the claimed guideline violations u id quot u NUM NUM NUM quot NUM NUM h yes i m enquiring about flight number bee ay two eight six flying in later today from san francisco NUM NUM could you tell me coughs souse me which airport and terminal it s arriving at and what
for each possible sense of the word identify a relatively small number of training examples representative of that sense s this could be accomplished by hand tagging a subset of the training sentences
however i avoid this laborious procedure by identifying a small number of seed collocations representative of each sense and then tagging all training examples containing the seed collocates with the seed s sense label
as noted in section NUM half of the examples occur in a discourse where there are no other instances of the same word to provide corroborating evidence for a sense or to protect against misclassification
label salient corpus collocates words that co occur with the target word in unusually great frequency especially in certain collocational relationships will tend to be reliable indicators of one of the target word s senses e.g.
columns NUM NUM illustrate differences in seed training options
in cases where there are multiple seeds it is even possible for an original seed for sense a to become an indicator for sense b if the collocate is more compatible with this second class
in this current work the one sense per discourse hypothesis was tested on a set of NUM NUM examples hand tagged over a period of NUM years the same data studied in the disambiguation experiments
we have also built models that allow individual english sounds to be swallowed i.e. produce zero japanese sounds
NUM for each pair assign an equal weight to each of its alignments such that those weights sum to NUM
the fundamental limitation of this property is coverage
we have yet to use this additional information
many of the translations are perfect technical program sez scandal omaha beach new york times ramon diaz
being english speakers the human subjects were good at english name spelling and u s politics but not at japanese phonetics
generate all katakana sequences with this model for example we do not output strings that begin with a subscripted vowel katakana
likely to be the next cb can be seen by contrasting the following two sequences which differ only in their final utterances NUM a susan is a fine friend
the resulting discourses are coherent but the determination of local coherence in the first case or the detection of a global shift in the second case requires additional inferences
a competition agent is established to deal with such conflicts
in section NUM we discuss the effect on perceived coherence of the use of pronouns and definite descriptions by relating different choices to the inferences they require the hearer or reader to make
here too we conjecture that the manner i.e. linguistic form in which a discourse represents a particular propositional content can affect the resources required by any procedure that processes that discourse
because ct u is only partially ordered some elements may from cf un information alone be equally likely to be cb un l
they propose that each discourse has an overall communicative purpose the discourse purpose dp and each discourse segment has an associated intention its discourse segment purpose dsp
NUM we presume utterances are processed in left to right order and that speakers make initial assignments of referent and meaning that may have to be retracted if material coming later in the sentence conflicts
the focus has shifted from terry to tony in the short subsegment of utterances e f so that use of he in g is confusing
and resources for domain specific semantic class dis mhiguation thus facilitating the generalization of semautic patterns ftom word based to class based representations
it should be noted though that the amount of training data available to the supervised algorithms may not be sufficient
firstly judges reflect that frequently just one indicative surrounding noun is enough to provide clear evidence for sense disambig tion
both judges are able c o perform sub scantially better than the most frequent heuristic baseline despite the seeming y impoverished knowledge source
mapping from the dom specific hierarchy to wordner 3rpically requires only the assignment of senses to the classes
surrounding nouns in the o na resnjk s approach refers to the other nouns in the noun grouping
zdegas work on one of the important input sources the conceptu parser is underway per ce
the algorithms are trained on the tr ng set and then used to dis tdguate the distinct testing set
competency evaluation for initiative setting how does an agent decide whether to ask its collaborator for help
figure NUM type constraints for words and some subtypes
structure and accusative object in second and third argument respectively
we use lexicalized forms when the meaning is not compositional
a lexical inheritance hierarchy facilitates the enforcement of type constraints
middle east technical university ankara turkey lcb onur bozsahin lcsl metu
the design has been tested as part of a hpsg grammar for turkish
thus it can not satisfy the subcategonzation requirements of verbs or postpositions
but the choice of the strategy also affects the design of lexical organization
we elaborate on the consequences of these phenomena in section NUM
run time execution of rules puts the burden on parsing or generation
figure NUM markup of part of a dialogue from the sundial corpus
section NUM introduce a number of mathematical operators used in the compilation process
table NUM n best hypothesis for the sentence can you
conversely let w e dom it
however in our case the transducer is nearly minimal
the lemma will later be used for the proof of soundness
the notion of local extension is formalized through the following definition
this consists simply of copying the transitions of the original transducer
the second reason for inefficiency is the potential interaction between rules
we will then turn the nondeterministic transducer into a deterministic transducer
this transformation is performed for each transducer associated with each rule
we then generalize the techniques to the class of transformation based systems
the situation will be even complicated as unknown words is under consideration fig NUM
the task of unknown word guessing is however a subtask of the overall part of speech tagging process
coverage the proportion of words guesser was able to classify but not necessarily correctly
this allows for the induction of more rules than from a lexicon derived from an annotated corpus
another important conclusion from the evaluation experiments is that the morphological guessing rules do improve guessing performance
we use for training a pre existing general purpose as opposed to corpus tuned lexicon
this resulted in about NUM higher accuracy in the tagging of unknown words
our morphological rules account for this difference by checking the stem of the word
inverting this gives us the sought for function f r
we then rederive the zipfian asymptote from the established recurrence equation
the mathematics are very similar to those used to rederive turing s formula in section NUM NUM
the discrete counterpart of an exponential distribution is a geometric distribution
parameterized by p the probability of some outcome occurring in one trial
stop stop in state qi at which point the se null quences l1 r1 l and r are considered complete
once a leader is chosen the participants act in a master slave fashion
it is evident that the more linguistic processing necessary to fill a slot the harder the slot is to fill correctly
an important advantage of the corelex approach is more consistency among the assignments of lexical semantic structure
the next sections describe how systematic polysem0us classes and underspecified semantic types can be derived from wordnet
these are either of the same type actorelation or of the simple types act or relation
the lexical items in those classes have a highly idiosyncratic behavior and are most likely homonyms
we found the muc NUM coreference task to be challenging and enjoyable for several reasons
however not all semantic objects will be realized in syntax
we do this for each lexical item and then group them into classes according to their assignments
figure NUM instances for the type act relation open dots the type act relation
the first provides a constraint based formalism that allows corelex lexicons to be used stralghtforwardiy in constraint based grammars
an example of these processing steps is given in figure NUM for the input dr
an empirical study on the generation of anaphora in chinese ching long yeh tatung institute of technology
observing the test data we found that nominal anaphora are not commonly marked with articles
for each anaphor its antecedent s position is classified as either topic or direct object
thus we employed the notion of discourse structure as the basis for enhancing the rule
for text NUM NUM speakers completely agree with tr2 and NUM speaker agrees with tr3
the overgenerated and undergenerated cases of pronouns and nominals can be obtained in a similar way
introducing the constraint of animacy of objects in the rule can resolve part of the problem
several restrictions are placed on domains
we next show the equivalence of the two problems
the description can be the same as the initial reference parts of the information in the
again the average matching rates of tr3 are sightly lower than tr2 for these two texts
in other words it means the recognition of a noun phrase
table NUM average number of senses per polysemous word in the wall street journal corpus for the top NUM
the sentence selectmn algonthm calculates the mformahveness for each sentence m a document the measurement represents the strength of relatmn between the goals sentences and the richness ofmformatron m a document these var ables are defined by the following three numerical values
pages long even though the cue phrase method is well tuned to these data we are aware that the hst of phrases we
to summarize structured documents such as manuals the hierarchical structure of the sections and subsections can be used to create goals these goals may control the inheritance of sub goals to be satisfied m the substructure such as the preface section
efficient human computer dialogue requires immediate utterance by utterance accommodation to the needs of the interaction
we were able to use some of the previous results specifically the optimality of a NUM context and the effectiveness of a smaller lexicon and binary feature vectors to obtain a direct comparison with the neural net results
furthermore shoe presses have using rock for granite roll two years ago we reported on advances in while the low error rate on ocr texts is encouraging it should not be viewed as an absolute figure
recalling that the treebank marks up the relationship between pre terminal and terminal as a unary tree and that susanne does n t do this the treebank regularly contains more trees than susanne
if the middle token is a potential end of sentence punctuation mark the descriptor arrays for the context tokens are input to the learning algorithm and the output result indicates whether the punctuation mark serves as a sentence boundary or not
more elaborate implementations such as the style program discussed above consider the entire word preceding and following the punctuation mark and include extensive word lists and exception lists to attempt to recognize abbreviations and proper nouns
NUM the style program which attempts to provide a stylistic profile of writing at the word and sentence level reports the length and structure for all sentences in a document thereby indicating the sentence boundaries
for satz the input is the context surrounding the punctuation mark to be disambiguated and the output is a score indicating how much evidence there is that the punctuation mark is acting as an end of sentence boundary
outputs which fall between the thresholds can not be disambiguated by the network which may indicate that the mark is inherently ambiguous and are marked accordingly so they can be treated specially in later processing
we attempted to counter this by dividing the abbreviations in the lexicon into two distinct categories title abbreviations such as mr and dr which almost never occur at the end of a sentence and all other abbreviations
in order to determine how much context is necessary to accurately disambiguate sentence boundaries in a text we varied the size of the context from which the neural network inputs were constructed and obtained the results in table NUM
most prefixes can be separated from the verb depending on their phonological level e example NUM ich mache die tiir zu
given a pronunciation lexicon this algorithm first extracts the most productive paradigmatic mappings in the graphemic domain and pairs them statistically with their correlate s in the phonemic domain
in fact the search problem is equivalent to the problem of parsing with an unrestricted phrase structure grammar which is known to be undecidable
representing words in the lexicon as perturbations of compositions has a number of desirable properties
it remains however to be seen how this model can be extended to take into account other factors which have been proven to influence analogical processes
the restriction of on g respectively p will be noted a resp p
an important property of our algortihm is that it allows to precisely identify for each pseudo word the lexical entries that have been analogized i.e.
a large number of procedures aiming at the automatic discovery of pronunciation rules have been proposed over the past few years connectionist models e.g.
this analog will in turn derive many more virtual analogs starting with rl once its suffixes will have been substituted during another expansion phase
the main purpose of the learning procedure is to extract from a pronunciation lexicon presumably structured by multiple paradigmatic relationships the most productive paradigmatic alternations
here r is the inverse of i p and denotes a matrix multiplication in which the left operand is first augmented with zero elements to match the dimensions of the right operand p
however the generm tendency can be extracted and the weight set is determined on the basis of this experiment
in methods of the latter type the validity of the heuristics is uncertain when the target text is changed
one problem was the variation for mccann erickson mccann
which holds all macros for the ingress and egress rule packages
we found two system problems that drastically reduced this score
this one change raised this particular document s score to 96r 93p
tense the tense of a sentence is analyzed as past or present
abstraction of a document is one useful tool for quick browsing of textual information
tence the proposed method is to create an abstract by determining important sentences according to features extracted from each sentence
the weights of features most previous systems can be considered to determine the weights of features according to human intuition
type of a sentence sentence types are fact conjecture or insistence
appositives for example are often good descriptor phrases
the types of the concepts and the relations form generalisation lattices which also help define a subsumption relation between graphs
in a practical scenario lowersem can be the knowledge base to which the generator has access minus any contentious bits
this allows the generator to choose an appropriate for the natural language perspective
syntactic stylistic preferences are helpful in cases where the semantics of two paraphrases are the same
a morphological post processor reads the leaves of the final syntactic tree and inflects the words
two causative interpretations 23a b and one manifestation 23c are therefore possible
they are mathematically sound as well as fat it rely relevant they re tect a sensible notion that of keystrokes an t turn out to be metrics under some hypotheses
this means that in average less that two nodes have to be edited in order to get the exact structm e the size of a structure in the tree bank being NUM NUM t NUM NUM nodes
commulalivity v s list b di t a h iangle inequality v a b c c s a dist a c dist a b dist b c
in fact it is possible to give a definition of an edit distance on forests which generalises the definitions on strings wagner fischer NUM and on 7arsala he sent and aslama he became converted verbs 3rd person singular past mursilun a sender and muslirnun a convert agent nouns
dtei hv to extinguish to turn off infinitive teindrai rod vicndrai future tense viendre is l b u ba rism in pbtce of venir to come compare in l ugllsh qocd for wc nl
the constraint eliminates for instance all words of the form txy with x and y two letters outside of the set lcb a e f h o t rcb but does not bar ill hhh eee which are solutions of this analogy
in this particular case saussure was interested in explaining the competition of honor with the older fornl honos honor is not a phonetic transformation of hon os by rhotacism but simply the result of a n alogy
experiments by us reveal that they especially the latter are quite important in the resolution of ambiguities and unknown words
these two issues have been intensively studied by the chinese language computing community in the last decade l NUM
global statistics are referred to statistical data derived from very large corpora as mutual information and t test in cseg tagl NUM
internal information i statistical information each candidate will be assigned a belief according to the statistics derived from the banks
an integrated system for chinese word segmentation and pos tagging which is being developed at the national key lab of intelligent technology and systems tsinghua university
unfortunately however no word segmenter and pos tagger for chinese with satisfactory performance in treating unrestricted texts are available so far
we will introduce them briefly in turn the detailed discussion of each part is beyond the scope of this paper
note that since the baum welch algorithm frequently overtrains a tagged text would be necessary to figure out what training iteration gives peak performance
tin this paper we ignore the problem of unknown words words appearing in the test set which did not appear in the training set
one weakness of this rule based tagger is that no unsupervised training algorithm has been presented for learning rules automatically without a manually annotated corpus
in the case a and b are being done simultaneously denoted by a ll b
with markov model based taggers there have been two different methods proposed for adding knowledge to a tagger trained using the baum welch algorithm
we have set up the experiments in this way to facilitate comparisons with results given in other papers where the same was done
once trained a sentence can be tagged by searching for the tag sequence that maximizes the product of lexical and contextual probabilities
initial state tagging accuracy on the test set is NUM NUM with accuracy increasing to NUM NUM after applying the learned transformations
the english data in celex are more reliable
for both speech recognition and grammar development we used NUM utterances NUM dialogues as a training set and NUM utterances NUM dialogues as a test set
tlmreli re the joined l l iselll aa i li fol llt3 ltses i i f s boxes llowever it is enriched witih among others n revised inleri tt e i syntax where the thematic roles m e derived according t o lisfs
ions is given in figure NUM the situai ion NUM rol ol yl i mly th s ribe t in figllre NUM all NUM e redrred to by al otll NUM ertmm verlts i c the elements of the partial fm t to give including our samph verbs vcrschc nkcn
NUM a they moved from boston to chicago
it can process only simple sentences
h2 the neighbor met the boy yesterday
b they moved to chicago from boston
the dichotomy of topic and focus concerns the sentence as a whole
sections NUM and NUM have introduced our treatment of topic and focus
what percentage of the test words is translated
assuming an average of NUM NUM sense tagged occurrences per word this will mean a corpus of NUM NUM million sense tagged word occurrences
suspectl0 had a motive to murder lord dunsmore
in order to acquire resolution rules for a nlp system effectively and efficiently various methods have been proposed
furthermore to take into account the effect of the anaphora resolution of anaphoric expression in english sentences on the accuracy of the identification of the antecedents of zero pronouns the antecedents of anaphoric expression in the english sentences were determined by hand and the accuracy with and without anaphora resolution was compared
for the sentence pairs which contain zero pronouns the syntactic and semantic structure of each japanese sentence was created using the japanese to english mt system alt j e as the japanese analyzer described in section NUM NUM and the partial syntactic structure of each english sentence was created using brill s tagger as the english analyzer described in section NUM NUM
v hen considering the application of these methods to a practical machine translation system for which the translation target area can not be NUM mired it is not possible to apply them directly both because their precision of resohtion is low as they only use limited information and because the volume of knowledge that must be prepared beforehand is so large
can not be aligned NUM NUM i according to these results the proposed method is effective for the automatic identification of japanese zero pronouns and their antecedents from japanese and english aligned sentence pairs and by using a large amount of aligned sentence pairs it is possible to identify antecedents of japanese zero pronouns for almost all types of japanese zero pronouns
these results shows that for the test set NUM out of NUM zero pronouns can have their antecedents identified using proposed method methodologically and most all of the antecedents of zero pronouns which can be identified from the japanese and english alig aed sentence pairs are correctly identified by using proposed method NUM NUM NUM out of NUM instances
from the point of view of the extraction of resolution rules of zero pronouns a technique to identify zero pronouns in a sentence in one language and their antecedents in a translation from aligned sentence pairs is needed
during r have27 a have have27 library book19 figure NUM the first tree incorporated into the description
lexieal depth can be evaluated in two dimensions
in substitution the root of the first tree is identified with a leaf of the second tree called the substitution site
multiple subject areas can be selected and prioritized
variance is used for expressing effectiveness of a context
the sub category for example that it is an organization is defined on the left side of the pattern
system maintenance was also difficult since the patterns were defined in both the matching engine and the pattern files
here we use NUM NUM NUM equal weight
thus pattern developers must pay special attention to the order of the patterns
the word to be segmented is written on the left side of the pattern
erie is a name recognition system developed for the multilingual entity task met in muc NUM
the segmentation pattern is used to further segment a word whose word boundary is given by majesty
since names can be expressed in many ways a hundred newspaper articles used for the pattern development were insufficient
in the course of this project most of our time was spent on the development of the engine generator
this was probably because the patterns for entity names were not well enough defined
furthermore by selecting potential heads on the basis of a head corner table comparable to the left corner table of a left corner parser it may use top down filtering to minimize the search space
null s u interested in discount red out departure time at NUM NUM
this observation leads to the notion of colored substitutions that takes the color information of formulae into account
the queries am routed to the running modsaf simulation and the available entities can be viewed over a www connection using a suitable browser
because of the use of oaa quickset can interoperate with agents from commandtalk NUM which provides a speech only interface to modsaf
the recognized entities as well as their recognition probabilities are sent to the facilitator which forwards them to the multimodal interpretation agent
the agent weights the results of both hmm and neural net recognizers producing a combined score for each of the possible recognition results
for use at NUM palms california where it is primarily used to set up training scenarios and to control the virtual environment
when the pen is placed on the screen the speech recognizer is activated thereby allowing users to speak and gesture simultaneously
speech recognition agent the speech recognition agent used in quickset employs either ibm s voicetype application factory or voicetype NUM NUM recognizers
a minefield of an amorphous shape is drawn and is labeled verbally and finally an m1a1 platoon is created as above
since any unimodal recognizer will make mistakes the output of the gesture recognizer is not accepted as a simple unilateral decision
we chose these five categories because they represented relatively different semantic classes they were prevalent in the muc NUM corpus and they seemed to be useful categories
building semantic lexicons will always be a subjective process and the quality of a semantic lexicon is highly dependent on the task for which it will be used
we decided to see how well the technique could work without this additional human interaction but the potential benefits of human feedback still need to be investigated
since new seed words are generated dynamically without manual review the quality of the ranked list can deteriorate rapidly when too many non category words become seed words
for example the words judged as NUM s for each category are shown in figure NUM figure NUM illustrates an important benefit of the corpus based approach
we use a very narrow context window consisting of only two words the first noun to the word s right and the first noun to its left
we asked the judges to rate the words on a scale from NUM to NUM because different degrees of category membership might be acceptable for different applications
in general we found that additional seed words tend to improve performance but the results were not substantially different using five seed words or using ten
given a text corpus and an initial seed word list for a category c the algorithm for building a semantic lexicon is as follows NUM
how one defines a true category member is subjective and may depend on the specific application so we leave this exercise to a person
however what about the example shown in figure NUM
the smaller the difference the more often the system will provide the correct part of speech if it translates at all
if governs h d m holds then establish the dependency relation between h and the m and add m to the set h
table NUM distribution of distances and
section NUM describes some experiments and their results
we find the tagging accuracy is very low
but the given context may contain much noise so there may be some activated clusters in which the senses are not similar with the correct sense of the word in the given context
we adopt depth first search strategy to determine the sub tree
section NUM presents some conclusions and discusses the future work
word sense disambiguation based on structured semantic space
the purpose of the project documented in this video was to demonstrate and explore some of the capabilities of a nlu interface to a ve system and to identify some of the research issues that need to be addressed in this area
the commands allow the user to control the playback of the simulation and its speed as well as various display characteristics such as viewpoint show me lhe lop down ou thewindow view and overlays display the map rings
in addition the user can move from one object to another by name or by description rather than by flying or pointing put me on the doomsday put me on the hostile ship or by specifying a particular location move me to NUM n NUM e lncrease my altitude to NUM feet
building the prototype involved the creation of an application specific dictionary and lexical semantics for nautilus a few minor extensions to its english grammar and the development of two sets of code one to translate the logical forms generated by nautilus into messages for the application software and one to interpret these messages and instruct viewer to produce the appropriate actions or responses
it also allows simulation objects to be referred to by description as sets and generically rather than just individually
commands are also used to manipulate time run the simulation forward backward set the clock to zero
however it must be stressed that this area of hci is still in its infancy and there are a number of research issues that will need to be addressed in order to realize the full potential of this technology
one major difficulty with interfaces to ve systems is that the user s hands and eyes are occupied in the virtual world so standard input devices such as mice and keyboards that require a physical support and or visual attention are impractical
he she can ask for information about the virtual world how many hostile ships are there or about a specific object what is the thunderbird s heading what is my viewing altitude
immersive interactive 3d computer display systems often called virtual reality systems or virtual environments are rapidly emerging as practical options for training command and control c2 hazardous operations visualization and other applications
when we were singing popular songs with mr park s friends at the edge of a well near the area of unlicensed buildings how many birds were there sitting around bread crumbs without any taste we observe the morpheme ga NUM times
features are used in parsing described below
b one b a two a c three c a f our a a two a a four a by means of this simple bottom up technique it is possible to compile finite state transducers that approximate a context free parser up to a chosen depth of embedding
let us consider the caseofa b b i b a a b a x applying to the string aba and see in detail how the mapping implemented by the transducer in figure NUM is composed from the four component relations
the final version of the paper has benefited from detailed comments by l onald m kaplan and two anonymous reviewers who convinced me to discard the ill chosen original title deterministic replacement in favor of the present one
if the four relations on the bottom of figure NUM are composed in advance as our compiler does the application of the replacement to an input string takes place in one step without any intervening levels and with no auxiliary symbols
with the addition of a category for foreign words the number of major categories used is NUM plus three tags for punctuation which is in no way a remarkable amount but the suc tags are composite
in the final replacement step the bracketed regions of the input string in the case at hand just a b a are replaced by the strings of the lower language yielding x as the result for our example
the effect of the left to right and longest match constraint is to factor any input string uniquely with respect to the upper language of the replace expression to parse it into a sequence of substrings that either belong or do not belong to the language
furthermore the number of readings connected with each word token is highly dependent on the linguistic description used as a basis for the tagging system its theoretical assumptions and the granularity of the system among other things
in addition to the need for additional linguistic resources clearer guidelines for developing these resources are needed
looking to the future approaches to learning extraction rules from examples is research with very high payoff
if the components of the text technology successfully process foreign languages text is that a sufficient test
in addition the shared software was immature but the development schedules necessitated that it be robust
system planning necessitates identification and acquisition of essential resources such as supporting data and software development tools
this is especially true for languages which are not traditionally the focus of natural language or computer applications
expect that systems shareable across government agencies require interagency investment far beyond the initial definition of an architecture
for ne named organizations named persons dates and times monetary amounts and percentages are found here
the lexical pattern matcher was developed in NUM to deal with grammatical forms such as names in english and japanese
based on the quality of our bigram based relevance feedback we also intend to experiment with a bigram method of segmentation
the top level model for generating all but the first word in a name class is pr w f i w f no
some rules mostly rules dealing with mute e and semivowels can be more easily expressed on the phonemes strings
similar rules are used to spell acronyms i b m gives ibe m NUM b
finally when we thought that the rules were good enough we took two text samples from different sources and tested both the taggers
as we mentioned earlier it is not very easy to change the behavior of the statistical tagger in one place without some side effects elsewhere
last cont pns fig NUM select b items therelbre substances but also plurals NUM the portion retains the constitution o1 the whole
consequetltly they can not bc tnnary bul relational predicates in ihe sense of i an91 l thai is terms which are predicates cmly with reference to some other entity
shape is assumed to be schematic vid
a portion has been oblained by a diflbrent process lhan the whole a cake has been obtained by baking it but a slice o1 cake by cutting it off tile cake
siiape and magn of elt portions fig NUM
they are individuations of pre existing parts of the whole
this accotulls fof the fact thai partitive constructions e.g.
figure NUM l luralizatiml i exical rule
they are idealisations of physical boundaries of the whole
comparing the results for english to german translation with german to english is difficult because of the different corpora used for the celex frequencies
it is beyond the scope of this paper to discuss the merits of one theory of the english syllable over another
for instance a word like aggravation has three tokens of the vowel grapheme a but all are phonetically different
choosing between katarina and catalina both good guesses for might even require detailed knowledge of geography and figure skating
we will then return a ranked list of the k best translations for subsequent contextual disambiguation either by machine or as part of an interactive man machine system
we believe that these parameters are relevant for the particular naive method described in the current section
in this experiment we test the performance of the morpho lexical probabilities on the task of analysis assignment
here one or more analyses of an ambiguous word are recognized as wrong and hence are rejected
this research was partially supported by grant number NUM of the israel council for research and development
in the case where l NUM we say that the word w is fully disambiguated
the determiner h the noun qph ngp d the coffee
the short context constraints use unambiguous anchors that are often function words such as determiners and prepositions
learning morpho lexical probabilities analysis and the frequency of the words in its sw set does not hold
the suffixes aize nize en airy nify all cue the change of state feature for their derived form as was discussed for aize above
stz called to a delicate situation NUM er habe schipke gesagt dai man nicht mit he have schipke said that one not with eiern werfen diirfe schon gar nicht auf eggs throw be allowed part partnot at den bundeskanzler
following data NUM weil das argument i einen mann j because the argument a man anfgeregt hat der das fest besuchte j daft upset has who the party visited that ranchen ungesund ist i
it is possible to have more than one extraposed phrase as shown in NUM and NUM NUM NUM a man i j came in with blond halr i who was smiling j
occasionally the sw sets defined for two different analyses are actually the same
given the unfortunate circumstance that for the foreseeable future some interactions will fail this measure remains necessary
cr further the restriction of upward boundeddeg hess applies to extraposition i.e. in contrast to fronting extraposition may not cross the sentence boundary NUM whoi did mary say s that john saw a picture of i in the newspaper
we find similar data with extraposition from fronted objects in english NUM which book j i did she write i last year that takes only two hours to read j NUM which woman j i did he meet i yesterday from the south of france j
as the state of the art progresses system errors are evolving into inappropriate responses rather than total system failure
this measure was used in order to demonstrate the practical viability of systems techniques when the hardware gets faster
measures for capturing the rate of success in situations where utterances are partially understood or perhaps even completely misunderstood are needed
besides the parameter k pebls also contains other learning parameters such as exemplar weights and feature weights
verb second position an only be ensltred if the constituent in sentence initial l osition is nonempty
mstanhatecl meaning template being rendered into coherent prose or as a passage xtractlon problem where certain fragments
for a segment s to be the n lughes t ranked referents m s where n ms a scalable value 4our sahence factors nurror those used by lappm and leass with fl te excephon of poss wluch is sens ve to possessive expmsmons and cntx wl ch is sensllav e to the chscourse segment m whlch a candldate appears
we thus avoid relying on high level inferences and very specific world knowledge postulates our goal being to determine the temporal structure as much as possible prior to the application of higher level inferences
here it would appear only one reading is possible i.e. the one where john gave mary her slice of pizza just after she stared or started to stare at him
as an example of how the temporal centering preference techniques can reduce ambiguity recall example NUM and the possible continuations shown in NUM
in 3a there is a causal relationship between mary s pushing john and his falling and the second event is understood to precede the first
e can precede el if dcu2 describes an event or dcu1 does n t describe an activity and dcu2 describes a past perfect stative
a if there is a temporal expression it determines the temporal relationship of the new dcu to the previous ones and defaults are ignored
the possibilities for rhetorical relations e.g. whether something is narration or elaboration or a causal relation can be further constrained by aspect
however explicit cues to rhetorical and temporal relations are not always available and these cases result in more ambiguity than is desirable when processing large discourses
for the eventive second sentence of 8b to be an elaboration of the first sentence it must occur in a stative form for example as a progressive i.e.
tempfoc the most recent event in the current thread which a subsequent eventuality may elaborate upon same event overlap come just after or precede
this means that we will get many wrong translations if a word is not included in the lexicon and has to be segmented for translation
but translation quality rests on the linguistic competence of the mt system which again is based first and foremost on grammatical coverage and lexicon size
nevertheless our results indicate that some systems focus on either of the two translation directions and therefore have a more elaborate lexicon in one direction
the frequency count has been disambiguated for part of speech by manually checking NUM occurrences of each word form and thus estimating the total distribution
this does not say anything about the distribution of the different noun readings financial institution vs a slope alongside a river etc
for training debugging and demonstration purposes realpro can also be used in interactive mode to realize sentences from ascii files containing syntactic specifications
the dsynts is a dependency structure and not a phrase structure structure there are no nonterminal nodes and all nodes are labeled with lexemes
first consider the simple example in figure NUM which corresponds to the sentence NUM NUM this boy sees mary
at each node each rule in the appropriate grammar deep or surface syntactic must be checked against the subtree rooted at that node
it has an application programming interface api available in c and java which can be used to integrate realpro in applications
default word order certain word order variations including so called topicalization i.e. fronting of adjuncts or non subject complements are controled through features
the development of realpro was partially supported by usaf rome laboratory under contracts f3060293 c NUM f30602 NUM c NUM and f30602 NUM c NUM and by darpa under contracts f30602 NUM NUM NUM and f30602 NUM c NUM
in addition realpro can also output an ascii representation of the dgraphs that a user application can format in application specific ways
full english morphology including a full range of pronominal forms personal pronouns possessive pronouns relative pronouns
the dsynts is lexicalized meaning that the nodes are labeled with lexemes uninflected words from the target language
so the shift from words to sentences is just of matter of reformulation
NUM x NUM x NUM x NUM NUM x NUM x NUM NUM after a simple matching of the words of this string with their lexicon entries figure NUM fpn me in mr
none of the correlations in tables NUM and NUM can be attributed to word frequency effects since the NUM words were all chosen with almost the same NUM frequency
NUM p k j converts japanese sounds to katakana writing
we use this to combine an observed katakana string with each of the models in turn
this yields a fatter NUM state NUM arc wfsa which accepts the correct spelling at a lower probability
NUM repeat NUM NUM until the symbol mapping probabil null ities converge
next we map english sound sequences onto japanese sound sequences
the next wfst converts english word sequences into english sound sequences
so we only aim to match or possibly exceed their performance
also a dot separator is used to separate words but not consistently
one can easily wind up with a system that proposes iskrym as a back transliteration of aisukuriimu
als a definitional sent en e such its dog c at noun at where the path on the right is identical to that on the left is written more succinctly as dog cat noun
both approaches to inference in datr aim to provide a system of deductioi that makes it possible to teterlnine formally for a given datr theory NUM what follows fl om the stateulellts in NUM the primary interest lies in deducing statements about the vahles associated with particular node path pairs defined within the theory
the semantics is presented as a set of inference rules that axiomatises the evaluation relationship for datr expressions
making use of defimlts the datrc theory given above can be expressed more succinctly as shown next
consider for examl le the following rule of in rence adapted from evans and gazdar
n the following sequences of descriptors in desc are denoted c j
inheritance of values permits appropriate generalizations to be captured and redundancy in the description of data to be avoided
the prelnises are detinitional sentences which can be read the value of path NUM at node nj ix inherited fl om the value of path p NUM at n2 and the vahle of path NUM NUM at node n2 is el respectively
we have proposed a possible theoretical explanation of analogy in terms of edit distances
the implementation of korean dialogue processing and the computation of social status is made based on ale system
NUM case markers nominative genitive plain ka i uy h0nol ific kkeyse dative accusative
chief section nora go out past dec chief section park went out NUM park kwacang nim i naka si ess e
the relative orders illustrated in NUM NUM are collapsed into the one illustrated in NUM
thus even if a sentence itself is looked at it is necessary to consider both addressee honorification and subject honorification
as shown in diagrams NUM NUM there is no conflict in the information provided by the attribute context
since it is possible and easy to include such contextual intormation in the ifpsg formalism that formalism is adopted here
first when the honorific suffix him attaches to an np the referent of the np is respected by speaker
let w be the word to be disambiguated and let NUM ll w rl r2 be the sentence fragment containing w
this gives us an interesting alternative perspective from the standpoint of algorithms that match the words between parallel sentences
in addition even if a derivational cue does exist the reliability on average approximately NUM percent of the lexical semantic information is too low for many nlp tasks
he proposed a matrix permutation method matching co occurrence patterns in two non parallel texts but noted that computational limitations hamper further extension of this method
for example it analyzed subsidize as sub side ize and thus produced the sidize change of state pair which for the relevant tokens was incorrect
examples are a synonymy new album the record three bills the legislation b hypernymy hyponymy rice the plant the television show the program c meronymy plants the pollen the house the chimney
april NUM a clarification can be invoked
partial automation included it the current version significantly reduces the manual effort
while the equivalence relations match the hyponymystructure does not situation NUM above
has eq hyponym when a meaning can only be linked to more specific ill records e.g.
the multilingual nature of this conceptual database raises methodological issues for its design and development
net database all language specific wordnets will be stored in a central lexical database system
the symbol NUM sam NUM said NUM NUM i stands for a feature which asks does the word said appear in the previous five sentences but not in the next five sentences
we expect model a to be inferior to model b for two reasons the lack of reuters data in it s training set and the difference of between one and two years in the dates of the stories in the training and test sets
as a third possibility the linking can be established through one of the languages
we have considered four possible designs a linking by pairs of languages
one proposed work around is to employ dynamic time warping to come up with an explicit alignment between the segments proposed by the algorithm and the reference segments and then to combine insertion deletion and substitution errors into an overall penalty
qualitative assessment of our algorithm as well as evaluation using this new metric demonstrate the effectiveness of our approach in two very different domains wall street journal articles and the tdt corpus a collection of newswire articles and broadcast news transcripts
for the wsj model the probabilistic metric p was NUM NUM when evaluated on 325k words of test data and the precision and recall for exact matches of boundaries were NUM and NUM for an f measure of NUM
this plot shows that when a segment boundary is crossed the predictions of the adaptive model undergo a dramatic and sudden degradation and then steadily become more accurate as relevant content words for the new segment are encountered and added to the cache
the above equations reveal that the probability of a word t involves a sum over all words s such that s e h s appeared in the past NUM words and s t is a trigger pair
by monitoring the long and short range models one might be more inclined towards a partition when the long range model suddenly shows a dip in performance a lower assigned probability to the observed words compared to the short range model
this motivates a quantitative measure of relevance which we define as the logarithm of the ratio of the probability the exponential model assigns to the next word or sentence to that assigned by the short range trigram model
the major alternative corpus architecture which has been advocated is a database approach where annotations are kept separately from the base texts
it may be less useful as a means of publishing corpora and may prove inefficient if the underlying corpus is liable to change
the query language of ims cwb which has the usual regular expression operators works uniformly over both attribute values and corpus positions
we are working on extending lt nsl in this direction e.g. to allow processing of the bnc corpus in its entirety
there is no reason why one should not attempt to use the strengths of both the database and the sgml stream approaches
we found that using this generic editor framework made it possible to quickly write new editors for new tasks on new corpora
one solution to this problem is syntactic underspecification e.g. grouping the nouns and adjectives under a single lexical category
the results of the first NUM sentences are summarised in table NUM
this actually did not come as a surprise since many main forms required by the suffix rules were missing in the lexicon
disregarding the semantic types completely on the other hand will cause syntactic constraints to govern both syntactic substitution and semantic unification
a data oriented semantic interpretation algorithm was tested on two semantically annotated corpora the english atis corpus and the dutch ovis corpus
every woman a man figure NUM same imaginary corpus of two trees with syntactic and semantic labels using the daughter notation
for instance parse for a woman whistles thus a parse tree can have many derivations involving different corpus subtrees
so far the data oriented processing method has mainly been applied to corpora with simple syntactic annotations consisting of labeled trees
if this type information is not available during parsing important clues will be missing and loss of accuracy will result
the complete interpretation of this utterance is user wants today itomorrow destination
for some texts this results in very few divisions being made
this level of accuracy was obtained consistently for a variety of different texts
if the dictionary does not contain inflected words that is if there are just the lemmas it may need some correction by the user or by the system in order to adjust its concordance with other related words
with relation to the new lemmas that do not have the information completed they might update or not the first of the tables if a entry for the unknown cases is included otherwise they would remain unchanged
this system will be easier to use there is no need to force users to know what the lemma and what the suffix of a word are and may have better results measured in terms of keystroke savings
there are some prefixes that can be used in some specific cases for example a prefix for verbs may indicate the absolutive case in the sentence but in general their frequency of apparition is not very relevant
the complexity of this system is also larger because in this case all the words of the sentence that appear before the current word are taken into account while in the previous approaches only one previous word was used
so the complexity needed to create a correct word including all the suffixes it needs in inflected languages may make it necessary to search for other prediction methods apart from all that were shown in the previous section
in this way the set of words that are candidates to be proposed by the predictor is restricted to the ones that match the most probable syntactic role in the current position of the sentence thus increasing the hint rates
so there is a need to define the syntactic rules typically left right usually being left and right some syntactic categories defined in the system that are used in a language
we are concerned with utterances requesting specific information
our model maintains for each agent a task initiative index and a dialogue initiative index which measure the amount of evidence available to support the agent holding the task and dialogue initiatives respectively
this allows our algorithm to deal with infrequent words or unknown proper nouns
figure NUM database information for the verb appear
we extended our annotations of the trains91 dialogues to include in addition to the agent s holding the task and dialogue initiatives for each turn a list of cues observed during that turn
in either case when the task dialogue obligation is fulfilled the initiative may be reverted back to the hearer who held the initiative prior to the request or interruption
st level phrasing and interpretation of this passage produces two relevant facts a job out for the firs t clause and a successor for the second
to date all the rules we have written for even complex domains such as the joint venture task in muc NUM have met this criterion
this was a difficult message for us and we scored substantially less well on this message than in average especially on the ne task
an equally effective approach and simpler is to ignore the pronoun and reason directly from th e successor fact to any contextualizing job out fact
for all the changes that the system has undergone the coarse architecture of the muc NUM version ofalembic is remarkably close to that of its predecessors
the phrases that are parsed by the phraser are subsequently mapped to facts in the inferential database a mapping mediated by a simple semantic interpreter
this in turn enables inference that may have been previously inhibited because the necessary antecedents were distributed ove r what were then distinct individuals
in controlled experiments we measured the tagger s accuracy on wall street journal text at NUM NUM based on a training set of140 NUM words
james person age num NUM num years old age person the treatment of age appositions is compositional as is the case for the interpretation of all but a few complex phrases
another interesting question is how much an organization lexicon might have helped had it been addedto our rule based phrasing algorithm not simply used by itself
in this paper the selected texts are restricted to the exposition type which explain an idea or discuss a problem
spud simultaneously constructs the semantics and syntax of a sentence using a lexicalized tree adjoining grammar ltag
no specification of idiomatic combination is complete without representing the pragmatic circumstances in which its use is appropriate e.g.
meanwhile some representation of entities and their salience is required to determine whether ellipsis is possible in context
however in the library widely different attributes can be appropriate even for physical objects of various types
periodicals meanwhile are best described by date of issue e.g. the may issue of language
semantic declarations such as the following represent this NUM area i s a basic has type i s a area that is area uses the specified semantics to provide a basic level description of a in terms of state NUM and information i
this approach captures naturally and elegantly the interaction between pragmatic and syntactic constraints on descriptions in a sentence and the inferential and lexical interactions between multiple descriptions in a sentence at the same time it exploits linguistically motivated declarative specifications of the discourse functions of syntactic constructions to make contextually appropriate syntactic choices
however whereas the planning procedures on which we base our system are used only for noun phrases we apply this procedure to the sentence as a whole using a rich semantic representation further although these procedures typically construct an abstract semantic representation we treat operators as entries with syntactic semantic and pragmatic properties
to rule out c s additional distractors the object relative clause anchored by pulled is chosen the informational coindexation between the foot n node and the verb in an object relative clause ensures that exerted does not apply because c is not the object of an exerting event according to information bp k c
except for the value of prefix dircii
we will investigate using cooccurrence information to match acronyms to full organization names and alternative spellings of the same name with each other
class is specified at synsemii oclcontenticr ass
when an unknown word is encountered in a sentence its context in that sentence can be of great importance when predicting its part of speech
for example muysken NUM studied the classification of affixes according to their order of application for a theoretical discussion on morphology
however all of their results assume that only one unknown word is present in each sentence or is within the tri tag range of the tagger
rappaport hovav and levin in press
the importance of dealing with unknown words in natural language processing nlp is growing as nlp systems are used in more and more applications
the distinction between closed class and open class words should help to refine the possibilities for an unknown word and enhance the information provided by the syntactic knowledge
if the sentence fails its first attempt to parse then it ks reparsed using the second choice list for the affix instead of the first choice list
an ngram tagger concentrates on the n neighbors of a word where n tends to be NUM or NUM ignoring the global sentence structure
the number of matches is important since ideally we want the recognizer to return all possible parses that occur when the full dictionary is used
new adjectives are formed in analogy to existing ones
our tag set has a three level structure as shown in figure NUM
to construct this model we have to answer the following questions
in section NUM i briefly discuss the utility of wsd in practical nlp tasks like information retrieval and machine translation
NUM to the subject representation language
this is encouraging as it demonstrates the feasibility of building a wide coverage wsd program using a supervised learning approach
NUM NUM constructing a basic tag context tree
mistake driven mixture of hierarchical tag context trees
the next step is to fill the gap between theory and practition
figure NUM constructing hierarchical tag context tree
wsd techniques that work well for refined sense distinction will apply equally to homograph dlsambiguation
NUM NUM constructing a hierarchical tag context tree
normalization is handled in the letter to sound rule set and in a preprocessing module
they are even differences between le petit robert and le grand robert dictionaries
hence we have to recursively repeat retrieval of the decision tree as long as the remaining suffix is not empty
we have presented a method of automatic extraction of subgrammars for controlling and speeding up natural language generation nlg
during training the user can interactively select which of the parser s readings should be considered by the ebl module
note that the mrs of the root node is used for building up an index in the decision tree
we showed how the method can be used to train a system to a specific use of grammatical and lexical usage
first there are many languages that developed a writing system only recently
depending on that classification different subsets of language specific rules can be activated
such rules are also necessary to formalize grapheme phoneme correspondences in speech synthesis architecture
this could be an entire dictionary or a list of proper names
computational linguistics volume NUM number NUM each of which retains its pronunciation
nevertheless often these rules are blocked if they cross a morpheme boundary
then if no rule has yet been applied rule NUM is tested
is pronounced o in moto loto solo
after hypotactic aggregation the un aggregated propositions are then combined using paratactic operators such as appositions or coordinations
these results turned our attention to the problem how to speed up the processing of correct sentences even further
as seen in the third event of the walkthrough article the fill can be an extended title such as vice chairman chief strategy officer world wide
sites have developed architectures that are at least as general purpose techniques as ever perhaps as a result of having to produce outputs for as many as four different tasks
ms marsh has many years of experience in computational linguistics to offer along with extensive familiarity with the muc evaluations and will undoubtedly lead the work exceptionally well
most systems achieved approximately the same levels of performance five of the seven systems were in the NUM NUM recall range and NUM NUM precision range
approximately NUM of the anaphors are personal pronouns including reflexives and possessives and NUM of the markables anaphors and antecedents are proper names including aliases
the task as defined for muc NUM was restricted to noun phrases nps and was intended to be limited to phenomena that were relatively noncontroversial and easy to describe
most if not all the systems that were evaluated on the ne task adopted the basic strategy of processing the headline after processing the body of the text
the article was relatively straightforward for the annotators who prepared the answer key and there were no substantive differences in the output produced by each of the two annotators
note also that even the best system on the third event was unable to determine that the succession event was occurring at mccann efickson in addition it only partially captured the full title of the post
probabilistic method provides a robust way to tackle with the unrestricted text
the code for coordination is shown in figure NUM
the significance of this restriction will be illustrated shortly
using higher order logic programming for semantic interpretation of coordinate constructs
figure NUM ccg derivation of harry found simulated
NUM x c on3 x x
derivation fails to yield all available quantifier scopings
indeed the object of the experiment is to disambiguate among wordnet senses of both verbs and nouns on the basis of the lexical semantic restrictions for the arguments of the verb and the lexical semantic associated to the noun
figure NUM shows the three dimensions of the matrix a words in a language indicated by yy b meanings indicated by a4 c languages indicated by psk
the optimal tag sequence for a given observation sequence of words is given by the following equation
the manual definition process is time consuming when a set of detailed grammatical classes is used
it is well known that for hmms the forward and backward probabilities tend exponentially to zero
a maximum memory requirement of 930kb has been measured in the experiments described in this paper
where l is the number of the known words and t is the number of tags
the stochastic solutions described by equations NUM and NUM are computed by multiplying several conditional probabilities
using the hmm tagger with the lexicon containing all the words from the brown corpus we obtained the error rate mean NUM NUM NUM with the standard error deb NUM NUM
although our primary goal was not to compare the taggers themselves but rather their performance with the guessing components we attribute the difference in their performance to the fact that brill s tagger uses the information about the most likely tag for a word whereas the hmm tagger did not have this information and instead used the priors for a set of pos tags ambiguity class
the divergence between pz and p is defined as d pilip2 d p2iip1 and is a measure of how difficult it is to distinguish between the two distributions
for example when we set the threshold 0s to NUM points the obtained ending guessing rule collection ending comprised NUM NUM rules the suffix rule collection without mutation suffix deg comprised NUM rules the suffix rule collection with mutation suffix NUM comprised NUM entries and the prefix rule collection prefix comprised NUM rules
for instance the ending guesser of xerox includes NUM rules whereas our ending guesser includes NUM NUM guessing rules the information listed in a general purpose lexicon can be considered to be of better quality than that derived from an annotated corpus since it lists all possible readings for a word rather than only those that happen to occur in the corpus
if the subtraction results in an non empty string and the mutative segment is not duplicated in the affix the system creates a morphological rule with the pos class of the shorter word cj as the class the pos class of the longer word ci as the r class and the segmented affix itself in the s field
not surprisingly their performance is quite poor if a word is assigned all possible tags the search space for the disambiguation of a single pos tag increases and makes it fragile if every unknown word is classified as a noun there will be no difficulties for disambiguation but accuracy will suffer such a guess is not reliable enough
NUM merge the most similar pair to a single new label i.e. a label group and recalculate the similarity of this new label with other labels
p i e pc e and pc3 e are probability distributions over environment e of cl e2 and c3 respectively
kiyono s approach performed a refinement of an original grammar by adding some additional rules while the inside outside algorithm tries to construct a whole grammar from a corpus based on maximum likelihood
we propose a method to group brackets in a bracketed corpus with lexical tags according to their local contextual information as a first step towards the automatic acquisition of a context free grammar
the simplest approach to pos class guessing is either to assign all possible tags to an unknown word or to assign the most probable one which is proper singular noun for capitalized words and common singular noun otherwise
when setting the s segment to an empty string and the m segment to a non empty string the schemata allows for cases when a secondary form is listed in the lexicon and the base form is not
i am indebted to melina alexa and john bateman for encouraging this work and to them both and wiebke mt hr renato reinau lothar rostek and ingrid schmidt for valuable help to improve this paper
special thanks go to oliver plaehn who implemented the annotation tool and to our six fearless annotators
this work is part of the dfg sonderforschungsbereich NUM resource adaptive cognitive processes project c3 concu rent grammar processing
note that if a stronger form is recognized when only the weaker one is correct it is counted as wrong
either there is some sort of structure in this segment more fine grained than would be obtained if pot int
while this might provide a satisfactory treatment of modification at the derivation level there are now three types of operations two adjunctions and substitution for two types of dependencies arguments and modifiers and the directionality problem for embedded clauses remains unsolved
however this derivation is ruled out by the restriction that only substitutable components can be substituted the subject component of the adore d tree is not substitutable after subsertion into the seems d tree and therefore it can not be substituted into the claims d tree
and sentences were assigned their correct speech act for comparison with those eventually selected by the discourse processor
the first is that the suggestion for meeting on wednesday in ds NUM is treated like an interruption
it is the structure resulting from this pattern matching process which forms the input to the discourse processor
and so we can meet tuesday at NUM NUM could be a suggest or a confirm appointment
however adjunction rather than substitution is used since in general the structure that is substituted may only form part of the clausal complement the remaining substructure of the clausal complement appears above the root of the adjoined tree
a sic is associated with the d edge between vp and s node in the seems d tree to ensure that no node labeled s can be inserted within it i.e. it can not be filled by with a wh moved element
a dtg is a four tuple g vn vt s d where vn and vt are the usual nonterminal and terminal alphabets s e v is a distinguished nonterminal and d is a finite set of elementary d trees
the fact that adjunction and substitution are used in a linguistically heterogeneous manner means that standard lag derivation trees do not provide a good representation of the dependencies between the words of the sentence i.e. of the predicate argument and modification structure
to agree with which is signaled by the fact that its component s root and frontier nodes are labeled s and vp respectively but the verb itself is not finite and therefore only projects to vp fin
to explain this i emphasize the dual functions served by pitch accents as markers of both propositional semantic pragmatic and attentional salience
for example in NUM john introduced bill as a psycholinguist and then he insulted him
what makes NUM felicitous is that the pitch accents on the pronominals contribute attentional information that can not be gleaned from text alone
however when uttered with contrastive stress on the pronouns i john introduced bill as a psycholinguist and then he insulted him
in this case the most probable function is displayed but the annotator has to confirm it
for example the average number of senses per polysemous noun is NUM NUM for the nouns which account for the top NUM noun occurrences in the brown corpus
from table NUM the average number of senses per polysemous word in the brown corpus for the remaining NUM word occurrences is only NUM NUM or less
committee based sample selection is applied to part of speech tagging to select for annotation only those examples that are the most informative and this avoids redundantly annotating examples
in each trial i00 examples were randomly selected to form the test set while the remaining examples randomly shuffled were used for training
in this paper we present the important factors in the generation process as well as its important steps
in this research we are studying the interaction between the text of a statistical report and its figures
to further illustrate the importance of simultaneous application of these factors let s look at figure NUM again
graphics only systems can get away with a simple type system as presented in NUM NUM
the writers intentions describe what to say and up to a certain point how to say it
if it is not it backtracks on the last allocation and tries the next best encoding for it
in figure NUM the goal is to present the evolution of the profits during the relevant time period
the difference can be seen in the organization of the graphs and in profits the wording of the text
we can use the following heuristic to overcome the problem for a given word wi in a trigram of wi NUM wi wi l with context heterogeneity x y
some research has focused on these problems separately such as mackinlay s apt system NUM NUM which focuses mainly on type based graph generation zelazny s work on graphs and messages NUM and casner s study of tasks goals NUM
it can be determined at this stage that a figure can not be generated because of physical reasons it is too big to fit not enough gray levels are available this low level work is quite involved because it has to take into account the NUM d constraints and the limitations of the media
there are a limited number of word bigrams x w and a limited number of word bigrams w y where w is the word air likewise for
since these functions words such as the a of will affect the context heterogeneity of most nouns in english while giving very little information we filter them out from the english text
note that this list consists of many single character words which have ambiguities in chinese english words which should have been part of a compound word multiple translations of a single word in english etc
on the other hand we suggest that words with productive context in one language translate to words with productive context in another language and words with rigid context translate into words with rigid context
one of the formal means provided by drt is the possibility to model components of psychological attitude states e.g.
an important question is how to come up with a good goal weakening operator
by using a check the speaker often seeks approving feedback from the hearer
the adverse recognition environment and the variability in user dependent features are the most frequent reasons of three kinds of recognition errors
non understanding is recognized by the dialogue system as soon as it happens because the system is not able to find any interpretation of the current user turn
in addition they can generate aliases of names automatically e.g. ana for all nippon airline and link variants of names within a document
on the other hand in languages like japanese where word boundaries are not explicitly marked by spaces indexing accuracy of individual words depends on accuracy of word segmentation
the expectation facility provides a list of expected meanings organized in a hierarchy and it is used for two purposes NUM if the incoming utterance is syntactically near the syntax for an expected meaning in the active subdialog the expectation provides a powerful error correction mechanism
in the main search screen cf figure NUM the user types in each query term including multi words like personal computer in each numbered box
the system indexes texts in different languages e.g. english and japanese and allows the users to retrieve relevant texts in their native language e.g. english
in translating an english query into japanese a company apple should be translated into a transliteration in katakana and not into a japanese word meaning a fruit apple
our internal testing of the japanese system against blind test sets of various japanese newspaper articles indicates that it achieves from high NUM to low NUM accuracy depending on the types of corpora
the system takes advantage of information extraction ie technology in novel ways to improve the accuracy of cross linguistic retrieval and to provide innovative methods for browsing and exploring multilingual document collections
thus this system can also be used for japanese monolingual users who want to query and browse in japanese a set of documents written in english japanese and spanish
or the user might ask who is related to shinshintou party a japanese political party and the user can find out all the people associated with this party
our program located some of these words
for example the clause combination sentence NUM below is preferred over the embedded combination sentence NUM below because in the latter the relative clause is twice embedded NUM intro to ai has many assignments which consist of writing essays
the results are shown in figure NUM
most of these words occurred only once
if lexical choice is not part of the syntactic realization component then all decisions regarding open class word selection must be made before the grammar is invoked they then must occur either as part of content planning or after all content has been determined and expressed in a language independent manner
avoiding encoding any assumptions about the mapping between domain and language has the benefit of portability the architecture and some knowledge sources of the generator can be reused for a variety of different applications in quite different mathematics and computer science department beer sheva NUM israel
at this stage the conceptual relation that will be realized as the head has been selected shown by the dotted line pointing to the node class assignt and the lexical chooser has decided to use a process i.e. ultimately a verb to realize it
NUM note that it is not the very idea of using an ontological upper model that we criticize here with all its advantages in terms of knowledge inheritance and reuse but the use of the most common linguistic realization of each concept as the main criteria for classification
this would have a smoothing effect
minimally syntagmatic decisions indude determining the main process which constrains the set of possible verbs s for example in paraphrase NUM of figure NUM this means choosing to map the relation class assignt as the main process of the sentence
when using paper based resources translators will often make references back to information they have previously found
tipster technology is being transferred in ways that give real users access to technology in useful ways
to support editing of text displayed in multiple languages oleada utilizes crl s multi attributed text widget
this possibility can be used to state the axiom which represents the specific semantic contribution of c ange si in temi it s poststate ehara terized by the state s2 of the person r0 being in an psychological attitude state one of whose coinponents c is a certain belief
libraries of procedures for user interface support with embedded functionality for information retrieval and information extraction
the users observed during the third phase were instructors who used the system to develop instructional material
many software tools already exist that could be useful to language translators instructors and learners
user designer discussions focus on real problems users are having working with the prototypes to accomplish real goals
the first goal is to determine worker goals and their strategies for accomplishing them
the users can then edit these files or use xconcord to print the results
zipf s law is commonly regarded as an empirically accurate description of a wide variety of linguistic phenomena but too general to be of any direct use
in section NUM we induce a recurrence equation from turing s local reestimation formula and from this derive the asymptotic behavior of the relative frequency as a function of rank using a continuum approximation
some typical word classes which is the part of results of subcorpus containing NUM words are listed below
although a nontrivial generalization it is in fact the case that for real valued NUM NUM NUM z x nx l n NUM z NUM
thus the agreement will resolve with an agreement
how best to implement this is still a research issue
two principal techniques are used to resolve definite noun groups
for example consider two documents that each mention ibm once
only the developers were able to define patterns in this system
c nielsen co co said vg
both approaches are made easier by fastspec and the compile time transformations
you have to see how that person relates to which verb
what we are doing is a tractable and useful special case
coreference resolution is done only for definite noun groups and pronouns
our evaluation scheme is more rigid and based on a larger dataset
it also contributes to system response in that the earlier more local shallower methods of analysis and transfer usually operate very quickly to produce an attempt at translation
an interlingual system is thus inherently more brittle than a transfer system which can produce an output without ever identifying a deep formal representation of the input
which returns the same set ofquantifiers
rather the input specification provides only a pointer into the attached kb
i use the term rhetorical relevance to refer to this sort of relevance
this paper describes the input specification language of the wag sentence generation system
ideational material thus does not need to be included within the input specification
NUM rhetorical relevance is dynamic it changes as the text progresses
the distinct contributions of the three meta functions are separated by the gray boxes
content what proposition is being negotiated between the speaker and hearer
responding moves reflect a far higher degree of ellipsis than initiating moves
interactional representation views the text as part of the interaction between participants
below we explore the nature of this semantic specification in more detail
for this purpose the vocal interface to the berlin system will be placed in a car and evaluated by french and german drivers in paris and karlsruhe respectively
this avm consists of four attributes abbreviations for each attribute name are also shown
but although general lexicons are readily available now this is not the case for specialised lexicons which contain for example technical terms relevant to a subject or family and brand names as can be found in journalistic texts
since this approach requires a certain number of examples of disambiguated verbs we have to carry out this task manually that is we disambiguate verbs appearing in a corpus prior to their use by the system
recent approaches to textcategorization focus more on algorithms than on resources involved in thisoperation
text categorization has emerged as a very active field of research in the recent years
definition NUM it would be coherent for s to utter u in discourse context ts if the utterance can be derived from an agent s linguistic knowledge assuming some set m meta of metaplanning decisions such that
the cost of the increased flexibility would be increased difficulty in mapping surface descriptions onto speech acts however because less effort would be required in sentence processing the total complexity of the problem need not increase
for instance due to the acquired mapping from gg273 element of sound in language to bg07 sound tone etc the verb accent in e26 is connected erroneously to syllable in c26
model NUM enhances model i by considering the dependence of pr s i t on the distortion probability dp d i i j l m where i and m are the respective lengths of s and t measured in number of words
we would like to thank liming yu from zebra english service union betty teng and nora liu from longman asia limited keh jiann chen and chu ren huang from academy sinica and perry chang from galaxy software services for making the dictionaries thesauri and corpora available to us
c13 ql o daxiang bizi hen chang elephant nose very long the topic prominence of mandarin sentences represents alignment connections with a large distortion in position leading to difficulty in estimating the likelihood of a connection according to translational position
that is most mandarin words are comprised of a single morpheme rather than a stem morpheme and a suffix serving grammatical functions such as case as in turkish and japanese number agreement or tense as in many other languages including english
in addition to the grammatical relation of subject a description of mandarin must include the topic element which can be characterized as follows first a topic always comes first in the sentence and is optionally followed by a pause in speech
for instance the translation for take mb051 carrying taking and bring in the collocation take effect is usually see fc04 seeing and looking as in example e21 c21
the core modules provide the information noted in section NUM NUM NUM morphology bilingual dictionary entry and examples from use
note that in contrast to commercially available systems the information is generated automatically so that it is available on line for any text
a more serious loss for the scenario template task was th e sentence about mr
the first column shows what other concepts are linked to the concept of mr
one can define privileged x which holds whenever x is required to take the feature f along similar lines
concept nodes and textref nodes are linked by an event with the internal action wordsused
it is an instance of the universal concept of all occurrences of that word
they prevent duplication of information by allowin g information to be inherited within the hierarchy
concepts are linked to lexical forms by a link named after the language of interest
the slot fill rule directly produces a list of the strings to fill the slot
NUM NUM lines of haskell plus some NUM NUM lines of c in NUM modules
the most severe problem was associated with reported speech that covered more than one sentence
further problems were created by the presence of a typographical error in the original text
NUM NUM probabilistic routing approach us ing multinomial distribution
the words are then counted and sorted by frequency
the advantage of truncated models is that they do not need to store nearly as many non zero parameters as untruncated models
in some cases the core is not explicit
set NUM contained abstracts from japanese technical papers
set NUM does not have an obvious distribution
each training set consists of a single document
cosine and mulrinomial distribution methods produced better results
in this paper we focus on cue occurrence and placement and present an empirical study of the hypotheses provided by previous research which have never been systematically evaluated with naturally occurring data
we apply a machine learning program c4 NUM to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previously thought to affect cue usage
if the answer is yes then we predict from the skip NUM transition matrix ml wt l wt
because they were also interested in the contrast between occurrence and non occurrence of cues they exhaustively coded for all of the factors thought to contribute to cue usage in all of the text
second some of the subtrees corresponded to clearly identifiable subclasses of the data such as relations with an implicit core which suggested that we should apply learning to these independently identifiable subclasses
cues are much more likely to occur in clusters where only informational relations occur than in core contributor structures where intentional and informational relations co occur x NUM NUM NUM
as such this method could also suffer from the data sparseness problem
these deal with latin american terrorist incidents and vary widely in terms of origin medium and purpose
table NUM shows the distribution for ore set of NUM turns with NUM NUM utterances
right peripherally modulo other extraposed constituents
order domains provide a natural framework for order variation and discontinuous constituency
figure NUM total compaction as a special case of compaction
a theory of linearization in head driven phrase
that determiners are to n domains
we leave this task for future work
thanks to bob kasper for helpful discussions and suggestions
ordering is achieved via linear precedence lp statements
one obvious point of reference is the engcg syntax which shares a level of similar representation with an almost identical tagset to the new system
when using the grammar formalism described above a considerable amount of syntactic ambiguity can not be resolved reliably and is therefore left pending in the parse
in this research based on the assumption that not all contexts are useful in every case effectiveness of contexts is also investigated
furthermore the more paragraphs are in an article the smaller the number of correct judgements
as a result word237 is regarded as a keyword while this is not
here the accusative reading is selected and linked to the main verb immediately to the left if there is an unambiguous clause boundary immediately to the right
for each article we extracted NUM NUM of its paragraphs as key paragraphs
for example xpi2j is shown in formula NUM by using formula NUM
this shows that our method is effective even in a restricted domain such as financial articles e.g.
zechner proposed a method to extract key sentences in an article by using simple statistical method i.e.
table NUM shows the location of key paragraphs extracted using our method and extracted by humans
furthermore the correct ratio does not depend on the number of paragraphs in an article
we call this article and each element general signal corp etc context
means that a nominal head nom head is a set that contains part of speech tags that may represent a nominal head may not appear anywhere to the left not NUM
if a feature is introduced by a subsort then the argument is added to the term that further instantiates its supersort
when a solution or debugging information is printed out uninstantiated features are omitted and shared structures are printed only once and represented by variables on subsequent occurences
while this abbreviation for feature paths is new for formal description languages similar abbreviatory conventions are often used in linguistic publications
NUM since a general treatment of disjunction would involve too much computational overhead we provide disjunctive terms only as syntactic sugar
since this must be done only when results are printed out as profit terms it does not affect the runtime performance
thanks to all the early users and testers for discovering bugs and inconsistencies and for providing feedback and encouragement
null a pretty printer is provided that produces a neatly formatted screen output of profit terms and is configurable by the user
every sort must only be defined once i.e. it can appear only once on the left hand side of the connective
sorted feature terms can be used in profit programs together with prolog terms to provide a clearer description language for linguistic structures
heuristic NUM NUM NUM and NUM to the most informed ones e.g.
we presented eight types of cues that affect initiative shifts in dialogues and showed how our model 1degin the maptask domain the task initiative remains with one agent the instruction giver throughout the dialogue
bonsai and the genus term or the core of the phrase e.g.
this heuristic assumes that senses are ordered in an entry by frequency of usage
been semantically tagged with a vector of semantic weights following formula NUM
the information placed in ldoce has allowed to extract other implicit information easily e.g.
heuristics NUM and NUM need external knowledge not present in the dictionaries themselves
all the heuristics except heuristic NUM can readily be applied to any other dictionary
heuristics NUM and NUM or combining lexical knowledge from several heterogeneous lexical resources e.g.
significance tests or association coefficients could be used in order to discard low confidence decisions
oa accusative object on the basis of its position which is not very reliable in german
a ten fold cross validation experiment on the first two million words of the wsj corpus shows an average generalization performance of igtree on known words only of NUM NUM
in a decision tree using system accuracy as an objective function for training typically results in poor performance NUM and some measure of node purity such as entropy reduction is used instead
transformation based part of speech tagging works as follows
extending the proof beyond binary trees is straightforward
we present the proof for the former case
the second transformation arises from the fact that when a verb appears in a context such as we do n t eat or we did n t usually drink the verb is in base form
if some rules are removed from a pure ccg grammar some parses will become unavailable
one algorithm finds only normal forms this simply and safely eliminates spurious ambiguity under most real ccg grammars
karttunen s approach must tease such parses apart and compare their various meanings individually against each new candidate
no constituent produced by bn any n NUM ever serves as the primary right argument to s
each ordinary ccg category is split into three categories that bear the respective tags from NUM
the latter case requires consideration of NUM s ancestors the nf properties crucially rule out counterexamples here
theorem NUM says all cases of spurious ambiguity can be eliminated through the construction given in theorem NUM
one might choose to say that two parses are semantically equivalent iff they derive the same phrase meaning
theorem NUM given distinct nf trees a o on the same sequence of leaves
the phrase h l est was analyzed in this way while the correct analysis is preposition determiner noun
if we use both the principled and heuristic rules the error rate is NUM NUM while NUM words remain ambiguous
distance between two values is measured using equation NUM an overlap metric for symbolic features we will have no numeric features in the tagging application
the transducer maps each inflected surface form of a word to its canonical lexical form followed by the appropriate morphological tags
our general conclusion is that the hand coded constraints perform better than the statistical tagger and that we can still refine them
the reason for this is obvious only a relatively small amount of time was allowed for writing the rules
in comparison the final set of non contextual rules introduces around NUM errors on the same set of NUM words
if we apply all the rules we get a fully disambiguated result with an error rate of only NUM NUM
the current system contains NUM rules consisting of NUM reliable contextual rules dealing mostly with frequent ambiguous words
the rules are obviously not very reliable but they are needed only when the previous rules fail to fully disambiguate
proper treatment of such an ambiguity would require verb subcategorisation and a description of complex coordinations of noun and prepositional phrases
since this would not be possible the np the dog would be discarded
for reasons of space the computation of outer domains can not be described fully here
only one reading was generated for each bag corresponding to one attachment site for pps
pruning the search space in these generators is important given the
this paper only considers ruh based approaches to this problem
sentences were generated using two versions of a modified chart parser
nk tmdly recursiw semantic ropresentactions cope stake
a form of a collection which is capable of responding to detectionquery
exact match means that each document must contain all of the arguments
exact or fuzzy the first one listed is the default
that is an operator consists of an operator field marker e.g.
for an sgml document these relationships are provided by a dtd
if which and destination are identical the entire collection is annotated
note that the data in a fax is not even byte aligned
we introduce our new synchronous system in section NUM and present our formal results and outline the proof techniques in section NUM
we suppose that in each vector v of a uvg dl there is exactly one privileged element which we call the synchronous production of v
we then finish the derivation to obtain the two trees in figure NUM and figure NUM with no synchronization or dominance links left
each vector v of g is constructed from a pair of synchronous vectors v v of gs as follows
intervaltype indicates the desires type of interval which may differ from statustype
consequently there were only NUM cancels issued in the production of the NUM NUM user utterances
all words containing n vowels none of which are pairwise consecutive have exactly n NUM substrings of the expression vlcl c2g gb v2 and according to theorem NUM for each of these one hyphen point can be derived
the permissible hyphen points of words are located between the syllables thus lemma NUM a the point following the maximal consonant prefix of a word and b the point preceding the maximal consonant suffix of a word do not constitute permissible hyphen points
the main difference is that stress markings are not applied to words whose letters are all written in capitals while the diaeresis mark is maintained in capital letters u consequently the transformation of any uppercase word to lowercase and back to uppercase again loses no information
nevertheless vowel sequences that contain consecutive stressed vowels or double vowel blends or consecutive vowels with diaeresis marks do not exist in any word pure greek or loan and thus this can be used as a general noussia hyphenator for modern greek elimination principle
NUM in our domain n represents the number of missing wires in the problem
we see from this model that dialogues normally begin with the introduction and assessment phases
on the other hand many of the patterns derived for the hyphenation of vowel sequences can not be applied to capitalized words because the most important discriminating factor in diphthong identification is stress marking and uppercase letters section NUM NUM NUM lack stress markings
using this convention the semantic annotation of the corpus trees is indicated as follows for every meaningful lexical node a type logical formula is specified that represents its meaning
in the examples below these schemata use the variable dl to indicate the meaning of the leftmost daughter constituent d2 to indicate the meaning of the second daughter constituent etc
dop estimates the probability of substituting a subtree t on a specific node as the probability of selecting t among all subtrees in the corpus that could be substituted on that node
in directive mode dialogues the computer will require verbal verification of the repair before transitioning to the test phase
the only novelty is a slight modification in the process by which a corpus tree is decomposed into subtrees and a corresponding modification in the composition operation which combines subtrees
as the experimental results in the next section show using a tree bank obtained in this way for data oriented semantic interpretation results in high coverage and good probability estimations
a sequence of more than three consonants at the beginning of the word is also possible as in the word f r NUM o r cr gdansk gdansk the city in poland although it is quite infrequent
to maintain it in the face of phenomena such as non standard quantifier scope or discontinuous constituents creates complications in the syntactic or semantic analyses assigned to certain sentences and their constituents
a chart item is associated with categories implying that the item is valid on the specified categories that begin the net fragment of the item
the smooth injective map recognizer simr algorithm presented in this paper is a bitext mapping algorithm that advances the state of the art on these criteria
thus the plan for achieving the purpose typically has two distinct parts NUM one or more utterances that serve to make the purpose manifest by expressing a belief or action for the hearer to adopt the core nucleus and NUM a set of subparts that contribute to achieving the purpose by manifesting subpurposes dominated by that purpose the embedded segments satellites
pronouns like proper names are treated as contextually restricted quantifiers where the contextual restriction may limit the domain of quantification to one indi
equation NUM s2 hang term e term h
for the qlf to be interpretable it is necessary to give the antecedent book term wide scope over the ellipsis in order to discharge the index
determining which terms are parallel non parallel is touched on in section NUM for parallel terms we have no choice about the ellipsis substitution
the strict substitution on the term in which it occurs it makes no difference whether the pronoun is given a strict or a sloppy substitution
for presentation purposes we only sketch the intended semantics of the simplified qlf notation used and a more detailed discussion is deferred until section NUM
the initial grammar is listed in table
we then try to find a modification to the hypothesis grammar such as the addition of a grammar rule that results in a grammar with a higher score on the objective function
in addition the availability of speech in the text and speech labeling method led to significantly higher reliability scores
for consensus scont phrases we found some differences between read and spontaneous speech for both labeling methods
an important issue in applying the t coefficient is how one calculates the expected agreement using prior distributions of categories
NUM we applied this metric to our segmentation data calculating weighted averages for pairwise scores averaged for each task
but scont and sf phrases exhibit similar prominence features and appear distinct from each other only in terms of timing differences
ticles these are items such as now first and by the way which explicitly mark discourse structure
while correlates have been identified in read speech they have been observed in spontaneous speech only rarely and descriptively
we have demonstrated that a theory based method for discourse analysis can provide reliable segmentations of spontaneous as well as read speech
higher higher higher higher longer shorter higher higher higher higher longer shorter lower lower lower shorter
we also compare the acoustic prosodic features of initial medial and final utterances in a discourse segment
in fact sentence NUM is the first main clause of one of the more problematic cases in the literature NUM john revised his paper before the teacher did and bill did too
this rules out the case in which for example the fact that john and bill are both persons would be used to establish their similarity when the fact that they are both men has already been used
before the re estimation algorithm is applied an rtn that faithfully encodes the input trees without any overgeneration is constructed from the NUM trees
paradise uses linear regression to quantify the relative contribution of the success and cost factors to user satisfaction
NUM when comparing agent a to agent b a similar table would also be constructed for agent b
null a key aspect of the framework is the decoupling of task goals from the system s dialogue behavior
performance function estimation must be done iteratively over many different tasks and dialogue strategies to see which factors generalize
in this case an evaluation over a larger subset of the user population would probably show significant differences
they also assume that automatic disambiguation will eliminate extraneous parses
since transitions of terminal and nonterminal types can occur together at a state terminal transitions are estimated as follows outside computation with chart
first the importance of closed class words will be discussed
the following notation is used in the proof
this hypothesis can be investigated by looking at the extent to which the speakers agree among themselves
in the future we will extend the property list by allowing multiple elements in the list
on the other hand a theory that allows for a partially specified interpretation must provide for refining that interpretation on the basis of subsequent utterances
among them of course also those found by the short cut check see subsection NUM NUM and for them the value set of superconcepts implicitly is a conjunction
antecedent anaphor pairs in the test data according to this classification are shown in figure NUM
typically it is also important to determine if the names refer to the same entity
thus NUM of the NUM speakers completely agree with tr3 while NUM agree with tr2
alternatively the collection could be tagged but the user might be required to specify names in the query
x in nlp many useful results can be generated from corpora but when can the results developed using one corpus be applied to another
the initial reference is reducible and the subsequent reference is the same as the initial reference
there were NUM NUM personal name word tokens in the manually marked set of NUM cases constituting the case law collection
table NUM all classes i missing classes common words i NUM NUM these results show that the criteria used to filter the oov lexicons allows us to produce reliable lexicons only NUM of the oov common words contained label errors
in the next subsection we improve this by considering the use of reduced and full descriptions
for example consider its use with the verb phrase construction np verb obj which is known as the t ba construction
although the number of rules is changing daily the evaluation was performed on a version of the grammar containing NUM rules
we continue this step until we pop from the agenda and add nc and later np to the agenda
match g c checks whether a subtree c can be matched by a symbol g NUM
this problem has inspired a modified formalism that enhances our ability to write and maintain robust large grammars by constraining productions with left right contexts and or nonterminal functions
if no complete parse tree is found for the input sentence a partial parse is returned such examples are shown without a number preceding the parse
d if g has the form a b check all the nodes of the subtree c if no node of category b is found return NUM
extend e c extends an edge c with the chart entry subtree c NUM create a new edge e
to evaluate our progress we have evaluated precision on a previously unseen sample of NUM sentences drawn from our corpus which contains hong kong legislative proceedings
because the domain of is ge it is parsed as a location noun and together with the leader is parsed as a locative phrase
the idea is to identify the inside probabilities used in generating an input sentence and to compute an outside probability using mainly those insides
the language consists of any sequence of four ys from the set lcb a b c d rcb within a constituent labeled x provided that the lp constraints a c b d are observed
this is a chart parser that constructs only active items except for categories that unify with the top category
firstly given a set of constraints of the form a c b d etc where each of a b c d is some kind of category description then add to each instance of the category that can appear within the relevant domains some extra features encoding what is not permitted to appear to the left or right on each category within that domain
for example lcb cat n count y number sing lex dog rcb lcb cat det number sing lex a rcb lcb cat verb number sing person NUM subcat lex snores rcb a rule consists of a mother category and a list of zero or more daughter categories
schema lcb cat vp subcat s rcb lcb cat vp subcat s rcb lcb cat s rcb sample entry lcb cat vp lex send subcat np np pp np np rcb note that this makes cat a boolean combination feature
the languages are english dutch german french spanish catalan russian chinese korean japanese
in the following sections we define a method for the automatic selection of the oest set of wordnet categories for nouns given an application corpus
sense n NUM breed strain stock variety animal group sense n NUM stock lumber timber
to compute the score of each set ci the parameters x NUM c and NUM in NUM must be estimated
due to the graph structure of wordnet different paths may connect each element cij of ci with different topmosts therefore we compute dm ci as
of course information of all these kinds might be added
as discussed in the latter paper high level tags reduce the problem of overambiguity and allow the detection of more regular behaviors in the analysis of lexical patterns
many language patterns from simple co occurrences to more complex syntactic associations among words occur very rarely or are never encountered in the learning corpus
clustering is achieved by means of the so called topic state
the NUM is computed for all the generated sets of categories c i and then normalised in the NUM NUM interval
accent is realized on those leaves that are marked as accented
this suggests that important parts of dyd s context model may be mirrored in the istformalism
first both adjectives depend on hotdog while in the derivation structure small is a daughter of spicy
this constraint does not prevent analogies from having multiple solutions
just like the statistical approaches in many automatic pos tagging programs our job is to select a constituent boundary sequence b with the highest score p bis from all possible sequences
NUM NUM determine the syntactic tags according to the intersection of the tag distribution sets of the open and close bracket on the constituent boundary if they can be found in statistical data s3
evaluating the parser against a smaller chinese treebank with NUM sentences it shows the following encouraging results NUM precision NUM recall NUM NUM crossing brackets per sentence and NUM labeled precision
if p a b p b c then the matching operation b c will be discarded
in bracket matching model these cases can be generalized as a matching restriction region mrr which is informally represented as the region rl rr in figure NUM
this section will propose some basic concepts and operations of the matching model to deal with the first problem and section NUM NUM NUM will give methods to resolve the second one
NUM would hesitations give even shorter and thus perhaps even more manageable segments if used as alternate or additional boundaries
therefore the basic matching algorithm can be improved by adding the following restrictions a to restrict the matching operations inside mrr and guarantee them ca n t cross the boundary of the mrr
the intuition behind this mewic is that the distance between the parent and the child should be closer if the probability of the parent is close to that of the child since that implies that whenever an instance of the parent occurs in the corpus it is usually an instance of the child
sense heuristic is used for the word sense disambiguation module and the conceptual distance metric is adopted for the semantic distance module it should be emphasized however that our al proach to s m tic class disambiguation need not be coupled with any specific word sense disambiguation algorithm
mapping this domainospeci c hierarchy to wordnet simply involves finding the specific sense s of m r motor vehicle NUM figure NUM a a simple domain specific hierarchy b the classes of the domain specific hierarchy as mapped onto wordnet together with the word to be dis mhigtmted plane
we will examine contexts where strings containing proper names occur
then given a sentence say the plane win be taking off in NUM minutes time
thus far two notable sense tagged corpora the semantic concordance of wordn et NUM NUM miller et al
for example if the program s reeponse is class politi whilst the answer is class lawyer since both e qses originated from the same level NUM class b m this response is considered correct when calculating the general semantic class accuracy
also the perforzzgiven a word win the following sentence segment NUM NUM w rz the 7features used are NUM h lz rl rl r2 NUM l r2 and NUM whereby the first NUM features are concatenations off the words
jutaig ga area of buildings NUM umul ga edge of a well NUM
based on wordnet the module will conclude that the concept node plane l is nearer to the semantic class node aircraft l and should hence be cl lssified as air craft
one can also view this transformation as successive unfolding of the frame predicates and the lexical rule predicates with respect to the interaction predicates followed by a folding transformation that isolates the original lexical rule predicates
for the test grammar the resulting extended search space of parsing with the basic covariation encoding leads to a performance that is on average NUM times slower than that with the expanded out lexicon
if we find a feature structure for a node qn that is identical to the feature structure corresponding to another node qm the arc leading to qn or the arc leading to qm is discarded
as the lexical rules themselves are already translated into a definite clause representation in the first compilation step the interaction predicates only need to ensure that the right combination of lexical rule predicates is called
only the verb form and some indices are specified to be changed and thus other input properties like the phonology the semantics or the nonlocal specifications are preserved in the output
furthermore due to the procedural interpretation of lexical rules in a computational system in contrast to the original declarative intention there can be sequences of lexical rule applications that produce identical entries
to ensure that no information is lost as a result of applying a lexical rule it seems to be necessary to split up the lexical rule to make each instance deal with a specific case
NUM the lexicon of the test grammar can be expanded out off line since the recursive complement extraction lexical rule applies only to full verbs i e lexical entries with a complement list of finite length
while a well founded set of speech act labels would be useful it has not been clear what the theoretical foundation should be
the envisioned configuration for hookah would give each analyst a hookah workstation for most simply an x terminal or comparable x based display
we have isolated a semantic module which allows the interpretation process to take into account the argumentative constraints imposed by linguistic clues
NUM automatic completely automatic query construction null NUM manual manual query construction NUM interactive use of interactive techniques to con null struct the queries the participants were able to choose between two levels of participation category a full participation or category b full participation using a reduced dataset NUM NUM of the full document set
the paper describes a paraphrase based approach native speakers are polled as to the essential equivalence of expressive patterns in specified discourse contexts
hobbs and kehler forthcoming describe the analysis of this case as well as others involving quantification
this relation says that y under the description associated with e2 is coreferential with x under the description associated with el
at each iteration of the do loop in determinize transducer for each s c q and for each w e such that i r w s the following this proves i
instead it was discarded and a unification based parser began a new parse for mt purposes on a text string passed from speech recognition
most importantly translation lexicons can only be used at the word level
characters match across languages only to the extent that they participate in cognates
table NUM time spent in constructing two gold standard tbms
the expanding rectangle search strategy makes simr robust in the face of tbm discontinuities
figure NUM s mr s expanding rectangle search
in defining sa trees we assume some naming convention for the elementary d trees in d and some consistent ordering on the components and nodes of elementary d trees in d for each i we define the set of d trees ti g whose derivations are captured by sa trees of height i or less
the spanish english bitexts were drawn from the on line sun microsystems solaris answerbooks
these features make simr the mostly widely applicable bitext mapping algorithm to date
it should be applicable to any text genre in any pair of languages
bitext maps identify corresponding text units between the two halves of a bitext
in speech recognition sequences of short speech segments must be recognized as phones and sequences of phones must be recognized as words
the location in NUM of an inserted elementary component a i can be unambiguously determined by identifying the source of the node say the node with address n in the elementary d tree a with which the root of this occurrence of a i is merged with when d edges are removed
table NUM tabulates the precision and recall values averaged over NUM long queries using l0 the NUM entry and l01 the NUM entry lexicons
based on this observation we made some adjustments to our lexicon and provide some experimental results of the lexicon effects on retrieval effectiveness
as discussed above our algorithm is not intended as a direct model of human learning of phonology
these theories propose a constraint called faithfulness which requires that the phonological output string match its input
the result of the first merging operation on the transducer of figure NUM is shown in figure NUM
at the end of each input string the transducer makes an additional transition on the end of string symbol
vowels may be annotated with the numbers NUM and NUM to indicate primary and secondary stress respectively
the underlying principle of the algorithm is to generalize by reducing the number of states in the transducer
this is because a separate state is only necessary for each distinct context in which segments behave differently
upon seeing a voiced stop the transducer jumps to the appropriate state without emitting any output
figure NUM shows the transducer induced from NUM NUM training samples and table NUM shows some performance results
NUM NUM b has three quantifiers too but unlike NUM a all the six ways of ordering the quantifiers are available
this dialog fragment is interesting in that it illustrates goal oriented behavior while simultaneously jumping between subdialogs
however the grammaticality of b opens up the possibility that the two conjuncts can be represented grammatically as functions of arity two similar to normal transitive verbs
this paper shows that quantifier scope phenomena can be precisely characterized by a semantic representation constrained by surhce constituency if the distinction between referential and quantificational nps is properly observed
NUM quantifier variable restriction body logical forms as notated this way make explicit the functional dependency between the denotations of two ordered quantificational nps
we conjecture that this can also be made to capture several related np semantics such as collective np semantics and or referential np semantics though we can not discuss further details here
geach s observation that NUM a has two readings suggests that the scope of the object must be determined before it reduces with the coordinate fragment
the advantage of ccg s very free notion every dealer shows host custoners of surface structure is that it ties abstraction or the equivalent as closely as possible to derivation
this correctly accounts for our intuition that NUM a has an apparently intercalating reading and that NUM b has only two readings
one flag indicates whether the subsystem is checked unchecked partially checked or suspicious
it yields as output theorems that are proven and status reports on the proof in progress
sentence generation and voice output produces the statement put the knob to one zero
conditions the switch must be on and the control must be set to NUM
in each case the system tracks the selected topic and responds in an appropriate manner
the dialog controller may choose to invoke the proposed interaction or it may select another action
the algorithm computes 6o t NUM v following the recurfences below NUM the time complexity of this algorithm is o tav a where t and v are the lengths of the two sen
a small e constant is chosen for the probabilities b z e and b e y so that the optimal bracketing resorts to these productions only when it is otherwise impossible to match words
it will announce significant losses for the fourth quarter
the equivalence classes are the models of the identity equivalence coreference relation
the all objects recall and precision scores are shown in figure NUM
the relational level in and out objects represent the personnel changes pertaining to that state
the full text of the task definition is contained in appendix c
mvi said the chief executive officer has resigned
template elemen t there are miscellaneous outstanding problems with the te task
leaving aside the fact that descriptors are common noun phrases which makes them less obvious candidates for extraction than proper noun phrases would be what reasons can we find to account for the relatively low performance on the org descriptor slot
an important ingredience of the processing is the notion of repair if the plan construction is faced with something unexpected it uses a set of specialized repair operators to recover
as can be seen in the figure the actually recognized dialogue acts are for this turn among the two most probable predicted acts
each node contains also information about the attitude of the dialogue participants concerning this certain item proposed rejected or accepted by one of the participants
to contribute to the robustness of the system the processing of the recognizer is divided into several processing levels like the turn level and the domain dependent level
for example the german utterance guten tag is translated to hello in the greeting phase and to good day in the closing phase
due to its role as information server in the overall verbmobil system we started early in the project to collect requirements from other components in the system
we also see in figure NUM how the phase information has been written into the boxes representing the utterances of turn b02 as segmented by the deep analysis
recurring problems in the system outputs include the information about whether the person is currently on the job or not and the information on where the outgoing person s next job would be and where the incoming person s previous job was
most of the systems fall into the same rank at the high end and the evaluation does not clearly distinguish more than two ranks see the paper on statistical significance testing by chinchor in NUM
this offers improvements over sequent formulations but raises alternative problems for example associative unification in general can have infinite solutions and is undecidable
the program clauses and agenda are read directly off the unfoldings with the only manipulation being a flattening of positive ilnplications into uncurried forln
let us assume a set ato t4 of atomic formulas NUM ary NUM ary etc formula constructors lcb a
tile focusing character is eml odied by creating in one step the objective of seeking all the arguments of all uncurried functor
note how the term unification computing the hierarchical structure can be carried out one way in the reverse order to the forward seglnent matchings
it is shown how a range of calculi can be treated by dealing with the highest common factor of connectives as linear logical validity
any multimodal calculus can be implemented this way provided we have a one way unification algorithm specialised according to the structural communication axioms
sublinear aspects of word order and hierarchical structure are encoded in labels in effect the term structure of quantified linear logic
this slot has a limited number of fill options and the right answer is almost always either in or out depending on whether the person involved is assuming a post in or vacating a post out
approach to describe verbs and detect similarities
nonterminal nodes contain information about the most probable or default classification given the path thus far according to the bookkeeping information on class occurrences maintained by the tree construction algorithm
trailiiilg data lllewis tagged orl us tmrc
this model has several prot l tns
let tls descrit e me princil le and iis algorithni briefly
helps i t process of llllkiiowii words
linear interpolation does not make optimal combination of information sources
probabilistic models have been widely used for natural language processing
we define transformations NUM u NUM v NUM
h2 is well defined since r is a partition of e let
NUM denotes a fixed finite alphabet and e the null string
the latter task can be done using the data structure originally introduced here
in the next section we will deal with the multi set case
the general pattern is for systems to have done better on the text slot than on the type slot for enamex tags and for systems to have done better on the type slot than on the text slot for numex and timex tags
let p be a node of tx associated with factor u x v
we represent l as a string multi set over alphabet e x e
these results show that human variability on this task patterns in a way that is similar to the performance of most of the systems in all respects except perhaps one the greatest source of difficulty for the humans was on identifying dates
function move link down p p u starting at
the efficiency of head transduction has allowed us to start experimenting with pruned word lattices from speech recognition with the aim of producing translations from such word lattices in real time
furthermore the assumed contextual information for example discourse structures may be difficult to access in a real implementation
the average matching rates of the texts generated by the test systems with native speakers results are shown in table NUM
the measure of agreement gets worse if only the zero pronoun nominal distinction is considered or if zero and nonzero pronouns are lumped together
there are nine anaphora where the kappa score including tr3 is less than that for the speakers alone in many other cases the results are better
indeed there is some question as to whether the notion of zero pronoun is the best way of accounting for the syntactic facts about languages such as chinese
thus we employ the animacy of the referent as a constraint to refine rule NUM and obtain a new rule rule NUM as shown in figure NUM
initial reference can be removed new information can be added to the initial reference or even a different lexical item can be used for a nominal anaphor
at the beginning of the second sentence it appears in a full description and then in four reduced descriptions in the rest of the sentence
NUM NUM a zhaolai tongyang daxiao de liangkuai tiepi get same big small nom two iron piece get two pieces of iron of the same size
pos eq car pos continuation cdr pos will for a more complicated example consider the two rules defining vp in the fragment above repeated here as NUM
develop test measures appropriate for testing these tools in an end to end system test and evaluate the tools
usability testing some modifications and testing against degraded ocr data will occur by january NUM
hnc and inquery via sovereign software are the first detection tools being integrated
results will be reported in monthly updates as well as in a final report
betac and idi are providing the technical leadership the integration and the gui
the ndic oasis idef model and the current ndic free text management architecture development will guide project activity
expand the prototype to include at least two tipster extraction tools and use the extraction tools to load a database
lockheed martin is supplying the tipster compliant document manager document viewer and extraction system used on other tipster demonstration projects
in tyson s comers va in an unclassified environment which replicates the hardware and software environment at ndic
other possible modifications include fine tuning the gui to the ndic users and integrating the system into the ndic environment
sclectannotations document or annotationset type swing or nil constraint sequence of attribute annotationset returns the possibly empty set of annotations from the document or annotationset which are of type type and which satisfy constraint constraint is a sequence of attributes where the ith attribute has name a i and value vi
NUM readsgml string parent collection externalld string document reads a string marked up with normalized sgml with all attributes and end tags explicit and generates a document with the specified externalld no attributes and an annotationset containing one annotation for each sgml text element marked in the input text
once a customizedextractionsystem has been created it can bc given a collection specifying the documents to be annotated the which argument and a collection where the annotations shall be placed the destination argument it will add to each document of the destination collection the appropriate templates in the form of annotations
although annotationsets are logically just sets of annotations and could be implemented like other sets e.g. as lists a special class is provided in the expectation that implementations may wish to choose a more elaborate implementation such as a sorted list or tree with one or more indexes in order to implement the operations more efficiently
the byte sequence may include subsequences representing text in multiple languages as well as non text material such as pictures audio and tables annotations annotationset information about portions of the document information about the document as a whole is stored in attributes a document inherits an attributes property by virtue of being a type of attributed object
createspan start integer end integer span the current span design is intended for character based text documents which may contain additional types of information such as graphical images audio or video which needs to be retained and displayed but which would not be further processed by components of the tipster architecture
collections in general are persistent and hence have names however the architecture also provides for volatile createcollection name string attributes sequence of attribute collection creates a named persistent collection createvolatilecollection attributes sequence of attribute collection creates an unnamed volatile collection
note that the values of attributes can be lists thus allowing for slots with multiple values and can be references to other annotations thus allowing for a hierarchy of filled objects and allowing for references to other annotations such as names which have been identified by a prior annotation process
this is a list of feature value pairs where the feature names are arbitrary strings and the values can be any of a number of types returns a member of the enumerated type indicating the type of attributevalue note attributevalue is made a separate class with an explicit typeof operator out of deference to languages such as c without dynamic type identification
thus it has a meaning like this
the modifiers copy and service are both applicable to e30 but copy eliminates all distractors while service does not so the former is selected yielding the final np the copy area
moreover they suggest that nonliteral language can also be represented using a nested environment whose contents are determined by treating topic environments as competing sources of information analogous to different agents views
before concluding the experimental results are demonstrated
the chunked result is shown as follows
assume there are two possible chunked results
table NUM experimental results for ta
the distribution of chunk length for definition NUM
this paper also presents a tag mapper
little quantitative data is available about lexical ambiguity and such data as is available is often confined to only a small number of words
we first formalize the concept of a paradigmatic relationship
a more special and yet typical example in regular german words the morphemeinitial substring chem as in chemisch is pronounced sse m whereas in the name of the city chemnilz it is pronounced kcm
labels on the arcs to fuge represent infixes fugen that german word forming grammar requires as insertions between components within a compounded word in certain cases such as wilhelm s platz or linde n hof
even though the evaluation experiments reported in this paper were performed on names in isolation rather than in sentential contexts the error rates obtained in these experiments table NUM correspond to the performance on names by the integrated text analysis component for arbitrary text
the data retrieval software did not provide a way to export a complete list of cities towns and villages thus we searched for all records listing city halls township and municipality administrations and the like and then exported the pertinent city names
many however are not decomposable such as henmerich or rimparstra e rimpar street at least not beyond obvious and unproblematic components stra e weg platz etc
the transition from root to the state first is defined by three large families of arcs which represent the lists of first names productive city name components and productive street name components respectively as described in the previous section
this approach was successively applied to the street name inventory of the four cities starting with mfinchen exploiting the result of this first round in the second city berlin applying the combined result of this second round on the third city and so on
or consider the word final grapheme string ie in batterie bat r i battery materie mat e ri matter and the name rosemarie r o zomari
no attempt was made to arrive at some form of morphological decomposition despite several obvious recurring components such as hild bert fried the number of these components is very small and they are not productive in name forming processes anymore
thus all weights in the text analysis components of gertts are currently based on linguistic intuition they are assigned such that after integration of the name component in the general text analysis system direct hits in the general purpose lexicon will be less expensive than name analyses see discussion
since the same node appears under both arg1 and arg2 we re done and have
the last two kinds of relations reltype and relhier just perform inheritance of constraints
for each simple type we just introduce a unit clause whose argument is just the type
partitioning the types in this manner helps us to construct definite clause programs for type constraint grammars
definition constrained type a constrained type is a type that interacts with a defined type
a defined type is a type that occurs as antecedent of an implicational constraint in the grammar
figure NUM recursive head transduction of a string
a disjunction can be embedded in another one if necessary
in summary the lexical chooser proceeds as follows
mapping semantic subconstituents to the complements of the head word
the right hand side shows the linguistic structure that is constructed
we call this subsequent stage involving paradigmatic decisions lexicalization proper
we call this initial stage involving syntagmatic decisions phrase planning
nor does it indicate which syntactic relations should be used
our criteria for a model for lexical choice are fourfold
a path is best understood as a pointer within the fd
this experience strongly supports modularization between lexical choice and syntactic realization
figure NUM head transducer for noun phrase dependents
by contrast if agreement is reached on a few tokenisation issues hyphens clitics the chances of two groups arriving at identical word frequency lists is very good
aggregate and mixed order markov models for statistical language processing
we store this function in a table and strip all semantic rules from the trees
this is because morphological variants can occur within the same document but they are less likely to do so in documents that are short
it is difficult to make much sense of these entries in isolation they have to be viewed in the context of the many contextual probabilities
a few of the evaluation sites reported that good name alias recognition alone would buy a system a lot of recall and precision points on this task perhaps about NUM recall since proper names constituted a large minority of the annotations and NUM precision
announced a major management shakeup mvi said the chief executive officer has resigned the big NUM auto maker is attempting to regain market share it will announce significant losses for the fourth quarter
the variety of tasks designed for muc NUM reflects the interests of both participants and sponsors in assessing and furthering research that can satisfy some urgent text processing needs in the very near term and can lead to solutions to more challenging text understanding problems in the longer term
there is a taskneutral date slot that is defined as a template element it was used in the muc NUM dry run as part of the labor negotiation scenario but as currently defined it fails to capture meaningfully some of the recurring kinds of date information
the inclusion of four different tasks in the evaluation implicitly encouraged sites to design general purpose architectures that allow the production of a variety of types of output from a single internal representation in order to allow use of the full range of analysis techniques for all tasks
the organization pointed to by the event object is the organization where the relevant management post exists the organization pointed to by the relational object is the organization that the person who is moving in or out of the post is coming from or going to
there was a large number of factors that contributed to the NUM disagreement including overlooking coreferential nps using different interpretations of vague portions of the guidelines and making different subjective decisions when the text of an article was ambiguous sloppy etc
a third significant reason is that the response fill had to match the key fill exactly in order to be counted correct there was no allowance made in the scoring software for assigning full or partial credit if the response fill only partially matched the key fill
however the organization portion of the te task is not limited to recognizing the referential identity between full and shortened names it requires the use of text analysis techniques at all levels of text structure to associate the descriptive and locative information with the appropriate entity
these examples suggest that reflexive pronouns choose their antecedents in some kind of local domain
the structures which were generated for some of the above examples are as follows NUM
but even here configurations exist in which intrasentential antecedents are possible the examples are given in english
the marker nodes sthat and srel are delimiters of local domains to which the binding principle verification functions are sensitive
in g meral the constraint applicatioil will not single out a uifique antecedent
6b the barber who shaved him told a clienti a story
it is discussed how these constraints can be incorporated adequately in an anaphor resolution algorithm
lqle work focuses on syntactic restrictions which are derived froin chomsky s binding theory
tile two experiments tagging and word overlap were found to be to be highly effective once the common causes of error were removed
one has to recall that in phrase structure trees the subject c commands the content of the vp
the following data substantiate the syntactic restrictions which are to be employed 2a the barbe shaves hirnselfi
this is because they can not be translated on a word by word basis
every instance of a semantic rule at a node has a semantic type associated with it
the configuration frequency of a node in the optimized lattice w then can be computed as thus a node in the optirnlzed lattice takes all cor fi ation frequencies w of itself and the above related nodes ips these nodes do not belong to the optimized lattice themselves and there is no higher node in the optimized lattice related to them
NUM table NUM summarizes these results and shows the breakdown across categories
if by that time there is a node abde in the lattice we then also create the node abd relate it to the nodes abcd and abde and set its configuration frequency to NUM if abcd had already been in the lattice we would simply incremented its configuration frequency abcd bcd NUM
important thing to note here is that we still constrain the atomic features a3 and acap together with their collocation feature a NUM can so a a cap has only the excess weight which differentiates p NUM cap from the product of p NUM and p cap the case ff there were no feature interaction
this method of building the feature collocation lattice ensures that along with true observations it contains hidden nodes which can provide generalizations about domain and at the same time there is no over generation of the hidden nodes no logically impossible feature combinations and no hidden nodes without generalization power are included
formally the feature collocation lattice is a NUM ple NUM c where NUM is a set of nodes of the lattice which corresponds tothe union of the feature space of the maximum entropy model and the configuration space NUM xuc w
then for each behavior variable we run the improved iterative scaling algorithm as described in section NUM and produce a s o o o fo el joint model with parameters z a0 an
for instance if we want to disambiguate part of speech of a word and we observed that in NUM of the times a noun is preceeded by a determiner and in NUM of the thnes it is preceeded by an adjective we can state these observations as constraints to the model
because of this it is desirable that a speech translator should be easily portable to new domains
next we turn to a description of the results and evaluation
this sequence is translated word for word using the glossary method giving result c a in the figure
NUM consequently we have used the dice coefficient as the similarity measure
these candidate translations are not necessarily correct translations from a performance perspective
we present work in progress on the machine acquisition of a lexicon from sentences that are each an unsegmented phone sequence paired with a primitive representation of meaning
semantic representations for a given utterance are merely unordered sets of sememes generated by taking the union of the sememe for each word in the utterance
we are further reducing the information present in the semantic input by removing all function word symbols and merging various content symbols to encompass several word paradigms
it then creates new words that account for uncovered portions of the utterance and adjusts words from the parse to better fit the utterance
a simple exploratory algorithm is described along with the direction of current work and a discussion of the relevance of the problem for child language acquisition and computer speech recognition
the corpus used for these examples contained NUM NUM sentences in each language
in the first the program starts with an empty dictionary early in the acquisition process and receives the simple utterance nina lcb nina rcb a child s name
finally it reparses the utterance with the old dictionary and the new words and adds the new words to the dictionary if the resulting parse covers the utterance well
our task orientation is a bit different because we are trying to construct a semantic lexicon for a target category instead of classifying unknown or polysemous words in context
english swedish swedish english and english danish are all of comparable difficulty
smadja mckeown and hatzivassiloglou translating collocations for bilingual lexicons application
finally we turn to a preliminary experiment which used the speech to speech evaluation methodology from section NUM NUM above
but then case e makes x4 coreferential with either john or the teacher depending on how the first ellipsis was resolved
we first tagged all definitions in the dictionary for words that began with the letter w
for example the sentence they danced across the lvom is ambiguous with respect to the word dance
less than half of those words will be like novel and we are examining them by hand
we have conducted experiments with hundreds of unique query words and tens of thousands of word occurrences
we have examined the lexicon as a whole and focused on the distinction between homonymy and polysemy
these rules superficially about parts of speech actually express essentially syntactic generalisations though indirectly and partially
NUM therefore two kinds of output were accepted for the evaluation i the unambiguous analyses actually proposed by the finite state parser and ii the engcg analysis of those sentences for which the finite state parser gave no analyses
on the other hand engcg does not spell out part of speech ambiguity in the description of i ing and nonfinite ed forms ii noun adjective homographs with similar core meanings or iii abbreviation proper noun common noun homographs
note that the automatic determination of the best k through NUM fold cross validation makes use of only the training set without looking at the test set at all
the distance between two examples is the sum of the distances between the values of all the features of the two examples
removal of tag NUM NUM words however decreases the number of relevants slightly from NUM to around NUM
in the present study we have focused on the comparison of learning algorithms but not on feature representation of examples
to understand why larger values of k are needed we examined the performance of pebls when tested on the wsj6 test set
our results indicate that although naive bayes performs better than pebls with k NUM pebls with k NUM achieves comparable performance
i know that she is NUM and that she came here
cross validation is a well known technique that can be used for estimating the expected error rate of a classifier which has been trained on a particular data set
this indicates that for a training data set when pebls has trouble even outperforming the most frequent classifter it will tend to use a large value for k
formulae also called syntactic types are built from a set of propositional variables or primitive types b lcb bl b2 rcb and the three binary connectives called product left implication and right implication
therefore we can read off the solution to f from this sequent by including in aj for NUM j m those three ai for which bi has an occurrence of bj say these are aj NUM aj NUM and aj NUM
definition NUM we define a lambek grammar to be a quadruple e r bs l consisting of the finite alphabet of terminals e the set jr of all lambek formulae generated from some set of propositional variables which includes the distinguished variable s and the lezical map l NUM NUM which maps each terminal to a finite subset off
moreover the attentive reader will have noticed that our encoding also extends to languages having more groups of n symbols i.e. to languages of the form n n n al a2 a k finally we note in passing that for this grammar the rules r and r are irrelevant i.e. that it is at the same time an sol grammar
since o does not appear in g each sdl proof of a lexical assignment must be also an i proof i.e. exactly the same strings are judged grammatical by sdl as are judged by l d note that since the lcb ax l l rcb subset of i already accounts for the cfr
the brill part of speech tags illustrate other information we would like to retain for the individual words
while we no longer have a monolithic grammar we are still able to take advantage of the syntactic regularities of both noun phrases and clauses
first each word in a sentence is looked up in a large english dictionary comlex syntax which provides syntactic information about each word
in this way the power of clause level syntax is provided to the pattern writer without requiring the pattern writer to keep these details explicitly in mind
a verb group consists of a verb and its related auxiliaries sleeps is sleeping has been sleeping etc
because we have a good broad coverage english grammar and a moderately effective method for recovering from parse failures this approach held us in fairly good stead
the development of our language analysis software and our participation in the mucs has been supported by the advanced research projects agency under a series of contracts
in our exam2these have some kinship to the metarules of gpsg which expand a small set of productions into a larger set involving the different clause level structures
in particular in our system we had to separately encode the active passive relative reduced relative etc patterns for each semantic structure
this was the best f score on the scenario template task although several other systems mostly with similar architectures got scores that were not significantly lower
our work has indicated the ways in which we can continue to obtain the benefits of syntax analysis along with the performance benefits of the pattern matching approach
we are also looking at the advantages that our approach offers for multilingual generation
by tagging them as separate categories one can search for separate features characterizing each class
intermediate verb is a term used in quirk et al NUM pp
every mapping rule has associated applicability semantics which is used to license its application
marking of major sections paragraphs and headings word tokenising sentence boundary marking part of speech tagging and parsing are all tasks which can be performed sequentially using only a small moving window of the texts
since catalogue entries are interpreted by tools as local to the directory where the catalogue itself is found this means that binding together groups of alternative versions can be easily achieved by storing them under the same directory
we are inclined to steer a middle course between a monolithic comprehensive view of corpus data in which all possible views annotations structurings etc of a corpus component are combined in a single heavily structured document and a massively decentralised view in which a corpus component is organised as a hyper document with all its information stored in separate documents utilising interdocument pointers
the indexing tools which come with ims cwb are less flexible than those of lt nsl since the former must index 61ms cwb already supports compressed index files and special purpose encoding formats would presumably save even more space
s th eil ie r outf would return the next p element dominated anywhere by text at any depth with the p element satisfying the additional requirement that it contain at least one s element at any depth with text containing at least one instance of their possibly misspelt
if alongside an inclusion semantics we have a special empty element repl which is replaced by the range it points to we can produce a patch file e.g. for a misspelled word as follows whether such a patch would have knock on effects on higher levels of annotation would depend inter alia on whether a change in tokenisation crossed any higher level boundaries
this means in the context of the penn treebank tagset find me sequences beginning with determiners other than the followed by optional adjectives then things with nominal qualities
lt nsl is not so good for applications which require a database approach i.e. those which need to access markup at random from a text for example lexicographic browsing or the creation of book indexes
because of the central role of corpus position it is necessary to tokenise the input corpus mapping each word in the raw input to a set of at null tribute value pairs and a corpus position
finally in order to back up our claims about the merits of sgml based corpus processing we present a number of case studies of the use of the lt nsl system for corpus preparation and linguistic analysis
the clusters to be merged ck and cl are identified by finding the cell k or k i where k l that has the minimum value in the dissimilarity matrix
in cases where the descriptor is an appositive the referenced organization is included in the pattern match otherwise if the appositive is a definite reference the stack of organization references is searched for th e putative antecedant
in the vinst system natural language generation is applied in various places
aggregation in the nl generator of the visual and natural language specification tool
the generation module takes the proved query and generates an nl answer
the vinst prototype is implemented in aais prolog and supercard on macintosh
int gen form refnr type featurelist used word list
the features can be unordered and the number can be arbitrary
in the second nl example figure 4c we see how the predicate grouping works
refnr is a reference number to the loxyexpression to be paraphrased
in principle then any rule can feed i.e.
eurotra was the european community mt research program
note that due to limited training data errors in f0 computation and variabilities in the acoustic marking of prosodic events across speakers dialects and so on one can not expect an error free detection of these boundaries
the tagger is a public domain rule based tagger
the terminology is divided into sul ject
there are two main reasons why complex transfer i.e.
for example in the first row the number NUM means that NUM of the NUM labels were classified as NUM the number NUM means that NUM of the NUM labels were classified as b3
new terms occur in each and every pate nt
this also saves lime tbr the user
and in wtfich order of priorily
tagging results presented in figure NUM are also shown as a reference
the wsj texts are re tagged manually using the atr syntactic tag set
we used wsj texts and the atr corpus for the tagging experiment
out of the set of events a decision tree is constructed
brown et al proposed the following method which we also adopted
artificial intelligence center the university of georgia athens georgia NUM NUM
if an explanation system could a invoke a knowledge base accessing system to select views and b translate the views to natural language figure NUM it would be well on its way to producing coherent explanations
tm multijudge stipulation the explanations written by each writer were parceled out to at least two judges i.e. rather than having one judge evaluate one writer s explanations that writer s explanations were distributed among multiple judges
an explanation system must be able to map from a formal representation of domain knowledge i.e. one which can be used for automated reasoning such as the predicate calculus to a textual representation of domain knowledge
to communicate complex ideas an explanation system should be able to produce extended explanations such as those in figure NUM which shows several explanations produced by knight from the domain of botanical anatomy physiology and development
at runtime if the explanation planner determines that inclusion conditions are not satisfied or if a topic is not sufficiently important given space limitations see below it can comprehensively eliminate all content associated with the topic
in addition to performing well on the evaluation criteria if explanation systems are to make the difficult transition from research laboratories to field applications we want them to exhibit two important properties both of which significantly affect scalability
explanation planning itself has two subtasks content deterruination in which knowledge structures are extracted from a knowledge base and organization in which the selected knowledge structures are arranged in a manner appropriate for communication in natural language
first it keeps the explanation planner at arm s length from the representation of domain knowledge thereby making the planner less dependent on the particular representational conventions of the knowledge base and more robust in the face of errors
if the pre tagging process has a relatively high recall then we hypothesize that the human will tend increasingly to trust the pre annotations and thereby forget to read the texts carefully to discover any phrases that escaped being annotated
we should note that the alembic workbench having been developed only recently in our laboratory was not available to us in the course of our effort to apply the alembic system to the muc6 and met tasks
there is a limit on how much one can reduce the timerequirements for generating reliable training data this is the rate required by a human domain expert to carefully read and edit a perfectly pre annotated training corpus
for example in annotating journalistic document collections with named entity tags one might want to simply pre tag every occurrence of president clinton with person of course these actions should be taken with some care since mistagging entities throughout a document might actually lead to an increase in effort required to accurately fix or remove tags in the document
NUM increasing manual annotation productivity through pre tagging a motivating idea in the design of the alembic workbench is to apply any available information as early and as often as possible to reduce the burden of manual tagging
the alembic phraser rule interpreter has been applied to tagging named entities sentence chunks simple entity relations template element in the parlance of muc6 and other varieties of phrases
but all such data will remain suspect as far as being considered part of an annotated training corpus until inspected by a human given the vagaries of genre and style that can easily foil the most sophisticated systems
on the basis of observing our own and others experiences in building and porting natural language systems for new domains we have come to appreciate the pivotal role played in continuous evaluation throughout the system development cycle
the planner is given the student s predictions plus a student model showing student errors and possible misconceptions
spelling correction is an important aspect of the input understander as students frequently misspell words abbreviate creatively and make word boundary errors two words joined together or a single word split in two
the heuristics using these mtds are aware of this
leem seop shim is currently at the department of information science and telecommunications hanshin university osan korea
circs m tutor picks a problem for the student to solve and obtains the correct answers from the problem solver
circslm tutor version NUM a dialogne based intelligent tutoring system its is nearly five years old
the core parameters and the causal relationships between them are shown in the concept map in figure NUM
transpositions elisions substitutions and similar errrors are counted and the most likely candidate is picked
chong woo woo is currently chair of the department of computer science kookmin university seoul korea
yoon hee lee is currently director of training and education institute of defense analysis seoul korea
the content does not reflect the position or policy of the government and no official endorsement should be inferred
what is required is a more tractable algorithm which given a wfss and its associated sign will be able to determine whether all remaining lexical elements can ever form part of a complete sentence which includes that wfss
one can also envisage applications of bag generation to generation fi om mini now at s 1arp l tboral ories of i mrope oxh rd science ark oxford ox4 4ca
l he tmje shows that the technique ctm yieht reductions in the number of edges both active aud inactive and time taken especially for longer sentences while retaining the overheads at an acceptable level
definition NUM NUM wo signs a NUM are directly connccled if there cxisl at least two paths patha palht3 such that a patha is token identical with b pathb
if this adjective were part of such a sentence brown would have to appear as a leaf in some constituent that combines with the dog or with a constituent containing the dog
in such signs the semantic argument will be referred to as an qndex and will be shown as n subscril t to a lexeme in the above exmnple the index has been giwm the unique type NUM
each term belongs to a single semantic field
within relative or subordinate clauses or with motion verbs
the content of each sub domain grammar will be determined automatically by running a comprehensive grammar over a corpus in which each sentence has a sub domain tag
the child had the old woman visit want the child wanted to visit the old woman
note the link between the unknown role in the job situation and the person he
we believe that the main challenge that the travel planning domain will impose on our translation system is the problem of how to effectively deal with significantly greater levels of ambiguity
the infinitival clause can be extraposed in control constructions tb but not in raising construction
because each of the sub domains will be semantically much more narrow the corresponding semantic grammars should be smaller and far less ambiguous leading to faster parsing and more accurate analysis
each document was indexed by NUM to NUM terms
while woz simulation of directive and passive modes is feasible the requirements for algorithmically determining the relationship between user focus and the computer goal make woz simulations of suggestive and declarative modes very difficult especially given the fast response time necessary for spoken interaction
based on the evaluation of the circuit fix it shop at two different levels of initiative we have observed the following phenomena directive mode dialogues tend to follow an orderly pattern consisting largely of computer initiated subdialogue transitions terse user responses and predictable subdialogue transitions
a dialogue s b dialogue s cycle s NUM cyde s r s p u l u NUM r s r s i u NUM r s
research in cognitive science and ergonomic design of dialogue systems have shown that human beings can only keep a few alternatives in their short term memory hence instead of presenting the listener with a long list of alternatives it is more efficient to phrase a question in a way that avoids mentioning the alternatives
lacking any formal models of initiative it would be very difficult for a wizard to accurately simulate the response patterns a computerized conversational participant would produce in a mixed initiative dialogue for a nontrivial domain that would be consistent from subject to subject
currently concerns include chinese personal names cn transliterated foreign personal names tfn and chinese place names cpn
in general we believe that the natural life cycle of experimental nl dialogue systems should be one of analyzing modeling building and testing so that the analysis of actual human computer dialogues can lead to the development of more effective systems
however with the potential for miscommunication as well as the potential for users to exploit their expertise and control of the dialogue to skip discussion of some task steps it is highly unlikely that the actual results will follow the idealized model
smith and gordon human computer dialogue the particular circuit being repaired is supposed to cause the led to alternately display a NUM and a NUM and the implemented domain problem solving component could detect errors caused by missing wires as well as a dead battery
select the computer s original goal else if mode declarative then search the domain knowledge hierarchy for a common relationship between the computer goal and the user focus if such a relationship exists then select as the next goal that the user learn about this
mental sessions are balanced section NUM NUM we must distinguish between the first five problems of each session where there was a single missing wire in each problem and problems NUM through NUM in each session which have two missing wires
consequently it is expected that the computer will still initiate many of the transitions to the assessment and diagnosis phases in order to provide assistance in these areas but that the user will be able to transition to other subdialogues as deemed appropriate
these documents are used to plan edf research activity
as will be illustrated later in this paper such an association is exceptionally important in attempting to understand ambiguities and in developing disambiguation strategies
moreover disjunctive overlapping ambiguity is a special case of critical ambiguity in tokenization since for the character string s al
given a complete tokenization dictionary it is obvious that all single character critical fragments or more generally single character strings possess unique tokenization
since the blueprint the blue print the character string the blueprint has hidden ambiguity in tokenization
o r jc NUM completive noun chance e.g.
we constructed local grammars based upon our description of the types of nominal phrases containing proper names
nevertherless it seems still hard to establish an exhaustive list of what we call proper nouns
the text slot has a sequence of paragraphs each of which contains a sequence of sentences
yuhaing ga popular songs e o NUM yen ang ga any taste
furthermore it can be viewed as the ultimate model for methods of string matching of any elements including methods for finding english idioms
to ensure a so called soft landing any practical application system must be designed so that every input character string can always be tokenized
merge tu1 tu2 if temporal units tu1 and tu2 contain no conflicting field fillers returns a temporal unit containing all of the information in the two otherwise returns lcb rcb
in rule NUM right is chosen again
infinitives without zu occur as the complement of modal verbs e.g.
that him the woman to beat seems that the woman seems to beat him
the structure of the sentence is given in 12b
the subject of infinitival clauses with zu is an empty constituent
null the syntactic structure of constituents corresponds to the gb x schema
when the parser reads verspricht the matching procedure applies
NUM dab ihn die frau zu schlagen scheint
weil das fahrrad niemand zu reparieren verspricht zu versuehen
nadvp time the meeting took long
np nadvp manner he put it
advp he headed home east that way
the influence of tagging on the classification of lexical complements
hlrther missing complements were found in parentheticals
lcb macleod meyers gri shman c s nyu
NUM to simplify the presentation in the remainder of this paper we will assume in most of the discussion that there is a total order with strict ordering between any two elements at those places where the partial ordering makes a significant difference we will discuss that
however a different interpretation one which retains some descriptive content provides the appropriate basis for an interpretation of the pronoun he in the slightly different subsequent utterance NUM historically he is the president s key person in negotiations with congress
however continuity of the house as a potential cb for 19c is reflected in the discourse segment being interpreted to be about the house and 19c being interpreted in the same way as 19b with respect to the house
one interpretation namely the individual who is currently vice president provides the appropriate basis for the interpretation of he in the subsequent utterance given in NUM NUM right now he is the president s key person in negotiations with congress
more generally events and other entities that are more often directly realized by verb phrases can also be centers whereas negated noun phrases typically do not contribute centers the study of these issues is however beyond the scope of this paper
each utterance u in a discourse segment ds is assigned a set of forward looking centers cf u ds each utterance other than the segment initial utterance is assigned a single backward looking center cb u ds
in summary these examples provide support for the claim that there is only a single cb that grammatical role affects an entity s being more highly ranked in cf and that lower ranked elements of the cf can not be pronominalized unless higher ranked ones are
by itself these additions are not enough spud must also take salience and basic level semantics into account in the evaluation of its alternatives
false na fm e na true figure NUM example feature coding of a potential boundary site
in each domain an extensive set of inferences presumed known in common with the user are required to ensure appropriate behavior
d refers to c just in case it distinguishes c from its distractors that is d applies to c but to no other salient alternatives
we present an algorithm for simultaneously constructing both the syntax and semantics of a sentence using a lexicalized tree adjoining grammar ltag
we have chosen to include the determiners in the basic np trees because of their importance for the semantics and pragmatics of the np
note that because trees are lexicalized and instantiated and must unify with the existing derivation spud can enforce collocations and idiomatic book19
the two books the set they comprise introducing a poset relation and the library are mentioned in NUM
given the additional input goal of communicating this fact the algorithm would proceed as before deriving the syntax book we have
NUM when there is no agreement other than that which would be expected by chance NUM
these two properties suggest that it may be possible to design a parsing strategy in which one first identifies a potential head of a rule before starting to parse the nonhead daughters
in order to construct a complete tree s for this head corner a rule is selected that dictates that a category np should be parsed to the right starting from position NUM
the head relation cm pm qm ch ph qh holds iff there is a grammar rule with mother cm and head ch
as a first step we modify the head corner relation to make sure that for all pairs of functors of categories there will be at most one matching clause in the head corner table
it should be noted that the general version of the head corner parser is not guaranteed to terminate even if the grammar defines only a finite number of derivations for all input sentences
unification is clearly not appropriate since it may result in a situation in which a more general goal is not searched because a more specific variant of that goal had been solved
the categories of the dau r das NUM startende bonusprograrnm for vielflieger
figure NUM te st system architecture rectangles represent domain independent language independen t algorithms ovals represent knowledge bases
in that sense bypassing allows for the violation of constraints
under the itg model word alignment becomes simply the special case of phrasal alignment at the parse tree leaves
the approach differs in its single stage operation that simultaneously chooses the constituents of each sentence and the matchings between them
automatic approaches to identification of subsentential translation units have largely followed what we might call a parse parse match procedure
with singletons there is no cross lingual discrimination to increase the certainty between alternative bracketings
we give here an algorithm for further improving the bracketing accuracy in cases of singletons
NUM unrestricted form grammars it is possible to construct a parser that accepts unrestricted form rather than normalform grammars
special cases of particular interest include applications where bracketing or word alignment constraints may be derived from external sources beforehand
abstr t the main application of name searching has be name matching in a database of names
this is because even within a single constituent immediate subtrees are only permitted to cross in exact inverted order
this means that the adjacency constraints given by the nested levels must be obeyed in the bracketings of both languages
joe occurred in NUM NUM documents within the NUM NUM document test collection and had a normalized idf of NUM NUM
guidelines and example marked up pages from case law text were prepared for use by the manual markers
in addition this table shows the average pairwise agreement of the coders and the expert a g which was assessed by averaging the individual scores not shown
for queries containing names there was retrieval performance improvement using name searching as simulated by proximity operators
special thanks are also due to kenneth r
it also indudes all possible pairs like
the basic case is unconditional obligatory replacement
our purpose in this paper is twofold
this expression is a description of the sigma star language
wdnl and the weight vector for category k is wc lk wc2k
we haveselected this synonymy information performing a categoryexpansion simdar to query expansion in ir
after locating categories in wordnet a term set containing allthe category s synonyms has been built
a set of predictors is typically computed from term tocategory co ocurrence statistics as a training step
before stepping into the actual results we provide acloser look to these elements
in this approach the terms used for the representation are justthe categories themselves
automatic text categorization is a complex and useful task for manynatural language processing applications
table NUM distribution of centering transitions
the three modules interact in cases of repair e.g. when the planner needs statistical information to resume an incongruent dialogue
this leads us to conjecture that the output from this algorithm can be used as a translator aid
user and application parameters infornm lion that typically correlates with the use of a i est suite for difl erent types of ewtluation and for different apl li ations e.g.
r msability of exisi ing test suilos is severely hmnl ered l y the it l mk of structure a ud a nn ta t ions
we chose three evaluators who are all native chinese speakers with bilingual knowledge in english and chinese
the tool instant iates the annotation schema see section NUM as a feral based input mask and provides for limited consistency checking of the field values
in its initial specification and in the early phase of the project tsnlp was greatly inspired by the conceptional and administrative contributions of siety meijer of university of essex
furthermore the choice of related terminology for the categolial and structural description contributes to i he comparability and consistency of the test items see section NUM for details
this is particularly the case when a phenomenon is illustrated by systematic variation over the parameters used to describe this phenomenon while all other parts of the test items remain constant
in tsnlp this aspect is addressed by requiring that each test item focuses only on a single t henomenon or rather subphenomenon or even feature which distinguishes it from all other test items
the units to be processed are not words but the speech acts of a text or a dialogue
in general tile systematicity of test data was greatly enhanced through the use of special purpose tools in the data construction and validation process see section NUM below
they can be used for e.g. the prediction of following speech acts to support the speech processing components e.g.
is it possible to achieve bilingual lex con translation by looking at words in relation to other words
all the above works point to a certain discriminatory feature in monolingual texts context and word relations
table NUM different word senses in bvg and hrd
these results explain in part the poor results obtained in our first experiment only NUM of the cases of bridging dds fall into the category which we might expect wn to handle
we report our analysis of a collection of NUM wall street journal articles from the penn treebank corpus and our experiments with wordnet to identify relations between bridging descriptions and their antecedents
an automatic search for a semantic relation in NUM possible anchor dd pairs relative to NUM bridging dds found a total of NUM relations distributed over NUM cases of dds
we propose to use wn s morphology component as a stemmer and to augment the verbal stems with the most common suffixes for nominalisations like ment ion
on the other hand specific houses schoolhouse smoke house tavern were encoded in wn as hyponyms of building rather than hyponyms of house fig NUM
due to wn s idiosyncratic encoding it is often necessary to look for a semantic relation between sisters i.e. hyponyms of the same hypernym such as home the house
the results for these NUM dds are summarized in table NUM overall recall was NUM NUM NUM NUM relations are due to the unexpected way in which knowledge is organized in wn
among these NUM NUM were based on different anchors from the ones we identified manually for instance we identified pound the currency whereas our automatic search found sterling the currency
bridging dds and wordnet as a first experiment we used wn to automatically find the anchor of a bridging dd among the nps contained in the previous five sentences
suppose w is a mona sense word and there are n occurrences of the word in a corpus i.e. wt we w NUM lists their neighbor words within d word distances respectively
in the dialogos corpus we have calculated the number of turns necessary for acquiring departure and arrival cities in the successful dialogues
these applications require speaker independent real time systems and the opportunity of having training sessions with the system can not be provided
this compels the designers of situation semantics to make meaning a triadic relation as we will now explain
by examining the dialogos corpus we collected evidence that some critical situations occur when the users make experience of repetitive recognition errors
as we can see from the example the dialogue module makes use of confirmation turns because it deals with potentially incorrect information
the test set included NUM NUM utterances randomly selected from corpus data collected during a field trial of dialogos with NUM unexperienced subjects
contextual information is sent to the lower levels of analysis by communicating the dialogue act produced by the system for addressing the user
it is an open issue if a spoken dialogue system has to generate a clarification subdialogue when faced with ambiguity or unclear input
these are stored in a temporary lexicon so that variations of the name in the text can be recognized and linked to the original occurrence
plans have been formulated to increase the sophistication of this selection process and to expand the system to bandie coreference of pronouns to organizations
using the probabilities assigned to each die for pl through p6 and the number of times each outcome occurred for nl through n6 and the total number of outcomes for n the following probabilities of producing each output given that a particular die was used are calculated
the cosine method worked best when the distinguishing terms for each class were the words which were more likely to be in the class than in the sum of the rest of the classes until the sum of the probabilities of the chosen words was at least NUM
the cosine measure is used when a document is represented as a multi dimensional vector and a document is defreed as more similar to class NUM than class NUM if its corresponding vector is closer to that of class NUM than to that of class NUM in ff idf a document is more similar to class NUM than class NUM if more terms match the class NUM terms than do the class NUM terms
sorting these probabilities we get the expected resuits set NUM is the output most likely to have been created with the fair die and set NUM the least and set NUM is the output most likely to have been created with the loaded die and set NUM the least
in choosing a representation of a class or a representation of a document much of the current research in classification and routing is focused on choosing the best set of terms in our case we call them distinguishing terms to represent it
use the high frequency words in each list which occur with low frequency on all of the other lists by selecting only the words which occur more often in one list than in all other lists combined until enough words have been chosen
the smart program independently calculates the scores for the distinguishing terms and for the document based upon the word frequencies in the entire collection available for classif mation and routing and takes the score as the sum of the products of the distinguisking term and document weights
sadly mercury falling makes ten summoner s tales seem brilliant by comparison lf s as if sting only made it because he looked at his calendar one day and realized by golly that it was time to make another record
in third experiment we implement our algorithm on NUM occurrences of the ambiguous word f l bianji it also has two senses one is editor the other is to edit
the minimal dictionary encoding this information is represented by the wfst in figure NUM a
an input abcd can be represented as an fsa as shown in figure NUM b
chang of tsinghua university taiwan r o c for kindly providing us with the name corpora
this is an issue that we have not addressed at the current stage of our research
sproat shih gale and chang word segmentation for chinese an abstract example illustrating the segmentation algorithm
with regard to purely morphological phenomena certain processes are not handled elegantly within the current framework
the first point we need to address is what type of linguistic object a hanzi represents
in this section we present a partial evaluation of the current system in three parts
a related point is that mutual information is helpful in augmenting existing electronic dictionaries cf
but we could locate some senses in the space which are similar with it according to their contexts and based on their definitions given in a dictionary we could make out the correct sense of the word in the context
there are also some complex structural changes encountered during transfer
in the chinese dictionary NUM NUM words have only one sense among which only NUM NUM words occur in the corpus we select NUM NUM most frequent mona sense words in the corpus to build the semantic space for chinese
john oksur pst hafifce john hafifce oksurdu john
the proportion of new underdispersed types in text slice k on the total number of new types pr u type k is given by avu k NUM pr u type k av k the plot of pr u types k is shown on the third row of figure NUM left hand panel
the time series smoother solid line for the absolute numbers of underdispersed types vu k and tokens nu k suggests an oscillating use of key words without any increase in the use of key words over time the dotted lines represent the least squares regression lines neither of which are significant f NUM in both cases
inspection of plots such as those presented in figure NUM for alice in wonderland suggests that the effects of lexical specialization appear in the central sections of the text as it is there that the largest differences between the expected and the observed vocabulary are to be observed differences that are highly penalized by the mse and chi squared techniques used to estimate the proportion of specialized words in the vocabulary
to ensure that a relatively good alignment is found early it is important at each stage to try matches before trying skips
to avoid floating point rounding errors all penalties are integers and the penalty for a complete mismatch is now NUM rather than NUM NUM
in others it was indecisive although it found the correct alignment of fish with piscis it could not distinguish it from three alternatives
covington an algorithm to align words approximation we can use the following penalties NUM o NUM skips NUM exact match NUM NUM
its only clear mistake is that it missed the hr correspondence in arbre drbol but so would the linguist without other data
nonetheless the aligner did reasonably well with them correctly aligning for example star with stglla and round with rotundus
NUM NUM matching tiling and derivation
in the former case x is coreferential with xl which is coreferential with john j giving us the strict reading
a crucial piece of our treatment of vp ellipsis is the explicit representation of coreference relations denoted with the predicate core
NUM john revised john s paper before bill revised bill s paper but after the teacher revised john s paper
handling these cases requires an account of how such dependencies are established which we discuss in hobbs and kehler forthcoming
then in expahding the vp ellipsis in the second main clause taking the similarity option for the event generates the desired reading
in long queries however many other terms are still available to remedy the removed crucial word
the method sorts systems into like and unlike categories
results of repeating the retrieval experiments using these two larger lexicons are shown in table NUM
i m going oreson strengthening the creative work he says
the presence of stopwords do not contribute much noise to ir
a threshold is used to extract the most commonly occurring ones
in theory we could continue to iterate but we have only done one round
they naturally do not work always but may work correctly often enough for ir purposes
when both tag NUM NUM entries and rule NUM are used for stopword removal exptyp NUM
a similar case appears in template element at the low and high end of the scores
this data is represented in a n x n dissimilarity matrix such that the value in cell i j where i represents the row number and j represents the column is equal to the number of features in observations i and j that do not match
in this paper we propose a framework structured semantic space as a foundation for word sense disarnbiguation tasks and present a strategy to identify the correct sense of a word in some context based on the space
however no corpus of any size will ever contain all possible uses of all possible words
we use NUM binary co occurrence features c1 c2 and ca to represent the presences or absences of each of the three most frequent content words c1 being the most frequent content word c2 the second most frequent and c3 the third
although NUM NUM shuffles are carried out the c program is fast
in addition each experiment was repeated NUM times in order to study the variance introduced by randomly selecting initial parameter estimates in the case of the em algorithm and randomly selecting among equally distant groups when clustering using ward s and mcquitty s methods
here NUM is the current value of the maximum likelihood estimates of the model parameters and 9i is the improved estimate that we are seeking p y si NUM i is the likelihood of observing the complete data given the improved estimate of the model parameters
the statistical significance of the muc NUM results proceedings of the fourth messag e understanding conference muc NUM
there is ambiguity for example whether a number refers to a date or a time but many potentially ambiguous sentences have only one possible meaning in the scheduling domain
a t verbmobil systems developed under the atis initiative and systems developed at sri a t t and mlt lincoln lab are examples of such successful spoken language understanding systems
to prevent human error the entire process of doing the statistical analysis is automated
now with the increasing success of large vocabulary continuous speech recognition lvcsr the challenge is to similarly scale up spoken language understanding
another nlp problem where combination of different sources of statistical information is an important issue is pos tagging especially for the guessing of the pos tag of words not present in the lexicon
our experience has been that for limited domains NUM to NUM coverage can be achieved in a few months with semantic grammars
semantic grammars allow our robust parsers to extract the key concepts being conveyed even when the input is not completely grammatical in a syntactic sense
in order to effectively deal with the significantly greater levels of ambiguity we plan to use a collection of sub domain grammars which will in sum cover the entire travel planning domain
the speaker playing the traveller is given a scenario such as you are traveling with your wife and teenage daughter to the pittsburgh arts festival
edit operations are insertion for instance e p leletion like l c and replacement like a s
the analysis shows that mbl and back off use exactly the same type of data and counts and this implies that mbl can safely be incorporated into a system that is explicitly probabilistic
we can see that isl which implicitly uses the same specificity ordering as the naive back off algorithm already performs quite well in relation to other methods used in the literature
the prograni determines the siblings NUM s name vp
we have also extended the set of structures recognized on the discourse level in order to identify speech acts such as suggest accept and reject which are common in negotiation discourse
processor attaches the current sentence to the plan tree thereby selecting the correct speech act in context it inserts the correct speech act in the speechact slot in the interlingua structure
fina lly analogy mso cxphdns incorrect brms or barbarisms ex mq le s of which re flmquent in child langua g
in case there exists such a valid path for z the translation of z by r is yl yna q
from a practical standpoint our second contribution will be a description of our implemented discourse processor which makes use of this extension of tst taking as input the imperfect result of parsing these spontaneous dialogues
a listance can be defined by assigning weights to these three per ations NUM for each of them for simplification
in this case it is possible to resolve the reference for the other day since it would still be on the stack when the reference would need to be resolved
most rese arch and development has focussed on these latter two applications rather than information retrieval
our results demonstrate the utility of language models that are intermediate in size and accuracy between different order n gram models
commercial products such as carnegie group s namefinder and isoquest s nametag are available to support these sorts of applications
the ordered proximity search treated joe woods as a single concept in which the terms comprising the concept were proximally ordered
the baseline model backed off to bigrams and unigrams the other backed off to the less than t times
our solution is to define translation rules as modular as possible and control application of rules with background conditions so that the best rule in the context should be chosen
this has obvious advantages if the goal of finding word classes is to improve the perplexity of a language model
in order to make out the clusters we first construct a dendrogram of the senses based on their similarity then make use of an heuristic strategy to select some appropriate nodes in the dendrogram which most likely correspond with the clusters
mixed order models are not as powerful as trigram models but they can make much stronger predictions than bigram models
this centroid region context vector is compared to all of the stem word context vectors in the vocabulary
to which represents an agent s intention to perform some action and 2a discourse segment purpose denotes the goal which the speaker s attempt to accomplish in engaging in the associated segment of talk
the user will be presented with a spherical array of nodes representing the world of information
although development and initial testing of the discourse processor was done with spanish dialogues the theoretical work on the model as well as the evaluation presented in this paper was done with spontaneous english dialogues
an ideal characteristic of this training is to have the map node vectors win the competition with equal probabilities
it is necessary to perform this loop hundreds and possibly thousands of times before training is completed
analogy is very general and its ts are seen in a number of other places
in our case we happen to know that the whole atis domain contains NUM distinct words
we have given a novel method for parsing these words by estimating the probabilities of unknown subtrees
at the end of the following section we describe an experiment in which we use this measure to evaluate the quality of translation in the english french version of slt
in this community it is generally assumed that grammars need to be as succinct as possible
however a different derivation may also very well yield the same parse tree for instance
we will not deal here with the algorithms for calculating the most probable parse of a sentence
the scenario template test shows even more overlap than the other two tasks
we strongly believe that such a two step approach is not optimal see section NUM NUM NUM
how can good turing be used for adjusting the frequencies of known and unknown subtrees
incorrect recognition of digits lost content words and misrecognized content words can cause the system to have high confidence in an incorrect interpretation
the cost matrix defines the cost for inserting or deleting words as well as the cost for a word substitution when such substitutions are allowed
we conjecture that provided a proper formalization of the other dg versions presented in section NUM their a p completeness can be similarly shown
the mutual information of two events x and y is defined as follows
the ordering of two linked words is specified together with their dependency relation as in the proposition object of verb succeeds it
in their discussion lombardo lesmo express their hope that slight increases in generative capacity will correspond to equally slight increases in computational complexity
discontinuities can easily be characterized since a word may be contained in any domain of nearly any of its transitive heads
third a domain e.g. dl in fig l can be constrained to contain at most one partial dependency tree
so we can define the number of the demanding classes in advance
mr ses cosine as metric to measure the similarity between two words
the results of the classification without introducing probabilities can be summarized in table i
finally we merge all resulting classes until the pre defined number is reached
the computer responds directly to user questions and passively acknowledges user statements without recommending a subgoal as the next course of action
very strong control means that the participant will select the subdialog and will ignore attempts by the partner to vary from it
this model is the recursive subroutine zmodsubdialog and it is entered with a single argument a goal to be proven
second the controller may halt an interaction that it deems unfruitful and send the system in pursuit of a new subgoal
the cross entropy rate of the two best grammars is lower than the source entropy rate because the corpus is finite and randomly generated and has been be overfitted
in the experiments that partitioned text into n and v chunks we use the chunk tag set lcb bn n bv v p where bn marks the first word and n the succeeding words in an n type group while by and y play the same role for v type groups
for example in the basenp tag set whenever a b tag immediately follows an NUM it must be treated as an i and in the partitioning chunk tag set wherever a v tag immediately follows an n tag without any intervening bv it must be treated as a bv
thus a set of partially completed trees will exist at all times and control will jump back and forth between them
a few responses during the experiment were as slow as NUM seconds or in some cases as much as NUM seconds which hampered the flow of the interaction
in transformational learning the space of candidate rules to be searched is defined by a set of rule templates that each specify a small number of particular feature sets as the relevant factors that a rule s left hand side pattern should examine for example the part of speech tag of the word two to the left combined with the actual word one to the left
awe will be defiberately vague about what such dominance and precedence relations represent obviously different researchers have very different conceptions about the relevence and implications of heirarchical phrase structure
such chunking models provide a useful and feasible next step in textual interpretation that goes beyond part of speech tagging and that serve as a foundation both for larger scale grouping and for direct extraction of subunits hke index terms
tree with substitution site NUM figure NUM examples of adjoining and substitution
u forms have heen previously used in nmchine translation as interlingual representations hut without being provided with a formal interpretation
the notion of s form cart now be delined through the nse of the s form b form encoding
this representation is called a scopeu depemh m NUM lotto or sqbrm
by renaming each such node a into a xi x
the only differeiice hetween this tree aml the l jform of l ig
and enriching it we have informally introduced the notions of s form and b form
john it is not the case that he likes every woman that pe
our discussion of scope being represented by node order has been infornml so far
when used as interlingual representations in machine translation systems u forms have several advantages
the semantic evaluation component needs predictions when it determines the dialogue act of a new utterance to narrow down the set of possibilities
in this paper we present a statistical approach for dialogue act processing in the dialogue component of the speech to speech translation system verbmobil
figure NUM shows the variation in prediction rates of three dialogue acts for NUM dialogues which were taken at random from our corpus
dimogue act determination in verbmobil is done in two ways depending on the system mode using deep or shallow processing
where f are the relative frequencies computed from a training corpus and qi weighting factors with qi NUM
these predictions are further sharpened by a user model and then are passed down to the signal processing level to improve speech recognition
we conclude with a preliminary evaluation of various aspects of the model on several english corpora
the text filtering results for muc NUM muc NUM tst4 and muc NUM tst2 are shown in figure NUM
the first has been used since muc NUM
figure NUM plum system official results
the discourse processor performs this conversion
the first was the relative lack of data provided
figure NUM measured progress on the new domain
any other inferences are also added to the database
dooner NUM in the walkthrough article
variable initiative dialog allows either participant to have control and it allows the initiative to change between participants during the exchange
domain specific knowledge is localized only in st
the processing modules are briefly described below
sentence boundary disambiguation has recently gained certain attention of the language engineering community
it was boiled down to NUM NUM in NUM hours of the processor t me
one of the most popular maximum entropy distributions is known as gibbs distribution
the basic constraint feature induction algorithm presented in della pietra et ai
in this paper we presented a novel approach for building maximum entropy models
this can provide a further generalization over the employed by the model features
in the basenp experiments aimed at non recursive np structures we use the chunk tag set i g b rcb where words marked i are inside some basenp those marked o are outside and the b tag is used to mark the left most item of a basenp which immediately follows another basenp
this operation in turn defines a subset of l l which includes all links between members of an
since the tag b is only used when basenps abut the basehne system tags determiners as i rule NUM takes words which immediately follow determiners tagged i that in turn follow something tagged NUM and changes their tag to also be i rules NUM NUM are similar to rule NUM marking the initial words of basenps that directly follow another basenp
the context free grammar compiled in the lr table is shown in fi gure NUM the crucial feature of this grammar is that nonterminals specify only the x projection level and not the category
figure NUM the time line representation for aspectual
i take this to mean that the icmh is confirmed only by a global assessment of the relation between the content of information and the average conflicts but not by pairwise comparisons of the grammars
because the lr table is underspecified with respect to the categorial labels of the input many instances of lr conflicts arise which can be teased apart by looking at the co occurrence restrictions on categories
computational linguistics volume NUM number NUM the action is unique while when the corner is a bar level projection there are multiple actions and they are the same independently of the input token
eliminating this property would be incorrect as it would amount to eliminating one of the crucial principles of gb namely move c which says that any maximal projection or head can be gapped
the sentence in 5a for example contains the chain maryi ti which encodes the fact that mary is the object of love represented by the empty category t
as a result of using a category neutral context free backbone to parse most of the feature annotation is performed by conditions on rule reduction associated with each context free rule which are shown in figure NUM
if b c and d together were not checked nlab would output ah ah lcb ai ai af af rcb
the chain selection problem csel given a node n of label l and an ordered list of chains c return the chain ci possibly none to which n has unified
central to all these benefits would be the notion of plug and play defining a set of modules and interfaces with sufficient precision that we could unplug one vendor s module and replace it with another vendor s without affecting system functionality
the modelling of pragmatic features of natural conversation to help aac users achieve a range of social conversational goals is considered in relation to the development of an aac system based on text pre storage and retrieval
phase ii of the tipster program had a twofold mission to advance the technology for document detection information retrieval and routing and information extraction from free text and also to facilitate the delivery of this technology to government customers
to give these parameters more chance to be trained during the robust learning process we tie together the parameters whose corresponding events appear less than qn times in the training set
the performances of the various models in terms of accuracy rate and selection power are shown in table NUM the values in the parentheses correspond to performance excluding unambiguous sentences
the improvement in resolutionof syntactic ambiguity by using more lexical contextual information however is not statistically significant s when the consulted contextual information in the syntactic models is fixed
to investigate the effects of model complexity and estimation error on the disambiguation task the following models which account for various lexical and syntactic contextual information were evaluated
for instance lcb b c rcb lcb a rcb is the pattern corresponding to the reduce action a bc in figure NUM
the mle approaches however may fail to achieve good performance in difficult tasks because the discrimination and robustness issues are not taken into consideration in the estimation processes
to establish a benchmark for examining the power of the proposed algorithms we begin with a baseline system in which the parameters are estimated by using the mle method
let the label ti in figure NUM be the time index for the ith state transition which corresponds to a reduce action and li be the ith phrase level
with the formulation each transition probability between two phrase levels is calculated by consulting a finite length window that comprises the symbols to be reduced and their left and right contexts
the inclusion of new words is similar to the one in the previous approach
words seldom employed can be replaced by others which are not in the dictionary
some other languages admit differences in gender for example in french voisin voisine
their results are normally measured in terms of keystroke savings ks NUM
so a word that is not at the lexicon can not be guessed
most of the methods seen in previous sections can be used for this purpose
that can be done by means of a syntactic analysis on the fly
even if prefixes and infixes are possible the basque language is declensed mainly by suffixes
their prediction makes sense mainly if the word is an auxiliary or a declined verb
other areas for future work are the systematic treatment of proper names outside the context of street names and of brand names trademarks and company names
for this set the street names for each of the four cities berlin hamburg ksln and miinchen were randomized
in a recall test these NUM strings accounted for NUM NUM of the original list of city names yielding a coverage of NUM NUM NUM NUM NUM NUM
on the one hand despite a significant improvement over the previous general purpose text analysis we have to expect a pronunciation error rate of NUM NUM for unknown names
in this paper we concentrate on street names because they encompass interesting aspects of geographical as well as of personal names
for evaluation purposes we compared the performances of the generm purpose text analysis and the name specific systems on training and test materials
no weights or costs are assigned to the most frequently occurring street name components previously intro null street name dachsteinhohenheckenalleenplatz
in evaluation experiments we compared the performances of the general purpose text analysis and the name specific system on training and test materials
some german street names can be morphologically and lexically analyzed such as kurfiivst en damm electorial prince dam kirche nweg church path
in all experiments NUM german dialogues with NUM dialogue acts from our corpus are used as training data including deviations
these probabilities must be considered an approximation to the real morpho lexical probabilities because of the following reasons
the experiment shows that using our method we can significantly reduce the level of ambiguity in a hebrew text
in order to demonstrate the complexity of the problem we should take a closer look at hebrew morphology
while we could use repeated applications of lemma NUM to turn a disjunction of n disjuncts into an alternative case form it will simplify the exposition to have a more general way of doing this as shown in lemma NUM
for these unseen vowels which consisted of the vowel uh and the diphthongs oy and ow all with secondary stress the transducer incorrectly returns to state NUM in this case we wish the algorithm to make the generalization that the rule applies after all stressed vowels
s lcb al w s c r rcb NUM return s the loop in lines NUM NUM repeatedly finds the single most profitable symbol a with which to augment the set s of profitable extensions
for such words we simply ignored the data and arbitrarily gave a uniform probability to all their analyses
by applying now the algorithm of the next section on these counters we can calculate the desired probabilities
first part of speech of translation equivalent may be specified through this operation since translation equivalents with different part of speech appear distinctly in the alternatives window
when the system pauses for interaction it shows initial selection of translation equivalents and translation area as in figure NUM b
in addition the user can change an inflection in a similar manner on an inflection selection window opened by the user s request
in intermediate steps a mixture of target language expression and source language expression are shown to give the current status of the interactive translation
this feature allows this system used as an add on function of any application enabling the user to work in a familiar document writing environment
on the other hand the user has only to repeat next i struction to obtain a result of automatic translation quality
in this section we show the basic steps of simple sentence translation in order to give a general idea about how the method works
with the steady decrease of network communication cost and equipment prices world wide computer networks and the number of its users are growing very rapidly
the method combining dictionary lookup function and user guided stepwise interactive machine translation allows the user to obtain clear result with an easy operation
other information is only presented to be read as in the case of a paper dictionary with all further work left to the user
way of crossing these apl roa hes
NUM a l c both promising apln oa chcs
photo NUM NUM NUM NUM NUM NUM NUM NUM fax takeda trl vnet
the notion of a syntactic hea d is simila r
such xtensh ns lntlst be ca refully
however the subject might just as well have asked a question during the dialogue
semantic information preprocessing for natural language interfaces to databases
the outline of the paper is as follows
suppose that the theory r consists of axioms
we assign the tags thing or attribute to argument positions of the lexical predicates according to what kind of restriction the predicate imposes on the referent at its argument position
a take e x y unknown y cmptt10 v take e x y unknown y erupt720
NUM is a specific principle subsumed by gp1 l background knowledge
we demand that an rldt be nonrecursive
using the example above and the assignments
i provide clear and sttfficient instructions to users on how to interact with the system
NUM p educe system lalk as nnich as possible during individual dialogue turns
the slightly stronger form b follows from a and the way possible prediction steps are defined
provide ability to initiate repair it s stem understandin has failed
the similarity between document j and category k is obtained with the formula
then we would like to say that s is ambiguous if it contains at least one disjunction
it should be done at a less specific level suitable for generating disambiguation dialogues understandable by non specialists
the mark tum or parag for a text must be used if there is more than one utterance
loken kim for their constant support to this project and to its funders cnrs and atr
the of a is the in which the differ and type way pi must be defined relative to each particular r
in the case above for example we might have the configurations given in the figure below
the system that focused on maximizing performance used the following hints or contextual templates the prefix the suffix the presence of particular characters in the prefix or suffix whether the candidate is an honorific e.g.
for the NUM categories used in thisstudy we have produced NUM terms
a s ed nn vb jj vbd vbn says that if by stripping the suffix ed from an unknown word we produce a word with the pos class nn vb the unknown word is of the class jj vbd vbn
the score for the ith rule as 3i NUM NUM p qp another important consideration for scoring a word guessing rule is that the longer the affix or ending of the rule the more confident we are that it is not a coincidental one even on small samples
this is especially the case with the ing words which in general can act as nouns adjectives and gerunds and only direct lexicalization can restrict the search space as in the case with the word going which can not be an adjective but only a noun and a gerund
for example the rule un vbd vbn jj says that prefixing the string un to a word which can act as past form of verb vbd and participle vbn produces an adjective j j
at the merging phase rules which have not scored high enough to be included into the final rule sets are merged into more general rules then re scored and depending on their score added to the final rule sets
although the learning process in these and some other systems is fully unsupervised and the accuracy of obtained rules reaches current state of the art they require specially prepared training data a pre tagged training corpus training examples etc
to put a perspective on that aspect we measure the overall tagging performance totalscore correctlytagsedwords totaiwords since the brown corpus model is a general language model it in principle does not put restrictions on the type of text it can be used for although its performance might be slightly lower than that of a model specialised for this particular sublanguage
the value of a guessing rule thus closely correlates with its estimated proportion of success p l which is the proportion of all positive outcomes of the rule application to the total number of the trials n which are in fact attempts to apply the rule to all the compatible words in the corpus
so if we try to translate the sentence in example NUM systran will not offer a translation although keyboard as a noun is in the lexicon
the accuracy of the taggers on the NUM NUM unknown words when they were made known to the lexicon was much lower than in the previous experiment NUM NUM for the xerox tagger and NUM NUM for brill s tagger
the amplitude is the same for every word at a given rank
each n best gives rise to a re evaluation of the current word score
the score we have used here is the global sentence acoustic score
it is not an exact measure but it has minimal knowledge requirements
we propose here a simple on line computing of the word confidence score
we may now ask the system for a solution to queries like wfs arthur sleeps d
moreover falsely rejected words can give rise to deletion repairing procedures
the result is a semantic representation built synchronously to the syntactical tree
initially all semantic features in an overspecified joker tree are marked 5cf
this allows to correct irrelevant matching of joker in the first pass
an example grammar to further motivate our approach we now show how to code a simple principle based grammar in our framework
of course composing rules is somewhat akin to programming and not all users will be inclined or well equipped to become involved in this process
another approach that we are interested in exploring involves supporting more indirect feedback or directives from the user that are rooted more closely to examples in the data
however much of the drudgery of this process can be removed if the most obvious and or oft repeated expressions can be tagged prior to the annotator s efforts
as the message understanding conferences move into their tenth year we have seen a growing recognition of the value of balanced evaluations against a common test corpus
we also would like to give users immediate feedback as to how a single rule applies correctly and incorrectly to many different phrases in the corpus
in addition to allowing users to define pre tagging rules we have developed a learning procedure that can be used to induce these rules from small training corpora
the score is determined by evaluating the corpus as currently annotated against the correctly annotated version using some evaluation function generally precision recall or f measure
other alternatives include setting a strict limit on the number of rules and testing the performance improvement of a rule on a corpus distinct from the training set
in these and other instances the tagging process can be accelerated by applying partial knowledge early on transforming the task once again into that of editing and correcting
the learning process is initiated by deriving and applying an initial labeling function based on the differences between an un annotated version and a correctly annotated version of the corpus
as another example specific types of weapons e.g. m NUM ar NUM m NUM or m NUM might not even be known to most users but they are abundant in the muc NUM corpus
most prefixes appear in distinct but semantically related rules resulting in polysemou s prefixes
this was done to minimize contextual effects e.g. seeing five category members in a row might make someone more inclined to judge the next word as relevant
typically the words near the top of the ranked list are highly associated with the category but the density of category words decreases as one proceeds down the list
one might consider these cases akin to acknowledge moves but with a negative slant
this makes it very difficult to develop a reliable coding scheme for complete game structure
although some semantic information is available in general purpose knowledge bases such as wordnet and cyc many applications require domain specific lexicons that represent words and categories for a particular topic
ambiguous in romance languages and can only have the former meaning
this gives four transaction types normal review overview and irrelevant
check does the question ask for a yes no answer or something more complex
for whether a move was an initiation type or a response or ready type
krippendorff also describes an experiment by brouwer in which english speaking coders reached a NUM
k NUM suggesting that the instructions conveyed his intentions fairly well
for agreed initiations themselves agreement was very high k NUM
we would like to thank our anonymous reviewers for their comments on the draft manuscript
overall each coder marked roughly a tenth of move boundaries as transaction boundaries
the communicative goal that we are concerned with is where the speaker wants the hearer to believe that the speaker believes some proposition
since both conversants expect the hearer to behave in this way the belief that there is an error can be mutually believed
but in these exceptional cases the sample is very small and the observed behavior may be due to chance
s the difference between low and high values of p is in the rate at which increasing data increases overall precision
the tagged result can be written out to an sgmlmarked file as shown in figure NUM
then we evaluate and compare the results of the mlr with those produced by the mdr
we construct the model so that NUM corresponds to same orientation and define dissimilarity as one minus the produced value
we found that retrieval performance decreased for NUM out of NUM phrases
or more actually formations the remaining NUM NUM conjunction tokens involve NUM NUM distinct pairs of conjoined adjectives types
links per adjective for classification performance over NUM and only NUM links per adjective for performance over NUM
this method has a few shortcomings it is based on a very limited set of types quantitative ordinal nominal it works on individual variables instead of global relations and it is not easily applicable to text
therefore it seems interesting to do a syntax approach for these types of languages because otherwise the problems of this approach are very dimcult to solve
in this table the approximation for the probabilities of the first three words is very good while the approximation for the fourth word is quantitatively poor but still succeeds in identifying the first analysis of lpny NUM v before as the dominant analysis
la hack NUM was written to do more sophisticated string matching
intuitively they are the smallest noun phrases in a parse
this is why we are presenting both official and unofficial scores
as a result multiple tokenizations of each article are maintained
other system components not developed explicitly for muc were written in lisp and c
we developed our tokenizer solely for th e muc coreference task because of specific tokenization requirements
it was trained on NUM sentences from the wall stree t journal treebank NUM
the wall street journal uses constructions similar to appositives to indicate relationships other tha n coreference
we postprocess the output of their tool to make it more appropriate for the coreference task
university of pennsylvania description of the university of pennsylvani a system used for muc NUM
as table NUM shows using anaphoric chains without anaphoric type identification helped improve the mlrs
unlike english japanese has so called zero pronouns which are not explicit in the text
however this is a subjective judgement
among them those NUM withhighest document frequency are selected
null binary tree ileal internal node intro label
both describe many events each of which involves one person s act
appropriateness is also a prerequisite for compilation of feature terms into fixed arity prolog terms
it can take up either the ongoing process or the resultant state
NUM NUM are cited from fillmore68 and e.g.
we confirmed that more than NUM of verbs are correctly classified
each item in the japanese co occurrence dictionary consists of a governing word
table NUM shows the determination process of the meaning of teiru
for the sentences with unknown words only NUM are parsed correctly
figure NUM shows the time line representation for each aspectual category of verbs
this shift of focus allows us to partition the problem we address into a series of smaller ones the solution to which may be within our reach
markedness in general and semantic markedness in particular have received considerable attention in the linguistics literature
this allowed us to study the different behavior of the tests for the two groups separately
application bridge agent the bridge agent generalizes the underlying applications api to typed feature structures thereby providing an interface to the various applications such as modsaf commandvu and exinit
figure NUM probability densities for the accuracy
consequently several tests for determining markedness have been proposed by linguists
the second purpose of our work is practical applications
the probability of obtaining by chance performance equal
favorably with the resulting loss gain in residual degrees of freedom
the mapping of the remaining tests to quantifiable variables is not as immediate
a quantitative evaluation of linguistic tests for the automatic prediction of semantic markedness
the main idea is to parse the sentence while it is being composed and to propose the most appropriate lemmas and suffixes
NUM NUM running the tests the sentence lists for adjectives nouns and verbs were then loaded as source document in one mt system after the other
this extension is useful for ot syntax but may have little application to phonology since the context free case reduces to the regular case i.e. ellison unless the cfg contains recursive productions
packfeet f f each foot is followed immediately by another foot i.e. minimize the number of gaps between feet
consider the following vector of o n primitive constraints ordered as shown NUM a vvev a s b
even if a fixed grammar can somehow be compiled into a fast form for use with many inputs the compilation itself will have to deal with this constant factor
unfortunately these constraints are not just reflected in the factors mentioning f or x since the allowed configurations of f and x may be mediated through additional factors
we have at this point a segment aligned parallel corpus with noise elimination
the machine state stores among other things the subset of v g that has already been seen so there are at least NUM itiersl states
to explore the reason for the errors we compute the distances between its definitions and those of the words in the activated clusters and find that the smallest distances fall in NUM NUM NUM NUM
in order to convey the influence of the german word order we provide a rough phrase tophrase translation of the entire text
auto continue function denoted by another anaphoric expression namely hilfsfunktion help function in u10
cb vn NUM cb un continue c smooth shift ss c u
as a consequence utterances without any anaphoric expression do not have any given elements and therefore no cb
let x denote the anaphoric expression under consideration which occurs in utterance ui associated with segment level s
thus the centering model is scaled up to the level of the global referential structure of discourse
this introduces an additional data type with its own management principles for data storage retrieval and update
in table NUM the symbol str denotes string equality n the natural numbers
it provides simple yet powerful data structures constraints and rules for the local coherence of discourse
this finding precludes a reliable prediction of segment boundaries based on the occurrence of shifts and vice versa
NUM the subtree t is attached to the foot node of fl and the root node of t i.e.
NUM a compose nodes realized from step NUM with nodes realized from step NUM
our approach is not without some disadvantages however it is well known that a considerable quantity of the semantics of human language is culturally and socially determined
this definition can be extended to include adjunction constraints at nodes in a tree
if b is the transitive closure then ak e b
of j and k being considered which has the property mentioned in NUM
before we introduce the algorithm we state the notations that will be used
a tree built from an operation involving two other trees is called a derived tree
notice that case NUM also covers step la and case NUM also covers step lb
assoc list ml assoc list m u lcb m end
this approach does not work for tals because of the presence of the adjunction operation
where in addition we have assumed that the lexicon probability p fle depends only on aj and not
in this paper we describe a dynamic programming dp based search algorithm for statistical translation and present experimental results
we used different preprocessing steps which were applied consecutively original corpus punctuation marks are treated like regular words
we define a modified probability p el for the language model depending on the alignment difference t
research and technology under the contract number NUM iv NUM a verbmobil and by the european community under the esprit project number NUM eutrans
to this purpose we exploit the monotony property of our alignment model which allows only transitions from aj i to aj if the difference NUM oj aj NUM is NUM NUM NUM
in many cases although not always there is an even stronger restriction the difference in the position index is smaller than NUM and the alignment
training efficiency is another issue in evaluating language models
NUM as a hidden variable model
table NUM perplexities of aggregate markov models on
in the surface form of utterance lb the information is missing that akkus accumulator links up with 316lt
an anaphora resolution module and an ellipsis handler based on this functional centering model has been implemented as part of a comprehensive text parser for german
our specification for the case of text interpretation says that cheap transitions are preferred over expensive ones with cheap and expensive transitions as defined in table NUM
still open are proper descriptions of deictic expressions proper names cf the alfa romeo driving scenario and plural or generic definite noun phrases
we have gathered preliminary evidence that the functional ordering of discourse entities in the centers seems to coincide with the grammatical roles of fixed word order languages
so the chance to properly resolve a nominal anaphor even at lower ranked positions in the center lists is greater than for pronominal anaphors
these updates are guaranteed to increase the overall log likelihood
for the police data set there were NUM categories associated with a set of NUM training responses
the lexicon is built from individual words and NUM word and NUM word terms from the training data
a multiple category rubric must be created to capture any possible response duplication that could occur in the examinees multiple response file
lexical entries not preceded by are relevant words from the set of training responses which are metonyms of concepts
the concept grammar rule templates for mapping and classifying responses were built from the NUM training set responses in NUM categories
all single xps and combinations of xps were matched against the concept grammars for each content category to locate rule matches
furthermore we will use the augmented lexicon from this second experiment to score a set of NUM new test data
our second set of results shows that developing new methods to augment the lexicon would improve performance significantly
as a last resort the word is assigned the tags for common noun verb adjective and abbreviation with a uniform frequency distribution
ahmet bug lcb in evden okula ahmet today home abl school dat ahmet went from home to school otob lcb isle NUM dakikada git i
figure NUM shows the path the generator follows while generating sentence NUM the solid lines show the transitions that the generator makes in its right linear backbone
our generator differs from his in various aspects we use a case frame based input representation which we feel is more suitable for languages with free constituent order
she can also deal with scrambling out of a clause dictated by information structure constraints as her formalism allows this in a very convenient manner
as a component of a large scale project on natural language processing for turkish we have undertaken the development of a generator for turkish sentences
all of these constituents except the verb are optional unless the verb obligatorily subcategorizes for a specific lexical item as an object in order to convey a certain usually idiomatic sense
the first sentence presents constituents in a neutral default order while in the second sentence bugiin today is the topic and ahmet is the focus NUM
however his generator is not complete in that noun phrase structures in their entirety postpositional phrases word order variations and many morphological phenomena are not implemented
a second reason for this approach is that many constituents especially the arguments of verbs are typically optional and dealing with such optionality within rules proved to be rather problematic
by quantifying their performance in segmenting a test set of NUM narratives from our corpus
continuing the example with binary pos vectors and for simplicity suppressing the parts of speech with value NUM the context becomes
precision is the ratio of hypothesized boundaries that are correct to the total hypothesized boundaries
ellipsis a series of periods can occur both within sentences and at NUM burlington road bedford ma NUM
although the colon the semicolon and conceivably the comma can also delimit grammatical sentences their usage is beyond the scope of this work
the procedure involves the following steps step NUM the raw corpus is tagged and from the tagged corpus the strings that obey the nounladjective noun expression are extracted
so the measures are at this point a description of which parameters to be used and not on the degree to which they should be used
as described in sections NUM NUM and NUM NUM the best results were obtained with a context size of NUM tokens and a hidden layer with NUM units
step NUM for these strings c value is calculated resulting in a list of candidate terms ranked by c value as their likelihood of being terms
where a is the examined n gram ca the context of a weight b the calculated from step NUM weight for the word b
for example the form shows of the verb to show in medical domains is very often followed by a term e.g.
atr also gives the potential to work with large amounts of real data that it would not be able to handle manually
therefore information from the modifiers of the candidate strings could be used in the procedure of their evaluation as candidate terms
in the resulting text of NUM NUM punctuation marks the style program had an error rate of NUM NUM while the satz system improved to NUM NUM
for example if there is an lit which produces abusive adj2 from abuse v1 the adjectival form will be unknown to the syntactic parser and its production would only be triggered by failure recovery mechanisms if direct lookup failed and the reverse morphological process identified abusevl as a potential source of the entry needed
the mappings produced by this scoring were then used by filtering heuristics
results of testing on the german news corpus are given in table NUM and show a very low error rate for both mixed case and single case texts
part of the work was supported by rome laboratory under contract number f30602 NUM c NUM
however lexical databases and especially wordnet have been often used for other text classification tasks like word sense disambiguation
we have tested our approach against training algorithms and lexical database algorithms reporting better results than both of these techniques
very low probability given no other information an analysis with a very low probability should be treated as a wrong analysis
at present this can be done manually by a speaker of the language and hopefully in the future it will be done automatically by a computer program
secondly wordnet by itself has been usedfor increasing the number of terms and so the amount of predictinginformation
lexicalizing such complex nps requires determining which relations in the complex np will appear as premodifiers and which as postmodifiers
although some meaningless terms occur and could bedeleted we have developed no automatic criteria for this at the moment
since the number ofcategories m reuters is NUM and two of them are composite these approachproduces NUM component vectors
the reliability of the probabilities we acquire using our method depends on the number of times the ambiguous word appears in the corpus
state ofthe art large vocabulary continuous speech recognition lvcsr technology automatically transcribes speech
when this argument is shared by other relations in the input conceptual network those other relations are realized as nominal modifiers
note that in case an analysis includes a particular attached particle this particle is also attached to each of its similar words
in the following sections we first present our general model for lexical choice illustrating it with a relatively simple example
furthermore in some situations different constraints come into play early on while in others they apply much later
figure i shows the typical language generation architecture used in many systems indicating the different places for lexical choice to occur
furthermore within the linguistic component there appears to be further consensus that the task of syntactic realization can be isolated
so the system will suggest the concept type cc pathology lbr tumor in ex
the next step is to make a guess about tbe concept type of the filled in element
this model is a classification and coding system of medical procedures
for finding the unknown surgical deed concepts multitale makes use of the fi ames as well
government supplied data in muc NUM and in met serve as the training data
the link between these two phrases is called post modification link
the guessing module uses the frames of the linking module
itule liis constraints the other approach is to maintain a bank of lrs and rely on their lhss to constrairi the application of the rules to only the appropriate cases in practice however it is difficult to set up the constraints in such a way as to avoid over or undergeneration a prior
the conditions on the fillers are found in the surgical deed lexicon and the type lexicon
thc subtype frame is specified for the finite form of the verb and the past participle
thanks to ray mooney for helpful discussions and the anonymous reviewers for their comments
since the content of the modifier is structure shared with an argument position within the telic this set of potential modifiers is further constrained by type constraints imposed by the relation in the telic role
compound forms such as hunting rifle or race car in NUM describe respectively an instrument which is used when hunting and a vehicle that is driven for the purpose of racing
if you allow lexical rules for compounds to apply at runtime during the parsing process then the storage problem is avoided but then they are really not any different from phrase structure schemata
if the lexical rules are used at a pre compilation stage in order to flesh out the lexicon allowing lexical rules for compounds will result in a massive increase in the size of the lexicon
exploiting these recursive properties of event denoting qualia is not an ad hoc move to account for the interpretation of complex nominals but is also motivated by the behavior of agentive nominals and their semantic contribution in context cf
in this framework which allows us to make fine grained distinctions between event types we can determine the selectional properties of di and da on the basis of the event type of the modifiers
we will show the schemata as rules here
in compounds where the modifying noun describes an individual in composition the modifier further specifies the type of an argument to a predicate in the telic agentive or constitutive role
such a hierarchy is useful e.g. for translation purposes
deliberate and clarification dialogues clarify query and clarify answer
in this paper main emphasis is on statistical dialogue act prediction in veftbmobil with an evaluation of the method and an example of the interaction between plan recognition and statistical dialogue act prediction
in a next version of the system it is envisaged that the semantic evaluation component and the keyword spotter are able to attribute a set of dialogue acts with their respective probabilities to an utterance
also the plan operators will be augmented with statistical information so that the selection of the best possible follow up dialogue acts can be retrieved by using additional information from the plan recognizer itself
the difference between the two experiments is that in ts1 only dialogue acts of the main dialogue network are processed during the test i.e. the deviation acts of the test dialogues are not processed
we implemented and tested both methods and currently favor the second one because it is insensitive to deviations from the dialogue structure as described by the dialogue model and generally yields better prediction rates
for each of these dialogues the plan recognizer builds a dialogue tree structure using the method presented in section NUM even if the dialogue structure is inconsistent with the dialogue model
the tag set is derived from higher level wordnet synsets
second some words function as sense primers for others
semantic tagging is thus crucial to all the above activities
information indeed is a typical abstraction that can be catalogued
collocationally derived lexical constraints as in the strong tea vs
several problems are tackled when a domain driven approach is used
many selectional constraints in argumental information have a semantic nature e.g.
the structural data sparseness means the lack of information on the grammar rules
although a space generally counts as a token boundary it can also be part of a multiword token as in expressions like at least head over heels in spite of etc
a world is divided into two universes its intension and its extension
given that the muc tasks only test certain aspects of the core system this means that much effort is expended on issues that will not affect the muc performance
therefore not only wordnet NUM NUM has been checked but our ideas have been developed and checked for validity and relevance using wordnet NUM NUM
using a more fine grained tagset however requires methods for adjusting the granularity of the tagset to the size and coverage of the corpus in order to cope with the sparse data problem
the opposite transformation is not always without loss of information
there is no one to one correspondence between uppercase and lowercase letters
figure NUM how could this type of false hnk selection between sattnwood NUM and satmwood NUM be prevented9
prototypical events these define restrictions on events by providing templates for events e g by imposing selectional restrictions on the roles in an event
cases of a vowel preceding vl or following v2 remain to be examined
phonetic transcriptions are presented for the reader who is not familiar with greek
to conclude formal definitions of diphthong and excessive diphthong sets would suffice
the resulting patterns tend to be more detailed and extended
ambiguity issues caused by the interpretation of the grammar rules will be resolved
morphological agreement provides enough information to make it possible fbr an atttomatic tagger to pick the right form in most cases
otherwise it splits the first consonant being hyphenated with the preceding vowel
now consider the case of a noninitial consonant or consonant sequence preceding vl
while living by all these commandments when designing a system to be used in normal situations is effectively impossible to obey them when designing for in car systems might appear to be even more difficult
as we observed in section NUM by using the symbol on the lower side of the replacement expression we can construct transducers that mark instances of a regular language without changing the text in any other way
the classification can also give hints about what could possibly be done about the errors
d the underspecified instances can later be automatically retrieved for either manual inspection or some more elaborate disambiguation device
not only have we successfully implemented all four tasks on our first attempt at muc but we managed to produce a deep analysis of a goo d part of the text in the formal evaluation set
apply abs r s r s
which is c equivalent to NUM
type atomic ype cat o atomic type rip
consider again the earlier problematic example la of coordination
figure NUM prolog implementation of ccg logical form operations
for each measure we recorded scores cumulatively choosing first the most promising sentence according to the opp then the two most promising and so on
NUM c is there a wire between connector eight four and connector nine nine NUM
the third constraint 4c states that the sum of the probabilities of the extensions e w must sum exactly to unity when every symbol is available in that context ie when e w e
the last row shows the accuracy figures of the naive bayes algorithm
NUM evaluation the goal of creating an optimal position policy is to adapt the position hypothesis to various domains or genres in order to achieve maximal topic coverage
the use of inferences such as foot is an end means that this theory is parametric on a knowledge base
errors in the annotated corpus were corrected by us
in this i differ from dorr where the amount of compilation is heuristic and based on practical experimentation
with respect to feature instantiation in particular it is predicted that precompiling syntactic features speeds up the parsing process
the primary objective of the project is to demonstrate the effectiveness of tipster sponsored data extraction technologies
the action performs operations on the text such as tagging a name with a classification
a name recognition rule consists of a pattern and an action
the second possibility is if the head is lexical but not a question word
thus this augmented parser could not recognize errors as soon as they are encountered
a minimum amount of lookahead even limited to these particular instances of aspectual adverbs would solve the problem
in the unextended algorithm the postulation and structural licensing of empty categories is always performed by the same mechanism
an empty category that is the foot of rightward movement must be licensed structurally before its antecedent is seen
the rules are partitioned to form processing phases that primarily recognize one class of name
typically patters recognize structural or contextual indicators of names and thus perform dynamic recognition
at this time attributes of the focus node and lower nodes are still undetermined except for lexical nodes
although basic model is as described it is apparently too bothersome to give an operation at every node
later reasoning processes could thus exploit general primitives augmented with domain specific lexico semantic phenomena
also it is not clear how to combine partial translations of two overlapping expressions except for direct editing
however when such ambiguities are multiplied the number of possibilities easily grows large making selection difficult
while writing in english the user can look up the system dictionary only by entering a japanese word
if the user chooses this alternative a new alternatives window containing literal translation appears as in figure NUM
null we give an example with denwa wo kakeru an equivalent expression for make a phone call
this function is realized using a standard hook and ime api of the operating system microsoft windows NUM
suppositions can be thought of as quoted propositions but with a limited syntax and semantics
in particular speakers knowledge about language is represented as a set of default rules
fourth turn repairs may occur after a self misunderstanding is recognized third turn repairs may occur after other misunderstanding
we now introduce a model of dialogue that extends both intentional and social accounts of discourse
russ recovers by reinterpreting t1 as an indirect request which his t4 attempts to satisfy
the language includes an infinite number of variables and function symbols of every sort and arity
our model extends the intentional and social accounts of discourse combining the strengths of both
another way to reduce one to many mapping between chinese and english words could be to use a morphological analyzer in english to map all english words of the same roots with different case gender tense number capitalization to a single word type
we postulate that the context heterogeneity of a given domain specific word is more similar to that of its translation in another language than that of an unrelated word in the other language and that this is a more salient feature than their occurrence frequencies in the two texts
NUM experiment NUM finding word translation candidates given the simplicity of our current context heterogeneity measures and the complexity of finding translations from a non parallel text in which many words will not find their translations we propose to use context heterogeneity only as a bootstrapping feature in finding a candidate list of translations for a word
immediately preceding w in the text b number of different types of tokens immediately following w in the text c number of occurrences of w in the text the context heterogeneity of any function word such as the would have x and y values very close to one since it can be preceded or followed by many different words
notice that although air and k have similar occurrence frequencies their context heterogeneities have very different
among the NUM words we selected there is one word service which occurred NUM times in the english text but failed to appear even once in the chinese text presumably the legco debate focused more on the issue of various public and legal services in hong kong during the NUM NUM time frame than later during NUM NUM
it is interesting to note that if we applied the same kvec algorithm to the english part of the text we would get a cluster of english words which contain individual translations to some of the words in the chinese cluster
for example the context heterogeneity of air is NUM NUM NUM NUM NUM NUM NUM NUM and the context heterogeneity of its translation in chinese is NUM NUM NUM NUM NUM NUM NUM NUM
in our first experiment we hand compiled a list of NUM word pairs as in tables NUM and NUM in english and chinese and then used NUM by NUM context heterogeneity measures to match them against each other
we have explained that there are various immediate ways to improve context heterogeneity measures by including more linguistic information about chinese and english such as word class correspondence and word order correspondence as well as by using a larger context window
the analysis of the results has been performed by comparing collision sets obtained by the two runs over a set of NUM sentences
avj denotes the conjunction of all feature values in the test example
we have no space to give details about this method but we must say that it is very important to obtain proper data for clustering NUM clusters figure NUM shows an example of such a cluster
in addition it marks its derived form as the antonym of a form derived by less if it exists less antonym
semantics one intuitively appealing idea is that humans acquire the meanings of words by relating them to semantic representations resulting from perceptual or cognitive processing
note that this solves the problems deriving from the csst having multiple final states or cycles involving the initial state
table NUM main features of the spanish to english spanish to german and spanish to italian text corpora
another cause of reduced recall is that some stems were not in the alvey lexicon or could not be properly extracted by the morphological analyzer
let us start with why it is a bad cue there may be no derivational cues for the lexical semantics of a particular word
in fact both un and de cue the change of state feature for their base and derived forms the change of state feature entails the telic feature
the nominalizing suffixes age and ment both produce derived forms that refer to something resulting from an event of the verbal base predicate
for example if we have evidence from the corpus that high performance is a more reliable association and general purpose a less reliable one then the noun phrase general purpose high performance computer an actual example from the cacm corpus would undergo the following grouping process general purpose high performance computer general purpose high performance computer
for example the lexical atoms extracted by this process from the cacm corpus about NUM mb include operating system data structure decision table data base real time natural language on line least squares numerical integration and finite state automaton among others
thus morphological cueing is best seen as one type of surface cueing that can be used in combination with others to provide lexical semantic information
thus for example under normal clarit processing the phrase the quality of surface of treated stainless steel strip NUM would yield index terms such as treated stainless steel strip treated stainless steel stainless steel strip and stainless steel as a phrase not lexical atom along with all the relevant single word terms in the phrase
a measure of the improvement that terminological np recognition implies over the activity of a shallow parser for la has been carried out
ssuch scanning is essential for some languages with no explicit word bounda ries
with the source and ta rget cfg skeletons in q is satisfied
wc show tha t thc rc exists a n attra ctivc
sequc tiees for the input sentence john should he a r
are unifiable with tra nsitiw a nd intra nsitive
john should hear from mary about the news if he returns home
thus monotonicity of he d cnnstraints holds throughout the parsing process
the first stage of the process is lexical analysis which breaks the input text a stream of characters into tokens
in disambiguation mode the input is the text whose sentence boundaries have not been marked up yet and need to be disambiguated
computational linguistics volume NUM number NUM the disambiguation is done before tagging no part of speech assignments are available for the boundary determination system
the individual tokens are next assigned a series of possible parts of speech based on a lexicon and simple heuristics described below
because the system is lightweight it can be incorporated into the tokenization stage of many natural language processing systems without substantial penalty
words containing a hyphen are assigned a series of tags and frequencies equally distributed between adjective common noun and proper noun
these are shown in the not labeled column of table NUM which gives the results of a systematic experiment with the sensitivity thresholds
as we showed in section NUM NUM the size of the training set used for the neural network affected the overall system error rate
the views and opinions in this paper are those of the authors and do not reflect the mitre corporation s current work position
informally a chain is a syntactic object that defines an equivalence class of positions for the purpose of feature assignment and interpretation
if chains intersect they share the same index and they have exactly one element in common as in 6b
in fact there are both theoretical and empirical reasons to think that this is the right way to idealize the data
the s attribution transformation is not restricted to languages with the properties of english it can also be extended to head final languages
this factor encodes the second and third restriction of the csel algorithm with the consequence that not all combinations are attempted
the rest of the column headings in the first row are various attributes which are relevant to the examples givens the entry for susan shows that its highest knowledge classification feature c0 is hument human entity sub categorized by the next classification feature cl as person in contrast to the second entry ibm which is an organization
the second rewrite feature of dxl is illustrated by the abstract rule below which for clarity s sake omits the needed variable assignments a b x c d y e c f b this rule specifies a left context of a after which three obligatory patterns are sought b d where b has some additional condition x placed on it
similarly the needed linguistic infrastructure has also only recently been developed the compilation from several sources of a large lexicon with parts of speech the development of the capability to find the roots of words prior t o lexical look up e.g. the root of placing is place and the morphological analysis capability to permi t guessing at the part of speech of unknown words based on their suffixes
all of the subsequent system processing is accomplished with specific pattern rules and involve s moving up and down the token stream checking tokens for particular attribute values and whe n successful either changing the attribute values of single tokens or replacing several tokens with a new one with a new type attribute whose chars attribute is the concatenation of those replaced
1k NUM clusters of ordinary words which share significant semantic features such as communication acts 2k and NUM isolated ordinary words which have particular significance of one kind or another 1k
on the lhs variables vl v3 are assigned to the three pattern elements those binding to vl and v NUM should in this example be understood as binding to single tokens and these are referenced on the rhs via the operator
roughly a third of the NUM person month effort was devoted to design and implementation of the data extraction language dxl another third went for overall system and knowledge bank development and the last third was focused on development of general and muc specific data class recognition rules usin g dxl
dxl rules have three components a left hand side lhs specifying the total pattern that is to be matched a right pointing arrow indicating that the lhs is to be rewritten and a right hand side rhs indicating what is to be substituted for the lhs with what actions
in this experimental method a data set is partitioned ten times into NUM training material and NUM testing material
most of the domain specific terms we are interested to are nouns or noun phrases that generally denote concepts in a knowledge domain
percentage unknown words was NUM NUM of the test words and overall tagging accuracy known and unknown NUM
copy all reports except for this one
an interesting subset of anaphoric expressions are inferential anaphors
it wouhl also l e use rid to stu ly the indu ed categories when intensional descriptions feature represeutations are used as input instead of extensional descrit lions phoitetnes
lcxcepl syllmde stru tm e in other words information about the rhyine of the last syllable of a noun is necessary and sufficient to predict the correct allomorph of the diminutive suffix
we conclude from this part of the experiment that the inaehine learning inethod has suc ee led in extracting a sophistieate l set of linguistic rules from the examph s in a purely data oriented way an l that these rules are formulated at a level that makes their use in the development of linguistic theories possible
now let us consider a more complex example
llistoricnlly dilh rcnt a nalyses of diminutive forreal ion rcb tav taken a lifferenl view of tile rules thai goveru the hoi of NUM he diminutiv sullix and ot the linguistic con el l s playing a role in these rules see e.g.
slna l linguisl i lomain for which different comlmting l hcories have rcb men pr t os xl ll fol whi ll liff r nt generalizal ions in terms of rules and linguistic categories have been proposed
edward is able to deal with all four types
NUM otherwise new node m is the end node of the arc originating from n with as label fi
the intuitive notion of salience has two important characteristics
box NUM NUM he nijmegen the netherlands
the part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory
table NUM summary of maximum matching results
for our experiment the training set consisted of NUM sentences NUM words the test set was a separate set of NUM sentences NUM words from the same corpus
a sequence of NUM transformations was learned from the training set applied to the test set they improved the score from f NUM NUM to NUM NUM a NUM NUM error reduction
if a word is found the maximum matching algorithm marks a boundary at the end of the longest word then begins the same longest match search starting at the character following the match
in most cases this simple approach was able to locate only one of the two necessary boundaries for recognizing full words and the initial score was understandably low f NUM NUM
a sequence of NUM transformations was learned from the training set applied to the test set they improved the score from f NUM NUM to NUM NUM a NUM NUM error reduction
for one it is weakly statistical but not probabilistic transformation based approaches conseo tly require far less training data than most o a is ical approaches
figure NUM enumerates the NUM segmentation transformations we define
in the next stage to allow for better training with very limited amounts of data we rebuilt the acoustic models using just the plp feature and signal energy
furthermore restrictions of specific sequences not possible in greek words would further confine these patterns
the difference is characterized as follows in parsing the inputs are sequences of words and the output is a structure produced by combining two adjacent trees into a single tree at each processing step in generation the inputs are a set of unordered words with dependency relationships derived from the interlingua NUM the results shown above were obtained from running the program on a sparcstation elc
transitions in i above are called shift transitions in ii are called reduce
we generally use symbols q r s to range over q and the symbol
regrettably we have as yet not been able to get hold of a copy of this paper
this leads to faster parser generation to smaller parsers and to reduced time and space complexity of parsing itself
generous help with locating relevant literature was provided by anton nijholt rockford ross and arnd ruflmann
this process makes use of a function pred from NUM n to NUM n specific to a certain context free grammar
observe that there is a direct one to one correspondence between transitions of a2la and productions of c2lr g
the first author is supported by the dutch organization for scientific research nwo under grant NUM NUM
secondly the static and dynamic complexity of parsing both in space and time is significantly reduced
we induce models that while being substantially more compact outperform n gram language models in medium sized domains
referring to the rules in table the parameter e is set to an arbitrary small constant
we used tagged wall street journal text from the penn treebank which has a tag set size of about fifty
after completing the implementation of our move set we plan to explore the modeling of context sensitive phenomena
to our knowledge neither algorithm has surpassed the performance of n gram models in a language modeling task of substantial scale
to evaluate our algorithm we compare the performance of our algorithm to that of n gram models and the inside outside algorithm
such grammars are poor language models as they overfit the training data and do not model the language at large
in the latter work mccandless uses a heuristic search procedure similar to ours but a very different search criteria
after removing unreachable rules this yields a grammar of roughly NUM nonterminals NUM terminals and NUM rules
in figure we display the viterbi parse of this data under the initial hypothesis grammar used in our algorithm
in the experiment described in this paper the symbols written are dependency relation symbols or the
the value of e varies from NUM to NUM e is close to NUM when all the relevant conflations are made and when no incorrect one is made
it is more complete and linguistically more accurate than simple stemming for the following reasons allomorphy is accounted for by listing the set of its possible allomorphs for each word
transformation for n p n terms the coordination types are first calculated by combining the pattern n1 p2 ns with possible expansions of a noun phrase with a simple paradigmatic struc
user entered information is used to construct a job schema object which can be considered as the user s ideal job
such rules carry with them a lot of baggage such as optional elements alternatives restrictions and so on
the job codes are hierarchically ordered so job title codes that differ over the first digit will refer to greatly different jobs
the parameters which are used to define job ads for tree are given by the job schema definition described above
at first glance this may look like old fashioned interlingual mt but there are two important differences
as such we can see that this parameter distance function would reflect commonsense judgements on the associated job titles
the stylised partial example of a filled schema in figure NUM gives an impression of the data structure
the lexicon matching phase returns for each source node i a set of runtime entries
a milder form of this kind of annotation is a bracketed natural language string
there is now greater understanding of the formal semantics of under specified and ambiguous representations
full scoping is irrelevant to the task or even the intended meaning
the translation system we have described employs only simple representations of sentences and phrases
two measures of translation acceptability are shown as judged by a chinese speaker
this puts the size of the model somewhere in between NUM gram and NUM gram model
the key point here is that the particle at has no natural similar word
for example the variant tempdrature et humiditd initiale de pair temperature and initial humidity of the air is a coordination where a determiner precedes the last noun air
this paper proposes a new approach for acquiring morpho lexical probabilities from an untagged corpus
using such training data three types of guessing rules are induced prefix morphological rules suffix morphological rules and ending guessing rules
unlike a morphological rule this rule does not check whether the substring preceding the ing ending is listed in the lexicon with a particular pos class
so for example if we had evaluated a guesser with something to NUM of them its coverage would have been NUM
words unknown to the lexicon present a substantial problem to nlp modules that rely on morphosyntactic information such as part of speech taggers or syntactic parsers
we tagged the fifteen subcorpora of the brown corpus by the four combinations of the taggers and the guessers using the lexicon of NUM NUM word types
this technique does not require specially prepared training data and uses for training a pre existing general purpose lexicon and word frequencies collected from a raw corpus
table NUM displays the metrics for the best scored by aggregate of the three metrics on the training and the test samples rule sets
in our tagging experiments we measured the error rate of tagging on unknown NUM we estimated the most likely tags from the training data
if the main clause is an event
rl serves as the current reference time for the following event e2
the event et is included in the current reference time r0
constructing a similar drs for such sentences gives the wrong truth conditions
in this section we present some applications of our analysis to related constructions
this state holds during the present and so its location time is n
scheme is universal quantification for the event and existential for the reference time
when bill left are processed before the main clause
having computed the alignment of trees across corpora one option is to compute either explicitly or in some form of stand off annotation a corpus combining the information from both sources thereby allowing the use of the distinctions made by each corpus at once
this section discusses some ways in which users will need to be able to interact with a text handling application
markups may be added manually or by automatic means such as a message handling application or a detection application
the reason for this is that often different types of output need to be manipulated thus becoming input
depending on the agency or the contract these roles may be combined
gue it is plausible to assume that multiple extraposition with distinct antecedents is subject to a nesting requirement the first extraposed phrase has to be associated with the last antecedent the second one to the next to last antecedent etc
at the pdr control gate the following tipster application documents are expected to be put under tipster cm control
the extent of tipster conformance will be determined on a per module basis and documented in the tacad
the result is that if the definitions for the root and variant had two or more words in common NUM NUM of the pairs were semantically related
dictionaries group senses based on part of speech and etymology but as illustrated by the word review senses can be related even though they differ in syntactic category
the first problem is that words are ambiguous and this ambiguity can cause documents to be retrieved that are not relevant
for example liable appears within the definition of liability and this is used as evidence that those words are related
second they have used very coarse grained distinctions e.g. river bank v commercial bank
we attribute these differences to the use of different test collections and in part to the use of different retrieval systems
however table NUM does not include cases in which the word appears in its definition but in an inflected form
null as we saw earlier in the paper it is possible for a query about aids the disease to retrieve documents about hearing aids
the predicates in p can be understood both as picking out particular subsets of the tree and as non exclusive labels or features decorating the tree
in restricting ourselves to the language of l NUM k p we are restricting ourselves to reasoning in terms of just the predicates of its signature
this research was supported by an equipment grant from sun microsystems and by arpa contract n NUM 94c NUM
a human information service presents the travel plan in a stepwise way generally giving at least one piece of new information with each turn
so it s understandable that the algorithm is doing better on words that it s seen during training as opposed to unknown words
when the paraphrasing will be carried out from a vl expression then we have to use preset linguistic primitives and words for the nl generation because there will not be any linguistic primitives
in this paper we show how to use the socalled aggregation technique to remove redundancies in the fact base of the visual and natural language specification tool vinst
figure 2c natural mode compact mode what we see is that the text can be aggregated in a different way and also that the subject grouping has not been fully applied on the phonenumbers
adjective form adj subjective predicative complement predcomp subject grouping sg and predicate grouping pg and many more
an idle subscriber tl has a phonenumber NUM and an idle subscriber tl has a phonenumber NUM and an idle subscriber t2 has a phonenumber NUM
the sentence types above are identical with the ones in the qlf except of the sentence type np and some others which are vinst specific
i introduction this paper describes the aggregation process in the natural language generator of the visual and natural language specification tool vinst and how the aggregation can be improved
redundancy typically occurs when the material selected for communication contains information that is duplicated in the text or else is so closely related that the reader can automatically infer one piece when reading another
finally iml lement ation issues and details involving the user interfaces of the tool are discussed
third on the basis of the computed alignments between the two corpora and the tree transformations they imply the possibility is now open to produce semi antomatically versions of those parts of the brown corpus covered by the penn treebank but not by susanne in a susannelike format
i know to marie and that she came here
though multiple analyses were considered ac null ceptable in the case of even semantically undecidable situations very few were actually needed only NUM words out of NUM NUM received two analyses for example it was agreed that more could be analyzed both as an adverb and as a pronoun in free trade will mean you destroy more
words followed by an expression of the form x y were initially tagged differently by the judges
for each contextually appropriate morphological reading all syn null tactic tags were introduced with a mapping program
three NUM NUM word texts were successively used a software manual a scientific magazine and a newspaper
i do n t think people get v inf v pres a great deal from bald figures
null NUM two experts in the engcg grammaticalrepresentation independently marked the correct alternative analyses in the ambiguous
4before an of phrase the pronoun numeral distinction of one was regarded as neutralised
this section reports on an experiment on part of speech and syntactic disambiguation by human experts the authors of this article
also absolutely no english specific information such as an affix list need be prespecified in the learner
in cases where the interaction protocol was violated the experimenter would issue a warning statement such as please be patient
the system being developed as a part of the tu language project also chose the structural l ransfer approach with a minimal amount of semantic analysis
proper coding in these situations required familiarity with what had actually occurred during the experiment familiarity that only the first author had
incorporating more metaknowledge about dialogue structure into the model should lead to more human like performance in handling initiative changes and miscommunication problems
at present there is no well developed model of human machine communi department of mathematics greenville nc NUM usa
suppose that the user s phone book contains two nicknames phil and bill
these miscommunications created various problems for the dialogue interaction ranging from repetitive dialogue to experimenter intervention to occasional failure of the dialogue
the other main source of difficulty in using the system was the enforcement of the single utterance turn taking protocol of the interaction
for example the relative amount of time spent in each subdialogue phase is likely to be highly dependent on the domain
consequently it was unnecessary for the experimenter to notify the user about such misunderstandings since they would not cause a problem
we also have a framework for accommodating new genres as yet unseen bundles of facets
there are at least two major ways of conceiving what the baseline should be in this experiment
we conclude that there is at best a marginal advantage to using structural cues
the NUM cues in our experiments can be combined to almost NUM different ratios
by contrast we require the answers to some basic theoretical and methodological questions
we will refer here to the attributes used in classifying genres as generic facets
this suggests problems with the labeling of the brow feature in the training data
we assume that the normalized rldt has certain properties
the normalized rldt is used to construct selectional restrictions
in all estimation methods word segmentation accuracies of d1 are worse than d2 while d1 is slightly better than d2 in using real word frequencies
according to the translation rules of aet axiom NUM and a logical consequence of a conjunctive context NUM the formula take e x y can be translated
after the candidates have been weighted according to these preference factors the highest rated candidate is selected and its form is modified by a post filter
a vp in such a relation is given a very high weight by the preference factor clause rel which in practice makes it an obligatory antecedent
the simplest and most important factor is recency if no other preference factors obtain the most recent syntactically possible antecedent is always chosen
after identifying NUM examples of vpe in the treebank we reserved NUM randomly selected examples from the wall street journal corpus for a blind test
this information is not available in the penn treebank and we do not use any forms of subject matching in the current version of the system
to capture the above data the syntactic filter rules out vps that contain the vpe in a sentential complement any other antecedent containment relation is permitted
the difference between the vpe res performance and the baseline is statistically significant by all three criteria based on a NUM analysis p NUM
analysis of the individual components of the system shows that each of the structural and discourse constraints used are strong predictors of the antecedent of vp ellipsis
while previous work provides important insight into the abstract syntactic and semantic representations that underlie ellipsis phenomena there has been little empirically oriented work on ellipsis
cases like this might be accounted for by assuming that there can be contrast between fields that are shared by data types having the same supertype
in a hou approach the contrast in this example might be predicted by unifying the representation of the second sentence with the entailment of the first
then we will have the following derivation for a quantity NUM to be defined later cil jl ail kl bkl jl derived by ail k i derived by bkl jl the key thing to observe is that cil jt generates two nonterminals whose inner indices match and that these two nonterminals generate substrings that lie exactly next to each other
since g is of size o rn NUM and iwl o ml NUM it takes o m NUM time to build the input to p which then computes 5rg w in time o t m2 t ml NUM
being able to predict the placement of contrastive accent is essential for the assignment of correct accentuation patterns in spoken language generation
i discuss two approaches to the generation of contrastive accent and propose an alternative method that is feasible and computationally attractive in data to speech systems
fortunately in data to speech systems like goalgetter the input of which is formed by typed and structured data a simple principle can be used for determining contrast
on the other hand it is based on a general principle which should be applicable in any system where typed data structures form the input for linguistic generation
i have sketched a practical approach to the assignment of contrastive accent in data to speech systems which does not need a universal definition of alternative or parallel items
in pulman s approach the contrast can only be predicted if the system uses the world knowledge that scoring an own goal means scoring for the opposing team
pitch accent should be assigned to those parts of the second sentence that express data which differ from those in the data structure expressed by the first sentence
because of the lack of actual measurements such as frequency on these abstract nodes we also decouple the partitioning and labeling components of our system and score the partition found under the best matching conditions for the actual labels
this approach called tilebars allows the user to make informed decisions about which documents and which passages of those documents to view based on the distributional behavior of the query terms in the documents
until very recently most information retrieval experiments made use only of titles and abstracts bibliographic entries or very short newswire articles as opposed to full text
in this work the structure of an expository text is characterized as a sequence of subtopical discussions that occur in the context of one or more main topic discussions
it will not be surprising if motivated subtopic segments are not found to perform significantly better than appropriately sized but arbitrarily segmented units in a coarse grained information retrieval evaluation
thus the parameter p in the simulation experiments measures how well we are able to predict each link independently of the others and the parameter k measures the number of distinct adjectives each adjective appears with in conjunctions
figure NUM macro stucture for text plan of figure NUM
we also thank t caldwell r
the sum of y of all states with a given lhs x is exactly baker s inner probability for x
this returns a description such as that shown in figures NUM or NUM to generate a description the text planner creates a text structure corresponding to the text plan configuration selected by the user
our design is based on initial interviews with software engineers working on a project at raytheon and was modified in response to feedback during iterative prototyping when these software engineers were using our system
an example of a description obtained in modifying the text plan of figure NUM is shown in figure NUM this description follows a format close to andersen consulting s standard for documentation
we are currently extending modex in order to give the user a better control over the text micro structure by replacing the set of predefined c text functions with customizable ascii specifications
white for their comments and criticism of modex
modex runs as a text plan editing
relations were particularly troublesome to both analysts and users
once the final state is reached a recursive procedure can recover the parse tree associated with the viterbi parse
in addition we call a structure of the form unit slot value a triple
it will not realize that clinton and bill clinton refer to the same person
here too efficient incremental computation saves time since the work for common prefix strings can be shared
we have demonstrated that compound noun interpretation requires the integration of the lexicon probabilistic information and pragmatics
the constellation shown in figure NUM may be characterized by another law which is pertinent here if we may assume that binary antosemy was intended
rules such as assume coherence serve to specify the necessary compound relation so long as context provides enough information
but established compounds may also have unestablished interpretations although as discussed in ss3 these will have minimal probabilities
general nn possessive NUM made of purpose patient deverbal i ndegn derived pp i i deverbal pp
since we have established that paths correspond to derivations it is convenient to associate derivation probabilities directly with paths
however this is also unsatisfactory because NUM overgenerates and ignores systematic properties of various classes of compounds
the rules in dice include default conditions of the form p q which means if p then normally q
in sdrt the values of u and b are computed as a byproduct of sdrt NUM update function cf
null suppose that the new information to be integrated with the discourse context is ambiguous between NUM bn
the subregions of the root system include the meristem which is where root system growth occurs
hypernymy troponymy the commutativity rule for antosemy and hypernymy or troponymy as a heuristw rule has already been suggested by fischer et al
the addition of lookahead is orthogonal to our extension to probabilistic grammars so we will not include it here
even though ahab is one of the main characters in moby dick and even though his name certainly belongs to the specialized vocabulary of the novel ahab is nonrandom word usage illustrated for ahab in moby dick
there are several sources of complexity
these rules choose or delete parses with specified features
1degeither of lc or rc may be empty
we are currently working on obtaining such statistics
it groups non lexicalized collocations turkish abounds with various non lexicalized collocations where the sentential role of the collocation has almost nothing to do with the parts of speech of the individual forms involved
the preprocessor then converts each parse into a hierarchical feature structure so that the inflectional feature of the form with the last category conversion if any are at the top level
tables NUM and NUM give the ambiguity recall and precision initially after hand crafted rules are applied and after the contextual statistics are used to remove parses all applications being cumulative
to accommodate the part of speech input to the parser the input sentence has to be part of speech tagged before parsing
we have evaluated the tagger performance on the test data both before and after training on the muc ii corpus
table NUM tagger evaluation on data set test table NUM shows that the tagger achieves a tagging accuracy of
b rules involving verbs need to be lexicalized to prevent misarsing due to an incorrect subcategorization
the head nouns which typically occur in prepositional phrases with the preposition omission are nautical miles and yard
since the grammar is domain and word specific it is not easily ported to new constructions and new domains
in the sections below we discuss one such method in terms of grammar design and some of its side effects x
instances of preposition omission are given in NUM where z stands for greenwich mean time gmt
a number of the sentences we consider to be misparses are t svntacuc mksparses but semanucallv anomalous
NUM recognizing names based on patterns of components
thereafter words are referred to by their hash values
the result can be checked and corrected at each step making it easier to obtain a desired result
co occurrence analysis selects collocates that span the space with minimal overlap optimizing the efforts of the human assistant
thereafter a group off fairly impenetrable tests occur
both recall and precision increase with increasing training data
this was done in increasing units of NUM texts
although apparently small in absolute terms on average this represents a NUM reduction in error rate
the results are shown in figure NUM below
relationship of performance to amount of training data
the systems are based on entirely different approaches
NUM recognizing names based on pre stored names
therefore as in figure NUM NUM we duplicate b so that each duplicated node corresponds only to a single me in
for instance two words pneumonia and cancer do not always co occur but they do co occur with words as doctor nurse and hospital forming the core of medical topics
therefore we varied threshold from NUM NUM up to NUM NUM by NUM NUM steps to make the input graph applied our algorithm to each input in order to detect the best threshold
in the beginning we tried to decompose input graph into maximum strongly connected components to obtain graphs of topics from the observation that nodes in a cycles are strongly related NUM
n l vn l n we define anchor distance as the maximum distance of the minimum distances of a b d and a c d
for instance the me ing of an ambiguous word can be decided by e xamln i g the duster it belongs to
in the complete graph of more than NUM nodes several anchor branches exist for each duplicate branch figure NUM NUM
figure NUM extraction of NUM NUM transitive graph d cl ste figure NUM subgrapl and their partial order
for instance star in cluster NUM is a sport player star that in cluster NUM is a singer star and that in cluster NUM is a movie star
some examples are economic topic although wall street journal articles have economic tendency clusters of economic topic can not be found in the dusters with more than
after this initial training we filter out all the features whose weight lie in this filtering range
in general the structure features that characterize long distance dependency can provide more relevant correlation information between words
finally the proposed integrated score function is adopted to select the most plausible normal form as the output
we argue that grammatical processing is a viable alternative to concept spotting for processing spoken input in a practical dialogue system
in addition a robust discriminative learning algorithm is also derived to minimize the testing set error rate
the correct part of speech parse trees and normal forms for the collected sentences are verified by linguistic experts
in the baseline system the values of parameters are estimated by using the maximum likelihood estimation method
by taking the sentence to meet spectrumanalyzer specification allow a NUM rain warm up before making any measurement
readers who are interested in details of the learning algorithm are referred to NUM
each cost measure is represented as a function ci that can be applied to any sub dialogue
from these results we manumly selected lusters which are judged to be semantically similar
however there are some words of which the memfings are not included these selected definitions
the procedm es for linking are the following five stages
table NUM the definition of sum in the dictionary
the results of experiments demonstrate the effectiveness of the proposed method
table NUM shows the definition of hmml er in the collins english dictionary
while in figure NUM they are not NUM NUM
a1 l reviation words in each figure and categories are shown in talile NUM
using a term weighting method articles wouhl be represented NUM y vectors of the form
ottr algorii liitm are i ah i o
figure NUM shows a case in which the author has marked the following four features for the warning action damage service cover 5actually this could also be interpreted as an ensurative warning meaning that the reader should make sure to damage the service cover although this is clearly nonsensical in this case
thus we may perform separate corpus analyses of a particular phenomenon for various languages and learn separate micro planning trees 3a cross validation test is a test where c4 NUM breaks the data into different combinations of training and testing sets builds and tests decision trees for each and averages the
the action is to be prevented rather than ensured performing the action would result in inconvenience but not in personal danger the user is likely to do the action accidentally rather than consciously the user is likely to be aware that performing the action would create problems
the corpus analysis results in a set of examples coded with the values of the function and form features
the learning algorithm used these examples to derive a decision tree which we then integrated into an existing micro planner
clementine can also balance the input to c4 NUM by duplicating training examples with underrepresented feature values
the first line in table NUM marked raw grep indicates the quantity of each type
table NUM time improvements due to optimizations
let t and tt be the source and the target trees
o iii a w a r e NUM j con sciou sne ss syste m 3iiij con sciou s NUM saf ety system 611i n ot badp NUM ll xunaware NUM uli unconscious NUM tlij adp NUM figure NUM the micro planner system network derived from the decision tree
other negative imperatives termed neg tc imperatives these include take care and be careful followed by a negative infinitival complement as in the following examples NUM to book the strip fold the bottom third or more of the strip over the middle of the panel pasted sides together taking care not to crease the wallpaper sharply at the fold
iii if a spoken utterance with its intonation center identified is analyzed then i and ii hold for sentences with normal intonation intonation center at the end
typically the position of the verb in tfa and often also the position of a complementation is ambiguous and in the present examples we give only one of the possible readings of the sentence
the word form is saved under label so contains information about the position of the complementation in systemic ordering and surf is the surface form noun group personal pronoun indexical word etc
this is similar with examples NUM NUM and with NUM the switch of the intonation center plays the same role as the switch of word order in the other examples
one analyser took the user s question to be a simple request to have the system s statement repeated in which case no guideline violation would have been committed by the system
function up link down p if s link p is defined then return s link p
in a data base it suffices to store only the kernels and references to the kernels from the utterances
the syntactic relations in the narrow sense are handled in the form of a dependency tree with the main verb constituting the label of its root and the branches being labeled by symbols denoting the kinds of complementation
if an item occurs in the topic it may be placed more to the left than would correspond to so the specific order of the elements of the topic is influenced by the speaker s discourse strategy
a constituent is judged to be correct only if both its bracketing and its syntactic label are correct
meaningful baseline evaluations are currently difficult to design for chinese parsing because of the unavailability of comparison standards
thus empirical performance is the true judge and our experience as described next has been quite encouraging
all those sentences were segmented by hand though we will use an automatic segmenter in the future
the not function the not function is denoted as b which means any constituent not labeled b
we can do a somewhat better job by using the domain knowledge supplied by a dictionary with semantic classes
morphology morphology is applied to an sgml tree whose leaves are individual word tokens and whose node s represent the structure of the document
lolita is designed as a core system supplemented with a set of small applications the former supplyin g basic nl facilities to the latter
concepts are connected with arcs such as specialisation and its inverse generalisation or instance inverse universal
since the ability of reference has many uses outside of the muc tasks a more general mechanism was designed and added to the core
in other words this matches an internal node labeled a which has a subtree with root labeled b
we use a generalization of the earley algorithm NUM NUM to parse grammars of our form
roughly NUM of the original corpus was assigned as the training corpus and the other NUM was reserved as the test corpus
we therefore consider our system complementary to one such as tribayes that predicts based on part of speech when possible
we also show the results for the remaining NUM confusion sets for comparison purposes but as expected these are n t as good
thus the distribution of the most frequent word between the the training and the test corpus will affect the performance of the system
in theory we should be able to increase the performance for each confusion set by tuning the various parameters for each confusion set
the horizontal axis in the figure represents the baseline predictor performance for each system even though it varies between the two systems
by eliminating the words the and of from the training and testing process we permit the remaining context to be used for prediction
however there are only one third as many training instances for amount the less frequent word as there are for number
n is the total number of the inputs and NUM is a coefficient defined as in equation NUM
if we assume related to mean contained in the same document then our error metric judges algorithms based on how often this happens
the first which we shall refer to simply as model a was trained using two million words from the bn corpus from the NUM NUM time period
the larger a the stronger ccd s influence on the system s output
this paper proposes an efficient example selection method for example based word sense disambiguation systems
let us take as an example the sentence below hisho ga shindaisha o toru
let x be the set of the residue realizing equation NUM
with these definitions ccd c is given by equation NUM
the paper reports the effectivity of our method through experiments on about one thousand sentences
thereafter we evaluated the relation between the applicability and the precision of the system
otoko joshu hisho shinbun zasshi kane heya kippu uma figure NUM a fragment of bunruigoihyo
in addition to the previous five categories we also experimented with categories for location commercial and person
they may however end up with a relative scoping as a result of taking the transitive closure of other scoping relationships
free movement of labor across national boundaries is an important aim of the european union NUM one of the prerequisites for this open labor market is accessibility of information about employment opportunities both from the point of view of people seeking work and of their potential employers
second we generalized the selection scheme giving several alternatives for optimizing the method for a specific task
the tree system stores job ads in a partly language independent schematic form and is accessed by job seeking users who can specify a number of parameters which are used to search the job database and who can also customize the way the information retrieved is presented to them
we would like to express our thanks to other partners on the project edy geerts and marianne kamoen vdab vlaamse dienst voor arbeidsbemiddeling en beroepsopleiding mick riley newcastle upon tyne city council and teresa paskiewicz and mark stairmand umist
as indicated in table NUM mismatch between training and testing segmentation hurts perplexity
the htk toolkit was used to generate the top NUM hypotheses for each segment
we were constrained to use the lattices that had been provided to the workshop
the next step will be modeling conversations and incorporating those models into a speech recognizer
it is convenient to use suffixes of the input string to represent the string positions of the input string as in dcgs
the other sentence is by speaker b which is a cradle for it
however our goal here is to first determine whether in fact this division is useful in the language model
the algorithm was approximate in that we did not keep track of all possible segmentations
mark johnson memoization in top down parsing top down parser written in continuation passing style will in fact terminate even in the face of left recursion
additionally the treatment of memoization in a cps is instructive because it shows the types of table lookup operations needed in chart parsing
for example the standard definition of the function square in NUM would be rewritten in cps as in NUM
after storing the caller continuation in the table entry each result already accumulated in the table entry is passed to the caller continuation
the continuation passing style encoding of cfgs discussed in the next section can be seen as a more functionally oriented instantiation of this kind of approach
a memo ed procedure constructs an entry in a memo table only after the result of applying the unmemoized function to its arguments has been computed
its wide range of senses made possible a highly specific level of tagging
besides hasten sra has conducted other research i n automatically trainable systems including co reference resolution for the sensemaker project
rather less attention however has been paid to their treatment in the context of language generation
therefore we enhance the constraint language as follows
one of the most important issues in the induction of guessing rule sets is the choice right data for training
finally we want to assume that generalisation is simplifying and can be performed within a bound of g m steps where m is the total size of the input constraint systems
parse forests are rooted labeled directed acyclic graphs with and nodes standing for context free branch null ing and or nodes standing for alternative subtrees that call be characterised as follows cf
a subformula coming from a shared subtree as z NUM in fig NUM has to be stated as many times as the subtree appears in an unfolding of the forest graph
nevertheless our method modulo small changes to handle failure may still prove useful when this restriction is not fulfilled since it focuses on computing the common information of disjunctive branches
third we assume the rule to rule hypothesis i.e. 3the graphical representation of an or node is a box surroux ding its children i.e. the and or graph structure of is o
figure NUM conjunctive part of udrs graphically
this means that we have to introduce some form of functional abstraction into the constraint language or anything equivalent that allows giving names to complex constraints and referencing to them via their names
in the t constraints we actually can suppress the existential quantifiers by adopting the convention that any variable other than the one of the current node is implicitly existentially bound on the formula toplevel
since we count the occurrence of strings generated from an arbitrary position in tile text with only the above observation only the right end position of a string can be assumed to determined a rigid expression
to evaluate its performance sra automatically converted the answer keys and edited the official scoring program configuration file
field ambiguity arises when the system has found a term that could refer to more than one database field
the alphabetic ordering has two advantages
cp the mother allowed her daughter not to the theater to go the mother did not allow her daughter to go to the theater
attachments are further constrained as follows formal attachments are restricted to adjacent constituents and are licensed by lexieal properties such as selection or agreement e.g.
NUM ambiguous this state is reached when one of three types of ambiguities exists in the sys null tem
hasten s performance got a boost from the latest upgrade to the scoring program and keys see l3
the second type of attachment requires a specific argument interpretation strategy ais to establish the link between the argument and the predicate which subcategorizes it
NUM in such a case a is considered a rigid expression that is used frequently in the text and a l is just a string that occurs in limited contexts
a second reason is to support identifying better features for automatic tagging
our method attacks the weaknesses of the parse parse match procedure by using NUM only a translation lexicon with no language specific grammar NUM a bilingual rather than monolingual formalism and NUM a probabilistic formulation for resolving the choice between candidate arrangements
the primary purpose of bilingual parsing with inversion transduction grammars is not to flag ungrammatical inputs rather the aim is to extract structure from the input data which is assumed to be grammatical in keeping with the spirit of robust parsing
bn where e is output on stream NUM and c on stream NUM define e i as the substring of e derived from bi and similarly define c i then xi generates e i NUM en
moreover it may even remain possible to align constituents for phenomena whose underlying structure is not context free say ellipsis or coordination as long as the surface structures of the two languages fortuitously parallel each other though again the bracketing would be linguistically implausible
the space of word pairs terminal pairs x w1 u lcb c rcb x w2 u lcb c rcb contains lexical translations denoted x y and singletons denoted x c or c y where x e w1 and y e w2
the local minima on the graph signify the boundaries as determined by the algorithm
thus it is useful to provide a replacement operator that implements these constraints directly
iters figure NUM outlines the construction of a simple tokenizing transducer for english
furthermore since the restrictions imposed by the case fillers in choosing the verb sense are not equally selective we consider a weighted case contribution to the disambiguation ccd of the verb senses
see the end of section NUM for a discussion about the choice of the term
sra will strive to make hasten easier to customize by non developers while enhancing its features to improve its extraction performance
as with most words the verb toru has multiple senses a sample of which are to take steal to attain to subscribe and to reserve
in particular experiments with actual corpus data should supplement the theoretical results based on uniform distributions
currently our scoring method only uses the lengths of candidate translations to break a tie in the similarity measure
finally we have carried out three evaluations of the system on three separate years of the hansards corpus
the case c NUM solid line represents the basic algorithm with no threshold changes
although these sentences are relatively simple automatically translating le as lf involves several problems
the third column gives the frequency of the word today in a subset of the hansards containing NUM NUM sentences
some word strings do not cover each other
it should be noted that this can be a critical problem for statistics based approaches NUM NUM NUM NUM NUM as the reconstruction of statistic classifiers is expensive
overall our system performed quite well as our position with respect to the best systems improved steadily since the beginning of trec
we have applied our noun phrase disambiguation method directly to word sequences generated using part of speech information and the results were most promising
null in the remainder of this paper we discuss particulars of the present system and some of the observations made while processing trec NUM data
at the same time other terms may be added namely those which are linked to some query term through admissible similarity relations
the parameters of this process were essentially the same as in trec NUM and an interested reader is referred to our trec NUM paper
proper names of people places events organizations etc are often critical in deciding relevance of a document
a notable exception is the new massive query expansion module used in routing experiments which replaces a prototype extension used in the trec NUM system
on the other hand we need to make sure that variants of the same name are indeed recognized as such e.g. u s
our goal here is to have the documents on the same topic placed close together while those on different topics placed sufficiently apart
hence the lemma is proven by contradiction
the grammar formalism of our systenl is an extension of the well known i atr h
it relies on a lexicalized tree grammar and on integrated repairing rules
in this experiment the applicability is the ratio between the number of cases where the certainty of the system s interpretation of the outputs is above a certain threshold and the number of inputs
this process involves a preliminary step of word stemming and stop list removal
each node corresponds to a cluster centroid vector for the high dimensional input vector space
realizing this we have identified numerous browsing paradigms to appeal to a broader audience
recall that each of the aforementioned textual entities is associated with a context vector
small nodes imply a relatively small number of documents for the given information theme
large nodes imply a relatively large number of documents for the given information theme
the determination of similar context is based upon the use of word stem co occurrence statistics
in recent years there has been an explosion in the amount of information available on line
one loop through the input vector set is not sufficient to train the node vectors
yet another possibility is for the user to use an entire document as a query
all unimodal graphical reference is considered deictic
table NUM shows the variety in use
automatic referent resolution of deictic and anaphoric expressions
deixis and anaphora that represents lexical meaning
if a cf s weight equals NUM the cf is discarded
edward responds in nl either written or spoken and graphics
the resolution of cataphors however requires a more lazy evaluation
will be the most salient one immediately after the pointing has occurred
adding incremental compilation functionality would improve this
the user is entering the command kopieer alle rapporten behalve dit
here the user can enter nl commands questions or assertions
the fragment above provides a default specification for syn args for verbs consisting of just one argument the subject np
as a result it is easier to improve the translation quality by adding and modifying examples and by modifying the thesaurus if necessary
if so the system performs the analogical matching process again on the identified portion from the input using examples of the corresponding smaller unit
an analogical system can generate natural sounding output more easily than a compositional rule based system because it directly uses the correspondences between source language and target language expressions
this paper presents a probabilistic formalization of analogical matching and describes how this model is applied to speech translation in the framework of translation by analogy
once the system finds the best matching exampies of the largest unit it checks whether there are portions that differ significantly between the input and the example
since language is productive a realistic analogical system needs to be able to handle linguistic constructions that do not have an exact match in the example database
the application domain for the prototype are expressions for traveling in a foreign country such as expressions related to making reservations or dining in a restaurant
ba ed on the probabilistic model of analogical matching we have implemented a prototype that translates from japanese to english in a limited domain
the pipelined system architecture shown in figure NUM separates speech recognition morphological analysis shallow parsing and recursive analogical translation into different modules
by treating syntactically similarity and semantic similarity as two separate aspects of the matching process we derive an improvement over methods that combine these two aspects
therefore it is desirable that we build clustering of the vocabulary in terms of mutual substitutability
the quality of one of the obtained compound classes is examined and compared to a conventional approach
this paper describes a data driven method for hierarchical clustering of words and clustering of multiword compounds
overall high error rates are attributed to the very large tag set and the small training set
NUM combine the dendrograms by substituting each leaf node of droot with the corresponding d sub
the resulting hierarchical clusters of words are then naturally transformed to a bit string representation of i.e.
this shows the improvement of the quality of clusters with increasing size of the clustering text
the obtained hierarchical clusters are evaluated via the error rate of the atr decision tree part of speech tagger
we give below a small fragment of such an fst in which is used as a morpheme boundary marker
on the other hand the second type is prone to being trapped by a local minimum
in this paper we adopt the merging approach and propose an improved method of constructing hierarchical clustering
to do this we proceed in two steps
the tool has been used to update and augment the french ltag developed at paris NUM a hierarchy has been written that gives a compact and transparent representation of the verbal families already existing in the grammar
having such a prototype for routes with all elements defined in terms of attribute value pairs it is relatively easy to re construct the route described by the linguistic input the reconstruction consists in recognizing the relevant elements and in assigning values to their attributes
it also ensures through syn constraints that inflections are marked in the right order cf figure NUM
NUM unification principle the unifications of partial descriptions and meta equations required by inheritance must succeed the unification of nodes with same constant is mandatory moreover two nodes with the same value for the meta feature function must unify
this is the first candidate which is proposed for validation to the user via tts and or the display depending on the kind of message an implementation of the validation protocol given in figure NUM the feedback messages are formulated by a generator module
of course the ultimate objective is the development of a good dialogue manager module as part of the vodis project and we believe that designing a dialogue manager which obeys the NUM commandments as far as possible is an indispensable step towards that objective
in general feedback aims at helping the user in keeping a good mental representation of the system commandments ii iv and a good representation generally increases the efficiency of the communication e.g. the chances of out of vocabulary input are reduced
fortunately the kind of noise in the car engine rotation tires wind etc is rather specific and highly correlated to the driving speed which is available all the time which means that distortion canbe compensated effectively
since these keywords embrace the functionalities of the original berlin system they are partitioned in a more or less comparable way thus when the interaction is dealing with hifi the user can not enter a destination for routeguidance
notice that in such cases repackaging of the message serves a purpose the user is provided with extra clues which are significant in that they provide additional information which may help the user in updating his model of the system
furthermore prenominal modifications are rather unfrequent in italian
experimental evidence of the proposed method are discussed
for our initial experiments we utilized a set of easily computable but fairly crude characteristics
thus we see that datr contains two instances of essentially the same declarative inheritance mechanism
the method for checking lexical coverage as introduced in this paper is one step in this direction
at r lcb i xi now NUM l
not to the first second or third
peter only pointed to the fourth lucky number
peter first pointed to the fourth lucky number
we begin with the fseeading of NUM
l eter only h tnded the
then these manually disambiguated versions were automatically compared
parsing speed varied greatly NUM NUM NUM words see
at least no rule based system with a convincing accuracy has been reported so far NUM as a rule data driven systems rely on statistical generalisations about short sequences of words or tags
ena or the linguist s abstraction capabilities e.g.
most of even the remaining ambiguities are structurally resolvable
during disambiguatiop the context can become less ambiguous
the other rule component is a syntactic grammar
constants and predicates can be used in rules e.g.
surface syntactic grammatical relations are encoded with dependency oriented functional tags
the precision is still comparable but the accuracy is lower since more of the entries were left unspecified
resolve deictic dt rf resolves the deictic term dt with respect to the reference frame rf
next monday friday 19th monday 22nd
the following describe these and other functions assumed by the rules below as well as some conventions used
thus the parser provides us with a sufficient input representation for our purposes on both sets of data
after a brief overview the rule application architecture is described and then the rules composing the algorithm are given
many theories that address how attentional state should be modeled have the goal of performing intention recognition as well
this paper presented an intercoder reliability study showing strong reliability in coding the temporal information targeted in this work
researchers can exploit speakers tendency to lengthen hesitations and to use them just before or after natural pauses
in both data sets there rules for non anaphoric relations rule nai all cases of non anaphoric relation NUM
the addition of a natural language generation module to generate appropriate verbal responses would improve feedback and smooth the flow of the discourse
nautilus keeps a history of prior references and their denotations which allows the use of anaphoric reference pronouns like it or them
this film shows a demonstration of a prototype system that illustrates some of the capabilities of nlu in an interface to a virtual environment system
valad was also used in the prairie warrior NUM military exercise at ft leavenworth in may NUM
if it is there can not be any more translations to be considered so champollion proceeds to step NUM
the NUM word valad is speaker independent and runs completely in software on off the shelf workstations
however the use of voice often allows the user to short cut many mouse clicks with a single query or command
null the use of natural language understanding in a human computer interface can help provide the richness of control required in complex interactive environments
the cross entropy of the learned model as applied to the training data in each case was about NUM NUM
its output shows promise for compilation of domainspecific technical and regional compounds terms
on the other hand the transducer
figure NUM application of a positive filter
this nondeterminism arises in two ways
no caret outside a bracketed region
figure NUM left to right longest match replacement
figure NUM contains the auxiliary definitions
figure NUM composition of directed replacement
if most specific starting fields tu
in this paper a compact graphically underspecified representation is proposed along with composition principles and a resolution routine based on context information
since the latter was the only path to access the foc partition the complete graph will collapse into a fully specified representation of is
it does not cover all the cases
we take here a more minimalist approach
but it is not trivial to convert that lattice into a chart i.e.
the number of new relevant documents found was shown to be correlated with the original number of relevant documents
on the periphery of the central phenomena are markables whose status as coreferring expressions is determined by syntax such as predicate nominal s motor vehicles international is the biggest american auto exporter to latin america and appositives mvi the first company to announce such a move since the passage of the new international trade agreement
to produce the translated sentence in normal language the transformation steps in the target language were inverted
o cu into cuesta una habitacidn doble incluyendo servicio de habitaciones cuesta para cinco noches
to study the dependence on the amount of training data we also performed a training wit la
transformation steps for both the source and the target languages in order to improve the translation process
table NUM examples from tile eutrans task o original sentence r reference translation
for this training condition the word error rate went up only slightly namely from NUM NUM rcb
the general domain of the task comprises typical situations a visitor to a foreign country is faced with
in addition a test corpus with NUM NUM sentence pairs different froln the training sentence pairs was constructed
however to keep the notation simple we will not make this explicit distinction in the subsequent exposition
mechanisms to help users in supervising system recall are still an open area of investigation in the hookah system
the automata become impractically huge due to intersections
what is striking is the large fi action of deletion errors
the two most inaportant transformation steps are categorization and word joining
figure NUM finite state representation of yasser arafat
in developing cogenthelp we encountered a need for just this sort of inferencing in order to support sensible layout
cogenthelp is a prototype tool for authoring dynamically generated on line help for applications with graphical user interfaces guis
these schemes do not necessarily correspond to any observable human human or human computer dialogue behavior
in this manner the practitioner sorts and prunes his list of possible pathologies
given the confusion matrix in table NUM p e NUM NUM p a NUM NUM and g NUM NUM
as goals get pushed and popped from the problem solving stack initiative changes accordingly
with any clustering approach there is always the tricky matter of determining a suitable distance measure
the doctor may know of thousands of possible diseases conditions allergies etc
this is partially illustrated in figure NUM which shows a sample cogenthelp generated help topic
i am unable to determine whether suspectl0 had an opportunity to administer the poison
each participant uses the continuous mode algorithm to determine who should be in control
the first of these goals is end user oriented whereas the latter two are developer oriented
the functional realizer consists of five principal components
this analysis produced some surprising results
we therefore designed and implemented a full scale realization component
it has two thematic roles subject and object
for example the exposition NUM edps are turing equivalent
its first step is to select an appropriate edp
this is a typical fragment of its semantic network
we chose biology as a domain for three reasons
the type checking system detects the problem
even so there is never quite enough data so smoothing will remain important
therefore r defines a function between an input language lt c x deg and an output language lo c y
with these advantages of cue based processing empirical grounding and speed come certain limitations
the general framework established for the traveler task aims at covering usual sentences that can be needed in typical scenarios by a traveler visiting a foreign country whose language he she does not speak
section NUM NUM describes the cost measures considered in paradise which reflect both the efficiency and the naturalness of an agent s dialogue behaviors
on the other hand they introduce the need for interfaces between processing levels
this integration can be done by using a sst as language and translation model since it has included in the learning process the restrictions introduced by the translation and the output language
we have implemented the above rules in a chinese natural language generation system that is able to generate descriptive texts
however further testing is necessary to demonstrate the reliability and usefulness of the approach
the instructions that were given to the judges are shown in figure NUM
they were given the original spanish and the english translations
unlike scalar and relative adjectives which are anchored in property and object concepts respectively in the underlying ontology deverbal adjectives are based on process concepts
because we are engaged in massive lexical acquisition we are obviously interested in lrs which are easily discoverable massively productive exception free
besides it is precisely the denominal and deverbal adjectives which are very hard to relate to a property concept directly so the lrs come in quite handy
in fact the non existence of a deverbal adjective positive or negative from the physical meaning of impregnate make pregnant is also an accidental gap
we believe therefore that lrs are worth discovering and activating only if they are clearly massproductive such as lrva which is central to this paper
their one telling difference from the truly qualitative predicating scalar adjectives is that the relative adjectives can not make the qualitative shift in the attributive position
these values are a crucial part of the lexical mapping lex map included in the semantics sem struc zone of a scalaradjective lexical entry
the NUM or so deverbal adjectives in our english corpus have received their entries as a result of the application of the deverbal adjective lr 12ii
a human must decide whether the adjective inherits all the senses of a polysemous verb because it is not always the case that it does unlike abusive
what it means again is that human judgment is necessary in deciding which if any case form of lrva to apply to each verb
dependency grammars represent sentence structures as a set of dependency relationships
NUM word local contexts boy subj chase head
table NUM lists words that appeared in the first local context
all of those similarity measures are defined directly by a formula
only words with top NUM likelihood ratio were used in our experiments
table NUM lists words that appeared in the second local context
we performed the complete tagger generation process on a NUM million words training set lexicon construction and known and unknown words case base construction and tested on NUM NUM test words
the initial reference is reducible and the subsequent reference is a reduced form of the initial reference without new information
in this procedure the predictions of the ml trigram model are discounted by an amount determined by the good turing coefficients the leftover probability mass is then filled in by the backoff model
best results were obtained in both dictionaries using the association ratio
we selected five verbs from this sub corpus show describe present prove and introduce and applied our algorithm assuming that the predominant senses of these verbs axe linked together and consequently that the five verbs would be placed in the same group by the clustering program
under this assumption we measured the reduction in ambiguity number of possible senses for each verb types as well as over all occurrences of the five verbs in the sub corpus tokens when the cluster based algorithm is applied
for example one of the senses of ask require as in this job asks for long hours is not linked to any of the other NUM words in the cluster and should therefore be removed
in a sentence with this syntactic structure such as fhe river appeared to the residents to be rising too rapidly appear can take only senses NUM and NUM for animate subjects and senses NUM and NUM for inanimate subjects
due to incompatibilities between the comlex and wordnet representations of syntactic information and the differences in coverage the process of linking the information sources can in some cases result in relatively underspecified rows of a restriction matrix or to spurious cells
if we consider a sense as linked with one of the senses of question if it is in the maximal subtree which includes that sense but no other senses of question we find the following links between question and the verbs ask inquire chal
plan based analysis may well proce necessary for certain purposes but it is quite expensive
it may be more consistent to consider this ratio within a constant window size e.g.
further the syntax of a prompt may become a factor in the final translation
figure NUM shows the sequential modular structure of the algorithm
fifth its output can be converted quickly and easily into an accurate sentence alignment
however both of these sohltions are t roblematic
data both before and after training on the muc ii database
NUM to note isthat s2stem faitm e
automatic english to korean text translation of telegraphic messages in a limited domain
the system architecture of cclinc is given in figure NUM
in the following sections we present an approach that avoids the problems of previous approaches yielding a very low error rate and behaving more robustly than solutions that require manually designed rules
this squib concerns what the underlying cause of this dependency is
b ivan loves hisk mother and jamesj loves hisk mother too
our spanish test set is that used for met comprised of articles from the news agency afp
where ff represents the relative weight of recall to precision and typically has the value NUM
this approach is perfectly valid as we am trying to estimate that which we have not legitimately seen in training
where old c y is the sample size of the model from which we are backing off
a name finder performs what is known as surface or lightweight parsing delimiting sequences of tokens that answer these important questions
punctuation marks all other words table NUM NUM word features examples and intuition behind them looked up in the vocabulary
the learning algorithm performs remarkable well nearly comparable to handcrafted systems with as little as NUM NUM words of training data
this means that the number of states in each of the name class states is equal to the vocabulary size ivi
this way we can gather likelihoods of an unknown word appearing in the bigram using all available training data
the example is thus not a counterexample for source determined approaches
given that candidate terms extraction in lexter is based on a morphosyntactical analysis our definition allows us to group collocation information disseminated in the corpus under different inflections the candidate terms of lexter are lemmatised and takes into account the syntactical structure of the candidate terms
a language independent conceptual system or structure may be represented in an efficient and accurate way but the challenge and difficulty is to achieve such a meta lexicon capable of supplying a satisfactory conceptual backbone to all the languages
the tailoring environment known as the alembic workbench has been built and used within our organization and we are making it available to other organizations involved in the development of language processing systems and or annotated corpora
when b is small namely only categories with high probabilities are assigned the category based and duster based approaches show comparable performance
since a beam search was adopted for the cluster based approach there was a possibility of falling to follow the correct path
co occurrences are usually gathered on the basis of certain relations such as predicateargument modifier modified adjacency or mixture of these
however searching through the bgh tree structure in a top down manner still enables us to save greatly on computational resources
in the following fr w v denotes the frequency that a noun w and a verb v are co occurring
we applied nakano s method on the data used in section NUM obtaining the accuracy of NUM NUM for NUM NUM words
p v NUM p v v is the prior probability that a randomly ex
unfortunately this characterization of the distinction between s cg and because is not supported by our corpus study
no testing was done on noninstructional texts and no claims are made concerning the applicability of the system s predictions in those areas
to test this hypothesis we paired each cue occurrence with all the other cue occurrences in the same turn
in future work we will consider other factors that may determine ordering as possible alternative accounts for this choice
we have introduced relational discourse analysis a coding scheme for the exhaustive analysis of text or single speaker discourse
two cues are alternatives when their use with a relation would contribute approximately the same semantic content s
for example c stands in the evidence relation to b
it will then discuss how the corpus analysis was performed and how the results were implemented in imagene an instructional text generation system
the study of cues must begin with descriptive work using intuition and observation to identify the factors affecting cue usage
in addition the text generator will provide a tool for the systematic construction of materials for reading comprehension experiments
NUM association for computational linguistics computational linguistics volume NUM number NUM lc pull out sharply for phone removal
we intend to use the system to prepare for future evaluations including muc7 and met2 and to carefully evaluate the alembic workbench as an environment for the mixed initiative development of information extraction systems in multiple languages
to construct its reference to have27 spud first determines which lexical and syntactic options are available
figure NUM ltag trees with semantic and pragmatic specifications the entity from all its alternatives
humans are able to take this additional information into consideration or ignore it depending on how relevant it is to the conversation
the information may have been volunteered in anticipation of a future request for information and as a result a dialogue manager which ignores it will not appear very natural
when the complete presentation is finished and thus acknowledged by the caller he may either finish the conversation or pose a new query
ifx is hearer new this goal is satisfied by including any constituent of type cat
further although the different states of the dialogue are pre specified the system automatically identifies what state it is in based on the user s utterance the result of the database query and knowledge of the previous dialogue state
figure NUM ltag trees with semantic specifications figure NUM ontological promiscuity makes it possible to
the use of full names for stations is very unnatural and confusing especially when the caller has used other descriptions to introduce them
the new tree is then substituted or adjoined into the existing tree at the appropriate node
we see that wh questions checks and questions for an extra travel plan mainly occur after the complete presentation of the travel plan
brill NUM require similar amounts gaizauskas pc
the distinction between arguments and adjuncts is expressed following x bar theory e.g.
unification failure results in the associated derivation being assigned a probability of zero
also although the almost NUM initial coverage on the heterogeneous
xp xp adjunct as opposed to government of
we would also like to thank karen kohl for permission to use her wordnet annotations for part one of levin s book as hints for wordnet senses for part two
as mentioned previously the problem with the semantic filter we have defined is that it is not sensitive to multiple word senses of the particular verbs in the semantic classes
but it also includes irrelevant senses such as sense NUM break dance the synonyms of which are dance do a dance perform a dance
we then describe a semantic filter designed to reduce the number of incorrect assignments made by the syntactic technique we show how this filter can be enhanced with a method that accounts for multiple word senses
without the semantic filter the syntactic filter provides up to NUM semantic class assignments for each of the NUM verbs giving NUM NUM assignments as shown in table NUM
as machine readable resources i.e. online dictionaries thesauri and other knowledge sources become readily available to nlp researchers automated acquisition has become increasingly more attractive
for novel verbs in the experiment which uses NUM of the verbs and tries to guess the rest the precision increases from NUM NUM to NUM NUM
the measure of success used in the purely syntactic approach is flawed in that the accuracy factor was based on the number of correct assignments in the five top ranked assignments produced by their algorithm
on the other hand the number of the branches is only about NUM NUM
the database specifies the case frame s associated with each verb sense
several of the factors posited to affect this ordering are discussed in section NUM but the full set of factors remains to be determined
analysis of unknown lexical items using morphological and syntactic information with the timit corpus
dict actually a set of files named dictl to dictl0
then the tagger learns various transformational rules by training on a tagged corpus
there have been several attempts to study the problem of learning unknown words
future work will investigate the effectiveness of the morphologicai recognizer
it consists of sperm cell generation and sperm cell transport
as a result NUM NUM tuples remained consisting of NUM NUM noun types and NUM NUM verb types
NUM this kind of coupling will enable more successful systems for two reasons improved speech recognition and more informative responses to the user
the different categories of noise would correspond to topics and typical sections of acoustic material from each category would correspond to keywords
sentence NUM is the source of the erroneous training tuple mexikanisch beschuldigen behsrde NUM mexican blame public authorities
thanks to esrc and ep src for funding
let word x have senses sl s2 sp and the dictionary definition of si be ysi y n y i y yt yn the similarity of x and rcb i is measured t y the imter l roduct of their normalised vectors and is detined as follows
hum in table NUM shows a sequential numt er wordl word whi h are added to the grou NUM of semantically similar nouns NUM tal le NUM shows for examl le new2 and york2 are xemanti ally similar and form a phn sal lexicon
NUM i states be ident perc loc thing NUM by NUM ii activities tinctions but are not articulated enough to capture other distinctions among verbs required by a largescale application
work on the acoustic level that is suited for integration into a dialogue framework like that depicted above has not advanced far enough yet
NUM i activities appeal matter NUM NUM act perc thing NUM on pert thing NUM by NUM ii states wear NUM NUM NUM
based on our experience handcoding small sets of verbs we estimate generating aspectual features for NUM entries would require NUM NUM personmonths four minutes per entry with NUM personmonth for proofing and consistency checking given unclassified verbs organized say alphabetically
this will force he system to develo p rules based more on context
thus for the beginning of a person many differences between robert l
right to left for person and left to right for companies
in many cases a second tag is not applied because the word o r
this paper discusses the two crl named entity recognition systems submitted for muc NUM
the first is a data intensive method which uses human generated patterns
NUM recognizing names based on character patterns numbers dates
the system has been successfull y tested on linux running on a pc
the method was more successful wit h organizations and locations then with persons
first the hash table of words is read and the corresponding decision tree
the results for three very different words are shown
we are aware of no attempt in the literature to represent and access aspect on a similar scale in part we suspect because of the difficulty of identifying the aspectual contribution of the verbs and sentences given the multiple aspectual types in which verbs appear
NUM constraints on center movement and realization the basic constraint on center realization is given by rule NUM which is stated in terms of the definitions and schematic in section NUM
variants NUM and NUM can be shown to be worse than NUM and NUM because they violate the centering rules presented in the next section
in the next section we describe the lcs representation used in a database of NUM verbs in NUM major classes we then describe the relationship of aspectual features to this representation and demonstrata that it is possible to determine aspectual features from lcs structures with minimal modification
because the house is the cb the cf 19b includes it as well as the door that is directly realized in the utterance
tm a violation of rule NUM occurs if a pronoun is not used for the backward looking center and some other entity is realized by a pronoun
to empirically test the claim made by rule NUM requires examination of differences in inference load of alternative multi utterance sequences that differentially realize the same content
this rule does not have the same direct implementation for interpretation systems rather it predicts that certain sequences produce a higher inference load than others
the variants NUM NUM differ only in their choice of realization of susan and betsy in particular in which is pronominalized and which is in subject position
we use the phrase inference load placed upon the hearer to refer to the resources required to extract information from a discourse because of particular choices of linguistic expression used in the discourse
we conjecture that the correct approach to take in these cases is to add the value free interpretation to cf and then load it for the interpretation of subsequent utterances if this is necessary
a pp with an object involved in a noun adjective zero derivation has a strong tendency to attach itself to the preceding noun as a modifier
the left node of t NUM represents that both a and b appeared only once after symbol a while the right node of t NUM represents only a occurred once after b
however the word model approach has the following shortcomings for agglutinative languages such as japanese and chinese the simple bayes transfer rule is inapplicable because the word length of a sentence is not fixed in all possible segmentations
by using the mistake driven mixture method the constituents of a series of hierarchical tag context trees gradually change from broad coverage tags e.g. noun to specific exceptional words that can not be captured by part of speech and subdivisions
to make the best use of the hierarchical context tree the mistake driven mixture method imitates the process in which linguists incorporate exceptional connections into hand crafted rules they first construct coarse rules which seems to cover broad range of data
the window size was a parameter of our implementation
in other words the method incorporates not only frequent connections but also infrequent ones that are often considered to be collocationah we evaluate several tag models by implementing japanese part of speech taggers that share all other conditions i.e. dictionary and word model other than their tag models
as described in the previous section frequency tables of each node consist of the set a at ally node s of a context tree let n ats and NUM als be tile count of element a and its probability respectively
in such cases the head corner parser can be said to run in left corner mode
the experiments include the grammar of the ov s system and the alvey nl tools grammar
earley parsing is intractable in general as the rule set is simply too general
underspecification can be exploited to obtain results required by certain techniques for robust parsing
to parse a list of daughter categories each daughter category is parsed in turn
improvement in speech recognition will stem from the dialogue manager acting as a kind of mediator between the speech recognizer and the user
we conclude that at least for some grammars head corner parsing is a good option
valad was used at the integrated feasibility demonstration at the jdef in washington dc in june NUM
another approach is to build a simple hidden markov model which gets trained for each category from the data in that category
prolog is a high level language this enables the application of partial evaluation techniques
moreover all the relations head link g n now contain the relevant information from the head comer table
the right hand part of the diagram shows the linguistic competence base lcb and the left the ebl based subgrammar processing component sgp
furthermore the index and the mrs of a template together define a normalization for the permutation of the elements of a new input mrs
to explore the areas for further improving the deep structure disambiguation system the errors for NUM sentences extracted randomly from the training corpus have been examined
when the error of the baseline system was examined we found that a lot of errors occur because many events were assigned with zero probability
for example when the model is operated in also mode the case score of the normal form in the previous example is expressed as
therefore the errors of this kind would have more serious effects on the case recall rate and the precision rate than the case structure accuracy rate
more precisely most syntactic errors result from attachment problems including prepositional phrase attachment and modification scope for adverbial phrases adjective phrases and relative clauses
the corpus is then randomly partitioned into the training set of NUM NUM sentences and the testing set of the remaining NUM NUM sentences to eliminate possible systematic biases
in the casedependent model the sense of a word is assumed to depend on its case role part of speech and the co word itself
by applying these algorithms the accuracy rates of NUM for parse tree NUM NUM for case and NUM NUM for sense are obtained
in contrast a statistics oriented corpus based approach achieves disambiguation by using a parameterized model in which the parameters are estimated and tuned from a training corpus
for instance the normal form in figure NUM c is decomposed into a series of case subtrees where prop
we have also conducted experiments on the susanne corpus data t2 and confirmed the effectiveness of our method
h interest setinterest lcb fj228 fb028 jell2 kao06 rcb d a share in a company business etc
she told her it was quite rare
we also show that when an unbounded number of these symbol classes are allowed within a transformation then the associated learning problem becomes np hard
this semantic characteristic of the phraser grammar is clearer still with st rules
the algorithm is divided into five distinct stages
here a transition is added to a new state i.e. a b to NUM the next state to be considered is NUM and it is built like state NUM except that the symbol b should block the current output
returning to our running example of section NUM the transducer obtained by composing the local extension of t2 right in figure NUM with the local extension of t1 right in figure NUM is shown in figure NUM
for example in NUM the word killed is erroneously tagged as a verb in past participle form and in NUM shot is incorrectly tagged as a verb in past tense
however current implementations of brill s tagger are considerably slower than the ones based on probabilistic models since it may require rkn elementary steps to tag an input of n words with r rules requiring at most k tokens of context
hence one has to entertain two possibilities namely NUM we are processing the input according to t7 and the transitions should be a b or NUM we are within the identity and the transition should be a a
definition let be a total order on p and let u lcb rcb be the al phabet with the two additional symbols and
the rule based tagger and the finite state tagger do not always produce the exact same tagging as the stochastic tagger they do not make the same errors however no significant difference in performance between the systems was detected
the uno system computes the fact that the following expressions differ in their information content an d that the first has strictly more information than the second x happened in may NUM x happened in may but not early may NUM
we somewhat randomly chose a batch of NUM wsj articles and using sgml like marks an opening mark te and a closing mark te we had marked all expression s of different syntactic categories that contained any information pertaining to time
we wanted the system to perform this task near perfectly because it would improve this generally needed capabilit y and because based on the existing literature we expected the distance expressed in a number o f sentences to be a very important factor in computing pronominal referents
the morphological analyzer can recognize and generate various form s of nouns and verbs for example cry cries crying cried adjectives for example angrier derive adverb s from adjectives for example slowly etc
occasionally though choosing a less likely step at one point leads to a parse with higher overall likelihood
we computed the worm feature for each of these test words and computed the euclidean distance between every word in these sets
the third test set hi consists of the nineteen japanese terms paired with their translations and NUM single english words in addition
the NUM value of a new word is high when there is a z th seed word which co occurs with it siguificantly often
in this case the chances for co occurrence between such seed words and all new words are very high close to one
however these works remain in the realm of solving ambiguities or choosing the best candidate among a small set of possibilities
NUM given all words in one text debentures is closely correlated with a subset of all words in the texts
following the above observations we propose the following algorithm for finding word or term translation pairs from non parallel corpora NUM
in the screen dump of the interface figure NUM we see a fragment of the dutch wordnet in the left box and a fragment of the spanish wordnet in the right box NUM the dark squares represent the meanings wms m the languages which are interconnected by lines labeled with the relation type that holds has hyperonym has mero madeof
figure NUM word relation matrix for debenture in both texts however most word pairs in truly non parallel bilingual corpus are less similar than those in figure NUM
these figures show that worms of the same words are similar to each other but worms are different between different words
having described the representations used it is now possible to describe the constraints that evaluate them
then step i NUM can be performed in time independent of input length
path weights in an fsa can not be more than linear on string length
this too seems like the right move linguistically although further study is needed
all these constituents are formally identicah each marks off an interval on the timeline
so must linguists if they are to know what their proposed grammars predict
no connection is required between nas and nas
here the left edges and interiors overlap but the right edges fail to
in each primitive constraint cr and NUM each specify a phonological event
the characteristic machine is defined in terms of dotted rules with transitions between them that are analagous to the conditions implied by formula NUM of section NUM when the machine is flattened e transitions are added in a way that is in effect simulated by conditions NUM and NUM condition NUM turns out to be implied by conditions NUM NUM
the left context vector of the following word
for example the adverb in mc n
null an analysis of the divergence between our classification and the manually assigned tags revealed three main sources of errors rare words and rare syntactic phenomena indistinguishable distribution and non local dependencies
a common tag class was created for vbn and prd to show that they are reasonably well distinguished from other parts of speech even if not from each other
performance is consistently better than for the evaluation on all contexts indicating that the low quality of the distributional information about punctuation marks and rare words is a difficulty for successful tag induction
vs the soldiers will come home
the method was systematically evaluated on the brown corpus
they require a relatively large tagged training text
the algorithm is evaluated on the brown corpus
the description we have given above amounts to a global memory model in which a datr query evaluator is a machine equipped with two memories one containing the current local node and path and another containing the current global node and path
this might be by banning multiple inheritance altogether restricting it so that conflicts are avoided providing some mechanism for conflict resolution as part of the formalism itself or providing the user of the formalism with the means to specify how the conflict should be resolved
syntactically however a datr description consists of a sequence of sentences where each sentence starts with a node name and ends with a period and contains one or more path equations relating to that node each corresponding to a statement in datr
our toy fragment is beginning to look somewhat more respectable a single node for abstract verbs a node for each abstract verb lexeme and then individual nodes for each morphological form of each verb but there is still more that can be done
the node from which inheritance occurs is that stored in the global context a query of love mor present will result in inheritance from love mor root via verb mor present while a query of do mor present will inherit from do mor root
NUM a path carl be used as the input tape and a value as the output tape recall that the datr default mechanism means that extensions to left hand side paths are automatically carried over as extensions to right hand side paths as discussed in section NUM NUM above
this allows us to employ the default mechanism to make the default mood active for arbitrary syn form values other than those that begin with the atom passive and thus just pick out syn form passive and its extensions for verbs in the passive mood
if we add these back in the complete definition looks like this the paths syn type and syn cat and also many others such as syn cat foo syn baz obtain their definitions from syn using the default mechanism just introduced and so inherit from verb
because we have now brought the global inheritance descriptor to the node corresponding to the global context for its interpretation global inheritance can now operate entirely locally the required global node is the local node come producing the desired result come mor past participle come
by default this dependency of subcategorization frame on mood will be inherited by all the descendants of tr verb whether these be instances of simple transitive verb lexemes or nodes defining specific types of transitive verbs ditransitives object plus infinitive verbs bet class verbs etc and their descendants
the fourth logical possibility is that the replacement operation is constrained by the lower context
after completing each alignment it backs up to the most recent tmtried alternative and tries a different one
when a trainable system relies on annotated texts for training some annotations are mor e useful than others
it therefore seems ironic to find such a small collection of training documents available to muc NUM participants this year
for example in one case the greedy algorithm segmented humanactivity as humana c ti vi ty
NUM we selected nine overlapping categories i.e. in which a document may berreuters NUM is available at http www research att com lewis
information extraction research at the university of massachusetts is based on portable trainable languag e processing components
the dictionaries supporting those specialists were also borrowed from the ir la b with some adjustments for muc NUM
badger is domain task independent and require s no adjustment in order to move from one application to another
every instance was correctly classified though NUM instance s were scored incorrect due to faulty string trimming
james from our example has been linked to more than one in and out and is classified as relevant
this leads to an in and out relationship which is eventuall y merged with the textually identical in and out already created
this tree returns a negative classification if the person and status are not found in the same sentence
so we ve stumbled upon a rule induced by resolve that probably would n t have been discovered manually
in this experiment we demon null strate that our algorithm can also improve the output of such a system
this paper describes the use of categories both in the training and translation processes for improving the eutrans translation systems
NUM NUM inserting subject boundaries stage NUM
the computation cost of this simiarity is not high for the components of equation i0 have been obtained during the early computation
there may in fact be just one organization involved the person could be leaving a post at a company in order to take a different or an additional post at the same company
it is important to recognize that nlu is not simply speech recognition where each individual utterance maps to a specific command
algorithm NUM can trivially be adapted to learn transformations in NUM where a left context is specified in place of a right context
second we present data that suggests how the dictionary can be filtered automatically for information extraction
signatures that pass both thresholds are labeled as relevancy signatures and are used to classify new texts
in this case the subject of the verb is extracted as the perpetrator of the murder
we are interested in using the concept nodes for two tasks information extraction and text classification
additional experiments suggest how a dictionary produced by autoslog ts can be filtered automatically for information extraction tasks
each concept node is triggered by a keyword but is activated only in certain linguistic contexts
there is a roughly linear relationship between the relevancy rate and the number of concept nodes retained
linguistic rules to create extraction patterns for a given set of noun phrases in a text corpus
table NUM phonemic correlates of x x ly
the paradigmatic cascades model offers an original and new framework for extracting information from large corpora
the top ranked element in this set is the pronunciation of x
each match increments the productivity of the related alternations f and g
in this section we introduce the paradigmatic cascades model
paradigmatic cascades a linguistically sound model of pronunciation by analogy
each new text was processed by circus and classified as relevant if it generated a relevancy signature
however it often makes mistakes and might attach the pp to the noun bogota
our new replacement operator goes in a class between the boolean operators and composition
one success for the systems as a group is that each of the six smaller organization objects and four smaller person objects those with just one or two filled slots in the key was matched perfectly by at least one system in addition one larger organization object and two larger person objects were perfectly matched by at least one system
it should be noted that human performance on this task was also relatively low but it is unclear whether the degree of disagreement can be accounted for primarily by the reasons given above or whether the disagreement is attributable to the fact that the guidelines for that slot had not been finalized at the time when the annotators created their version of the keys
the results of this case study illustrate the reliability of lexical semantic methods
the above instance of ts can easily be constructed in polynomial deterministic time with respect to the length of g k rcb
crl is using tipster technology to develop oleada which is an integrated set of computer tools designed to support language learners and instructors
annotations on doc null uments automatically added by tipster language analysis modules can also be viewed and changed through the same annotation tool
often new technology is not delivered in a conveniently usable manner and systems may not provide functions that are immediately useful to professionals
the word frequency tool also works with tipster documents and collections and takes advantage of word segmentation annotations to count chinese and japanese words
these resources included dictionaries glossaries thesauri and other data including translation memory parallelaligned source and target language text
where s is the root nonterminal
NUM NUM bits NUM per word
the validation suite implements a series of tests each of which tests some aspect of architecture compliance
NUM the prospect was challenging and indeed a bit daunting for a group none of whom had written an architecture before
contractors found that they had to extend or modify the architecture to meet the needs of specific applications
it has also prompted the development of an initial version of a validation suite by new mexico state univ
modules within the architecture communicate primarily by passing documents and collections and by adding annotations and attributes
the annotations would point to segments of the original text the original text would be maintained unchanged
the dialog system exclusive of the parser and error correction code consists of about NUM NUM lines of prolog and this includes some comments apportioned as follows dialog controller procedural mechanisms including ipsim NUM dialog controller knowledge base NUM domain processing procedural mechanisms NUM domain processing knowledge base NUM linguistic interface including much language generation code NUM miscellaneous NUM
we need to specify the error conditions for the operations in the architecture and the mechanisms for error signaling
the initial specification was programming language independent but included some basic guidelines for implementations in c and lisp
null starting from these ideas the cawg set out in april NUM to knit together an architecture
the difference in the greedy scores for english and thai demonstrates the dependence on the word list in the greedy algorithm
table NUM shows the results of this crossclassification of the adjective pairs
a few notational preliminaries we will denote the sentence pairs by e c where the english sentence e el et and the corresponding chinese sentence c cl ev are vectors of observed symbols that is lexemes or words
an implicit node is a node not explicitly represented in the suffix tree that splits the label of some edge at a given position
although an attempt is made in this case to fit the english constraints the main difficulty is that the translation so was missing from the automatically learned lexicon also the simple grammar lacks infinitival clauses
the s category not to be confused with the start symbol so is a placeholder for miscellaneous items including punctuation and adverbs and functions as a fallback category similar to the a nonterminal in the generic bracketing grammars
we describe two new strategies to automatic bracketing of parallel corpora with particular application to languages where prior grammar resources are scarce NUM coarse bilingual grammars and NUM unsupervised training of such grammars via em expectation maximization
used the best of the transducers obtained in the spanish to english text experiments
the next two functions are used to move a links up and down two aligned paths
we would like to thank beats forsmark nathalie kirchmeyer carin lindberg thierry reynier and jennifer spenader for carrying out judging tasks
make a dendrogram dsub out of the merging process for each class
example NUM in order to eliminate geminates one possibility is to analyze the last character sent to the output buffer
the guarantee that parsing will produce a tail recursive tree facilitates easily identification of those nesting levels that are associative and therefore arbitrary so that those levels can be flattened by a postprocessing stage after parsing into non normal form trees like the one in figure NUM c
the previous three experiments showed that our rule sequence algorithm can produce excellent segmentation results given very simple initial segmentation algorithms
in this case node u is never reached by the algorithm and no a link is established for this node
while this is a low segmentation score this segmentation algorithm identifies enough words to provide a reasonable initial segmentation approximation
NUM NUM relationship between ils and informational structure
should the various rst intentional relations be incorporated into a synthesized theory
a come home by NUM NUM
instead the relative ordering of core nucleus and embedded segment satellite is highlighted
we now turn to a point of contention between the two theories
the definition of ils comprises one of the major claims in g s
one intention satisfaction precedes another when it must be realized before the other
that is dominance in g s corresponds closely to nuclearity in rst
g s is formulated in terms of the interdependence of three distinct structures
rosenfeld argues at length against naive linear combinations in favor of maximum entropy methods
tained by dropping trigrams that occurred less than t times in the training corpus
the s ec0nd shows the fraction of words in the test set that were assigned zero probability
table NUM shows the final perplexities on the training set after four iterations of em
we trained aggregate markov models with NUM NUM NUM NUM and NUM classes
table NUM most probable assignments for the NUM most frequent words in an aggregate markov model with
as we have mentioned in section NUM our algorithm does not cover all cases of tfa occurring in english sentences
future research in the domain of automatic processing of tfa thus may concentrate on solving further problems connected with secondary cases
in any case for practical applications it will be necessary to work with preferences excluding the least probable readings
it also handles just the verb and its complementations deeper embedded elements are left aside for the time being
it determines the appurtenance of an element to topic or to focus but does not specify cd within topic
passonneau and litman discourse segmentation table NUM performance on test set for higher boundary thresholds
thus the present paper also does not aim at a complete solution that would handle all possible cases appropriately
the unmarked case when the verb belongs to the focus is left without a specific notation mark here
for example the tree initially branches based on the value of the feature before
this allows relative information about previous boundaries to be used in deriving the global pro
both methods rely on an enriched set of input features compared to our previous work
however this low error rate is achieved at the expense of the other metrics
the precision of the additive algorithms is indeed higher than any of the algorithms alone
NUM however precision is low and both fallout and error are quite high
the np results are very similar to the training set except that precision is worse
our first method relies on an analysis of the errors made by the best performing algorithm
argument structure can be represented in terms of unordered trees with crossing branches
in order to reward a full phrase mention in a sentence over just a partial overlap with a multiword keyword phrase we used a formula sensitive to the degree of overlap
for positions with equal scores different policies are possible one can prefer sentence positions in different paragraphs on the grounds that they are more likely to contains distinctive topics
we measured word overlap as follows first we removed all function closed class words from the abstract and from the text under consideration
if we produce an extract of about NUM of the average length of a text i.e. NUM sentences the coverage score is NUM NUM
this peak occurs precisely where most texts have their second or third paragraphs recall that the average text length is NUM to NUM paragraphs
both peak at position p2 NUM and decrease grad ually in the x direction and more rapidly in the y direction
ppt and spp prevent us from forming a rule such as 251h sentence in the lo0lh paragraph when ppt is NUM and spp is NUM sps suggests how many sentences to extract
notice that the topmost segment of each column in figure NUM represents the contribution from matches of at least five words long since we only have cm up to m NUM
figure NUM shows dhit scores for the first NUM paragraph positions and figure NUM dhit scores for the last NUM positions counting backward from the end of each text
on the other hand the latter figure does not support the last sentence hypothesis it suggests instead that the second sentence from the end of a paragraph contains the most information
a head transducer reads from a pair of source sequences a left source sequence l1 and a right source sequence rt it writes to a pair of target sequences a left target sequence l2 and a right target sequence r2 figure NUM
in particular we choose d to be an exponential distribution with mean l p a parameter that we fix at the approximate mean document length for the domain dt i j 7t e li jl
repns is defined as the intersection of many automata exactly like NUM called tier rules which ensure that brackets are properly paired on a given tier such as f foot
ae iia6 i its is the most geucral event predicate
thus if one is committed to the use of sgml for corpus based nlp then one needs to have specialised software to facilitate the viewing and editing of sgml
e peter erst vier r angestellte kannte
yesterday being in munich we have considered only temporal adjuncts so far
in our framework conditions sllhshlrte tildrent types of i roje ction
implicatures NUM NUM the l irst of a qucuce intert re tation
the negation test which is comlnonly used to detect presuppositions supports these strueturm assumptions
at first glance it seems that what we have said above applies to this case also
we t lnit this r ther cmlonicm structuring for the other examples
similarly an english dependency sequence for yes no questions modal actor head object temporal is converted into the chinese sequence actor temporal modal head object ma the transducer stopping in state q6 ma being the relation between the head verb and the chinese particle for yes no questions
one question which arises in respect to using sgml as an i o format is what about the cost of parsing sgml
it is claimed that there is no easy way in sgml to differentiate sets of results by who or what produced them
the text generator operates as follows each s template attempts to get a sentence generated from it into the text
for example sgml dtds such as the tei include a resp attribute which identifies who was responsible for changes
a translator based on head transducers consists of the following components a bilingual lexicon in which entries are 5tuples w v m q c associating a pair of source target words with a head transducer m an initial state q and a cost c
the categories of the grammar are defined as follows NUM
tim paper has presented a method for providing interpretations word by word for basic categorial grammar
a t n a t c p represent respectively an adjective phrase and a prepositional phrase coordinated with the prepositional phrase of the original term
since we focus on french a language with a rich declensional inflectional and derivational morphology we have chosen the richest and most precise morphological analysis
accounting for variants which are not considered in our framework would require the conception of a novel framework probably in cooperation with a deeper analyzer
NUM noun adjective variations the two ways to modify a noun a prepositional phrase or an adjectival phrase are generally semantically equivalent e.g.
transformation of a term with a compound structure into a noun phrase structure such as consommation de l oxyg ne consumption of the oxygen
this system relies on a full fledged unification formalism and thus is well adapted to a fine grained identification of terms related in syntactically and morphologically complex ways
the rule required is similar to function composition in cg i.e.
as an example reconsider the string john likes sue
this will be discussed in the final section of the paper
m c illustrate the problem by considering the fragment mary thinks john
partial syntax trees can be regarded as performing two main roles
the second is to provide a basis for a semantic representation
the resulting grammar is equivalent to ab categorial grammar plus associativity
as a consequence extraposed vp adjuncts can not be distinguished from vp adjuncts in base position which is clearly undesirable
ll NUM i do n t see much argument myself any longer against differential rents
ll the antecedent the category from which the dislocated element is extraposed is a noun in these cases
defining extra in this way we can rely on the nonlocal feature principle for percolation no additional mechanism is required
otherwise the phrase has a right periphery and extra elements can be bound on is rather unusual in standard hpsg
to formalize the notion of periphery we introduce a new feature periphery per which is located under local
the phrase structure for extraposition outlined so far has to be constrained further since it allows adjuncts to adjoin higher than extraposed elements which is clearly wrong
languages in which the right vp boundary is clearly marked as e.g. by the non finite verb in verb second languages can provide evidence for extraposition with verbal antecedents
part of the work was carried out as part of the verbmobil project while the author stayed at the institute for logic and linguistics ibm germany heidelberg
in fact w is taken from the set v1 consisting of the source language vocabulary augmented by the empty word e and v is taken from v the target language vocabulary augmented with e
note that a bigram tagger trained on our training set would not correctly tag the first occurrence of as
the same is true of the nonlexicalized transformation based tagger where transformation templates do not make reference to words
NUM since gpsg is presumed to license roughly context free languages we are not concerned here with establishing language theoretic complexity but rather with clarifying the linguistic theory expressed by gpsg
the context sensitive languages on the other had can be characterized by linear bounded automata they can be processed using an amount of memory proportional to the length of the input
a vast amount of on line text is now available and much more will become available in the future
an exception to this generalization arises when the word is also one word to the right of a determiner
this allows intermediate results in brill transformation based error driven learning classifying one object to be available in classifying other objects
there are a number of large tagged corpora available allowing for a variety of experiments to be run
in terms of models one can understand gb to define a universal language the set of all analyses that can occur in human languages
thus we can use such descriptive complexity results to draw conclusions about those abstract properties of such mechanisms that are actually inferable from their observable behavior
ultimately it has the potential to reduce distinctions between the mechanisms underlying those theories to distinctions between the properties of the sets of structures they license
p x is true of a set iff it includes all nodes not free for f and is closed wrt propagate
for example while the explanations of filler gap relationships in gb and gpsg are quite dramatically dissimilar when one focuses on the structures these accounts license one finds some surprising parallels
p half l cd NUM NUM p half l dt NUM NUM p half i j j NUM NUM p half inn NUM NUM p half l pdt NUM NUM p half l rb NUM NUM p half i vb NUM NUM
in gb it is presumed that all principles are universal with the theory being specialized to specific languages by a small set of finitely varying parameters
as a result of these observations the tu language project team has chosen the ibm computer manuals as their translation domain
also dependent on the verb an object of an english sentence may be mapped to different case markings in turkish
depending on the success of the system the lexicons and the transfer module might be modified to tackle other translation domains in the future
produces various parses for an input sentence the best parse is filtered by the system which conveys the intended meaning of the sentence
this characteristic also affects the word order of the sentences which can be described as sov where the verb is positioned at the end
in the mt system being developed these and other different characteristics of the turkish language are handled in the transfer and generation components
as more and more computer companies enter the turkish market a growing demand for english to turkish translation of computer manuals has emerged
t he transfer phase of our mt systeln performs structural transfer between the respective case frames of the analyzed english sentence and targetted turkish output
in the english passive tbrm the surface subject can correspond to both the direct object or the indirect object of the active form
the greatest difficulty encountered with this approach is handling the complex transfer issues that arise due to the differences between the two languages
this will result in a less useful representation of the input space
kqml aims at the standardization of both a protocol and a message format for communication among independent processes over a wide area network
but as we take larger and larger corpora the resulting empirical distributions converge to NUM NUM NUM NUM
in any case any natural language must allow us to directly refer to single characters
assume that items xl x are already in the sample and we wish to choose xn l
we arrive at the same weights m2 we considered above defining dag weights c NUM x
the astute reader will note that there is a problem with the null field if l g is infinite
earlier we alluded to the fact that these algorithms are computationally intensive
although the proof itself is easy to follow the result has nonetheless been a surprise
here we were free to choose rule NUM instead of rule NUM to expand the right hand a node
in other words about NUM close dictionary tokenization accuracy can be achieved efficiently without disambiguation
s is ill formed on dictionary d if itd s NUM
one is the tendency to bring every possible knowledge source into the character string generation operation
the hypemyms of kidnapping axe capture felony crime evil doing wrong doing activity
after evaluation of the separate phases we cornblued the best algorithms of the two phases and evaluated the performance of our semantic class disambiguattion approach
as a baseline we again sought the most frequent heuristic which is the occurrence probability of the most frequent senantic class entity
the extent of support depends on the information content of the subsumers of the nouns in word net whereby information content is defined as negative log NUM NUM NUM hood
through the use of general word sense disaznbiguation algorithms and semantic distance metrics our approach correlates the performance of semantic class disambiguation with the improvemen in these actively researched fields
a simple implementation of the s n n c distance module can thus be just a waversal of the taxonomic l b is a of word net
the most frequent baseline is obtained by following the stxategy of always picking sense NUM of wordnet since wordnet orders its senses such that sense i is the most likely sense
the semantic distance module should infer the close distance between the two concept nodes kidnapping aud attack NUM and thus col rectly classify lddz ppin
in this case td s is the poset
that is the system s answers are mapped from its more complex internal representation an ilt see section NUM NUM into this simpler vector representation before evaluation is performed
it allows letters that have a special meaning in the calculus to be used as ordinary symbols
for the sake of generality we allow prefix and suffix to denote any regular language
the number of auxiliary markers is an important consideration for some of the applications discussed below
the definition of the upper lower relation is presented in the next section
NUM however that this constraint does not by itself guarantee a single output
transitions that differ only with respect to the label are collapsed into a single multiply labeled arc
the second transducer inserts an end 0f token mark after simple words and the listed multiword expressions
expression is replaced by the insertion formula in figure NUM insertion expression in the definition of
with the expressions we can construct transducers that mark maximal instances of a regular language
fortunately models used in natural language processing often assume independence between most model parameters
based sample selection which reduces redundant annotation of examples that contribute little new information
this would be of considerable advantage in developing selectively annotated corpora for general research use
NUM committee based selection ignores such counts focusing on parameters which improve the model
the test set was a separate portion of the corpus consisting of NUM NUM words
dividing by log k normalizes the scale for the number of committee members
this avoids redundantly annotating many examples that contribute roughly the same information to the learner
an example is selected for labeling if the committee members largely disagree on its classification
model parameters for which acquiring additional statistics is most beneficial can be characterized by the following three properties NUM
such modeling is absent in batch selection and we hypothesize that this is the reason for its lower effectiveness
frequency NUM was chosen because it is a medium frequency for all three word classes
the list of criteria for self evaluation consisted of technical linguistic and ergonomic issues
example NUM shows the english adjective hard frequency rank NUM with its translations
the second question is about the percentage of the test words that are correctly translated
carefully going through NUM words each for NUM systems including dictionary look up for unclear cases takes about NUM days time
of course the introduced method can not claim that the relative lexicon sizes correspond exactly to the computed percentages
we can check if these nouns get the correct gender assignment if we look at the form of the determiner
looking at the frequency figures we decided to take the NUM most frequent adjectives nouns verbs
these experiments were performed to judge the information content of the translations the translation quality and the user friendliness
the evaluation of machine translation mt systems has been a central research topic in recent years cp
this core platform provides two main facilities analysis which converts tex t to a logical representation of its meaning and generation which expresses information represented in thi s logical form as text
the second group are sentences prepared to give a context for a multiple tagged word
the statistical translation uses two sources of information a translation model and a language model
for NUM NUM training sentences to NUM NUM for NUM NUM training sentences
o pot favor querr lcb a qua nos diese las llaves de la habitacidn
r could you ask for nay taxi for room number three two two for me
wer decreases from NUM NUM c c for the zerogram model to NUM NUM for the bigram model
a how much does a double room hlcluding room service cost for five nights
actually the figures for sentence error rate are overly pessimistic
this test corpus contained NUM NUM spanish a nd NUM NUM english words
section NUM gives the semantics for the notation and argues that qlf is best understood as providing descriptions of semantic compositions
in this case the two terms term h in ellipsis and antecedent are both discharged i.e.
this ensures that the term for mary in the ellipsis gets a parallel scope to the term for john in the antecedent
one thus merges the category information from source and antecedent to determine what verb phrase form should be substituted for the original
order independence one of the reasons for the computational success of unification based syntactic formalisms is the order independence of parser generator operations they permit
certain languages such as english and french are among the most complex languages to construct letter to sound rules for
null second the consideration of a sentence itself is not enough because honorification phenomenon is related to the sociolinguistic factor such as social status
as shown in NUM the conjunctive kuliko is not inserted after the last sentence in a dialogue because no further sentence follows
each element of the set contains the inlormation about who honors whom which is collected during a parsing of each sentence in a dialogue
when the inference rule in NUM is applied to 33b and 33e the result is NUM
speaker shows honor to an object referent and that the social status of the ohject referent is higher than that of speaker and addressee
their approach however can not gain access to speaker who is a sentence external individual because only a sentence itself is considered
thus the inference in NUM is the result of computing relative social status of the individuals involved in dialogue NUM
finally the constituent towatuli si ess supnikka contains the humble form of the verb towacwu the honorific infix si and the honorific verbal ending supnikka
the above dialogue occurs between the person s and the person l in their utterance the person k and the person p are mentioned
this required understanding the way the mapquest web site handles these map navigation commands
this is probably due to the fact that many english verbs in the low frequency class are rare uses of homograph nouns e.g. to keyboard to pitchfork to section
table NUM gives additional evidence that personal translator has the most elaborate lexicon for english to german translation while german assistant and systran have the least elaborate
a look at some common nouns that received different translations from our test systems reveals that there are big differences in this dimension which are not reflected by our test results
this does not necessarily entail a very large vocabulary since corpus studies and similar language elicitation exercises can provide a relatively small core vocabulary
this finite state tagger will also be found useful when combined with other language components since it can be naturally extended by composing it with finite state transducers that could encode other aspects of natural language syntax
c recovery strategy although misunderstandings often occur in conversations speakers have the ability to recover from these and other deviations in communication
therefore imdy u i imdy v l NUM x m k thus imdy u mdy v li k
the new tagger operates in optimal time in the sense that the time to assign tags to a sentence corresponds to the time required to follow a single path in the resulting deterministic finite state machine
the deterministic version of the transducer t3 is shown in figure NUM whenever nondeterminism arises in t3 the deterministic machine computational linguistics volume NUM number NUM figure NUM subsequential form for t3
at such speeds the time spent reading the input file breaking the file into sentences breaking the sentences into words and writing the result into a file is no longer negligible
this generic template can be used in the design of future dialogue management systems highlighting important features and the mechanisms required to implement them
NUM in figure NUM the dictionary lookup includes reading the file splitting it into sentences looking up each word in the dictionary and writing the final result to a file
in this section we will see how a function that needs to be applied at all input positions can be transformed into a global function that needs to be applied once on the input
what is made clear is that we need to conduct further research into explicitly quantifying each feature for this approach to be worthwhile
given that the systems surveyed performed just one or two tasks it is not surprising that functional perplexity is not ranked highly
for example the procedure associated with although instructs the analyzer that the textual unit that pertains to this cue phrase starts at the marker and ends at the end of the sentence or at a position to be determined by the procedure associated with the subsequent discourse marker that occurs in that sentence
however given that the structure that we are trying to build is highly constrained such a prediction proved to be unnecessary the overall constraints on the structure of discourse that we enumerated in the beginning of this section cancel out most of the configurations of elementary constraints that do not yield correct discourse trees
the value of the statistic ranges from NUM indicating that high ranks of one variable occur with low ranks of the other variable through NUM indicating no correlation between tile variables to NUM indicating that high ranks of one variable occur with high ranks of the other variable
to evaluate our algorithm we randomly selected three texts each belonging to a different genre NUM an expository text of NUM words from scientific american NUM a magazine article of NUM words from NUM me NUM a narration of NUM words from the brown corpus
particularly important is the fact that the theoretical foundations of sumita et al s analyzer do not seem to be able to accommodate the ambiguity of discourse markers in their axe independent of each other against the alternative hypothesis that the rank of a variable is correlated with the rank of another variable
the most important units of a textual span are determined recursively they correspond to the most important units of the immediate subspans when the relation that holds between these subspans is paratactic and to the most important units of the nucleus subspan when the relation that holds between the immediate subspans is hypotactic
and the microsoft office NUM summarizer recalled NUM of the important sentences with a precision of NUM
water formed in because ofthe low this way would atmo het c
NUM determine the set d of all discourse markers and the set ur of elementary textual units in t
each text fragment contained a window of approximately NUM words and an emphasized occurrence of a marker
in all cases the taggers sense selections were compared to those made by two of the authors who have years of experience in lexicography
we give the results in percentages here however calculation of the significant effects is based on analyses of variance carried out on the raw data
again the taggers probably understood the first most frequent and often most salient sense easily and were reluctant to consider more fine grained sense differentiations
we found the predicted main effects for degree of polysemy pos and the order in which the senses were presented in the dictionary booklet
for words with only two senses in wordnet the position had no significant effect on the rate of agreement between taggers and experts
we therefore predicted a tendency on the part of the taggers to select the first sense even when it was not the one chosen by us
the mean number of wordnet senses for the verbs in the text was NUM NUM for adjectives NUM NUM for nouns NUM NUM for adverbs NUM NUM
the expected utterances from subdialogs other than the current one can indicate that a shift from the current subdialog is occurring
given that they recognized that the first sense was appropriate selecting it meant that they did not have to examine and compare the remaining senses in search of an even better choice
note that the rules are specified with respect to the innermost proof unit containing a proof node
the textual closeness is used as a measure of the level of focus of an individual reason
for nodes in a box a referring expression must have been generated in the text
the naturalness of this segmentation is largely due to the naturalness of the hierarchical planning operators
the hierarchical planning splits the task of presenting a particular proof into subtasks of presenting subproofs
here the slot derived formula is filled by a new conclusion which this pea aims to convey
it can be inferred by applying the filler of method to the filler of reasons as prernises
the controlling attentional space is the innermost proof unit that contains the active attentional space
proverb s hierarchical planning is driven by proof patterns that entail or suggest established ways of presentation
extraneous concepts had to be removed before the rule generation process so that the concept structure information in the concept grammar rules would be precise
the scoring program assigns points to an essay as rule matches are found according to the scoring guide see figure NUM
intuitively the reduction ought to improve performance by disallowing the distantly located words in long sentences to have any influence on the prediction of the confusion word because they usually have little or nothing to do with the selection of the proper word
one at a time the words from the confusion set are inserted into the sentence at the location of the word to be predicted and the same transformations that the training sentences undergo are applied to the test sentence
thus lsa is doing better than the bayesian component of tribayes but it does n t include part of speech information and is therefore not capable of performing as well as the part of speech trigram component of tribayes
next inflectional suffixes were automatically removed from the words in the parsed sentences since inflectional suffixed forms are not included in the lexicon
a quick examination of the context in which both words appear reveals that a significant percentage NUM of all training instances contain either the bigram of the confusion word preceded by the followed by of or in some cases both
because the baseline score captures information about the percentage of the test corpus that should be easily predicted i.e. the portion that contains the most frequent word we propose a comparison of the results by examination of the respective systems improvement over the baseline score reported for each
however we hypothesize that the non cooperative behavior may be partially due to the artificial experimental conditions we are planning to experiment the current version of dialogos in a real environment with users that really need to take trains for traveling all around italy and that will use the system for having timetable information
the result is a large wfsa containing all possible english translations
however clarification subdialogues may be avoided if the dialogue expectations allow to choose an interpretation of ambiguous input
a notable exception is the approach taken by the universi of massachusetts
bayes rule lets us equivalently maximize p w
database servers expert agents that have access to knowledge bases which are updated periodically and which contain information that is less likely to change over the course of a summarization session e.g.
kqml is used to create facilitators which provide the interface between heterogeneous applications which run on various machines and which are written in various programming languages
they are connected to the rest of other kqml performatives such as ask all ask one register tell or sorry have also been implemented
the output consists of short summaries that convey information selected to fit the user s interests the most recent news updates and historical information
we have described an agent based system which allows for summarization of multiple articles from multiple sources in an asynchronous fashion while taking into account user preferences
the world book facilitator parses the entries for each country into a lisp like format and provides access to them to the planner
whenever a new message becomes available e.g. figure NUM the muc facilitator will reply with an appropriate message
we have used agents of various types in a modular way the modules through the intermediary of facilitators that convert from the template format to kqml and vice versa
several components related to interoperability are also fully implemented e.g. the subscription package in kqml and the query response interface to the muc and world book facilitators
as suggested by robert macintyre NUM it is hout this paper
we distinguish six degrees of automation NUM completely manual annotation
NUM NUM sentences NUM NUM words
this uniquely identifies a phrase category
the task is to assign category vp
then the average accuracy was calculated
table NUM tagging accuracy for assigning grammatical
then it could detect the finite verb that completes the sentence
if we tell you that the document that we are looking for has the keyword boycott then we have narrowed the search space down to just NUM d documents
we use dijkstra s shortest path algorithm lcb dijkstra NUM to extract the most probable one
in the case fillers of figure NUM for example ccd acc is greater than ccd nom see fujii et al s paper for details
each alignment contributes counts in proportion to its own weight
we denote that exampie with emax where the max function chooses the example with the maximum conditional probability NUM emax trmxe examples p eli our approach to determining ernax is as follows
however in our method similarity value such as vsm wl wa can be reasonably measured because sbls xl x2 and x3 can be well defined with sufficient statistics
in this paper we aim at intergrating the advantages of the two above methodological types or more precisely realizing statistics based word similarity based on the length of the thesaurus path
null we would like to avoid having to manually construct the different sub domain grammars for several reasons
also there are various ways of referring to the composition being discussed for instance by name k NUM with a definite noun phrase or with a pronoun
metrical structure is most conveniently represented by binary trees in which one daughter of each node is marked as strong and the other as weak
as a result the default accent rule swaps the strong weak s w labeling between hear and k NUM before the accented labels are assigned
this preference is stored in the dialogue state a part of the context model in which all those properties of the dialogue history are recorded that are relevant for monologue generation
we will see that very similar rules which are also based on the information in the discourse model are used to determine which words in the sentence are to be accented
but linguistic contexts have a peculiarity they change during processing discourse entities are added objects and expressions move into and out of focus as sentences are generated or interpreted
existing speech synthesis systems e.g. bell labs newspeak program have typically de stressed all content words that had occurred in the recent past
scores for each pair s alignments should sum to NUM
these requirements have been incorporated in the text generator which also presents the sentences in such a way that the text shows a certain coherence
when the generation module outputs a sentence the generated structure contains all the syntactic information that was present in the s template from which it results
these pairs can be given weightings but the emphasis of the approach is on the list of valid pairs rather than the weightings assigned to each pair
an unfulfilled goal is pushed forward or stored for later processing
contributions are planned as reactions to the changing context and no dialogue grammar is needed
finally conclusions and filture directions are given in section NUM
a communicative situation c communicative context role
consideration may i say this NUM
pressure on the agent to react in a particular way
evocative intentions put live rights and obligations of the agents
let us assume that our database of already analyzed examples contains an ad which includes the following knowledge of dutch an advantage and which is linked to a schema with slots filled roughly as follows null skills language lang nl skills language keq an advantage now suppose we want to process ads containing the following texts knowledge of the english language needed
NUM dialogue contributions are constructed in three phases corresponding to the three main processing tasl s
evaluation of the user goal concerns an appropriate joint purpose and determines the next system goal
in this section the parsing experiments on texts of two domains are reported
then we apply each of the grammars to some texts of different domains
undoubtedly these conclusions depend on the parser the corpus and the evaluation methods
this establishes a one to one correspondence between subsets of the udrs and lfg formalism
atomic attribute value pairs can be included as unary definite relations
they can not exceed the top level drs it i.e. li it
that is about NUM NUM to NUM NUM words or about NUM NUM to NUM NUM sentences
thus the definition implements the garbage in garbage out principle
the base cases of the definition are provided by the three remaining clauses
note that NUM NUM is a partial function on udrs representations
it also assumes that discourse referents in quantifier prefixes are disjoint
this is the case in particular for the romance and love story domain
the model addresses both classes of misunderstanding see section NUM NUM NUM
fiction domains in the brown corpus are very similar in terms of syntactic structure
the reason for this is that the undisambiguated filters contain numerous assignments which are correct but are included only accidentally
since verbs may occur in multiple classes the number of possible assignments of ldoce verbs into classes is NUM
the observations using the brown corpus demonstrate domain dependence and idiosyncrasy of syntactic structure
table NUM also shows the performance of two other semantic filters based on hyponyms
this paper addresses the problem of large scale acquisition of computational semantic lexicons from machine readable resources
for the NUM known verbs the filter made NUM assignments to semantic classes
in the table results are shown in the form of recall precision
we also built a filter based on the union of synonyms with hyponyms of hypernyms
to see the effect of disambiguation compare the difference between undisambiguated and disambiguated synonyms
table NUM undisambiguated synonyms the semantic filter were assigned to their correct classes
our main result is that the semantic field substantially reduces the number of incorrect assignments given by the syntactic filter
the information losing aspect of transliteration makes it hard to invert
the most desirable feature of an automatic backtransliterator is accuracy
c a referring expression is free i.e.
dei ending on the type of i
lla pauli accepts the decision for himi
12b pauli rcviscs the decision for himselfi
13d pauli revises hisi decision for himselfi
the practical behavior of the algorithm fulfilled the expectations
but afl era first decision e.g.
b a pronominal is free i.e.
figure the kontext anaphor resolution algorithm
this could be because of conflicting information from the user or speech recognition errors
if found they are all moved to form a continuous string at the position of the rightmost occurrence
the conceptual schema tree is transformed into a text plan tree representing the rhetorical structure of the claim text
if an english version is generated directly it will produce a list of individual sentences describing the invention
content specification in our system is a process of interactive traversal of a conceptual schema of patents about apparatuses
the latter can occur both in the conceptual schema tree nodes and in the values of the template slots
the planning stage is guided both by constraints on the patent claim sublanguage and the general constraints on style
in the final string the boundaries between the templates are retained the string is bracketed
after the initial sorting the procedure checks for occurrence in the sibling templates of the same predicates
labels are assigned by a morphological analysis module to strings in input templates and nodes in conceptual schema instances
our hypothesis was that this measure is appropriate because the patents were written by expert patent specialists who actu1
we start with the two start symbols which are linked
therefore building new information extraction systems requires an integrated environment that supports NUM the development of a domain specific annotated corpus NUM the multi faceted analysis of that corpus NUM the ability to quickly generate hypotheses as to how to extract or tag information in that corpus and NUM the ability to quickly evaluate and analyze the performance of those hypotheses
usually likes to be robbed just to see the disappointment because he holds no money
NUM but unfortunately i had a little money
the talking subject refers to the person who pronounced the words
the following example illustrates these constraints i was robbed yesterday
NUM but luckily i had a little money
NUM but luckily i had little money
NUM but unfortunately i had little money
the structure is computed recursively on the a structure
figure NUM a structure for i was robbed yesterday
our NUM NUM million words and the verbs are all common so it is likely that considerably more exemplars of each verb were available
we hope that this decision to minimize the training corpus can be reconsidered for future evaluations
persons with multiple links from in and out objects are classified as relevant
james and status out the tree returns a positive classification
wrap up used NUM different c4 NUM decision trees in its processing
recall and precision had not leveled out after NUM training texts
two of this year s lessons are painfully obvious to us
this conundrum has all the earmarks of a no wi n situation
other lessons are more subtle and were not immediately obvious at least to us
it appears that NUM trainin g texts are not enough for crystal s dictionary induction algorithm
the grammar thus consists of a set of rules and a set of lexical entries for each rule an element of the right hand side is identified as the head of that rule
second the results of this task will be evaluated by the probability assigned to the correct state of affairs with respect to an entire coreference set and not by the number of correct antecedents assigned to anaphoric expressions
in the example below the words life and manufacturing are used as seed collocations for the two major senses of plant labeled a and b respectively
a human judge must decide which one but this can be done very quickly typically under NUM minutes for a full list of NUM NUM such words
this can be done automatically using words that occur with significantly greater frequency in the entry relative to the entire dictionary
this approach is least successful for senses with a complex concept space which can not be adequately represented by single words
words not only tend to occur in collocations that reliably indicate their sense they tend to occur in multiple such collocations
2here i use the traditional dictionary definition of collocation appearing in the same location a juxtaposition of words
comparative performance column NUM shows the relative performance of supervised training using the decision list algorithm applied to the same data and not using any discourse information
different positions often yield substantially different likelihood ratios and in cases such as pesticide plant vs plant pesticide indicate entirely different classifications
there is additional hope for these cases however as such isolated tokens tend to strongly favor a particular sense the less bursty one
quick hand tagging of a list of algorithmically identified salient collocates appears to be worth the effort due to the increa3ed accuracy NUM NUM and minimal cost
this is the problem in making a lexical entry list in dictionary construction
baayen and lieber NUM studied the productivity of certain english affixes in the celex lexical database in an effort to study the differences between frequency of appearance and productivity
if the word ks defined in the lexicon its definition consisting of the word its part of speech and various features affecting its us is used to parse the sentence
table i shows the total number of deletions and insertions as well as each as a percentage of the total number of parses for all the sentences in the corpus NUM
one of the problems facing natural language parsing nlp systems is the appearance of unknown words words that appear in sentences but are not contained within the lexicon for the system
assuming that all the other words in the sentence are in the lexicon then based on purely syntactic knowledge the unknown word must be a finite tense verb either past or present tense
badecker and caramazza NUM discussed the distinction between inflectional and derivational morphology as it applies to acquired language deficit disorder and in general to the theory of language learning
this system is a post mortem error handling technique if and only if the sentence fails to parse the parser tries again using a more liberal interpretation in its word look up algorithm
the program automatically assigns grammatical functions
figure NUM rightward sorted strings starting from
this is the point at which the insufficieney of mcca categories and wordnet synsets becomes visible
this suggests that categorical systems used for tagging need to be augmented with more precise lexical semantic information
for exrmple detached ro es has a total of NUM words with an average expected fi equency of NUM
the general principles of category development followed in these procedures are described in litkowski in preparation
these scores are then subjected to analysis to provide additional results useful in social seience and information retrieval applications
they recapitulate the wordnet synsets by acting as supemategories similar to those identified in hearst sch ltze
analysing the interaction between different tests and refining the weightings used for each
while the sense tagging results are fairly encouraging the part of speech tagging results arc at present relatively poor
the part of speech tagging was also tested on the same texts to similarly strict criteria i.e.
selectionai preference pattern matching has proved one of the most useful of all tests
a good example is the sentence the head asked the pupil a question
thus all the senses can be correctly assigned just by using selectional preferences
where there are cross references in the dictionary or where there is genuine ambiguity
three other part of speech taggers were run on the same texts for comparison
our method is more ambitious but intrinsically less efficient than hidden markov model approaches
the verb asked with two objects can only have the pattern human asked human communication
we now need to address how we evaluate a derivation
so one of the following propositions will be adopted
speaker agt agt is the current speaker
a then accepts the refashioned referring expression in line NUM
the basic vocabulary consists of five disjoint sets gfs subcategorizable grammatical functions gf non subcategorizable grammatical functions sf semantic forms atr attributes NUM is an o eration mapping a into one of its disambiguations c
for the reverse mapping assume a consistent udrs labeling e.g. as provided by the v mapping and a lexically specified mapping between subcategorizable grammatical functions in lfg semantic form and argument positions in the corresponding udrt predicates ii gel g2
after defining dtg in section NUM we discuss in section NUM dtg analyses for the english and kashmiri data presented in this section
these distances are measured crudely i.e. by character length so as not to be dependent on the accuracy of methods for identifying more complex boundaries e.g. clause sentence and discourse segment boundaries
the edge from the root of r to the root of the subtree ri is labeled by li NUM i k defined as follows
that is an elementary structure can not be subserted into more than one structure since this would be counter to our motivations for using subsertion for complementation
an i na rcb specifies some elementary d tree a e d a component of a and the address of a node within that component of a
if a node is associated with a sac containing a pair d a then the d tree a can be d sister adjoined at r
since the direct object of kor has wh moved out of its clause the d edge connecting it to the maximal projection of its verb has no sic
b rameshan kyaal chu baasaan ki rameshzrg what is believenperf that me kor ti izrg do what does ramesh beheve that i did
the string language l g associated with g is the set of terminal strings appearing on the frontier of trees in t g
knowledge acqulsition classification of terms in a thesaurus from a corpus
special requirements robustness and efficiency lead to a NUM layered hybrid architecture for the dialogue module using statistics an automaton and a planner
in the remainder of this paper first the requirements of the verbmobil setting with respect to functionality and design of the dialogue component section are introduced
therefore we chose a hybrid NUM layered approach see fig NUM where the layers differ with respect to the type of knowledge they use and the task they are responsible for
while hierarchical planning spans out an attentional hierarchy of the discourse produced local navigation fills details into the primitive discourse spaces
like previous approaches for modeling taskoriented dialogues we base our ideas on the assumption that a dialogue can be described by means of a limited but open set of speech acts e.g.
while the first is used to describe utterances which state date s or places to be negotiated the latter corresponds to contributions that contain a mutual agreement concerning a given topic
prediction akzeptanz ablehnung while the finite state machine accepts the sequence of speech acts without failure the predictions made by the statistical module are not correct for de006 NUM
therefore we have first to devise an architecture for natural language generation that facilitates a natural and effective segmentation of discourse
metonyms within content categories had to be manually classified since such relations were often not derivable from real world knowledge bases
we invite practical systems developers to help us assess their products against this generic template allowing us in turn to maintain and refine the theoretical generic model to keep step with practical developments
in short the preliminary nature of the task design is reflected in the somewhat unmotivated boundaries between markables and nonmarkables and in weaknesses in the notation
this evaluation is called the met multilingual named entity and like muc NUM was carried out under the auspices of the tipster text program
note that the number of instances of percentages in the test set is so small that a single mistake could result in an error of NUM
the experimental configuration resulted in a three point decrease in recall and one point decrease in precision compared to the performance of the baseline system configuration
nearly half the sites chose to participate in all four tasks and all but one site participated in at least one sgml task and one extraction task
in the answer key for the walkthrough article there are NUM enamex tags including a few optional ones six timex tags and six numex tags
for common noun phrases the systems were not required to include the entire np in the response the response could minimally contain only the head noun
the introduction of two new tasks into the muc evaluations and the restructuring of information extraction into two separate tasks have infused new life into the evaluations
as indicated above markables include names of organizations persons and locations and direct mentions of dates times currency values and percentages
the article contains about NUM words and approximately NUM coreference links of which all but about a dozen are references to individual persons or individual organizations
one route to this happy state of affairs would be to develop efficient processing mechanisms for the richer devices directly
the feature value of f above holds only of lcb NUM a rcb and lcb NUM b rcb
however to express the constraints we need to express the encoding has to be a little more complex
this means that we are losing information we can not now use the stem feature to distinguish these verbs
in order to do this we have to make our original lattice a distributive one making new disjunctive types
however the notion of generalization captured in this way is not distributive because the lattice is not
furthermore what in the original lattice was the glb of two types is now the lub and vice versa
we will often assume that such defaults have been declared to make the various example rules and entries more succinct
pp meanings are functions from vp meanings to vp meanings or more generally from predicates to predicates
safety training and combat train were terms related to a type of training with regard to personal safety
if the forms are identical strings as in the frequently repeated dooner or mccann in th e walkthrough article then they are merged
and all other cases were counted as wrong as well for example recognizing a suggestion as an acceptance
in some cases the discourse processor is not able to assign a speech act based on plan inference
and there are no semantic clues in the sentences themselves to let the hearer know which week is intended
although such a specification is clearly open ended we approximate the full set of constraints in terms of two parameters of the discourse context a reasonable degree of intimacy between speaker and hearer and an informal register of conversation
we approach this problem by modeling attentional state as a graph structured stack rather than as a simple stack
when the response is attached to the suggestion the rest of the time expression can be filled in
because the speech acts for the test dialogues were coded by one of the authors and we do not have reliability statistics for this encoding we would draw the attention of the readers more to the difference in performance between the two focusing mechanisms rather than to the absolute performance in either case
various type logical categorial formalisms or strictly their implicational fragments differ from the above system only in imposing further restrictions on resource usage
the point rather is that such a combination will typically not happen as a component in a proof of some other overall deduction
NUM the method involves compiling the original formulae to indexed first order formulae where a higher order initial formula yields multiple compiled formulae e.g.
the local grammar proposed so far should be completed by the description of the following transformation
in an incremental processing context the words of a sentence are delivered to the parser one by one in leftto right order
for the morphological recognition module in this system we constructed a list of suffixes and prefixes by hand using lists found in quirk et al NUM
graph based approaches require the entire dialogue state transition graph for an application to be pre specified
tokens next to punctuation marks and tokens with rare words as neighbors were not included
what these approaches have in common is that they classify words instead of individual occurrences
but even with high frequency words the simple vector model can yield misleading similarity measurements
we also gain efficiency since we can manipulate smaller vectors reduced to NUM dimensions
these phrase constraints could then be incorporated into the distributional tagger to characterize non local dependencies
table NUM precision and recall for induction based on word type and context
note that the information about left and right is kept separate in this computation
again an svd is applied to address the problems of sparseness and generalization
these examples demonstrate the importance of representing generalizations about left and right context separately
in the preceding stages of building the skeletal sentence structure and covering the remaining semantics the generator is mainly concerned with consuming the initial semantic structure
the generation architecture makes explicit the decisions that have to be taken and allows for experiments with different generation strategies using the same declarative knowledge sources
this captures the intuition that the generator should try to express as much as possible from the input while adding as little as possible extra material
we consider uppersem to be a generalisation of builtsem and lowersem a specialisation of builtsem in terms of the conceptual graphs that represent them
the notion of semantic head and their connectivity is a way to introduce a hierarchical view on the emantic structure which is dependent on the language
in protector we will use a much more sophisticated notion of what it is for a conceptual graph to match better the initial semantics than another graph
if a node is visited more than once grammar rules determine when and how much of its content will be uttered NUM
null integration it should be possible to incorporate the semantics of the mapping rule into the semantics of the current structure being built by the generator
d tree grammar dtg NUM is a new grammar formalism which arises from work on tree adjoining grammars tag NUM
we have augmented the dtg formalism so 7in dialogue and question answering for example the syntactic form of the generated sentence may be constrained
in such cases a simple top down parser will be incomplete and a left corner parser will resort to buffering the input so wo n t be fully 2note that ccg does n t provide a type for all initial fragments of sentences
it is the choice of this particular transition at this point which allows verb phrase modification and hence assuming the next word is sue an implicit bracketing of the string fragment as john likes sue
state application and state prediction together provide the basis of a sound and complete parser NUM parsing of sentences is achieved by starting in a state expecting a sentence and applying the rules non deterministically as each word is input
here an np s was expected but likes only provides part of this nit differs in not being a rule of grammar here the functor is a state category and the argument is a lexical category
the main reason is that applicative cg is a much simpler formalism which can be given a very simple syntax semantics interface with function application in syntax mapping to function application in semantics NUM NUM
woman x found john x g p mary x where p is a function from a left argument mary of type e and a whargument also of type e
lcb w x y x z where w y z considering this informally in terms of tree structures what is happening is the replacement of an empty node in a partial tree by a second partial tree i.e.
the addition of extra operations also means that for any given reading of a sentence there will generally be many different possible derivations so called spurious ambiguity making simple parsing strategies such as shift reduce highly inefficient
the prefix t n a t identifies the table t to which this item belongs assigns this item a unique identifying number n provides the number s of the item s a which caused this item to be created and displays its tag t p for program t for table and s for solution
lex opzettelijk adv d lex ont2ijken i add adjunots s np np i lex lijkt te i y add adjuncts s np s np io division io i y
table NUM lists the number of polysemous words in each part of speech making up the top NUM top NUM of word occurrences in the brown corpus where the polysemous words are ordered in terms of their occurrence frequency from the most frequently occurring word to the least frequently occurring word
bc50 consists of NUM NUM occurrences of the NUM words that occur in NUM text files of the brown corpus
while inquery is optimized for quickly searching one or more multi gigabyte document collections inroute is optimized for quickly comparing a steady stream of documents to a large number of profiles
the user may also query by example selecting articles which contain the sort of information they are interested in and allowing the system to build a query to locate similar articles
the analysis of this data will attempt to evaluate the user acceptance of the new features in prides such as the internet delivery mechanism relevance ranking and automatic query refmement
the user can list their mail hit and save folders and then open any folder to see a list of the folder contents
inference networks are ideally suited for the uncertainties encountered when matching a person s statement of an information need with a document expressed in natural language
to fulfill the requirements for the prides pilot system and simultaneously lay the foundation for the future system the prides architecture is comprised of three layers
in addition to using inference networks inquery incorporates several different methods of combining evidence enabling a rich query language in which to express information needs
the user interface is implemented using a world wide web browser a web server and the hypertext markup language html to provide custom screens
indeed the wordnet software has an option for grouping noun senses into a smaller number of sense classes
upgrade the api for robustness in an integration environment
where custom software was necessary for the prides system it was designed within the layered architecture approach described above in order to guarantee maximum flexibility scalability and extensibility
the method returned satisfying results regardless of the size of the input file
however a surprisely large numbe r of invmid strings were also extracted
figure NUM cross entropy of grammar across domains
figure NUM size and precision press report
in other words if the size of the training corpus is the same using a training corpus drawn from a wide variety of domains does not help to achieve better parsing performance
one of our basic claims is the following
figure NUM size and precision romance love
figure NUM size and recall romance love
sending of the sentence transformed in an atibmative form and pret rocessed tilt tbllowing messages will be sent send pret i ansf hfform asse rt sentence should i correct the paper and address with the linguistic conl extual laws t resented in the tirst part
the main verb angle in example e25 is given a paraphrased translation g to change the angle
we observed the same phenomena in the previous experiment
only strings that have possible boundaries are generated and their occurrence counted
despite the first character after a NUM being
in our choice of the linguistic filter we lie somewhere in the middle accepting strings consisting of adjectives and nouns
nns inatinpt vbd cs at jjnnnn hvz ben vbn in
in addition the simple but effective chunker can also be applied to many natural language applications such as extracting the predicate argument structures NUM NUM grouping words NUM and gathering collocation NUM
where pi denotes part of speech i f pi pi l is the frequency of which pi l follows pi f pi and f pi l are the frequencies of pi and pi l and n is the corpus size in terms of the number of words in training corpus
and cc rb rb nc plus in jj nn fw cc with in in ri nc without in ri physics nn politics nn nns mathematics nn associates nns vbz am bem fw ai hvz bez ber those words which can not be found in lob corpus are removed
it should be pointed out that this compilation result is quite a dramatic improvement on more naive on line approaches to ttpsg processing
we plan to apply earley deduction to our scheme in the near future and experiment with program transformation techniques and bottom up interpretation
this is unsatisfactory at least from an hpsg point of view since hpsg feature structures are supposed to be maximally specific
the research reported here was carried out in the context of sfb NUM project b4 funded by the deutsche forschungsgemeinschaft
since we assume a closed world interpretation of the type hierarchy we really only need to compute proper definitions for minimal types
he list ne listcon hd ttvp listtypc
each hierarchy relation of a type references the constraint relation and makes sure that the constraints below one of the subtypes are obeyed
hpsgii gives a closed world interpretation to the type hierarchy every object is of exactly one minimal most specific type
for hiding types we do exactly the same thing except that we do n t have any structure to begin with
null we will stick to an avm style notation for our examples the actual program uses a standard feature term syntax
finally this formula is simplified as a markov character bigram model shown below
after the repair processing the number of the errors is reduced to NUM
only NUM NUM of repetition repairs occur across more than NUM utterances issued by other speakers
table NUM shows the results when the glottal stop information is used to enhance the baseline model
in chinese conversation some words or phrases are frequently repeated but they are not repairs
achieve the precision rate of NUM NUM and the recall rate of NUM NUM
let s s i s NUM s3 sn be a syllable string and c cl c NUM c NUM cn be one corresponding character string
mandarin chinese has approximately NUM NUM syllables NUM NUM commonly used characters and more than NUM NUM words
because the repetition repairs form the majority we focus on the repetition repairs in this paper
total NUM NUM of repetition repairs occur between two consecutive utterances without interrupting by other speakers
figure NUM after lexical analysi s
perhaps uncharitably we can view optical character recognition ocr as a device that garbles perfectly good katakana sequences
given a pronunciation p we may want to search for the word sequence w that maximizes p wtp
translators must deal with many problems and one of the most frequent is translating proper names and technical terms
each alignment is scored with the product of the scores of the symbol mappings it contains
coherence marking some particles can be employed to facilitate the embedding of the utterance within the context and to check the common basis of the participants
in addition to the basic patterns middle verbs and symmetric verbs are handled
c nielsen co ng said vg george
but should we generalize united steel workers to union or to organization
determining the correct level of generalization of the hypothesized rule is a difficult problem
in addition we look for a previous object of the right domain specific type
we have done this on a small scale five texts for one trec topic
if the topic concerns ibm references to the computer company will increase the score
in the sample text this phase results in the following labeling a
v have indicates some form of the verb have
then the subject codes of each sense of each word are compared with the subject domain for the sentence and the number of matches noted
this examples text forms a convenient hand sense tagged corpus though with only one word the headword sense tagged in each example
an example of how different taggers can interact is given by the following two sentences he was fired with enthusiasm by his boss
all the following examples are taken from the reuters categoryset and involve words that actually occur in the documents category
when an argument is encountered the class specified in the selectionai preference pattern is matched against the possible classes for the word
repair indicates problems in planning and performing the output signals a new start and thereby is also a turn holding signal
the adjective class is matched against the class of the noun which it modifies using much the same scoring system as for the verbs
we focus here on particles in german suggest a framework for representing their roles in utterances and sketch an approach for adequately translating them into english
the next stage of our research is to use the test corpus section NUM as a training corpus to fine tune the weightings
to improve the translations for the second phase of the verbmobil project we propose to build upon the framework of discourse functions
the form is divided into four major sections
a complete sample dialogue taken from the system s present performance will serve as a reference throughout the paper
refinements can 2cancellations of reserved slots due to a high priority requcst are a straight forward extension of the present coverage
the tverage anlbiguity of a colilpoi lid ilolilt is NUM NUM and this low anibiguity niust ha re eonl ributed l o tile iiigli grecnlent ratio of tile proposed indexing method with lil lill l indexing
domain action hour of appointment duration etc a different set of rules is used
the e mails were manually analyzed and annotated with major syntactic and semantic features as well as speechact information
for instance any time slots the owner does not wish the agent to use can be blocked
it is based on finitestate automata that were defined with help of an annotated corpus of e mail messages
as soon as a dialogue is completed the assigned virtual system can be reused to process another one
in order to ensure correct processing a manager may operate in only one virtual system at a time
solving anaphoric and deictic relations involves a rather complex machinery which borrows many concepts from discourse representation theory
either they contain expressions which need to be delimited in order to be pragmatically plausible underspecification e.g.
since the problems associated with discourse particles are largely absent when processing written language computational linguistics has for most of its history not dealt with these problems
distinguishing elements in an open class requires semantics while in a closed class it can be done on syntactic grounds only
there is still no NUM NUM mapping between particles formulas and discourse functions in analysis nor between discourse functions and their realization in the target language
to represent this decision the lexical chooser copies information from the top level semantic representation in the semr feature under process cf
these instances of misunderstanding are reflected in the semantic frame
a word is morphologically ambiguous if k NUM the number and character of the analyses depend on the language model
test group1 consisted of NUM ambiguous word types chosen randomly from all the ambiguous word types appearing more than NUM times in the corpus
to illustrate the whole process let us reconsider the ambiguous word hqph flpn and its three different analyses
the well known new mexico example in information retrieval describes an oft encountered problem when single word searches are employed searching for new and mexico independently will retrieve a multitude of documents that do not relate to new mexico
suppose a word w has k different analyses then a1 ak will be used to denote these k analyses
to train the translation probabilities p j fc we use a bilingual orpus consisting of sentence pairs NUM NUM s
looking at such alignments produced by a hmnan expert it is evident that the mathematical model should try to capture the strong dependence of aj on the previous aligmnent
the organization of the paper is as follows
thus l he resulting training procedure is straightforward
in addition we assume that the t lcb mm alignment probabilities p i i depend only on the jump width i i
to achieve this goal the approach uses a first order hidden markov model hmm for the word alignment problem as they are used successfully in speech recognition for the time alignment problem
although such tools are becoming more widely available in many languages they are still hard to find
lcb vogel ney t illmann inf ormat ik
the description on above is only word w belong to a certain class in i certain level without consider the affection from its upper levels
the following shows the actual contents of each of the three cd roms disks NUM NUM and NUM
first the better results are very similar and it is unlikely that there is any statistical difference between them
figure NUM the tncb after brown is moved to dog the big brown dog barked pa k he figure NUM the final tncb after big is moved to brown dog
again for more details on the various runs and procedures see the cited papers in the trec NUM proceedings
this first involves deleting tncb NUM noting it and raising node NUM to replace node NUM we then introduce node NUM above node NUM and make both nodes NUM and NUM its children
computational linguistics volume NUM number NUM ambiguity problem all we need to do is define a new set of rules for generation of sw sets in that other language
mij in NUM is shown in formula NUM
according to luhn s assumption o frequently appears throughout paragraphs
we have conducted three experiments to examine the effect of our method
we extracted keywords using this feature of the degree of context dependency
the results are shown in table NUM
the authors would like to thank the reviewers for their valuable comments
table NUM the words and their frequencies
NUM NUM key paragraphs experiment effectiveness of the method
the result is shown in table NUM
table NUM the results of comparative experiment
fortunately lt nsl does n t require this
the details of this are a matter of ongoing research but an important motivation for the architecture of lt nsl is to allow such edits without requiring that the read only information be copied
while it is possible if sentence boundaries are marked in the corpus to restrict the search to within sentence matches there are few facilities for making more refined use of hierarchical structure
this makes it easier to be clear about what happens when a different view is needed on fixed format read only information or when it turns out that the read only information should be systematically corrected
NUM NUM sggrep and the lt nsl query language the iei provides the program mer with two alternative views of the nsgml stream an object stream view and a tree fragment view
a side effect of the proof may be some voice interactions with the user to supply missing axioms as described above
there is only limited access to structural information
the english atelic manner verb march and the telic pp across the field from NUM is best translated into spanish as the telic verb cruzar with the manner marchando as an adjunct similarly in changing the weekend verbs i.e.
however in certain circumstances the approximation above will generate probability mass for an impossible case specifically when it is known a priori that x is incompatible with one of the templates y1 y i
null our periphery definition entails that in a sentence which contain more than one projection with a right periphery multiple locations for extraposition exist correspondingly
cf the following german data which include the extraposition of adjuncts in NUM and NUM and that of complements in NUM and i0
net will part everyone be allowed NUM es wird wohl jeder vp einen hund ffittern der hunger hat dfirfen
rather than having no appropriate senses for this syntactic pattern we map it to wordnet s verb frames something s adjective noun and somebody s adjective by analyzing experiment results regrsssively
d the fact that no island constraints for extraposition exist follows from our use of extra island restrictions are formulated for slash and hence do not apply to extraposition
up thanks go to anette frank tibor kiss jonas kuhn kai lebeth and stefan miiller for comments and suggestions in connection with the research reported here
judges are told to assume that they have the option of aborting translation if recognition is of insufficient quality judging a recognition hypothesis as unacceptable corresponds to pushing the abort button
certainly the lexical rules are proposed as a tool for generation of new schemata or new classes in a inheritance network
they use two main devices for lexicon representation inheritance networks and lexical rules
first these solutions use inheritance networks and lexical rules in a purely technical way
for instance all passive trees or all trees with extracted complements can be generated
we present and a tool that automatically generates the tree families of an ltag
out of this syntactic database and following principles of well forrnedness the generator creates elementary trees
the proposed type of hierarchy is meant to be universal and we are currently working on its application to italian
the tool was used to generate tree families of the french grammar using a hand written hierarchy of syntactic descriptions
semantic structure is built up from linguistically relevant and universally accessible elements of verb meaning
the lcs structures the components of our lcs templates correlate strongly with aspectual category distinctions
the nature of the alternations between states and events is a subject for future research
with the changes we can automatically assign aspect to some NUM verbs in existing classes
he also points out that these properties are difficult to obtain directly from corpora
an exhaustive listing of aspectual types and their corresponding lcs representations is given below
the formal specification of the aspectual feature determination algorithm is shown in figure NUM
similarly stative verbs appeared with event interpretations and punctiliar events as durative
the durative feature denotes situations that take time states activities and accomplishments
a single algorithm may therefore be used to determine lexical aspect classes and features at both verbal and sentential levels
for each of the seven verbs for which we undertook a corpus analysis we calculate the token recall of our system as the percentage over all exemplars of true positives in the corpus
the system we have developed is straightforwardly extensible to nominal and adjectival predicates the existing grammar distinguishes nominal and adjectival arguments from adjuncts structurally so all that is required is extension of the classifier
this demonstrates the value of the classifier as a filter of spurious analyses as well as providing both translation between extracted patterns and two existing subcategorization dictionaries and a definition of the target subcategorization dictionary
NUM a patternset extractor which extracts sub null categorization patterns including the syntactic categories and head lemmas of constituents from sentence subanalyses which begin end at the boundaries of specified predicates
in the case of pp i NUM arguments the pattern also encodes the value of psubcat from the pp rule and the head lemma s of its complement s
many of these are not exploitable automatically because they rest on semantic judgements which can not yet be made automatically for example optional arguments are often understood or implied if missing
evaluating putative entries on binomial frequency data requires that we record the total number of patternsets n for a given predicate and the number of these patternsets containing a pattern supporting an entry for given class m
the experiment used the same probabilistic parser and tag sequence grammar as are present in the acquisition system see references above although the experiment does not in any way rely on the
NUM a pattern classifier which assigns patterns in patternsets to subcategorization classes or rejects patterns as unclassifiable on the basis of the feature values of syntactic categories and the head lemmas in each pattern
usenet newsgroup with emphasis on the following seven facts company name position title experience skill location benefit salary and contact information
it would be helpful to offer first the items that are most likely to be appropriate
we have built a system that attempts to provide any user with the ability to efficiently create and customize for his or her own application an information extraction system with competitive precision and recall statistics
the output of the scanning process for each article is a semantic network for that article which can then be used by a postprocessor to fill supported by fellowships from ibm corporation
groups of words that comprise these entities are collected together and con null sidered as one item for all future processing
as illustrated in figure NUM there are three main stages in the running of the system the training process rule generalization and the scanning process
in addition to wordnet the system uses ibm s languageware english dictionary ibm s computing terms dictionary and a local dictionary of our choice
for ne little is actually required beyond careful document management and printing routines
we participated in two officially scored tasks at muc NUM named entities and template elements
we exploited inference rules in several primary ways for the te and st tasks
interpretation procedures can thus remain compositional which makes them substantially simpler to write
the headword of each phrase is also identified
figure NUM recall vs degree of generalization
with this error analysis behind us we pursued a number of post hoc experiments
most interesting among them was a simple attempt at improving recall on organization names
knowing now that the addition of a stack valued feature suffices to capture the basic hierarchical structure of language additional features can be used to deal with other syntactic relations
language system but also on details of actual language use in a language community NUM this research was funded by a research studentship from the esrc
the next section sets out some of the properties that we might require from such a performance grammar and offers a formalism which attempts to satisfy these requirements
for example factoring out items on the stack as in NUM removes from the model the disinclination for long states inherent in the original corpus
considering the first of these points namely a close relation to a simple probabilistic model a good place to start the search might be with a right branching finlte state grammar
dop attempts to combine these two traditions and produce performance grammars which should not only contain information on the structural possibilities of the general NUM
it goes on to investigate ways in which a corpus pre parsed with this formalism may be processed to provide a probabilistic language model for use in the parsing of fresh texts
NUM sentences of less than NUM words were chosen randomly from other texts in section n of the brown corpus n09 n14 and fed to the parser without alteration
it would not be difficult to make a small extension to the present model to capture such information namely by introducing an additional feature containing the lexical value of the head of a phrase
how can such a simple formalism in which syntax is reduced to a string of category states hope to capture even the basic hierarchical structure the familiar tree structure of linguistic expressions
our system currently does n t handle entity crossreferencing
for example polysemy is not properly handled
the corresponding fd is shown in figure NUM
we have implemented a tcp ip interface to surge
for our task symbolic text generation precision is more important than recall it is critical that the extracted descriptions are correct in order to be converted to fd and generated
figure NUM shows the web interface to profile
in the previous example if president bill clinton is used in a sentence then head of state can be used as a referring expression in a subsequent sentence
this is because when the training data entails a specific classification with high certainty most in a probabilistic sense classitiers consistent with the data will produce that classification
in addition it computes the probabilities of the second best functions of each daughter node
this paper investigates an approach for optimizing the supervised training learning phase which reduces the annotation effort required to achieve a desired level of accuracy of the trained model
as a representative task for probabilistic classification in nlp we experiment in this paper with sample selection for the popular and well understood method of stochastic part of speech tagging using hidden markov models
we use the average entropy rather than the entropy over the entire sequence because the number of committee members is small with respect to the total number of possible tag sequences
here we examined the number of lexical and bigram counts that were stored i e were non zero during training using the two member selection algorithm and complete training
corn null mon features are the words involved in the attachment such as the head verb or noun the preposition and the head word of the pp
first it was found that the simplest version of the committee based method using a two member committee yields reduction in annotation cost comparable to that of the multi member committee
when generating a committee of models however we are not interested in the best model but rather in sampling the distribution of models given the statistics
this error could also table NUM tagging accuracy for assigning phrase categories depending on the manually assigned category
that is the weakest strength of word cluster will be assumed to have an implicit spelling error
using equation NUM the strength of association of words in a chain can be calculated
this work attempts to provide a computational solution called word filtering to handle those three points prior to parsing
implicit spelling errors one of ill formedness usually encountered in documents are caused by either carelessness or lack of knowledge
the association between words in the boat shake is stronger than in the boat ox
surface ordering of dependent phrases of either the source or target is not taken into account in the transfer mapping
number ofta s number of words percentage both word boundary and tag ambiguity increase the complexity in syntax analysis
thus collected statistics not only emphasize on the frequency of using individual words but also on the cluster of words
however this improvement is relatively small around NUM reduction in the number of utterances containing translation errors
words having an occurrence below or equal to this threshold in the training text are counted as less probable words
in table NUM the number of pos tags used for each language and each set of grammatical categories is shown
the optimal criterion is to choose the tags that are most likely to be computed independently at each word event
methods for the estimation of these probabilities have already been proposed e.g. the use of word endings morphology
the study provided encouragement for a divide and conquer analysis strategy in which parsing and perhaps translation of pause units is carried out before or even without attempts to create coherent analyses of entire utterances
a val can be an atom or a variable drawn from a predefined finite set of possible values z the ith element in the tuple corresponds to the j z i th element in rule expressions
phone recognition is generally handled using hidden markov models hmms word recognition is often handled using viturbi style search for the best paths in phone lattices and sentence recognition is handled through a variety of parsing techniques
the paper sketches work in six areas interactive disambiguation system architecture the interface between speech recognition and analysis the use of natural pauses for segmenting utterances dialogue acts and the tracking of lexical co occurrences
if by consensus several patterns can yield paraphrases which are judged equivalent in context and if the resulting pattern set is not identical to any competing pattern set then it can be considered to define a communicative act
given a sufficiently rich logical language the meaning of a natural language sentence can be represented as a description in this sense by assuming sentences refer to entities in a discourse model cf
we have presented the difficulties of grapheme to phoneme conversion for english and french
english and french have interacted and continue to interact with each other
external characters ele2e3 is a string
in the output buffer resulting in scandal ous ness
for speech synthesis one output string is needed for a word
see section NUM NUM for stress assignment
in french the situation is similar
these rules may be context sensitive or context free
notice that many of the rules seem to be employed more often by men than by women
this will require extending our tag based probability estimation step to parse the phone strings from the forcedviterbi
the tree set for book includes trees with definite and indefinite determiners since the hearer can uniquely identify book19 the definite tree is selected and substituted as the leftmost np as in figure NUM
bamboo sword figure NUM an example of the morpheme network
finally the use of a humble verb form indicates that an object referent is respected by speaker and that the social status of an object referent is higher than that of a subject referent
the linguistic realization of these types of honorifieation is manifested by specific morphemes such as an honorific suffix honorific case markers an honorific infix honorific verbal endings and hmnble verb forms
an interesting future research direction is to construct a theory that handles markov processes
in the computation of social status it is necessary to know the binary relation such as the person a is respected by the person b in the honorification system the person who respects others is always speaker
in hpsg which adopts a sign based approach the information about sentence external individuals such as speaker and addressee as well as the information about the persons mentioned in a sentence can be included in a lexical sign
NUM indsp indo when the honorific infix si occurs in a verb the social status of a subject referent is higher than that of speaker and addressee as represented in NUM
if a dialogue is coherent the order of the social status of the individuals involved in the dialogue is produced whereas when a dialogue is found incoherent the reason for incoherence is produced
the information about who honors whom and about relative social status of the individuals involved in a sentence is collected at sentence level by the background and social status consistency principle stated in NUM
our approach sets a new direction of processing korean in that it considers and implements the important fact that a korean sentence is constrained by relative social status of the individuals involved in the sentence
the advantages of including contextual information in the implementation are that it is possible to catch the context where a sentence is felicitous and it is also possible to detect whether a dialogue is coherent
we have proposed an estimation method from ambiguous observations and a credit factor
the result of this labeling is a vector of phone likelihoods for each acoustic frame
to accommodate for rule features each rule may be associated with an n j tuple of feature structures each of the form attributel vall attribute val2
let f be the most specific field in tucurrent above the level of time of day
a temporal unit is also the representation used in the evaluation of the system
for example our system development was inevitably focussed more on some types of slots than others
although content dependent features of conversation can be modelled to some extent within a phrase storage approach this will have to be supplemented by a phrase construction component
if a two level grammar is compiled into an automaton denoted by gram and a lexicon is compiled into an automaton denoted by lez the automaton which enforces lexical constraints on the language is expressed by
first anaphoric chains and competing discourse entities were manually annotated in all of the seen data
among the second less constrained data there are four training dialogs and three test dialogs
orders can be assigned to units for example in figure NUM an m1a1 platoon on the bottom left has been assigned a route to follow
we are also developing a capability for automatic logging of spoken and gestural input in order to collect more fine grained empirical data on the nature of multimodal interaction
hayer vp NUM v np NUM i i have t during r having have having hayer havee c
the multimodal command involves speech recognition of only a three word phrase while the equivalent unimodal speech command involves recognition of a complex twenty four word expression
speech recognition operates in either a click to speak mode in which the microphone is activated when the pen is placed on the screen or open microphone mode
we have presented an architecture for multimodal interfaces in which integration of speech and gesture is mediated and constrained by a unification operation over typed feature structures
for example if overlapping with or just after the gesture the user said barbed wire then the line feature interpretation would be preferred
the horizontal range of segment j corresponds to a horizontal gap in simr s first pass map
the linguistic data consortium plans to publish both the maps and the alignments in the near future
even so gsa performs at least as well as other alignment algorithms and usually better
therefore gsa also considers the confidence level with which the length based alignment algorithm reports its re alignment
a naive solution is to merge these blocks and then to re align them using a length based method
whenever the component sentence lengths suggest a more fine grained alignment simr s output is not trusted
in a separate development bitext i have found that simr is usually wrong in these cases
simr s initial output has more expressive power than the alignment that can be derived from it
each cell in the grid represents the intersection of two sentences one from each component text
different parameter settings considered by the optimization process resulted in different bitext maps for the development bitext
the drcc component of pro verb models this behavior with the following four reference rules
as an example let us look at the pea with the name derive below
for trivial proofs that demonstrate no characteristic patterns however this technology will fail
to be legal a rotation must preserve symbol order on both output streams
only when none of them is applicable will a local navigation operator be chosen
cr now accepts only the sequences of tuples which appear in contexts in the grammar but including the partitioning symbols p however it does not force surface coercion constraints
for example if s is a tuple of strings and 0p s is an operator defined on s the operator can be extended to a relation r in the following manner
the chart size is n2 NUM n NUM for n word sentence
note that in r5 and r6 NUM the lexical expressions in both rules ignoring 0s are equivalent NUM both rules are composite and NUM they have different surface expression in r
NUM compute the new probabilities based on the newly counted frequencies
the algorithm can be used for class based or tag based dependency grammar
the new usage count of a dependency relation is calculated as follows
we thank stefan brandle reva freedman and michael glass for continued enhancements to v NUM as part ol their research on v NUM and for writing this document
there is an exception for chart entries of n lth column
james are merged relying on the alias information provided by nametag
the display shows the individual credit assignments as well as the recall precision subtotals fo r
the best l and the best lr always share the same m
in many cases this is a good assumption it provides what one may call the author s perspective of the text
constructs covered by the grammar include verb second and verb final clauses
the system is not always able to determine constituent heads correctly
where ge is the abbreviation of geology
we have made two extensions to the form of standard context free grammars
unlabeled words often indicate inadequacies with lexicon coverage rather than the grammar
results are shown in table NUM
experiments show promising performance on chinese sentences
in german verb prefixes can be separated from the verb
therefore this measure is a kind of weighted precision NUM
again not to be used with rules that can cause circular derivations
such errors may be avoided with further development of the grammar
word classes relevant to lexical rule application are automatically detected and the corresponding finite state automata are refined in order to avoid lexical rule applications that are guaranteed to fail
by an occasional distorted use of the partof relation applied to individual concepts transitivity was invalidated this pertains to cases of the type the alps are part of yugoslavia the alps are part of france ere with respect to generic concepts not individual ones as the alps there is also an acceptable example of an implicit exclusive disjunctive partof value set
we suggest that the use of this lexical semantic information in tagging may provide considerable benefit in analyzing tagging results
usuajly for medicinal or relmmtlon purposes figure NUM the entailment hnk from rub i7 to touch NUM is redundant however the entailment hnk from massage NUM to rub NUM is not redundant or could massage NUM be a troponym of rub NUM NUM
interestingly it was found that most of the proposed cas seem valid for both english and japanese only two out of NUM cas seem to be monolingual for the corpus in question
however if this single valued binary relationship is split here into two binary ones linked to the components of the disjunction then all antonym value sets must be interpreted as disjunctmns and transitivity need not hold for n ary antosemy with n NUM on the other hand the antonym value set of trust NUM is to be interpreted as a conjunction
there are two main ideas behind this algorithm
let n e c be the count of taking choice elc in positive instances resulting from processing the source sentences in a training corpus
there is also an inherent difficulty in evaluating the translation task a single source utterance has many valid translations and the validity of translations is a matter of degree
here the final automaton has NUM n states
these glosses are attached to the concepts but sometimes they are not available and sometimes they are intermixed with or replaced by usage examples
again the procedures for analyzing mcca categories seem to require this type of information
standard operations include intersection union difference determinisation and minimisation
however the approximation becomes exact when conditions NUM NUM are added
NUM NUM how much floor did you lay today
NUM NUM i sold you two cups of coffee
NUM NUM carol feels intense anxiety before every dinner party she gives
NUM NUM reed bought every compaq in the store
only two coffees are sold in this store ethiopian and costa rican
NUM NUM i ordered a pizza not a slice of pizza
these men mark twain and samuel clemens are the same man
NUM NUM that what the directionality should be is not always clear
occurs with admits the contrast of a determiner singular and plural proper name
the original forward backward algorithm calculates the probability of the partial observation sequence given the state of the hmm at the time position of word in the input sentence
b i w t is an output probability of a pair of word w and tag t on the state x i
the suggestion then is that the low and high roads be traveled in tandem and that even systems aiming for full automaticity recognize the need for interactive resolution when automatic resolution is insufficient
since the coordinator surveys the whiteboard in which are assembled the selected results of all components all represented in a single software interlingua it is indeed well situated to provide central or global coordination
in this paper we describe a modern version of a similar approach given a large corpus in two languages our system produces translations of common word pairs and phrases that can form the basis of a bilingual lexicon
using xtract on three parts of the english data in the hansards corpus each representing one year s worth of data we extracted three sets of collocations each consisting of NUM randomly selected collocations occurring with medium frequency
the result as well as its verbalization is given below subset set f g the set f is a subset of g actually for mathematical texts we have only used two embedding rules with the other being the dual of rule b NUM where p and q change their places
in this example the combination of the tree in fig NUM and the first tree in fig NUM is compatible and will lead to the verbalization b since c1 and c2 are parallel
technically speaking the text structure in proverb is a tree recursively composed of kernel subtrees or composite subtrees an atomic kernel subtree has a head at the root and arguments as children representing basically a predicate argument structure
starting from a list of pms as the initial text structure the microplanner progressively maps application program concepts in pms into text structure objects of some textual semantic type by referring to upper model objects as an intermediate level
composite subtrees can be divided into two subtypes the first has a special matrix child and zero or more adjunct children and represents linguistic hypotaxis the second has two or more coordinated children and stands for parataxis
before running champollion there are two steps that must be carried out source and target language sentences of the database corpus must be aligned and a list of collocations to be translated must be provided in the source language
since in some instances parts of a sentence can be translated on a word by word basis a translator must know when a full phrase or pair of words must be considered for translation and when a word by word technique will suffice
now suppose that we have two consecutive derivations with r1 m1 c1 and r2 m2 c2 as its premises called reasons the rule of inference called method and the conclusion
although the handling of paraphrase generation already increases the flexibility in the text the default verbalization strategy will still expand the text structure by recursively descending the proof and formula structure and thereby forced to keep these structures
tools in french such as a morphological analyzer a tagger a list of acronyms a robust parser and various lists of tagged words would be most helpful and would allow us to improve our results
we wish to thank pascale fung and dragomir radev for serving as evaluators thanasis tsantilas for discussions relating to the average case complexity of champollion and the anonymous reviewers for providing useful comments on an earlier version of the paper
the dice coefficient on the other hand combines the conditional probabilities p x NUM i y NUM and p y NUM i x NUM with equal weights in a single number
however since word boundaries in the morpheme network may or may not cross on the input character sequence we can not directly apply this method to the extended algorithm
null let us consider the sentence in figure NUM two sequences of grammatical functions are to be determined namely the grammatical functions of the daughter nodes of s and vp
the replaced formulae are free of the under flow problem and their use also obviates the need to calculate the weighted sum of path probabilities of the k th ambiguous observation pk
as a consequence we have adopted a bootstrapping approach and gradually increased the degree of automation using already annotated sentences as training material for a stochastic processing module
the precision of the hmms are better than the precision of the tag bigram model despite the number of parameters of the tag hmm being smaller than that for the tag bigram model
native greek speakers are able to hyphenate most greek words fully and unambiguously
with this delinition and a weight of NUM for each of the three edit operations tile distance between mathematical and physics becomes NUM
whereas the second equation stands for a conservation of grammatical categories n as opposed to a but a change in meanings
l igure NUM analyses from the tree bank and y analysis of the prototyt e sentence obtained by analogy
tlaving defined what we un lerstand by analogy in a formal way we inspect some o its properties
they answer the correction problem what is the minimal number of cdit operations needed to lransjbrm one word into anolhcr one
in one experiment the recall is NUM NUM a quite good figure which shows thai the technique is promising
two resource critical sections are written in c the parser and the semnet data structure and its access functions
we are exploring techniques for introducing part of speech information into the lsa space so that the system can make better predictions for those sets on which it does n t yet measure up to tribayes
in particular operations are provided to support the sequential scanning of a document annotationsat nextannotations and to support thc extraction of annotations meeting certain criteria sclcctannotations
if the input violates these constraints e.g. unmatched start tags or violates sgml syntax e.g. unmatched quotation marks within tags an error will be signaled
it is therefore possible to have objectreferences to documents in collections which are not currently open it is even possible to have references to documents which have been deleted from a collection
it may be desirable to have a second external representation which much more closely parallels the internal property structure of the annotations particularly if annotations are to be exchanged over a network
this translation would be performed by a component which will guide an analyst in producing a customizedextractionsystem this interactive translation component is labeled customize below
ddmmyy NUM if the addressee source annotations are recorded when the document is indexed for retrieval it will be possible to perform retrieval selectively on information in particular fields
in the present architecture these declarations only serve as documentation future generations of the architecture may seek to do type checking based on these declarations see appendix a NUM
such information sharing will be workable only if there are precise formal descriptions of the structure of these annotations and if the modules which create annotations adhere to these descriptions
standards for other linguistic annotations such as phrase structure word senses and predicate argument structure may be added as more progress is made in defining these annotations for muc evaluation
at present the development of extraction engines from a description of a class of events a scenario is a black art practiced by a cadre of information extraction specialists
the non tipster muc NUM participants could choose which of the NUM domainlanguage pairs they wished to be evaluated against
the symmetric learning approach requires only the translation of a limited number of words tie words
this entire extraction task was significantly more difficult than previous extraction tasks when measured along several dimensions i.e.
this general framework extends to other perturbations
we propose that referring expressions can be represented by plan derivations and that plan construction and plan inference can be used to generate and understand them
the mutual responsibility that the agents share not only concerns the goal they are trying to achieve but also the plan that they are currently considering
from its inception the tipster text program has been a jointly planned funded and managed program
as it relies upon simple pos tagging it is widely portable to other languages as soon as np grammars are available
for example combined with some stative verbs durch signifies verb during a certain period of time as in NUM durch leben through live durchleben live through specifying the set of adequate bases implicitly by selection restrictions allows to elegantly capture generalizations
for example while eilen to haste is an activity etw
a verb can only combine with prefixes for which an instance is specified at prefix
a prefix can only be combined with verbs with an adequate feature value at prefix
end of sentence punctuation is used very consistently and almost identically in english and in french
the input to our system is quite unlike the inputs to other generators
the actual text plan tree for our example is illustratzd in figure NUM
the input to this stage is the bracketed string of english strings
we have developed a system which helps an inventor to compose patent claims
knowledge about the invention is elicited from the inventor interactively
most of text planning and realization is carried out automatically
the second peculiarity seems inherent only to the legal sublanguage
the sublanguage for such a system has two crucial peculiarities
zone NUM contains the verb s semantic class label
realization is carried out left to right segment by segment
nouns np for noun phrases etc ccg allows infinitely many slashed categories
NUM the answer is the ambiguity
the best threshold differs in topics
put a triangle graph including e into a
we describe our results in terms of a baseline prediction system that ignores the context contained in the test sentence and always predicts the confusion word that occurred most frequently in the training corpus
figure NUM threshold and the output
therefore a higher threshold is preferred
each utterance is represented by one web page
the result is shown in figure NUM
once the lsa space for a confusion set has been created it can be used to predict the word from the confusion set most likely to appear in a given sentence
in the remaining four cases satisfaction of the expectation the target contrast item is delayed by NUM NUM sentences elaborating the source contrast item e.g.
since it is a statement informing the listener of the speaker s schedule a possible speech act is state constraint
below is a description of the different approaches implemented for calculating the match between a document and a class profile
for ex null ample consider the set of outcomes produced by rolling one of two single six sided dice
a layer is a fragment of network that corresponds to a nonterminal
this complexity can be significantly overcome when the redundant computations are avoided
a parse is composed of dark headed transitions
new weights are calculated from the corpus of documents to be classified and routed
each of the selection methods requires a weight to be calculated for each distinguishing term
let us assign the expected probabilities for the outcomes for each of the two dice
this is done here for each selection method for the last set of distinguishing words
bin NUM returns the states to which layer NUM returns
the outputs are the counts of how many of the distinguishing terms from each class are evident in a document
and NUM using the inside and outside probabilities
in the derivation structure seem is a daughter of adore the direction does not express the actual dependency and claim is also a daughter of adore though neither is an argument of the other
NUM includes three items the first one v NUM NUM is produced by the initialization the next two v NUM NUM n arid n NUM NUM are produced by the predictor a n headed subtree beginning in position NUM must be recognized and in case such a recognition occurs the governing v can pass to state NUM
the input sentence is accepted because of the appearance in the last set of the item v NUM NUM encoding that a structure headed by a verb i.e. a root category ending in a final state NUM and covering all the words from the beginning of the sentence has been successfully recognized
to disambiguate a polysemous word w in a context c which is taken to be the sentence containing w the system scores each sense s of w as defined in ldoce with respect to c using the following equations
in a standard model the logarlthm of the probability of occurrence of a conceptual set lcb x x xm rcb in the context of the conceptual set lcb y y y rcb is given by
f x y is looked up directly from the conceptual co occurrence data table fix and f y are looked up from a pre constructed list off dc values for each defining concept dc
this is needed to rectify the bias towards the sense s with defining concepts of higher average mutual information over the set of all defining concepts which is intensified by the ambiguity of the context words
to minimize the size of the table and the processing time all the closed class words and words which are rarely used in definitions e.g. the days of the week the months are excluded from the list
the contents of the entries in the parse tables are sets possibly empty of predict and scan
if conversely the algorithm halts for some input g or then there necessarily must be a dependency tree rooted in ho completely covering a
the list of features can be used in two ways to evaluate the genericness of a dialogue manager and to ascertain whether a dialogue manager is suitable to a particular application
in order to combine complements and adjuncts into predicate argument structures special automata for verbs are then activated over the sequence of constituents analyzed so far
the backbone of an il expression is thus the following sentence structure contains all the il expressions obtained from the analysis of a single sentence
semantic representations produced by sines are mapped into a format suitable for the pasha ii client by the imas component information extraction module for appointment scheduling
this architecture represents a virtual system also called operation context which is a highly complex object consisting of a variety of interacting managers
the robustness requirement is fulfilled by recognizing failures within the server during semantic analysis and possibly within the client systems and by clarification dialogues cf
existing automata can be 4if no vcrb is found a dummy entry triggers proccssing of verbless expressions which occur frequently in c mail communication
given the use of distributed calendar systems techniques used by both human and machine agents for cooperatively scheduling appointments must be based on negotiation dialogues
the initiator s broadcast proposal is triggered by its owner who determines partners duration and an interval within which the appointment should be scheduled
although agent systems allow users to automate their scheduling tasks to a considerable degree the circle of participants remains restricted to users with compatible systems
at this stage the only concept that remains to be consumed is k
the constraints of still valid for it to identify the referent corresponding to entity1
the constraint on the modifiers action that terminates the addition of modifiers is then evaluated
the probabilities of the rules are conditioned on the parent rule and on the trigram centered at the first input symbol that would be covered by the rule
in particular the multiple pp attachment problem results in sparser data which must be used to resolve greater ambiguity a strong test for any probabilistic approach
lazy pronouns lazy pronouns can be accounted for similarly
to our knowledge however these investigations have only considered the problem of attaching the first pp i.e. in a iv np pp configuration
attempts to resolve the problem of pp attachment in computational linguistics are numerous but the problem is hard and success rate typically depends on the domain of application
the authors point out that prepositions are the most informative element in the tuple and that taking low frequency events into account improves performance by several percentage points
their first arguments are similar since they are identical clause NUM
if b and d are chosen the jtbt reading results
if b and c are chosen the jtjt reading results
john revised his paper and the teacher followed suit
john revised his paper and bill did the same
we then go on to describe the key aspects of the implementation
thus in a tree with substitution sites adjoining must be limited to nodes on the path from the left sister of the most embedded site to that sister s rightmost descendent
with our prolog based interpreter parse times axe around NUM NUM sec
the gui was developed by carsten hess
let us illustrate the problem and its solution with a schematic example
this should be done in the phrase structure or procedural attachment part
the paper is organised as follows
figure NUM defining the relation wf phrase
figure NUM a relation encoding the hfp
figure NUM a universal constraint on phrases
thus the grammarian might now write transitive verb kick
unfortunately it is not possible to combine these conflicting requirements
all the other positions are simply linked by shared variables
an intransitive verb would of course just be subcat fl
this technique certainly reduces the number of items in the lexicon
rcb the various vp rules are recast using the mnemonic symbols
this extension can also be generalized to products of large sets
under these circumstances there is a simple extension of this encoding
various simple kinds of typing can be superimposed on this formalism
for instance the current task initiative indices take the following form rat speaker z and rat hearer NUM z that for o is decremented by
considering that the pattern development was done in only two weeks our scores are quite satisfactory
similarly the s represent the end of the root and r is the continuation this time reversed leftwards into the root from the r1
for example for english the sequences ed ly and un ing are among those produced the asterisk representing the unspecified root
morphophonological interactions may be quite complex and the purpose of morphological processing is to derive syntactic and semantic analyses from words and vice versa for the purpose of full nlp
methods for the automatic compilation of rules from a notation convenient for the rule writer into finite state automata have also been developed allowing the efficient analysis and synthesis of word forms
however for other languages including french it leads to excessive numbers of spelling patterns because there are many obligatory rules with non trivial contexts and feature specifications
because of the separation between lexical and morphological representations these timings are essentially unaffected by in core lexicon size as full advantage is taken of prolog s built in indexing
now in average the structures delivered are far from the exact structure by NUM NUM node with a standard deviation of NUM NUM
generation is also quite acceptably fast running at around NUM words per second it is slightly faster than analysis because only one spelling rather than all possible analyses is sought from each call
this indicates to the user that if chef is given a lexical entry consistent with the constraint cdouble n then only the first analysis will be valid otherwise only the second will be
this entails that the dm will not be confronted with an n best list of recognized key words but with a more complex structure a kind of list of parses annotated with confidence scores from both the sr and the semantic parser
compared to plum s previous performance in muc NUM NUM and NUM our progress in muc NUM was much more rapid and our official score was higher than in any previous template fill task
we tried many variations on this theme for matching plum shogun frames as well as combining various matching approaches with the use of simple statistical methods on individual frames to judge their likelihood of matching the key
the level of constraint should not be measured when the system is recovering from deviations in the dialogue since focussing the user may be necessary for recovering from the deviation in as few steps as possible
the work reported here was supported in part by the defense advanced research projects agency technical agents for part of the work were rome laboratory under contract number f30602 NUM c NUM and fort huachucha under contract number dabt63 NUM c NUM
one approach to the three tasks is to have a full information extraction system apply all its knowledge to all three tasks simply providing three different levels of output
these preliminary results are quite encouraging since they are better than any previously reported scores for a learned system and since they are approaching the scores of the state of the art for manually built rule based systems
the following problems are evident lack of punctuation lack of reliable mixed case to signal names transcription errors when input is outside the NUM NUM word vocabulary which is problematic for infrequent names
scoring just these templates produced a combined result with a better f score than that of shogun alone though not nearly so good as the score for choosing just the right individual frames in the previous experiment
since the sgml marked text can be straightforwardly aligned with the answer keys the training algorithm simply counts the frequency of events and normalizes with respect to the event class to estimate the parameters of the model
our combined system was based on the output of our own bbn plum system configured for ejv and the output of the shogun system developed at ge and made available to us through lockheed martin
while we uncovered many filters that had some predictive value none of the tests we devised was of high enough quality to allow us to raise f scores for the combined system over those of shogun alone
if that is the case the algorithms should be effective to other language pairs
null NUM lose cover and test a s recornmended in operation section
both imperative infinitive and imperative simple expressing generated occur first while a gerundive expressing generating occurs second
for appears only with np and marks only generated elements
unlike in portuguese ordering does play a role in french
the overlap in the syntax of generation vs enablement sentences is confined to expressions of purpose
erie s pattern matching engine processes the patterns in the order of definition
apr s ddpoussidrage appliquerdeux couches de peinture vinylique
english on the other hand had the opposite characteristics
imperative finite nominm figure NUM l ench generation
figure l expressions of eneral ion portuguese
the results of the test samples are listed in figure NUM
in that evaluation a number of systems scored over NUM on the named entity recall and precision metrics providin g a sound basis for good performance on the coreference task for individual entities
as shown in the table below performance on th e ne task overall was over NUM on the f measure for half of the systems tested which includes systems fro m seven different sites
note that although named entity coreference and template element are defined as domain independent tasks the articles that were used for muc NUM testing were selected using domain dependent criteria pertinent to the scenari o template task
scenario template st drawing evidence from anywhere in the text extract prespecified eventinformation and relate the event information to the particular organization and person entities involve d in the event
finally the current notation presents a set of issues such as its inability to represen t multiple antecedents as in conjoined nps or alternate antecedents as in the case of referential ambiguity
although the management scenario contained only five domain specific slots disregardin g slots containing pointers to other objects it nonetheless reflected an interest in capturing as complete a representation of the basic event as possible
as defined for muc NUM the st task presents a significant challenge in terms of system portability in that the test procedure required tha t all domain specific development be done in a period of one month
however the task does not require the system to extract all descriptors of an entity that are containe d in the text it requires only that the system extract one or none
the top scoring system the baseline configuration of the sra system labele d satie base in appendix a achieved an f measure of NUM NUM and a corresponding error score of NUM
looking at the document section scores in the table below we see that the error score on the body of the text was much lower than on the headline for all but a few systems
such redundant nodes might develop because we explicitly put all the atomic features into the lattice but some of them never act on their own
i figure i this figure shows a feature lattice where thick orcles repre mt reference nodes and filled circles represent obsolete hidden nodes
this means that we constrain only features with reliable estimates and at the same time we drastically decrease the computational load
when we appl ed the atomic feature selection algorithm section NUM we in NUM minutes bo led the lattice down to NUM NUM nodes
we can also use all the nodes from the lattice reference and hidden as the extended configuration space w
in this case the computational load will increase proportionally to the number of hidden nodes but the model itself will be fit more accurately
the model induction procedure has two parts feature selection and parameter estimation both of which agree with the principle of maximum entropy
NUM if the greatest aa computed at the previous step is smaller than a certain threshold the algorithm has converged and we exit
such decomposition of complex features into simpler ones provides an elegant way of representing cases with interactions of many overlapping features of high complexity
by letting types a and b be zero and others be nonzero we obtained a new rule rule NUM
our case representation is at this point simpler only the ambiguous tags not the words themselves or any other information are used
in the unknown words case base the trie representation provides an automatic integration of information about the form and the context of a focus word not encountered before
if it is found its lexical representation is retrieved and its context is determined and the resulting pattern is looked up in the known words case base
NUM this is not necessarily a trivial task as of course there is no physical evidence for zero anaphora in text
thus in the following we will not make any refinement to the long distance cases because little progress would be obtained
in these approaches a tag sequence is chosen for a sentence that maximizes the product of lexical and contextual probabilities as estimated from a tagged corpus
the nominal descriptions investigated in the remainder of this section are thought of as noun phrases of the above scheme without articles
on the other hand if a reduced description is decided on only the substance is taken into the semantic structure
minimal distinguishing descriptions pursue efficiency in producing an adequate description that can identity the intended referent unambiguously with a given context set
thanks to antal van den bosch ton weijters and gert durieux for discussions about tagging igtree and machine learning of natural language
our goal is to adhere to the concept of memory based learning with full memory while at the same time keeping memory and processing speed within attractive bounds
here we give a simple and general but less than optimal implementation using association lists
this is not surprising to anyone familiar with logic programming approaches to natural language processing nlp
informally the resulting function recognizes the union of the substrings recognized by fa and fb
first a top down parser using a left recursive grammar typically fails to terminate on some inputs
a sentence may be divided into tokens
whether the utterance is a repair
figure NUM agent b dialogue interaction danieli and
figure NUM agent a dialogue interaction danieli and
the baseline lexicon has correct entries only for the most likely translation and for the second most likely translation
it allows many experiments to be run without concern about the cost availability and reliability of human evaluators
to maximize precision for the best of three or more translations only the cognate filter should be used
the upper bound on performance for this task is plotted at NUM NUM see end of section NUM
the remaining candidate translations from all training sentence pairs were pooled together and fed into a fixed decision procedure
fifteen thousand sentence pairs were randomly selected and reserved for testing one hundred thousand were used for training
this makes it practical to train on small hand built corpora for language pairs where large bilingual corpora are unavailable
the baseline lexicon induced with no filters contains correct translations only in the first and sixth positions
it is based on the assumption that if a candidate translation pair s t appears in an oracle list of likely translations then t is the correct translation of s in their sentence i pair and there are no other translations of s or t in that sentence pair
slang some word pairs seem unlikely to be translations of each other such as collusion and its first three candidates it pull t cat f tail
in addition we not only obtain a score from the dtw matching between pairs of words but we also reconstruct the dtw paths to get the points of the best paths as anchor points for use in later stages
put another way there is always a cluster of good overlaps but the general tendency is to have fairly poor overlaps
interestingly we still got useful results with these impoverished parses although fewer semantic classes had uniquely identifying syntactic signatures under these conditions
NUM of them mriquely identify a semantic lass meaning that NUM NUM of the classes have uniquely identifying syntactic signatures
note that this algorithm assmnes that there is a canonicm set of ldoce codes tbr each of levin s semantic classes
we have automatically classified NUM NUM unknown verbs i.e. those not occurring in the levin classification using this technique
in the first experiment verbs that appeared in different classes collected the syntactic information fl om each class it appeared in
not surprisingly but not insignificantly this relationship was very clear since this experiment avoided the problem of word sense ambiguity
therefore we can turn to the extensions of the functions the actual groupings of verbs based on these two separate criteria
the first phrases chosen must always be the most reliable
a syntactic signature for a verb by definition is the union of the frames extracted from every example sentence for each verb
walker finally claims that the content of the cache rather than the intentional discourse segment structure determines the accessibility of discourse entities for anaphora resolution
it seems to us that these kinds of phrases may override text grammatical structures as evidenced by referential discourse segments and rather trigger other kinds of search strategies
NUM viele der kleinen macken verzeiht man dem hl NUM wenn man erste ausdrucke in h inden h ilt
u2 and ua simply continue this segment block NUM of the algorithm so lift does not apply
upon initialization the beginning as well as the ending of the initial discourse segment are both set to NUM
NUM ohne diesen ausdruck sucht man vergebens nach einem hinweis darauf warum die auto continue funktion in der postscript emulation nicht wirkt
hence block NUM of the algorithm applies leading to the creation of a new segment at level NUM
word bits for all the words in the vocabulary
the adjuncts are not usually marked in the verbs because most of the verbs may have e.g.
even verbs which are always considered to be transitive like hit for example can be used intransitively if the action is considered to be habitual
in addition syntactic preference also depends on type of head category and modifier category
we will now illustrate some of the idiosyncracies and peculiarities of names that the analysis has to cope with
we describe the name analysis and pronunciation component in the german version of the bell labs multilingual text to speech system
table NUM performance of the general purpose and the name specific text analysis systems on training and test data sets
the orthographic strings are annotated with symbols for primary and secondary lexical stress
the old and the new versions of the tts system were run on the training and the test set
table NUM comparison between the general purpose and the name specific text analysis systems on training and test data sets
the net improvement by the name specific system over the generic one on the test data is thus NUM
if several syntactic functions of a word have dependency relations they form a dependency forest
we find a different pattern for multiple extraposition involving distinct antecedents NUM its struck a grammarian j last month who analyzed it j that this clause is grammatical i
inside a set of goals the planning process is divided in NUM steps we first find the intentions that are compatible so that each schema takes into account as many intentions as possible while keeping each one readable
by designing our own graphical realizer in prolog the same language as the rest of the system we were able to precisely integrate it in the decision process thus allowing more accurate heuristics and a backtracking approach for more complex cases
if we look at the content word preceding air in the concordance and the content word following it we notice that air is not randomly paired with other words
properties annee etiquette dollar pluriel profit dollar pluriel depense variables that can be part of a relational key a inee compa nie variables that ca n t be part of a relational key
for example a variable such as profits can wind up as a key if its values are all different but it is rarely desirable to express a set of variables such as years and company names as a function of profits
for this we had to develop a postscript generation system in prolog in or null der to determine the exact position of each element character line axis etc of a generated graph
there was a highly significant difference for the agreed upon choice between the first and subsequent positions in the case of verbs and adjectives and words with eight or more senses in the frequency order condition p NUM NUM
he succeeds lance r primis collect sem succession NUM in sem person NUM name quot john doon out sem person NUM name quot james quo the analyzer had created the semantic person representations during a previous processing phase and linked the m to the originating text
although a representation at the level of an entity relationship diagram would have been quite useful especially for long reports and global relationships between sets of data we chose to limit the input to a table like structure which is easily obtainable from a spreadsheet
so when we apply context heterogeneity measures to word pairs in english and french we might map the left heterogeneity in english to the right heterogeneity in french and vice versa
if this inequality can be maintained for our method that is sbl a b sbl c d the similarity measurement is taken to be successful
one important goal of our module is to provide top down information for the other modules of verbmobil e.g. to reduce the search space of the word recognizer
a limited validation test has been set up
probably recall will drop while precision could raise
1english surgical procedure quintuple coronary bypass
NUM NUM the linguistic string project medical language processor
this was in line with results earlier achieved
3currently the files are transmitted by e mail
nevertheless these figures are temporary as examination of the sentences showed that very few words had more than one semantic label so that the medical subselection stage did not have a big impact
the basic idea was that when treating a patient it is considered to be helpful to reread the admission history the discharge summary or other important parts of the medical record
NUM enclosed you can find the operation report
nevertheless they may be an erroneous chain which have implicit spering errors
morfologische analyze van hot woord 6cril
NUM NUM the morl hoh gical mmlyser
since large cow rage ana lysis
em inforlnal ion is direcl ly
pare en NUM dens une encyclopddie thdologique
in this context terminology acquisition is defined as a twofold process
they both analyze corpora of arbitrary length
a long list of potential analyses is potentially of very little use
polysemy and quasi synonymy often makes the ontological reading of linguistic data difficult
the axioms may then be used by the theorem prover
such individuals could probably fix the circuit without any assistance
s h is the global acoustic score of the hypothesis h
figure NUM shows the evolution across seven n best of an ill recognized sentence score profile
smith and gordon human computer dialogue figure NUM subdialogue transition as a finite state network
this transition model is also depicted in the finite state network of figure NUM
the time allowed for the second and third sessions was two hours each
prompt the listener has control because the speaker is abdicating control
the diagnosis to assessment transitions are indicative of attempts at error correction
this system answers user queries about train schedules and services
did the order in which subjects were given the initiative affect their performance
repair subdialogue the change should be dependent on the task domain
now what happens if the user uses multiple pointing gestures within one utterance as in the example zet deze file hiers en dezes daart
the relationship between a verb in final position a verb in second position and the empty head can be summarized as follows for each final finite verb form there is a corresponding finite verb form in second position which licenses a verbal projection whose empty head shares its local information with the corresponding final verb form
psg makes crucial use of head traces to analyze the verb second v2 phenomenon pertinent in german i.e. the fact that finite verbs appear in second position in main clauses but in final position in subordinate clauses as exemplified in la and lb
the algorithms have three parameters a threshold NUM and two update parameters a promotion parameter o and a demotion parameter ft
in this system an lh s is processed by a bottom up chart parser that takes word lattices as tthis work was partimly funded by the gc imtn vedcral ministry for research and technology bmiw in the framework of the verbmobil project under r nt NUM iv NUM v verbmobil
if the number of possible trace locations could be reduced significantly the parser could avoid a large number of subanalyses that conditions a c would rule out only at later stages of the derivation
in particular very little is known about their generalization performance that is their behavior on documents outside the training data
table NUM shows the recognition results in percent for the NUM NUM classifier and for the b3 not b NUM classifier using the s3 positions as reference first column again not counting turn final boundaries
these segments however are also often separated by an s3 boundary so that the error rate is likely to drop considerably if a segmentation of utterances into syntacticmly well formed phrases is performed prior to the trace detection
for the classification reported in the following we employ three main labels NUM syntactic boundary obligatory s3 syntactic boundary impossible and NUM syntactic boundary optional
the techniques that are used are variants and extensions of the classic k nearest neighbor k nn classifier algorithm
performance was further summarized by a break even point a hypothetical point obtained by interpolation in which precision equals recall
in the testing phase the same process is repeated on the test collection only that the hypothesis is not updated
this motivates the need for smoothing methods which reestimate the probabilities of low count events from more reliable estimates
these two examples create a paradox apparently neither type of analysis nor any previous analyses we are aware of can explain both
words instead of similarity between patterns that are a possibly complex combination of many features
common mistakes made by the systems included missing the date expression the 21st century and spuriously identifying NUM pounds which appeared in the context mr
we call these interesting concepts the interesting wave front
this paper reviews the currently available design strategies for software infrastructure for nlp and presents an implementation of a system called gate a general architecture for text engineering
by software infrastructure we mean what has been variously referred to in the literature as software architecture software support tools language engineering platforms development enviromnents
the composition of the back off sequence following from this can be seen in the lower part of figure NUM
what would be the topic of this sentence
for example part of speech tags phrase structure trees logical forms discourse models can all be seen in this light
graph structured information might be present in the output of a parser for example representing competing analyses of areas of text
the definition of annotations in tipster forms part of an object oriented model that deals with inter textual information as well as single texts
the tipster architecture is designed to be portable to a range of operating environments so it does not define implementation technologies
it would seem therefore that we are on safe common ground if we start only by committing to provide a mechanism which manages arbitrary information about text
as creole expands more and more modules will be available from external sources including users of other tipster systems
as part of the verbmobil real time speech to speech translation project ice has addressed two key problems for this type of system viz
the abstraction based approach to managing information about texts is primarily motivated by theories of the nature of the information to be represented
turning the switch up is necessary
the most specific schema is the schema with zero mismatches which corresponds to the retrieval of an identical pattern from memory the most general schema not shown in the figure has a mismatch on every feature which corresponds to the 3note that mbl is not limited to choosing the best class
one of the heuristics we therefore use is that the pattern may only apply if both head nouns carry the same corelex tag or if the tag of the second head noun subsumes the tag of the first one through a dotted type
less obviously segmentation also often fits this description
example NUM f say it start again
there is currently no best practice methodology available which specialises software engineering best practice to the particular purposes of dialogue engineering that is to the development and evaluation of sldss
and where the games ended NUM
the move coding analysis is the most substantial level
it must also have been shown that different developers are able to use the new method or tool with approximately the same result on the same corpus system or development process
for instance there are NUM different noun stems with NUM NUM instances that have each NUM out of the NUM basic senses assigned to them in NUM different combinations a subset of NUM NUM possible combinations
the same level of agreement k NUM
we would not argue however that the delayed feedback strategy is impossible to implement and suecessflally use for flight information systems of the complexity of the intended sundial system
the tables were structured by guideline and showed the violations of a particular guideline that had been identified by one of the nob is nob s annotation of the nob sub corpus
the rule that closes off the subcategorization needs to have the relevant selectors value added as in the example above
this might conveniently be stated by a declaration something like subcategorization feature name categories mnemonics maximumlength
the vp rule schema now uses the current selector to choose the appropriate symbol from the complement it is combining with
given the importance of the cat feature for efficient indexing and lookup this might be for practical purposes unwise
researchers who work on reversible NUM words are traditionally divided into a open class words such as nouns verbs adjectives and adverbs and b closed class words also called fimction words such as articles pronouns and conjunctions
a condition arb restricts this set of pairs to only those for which some relation r holds where r denotes a subset of the cartesian product of the sets of type objects and type r objects
a bsf is a propositional description
for example whether the six programming assignments should be viewed as a plus of ai or a minus will depend both on hearer NUM goals and on what action the speaker NUM thinks the hearer should pursue i.e. take ai or not
if the constraint is available it can influence the choice of that word if not then if the word is selected based on other constraints it will trigger the constraint which may in turn trigger selection of other words
in the architecture we are describing the lexical chooser must meet the requirements of the underlying application which feeds it its input on the one hand and on the other hand it must produce an output acceptable by the syntactic realization component
since the main verb has not yet been selected the lexical chooser can not proceed further and determine which participants or verb arguments will be inserted in the clause and how they will map to the arguments of the input semantic relation
given a request to communicate a language generator typically must select information from an underlying domain representation and determine how to order this information ultimately realizing the representation as sentences by selecting words and linearly ordering them under the syntactic constraints of the language
in an effort to make domain representations independent of language there may be a variety of different words that can be used to express any concept in the domain and a language generator must choose which one is most appropriate in the current context
out of the eight interpretations accepted two are implausible for a human reader
NUM the xtag tagger which is an implementation of ken church s parts tagger NUM and adwait ratnaparkhi s maximum entropy tagger NUM
for example in the phrase john smith president of acme a former worker at eastern john smit h is coreferent with both president and a former worker
bride of cognia c resolution of pronouns and lower case anaphors was handled by a program called bride of cogniac whic h is an extension of cogniac NUM
we focused on building high precision component s on the assumption that many high precision moderate recall components when linked together would yield a system with good overall recall
sentence final punctuation i s defined to include only periods exclamation points and question marks we do not attempt to mark sentence boundaries indicated by semi colons commas or conjunctions
the parser we use has been developed over the past NUM months by michael collins and is a continuation of the work on prepositional phrase attachment described in NUM
for example chief executive officer and international business machines are both basal noun phrases but chief executive officer of international business machines is not since it contains nested noun phrases
paradigmatic decisions choosing among alternative lexicalizations inside a particular thematic structure e.g. the choice of the verb to require to express the assignt type relation in paraphrase NUM instead of to involve in NUM
that is when one or more of the words which comprise a hyphenated word exists on their own within the article then th e hyphenated word is split into multiple tokens
the majority of the strings annotated are noun phrases detected by the noun phrase detector bu t some sub noun phrase units are annotated as well
however because no deep syntactic analysis is performed the patterns can only approximate subjects and objects in this way and i therefore do not refer to these patterns as subject verb and verb object respectively
this mapping is domain specific and is completely contained in the lexicon en13 under different conditions the lexical chooser could select one of the other verbs represented in the entry such as to require or a construction such as in class there is assignment
the defnition of p3 llnl v n2 is analogous to that of pbo wnlw x
it is based on an unsupervised learning procedure to collect test and training data and the back off model to make assignment decisions
it makes use of an unsupervised learning procedure to collect training and test data and the back off model to make assignment decisions
the model developed within the context of speech recognition consists of a recursive procedure to estimate n gram probabilities from sparse data
a long standing debate in the computational linguistic community is about the generality of lexical taxonomies
some relations are not predicted by wordnet as for example pattern iii
reestimation and best first parsing algorithm for probabilistic dependency grammars
these are the joint task force jtf reference architecture and the tipster architecture
first goals of the form distinguish x as cat instruct the algorithm to construct a description of entity x using the syntactic category cat
the lexical resources contain information about words such as their partof speech and their meaning
in our work we incorporate such features by using a pair of language models as described below
for example nametag has separate phases for recognizing personal names and organizational names
with respect to the tipster architecture a tipster compliant version of nametag will be used
what we believe to be crucial is the association between tokenization ambiguity and the maximization or minimization property of the partially ordered set on the cover relation
in both cases since cd s c c td s there must be itd s NUM
one of the obvious and immediate results is the concept of critical tokenization which is simply another name for the minimal element set of a poset
if not specified otherwise in this paper when referring to a complete dictionary or tokenization dictionary we mean the dictionary after the completion process
furthermore by contrasting critical tokenizations we can easily identify a few critically ambiguous positions which allows us to avoid wasting energy at useless positions
in this paper we have also discussed some important implications of the notion of critical tokenization in the area of character string tokenization research and development
however criticality which is what is being explored in this paper would most probably still not be captured in such a carefully generalized ambiguity definition
the cover relationship between tokenizations was recognized and the set of tokenizations was proven to be a poset partially ordered set on the cover relationship
among the eight procedures based on both analytical inferences and experimental studies both forward maximum tokenization and backward maximum tokenization are recommended as good solutions
ttle binding force of a wor l is a rues sure of how strongly the charact rs conll osing th word are bound t g ther as a single unit
if cd can be an infix preceded by ab the linked list pointed at by cd as an infix will be searched for the longest possible sutfix to combine with abed as its prefix
this algorithm iilays a key role in post processing the outtmt of a character or st eech recognizer in determining the proper word sequence c rrest onding to an input line of cha raeter images or a speech wav fol tn
words can NUM r h cd NUM y adding or leh ting woms to or from the h xi on as well as adjusting word t in ling forces
as it stands now given an input line of text the word segmentor can proce ss on the average NUM NUM characters per se ond when running on an ibm risc system NUM 3bt workstation with a col rect word identitication rate of NUM NUM
this can be done by checking whether all the chains satisfy the well formedness conditions
these word graphs are the latest word graphs that were available to us they are real output of the current version of the speech recognizer as developed by our project partners
this implies that in order to see whether a result item is applicable we check whether the interval covered by the result item lies within the extreme positions of the current goal
a simplified version of the recover predicate may be defined in which we only recover the semantic information of the root category but in which we do not build parse trees
in order to compare paths in the best first search method we have experimented with score functions that include some or all of the following factors the number of skips
based on the experiments discussed in section NUM it can be concluded that a specialized left corner parser is only about NUM faster than a head corner parser running in left corner mode
parse right ds hit q0 q e parse h qo ql NUM e parse right ds t qi q e
i will start with considerations that lead to the choice of a head driven parser i will then argue for prolog as an appropriate language for the implementation of the head corner parser
we should not let these error analyses obscure alembic s achievements however
there does not exist a generally agreed upon method to measure the efficiency of parsers for grammars of the kind we assume here i.e. constraint based grammars for natural language understanding
an example might be the following where we assume that the category vp is never assigned to a lexical entry which is a subset of the table in NUM
in gb theory empty categories can be freely coindexed with an antecedent from which they inherit their features
it is important to note that our
the result is an iteratively improved labeling of the source text
this may account for much of the io training to testing gap in chinese
figure NUM brill s rule sequence architecture as applied to phrase tagging
tim l agging prol h nt is dolim d as liuding t lw tttosl likely t tg s tltlclt
modal qualio quality t material word quality r logical arbitraty place relation sequence generalized possession quantifications
where can be either a logical a or a logical v and p stands for a logical predicate
a perfect match results in a NUM NUM
meteer s text structure is organized as a tree in which each node represents a constituent of the text
for every domain of application domain specific concepts must be identified and placed as an extension of the upper model
on account of this we carry out aggregations before concrete resources for the apos like object and class ascription are chosen
they define how separate text structures can be combined and ensure that the planner only builds expressible text structures
aggregation rules implemented NUM NUM there are comparatively few detailed discussions in the literature
as a result of checking for such attributes the values returned will be set on the token
the application of an aggregating rule before the expansion of a leaf node may trigger the insertion of cue words
to determine the layout parameters which will be realized later as texcommands in the final output text
for start states the relation start NUM should hold and for final states the relation final NUM should hold
parsing uncertain input might be necessary in case of ill formed textual input or in case of speech input
for example if a natural language understanding system is interfaced with a speech recognition component chances are that this co t is uncertain about the actual string of words that has been uttered and thus produces a word lattice of the most promising hypotheses rather than a single sequence of words
first i give a simple algorithm to encode any instance of a pcp as a pair consisting of a fsa and an off line parsable dcg in such a way that the question whether there is a solution to this pcp is equivalent to the question whether the intersection of this fsa and dcg is empty
for example we can restrict the attention to dcgs of which the context free skeleton does not contain cycles
i show that if the above mentioned intersection problem were decidable then we could solve the pcp too
the main findings of this paper can be extended to other members of that family of constraint based grammar formalisms
the reason is that this construction typically yields an enormous arnount of rules that are useless
finally we add the corresponding constraints from the dcg to the grammar rules of the parse forest gral nrrlaro this has the advantage that the result is still sound and complete although the size of the parse forest grammar is not optimal as a consequence it is not guaranteed that the parse forest grammar contains a parse tree
we do not wish to define the sets of terminal and non terminal symbols explicitly these can be understood from the rules that are defined using the relation rule NUM and where symbols of the are prefixed with in the case of terminals and in the case of non terminals
we can gel each l robabilit y va hte front the t aggcd coritus which is i rq arcd for l raining by
we need a representation for right context strings
our main contribution is summarized in what follows
they consider all pairs of factors with lengths bounded by n occurring at aligned positions within some pair in l and update the positive and the negative evidence of the associated transformations
for each u j NUM j d NUM we charge a constant amount of time to the symbol in w corresponding to the last symbol of uj
in this case each leaf holds a record called count of the number of times the corresponding suffix appears in the entire multi set which will be propagated appropriately when computing factor statistic
we are interested in the set of transformations that are associated with the highest score in a given aligned corpus and will develop algorithms to find such a set in the next sections
function move link up p p starting at p and p simultaneously scan the paths to the roots of t and t respectively using function fast scan
from each node au in the suffix tree au some factor mccreight s algorithm creates a pointer called an slink to node u which necessarily exists in the suffix tree
heuristics various heuristics are used to gel together quiet clauses in the document
we are developing a program that assigns messagelevel event structures to newswire texts
each module then passes its constraints to the event manager
the third module identifies sentences containing a subset of cue phrases
templates are merged with earlier ones unless they contain incompatible slotfills
they are designed to run in parallel on the same text
the phrases overtly referring to time and location have been underlined
i would like to thank chris mellish and the anonymous referees for their helpful comments
conclusions and future work we have manually segmented NUM texts and have compared them against computer generated grids
we also define several special ones to characterize suppositions actions and sequences of turns
department of computer science national tsing hua university hsinchu NUM taiwan roc
future work will include more sophisticated methods for verb sense disambiguation and methods of acquiring seeds the acquisition of which is currently based on an existing dictionary
as pointed out in section NUM the generalization of examples NUM NUM is another method for reducing the size of the database
considering the two restrictions we compute interpretation certainties by using equation NUM where c x is the interpretation certainty of an example x
tuf x s which is the training utility function for x taken with sense s can be computed by equation NUM
we conducted a six fold cross validation as described in section NUM NUM but in this experiment each method selected some proportion of the training set as samples
the overall control flow of systems based on selective sampling can be depicted as in figure NUM where system refers to dedicated nlp applications
in this diagram outputs refers to a corpus in which each sentence is assigned the proper interpretation of the verb during the execution phase
in consequence one can expect that the size of the database which is directly proportional to the number of training examples can be decreased
we compute tuf x by calculating the average of each tuf x s weighted by the probability that x takes sense s
we compared the performance of our example sampling method with random sampling in which a certain proportion of a given corpus is randomly selected for training
and if all interactions were successful we might believe that the task was simply not challenging enough
the model unifies theories of speech act production interpretation and the repair of misunderstandings
figure NUM derivation tree for the sentence show me the flights from boston to philadelphia this tree the elementary tree that the word anchors head word the word on which the current word is dependent on if the current word does not
a total of NUM sentences average length of NUM words per sentence which had been completely parsed by the xtag system were randomly divided into two sets a training set of NUM sentences and a test set of NUM sentences using a random number generator
exactly one node on the frontier of an auxiliary tree whose label matches the label of the root of the tree is marked as a foot node by a the other nodes on the frontier of an auxiliary tree are marked as substitution sites
the following is the definition of the lattice of candidates representing ambiguous word and tag sequences called the morpheme network
credit r s the credit factor of the connection between the r th and the s th morphemes
the number of segmentation ambiguities of japanese sentences is large and these ambiguities complicate the work of a japanese tagger
i would like to know whether or not the noise problem occurs in other language models such as the hmm
another promising avenue for research is the development of improved methods to assign the credit factor without using rule based taggers
the hmm is very capable of modeling language if the training data is reliable
of course any algorithm for estimation from untagged corpora can not determine whether the connections are correct or not
the trellis that is often used to explain the originm forward backward algorithm is extended into a network trellis
let us introduce synchronous points on a input characters sequence to facilitate synchronization of the calculation of forward backward probabilities
in the next section i describe an extension to the forward backward algorithm for determining hmm parameters fi om ambiguous observations
NUM derivation of m q3 lcb a rcb u i vocalisrn tape c2 vxlnlc3 v21c4 a pattern tape NUM idlhlulnlrliljl lal isurfacetape
a scoping is chosen for r and c and a dependency function is constructed in the normal way but when it comes to partitioning the function and generating a quantifier for c some care must be taken
choose a scoping for x and y and so assign a value to scpx construct a dependency function choose a partition choose quantifiers qx and qy must be consistent with constraints
evaluation of tc and other text classification operations exhibits greatheterogeneity
NUM the nature of the speech translation task speech translation is in many respects a particularly difficult version of the translation task
notice the use of ellipsis to indicate that there can be tuples separating lex and llc as far as the tuples in llc are the nearest ones to lex
NUM if variable r is in wide scope position in 17b then qr must be of the form exactly n but is not generated in the final output
previously analysts shared both an analytical role and a data entry role hookah substantially reduces the data entry task and shifts the balance toward supervising and correcting the extraction system
the hookah architecture is presented in figure NUM
user interface once data is prepared off line it is
retraining analysts for this new job function may prove to be costly
dea is in the initial stages of converting to softcopy report dissemination
several lessons for transitioning tipster technology have emerged from the project so far
communication with the naddis database proceeds through the naddis interface
an operational prototype currently exists and is undergoing testing at dea
we are still investigating ways to evaluate the performance impact of hookah on the dea analyst
project hookah has provided considerable experience in the importance of the user interface for extraction systems
generation consists of going through the list of all possible quantifiers and checking whether or not each one is consistent with the appropriate variable in the current dependency function partition
as the annotation scheme described ill this paper focusses on annotating argunlent structure rather than constituent trees it differs from existing treebanks in several aspects
we have argued that the selected approach is better suited for producing higl quality interpreted col pora m languages exhil iting free constituent order
during annotation the highest rated granmlatical fimction labels gi a re calculated using the viterbi algorithnr and a ssigned to the structure i.e. we
a problem for the rudimentary a rgument structure representations is tile use of incomplete structures in natural language i.e. t henornena such as coordination and ellipsis
since the requirements for such a formalism differ from those posited for configurational languages several features have been added influencing the architecture of the scheme
separate labels are defined for dependencies that do not fit the complement modifier dichotomy e.g. pre gl and postnominal genitives gr
due to the substantial differences between existing models of constituent structure tile question arises of how the theory indcp ndcnc requirement can be satisfied
while in the first phase each annotator has to annotate structures as well as categories and functions the refinement can be done separately for each representation level
most linguistic theories treat nps as structures hea led by a unique lexical item no m however this idealised model needs severa l
in such cases an additional edge is drawn from tim embed led vp node to the controller thus changing the syntactic tree into a graph
this would amount to systematically comparing cta et with results obtained in speech to text evaluations divided up according to error categories such as those in our taxonomy
at first sight it would appear that the update of lmixn would require contributions from an arbitrarily large subtree since u may be arbitrarily large
that is ln s is the product of the predictions of the node on all the observation sequence suffixes that ended at that node
the set of words is in principle unbounded since in natural language there is always a nonzero probability of encountering a word never seen before
in the latter case its sons are either a single wildcard with probability rio or actual words with probability NUM f
moreover in many language modeling applications we need to predict only that the next event is a new word without specifying the word itself
among problems of this approach are as melby pointed out excessive interaction and necessity for special training for interactive operations
we are currently developing an alternative approach for cases when there is a known arbitrarily large bound on the maximal size of the vocabulary u
a single step of the random walk was performed by going down the tree following the current context and stop at a node with the probability assigned by the algorithm to that node
moreover we are only interested in the predictions of the mixtures the likelihood values are only used to weigh the predictions of different nodes
interactive operations are similar to those of kana kanji conversion although they are further extended to be capable of controlling syntactic transformations
it is that if we are to count the objects we should be counting are ones with a linguistic pedigree
men lugs NUM l cell NUM NUM i ice NUM NUM i panel NUM NUM treat NUM NUM NUM a word with the same me nlng but used in different contexts NUM words
the truncated texts were randomly assigned to either corpus NUM or corpus NUM and frequency lists for each corpus were generated
we denote v as the set of nodes words e as the set of branches co occurrence relations g v e as an input graph and i1 NUM as the number of nodes
clusters which are merged between threshold NUM NUM and NUM NUM were those within a topic a b c or d e f but topics of different clusters are merged at NUM NUM clusters NUM and NUM
consequently one of the most important future work is to integrate two stages the first stage of malc ng input graph with the static threshold and the second stage of clustering into a single stage with dynamic threshold
if it is captured into a subgraph and becomes e in step NUM the subgraph extends to the size of maximal subgraph if it gets larger the subgraph contradicts being maximal as the result of the last section
the number of resulting clusters depends on the input graph as follows when the threshold value is too high the output is NUM on the contrary when it is too low then the output becomes NUM
since the error term tends to increase with frequency cbdf scores for will only be comparable if words of the same span of frequencies are used in the comparisons
indeed some terms generally uniterms have multiple senses for example base and produce a great part of the noise
in this manner the user and the system are essentially cooperative avoiding the problem of excessive questioning by the system
the requirement specifications are defined as follows i to strictly prohibit impermissible hyphen generation ii to generate a hyphen list that is as complete as possible
tokens NUM NUM words NUM NUM tags NUM average number of tags per token NUM NUM table NUM NUM
the first stage of training is learning rules to predict the most likely tag for unknown words
the figures given in table NUM NUM and NUM NUM were obtained from the training files
the tagger was tested on the same test file as for the statistical experiments
the first step of pos tagging is obviously a definition of the pos tags
the following tables show a detailed analysis of the errors of the trigram experiment
in the third experiment we deleted the morphological information for nouns and adjectives alltogether
we have also included the results of english tagging using the same xerox tools
therefore lemma NUM the substrings of grammar rules c1 c2 and c3 are contained in the set of the expression vlc NUM c2c c3 v NUM
the rules presented here have been used for the development of a hyphenator program included in the microsoft word for windows NUM NUM and NUM NUM greek version already on the market
by definition this process could not be automatic because hyphens were not included in the lexicon or the corpus but there were far too many matches to be examined manually
according to the informal definition of syllable given above a syllable has at least one vowel and thus the consonant prefixes and suffixes of a word can not constitute entire syllables
then the user can undo the translation correct selections and try again for example see figure NUM
the declaration of a package of annotation types would consist of a package name declaration followed by one or more annotation type declarations
extraction is a special type of annotation and accordingly the extract operation is a variant of the annotate operation section NUM NUM
in the future it is expected that there will be more general extraction engines which can be customized by users to specific needs
any text not explicitly encapsulated in a query language operator is assumed to be implicitly encapsulated by the sum operator described below
detectionneeds are independent of the specific retrieval engine employed while detectionqueries retrievalqueries and routingqueries are specific to a particular retrieval engine
the user s request for documents is initially prepared in the form of a detectionneed a document with a variety of sgml delimited fields
for instance in compressed video the information contained in a sequence of frames can not be located using starting and ending byte
a tipster implementation can support this capability by allowing a bytesequence to be created as a reference to a portion of this data store
getannotation document or annotationsct id string annotation returns the annotation whose id slot is equal to the desired value
there will be annotators for different types of annotations for example for tokenization for sentence segmentation for name recognition etc
in this model a small value for l w indicates that the word w typically carries less information that the word that precedes it
for each k NUM the ski p k transition matrix m wt k wt predicts the current word from the kth previous word in the sentence
the m step of the algorithm is to update the parameters ak w and mk w w to reflect the statistics in eq
figure NUM shows the tagging error rates plotted against various clustering text sizes
each iteration consists of two steps an e step which computes statistics over the hidden variables and an m step which updates the parameters to reflect these statistics
as described before target users of our method are those who have basic knowledge to read and understand the target language
all our experiments used a vocabulary of sixty thousand words including tokens for punctuation sentence boundaries and an unknown word token standing for all out of vocabulary words
the goal of this section is to establish the proper tension between model complexity and data complexity in the fundamental units of information
therefore our estimate of a ly should be conditioned on the fact that the longer context xy did not occur
accordingly we add a new parameter to the model only if doing so will decrease the total codelength of the data and the model
each symbol is predicted in its own context and the model s current predictions need not be estimated using the same set of histories
we feel that the use of morphological recognition a small lexicon of closed class words and a dictionary of known open class words can be used to help our parser to determine the parts of speech for unknown words as they occur
the first approach is to attempt to construct a complete lexicon then deal with unknown words in a rudimentary way m for example rejecting the input or interacting with the user to obtain the needed information about the unknown word
tagging systems make only limited use of the syntactic knowledge inherent in the sentence in contrast to parsers
kupiec s hidden markov model uses a set of suffixes to assign probabilities and state transformations to unknown words
we will examine the effects of combining morphology and syntax while using a deterministic system to perform parsing
they can not have a closed class part of speech since the closed class words are enumerated in the dictionary
morphological reconstruction researchers process an unknown word by using knowledge of the root stem and affixes of that word
of the two suffixes general offer more effective constraints on the possible parts of speech of a word
typically the ly affix is attached to adjectives to form adverbs e.g. happy happily or to nouns to form adjectives e.g. beast beastly however the word butterfly was not formed by this process but rather by compounding the words butter and fly
after all of the parse trees have been generated each run is compared to the control run and three numbers are calculated for each sentence in each run the number of matches deletions and insertions
others will make heavy use of prefabricated patterns such as the tourist phrases found in a travel book whose use may precede a complete understanding of meaning or structure
the experimental results obtained from the proposed algorithm with respect to word alignment are presented in this section
sproat shih gale and chang word segmentation for chinese raphy k
a moment s reflection will reveal that things are not quite that simple
methods that allow multiple segmentations must provide criteria for choosing the best segmentation
for example suppose one is building a tts system for mandarin chinese
this larger corpus was kindly provided to us by united informatics inc r o c
in a few cases the criteria for correctness are made more explicit
the model described here thus demonstrates great potential for use in widespread applications
their results are then compared with the results of an automatic segmenter
finally for re the derived form entails that its result state held previously e.g. if one recentralizes something then it must have been central at some point previous to the event of recentralization presups rstate
for aize en and airy a bit more can be said about the result state it is the base predicate rstate eq base e.g. the result of formalizing something is that it is formal
much relative work on word classification has been done
the axiom states that if a change of state predicate describes an event then the result state of this predicate holds at the end of this event and that it did not hold at the beginning e.g. if one wants to phological analyzer is also able to construct a base on its own when it is unable to find an appropriate base in its lexicon
finally aize en and airy cue the following feature for their bases if a state holds of some individual then either an event described by the derived form predicate occurred previously or the predicate was always true of the individual ize dependent e.g. if something is central then either it was centralized or it was always central
the derived forms of age entail that an event occurred and refer to something resulting from it event and resultant e.g. seepage entails that seeping took place and that the seepage resulted from this seeping
word sense disambiguation is necessary because one needs to know which sense of the base is involved in a particular derived form more specifically to which sense should one assign the feature cued by the affix
it strips off aize from a word if it can find an entry with a reference form of the appropriate orthographic shape and has the features uninflected latinate and adjective
the majority of the missed re verbs were due to the fact that the system only looked at verbs starting with re and not other parts of speech e.g. many nominalizations such as reaccommodation contain the re morphological cue
the lexical semantic information commonly utilized includes verbal argument structure and selectional restrictions corresponding nominal semantic class verbal aspectual class synonym and antonym relationships between words and various verbal semantic features such as causation and manner
to cope with this problem we provide a bookkeeping mechanism that preserves all partial syntax trees generated during translation
for example for a user who is competent in english our system will be useful as an online dictionary
generally if two syntax tree nodes share a child leaf node one is an ancestor of the other
in this case as the translation equivalent is shown as a blank no morpheme appears in the translation
the system has an interactive interface similar to kana kanji conversion method and initially serves as a dictionary look up tool
consider a situation where the user is writing a message in english using an editor of a mail program
after the user confirms selections of translation equivalents and translation area on b the user invokes translation
this is a major reason personal ej english to japanese machine translation systems are gaining popularity in japan
here the word phone call is highlighted corresponding to the interpretation as make a phone call
although they might not always recognize a misunderstanding when it occurs discourse participants are aware that misunderstandings can occur
in addition the model avoids open ended inference about goals by using expectations derived from social norms to guide interpretation
for example one speaker might take an utterance as an assertion while another understands it to be a request
it is possible that a misunderstanding will remain unnoticed in a conversation and the participants continue to talk at cross purposes
after russ hears t3 he decides that his interpretation of mother s first turn as a pretelling is incorrect
more specificall a time phrase in preverbal position tends to denote punctual time while that in postverbal position signals durative time as in in contrast both kinds of time phrase appear in postverbal position in english
differences in expectations might very well be one thing that new acquaintances must resolve in order to avoid social conflict
from russ s perspective this example demonstrates the detection of a self misunderstanding and the production of a fourth turn repair
the expanded model is obtained by an iterative procedure which starts with the initial sst
based on the character alignment words are subsequently aligned based on a modified version of brown et al s model NUM the authors report that NUM NUM of NUM NUM words in a noisy document are correctly aligned
in our model defaults will have one of three priority values strong weak or very weak
our implementation actually represents the dcg terms as a feature structures
individual words are represented by means of finite state automata with arcs labeled by phones
these results approximately fit the expectations from the theoretical complexity bound
each hmm consisted of six states following a left to right topology with loops and skips
make a dendrogram out of this process
efficient processing is achieved through user supplied delay patterns that work on both relations and implicational constraints as well as preferred execution of deterministic goals at run time
we can also query for a term like word a subcat he list and check it against the implications alone as it contains no relational goals
instead of delaying all constraints on a type until some condition is met one wants to be able to postpone the application of some particular universal principle
w phrase phrase a dtrs headed struc a h pa cap wf phrase phrase a dtrs headed struc acap
the definite clause part of our system is very similar to the one of cuf both use delay statements and preferred execution of deterministic goals
n o n41og n total time complexity
the structure of asl is radically different from that of english being much more similar to that of chinese or the native american language navaho
introduction a significant part of the development of formalisms for computational linguistics has been concerned with finding the appropriate data structures to model the linguistic entities
in the following section we describe an algorithm that circumvents this problem
name conflicts that would force variable renaming can not occur
these semantic similarities allow the ke to build conceptual fields in the early stages of the ka process
in our pp attachment example the blowup caused by this is in fact exponential
first structural information is encoded separately from lexical information
the system called icicle interactive computer identification and correction of language errors is designed to be a general purpose language learning tutor
a good example in a legal domain in italian basili pazienza and velardi verb classification agent ab abstracrion identify estimate i de ribe d cognitweprocess i
4it should be pointed out that the resource management in this calculus is very closely related to the handhng and interaction of local valency and unbounded dependencies in hpsg
in specifying the speech act there are sev null eral important things which need to be specified null speech function what does the speaker requires the hearer to do in regard to the encoded proposition NUM this is called in systemics the speech function
let us in parallel to sdl consider the fragment of it in which r and r are disallowed
the latter being handled with set valued features slash que and kel essentially emulates the permutation potential of abstracted categories in semidirectional lambek grammar
it remains to be shown that there is actually a proof for such a sequent it is given in figure NUM
this result indicates that efficient parsing for grammars that allow for large numbers of unbounded dependencies from within one node may be problematic even in the categorial framework
to our knowledge the question whether the lambek calculus itself or its associated parsing problem are np hard are still open
first of all since we do n t need products to obtain our results and since they only complicate matters we eliminate products from consideration in the sequel
did bk 3m NUM k am NUM dlb cs a3 k 3m k zm
we can use lemma NUM yet again to replace t with a set of left anchored right auxiliary trees
the fact that tigs can generate path sets more complex than regular languages is shown by the following example
simultaneous adjunction in tig allows these auxiliary trees to be chained together in every possible way root to foot on
from the perspective of this difference a tig is trivially a tag without the need for any alterations
figure NUM illustrates the operation of the gnf procedure when applied to the same cfg as in figure NUM
schabes NUM that tag extended with adjoining constraints not only strongly lexicalizes cfg but itself as well
if all the frontier nodes of an initial tree are empty the tree is referred to as empty
tree insertion grammar tig is a tree based formalism that makes use of tree substitution and tree adjunction
null NUM analysing the relations between corpus induced and human deduced categories in this section we propose a method to analyze the relations between a domain general ontology such as wordnet derived by linguists seeking language neutral principles and our example driven clusters derived by ciaula
then the dialogue manager counts a number of retrieved information
his experiments used the NUM NUM word vocabulary wall street journal corpus a predecessor of the nab corpus
the template with the highest score yields the semantic representation
these rates show that the language processing part worked well
the latter two parts are described in sections NUM and NUM
figure NUM illustrates an example of map menu and history
experiments showed that our interpretation mechanism is suitable for understanding the recognition result of spontaneous speech
figure NUM an example of map menu and history
on the other hand the difference in perplexity for higher values of m is not very dramatic
figure NUM decision tree for core placement
in the two level framework as it is well known morphographemics is modelled in two level rules tlr and morphotactics either in continuation classes or in unification word grammars wg
the input to catmorf is the set of textual items to which the text handler has not been able to assign a tag
note that this framework does not avoid the specification of morpbotactical contexts for those morphographemic changes which involve interaction between tlrs and the wg
it assigns a tag to the textual items that be handled by catmorf numbers dates proper names i e the usual pre process
NUM rules cover nominal inflection and derivation processes whereas only NUM rules cover verbal inflection thus few rules can be considered as applicable to both inflections
we believe that the use of a standard linguistic theory such as ccg made it possible to develop a grammar checker in a very short time frame and limited man power as the existing large standard lexicons are be made readily available for it
the current project started as part of a collaboration between the computer and information science department and the english language programs at the university of pennsylvania in an effort to provide a computational tool for students who are learning english as a second language
ca tmorf assigns as many tags to each wordform as morphological analyses are allowed by its NUM items dictionary and its two level and wordgrammar rules
the original public domain lexicon contains about 37k quintuples index entry pos cat fs where pos and cat are associated with a part of speech such as v or n and a set of categories respectively for the lexical item associated with entry
the remaining exceptions occur when the purpose is considered optional or contrastive and are handled by optionality and contrastiveness respectively
a baseline measure obtained by choosing the majority class
we conclude that aggregate and mixed order models provide a compelling alternative to language models based exclusively on n grams
we can then define standard truth tables over datr paths or false false false
this path extension carries over to any paths occurring on the right hand side as well
NUM bear inmindthatthefollowing are not synonymous come syn intransitive
the local context is initially set to the node and path specified in the query
the syntax of datr distinguishes four classes of lexical token nodes atoms variables and reserved symbols
figure NUM combined communicative goals corre lation and evolution
examples 5a b also show some problems inherent in relying on brittle features such as capitalization when determining sentence boundaries
there are indeed cases where in order to perform the correct operation more than one elementary tree must be spanned
excess of partial evaluation off line increases the size of the grammar which might in turn slow down the parse
when the training set was reduced to NUM NUM words accuracy dropped to NUM NUM
null at the syntactic level interaction occurs when the parser faces difficult ambiguities for instance when the resolution of an ambiguity depends on contextual or extra linguistic knowledge as in the case of some prepositional phrase attachments or coordination structures
we trained the transformation based tagger on the same corpus making the same closed vocabulary assumption
furthermore allowing even light accent in the unelided form is enough to falsify a discourse determined analysis
every boyi was hoping that mary would ask him out but the waiting is over
how such examples are to be handled within source determined analyses is a subject for future study
there are other cases that do appear to be problematic for source determined analyses proposed to date
c ivan loves hisi mother and jamesj loves hisj mother too
NUM ivan loves his mother and jamesj loves hisk mother
NUM john told mary to hand in his paper before bill hands in his paper
NUM a love ivan mother kris p ivan
nonetheless it is our sense that something quite different is happening in this particular case
t division of engineering and applied sciences NUM oxford street cambridge ma NUM
for instance the phonetic sequence sa leads to a terminal node in the trie connected to the lexical entries corresponding i i to the feminine possessive determiner sa her and ii to the demonstrative pronoun a that
computational linguistics volume NUM number NUM contextual transformations that can make reference to words as well as part of speech tags
three values are shown for each of the six variations in the experiment the mean overlap the median overlap and the percentage of perfect overlaps overlaps of value NUM NUM
the other parsing experiment is the intensive experiment where we try to find the best suitable grammar for some particular domain of text and to see the relationship of the size of the training corpus
as a demonstration that such predictable relationships are not confined to an insignificant portion of the vocabulary levin surveys NUM verbs grouped into NUM semantic classes in part two of her book
lb this end we created a database of levin s verb classes and example sentences from each class and wrote a parser to extract basic syntactic patterns from tire sentences NUM
it turns out that a very simple strategy works well namely flat parses that contain lists of the major categories in the sentence the verb and a handfifl of other elements
however levin s class NUM NUM is not the correct class for attempt since this sense of try has a negative amuse meaning e.g. john s behavior tried my patience
furthermore we found that of the NUM words appearing in the text there were NUM words that were not found in a standard thai dictionary
since the number of potential empty categories is at most 2f for f binary features this gain is expressed as f
to compose the syntactic signatures for each verb we collect all of the syntactic patterns associated with every class a particular verb appears in regardless of the different classes are semantically related
more specifically we define the overlap index 3an example of the intensional characterization of the levin classes are the definitions of lexical conceptual structures which correspond to each of levin s semantic classes
for non fiction domain texts a b e and j the performance of the fiction grammar is notably worse than that of the same domain grammar or the same class grammar
in the following sections we firstly describe the necessity for making this statistical ol servad m for extracting open comtlounds from thai text corpora
for instance to check the givenncss of the vp in NUM reads a book about x has to be entailed whereas on the basis of the marking in NUM reads y has to be entailed
since the generation of these objects is independent of the relevance criteri a imposed by the scenario template st task there are many more organization and person objects i n the te key than in the st key
for example if NUM is the right solution this will be discovered even if reads a book about x is not checked since in this case a book about x will be contextually given as well
language typology is the study of similarities and differences between languages formalized in terms of parameters such as word order and morphological structure
an antecedent a such that the existential closure of a entails the result of existentially binding f variables in the existentially closed f skeleton of t where the existential quantifier binding f w riablcs quantifies over contextually salient values
parsing is carried out with the same memory size but when the training corpus grows and the grammar becomes large some long sentences ca n t be parsed because of data area limitation
however a u l is an illegible string and can not be used on as indivi lcb lual basis in general text
so when a certain constituent e.g. again the vp in the abow examples is checked for givenness it suffices to assume f marking of the maximal potentially f marked subconstituents i call this the maximality assumption
they examine the feasibility of aligning the english french hansards corpus using the smt model on both the sentence level and the word level
the template element te task requires extraction of certain general types of information about entitie s and merging of the information about any given entity before presentation in the form of a template or object
NUM indirect head f marking principle in a head complement structure where none of the head daughter s arguments have yet been saturated NUM the o sem of the head daughter is s linked to the o sem value of the complement daughter
the task places heavy emphasis on recognizing proper noun phrases as in the ne task since all slot s except org descriptor and per title expect proper names as slot fillers in string or canonical form depending on the slot
an alternative more empiricist approach is to look at the behavior of features in the set of examples used for training
again we pop np from the agenda and create the initial edge relph np vn lcb np rcb we find this edge can not be extended by any entry and is not finished so we go to step NUM and pop the next entry from the agenda
NUM if the edge e is finished i.e. a subtree then add e i to the agenda else for all chart subtrees c i beginning at end el NUM if g is the active symbol in the rhs of e i and match g c returns NUM call extend e i c
it would not be expected to hold for so called scrambling or free word order languages or heavily inflected languages
note that this hypothesis is for fixed word order languages that are lightly inflected such as english and chinese
2d 3e demande l addition et que quelqu un paie
2a je sais son psge et son adresse
for this reason the experiencing is not controlled nor intentional it is stative
in 9b triste has the head on the agentive and receives its causative sense
in some specific contexts the causative sense is also possible with individuals NUM
the question is then what prevents this adjective from having the head on the agentive role
that semantic frames for different languages share common core arguments is more plausible than that syntactic frames do
a parse may be available for one of the languages especially for well studied languages such as english
higher precision could be also achieved without great effort by engineering a small number of broad nonterminal categories
word alignment is difficult because correct matchings are not usually linearly ordered i.e. there are crossings
the mismatch can be exacerbated when the monolingual grammars are designed independently or under different theoretical considerations
previous approaches to phrasal matching employ arbitrary heuristic functions on say the number of matched subconstituents
for instance even messy alignments such as that in figure NUM can be handled by interleaving orientations
itgs inherently implement a crossing constraint in fact the version enforced by itgs is even stronger
the mbl framework is a convenient way to further experiment with even more complex conditioning events e.g. with semantic labels added as features
the label on source node v corresponds to the label on target node v in the bilingual dictionary
thanks to andrew appel carl de marken and dafna scheinvald for their critique
documents added after firstdocument is called may or may not be encountered during the loop
they depict the option of selecting all regions found on the map figure 4a shows all the regions that were found by the system
while such a synecdochical use of designations carefully applied in discourse does not lead to polysemy it would lead to an absurd overload of polysems in a dictionary if the principle would be transferred to it even if reduced to inherttance of designations i.e. top down not bottom up and along the generic relation only
each document includes as one property an annotationset holding the annotations on that document
the resulting grammar has the interesting property that it combines the strong tendency towards lexicalism and positing general combinatoric rule schemata present in frameworks such as hpsg with relatively specific grammar rules to facilitate efficient processing
accordingly the context ouestablish has a single positive extension u
the most straightforward method for evaluating concept accuracy in this setting is to compare the normal form of the update produced by the grammar with the normal form of the annotated update
the user is provided a media player type interface with buttons for play stop and stepping forward and backward
semantic accuracy consists of the percentage of graphs which receive a fully correct analysis match percentages for precision and recall of semantic slots and concept accuracy
the string that is being compared with the actual utterance is defined as the best path through the word graph given the best first search procedure defined in the previous section
in fact we have found that the few word graphs which can not be treated efficiently almost exclusively represent cases where speech recognition completely fails and no useful combinations of edges can be found in the word graph
in words for determining which triple has minimal score i.e. is optimal the number of skips has strictly the highest importance then the number of projections and then the acoustic scores
hnc has developed a hardware architecture that is designed to handle neural networks and in particular the compute intensive processes they model
in example NUM above then the fragment the international telephone services together with the two skeleton representations the international telephone services the international telephone services is not minimal because it and its two representations can be reduced to the subfragment international telephone services and its two representations which are minimal
each sdu is then assigned one of four grades for translation quality NUM perfect a fluent translation with all information conveyed NUM ok all important information translated correctly but some unimportant details missing or the translation is awkward NUM bad unacceptable translation NUM recognition error unacceptable translation due to a speech recognition error
elman NUM by forcing the learner to ignore more complex potential triggers that occur early in the learning process
the entire community would benefit from more refined measured values and a better understanding of how the differences in human performance influence the results
besi les the actual input string annota t ing and documentation inr rmation sill her date id numl er it the item format its length category and well formedness eo le iii the morpho syntactic catego tentially ontroversial t hrase structttre eonfigul ations ilcl thus avoids imposing a specifi onstituent stru ture lint still ean be mapi ed onto one
as in this setup the evaluation situat ions ranged froilt user level black box ewdua tion of a ommercial prodttct to glass box diagnosis of a research NUM rol otylm tamer develol ment the i i ki syslcm a tilllltber of interc st ing resull s were ol tained on both t hc adequacy of tim tsni i sly
the parser has been tested on a NUM words application s
the tsdb NUM inll leanelfl ation is a small and etlicient relational database engine in ansi c NUM was designed with an open and dot unrented interface layer see figure NUM that enalfles test suite users to NUM idirectiona lly link an al l lication being tested to t he database and run automated retrieve NUM recess and comi arc cycles
the scoring stops in the case of the amplitude reaching zero
spoken dialogue systems enable people to interact with computers using speech
in a practical application a dialogue module with lab
this re evaluation decomposes into two factors a re scoring potential v and a re scoring amplitude as
substitution sites bear syntactic and semantic constraints on their possible substitutors
standard cots web server products provided the capabilities needed to define the prides user interface
we then compute the similarity between words a and b bj the cosine of the angle between the two vectors g and b
by providing different arguments for a slot several variants can be derived from the same mu at run time
a duration model is a rule based system calculating durations taking into account parameters such as lexical stress position of phonemes word initial word medial word final sentence final length of the argument phonetic context of phonemes left right neighbor consonant cluster open closed syllable etc
in a first step a duration is calculated for each of the phonemes in the argument see section NUM NUM NUM
by doing so the important topic of generation of natural prosody is not touched upon see e.g.
still the approach aims at achieving linguistic flexibility for the utterances and attaining a natural sounding prosody
for the argument to be realized correctly linguistic constraints on the slot must be taken into account
also the mus and the underlying carriers can be re used to compose new messages without any loss in speech quality
for the prides detection engines prides uses the acsiom products inquery and inroute
in order to select the appropriate carrier morpho syntactic information about the argument must be available in a dictionary
if not det will not necessarily be useless but will be less useful in circumstances in which the objective number of dialogue design errors matters
the tda consists of a set of tipster compliant search engines and database management software
when used prior to implementation det acts as a design guide when applied to an implemented system det acts as a diagnostic evaluation tool
the distinction between use of det for diagnostic evaluation and as design guide mainly depends on the stage of systems development at which it is being used
this will add a new dialogue and task type as well as the new circumstances of a field trial to the generality test of the tool
following these steps the final task will be to define an explicit and simple training scheme for how to become an expert in using the tool
following the reasoning of the preceding paragraph the analysers proceeded to distil the different types of guideline violations or dialogue design errors identified in the corpus
we take this to mean that when annotating spoken dialogue transcriptions it can be waste of time and effort to annotate the same design error twice
it is not an easy design task to get the system s dialogue contributions right at all times when this distinction has to be transparently present throughout
the sundial dialogues are early woz dialogues in which subjects seek time and route information on british airways flights and sometimes on other airline flights as well
2b kopieer alle rapporten behalve dits
grosz and sidner propose a complex system of rules
whih constructing the decision tre e see prey oils section several t honologically relevant cat gories are discovered by the value grouping mechanism in c4 NUM including the nasals the liquids the obstruents the short vowels mtd the bimoraic vowels
anaphoric expressions are only possible in the nl mode
the nal uraj ategorics or feal ures wlfi h are hyllothesised in her rules in lu h obst r uents sonorwnl alld the lass of bimoraic vowels consisting of long vowels diphtongs and schwa
linguistic knowledge acquisition bottleneck the fact that lexical an t grammatical knowledge usually has to be reformulated t i iii scratch whenever a new application has to be built or an existing application ported to a new domain and to solve problems with robustness and coverage inherent in knowledge based the ory oriente d
knight ridder information inc participated in muc NUM with vanf valu e adding name finder the system used by knight ridder information i n production for adding a company names descriptor field to online newspape r and newswire databases
a direct port was not feasible at the same time sri decided that a declarative grammar like description of fsm was more intuitive and easier to work wit h than the graphical tools they were using at the time
given a ndfsm skand a sequence of symbols ss all the paths are followed the longest matching sequence 3t1 i is considered the result of the application the actions corresponding to all the longest paths are executed a single output symbol i e a head word with a bunch of attributes is sent to the next stage
text processing flow NUM the basic tokenizer written in lex handles lexicon lookup capitalization simple money number phone number and date expression processing simple unknown names processing multi word lexicon entries are handled here as well
NUM the evidence combiner c merges similar names and assigns types to the names whose type could not be derived from the form or from the context database lookup happens here as well
in our case such efficiency made it possible to build a rule set consisting of many quite specific rules such that althoug h each one has a limited application together they cover a large area
under the contract we had sri delivered a transcription of fastus rules in the new declarative language although they did not have their own interpreter nor even a complete languag e definition at that time
two future tasks will be concentrated on training rules building tools the author plans to develop a learn by example system conceptuall y similar the autoslog NUM and the like non boolean evidence combining
stands for a directional meta variable for lcb rcb
the possible cases of combinatory rule application are summarized as follows NUM a
an even more crucial problem lies in the fact that practically all algorithms proposed so far contend themselves with producing a set of descriptors rather than natural language expressions
NUM a preliminary interlingua design for the travel domain contains about NUM concepts arranged in an is a hierarchy semantic features to represent the meaning of closed class items and a list of five basic speech acts which each have several sub types
finally the encoding of lexical rules and their interaction is advanced using constraint propagation to allow coroutining of its execution with other grammar constraints
computational linguistics volume NUM number NUM this means that the more lexical entries in a word class the greater the saving in space
in practice letting d be the uniform distribution is unreasonable since for large corpora most randomly drawn pairs of sentences are in different documents and are correctly identified as such by even the most naive algorithms
as a simple baseline we compared this performance to that obtained by four simple default methods for assigning boundaries choosing boundaries randomly assigning every possible boundary and tested on a similarly sized portion of unseen text
similarly the end of an article is often made with an invitation to visit a related story hence a sentence beginning with see boosts the probability of a segment boundary by a large factor of NUM NUM
for a concrete example if si vladimir and ti gennady then fi NUM if and only if vladimir appeared in the past n words and the current word w is gennady
this is likely to help if all of these sentences are in the same document as the current word for in that case the model has presumably begun to adapt to the idiosyncracies of the current document
a string edit distance such as this is useful and reasonable for applications like speech or spelling correction partly because it measures how much work a user would have to do to correct the output of the machine
in hindsight we can explain this feature by noting that in wsj data the style is to introduce a person in the beginning of an article by writing for example wile e coyote president of acme incorporated and then later in the article using a shortened form of the name mr
the tdt corpus is a mixed collection of newswire articles and broadcast news transcripts adapted from text corpora previously released by the linguistic data consortium in particular portions of data were extracted from the NUM and NUM language model text collections published by the ldc in support of the darpa continuous speech recognition project
after feature induction was carried out as described in section NUM a simple decision procedure was used for actually placing boundaries a segment boundary was placed at each position for which the model probability was above a fixed threshold or with boundaries required to be separated by a minimum number of sentences e
resulting classes of left null right binary tree
a further advantage is obtained because techniques such as partial evaluation can be applied
several authors have suggested parsing algorithms that may be more suitable for lexicalist grammars
this sixth argument is the reference to the result item that was actually used
first we add an output argument to the parse predicate
in the latest version we use different goal weakening operators for each different functor
the head corner predicate constructs in a bottom up fashion larger and larger head corners
small from qo q is a head corner of cat from po p where po p occurs within eo e
it is assumed furthermore that lexical lookup has been performed already by another module
as the names suggest there are many parallels between left corner and head corner parsing
the column headed dec93 reports results on unsupervised training data while the column entitled dec93a contains the results from using models trained on the partially annotated corpus
another interesting case is the formal language token list which trains to a a of NUM NUM indicating that it frequently generates no english text
it appears that the computation of the likelihood which is the sum of e f e f product terms is exponential
we resort to a top n approximation to the em sum for the general model summing over candidate clumpings and alignments proposed by the poisson fertility model developed below
the hw and bg suffixes indicate the results when p e f is computed with a headword or bigram model
to illustrate how well fertility captures simple cases of embedding trained fertilities are shown in table NUM for several formal language words denoting time intervals
we view this interpretation as translation from a natural language expression e into an equivalent expression f in an unambigous formal language
while the architecture provides a means for handling a wide range of user needs as well as a specification for the way in which the relevant data are input into the architecture it does not specify which of those user needs a particular application must satisfy nor does it specify the manner in which the user interface component should operate
the domination links are introduced to allow for the possibility of adjoining
in the trees shown nodes detected as foot nodes are marked with
we also produce a second tree with similar properties for the infinitive marker to t6
note that because of this termination criterion the adverb tree projection will stop at this point
if there is mutual selection we have to stipulate one of the daughters as the sd
however simply blocking the reduction of a sf whenever its value is unspecified is n t sufficient
substitution at the nodes on the frontier would yield the string what kim gives to sandy
basic algorithm take a lexical type l and initialize by creating a node with this type
for example the head subject schema in german would typically constrain a verbal head to be finite
this has the effect of inserting the substring kim wants into what to give to sandy
essentially none of these tagging e rors had to do with the use of the syntactic portion of our tags all of the errors were semantic the same was true in the two tagging consistency experiments related above
i percentage of parses which exactly match one of the human produced parses exact match or which match bracket locations role names and syntactic part of speech tags only syntactic exact match
we chose to use the least enclosing node that is the lowest non pretermiual node in the parallel tree which spans at least the set of words spanned by the node in the atr parse
a simpler but probably adequate approach would combine the two models p a and p aif heuristically using p aif to rescore the n best parses found by the model p a
NUM overall we expect that conversion models which take full advantage of the existing database as well as of the parallel corpus as outlined above should produce data of high enough quality to use as training data for our parser
so called head slot and slot slot ordering rules describe the precedence in projective trees referring to arbitrary predicates over head and modifiers
there are a variety of characteristics of context that one might add to improve the models
all the participating sites als o submitted systems for evaluation on the te and ne tasks
definitions of relations of this sort include specification of the constraints on the nuclei and the combination of nuclei as well as a specification of the effect of the expression
trees are combined through the identification of the root of one tree with a leaf of identical category of another tree
the named entity ne task requires insertion of sgml tags into the text stream
distribution of ne tag elements in figure NUM subcategories of enamex in test set test set
for muc NUM the entities that were to be extracted were limited to organizations and persons
some practitioneers of dg have allowed word order as a marker for translation but they do not prohibit non projective trees
summary scores for all systems evaluated are contained in appendix b
coreference set NUM and possible partitions of coreferential templates in the set as coreference configurations
while debito publico estero foreign public debt produces to the following ratio
on the contrary more language oriented methods are those where specialized grammar are used
linguistic principles characterize classes of surface forms as potential terms step NUM
semantic features selectional restrictions for complex terms and or unknown words
the problem of detecting terms in textual corpora has been approached in a complex framework
NUM noun a p con9 a p deg term NUM noun term
hence one end point of every edge is assigned to the vertex cover i.e. it is marked
the specific nature of our tests required the definition of particular performance evaluation measures
td is hierarchically organized in separate sections where singleton terms dominate all their specified subconcepts
concepts are lexicalized in surface forms via a set of operations that imply semantic specifications
the precision for each similarity calculation method did not differ greatly and the use of the length of the path in the bunruigoihyo thesaurus bgh slightly outperformed other method on the whole
in an attempt to achieve both robustness and translation accuracy when faced with speech disfluencies and recognition errors we use two different parsing strategies a glft parser designed to be more accurate and a phoenix parser designed to be more robust
in follow on 6e0 assigning he terry results in a continue whereas assigning he tony results in a smooth shift and so terry is preferred
issues of the interaction between turn taking and changes in centering status remain to be investigated
we defined various centering constructs and proposed two centering rules in terms of these constructs
a pronoun could also be used in other grammatical roles to refer to the door
furthermore centers are semantic objects not words phrases or syntactic forms
definition NUM measure let ii ii be a measure for the encoded input length of a computational problem
we also abstract from sidner s focusing algorithm to specify constraints on the centering process
it demonstrates that the attentional state properties modeled by centering can account for these differences
we describe how the attentional state properties modeled by centering can account for these differences
NUM a john went to his favorite music store to buy a piano
parallelism would suggest different preferences for the cb 12e in the two sequences
in all the results presented from this point on all the algorithms use the threshold range modification
in translating terms from japanese to english in the browsing mode the indexing module identifies names correctly avoiding the first type of translation errors
slots which are not applicable to this type of incident a kidnapping are marked with an
there had originall y been consideration given to using a more varied test corpus drawn from several news sources
the coreference task like the named entity task was annotated using sgml notation
pushing improvements in the underlying technology was one of the goals of semeval and its curren t survivor coreference
much of the energy for the current round however went into honing the definitio n of the task
the first line identifies this as organization object NUM from article NUM
a coref tag has an id attribute which identifies the tagged noun phrase or pronoun
the participants and the tasks they participated in are listed in figure NUM
we on the other hand seek a solution that can be used in an on line algorithm
thus marking the ivi k complement vertices actually requires marking ivi k times ie identical vertices
furthermore in disallowing swallowing we were able to automatically remove hundreds of potentially harmful pairs from our training set e.g. b aa r b er sh aa p b a a b a a
in addition the user can constrain sources and the date range of documents and also sort the results by date title and sources
with this approach the english sound k corresponds to one of NUM ka y ki ku ke or ko depending on its context
this type of search capabilities can not be offered by typical information retrieval systems as they treat words as just strings and do not distinguish their semantic attributes
best and average error per response fill organization object slot scores for te task
the same set of articles was used for te as for st therefore the content of the articles is oriented towar d the terms and subject matter covered by the st task which concerns changes in corporate management NUM
the indexing module creates and loads indices into a database while the client module allows browsing and retrieval of information in the database through a web browser based graphical user interface gui
there are cases in which times are considered as points e.g. it is now 3pm
many of the rules calculate temporal information with respect to a frame of reference using a separate calendar utility
similarly there are reasonable levels of agreement between our evaluation temporal units and the answers the naive coders provided
all consistent maximal mergings of the results are formed and the one with the highest score is the chosen interpretation
ex how about the 3rd week in august let s see monday sounds good
an important property of the algorithms investigated here is that they do not require a feature selection pre processing stage
also numbers are mistaken by the input parser for dates e.g. phone numbers are treated as dates
this information includes a certainty factor representing an a priori preference for the type of anaphoric or non anaphoric relation being established
the result of applying the approximation algorithm is a 3state automaton recognizing the language e a b
the use of prolog rather than c or c causes large overheads in the memory and time required
each has the property that the symbols d a and n occur only in the combination d a n
terminal symbols may be any prolog terms so the terminal alphabet is implicit
here we have left the scope node as an uninstantinted meta variable s
although the management post and information associated with it are represented in the succession event object that object does not actually represent an event but rather a state i e the vacancy of some management post
where w is the noun verb adjective to be assigned a weight n the number of the first candidate terms considered null t w the number of candidate terms the word w appears with ft w w s total frequency appearing with candidate terms f w w s total frequency in the corpus
for each of these adjectives nouns and verbs we consider three parameters NUM its total frequency in the corpus NUM its frequency as a context word of the first candidate terms NUM the number of these first candidate terms it appears with
there are cases where the verbs that appear with terms can even be domain independent like the form called of the verb to call or the form known of the verb to know which are often involved in definitions in various areas e.g. is known as the singular existential quantifier is called the cartesian product
we said that for this prototype we considered the adjectives nouns and verbs that surround the candidate string
NUM a is the examined string lal the length of a in terms of number of words f a the frequency of a in the corpus ta the set of candidate terms that contain a p t the number of these candidate terms
in this work we study three mistake driven learning algorithms for a typical task of this nature text categorization
the information is maintained however so that later expectation driven processing can use it if necessary
a semantic rule creates a semantic representation of the phrase as an annotation on the syntactic parse
both kinds of resources are still available only for a limited number of languages so only one of the two methods may be a viable option in any given situation
the best it can do for compound words like au chaurange and right away is to link their translation to the most representative part of the compound
consequently we created our ow n additional data and answer keys for ne and st
by contrast several groups this year achieved an f in the 50s in NUM calendar days
it therefore should work equally well on other text not specific to change in corporate officers
these semantic rules can add additional long distance relations between semantic entities in different fragments within a sentence
the knowledge bases of te are inherited by st and do not includ e domain specific knowledge
a ddo can have multiple trigger fragments if the discourse component determines that the triggers co refer
this was created by retrieving article s using the university of massachusetts document retrieval engine inquery
we also evaluated system changes on a daily basis using the scores from the training development set
this reduces the time complexity of each iteration from o n NUM to o n given that n is the total number of examples in s
one plausibile solution would be to select a point when the increment of the total interpretation certainty of remaining examples in x is not expected to exceed a certain threshold
note that in this figure whatever example we use for training the interpretation certainty for the neighbors x s of the chosen example increases
which ranges from NUM to NUM is a parametric constant to control the degree to which each condition affects the computation of c x
let s be a set of sentences i.e. a given corpus and t be a subset of s in which each sentence has already been manually disambiguated for training
both sampling methods used examples from ipal to initialize the system as seeds with the number of example case fillers for each case being on average of about NUM NUM
however example based systems NUM NUM NUM do not require the reconstruction of the system but examples have to be stored in the database
figure NUM comparison of the initial word frequency estimation methods
a self organlzing japanese word segmenter using he ristic word identification and re estimation
we are currently investigating solutions to all of these problems in a highly experimental setting
NUM if the interpratation of x is correct NUM p otherwise in equation NUM p is the parametric constant to control the degree of the penalty for a system error
NUM a chinese discourse say a paragraph of written text therefore consists of a sequence of sentences and the corresponding intentions altogether form the intention of the discourse
figure NUM initial word list size and word segmentation accuracies
it seems further re estimation brings no signi cant change
top performance o n person objects came close to human performance while performance on organization objects fel l significantly short of human performance with the caveat that human performance was measured on only a portion of the test set
in the future this work needs to be further developed to deal with anaphora in other types of texts and the use of connectives in generated text to create cohesive discourse
as for the other discourse factor high noteworthiness the condition of animacy noticed by chen can be determined according to the features of the referent and hence is easily implementable
robust learning smoothing and parameter tying in addition the syntactic and lexical weights are adjusted as follows
the parameters obtained in such a way frequently fail to attain an optimal performance when used in a real application
such a formulation is particularly useful for a generalized lr parsing algorithm in which context sensitive processing power is desirable
meanwhile the syntactic parameter component corresponding to the top incorrect candidate would be adjusted according to the following formulae
maximizing the likelihood values on the training corpus therefore does not necessarily lead to the minimum error rate
statistical approaches to natural language processing generally obtain the parameters by using the maximum likelihood estimation mle method
sp is defined as the average selection factor sf of the disambiguation mechanism on the task of interest
in the training set there were NUM NUM unambiguous sentences while NUM sentences were unambiguous in the test set
the definition and implementation of the evaluations reported on at the message understanding conferenc e were once again a community effort requiring active involvement on the part of the evaluation participants a s well as the organizers and sponsors
the maximum likelihood estimate pml for the probability of an event e occurring r times is defined as follows r
in order to implement the variations in the constituent order dictated by various information structure constraints we have used a recursively structured finite state machine instead of enumerating grammar rules for all possible word orders
this paper describes tactical generation in turkish a free con stituent order language in which the order of the constituents may change according to the information structure of the sentences to be generated
tactical generation in a free constituent order language
in addition to the content information our generator takes as input the information structure of the sentence topic focus and background and uses these to select the appropriate word order
this work was supported by a nato science for stability project grant tu language
this however does not mean that word order is immaterial
in this process a generation grammar and a generation lexicon are used
the word order information is lexically kept as multisets associated with each verb
our implementation environment is the genkit system developed at carnegie mellon university center for machine translation
our implementation is based on the genkit environment developed at carnegie mellon university center for machine translation
this hypothesis runs counter to the standard practice in information retrieval of weighting words by idf favoring extremely rare words no matter how they are distributed
table NUM showed that variance and entropy can also be used as a measure of content at least among a set of words with more or less the same word frequency
we showed in section NUM that deviations from poisson in one year of the ap can be used to predict deviations in another year of the ap
figures NUM and NUM compare idf and log NUM o NUM for the NUM words in table NUM and find that idf and log lo i2 are reasonably stable across years
the two parameters tx and NUM can be fit from almost any pair of variables considered thus far e.g. f idf t NUM h
for the different polysemy groups the choice most often made was in first position for low and medium high polysemy words but for high polysemy words NUM or more senses the most frequently selected sense was less often in the first position
a better approach is to have the system verify its interpretation of an input only under circumstances where the accuracy of its interpretation is seriously in doubt or correct understanding is essential to the success of the dialog
we now turn our attention to an empirical study of strategies for selective utterance verification that attempt to select for verification as many of the misunderstood utterances as possible while minimizing the selection of utterances that were understood correctly
as pointed out earlier the dependency relation of elements in japanese sentences are fairly complicated due to relatively free word orders
engage in a verification subdialog using this decision rule and comparing it to strategy NUM the over verification rate drops from NUM NUM to NUM NUM while the under verification rate rises from NUM NUM to NUM NUM i.e. the percentage of utterances correctly understood falls from NUM NUM to NUM NUM
engage in a verification subdialog this basic capability for verification subdialogs was not available during the NUM dialog experiment
in that situation the over verification rate was NUM NUM while the 4consequently the under verification rate is NUM NUM
general expectations for the meaning of user responses to a goal of the form goal user ach prop include the following a question about the location of obj
in the case of an action completion or a property status there is also a main expectation for either that the user completed the action e.g. done or the switch is up or that the property status is verified e.g. wire connecting NUM and NUM
most of the systems fall into the same rank at the high end and th e evaluation does not clearly distinguish more than two ranks see the paper on statistical significance testing b y chinchor in this volume
where any combination of types contributes to more than one overall analysis it need only be computed once
in this approach these inferences are no longer required their effects having been compiled into the semantics
for example in the proof NUM since the higher order functor s argument category i.e.
of course the system of labeling that is in use where the constraints of the real grammatical logic reside may well import word order information that limits combination possibilities but in designing a general parsing method for linear categorial formalisms these constraints must remain with the labeling system
both gpsg and hpsg use slash features to percolate features to gaps
operations on feature annotation are performed by constraints represented as ovals
secondly experimental results show that an entirely deductive approach is inefficient
modularity corresponds to maximal succinctness when all independent principles are stated separately
it is most appropriate when the entity type is highly predictive of its role in the event
feature structure formalisms also use rule schemata to capture similarities among grammar rules
the distinction between these two approaches can be used as a conceptual tool for analyzing new domains
thus they lose generality without exploiting all the available information
this information is stored in a table called a co occurrence table
to explore the emergence and persistence of structured language and consequently the emergence of effective learners pseudo random initialization was used
table NUM shows the pattern of preferences which emerged across NUM runs and how this was affected by the presence or absence of memory limitations
partially ordering the updating of parameters can result in experimentally effective learners with a more complex parameter system than that studied previously
in the case of grammar learning this is a co evolutionary process in which languages and their associated grammars are also undergoing selection
in this model the issue of ambiguity and triggers does not arise because all sentence types are treated as triggers represented by p setting schemata
figure NUM summarizes crucial options in the simulation giving the values used in the experiments reported in ss4 and figure NUM shows the fitness functions
a lagt can live for up to ten interaction cycles but may die earlier if its fitness is relatively low
in general no relationships between words have been directly encoded in stochastic n gram taggers
japanese is an sov language with 3throughout double quotes around language names are used as convenient mnemonics for familiar combinations of parameters
how many percentage points of all words in running text are retain a different analysis after the differences due to inattention have been omitted
however there were three situations where a multiple analysis was accepted when the judges disagree about the correct analysis even after negotiations
also the structure of adverbials as well as prepositional and adjective phrases is given though some of the attachments of adverbials is left underspecified
first we develop a general model of coreference between any two templates and apply it to pairwise combinations of templates in a given coreference set without regard to the other templates in the set
as there are many scenarios that will never be encountered in a corpus of training data of any reasonable size it would be hopeless to attempt to estimate a conditional distribution for each possibility directly
a fairly coarse grained set of characteristics also allows us to restrict ourselves to a relatively small set of training data likewise we will not want to encode a large set of data for each new domain
evidential approach but consisted of characteristics of context for NUM template pairs in the first training set NUM pairs in the second training set and NUM pairs in the third training set
one of the goals of this effort is to allow the ability to train up probabilities in new domains quickly which requires an approach that is successful with a limited amount of training data
NUM however the probability that c and d corefer in the final distribution is only NUM NUM the sum of the probabilities of the two partitions in which c and d occupy the same cell
while considering feature sets for all pairs may wash out the training data for the pairwise probability model somewhat the evidence provided by all pairs appears to more than make up for the difference
another version of the queries was constructed in which part of speech variants were retained if the meaning was related 4in actuality we indexed it with whatever tags were used by the tagger we are just using noun and verb for purposes of illustration
in motivating our approach we noted that we can not expect to have the amount of training data necessary to directly estimate distributions for all the possible scenarios with which we may be confronted
linguistic extraction however is not enough
as the guidelines and training set drifted further apart this led increasingly to the same inconsistencies we experienced with chinese
second alembic supports the developer through a growing suite of tools chief among them the phrase rule learner
pani h enolieh japaneee chincec figure NUM name tagger rankings by language
aside from date and money patterns the entirety of the chinese rule sequence was acquired through a machine learning process
part of these differences can be attributed to inconsistencies that were eventually detected in the final test data
the development process for him consisted largely of kanji pattern matching as opposed to bona fide reading
in the course of met we ported the alembic name tagger to all three of the target languages
first the inherent speed of the system NUM NUM NUM NUM words per minute enables a rapid evaluation methodology
with help from a good dictionary and atlas we were able to understand the training texts well enough to grasp their critical semantics or as much of the semantics as was needed for the purpose of name tagging
first the initial labeling breaks the string into components on the basis of part of speech taggings none associaci6n none de none mutuales israelitas argentinas none the first rule searches for organizational head nouns e.g. associaci6n and others and marks any matching phrase as an organization orgex in our local met dialec0
at a minimum the string must record all moments where there is an edge on some tier
the alternatives window for denwa is shown in figure NUM
that node serves as a kind of lexical node in subsequent translation
the dxl language now that we understand it is wonderfully powerful and flexible
to control quality some kind of human interaction will be inevitable
in section NUM we describe the basic model and associated operations
section NUM gives further explanation about disambiguation capability of the interactive operations
harry morgan raffler jr vice president and frank
further disambiguation capability of this operation will be discussed in section NUM
figure NUM shows translation steps for a sentence with a relative clause
two of the four bnf pattern definitions are given at the bottom of the figure
specifying a feature forced penman to make a particular linguistic decision
many paths generation leads to a new style of incremental grammar building
new companies will have as a goal the launching at february
a procurements of guns by a americans will be an effortlessness
you may be obliged to eat that there was the poulet
each island corresponds to an independent component of the final sentence
for evaluation we compare english outputs from these three sources
statistical methods give us a way to address a wide variety of knowledge gaps in generation
although we do not expect to achieve perfect recall on this criterion after general usage entries have been filtered out the number is useful insofar as it provides a sense of how recall for this corpus correlates with precision
always noun attachment NUM NUM most likely for each preposition NUM NUM average human NUM head words only NUM NUM average human whole sentence NUM NUM always noun attachment means attach to the noun regardless of v nl p n2
this is effectively a comparison of the maximum likelihood estimates of pll nl and p pi rcb v a different measure from the backed off estimate which gives i5 liv p nl
a pp attachment algorithm must take each quadruple v v n1 nl p p n2 n2 in test data and decide whether the attachment variable a NUM or NUM
if we ignore n2 then the ibm data is equivalent to hindle and rooth s v hi p rcb triples with the advantage of the attachment decision being known allowing a supervised algorithm
the second problem is tagging ambigui which occurs when there is more than one tag for one word
our assessment of the system was designed to reasonably approximate the post processing that would be done in order to use this system for acquisition of translation lexicons in a real world setting which would necessarily involve subjective judgments
there are three nontrivial problems of thai morphological processing word boundary ambiguity tagging ambiguity and impficit spelling errors
we summarize significant differences between test and training sets in table NUM
there are three operations involving the cache and main memory
discourse processes execute on elements that are in the cache
thus return pops are not problematic for the cache model
the stack model does not predict a function for the irus
consider the variation of dialogue a in dialogue b in figure NUM
such a process must decide what information is relevant at each point of the unfolding discourse
a speaker uses indefinite deixis to indicate that he believes the entity unknown to the hearer
null expectations about what will be discussed also determine operations on the cache
wag however takes the speech act as central the semantic specification is a specification of a speech act
relations between the participants can also be specified for instance parent child or doctor patient relations
and they bring the child to us every day for babysitting
this focus space supports the interpretation of the proforms in a 8a
most of these points are illustrated by the input in figure NUM phenomena currently not handled automatically include certain types of fancy syntax such as clefts and it clefts though these can be generated by specifying the surface structure in the input as well as long distance dependencies such as these are books which i think you should buy where which is an argument of buy
depending on its content the template is unified with a prefabricated structure specifying linguistic oriented input to the generator
we are currently experimenting on using some other similarity measures between word pairs from non parallel corpora
chinese tokenization is a difficult problem and tokenizers always have errors
many adjectives can also act as adverbs with no morphological change
we start with a uniform weight distribution
the routine was the following NUM
we adapt cg constraints described above
this is a partial rule about coordination
lower layer states are checked only if the system is already in a sub dialogue
there are two common methods for statistical estimation the maximum likelihood estimation method
the goodness and badness of selection of a model directly affects classification results
method when we use our current method of creating clusters
we propose a method of document classification based on soft clustering of words
it then calculates the probability of a document with respect to a category as
in contrast sable automatically constructs an explicit translation lexicon the lexicon consisting sable exhibit plateaus of likelihood
although we have data from only one annotator table NUM shows the clear differences between the two results
tie scores or the absence of a NUM of NUM plurality were treated as the absence of an annotation
exact matches such as cpio cpio or clock clock comprised roughly NUM of the system s output
so in this case you could in fact decide to choose both specific and general
thus from the evaluator s perspective the task appeared to involve a single sample of NUM translation lexicon entries
a bitext map is an injective partial function between the character positions in the two halves of the bitext
robustness the system performs well even in the face of omissions or inversions in transla null tions
the results show that up to NUM of the translation lexicon entries produced by sable on or above the 2again this sample of data was produced by an older and less accurate version of sable and therefore the percentages should only be analyzed relative to each other not as absolute measures of performance
the most likely sequence of tagged word is the one that maximizes the chain probabilities
if rl and p2 are set to true we get young dog if q2 is selected we choose the right branches of nodes NUM and NUM and get puppy
in many situations there may be certain specifications in the input discourse consideration indication of preferences etc that may not be crucial to the adequacy of the resulting expressions
in his algorithm kay proposes to use two devices to establish which phrases interact and when phrases can be folded together under a disjunctive edge
before we continue with examples of more complex boolean conditions we explain how the boolean arrays are constructed and what exactly is their logical interpretation
the generation from a disjunctive input proceeds just as before as if the disjunction is ignored and all the semantic facts are given equal status
a more interesting problem that a chart with boor can conditions can address is how to use ambiguous semantics as an input to the generation process
thus the ability to recognize equivalence is an important aspect of chart processing and it is essential that it will be available to the generation process
now this situation is interesting because this fact is already contained in one of the branches of node i l as we have already seen
the lnotivation for this is to enable a situation particularly in machine translation where the resolution of ambiguity is postponed to after the generation process
in our corpus more than NUM of sentences include word boundary ambiguity
moreover three of the four filters prove useful even when used with large training corpora
i would like to thank timo jpsrvinen jussi piitulainen past tapanainen and two eacl referees for useful comments on an earlier version of this paper
the tokeniser is a rule based system for identifying words punctuation marks document markers and fixed syntagms multiword prepositions certain compounds etc
NUM one of these two corpus versions was modified to represent the consensus and this consensus corpus was used as the benchmark in the evaluation
this is probably due to the fact that while the parsing grammar always requires a regent for a dependent it is much more permissive on dependentless regents
clause boundaries and hence the internal structure of clauses could probably be determined more accurately if the heuristic part of the grammar also contained rules for preferring e.g.
because of the known feasibility of the linguistic rule based approach at related levels of description the success of the data driven approach in part of speech analysis may appear surprising
in the linguistic approach the generalisa null tions are based on the linguist s potentially corpus based abstractions about the paradigms and syntagms of the language
is the level of parts of speech somehow different perhaps less rulegoverned than related levels NUM we do not need to assume this idiosyncratic status entirely
for instance the premodifier tag n only indicates that its head is a nominal in the right hand context
a preposition is followed by a coordination or a preposition complement here hidden in the constant prepcomp that accepts e.g.
this part of speech tagging is the principal role of the unix preprocess and it is itself supported by a number of pretaggers e g for labeling dates and title words and zoners e.g. for word tokenization sentence boundary determination and headline segmentation
this fraction can be computed either by token or by type depending on ghe application
requires determinization and the determinization of automata representing expressions of the form a where c is a regular expression is often very expensive specially when the expression a is already complex as in this case
in order to compare the performance of the mgorithm presented here with kk we timed both algorithms on the compilation of individual rules taken from the following set k NUM NUM
however we hope to also be able to use the compiler in serious applications in speech deterministic automaton r representing ps e NUM p 0e NUM e NUM versus the log of the number of arcs in the automaton obtained by determinization of r
the first ruleset consisting of pronunciation rules for the orthographic vowel NUM contains twelve rules and the second ruleset which deals with the orthographic a b c k k e NUM NUM
in the actual application of the rule compiler to these rules one compiles the individual rules in each ruleset one by one and composes them together in the order written compacts them after each composition and derives a single transducer for each set
consider for example the righthand intersectand namely NUM p NUM NUM NUM which is the complement of NUM p NUM NUM NUM as previously indicated the complementation mgorithm
on the horizontal axis is the number of arcs of the non deterministic input machine and on the vertical axis the log of the number of arcs of the deterministic machine i.e. the machine result of the determinization algorithm without using any minimization
these pns select i eutities usually substances wheat rice but also possibly individuals lelnou as conventioualised in spanish as internally structured in gajos
then bible was used to select among several possible generalizations of the two tag sets
we present a compact hierarchical organization of syntactic descriptions that is linguistically motivated and a tool that automatically generates the tree families of an ltag
n the english grammar for instance there are trees for wh questions and trees for relative clauses that are adjoined to nps
we have reported an experimental study for extracting key paragraphs based on the degree of context dependency for a given article and showed how our context dependency model can use effectively to extract key paragraphs each of which belongs to the restricted subject domain
in table NUM if the number of keywords which belonging to the third paragraph is larger than that of the fourth the order of key paragraphs is NUM NUM NUM NUM otherwise NUM NUM NUM NUM
stage three extraction of key paragraphs the sample results of clustering is shown in table num in table NUM shows the order of clusters which we have obtained and the number shown under cluster shows the paragraph numbers
on the other hand the deviation value of o in the article is larger than that of the paragraph since in article o appears in a particular element of the article general signal corp
where n is the number of nouns in an article and niy is as follows lcb o NUM nj nis NUM where i nj is a frequency with which the noun nj appears in paragraph pi
other heuristics as we discussed in keywords experiment it might be considered that some heuristics such as location of paragraphs are introduced into our method to get a higher accuracy of keywords and key paragraphs extraction even in these articles
this shows that using only location heuristics the key paragraph tends to be located in the first parts is a weak constraint in itself since the results of our method showed that the correct ratio attained NUM NUM
secondly we could obviously use the tool to build a grammar for another language either from scratch or using the hierarchy designed for french
so the principle should be a principle of predicatefunctions co occurrence the trees for a predicative item contain positions for all the functions of its actual subcategorization
furthermore the deviation value of o in the domain is larger than those of the article and paragraph since in the domain o appears frequently in a particular context economic news
whom has peter s mother praised
hans hat otto ein bv n gegeben
an underspecified hpsg representation for information structure
as the focus for the example
lsthe feature max f is actually redundant
a binary branching structure is assumed
additional knowledge may introduce further solid arrows
also a resolution routine was presented
NUM notice in particular how the datr default mechanism completes most of the truth table rows without explicit listing
the yield of this plan derivation can then be given as input to a module that generates the surface form of the utterance
however instead of explicitly including the atom love in the morphological form the value definition includes the descriptor mor root
with hindsight this may have been a bad design decision since similarity of syntax tends to imply a similarity of semantics
roughly speaking this is to be interpreted as inherit the value of mor root from the node originally queried
effectively syn form is being used here as a parameter to control which specific form should be considered the morphological form
do mor verb syn verb mor present verb syn cat verb syn type verb
such a statement would respect global inheritance but not local inheritance and might be useful to achieve some exotic effect
this model is the basis of at least one implementation of datr but it is not of course declarative
such specifications may provide a new node a new path or a new node and path to inherit from
standard deviations on the NUM experiments are between brackets
in this distributed data model accessing a document via a document server gives access to a document s contents and to attributes and annotations of a document
in this model all components run as servers and the application code which implements the logic of the application runs as a client of the component servers
this model also provides adequate support for the integration of static knowledge sources such as dictionaries and of ancillary tools such as codeset converters
various versions of this architecture have been developed in c c and lisp but no support is defined for integration of heterogeneous components
in the basic document class provided in the architecture a document is identified by its name url to the location of the document s content
each component can talk directly to each other and thus all components need to incorporate some knowledge about each other at all three levels mentioned above
a database version uses a commercial database management system to store and retrieve collections attributes and annotations and also documents through an import export mechanism
after algorithm NUM has recognized a given input the set of all parse trees can be computed as tree q n o n where the function tree which determines sets of either parse trees or lists of parse trees for entries in u is recursively defined by i tree a q i j is the set lcb a rcb
for determining the order of the time complexity of our algorithm we look at the most expensive step which is the computation of an element xfl e ui j from two elements x q e ui k and t3 e uk j through x q fl xfl e t2lr
we have also observed that in the open microphone mode multimodality allows erroneous speech recognition results to be screened out
for example the gesture in figure NUM is used for unimodal specification of the location of a fortified line
systems capable of integration of speech and gesture have existed since the early NUM s
the speech recognition agent is built using a continuous speaker independent recognizer commercially available from ibm
our initial application of this architecture has been to map based tasks such as distributed simulation
this integration method allows the component modalities to mutually compensate for each others errors
pruning the dynamic programming condition for pruning suboptimal partial analyses is as follows
a word may have several or no r dependents for a particular relation r
the main justification for artificial semantic representation languages is that they are unambiguous by design
this may not be as critical or useful as it might first appear
the attachment information was used to generate additional negative and positive counts for dependency choices
this was a previously unseen test set not included in any of the training sets
these examples serve to further restrict the assumptions needed to support a discourse determined approach elided vps exhibit the discourse behavior of deaccented vps
thus the parallelism argument per se does not distinguish a source determined analysis such as the equational analysis from a discourse determined analysis
unlike simple n gram models head automata models yield an interesting distribution of sentence lengths
sentence NUM has the two readings corresponding to whether james likes ivan s mother and father or his own mother and father
hardt presents further examples such as NUM of switching reference in which the source and target are structurally different
of course this particular choice of parallelism between the two clauses is not the only one nor is it the most natural one
such extra accent and deictic gesturing are capable of forcing a reading in which the second pronoun refers to kris not ivan or james
it is assumed that state names are integers to rule out cyclic word graphs we also require that for all transitions from p0 to p it is the case that p0 p transitions in the word graph are represented by clauses of the form wordgraph trans p0 sym p score which indicate that there is a transition from state p0 to p with symbol sym and acoustic score score
to this end a rule is selected of which this category is the head daughter then the other daughters of the rule are parsed recursively in a bidirectional fashion the daughters left of the head are parsed from right to left starting from the head and the daughters right of the head are parsed from left to right starting from the head
further panebmt can index certain word pairs to in effect precompute some two word chunks
figure NUM l angloss machine q r mslation system
figure NUM shows the set of translations generated fi om one sentence
the suhstring with the best score is then selected as the aligned match for the chunk
null NUM sentence pairs stern from the pai lcb o corpus and NUM pairs from evaluations
not only was the corpus fairly small the text which was used was not flflly indexed
so for example waiters in spain are expected to serve snacks whereas in belgium they do not
we trove added atomic features associated with each constant such as category index quality i.e.
during bss aic removed feature l2 from the model bic removed l1 l2 r1 and r2 g NUM x NUM removed no features and the exact conditional test removed c2
we believe this to be a preferable means to approaching a sound and complete knowledge base
various methods for pos tagging have been proposed in recent years
moreover gaps frequently arise in dictionaries and thesauri in specifying this kind of virtual polysemy
thus the algorithm described in this paper can readily apply to other mrds besides ldoce
table NUM displays a word by word performance of the algorithm
NUM for simplicity the parameter is set to i
an illustrative example demonsu ates the effectiveness of the algorithm
directly using dictionary senses as the sense division has several advantages
not reachable via directed arcs from another node in the target graph gn of such entries
however the aim of the generation method we advocate here goes beyond rendition of fully specified semantics
even so the algorithm is quite able to skip affixes when appropriate
in the absence of known sound correspondences it can do no more
this model is coded entirely as a bilingual lexicon with associated cost parameters
if tile later then we know that the original dependent disjunction in already nn lulai
this paper introduces all etficient normal tbrm for processing dependent disjunctive constraints and an operation for compilation into this normal form
the key insight is that solving disjunctions of the base constraints is no longer necessary since they are purely conjunctive
disjunctions are replaced by conjunctions of im plications from contexts propositional formulae to the base constraints fie
they then collapse this set down to a single feature structure where nodes are labeled with dependent disjunctions of types
when a group of dependent disjunctions is split into slnaller groups an exponential amount of redundant information is reduced
that the corresponding disjuncts of every disjunet in the group inust be simultaneously satisfiable
contexted constraints the usefulness of the alternative case form only becomes apparent when considering dependent disjunctions
sin this example equivalent alternative variables have been replaced by representatives of theirequivalence chess
and cutting each of these ropes in half again one thereby obtains four ropes of two feet each
it might be asked why the cardinality of a plural demonstrative noun phrases might be allowed to be one
surely a unit different from that of a serving is invoked in the case of weight
proper names are constantly being added to english and once added they are subject to such conversions
they include nouns such as duck chicken turkey and lamb to mention just a few
instead semanticists have tried to distinguish english mass and count nouns on the basis of what they denote
it is also true that a conjoined noun phrase is plural even if its conjuncts are singular
in this formalism the lexical items are associated with the syntactic structures in which they can appear
but even in their guise as count nouns they satisfy the criterion of the divisity of reference
here are some well known examples coffee tea beer hamburger cheese and wheat
those functions rate w as similar to wl if roughly p w21w is high when p w21 wj is
by a huge margin therefore we conclude that information from other word pairs is very useful for unseen pairs where unigram frequency is not informative
according to this formula w2 is more likely to occur with wl if it tends to occur with the words that are most similar to wi
we concentrate here on the problem of estimating the probability of unseen word pairs that is pairs that do not occur in the training set
we compare four similarity based estimation methods against back off and maximum likelihood estimation methods on a pseudo word sense disambiguation task in which we controlled for both unigram and bigram frequency
we found that all the similarity based schemes performed almost NUM better than back off which is expected to yield about NUM accuracy in our experimental setting
the text handling module thm receives as input the plain text between sgml marks
now we could directly replace p w2 wl in the back off equation NUM with psim w21wl
considerable latitude is allowed in defining the set wx as is evidenced by previous work that can be put in the above form
only around NUM nouns and around NUM verb forms have been added to the system by hand
a method for deriving a verb subcategorization lexicon from a corpus according to an example based learning technique applied to robust parsing data is described in basili et al forthcoming
social for which a general agreement exists
first ambiguity of words 2the t11 NUM ng phase has been evaluated over different corpora but results will be discussed over a collection of publications on remote sensing sized about NUM NUM words
the essential difficulty in separating word senses when conflating data are derived from distinct senses is due to the fact that simple collocations are often the surface results of independent linguistic phenomena
let for example future earth observation satellite systems for worldudde high resolution observation purposes require satellites in low earth orbits supplemented by geostationary relay satellites to ensure intermediate data transmission from leo to ground
v n model co require cg n v require cg satellites ob v n process co require cg n v require cg sensors ob v n satellite 0b require cg n v requsre cg beam antenna 0b
second since the training is performed on an unbalanced corpus and also for verbs that notoriously exhibit more fuzzy contexts we introduced local techniques to reduce spurious contexts and improve reliability
wordnet high level classes has several advantages
results from large scale experiments are reported NUM
case a primitive representation e.g. to loc at loc is extracted from a set of predefined mappings
as is customary the surface and lexical descriptions in rules are related by four types of operators
NUM a verb together with its semantic class uniquely identifies the word sense or lcs template to which the verb refers
we estimate that it would take at least NUM months to build such a lexicon from scratch by human recall and data
be loc thing NUM at loc thing NUM thing NUM touchingly NUM
the main technical characteristics of our analyzer the system has been written in sicstus prolog
we focus on the probleln of building large repositories of le rical cojtceplual structure lcs representations for verbs in multiple languages
catmorf s internal structure figure NUM conforms to the two level paradigm
the wg has NUM rule for verbal inflection and NUM rules for nominal processes
when there is total agreement NUM
NUM rules cover nominal inflection NUM rules cover verbal inflection
the wordnet verb taxonomy is based on the roponymy relation which is defined as the co occurrence of both lexical implication and temporal co extension between two verbs
tral module of a tagger intended to deal with free input
his approach is very different from ours and uramoto s
the parameter k was set to NUM for our method
the main difference between category based and cluster based approaches resides in the cluster construction
the stored entries are all minimal signs and i hey are usually not very in i eresdng to the lexicon user
since no special syntactic notions are assumed we must here decide on an existing syntactic theory before the mapping rules can be defined
the entry specifies a particular word form contains a conceptual structure with three arguments and lists the syntactic functions realizing these arguments
the whole entry is generated by a series of derivations where each derivation adds a piece of information to the final lexical entry
the system then chooses the correct inflectional paradigm and it can start trying out the different expansion rules to generate complete lexical entries
conceptual e r pansion rules are rules that extend the semantic part of the signs without combining them with other sign structures
the rule in figure NUM b expands the basic entry for paintv into the more specialized entry for the past form paintedv
section NUM explains the use of lexical expansion rules whereas some concluding remarks and directions for further work are found in section NUM
in the sign expansion approach the lexicon is viewed as a dynamic rule system with lexical frames and various kinds of expansion rules
the most specific generalization does not necessarily provide additional constraining information
a covariation treatment of hpsg lexica therefore can be particularly profitable
to ensure termination in case of direct or indirect cycles we use a subsumption check
the first of these is a variation on traditional beam search
we use a new search algorithm to simultaneously optimize the thresholding parameters of the various algorithms
NUM we wanted a metric of performance which would be sensitive to changes in threshold values
on the other hand the probabilities also introduce new opportunities
in section NUM we discuss implementation results and illustrate the efficiency of the proposed encoding
performance measures such as precision and recall will remain virtually unchanged
in this paper we examine thresholding techniques for statistical parsers
this paper proposes a new computational treatment of lexical rules as used in the hpsg framework
in stark contrast beam thresholding only compares nodes to other nodes covering the same span
we designed a variant on a gradient descent search algorithm to find the optimal parameters
idiosyncrasies peculiar pronunciations that can not be described by rules and that even native speakers quite often do not know or do not agree upon e.g. oeynhausen c nhauzon duisdorf dy sd f or du sd f or du isd f
as a test data we selected NUM articles each of which belongs to one of these NUM domains
once the highest rated antecedent has been identified it may be necessary to modify it by removing an argument or adjunct that is incorrectly included
the vpe res system achieves an NUM NUM success rate according to head match in the blind test data from the wall street journal corpus
NUM the syntactic filter for vpe also rules out local antecedents in a sense it rules out antecedents in certain containment configurations
the weights of all potential antecedents that do not match the vpe auxiliary category are multiplied by our standard penalty value which is NUM
the coder selected ignite another war among the world s giants while vpe res selected threatened to ignite another war among the world s giants
for instance if we know that the basic syntactic category of a word because is col junction and it is part of a conjunction group then this is an indication to close the current frame and trigger a new fl ame for the next utterance
this compares favorably with lappin and leass s result especially considering that computer manual text is a good deal more restricted than newspaper text
the vpe res system incorrectly selects seems as the antecedent because it does not recognize that the vp headed by seems improperly contains the vpe
l phis is ahnost a literal translation of the germau utterance l ienstags um zehn ist bei mir nun wiederum schlecht weft ich da noch trainieren bin ich denke wir sollten das ganze dann doch auf die niichste woche verschieben geht es bei ihnen da
our guideline for the choice of these dialog acts was based on l the particular dommn and corpus and NUM our goal to learn rather few dialog at gories but in a robusl n anucr NUM
a large number of parameters will inevitably be required for such a formulation and a large training corpus is thus needed for training
for comparison the corresponding results before learning i.e. the baseline results are repeated in the upper row of each table entry
thus minimizing the error rate on the training corpus does not imply minimizing the error rate in the task we are really concerned with
the transition probability between two phrase levels say p l7 i c6 is the product of the probabilities of two events
however in such a formulation the lexical scores as well as the syntactic scores are assumed to contribute equally to the disambiguation process
under such circumstances the associated probabilities for these two reduce actions will be identical and thus will not reflect the different preferences between them
however this measure is unable to identify which model is better if the average number of alternative syntactic structures in various tasks is different
the accuracy figures are given in terms of translation word error rate a measure we believe to be somewhat less subjective than sentence level measures of grammaticality and meaning preservation
sentence level segmentation could be expressed in terms of links into the phrase level segmentation as presented above
this involved running the systems on the sample utterances starting initially with uniform costs and presenting the resulting translations to a human judge for classification as correct or incorrect
for example adding words to a lexicon should occur less often than if every application had its own lexicon
from the observation we can estimate that the cause of the results was not our clustering technique
the input stage is followed by several stages of pattern matching
in our system numerical predictions based on the more local utterance level are generated by tile parser
we can if we wish use parameters in evaluable paths that resolve to true or false
if we have the sentence p t barnum took the helm of f
or may be part of a verb phras e he enjoys driving cars
these benefits are lost when we encode individual semantic structures
the only substantial processing for response generation occurs in the scenario template task
james is vacating but not on the positions mr
this information is encoded in a set of context based scores produced by the discourse processor for each ilt
relative clauses fred who runs ibm
in any case problem NUM still loomed
this could be ace omplished for exa3nl h with a decision tree le trning
thus we can simplify further as follows this corresponds directly to the individual costs that we use for the dynamic programming equation
the essential elements of the analysis are as follows bare verb mor past participle mor root
the probability of a lexical rule a NUM NUM x y is ba x y NUM NUM
the definition of the left to right longest match replacement can easily be modified for the three other directed replace operators mentioned in figure NUM
the statistics were collected over the roughly seven million words of mixed broadcast news and reuters data comprising the tdt corpus see section NUM
though the trigram prior was trained on NUM million words the trigger parameters were only trained on a one million word subset of this data
in this work we rely exclusively on simple lexical features including a topicality measure called relevance and a number of vocabulary features that are induced from a large space of candidate features
furthermore it is not possible to cheat and obtain a high score with this metric spurious behavior such as never hypothesizing boundaries and hypothesizing nothing but boundaries are penalized
the difference is quantifiied in table NUM which shows that p NUM NUM for model a while p NUM NUM for model b
a soap commercial for instance does n t benefit a long range model in assigning probabilities to the words in the news segment following the commercial
often a long range model will actually be misled by such irrelevant context in this case the myopia of the trigram model is actually helpful
because the architecture is modular and identifies equivalent components a basis on which to compare these variables may exist
selection of a pair of dependent words w i and v and transducer m i given head words w and v and source and target dependency relations el and r2
NUM for example caution has to be exercised in the use of datr variables for two reasons
yet an algorithm that places a boundary a sentence away from the actual boundary every time actually receives worse precision and recall scores than an algorithm that hypothesizes a boundary at every position
on the other hand if the present document has just recently begun the long range model is wrongly conditioning its decision on information from a different and presumably unrelated document
for the same reason reconfiguration and extension of an architecturally compliant application will be easier than for a non compliant application
tam1 opt agr 3sg let him carve and the form oyun gives rise to the following parses ioutput of the morphological anajyzer is edited for clarity and english glosses have been given
a negative filter that deletes all the material between the two sgml codes including the codes themselves is expressed as in figure NUM
the work on tokenizers and phrasal analyzers by anne schiller and gregory grefenstette revealed the need for a more efficient implementation of the idea
one is a transfer model with monolingual head automata for analysis and generation the other is a direct transduction model based on bilingual head transducers
most datr descriptions consist only of definitional statements and include at most one statement for each node path pair
the discourse processor must be able to deal with more than one semantic representation as input at a time
given a new text annotated with all morphological parses this time the parses are not projected we proceed with the following steps for disambiguation lier are then repeatedly applied to unambiguous contexts until no more ambiguity reduction is possible
the current features must be refined and more features may be need to be added
directionality means that the replacement sites in the input string are selected starting from the left or from the right not allowing any overlaps
practical experience shows that the presence of many auxiliary diacritics makes it difficult or impossible to compute the left to right and longest match constraints in such cases
we are grateful to jishen he for building the chinese model and bilingual lexicon of the earlier transfer system that we used in this work for comparison with the head transducer system
depending on the amount of unambiguous tokens in a context our rules can have one of the following context structures listed in order of de null creasing specificity i llc ic rc rrc NUM llc ic
the context c is an equivalence class of states under which an action is taken and the event e is an equivalence class of actions possible from that set of states
NUM tile classification system builds a feadtlre vc tor of a new doclllllel t coiilljal es il with the feature vectors of each doniain an lcb l dcl erlnhies the doniahi whh h l he docunie nt
preventative expressions are used to warn the reader not to perform certain inappropriate or potentially dangerous actions
department of philosophy university of pennsylvania philadelphia pa
t computer and information science university of pennsylvania philadelphia pa
grosz and sidner argue that global coherence depends on the intentional structure
this confusion is avoided in the sequence of discourse NUM
the same sentence uttered in different discourse situations may have different centers
we will say that u directly realizes c barbara j grosz et al
NUM see section NUM for some recent references related to this issue
a unique cb each un has exactly one backward looking center
if more probable senses are preferred by the system the proliferation of senses that results from unconstrained use of lexical rules or other generative devices is effectively controlled
the dry run took place in april NUM with a scenario involving labor union contract negotiations and texts which were again drawn from the NUM NUM wall stree t journal
the old style muc information extraction task based on a description of a particular class of events a scenario was called the scenario template task
we may hope that once the task specification settles down further evaluations coupled with th e availability of coreference annotated corpora will encourage more work in this area
the scenario definition was distributed at the beginning of september the test dat a was distributed four weeks later with results due by the end of the week
the template has slots for informatio n about the event such as the type of event the agent the time and place the effect etc
said friday it has set up a joint venture in taiwan wit h a local concern and a japanese trading house to produce golf clubs to b e shipped to japan
the second goal was to focus on portability in the information extraction task the ability to rapidly retarge t a system to extract information about a different class of events
we gloss this complex disjunctive formula as vl i n update t a j3i
slots are filled only if information is explicitly given in the text or in the case of the country can be inferred from an explicit locale
it was decided however that multiple sources with different formats and text mark up would be yet another complicatio n for the participants at a time when they were already dealing with multiple tasks
these cover the majority of compounds but for the remainder the interpretation is left unspecified to be resolved by pragmatics
indeed this rule offers a way of checking whether fully specified relations between compounds are acceptable rather than relying on expensive pragmatics to compute them
if on gl t2 is selected next the only way to link it to the partial expression generated so far is via a relative clause but this slot is already filled
the existing algorithms attempt to identify the intended referent by determining a set of descriptors attributed to that referent or to another entity related to it thereby keeping the set of descriptors as small as possible
hence the main achievement of our approach lies in providing a core algorithm that makes few assumptions about other processing components and improves the flow of control between modules
major problems for the future are an even tighter integration of the algorithm in the generation process as a whole and finding adequate concepts for dealing with negation and sets
if a lf contains an underspecified ele null ment e.g. arising from general nn this must be instantiated by pragmatics from the discourse context
a common mistake is to write it as samma iyy
for example dhruji will be interpreted as duhrij
the lex element is a tuple itself of pattern root vocalism
the numbers between the surface tape and the lexical tapes indicate the rules which sanction the moves
it is common among learners to make mistakes such as kteb or nhat
a common mistake is to place the cursor one extra position to the left when entering diacritics
this paper introduces a spelling correction system which integrates seamlessly with morphological analysis using a multi tape formalism
semitic is known amongst computational linguists in particular computational morphologists for its highly inflexional morphology
in our morphographemic model we add a similar formalism for expressing error rules NUM
to determine the referent of an anaphoric expression the interpretation component retrieves the most salient semantically appropriate referent
we devised a slightly improved version which we term the longest match string frequency method
in the following sections we ffirst describe the statistical language model and the word segmentation algorithm
we used the viterbi reoestimation procedure to refine the word unigram model because of its computational efficiency
there are two types of major changes in segmentation with re estimation word boundary adjustment and subdivision
however we do n t know how we can estimate the initial word bigram frequencies from scratch
we present a method that takes as input a syntactic parse forest with associated constraint based semantic construction rules and directly builds a packed semantic structure
writing c x1 xk shall indicate that x1 xk are the free variables in the constraint
when generating candidate choose or delete rules for contexts where rc is a derived form and rrc is empty we actually generate two candidates rules for each ambiguous token in that context NUM if llc ic and rc then choose delete pi
but note also that the influence of the actual semantic operations prescribed by the grammar can be vast even for the simplest constraint systems
firstly we can store different templates under a common prefix which allows for efficient storage and retrieval
null moving to parse forests the semantics of an or node u l uk is to be defined as
however at least the simplifications true a c c and c a true c should be assumed
the packed semantics construction algorithm is given in fig NUM it enforces the following invariants which can easily be shown by induction
in support of the syntactic phrase finder or phraser as we call it the input text must be tagged for part of speech
as with several other veteran muc participants mitre s alembic system has undergone a major trans formation in the past two years
we have found this approach to provide almost an embarrassment of advantages speed an d accuracy being the most externally visible benefits
if the antecedents of the rule are satisfied by a phrase then th e action indicated by the rule is executed immediately
rules can test lexemes to the left and right of the phrase or they can look at the lexemes in the phrase
organizationally headed noun phrases are labeled as org regardless of whether they are simple proper names or more complex constituents such as th e
this happened because we did not successfully identify mccann as an organization thus precluding the formation of the job out phrase
1we put about one staff week of work into the st task during which we experienced steep hill climbing on the training set
sorry to hear that hedge e.g.
uniqueness appropriateness coping with the unexpected
people have a variety of implicit and explicit goals when engaging in conversation among which a broad distinction between social goals and goals concerned with getting things done can be discerned
not surprisingly there was a tradeoff
lowering the threshold had the opposite impact
NUM NUM strategy NUM parse cost context combination
NUM NUM strategy NUM using context only
be confused by the speech recognizer
other domains for information deemed essential to continuing progress
the model is easy to build to maintain and to expand and it is computationally fairly inexpensive
there is also a natural ranking for the candidate nodes the closer to NUM the weight a of a constrained node is the less it is important for the model
our current implementation uses a breadth first search to limit the computational complexity of model selection
accordingly this tree may be encoded with an enumerative code using l d bits
and if there are too many parameters then overfitting occurs and predictive performance degrades
the correct translation is the third candidate
framis NUM proposes a methodology to extract selectional restrictions at a variable level of abstraction from the penn treebank
for each susanne tag the frequency of lob tags is calculated and the most frequent lob tag is regarded as the result
the only problem we encountered concerned the use of two pronouns in one sentence he lives in her town
similarly npl and jj nn and vnd and vnd and nr have the same situation
lob corpus which is a million word collection of present day british english texts is adopted as the source of training data
by the way much more reliable statistic information can be trained from the large scale tagged corpus so that the feasibility of the chunker is assured
after examining all the susanne tags by these three steps three cases have to be considered NUM unique tag
however that introduces another problem the more the general definitions we use the larger the tagged corpus we need
research based on a treebank i.e. a corpus annotated with syntactic structures is active for many natural language applications NUM NUM
in this paper we propose a probabilistic chunker to help the development of a partially bracketed corpus i.e. a simpler version of a treebank
smoothing in this way allows us to report only the coreference configurations with nonnegligible probability along with a single probability that is assigned uniformly to the remainder of the possible configurations
because we know that a and c can not corefer the coreference configurations in which a and d corefer and the configurations in which c and d corefer are mutually exclusive
be translated into compound words in english
in reality the pairwise probabilities for this model were trained with an adapted set of training data as explained below and so these numbers axe in actuality a bit different
therefore we modify the above approximation to apply only if x and each of y1 yn NUM are compatible otherwise the probability mass assigned is used for normalization
thus unifying any two boolean vector terms that results in the term encoding such a bitstring will fail if all the elements are zero then all the arguments will be linked and we will be trying to unify NUM and NUM everything else will work just as before
function words in chinese or japanese are frequently omitted
the final result after flattening sentence NUM is as follows null
this observation holds assuming that the translation lexicon s coverage is reasonably good
the notation concisely displays the common structure of the two sentences
the basis of the approach is a new inversion invariant transduction grammar formalism
grammar based bracketing methods can not directly produce results of a comparable nature
the method proceeds depth first sinking each singleton as deeply as possible
consider the following bracketing produced by the algorithm of the previous section
a matched terminal symbol pair such as z y is called a couple
certain punctuation characters give strong constituency indications with high reliability
the bracket alignment includes a word alignment as a byproduct
for example air pollution is translated into
NUM whenever an undefined construction was detected during the joint examination the grammar definition manual was updated
due to a lack of space we can not develop all the aspects of this work NUM
we have added phenomena such as some causative constructions or free order of complements
context heterogeneity is such a correlation feature
in this article we report on a double blind experiment with a surface oriented morphosyntactic grammatical representation used in a large scale english parser
these modest results demonstrate that lexical and corpus methods can be applied to query translation in a large scale multilingual text retrieval scenario although at a fair penalty in performance
NUM the ep derived queries produced performance which was NUM NUM worse than the reference queries except at higher recall levels NUM NUM NUM at which they performed better than the method NUM queries
the parser will attempt to match those constituents for which a partial decomposition and matching can be found parsing the rest largely according to the english grammar backbone
this tactic bears closer resemblance to our approach but still requires ad hoc heuristics to determine exactly how the matching task influences the monolingual parses that are chosen
in contrast in a parallel bracketed corpus the bracketed sub constituents are themselves parallel in the sense that explicit matching relationships are designated between sub constituents of each half
the translation lexicon contained approximately NUM NUM english words and NUM NUM chinese words and was not manually corrected for this experiment having about NUM translation accuracy
productions of the a ui v form list all word translations found in the translation lexicon and the others list all potential singletons without corresponding translations
the query translation methods that we applied to produce new spanish queries were of two major types methods that used a prepared lexicon and methods that used a parallel training corpus
the number of possible unique deletions that can be performed on a NUM word query is quite large however making the direct examination of all possible modified queries effectively impossible
the final query translation method was a radical departure from the others but is derived from earlier work by NUM and NUM
high frequency terms from parallel text in text the terms that occur with the highest frequency are rarely of statistical significance and are more often than not merely redundant
some repetition of terms is apparent in the resulting queries because all senses of each term were used with no attempt to disambiguate the contextual usage of the english terms
the corpus was NUM NUM gb of spanish and english translations from the united nations containing proceedings of meetings policy documents and notes on un activities in member countries
the queries and corpus are monolingual however so testing a multilingual system is only possible if the query set or the corpus is translated into a different language
the differences between the two results are then a reasonable measure of the effectiveness of the translation process in preserving the characteristics of the original query that contribute to retrieval
since there is no matching spanish concept related to flesh NUM the dutch wordnet thus in its turn suggests a new potential ili record for the spanish wordnet
we are not aware of any surface form based algorithms that achieve similar results
sentential and discourse usages of cue phrases
the elementary units of complex text structures are non overlapping spans of text
this task is facilitated by the guidelines given on the form of the hierarchy
it should be possible to store domain labels for non english meanings e g all spanish bullfightmg terms should be linked to ill records with the domain label bull fighting
one problem is that there are so many potential metrics that can be used to evaluate a dialog system
the dm can provide information to the user via text to speech tts synthesis and via a small display
to prevent this from happening often subjects were instructed at the start of their participation to plan their utterance completely before speaking
the next section explains how to combine with a set of ci to yield an overall performance measure
because john is such a generous man b whenever he is asked for money c he will give whatever he has for example d he deserves the citizen of the year award
the elementary tree corresponding to suppose is shown in figure 4a ii with the interpretation of you need money corresponding to the left daughter labeled b
the way in which discourse features express connections back to the previous discourse has been described in the literature in terms of adjoining at the right frontier of discourse structure
cf25 in these cases the target contrast item is cued by on the other hand in three cases and at the same time in the case given above
for example a dialogue model may embody the expectation that a suggestion made by one dialogue participant would eventually be followed by an explicit or implicit rejection acceptance or tabling by the other
subordinate conjunctions e.g. just as although when etc can lead to similar expectations when they appear in a preposed subordinate clause eg
our attention was called to this by a frequency analysis of potential cue phrase instances in the brown corpus compiled for us by alistair knott and andrei mikheev hcrc university of edinburgh
as the previous examples have shown the same phenomenon that occurs inter sententially in examples NUM NUM occurs intra sententially in examples NUM and NUM suggesting that the two processors may be based on identical principles
table NUM accuracy on the pp attachment test set
since this prototype will be tested by drivers in the car it runs on a stand alone machine
example NUM illustrates the expectation that following a clause marked on the one hand the discourse will express a constrasting situation here marked by on the other
the situation is expected to change when the range of user s inputs is widened at a later stage
to backup this claim let us first describe the planned second prototype in somewhat more detail
the first method error analysis tunes features and algorithms based on analysis of training errors
as with the learning NUM algorithm discussed below performance does not suffer
word1 is assigned this lexical item if cue1 is true na not applicable otherwise
each horizontal bar thus represents the number of subjects assigning a boundary at a particular interphrase location
the remaining NUM of the NUM narratives in the corpus are reserved for future research
feature is defined in a manner that depends on incremental assignment of boundaries and coding of features
pause and cue each depend on only a single feature while np relies on three features
as we will see the performance of our algorithms improves with the amount of knowledge exploited
see figure NUM for boundaries assigned by the resulting algorithm ea for error analysis
rr NUM NUM before nonboundaries for tj NUM the average durations are NUM
likewise fss bic adds fewer interactions than fss aic
the exact conditional test suffers from the reverse problem of bic
consequently the algorithm that we developed for verifying aspectual conformance of the lcs database is also directly applicable to aspectual feature determination in lcss that have been composed from verbs and other relevant sentence constituents
during both bss and fss model selection also performs feature selection
overall aic selects the most accurate models during both bss and fss
evaluation criteria fall into two broad classes significance tests and information criteria
the information criteria do not require the setting of any such cut off values
of interactions in model figure NUM bss recall interest
decision tree induction has been applied to word sense disambiguation e.g.
the joint parameter estimate is formulated as a normalized product
we describe an algorithm for both unsupervised and weakly supervised training of a rule based part of speech tagger and compare the performance of this algorithm to that of the baum welch algorithm
for composed lcs s modifications described above reveal similarities between verbs that carry a lexical aspect feature as part of their lexical entry and sentences that have features as a result of lcs composition
english words with multiple senses also tend to be wrongly translated at least in part e.g. means
most texts have under NUM paragraphs NUM NUM of para
figure NUM precision scores show individual contribu tion from window size NUM to NUM
figure NUM cumulative precision recall scores of top ten opp selected sentence positions of window size NUM
he conducted seventeen experiments to verify the significance of these methods
the average text length is NUM sentences NUM NUM paragraphs
thus the optimal position policy for the ziff davis corpus is the list
of levin s ran kerbs NUM NUM NUM NUM we assign it the template in NUM i with the corresponding lisp format shown in NUM ii
sometimes however keywords consist of multiple words such as spreadsheet software
we define some terms and three measures used to assess the quality of the opp selected extracts
we will also experiment with our primary target applications information retrieval and translation assistance
this way the core grammar is clearly separated from optional linguistic descriptions and heuristics
this reduces structural ambiguity in the parser output with a very small error rate
subject assignment or verb segmentation may be performed by more than one transducer
further down in the sequence transducers may allow for verb subject constructions outside the previously considered contexts
we are currently conducting wider experiments to evaluate the linguistic accuracy of the parser
if the constraints attached to a given transducer are not fulfilled the transducer has no effect
however a bypassed construction may be reconsidered at a later stage using different linguistic statements
the revisions are meant to reduce the impact of the most frequent errors of the tagger e.g.
the less frequent phenomena apply only to segments that are not covered by previous linguistic description stages
this type of useful metric has not been used in previous work on genre
specifically we calculate the likelihood value for each category with respect to the document by
we then classify it into the category having the largest likelihood value with respect to it
moreover the number of parameters in word based distributions is too large to be efficiently stored
we then assign individual words to those clusters in whose related categories they most frequently appear
l dlcl and l dlc2 according to eq NUM
recall is defined as the percentage of the total documents in all categories which are correctly classified
we also thank naoki abe of nec for his important suggestions and mark petersen of meiji univ
by assigning words to clusters it can drastically reduce the number of parameters to be estimated
hereafter we will use only the term likelihood and denote it as l dlci
if the most important relevant terms for the encoding task essentially the h diag diagnosis and the h ttchir surgical deed words are already highlighted the human encoder is able to detect them more rapidly so that the encoding speed can be improved
this resolves the inconveniences and difficulties of the former two options but forces an excessive dependency on the lexical and conceptual structure of one of the languages involved
table NUM onl ributions of pangloss l hlgines
syntactic information are associated to single verbs while semantic information are associated to the whole synset i.e. semantic participants are shared among all the verbs belonging to the synset
there are two things to note about this definition
these then let us capture fsds
we in essence define this inductively
we look briefly at two of these issues
this approach abandons the notions of grammar mechanism and derivation in favor of defining languages as classes of more or less ordinary mathematical structures axiomatized by sets of more or less ordinary logical formulae
the languages in a class for instance will typically exhibit certain closure properties e.g. pumping lemmas and the classes themselves admit normal forms e.g. representation theorems
so adjective noun pairs do as a rule strongly favor one particular sense and this is as true of pairs with many instances as of those with few
a model theoretic framework for theories of syntax
even as a premodifier of a noun the adjectives in this construction often relate semantically to the verb phrase e.g. this is a hard program to carry out
in particular we integrated the hierarchical structure of wordnet as an external kb while an isa function uses the wordnet hierarchy in order to check subsumption relationships between wordnet synsets
for this purpose the topics and sets of related words in longman lexicon of contemporary english lloce are used in this work
in the experiments reported here we generated four different combinations of phrases the results from these different phrase sets are discussed in the next section
in the experiments described in the following section the clarit noun phrase extractor is used to extract all the noun phrases from the NUM megabyte text corpus
such bias in the parameters of the modification structure probability will be propagated to the word modification parameters when the parameters are iteratively updated using em algorithm
for example the modification structure parameters naturally prefer left association to right association in the case of three word noun phrases when the data is sparse
while much effort has been made to apply nlp techniques to ir very few nlp techniques have been evaluated on a document collection larger than several megabytes
the log likelihood of generating a noun phrase given the set of noun phrases observed in a corpus np lcb npi rcb can be written as
the content of informal conversational exchange is often subordinate to its social aspects
in addition we are interested in using computer based training for the aid
this website will required a browser running java and is platform independent it has been tested with netscape on linux solaris2 NUM
whether this is actually the case depends primarily on the quality of the external components the descriptor selection and the lexicalization component and to some minor extent on the parameterization of the structural appearance of the referring expression to be produced
for example in pictalk variations in style can be expressed in the
in contrast to this lack of inflectional morphological complexity mandarin is relatively rich in other types of morphological combinations including compounding
schedule of the intended demo sessions other informal sessions can be scheduled in order to allow people to parse their own sentences or texts
at present the script for the computer is entirely pre programmed
during bss the hypothesized model with the largest negative ic value is selected as the current model of complexity level i NUM while during fss the hypothesized model with the largest positive ic value is selected as the current model of complexity level i NUM
ve now extend sdrt and dice to handle the probabilistic information given in ss3
anaphora ca n t be resolved or no rhetorical connection can be computed via dice
for each sense we report a conventional name which unambiguously identifies the synset and the argumental positions admitted for that sense with the indication of the selectional restrictions
these four parameters are the morphological structure of words the number of syllables per word topic prominence and word order
the results presented in table NUM second column show the significant improvements achieved in positivewinnow performance when normalization is used
it is important to use any methods we can e.g.
although the definition of detailed selectional restrictions was highly time comsuming our experience shown that this approach obtains good results both in the discrimination rate and in the precision
it is additionally influenced by two parameters refs which specifies those referent which must be directly related to the chosen descriptor and pprops which entails a list of properties whose lexical images are likely to fill yet empty slots
we concentrate here on the text processing domain with the characteristics mentioned above and explore this space of choices in it
today fatma is going to read a bookie in yes no questions if a non verbal element is being focused by the question morpheme and the answer is no the system provides a more natural and helpful answer by replacing the focus of the question with a variable and searching the database for an alternate entity that satisfies the rest of the question
a few drastic examples should be sufficient to illustrate some of the problems that might occur due to ignoring these issues a the bottle which is on a table on which there is a cup which is besides the bottle
for example the verb see is assigned the lexical category s see x y lcb nn x na y rcb and the noun fatma is assigned nn farina where the semantic interpretation is separated from the syntactic representation by a colon
for example the following is a derivation of a transitive sentence with the word order object subject verb variables in the semantic interpretations are in fact all six permutations of this sentence can be derived by the multiset ccg rules and all are assigned the same propositional interpretation see fatma ahmet
b two constituents can combine if and only if i their syntactic semantic categories can combine using the multiset ccg application and composition rules ii and their ordering categories can combine using the rules below every verbal category in multiset ccg is associated with an ordering category which serves as a template for the is
for instance all six permutations of the transitive sentence below are possible since case marking rather than word order serves to differentiate the arguments a athe accusative dative genitive ablative and locative cases are associated with specific morphemes NUM a fatma ahmet i gsrdii
this function will result in a complete is only if it finds the obligatory sentence initial topic and the immediately preverbal focus constituent its other arguments the ground are optional and can be skipped during the derivation through a category rewriting rule xi y x that may apply after the application rules
for instance a transitive verb has the category si lcb nn wa rcb a function looking for a set of arguments a nominative case noun phrase nn and an accusative case noun phrase na and resulting in the category s a complete sentence once it has found these arguments in any order
thus the derivation reflects the single surface structure for the sentence while compositionally building the as and the is of the sentence in 5another is is available where the topic component is marked as inferrable for those cases where the topic is a zero pronoun instead of an element which is realized in the sentence
multiset ccg is flexible enough to handle free word order languages that are freer than turkish such as warlpiri through the use of unrestricted composition rules but it can also handle languages more restrictive in word order such such as korean by restricting the categories that can take part in the composition rules
thus z denotes the literal square bracket as opposed to which has a special meaning as a grouping symbol NUM is the ordinary zero symbol
if the list of multiword tokens contains hundreds of expressions it may require a lot of time and space to compile the tokenizer even if the final result is not too large
it has to replace any string of a s by x or copy it to the output unchanged depending on whether the string eventually terminates at b
for many applications it is useful to define an null other version of replacement that produces a unique outcome whenever the lower language of the relation consists of a single string
we use three auxiliary symbols caret left bracket and right bracket assuming here that they do not occur in any input
the first step shown in figure NUM composes the input string with a transducer that inserts a caret in the beginning of every substring that belongs to the upper language
for example white space space is a normalizing transducer that reduces any sequence of tabs spaces and newlines to a single space
with these two kinds of constraints we can define four types of directed replacement listed in figure for reasons of space we discuss here only the leftto right longest match version
the most difficult issue in derivations is the semantic composition for instance the ci morpheme with allomorph s ct ci cu cii ft fi u fii adds the meaning doer user of something 7a seller lover of something 7b or habitual 7c
for instance the new argument becomes the subject causer and the old subject agent is demoted down the grammatical hierarchy NUM to direct object or indirect object depending on the valency of the verb morphophonemic rules the rules for inflectional and defivational morphology might also take into account the archiphonemes that are not marked for certain features
one way of dealing with nouns then is to keep two entries in the lexicon one for unmarked form which may receive case marking and scramble and one with lexically assigned case accusative which may not scramble
NUM a gok rahat koltuk very comfortable couch very comfortable couch b gok gift koltuk c rahat gift koltuk comfortable double couch comfortable twin couch the fragments NUM of the type constraints for these subtypes are given in figure NUM the controlled use of type constraints at different levels of the lexical hierarchy eliminate the need to enumerate type specific lexical rules to achieve the same effect
for instance a qualitative adjective e.g. rahat comfortable is distinguished from a quantitative one e.g. gift double by its choice of modifiers the latter does not allow intensifiers NUM
the problem is further complicated by the rich inventory of derivational affixes for both paradigms as exemplified in NUM hankamer NUM argues convincingly that full listing of every word form in the lexicon is untenable for agglutinative languages
for instance if inflections and derivations are handled by lexical rules the morphological features need not be kept in the lexicon since the lexical rules will reflect the changes in syntactic and semantic requirements coming from morphology
using features like tanimate fartifact fcontainer and fperiod one can define semantic fields for the derivational morphemes
NUM a kuru yaprak dry leaf dry leaf b meyve kuru su fruit dry poss dried fruit c ya h hantm age adj lady old lady d biitiin ya h lar all age adj plu all elderly in what follows we will describe different kinds of lexical rules for type constraints and handling changes in grammatical roles or subcategorization requirements
the alternatives to this approach for turkish have also been explored e.g. the modularization of syntax and morphology by keeping them and their lexicons as separate systems that communicate with each other NUM or integrating morphology syntax and semantics thus treating morphotactics in the same manner as syntax with respect to semantic composition NUM
hcm then defines for each category ci a distribution of the clusters p kj ci j NUM m
the result will be a complete redefinition of the lexical relations while for the semantic relations those originally defined for english will be used as much as possible
insert unify updates the data structure fd by incrementally inserting mappings of selected descriptors c43 s13 unless check scope detects a global problem c44 s NUM
the unknown words which initially cover about NUM NUM percent of the text are reduced to NUM NUM percent when all the available text is used as training data
the stochastic model is successful for only half of the unknown words for the italian text and for approximately two out of three unknown words for the english text
this distribution is measured in a different open testing text i.e. a text that may include both known and unknown words
in order to reduce the huge cost of manually creating such corpora the development of automatic taggers is of paramount importance
this material was assembled and annotated in the framework of the esprit NUM NUM project linguistic analysis of the european languages
defined by the selection of the most probable tags that have been assigned to the less probable words of the training text
these techniques do not increase the prediction error rate or have only minimal influence on it as proven in the experiments
the testing material consists of newspaper texts with NUM NUM NUM NUM words for each language and an english eec law text with NUM NUM words
the probability distribution of some grammatical classes of the unknown words changes significantly when the size of the training text is increased
texture analysis does an even better job in noise suppression
for instance the following sentences are freely translated la
it was only a quarter to eleven
however the plotalign algorithms constitute a functional core for processing noisy bitext
the prospects for english japanese or chinese japanese in particular seem highly promising
it does a pretty good job of identifying a long line segment
figure NUM figure NUM displays the result of word alignment by a
predictive methods that will reduce the cognitive load on the user or that will allow the user effective access to a larger set of utterances
therefore our emphasis on casual conversation leads us to focus more on supporting those social aspects of conversation rather than on the delivery of information
there follows an empirical analysis of the system in which we compare the system output to coder choice based on our three success criteria
the judging interface is structured as a hypertext document that can be accessed through a
at the moment our s cstem is not capable of dealing with a sentence containing i in known words cf
am NUM NUM ward a neutrmnat on ttumint sources ihdicated NUM sti ike knowfiwords in an in house exl ei ment
the parse tree directly encodes the knowledge that sterett and kirov are ship names ssn NUM a submarine name and z stands for greenwich mean time
note that the system will provide an indication or flag to the user showing whether the translation is produced by tina genesis or by the word tbr word fallback system
the primary target application is enhanced commuilication among military forces in a mull lingual coalition environment where translation utilizes a common coalition language as a military infierlingua
this paper is organized as follows in section NUM we tescribe our system architecture along with the grammar rules which drive the ore systein
is it the case that suspect7 is the murderer of lord dunsmore
is it the case that suspect9 is the murderer of lord dunsmore
dynamically modifying the user model based on on going problem solving is difficult
fwatson now has enough information to prove that suspect7 is the murderer
c what is the led displaying
c in the lower left corner
no initiative setting algorithm can do better
the initiative is set throughout the dialogue
if the algorithm finds all initial candidates dissimilar a second run of the algorithm is executed with candidates expanded to all topics in lloce
next compare the right hand length probabilities in NUM and NUM
next compare the right hand length probabilities in NUM and NUM
we then extracted NUM sentences from a part of the in training as our test data and analyzed the sentences
for example in the parents reclaimed the child under the circumstances NUM there are two interpretations
the number NUM accuracy for both methods increases drastically as many test sentences have only two interpretations rcb
the lengths of vps in the latter are equal while the lengths of vps in the former are not
in this study a classification of excellent or poor was automatically assigned to an ap biology essay
for part c1 the categories were recognition cutting alternate and detail point
final computer based essay scores are based on the system s recognition of conceptual information in the essays
this paper describes a prcexype for automatically scoring college board advanced placement ap biology essays
these sentences are represented by the csr in 2a and in 2b
accuracy acc indicates percentage of agreement between the computer based score and the human rater score
inevitably there would have been typographical errors and other kinds of human error
one hundred excellent essays from the original NUM essays were selected to train the scoring system
so far we have described possible dialogue continuations and interpreted them in the context of the dialogue history as pursuing a particular communicative goal
is expressed uniquely as compared to its paraphrases in the training set
the set of essays used in this study had been scored by human raters
instead in order to achieve efficient and cooperative dialogue system utterances must be generated using natural language generation nlg techniques
we thus see the results of translation becoming steadily more accurate and comprehensible as processing proceeds
note that the parser excludes the sense write publish since the indirect object must be introduced by the italian prepositions su or per in english on or fort while in this example we have the preposition a to
the figure shows for each experimental setting the number of total readings produced by the parser the discrlmination rate i.e. the rate of the lectures rejected NUM z NUM and the precision i.e. the rate of correct lectures NUM z
thus the actual goal depends on the state of the task concept under consideration and the system s beliefs concerning that state
it is also foreseen the use of corpora to extract contextual information to be used during the disambiguation process
a condition for using wordnet coupled with the geppetto environment is to bring it in a format effectively usable
plausibility of wordnbt senses for describing a lexical entries e usability of wordnet for carrying out lexical discrimination
let us first consider the results obtained in the second experimental setting which best approximate the human judgment
however an important drawback in wordnet is the lack of relations among related senses of the same word
we argue that in any system of reasonable size the number of templates would be too large to determine a priori
this is a valid approach in system initiative type systems and in systems where utterances stand in a one to one relation to communicative goals
after each parsing stage a corresponding translation operation takes place on the resulting constituent lattice
note that y e NUM NUM with each of these endpoints associated with one of the possible outcomes
the program sketched above is difficult or arguably impossible to implement in a general setting
in this paper we show how these constraints influence the design and subsequent implementation of the dialogue manager module and how the additional requirements fit in with the NUM commandments
in general change of status information as far as it is directly relevant for the user is handled via specific system initiated routines
sometimes it may be useful to violate these commandments most notably in non standard situations e.g. after an error has been detected
the first alternative ignores error handling iv b and is obviously in conflict with the underlying philosophy of the handling of user s input
consider for instance a sample news article given in fig NUM
the dm methods described in this paper are also intended to be simple but robust and that is why the prevention and handling of speech errors plays a central role
the results of these nition user ich m chte nach himmelpforten fahren on such a list will cause problems for which alternative strategies have to be chosen
the second alternative however disobeys commandments NUM looking back too much and iv forcing unnecessary validation and both violate v by not being adaptive
if the user wants to say something s he can indicate this by pressing a button located near the steering wheel the push to talk ptt button
derivation of syntax and semantics but they do not construct the semantics it is an input to their system
this noise elimination could have happened because many spurious terms had been manually removed from the queries inqi02 had an average of about NUM terms as opposed to nearly NUM terms in 1nqi01 or could have come from the use of the proximity operators
inq201 university of massachusetts at amherst recent experiments with inquery by james allan lisa bellesteros james p callan w bruce croft and zhihong lu used a version of probabilistic weighting that allows easy combining of evidence an inference net
in trec NUM all the adhoc topics had samples re judged by two additional assessors with the results being about NUM agreement among all three judges and NUM agreement between the initial judge and either one of the two additional judges
in general groups seem to be getting about NUM improvement over their own baselines less for eth and pircs with that improvement coming in different percentages from passage retrieval or expansion depending on the specific retrieval techniques being used
the number of participating systems has grown from NUM in trec NUM to NUM in trec NUM including most of the major text retrieval software companies and most of the universities doing research in text retrieval see table for some of the participants
for example in trec NUM and trec NUM the top NUM documents from each run NUM runs in trec NUM and NUM runs in trec NUM could have produced a total of NUM and NUM documents to be judged for the adhoc task
the average number of terms in the queries is widely varied with the city group averaging around NUM terms NUM terms from expansion the inquery system using around NUM terms on average and the comell system using NUM terms on average
ments in the probabilistic retrieval of full text documents by william s cooper aitao chert and fredric c gey is a modification of the brkly6 run with that modification being the manual expansion of the queries by adding synonyms found from other sources
these topics tended to have for passage retrieval and topic expansion fewer relevant documents but also tended to be topics for which the systems bringing terms in manually such as by manually selecting from a thesaurus or outside sources also did well
null the total number of relevant documents found has dropped with each trec and that drop has been caused by a deliberate tightening of the topics each year to better guarantee completeness of the relevance judgments see below for more details on this
when the page is exited the contents of the completed form are stored for further use
the complex entry of figure NUM is produced automatically from these three types of lexical information
the results of these experiments are shown in table NUM
of these NUM are intransitive or have a locative prepositional phrase complement
when appear is encountered in a particular syntactic structure the program consults the
we expect that this will partly alleviate the increase in the error rate
each stage will be separately measured as well as their combined effectiveness in pruning senses
another NUM tokens are followed by to and a subject controlled infinitive
lcb hj ing vh becky kathy c s columbia edu
this cluster would look odd were not the domain considered
the translation makes some sense but fails to convey the sense of the source utterance
NUM we then constructed a separate test text consisting of NUM NUM test cases with a lower bound of NUM NUM
this configuration produced a total of NUM errors out of NUM NUM test cases for an error rate of NUM NUM
training data for the neural network consist of two sets of text in which all sentence boundaries have been manually disambiguated
training data for the induction of the decision tree were identical to the training set used to train the neural network
according to wasson it is not likely however that the results would be this strong on lower case only data
exclamation points and question marks can occur within quotation marks or parentheses as well as at the end of a sentence
therefore a very simple successful algorithm is one in which every potential boundary marker is labeled as the end of sentence
NUM NUM false negative resulting from an abbreviation followed by quotation marks related to the previous two types
the results reported in the previous sections were all obtained using the prior probabilities in the descriptor arrays for all tokens
however we will also experiment with other sequences
i i i exopbonc lc i i endophonclc i
NUM fax NUM NUM NUM NUM email hovy isi edu
NUM remove a portion of a pre spl expression
table NUM effect of truncating trigrams that occur
the second covers specific subtasks of sentence planning
each successful transfer attempt results in a target language string being added to a targetside lattice
both for mutual information and the dice coefficient this involves comparison with an experimentally determined threshold
however the average mutual information of the variables is now NUM NUM would remain the same
therefore we should select a similarity measure that is based only on NUM NUM matches and mismatches
in this section we discuss the properties of similarity measures that are appropriate for our application
champollion selects the group of words with the highest cardinality and correlation factor as the target collocation
champollion uses statistical methods to incrementally construct the collocation translation adding one word at a time
as a phrase however prouver son adhdsion carries the same meaning as the source phrase
table NUM gives concrete examples from this experiment in which the dice coefficient outperforms specific mutual information
officielles langues NUM NUM an example sentence in french where the selected translation is used is also shown
in the worst case as we show below the answer to this question is affirmative
but for some purposes presenting all arguments in a canonical order might be more adequate
these rules are applied after the last stage in the tables above
the stages of generation are NUM building an initial skeletal structure NUM attempting to consume as much as possible of the semantics uncovered in the previous stage and NUM converting the partial syntactic structure into a complete syntactic tree
the elementary structures are tree descriptions NUM which are trees in which nodes are linked with two types of links domination links d links and immediate domination links i links expressing reflexive domination and immediate domination relations 3called d trees hence the name of the formalism
note that the connectivity condition restricts the choice of mapping rules so that a rule that matches part of the remaining semantics and the extra semantics added by previous mapping rules can not be chosen e.g. the bad mapping in figure NUM
the intent is to permit substitutions for words which sound very similar such as do and two to too words that are likely to NUM of the NUM dialogs which were not completed misunderstandings due to misrecognition were the cause in NUM of these failures
even if the o analysis is equivalent to earley s the phase of precompilation into the parse tables allows to save a lot of computation time needed by the predictor
in addition according to the difference of word including punctuation number in the sentence all sentences in the treebank can be further classified as two sets
the initial state annotator and learned unsupervised transformations are then applied to unannotated text which is then input to the supervised learner along with the corresponding manually tagged corpus
b cat state j cat which represents the arc of the transition graph of the category cat entering the state state and labeled cat
the expansion of a state s takes into account each symbol y that ilmnediately follows a dot y c c u lcb rcb
the improvement is due to a precompilation of the dependency rules into parse tables that determine the conditions of applicability of two primary actions predict and scan used in recognition
for instance the two rules for the root category v erb specify that a verb v can dominate one or two nouns and some prepositions
we are encouraged by these results and expect an improvement in performance when the number of transformation templates provided to the unsupervised learner increases beyond the four currently used
the next step in our research will be to relax the condition of projectivity in order to improve the expressive power and to deal with phenomena that go beyond the context free power
so to allow a number of iterations one or more y s follow or no iteration the first symbol in follows in the next step
we use two primary actions predict that corresponds to the top down guessing of a category and scan that corresponds to the scanning of the current input word
we evaluated the performance of these different relations by examining the degree of class coverage of the relation using a prototypical verb from each class
clearly the semantic filter constrains the possible assignments but the question to ask is whether the constraint improves the accuracy of the assignments
to answer this we first examined the NUM verbs in ldoce that also appear in levin to see if they matched levin s categorization
these assignments are particularly interesting because we know they are correct and we can see how high the program ranks the correct assignments
one of our goals is to assign new verbs i.e. all of the verbs in ldoce to the semantic classes of levin
in particular recall is the number of correct categorizations the algorithm gives divided by the number of correct categorizations already given in the database
a better measure of the efficacy of the algorithm would be to examine the ratio of correct assignments to the total number of assignments
we describe semantic filters designed to reduce the number of incorrect assignments i.e. improve precision made by a purely syntactic technique
here k is the number of relevant links in the sentence for instance feet in nlab and n is given by the size of the set of features collapsed by lifting some of these checks hence NUM NUM NUM and NUM respectively
we also evaluate the performance of the selected features and their estimated parameters in the subcategorization preference task
we also evaluated the performance of the selected features and their estimated parameters in the subcategorization preference task
we evaluate the performance of the selected features and their estimated parameters in the following subcategorization preference task
verb noun collocation is a data structure for the collocation of a verb and all of its argument adjunct nouns
the decrease of the case coverage in the independentframe one frame models is caused by the overfitting to the training data
we use held out verb noun collocations of the verbs vl and v2 which are not used in the training
in the development of parsers for syntactic analysis it is standard practice to posit two working levels the grammar on the one hand and the algorithms which produce the analysis of the sentence by using the grammar as the source of syntactic knowledge on the other hand
NUM daily z slowly NUM every uhere counter examples NUM person name NUM h NUM i japan honshu e u s
the hand crafted rules are applied first
phrases may be formed in a similar way NUM
ments are of the following f function sign
res op arg is the categorial notation for the element
figure NUM operators in the proposed model
phon also allows efficient lexicon search
NUM yoksul la t r zl makta lar poor v caus pass adv pers
lexical entry syntactic category semantic category fi
examples of different operator combinations are given in figure NUM
they are being made poor impoverished
he motivation is that typicmly we have a strong localization effect in aligning the words in parallel texts for language pairs fi om ndoeuropean languages the words are not distrilmted arbitrarily over the senteuce ositions but tend to form clusters
in typical cases we can assume a sort of pairwise dependence by considering all word pairs fj ei for a given sentence pair i NUM j elqlj we further constrain this model by assigning each french word to exactly one english word
after reviewing the statistical approach to machine translation we first describe the conventional model mixture model
in this paper we describe a new model for word alignment in statistical translation and present experimental results
the mixtm e model can be interpreted as a zeroth order model in contrast to the first order tlmm model
the alignments have a strong tendency to preserve the local neighborhood when going from the one langnage to the other language
the difference to the time alignment hmm is that there is no monotony constraint for the possible word orderings
in this paper we address the question of how to define specific models for the alignment probabilities
for example some words judged as NUM s for the energy category are spill pole tower and fields
their scheme emphasizes the importance of organizing the high level structure of a text according to its topical content and afterwards incorporating the necessary related information as reflected in discourse cues in a finer grained pass
specifically the goal is to identify the regular expressions of the patterns and the exact hyphen points for each formal pattern
in contrast the small size of the german lexicon decreases the required memory
after lexical analysis which effectively includes part of speech tagging it is determined that the word a is unlikely to precede are and so a is dropped from the translated sequence b thus translating recognizer hypothesis NUM using the glossary based method
there is no significant variation in the chi square test results for additional training text
this feature is true when phrase NUM and phrase NUM ar e compatible number and gender and when phrase NUM is the most recent subject in the text
figure NUM initial seed word lists
figure NUM instructions to human judges
the processing of badger is not significantly different than that of the circus system used i n previous muc evaluations NUM NUM NUM
scores using this dictionary are remarkably close to the official score report based on a fully trained crystal dictionary as shown by the following score report
it determines when two noun phrases refer to the same entity and should therefore be merged in order to consolidate feature descriptors into a single entity description
our natural language based temporal reasoner was developed and tested on more than three hundred s NUM wall street journal articles
the dictionary is used by the morphological analyzer for supplying each input word with syntactic semantic and pragmatic information
enamex type person quot dooner enamex that we are looking for qualit y acquisitions and ammirati paris is a quality operation
theory and practis e we are committed to addressing research problems with a strong promise for facilitating processing natural language input
we have also been experimenting with flexible processing such as undoing various decisions and independent processin g of related tasks
the knowledge base interpreter implements the interpreter of the sets of type equations encoding taxonomic temporal and geographical knowledge
adverbial and adjectival modification at all syntactic levels adverbial and adjectival modification also contributes to the flatness of our taxonomies
handling general negation in natural language allows the uno system to correctly compute possibl e interpretations of the sentence mr
there are some people and entire agencies that i would love to see be part of the mccann family
some of the nonstandard quantifiers that our system can handle include vague quantifiers involving th e determiner many
zeros tli l sl rong t ron ltlls ill order to assess NUM he slr ttegies proposed in NUM
this strategy relies on the following steps immediate attachment provisional and definitive interpretation the testing of constraints creation of chains and restructuring
in the example NUM the pronoun sic is the direct object of the infinitival clause although it is attached to the main clause
italian has two pronominal systems cabj rese NUM weak l ronouns th tt iilust always be c liticized to the verb e.g.
finally centering provides an interesting fraltlt work for studying the functions of pronouns as tim ot servation that the ct is ofl e ll
therefore the coindexation only applies when both arguments controller and eontrollee are available the infinitival cp and the indirect object in NUM
we have discussed the parsing strategy in detail and shown that it is adequate for the treatment not only of finite clauses but also of non finite clauses
an infinitive with zu projects a non finite clause cp to which an empty subject is added spec ip
indeed to list all the possible orders leads to an increase in the grammar size and a corresponding decrease in performance
when the past participle is read the arguments are matched with the argument structure of gelesen die kinder as subject and diesen bericht as direct object
modals are treated on a par with auxiliaries i.e. they are taken to select an infinitival vp as complement and are not associated with an argument table
vp hktte besuchen wollen yp vp vv vv t lcb tj h itte besuchem
thus the grammatical function of an argument depends not only on its position but also on case and agreement information and scrambling ordering constraints
since there is as yet no trec track for a complete evaluation of chinese ir systems we have conducted an in house evaluation with limited resources to determine if the quality of retrieval appeared to be in line with our performance in other languages
an event is defined to be either a type of labeled edge written e.g.
this suggests the need to develop resources such as lists of word plus part of speech grammars lexicons with syntactic features and at least high level semantic categories person organization product event state of affairs etc
a makeshift substitute for this interaction is to run the name segmenter to identify guaranteed names from name lexicon run the segmenter and then run the name recognizer again this time to identify possible names from the still unsegmented characters
for example although research indicates that a bigram model for languages like chinese may be very effective a user may be disconcerted to see the second character of one word juxtaposed with the first character of the following word as a search item
query expansion with related terms one of the objectives of information retrieval with respect to the user is to render the technology more accessible by diminishing the gap between the retrieval performance of an expert or trained user and that of a novice or casual user
note that the error rate can be well under NUM if either the spelling of the language supports ending analysis or there is a sizable list of words and their parts of speech e.g. a dictionary listing part of speech for each entry
the asch encoding was evolved and standardized on the english language so input and display of any other language presents problems for ascu odented display technology and languages such as c where even the datatype char is ambiguous and not guaranteed to support more than NUM bit ascii
with a corpus of roughly NUM NUM words marked by part of speech the overall error rate on newswire was below NUM even though without a large list of words plus parts of speech and the error rate on unknown words was only half that of chinese
a chinese name recognizer must look for sequences of unsegmented or poorly segmented characters and try to identify a traditional family name followed by two characters that could be a given name i.e. not otherwise segmentable as a word or part of a word
the application of plan operators depends on the validity of constraints
since the dialogue can be processed properly by the finite state machine no repair is necessary
in the table below the results for two experiments are shown
another level of processing is an implementation of an information theoretic model
NUM to provide contextual information for other verbmobil components
verbmobil combines the two key technologies speech processing and machine translation
this paper analyses the relation between the use of similarity in memory based learning and the notion of backed off smoothing in statistical language modeling
the weights of the linear interpolation are estimated by maximizing the probability of held out data deleted interpolation with the forward backward algorithm
each bucket can further be decomposed into a number of schemata characterized by the position of a wildcard i.e. a mismatch
off ordering using a domain independent heuristic with only a few parameters in which there is no need for held out data
usually not all features x are equally important so that not all back off terms are equally relevant for the re estimation
using these vectors in combination with the ig weights mentioned above and a cosine metric we got even slightly better results
however many possible conditioning events are not present in the training data yielding zero maximum likelihood ml estimates
this probability is estimated from the data set by looking at the relative joint frequency of occurrence of the classes and pattern x
the difference between them lies in that is a time particle and therefore is parsed with its classifier as a time phrase whereas is a general noun and is parsed with its classifier as a general np
a partial solution for such cases was implemented in the revised algorithm we used for morpho lexical probabilities calculation
this representation allows the system to compute entailed logical context independent deductive inferences and facilitates computing context dependent non monotonic inferences including implicature specialization and generalization
detached portions similmly stlape and magn of NUM tcitd pns fig NUM
pns are not straightforwardly referential as they predicate wilh reference io another entity
shape and measure if considered relevant are inherent to the portion itself
both individuals and substances may be refered to cumulatively that is bc construed as an indiffercntiated unassment
bul zero detcrlninalion is not exclusively a ressource to refer to substances it is the way of expressing cumulative rclcrence
agent1vi lcb if cousidercd relevant will be a process of filling the container
a portion always conveys a measure wilh relation to tile total magnitude of tile whole
therefore this quale will be a signed to one of both types or a coherent subtype
as defined ill pus951 sltape md magnitude magn are features of the formal role
those features can be either unary features i.e.
evaluating automated and manual acquisition of anaphora resolution strategies
this can be accomplished by allowing the user to interact with the produced classifters tracing decisions back to particular examples and allowing users to edit features and to evaluate the efficacy of changes
table NUM shows the results of six different mlrs and the mdr for the four types of anaphora while table NUM shows the results of the mlr NUM with different sizes of training examples
both the mlrs and the mdr used the character subsequence the proper noun category and the semantic class feature values for name org anaphora in mlr NUM using anaphoric type identification
however they note a problem with their decision tree in that it is not guaranteed to return consistent classifications given that the preference relationship between two possible antecedents is not transitive
table NUM recall and precision metrics for evaluation
the generative lexicon provides a useful framework for potentially infinite sense modulation in specific contexts cf
we also argue that the place of lrs in the computational process is a complex issue
each entry in the bilingual lexicon specifies a way of mapping part of a dependency tree specifically that part matching as explained below the source fragment of the entry into part of a target graph as indicated by the target fragment
edges in the parsing lattice or chart are tuples representing partial or complete phrases headed by a word w from position i to position j in the string w t i j m q c
for any node n of s for which target nodes fi g l n and fj g l n are defined these two nodes are identified as a single node f n in t
a lexical parameter p m qlr t w is the probability that a local tree immediately dominated by an r dependent w is derived by starting in state q of some automaton m in a lexieal entry w m
for language centered applications like translation or summarization for which we have a large body of examples of the desired behavior we can think of the task in terms of the formal problem of modeling a relation between strings based on exampies of that relation
this trend is now reversing itself in part because statistical methods reduce the burden of detailed modeling required by constraint based grammars and in part because statistical models for converting natural language into complex syntactic or semantic representations is not well understood at present
in section NUM we present a general framework for associating costs with the solutions of search processes pointing out some benefits of cost functions other than log likelihood including an error minimization cost function for unsupervised training of the parameters in our translation application
in order to take advantage of more of the information available in our training data we experimented with cost functions that make use of incorrect translations as negative examples and also to treat the correctness of a translation hypothesis as a matter of degree
this leads to greater efficiency as it avoids repeating matching operations during the search phase and it allows a static analysis of the matching entries and source tree to identify subtrees for which the search phase can safely prune out suboptimal partial translations
bloksma et al criticized that wordnet practice
an example is shown in figure NUM
dooner does n t see a creative malaise permeating the agency one of which is that mr dooner sees something else
in this approach the learned linguistic information is represented in a concise and easily understood form
our treatment of transcategoriality allows for a lexicon superentry to contain senses which are not simply enumerated
it may be justified by feature inheritance
flat taxonomies are highly desirable because among others they facilitate the ease and quality increas e of knowledge base maintenance
cogenthelp takes as input various human written text fragments or help snippets indexed to gui resource databases which provide some useful helprelated information in the form of types labels locations and part whole relations for gui widgets
now since the various messages associated with a widget have slightly different inclusion conditions it makes sense to localize these inclusion conditions to a text planning rule for each message the common parts of these conditions are shared via inheritance
the final goal is to lower barriers to adopting the technology which has principally meant providing an authoring interface which makes the benefits of the system available at a reasonable cost in terms of the understanding and effort required of the help author
NUM the initial state annotator assigns each word its most likely tag as indicated in the training corpus
at the same time however there is a need to know whether any of these messages will in fact appear in order to decide whether to include the second paragraph in the upper right frame as well as the italics element
the main idea of cogenthelp is to have developers or technical writers author the reference oriented part of an application s help system NUM in small pieces indexed to the gui components themselves instead of in separate documents or in one monolithic document
paris and vander linden NUM knott et al NUM hirst and di marco NUM traditionally most applied nlg systems have focused on niches where texts can be generated fully automatically such as routine reports of various types e.g.
although cogenthelp is by no means a typical nlg system insofar as it is incapable of generating useful texts in the absence of human authored help snippets it does employ certain natural language generation techniques in order to support the software engineering goals described above
prediction should be much more effective than with free text since the slots will provide fine grained syntactic and semantic constraints
experience with this suggested that an approach which allowed for more flexible combination of fixed and free text might have advantages
though documents generally concentrate on a single topic they may sometimes refer for a time to others and while a document is discussing any one topic it will naturally tend to use words strongly related to that topic
however we will not discuss these differences in this paper as they are not relevant to the present work
the tipster demonstration software program shows how the architecture can meet the tipster goals and requirements
for example there are two ways to align the pair l
note also that our model translates each english sound without regard to context
NUM p jle converts english sounds into japanese sounds
at that level human translators find the problem quite difficult as well
these items are commonly transliterated i.e. replaced with approximate phonetic equivalents
this section describes how we desigued and built each of our five models
it would prefer exactly those strings which are actually grist for japanese transliteratots
due to memory limitations we only used the NUM NUM most frequent words
for each english japanese sequence pair compute all possible alignments between their elements
in greek this representation contains much of the pronunciation information which is the ultimate basis for hyphenation in every language
the particular semantic features name of the relations and of the argu null conceptual input to the lexical chooser in fd format
they define the notion of implicit recovery ir as a measure of the ability of a system to filter the output of the parser and interpret it using contextual knowledge
one of the three features concerning segment structure trib pos inten structure infor struczure appears as the root or just below the root in all trees in table NUM more importantly this same configuration occurs in all trees equivalent to the best tree even if the specific feature encoding segment structure may change
learning turns out to be most useful for corel where the error reduction as percentage from baseline to the upper bound of the best result is NUM aii our experiments are run with groupin NUM turned on so that c4 NUM groups values together rather than creating a branch per value
i feature type feature dencription segment ntructure trib pos relative position of contrib in segment t number of contribs before and after core inten structure intentional structure of segment infor structure informational structure of segment nten tional structure indicates which contributors in the segment bear the same intentional relations to the core
it is interesting to note that the tree induced on gorel the only case in which synrel is relevant for occurrence indudes the same distinction as in figure NUM namely if the contributor depends on the core the contributor must be marked otherwise other features have to be taken into account
from the generation perspective cue usage consists of three distinct but interrelated problems NUM occurrence whether or not to include a cue in the generated text NUM placement where the cue should be placed in the text and NUM selection what lexical item s should be used
crossvalidation obviates this problem by running the algorithm n times n NUM is a typical value in each run n l th of the data randomly chosen is used as the training set and the remaining th used as the test 3we will discuss only decision trees here
given the time constraints it was not possible to ask more detailed questions about each feature although the respondents were encouraged to give examples
to bring the power of these tools and others to language translators instructors and learners requires usable user interfaces that help users accomplish their tasks
for example system NUM verbmobil regarded the interaction strategy to be of low importance since it is a minimally intrusive system which facilitates the dialogue between two humans
this concept thus partially matches the spanish carne NUM
the output from this stage of the algorithm is a list of sentence break numbers NUM n with n number of sentences in the document and a lexical correspondence measure
figure NUM gives a simplified overview of how the different modules are interconnected
z therefore the characteristic distribution pattern of topical content words which contrasts markedly with that of non topical and non content words could provide a useful aid in identifying the semantically relevant words within a text
NUM now sets a p a b and b are generated as described and then the formula above is applied which assigns a correspondence value to the sentence break currently under consideration
again the local minima indicate where the algorithm considers a subject boundary to occur and the vertical lines are the obvious breaks in the text mainly before new headings as judged by the author
it is important for our algorithm that morphological differences between semantically related words are resolved so that words like bankrupt and bankruptcy for example are identified as the same word
obviously both are needed to form meaningful sentences but intuitively it is the content words that carry most weight in defining the actual topic of discourse
the advantage of using a text such as this is that there can be no doubt from any human judge as to where the boundaries occur i.e. between articles
after we collect a series of descriptions for each a large number of threads of summaries on the same topic from the reuters and upi newswire used up to NUM different referring expressions mostly of the type of descriptions discussed in this paper to refer to the same entity
and type NUM variants the concept of a grammar of syntactic transformations is motivated by well known observations on the behavior of collocations in context e.g.
in an analogous way a dialogue can include more than one task whether it is to book tickets for a performance or to enquire about flight times
however the relationship between entropy and word prediction is somewhat complex
j that the seventh and fifteenth features ask about can be attributed to the large number of news stories in the data having to do with the o j simpson trial
rp NUM rp NUM rpn comes from statistical data s5 defined in section NUM in addition ffrp i is a word component then set rpi NUM
a constituent boundary parse b is therefore given by b bl b2 bn where b i is the boundary tag of the th word and n is the number of
for example the rule v n vp NUM NUM np NUM NUM NUM indicates that a syntactic constituent composed by a verb v and a noun n can be reduced as a verb phrase vp with the probability NUM NUM and as a noun phrase rip only NUM NUM
in the absence of an available annotated chinese corpus we had to build a small chinese treebank for training and evaluating the parser which consists of the sentences extracted from two parts of chinese texts NUM test set for chinese english machine translation systems text a NUM singapore primary school textbooks on chinese language text b
its parsing probability p ph can be calculated through the following formula p ph h p rp p ph rp rp2 rp NUM i l
NUM syntactic tag distribution on a boundary s3 this group of data expresses the possibilities for an open or a close bracket to be the boundary of a constituent with certain kind of syntactic tags under different pos context
b to reduce the mrr as a constituent mc rl r r aitvr all matching operations inside mrr have been finished so as to make it as a whole during the following matching operations
there arc many regional restricted constituents in natural language such as reference constituents in the pair of quotation marks and the regular collocation phrase zai de shikou when in chinese
while it is the task of the semantic evaluation module to extract time information from the actual utterances the dialogue module integrates those information in its thematic memory
however treating the sentence as a basic unit loses meaning when the sentence is incomplete or illformed
to cope with this problem the original probability can be modified by a popular technique into the following formula
after mapping onto cle tags application of the phrasal phase which implements bottom up parsing is straightforward
the focus is on these two issues no attempt is being made to produce a complete product NUM
different components and configurations of the system will be compared for example the error rules v p b t s
altering this would provide some right hand context information which would among other things facilitate handling space addition
null a the the brown bear the i the brown bear b ate the nice friendly ate i the i nice friendly for example a produces NUM segment and b produces NUM segments whereas ate the nice friendly cat would produce NUM segment
transposition of a space could be dealt with by setting up an expectation upon discovering deletion of the last character of a word that the deleted character may be attached to the beginning of the next word
the error rules are applied when ordinary morphological rules fail which is usually a place p b t s would mark as in error but the rules do n t ignore error locations p b t s accept as allowable letter combinations
if that branch fails this agent s second ranked branch is compared to the other agent s first ranked branch with the winner gaining initiative
spoken utterances contain a lot of disfluencies such as pronunciation errors word selection errors word fragments and repairs
the order of the steps in the travel plan are the guiding principle behind the order in which the elements are presented
the successor of these projects is the european project arise which aims to improve the previous versions of the different systems
we have taken a first step in this direction by extracting presentation scenarios for different dialogues situations from our sample corpus
the alparon research group in delft aims to improve automated speech processing asp systems for information retrieval and information storing dialogues
NUM towards a new strategy of information presentation analysis of information presentation in vios and ovr dialogues shows an important difference in strategy
in this method costs are computed by tracing the events involved in producing translations of sentences from a source training corpus a bilingual speaker classifies the output translations as positive or negative examples of acceptable translations
the alparon project aims to improve vxos openbaar vervoer reisinformatie s ova automated speech processing system for public transport information by using a corpus based approach
dialogue management will have a predominant role in this precursor as our study has shown dialogue management to be the significant difference between current asp systems and human human dialogues
in addition it can help build explanations that are coherent
first it required us to grapple with difficult representational problems
elaboration nodes specify optional content that may be included in explanations
we discuss the salient aspects of each type of node below
knight s user sensitive explanation generation is not addressed in this paper
the views in the explanation plan are grouped into paragraph clusters
the structure beginning with circum creates the subordinate infinitival purpose clause
the selected view controls the content of the explanation and the reasoning that produced that content
since f is a group 1v NUM by the uniqueness of solution
another kind of problem concerns the magnifique descente
the fastest system processes at about NUM words per minute whereas the slowest system reaches only NUM words per minute
we therefore propose to use a special word list with words in different frequency ranges to probe the lexicon efficiently
the mt systems under investigation translate between english and german and we employed our evaluation method for both translation directions
m missing word the source word is not translated at all and is missing in the target sentence
and even if they are set it does not follow that they are all optimally used during the translation process
langenscheidts t1 and telegraph are second best with about the tor rank third while systran clearly has the lowest scores
systran does not offer a translation of a word if it is in the lexicon with an inappropriate part of speech
taking the most frequent adjectives nouns and verbs is not very informative and mostly serves to anchor the method
note that the NUM mt systems give three different translations for hard all of which are correct given an appropriate context
with the sentence we were trying to get each system to translate a given word in the intended part of speech
in a similar way the second utterance in the following sequence NUM prefers the vl interpretation but allows for the vf
the value free interpretation is needed in the sequence 25a c whereas the value loaded interpretation is needed in 26a c
centering NUM as ambassador to china he handled many tricky negotiations so he does well in this job
at the level of linguistic structure discourses divide into constituent discourse segments an embedding relationship may hold between two segments
the task of completing and revising this draft became more daunting as time passed and more and more papers appeared on centering
these mal rules allow sentences containing errors to be parsed with the grammar and enable the system to flag errors when they occur
we consider the relationship between coherence and inference load and examine how both interact with attentional state and choices in linguistic expression
we briefly list several major claims in this section and elaborate on the evidence or motivation for each in subsequent sections
NUM centering constrains realization possibilities rule NUM discussed in section NUM stipulates one constraint centering imposes on realization
this paper deals with the automatic translation of route descriptions into graphic sketches
b other reasons in a closed attentional spac e are structurally distant
if a reason is both structurally and textually close it will be omitted
NUM an explicit form will be used for reasons that are both structurally and textually far
the node to be presented next is suggested by the mechanism of local focus
let us look at one such operator which handles proof by case analysis
this paper deals with the reference choices involved in the generation of argumentative text
using the maxim that union gives strength we create contexts so that features not relevant to a context position are not included thereby treating context that differ in these features as same
if these features along with their possible values are included in context positions where they are not relevant they split scores and hence cause the selection of some other irrelevant rule
both the verb entry and the adjective entries are much more complex than those for nouns and denominal entries
ferred to by these variables and marked by a caret are used
related to suppletivism we discover quite a few deverbal adjectives shared by several verbs on a synonymous sense
abuse has two more senses abuse v2 violate a law or a privilege and abuse v3 assault physically
perhaps the most easily discoverable lrs involve e l e2 pairs which are morphologically related especially if their share the roots
obviously the semantic analysis of adjectives shares many problems with the semantic analysis of anything in natural language
the existing literature on adjectives also shows a predictable scarcity of systematic semantic analyses or lexicographic descriptions of adjectives
obviously this kind of supersuppletivism would be impossible and arbitrary if implemented outside a justified situated ontology
some of these rules are transcategorial i.e. e NUM and e2 belong to different lexical categories
each sublexical entry is associate with a feature structure
for correct probability estimation we have to include the immediately preceding unambiguous class cu actually belonging to the preceding subsequence ci or cm
correctness that p is properly defined
we then have the following properties
there exists a qf c f s t
unlike in an hmm once a decision on a tag has been made it influences the following decisions but is itself irreversible
we define transformation based systems as follows
the emission function is defined by
12a special case can be added for epenthetic rules
the expression of sublexicon i with r entries becomes
section NUM describes a regular formalism with rule features
are n tuples of alphabetic symbols or the empty string e
this phenomenon becomes stronger in taggers based on the hmm where the accuracy of the p w j t estimation is proportional to the word and the tag frequency of occurrence in the training text
in the tagset of main grammatical classes this distance is minimized for threshold values less than three four or five depending on the training text size
on the other hand prepositions in the french text have a NUM NUM greater probability which is also the most significant difference between the distributions of the two languages
the results of the tests shown in figures NUM and NUM include threshold values up to NUM because the difference between the distributions for values greater than NUM increases significantly
another question that remains unanswered is to what extent the linguistic information he considers can be handled or at least approximated by finite state language models and therefore could be directly interfaced with the segmentation model that we have presented in this paper
a set of symbols and keywords a sentence separators set and the maximum length of a sentence are the only manually defined parameters when the hmm taggers are applied
in figures NUM and NUM the tagger speed and the memory requirements after the last memory adaptation process are presented for all taggers and languages and for the extended tagset
the greek and italian corpora have a great number of lexical entries different word forms for the same amount of NUM NUM word training text as shown in table NUM
the first module extracts from the model parameters the intra tag and the word tag conditional probabilities requested by the second module which computes the optimum solution by multiplying the corresponding conditional probabilities
in this case the model hypotheses are not satisfied e.g. there are strong intra tag relations in distances greater than the model order idiomatic expressions language dependent exceptions etc
in this paper i argue that given the current state of the art capability of automated machine learning algorithms a supervised learning approach using a large sense tagged corpus is a viable way to build a robust wide coverage and high accuracy wsd program
in the rest of this paper i will assume that broad coverage high accuracy wsd is indeed useful in practical nlp tasks and that resolving senses to the refined level of wordnet is a worthwhile task to pursue
NUM if a reason is structurally close but textually distant first try to find an implicit form if impossible use an explicit form
this level of focus decreases either when a attentional space is moved out of the foreground of discussion or with the increase of textual distance
and if one is interested in tts one would probably consider the single orthographic word acl to consist of three phonological words ej s i l corresponding to the pronunciation of each of the letters in the acronym
in reichman s theory although four levels of focus can be established upon activation only one is used in the formulation of the four reference rules
because reasons are intermediate conclusions proved previously in context their reference choices have much in common with the problem of choosing anaphoric referring expressions in general
with the increasing size of proofs which proverb is getting as input investigation is needed both for longer proofs as well as for more concise styles
NUM first we no longer depend on the size or even existence of an annotated training corpus
since the mutative segment can be an empty string regular morphological formations can be captured as well
the ending guessing rules constitute the backbone of the guesser and cope with unknown words without clear morphological structure
which makes the accuracy drop caused by the cascading guesser to be less than NUM in general
when the unknown words were made known to the lexicon the accuracy of tagging was NUM NUM NUM NUM
using these replicates we calculated the mean and standard error of the whole bootstrap distribution as follows
the direct evaluation phase gave us a basis for setting the threshold to produce the best performing rule sets
this means that among very confident rules with very high scores there are many quite general ones
thus we will try to maximize recall first then coverage and finally precision
so if the guesser had assigned only jj its precision would have been NUM
in any case we would call the criteria which are based on the reasoning of the system internal ones
generating acceptable speech requires syntactic and semantic information that is hard to extract from unazmotated text
the above mentioned parameters are defined as follows no of correctly assigned words precision no of fully disambiguated words no of fully disambiguated words applicability no of ambiguous words the results obtained for full disambiguation are shown in table NUM
but in natural language the effect from left neighbor and right neighbor is asymmetric that is the effect is directional
the senses marked with are those that reach some of the categories marked in bold in the figure of the bestperforming set selected by the scoring function NUM
morphological information in the tagset for example helps to identify the objects and the subject of a sentence
thus top down splitting techniques can learn from bottom up idea s strong points to offset its obvious weakness and keep the advantage of itself
this rule set had to be rigorous have a minimum of ordering constraints such that new rules could be added at random with a minimum of liability complete with a large number of rules covering large sequences computational linguistics volume NUM number NUM including morphs both free and bound optimally parsed in order to make use of morphological information relevant to allophonic variation as well as to stress
the resulting dcg fsa pair for the example pcp is given in figure NUM proposition the question whether the intersection of a fsa and an off line parsable dcg is empty is undecidable
one example is the processing of an unknown sequence of words e.g. in case there is noise in the input and it is not clear how many words have been uttered during this noise
limit the fsa rather than assuming the input for parsing is a fsa in its full generality we might assume that the input is an ordinary word graph a fsa without cycles
for each pair of strings from the lists a and b there will be one lexical entry deriving the terminal z where these strings are represented by a difference list encoding
x q lq for all q0 q furthermore for each transition NUM qi or qt we have a rule orqiqk or
in that case we are not longer dealing with dcgs but rather with cfgs which have been shown to be insufficient in general for the description of natural languages
let us notate the set of previously unseen or novel members of a category x as unseen x thus novel members of the set of words derived in lcb meno will be denoted unseen f
no global properties of the text are considered and no explicit planning is involved
however there are extra conditions to guard the wellformedness and effectiveness of presentations
this yields the optionality of genitives while preserving tile underlying semantics as shown in NUM
wellnigh impossible in light of the greater ordering possibilities gra nted hy the flexible german word or ler
however it appears that human writers also have some active tendency to avoid mixing senses within a discourse
first the statistical prediction model assigns a suitable constituent boundary tag to every word in the sentence and produce a partially bracketed sentence figure NUM c
an important aspect of discourse knowledge is the relative importance of subtopics with respect to one another
for the most part the system is context free
note that most training examples will exhibit multiple collocations indicative of the same sense as illustrated in figure NUM
regardless of origin this phenomenon is strong enough to be of significant practical use as an additional probabilistic disambiguation constraint
the algorithm will be illustrated by the disambiguation of NUM instances of the polysemous word plant in a previously untagged corpus
the primary stage picks up james john dooner kevin goldman robert l
animal and a beds too salty to support a heavy seas damage and a vinyl chloride monomer
these patterns were devised to take advantage of all the loca l contextual clues we could come up with including upper vs lower case information and descriptive appositives
the former omission was deliberate due to too many spurious matches when it was included the latter was a construct we did not think to include
the basic matching algorithm based on the complete matching principle is inefficient because many ungrammatical or unnecessary constituents can be produced by two matching operations
at the destination site of the movement whether conjunction or adjunction a new well formed node is created
when two maximal tncbs are conjoined nodes dominating the new node which were previously ill formed become undetermined
the emphasis on diverse experiments evaluated within a common setting has proven to be a major strength of trec
four groups used the baseline and NUM corruption level only two groups tried the NUM level
disks NUM and NUM were also used for the adhoc task and disk NUM for the routing task
the documents are uniformly formatted into sgml with a dtd included for each collection to allow easy parsing
there is a narrative section which is aimed at providing a complete description of document relevance for the assessors
this ordering must respect the linguistic constraints which have been transferred into the target signs
we then scan the tncb say top down from left to right looking for a maximal tncb to move
the experiments with short topics has continued and further results can be seen in NUM
the run from siemens siemsl was made as a baseline for database merging and therefore had less expansion
the monotonicity constraints on the other hand might constitute a dilution of the shake and bake ideal of independent grammars
in order to recognize a sentence of n words n l sets si of items are built
his missing axiom approach demonstrates collaboration and communication between two agents wl o possess complementary knowledge if the agent s information is not sufficient to allow completion of the proof the agent is set to do the agent attempts to provide the missing axioms through interaction
it also has access to three static knowledge bases communicative principles knowledge about rational cooperative communication application model knowledge about tasks and roles and world model general knowledge about the entities and their relations in the world
the top n candidate evaluation is useful because in a machine aided translation system we could propose a list of up to say ten candidate translations to help the translator
according to the joint purpose rule NUM the user s c goal is thus adopted and the system also takes the initiative since the user contribution is non expected an information seeker is expected to start with a question or a request
the joint purpose becomes new indir request with user wants to have a car as the content i.e. the communicative strategy is to share the user s want to have a car and check if this want can be satisfied within the application model
textl is segmented by the points NUM NUM NUM NUM and text2 is segmented by the points NUM NUM NUM NUM
the constraints of rational cooperative communication p ovide the framework in which to deal with contributions communicators have a joint purpose they obey communicative obligations and they trust that the partner behaves so that these constraints are tifffilled
the error rate is improved when new texts are used to update the stochastic model parameters
the first lexical item found in fl x is the analog of x
given two strings x and y pref x y resp
is computationally tractable even if extremely ressource consuming in the current version of our algorithm
let us examine carefully how these two aspects of the pronunciation procedure are implemented
we then go through the details of the learning procedure which essentially consists in an extensive search for such relationships
a transcription is judged to be correct when it matches exactly the pronuncia tion listed in the database at the segmental level
f and g are termed the paradigmatic alternations associated with the relationship a b NUM c d
of course when the search fails this procedure fails to propose any pronunciation
by associating entities not just with salient attributes but also with salient actions and salient figurations we capture collocations semantic collocations and idiomatic compositionality using a uniform mechanism
supose now that we wish to infer the pronunciation of a word x which does not appear in the lexicon
we finally apply to the analogs pronunciation the correlated series of mappings in the phonemic domain to get the desired pronunciation
n ox n ox b v go down v shake v go down v shake b n the boat n the boat n the boat n the boat
NUM hobbs and shieber NUM park NUM saint dizier NUM
for example the following are all acceptable for model NUM
of course the domains and ranges of these sub functions are appropriately adjusted
alternatively ifs is given wide scope the following dependency function is computed
in summary we have improved the standard evaluation method for speech translation by developing a feasible alternative with a more finegrained taxonomy of acceptability
the algorithm does not deal with collective interpretations like this
which returns the following quantifiers a some sing at least one
rx contains an embedded np process res ry returns ys qy scpy
the details of this have not yet been worked out
the gencration algorithm of necessity incorporates quantifier scoping
with ma irmm entropy h p p argmaxh p NUM p
we adopted the maxlmum entropy model learning method and applied it to the task of model learning of subcategorization preference
this requirement means that we would like p to lie in the subset of
they reported that dependencies were discovered only at the slot level and not at the class level
this paper proposes a novel method for learning probabilistic models of subcategorization preference of verbs
especially we propose to consider the issues of case dependencie and noun class generalization in a uniform way
let ps be the training corpus consisting of traln ng events of the form v ep
when c equals to NUM NUM both rc and rh are slightly h gher than when a equals to NUM NUM
in bangla for example the default ordering is subj iobj obj
the introduction of a new type of underspecification metavariable
any permutation of the underlined phrases and the verb should give identical results
the next point to be considered is binding actual function names to nameholders
the lhs of the rule contains the mother of the head
the classical way of handling such situations is to use alternation or disjunction
the modification of encoding schemata NUM to schemata NUM
the evaluation process naturally satisfies the uniqueness property for sentence level grammatical functions
figure NUM shows a tree that contains traces and visible constituents
the gen case marker normally marks a genitive qualifier of a noun
for example for management succession we have complex noun groups for companies persons and positions
there are various constraints of consistency compatibility and distance that govern whether or not the two merge
our performance on the muc NUM coreference task was a recall of NUM and a precision of NUM
it also labels prepositions and other particles such as the possessive marker relative pronouns and conjunctions
middle verbs are verbs whose object can appear in the subject position and still have an active verb
when one sees a union it can only go into the union slot of a negotiation event
one can think of it as having five phases each building up larger structures from the input
NUM name recognition NUM basic phrase recognition NUM complex phrase recognition NUM clause level event recognition
the crucial reason we believe lies in the knowledge
for the walk through article visual inspection shows that approximately half of the parses are reasonable
the speed is about NUM characters per second on pentium NUM
obviously a much more serious combinatorial problem is encountered here
common words candidates for chinese place names candidates for chinese personal names
the type of relationship may vary depending on which of several categories the event belongs too
null the metarule for relative clauses with a gapped subject as in the company which resumed talks
the parties are those referred to by the subject NUM and the prepositional object NUM
both components use the role filler class restrictions the cardinality information and the role set restrictions from the knowledge base and they use the same cfs with the same initial significance weights and the same decay functions of the context model
we have compared the capabilities of this model with two alternative models both empirically using a test set of NUM user generated referring expressions obtained from interactions with edward and analytically studying the inherent limitations that follow from the models designs
consider for instance the interpretation of dit this one in sentence 2a versus the interpretation in sentence 2b following the nl command NUM NUM zoek het rapport over gr2
finally we present some data on the frequencies of use of the two most common words that can feature in both deictic and anaphoric expressions viz dit and deze two demonstrative pronouns respectively neuter and non neuter
the linguistic cfs are major constituent referent cf subject referent cf nested term referent cf and relation ce major constituent referents are the referents of the subject the direct object the indirect object and the main modifiers of a sentence
the secretary is called hil for all referents that are in context starting with the one with highest salience their associated individual instances computational linguistics volume NUM number NUM are retrieved and matched with the class of the phrase
however in the session of subject NUM we discovered an computational linguistics volume NUM number NUM error in the interpretation of a dozen sentences this subject keyed in just for curiosity after she had completed the NUM tasks
in the final sentence of table NUM for example the referent of the phrase het artikel the article is the most salient individual instance belonging to the class karticle or to any of its subordinate classes
indication by the system is done by means of a simulated pointing gesture a fat animated growing arrow to a particular icon for instance generated upon the question which e mail message is about parsing
in order to produce cohesive text it will be necessary to a select inflectional forms of predicates to facilitate continuity of exposition e.g. using a participial form instead of a regular finite form to connect two phrases b to treat coreference issues by either pronominalization or ellipsis our system does not use definite descriptions and c to realize discourse relations through inserting punctuation and conjunctions
patent law prescribes that an invention is described by specifying in order a the title of invention b its components and components of components as required c properties attributes of components shape material dimensions etc and d relations among the components spatial connection purpose etc
templates can be concatenated to the end of the string which resulted from the linearization process of the template processed immediately before the current one or inserted into the string corresponding to its parent template immediately following the case role of the parent template on which the child is linked
thus in figure NUM the linearization pattern NUM NUM NUM where i NUM and NUM are case role ranks and shows the position of the predicate will match for example the following phrase from an actual claim NUM the splice holder is mounted NUM on the cover part NUM to form a rotatable splice holder
in fact our system performs this kind of generation for the purposes of allowing the user to check the draft before it is submitted to the claim generation stage in this way it is guaranteed that the list of templates contains all the required information
the plan structure is obtained by first clustering input templates according to the conceptual schema node to which they belong building an hierarchical structure a tree or a forest for templates in each cluster and finally hierarchically connecting all such structures
than one component of the invention should appear as early as possible if a content element is described by a single template it might be amenable to realization as a prenominal modifier such elements should appear as early as possible etc
examples are biology computers law cooking
nevertheless few research was reported to provide both thematic role and word sense information with statistical approach
expectations equate action to the current action
thus the total cost can be represented as
tol rcb i lcb aliz rtion t es n lcb rcb t alh w a thai i oml hmmnt iz lcb NUM that arthur williams had to be locat ed they agreed
we discuss each in the following paragraphs
then the syntactic score of the tree l a in figure NUM is defined as follows NUM NUM
the results of the deep structure disambiguation system with the aiso cd model is summarized in table NUM
it must be complemented with lexical depth and grammatical coverage
table NUM dialogue acts expressed by the caller in the information phase of an ovr dialogue
after acquiring NUM patterns of pp attachment the parser can correctly resolve approximately NUM of the ambiguity
5however in dictionary definitions the headword and the genus term have to be the same part of speech
the attach null ment is conditioned by the relevant head words a NUM gram of the vp
given the preferred configurations c c and c we now must determine the best of the five possible configurations c5 for the the tests NUM to NUM simply use the attachment values c c and c to determine c the best configuration
while there have been a number of recent studies concerning the use of statistical techniques for resolving single pp attachments i.e. in constructions of the form v np pp we are unaware of published work which applies these techniques to the more general and pathological problem of multiple pps e.g.
in particular we investigate the pp attachment problem in cases containing two pps v np pp1 pp2 and three pps v np pp1 pp2 pp3 with a view to determining whether n gram based parse disambiguation models which use the backed off estimate can be usefully applied
by frequency NUM dictionary senses have semantic codes and NUM of dictionary senses have pragmatic codes
semantic distance gives the best precision for lppl but chooses an average of NUM NUM senses for each genus
informatikoak saila universitat polit cnica de catalunya euskal herriko unibertsitatea barcelona catalonia donostia basque country lcb g
for this purpose we derived a list of links for each word in spanish and french as follows
in this example two links would be produced vin wine vino and vin wine wine coloured
that is even those heuristics with poor performance can contribute with knowledge that other heuristics do not provide
the difference in performance between the two dictionaries show that quality and size of resources is a key issue
a second approach based on statistical learning was used to create a learned spanish namefinder
the first approach is pattern based and has an architecture as shown in figure NUM
the final general challenge is represented by the lack of available linguistics resources for chinese
one component is a training module that learns to recognize the met categories from examples
the original training and understanding modules were not completed until the first half of march
first the learned system could be retrained in a matter of five or ten minutes
text can be mined using simple techniques such as regular expression patterns to effectively find critical vocabulary items
some of the techniques we used are therefore applicable in all languages where significant amounts of online text are available
for example a location name a title of a person and a person name often will co occur
in addition different categories will occur contiguously so that correctly recognizing a category is needed to locate the others
this means that whenever a constituent is addedto a domain as a single element its information content will be condensed to categorial and phonological information
ri e the initial placement of a preverbal constituent in a verb second clause is a consequence of lp constraints within a flat clausal order domain
in reape s approach there are in essence two ways in which a sign s dom value can be integrated into that of its mother
in fact as will become clear below total compaction and partial compatcion are not distinct possibilities rather the former is a subcase of the latter
along similar lines note that extrapositions from topicalized constituents noted by nerbonne as a challenge for his proposal do not pose a problem for our account
thus the domain object of the relative clause in the np domain is tokenidentical to the one in the vp domain
to see how this improves the analysis of extraposition consider the alternative analysis for the example in NUM given in figure NUM
messages to corba idl interface definition language for the exercise initialization project
this is accomplished by selecting the highest scoring unified interpretation of speech and gesture
the paper briefly describes the system and illustrates its use in multimodal simulation setup
the configuration of agents used in the quickset system is illustrated in figure NUM
instead the recognizer produces a set of probabilities one for each possible interpretation of the gesture
one can view this as human human collaboration mediated by the agent architecture or as agentagent collaboration
for example the user can ask commandvu fly me to this platoon gesture on the map
quickset has been delivered to the us navy nrad and us marine corps
ses where the tagger assigned a correct grammatical function or would have assigned if a decision is forced
next we remove stopwords numbers and any words with a corpus frequency NUM we used a stopword list containing about NUM general nouns mostly pronouns e.g. he she they and determiners e.g. this that those
the algorithm obviously is nondeterministically polynomial in the length of the input
sg5 provide clear and sufficient instructions to users on how to interact with the system
cyclic terms arise naturally in nlp through unification of non cyclic terms e.g. the subcategorization principle and the spec principle of hpsg
such a change of the feature geometry makes it necessary to change the path in all references to a feature
in the past decade there have been diverging trends in the area of linguistic descriptions and in the area of processing models
consider the agreement features person with values NUM NUM and NUM and number with values sg and pl
lexicons which make use of these abstractions can be re used in different kinds of applications where different datastructures represent these abstractions
this is for example the case with a grammar that uses feature terms for grammatical description but whose input and output e.g.
profit allows the use of sorted feature terms in prolog programs and logic grammars without sacrificing the efficiency of prolog s term unification
a prolog term can have a feature term as its argument and a feature can have a prolog term as its value
note that conjunction also provides the possiblity to tag a prolog term or feature term with a variable var term
there is little room for improvement at this level of description see chapter NUM NUM NUM
we used only linguistic intuition and a very limited set of sentences to write the NUM constraints
NUM or they refer to intervals which are not explicitly mentioned in the sentence temporal anaphora
positive and negative counts for cost assignment were collected from two sources for both systems and an additional third source for the transfer system
bounding effects on discontinuities are described by specifying that certain dependencies may not be crossed
there was no restriction on utterance length or atis class dialogue or one off queries etc in making this selection
the degree of similarity depends on the adopted threshold value
a good example el that is a probabilistic contcxt free grammar
this is not the same as the synonym relationship which is based on semantic similarity
using a hybrid system of corpusand knowledge based techniques to automate the induction of a lexical sublanguage grammar
for rare neighbors the algorithm simply records the neighbor s pos a compromise to keep the size of the arrays manageable while providing some information on the syntactic context
most of these are new counts i.e.
each context digest for verbs then contains NUM NUM possible entries
typically any given verb is a vector which silnultaneously belongs in several neighborhoods
rather a hybrid system shoukl be developed where the strengths of both paradigms arc combined
these phrasal boundaries are of variable length and can in fact span the whole sentence
if authors for example wish to prevent the reader from performing the action of dismantling the frame of the device and they decide that the reader is unaware of this danger that the action is consciously performed and not unsafe drafter produces the following text do not dismantle the frame
many systems includ o ing ones from nyu bbn sri sra and mitre have taken steps to make the process of customizing a system for a particular domain an easy one
then the second step identifies which of the associated trees are applicable by testing their pragmatic conditions against the current representation of discourse
similarly many problems in natural language processing in particular parsing and generation can be expressed as transductions which are calculations of such correspondences
nodes of the graph are of two different types called and nodes and or nodes respectively and each directed arc connects nodes of different types
we give a simplified syntactic representation for NUM in figure NUM and a simplified semantic representation for 2b in figure NUM
these systems have been borrowed from translation theory a subfield of formal language theory or have been originally and sometimes redundantly developed
for a parse tree t in g we denote as t t the set of all parse trees in g that are synchronous with t according to gs
its composition follows the principles established by the brown and lob corpora with adjustments for the fact that it should cover the most common genres of the swedish of the NUM s
this is enforced by requiring that the links establish a bijection between nonterminals in the two synchronously derived sentential forms that is each nonterminal must be involved in exactly one link
unlike the standard derivation tree for uvg dl the vector derivation tree clearly shows how the vectors rather than the component rules of the vectors were combined during the derivation
between man and machine the automatic tagger was run on NUM NUM words of text not used in the training of the tagger
actually almost all errors concern function words and a scrutiny of them makes it clear how doubtful the whole concept of correctness is in this connection
to remedy this situation it would probably be necessary to have a phrasal lexicon as most instances of naked singular nouns appear in lexicalized phrases
it can be used as adverb preposition or subordinating conjunction and all the six possible mistagged combinations do occur but with quite varying frequency
some clear patterns among the errors can be discerned and the sources of the errors as well as possible alternative methods of remedy are presented and discussed
preposition instead of subjunction appears NUM times subjunction instead of preposition NUM times altogether NUM of the NUM errors connected with the word om
returns nil if no more documents are found in the collection
ts2 uses another NUM dialogues with NUM speech acts as test data
actions therefore are employed to interact with other system components
in order to evaluate the statistical model we made various experiments
in order to get good translations context plays an important role
NUM to control clarification dialogues between verbmobil and its users
these additional attributes are not shown in the example below
our final example involves an annotation which effectively modifies the document
a package will typically include a set of related annotation types
these arguments have the same significance as for selectannotations above
it is an error if no annotation has the specified identifier
an annotation associates a type with a span of the document
the document is the central object class in the tipster architecture
an abstract class for objects which have attributes is defined as
all candidates whose second vowel has both a diaeresis mark and a stress mark do always split e.g. ma ov ma fu may 7rpo c rap l pro fparksi preexistence e a c awcr eksa flosi immateriality
energy flmci ion repre sents the degree of unstability of current stntc of raiidoni vl triables in m rf
a clique is defined as the set of random variables that all of the pairs of random variables are neighborhood in it
NUM is similar to gibbs distribution which is the primary probability distribution of m lcb f model
at first glance the problem seems insoluble because the given information is insufficient to determine the probabilities pi x
corresponds to tile posterior listributton t tiw ill the ia g illg iitod ti
by further taking into account theorem NUM the assumption is that vlci c2c cg v2 is hyphenated either as vl ci c2c c3 v NUM or exclusively as vlc NUM c2c c3 v NUM
theorem NUM the strings of the expression vlcl c2c c3 v2 are hyphenated as vl cl c2c c3 v2 if c1 c2 c cc u c otherwise they are hyphenated as vlcl c2c c3 v2
if non existent patterns were eliminated i.e. those consisting of either two consecutive stressed vowels or of two consecutive vowels with diaeresis marks the degree of completeness of the hyphenator on a vowel pattern basis could be then computed as NUM NUM NUM NUM NUM NUM NUM NUM NUM
to present the proof in its entirety it would be sufficient to give two contradictory examples where the points preceding vl and following v2 are not permissible hyphen points e.g. c v a av hl courtyard and rc a pa li6s old
however two different interpretations can be given namely that i only one hyphen point is specified by the rules i.e. the point preceding or exclusively following the first embedded consonant cl or that ii two additional hyphen points are permissible those preceding the first and following the second vowel
if these principles had been used in the examination of consonant splitting the set of all subword patterns of table i would have been restricted to the set of expression vlc NUM c2c3c4 v NUM similarly prefix and suffix consonant sequences would be restricted to ocl c2c3 and cdeg respectively
viterbi algoritllm guarantees optinial sohltion tilt it canilot bc used in the probleln which has very huge search space
each connective has a right r and left l rule showing respectively how to prove and how to use a type containing that connective
for example the following rules of permutation p and association a undermine sensitivity to the linear order and bracketting of assumptions respectively
there is also a value none that indicates when the position i to the left or right is occupied by a word that is not among the NUM most frequent and a value null indicating that the position i to the left or right falls outside of the sentence boundary
the algorithm assumes that least common ancestors are preserved in the alignment
x r y indicates that both orders are possible for its subeomponents and the move to xey or yox involves forgetting one of these possibilities
x ay suggesting x y xo y as a linking theorem of a mixed logic revealing the natural relation between xo y and x y
this suggests the translation x ay for which we observe x ky c ky x
the conjuncts of i can be derived and coordinated as s np since np np s t pp s pp is a theorem as in NUM
suggests that both views are possible and may even be compatible for realisation within a single system further extending the possibilities for the multimodal systems that can be constructed and for their potential utility
or x ay or ax y i.e. with either or both of the product subcomponents modalised indicating that subcomponents x and y may legitimately appear in either order
the above terms however encode distinctions unwanted for this purpose but can easily be simplified to terms using only a single abstractor a and with application notated by left right juxtaposition e.g.
crl has also made significant progress in its research in multi lingual query generation
NUM analyze the training corpus using the known probabilities and recalculate the frequency of each dependency relation based on the analysis result
the simplified and more efficient memorization technique that i use see section NUM however does not solve this problem
this has resulted in the construction of imagene an instructional text generation system that embodies a model of the forms of expression consistently used by instructional text writers over a broad range of instruction types
the input for the parser consists of a test set of NUM NUM word graphs randomly taken from a corpus of more than NUM NUM word graphs
the second type is not based on an action in the process structure at all but rather is a span added by the system networks to signal a state resulting from an expressed action
in addition we have developed a large scale document management system which allows documents used in a tipster compliant system to be handled in a nniform manner
purpose expressions arise in the context in which actions are viewed as being related hierarchically that is in which one higher level action is realized by the execution of a set of lower level actions
the sentence builder uses the lexical information given in the process structure just described to translate the text structure described above into the appropriate sentence specification to be passed to penman for surface realization
although the corpus study has become a common methodology in natural language generation seldom are the representation and analysis techniques given in any detail and detailed evaluations of the resulting text are not provided
definitions of relations of this sort specify constraints that apply to the nucleus n the satellite s and the combination of the two and specify the effects of the expression
rst was attractive for the imagene project because of its ability to represent the hierarchical structure of text with rhetorical structures that matched the level of analysis required for the study of expressions of procedural relations
cervantes a system supporting text analysis
this ambiguity can reliably be resolved with a simple and obvious grammar rule that disallows verbs after determiners
the key idea is that the linguistic concept head can be used to obtain parsing algorithms that are better suited for typical natural language grammars
this parser is developed for the ovis system a dutch spoken dialogue system in which information about public transport can be obtained by telephone
the system also uses a gazetteer consisting of approximately NUM names of cities states and countries
synsets this task seems fairly more complex than ours as we estimated an average of NUM NUM synsets per noun on a set of NUM nouns of the rsd
the available german word level grammar of x2morf was rewritten to conform to the feature structure notation employed by fuf
designed to work together llow this can be done in an organized way is the topic of this paper
it is generally acknowledged that developing a successful computational model of interactive natural language nl dialogue requires extensive analysis of sample dialogues
syntactic annotation of the tree bank is conventional
a string s as the most appropriate interpretation of s
could you please repeat your destination
the details of this procedure have not been specified
a tokenizer for french for example needs to recognize de plus moreover en plus more en plus de in addition to and de plus en plus more and more as single tokens
a dop model for semantic interpretation
no provisions were taken for unknown words
we now discuss these four steps
we can also observe that there are only small differences between the figures in table NUM and table NUM as far as the high and medium frequency classes are concerned
having a b c e v and r as a relation they can be described as follows reflective ara
telegraph offers NUM semantic features ani null mate time place etc german assistant NUM and langenscheidts t1 NUM power translator offers few semantic features for verbs movement direction
but since the results of working with one medium and one low frequency class show clear distinctions between the systems it is doubtful that the additional cost of taking more classes will provide significantly better figures
the latter reflects the figures in the langenscheidts t1 manual where they report an inbalance in the lexicon of NUM NUM entries for german to english and NUM NUM entries for the opposite direction
that means if a word is frequently used as an adverb and seldom as a verb the count of the total number of occurrences will be attributed to both the adverb and the verb stem
this database also contains frequency data which for german were derived from the mannheim corpus of the institut fiir deutsche sprache and for english were computed from the cobuild corpus of the university of birmingham
to avoid confusion with theory specific constructs we use the generic term argument structure to refer to our annotation format
dr a9 there is a train leaving at NUM NUM p m dt gerbino NUM like previous approaches to evaluation performance evaluation using paradise requires a corpus of dialogues between users and the agent in which users execute a set of scenarios
other cases assumed to occur with low probability such as for example the neighbor gave the boy a book or the neighbor gave him the book are not taken into account
c complementarity a design error case identified by one annotator
NUM for example table NUM shows a hypothetical confusion matrix that could have been generated in an evaluation of NUM complete dialogues with train timetable agent a perhaps using the confirmation strategy illustrated in figure NUM
to be able to characterize the procedure determining some of the main points of tfa and to illustrate the output language of our parser we have to add a brief discussion of certain issues concerning word order
whenever an attribute value in a dialogue i.e. data avm matches the value in its scenario key the number in the appropriate diagonal cell of the matrix boldface for clarity is incremented by NUM
all dialogues resulting from execution of this scenario in which the agent and the user correctly convey all attribute values as in figures NUM and NUM would have the same avm as the scenario key in table NUM
at the top level this model posits that performance can be correlated with a meaningful external criterion such as usability and thus that the overall goal of a spoken dialogue agent is to maximize an objective related to usability
for the present stage of research it has been possible to account only for the primary shape of sentence structure the verb with its arguments and free modifications and for the prototypical cases of tfa
one can represent this information about the interaction of lexical rules as a more complex finite state automaton which can be used to avoid trying lexical rule applications at run time that are bound to fail
in addition to the three principal funding agencies additional funds were obtained from a variety of other sources at critical junctures in the program
the a sentences are ambiguous in that the penultimate sentence part in some readings and thus in some dependency based syntactic representations of these sentences belongs to the focus and in others to the topic
in the existing literature we can not find other work on the generation of chinese referring expressions or indeed on the full evaluation of anaphor generation for any other language which means that we have no real working systems to compare with
NUM NUM no romanow ever turns down a free ticket
this completes the construction since the set of parse trees represented by 7r is included in the set of parse trees represented by 7rq
not all words for emotions have a count noun counterpart
the constraint is between the verb and its object and any number of words may occur between these two elements e.g. you will be setting a gorgeously decorated and lavishly appointed table designed for a king
this clearly shows how practical applications of language engineering have to conform in unforeseen ways to the real world
at each encountered node of t create an a link to the paired implicit node of t
the labels of the transitions from one state to another are disjunctions of the lexical rule predicate indices i.e. the lexical rule names constitute the alphabet of the finite state automaton
if a nominal anaphor n is at the beginning of a sentence NUM or is the first mention of the referent in a sentence then a full description is preferred otherwise if n is within a sentence or has been mentioned previously in the same sentence without distracting elements then a reduced description is preferred otherwise a full description is preferred
however all of the metrics are commutative i.e. distance from concept a to b is the same as chat from b to in semantic class disambi tion a distinction is necessary since the taxonomic links indicate membership relationships which are not commutative aircraft l is a vehicle l but vehicle l need not be an aircraft l
the resulting optimal tag set is shown in table NUM
all the relevant entries will be rank orldered for appropriateness
automatic evaluation and uniform filter cascades for inducing n best translation lexicons
there was no way to objectively judge lexicon precision
precision is the fraction of lexicon entries that are correct
this arrangement allows for fair comparison of different filter combinations
the word alignment filter exploits this observation as illustrated in figure NUM
the bible approach is suitable for many different evaluation tasks
there was no way to uniformly combine the different kinds of filters
the presented framework can be used as a method of enhancing an mrbd
furthermore since the same sample for a given no is used for all values of c any such random variation due to small sample size will be replicated in all curves of figure NUM
first we no longer require weights to sum to one for rules with the same left hand side
for example the dutch vleeswaren l meat products has an eq synonym relation with meat NUM the flesh of animals where the sense numbers do not necessarily correspond with word netl NUM numbers and a has hyperonym relation to the synset voedsel l
the underlining in figure NUM shows aba aba aba aba a x a a x x a x the four alternate factorizations of the input string that is the four alternate ways to partition the string aba with respect to the upper language of the replacement expression
both the top concepts and the domain labels can be transferred via the equivalence relations of the hillrecords to the language specific meanings and next via the language internal relations to any other meaning in the wordnets as is illustrated in figure NUM for the top concepts object and substance
words judged inappropriate in each cluster are attached t words tmdecidable being suitable in their clusters are put
they are expected to at least be of a qualitative value to the user
the norwegian lexicon was created from a NUM million word corpus with a similar composition
subsequent studies have included individuals with writing difficulties due to linguistic and or dyslectic difficulties as well
the lack of standardization of test conditions prevented any cross linguistic or cross product comparison of keystroke savings
the inclusion of trigrams involved an extension of scope compared with the current version of profet
the unigram word lexicon was then hand tagged and prediction tests run with vs without semantic information
for perfect adaptation of lexicon to test text maximum savings of around NUM were obtained
our hypothesis is that certain aspects of the disabled individual s writing will improve with the appropri
ate use of and training with the new version of prolet with its augmented functionality
the five subjects with dyslexia have reading and writing difficulties as their main problem
phrases comma splices missing hyphens missing punctuation at the end of a segment and questions with a final period instead of a question mark
the following sentence from an ibm manual illustrates both cases the format is defined in the file which was not included by the header file
for each term the frequency is stated and the user has the choice between having the terms sorted either in frequency order or alphabetical order
it has not yet been included in the c version of easyenglish and we give here an example of its use produced by the prolog version
high precision is attained by the use of a high quality robust broad coverage grammar esg that delivers dependably consistent parses with great detail
users generally express enthusiasm about using easyenglish and the ibm translation centers have reported that they find the easyenglished documents easier to deal with
dokumentationsdeutsch is not defined by a list of allowed constructions but rather by a list of forbidden constructions allowing most of standard german syntax
if that interpretation is not the desired one it is up to the user to construct a rephrasing that will result in the desired interpretation
this is an ordinary feature that we use in conjunction with the bdc dictionary which defines semantic domains
the multilingual interface has the following objectives it should offer new or better equivalence relations for a set of word meanings it should offer better or alternative language internal configurations for a set of word meanings it should highlight ill formed configurations it should highlight ill formed equivalence relations null
in a sense this function allows a measure of redundancy in the grammar specification and thereby improves robustness
finally the pi eferi lcb ed centei lcb or cp is the highest rauked member of the of list
if there is no such difference in the semantic restriction score the standard word ordering ga wo and hi seems to let the listener to interpret e.g.
e experienced that x was made to eat y by z it is still grammatical but is much more difficult to get the meaning of it because it has four arguments for the single verb
upon this empirical study and development of over NUM NUM japanese verbs and adjectives we propose an architecture for verb subcategorization that represents the mapping information between surface case frame and deep case thematic role frame
most nominative cases in japanese verbs including agc ru to give have strong preference for human animate attribute so that a meaningful difference between semantic similarity of x to animate object and the similarity of y to other kind of concrete object leads to allocate nominative case on x and accusative case on y in either NUM NUM a or NUM NUM b
for example deha in fig l could only be used with animate plural nouns such as kotira our side but it certainly could mark the nominative case
the lexicon by this design has comprehensive information on both the surface frame and the deep frame and the correspondences between them which are embedded in a code yet the number of codes has been controlled under a manageable figure of several hundreds so that the coding system could evade the potential combinatorial explosion
the lexicon developed by this design has comprehensive information on the correspondences between the surface case frame and the deep case frame and yet restrains the potential combinatorial explosion of the number of verb subcategorization frames by carefully identifying superficially different frames with an idea of alternative case markers and semantic roles and by introducing the notion of surface case frame permutations
slot nome nom acc dat with deep cose agent pat goal n a frame source ient fig NUM deep case overlop fig NUM shows ahnost the same deep case frame as in fig NUM that shows the subcategorization frame of verb ageru give
while reading each new wordnet entry for a given word taggers must modify the corresponding entry in their mental lexicons
these mismatches may be due to a mistake in the eqmvalence relations interlingual links a mistake in the language internal relations a language specific difference in lexicalization by using the cross language comparison and the tools described in section NUM a particular series of mismatches can provide criteria for selecting that part of the semantic network which needs inspection and may give clues on how to unify diverging semantic configurations
depending on the interaction environment dialogue initiative may reside with the computer with the user or may change during the interaction
at the same time the fact that the inter lingual index or ili is unstructured has the following major advantages null complex multilingual relations only have to be considered site by site and there will be no need to communicate about concepts and relations from a many to many perspective
NUM ing processed by lsa each sentence undergoes the following transformations context reduction stemming bigram creation and term weighting
roughly speaking text categorization proceeds in two steps first for each of the given categories estimate the likelihood that it is a correct category of a document and second decide whether to assign the category to the document based on the estimate the rule is to use a suitable cutoff point to determine a choice
p c doc f d doc f d p t i c f t token f d p t l d fd t token f d p t fd t token f d
since the title length stays rather constant over the test corpus the possibility that an actual topic is identified by chance would be higher for short texts than for lengthy ones we find NUM of indices to be actual at NUM while the rate goes down to NUM at NUM
table NUM and table NUM show break even points of experiments using the fixed length and proportional length strategies respectively
the combination of subtree t and subtree u written as t o u yields a copy of t in which its leftmost nonterminal leaf node has been identified with the root node of u i.e. u is substituted on the leftmost nonterminal leaf node of t
to implement this idea we introduce the special symbol on the right hand side of the replacement expression to mark the place around which the insertions are to be made
although they do not consider a computational model for participating in mixed initiative dialogues their observation that there are speaker specific plans or goals underlies the model that we propose
with the computer running in declarative mode the experimenter chose to make such statements once every NUM NUM user utterances but only once every NUM NUM user utterances in directive mode
it is hypothesized that when the computer has yielded the initiative users are more likely to attempt to redirect the computer s focus when an error situation occurs
the small number of subjects and the design of the experiment make it difficult to observe differences within a given level of initiative as subjects gain additional expertise
for the assessment phase the test statistic is NUM NUM with a corresponding p value of NUM NUM for NUM degrees of freedom
ment problems according to type such that problem k of both sessions NUM and NUM was the same type of problem
consequently many of the initial disagreements in coding were due to a lack of familiarity with what transpired during the experiment
furthermore in exit interviews conducted after they had completed participation none of the subjects indicated any difficulty with or dislike of planning utterances in advance
the NUM dialogues analyzed were produced from experiments with a variable initiative spoken natural language dialogue system organized around the paradigm of the missing axiom theory for language use
the following simple expressions appear freqently in the formulas the empty string language the universal sigma star language
ci2 ci3 ci4 rcb and is NUM NUM NUM NUM
sets have an increasing level of generality
figure NUM an example of synsets hierarchy
words should be evenly distributed among categories
duces the initial ambiguity of the corpus
we do not discuss the data here for the sake of space
thus the intersection of a fsa and a cfg is a cfg that exactly derives all parse trees
there are rules xoqoq xlqoql x2qlqa
in extrapolating from the analyses pr ist gives we find that his analysis generates only two of the five readings
this shows the word or relevance score for each feature together with the value of e x for the feature after iterative scaling is complete for the final model
there is no way to get x4 coreferential with bill once we have set x3 to something other than bill
the tdt corpus was constructed as part of a darpa sponsored project intended to study methods for detecting new topics or events and tracking their reappearance and evolution over time
when the parser fails to generate a unified parse it outputs partial parses in such a manner that fewer partial parses cover every word in the input sentence
the results of our experiments show the effectiveness of this method moreover implementation of this method on a machine translation system improved the accuracy of its translation
de o NUM cardinaj i i i cardinal l i
for example figure NUM shows an incomplete parse of the following sentence which is the 43rd sentence in a technical text that consists of NUM sentences
a i found the NUM guideline violation types which were also found by a2 plus another NUM guideline violation types
the different states effectively code the presence of different modifier types
furthermore there is a general combination rule that simply concatenates astrings and concatenates b strings
this process of adding values to a token onl y when a rule has inquired of the attributes is known as lazy annotation and is much more economical than attaching all possible attribute values slavishly to all known knowledge bank entries
NUM mandatory fields this state is needed only for applications in which values for certain fields must be known before a query can be issued
different sets of beliefs can yield different bases for parallelism and indeed different judgments about whether parallelism occurs at all
this holds if their properties foot f l and top t z are similar
in fact it appears that for this set of most ambiguous words of english more training data may be beneficial to subsets of test sentences of our sense tagged corpus as shown in table NUM
unfortunately there was not enough training to produce effective decision trees s o wrap up did n t exactly get a fair trial here
badger s cn output was put to better use in st where wrap up used cn patterns in order to induc e relations between entities
while it is apparent that the serial architecture is far from ideal our most glaring weakness was the recall of th e organization specialist
however we discovered that NUM of the recall in the co dry run test materials was based o n references to people and organizations
this version of resolve suffered a small decrease in recall NUM but a much larger increase i n precision NUM
we also noticed that crystal s dictionary was too sparse to cove r all the useful morphological variants for important verbs and verb phrases
this is primarily a noun phrase analysis challenge with additional points to be won from correc t merging and consolidation across multiple noun phrases
if nps were not processed correctly by the string specialists then resolve is right to reduce its confidence i n the same type feature accordingly
the organization and people specialists used for ne were based on code heavily modified developed in the informatio n retrieval laboratory at umass
badger then applies cn definitions from crystal s dictionary and finds four cns that apply to the firs t segment three that extract mr
eq NUM gives the probability that word wl was assigned to class c based on the observation that it was followed by word w2
thus we get different results for the aba depending on whether we start at the beginning of the string or in the middle at the b
although transaction coding has some problems the computational linguistics volume NUM number NUM coding can be improved by correcting a few common confusions
although one coder tended to have longer games and therefore fewer beginnings than the others there was no striking pattern of disagreement
however this actually led to a degradation in performance with the corpora we tried because they were a poor model for our data we suspect this is because our user makes frequent use of questions imperatives and interjections
somewhat surprisingly the treebank corpora turned out to be a good model for our data with respect to the most likely pos associated with a word and we obtained about NUM tagging accuracy simply by choosing the most frequent tag on this basis
instead in cogeneration input is partially specified as a series of text units by the user and the job of the generator is to combine these units into grammatical coherent sentences which are idiomatic ap propriately polite and so on
achieve NUM keystroke savings with input which like that of our user averages NUM letters per word it is necessary to predict the word on average after NUM NUM letters have been input assuming that the space is predicted
secondly consider the trivial example of a language which consists of NUM equiprobable words each of NUM letters written without spaces all of which have the common prefix zzz i.e. zzza zzzb zzzy
for our current purposes even distinguishing between less related usages such as the luggage use of trunk and the american english part of car use is probably unnecessary since a more general class such as container would capture most of the relevant behavior of both
because none of the classical terms fits exactly we have chosen a novel term directed transduction to describe a relation induced by the definition in figure NUM
the duplication of textrefs resulted in the loss of many of the co references involving mr
these are considerably lower than the scores for the formal evaluation p r NUM NUM
james and the third gives a broad grammatical classification for the text sequence
a later stage of analysis examines the recently built pieces of net and attempts to unify those which are similar
the cure part of diagnostic analysis suggests ways of repairing system dialogue behavior
the remainder of the succession event information is then established using one of two techniques
whether the core the contributor is a segment or a minimal unit further subdivided into action state matriz
in the research presented here a window of tour was adopted i.e. for words of interest in the domain of physical chemistry co occurrence counts were kept between those words and their immediate left neighbors wi l wi immediate right neighbors wi wi l and left and right neighbors that are two words away wi NUM wi and wi wi NUM respectively
one of suitable measures for representing effectiveness of a context is dispersion of the context on labels
this algorithm is an extension of forward backward algorithm which infers the parameters of a stochastic context free grammar
in other cases however the process can bail out immediately in event coreference consider the sentence john revised his paper teacher t are similar by virtue of being persons
the grammar is learned from the wsj bracketed corpus where all nonterm nals are omitted
where a is a set of all labels and a is one of its individual members
these sharp peaks can be used as a step to terrnln te the merging process
the decision tree therefore represents a very useful mechanism for determining the semantic level at which the decision on the pp attachment is made
there are no matches found for q1 and the algorithm moves to q2 finding quadruple q4 as the only one matching such criteria
the use of the same training set for both the pp attachment and the sense disambiguation provides a positive bias in favor of correct attachment
however it is necessary to maintain the understanding that it is the pp attachment rather than the sense disambiguation that is our primary goal
this is because training examples with an error or with a word not found in wordnet could not fully participate on the decision tree induction
unfortunately at the time of writing this work a sufficiently big corpus which was both syntactically analyzed and semantically tagged did not exist
branches that lead to empty subnodes as a result of not having a matching training example for the given attribute value are pruned
actually proper nouns important in number and in frequency are one of the most problematic units for all automatic analyzers of natural language texts
similar contextual situations these include information on the pp attachment are found in the training corpora and are used for the sense disarnbiguation
in natural language processing however we rely mostly on the sentential contexts i.e. on the surrounding concepts and relations between them
simr s localized search strategy provides a vehicle for a localized noise filter
in those parts the token type can provide valuable clues to correspondence
simr was evaluated on hand aligned bitexts of various genres in three language pairs
table NUM summarizes the amount of time invested in each new language pair
each bitext defines a rectangular bitext space as illustrated in figure NUM
simr is faster and significantly more accurate than other algorithms in the literature
a noise filter can make it easier for simr to find tpc chains
the upper right corner is the terminus and represents the texts ends
he suggests a deterministic annealing procedure for clustering
let p and q be the probability distribution
to a first approximation tbms are monotonically increasing functions
all simr needs is a place to start the trace
there are switches that can be set for example to turn all punctuation off to turn it all on or to normal pronunciation where very few punctuation marks need to be pronounced
grapheme to phoneme technology is also useful in speech recognition as a way of generating pronunciations for new words that may be available in grapheme form or for naive users to add new words more easily
the rule having the longest match between the set of all the is strings of the block and the string beginning with the next character to be processed in the input text is searched first
some problems result also from a different but acceptable elision of mute e as in chemin de fer briqueterie petit neveu amenuiser point de vue porte b b redevenir
NUM the grapheme pattern and the left context pattern are reversed by the rule compiler that is stored in right to left order so that they are stored in the direction that they are actually used
there are some special pairs like NUM and l syllabic l that get deleted even if there is a morpheme boundary between them
tests can be done NUM with or without an exception dictionary lookup running before the rules NUM on text extracted from papers books magazines
the rule compiler does not perform any sophisticated checking of the rules it does not check that the rule set is complete nor does it check that long rules are always presented before short rules
this is a pragmatic approach based on failures of systems that use hard coded rules that the linguist would be forced to program or the programmer would be forced to articulate
with two buffers the writing of the left context of a rule is easier because the input string is only modified at the end of the block of rules
furthermore at the time a description is needed it limits the amount of online full text web search that must be done
we will also look into connecting the current interface with news available to the internet with an existing search engine such as lycos www lycos com
profile can also be used in a real time fashion to monitor entities and the changes of descriptions associated with them over the course of time
these full expressions are used as input to the description finding module which uses them to find candidate sentences in the corpus for finding descriptions
in such cases the information may be readily available in other current news stories in past news or in online databases
in this paper we describe a method for automatic creation of a knowledge source for text generation using information extraction over the internet
the last category arises from zipfian behavior of terms and is standard for ir processing features with frequencies that are too high or too low have adverse effects on retrieval effectiveness and efficiency
however for this query it is crucial
thus the model will tend to produce errors when it sees this input phone in a similar gildea and jurafsky learning bias and phonological rule induction ih m p oal r t ah n s iiii NUM ih m p oal dx all n t s figure NUM alignment of importance with flapping r deletion and t insertion
but adding three domain specific learning biases to ostia allowed it to successfully learn transducers implementing simple phonological rules of english and german faithfulness underlying segments tend to be realized similarly on the surface community similar segments behave similarly and context phonological rules need access to variables in their context
because our biases were applied to the learning of very simple spe style rules and to a non psychologically motivated and nonprobabilistic theory of purely deterministic transducers we do not expect that our model as implemented has any practical use as a phonological learning device nor is it intended as a cognitive model of human learning
as an example of a retrieval we have shown in table NUM comparing the trec NUM chinese experiment using bigram representation with our method of text segmentation in the pircs system
for the theoretical property of language identification in the limit we must be guaranteed that the alignments used are correct that is the alignment must not show an output symbol to correspond to an input symbol that comes after the input symbol that in the target transducer generates the output symbol
d iteration using the newly identified short words of step c all tagged useful for segmentation purposes we expand our initial lexicon in step a and re process the corpus
for short queries with a large lexicon stopword elimination can lead to some improvements but runs the risks of accidentally deleting a crucial word in a query that can adversely affect retrieval significantly
furthermore we show that some of the remaining errors in our augmented model are due to implicit biases in the traditional spe style rewrite system that are not similarly represented in the transducer formalism suggesting that while transducers may be formally equivalent to spe style rules they may not have identical evaluation procedures
rather the machine goes to state NUM and waits to see if the next input symbol is the requisite unstressed vowel depending on this next input symbol the machine will emit the t or a dx along with the next input symbol when it makes the transition from state NUM to state NUM
one example of such state explosion is the german rule to devoice word final stops sonorant NUM continuant voiced in this case a separate state must be created for each stop subject to devoicing as in figure NUM
this would be acceptable for referential np semantics
the following category is used for but
session NUM was scheduled for three or four days later
figure NUM investigate two dialects of
b every dealer shows most customers at most three cars
sthis should not be taken as denying the reality of the uvc itself
b most boys think that bill danced with two women
a 3ohn thinks that every man danced with two women
it will provide information only as a direct response to a user question
the system must output statements that are compatible with its level of initiative
c most boys think that every man danced with two women
as before user requests for clarification of the previous goal have priority
NUM the system is ready for your next utterance
then they were released to do up to ten problems
the proper choice is determined by the expectations produced based on the situation
in case of a comment to the experimenter
a variable initiative dialogue system is just the first step toward the more important objective of a mixed initiative dialogue system
we experiment the two hypotheses on selectional restrictions presented in section NUM i.e. the one with general wordnet frames and the other with more refined selectional restrictions
from these results it can also be seen that expressions typical to newspapers have been extracted
sometimes the translation of non linguistic text is completely erratic especially where white space is concerned
then simr will be better able to follow the variations in the slope of the tbm
simr judges the cognateness of each token pair by their longest common subsequence ratio lcsr
space the true points of correspondence trace the true bitext map parallel to the main diagonal
the first matching predicate relies on orthographic cognates and a stop list of closed class words for both languages
most of simr s effort is spent searching for tpcs one short chain at a time
since the matching predicate does not require perfect accuracy the induced lexicons need not be perfect
the heuristic was introduced after inspection of several scatterplots in bitext spaces revealed a recurring noise pattern
chains of only a few points are unreliable because they often line up straight by coincidence
crl has also developed a tipster architecture validation suite which allows the testing of tipster compliant document managers
crl has used the architecture as the foundation for other dod programs oleada and temple see separate s mmaries
it is also continuing to develop language technologies to support document detection and information extraction in a variety of languages
this multilingual information retrieval capability is being designed so that it can be integrated into any statistical information retrieval system
prototype document managers supporting the architecture were implemented and used to support the tipster NUM and NUM month damonstration systems
this includes a sophisticated editor which allows the display and editing of annotations on documents
the crl has provided mulfilingual human computer interface software which conforms to the tipster architecture
a mature version of the document manager software has now been developed and distributed
approach crl has provided general purpose enabling technology for several aspects of the tipster phase h program
other types of information about text type text structure and more finely grained distinctions with respect to referential types e.g. modeling pronouns differently than other definite nps would all likely further improve the model although for some of these additional training data would be required and more domain and genre dependence may result
a rail depot was found NUM km southwest of the capitol of raleigh consisting of extensive admin and support areas similar to the ammunition depot in fairview two material storage areas extensive transshipment facilities some of which are under construction immediately east of the depot and several training areas
this sentence has two readings one in which the teacher revised john s paper the strict reading and one in which the teacher revised his own paper the sloppy reading
the resulting distribution for our example is NUM a b d c NUM
the question is then how to take these sources into account given that they may be partially contradictory
we have implemented simple methods for pruning very low probability configurations during processing and for smoothing the resulting distribution
therefore the preferences for particular coreferential dependencies can change when considering the larger picture of possible coreference sets
this approach yields a probabilistic model as given that is the probabilities sum to NUM without normalization
the latter step is accomplished when necessary by eliminating certain low probability configurations at the end of processing
NUM b c a yes NUM c c b a no NUM d cc no NUM d c b a yes we therefore model the probability of this coreference configuration as the product of each of the corresponding pairwise probabilities
we distinguished between three values the percentage of correctness for coreference sets of cardinality NUM call this p2 the percentage for coreference sets of cardinality NUM call this p3 and the percentage for coreference sets of cardinality NUM or more call this p NUM
we capture this by having the agent that we are modeling the system adopt the belief that it is mutually believed that the speaker intends to achieve the goal by means of the plan
the new referring plan labeled p34 is shown in figure NUM with the expansion circled we have abbreviated plan derivation p34 for the weird creature in the corner
figure NUM thirty ninth sentence of chapter NUM and a part of its parse
NUM NUM intersection of fsa and off line parsable dcg is undecidable
the operator s side as seen in figs NUM and NUM
table NUM results of completing incomplete parses on the basis of discourse information
figure NUM example of an incomplete parse obtained by the peg parser
repeated aj n v aj pp det within the the as NUM system
assuming that no intervening errors are encountered the evaluator will eventually reach the constraint on the terminating instance of modifiers cand object with cand instantiated to a non null set
if there is none then this constraint is unsatisfiable and so the evaluation of this plan stops with this action marked as being in error since no object matches this part of the description
NUM another approach would be to use the identity of the action in error to revise the beliefs that the agent has attributed to the other conversant and to use the revised beliefs in refashioning the plan
for instance the utterance no the red one could be interpreted as an s reject of the color that was previously used to describe something and an s actions for the color red
as peter a heeman and graeme hirst collaborating on referring expressions with traum it is the proposals that are refashioned before they are integrated into the shared plan rather than the shared plan
second their model gives special status to the role of the current referring expression current plan participants judge and refashion the current referring expression directly rather than recursively modifying modifications e.g.
we assume that the parser can determine from context that the no is rejecting the surface speech actions that were previously added and so the parameter of s reject is a list of these actions
each sentence in the whole text given as a discourse is processed by a syntactic parser
the confidence scores succeed in pointing is as illrecognized the alignment considerations will then classify it as an insertion
we present here a method based on a statistic NUM class model dedicated to oov proper names
fig NUM substrings to be extracted here 3rd condition means that when a string for instance a in fig l is extracted from a certain location within the source text any substring b t that is included within the string a is not subject to extraction
NUM problems of n gram statistics nagao and mori s method obviously fulfills requirements of conditions NUM and NUM but not condition NUM it is expected that the accurate frequency of any substring a is obtained subtracting the frequency by the frequency of the other substring which is included in substring o NUM
the characters of string word i is compared with that of the next string word i NUM from the beginning
for example in machine translation there are many expressions that are difficult to be translated literally
by referring to the spt NUM the strings to be extracted are determined and their frequencies are calculated
thus using the output results we can easily obtain interrupted collocational expressions as well as uninterrupted ones
and examples of substrings with high frequency and with much characters in total are shown in table NUM
in this case boundary conditions of sentences and mutual relationship between the extracted substrings need to be considered
hence collocational substrings are extracted and counted taking notice of the order of the appearance of each substring
however the frequency of such a pair is limitted to NUM then there is no need to consider
the rules never discard a last reading so every word retains at least one analysis
dummy represents all prepositions i.e. the parser does not address the attachment of prepositional phrases
usually this practice resulted in one analysis per word
however there were two types of exception NUM
our syntactic tags start with the sign
a represents the postmodifying adverb enough
the input did not contain the desired alternative due to a morphological disambiguation error
next we describe some basic features of the rule formalism
also the contributions of the linguistic and statistical language models to the hybrid model are estimated
briefly what the algorithm does is NUM start with a random weight assignment
under this heuristic rule the experimental results are showia m table NUM
these three cases form about NUM of one tag chunks
table NUM experimental results after applying the first heuristic rule
that is ati and np belong to different chunks
two susanne tags may be mapped into one lob tag
through the tag mapper ps is converted into pi
the chunker partitions the part of speech sequence into segments called chunks
lancaster oslo bergen lob corpus and susanne corpus are adopted
then a tag mapper and a probabilistic chunker are described
the evaluation criterion adopted in this paper is not very strict
where c is the total number of classes
note then that salience is determined for explicitly mentioned and inferable entities and depends not only on recency of mention but also on facts about the conversational situation and real world relationships between objects
to constrain the situations in which this is an appropriate thing to say we need to determine the circumstances in which bp k c is as salient as k
item NUM is tagged program because it contains a negative literal that is not memo or delay the resolution of this literal with the program clauses for lex NUM produces item NUM containing the constraint literals associated with lijkt re
making the equality constraints explicit we see that the abstracted goal is obtained by merely selecting the underlined subset of these below x xl x2 x3 xl c x2 l xa r
nevertheless these descriptions share features in that one always describes its type sometimes the service it provides and most rarely its location
spud then evaluates the distractor set since copy action is a new reference spud checks whether any distractor is also fast at an action which is at least as salient as copy action
to see how spud uses these specifications let us say that we have a copier c42 which is the sole fast copier at making copies in the library
for example even if both room and area are basic a room will be still be described using room because all rooms are areas but not all areas are rooms
we assume that information of these four kinds is available in a model of the current discourse state and that the applicability conditions of constructions can freely make reference to this information
at any point an entity is either new or old to the hearer according to whether or not the hearer has at least implicit knowledge of the existence of the entity
unfortunately the coroutining approach which requires that constraints share variables in order to communicate seems to be incompatible with standard memoization techniques which this research was largely conducted at the institut ffir maschinelle sprachverarbeitung in stuttgart
in the library domain shared knowledge includes such things as rules about how to check out books while speaker knowledge includes such information as the status of books in the library
more than the surrounding context to build adequate contextual representations
this is to reduce the confounding effects of lexical ambiguity
choosing the word most typical in context using a lexical co occurrence network
ideally they should be chosen by a credible human informant
table NUM accuracy of several different versions of the iexical choice program
for each annotation is shown its id type span and attributes
the problem is of course that authors are n t always typical
for comments and advice i thank graeme hirst eduard hovy and stephen green
thus we can not expect perfect accuracy in this evaluation
after his so alh d prel roc essing
p eq car p x list cdr p
sentences NUM and NUM below generated by cook illustrate cross ranking constraints
surge is the data part of the package an encoded knowledge source usable by any generator
in this paper alts are represented using the following standard notation for disjunctions in feature structures
the order of constraint application is determined dynamically through unification allowing for different orderings as required
perspective class assignt focus ai NUM the six ai assignments require programming
the value of the alt keyword is a list of fds each one called a branch
in addition the roles feature is added as a generic argument structure for the clause
two types of models are studied in particular
during unification the tests probe both the input conceptual network and the linguistic tree under construction
wide coverage syntactic grammar of english implemented in fuf and usable as a syntactic front end portable across domains
however unlike other deviations bilingual collocation is not easily bounded within a couple of classes
the corpus provides training and testing materials thereby allowing knowledge to be derived and evaluated objectively
nevertheless this work presents a functional core for processing bilingual corpora at lexical and conceptual levels
this work was partially supported by roc nsc grants NUM NUM e NUM NUM NUM NUM e NUM NUM NUM NUM e NUM NUM
both thesauri cover just over NUM of the words in the test sets
assemble a prototype which includes at least two tipster detection tools working on source material which represents ndic data unclassified
we are also thankful to j p chanod and the anonymous reviewers for many useful suggestions
in the process of word alignment the translation of each source word is identified
however there is insufficient evidence to support a class to class mapping from mb051 to fc04
to do so the class bytesequence is introduced
the result of this process would be a customizedextractionsystem
not defined by the tipster architecture manner
the default value for numeric arguments is NUM
each query language operator has the following syntax
NUM names as defined for muc NUM
with an information extraction system covering terrorist events
this can be done by introducing additional annotations
status is the current status consistent with type
the interval value behaves according to the intervaltype
for example r must be scoped relative to c because they are both arguments to the predicate of the possibilities are r c and t3 r similarly r and s are arguments to the predicate saw and may be scoped either r s or s r the relative scoping of c and s is never considered directly because they do not participate directly in any single predication in the sentence
NUM focus of lcb rl r2 rcb power lcb cl c2 rcb rl lcb cl rcb r2 lcb c2 rcb compt of lcb r3 r4 rcb power lcb rcb r3 lcb rcb r4 lcb rcb
sentence NUM has the following pas saw every r rep r a s sample s quantifier scoping since the particular scoping framework underlying the generation algorithm is novel a brief explanation is appropriate
this work was funded by the german federal ministry for education research and technology bmbf in the framework of the verbmohil project under grant 01iv101k NUM
viewing the model in this way we can derive an em algorithm to learn the mixing coefficients ak w and the transition matrices NUM mk w w
it provides a mouse and menu interface to configure and start other processes
the length of time to wait is a parameter that can be set in the recognizer
it reports process status not running initializing running or dead
thus x means that a sequence of zero or more instances of z may occur
oaa makes use of a facilitator agent that plans and coordinates interactions among agents during distributed computation
generally these commands access functions that are also available using the gui but not always
sources of this information can include linguistic context situational context and defaults
this module that generates the recognition grammar for commandtalk is described in section NUM
nuance is a commercial speech recognition product based on technology developed by sri international
the ci agent decides these questions based on a combination of phrasing and context
this determination might be made solely on the basis of a direct comparison of the two strings or more knowledge might be used e.g. models of a variant spelling or representation of names b keying errors c phonetic models or d record linkage
nevertheless by combining several predictions of this form for different values of k we can create a model that is intermediate in size and accuracy between bigram and trigram models
hcm then counts the frequencies of clusters in each category see tab NUM and estimates the probabilities of clusters being in each category see tab NUM NUM suppose that a newly given document like d in fig i is to be classified
the same stands for the features b and c the frequency of seeing the feature c is the frequency of not seeing the feature b because they are mutually exclusive and thus all the configurations without feature b will account for the presence of the feature c if we do n t put it into the lattice
this approach is naturally suitable for distributed computation but as it was pointed out in rosenfeld NUM it is not a good way to proceed because every behavior is activated only by a fraction of possible factors but we will estimate their joint model on the whole space of possible configurations w
p wi NUM 2e exc exxxc fxc w where NUM t is a set of instantiated by their values atomic features of the model w is a described by the model entity which can be represented as a configuration of the
for instance if our atomic feature set t includes word length NUM NUM NUM NUM NUM its capitalization cap and whether it ends with the full stop fstop and we want to obtain the probability of spelling the word mr
in this paper we propose to adjust the first method with restrictions similar to that of the second method
the back off method in fact does not combine different knowledge sources but rather ran them
apart from being a distribution of maximum entropy this distribution also po6sesses a very important property of model decomposition
tribution will closely model some reference distribution which is usually taken as the empirical distribution of the configuration space
in our delayed encoding proposal the modified locate operation should leave the name of the functional role played by the np as underspecified to force the locate operator to behave in this manner we propose
observations show that in most well formed sentences the agreement schema of the verb for any function g is satisfied by at most one constituent np of the sentences provided some order of processing the agreement schema of different gf s is maintained
a sentence is well formed if and only if all the m structure schema for the verb are satisfied and all nameholders in the scope of the sentence are bound to names i.e. at the end the symbol table is empty
the parser must ensure evaluation of an encoding schemata of a constituent np in the context of the agreement schema of the verb somewhat like handling a forward reference where an item referred to is defined later than the places where it has been referred to
a new metavariable augmentation of the scope of the locate operator and a special type of schema called m structure to be projected by the verb are some of the salient features of our technique
the mapping ctog is therefore nearly one to one in the context of the agreement schema of the verb and the sengupta and chaudhuri delayed syntactic encoding agreement schema may serve as test criteria for selecting grammatical functions from internal properties of nps
the metavariables generate placeholders for hitherto anonymous grammatical functions which we shall call nameholders and denote them by actual name variables nl n2 locate ing of schemata NUM creates such a nameholder n say in the scope of the functional placeholder f say for the t metavariable and simultaneously stores the pair f n in the symbol table
to express this more formally let g lcb gl g2 rcb be the set of relevant gf s c lcb cl c2 rcb be the set of np case markers and ctog be a mapping from case markers to gf s such that ctog c c e c is are the grammatical function s predictable from c
the probability distribution for these models has the form
it has been argued that while the techniques of name recognition and matching used in database searching and in information extraction can be adapted to the text retrieval problem that the retrieval application is sufficiently different from beth of the other two applications as to require very different approaches
mixed order markov models express the predictions p wt wt NUM wt NUM wt m as a convex combination of skip k transition matrices m wt k wt
the main conclusions are that name recognition in text can be effective that names occur frequently enough in a variety of domains including those of legal documents and news databases to make recognition worthwhile and that retrieval performance can be improved using name searching
although it would be possible to have the user as in the boolean situation tag query terms as names this would seem to violate the underlying philosophy of natural language input search systems i.e. that the user communicate with the search engine in ordinary natural language
our experiment with open closed compounds indicated that these forms are almost always related in meaning
it is very rare that the order could be reversed to produce a different concept
polysemy is important because the related senses constitute a partial representation of the overall concept
this paper discusses research on distinguishing word meanings in the context of information retrieval systems
we conducted experiments to determine the effectiveness of the two methods for linking word senses
unless we recognize the inflected form we will not capture all of the instances
we found that NUM of the sense pairs with one word in common were related
we conducted several experiments to determine the impact of grouping morphological variants on retrieval performance
by grouping morphological variants we are helping to improve access to the shorter documents
definition NUM the inside probability denoted by pi i s t of state i is the probability that layer i generates the string positioned from s to t starting at state i given a model
it can be shown that the complexity of the inside algorithm is o n3g NUM and that of the outside algorithm is o n4g NUM where n is the input size and g is the number of states
out of NUM NUM wall street journal trees of the penn tree corpus NUM NUM trees corresponding to sentences with NUM words or less were chosen and the programs written in c language were run at a sparcl0 workstation
in equation NUM p f i s NUM is the probability that sequence wa l is generated by layer i left to state i and pl j t l b is the probability that sequence wt l b is generated by layer i right to state j
where x e bout layer i y c bin layer i f first layer i e last layer i c c lcb nonterminal rcb layer i layer j and layer x layer y
in this paper we consider a probabilistic recursive transition network prtn as an underlying grammar representation and present an algorithm for training the probabilistic parameters then suggest an improved version that works with reduced redundant computations
suppose a net fragment i j begins with np and adjp then given a sentence fragment ws t adjp may not participate in generating ws t while np may
in the third training set the NUM coreference sets gave rise to characteristics for NUM pairs of templates
table NUM perplexities of bigram models smoothed by
in the search phase often indicated by a silence since the operator is searching the information service applies the database query and chooses the right travel plan
when referring to anaphor in the chom skyan sense the notion reflexive reciprocal pronoun is used
as a proper base for comparison the theoretical analysis is restricted to the contribution of intrasentential antecedent search
in examph sa the barbcri told the elientj a story while hek shaved himl
an even more stringent restriction holds for nonpronominal nouns ta the barber shaves the barberi
a topic which should be subject of further research is the interdependency between parse tree construction and anaphor resolution
table NUM perplexities of smoothed mixed order mod
cooperative problem solving involves maintaining a dynamic profile of user knowledge termed a user model
the sampling process basically cycles between the execution and the training phases
the rule above represents a local rule the test checks only neighbouring words in a foreknown position before or after the target word
NUM finally there is a single main verb which is indexed to the root s in position go
the verbs as well as other elements have a valency that describes the number and type of the modifiers they may have
we maintain that one is generally also interested in the linear order of elements and therefore it is presented in the tree diagrams
the former denotes subject verb indirect object and object and the latter subject verb object and object complement
the verb serves as the head of a clause and the top element of the sentence is thus the main verb of the main clause
because the dependencies are supposed to form a tree we can heuristically prune readings that are not likely to appear in such a tree
the rules are extracted from the real grammar and they are then simplified some tests are omitted and some tests are made simpler
note how low values of al w are associated with prepositions mid sentence punctuation marks and conjunctions while high values are associated with contentful words and end of sentence markers
on the contrary rich indexing is slightly less accurate but recall is much higher
iterative experimental tuning has resulted in wide coverage linguistic description incorporating the most frequent linguistic phenomena
table NUM precision and recall of term variant extraction on agr
with such a definition any type NUM variant is a type NUM collocation
the controlled terms are transformed into grammar rules whose syntax is similar to patr ii
selection of the correct links occurs during subsequent term expansion process with collocational filtering
second is the problem of difficulties in identifying related terms across parts of speech
electrophoresed on a neutral polyaerylamide gel is a type NUM variant of gel electrophoresis
type NUM variations are classified according to the nature of the morphological derivation
tion the simultaneous equation used in our method is expressed by equation NUM where a is a matrix comprising only the values NUM and NUM and b is a list ofvsm s see equation NUM for any possible combinations of given words
formally this is expressed by equation NUM where score s is the score for verb sense s nc denotes the case filler for case c and gs e denotes a set of case filler examples for each case c of sense s for example ps lcb kate kigyou rcb for the ga case in the to employ sense in figure NUM
what we evaluated here is the degree to which the simultaneous equation was successfully approximated through the use of the technique described in section NUM in other words to what extent the original statistics based word similarity can be realized by our framework
return the lcb implicit node corresponding to the last matching symbol
in order to maintain structural coherence the new word attached via tree lowering must be preceded by all other words previously attached into the description
then we say that is the statistic of factor z in w
the advantages of this approach is the possibdity to implement a fast and flexible access to the synsets hierarchy and in particular an efficient isa functionality as required for the semantic checking during the parsing
a he went to the house by car
figure NUM shows an example of an event with a current word like
if the classification of to were dependent upon the classification of yet another word this would have to be built into the decision tree as well
a simple example with a five word vocabulary is shown in figure NUM
the same clustering technique is then applied to the classification of multiword compounds
for each i NUM i c do the following
this dendrogram droot constitutes the upper part of the final tree
the same argument holds for an example based machine translation system
the suffix tree alignment data structure allows simultaneous scoring for all transformations
this takes time o if p l n
for example given the query type describe process the edp selector will return the explain process explanation design package
unlike a domain such as introductory geometry biology can not be characterized by a small set of axioms
this facilitated a unique experiment in which the representational structures were not tailored to the task of explanation generation
this paper reports on a seven year effort to empirically study explanation generation from semantically rich large scale knowledge bases
one of the largest knowledge bases in existence it is encoded in the km frame based knowledge representation language
the content specification expression associated with reference process names the kb accessor find ref conc and the global variable primary concept
1deg the explanation planner invokes the edp selector which chooses an explanation design package from the edp library
the content associated with topic nodes that are grouped together will appear in a single paragraph in an explanation
a probabilistic descent method is used for adjusting the weights amari NUM
t iteration time figure NUM the block diagram of a viterbi training model for word identification
in this paper an unsupervised approach for constructing a large scale chinese electronic dictionary is surveyed
however it may not take into account the features for forming a word from characters
columns NUM NUM are shared because the postfiltering is applied immediately after the basic model
therefore special attention should be taken when interpreting the performances reported in the following sections
hence it is not surprising that the performance for the NUM grams and NUM grams is poor
it indicates how much percentage of word candidates are recognized as words by the standard dictionary
with the large seed corpus the weighted precision and recall are NUM and NUM
therefore it is worth while trading off the precision requirement with the cost of dictionary construction
each a link is denoted by indexing the incident nodes with the same integer
the updates for mixed order markov models are given by s w wt k c k NUM a w NUM note that the ml estimates of mk w w do not depend only on the raw counts of k separated bigrams they are also coupled to the values of the mixing coefficients aa w
comparing row NUM and row NUM the average number of candidates are much larger than the rank of assumed topic
idf w log p o w o w c NUM
we also postulate that NUM noun verb is a predicate argument relationship on the sentence level and noun noun relationship is associated on discourse level
the strength of one occurrence of a verb noun pair or a noun noun pair is computed by the importance of the words and their distances
the word association norms are based on three factors NUM word importance NUM pair co occurrence and NUM distance
using equation pn pv NUM and equation NUM we could derive par and pv as equation NUM and equation NUM show
in general when a graph could not be separated by duplicating its subgraph then the subgraph is regarded not to have ambiguity
the criterion was whether the use of this adjective ascribes in general a positive or negative quality to the modified item making it better or worse than a similar unmodified item
this aggregation operation increases the precision of the labeling dramatically since indicators for many pairs of words are combined even when some of the words are incorrectly assigned to their group
this measure ranks the words according to how well they fit in their group and can thus be used as a quantitative measure of orientation refining the binary positive negative distinction
we are currently combining the output of this system with a semantic group finding system so that we can automatically identify antonyms from the corpus without access to any semantic descriptions
we also thank dragomir radev eric siegel and gregory sean mckinley who provided models for the categorization of the adjectives in our training and testing sets as positive and negative
evaluations on real data and simulation experiments indicate high levels of performance classification precision is more than NUM for adjectives that occur in a modest number of conjunctions in the corpus
to find pmin we first construct a random partition of the adjectives then locate the adjective that will most reduce the objective function if it is moved from its current cluster
to measure the effect of p and k which are reflected in the graph topology we need to carry out a series of experiments where we systematically vary their values
the third phase of our method assigns the adjectives into groups placing adjectives of the same but unknown orientation in the same group
we have built an integrated dialogue processing system the circuit fix it shop which is parameterized for a key system behavior initiative NUM we have tested the system in NUM dialogues totaling NUM NUM user utterances while varying levels of system initiative
the difference in the relative number of notifications is largely due to the fact that in directive mode the computer frequently ignored the statements it misunderstood as the misunderstandings often were in conflict with the computer s current task goal
the large relative difference in percentages for transitions from diagnosis to either repair or test in the two modes is also expected given that users who take the initiative can make the repair themselves without discussing it with the computer
while their dialogue control algorithms are not identical to ours their results are complementary as they show that performance differences as a function of the computer s level of control may be prevalent in database query interactions as well
a detailed examination shows that NUM of the NUM control shifts were caused either by the user attempting to correct a computer misunderstanding section NUM NUM NUM or by the user initiating a task topic change by asserting new task information
select as the next goal an uncommunicated fact relevant to the user focus else if mode passive then select as a goal that the user learn the computer has processed the user s last utterance figure NUM computerresponse selection algorithm
the system was originally implemented on a sun NUM workstation with the majority of the code written in quintus prolog and the parser in c the system assists users in the repair of a radio shack NUM in one electronic proiect kit
when the computer had the initiative the directive mode dialogues very few subdialogue transitions were ever initiated by the user other than to the final test phase when the repair would cause the circuit to begin to function normally
three passes can each provide analysis when anomalies are detected for correct sentences the first pass is sufficient
for example the concept crime is found to co occur frequently with the concept punishment
clearly the two curves are very close and the monotonic decrease in test set perplexity strongly suggests little if any overfitting at least when the number of classes is small compared to the number of words in the vocabulary
our approach has overcome the data sparseness problem by using the defining concepts of words
the analysis of a sentence proceeds at two levels the lexical level and the syntactic level
attachment combines the current constituent with the constituents which immediately precede this current constituent in the chart
he trained a maximumentropy model consisting of unigrams bigrams trigrams skip NUM bigrams and trigrams after selecting long distance bigrams word triggers on NUM million words the model was tested on a held out NUM thousand word sample
m tssen must and exceptional case marking eels verbs e.g.
for instance an infinitival verb projects the structure in figure NUM from vp to cp
b sic hat ihmi tj erlaubt cv pro das bueh anzusehen j
this phenomenon is called infini ivus pro parlicipio ipp or ersatzinfini iv
as illustrated in NUM an infinitival clause can precede or follow its controller
die mutter erlaubte ihrer toehteri nicht proi ins theater zu gehen
a constituent of the left context is attached to the current constituent left attachment
NUM gestern hat siei der professor versucht ti zu kfissen
this article originated from inspiring discussions with david milward and slava katz
following the system s statement that i m sorry there are no flights leaving crete today the user asked did you say there are n t any flights leaving crete today
we use u here to stress again that it is the utterance not the string of words
several extensions to the theory presented here are needed to handle plural quantified noun phrases and indefinites
each scenario execution has a corresponding avm instantiation indicating the task information requirements for the scenario where each attribute is paired with the attribute value obtained via the dialogue
in this segment the house referred to in 19a is an element of the cf 19a
the particular cases that have been identified involve instances where attention is shifted globally back to a previously centered entity e.g.
for example if b were followed by otherwise from the outside it appeared quite normal
they remain useful for illustrating the original points if the time of original writing is taken into account
NUM because this preference might be attributable to parallelism the last utterance in NUM provides a crucial test
this example suggests that pronominalization and subject position are possible linguistic mechanisms for establishing and continuing some entity as the cb
cb john referent he c john wanted to meet him urgently
in other circumstances however as the examples below illustrate the cb may be realized in other grammatical roles
to achieve and demonstrate an acceptable degree of generality the tool must be iteratively developed and tested on systems and application domains and in circumstances that are significantly different from those available in house
the preferred referent for the pronoun in example 7d is bob dole whereas the preferred referent for the pronoun in example 7d is bill clinton
that is this very property renders such an approach incapable of modeling the preferences associated with an addressee s immediate tendency to interpret pronouns as example NUM demonstrates
while the subject pronouns in follow ons 6e1 e3 may all display this ambiguity to a certain degree the preferences associated with them appear to be consistent among the three variants
however certain examples demonstrate that bfp s utilization of the centering rules does not model this tendency which in turn limits the ability of their algorithm to account for the data
NUM association for computational linguistics computational linguistics volume NUM number NUM engender different inferences on the part of a hearer or reader
their rules also account for the oddness of sentence 3e since assigning he to tony results in a smooth shift whereas assigning he to terry results in a continue
in follow on 6e2 assigning he terry results in a rough shift whereas assigning he tony again results in a smooth shift and so tony is preferred
moreover the system allows interactive information discovery from a multilingual document collection by combining ie and mt technologies
we use automated methods as much as possible to reduce the cost of creating a large name lexicon manually
the client module lets the user both retrieve and browse information in the database through the web browser based gui
the world wide web www for example is becoming a vast depository of multilingual information
the users would not know the query terms in japanese even if the search engine accepts japanese queries
both indexing servers are intelligent because they identify and disambiguate names with high speed and accuracy
since there is no space between first and last names in japanese this must be automatically determined
for example the system can be customized to index product names and financial terms for a business application
for example it is not easy for a monolingual english speaker to locate necessary information written in japanese
the list provides information on the title length source language and date of each article
therefore it is important to maintain a record of which objects have been introduced in the text and how and when they have been referred to
one innovation of muc NUM was the use of a nested template structure
the highest performance overall was NUM recall and NUM precision
wc now had to r xhlce
the delayed feedback strategy is natural in human human communication but might be considered somewhat dangerous in sldss because of the risk of accumulating system misunderstandings which the user will only discover rather late in the dialogue
figure NUM sample named entit y annotation
a sample scenario template is shown in the appendix
the implication is that several of the guidelines in figure NUM such as ggll sg6 sg7 on background knowledge and gg13 sg9 sg10 sgi NUM on meta communication are not likely to be violated in the transcribed dialogues
however if resource limitations enforce restrictions on the number of dialogue design errors which can be repaired the number and severity of the different dialogue design errors will have to be taken into account
the search reclangle is anchored at the top right corner of the previously found chain
section NUM NUM explains why simr will not be led astray by false points of correspondence
from the above claim it follows that 7rq can be processed in time polynomial in the size of r
in a derivation all or no rules from a given instance of a vector must be used
in this way once a nonterminal is rewritten through the application of a pair of rules to two
a production may introduce a synchronous nonterminal whose counterpart in the other grammar has not yet been introduced
a parse forest in g represents a set t of parse trees in g if the following holds
length based methods assign very low probabilities to such pattern sequences and usually get them wrong
sentences on x axis figure NUM sentence boundaries form a grid over the bitext space
of the three possibilities table NUM conservatively reports the highest error estimates for simr
fortunately the noise in simr s output causes alignment errors in very predictable ways
however like any heuristic filter this one will reject some perfectly valid candidates
o i i NUM NUM position in text NUM e NUM NUM i NUM NUM
the matching predicate considers a token pair cognates if their lcsr exceeds a certain threshold
the final column lists the maximum space requirements per word graph in kbytes
these scores are negative logarithms of probabilities and therefore require addition as opposed to multiplication when two scores are combined
table NUM in the first table we list respectively the total number of milliseconds cpu time equired for all NUM
in particular it says for each referentially used noun phrase whether and where in the discourse the object that it refers to was described earlier
for example d200 consists of words appearing more than NUM times in training NUM
the second training set training i is used to train various word segmenters
however the character bigram for the word model must be computed from segmented texts
by the heuristic word identification method where the recall and precision are NUM NUM
it assigns too little probability to longer word hypotheses especially more than thee characters
furthermore standard versions of drt do not contain information about the exact place of occurrence of expressions nor do they contain information about paragraph structure
one may also argue that we could use the character bigram in the word model
at the moment we exploit linguistic information about the syntactic type obligatoriness and position of arguments as well as the set of possible subcategorization classes and combine this with statistical inference based on the probability of class membership and the frequency and reliability of patterns for classes
patterns provide several types of information which can be used to rank or select between patterns in the patternset for a given sentence exemplifying an instance of a predicate such as the ranking of the parse from which it was extracted or the proportion of subanalyses supporting a specific pattern
thus both predict that seem will occur with a sentential complement and dummy subject but only anlt predicts the possibility of a wh complement and only comlex predicts the optional presence of a pp to argument with the sentential complement
all classes for seem are exemplified in the corpus data but for ask for example eight classes out of a possible NUM in the merged entry are not present so comparison only to the merged entry would give an unreasonably low estimate of recall
one of the major problems in unsupervised word segmentation is the treatment of unseen words
the tagger lemmatizer grammar and parser have been described elsewhere see previous references so we provide only brief relevant details here concentrating on the description of the components 2the analysis shows only category aliases rather than sets of feature value pairs
he reports that for a test sample of NUM tokens of NUM verbs in running text the acquired subcategorization dictionary listed the appropriate entry for NUM cases giving a token recall of NUM as compared with NUM NUM in our experiment
it is impossible to build a word segmenter for a new domain without human intervention
the corpus data for seem contains examples of further classes which we judge valid in which seem can take a pp to and infinitive complement as in he seems to me to be insane and a passive participle as in he seemed depressed
the process of evaluating the performance of the system relative to the dictionaries could in principle be reduced to an automated report of type precision percentage of correct subcategorization classes to all classes found and recall percentage of correct classes found in the dictionary entry
the table NUM attribute by entry structure is convenient for exposition but given there are now well over a hundred total possibl e attributes such a structure would be very space inefficient
names and organizations that are amenable to this app oach get as many instances as possible and enter in the knowledge bank and then check for them in any new input
the subsequent reference simply to washington was correctly identified by the previously seen by being a sub string of in this case the closest prior reference that of ms
in the last pass step there were several rules which attempted to use context inferences an d other heuristics to identify token sequences which were likely place org or person instances
in the first row of table NUM the first key column indicates the lower case look up character s that will be matched against the chars values from the input text
we also wanted a system that was easily extendible serviceable by programmers supportive of informed guessing but giving confidenc e and basis and eventually capable of learning extensions
name searching is a term that has been used in a variety of ways
NUM is the monoid over NUM where
ill other words nothing new an really be said
but the set of rationals is mathematically well equipped
may t e the mmtysis or genera lion of sentences
proportions in ii are thus well understood and safely solved
addition defines a commutative group and multiplication makes it a field
dist mathematics physical dis t physics
this fact is problematic for accounts of vp ellipsis that operate only within the minimal clauses
in general the prototype will not be found in the tree bank
figure NUM a short cut in the generic noun hierarchy
the utterances evoke the same time or the second is more specific than the first
these homographs belong to NUM pairs of noun concepts
the system uses the node s context vector to perform dot products with every document context vector in the corpus
if a display of misunderstanding occurs during a subsequent turn by the same speaker who generated the misunderstood turn and the hearer then reinterprets the earlier turn and produces a new response to it then we say that they have made a fourth turn repair
feature constraints and cases where the rules will not apply if those constraints are broken are shown
one is simple sentence set in which every sentence has no more than NUM words
official NUM languages NUM the numbers indicate the frequencies of the input words in the english corpus
the examination of consonant splitting has not set any restrictions on the maximum length or even on the existence of certain consonant sequences
our search engine computes the normalized idf nidf in the following way
anaphoric expressions result in this context structure being examined for possible candidates which have appropriate feature and semantic information attached if more than one candidate exists then a new node is created to represent the alternatives and is linked to each of them
the following is a list of the changes made fixed problems with reported speec h prevented duplication of textrefs allowed additional time and memory for parsing which enables the parsing of the step down sentence
this further suggests that backed off estimation may be successfully integrated into more general syntactic disambiguation systems
other events which are erroneously connected with mr jame s or mr dooner include guiding attributed to mr james figure NUM and acquiring materialisir NUM to mr dooner
the muc NUM competition has provided an opportunity for the laboratory for natural language engineering to evaluate the approach used in the lolita system on some very specific tasks as well as a chance to strengthen the system s performance in the domain of newspaper articles
context is important because it allows speakers to use the same set of words for example do you know what time it is to request the time to express a complaint or to ask a yes no question
asher s account extrapolating from an example he discusses p
thus the points that remain to be examined in regard to their hyphen permissibility are elements of set pfo fk
the corpus is tokenized morphologically analyzed lemmatized and parsed using a standard cfg parser with a hand written grammar to identify clauses containing a finite verb taking a nominative np as its subject and an accusative np as its object
dsp identify two kinds of analysis in the vp ellipsis literature
precisely the set of diphthongs and excessive diphthongs is a proper subset of 0clf2 fl f2 e v u 2v rcb
identify collocations or phrases which can not be translated on a word by word basis in the source language
while this is an appropriate translation for the canadian parliament in different contexts another translation would be better
at first the word senses of the quadruple are disambiguated by the algorithm described in chapter NUM which is modified to exclude the sdt iteration cycles
this important feature is maintained in our approach by small homogenous leaves at higher levels of the decision tree which usually accommodate the low count training examples
however he would then be accountable for justifying his action as well as for displaying his acceptance of mother s displayed understanding e.g. by including an explicit rejection of her offer otherwise she might think that one of them has misunderstood
we based our word sense disambiguating mechanism on the premise that two ambiguous words usually tend to stand for their most similar sense if they appear in the same context
collins and brooks have also demonstrated the importance of low count events in training data by an experiment where all counts less than NUM were put to zero
it turned out that for the size of a training set smaller than NUM examples learning is rather unreliable and dependent on the quality of the chosen quadruples
the relevant defaults are repeated here default NUM intentionalact sl NUM a ts shouldtry sl s2 a ts d try s1 NUM a ts
the prepositional statistics indicates that there were no matches found for the given quadruple and the attachment was decided based on the statistical frequency of the given preposition
attachment of these had to be based on a partial quadruple and was usually assigned at a higher level of the decision tree which reduced the overall accuracy
additionally because the words of the input sentences for the pp attachment are to be assigned senses in the same manner the sense disambiguation error is concealed
NUM the attribute is either a verb noun or a description noun NUM its values correspond to the concept identificators synsets of wordnet
for reliability of their tagging scheme
another possibility is to simply substitut
note however that the result of the redundancy check is valid if and only if the premises are valid if one of the non redundant hyponym links is wrong and has to be removed the link diagnosed as redundant may be correct and non redundant
lexical analysis NUM NUM np np
because of its syntactic nature the form feature coding was very robust
NUM NUM NUM indexing of the head corner table
the implementation consists of two steps
this is illustrated with an example
most of these items will be useless
this head corner parser generalizes the left corner parser
for ahab all NUM NUM permutations revealed full dispersion d NUM which suggests that the probability that the low empirical dispersion of ahab d NUM is due to chance is much less than NUM NUM the content words singled out as being signifi5 i am indebted to an anonymous referee for pointing out to me that z scores are imprecise
we would like to thank the behavior design corporation bdc for providing us with the parsed corpus
the residuals do not reveal any significant trend f NUM NUM NUM which suggests that the underdispersed vocabulary is indeed responsible for the main trend in the progressive difference scores d k of the vocabulary and hence for the divergence between e v n and v n
so the conversational moves or clarifications can be generated and understood within the planning paradigm
vu k and nu k numbers of underdispersed types and tokens in text slice k acf auto correlation function pr u type and pr u token proportions of underdispersed types and tokens d k and du k progressive difference scores for the overall vocabulary and the underdispersed words
figure NUM illustrates the problems that arise when NUM is applied to three texts alice in wonderland by lewis carroll upper panels moby dick by herman melville middle panels and max havelaar by multatuli the pseudonym of eduard douwes dekker bottom panels NUM all panels show the sample size n on the horizontal axis
in order to ascertain the potential relevance of syntactic constraints referred to by halle we may proceed as follows if sentence level syntax underlies the misfit between the observed and the expected vocabulary size then this misfit should remain visible for randomized versions of the text in which the sentences have been left unchanged but in which the order of the sentences has been permuted
denoting the probability of wi by pi the expected total number of word types with frequency m in a sample of m tokens e v m re i is given by
the bottom panels show that the progressive difference scores du k for the underdispersed words capture the main trend in the progressive difference scores of the total vocabulary d k quite well the residuals d k du k do not reveal a significant trend f NUM NUM NUM NUM p NUM
in addition the contrast relation signalled by but is justified by the contrasting predicates before and after provided their corresponding pairs of arguments are similar
we demonstrate the depth of our approach by showing that unlike previous approaches the algorithm generates the correct five readings for example NUM without appeal to additional mechanisms or constraints
parallelism is computed by determining the most specific common denominator of a set of representations which results from unifying the unifiable aspects of those representations and generalizing over the others
other approaches based on parallelism our aim in this paper is to present the theory of parallelism at an abstract enough level that it can be embedded in any sufficiently powerful framework
the resolution of vp ellipsis is driven by a need to maximize parallelism or in some cases contrast that is very much in the spirit of what we present
if choice a is taken in the second clause then the similarity choice in the fourth clause must be f if b then g
our mechanism is more natural because of the alignment of parallel elements between clauses when establishing parallelism and it is this property which results in the underivability of the missing reading
two properties are similar if two corresponding properties can be inferred from them in which the predicates are the same and the corresponding pairs of arguments are either coreferential or similar
in general however in neither of these approaches has enough attention been paid to other interacting phenomena to explain the facts at the level of detail that we do
before examining the problem more fully it is useful to consider work that has already been done on the problem
while there may be good reasons for deciding to procede on this basis one of the reasons they give is definitely a bad one and this is the principal subject of this note
then add a determinized minimized version of the result to to lj where j is the highest numbered tier it now mentions
on pages NUM NUM they remark that if meaning is to be a dyadic relation it is necessary that the complement of a situation should at least sometimes be another situation
in this case the analysis proceeds as before saving that the collection ff as above is now a set
then xva collection of visual alternatives for a xnvo
there are a number of closed class parts of speech including determiners prepositions conjunctions predeterminers and quantifiers
in general we can not assume xnvo xvo
the objective is thus to answer the question of how complex a rule must be to account for the complexity of anaphor generation exhibited by the test data
it is barwise and perry s contention p
sixth message understanding conference muc NUM
table NUM name recognition and retrieval for NUM
on the other hand in general a description with more distinguishing information is used for the second anaphor if distractors have entered into the context set
practically name matching becomes a matter of determining whether the surface forms of the two names being matched are close enough as to indicate that it is plausible that they refer to the same individual
this plq er lescril es a hit i triven nlet hod for hiera rchicm chlstering of words ill whicii a la rge vo aj ul ry of i ii
NUM we should note however that there is one reading pings generated by a scheme similar to hobbs shieber NUM
b most boys think that every man danced with but doubt that a few boys talked to more than two women
in particular missing readings include the one in which every girl admired the same saxophonist and most boys detested the same but another saxophonist
this yields the following situational analysis of attitudinal reports involving epistemic perception
they are roughly interpretable as b and NUM NUM a john believes that a republican will win
in the next function we use function fast scan introduced in section NUM NUM but we run it upward the tree with the obvious modifications
we use an amortization technique and charge a constant amount of time to the symbols in w and w for each node visited in this way
w w is the number of different positions at which factors u and v are aligned within w w
the first result is easy to show by observing that in an aligned corpus there are polynomially many occurrences of transformations with a bounded number of alternations
it then follows that at step NUM algorithm NUM finds the transformations with the highest score among those represented by nodes of tx and tr
part of the present research was done while the first author was visiting the center for language and speech processing johns hopkins university baltimore md
integer e p computed at step NUM is the number of times a suffix having u x v as a prefix appears in strings in lx
suffix trees and suffix tree alignments can be generalized to finite multi sets of strings each string ending with the same end marker not found at any other position
in standard cg function application the functor and argument can correspond to a word or a phrase
good tufing obtains good estimates for r n if nr is large
processing in verbmobil in contrast to many other nl systems the veabmobil system is mediating a dialogue between two persons
before the concluding remarks in section NUM we discuss aspects of robustness and compare our approach to other systems
however since we deal exclusively with spoken unconstrained contributions utterances are sometimes just pieces of linguistic material
mean accuracies for dop1 for NUM different training test sets from atis
these data are partly provided by other modules of verbmobil or computed within the dialogue module itself see below
more than NUM of them have been annotated with dialogue related information and serve as the empirical foundation of our work
parse accuracy for word strings from the atis corpus by dop2
in the future we will have to focus on the problem of glueing fragments together
thus we must estimate the total number of possible np subtrees
jimenez et al NUM i ii i a robust gb based parser iii i a transfer based translation module and iv a speech synthesis module
in an sentence such as NUM the possessive son could refer either to jean to marie or less likely to some other person depending on contexts
this may prove particularly troublesome for systems that attempt term clustering in order to create meta terms to be used in document representation
applying linguistic constraints the parser will try to disambiguate these words to produce a set of ranked gb style enriched surface structures as illustrated in NUM
all of the cited performance figures above also appear to derive from manual checks by the investigators of the system s predicted output and it is hard to estimate the impact of the system s suggested chunking on the judge s determination
at each site where this baseline prediction is not correct the templates are then used to form instantiated candidate rules with patterns that test selected features in the neighborhood of the word and actions that correct the currently incorrect tag assignment
however the treebank parses do also frequently classify conjunctions of ns or nps as a single basenp and again there appear to be insufficient clues in the word and tag contexts for the current system to make the distinction
a disabled rule is then reenabled whenever enough other changes have been made to the corpus that it seems possible that the score of that rule might have changed enough to bring it back into contention for the top place
performance is stated in terms of recall percentage of correct chunks found and precision percentage of chunks found that are correct where both ends of a chunk had to match exactly for it to be counted
rule NUM changes n to bn after a comma which is tagged p and in rule NUM locations tagged bn are switched to bv if the following location is tagged v and has the part of speech tag vb
these putative errors combined with the claimed high performance suggest that nptool s definition of np chunk i s also tuned for extracting terminological phrases and thus excludes many kinds of np premodifiers again simplifying the chunking task
existing efforts at identifying chunks in text have been focused primarily on low level noun group identification frequently as a step in deriving index terms motivated in part by the limited coverage of present broad scale parsers when dealing with unrestricted text
however empirical comparisons between runs with and without rule disabling suggest that conservative use of this technique can produce an order of magnitude speedup while imposing only a very slight cost in terms of suboptimality of the resulting learned rule sequence
s forms and b forms are completely equivalent representations
with an empty set of free variables
the following are several significant features of our aggregation rules
in pro verb currently four such rules are integrated
the next category of aggregation rules handles parallel structures which are not identical
three of them build a sequence of some transitive relations into a chain
note that b stands for a conclusion which will not be examined here
the resource tree of the first alternative is given in fig NUM
in the next section we first give a brief overview of proverb
set f a subset f g NUM
fig NUM shows a fragment of the hierarchy of textual semantic categories
the hierarchy of textual semantic categories is also a domain independent property inheritance network
the accuracy achieved by our improved exemplar based classifier is comparable to the accuracy on the same data set obtained by the naive bayes algorithm which was recently reported to have the highest disambignation accuracy among seven state of the art machine learning algorithms
since the primary aim of our present study is the comparative evaluation of learning algorithms not feature representation we have chosen for simplicity to use local collocations as the only features in the example representation
although this top down splitting method has the advantage we mentioned above it has its obvious shortcomings
the goal of a naive bayes classifier is to determine the class ci with the highest conditional probability p ci a vj since the denominator p avj of the above expression is constant for all classes ci the problem reduces to finding the class ci with the maximum value for the numerator
thus the most important part of the model building is the feature selection procedure
an improved version of our approach will lmndle an idiom after some inore base lexeines appeared
we can look at the constraints as at employed by the model features NUM
the nodes from the lattice also serve as potential constraint features to our model
figure NUM this figure shows the redistribution of configuration frequencies in the optimized feature
2degwe want to thank david palmer for making his test data available to us
tom has on the meeting a big uek shot
eq make a bloomer NUM and jmdm
we also included into the constraint set the actual spellings of the most frequent words
the accuracy of the classification of the produced model reached NUM NUM on unseen words
following the basis of equation NUM the interpretation certainty of x is small in both figure ll a and ll b
in other words by selecting an appropriate example as a sample we can get more correct examples in the next cycle of iteration
our system is based on such an approach or more precisely it is based on an example based approach NUM
in contrast since the ranges are diverse in the accusative it would be feasible to rely more strongly on the similarity here
dagan et al proposed a committee based sampling method which is currently applied to hmm training for part of speech tagging NUM
during the execution phase the system generates an interpretation for each example in terms of parts of speech text categories or word senses
ideally the sampling size i.e. the number of samples selected at each iteration would be such as to avoid retraining of similar examples
table NUM the relation between the length of the path between two nouns x and y fen x y
this happens when the case fillers of two or more verb senses are not selective enough to allow a clear cut delineation among them
this requires tentatively choosing a discourse level act on the basis of the decomposition relation and then attempting to abduce either that it is an intentional display of understanding or that it is a symptom of misunderstanding
from russ s perspective it displays acceptance because a surface request is one way to perform an askref an act that is expected according to russ s model of the discourse after the first turn
the result is NUM occurrences are tagged with the second sense NUM occurrences wrongly tagged and the others tagged with the first sense NUM occurrences wrongly tagged
this information is extracted from tile phraseo lex syntax tree
we briefly describe some of aspectual forms used in the experiment
these include time distance and any quantity of contents
it means a state holding before a speaker s eyes
table NUM the determination process of the meaning of teiru
processes modifiers modify verbs which have process p
synta tic inibrmation is encoded in feature structures
this does n t necessarily imply that the verb itself is instantaneous
as the process of translating extractionneeds becomes more formalized the fill rules will accordingly also become more formalized
in order to specify the sense clusters we only need to determine a sub tree of t which makes NUM get its biggest value
ken has been wearing that kimono since this morning
finally in section NUM we show some possible extensions
the sense distinctions in the dictionary are the same as those in the modem chinese dictionary and for each sense in the collocation dictionary some words are listed as its collocations
a separate process will then search for strings that express more than one interpretation if such strings are found we say that the ambiguity of the source language is preserved by the target language
like a packed parsing forest which represents nmltiple parsing results the chart generator produces a packed generation forest to represent the various string realizations of the semantics
another expression can be obtained by choosing q2 at node NUM this leads to node NUM on whose right branch the adverb quickly expresses quick e
note that at node NUM we can only choose the left branch because otherwise the condition of the third slot would also be satisfied contrary to the mutually exclusive nature of the semantic alternation
the next two simplified examples demonstrate how logical forms which contain disjunctions can be processed by the generator and how the rich logical annotations relate the various paraphrases to the alternations in the semantics
however if both daughters express a particular semantic item the boolean expressions of the corresponding slots need to be disjoined from the point of view of the mother they are alternative renditions
in the method we propose here these forests are annotated with information that enables keeping track of the rela tion between pieces of the semantics and the various phrases that express them
for instance in translation one might prefer to maintain the source language subject as the target language subject but be willing to accept a translation which violates this if generation would otherwise fail
the interesting action is in the fifth slot
this explains the reason that expression of the fact loud e is conditioned on the choice q2 the 5th slot of the array in node NUM
the remainder of the paper is organized as follows section NUM defines the notion of semantic space and discuss how to outline it by establishing the context vectors for mono sense words
similar tendencies are also observed for the other models
values in parentheses correspond to performance excluding unambiguous sentences
the formulation for the syntactic scoring tung hui chiang et al
for the lexical parameters corresponding to the correct candidates
NUM NUM robust learning on the tied parameters
the tying procedure includes the following two steps
however the spelling rules make no reference to present 3s it is simply a device allowing categories and logical forms for irregulax words to be built up using the same production rules as for regular words
this paper is organized as follows
here the li s are variables later instantiated to single characters at the beginning of the root and l is a variable which is later instantiated to a list of characters for its continuation
since muc NUM we have improved identifinder s prediction of aliases once a name has been seen and added rules for low frequency cases e.g. for names that are quite unlike western european names
in this context highly efficient word analysis and generation at run time are less important than ensuring that the morphology mechanism is expressive is easy to debug and allows relatively quick compilation
the use of a gui and a database in place of files of source code and data which must be edited as text represents a fundamental advance in making natural language technology widely available
production rule tree NUM is that for a single application of the rule adjp adjp fem which describes the feminine form of the an adjective where the root is taken to be the masculine form
using conceptual co occurrence data contextual information from the salient but less frequently used words in the sentence will also be utilised through the salient concepts in the conceptual expansions of these words
one way to acquire morpho lexical probabilities from a corpus is to use a large tagged corpus
the key issue is prior translation of the foreign language material
the sw sets for every analysis are generated using this module
we will use the abbreviation morpho lexical probabilities to denote this term
we use the term misleading words for such ambiguous similar words
this kind of situation is not unique for the word xwd
the probabilities for each iteration are given below iteration no
et al use the sum of two relative entropies as the similarity metric to compare two words
let us start with a few definitions and terminology that will be used throughout this paper
in the pure language recognizer the word candidates are all from a single language dictionary whereas the mixed language dictionary contains words from two dictionaries
trec NUM therefore provided the first opportunity for more complex experimentation
and the normalized results of the left right and the right left binary tree also must be normalized together
parameter estimation is only a small part of the overall model estimation problem
no human knowledge is required for training to occur
however translation is time consuming costly and subjective
NUM NUM electrical engineering code NUM information engineering item NUM NUM computers detailed item small items NUM NUM memory ilnit more detailed item j ndc is tile most popular library cl ssification in jal an and it has tile hierarchical domains
the window has NUM target stem and multiple neighbor stems
the pel plexity which is the inverse of the probability over the whole text is measured
as for verbs we started from the italian wordnet senses and then we faced to the problem of mdividuatmg the proper selectional restrictions for each argumental position of the verb subcategorization frame as seen before
the quality of the experimental results showed that our apl roach enables document classification with a good ac e lracy and suggested the possibility for jat anese documents to t e represented on the basis of kanji characters they contain
NUM training text can be presented in any order
in chinese the characters are called ideographs
if we are given a new document the feature vector of which is x the classification system can compute the angle NUM with each vector vi which represents the domain i and find vi with rain NUM vi z
as a consequence they share a common context vector
conceptual types are assigned either manually in italian since no on line resources are available or automatically using wordnet
rather our purpose is to identify commonalities and discrepancies and to investigate the possibility of profitably integrating the two approaches
in some cases ciaula clusters received a pertinent wordnet sense label but in some cases they did not
the best wordnet label syns for the cluster c is the one that maximizes g figure NUM labeling algorithm of ciaula clusters
different ciaula clusters received the same wordnet label and this was used as a hint to further structure the induced classification
the algorithm determines the coordinate space of the context vectors
class NUM to record affected property class NUM to record agentive cognitwe process class NUM to record location place
we could specify features that were described at a very general level in word net and detect semantic restrictions specific to the sublanguage not accounted for in wordnet
the wordnet argument structure for verbs however simply provides a qualitative description of the possible phrasal patterns in which verbs in a given synset can be used
it is worth noticing that even in these cases we still achieve useful information since the wordnet argument structure can be further specified by domain specific semantic constraints
an exanlple artic le of encyclopedia lloibonsha is shown in figure NUM unfortunately tile articles are not classified hut there is the author s llaliie at the end of each article and his specialty is notified in the preface
it makes it easy to set command line arguments and maintain consistent command line arguments across processes
the ci agent needs to determine when a command is given to a unit should be carried out
the nuance recognizer like all other practical recognizers requires a grammar that defines a finite state language model
for nonterminals with recursive rules we eliminate the recursion by introducing regular expressions using the kleene star operator
so if we naively generated all possible complete instantiations of this rule we would get at least NUM rules
finally we form the disjunction of all the right hand sides of the nonrecursive rules which we may call non rec a
the set of messages that the modsaf agent responds to is defined by the modsaf agent layer language mall
gemini is a research system that has been developed over several years and includes an extensive grammar of general english
larger lexicons can give incremental improvements
this mediated communication makes it possible to hot swap or restart individual agents without restarting the whole system
for commandtalk however we have developed an application specific grammar which gives us a number of advantages
to this corpus we added the most popular male and female NUM each names given to newborn children in the years NUM NUM in both the former east and west germany according to an official statistical source on the internet
likewise our grammar proposes both his box and the box of he him but the former is statistically much more likely
a literal translation of the input sentence was something like as for new company there is plan to establish in february
or consider they planned increase in production where the model drops an article because planned increase is such a frequent bigram
note for example the choice of obtain in the second example of the previous section in favor of the more formal procure
to attack these problems we have built a hybrid generator in which gaps in symbolic knowledge are filled by statistical methods
these two categories of gaps include interlingual analysis often does not include accurate representations of number definiteness or time
this essentially assumes that our generator produces valid mappings from i but may be unsure as to which is the correct rendition
the process of selecting words that will lexicalize each semantic concept is intrinsically linked with syntactic semantic and discourse structure issues
any input features that are not matched by the selected rule are collected in rest and recursively matched against other grammar rules
we thus get by with a very small exception table and furthermore our spelling habits automatically adapt to the training corpus
furthermore each word and phrase has an associated head word represented as a feature value that is propagated from the z or zp on the right hand side of the above rules to the left hand side
verb subca tegoriza tion is th ii encoded a s
first the percentage of proper names is likely to be much higher in the onomastica database no numbers are given in the report in which ease higher error rates should be expected due to the inherent difficulty of proper name pronunciation
a big advantage to using this form comes from the fact that any partial parse that exceeds the currently known minimum can be abandoned immediately at great savings in computation time
to that used in unification grammars although the hea ds in our pa tterns
unfortunately fin those wit axe not na tiv
fronl solll NUM t ta rget derivation trees
rules a nd structure sha ring vijay sha nker
sink noun sink verb or stone wall stonewall
we allow rldts to form theory hierarchies where parent theories can use results of their children s normalization process as their own logical part
however we found that only NUM NUM were actual cases of related meanings
most of the research on lexical ambiguity has not been done in the context of an application
the relationships described in a thesaurus however are really between word senses rather than words
we found that the use of phrases acts as a filter for the grouping of morphological variants
there is currently no preference for which words are used in a parse save to minimize mismatches and unparsed portions of the input but obviously a word grammar could be learned in conjunction with this acquisition process and used as a disambiguation step
these experiments were done with four different test collections which varied in both size and subject area
however of the sense pairs that were actually related twothirds had only one word in common
a parameter is updated on parse failure and if this results in a parse the new setting is retained
similarly german composita like terminvorschlag axe decomposed into its compounds e.g.
this single information structure serves as input to semantic evaluation and transfer
the responsibility for the contents of this paper lies with the authors
1for a more detailed overview of different approaches to mt see e.g.
meanings output by the minimum distance parsing algorithm described below have a corresponding utterance cost which is the distance between the user s input and an equivalent well formed phrase
finally section NUM summarizes the results
these lagts were initialized with p settings consistent with a minimal inherited cgug consisting of application with np and s atomic categories
that really does n t suit me well
NUM a das paflt mir echt schleeht
in five of the runs with memory limitations only in parsing there appeared to be a slight preference for defaults emerging
in seven of the ten runs with memory limitations only in learning a clear preference for default learners emerged
because many words are polysemous have multiple meanings and synonymous have meanings in common with other words the evidence available in the text tends to be somewhat noisy
the algorithm for the parser working with a gcg which includes application composition and permutation is given in figure NUM
let the number of words in the manually segmented corpus be std the number of words in the output of the word segmenter be sys and the number of matched words be m recall is defined as m std and precision is defined as m sys
the parser operates on two data structures an input buffer or queue and a stack or push down store
ia fact using the estimated word frequencies obtaiued by the heuristics results in poor segmentation accuracy NUM we found however that it is very effective to use the character type based word segmenter as a lexical acquisition tool to augment the initial word list
greenberg NUM hawkins NUM was constructed as a partial gcug with independently varying binaryvalued parameters
therefore as a next step of our research we are thinking of using the proposed unigram based word segmenter to obtain the initial estimates of the word bigrams and the word based character bigr m which will then be refined by a re estimation procedure
talk about the association of english and the association of linguistics and the dictionary has r linguistics language language study association and talk
we add the extracted word the word segmentation accuracy of the character type based method was less th NUM while other estimation methods achieves around NUM NUM as we show ia the next section
below are definitions of some terms as used in the discussion of tipster documents
they should therefore work well on other text not specific to change in corporate officers
figure NUM shows progress in porting plum to st evaluating periodically on blind test material
furthermore plum s performance was higher than in any of the previous full template muc tasks
a programmer with no computational linguistics background performed a usability evaluation of the nlu shell toolkit
in muc NUM and muc NUM the output of information extraction has been a multi level object oriented data structure
as before it takes declarative finite state descriptions regular expressions defining document structures
template element te extraction of organizations persons and properties of them
in around NUM of the runs language s emerged and persisted to the end of the run
the template merging experiment provided substantial range in recall versus precision i.e. undergeneration versus overgeneration
these inverse relationships are not linear but this will not matter to the arguments presented here
one example is types of establishment e.g.
the approach is not without its problems
examples are adjectives used to describe suitable applicants e.g.
some hierarchies are trivially simple for example full time part time
filled or partially filled schemas on demand
the matches are then scored accordingly
a more interesting example is geographical location
different languages have evolved different conventions on using such particles which renders the task for spoken language translation quite difficult
we asked what percentage of taggers selected the most frequently chosen sense and did the syntactic class membership of the words their degree of polysemy or the order of the senses in wordnet have an effect on the rate of agreement
a skeletal fd for the selected category enriches the semantic input
it is sent to the performance evaluation model
in japanese it is possible and even common to use a number of discourse relations in one sentence
first it proposes an underspccified treatment also for these cases along the lines of quantitiers and other operators
the latter has finer tags than the former
secondly it suggests some typical orders in which the scopal underspecification among discourse relations can be resolved
for the sentence in fig NUM the lud representation can be implemented like in NUM
this explains the stipulated scope relation between the topic wa and the explanative node in NUM
section NUM discusses possible resolutions in which a relationship between semantics and discourse structure plays an important role
this has lead to the decision that a discourse relation element should be directly subordinated to the top hole
the same explanation holds for the scope difference which is observable between the two sentences in fig NUM
first there is a topic relation which is expressed by a so called topic phrase marked by wa
they have NUM and NUM tags respectively
example NUM g and then below that what ve you got f a forest stream
since reply y moves are elicited responses they normally only appear after query yn align and check moves
words extracted from susanne corpus for some susanne tags
once the pp has been attached as an argument of the verb it can never be reanalysed as the adjunct of the preceding np because the np will precede the pp before reanalysis and dominate it after reanalysis which is against the exclusivity condition on trees i.e.
let d be the current tree description with the first right attachment site a let s be the subtree projection of the new word whose root r is of identical syntactic category as a the updated tree description is s ta d where a is unified with r
in addition when assessing segmentation it is important to choose the class of possible boundaries sensibly
the game coding results come from the same study as the results for the expert move cross coding results
the third is accuracy which requires coders to code in the same way as some known standard
the above procedure forms the second heuristic rule
however it is possible that in other languages or communicative settings this behavior will be more prevalent
example NUM f is this before or after the backward s g this is before it
note that the expert interacted minimally with the coders and therefore differences were not due to training
overview transactions were too rare to be reliable or useful and should be dropped from future coding systems
the first feature the expansion of the root node of the tree is the focus word then context features are added as further expansions of the tree until the context disambiguates the focus word completely
besides restricting search to those memory cases that match only on this feature the case memory can be optimised by further restricting search to the a training set t of cases with their classes start value a full case base an information gain ordered list of features tests fi fn start value f1 fn
all leaf node daughters of a mother node that have the same class as that node are removed from the tree as their class information does not contradict the default class information already present at the mother node
we extended the algorithm described there in the following way in case a pattern is associated with more than one category in the training set i.e. the pattern is ambiguous the distribution of patterns over the different categories is kept and the most frequently occurring category is selected when the ambiguous pattern is used to extrapolate from
a memory based approach has features of both learning rule based taggers each case can be regarded as a very specific rule the similarity based reasoning as a form of conflict resolution and rule selection mechanism and of stochastic taggers it is fundamentally a form of k nearest neighbors k nn modeling a well known non parametric statistical pattern recognition technique
finding the classification of a new case involves traversing the tree i.e. matching all feature values of the test case with arcs in the order of the overall feature information gain and either retrieving a classification when a leaf is reached or using the default classification on the last matching non terminal node if a feature value match fails
in the preliminary experiments described in this paper we limited this information to the possibly ambiguous tags of words retrieved from the lexicon for the focus word and its context to the right and the disambiguated tags of words for the left context as the result of earlier tagging decisions
complexity of searching a query pattern in the tree is proportional to f log v where f is the number of features equal to the maximal depth of the tree and v is the average number of values per feature i.e. the average branching factor in the tree
furthermore not having to use a tagger makes it much simpler to implement a practical system which adapts to the user and to the type of text
the major part of the terminology is usually represented by nouns or nominalizations
this would possibly lead to significant improvements in performance on the basic event relate d elements and to development of good end user tools for incorporating some of the domain specific patterns into a generic extraction system
the nlp module of the kawb consists of a word tagger e.g.
terms usually have a particular set of modifiers which represent different properties
first we can sort terms with the same head word by length
more complex patterns can be used for the description of complex groups
when this is done we get a clustering of short lexico semantic paradigms
figure NUM illustrates generalizations for the types body part and disease
this is useful because very large corpora frequently exist for many domains
this is known as a conceptual analysis of the acquired lingistic data
the main insight of the present work is that words are a happy medium sized text unit at which to map bitext correspondence
for instance abbreviation of several offer constellations that represent the initiate choice as in do you want the rate or the total cost of a call
as systems are tested in more challenging environments the base level accuracy of the input signal remains an important benchmark in measuring system performance
this measures whether or not the interaction was successful i.e. was the desired information obtained or the required task completed
in the next section we examine some of the measures relevant to correctness and timing and discuss their relevance for future evaluation of snlds
over the course of time a few systems have been constructed in sufficient detail and robustness to enable some evaluation of the systems
this training involved recording of voice patterns for speaker dependent speech recognition as well as training on the restricted vocabulary and syntax that systems required
there has recently been considerable interest in the use of lexically based statistical techniques to resolve prepositional phrase attachments
our hypothesis is that this information will be useful in determining the attachments of subsequent pps as well
in agglutinative languages affixes are attached to stems to form a word that may correspond to an entire phrase in a language like english
this is the most effective indicator
in section NUM we present an overview of our spoken dialogue system through multi modalities
to do this process by computer we realized a filtering process NUM b
the other is to modify networks so that they can be accepted as semantically correct
furthermore the recognizer processes interjections and restarts based on an unknown word processing technique
the speakerindependent hmms were adapted to the test speaker using NUM utterances for the adaptation
recently many multi modal systems which combine speech with touch screen have been developed
some spoken language systems focus on robust matching to handle ungrammatical utterances and illegal sentences
based on these considerations we developed a cooperative response generator in the dialogue system
therefore we use the display output map and menu as well as speech synthesis for the response
this statement must be kept in proportion however
results of the controlled iz exing
this allows for integration with most existing commercial off the shelf oo modeling tools
we wish to address the particular difficulties faced by the deaf writer learning english and to create a system with the capabilities of accepting input via an essay written by a user possibly several paragraphs in length analyzing that essay for errors and then engaging the user in tutorial dialogue aimed toward improving his her overall literacy
the seven syntactic patterns for terminology extraction
the probability of an interpretation i of a string is the sum of the probabilities of the parses of this string with a top node annotated with a formula that is provably equivalent to i let ti4p be the i th subtree in the derivation d that yields parse p with interpretation i then the probability of i is given by
the framework is based on combinatory categorial grammars and it uses the morpheme as the basic building block of the categorial lexicon
in addition responses should encourage both deductive and inductive learning where in the former a standard practice for many foreign language classrooms the student is introduced to the rule and is expected to use it to construct specific examples in the latter the student is not directly told the rule but is encouraged to generalize to the rule from specific correct examples
inheritance network without meta rules like the solutions described in section NUM our system uses a multiple inheritance network
representation and semi automatic generation we propose a system for the writing and or the updating of an tag
the rules for the organization of a family its coherence and completeness are flattened into the different trees
while researchers generally agree that a dictionary word should be tokenized as itself they usually have different opinions on how a non dictionary word critical fragment should be tokenized
the focus is then shifted to the problem of defining scores for evaluating each possible tokenization and to the associated problem of searching for the best path in the word graph
foot anchor or substitution node canonical syntactic function and actual syntactic function
consequently while they proposed many sophisticated algorithms for the discovery of ambiguity and certainty they never were able to arrive at such a concise and complete solution
understanding the importance of such a distinction we will use the more generic term token rather than the loaded term word when we need to highlight the distinction
similarly from ab cd we can bring back a b c d ab c d and a b cd and from abc d we can recover a b c d ab c d and a bc d
while the existence of tokenization ambiguities is jointly described by critical points and critical fragments the characteristics of tokenization ambiguities will be jointly specified by critical ambiguities and hidden ambiguities
we are currently developing a computational model that captures the way that english is acquired as a second language and gives us a framework upon which to project a student s location in that process
in datr this might be expressed as follows verb cat verb
french where past participles inflect for gender and number
consider for example the definition of do we gave above
section NUM NUM deals with implicit specification via datr s default mechanism
sides of datr equations are sequences of zero or more descriptors
NUM descriptors are defined recursively and come in seven kinds
moreover long involved utterances of a manual language are parceled into small parts that are recursively reinforced referring back to previous details as each new piece of information is added another characteristic atypical of spoken language
after we have chosen a subset of the atomic features for our model we restrict our feature lattice to the optimized lattice
bank2 noun mor root bank sem gloss financial institution
the exception occurs with a vowel shift error ss3 NUM NUM
this method in fact resembles the backward sequential search bss proposed in pedersen bruce NUM for decomposable models
in order to penalize large errors more heavily root mean squared rms distance is minimized instead of mean distance
next we discuss current trends in the field and motivate a set of requirements that have formed the design brief for gate which is then described
the underlying module can be an external executable written in any language the current creole set includes prolog lisp and perl programs for example
because gate places no constraints on the linguistic formalisms or information content used by creole modules the latter problem must be solved by dedicated translation functions e.g.
we believe that the environment provided by gate will now allow us to make significant strides in assessing alternative le technologies and in rapidly assembling le prototype systems
NUM from the point of view of efficiency the original lt nsl model of interposing sgml between all modules implies a generation and parsing overhead in each module
gate as will be seen below is more like a shell a backplane into which the whole spectrum of le modules and databases can be plugged
during training a set of examples the training set is presented in an incremental fashion to the classifier and added to memory
based on such a corpus the tagger generator automatically builds a tagger which is able to tag new text the same way diminishing development time for the construction of a tagger considerably
the algorithm is based on the following lemma lemma let b be an n x n upper triangular matrix and suppose that for any r n e the transitive closure of the partitions NUM i j r and n r i j n are known
even if we are able to identify the derived tree NUM rooted at m we have to first identify fl before we can check for adjunction fl need not be realized as a result of the composition operation involving the nodes from the first and last NUM NUM s say r NUM NUM
the approach in its basic form is computationally expensive however each new word in context that has to be tagged has to be compared to each pattern kept in memory
but if the adjunction was by a grown auxiliary tree as shown in figure he then the minimal nodes include the roots of NUM NUM 3s NUM and the node m
when we refer to a node m being realized as a result of composition of two nodes ml and rnp we mean that NUM an elementary tree in which m is the parent of ml and m2
but first we make an observation given any two symbol positions r rt rt r NUM NUM and a node m spanning a tree i j k l such that i rs and i rt with j and k in any of the possible combinations as shown in figure NUM
ideas related to the ones discussed here have been presented on numerous occasions
after the text planning is finished the decision of anaphoric forms and descriptions is then carried out by traversing the plan tree
sion definite clause grammars based on pure proloc involving no nonlogical devices like cut var NUM etc
to mitigate the monotony constraint we plan to reorder the words in the source sentences to produce the same word order in both languages
the accompanying reduced descriptions can then be explained as being intended to contrast with the emphasis at the beginning of sentences
the bottom up approach contributes to robustness in the obvious way if a single analysis can not be found for the whole utterance then translations can be produced for partial analyses that have already been found
our specifications define the properties and identities of objects e.g. attributes of books parts of the library the taxonomic relationships among terms e.g. that a service desk is an area but not a room and the typical span and course of events in the domain e.g. rules about how to check out books
during the first phase acoustic scores are ignored
thus such word graphs are acyclic weighted finite state automata
we will report only on the most efficient version
it therefore uses many of the ideas presented above
this may come as a surprise at first
in the head corner parser parse goals are memorized
d applies to a sequence of entities when substituting them for the variables in d yields a true formula
our results show cases in which this rule is not followed by technical writers that is when the purpose is neither global optional nor contrastive
as an example of this methodology consider the issue of slot that is the determination of which span in a rhetorical relation should be expressed first
it is well known that such parsing methods suffer from two major problems
natural language provides an extensive set of lexical and grammatical forms for expressing concepts many of which may taken out of context appear to be interchangeable
NUM this paper will add a reference to the end of all examples that have come directly from the corpus indicating the manual from which they were taken
this notion of purpose which will be detailed in the next section is one of actions that are to be realized through the execution of expressed sub actions
as an alternative the current study has employed the following four step process for identifying both the relevant forms of expression and the contexts in which they are used
in sections NUM and NUM of this paper we therefore consider the characteristics an evaluation should have and describe one we have carried out discussing the extent to which it meets the desired criteria
additionally it is important that the cognitive demands made of pictalk be limited and therefore the number of possible decisions that are required in order to select utterances must also be limited
the types of centering transitions we make use of cf
table NUM centering data for text fragment NUM
only with respect to the troublesome alfa romeo driving scenario cf
table NUM grammatical role based ranking on the c
crucial for the centering model is the way the forward looking centers are organized
table NUM centering data for text fragment NUM
NUM a ein reserve batteriepaek versorgt den 316lt ca
this candidate is presented for validation to the user via a feedback message which tells the user that the system will switch to the radio and start playing the last selected radio station e.g. switching to radio station bbc NUM
b durch diesen neuartigen akku wird der rechner ffir ca NUM stunden mit strom versorgt
we applied our constraints to japanese examples in the same way
at a minimum it is clearly reasonable in many contexts to feed back to the source language user the words the recognizer believed it heard and permit them to abort translation if recognition was unacceptably bad
structure component returns the named component of the structure
initial testing proved that these three sections covered the form and content of most enquiries within the domain but to account for unforeseen material the judge is also presented with a miscellaneous category
since in a categorial grammar the category for a lexical item includes its arguments the process of generalization of the parse can also be immediate in the same sense of our approach
where w and x are variables over all words in the training corpus and z and t are variables over all parts of speech
the transformation based tagger obtained the same accuracy with NUM NUM tags per word one third the number of additional tags as the baseline tagger
the parser retrieves the elementary trees that the words of the sentence anchor and combines them by adjunction and substitution operations to derive a parse of the sentence
since the features in ltags are finite valued and only features within an elementary tree can be co indexed the stapler performs termunification to instantiate the features
the derivation tree can also be interpreted as a dependency tree NUM with unlabeled arcs between words of the sentence as shown in figure NUM c
the process of combining the elementary trees to yield a parse of the sentence is represented by the derivation tree shown in figure NUM b
address of operation the substitution and ad null junction links are to be assigned a node address to indicate the location of the operation
the fst constructed from the generalized parses of the NUM atis sentences used in experiment l a has been used in this experiment as well
this method of retrieving a generalized parse allows for parsing of sentences of the same lengths and the same pos sequence as those in the training corpus
this task is extremely straightforward since the types initial or auxiliary of the elementary trees a dependency link connects identifies the nature of the link
the agreement among coders a is shown in table NUM
we can modify the transformation based tagger to return multiple tags for a word by making a simple modification to the contextual transformations described above
for nouns and verbs the corresponding superordinate synonym sets were presented adjectives were 2we had made a few minor alterations to the text for example we omitted short phrases containing word senses that had previously occurred in the text
speech and text are in some ways very different media a poorly translated sentence in written form can normally be re examined several times if necessary but a spoken utterance may only be heard once
however it is difficult to compare taggers using this figure as the accuracy of the system depends on the particular lexicon used
to our knowledge this is the highest overall tagging accuracy ever quoted on the penn treebank corpus when making the open vocabulary assumption
since a transformation list is a processor and not a classifier it can readily be used as a postprocessor to any annotation system
though it would be difficult to implement a modestly effective prediction system could reduce the cognitive load on the pictalk user
a delivered system comprises a set of creole objects the gate runtime engine gdm and associated apis and a custom built interface maybe just character streams maybe a visual basic windows gui
thus it contains both il expressions committed by the client and semantic input structures from generation
the judge is first shown a text version of the correct source utterance what the user actually said followed by the selected recognition hypothesis what the system thought the user said
future work includes extensive in house tests that will provide valuable feedback about the performance of the system
of course gate does not solve all the problems involved in plugging liverse le modules together
working with gate vie the researcher will don the outset reuse existing components i he overhead for doing so being much lower than is conventionally the case instead of learning new tricks for each mo lule reused tile common apis NUM m ge s alc ie
we are planning to enhance the sgml capabilities of this model by exploiting the results of the multext project
as we built our muc system it was often the case that we were unsure of the implications for system performance of using tagger x instead of tagger y or gazeteer a instead of pattern marcher b in gate substitution of omponents is a t oint and click operation in tile ggi interface
as more of this work is done we can expect the overhead involved to fall as all results will be available as cit eole objects hi the early stages sheflmd will provide some resources for this work in order to get the ball rolling i.e. we will provide help with creoleising existing syst ems and with developing interface routines where practical and necessary
architecture overview gate presents le researchers or developers with an environment where they can use tools and linguistic databases easily and in combination launch different processes say taggers or parsers on the same text and compare the results or conversely run the same module on different text collections and analyze the differences all in a userfriendly interface
based on this intuition we believe it would be advantageous to identify these content words in a text
high reliability indicates that the encoding scheme is reproducible given multiple labelers
once the text is loaded the user may ask that it be analyzed by the system
it also provides a range of specialized exception handlers to ensure robustness see section NUM NUM
one way in which compounds can be further disambiguated is through the incorporation of a statistical model as one of the heuristics employed in determining the appropriate interpretation
in compounds where the modifying noun denotes an event the composition in the compound frequently involves co composition between the qualia structure of the head and modifier
finally by restricting the form of the commandtalk grammar we are able to automatically extract the grammar that guides the speech recognizer
in such an approach one could train on a data set comprised of compounds paired with an indication of the relation holding between the head and the modifier
in addition to its theoretical relevance the approach to the semantics of complex nominals described here has important applications in the construction of natural language processing systems
another common function of modifiers in complex nominals is to specify a subpart of the denotation of the head noun or the material of which it is composed
in the case of lemon juice the head juice will have a squeeze act as its agentive and the object squeezed will be listed as a default argument
as described in the next section our approach handles focus effectively
a b ich wiirde sie gem am montag dem NUM NUM NUM wegen der bevorstehenden projektbegutachtung treffen
in particular lla refers to weapons that bring about destruction llb to a card that brings about a credit and so on
the approach described here needs to be integrated with further mechanisms and heuristics in order to determine the best guess for complex nominal interpretation in any given case
in what follows h is the initiator but cosma also copes with machine initiated dialogues cf
note that all of these steps are required to develop the performance function
in the first ambiguous verbs in wordnet have been evaluated the automatic classification is compared with the wordnet initial description
through experiments our parser can achieve high paxsing accuracy to some extent compared with other previous approaches with less computational cost
a framework to produce a semantically tagged corpus in a domain specific perspective using as source a general purpose taxonomy i.e.
although semantic information is crucial to the induction of most lexical knowledge accessing it is often impossible
table NUM counts the wn synsets of some of the most ambiguous verbs found in our rsd corpus
the tagged version of the source document results as follows future earth lo observation ac satellite ob systems co for worldwide high resolution at observa
the larger is the set of basic classes the larger is the size of the search space
for such verbs a recall of NUM is obtained over their unique and confirmed senses
the relevance of word classes for a variety of lexical acquisition tasks has been described in several works
this would however increase the total number of rules to a size that would be too large to deal with
in our case this alignment is achieved by a dynamic programming method NUM for each n best an alignment value is defined from the words alignment
the unit probabilities for and are as follows
both algorithms have o n s time complexities
the reestimation algorithm is a variation of the inside outside algorithm adapted to pdg
it is based on complete link and complete sequence of non constituent concept
NUM continue NUM through NUM until the new entropy previous entropy
NUM osed word lasses in die i uf iinl lehlcnt fl ion hese at egori NUM are elf i ile t into phrase sl rlletllre rules
the reuse of existing resources does not only save efforts but to a hopeflflly much minor extent also creates new tasks to be solved i.e. the integration of resources not having been the work reported here h s been carried out within tit
l his is an inq orlant st e l in bringing natalra language processing techniques closer to real world al plications where the minimizing of adaptal ion el and the maximal use of existing resources is crucial for success
i ll provides the means co specify a sul sump don or jeriug of l lpes whi h is useful lcb express gem ralizalions an it illa ro 1he hallislll
although the main components to be integrated fulfill reusability requirements fu f being a fairly general and modular generation engine the hpsg grammar being a declaratively written resource integration of these resources into a unified system couhl only be achieved after suitable adaptation
the actual implementation additionally allows for the specification of arguments via external macros accounting for a more principled treatment of case assignment argument reduction and slash extraction a ditferentiation between lexemes and stems to account for a treatment of inflection by the morphology component
we use only NUM domains a b e j k l n and p for this experiment because we want to fix the corpus size for each domain and we want to have the same number of domains for the non fiction and the fiction domains
frequency of the partial tree in a domain should be NUM times greater than that in the entire corpus NUM it occurs more than NUM times in the domain the second condition is used to delete noise because low frequency partial trees which satisfy the first condition have very low frequency in the entire corpus
the difference of the accuracy of the grammars of the same domain and the other domain is quite large
it is very interesting to see that the saturation point of any graph is about NUM to NUM samples
hence only NUM NUM NUM sequences were initially non ambiguous while NUM NUM NUM NUM NUM were ambiguous
all consonant tokens are also subdivided according to whether their two character prefix is contained in the cc set or not
for each domain the list of partial trees which are relatively frequent in that domain is created
the grammar of non fiction and fiction domains are created from corpus of NUM samples each from NUM domains
all of the above rules are negative in that they indicate impermissible hyphen points within particular substrings of consecutive vowels
nontrivial consonant sequences are also designated by a flag indicating the occurrence of a p r suffix
when we started this work it was not clear to what extent a symbolic segmentation parser and a connectionist learning dialog act network could be integrated to perform an analysis at the semantics and dialog level
word hyphenation could be bypassed by stretching out this space but this would effect the appearance of the document
the substrings of lemma NUM a comprise the set of all maximal prefix and consonant sequences of words
thus in order to cover hyphenation of such loanwords the patterns of table NUM must not be eliminated
theorem NUM gives further support to the proposition that grammar rules are not capable of completely hyphenating all nl words
conventions values are in bold face and variables are in italics
the resulting set of spanish terms became the spanish query
the system perfbrms well on both well formed and ill formed l cst it cans illustrating the phcliollt lt ll of agi NUM NUM iciil ill clahscs as well a in liolllt l hrast s
building on the c version of the tsnlp database tsdbl a bidirectional interface to the application was established allowing the instantiation of a dfki user application profile for tile storage of application specific data including performance measures and a semantic specification of the expected output
currently we use n gram dialogue act probabilities to compute the most likely follow up dialogue act
in ad lition to l he parts of the annotation s henta that follow a formal speeifi ation there is room for textual conmmnts at the wn ious levels to accommodate informatioi that annot or need not be forlnalized
rib achiove th se two goms of sl cili ity and reusalfility the tra ditional notion of a l est suite as a monoliflfi set of test it olns has l n
it was used for the task of dialogue act prediction by e.g.
backgro md and motivation ewduation of ni p q l lications plays mi im reasingly iml ortanl role in both he a adtnni mtd in lusl ria NUM ni onmumiti s
statistics in dialogue processing is used to predict follow up dialogue acts
they are represented in an additional subnetwork which is shown at the bottom of figure NUM
therefore our model provides robust techniques for the processing of even highly unexpected dialogue contributions
an important application of the statistical prediction is the repair mechanism of the dialogue plan recognizer
the type inventory of street name components was then used to collect lexically and semantically meaningful components which we will henceforth conveniently call morphemes
in an hmm the forward probability of a given state corresponds to the probability of reaching that state from the start state
there is a subtlety about what it means for a node n k to be more likely than some other node
the outside probability of a node n k is the probability of that node given the surrounding terminals of the sentence i.e.
we ca n t threshold out these nodes because even though they are all bad none is much worse than the best
cells covering shorter spans are filled in first so we also refer to this kind of parser as a bottom up chart parser
thus fin l contains the score of the best sequence covering the whole sentence maxl p l
that is from pass to pass only information about where words are likely to start and end is used for thresholding
we will discuss the importance of supporting confirmation turns and clarification and correction subdialogues
al NUM that has NUM NUM words including more than NUM station names
the second problematic issue is related to the impact that recognition errors have on the user be null havior
in order to improve speech recognition performance the contextual knowledge may be used as a constraint for the language models
the speech community usually classifies these errors into deletions insertions and substitutions
however we have obtained some data that may give some insights on the issue
clarification subdialogues may also occur in case of parser outputs that contain inconsistent related information
more specifically they identify some severe requirements that spoken dialogue modules have to meet
interactions with spoken language systems may present breakdowns that are due to errors in the acoustic decoding of user utterances
in t2 u the user utterance contains an hesitation when uttering the name of the departure city milano
the next step will be to take a set of communicative goals chosen for aggregation and the content selected by them and pass this to a natural language generation system
an anti greedy algorithm ag instead of the longest match take the shortest match at each point
however there is a strong relationship between n ts and the number of hanzi in the class
full chinese personal names are in one respect simple they are always of the form family given
this is to allow for fair comparison between the statistical method and gr which is also purely dictionary based
the horizontal axis in this plot represents the most significant dimension which explains NUM of the variation
NUM by a similar argument the preference for not splitting could be strengthened in lb by the observation that the classifier tiao2 is consistent with long or winding objects like ma3 1u4 road but not with ma3 horse
therefore in cases where the segmentation is identical between the two systems we assume that tagging is also identical
such properties since they are present in x of ni but they were not in n1 are assumed to be carried to the coustruclion by the noun x
NUM in diesem jahr erwartet die okonomin in this year expects the economist eine hohe inflationsrate
test and training tuples are obtained from shallow structures containing a verbal constituent and two nominative accusative nominal constituents
further in case both heads in a test tuple are pronouns the tuple is not considered
NUM weil die okonomin eine hohe inflationsrate because the economist a high inflation rate erwartet
there were no training tuples in which the compound noun altersgrenze occurred as the subject object of the verb
for instance in sentence NUM the verb trainieren to train occurs with two ncs
the next section evaluates the decision algorithm as well as the training data obtained by the learning procedure
null following are examples of test tuples for which a decision was made based on values of p2
the overall accuracy of the decision algorithm was almost NUM higher than the baseline of NUM NUM established
in case n2 but not nl is a pronoun redefine ci and ti as follows
it should be noticed that a quarter of the non fiction domain corpus and one eighth of the all domain corpus consists of the press report domain corpus
we assume so ci io81 aualysis which cousiders of in lhis kiud of collslrtlclious a lnere surface case marker
we found that an optimal solution to the problem of balancing local density against global frequency was rather elusive
rather a covering grammar could be used more suited to the purpose of parsing
euroti lcb a was a transfer based multilingual mt project
each of the data structures is the direct implementation of linguistic objects with different information contents
however fong s types are a mechanism to interleave constraints and phrase structure rules automatically
rightward movement requires an extension of the algorithm to incorporate the empty category in a chain
third the closest head is always chosen as a potential chain to which to unify
for instance the configuration does not depend on the categorial labeling of the head node
clearly both notions constitute an attempt to partition the set of principles into smaller subsets
syntactic features case NUM barrier strong agr d
improvcm nt in tra nsla tion
w a lso describe how our pa tterns
pe rha ps our t a ttern based
handling aml iguous parses is a difficult task
the desired effect can be reached if a rule schema is used for the introduction of nonlocal dependencies
wird er das m lrchen figure NUM analysis of seiner tochtcr erziihlen wird er das mfirchen
an infinite lexicon is both not very nice from a conceptual point of view and an implementational problem
NUM gewuflt daft peter i sehlggt habe known that peter hit have ich siei
a verb with some of its arguments may appear in the vorfeld leaving other arguments in the mittelfeld
NUM a miissen wird er ihr must will he her ein m irchen erz ihlen
NUM it is clear that we want the matrix verb to behave in a very well defined way
the list of chains given as input is ordered by the structure building algorithm when new chains are started they are added at the end of the list
an introduced nonlocal dependency is licensed by an actually present element in the syntax analysis of a string
in german ahnost any complemellt of a verb can be fronted subjects as well as objects
mary seems to like john john thinks that mary loves bill john thinks that mary runs mary thinks that john seems to like bill who does john love
as the previous section on phrase structure has shown computing features is not always profitable as some features reduce the search space while others do not
this sort of information ould be ranch more easily recorded in the hierarchical structure introduced for muc NUM in which there was a single template for an event which pointed to a list of temi lates one for each particii mlt in tile event
given this level of performance there is probably little point in repeating this task with the same ground rules in a future muc although there might be interest in processing monoease text and in performing comparable tasks oil a more varied corpus and for languages other than english
despite these distractions a few interesting early results were ol tained regarding eoreference methods we may hot e that once the task specification settles down the availability of coreferenceaimotated corpora and the chance for glory ill fltrther evaluations will ein ourage more work in this area
the first goal was to identit y from the component technologies being developed for information extraction flmctions which would be of NUM ractical use would be largely domain indet endent and couhl in the near term be performed automatically with high ac uracy
a better filtering can only be achieved if it is informed by other knowledge sources
suppose that a corpus consists of only two trees we employ one operation for combining subtrees called composition indicated as o this operation identifies the leftmost nonterminal leaf node of one tree with the root node of a second tree i.e. the second tree is substituted on the leftmost nontermi null nal leaf node of the first tree
where k is the length of the character sequence and olqk represents unknown word
the salience value sv of an individual instance inst at any given moment is obtained simply by adding the current significance weights of the cfs which have that instance in their scope
verbs with typical complements over verbs without complements
this data had been much less exposed than th e earlier wall street journal data and so was deemed suitable for the evaluation participants were required to promise not to look at wall street journal data from this period during the evaluation
within the industry object the product service slot has to list not just the specific product or service of the joint venture but also a two digit code for this product or service based on the top level classification of the standar d industrial classification
ik NUM x y cognizer x atalking to x y jq NUM x y cognizer x atalking to y x
inferential anaphors are references to individual instances that are not explicitly introduced in the dialogue but are implicitly introduced by associated instances e.g. the secretary in the sentence pair the nici has NUM employees
when operating in the action mode i.e. selecting and manipulating graphical representations the gestures can be taken to refer to the objects at the positions indicated since screen positions can not be manipulated
grosz and sidner mention this distinction but do not however provide a thorough analysis of all syntactic semantic and pragmatic rules they envisage to play a role in either focusing or centering
in keeping with the hierarchical object structure introduced in muc NUM it was envisioned that the mini muc would have an event level object pointing t o objects representing the participants in the event people organizations products etc
a major constituent referent cf has an initial significance weight of NUM all significance weights have been determined by trial and error and as will be shown in section NUM work fine
dit this deze files these files personal pronouns e.g. hij he het it and adverbs e.g. daar there
while our results indicate that we have not solved the whole problem of combining non context and context based predictions for disambiguation they show that the discourse processor is making usefld predictions and that we have combined this information successflllly with the non context based predictors
as far as tile systetn in ge neral is colmerned graded constraints only give preferences they do not rule out inferencing and attachment possibilities thtls introducing new constraints will not damage the broad coverage of the system
while a broad range of ambiguities can i e hal died well in non context basel manner some ambiguities must be treated in a contexl se nsi tive manner in order to be translated correctly
for example we were able to achieve almost perfect performance on the state vs query if ambiguity missing only one case with the genetic programming approach thus for this ambiguity we can trust the discourse processor s prediction
as far as the discourse processor is concerned it would be possible to achieve the same effect by adding more elimination constraints but this wouht make it necessary to introduce more fine tuned plan operators geared towards specilic cases
a key feature of our approach is that it allows multiple hypotheses to be processed through the system in parallel and uses context to disambiguate among alternatives in the linal stage of the process where knowledge can be exploited to the fullest extent
the second method the parser uses to score the ii l s makes use of penalties mammlly assigned to different rules in the l arsing grammar rl he resulting score from this method is called the gr ammar pr cfercucc score
this is an example of what we call the state vs query i f ambiguity in spanish it is impossible to tell out of context and without information about intonation whether a sentence is a statement or a yes no question
note that simply extending the discourse processor to accept multiple ilts is not the whole solution to the disambiguation problem finer distinctions must be made in terms of coherence with the context in order to produce predictions detailed enough to distinguish between alternative llts
the next most common type second turn repairs occur as the reply to the problematic turn e.g. as a request for clarification
misconceptions are errors in the prior knowl null edge of a participant for example believing that canada is one of the united states
NUM it would have been possible to characterize actual belief using an appropriate set of axioms such as those defining a weak NUM modal logic
these two notions of expectation are complementary and any dialogue model that uses speech as input must be able to represent and reason with both
allowing defeasible beliefs is a step in the right direction however the approach still misses the point that participants are able to negotiate meanings
NUM earlier speaker s2 performed act aintended NUM actions aintended and asimilar can be performed using a similar surface form NUM
for a hearer to interpret an utterance as a particular metaplan or as a manifestation of misunderstanding he needs a model of his understanding of
for reference ponte and croft report scores of f NUM NUM and NUM NUM for their probabilistic chinese segmentation algorithms trained on over 100mb of data
1deg NUM we assume that these attitudes are a function of discourse or illocutionary level of speech acts rather than the surface or locutionary level
we started with a training set only slightly larger than the test set NUM sentences and repeated the maximum matching experiment described in section NUM NUM NUM
thus an entirely new word can be treated simply as a word that has been observed at all the nodes of the pst
pruning is performed by removing all nodes from the suffix tree whose counts are below a threshold after each batch of k observations
a trigram model for instance is a pst of depth two where the leaves are all the observed bigrams of words
a wildcard symbol is available in node labels to allow a particular word position to be ignored in prediction
our initial experiments used the brown corpus the gutenberg bible and milton s paradise lost as sources of training and test material
instead we use a recursive method in which the relevant quantities for a pst mixture are computed efficiently from related quantities for sub psts
here we can only discuss some exemplary cases such as lfg analyses of n and np pre and postmodification
second mistakes in reference resolution can cause the extraction of erroneous semanti c representations even if the egraph match is correct
in the f structure domain modifiers are collected in an unordered set while in the range we impose some arbitrary ordering
fifth for muc NUM about NUM of the management scenario template fills are contained in the person and organi zation objects
presumably the fourth egraph quarter includes generally applicable examples while the first three egraph quarters include unusual o r redundant examples
again NUM NUM can be extended to the non subcategorizable grammatical functions discussed above
we eliminate r and give a direct and underspecified interpretation in terms of adapting qlf interpretation rules to fstrueture representations
induction for with possibly reeursive values i of grammatical functions on the assumption that for each i
r a nuclear scope f structure c nf s is is an f structure resulting from exhaustive at plicatiou of d14
to translate an f structure we call on r with the first argument set to a dummy grammatical flmction sigma
the evaluation results of the speech recognizers are given with others results in table NUM
the planning process starts with the administrator which places a pre spl fragment onto the blackboard and activates a module
the language model is usually trained on fluent text
it also provides a powerful means to access the knowledge that underlies the ve by allowing the user to ask questions of the system
the corresponding spanish text is quatro personas fijeron matadas en el ataque por el group contras sendero luminoso
once the mechanism for multiple references has been established the next step is to consider the actual training algorithm
tie words are used to connect the context vector space for multiple languages through a unified hash table
it is this property that translates the problem of assessment of similarity of content for text into a geometry problem
this unified set of context vectors is the basis for formation of document context vectors
note that attack and ataque are a tie word pair
since these words should have the same context vector some form of connection must be made between the words
the successful development and testing of the one step learning law offers the possibility of much faster context vector training
the value of this approximate solution is that it provides adequate performance with only a fraction of the computational requirements
once the correction is made we move the learning window to next location and the learning operation is repeated
likewise the number of input vectors as well as the number of map node vectors will determine the scale of the problem
since reihe series in german is a singular noun and kontakte contacts plural the actual object but not the subject agrees in number with the verb so the incorrect tuple reihe suchen kontakt o series seek contact is obtained from this sentence
to handle semantic collocations now requires only a representation of how certain lexical items depend on hidden parameters for actions and events
the heuristic rule is based on the observation that in the constructs stipulated by the rule although the object may potentially precede the subject of the verb this does not usually occur in written text
each pattern is represented as a pair of current symbols reduced symbol
selection power supplements accuracy rate when two language models to be compared are tested on different tasks
moreover the number of parameters is reduced to less than NUM NUM of the original parameter space
this means that smoothing unreliable parameters is absolutely essential if only limited training data are available
to achieve better performance for a real application one must deal with statistical variation problems
the learning rules for adjusting the lexical parameters can be represented in a similar manner
let the correct syntactic structure associated with the input sentence be syn
for example a fllll stop all indicate an abbreviation or the end of the sentence
for instance the training tuple gesellschaft erwarren umsatz NUM society expect turnover is obtained from the structure NUM above with the case accusative rule since the nc headed by the masculine noun umsatz turnover is unambiguously accusative and hence the object of the verb
for example the form and can be viewed as a syntagm coordinator or as a proposition coordinator
the correct parts of speech and parse trees for the collected sentences were verified by linguistic experts
in the current system there are NUM NUM distinct lexicon entries extracted from the NUM NUM sentence corpus
responses need to be appropriate but they do not need to be ideal or precise to meet participants goals in much social conversation
determining the subject object of an ambiguous construct such as NUM with a knowledge based approach requires at least a lexical representation specifying the classes of entities which may serve as arguments in the relation s denoted by each verb in the vocabulary as well as membership information with respect to these classes for all entities denoted by nouns in the vocabulary
figure NUM a partial example of a filled job schema
original text urgent p t waiters required urmston area
company x2 red herring rest aurant
translation tables are provided for each term containing the names used in the different languages
note that the example shown in figure NUM is rather simplified for the purposes of illustration
this type of discrimination is illegal in the uk where it would violate sex equality legislation
let us now indicate how the rules are meant to be used by the generator module
figure NUM presents the process by which a text corpus is transformed into some intuitive visual paradigm that users can easily relate to and understand
to a great extent the design of each of these modules is not especially innovative
hnc has developed an underlying information representation technology and a concept for information visualization that can solve the problem of effectively browsing large textual corpora
the content delimitation module determines the material to be included into each separate spl expression
peribrnlance figure of NUM using the NUM head words alone
the accuracy of NUM NUM is close to the hmna n
finally more training data is almost certain to improve results
there are a few possible improvements which may raise performance further
they use a maximum entropy model which also considers subsets of the quadruple
katz87 describes backed off n gram word models for speech recognition
the training and test data were both the unprocessed original data sets
all strings name name were then replaced by name
the table below gives accuracies for the sub tuples at each stage of backing off
we have excluded tuples which do not contain a preposition from the model
this implies the ability to view and modify each of the parts of the application that are linked to those slots
manager concerned with the cost of a new application j needs to know immediate staffing impact of a new application
different users of a tipster application will typically have different types of interactions with the application to accomplish different tasks
this discussion is organized around typical interactions of the end user the application developer and the system support officer
requirements for other functions such as machine translation or optical character recognition must be met outside the tipster architecture
an application may contain a detection component an extraction component a clustering component or any combination thereof
all designs will be kept on record both in design to and as built form with the tipster program office
the tipster architecture is intended to facilitate the deployment into the workplace of advanced document detection and information extraction software
for example some information may be gleaned from the words in a table without actually processing the table structure
clicking the mouse button once on a node a pop up menu reveals among other choices the information theme the node represents
user model information is maintained as a set of axioms acquired from inferences based on user input
thus there are speaker specific plans instead of simply joint plans as in the litman and allen model
the document selected will appear in a separate window along with a highlighting tool to further examine the relevant parts of the document
NUM association for computational linguistics computational linguistics volume NUM number NUM cation p
wizard of oz woz dialogues result from an experimental technique that is one way of addressing this dilemma
an interactive translation support facility for non professional users
figure NUM change of generation style
NUM signal the end of translation
our system is running as a daemon
there are two major sources of violation
figure NUM shows translation steps for two sentences
these users 2these characteristics are inherited essentially from a kana kanji conversion interface
the user should be able to ignore them unless they want to
the system dictionary contains about NUM NUM japanese entries and NUM NUM idiomatic expressions
possible diagnostic steps are voltage measurements or an led observation under a different physical configuration of the circuit
in order to access the argument in the telic the content value of the modifier is structure shared with the first default argument in the content of the head
the numbers in the left hand column refer to two word noun phrases that identify entities e.g. bill clinton
we believe that a blackboard architecture with separate modules a situation discr mination
the manuals are written using the pace perkins approved clear english guidelines with the aim of producing clear unambiguous texts
null for most of the work described here the sentence was dynamically truncated NUM words beyond the hypertag marking the close of the subject
in feed forward networks trained in supervised mode to perform a classification task different penalty measures can be used to trigger a weight update
if the number of candidate strings is within desirable bounds such as for the head detection task no rules are used
NUM language representation ii different network architectures have been investigated but they all share the same input and output representation
at this stage the data is ready to present to the neural net figure NUM gives an overview of the whole process
NUM will generate strings of tags including then the performance of the pump must be monitored
our approach uses a similar concept but differs in that embedded syntactic constituents are detected one at a time in separate steps
multi layer networks which can process linearly inseparable data were also investigated but are not necessary for this particular processing task
a detailed error analysis yielded the following types of remaining problems syllabic stress saarbriicken za br yk n but zweibriicken tsv aibryk n
to assess the value of this filtering mechanism the muc6 evaluation corpus was processed without the illmr
corporate subsidiaries NUM corporate name changes NUM missing name NUM incomplete name variation NUM unusualfirstname
in the following example the slot can be either name or descriptor depending on the mapping
shareholders approved changing the name of this trash hauling recycling and environmental services concern to wmx technologies inc
ill summary our system has incorporated many new techniques for associating coreferential information as part of our tipster research program
sixty eight of the NUM possible descriptors were missed because the system did not recognize the noun phrase as describing an organization
improvement of name recognition is an on going process as the system and its developers are exposed to more and more text
association by reference once an organization noun phrase has been recognized the reference resolution module seeks to find its referent
associating an organization name with a descriptor requires resolving coroferences among names noun phrases and pronouns
this paconcludes that most of the techniques have been beneficial to our performance and suggests ways to further improvement
the line p1p3 is bisected and this point is labeled a p2 is perturbed by a constant amount not deependent on the distance between a and p2 towards a this new point is labeled b and becomes the new p2
table NUM shows that within the facet genre our systems do a particularly good job on reportage and fiction
if the machine were to guess randomly among k categories the probability of a correct guess would be NUM k
rather factors are used to validate hypotheses about the functions of various linguistic features
derivative cues are ratios and variation measures derived from measures of lexical and character level features
for binary decisions the simple perceptron fits a logistic model just as lr does
w e ended up using NUM of the NUM texts in the brown corpus
thus the diagonals are correct guesses and each row would sum to NUM but for rounding error
however it is less prone to overfitting because we train it using three fold cross validation
computational linguists have been concerned for the most part with two aspects of texts their structure and their content
the NUM NUM morphemes recall NUM NUM component types out of the total of NUM NUM or NUM NUM leaving NUM NUM types or NUM NUM that are unaccounted for residuals by the morphemes
referring to an entity in the same sentence then its resolution may depend on one of the previous prepositional phrases
the pronoun its can not be resolved by the anaphora resolution module because it is preceded by unattached pps its resolution is skipped
the anaphoric pronouns have to be resolved first so as to determine what semantic class they refer to the pp attachment procedure can then be applied
in a yellow pages application for example the user may ask about a phone number an email address or a postal address
next we present empirical results from experiments with autoslog ts using the muc NUM text corpus
the other words represent the s rrounding context used to construct a concept node
the new intuition presented here that expectations convey a dependency between the current discourse unit and future discourse material a dependency that can be stretched long distance by intervening material more fully exploits tag s ability to express dependencies
but the phs conceded that the new radioactive particles will add to the risk of genetic effects in succeeding generations and possibly to the risk of health damage to some people in the united states cb21
we can call a rf rooted at the left sister of the most embedded substitution site the inner right frontier or inner rf in figure NUM i the inner rf is indicated by a dashed arrow
because we have not yet implemented a parser that embodies the ideas presented so far we give here an idealized analysis of examples NUM and NUM to show how an ideal incremental monotonic algorithm that admitted expectations would work
of these NUM have their expectations satisfied immediately NUM for example null suppose john jones who for NUM filed on the basis of a calendar year died june NUM NUM
outside of cue phrases we have identified imperative forms of suppose and consider as raising expectations but currently lack a more systematic procedure for identifying expectation raising constructions in text than hand combing text for them
it may not be obvious that there could be uncertainty as to whether the current discourse unit satisfies an expectation and therefore substitutes into the discourse structure or elaborates something in the previous discourse and therefore adjoins into it
adjoining adds to the discourse structure an auxiliary tree consisting of a root labeled with a discourse relation an empty foot node labeled and at least one non empty node figures lc and ld
now these are very likely not the only kinds of expectations to be found in discourse whenever events or behavior follow fairly regular patterns over time observers develop expectations about what will come next or at least eventually
as usual with commandments some are conceptually clearer and easier to obey than others
for this approach to work the feedback messages have to meet certain criteria
the result of a ptt push action is that the speech recognition unit is activated
the vocal interface is speaker independent and is developed for german and french
the tipster architecture gives the developer several sources of help
the evaluation of the first vodis prototype may give some indications in this respect
discussion of information requests and outputs are combined in section NUM NUM
only interrupt the ongoing dialogue in urgent situations and justify the interruption
we investigate the utility of an algorithm for translation lexicon acquisition sable used previously on a very large corpus to acquire general translation lexicons when that algorithm is applied to a much smaller corpus to produce candidates for domain specific translation lexicons
if we assume that translation pairs in the collins mrd are not specific to our chosen domain then domain specific translation lexicon entries constituted only NUM of sable s unfiltered output on or above the 2nd plateau and NUM on or above the 3rd plateau
NUM if you re completely at a loss to decide whether or not the word pair is valid just put a slash through the number of the example the number at the beginning of the line and go on to the next pair
the authors wish to acknowledge the support of sun microsystems laboratories particularly the assistance of gary adams cookie callahan and bob kuhns as well as useful input from bonnie dorr ralph grishman marti hearst doug oard and three anonymous reviewers
it uses system resources to identify and respond appropriately to user interruptions
each point of correspondence x y in the bitext map indicates that the word centered around character position x in the first half of the bitext is a translation of the word centered around character position y in the second half
confirmation strategies are tailored to the particular operating environment and the specialised domain
for example if french imm6diatement were paired with english right you could select i because the pair is almost certainly the computer s best but incomplete attempt to be pairing imm4diatement with right away
examples like NUM seem to suggest that this is possible
twenty minutes later cocu scored for psv
figure NUM data structures expressed by NUM b and c
the annotator was informed that these annotations were the computer s best attempt to identify the part of speech for the words it was suggested that they could be used as a hint as to why that word pair had been proposed if so desired and otherwise ignored
player nilis c card event minute NUM
in the near future the proposed approach will be implemented in goalgetter
b shortly after the break the referee handed nilis a yellow card
however we argue again that this task is best integrated into the parser the task is complex enough to warrant a probabilistic treatment and integration may help parsing accuracy
the rest of the phrase is then generated in different ways depending on how the gap is propagated in the head case the left and right modifiers are generated as normal
adjuncts in the penn treebank we add the c suffix to all non terminals in training data which satisfy the following conditions NUM
each sentence tree pair s t in a language has an associated top down derivation consisting of a sequence of rule applications of a grammar
a pcfg can be lexicalised NUM by associating a word w and a part of speech pos tag t with each non terminal x in the tree
however this saving is partially nullified by the additional operations incorporated especially by the application of lexicalization operators and scoping verifications
NUM generate modifiers to the right of the head with probability NUM ii NUM m NUM n ri ri lcb p h h
np c marks is immediately generated as the required subject and np c is removed from lc leaving it empty when the next modifier np week is generated
the basic route follower coding identifies whether the follower action was drawing a segment of the route or crossing out a previously drawn segment and the start and end points of the relevant segment indexed using numbered crosses on a copy of the route follower s map
although align moves usually occur in the context of an unconfirmed information transfer participants also use them at hiatuses in the dialogue to check that everything is ok i.e. that the partner is ready to move on without asking about anything in particular
one exception in the map task occurs when a participant is explaining a route for the second time to a different route follower and asks for confirmation that a feature occurs on the partner s map even though it has not yet been mentioned in the current dialogue
however map task participants do not always proceed along the route in an orderly fashion as confusions arise they often have to return to parts of the route that have already been discussed and that one or both of them thought had been successfully completed
moreover it is possible for a set of coders to agree on where the game begins and not where it ends but still believe that the game has the same goal since the game s goal is largely defined by its initiating utterance
sponsored by the university of pennsylvania three non hcrc computational linguists and one of the original coding developers who had not done much coding move coded a map task dialogue from written instructions only using just the transcript and not the speech source
other types of subdialogues are possible such as checking the placement of all map landmarks before describing any of the route or concluding the dialogue by reviewing the entire route but are not included in the coding scheme because of their rarity
n NUM k NUM on whether the initiation was a command the instruct move a statement the explain move or one of the question types query yn query w check or align
they are also quite often questions that serve to focus the attention of the partner on a particular part of the map or that ask for domain or task information where the speaker does not think that information can be inferred from the dialogue context
further like many sentence planners we assume that there is a flexible association between the content input to a sentence planner and the meaning that comes out
the only difference between the algorithms is that in this case the weights are updated in an additive fashion
this process begins and ends with the corpus providing an empirically based approach to identifying the range of lexical and grammatical forms that are used in real text and to determining the contextual issues that are relevant to choosing among them
we have a ontinui as its cb is ohn the higlms entity on the cf list of 2t and so is its c1
coverage cm tests similarity between e and a in a very loose sense
NUM corresponds to a simple state
this result is extremely promising and confirms the opp selected extract bearing important opp selected positions
the former indicates that the closer a sentence lies tence positions in a paragraph
also s is the start category of the dcg
a hierarchy of domains labels which relate concepts on the basis of scripts or topics e.g.
due to the unavailability of large scale i because the senses in the semantic space are of mono sense words we do n t distinguish words from senses strictly here
learning then goes on as a number of iterations over the training corpus
b the score of the candidate rule is then computed as
for such cases one has to take into consideration additional pieces of information
table NUM average parses recall and precision for text
table NUM average parses recall and precision for text
some statistics on these texts are given in table NUM
we have applied our learning system to two turkish texts
figure h the structure of the constraint based morphological disambiguation system
i combining hand crafted rules and unsupervised learning in constraint based morphological disambiguation
for example in a flight arrival departure application if the system prompts the user for either the arrival city or departure city and the user just says newark the field to which the term belongs is ambiguous
in the first case the system informs the user of the limitations of the system switches the dialogue to the initial state and permits the user to revert to some query within the bounds of the system
in the above example if there is also a book entitled dickens in the database then class ambiguity exists since it is unknown whether the user meant the author or the title
the resulting functional description which encodes the functional structure for the entire content of the message specification is then passed to the surface realizer
lexicon physically distributed throughout the knowledge base each concept frame has access to all of the lexical information relevant to its own realization
by performing a series of these experiments one can determine which aspects of the system and its representational structures contribute most significantly to its success
by assembling these diverse mechanisms into a single architecture he demonstrates how the complexities of explanation planning can be dealt with in a coherent framework
for example a schema for defining a concept includes instructions to identify its superclass to name its parts and to list its attributes
because the differences between knight and the biologists were narrow in some cases we measured the statistical significance of these differences by running standard t tests
comparing differences in dimensions knight performed best on correctness and content not quite as well on writing style and least well on organization
because carefully evaluating multiple dimensions of explanations is a labor intensive task time considerations required us to limit the number of explanations submitted to each judge
although one can easily count the number of examples that an induction program classifies correctly there is no corresponding objective metric for an explanation generator
the view produced in this execution will eventually be translated to the sentence embryo sac formation is a kind of female gametophyte formation
sas most ratios involve a NUM for some observed value smoothing is crucial
it is straightforward to extend this to k senses using k sets of seeds
similarly the one sense per discourse constraint may also be used to correct erroneously labeled examples
regrettably this algorithm was only described in two sentences and was not developed further
translation of a hebrew verb object pair such as lahtom sign or seal and h
this property may be utilized in two places either once at the end of step
the algorithm should begin with seed words that accurately and productively distinguish the possible senses
circled regions are the training examples that contain either an a or b seed collocate
and golgi apparatus of plant and animal cells computer disk drive plant located in
it is very much stronger for collocations with content words than those with function words
table NUM test data evaluation results on mixed gram
second many function words like prepositions and articles are omitted
misparsing reduced by omissions has a far reaching consequence in machine translation
relv on svntactic rules defined in terms of part of speech
the key to the technique is to adequately mix semantic and syntactic rules in the grammar
apart from a few obvious observations given in section NUM NUM such a comparison would require a detailed examination of the corpora and the taggers errors by experienced linguists
but then the relation between the active object and the passive subject is lost
sometimes ci l does not need to be intersected with the input because they do not mention any of the same tiers
for instance one can generate all the passive trees or all trees with extracted complements
a dominance link can be further specified as a path of length superior or equal to zero
but some information is not taken into account the lexical rules do not update argument index
a description can leave underspecified the order of some daughters leading to several minimal trees
first these solutions use inheritance networks and lexical rules in a purely technical way
this resulting hierarchy will then be more transparent and will benefit from more declarativity
to get elementary trees from these classes we need to translate the partial descriptions into trees
the corresponding terminal class w0n0vnl pass inherits the canonical subcat strict transitive and the redistribution personal full passive
use of the architecture for government procurements will also shorten the development process for new text handling applications because a basis for design already exists and is understood by vendor and customer alike
this rule works for instance for book booked water watered etc
sshorter than NUM characters 9this however was not an entirely fair comparison because of the differences in the tag sets in use by the taggers
the remaining descendants in the optimal parse tree are then given recursively for any q s t u v by
at the scoring phase each rule is scored in accordance with its accuracy of guessing and the best scored rules are included into the final rule sets
in english however since most of letter mutations occur in the last letter of the main word it is possible to account for it
stands for all transitions labeled with a letter that does n t appear as input on any outgoing arc from this state
our work relies on two central notions the notion of a finite state transducer and the notion of a subsequential transducer
the intended use of the rules in the tagger defined by brill is to apply each function from left to right
for instance the minimal dom fa decomposition of daaaad is d aaa ad
thus w NUM w NUM the following lemma will also be used for soundness
furthermore the space required for part of speech taggers is also an issue in commercial personal computer applications such as grammar checking systems
finite state devices have important applications to many areas of computer science including pattern matching databases and compiler technology
usually these two modules are written in different frameworks making it very difficult to integrate interactions between the two modules
we say that f2 is the local extension NUM of fl and we write f2 locext fl
on the other hand if the evaluation indicates that the agent should maintain her original belief she should attempt to provide sufficient justification to convince the other agent to adopt this belief if the belief is relevant to the task at hand
a candidate loci tree contains the pieces of evidence in a proposed belief tree which if disbelieved by the user might change the user s view of the unaccepted top level proposed belief the root node of that belief tree
however since this is a collaborative interaction the actual modification can only be performed when both sl and s2 believe that node is not acceptable w that is the conflict between sl and s2
and attacking bel itself cause mckeown s focusing rules suggest that continuing a newly introduced topic about which there is more to be said is preferable to returning to a previous topic ovickcown NUM
where cil stands for the cardinality of cluster i and d z y is the dissimilarity between adjectives z and y
of course standard grammar checkers do also try to supply checks that are relevant for non fictional genres
tim fail soft mt ehanism works at all levels of representation
controlled languages have been invented to solve the problems associated with readability and translatability with slight regard to ensuring grammaticality
for a company that has to pay for document translation on a per word basis every repeti
4we define precision to be the number of relevant error reports divided by the total number of error reports
we have developed a system that we believe strikes a useful balance between cl checking and standard grammar checking
being able to properly identify revised parts means that the user can elect to check only revised parts
but even when the attachment is off easyenglish can often point out other attachment possibilities to the writer
this module originally written for use with lmt was modified slightly to point out ambiguous pronominal references
if the logical subject is not available the passive is pointed out but no rephrasing is offered
results and an implemented prototype of a multilingual translation system
the authors would like to thank dr manabu okumura jaist japan mr timothy baldwin titech japan michael zock and dan tufts limsl france for their comments on an earlier version of this paper
converting certain english ing forins into l anish relative clauses
word sense disambiguation is a crucial task in many nlp applications such as machine translation NUM parsing NUM NUM and text retrieval NUM NUM
however it is obvious that we can increase the total interpretation certainty of x s when we use a for training as it has more neighbors than either b or c
figure NUM a the case where the interpretation certainty of the enclosed x is great figure NUM b the case where the interpretation certainty of the the enclosed x is small figure NUM the concept of interpretation certainty
then c s contribution to v s sense disambiguation ccd c is likely to be higher if the example case filler sets lcb gsi c i i NUM n rcb share less elements
to avoid this problem a method used in efficient database search techniques NUM NUM in which the system can search some neighbor examples of x with optimal time complexity can be potentially used
titles paragraphs text segments that should not be translated etc
algorithm and exl anding our bilh gua dictio nnry
NUM a log linear regression model combines informa null tion from different conjunctions to determine if each two conjoined adjectives are of same or different orientation
is the corresponding measure for arcs
we will also experiment with the non gr edy
score of NUM was awarded for each match using our bilingual dictionary
the summation is evaluated for all o d possible pairings
in our cur rent approach disjunctions are r prcse ntetl
we thank xingong chang for his work on implementing the mal rule grammar for the asl writing project
input simplification is also a key premise in pidginization creolization accounts of acquisition hat83
this is intended to capture sets of features which are acquired at approximately the same time
null so too are both proper and regular nouns and one and two word sentences
we discuss a dual component linguistic model that attempts to reflect the generation process of the learners
the linguistic model helps the system identify errors along with their probable source s
knowing the underlying reason for a mistake is crucial to the goal of providing effective tutoring
they are classified into two types
tagging is performed left to right
job waiter jobcode NUM number of jobs several location urmston worktime NUM skills experience essential application phone NUM NUM
it is unnecessary to keep the dropped parameters after smoothing thus this method of smoothing helps reduce the memory overload when merging parameters
we found that syntactic links represent good descriptors for candidate terms clustering since the clusters are often easily interpreted as conceptual fields
regional network is extracted by lexter and a NUM otherwise national line is not extracted by lexter
the table also shows that punctuation and interjections rank between the function words at the top and the content words at the bottom
both represent an attachment of np to p and the length of p is NUM in both terms
however for the same reason as described above the data sparseness problem can not be resolved completely
in the present paper we mainly describe our solutions to subproblems b and c
the lexical likelihood values of the two interpretations in NUM and NUM are thus calculated as
thus the right hand probability in NUM is likely to be higher than that in NUM
for example in rain washes the fertilizers off the land NUM there are two interpretations
we conclude that the most effective way of improving disambiguation results is to increase data for training lexical preference
the left hand probability in NUM is likely to be lower than that in NUM
thanks are also due to john lafferty nata a milid frayling xiang tong and two anonymous reviewers for their useful comments
for each speaker the number for each test text is the average of matches with the other eleven speakers
the speaker was asked to annotate which form he or she preferred for each anaphor position on the test sheets
both figures show that the preference rule is promising in the choice of full or reduced descriptions for nominal anaphora
as in the first experiment we trained two language models on linguistic segmentations and acoustic segmentations respectively
the number of overall matched cases is thus NUM NUM NUM NUM out of NUM NUM anaphora in total
as for text NUM NUM speakers completely agree with tr2 while the others partly agree with tr2 and tr3
sentences have a given new or topic comment structure which is especially pronounced in conversational speech
in addition the constraints for pronominal anaphora could be improved and the implementation extended to satisfy other types of applications
it should be emphasized that having these morpho lexical probabilities enables us not only to use them rather naively in the above mentioned strategy but also to incorporate these probabilities into other systems that exploit higher level knowledge syntactic semantic etc
categories of verbs in figure3 thick line segments signify regions dashed line segments signify unbounded ends of regions and large open dots signify points in time boundaries or punctate events
from the above comparison we judged that the annotations we made were highly reliable for the purpose of the experiment
a simple way to refine the previous anaphor generation rule is to let the nonzero parts in the rule be nominal
the process of computing what to say and the process of computing how to say it are in the general case interleaved processes
segment y a nd the one thing that struck me about the three little boys that were there is that one had ay uh i do n t know what you call them but it s a paddle and a ball is attached to the paddle and you know you bounce it
it is possible that a larger document collection might increase the frequency of most phrases and thus alleviate the problem of low frequency
this contrasts with the relatively isolated occurrences of the word software in the middle of the document which are deemed to be little more significant than several occurrences of the word the a function word
where a1 and a2 are the lagrange multipliers corresponding to the two constraints mentioned above and are given by the following formulas
for example a distance function for the job title parameter as represented by job title codes illustrated in figure NUM could be given by NUM
a proposal for task based evaluation of text summarization systems
in fact sorting the NUM subjects into comparable pairs i.e. subjects assigning a similar number of boundaries a reliability metric that ranges between NUM for perfect reliability and NUM for perfect unreliability krippendorff s a discussed below gives a wide spread of reliability values from NUM to NUM
because our analysis realizes this relation distinction in a form different from both intention dominance and nuclearity we have chosen the new terms core and contributor
in this section we describe our approach to the analysis of a single speaker s discourse which we call relational discourse analysis rda
all elements of the analysis from the largest constituents of an explanation to the minimal units are determined by their function in the discourse
each relation is labeled with both its intentional and informational relation with the order of relata in the label indicating the linear order in the cliscourse
the proposed inductive method puts a specific attention to the linguistic description of what terms are as well as to the statistical characterization of terms as complex units of information typical of domain sub
where o is the number of words in the corpus that appear in at least one of the synsets of w
there is no hope for any inductive method making use of simple lex null ical collocations instead of class based collocations e.g.
it takes as a direct object the word information only once that is an evidence too small to support any probabilistic induction
in a corpus on remote sensing rsd NUM for example we computed an average ambiguity of NUM NUM senses i.e.
several approaches to la rely on some forms of declarative descriptions of source data bracketed or pos tagged corpora are just examples
hierarchy nodes a domain specific semantics is obtained through the selection of the suitable high level synsets in the wordnet hierarchy
we hence decided to adopt as initial classification for verbs the NUM semanticaliy distinct categories verb semantic fields in wordnet
there also appeared to be many similar modules in the diverse systems
ahnost all items in the corpus are marked as sentences although not all fulfil that grammatical role
therefore it is necessary to develop a new theory of punctuation that is suitable for computational implementation
their usage is discussed and suggestions are made for possible methods of including punctuation information in grammars
carenlly examined to letermine whether there was arty justitication for a particular rule pattern given the content of the seutenee
s pl s an exception since the mother category is not really a sentence NUM
note also that nunberg s principle of quotetransposition is still necessary if this rule is to remain in its current form
the first NUM simply states that a dash interpolation can contain an identical category to the phrase it follows
the mothers should really all be top category since the full stop is used to signal the end of a text unit
intuitively this s ems very wrong since punctuation is such an integral part of many written languages
the algorithm updates its hypothesis only when a mistake is made as follows NUM if the algorithm predicts NUM and the label is NUM positive example then the weights of all the active features are promoted the weight wit is multiplied by o
three characteristic propertie s of this domain are a very high dimensionality b both the learned concepts and the instances reside very sparsely in the feature space and consequently c there is a high variation in the number of active features in an instance
while the weight of f in the weight vector of the category w f c may be fairly small its cumulative contribution might be too large if we increase its strength s f d in proportion to its frequency in the document
a linear text classifier represents a category as a weight vector wc w fl c w f2 c w fn c wl w2 wn where n is the total number of features in the domain and w f c is the weight of the feature f for this category
specifically we measured the effectiveness of the classification by keeping track of the following four numbers pl number of correctly classified class members null p2 number of mis classified class members nl number of correctly classified non class members n2 number of mis classified ion class members null in those terms the recall measure is defines as pl pl p2 and the precision is defined as pl pl n2
state of the art ir systems determine the strength of a term based on three values NUM the frequency of the feature in the document t NUM an inverse measure of the frequency of the feature throughout the data set id and NUM a normalization factor that takes into account the length of the document
the subjects were three people with no previous involvement in the project
this research has also been partially supported by nserc research grant 0gp121338 and by the institute for robotics and intelligent systems
figure NUM list the language specific ordering parameters used to define the full set of grammars in partial order of generality and gives examples of settings based on familiar languages such as english german and japanese NUM english defines an svo language with prepositions in which specifiers complementizers and some modifiers precede heads of phrases
in order to test the preference for default versus unset parameters under different conditions the five parameters which define the difference between the two learning procedures were tracked through an other series of NUM cycle runs initialized with either NUM default learning adult speakers and NUM unset learning adult speakers with or without memory limitations during learning and parsing speaking one of the eight languages described above
each step for a learner can be defined in terms of three functions p setting grammar and parser as parseri grammar p setting sentence j a p setting defines a grammar which in turn defines a parser where the subscripts indicate theoutput of each function given the previous trigger
generalized categorial grammar gcg extends cg with further rule schemata the rules of fa ba generalized weak permutation p and backward and forward colnposition i c bc are given in figure NUM where x y and z are category variables is a vm iable over slash and backslash and
this suggests that if the human language faculty has evolved to be a right branching svo default learner then the environment of linguistic adaptation must have contained a dominant language fully compatible with this minimal grammar
critical period yes figure NUM the simulation options cost benefits per sentence NUM NUM summed for each lagt at end of an interaction cycle and used to cal culate fitness functions NUM NUM
basic categorial grammar cg uses one rule of application which combines a functor category containing a slash with an argument category to form a derived category with one less slashed argument category
for example the atomic categories n np and s are each represented by a parameter encoding the presence absence or lack of specification t f of the category in the u g
an organization object consists of NUM the organization s name NUM all aliases for that name found in the text NUM one descriptor phrase NUM the organization type NUM the organization s locale an d NUM country
effort the ne and te modules of louella were developed over the spring and summer of NUM b y two experienced system developers one focusing on the ne task and the other on the te task wit h an emphasis on reference resolution
james NUM years old is stepping down as chief executive officer on date lcb NUM rcb july NUM and will retire as chairman time relative lcb NUM rcb at the end of the year
overall louella s performance was near the top in all tasks with f measures within si x percentage points of the top f measures in named entity within four in template element an d within five in scenario template
in most cases the sentence clues which will tell the system whether a person is in or out of a position and whether the person is still on the job are also the clues for the succession event itself
the top level template of interest is the succession event which is comprised of succession org an organization template element post a string fill in and out a relational object about each person involve d may be more than one
the st effort runs the gamut from domain specific application design through rul e construction and specialized coding however the lockheed martin nltoolset system provides a basic framework for building an information extraction application which greatly reduces th e amount of effort required
if an organization changed or plans to change its name the old or future name is linked to the current name and the system symbol for the current name is used by all references to eithe r name
this message was improved by expanding the definition for one of the floating phrases i e macros which make up all ingress and egress patterns and by inserting a buffer phrase into one of the egress patterns
figure NUM the la derived from wrote
dependency feature strueture dfs of the
there are two potential causes of non termination
the other is a partim unification routine
consider the rule schema in figure NUM
consider the parse tree in figure NUM
the grammar for the speech recognition module consists of a vocabulary of just over NUM words and a set of about NUM rules that support the recognition of approximately NUM NUM million utterances
the original np algorithm assigned boundaries wherever the three values coref infer global pro
even if the parser has been tested only in the direction giving domain where the behavior of prepositions is very consistent it shows that a mixture of lexical and structural information is needed to solve the problem successfully
NUM the purpose expression is therefore stated first to set the appropriate context for interpreting the prescribed sub action
then in order to apply his method to key paragraphs extraction we calculated the sum over all sentences for each paragraph and sorted the paragraphs according to their weights
this would correspond to the knowledge that could be extracted from an on line dictionary or through morphological and distributional analysis
the most accurate stochastic taggers use estimates of lexical and contextual probabilities extracted from large manually annotated corpora eg
expanding the training set to NUM NUM words and testing on the same test set accuracy increases to NUM NUM
in this experiment a training set of NUM NUM words and a separate test set of NUM NUM words were used
a better method is to identify groups of words that create meaningful phrases especially if these phrases denote important concepts in the database domain
in addition the top n highest idf matching terms simple or compound were counted more toward the document score than the remaining terms
we have also demonstrated that overtraining a problem in baum welch training is not a problem in transformation based learning
in table NUM we show tagging accuracy on a separate test set using different sizes of manually annotated corpora
we presented in some detail our natural language information retrieval system consisting of an advanced nlp module and a pure statistical core engine
this algorithm works by iteratively adjusting the lexical and contextual probabilities to increase the overall probability of the training corpus
with unsupervised learning the learner does not have a gold standard training corpus with which accuracy can be measured
the user can also ask about the state of various aspects of the simulation is the simulation running what is the time increment
this approach also works for anaphoras
objects remain selected until the user points to another object or explicitly deselects the selected object
for example the time interval of the relation live in i expressed by koen woont in nijmegen
in the bottom left corner of the viewport a garbage container and a copier are displayed
the viewport shows only part of the model world window which in principle extends indefinitely
in section NUM NUM we briefly describe the way the two alternative referent resolution models work
that is an indicated cf and a selected cf are created for each of them
find the report about gr2 2a kopieer alle rapporten behalve dit
the pointing gestures that the system produces have been designed not to interfere with user selection
their model consists of two separate mechanisms each resolving a specific type of referring expression
here a clearer view on the variety among subjects in the way of referring is presented
the purpose expressions can be textually placed in the slot either before or after the expression of their sub actions
use the original seril feature to represent the semantic value that will be chosen when the p is combined with an np p lcb lex for sem chosen semvalues for benefactive for time period for directional rcb
thus difficult to evolve image maps superimposed over bitmaps of each application window
it is for example rather common for a standard grammar checker to discourage repetition
thus many initiative changes are done implicitly based on which goal is being solved
a typical case might be a feature encoding the identifier of a particular lexical item in english for example the various forms of be often require extra constraints or relaxation of constraints which do not apply to other verbs
the intention of the present paper is to describe them all in an accessible form hence the more tutorial tone than is usually found in this journal and thus attempt to narrow the gap between rich grammatical formalisms and efficient practical implementations
how this is done depends entirely on what features and categories are involved we could use boolean combinations of atomic values category valued features or as in the example below a pair of term valued features left and rlght
we define for each domain five features earlier called store left right in and out whose values will be tuples of length n where there are n different categories figuring in the partial order declaration
in section NUM we provide background on automated documentation and identify where cogenthelp fits in this picture
the feature and value will be of the form selectors a a b b c c
the technique has a wide variety of applications and can be astonishingly effective in reducing the number of explicit alternative entries or rules that need to be written at the cost of a few extra features that cost nothing in terms of processing time
is in control and the other agent is passive the master slave assumption
cogenthelp generates html help files which can be displayed on any platform for which a web browser is available
3a darpa sponsored technology reinvestment program for rapid object application development led by andersen consulting
in this paper we will only work towards a solution for the first two problems
to make feature ranking computationally tractable in della pietra et al NUM and berger et al NUM a simplified process proposed at the feature ranking stage when adding a new feature to the model all previously computed parameters are kept fixed and thus we have to fit only one new constraint imposed by a candidate feature
i present a simple example in figure NUM for the sake of clarity
we intend to test the hypothesis flint this method extrapolates to dl the above types of modification as well
this move is again related to file issue of grain size of semantic description
the frst such relation is owned by as in federal adjl in the sense of owned by a federation
the mikrokosmos analyzer treats modification by attempting to merge the meanings of the modifiers into the meanings of the modified
the work was based on the set of over NUM NUM english and about NUM NUM spanish adjectives obtained from task oriented corpora
big will then be assigned a vahte of NUM NUM value on the size scale
NUM and NUM below are shown only partially where they conwast with NUM
we deliberately settle on a grain size of description coarser than the most detailed semantic analysis possible NUM
once an organization or person has been linked to all its variations in the article the te sys tem chooses the best name for the element and relegates the rest of the names to the alias catego ry
in the combination step the fragments from a partial parse are assembled into a set of meaning representation hypotheses
the method is suitable for speech translation because it allows efficient bottom up processing
the positions to the right of the head are numbered analogously with positive integers
for instance from the parse tree for slenose serve de le tronc eommun gauche NUM cf
the head transducers in the experimental system have a wider range of output positions than input positions
this restriction means that the transduction search can be carried out with the type of algorithm used for
this is particularly important when the input is in the form of word lattices
we believe that head transduction models have certain advantages that help satisfy these requirements
sri has been focusing its research efforts on developing the domain portability tools necessary for users to customize generic ie and capture previously unidentified entities in text
details of the experiment are presented in alshawi buchsbaum and xia NUM
the method used to assign the cost parameters for the model can be characterized as supervised discriminative training
sri participated in the architecture working group awg meetings and aided in the design testing and implementation of the tipster document manager architecture
in the nuance regular expression notation indicates a sequence and indicates a set of disjunctive alternatives
in this paper we concentrate on the first issue
it ilnplies to transduce the trees resulting fl om different parsers to a common fornlat
however the computation of NUM is much more complex than NUM since it requires to examine a given derivative before it can be positioned in the stack
the positive outcome of the experiment is that half as much training data would have given almost equivalent performance
and a few systems such as clarit which uses simplex noun phrases attested subphrases and contained words as index terms and new york university s trec systems which uses head modifier pairs derived from identified noun phrases have demonstrated the practicality and effectiveness of thorough nlp in ir tasks
however a frequency of NUM in a corpus of NUM sm words is quite high
the jk system is half this size
this paper presents the results of an intercoder reliability study a model of temporal reference resolution that supports linear recency and has very good coverage the results of the system evaluated on unseen test data and a detailed analysis of the dialogs assessing the viability of the approach
for each language we have a held out development test set and a held out blind test set
is calculated as follows where the numerator is the average percentage agreement among the annotators pa less a term for chance agreement pc and the denominator is NUM agreement less the same term for chance agreement pe
resolved to mon NUM aug how about NUM resolved to 2pm mon NUM aug hmm how about NUM resolved to 4pm mon NUM aug see also NUM NUM in the example from the corpus
in the case of anaphoric relations this factor gets adjusted by a term representing how far back on the focus list the antecedent is in rules a1 a4 in section NUM NUM the adjustment is represented by distance factor in the calculation of the certainty factor cf
however so that the instructions will be applicable to a wider class of dialogs we decided to be conservative with respect to filling in an ending time given the starting time or vice versa leaving it open unless something in the dialog explicitly suggests otherwise
the worm vectors are then sorted according to w w w i
it would be impractical to use a parser with the speed of one or two sentences per second
in addition we are also able to provide a more detailed error analysis of the english segmentation since the author can read english but not thai
however single words are rarely specific enough to support accurate discrimination and their groupings are often accidental
this hinders somehow the decoding so that the right solution is sometimes just not available
complex nps are defined as a sequence of simplex nps that are associated with one another via prepositional phrases
for our purposes we need to be able to identify all simplex and complex nps in a text
in this sense extraction of such small compounds is a step toward a shallow interpretation of noun phrases
they can be reactivated by the parsing if necessary to achieve a complete analysis
enablement in english is expressed most fl equently by sequenci
for enablement some discourse markers are exclusive and some ambiguous
null the approach also reveals some interesting facts about the individual languages
three strong patterns emerge in the data
figure NUM expressions of generation french
figure NUM expressions of generation english
figure NUM expressions of enablement portuguese
figure NUM expressions of enablement english
purpose is also a frequent interpretation
me maximum entropy principle laynes57 provides the method to combine information sources consistently and the ability to overcome overestimation problem by maximizing entropy of the domain with which the training data do not provide information
it orl i algorilhnt l orncy73 is the one gonerally used to liw l t he t a g so luencc which safislies NUM aim i ttis algoril hnt
posterior t rol alfility of l hc words sequence o win lcb low size n csl ecially NUM in this lliodel i y colillting the entry oil training data
where hi is l elilljel i tllre is norlnalizing coil sl tllt called partition ftlllclioil aald u is etlergy fimct ion
the adequacy of this definition is judged by the effects centered discourse segmentation has on the validity of anaphora resolution cf
we can run out of new inferentially independent properties of arguments
clause NUM b partially satisfies that expectation by raising a hy null pothetical situation along with the expectation of learning what is true in such a situation
note that the standard definition of drt does not provide any description of types or other abstract entities
discourse NUM shows that for the german preterite the initial point is focussed by the viewpoint
this research was supported by a phd scholarship hspii aufe awarded by the german academic exchange service daad
the following discourse will furthermore show that also the imperfective view is not applicable to the german preterite
NUM however she restricts her analysis to single sentences and neglects the effects viewpoints can have in a discourse
in la the vp fuhr nach hause refers to a completed event and therefore contains an end point
at first the defendant had an accident and then he drove home NUM der angeklagte hatte einen unfall
in particular we investigated the effects this viewpoint has on a discourse level and compared it with english
note that the english translation of lb is therefore only correct if an imperfective view is used
the experiment with part of speech tagging indicated that taggers make a number of errors and our current work is concerned with identifying those words in which a difference in part of speech is associated with a difference in meaning e.g. train as a noun and as a verb
if the translation is the same as in the corpus then it is judged as correct
although one can only speculate now on the reason for this phenomenon it does make a difference to incremental analysis as we try to show in section NUM NUM
NUM a ocuk top a kaleci ye bakar ken vurdu child ball dat goalkeeper dat look adv hit the child hit the ball facing the goalkeeper b ocuklar yiiriir ken tan toplamt lar children walk adv stone picked the children had picked stones while walking c uzun kol lu g6mlek long sleeve adj shirt shirt with long sleeves d
NUM execute a path through the head automaton m starting at state q and ending at state q with a finite stop action cost c olq m
the work on cost functions and training methods was carried out jointly with adam buchsbaum who also customized the english model to atis and integrated the translator into our speech translation prototype
a head automaton is a weighted finite state machine that writes or accepts a pair of sequences of relation symbols from r rl r
the machine consists of a finite set q0 qs of states and an action table specifying the finite cost non zero probability actions the automaton can undergo
c discriminative the positive counts as in the probabilistic method together with corresponding negative counts from bad translations or incorrect attachment choices were used to compute log likelihood ratio costs
for primary nodes nl and nj of two distinct entries ei and ej gi ni and gi nj are distinct
lexical matching phase the algorithm for lexical matching has a similar control structure to standard unification algorithms except that it can result in multiple matches
the user may ask information to the system which provides guidance for the user decision
finally we return to the more general discussion of representations for machine translation and other natural language processing tasks arguing the case for simple representations close to natural language itself
when a node with label w immediately dominates a node with label w via an arc with label r we say that w is an r dependent of the head w
they also would like to acknowledge the aid given by jose marl arriola kepa sarasola and ruben urizar who work in the ixa taldea of the computer science faculty above mentioned
we investigated two approaches for relating senses with respect to morphology and part of speech NUM exploiting the presence of a variant of a term within its dictionary definition and NUM using the overlap of the words in the definitions of suspected variants
the authors would like to acknowledge the work of the rest of the members of the laboratory of human computer interaction of the computer science faculty of the university of the basque country
when the user starts typing a string of characters a the predictor offers the n most frequent words beginning by this string in the same way they are stored in the system
temp relns e2 precedes el e3 just after el b
the correct temporal relations are shown in NUM
consider NUM a sam rang the bell
if there are no existing threads a new thread is started
it fell through a hole in his pocket e
cue word cues to rhetorical structure e.g. because
sem aspect contains the semantic aspect event state activity
within a rule it could be possible to define concordance amongst the components of the right part either in gender and or in number
rhet reln the relation between this dcu and a previous one
it would be undesirable for the temporal processing mechanism to postulate an ambiguity in this case
if we replace words with senses we are making an assertion that we are very certain that the replacement does not lose any of the information important in making relevance judgments and that the sense we are choosing for a word is in fact correct
the reading bill said that john revised bill s paper is missing
possibilities for word prediction methods that cope with the enormous number of different inflexions of each word are proposed using basque as the target language
then the method uses an amount of time o nn4
the problem we faced was how to logically arrange descriptions of widgets within a help page or set of help pages describing a window which is the basic unit of organization in cogent help
the text planner builds up html trees starting from an initial goal using information provided by the ikrs following the directives coded in cogenthelp s text planning rules
cogenthelp currently works with applications built using the neuron data cross platform gui builder while it is not dependent on this product in any conceptually substantial way using other gui builders would require a porting effort
note that while this type of inferencing is motivated by text planning needs it is still about the domain rather than about natural language communication and thus does not belong in the text planner itself
it is designed to support efficient navigation through the help system through the use of intelligent functionally structured layout as well as through an expandable collapsible table of contents and thumbnail sketch applet
in the case of the window shown in figure NUM the clustering procedure performs exactly as desired yielding precisely the groupings used in the description of this window given above
this design provides maximal realism for the author especially since one can switch between editing and browsing mode at the click of a button to preview the generated help
which require the help author to start from scratch each time the skeleton is regenerated in response to gui modifications cogenthelp supports help authoring throughout the software life cycle
to illustrate the problem consider the sample application window shown in figure NUM from a prototype of an application under development by our trial user group at raytheon
in the training phase given a collection of examples we may repeat this process a few times by iterating on the data
ceiving a strict interpretation from the second
for example possible classes categories may be lcb bond rcb lcb loan rcb lcb interest rcb lcb acquisition rcb
note that the total number of a links is o iwl
the strength is usually a function of the number of times f appears in d denoted by n f d
using NUM and a set of parameter distance functions that conform to the properties given as NUM NUM it is possible to quantify the difference between any job schema instance held in the database and the ideal job schema object specified by the user
a document is considered as a positive example for all categories with which it is labeled and as a negative example to all others
only rarely occurs in text categorization and thus the main use of the negative features is to tolerate the length variation of the documents
the input to this step consists of a polysemous word w0 and its selectors lcb l i l i v2 ivy rcb
it has two local contexts subject of employ subj employ head and modifiee of new adjn new rood
these techniques have the advantage that they are better understood from a theoretical standpoint leading to performance guarantees and guidance in parameter settings
we treat unknown proper nouns as a polysemous word which could refer to a person an organization or a location
the other three senses are NUM curiosity NUM interest group and NUM pastime hobby
in april beginning april am i on vacation end april have i still time in april
the algorithm does not require a sense tagged corpus and exploits the fact that two different words are likely to have similar meanings if they occur in identical local contexts
in our approach a local context of a word is defined in terms of the syntactic dependencies between the word and other words in the same sentence
where ic is a local context and c lc is a set of word frequency likelihood triples
NUM a local context of a word consists of an ordered sequence of NUM surrounding part of speech tags its morphological features and a set of collocations
search space for the deternfination of a verb s arguments
these phenoinena i ei resent two are as
ncither all the particular order of auxilia ries
very many of the errors to be discovered by the system can be traced down to mismatches of vmues of features projected into the synta ctic
this paper describes an efficiency supporting tool for one of the two grarnmar checker technologies developed in the fi amework of the peco2824 joint research project sponsored by the european union
consider the following interaction where the system actually misrecognized all the values provided by the user
the idea of detecting some particular cases of this error by a finite state automaton results from the combination of the following observa tions
in other words there is a price to be paid for the speed up of the error checking process by means of tile techniques proposed
where a t least one of them was not analyzed and hence also tile pa rsing including the pa rsing with rela xed
while the bulgarian system remained in more or less a demonstrator stage only the czech one satisfied macron s requirements as to syntactic coverage
holl etc do not qualify due to their part of sl eech ambiguity which means that ill senten es containing them this stra tegy
all the renlaining expressions have clear mnemonics and also the classes which they stand for do not contain elements which are ambiguous as to part of speech
the det e ti m of the latter error is also based on the czech interl unct ion
however it seems t o be self evident that the core idea is transferable t tiler languages
of a verb trace could be reduced from NUM to NUM while only NUM necessary trace positions remmned unmarked
are fairly straightforward in information seeking dialogues hence we only find a small set of communicative goals
we start with the nonterminals v lcb s rcb and the production set r
humans achieve efficiency by pursuing several goals at a time instead of dealing with single goals in a strict sequential order
the continuations are represented as partial trees and those chosen extends the parse tree at the appropriate open end
t ouowlng gv l maritk john lul
template c says that a verb phrase consists of an object followed by a verb
examination of the proof reveals that we have also shown the following two corollaries
this is a part of our overall effort in text and speech translation for limited dommn multilingum applications
in this paper we have described our ongoing work in automatic english to korean text translation of telegraphic messages
cbuftgefpart of the origiiial inesstgge NUM nd
for a fairly complex sentence containing NUM words it takes about NUM seconds to translate
both papers can be found in the anlp NUM conference proceedings
machine agents interact with each other in their own formal language
we are concerned with meetings all participants should attend and the date of which is negotiable
the server offers nl dialogue service to multiple client agent systems
his calendar is managed outside the scope of the systems
human language makes agent services available to a much broader public
systems available on the market allow for calendar and contact management
in a nonmonotonic model this constraint is relaxed to allow compact dictionaries discontinuous backoff and arbitrary context switching
the nl server is implemented in common lisp and c with a graphical surface written in tcl tk
the third person plans his appointments himself and interacts with other participants through nl e mail messages
any account applying only to the latter would miss an important generalization
lexical atoms are discovered at the same time during simplex noun phrase parsing
in the parsing stage each simplex noun phrase in the corpus is parsed
this is different from many current statistical nlp techniques that require a training corpus
NUM thus lexical atoms and small nominal compounds should make good indexing phrases
thus it may suffice to have a shallow and partial representation of the content of documents
this requires that the nlp used must be extraordinarily efficient in both its time and space requirements
an often cited example is the contrast between junior college and college junior
NUM lexical atoms are given score NUM this gives the highest priority to lexical atoms
word based indexing can not distinguish the phrases though their meanings are quite different
on the other hand some studies hope to build spoken language translation systems using a certain interlingua method
some words have several acceptable pronunciations aof t aut ut ananas anana ananas dompter dsmpte dste bat babil blet chenil exact but as but only one is stored in the electronic dictionary
however it can also be a difference of one or more segments deliberate di llborit adjective vs di llboreit verb use juls noun vs ju z verb differ in terms of only one segment
in fact at this stage in the technology it is still the rule set and not the dictionary that is the more dominant although this is beginning to change primarily due to the need for more complex lexical entry containing information on syntax semantics and even pragmatics for more natural prosodics in text to speech tasks
with the word finishing a context sensitive rule in ing would for instance produce the phonemes for ing plus a mark NUM indicating that the syllable is unstressed add a morpheme boundary mark in the input string which is then finish ing and continue the conversion from right to left starting on h of finish
similarly even if a rule to convert e to to handle words such as entree ntrei entente or entourage could be written it would be much easier and more efficient to put the words it affects in a dictionary because there are so few of them
it should be pointed out that the use of text in text string was not ascii but an encoded alphabet in which some grapheme pairs like qu gu and certain others were encoded as single letters because doing so made it unnecessary to have a large number of unnecessary blocking rules in the rules for the grapheme u
divay and vitale grapheme phoneme translation some characters may or may not be pronounced depending on the application punctuation spelling for instance NUM kg is a singular one kilogram but NUM kg is pluralfi ve kilograms
dictionaries and sets of rules will have to continue to coexist either as a dictionary of exceptions and a large set of rules or as a large dictionary and a set of rules to deal with exceptions
in this paper we concentrate only on the macroplanner and the drcc component
for example for rule NUM an anaphor is determined to be immediate if its antecedent occurs in the immediately preceding clause otherwise it is long distance
annotations associate arbitrary information attributes with portions of documents identified by sets of start end byte offsets or spans
in her theory there are seven status assignments a discourse space may have
step c i construct a similarity matrix NUM
none of these is similar to the body part meaning of heart
furthermore the vector derivation tree of synchuvg dl shares with the the derivation tree of dtg the property that it reflects linguistic dependency uniformly however while the definition of dtg was motivated precisely from considerations of dependency the vector derivation tree is merely a by product of our definition of synchuvg dl which was motivated from the desire to have a computationally tractable model of synchronization more powerful than synchtag NUM
it consists in restricting the cl checking to the detection of structural syntactic ambiguity complexity and violations of vocabulary constraints
when the module pre dates integration this is called a wrapper as it encapsulates the module in a standard form that gate expects
some systems support automatic substitution since we deal with truly ambiguous constructions we have to involve the user in making the choice
this accounted for approximately NUM of the errors
we think it is more user friendly to show the user exactly how the construction may be ambiguous and let her make her own choice
the latter stage gives us the bij probabilities directly
the parse forest representing the set of all parse trees in g with no more than q vectors can be constructed in an amount of time bounded by a polynomial function of q let gs be a synchuvg dl g and g its left and right uvg dl components respectively
as a result of these two generalizations nonmonotonic extension models can outperform their equivalent context models using significantly fewer parameters
the way these predicates interconnect is represented in figure NUM
it is not clear how well the system would be able to handh texts where multiple threads of contents occur possibly one couhl employ the method of texttiling here see e.g. which helps determining coherent sections within a text and thus could guide the abstracting system ill that it would be able to track a sequence of multit le topics in a text
i wish to thank lily supervisors steve finch and richard shillcock for vahlal le discussions suggestions and advice also i am grateful to chris manning for his comments on an arlier draft as well as t o the t wo allollyi lolls r wiewei NUM whose reillarks gre al ly helped in improving t his l aper
aln fact it t urned out that fact ors which couhl NUM e thought of as l ecitic for newspaper articles su h as increased weights for title words or sentences in the beginning did not have a sign titan eriect m the sys elll s per orntance
all tf idf values of the on eat words NUM for each sentence s NUM sort the sentences according to l heir weights and extract the n highest weighted sentences in text order to yield he abstract of the docllhleltt
this paper describes a system for generating text abstracts which relies on a general purely statistical principle i.e. on the notion of relevance as it is defined in terms of the combination of tf idf weights of words in a sentence
in corpora where abstracts are not already provided it might facilitate the retrieval process a lot if text abstracts could be generated automatically either off line to be stored together with tile texts e.g. as ranked sentence numbers or on line in accordance with the user s query
1degthe tf idf nmtho t proved itself better than all the other methods of weight computation which we tested see in particular those using a combination of w rious other heuristics as proposed e.g. in
encoding a finite state automaton as definite relations is rather straightforward
we can also select one description over another based on how recent they have been included in the database whether or not one of them has been used in a summary already whether the summary is an update to an earlier summary and whether another description from the same category has been used already
italy npnp s former jj prime jj minister nn silvio npnp berlusconi npnp figure NUM retrieved description for silvio berlus coni
currently the sys null tem includes an extensive finite state grammar that can handle various pre modifiers and appositions
currently we identify concepts such as profession nationality and organization
this would not be possible if only canned strings were used with no information about their internal structure
like this research our work also aims at extracting proper nouns without the aid of large word lists
this resulted in a list of NUM unique entity names that we used for the automatic description extraction stage
in this section we describe the extraction component of pro file the following section focuses on the uses of
null after several iterations the seed word list typically contains many relevant category words
dutch hoofd only refers to human head and kop only refers to animal head while english has head for both
all states including the initial state are final states
we have mixed stochastic and symbolic reasoning
pragmatics screens the lfs for acceptability
in this paper we concentrate on compound nominals
but the president hates the diet
as we will see in ss4 this means pragmatics
integrating symbolic and statistical representations the lexicon pragmatics interface
the relationships operate upon five different types of data entities word meanings instances ill records domains and top concepts
in cases where none of the entity parts are in a list of known things we use surrounding con text to identify the name
this document also contains an example of the difficulty in recognizing when a company nam e is being used as a modifier to a product
in this way the aligned wordnets can be used to help each other and derive a more compatible and consistent structure
by theorem NUM critical tokenization is in essence the union of the whole tokenization set and thus the compact representation of it
he claims that while a single occurrence of a topically used content word or phrase is possible it is more likely that a newly introduced topical entity will be repeated if not for breaking the monotonous effect of pronoun use then for emphasis or clarity
in summary the critical tokenization is crucial both in knowledge development for effective tokenization disambiguation and in system implementation for complete and efficient tokenization
furthermore the computational bookkeeping necessary for the delaying mechanism is very expensive
the proximity operator specifies that the query words must be adjacent and in order or occur within a specific number of words of each other
the only lexical knowledge used by our parser is a part of speech dictionary for syntactic processing
the first step toward remedying this procedure is to build a rigorous syntactic framework which can be used as a tem plate for rule variations
another divergence between what katz has done so far and what the task of subject boundary insertion requires is that he decides to ignore the issues of coincidental repetitions of non topically used content words and simply equates single occurrence with non topical occurrence and multiple occurrence with topical occur
in the following sections we first overview related work in the area of information extraction
murax also extracts information from a text to serve directly in response to a user question
phrases that we use represents a variety of different syntactic structures for both pre modifiers and appositions
we present an evaluation of the approach and its applications to natural language generation and summarization
see section NUM for more discussion of different computational approaches
database of profiles for each retrieved entity we create a new profile in a database of profiles
here is an example iv der direktor der firma abc sat im c f
while not fully automatic this approach yields rich and highly reliable seed sets with minimal work
in particular when the system encounters unknown words these will be considered equivalent to the null word
availability the basic system can be accessed for testing through the mail server ne tag crl nmsu edu
the second autolearn is a system which learns automatically from training data
the subject field of the message should consist of the word tag
in particular for organizations the headline is not processed apart from this last stage
unambiguous and ambiguous human first and last names titles and human position words
figure NUM shows the full set of four lexical rules
NUM differences between robert l james NUM between robert l james NUM etc
james mccann erickson was missed as no hyphenated company pat tern had been added
7indeed any supervised classification algorithm that returns probabilities with its classifications may potentially be used here
also it is fast and can be efficiently ilnplemented for both on line and off line purposes
the processing strategy is top down and depth first
we call such a rule c rule
figure NUM contains a sample gil expression
if not treated early on in a spoken dialogue system they weaken the dialogue interaction caught between running the risk of confusing the user with irrelevant interactions or annoying the user with repetitive confirmation checks
it is an lr type parser using a shift reduce algorithm with packed parse forests
the true value of the experimental approach can be seen when comparing the insertion rates
however we still believe that a simple morphological recognition module can be useful
for each of these affixes we constructed two lists of parts of speech
it was originally designed as a corpus for training phoneme recognition for speech processing
if the sentence fails to parse the parser goes on to pass three
the morpholo cal recognizer is used to determine definitions for all open class words
further it is a verb which takes a noun phrase as its object
thus the syntactic information can augment the morphological information and vice versa
in such an approach the need to generalize from input strings to input finite state automata is also clear
the connection predicate can be specified simply as the reflexive and transitive closure of the transition relation between states
the head corner parser is one of the parsers developed within the nwo priority programme on language and speech technology
we compare the sentence associated with the best path in the word graph with the sentence that was actually spoken
computational linguistics volume NUM number NUM assertz fail conn a b fail
suppose however that a specific robustness module is interested in all maximal projections anywhere in the sentence
an arbitrary nonzero probability is given to all chinese words and symbols that do not exist in the vocabulary
although our method is to calculate distributional classification it still demonstrates that it has powerful part of speech functions
the average mutual information i between events x x2 x n is defined similarly
if the height of the binary tree is h the number of all possible classes will be NUM h
therefore no packing is possible and the recognizer will behave as if it is enumerating all parse trees
in such cases the first phase may be said to be unsound in that it allows ungrammatical derivations
the probability is also introduced in our algorithm to merge the resulting classes from left right and right left binary tree
to utilize this directional information we induce the left right binary and right left binary tree to represent this property
in some sense expansion just re plays the derivation obtained in the past
remember that the relative order of the elements of a mrs is immaterial
the cm review process will result in a document which details the ways in which an application or vendor product conforms to the architecture design document and is in agreement with the tipster architecture design
first there is no case distinction in japanese whereas english names in newspapers are capitalized and capitalization is a very strong clue for english name tagging
NUM yaz tct lar a gor ev ler i bil dir il me mipti write vton plu dat able vton plu acc know caus pass neg asp tense
child nom book acc read tense NUM sg
causatives can be modelled in a similar vein
figure NUM lexical rule for non referential objects
clearly this ambiguity can not be resolved without incorporating into lexical semantics a qualia structure a la pustejovsky NUM or lexical semantic constraints NUM
there are other linguistic phenomena that are on the boundary of lexicon and syntax which we opted to contain in the lexicon e.g. non referential objects and valency change in the causatives
figure NUM also shows the derivation of the semantic representation for the case marked np at x y is a second order predicate that holds between a term z and a predicate y
this is achieved by modifying the head feature mod while the nominative marked noun has null value a modsyn value with verbal head is introduced in the head feature of the locative noun
for example entries for words beginning with ma but ending with any letter could be found by entering ma
if the object is non referential or indefinite cf
we have been incorporating these types of constraints
then the term translation module utilizes type information obtained by the indexing module to decide which translation strategies to use thus overcoming the second type of error
while there are a number of measures which can be used for representing the sir ilarity of labels in the step NUM measures which make use of relative entropy kullback leibler distance are of practical interest and scientific
the reason for using the modified version of contexted constraints in lemma NUM is that we can separate the representation of disjunctions into a conjunction of the values that the disjuncts can have called the alternatives and the way in which the we can choose the values called the cases
could contain ev m m re it f h m case case must ontain cxacl ly the disjuncl s ill cas aild l hcrcforc as t o j NUM ts
t o ilclak this t tur sl ructur wen more dlichuit by st litl ing all gro ip d into two new gt llps dl a nd NUM as showu lmlow
so for instance in the above example n might equal lcb NUM NUM NUM NUM rcb since there are four disjuncts in the original ease form while n might equal lcb NUM NUM rcb and n lcb NUM NUM rcb since the smaller case forms each contain two disjuncts
lemma NUM alternative case form NUM v c NUM is satisfiable iff al NUM c a a2 c a al v a2 is satisfiable where al and a2 arc new propositional variables
this the orem also allows us to trotform other comt inat orial short cuts su h as noting that if t he nunltmr of disjuncl s in the origimtl case torm is prime hen it is already modular
the parser is constructed by using decision tree learning techniques and can succeed up to NUM NUM of bracketing accuracy both recall and precision when tr ing with the wsj corpus a fully parsed corpus with nontermlnvj labels
where the arguments appended to each predicate hold input and output sentence respectively and where an accept predicate is inserted before each literal of the rule body NUM accordingly n sleep verb n n noun n
although the pure dg formalism proved to be particularly practical for integration of idioms and exceptions its lack of constituent symbols i.e. non terminals would have lead to a grammar of enormous size and made it difficult to integrate special latin constructs such as accusative cum infinitive or ablative absolute
NUM dependency grammar as context free grammar whereas context free grammars differentiate between terminals coding the words of a language and non terminals representing the constituents that are to be expanded the symbols of a dg uniformly serve both purposes like terminals they must be part of the sentence to be accepted or generated and like non terminals they call for additional constituents of the sentence
people from all walks of life has made the issue of generalized information extraction a central one in natural language processing
this paper proposes a method for correcting chinese repetition repairs and demonstrates the effects of repair processing in chinese homophone disambiguation
thus with a certain confidence c we can assume that if we used more training data the rule estimate would be not worse than the 7rl
e t l NUM can be looked up in the tables for the t distribution listed df df in every textbook on statistics
when we applied the ending guessing rules to these words the words return and stop were correctly classified as noun verbs nn vb vbp and only the word cost failed to be guessed by the rules
for instance in the sentence smaller dna fragments mave faster than larger ones the terms smaller dna fragments move faster larger are considered to be the most meaningful terms in the sentence
it shows the overall error rate on unknown words and also displays the distribution of the error rate and the coverage between unknown proper nouns and the other unknown words
an interesting behavior is shown by precision first it grows proportionally along with the increase of the threshold but then at high thresholds it decreases
brill s system has two transformations that our schemata do not capture when a particular character appears in a word and when a word appears in a particular context
therefore scoring systems must be able to recognize paraphrased information in sentences across essay responses to identify paraphrased information in sentences the scoring system must be able to identify similar words in consistent syntactic patterns
for instance in figure NUM it is reasonable to classify the brackets NUM c2 c4 and c5 into a same group and give them a same label e.g. np noun phrase
for the example in figure NUM the numbers of appearances of c1 c2 c5 art cz vi prep c2 null and vt es adv are collected from the whole corpus
in our experiments we restricted ourselves to the production of six different guessing rule sets which seemed most appropriate for english suffix deg suffix morphological rules with no mutative endings NUM
in our work like shirai s approach we make use of a bracketed corpus with lexical tags but instead of using a set of human encoded predefined rules to give a name a label to each bracket we introduce some statistical techniques to acquire such label automatically
dna s used as markers standards resolution concentration of gel apparatus use of wells gel material past b describe the results you would expect from electrophoretic separation of fragments from the following treatments of the dna segment shown in the question
change in i NUM band fragment change in iii NUM bands fragments alternate y no longer recognized and cut detail point y site might become an x site accordingly the computer rubric categories were the following
n z y this determines the rank r f as a function of the relative frequency f
finally assuming the asymptotic behavior of eq NUM we rederive the recurrence equation NUM
the two cases were generalized to a single spectrum of reestimarion formulas and corresponding asymptotes parameterized by one real valued parameter
in fact it is tempting to interpret turing s formula as smoothing the relative frequency estimates towards a geometric distribution
it states that asymptotically the relative frequency is inversely proportional to rank a
utilizing the fact that the relative frequencies should be normalized to one we find that
let r x be the rank of the last species with frequency count x
the alignments of mouth with mund and eye with auge gave the aligner some trouble in each case it produced two alternatives each getting part of the alignment right
if it turns out that the actual f marking is more restricted this will be detected at a lower level
somewhat surprisingly it was not necessary to use information about place of articulation in this evaluation metric although there are a few places where it might have helped
the s in this is aligned with the wrong s in dieses because that alignment gave greater phonetic similarity taking off the inflectional ending would have prevented this mistake
even so on spanish and french chosen because they are historically close but phonologically very different the aligner performed almost flawlessly tables NUM and NUM
that is of the three alignments ab c a bc abc a dc ad c adc only the third one is permitted pursuing all three would waste time because they are equivalent as far as linguistic claims are concerned
one reason is that alignment deals with syntagmatic rather than paradigmatic relations between sounds what counts is the place of the sound in the word not the place of the sound in the sound system
for example the correct alignment of latin dcr with greek did ymi is do didomi null and not do d o do didomi didomi didomi or numerous other possibilities
otherwise the aligner would start by generating a large number of useless displacements of each string relative to the other all of which have high penalties and do not narrow the search space much
discourse processing of dialogues with multiple threads
after the discourse see you then
or friday would also possibly work
when can you meet next week
all constituents that are not f marked need to be given where givenness is defined an entaihnent by prior discourse
that is the forward maximum tokenization the backward maximum tokenization and the shortest tokenization are all true subclasses of critical tokenization
this led us to bring forward the lexical matching stage during the expansion of the topmost stack element all its derivatives are looked for in the lexicon
the second one transforms reactor into factor and reaction into faction and consists in exchanging the prefixes re and f
the number of correct phonemes in a transcription is computed on the basis of the string to string edit distance with the target pronunciation
the first one transforms reactor into reaction and factor into faction and consists in exchanging the suffixes or and ion
the second series of experiments is intended to provide a more realistic evaluation of our model ill the task of pronouncing unknown words
under this view tile orthographical representation of individual words is strongly subject to their phonological forms on an word per word basis
we expect such a model to bias the search in favor of short words which are more represented than very long derivatives
the idea is to rank the stack of analogs according to the expectation of the number of lexical derivatives a given analog may have
the top scoring system the baseline configuration of the sra system achieved an f measure of NUM NUM and a corresponding error score of NUM
the error scores for persons dates and monetary expressions was less than or equal to NUM for the large majority of systems
NUM the user only determines the components of a new phrase local tree of depth NUM while both category and function labels are assigned automatically
two of the slots vacancy reason and on the job had to be filled on the basis of inference from subtle linguistic cues in many cases
further simplification may be advisable in order to focus on core information elements and exclude somewhat idiosyncratic ones such as the three slots described above
errors of these kinds result in a penalty at the object level since the extracted information is contained in the wrong type of object
the three larger organization objects that none of the systems got perfectly correct are for the mccann erickson creative artists agency and coca cola companies
of the NUM texts in the test set NUM were relevant to the management succession scenario including six that were only marginally relevant
in particular problems remain with normalizing various types of date expressions including ones that are vague and or require extensive use of calendar information
tiffs means that the adjacency constraints given by the nested levels must be obeyed in the bracketings of both languages
we confine ourselves to bracketing transduction grammars btgs which are iitgs where constituent categories ate not differentiated
the algorithm employs a rebalancing strategy remniscent of balanced tree structures using left and right rotations
because the btg is in normal form each bracket can only hold two constituents
the mapping is many to many with an average of NUM NUM chinese translations per english word
in the monolingual view extra brackets appear in one language whenever there is a singleton in the other language
although finite state transducers have been well studied they are insufficiently powerful for bilingual models
we refer to the direction of a production s l2 constituent ordering as an orientation
with singletons there is no cross lingual discrimination to increase the certainty between alternative brackeaings
we now introduce an algorithm for further improving the bracketing accuracy in cases of singletons
they are all treated as siblings of nk s regardless of their position in situ or extraposed
however fill or partial disambiguation takes place in context and the annotators do not consider unrealistic readings
this work is part of the dfg somlerforschungs null we wish to thank ta nia avgustinova berthold crysmann la rs konieczny stephan oepen karel oliva christian wei6 and two anonymous reviewers lcb or their help ul comments on the content of this paper
dependency type complemcnls are fllrther classified according to features su h as category and case clausal complements oc accusative objects oa datives da etc modifiers are assigned the label mo further classification with respect to thematic roles is planned
consider qui verbs where the subject of the infinitival vp is not realized syntactically but co referent with the subject or object of the matrix equi verb NUM er bat reich zu kolnlnen he asked me to come mich is the imderstood subject of komm u
their further classification must reflect different kinds of linguistic information morphology e.g. case inflection category dependency type complementation vs modification thematic role etc NUM however there is a trade off between the granularity of information encoded in the labels and the speed and accuracy of annotation
as modern linguistics is a lso becorning rnore aware of the irnportance of larger sets of m turally occur null ring data interpreted corpora are a valuable resource for theoreticzd and descriptive linguistic research
categories no special nlechanisnls tbr handling disconti null nuous constituency the current tagset conlprises only NUM node labels and NUM function tags yet a finely grained cla ssification will take place in the nea r future
NUM my uncle peter smith NUM tier sehr 41iickliche the very lta ppy tire very ha pl y one in NUM different theories make different headedness predictions
state incorporated it was occupied by prussian troops and incorporated into prussia i t NUM the category of the coordination is labeled cvp here where c stands for coordination and vp tar the actual category
while our algorithm will necessarily miss some valid translations this is a worst case scenario
NUM does the filtering process we use sometimes cause our algorithm to omit a valid translation
first because they are opaque constructions they can not be translated on a word by word basis
for interlingua systems identification of collocations and their translations provide a means of augmenting the interlingua
a bilingual list of collocations could be used for the development of a multilingual information retrieval system
this sampling problem which generally affects all statistical approaches was not addressed in the paper
in contrast NUM note that the correct translation is really a single word in contemporary french
we carried out three tests with champollion using two database corpora and three sets of source collocations
here we examine two more computational linguistics volume NUM number NUM cases associated with specific tests
of the four candidates aujourd hui shown in bold is the only correct translation
first the out of vocabulary rate is significantly higher
expanding the domain of a multi lingual speech to speech translation system
in this paper we describe our recent preliminary efforts to expand the domain of coverage of the system from the rather limited appointment scheduling domain to the much richer travel planning domain
travel planning contains a number of semantic sub domains for example accommodation events transportation each of which has a number of sub topics such as time location and price
we experimented with adapting the esst acoustic models by using the etd speech as adaptation data but both the mllr and map adaptation methods did not reduce the word error rate any further
but from a procedural perspective it captures try to attach the drs based on the most probable senses first if it works you re done if not try the next most probable sense and so on
however they assign numerical penalties or preferences to inference chains based on domain specific information
in addition to annotations which provided information about segments of a document a document could have attributes which specified information about the entire document e.g. its source or creation date
a retrieval engine would take a collection and a query and return a smaller collection of relevant documents with an attribute on each document indicating its degree of relevance to the query
the demos to show the benefits of the architecture and to push its further development the government asked the cawg to create an integrated demo system for the NUM month tipster meeting in november NUM
the cawg initially met once a month in washington in the intervening four weeks the committee chair prepared a revised design document to reflect the changes and additions proposed at the previous meeting
it has n t quite worked out that way
how could delivery of this technology be improved
the objective was certainly quite ambitious to create an architecture which could satisfy the needs of a wide range of text analysis applications and could be implemented efficiently enough to support operational systems
the government proposed an approach of identifying a core set of services needed by a broad range of text analysis applications and defining a standard set of functions and interfaces for these services
2bbn hnc martin marietta and the univ of
this demo led to several revisions to the architecture
figure NUM the complete event type c
scribe types of eventualities is currently being investigated
the use of clause level syntax to generate syntactic variants of a semantic pattern is even more important if we look ahead to the time when such patterns will be entered by users rather than computational linguists
the same or similar techniques can be used at retrieval time when parsing a user s query
several factors each cause the quality of information extracted to decrease on speech input
here we consider this second process
if a proposed candidate is explicitly or implicitly accepted by the user the dm sends a message containing the validated task to the control unit requesting modification of the status of the berlin system according to the user s wishes
ontological promiscuity offers a simple syntax semantics interface
remove i oej if NUM c vfin barrier svoo link not NUM svoo
this however is a matter of output formatting for which the system makes several options available
figure NUM a dependency structure for the sentence joan said whatever john likes to decide suits her
the rule states the first noun phrase head label to the right is a subject c sub j link subj exists and is followed up to the finite verb c f in a verb chain v ch which is then followed up to the main verb
total in addition figure NUM shows the total processing time required for the syntactic analysis of the samples
if then a verb with subcategorisation for objects is encountered an object link from the wh pronoun is formed
the overall result is that the rules in the new framework are much more careful than those of engcg
our grammar incorporates two additional principles
null this setup makes it possible to operate on several layers of information and use and combine structural information more efficiently than in the original constraint grammar framework without any further disadvantage in dealing with ambiguity
for example the subject label sub j is chosen and marked as a dependent of the immediately fol null lowing auxiliary auxmod in the following rule select subj if 1c auxmod head to get the full benefit of the parser it is also useful to name the valency slot in the rule
composition in steps like this one
this proved to decrease the number of required iterations to fit the simplified model by about tenfold which makes a tremendous saving in time
figure NUM sample sdts and derivation
kupiec et al present the results of a study where NUM of the sentences in man made abstracts were close sentence matches i.e. they were either extracted verbatim from the original or with minor modifications p NUM
a murder mystery domain was created with NUM suspects
the dreaded occur check must be performed
NUM NUM grammar development in sorted feature formalisms
abstraction also ensures that databases e.g.
NUM NUM efficient processing based on logic grammars
NUM NUM combining logic grammars and sorted feature formalisms
terms NUM NUM compilation of sorted feature terms
a sentence that allows an ambiguous fragment to have multiple word boundaries will end up with more than one interpretation
in section NUM we compare our model with others and explore areas for future research in section NUM
it is inevitable that the system would make such a mistake as our linguistic descriptions have not yet covered this phenomenon
the decision about which structure should win is therefore a random one as both have an equal probability of success
the computational temperature is in turn used to control the amount c f randomness in the local action of codelets
among all codelet instances that exist in the coderack only one of them is stochastically selected to execute each time
in contrast people are extremely flexible in their perception of word boundaries of ambiguous fragments appearing in different sentential contexts
decision making is stochastic with the amount of randomness being controlled by a parameter known as the computational temperature
since rare patterns are the majority the quality and coverage of lexical learning may result severely affected
there are reported cases in which the use of wordnet worsened the performance of an automatic indexing method
it is widely accepted that tagging text with semantic information would improve the quality of lexical learning in corpus based nlp methods
this may well cause a shift of the reference scoring function as compared with the real scoring function
however overambiguity of on line thesaura is known as the major obstacle to automatic semantic tagging of corpora
selected categories vary according to the domains but the size of the best set stays around the NUM NUM categories
on the contrary experiments are described in which head corner and left corner parsers implemented with selective memorization and goal weakening outperform standard chart parsers
the model parameters for category selection has been tuned on semcor but a correctly tagged corpus is not strictly necessary
however nothing can be said on the appropriateness of a specific sense aggregation and or sense elimination for a word
most linguistic theories assume that among the daughters introduced by a rule there is one daughter that can be identified as the head of that rule
however transformations toward the end of the list contribute very little to accuracy applying only the first NUM learned transformations to the test set achieves an accuracy of NUM NUM applying the first NUM gives an accuracy of NUM NUM
NUM the training we did here was slightly suboptimal in that we used the contextual rules learned with unknown words described in the next section and filled in the dictionary rather than training on a corpus without unknown words
this work has been supported by the nato
to produce the surface form of the generated words
if for typical input word graphs the number of transitions per state is high such that almost all pairs of states are connected then this may be an option
null with all the complex transfer issues resolved in the transfer phase the corresponding turkish case frame is generated which is then translated froln its prolog notation into the lisp notation required by the generation module
the adjective weak has to be mapped onto an adverb hafifce and the verb give s default translation into the verb ver has to be blocked when it is used with the dependent noun cough
an example sentence and parts of the corresponding english and turkish case frames can be seen below other in a number of ways because the generator requires additional information to form an equivalent turkish representation
in the first sentence the topic is found to be program and the focus is kullanici whereas in the second sentence the topic and the focus are kullanici and program respectively
the difference between the two sentences is distinguished by the order of the phrases in the target sentence as seen in the example below NUM this program was given to the user
an english to turkish machine translation system using structural mapping
NUM to generate an intermediate representat ion
we think that such an expectation in the case of the epa reading of erst is only optional
the decisive feature however why the above argumentation for the incompatibility of the r reading and the presence of temporal localizations does not go through is the fact that the background temporal localization does not uniquely ix the occurrence time of the event with regard to the time fralne of the presupposed plan or expectation
NUM petra war iiberrascht well a peter erst on stuttgart war r b peter erst urn NUM NUM v in stuttgart war r NUM confirms what we have said so far
this is not the case it there is no background event i ype at all i.e.
this index triggers the projectiou routine that is sl ecific to the respective resolution l robhm
i lcb eea NUM a can not have the r reading
the algorithm selects all new nodes which have a named individual ran k
now if this is true and it the ev ml descrit tion contains a teml orm location hi t hc focus this information can not be used att ibu tively because it contributes to the antecedenl de scription and to the distinction of this antecedent fi om its alternatives
l ocns 13ackgr mnd cr terion the f l a reading is ac l tm h only if the scol c of c rst is structured into focus and bac kgrouml in such a way that the background is a specitic event type
figure NUM non local derivation in nlsynchtag
one set of weights is better than another if its divergence from the empirical distribution is less
a random field defines a probability distribution over a set of labeled graphs f called configurations
in short in the av case the erf weights do not yield the best weights
more extremely if we generate a random corpus of size NUM from ql it is quite
finally in d the only remaining node is the bottom most node labeled a
a prerequisite for gibbs sampling is that the configuration space be closed under relabeling of graph nodes
deg odeg figure NUM rule applications in a parse tree
NUM intersections between textref sequences of a particular concept are removed
features can be combined by adding connecting arcs as shown in figure NUM for example
eriday thursday saturday sunday monday wednesday tuesday
however such methods clearly have a number of limitations
this potentially allows disambiguation performance to be examined at any time
also present are groups of words whose members all commonly precede or follow a particular particle
ninety pounds years days minutes hours double es
for these reasons current work is focusing on the problem of disambiguating words given statistical context
this process of developing concepts for abstract words is one which psychological research has tended to ignore
following the creation of these vectors heirarchi null word or immediately following the target word
thus one can usefully examine the state of the clusters at any point during learning
chinese language tutoring a mixed english and chinese grammar allows detection of students o f chinese using english constructions and diagnosis of problems
the most important part of th e core is the large knowledge base which we call the semantic network semnet or net for short
the lowest cost set is ordered on the basis of several heuristics on the form of the tree for example preferring a deeper tree
the order used has been developed by tria l and error to get the desired meaning in the majority of cases in a small test set
m a a a rcb c rcb v a a rcb i6mj6n aj6nic m where each a rcb is a new propositional variable and n lcb l n rcb
NUM many matches if none of the previous states are reached the database query must have resulted in too many matches i.e. not enough information was supplied by the user to match only a single or a few database items
japanese sound sequences in lower case g o r u h u b o o r u and katakana sequences naturally t
we also measured the performance of the system with half the training data or slightly more than NUM NUM words of text
when word separators are removed from the katakana phrases rendering the task exceedingly difficult for people the machine s performance is unchanged
it is robust against ocr noise in a rare example of high level language processing being useful necessary even in improving low level ock
high level systems for the purpose form system network
they are analogous to penman s sentence level inquiries
imagene s network consists of approximately NUM systems
all other examples are contrived for explanatory purposes
clauses and phrases are represented in separate tables
the sequence and concurrent relations are such relations
that this structure is currently built by hand
the form feature specifies the general grammatical form
however the given estimation is necessary in order to normalize the sum of the probabilities p clw to one
there are two types of results that imagene supports
the first is the analysis of instructional text itself
the argmax operation denotes the search problem
this case corresponds to skipping a word
table NUM shows the effect of the global reordering for two sentences
was obtained as opposed to NUM NUM 7c presented in this paper
o rep seme la cuenta de la l abitacidn ochocientos veintiuno
a we booked two double rooms with a bathroom please
these general categories of performance measures can be broken down into more precisely defined and quantifiable measures
van den berg et al indicate how a corpus of this sort may be used for data oriented semantic interpretation
finally figures NUM and NUM show results for differing training set sizes using subtrees of maximal depth NUM
the slot value assignments are defined in a way that corresponds closely to the linguistic notion of a ground focus structure
the semantic types not occurring in any of these pairs are grouped together and treated as equivalent
these distributional differences are reflected in our corpus and the ordering difference i is of particular interest
in everyday conversation humans are adept at marking topic boundaries and changes
seldom in human to human conversation does the dialogue break down
keywords evaluation comparisons generic model standards
to begin with seven such features are described below
in retrospect the ranking of each feature needs to be made consistent
it is interesting to note that different respondents interpreted the ranking differently
by incorporating anaphora a speaker can reduce redundancy and economise their speech
the low ranking of the interaction strategy reflects the application of the system
the inadequacies of speech recognition technology introduces additional potential deviations
without modelling ellipsis dialogue can appear far from natural
the corpus study results in rules for cue selection and placement that will then be exercised by our text generator
it should be noted that the term type is in this context rather vague
in both cases one works with transcribed data to which the guidelines are then applied
current communication devices designed for non speaking users are inadequate to support conversation because the speed with which a user can input information is typically very limited
each lexical rule used for compounds will license a great many modifiers for large number of potential heads
the function of the modifier bread is to specify the third argument of the cut act relation
we have chosen to use phrase structure schemata rather than lexical rules on the basis of storage considerations
the lexical representation of the compound also contains an attribute dtrs containing a head and a mod value
the preferred interpretation of this compound is that it is a knife which is used to cut bread
the content of the chosen candidate is then matched against the outputs of the various phrase structure schemata used for italian
an important feature of this approach is that it utilizes resources which are independently needed for analysis of the languages involved
the most obvious of these is the use of a cross linguistic approach to complex nominals in machine translation
the most likely interpretation from the candidate set is picked on the basis of contextual and statistical models
by an indirect route it allows the automatic identification of the predominant sense of a word in a given text or subject topic
the pertinent information in the three databases for this word is listed in parts a c of figure NUM
yet the cell for NUM x pp t0 iiif rs is generated in our matrix because of the overly general specification of verb frames in wordnet
the process is highly domain dependent i.e. the same set of words will be partitioned in different ways when the domain changes
it also supplies limited subcategorization information in the form of allowed sentential frames verb frames for each sense
this increased difficulty applies even to statistical methods because of the large number of alternatives and the likely closeness in meaning among them
second it reduces the ambiguity of a given word without sacrificing accuracy insofar as the three input knowledge sources are accurate
to further restrict the size of the set of valid senses produced we are currently exploring domaindependent automatically constructed semantic classifications
it reduces the ambiguity by NUM but has the combined error rate of both methods in this case NUM
in another experiment we looked at a specific corpus taking into account the frequency distribution of the verbs in it
in general this assumption is wrong but for the atis domain it may not be unreasonable
figure NUM synchuvg dl grammar for quantifier
they can follow the verbs which are dynamic d
utterances in dialogues are often closely related for instance one utterance may be a prompt and another utterance may be its response and the proper translation of a response often depends on identification and analysis of its prompt
translation equivalent selection enables the user to directly manipulate target language expression
firstly we list the proportion of utterances for which the corresponding semantic units exactly match the semantic units of the annotation match
this is convenient in this application because most ambiguities that arise such as ambiguities of scope do not need to be resolved
from a linguistic perspective the ovis grammar can be characterized as a constraint based grammar which makes heavy use of multiple inheritance
the following is repeated until a final node is found with an optimal triple NUM pop an optimal element from the queue
irrelevant differences between annotation and analysis were ignored for example in the case of the station names cuijk and cuyk
each path should be an instance of some major category from th grammar such as s np pp etc
speech act based ai approaches normally make reference to mental attitudes and often provide links between the surface form of the utterance and the mental attitudes of both the speaker and hearer
in particular previous approaches have lacked any way to model the propagation of belief within the system itself and instead have made use of precomputed and fixed nestings of mental attitudes
instead we have put forward a theory of speech acts where only the minimal set of beliefs is ascribed at the time of the utterance
in this paper we will present an approach to speech act processing based on novel belief modelling techniques where nested beliefs are propagated on demand
utterances in a dialogue are modelled as steps in a plan where understanding an utterance involves deriving the complete plan a speaker is attempting to achieve
an ascription based approach to speech acts abstract the two principal areas of natural language processing research in pragmatics are belief modelling and speech act processing
the structure of this paper is as follows in section NUM we review and discuss previous speech act approaches and their representation of mental attitudes
because of this potentially infinite regression it has proven difficult to use an axiomatic definition of mutual belief based in terms of simple belief in computational implementations
however previous speech act ba sed approaches have been limited by a reliance upon relatively simplistic belief modelling techniques and their relationship to planning and plan recognition
excluding punctuation this accuracy is NUM NUM
a chinese word segmenter and pos tagger which unifies these two procedures into one model is introduced in this paper
nd rules have usually standard verbalizations
we start with the definition of topic
authority whose permission as well obtained sources say
figures are in break even point
we estimate the component probabilities by
however the re ordering of the precedence of the two relevant word features had little effect when decoding spanish so they were left as is
ideally we would have sufficient training or at least one observation of every event whose conditional probability we wish to calculate
had we used only one quarter of the data or approximately NUM NUM words performance would have degraded slightly only about NUM NUM percent
to give a sense of the size of NUM NUM words that is roughly half the length of one edition of the wall street journal
the entire system is implemented in c atop a home brewed general purpose class library providing a rapid code compile train test cycle
cseg tagl NUM is implemented in windows environment with visual c i NUM programming language
by that we mean that the text of the document itself including headlines but not including sgml tags was NUM NUM words long
we ran a sequence of experiments in english and in spanish to try to answer this question for the final model that was implemented
missing information in the labels is the main source of errors
the inspection of tagging errors reveals several sources of wrong assignments
there are NUM verbs modified by the classified adverbs more than NUM times
NUM marks the difference between almost and fully reliable predictions
the probability of an alternative is within some larger distance
figure NUM shows a screen dump of the graphical interface
be fixed by inspecting the context and detecting the associated np
the most frequent error was the confusion of s and vp
suitable values for NUM and NUM were determined empirically cf
details about the accuracy are reported in the next section
that is one of the reasons why this type of ambiguity is called hidden
the first reason to analyze speech acts in terms of obse able linguistic patterns then is the measure of objectivity thus gained the discovery process is to some degree empirical data driven or corpus based
here we want to stress that our primary task was not to evaluate the taggers themselves but rather their performance with the word guessing modules
by taking advantage of these properties the tokenization problem can be greatly simplified
in a locally coherent discourse segment shifts are followed by a sequence of continuations characterizing another stretch of locally coherent discourse
theorem NUM explicitly and precisely states that tokenization ambiguity is the union of critical ambiguity in tokenization and hidden ambiguity in tokenization
consequently it must be crucial and beneficial to pursue an explicit and accurate understanding of various types of character string tokenization ambiguities and their relationships
the first type is when guessers provided broader pos classes for unknown words and the tagger had difficulties with the disambiguation of such broader classes
the major topic in the development of word pos guessers is the strategy which is to be used for the acquisition of the guessing rules
the only difference with the scoring used for delete rules is that the score of a parse pi here is a weighted sum of the quantity incontext c pi count pi evaluated for three contexts in the case both the lc and rc are unambiguous
in general the ambiguities of the forms that come before such a form in text can be resolved with respect to its original or intermediate parts of speech and inflectional features while the ambiguities of the forms that follow can be resolved based on its final part of speech
if in this case the token bit is considered to neighbor a token whose top level inflectional features indicate it is a verb it is likely that bit will be chosen as an adverb as it precedes a verb whereas the correct parse is the determiner reading
we can make a number of observations from our experience hand crafted rules go a long way in improving precision substantially but in a language like turkish one has to code rules that allow no or only carefully controlled derivations otherwise lots of things go massively wrong
in the example above vbd vbn is the class of the rule and j is the r class
although the additional impact of choose and rules that are induced by the unsupervised learning is not substantim this is to be expected as the stage at which they are used is when all the easy work has been done and the more notorious cases remain
we order all candidate rules generated during one pass over the corpus along two dimensions a we group candidate rules by context specificity given by the order in section NUM NUM b in each group we order rules by descending score
almost all of these collocations involve duplications and have forms like w x w y where w is the duplicated string comprising the root and certain sequence of suffixes and x and y are possibly different or empty sequences of other suffixes
libxpro was deployed bex aus it in awdlm le for both apple macintosh and personal comlml ers running ms windows NUM and has a very wi t distribut ion
we also discuss processing issues such as run time generation versus pre compiling of word forms
the over segmentation of tfn1 and tfn2 in the sentence NUM will result in the synthesized speeches choppy
in our model they are embedded in lexical rules for inflections and derivations
in short both the cover relation and critical tokenization have given us a clear picture of character string tokenization ambiguity as expressed in theorem NUM
we have been testing our lexicon design as part of an hpsg grammar for turkish NUM
two main obstacles block the progress of chinese word segmentation one is ambigury another is unknown word
after checking alias and same string the featur e most recent compatible subject is checked
resolve handles all the crucial merging decisions used by consolidation and wrap up
the semantic lexicon has NUM entries
crystal had limited recall for these cns
i i i i person is role no
the p o s tag lexicon has NUM entries
both times i t misclassified the instances
most notably it pays to engineer the best possible string specialists
do performance evaluations succeed in bringing theoretical ideas closer to real world systems
some a l riori remarks on theoretic subtm ies and on the eml loyed ret resentation are in t lace
because howev r the ante edent decisions are performed in isolation invalid index distributions may m ise
this is achieved by supplementing the straightforward se quential strategy with a dynamic reveritication of the binding restrictions in the antecedent selection stet
according to binding principle a a reflexive pronoun requires constructively a local antecedent step l b i
in accordance with intuitive judgement a local instance of the np storyj blocks the eoindexing of the possessivc pronoun and its dominating noun
thus the pre ference rule suggests peter as the mtecedent for lie and brother as the antecedent for him
a direct iml leme ntation of this generate md test NUM ro edure yields an exponential time complexity
because the aim of aimi hor resolution for a specific language is restricted the rei resentation an be simplifiexl
it has been demonstrated that in general the construction of appropriate representations for binding domains may necessitate semantic or pragmatic inferencing
this now yields a list of candidate subject boundaries and an associated confidence measure for each one
the existence of a set of closed class words allows the construction of a dictionary in such a way as to facilitate the detection and analysis of unknown words
NUM all open class variants for unknown words if the sentence falls its second parse attempt it is reparsed assigning all open class lexical categories to every unknown word
if it is not in the lexicon we assume that it can only be an open class part of speech and it could possibly be any of them
obviously the more words in a sentence that are defined in the lexicon the more the syntactic knowledge can limit possible parts of speech of unknown words
the first choice list contains those parts of speech that are most likely to be found in words ending suffix or beginning prefix with that affix
the test corpus is a set of NUM sentences from the timit corpus NUM a corpus of sentences that has no underlying semantic theme
dict9 contains NUM of the words from dictl0 dict8 contains NUM and so on down to dictl which contains NUM of these words
at the same point in the experimental run NUM missing there are only NUM NUM insertions per sentence less than one hundredth of the baseline value
a method to cope with unknown words can not be based on knowledge of the root if the root is also unknown
light NUM uses morphological cues to determine semantic features of words by using various restrictions and knowledge sources
however to date the tipster scoring categories correct partial incorrect spurious missing and noncommittal have not been applied to classes of data based on structural distinctions in the language or on semantic subclasses more finely differentiated than the ne types person location organization time date money and percent
when the interaction predicate is finally called as a result of syntactic information being present many of its possible solutions simply fail
the lexical entries belonging to a particular natural class all call the interaction predicate encoding the automaton representing lexical rule interaction for that class
this is an easier task than assessing the quality of whole texts
then we examine the more complicated texts texts NUM and NUM
lexical rules on the other hand are usually not NUM this approach is for example taken in the ale system
the computational treatment of lexical rules that we propose in this paper is essentially a domain specific refinement of such an approach to lexical rules
contrary to the mlr setup the dlr formalization therefore requires all words feeding lexical rules to be grammatical with respect to the theory
a comparison with other computational approaches to lexical rules section NUM and some concluding remarks section NUM end the paper
pruning the finite state automaton representing global lexical rule interaction only involves restricting lexical rule interaction in relation to the lexical entries in the lexicon
in the third compilation step the finite state automaton representing global lexical rule interaction is fine tuned for each base lexical entry in the lexicon
compared to free application the finite state automaton in figure NUM limits the choice of lexical rules that can apply at a certain point
one thus needs to distinguish the lexical rule specification provided by the linguist from the fully explicit lexical rule relations integrated into the theory
other categories do not fall out so neatly
any d t haract ex word is divided up into a 2chara ttu prefix and a NUM haract n suffix NUM oth stored in tile bin table or NUM character words with ch ar indications of tll ir r spc tivc status
another meaning component is seen in approve and disapprove namely the negative or pejorative prefix again requiring a lexical rule as part of the category s definition
in the simple use the distance between transcripts of nursing home patients staff and administrators was used as a measure of social distance among these three groups
another complex category is normaave consisting of NUM words with an average expected frequency of NUM percent and a range over the four contexts of NUM to NUM percent
since mcca categories do not exactly correspond to wordnet subtrees but frequently represent a bundle of syntactic and semantic properties we believe that the tagging results are epiphenomenal
this measure was combined with various ch terist cs of nursing homes size type location etc for further analysis using standard statistical techniques such as correlation and diseriminant analysis
examination of the 48agglomerative techniques cluster the two closest texts with whatever distance metric and then successively add texts one by one as they are closest to the existing cluster
this section of the graph reflects a long article which contains a number of different subtopics
louella uses the same pattern matcher for information extraction that it uses for text reduc tion however there is a difference in the way the pattern matcher is used
this cut off criterion is arbitrary and in our implementation can be specified at run time
the cn in NUM may make the word segmentation and pos tagging of the whole sentence totally wrong and further the pronunciation of the character totally wrong should be pronounced as shah4 if it is referred to a surname whereas as danl if adjective or adverb
the number of nearest neighbors to consider in equation NUM increases with the word s frequency
a same length relation is devoid of e
in addition to the segmentation and lexical look up stages of ou r system early passes of the reduction phase identify time date money and percent components
the sentence tagging component is both simple and conservative
this is driven by a sequence of phrase finding rules
note that these rule sequences encode a semantic grammar
merging two forms takes place in several steps
we will touch on this point again below
the sort verb prefix specifies typical lea null tures of verb prefixes
a priori there is no reason to think that either susan or betsy alone is the cb of utterance 6b
if the cf ranking depended on pronominalization alone the fourth utterance would allow either susan or betsy to be the highest ranked cf
rather it must be the case that susan is the cb at utterance b at each of the variants
the first of these suggests a strong interaction between dialogue verbs and centering which is also apparent in direct speech dialogue examples
that NUM is a more coherent discourse than NUM can be explained on the basis of this difference
note that rule NUM does not preclude using pronouns for other entities so long as the cb is realized with a pronoun
to simplify notation when the relevant discourse segment is clear we will drop the associated ds and use cb u and cf u
computational linguistics volume NUM number NUM the cb 25b and the cb 26b are both directly realized by the anaphoric element he
this research was partially supported by the studienstiftung des deutschen volkes and erasmus
in particular using adjunction in this way can not handle cases in which parts of the clausal complement are required to be placed within the structure of the adjoined tree
however work on semantics and morphology in hpsg is relatively scarce
by definition of sisteradjunction all substitution nodes and all nodes at the top of d edges can be assumed to have sacs that are the empty set
the entries in this array recording derivations of substrings of input contain a set of elementary nodes along with a multi set of components that must be in rted above during bottom up recognition
the direct object of to adore has wh moved out of the projection of the verb we include a trace for the sake of clarity
however we would have to stretch the edge over two components which are both ruled out by the sic since they violate the projection from seems to its s node
in developing dtg we have tried to overcome these problems while remaining faithful to what we see as the key advantages of tag in particular its enlarged domain of locality
been d sister adjoined at some node with address n in a in which case li will be the pair d n where d e lcb left right rcb
it is therefore natural to interpret these operations as establishing a direct linguistic relation between the two lexical items namely a relation of complementation predicateargument relation or of modification
for the purposes of the larger project of which this annotation project is a part the words are annotated with information in addition to the wordnet sense tags
wordnet does not provide entries for all idioms and the entries it does provide do not always include a sense for the occurrences observed in the corpus
we consistently break out certain uses of verbs to a greater extent than wordnet does in particular idioms and verbs of intermediate and auxiliary function
at the end of the paper we share some strategies from our coding instructions for recognizing idioms and show some challenging ambiguities we found in the data
our coding instructions specify that the tagger should attempt to identify idioms even if wordnet does not provide an entry for it
some of these special case uses can be identified with good accuracy with simple grammars while the more semantically weighty uses of the same verb generally can not be
strategies described in the coding instruction for recognizing idioms are described as well as some challenging ambiguities found in the data
figure NUM partial lexical entry for durcheilen
wordnet s structure with the alignment of hierarchical information and the addition of synsets and sample sentences was especially helpful
that is had is a form of the main verb have occurring as wordnet sense number NUM
complex words have a headed binary structure with the affix as head
we especially want to thank roger havenith the tsnlp project otficer at NUM g xiii for his help throughout the project and tile two external reviewers dan flickinger and john nerbonne for their constructive comments and suggestions
the selectional preference pattern tagger checks verb complementation and selectional preferences and also adjective selectional preferences
these are p and p around noun phrases acting as subjects i.e.
the cide database contains detailed information on both single words and multi word units
the tagger at present works on one sentence at a time
more time also needs to be spent on rule development
as a learner dictionary cide contains much examples text
these results clearly need to be improved dramatically before automatic sense tagging can prove practically useful
thus most valid pairs are given a standard weighting of NUM
it is thus possible for a number of positional factors to outweigh more concrete material factors
some work on expanding the scope of the ne task has been carried out in the context of a foreignlanguage ne evaluation conducted in the spring of NUM
the association of shortened forms of the name with the full name depends on techniques that could be used for ne and co as well as for te
the lowest error scores were NUM on vacancy reason median of NUM and NUM on onzi he job median of NUM
the slot that most systems performed best on is newstatus the lowest error score posted on that slot is NUM median of NUM
performance on this particular article for some systems was higher than performance on the test set overall reaching as high as NUM recall and NUM precision
using a simple counting scheme the algorithm obtains recall and precision scores by determining the minimal perturbations required to align the equivalence classes in the key and response
however utterance NUM indicates an error in the current model and the system removes this assertion from the user model
these scores indicate that pronoun resolution techniques as well as proper noun matching techniques are good compared to the techniques required to determine references involving common noun phrases
viewed from the perspective of the te task the walkthrough article presents a number of interesting examples of entity type confusions that can result from insufficient processing
to its credit however it did recognize that the event was relevant only two systems produced output that is recognizable as pertaining to this event
we must note however that currently this advantage of our system i s rarely realized in practice because it strongly depends on a correct quality parse of input sentences
an nlp system that needs to perform tasks beyond information extraction and to exhibit some in depth processing such as question answering virtually always calls some external specialists typicall y knowledge representation systems
enamex type person james enamex is filled with thoughts of enjoying his three hobbies sailing skiing and hunting NUM
NUM it promises to be a smooth process which i unusual n th e volatile atmosphere of the advertising business
previously we were missing bare numeric years such as the NUM election 1980s pre NUM and dates such as NUM NUM
NUM namex type organization mccann enamex has initiated a new so called global collaborative system mposed of world w ccuunt directors paired with creativ ertners
somewhat expected and unexpected problem s given our extremely ambitious goals for such a short period of time and particularly knowledge engineerin g large knowledge bases prevented us from doing coreference
NUM through NUM show one such derivation which results in readings where three cars outscopes most customers but every dealer must take either wide or narrow scope with respect to both most customers and three cars
however even without such a quality parse the system is capable of automatically computin g relations such as the relation between the following two expression s possible acquisition
lsas we can see in figure NUM a b there m no way quantifiers inside can be placed between the two quantifiers two three correctly excluding the other two readings
this section examines quantifying in to show a that quantifying in is a powerful device that allows referential np interpretations and b that quantifying in is not sufficiently restricted to account for the available readings for quantificational np interpretations
if we modify NUM further so that each tier rule from repns is intersected with the candidate set only when its tier is first mentioned by a constraint then the automata are pruned back as quickly as they grow
when algorithm NUM is implemented literally and with moderate care using an optimizing c compiler on a 167mhz ultrasparc it takes fully NUM NUM minutes real time to discover a stress pattern for the syllable sequence NUM
other other b during a NUM ii a ends a candidate that does see a j during an c can go and rest in the right hand state for the duration of the a
or otp is a formalization of ot featuring a homogeneous output representation extremely local constraints and a simple unrestricted gen linguistic arguments t or otp s constraints and representations are given in eisner
to get the exact original meaning we would have to decompose into so cmled unranked constraints whereby ci r is defined as c r ci r
the key insight is that among candidates with a fixed number of syllables and a single floating tone align a l h l prefers candidates where the tone docks at the center
we may encode each timeline as a string over an enormous alphabet e if tiersl k then each symbol in e is a k tuple whose components describe what is happening on the various tiers at a given moment
as an example there may be a factor mentioning f and c some of whose paths are incompatible with the input factor because the latter allows c only in certain places or because only allows paths of length NUM
unlike previous work the algorithm categorizes word tokens in con ezt instead of word ypes
the context vectors used so far only capture information about distributional interactions with the NUM most frequent words
intuitively it should be possible to gain accuracy in tag induction by using information from more words
the disambiguated texts were also proofread afterwards
the class based scheme makes it more likely that meaningful representations are formed for all words in the vocabulary
tables NUM and NUM present results for word type based induction and induction based on word type and context
the f score increases for all tags except cd with an average improvement of more than NUM NUM
both occurrences are miscategorized since its context vectors do not provide enough evidence for the verbal use
in this paper we describe an experiment on fully automatic derivation of the knowledge necessary for part of speech tagging
we randomly selected NUM NUM word triplets from the corpus and formed concatenations of four context vectors as described above
initially partial represents the syntactic semantic correspondences which are imposed on the generator
alternative processing strategies using the same knowledge sources can therefore be envisaged
ldeg the skeletal structure thus generated is fred limp ed
issue of the order in which internal generation goals are executed
the semantic representations used are variations of a predicate with its arguments
d trees allow us to view the operations for composing trees as monotonic
figure NUM shows a simple conceptual graph which does not have cycles
we argue that the input to generators should have a non hierarchical nature
in the syntactic part of this structure we have no domination links
we will say more about how this is done in section NUM
the org locale fix would actually have given us the highest f measure on that category NUM NUM
it was designed to share as much of the processin g sequence between tasks as possible
NUM free word order is a function of several interacting parameters such as category case and topic focus articulation
the bonus program for frequent fliers starting in NUM figure NUM example for automation level NUM the user
varying the order of words in a sentence yields a continuum of grammaticality judgments rather than a simple right wrong distinction
tparenthesis denote disjunction over the given values
when the combined corpus has less than NUM NUM occurrences of w the max imum number of available occurrences of w is used
the equivalence relations of these compatible fragments can then directly be compared
on the other hand it is plausible that a domain specific grammar can produce better results than a domain independent grammar
we would like to thank our colleagues in particular prof ralph grishman and ms sarah taylor for valuable discussions and suggestions
for example in the cross comparison experiment we have seen that domains k l and n are very close
note that the small declines of some graphs at large number of samples are mainly due to the memory limitation for parsing
also it should be noted that we do n t need a very large corpus to achieve a relatively good quality of parsing
a major concern in corpus based approaches is that the applicability of the acquired knowledge may be limited by some feature of the corpus
in particular the notion of text domain has been seen as a major constraint on the applicability of the knowledge
it is a probabilistic bottom up best first search chart parser and its grammar can be obtained from a syntactically tagged corpus
see rule na1 in section NUM NUM NUM a forward time is calculated by using the dialog date as a frame of reference
o t where r is the standard deviation for x user agent us el utt e2 rep
postgraphe uses the same planning mechanism to generate text and graphics
instead it uses a set of inter variable or intra variable goals
this is obviously impossible as it leads to massively exponential behavior
the focus of our research is the integrated generation of text and graphics in statistical reports
profits the profits were at their highest in NUM and NUM
postgraphe a system for the generation of statistical graphics and text
some of these intentions are further divided into more specific subtypes
the study of intentions is a major topic of our research
statistical reports are particularly interesting because the reader can easily be overwhelmed by the raw data
the associated text describes the over NUM all evolution and points out an interesting irregularity
the rule covers the predicate grouping rule reported in NUM
therefore di lcb t offers set the possibility to test its results against a semantic background that e.g.
they cover the more syntactic rules reported in the literature at a more abstract level
it is worth noting that there is a practical advantage of this two staged process
two apos are parallel in the sense that they have the same parent node
to this end we adopted the text structure of meteer as our basic representation
one interesting rule is to distinguish between general rhetorical relations and domain specific mathematical concepts
some of their rules nevertheless make decisions which we would call reference choice
it uses different configurations of the same fast indexing engine called nametag tm for different languages
in addition the client module is integrated with a commercial mt system for rough translation
in this section we give a tour of the system
hiragana names are transliterated into english using the hiragana to romaji mapping rules
more and more multilingual information is available on line every day
an intelligent multilingual information browsing and retrieval system using information extraction
ononelhand bethleadc did opinion c changeconcerning te ohs t
in this paper we present a general approach to lexical choice that can handle multiple interacting constraints
the problem of determining what words to use for the concepts in the domain representation is termed lexical choice
this output fd is then fed to the surge syntactic realization component to eventually produce the expected sentence
the conceptual representation in the input is encoded as an fd under the semr semantic representation attribute
this is accomplished by adding a lex cset lexical constituent set declaration to the lexical fug
the next task of the lexical chooser is to select a word or phrase to realize the relation class assignt
the lexical entry for the head word is responsible for providing a lexical item with its lexical features
the best thresholds were obtained in the range of NUM NUM points
these operations of phrase planning are possible in this approach because the conceptual input is not already linguistically structured
they are mapped to clausal participants e.g. carrier attribute only once the verb is chosen
rule of bec the initial state init e of the l ra nsition
arguably in such rules the second daughter should not be gapped
in addition the architecture would make it easier to conduct research on individual components of a text analysis system by creating an environment of standardized modules
this is comparable to many efforts at programming language standardization where an initial specification is gradually revised over several years in response to user and developer feedback
through this marketplace of tipster modules we will be able to meet our goal of facilitating the transition of advanced text analysis techniques to operational systems
it has been refined through feedback from the demos developed by the cawg for the NUM month and NUM month tipster meetings and from the tipster compliant systems now being implemented
we did have an architecture after six months so we might have stopped there and let the government mandate that people use that architecture as best they could
this experience has led to an extended discussion of the semantics of some operations and plans to include both more details and some examples in the design document
a few more changes were made to the architecture document as a result of problems which came up during the NUM month demo
after two years we are closer to meeting the goals set for the architecture but we are still not done
the probabilities given here are based on productivity figures for fabric container compounds in the bnc using wordnet as a source of semantic categories
we want the pragmatic component to utilise this knowledge while still maintaining sufficient flexibility that less frequent senses are favored in certain discourse contexts
we discuss the inadequacies of approaches which consider compound interpretation as either wholly lexico grammatical or wholly pragmatic and provide an alternative integrated account
but we will ignore this here and assume pragmatics has to distinguish between alternatives which differ only in the sense assigned to one compound
these modifications and extensions are now beginning to be presented to the cawg and to the government s architecture committee for possible revision of the standard architecture
thus a name annotator would take a collection and add to each document in the collection some annotations indicating the proper names found in that document
and that thus turing s asymptotic law is
the three parameters of this smoother are estimated by requiring that e v n v n that e v n NUM v n NUM and by minimizing the chi square statistic for a given span of frequency ranks
the upper right panel reveals that for the chronologically ordered series of samples from de telegraaf in the uit den boogaart corpus NUM randomly sampled text fragments with on average NUM word tokens only NUM text chunks reveal a significant difference between e v n and v n
below we give an algorithmic interpretation of clark and wilkes gibbs s collaborative model where present judge and refashion are the conversational moves that the participants make and ref re and judgment are variables that represent the referent the current referring expression and its judgment respectively
from rule NUM and belief NUM the system adds the belief that it is mutually believed that the user has the goal that the system knowref and from rule NUM and belief NUM it adds the belief that the user believes that the plan achieves its goal
we have also acknowledged that a lexical database algorithm can rival training algorithms in real world situations
textual and inter textual cohesion on the accuracy of theoretical estimates of vocabulary size the growth rate of the vocabulary and good turing adjusted frequency estimates in the belief that knowledge of how nonrandomness might affect these measures ultimately leads to a better understanding of the conditions under which these measures may or may not be reliable
this techniqueresults in NUM term weights vectors NUM coming from wordnet and NUM fromtraining
information making morphological reconstruction of less value for coping with unknown words
the control regime confirms results of the server or it activates the server s backtrack mechanism if the semantic representation received does not fit within the current dialogue step or it issues a request for repair if backtracking should not yield any further results
the behavior and function of the workflow manager are determined by the following sequence of operations identifying and formulating a workflow goal decomposing it into subgoals determining and allocating resources for achieving the subgoals elaborating and eventually executing an operation plan
note that this strategy can be instantiated in different ways as becomes clear from dealing with expression such as next week only a selection of free time slots can be provided here which is explicitly marked using e.g. for instance
words date expressions etc a fast lexical and morphological processing of NUM NUM million german word forms a shallow parsing module based on a set of finite state transducers a result combination and output presentation component
the same holds for failure messages such as NUM and for specifications of free time slots as in NUM where simple rules of aggregation take care not to repeat the full date specification for each clock time mentioned
in order to determine the coverage of the sublanguage relevant for the application and to measure progress during system development a corpus of NUM e mails was selected as reference material from several hundred e mails collected from the domain of appointment scheduling
grammar development is guided by a frequency based priority scheme the most important area temporal expressions of various categories followed by basic phenomena including different verbal subcategorizations local and thematic pps and the verbal complex are successfully covered
for ccl a parser a printer and an inference engine are available cci contains various kinds of interlace objects containing higher level protocols and methods for reliable tcp ip based communication data encoding decoding and buffering as well as priority and reference management
for each cluster member the local and global membership is shown
these were pild and bild analogized with wild and pornb analogized with tomb
this process resulted in the final NUM pos tags
formally the preference score g is a real valued function defined by
central to our project is a notion of discrete reusable system components some of which are intended to work collaboratively in software mechanisms some to provide generic functionality that can be tailored or augmented to suit particular applications
in contrast c1 c2 and c3 specify permissible hyphen points
the accuracy of the procedure for tuples for which a decision was made based on training pairs triples p2 and p3 exceeded NUM
in contrast when the core is second if a cue occurs it can occur either on the core or on the contributor
table NUM NUM number of bigrams with frequency x
discourse cues are words or phrases such as because first and although that mark structural and semantic relationships between discourse entities
for neither gorcl nor impl core does any individual feature perform better than the baseline and for core only one feature is sufficiently predictive
the tennis player trains the whole year in sentence NUM below the word morgen tomorrow is an adverb
table NUM NUM number of trigrams with frequency x
figure NUM describes the algorithm more formally
in this case the optimal tagging procedure is
finally the paper outlines the potential for generalization to other languages
an example is shown in figure NUM
step d the sense s is assigned to all occurrences of w in the input text
a single consonant between two vowels is hyphenated with the succeeding vowel
definition NUM y ee combination r the free combination of two ease forms is the disjunctive normal form of their conjunction case r case dnf case a case the two ease forms case and case are i nf formulct
a second alternative is to allow the lvscr system to enter into the transcription a symbol meaning something outside the vocabulary
while the nlu shell users need detailed familiarity with extracting formatted data from natural language they do not need to be programmers
for instance organizations have full names aliases descriptions locations and types company government or other
to the extent covered by this evaluation the nlu shell is a successful first prototype of a gui based natural language processing application builder
this occurred because these other frames relied on the match provided by the good frame in order to be matched to the key themselves
this template organizes a set of joint venture frames called tie up relationships or turs one for each joint venture mentioned in the story
a combined system could use the outputs of several different systems to produce performance levels potentially superior to those of any one component alone
pattern matching techniques will still have a crucial role for domain specific details but we believe overall improvement will be achieved by deeper understanding
each rule now has the form3
generation of s c bought gap
marks and brooks are in subject
it compares the prior distribution p cls with the posterior distribution p clv s NUM p elv s scales up the strength of the association by the frequency of the relationship
using the wordnet hierarchy as a source of backing off knowledge in such a way that if n grams composed by c are n t enough to decide the best sense are equal to zero the tri grams of ancestor classes could be used instead
the second advantage is that as long as the prior probabilities p c involve simpler events than those used in assoc p cls the estimation is easier and more accurate ameliorating data sparseness
NUM NUM model NUM traces and wh movement
NUM NUM part of speech tagging and parsing
furthermore equations are kept in NUM normal form
finally let us briefly examine some cases of adverbial quantification
this gives us a decidable test for validity of an equation
consider the following discourse NUM a jon likes sarah b
yes and he always takes sue to jo s mother
higher order unification has been shown to be a powerful tool for constructing the interpretation of nl
we see that this lifts the constant ac from the argument position to the top
section NUM NUM do we need a more elaborate formalization which we will discuss there
various new spurious ambiguities arise if it is permitted freely in the grammar
ccg then provides a derived interpretation in the model for the complete tree
the role of the grammar is to state which combinations are allowed
are the two analyses of vp vp vp vp vp semantically equivalent
the technique eliminates all spurious ambiguities resulting from the interaction of these rules
such ambiguities are left to future work
let us adopt a standard model theoretic view
efficient normal form parsing for combinatory categorial grammar
the association of a canonical subcategorization frame and a compatible redistribution gives an actual subcategorization namely a list of argument function pairs that have to be locally realized
the difference lies principally in the definition of quasi trees first seen as partial models of trees and later as distinguished sets of constraints of such features
the semantic network in the biology knowledge base that represents information about embryo sac formation was shown in figure NUM when knight is given the task of explaining this concept NUM it applies the explain process edp as illustrated in figure NUM computational linguistics volume NUM number NUM its traversal of this tree it begins with process overview which has a high centrality rating and an inclusion condition of true
given the centrality of content determination for explanation generation it is instructive to distinguish two types of content determination both of which play key roles in an explanation system s behavior local content determination is the selection of relatively small knowledge structures each of which will be used to generate one or two sentences global content determination is the process of deciding which of these structures to include in an explanation
after a brief description of the organization of the syntactic hierarchy we will focus on the use of partial descriptions of trees
the canonical subject argument NUM in a passive construction even when unexpressed is still an argument of the predicate
redundancy makes the tasks of ltag writing extending or updating very difficult especially because all combinations of phenomena must be handled
but for ltag representation inheritance networks have to include phrase structure information also and lexical rules become lexico syntactic rules
its classes contain information on the arguments of a predicate their index their possible categories and their canonical syntactic function
equality of nodes can also be inferred mainly using the fact that a tree node has only one direct parent node
the underspecified link between the s and v nodes allows for either presence or absence of a cliticized complement on the verb
the inside outside algorithm is a probabilistic parameter reestimation algorithm for phrase structure grammars in chomsky normal form cnf
there is however an added value also here because each disambiguated type can generate any number of context dependent interpretations
the associations of a noun with other nouns and verbs are supporting factors for it to be a topic
we postulate further that the noun in the topic sentence play important roles in the whole discourse
for example NUM verbs in lob corpus have relationships with the word problem in different degrees
those words that appear more than one haft of the documents in lob corpus have negative log p
the connective strength of a noun ni NUM i m is defined to be
they are based on different levels the paragraph and sentence levels for noun noun and noun verb pairs respectively
the other type of generator applies a greedy algorithm to an initial solution in order co find a grammatical sentence NUM oznafiski et al NUM NUM
the original was written on a roll or codex which fell into disorder or was accidentally damaged
the grammar used for the experiment consisted of simplified feature based versions of the NUM rules in gpsg there were NUM rules and NUM lexical entries
a technique fl r pruning the search space of a bag generator has been implemented and its usefulness shown in lhe geueration of different types of constructions
as our experiments show the previous topics have the tendency to decrease their strengths in the current paragraph
our topic identification algorithm demonstrates the similar behavior see rows NUM and NUM
we expect to use these categories directly in the symbolic grammar to control preposition selection and to provide schemata for compound nouns for instance
however in korean there are no typographical markers such as upper case vs lower case while one assumes that the nouns such as in NUM could be semantically and syntactically different from those of NUM NUM z l
the transformation rules invoke the tree transformer when the features they are associated with are chosen
null four general types of transformation are generally required during sentence planning symbols preceded by
the sentence structuring module determines the structure of the sentences to be encoded in the spl
the first is sentence planning as a task including the organization of the planning process
each module contains a feature system network that discriminates arbitrarily finely to a desired state
this syntactically neutral role situation enables the exophoric choice module to generate different internal clause structures
a few sentence planners have combined some of these tasks in a single engine
this is in accordance with the increasing delicacy of phenomena the modules deal with
the reasoning is that if an example s classification is uncertain given current training data then the example is likely to contain unknown information useful for classifying similar examples in the future
in particular we found that the simplest version of the method achieves a significant reduction in annotation cost comparable to that of other versions
for our example we need to sample the posterior density of estimates for a namely p a a s
during training the values of the parameters are estimated from a set of statistics s extracted from a training set of annotated examples
classification is based on a score function fm c e which assigns a score to each possible class of an example
our approach to measuring disagreement is to use the vote entropy the entropy of the distribution of classifications assigned to an example voted for by the committee members
if cl c select e for annotation NUM if e is selected get its correct label and update s accordingly
furthermore as batch size increases computational efficiency in terms of the number of examples examined to attain a given accuracy decreases tremendously figure NUM NUM
we propose a solution as in NUM NUM a rules involving verbs and prepositions need to be lexicalized to resolve the prepositional phrase attachment ambiguity cf
the transformation based part of speech tagger operates in two stages
however relying on a semantic module for ambiguity resolution implies that the parser needs to produce all possible parses of the input text andcarry them along thereby requiring a more complex understanding process
for the misparse illustrated in figure NUM utilizing the lexicalized rules in NUM prevents iji0 z from being analyzed as part of the subsequent noun phrase as in figure NUM
in figure NUM the prepositional phrase with NUM rounds is u ronglv attached to the noun phrase the contact as opposed to the verb phrase vp active to which it properly belongs
i NUM z instead of at i NUM z examples of misparses due to an incorrect verb subcategorization and a pp attachment ambiguity are given in figure NUM and figure NUM respectively
an advantage of integrating a part of speech tagger over a lexicon containing part of speech information is that only the former can tag words which are new to the system and provides a way of handling unknown words
all hostile recon aircraft outbound NUM z at NUM z if we try to parse sentences containing such omissions with the grammar where the rules are defined in terms of syntactic categories i.e.
figure NUM parse tree with correct verb subcategorization
figure NUM misparse due to omission of preposition
also the reliable parts of the engcg s morphological disambiguator by atro voutilainen are applied
box NUM fin NUM university of helsinki finland lcb pasi
also the rules are independent and they describe syntax in a piecemeal fashion
the parser applies the engtwol lexicon designed originally by juha heikkilps and atro voutilainen
the parser has been tested in sun workstation and in pcs under linux
the syntactic analysis is modest in time and space requirements the size of the process the syntactic analysis only is less than NUM mb and it runs in a pentium NUM mhz machine at the speed of NUM words per second
by now some NUM million words have been parsed
we have tested the parser on bigger texts to test its usability in corpus linguistic and lexicographic work
our main goal is to describe a syntactic analysis of sentences using dependency links that show the head dependent relations between words
this toolkit makes it possible to directly edit and add relations in the wordnets
NUM type ii in a sequence of chinese characters s al aibl bjcl ck if al aibl bj and bl bjcl ck are each a word then s is an overlapping ambiguous segment or in other words the segment s displays disjunctive ambiguity
c n from the alphabet there exists at least one word string w wl wm with s as its generated character string g w s theorem NUM a dictionary d over an alphabet g is complete if and only if all the characters in the alphabet are single character words in the dictionary
in spite of this strong constraint since all ns are mono syllabic ambiguity problems are often hard to handle
in practical terms what this amounts to is exploiting re entraney in qlfs
any uninstantiated recta variables in the antecedent are thus re entrant in the ellipsis
taking the first conjunct as the antecedent we can set up an
but no such increase in complexity is required under the present treatment
the substitutions specify what these changes are
in doing so we readily deal with phenomena like scope parallelism
lipsis the qlf is rendered uninterpretable which is as required
we need to distinguish between parallel and non parallel terms in ellipsis antecedents
so a second identical set of ten was undertaken except that the initial population now contained two sov v2 german speaking unset learner lagts
figure NUM the disjunctive hypernym implemented in a way
the representation for the antecedent clause in our logical form appears on the left hand side of figure NUM note that a core relation links xl the variable corresponding to he eventuality e13 to its antecedent j the entity described by john eventuality ell
NUM a semantic net needs proper representation of lexical gaps
the database ineludes another three indirect redundant entailments of this type
the database contains exactly three constellations of the type in question
may a troponym of x also be an entailment of x
we explain this by the following examples from wordnet NUM NUM
for sentence NUM the correct readings result if his is linked to he and he to john for sentence NUM the correct readings result if both pronouns are linked to john
therefore it was modeled in terminologyframework as a term term relation
the stronger correlations found for intonational features of the text and speech labelings suggest not only that discourse labelers make use of prosody in their analyses but also that obtaining such data can lead to more robust modeling of the relationship between intonation and discourse structure
we show the generality of the approach by demonstrating its handling of several other examples that prove problematic for past approaches including a source ofellipsis paradox so called extended parallelism cases and sloppy readings with events cases
changes in linguistic structure and hence atten null tional state depend on the discourse s intentional structure this structure comprises the intentions or discourse segment purposes dsps underlying the discourse and relations between dsps
the observed percentage of sbeg labels prior distribution for sbeg average of the pairwise scores and standard deviations for those scores are presented in the average g scores for group t segmenters indicate weak inter labeler reliability
while it may appear somewhat surprising that results for both labeling methods match so closely in fact correlations for text and speech labels presented in table NUM were almost invariably statistically stronger than those for text alone labels presented in table NUM
ongoing research is addressing the development of automatic classification algorithms for discourse boundary type the role of prosody in conveying hierarchical relationships among discourse segments individual speaker differences and discourse segmentation methods that can be used by naive subjects
we first calculated the prior probabilities for our data based simply on the distribution of sbeg versus non sbeg labels for all labelers on one of the nine direction giving tasks in this study with separate calculations for the read and spontaneous versions
for purposes of intonational analysis we take advantage of the high degree of agreement among our discourse labelers and include in each segment boundary class sbeg sf and scont only the phrases whose classification all subjects agreed upon
results for group t are given in table NUM and for group s in consensus sbeg phrases in all conditions possess significantly higher maximum and average f0 higher maximum and average rms shorter subsequent pause and longer preceding pause
case f makes x4 coreferential with john and case g makes it coreferential with the teacher
although not restricted to nominal phrases our reference decisions are similar to those concerning nominal subsequent referring expressions
concretely proverb uses an architecture that models text generation as a combination of hierarchical planning and focus guided navigation
the tsg text structure generator module subsequently produces the text structures as the output of the microplanner
to account for this phenomenon concepts like activat null edness foregroundness and consciousness have been introduced
the structural closeness of a reason reflects the foreground and background character of the innermost attentional space containing it
this reflects a natural shift of attention between a subproof that derives a formula of the pattern NUM p z node NUM NUM x e u and the subproof that proceeds after assuming a new constant u satisfying p node NUM u e u
within a discourse space four levels of focus can be assigned to individual objects high medium low or zero since there are four major ways of referring to an object using english namely by using a pronoun by name by a description or implicitly
suppose furthermore that in order to connect n to np a rule is selected that requires a category adjp to the left of n
this example illustrates how the use of two pairs of string positions reduces the number of possible lexical head corners for a given goal
the striking property of this parser is that it does not parse a phrase from left to right but instead operates bidirectionally
an important goal of the programme is the implementation of a spoken dialogue system for public transport information the ovis system
again this strategy avoids the disadvantages of parsers in which rule selection is uniformly driven by either the left most or right most daughter
parsing on the basis of a finite state automaton can be seen as the computation of the intersection of that automaton with the grammar
this is a source of inefficiency if it is difficult to determine what the appropriate lexical head for a given goal category is
two examples of companies that develop customized name matching systems of this sort for business and government clients are language analysis systems inc and search software america
merge upper tu1 tu2 like the previous function except includes only those field fillers from tu1 that are of the same or less specificity as the most specific field filler in tu2
NUM a three frenchmen visited five russians
at the end of the table are the average numbers for the speakers agreement among themselves
the analysis of the detailed results showed that the implemented system performs quite well for instance NUM accuracy vs a lower bound of NUM on the unseen cmu test data
this number need not be identical with the sense number shown by wordnet s read only browser
to achieve this algorithm through recognition of a dependency grammar the marking process will be encoded as the filling of appropriate valencies of a word s by words representing the vertices
the feature graduality characterizes events in which some kind of change is included and the change gradually develops
the feature telicity distinguishes between culminative events t and nonculminative events t
we carried out an experiment to classify japanese verbs into six categories in the table NUM by means of corpus data
this brief overview of current dg flavors shows that various mechanisms global rules general graphs procedural means are generally employed to lift the limitation to projective trees
to be relevant it must be the case that the pos t definitely was is now or will be vacant if the vacancy is contingent on other events or the decision to replace an officer is otherwise not yet definite the text is nonrelevant
if the text provides only an indirect indication of a relevant post e.g. via a verb phrase such as will run the company or an idiom such as took the helm the fill is th e string no title
if there is obviously a major employment ga p between the person s stints at the two organizations the other org fill may be marked optional in th e answer key NUM a consultant to a company is not an employee of the company
similarly any successions to a post other than the most recently reported succession to that post are no t relevant thus accounts of people who held a particular post prior to the news of the most recent succession to tha t post are nonrelevant
left bolted stepped down was pushed aside if person is identified as having previously held the post via some special word or phrase not just or only by use of past tense se e
special usage notes NUM instantiate separate in and out objects for each move even when this results in the creation of identical in and out objects as in the case where a particular person is acquiring or vacating more than one post at a particular organization
reassignment would provid e an analyst only with the information that the article contained some specific reference to the person s departure and or move to a new job that reference may be enough to inform the analyst what the circumstances for the departure were
if the two posts are at the same company or at related companies see section NUM NUM NUM below and the posts are not mutually exclusive the person is not giving up the old post to acquire the ne w one
are relevant posts as are directorships with special titles such as managin g director or director of the european subsidiary the latter may actually represent a separate usage of th e word director
the categories that are to be used for this slot are defined basically as follows and their usage is describe d more fully in appendix a yes the person held the post as of the date of the article
this may reflect the more complex linguistic environment in which incorrect absolute settings are more likely to handicap rather than simply be irrelevant to the performance of the lagt
finally each aggregate in NUM NUM bears the relation of endorsing to some aggregate in NUM NUM
finally suppose that there is an endorsement of the female committees by the male committees as depicted below
for example anger disgust grief astonishment and esteem do not
the conversion of these nouns to count nouns can fall under any of several types
to see how the principles work consider this sentence with plural quantified noun phrases
there are cases where plural demonstrative noun phrases and interrogative noun phrases exhibit scope like interpretations
in english the distinction between mass nouns and count nouns has clear morpho syntactic criteria
these features serve to constrain the free assignment of the morpho syntactic features of pl
the fred i am speaking of is different from the fred you were speaking of
i divide this discussion into two parts as suggested by the term conversion
i am grateful to pasi tapanainen jean pierre chanod and annie zaenen for helping to correct many terminological and rhetorical weaknesses of the initial draft
i am particularly indebted to kaplan for writing a very helpful critique even though he strongly prefers the approach of kaplan and kay NUM
the input by touch screen is used to designate the location of map around mt fuji which is a main location related to our task on the display or to select the desired item from the menu which consists of the set of items responded by a speech synthesizer
thus although the simulation does not model the development of expressivity well it does appear that it can model the emergence of effective learning procedures for some full languages
rules for anaphora establish the antecedents for anaphora and afterwards it is checked whether the resulting discourse model is well formed e.g. by checking whether each pronoun has an antecedent whether definite descriptions have been used appropriately etc
these examples show cases where a two word collocation is translated as one word e.g. health insurance a two word collocation is translated as three words e.g. employment equity and how words can be inverted in the translation e.g. additional costs
if word order is variable in the target collocation champollion labels it flexible for example to take steps to can appear as took immediate steps to steps were taken to etc otherwise the correct word order is reported and the collocation is labeled rigid
given the information represented about the composition k NUM in the database example sentences derived from the abovementiuoned s templates include k NUM was written by w a mozart in NUM and this quodlibet was written by the composer in march NUM
for example an occurrence of k NUM or of this composition may become given and hence de stressed de accented due to an earlier reference to k NUM you have selected k NUM
however setting up structures of this kind would have required a tremendous amount of work since generation requires many kinds of information that are neither routinely represented in existing versions of drt nor trivial to calculate on the basis of them
since few of the NUM NUM points in each sample meet the criterion of having dice ab x greater or equal to the threshold for high final threshold values the estimate of the percentage of failures is more susceptible to random variation in such cases
the last column and the last row show the total number of collocations correctly and incorrectly translated when the dice coefficient represents candidate translations in french for the credit cards example cartes cartes credit cartes credit taux and cartes crddit taux paient
for example running the algorithm at an initial threshold of NUM NUM and a final threshold of NUM NUM gives a failure rate of NUM NUM much less than the failure rate of NUM NUM which corresponds to a constant threshold of NUM NUM for both stages
we see from table NUM that as for the today example in table NUM the si scores are very close to each other and fail to select the correct candidate whereas the dice scores cover a wider range and clearly peak for the correct translation
clearly NUM NUM NUM NUM and i ri yi for i NUM for a particular translation with i NUM words to be generated at least one of its i subsets with i NUM words must have survived the threshold
with c text c mozart composed k NUM speaker c dyd previous sentence c de c lcb x y rcb conditions c lcb x w a mozart y k NUM x
then the expected average number of candidate translations with i NUM words examined by champollion will be and the sum of these terms for i NUM to m plus the terms q and 2q gives the total complexity of our algorithm
using the index file on the english part of the corpus we collect all french sentences that match the source collocation and produce a list of all words that appear in these sentences together with their frequency in sentences in this subset of the french corpus
contrary to previous studies on heavily restricted dependency grammars we prove that recognition and thus parsing of linguistically adequate dependency grammars is a t complete
on the other hand l does not have this special role on the right hand side of which does not quantify universally over an interval
speed was particularly an issue for muc NUM because of the relatively short time frame NUM month fo r training
this will be difficult however if the scenario requires the addition of some grammatical construct albei t minor
the position of the vp node in figure NUM is equal to that of anfgegeben and the position of the np in figure NUM is equivalent to that of bonusprograrara
today s dgs differ considerably from gaifman s conception and we will very briefly sketch various order descriptions showing that dgs generally dissociate dominance and precedence by some mechanism
in this paper we first describe our theory of machine learning of natural language section NUM and then describe the corpora in ten languages that we used for experimental purposes section NUM
this need not hold in other situations
the type abstraction step is based on the standard assumption that the word specific lexical semantic types can be grouped into classes representing morpho syntactic paradigms
we are already deep into our next csli ventura hall stanford ca NUM NUM NUM association for computational linguistics computational linguistics volume NUM number NUM area of application machine understanding of physics word problems and we have not needed to change the general formulation of our theory to accommodate this quite different problem of language learning
but this combination also occurs in nps like NUM leule NUM monate NUM people NUM months which are mis tagged since they are less frequent
no such corpus exists for modern hebrew
key concepts in our learning theory are probabilistic association of words and meanings grammatical and semantical form generalization grammar computations congruence of meaning and dynamical assignment of denotational value to a word
this has the advantage that it is not necessary to maintain a complex semantic structure that incorporates the complexity of all languages involved
investigation revealed the bulk of this to be attributable to morphological error
pbhr stands for previous best hypothesis rejection and p np for phbr without punctuation hypotheses
word completion a first step toward
the probability of generating hypothesis t at position
the active vocabulary is also represented as a trie
iao inlxmdcd ix t prt xiimd t i hc iiiilhi cf o c eysl rokes s tved within words
word completion a first step toward target text mediated imt
thus tagging for part of speech alone would not solve our problems
the remainder of th e time was a steady effort of studying texts adding patterns and reviewing outputs
word t given a preceding target text t anti a source text s
the third example in the next section serves to clarify this point
the only connection between know and beans is that the finite verb allows the extraction of beans thus defining order restrictions for the np
this test group consisted of NUM words
the rules described in this section see figure NUM apply to individual temporal units and return either a more fully specified tu or an empty structure to indicate failure
the patterns are translated to lis p procedures which are then compiled so the pattern matching can proceed very efficiently
borrowing a type distinction from the area of machine translation mt we can classify this system as that of human aided nlg as opposed to fully automatic nlg
in fact the first action of this procedure is to substitute demarcation points for any cluster of brackets in the string
correspondingly the right hand sides of the realization rules include instructions to carry out the above types of actions
a label string consists of a grammatical class symbol see table NUM and a unique ordinal number for each distinct string
the cluster level subtrees of the text plan tree are organized by grouping the templates into what will become sets of siblings at different levels in the text plan tree
the result of the interactive content acquisition stage is a shauow level representation which can be considered a draft to be automatically revised into the final text of the claim
the list of case roles for the sublanguage is as follows agent theme co theme place manner purpose means condition time
we intend to a extend the system into multilingual generation we have already acquired a lexicon and grammar of russian for the patent disclosure sublanguage
the procedure also moves all templates whose predicates are prepositions to the leffmost positions in the string in order to facilitate their realization without introducing a full clause
c assign an especial tag that is not in the current syntactic set to every unlabeled constituent after above two processing steps
the statistics extracted from a large scale treebank will show useful syntactic distribution principles and be very helpful for disambiguation in a parser
based on them it is easy to determinate the suitable syntactic tag for a parsed constituent according to its internal structure components
however it is su cient to calculate as follows
we define clusters as maximal subgraphs in this partial order chain
we tried to find out the special structure in linguistic graph
transitive word b does not have two sided meanings ambiguity
this graph theoretical approach using graph is characteristic and it differs from the conventional clustering method
when the threshold is NUM NUM such dusters were most numerous
his clustering method is so called dlllrnage mendelsohn decomposition in graph theory
its starting branch is encountered without fail in the above algorithm
they are classified as follows numbers in parenthesis are cluster number in appendix i
cell in cluster i means cells of tissue whereas that in cluster NUM means battery
a large number of cardinal noun pairs forms a numerical component nm like NUM millionen NUM prozent etc NUM million NUM percent
the application of this transducer to the input aba produces four alternate results axa ax xa and x as shown in figure NUM since there are four paths in the network that contain aba on the upper side with different strings on the lower side
for example the expectation that the user is going to report the setting of a switch would be represented as obs phys state prop switchl state propvalue truthstatus
the user may receive an unexpected response from the system and then speak again but with increased volume or nonstandard vocabulary this may yield worse machine responses and even more extreme behavior from the user
the purpose of the testing was to gather general statistics on system performance and timing to study the effects of mode and to judge the human factors issues learnability and user response
some words such as not or major nouns make a large difference in sentence meaning other words such as articles may not carry significant meaning in a given context
thus in the example listed above where the led is being observed the expectations are as follows these are passed to the controller at the time of the request for the led observation
for example in the implemented system the top level device is a circuit called the rsl11 its subsystems are the power circuit the t1 and t2 circuits and the led circuit
thus the machine must be able to carry out its own problem solving process and direct the actions to task completion or yield to the user s control and respond cooperatively to his or her requests
this technique increases the amount of evidence used topredict category occurrence
since they miss a natural intonation pattern they are awkward to understand
it shows the amount of utterances the information service applies in one turn
whereas the present description focuses an its formal properties and suitability for computational work
on the other hand the integrated approach reports better general performance than the training approach
no interruptions by the caller are possible see figure NUM
so no feedback from the user to the system is possible
nevertheless during communication problems may arise
table NUM an example of a scenario
the task is to assign the functions modifier mo to adv head so to wee and direct accusative object oa to ne
it also shows the order in which the elements should be uttered
each line in this scenario refers to a separate chunk of information
the difference that recourse to lists can make in performance is seen by comparing two runs made by sra labeled satie base
examples mined mine keyed key joined join
examples mining mine keying key joining join
is empirical but allows us to guarantee excellent classification results
in routing the top ranked documents for each class are returned
the methods were tested against a small set of available documents
the method we use now described in section NUM NUM
consider two different classes each represented by a training set
most likely words then the weight for each word is calculated
NUM use all of the words in the training set
to help illustrate the procedure a small example is described
act and thatt of attack NUM are bakery crime evil doing
for fair comparison of each metric the best thresholds are arrived through exhaustive searching of a reasonable space NUM
upward traversal of links towards the unique beginners are weighted consistently at NUM NUM whilst downward traversal of links towards the leaves
also indicative nouns may not just hold is a relationships which are the only relationships exploited by both algorithms
the semantic class attack for e ample is mapped onzo both senses i and NUM
the semantic classi cation of words refers to the abstraction of ambiguous surface words to unambiguous concepts
togp c and p c is the probability of encountering an instance of concept c
NUM n word net these are NUM unique beginners of the taxonomy instead of a co on root
sentence NUM talks about all three representatives while sentence NUM talks only about the subset lcb r2 r3 r4 rcb
the check for cardinal quantifiers is defined in terms of two sub checks one for a monotone increasing quantifier and one for a monotone decreasing
an obvious improvement to the algorithm would be to generate a preferred sentence or to rank the outputs as to how well they describe the model
the algorithm for conceptual co occurrence data collection is shown in figure NUM
the orthodox approach to quantifier scoping is embodied in hobbs and sheiber s algorithm and it permits all permutations of quantifiers such that there are no unbound variables in the resulting logical form
conclusion an algorithm has been described for generating quantifiers in english sentences which describe small models containing collections of individuals which are inter related in various ways
to check the consistency of a particular quantifier with a particular variable it is first necessary to compute the variable s candidate set focus set and focus maximum and focus minimum
results are shown in table NUM
however fully automatic lexically based approaches
the whole ldoce is pre processed first
the results are listed in table NUM
approaches in a large proportion of cases
a comparison of the automatically expanded crnlea run and the manually expanded ass ctvi run shows minimal difference in average precision but superior performance in NUM of the topics for the manual expansion as opposed to only NUM of the topics having superior performance for the automatic cornell run
the cityal run tended to miss more relevant documents than the crnlea run NUM topics were seriously hurt by this problem but was better able to rank relevant documents within the NUM document cutoff so that more relevant documents appeared in the top NUM documents
reation of a terminal class totally defil ed
two main types of term expansion were used by these top groups term expansion based on a pre constructed thesaurus for example the inquery phrasefinder and term expansion based on selected terms from the top x documents as done by city comeu and pircs
some splitting of hyphenated words is also done
sample trec NUM trec NUM topic top head NUM pster topic description num number NUM dora domain science and technology title topic natural language processing desc description document will identi y a type of natural language processing technology which is being developed or marketed in the u s
the curves show system performance across the full range of retrieval i.e. at the early stage of retrieval where the highly ranked documents give high accuracy or precision and at the final stage of retrieval where there is usually a low accuracy but more complete retrieval
in our running example the transducer in figure NUM has some nondeterministic paths
we can encode this analysis literally in terms of threading
so we have to encode it into a unification operation
this will allow us to automatically construct the selector list
however where all the categories that figure in subcategorization will
we represent a set as a tuple of values e.g.
perhaps the most obvious source of lexical disjunction is subcategorization
this is clearly not what the grammarian would have intended
this completes our definition of a basic unification grammar formalism
this row will represent the glb of the two types
we further restrict our attention to hierarchies of atomic types
to the left we see the results of one of the shallow analysis components
this is often done in tag literature and hopefully it will be clear what is intended
therefore the subject will have to raise out of its clause for agreement and case assignment
NUM a rameshan kyaa dyutnay tse rameshzrg whatnom gave youdat what did you give ramesh
dtg are designed to share some of the advantages of tag while overcoming some of its limitations
in tag modification is performed with adjunction of modifier trees that have a highly constrained form
in the past variants of tag have been developed to extend the range of possible analyses
the derivation requires that the s node of seems be inserted into the si s d edge of claims
each node is labeled with a terminal symbol a nonterminal symbol or the empty string
thus they fail to address the first problem we discussed in section NUM NUM
a second approach to collecting relevant distributional information is to keep co occurrence counts of the nearest lexical neighbors of a word usually within a fixed distance or window
experiments with pundit a broad coverage symbolic nlp system have shown that the category space can successfully bc used to induce features like transitivity and subcategorization for clauses and infinitival complements
the category space can be clustered by comparing pairs of context digests using the cosine similarity measure such clusters contain words whose syntactic behavior is substantially similar
tim generalizing effect of svd causes the category space for verbs to become much less sparse NUM NUM percent of the entries now have non zero ounts
the system described in this paper was tested instead by comparing the number of successful parses of a held out test corpus before and after customizing the lexicon
brcnt in fact takes the fronl scratch to an extreme and models his system aftcr the way a child learns to understand hmguage
by combining only the first k linearly independent components a reduced model is built which disregards lesser terminology variations because k is smaller than the number of rows terms
in enamex type location atlanta enamex is that caa an d other ad agencies such as fallon mcelligott will continue t o months timex says now that he has reinvented himself he wents to d o the same for the agency
rules may be optional op is or obligatory op is c
run time speed need only be enough to make the time spent on morphology small compared to sententia and contextual processing
in the absence of a lexical string for the root the correct treatment of obligatory rules is another problem for compilation
the default rule that copies characters between surface and lexical levels and the boundary rule that deletes boundary markers are both optional
a partition breaks an obligatory rule if the surface target does not match but everything else including the feature specification does
at run time an inflected word is analyzed nondeterministically in several stages each of which may succeed any number of times including zero
for example polish nouns with stems whose final syllable has vowel NUM normally have inflected forms in which the accent is dropped
we decided t o do it in order to experimentally substantiate our belief that reference mechanism for named entities o f different types is basically the same for all entity types and that references can be computed by the same piece of code
the algorithm proceeds bottom up elimidming as malay brackets as possible by making use of the associafivity equivalences abel a bc lab c and
spelling patterns are indexed according to the surface for analysis and lexical for generation affix characters they involve
the debugging tools help in checking the operation of the spelling rules either NUM in conjunction with other constraints or NUM on their own
the interface is from cascading menus which allowed the initial screen to be simple at times awkward to use when navigating among the tools
lemma3 for any inversion invariant transduction grammar g there exists an equivalent inversion transduction grammar g where t g t g such that g does not contain any productions of the form a b
additionally we realized that we needed to gather more data to begin to learn about what each system was doing well compared to the other
the most exciting lesson we learned is that near human performance in named entity recognition is within the state of the art for mixed case english
for each story this output consists of a set of interconnected frames specifying the content of the joint ventures as described in the source text
this allows arcs that previously had different output strings to merge as for example in the arc from state NUM to state NUM of figure NUM which is a generalization over the arcs into state NUM in figure NUM
since our segment set distinguishes three degrees of stress for each vowel the alphabet size is NUM we believe this was simply too large for the algorithm without some prior concept of vowel and consonant
according to this metric clearly a grammar that does not contain trivial rules mapping an underlying phonology unit to an identical unit on the surface is preferable to an otherwise identical grammar that has such rules
an examination of the final few errors three samples in the induced flapping and three rule transducers in section NUM NUM NUM turned out to demonstrate a significant problem in the assumption that an spe style rule is isomorphic to a regular relation
because the decision tree specifies a state transition and an output string for every possible combination of phonological features one can no longer fall off the machine no matter what the next input segment is
in this paper we suggest that an alternative to the purely nativist or purely empiricist learning paradigms is to represent the prior knowledge of language as a set of abstract learning biases which guide an empirical inductive learning algorithm
other scholars have argued that a purely nativist parameterized learning algorithm is incapable of dealing with the noise irregularity and great variation of human language data and that a more empiricist learning paradigm is possible
these values are attached to the new node s children and the algorithm is run again on the children s subsets until each leaf node has a set of samples that are all of the same category
the basis of the bracketing strategy can be seen as choosing the bracketing that maximizes the probabilistically weighted number of words matched subject to the btg representational constraint which has the effect of limiting the possible crossing patterns in the word alignment
productions are interpreted as rewrite rules just as with context free transduction grammars with one additional proviso when generating output for stream NUM the constituents on a rule s right hand side may be emitted either left to right as usual or right to left in inverted order
in this work we further elaborate on the error driven learning paradigm
algorithm NUM can be executed in time o nn2
then the interpretation of the resulting transformation is the usual one
again we only give an outline of the proof below
l membership in np is easy to establish for ts
we now turn to a more general kind of transformations
hence algorithm NUM runs in time o nn
figure NUM the table reports the values of 8bi bi
if pl is the root node return implicit node
the annotators problems with vacancy reason may have had more to do with understanding what th e scenario definition was saying than with understanding what the news articles were saying
the succession event object points down to the in and out object which in turn points down to person template element objects tha t represent the persons involved in the succession event
sites have developed architectures that are at least as general purpose techniques as ever perhaps as a result of having to produce outputs for as many as four different tasks
the vast majority of cases are simple ones thus some systems score extremely well well enough in fact t o compete overall with human performance
errors on the text slot are errors in finding the right span for the tagged string and this can be a problem for all three subcategorie s of tag
fifteen sites participated in the ne evaluation including two that submitted two system configurations fo r testing and one that submitted four for a total of NUM systems
thus each of the five person objects in the key and seven of the ten organization objects in the ke y were matched perfectly by at least one system
most if not all the systems that were evaluated on the ne task adopted the basic strategy of processing the headline after processing the body of the text
we believe that this result further supports the claim that taggers in the two conditions proceeded differently taggers working with a randomly ordered list of senses did not rely on the first sense being the correct one
note that the text filtering definition of precision is different from the information extraction definition of precision the latter definition includes an element in the formula that accounts for the number of spurious template fills generated
identification of certain common types of names which constitutes a large portion of the named entity task and a critical portion of the templat e element task has proven to be largely a solved problem
one might argue that the problem of unknown category words is due to the tiny size of the atis corpus
in order to create such an extension we need a method that estimates the probabilities of unknown subtrees
we only need to establish the unknown words of an input sentence and label them with all lexical categories
the output contains several well formed meaningless clauses and also cliches such as conserving our rich natural heritage suggesting that the model captured some longer term statistical dependencies
figure NUM day day of week detail of the thematic structure
on the table figure NUM derivation and parse tree for she displayed the dress on the table
to the best of our knowledge our method outperforms other statistical parsers tested on penn treebank word strings
for each turn we store e.g. the speaker identification the language of the contribution the processing track finally selected for translation and the number of translated utter null ances
this is common practice in corpus linguistics where the estimations are usually restricted to the domain under study
moreover within verbmobil there are different processing tracks parallel to the deep linguistic based processing different shallow processing modules als0 enter information into and retrieve it from the dialogue module
we shall also allow unit program clauses x o to be abbreviated x
the termination condition is met by a unit agenda with its unit database
in the case of nl invalidity tile term unification would fail
is free of spurious ambiguity and i r
higher order coding allows emission of hypotheticals to be postponed until they are germane
we go on to briefly mention multimodal calculi for the binary connectives
for nl the term labeling provides a clausal implementation with unification being nonassociative
there is a further problem which will be solved in the same move
in the ed rules the succedent is atomic
the assignments are compiled as shown in NUM
the problem is that in some cases a constituent in one sentence may have several potential matches in the other and the matching heuristic may be unable to discriminate between the options
also in contrast to the applications discussed here which deal with analysis and annotation of parallel corpora we are working on incorporating the sitg model directly into our run time translation architecture
the time complexity of this algorithm in the general case is o n3t3v3 where n is the number of distinct nonterminals and t and v are the lengths of the two sentences
the second expressiveness desideratum for a matching formalism is to somehow limit the rank of constituents the number of children or right hand side symbols which dictates the span over which matchings may cross
the prepositional bias has already correctly restricted the singleton the c to attach to the right but of course the does not belong outside the rest of the sentence but rather with authority
in other words itgs appeal to a language universals hypothesis that the core arguments of frames which exhibit great ordering variation between languages are relatively few and surface in syntactic proximity
if in particular cases this assumption does not hold however the damage is not too great the model will simply drop the offending word matchings dropping as few as possible
an inconvenient problem with ambiguity arises in the simple bracketing grammar above illustrated by figure NUM there is no justification for preferring either a or b over the other
while grammatical differences can make this problem unavoidable there is often a degree of arbitrariness in a grammar s chosen set of syntactic categories particularly if the grammar is designed to be robust
where NUM t c ti ig is tit reqttency obt aincd fronl l rainhlg dal a
part of speech tagging which assigns the most likely tag to each word in a given sentence is one of tire problems which can be solved by statisticm approach
as gibbs distribution can be used to describe a posteriori probability of tagging we use it in ma ximum a posteriori map estimation of optimizing process
given consistent constraints a unique me soluton is guaranteed to exist and to be of the form where the ar s are some nnknown constants to be found
the a priori probal ilit y p NUM of tag sequence NUM is gibbs distribution because the randora variable NUM of tagging is mrf
as is standard we use suffixes of the input string for string positions
when finally only a consistent basic constraint remains this is an answer constraint c
figure NUM depicts the proof of a parse of the verb cluster in NUM
this allows constraints to be passed out ofa memoized subcomputation
returns program and the selection rule chooses the left most such literal
this section presents a coroutining memoizing clp proof procedure
this allows constraints to be passed into a memoized subcomputation
figure NUM the bn analysis of NUM
the results therefore indicate that the expert choice being the first made the decision process for the taggers much easier by eliminating the need for a difficult comparison of all the available senses and in the frequency condition by the fact that the first sense was generally the most salient one
we adopted the stop condition suggested in berger et al NUM the maximization of the likelihood on a cross validation set of samples which is unseen at the parameter esti tion
maximum entropy framework proved to be expressive and powerful for the statistical language modelling but it suffers from the computational expensiveness of the model building
we also will need the indicator function similar to one in equation NUM to indicate whether the relation c holds from node i to node k
because of the dual interpretation of the nodes a node can also be associated with its feature frequency count i.e. the number of times we see this feature combination anywhere in the lattice
to support genermizations over domain we also want to add to the lattice those nodes which were not seen on their own but only as common parts of other nodes in the lattice
this can lead however to the overlltting of the model and such model also will not possess any generalization power so its performance on unseen cases might be rather poor
in addition the short time frame allocated for domain specific development naturally makes it very difficult for developers to do sufficient development to fill complex slots that either are not always expected to be filled or are not crucial elements in the template structure
so one of the directions in our future work is to find efficient ways for a decomposition of the feature lattice into non overlapping sub lattices which then can be handled by our method
then constraining all the nodes we compiled a maximum entropy model in about three hours and then using the constraint removal process in two hours boiled the constraint space down to NUM
one reason for low performance is that an organization may be identified in a text solely by a descriptor i.e. without a fill for the org name slot and therefore without the usual local clues that the np is in fact a relevant descriptor
for NUM of the words the offset from correct alignment is at most NUM
the em algorithm iterates between two phases to estimate ltp and dp until both functions converge
e26 the accent in the word important is on the second syllable
moreover class based models offer the advantages of a smaller storage requirement and higher system efficiency
this paper presents an algorithm capable of identifying the translation for each word in a bilingual corpus
therefore we contend that a more successful alignment can be achieved using a class based approach
the closed test set consists of NUM examples and their mandarin translations randomly selected from the lecdoce
in such a circumstance they recommend aligning words directly without the preprocessing phase of sentence alignment
analytical results demonstrate the strengths and limitations of the methods and suggest possible improvements to the algorithms
the english examples range from NUM to NUM words long average example length is NUM NUM words
nyu system kim in as vice chairman of wpp group where the vacancy existed for other unknown reasons he may or may not be on the job in that post yet and the article does n t say where his old job was
NUM NUM evaluation of name recognition and retrieval performance
the generalized context vectors were input to the tag induction procedure described above for word based context vectors NUM NUM word triplets were selected from the corpus encoded as NUM NUM dimensional vectors consisting of four generalized context vectors decomposed by a singular value decomposition and clustered into NUM classes
the recursive behavior is one of the reasons to create more than one table of probabilities which stores the probability of apparition of a suffi immediately after the previous one
but the variations that the word adiskide same meaning as friend or amigo may have in basque make it impossible to store them in the dictionary of the system
as we mentioned in the introduction in non inflected languages it is feasible to include in the dictionary all the forms derived from each lemma taking into account that the number of variations is quite small
the first one includes the lemmas of the language alphabetically ordered with their frequencies and some morphologic information in order to know which possible declensions are possible for a word
when a word is syntactically ambiguous that is when more than one category is possible for a given word one entry for each possible category may be created
finally the problem of verb formation in basque is not solved and the most frequent verb forms are included in the dictionary in the same way as in the second approach
figure NUM the transducer compiled from the sub grammar that performs the decomposition of the fictitious
in our study proper names were only covered in the context of street names
we concentrate on street names because they encompass interesting aspects of geographical and personal names
the addition of name specific rules presupposes that the system knows which orthographic strings are names and which are regular words
after the evaluation the name analysis transducer was integrated into the text analysis component of the german tts system
the second version purely consisted of the name grammar transducer discussed in the previous section
in NUM out of these NUM cases NUM NUM both systems were wrong
we retrieved all available first names from the records of the four cities and collected those whose frequency exceeded NUM
the final state end can only be reached from first by way of suffix
they take the NUM most common words in the corpus find their rank within each genre and calculate a spearman rank correlation statistic
the axis of abscissas de null same input ml n transitive graphs are included in m2 n transitive graphs when ml m2 and m nl transitive graphs are included in m n2 transitive graphs when n2 hi
his method involves counting a range of linguistic features in each text and then using factor analysis to determine which of the features co occur
there is also a theoretical problem it is not clear what it means to say that corpora of different sizes are equally homogeneous
we can not use the x NUM statistic for testing the null hypothesis but nonetheless it does come close to meeting our requirements
our method provides figures which can be directly compared for corpus homo hetero geneity and for corpus dis similarity
also by way of rule NUM the system adds the belief that the user also does since the user had proposed it
we therefore conclude that d k and that v is a clique in g of size greater equal than k
the model assigning the highest probability is assumed to be most adequate and the corresponding label is assigned to the phrase
consequently a parser based on such a grammar is compact and theoretically easier to debug maintain and update NUM in practice however designing and implementing faithful and efficient parsers is not a simple matter
to keep the ci informed about the objects in the simulation and their properties the modsaf agent notifies the ci agent whenever an object is created modified or destroyed
was given the highest grade of any marine corps portion of the exercise in addition to these milestones commandtalk has been included in demonstrations of leathernet to numerous vips including general
in the nuance regular expression notation the kleene star operator precedes the iterated expression rather than following it as in most notations for regular expressions
the agent library supplies common functionality to agents in multiple languages for multiple platforms managing network communication icl parsing trigger and monitor handling and distributed message primitives
to produce the recognition vocabulary and grammar for commandtalk we have implemented an algorithm that extracts these from the vocabulary and grammar specifications for the natural language component of commandtalk
for example if the only rules for the nonterminal a are then the regular expression defining a would be bc de
it is expected that performance would be even greater over a larger corpus
the template represents recall of only NUM with precision of NUM
fare l the food and drink that are regularly consumed and sustento NUM nourishment NUM a source of nourishment all linked to ill records different from the dutch case
named entity our official ne scores for the walk through document were 91r 88p
he also judged the tool to be an interesting utility and consented in setting up a larger experiment to measure exactly the impact of the tool on the daily routine of the medical encoders
a preliminary effort at linking sub parts of succession events was attempted for muc NUM
the nlp processing of a load of pdss can be done in batch during the night so that the throughput of the encoder is not affected in the negative sense
figure NUM shows the menu page and pds page in which words concerning the diagnosis h diag the surgical procedure tt ttchir and the bodypart h ptpart are marked
this general strategy will be illustrated by a practical application namely the highlighting of relevant information in a patient discharge summary pds by means of modern hypertext mark up language html technology
nevertheless the experience did prove to be valuable as the collaborating doctor who had never heard of nlp before said he was positively surprised and impressed by the capabilities of the system
but before any application of such an extent can be envisaged for dutch the words of the dictionary database all have to receive the appropriate semantic label s
the documents are morphologically and syntactically analyzed by the dmlp first the resulting parse trees being made conform to the lsp format and subsequently passed NUM on to the lsp mlp
if the id number is added to the original document as a pseudo html code the same mechanism as mentioned above can be used to highlight the sentences containing the relevant information
figure NUM shows a possible hierarchy for the concept digital computer
the current system draws a performance lower bound for future systems
we need a method of identifying the most appropriate concepts somewhere in middle of the taxonomy
we have implemented a prototype system to test the automatic topic identification algorithm
a concept itself is considered as its own direct child
we then borrowed two measures from information retrieval research null
for example john bought some vegetables fruit bread and milk
on the other hand a concept lower in the hierarchy tends to be more specific
this prohibits b k x with k i on the pair 2categories are in the result leftmost representation and associate left
once the other recognizes the judgment that the plan is in error the criteria for him entering will be fulfilled for him as well
this derives l11 a NUM NUM entry lexicon from the collection based on our segmentation procedure
the conditions specify that plan is the current plan of a collaborative activity and that the speaker believes that there is an error in it
wordnet does not provide sense information for auxiliary uses of verbs
using a pilot version of the ovis system a large number of human machine dialogs were collected and transcribed
such cases are borderline hovering in between two distinct meanings
representing split idioms is also a problem with this scheme
as mentioned above for rule NUM this last condition prevents the system from trying to accept a plan that it has itself just proposed
note that the fact that this problematic case does n t show up in the correct analysis of normal nl sentences does n t mean that a parser would n t have to try it unless some arbitrary bound to that number is assumed
hence o may not occur in the cut formula a of a cut application and any subformula b o a which occurs somewhere in the prove must also occur in the final sequent
the human annotator s tags are included in the individual word annotations
when we assume the final sequent s rhs to be primitive or o less then the o r rule will be used exactly once for each positively occuring o subformula
a drawback of the pure lambek calculus is that it only allows for so called peripheral extraction i.e. in our example the trace should better be initial or final in the relative clause
moreover the parsing problem also lies within np since for a given grammar g proofs are linearly bound by the length of the string and hence we can simply guess a proof and check it in polynomial time
word237 and word238 are representative words which are the result of linking noun with their semantically similar nouns
therefore the number of words which appear in both paragraph NUM and NUM was larger than any other pair of paragraphs
this shows that it is too difficult even for a human to judge whether a paragraph is a key paragraph or not
miy in formula NUM is shown in NUM where k is the number of different words and l is the number of contexts in paragraph
we call this domain and each element economic news or international news a context
we used watan null formula NUM shows the value of x NUM of the word i in the domain j
a larger value of x means that the word i appears more frequently in thje domain j than in the other
in table NUM shows that the number of the correct data is smaller than that of an extraction ratio
data manually annotated with lexical semantics clearly has many applications in nlp
a degree of context dependency is a measure showing how strongly each word related to a given context a particular context of paragraph article or domain
examining the results of human judges when the number of paragraphs was more than NUM the number of paragraphs marked with a is large
by expanding the hyperonymyrelations for carne l we see that the spanish wordnet gives three hyperonyms tejido NUM tissue NUM a part of an organism comlda l
the first lower level but more efficient provides data structures and access functions such as getnextbit and printbit where there are different types of bits for start or empty tags with their attributes text content end tags and a few other bits and pieces
the lt nsl programs consist of mknsg a program for converting arbitrary valid sgml into normalised sgml NUM the first stage in a pipeline of lt nsl tools and a number of programs for manipulating normalised sgml files such as sggrep which finds sgml elements which match some query
searching NUM of the british national corpus a total of NUM NUM words NUM mb is currently only NUM times slower using lt nsl sggrep than using fgl ep and sf rre p allows more complex structure sensitive queries
in addition to the normalised sgml the mknsg program writes a file containing a compiled form of the document type definition dtd NUM which lt nsl programs read in order to know what the structure of their nsgml input or output is
if the new attribute were sparse it would be possible to reduce the space cost by switching for that attribute to a more space efficient encoding NUM the ims cwb is a design dominated by the need for frequent fast searches of a corpus with a fixed annotation scheme
because of this agents will have a belief regarding the validity of the plan and an intention that this belief be mutually believed
let sn wi be the score of the word wi having observed n best hypothesis up to rank n NUM where vn wi is the potential for rescoring the word wi according to hypothesis hn the sentence hypothesis at rank n and ash is the rescoring amplitude at rank n
best hypothesis can you give me is a budget first evaluation of the filtering hints that it may be a good guidance but not a sufficient criterion some parameter settings such as the threshold remain problematic
first although the parser belongs to the family of robust parsers since it can process ill formed sentence it is still able to reject a subset of ill formed sentences which may be produced by a recognizer
the last two lines of the table distinguish between two kinds of wrongly filtered sentence the first appear well formed to the parser there is no way to recover from those the second contain anomalies detected by the parser there might be some way to repair or reject those ones
each pass will in turn modify the result of the previous pass and hand it back to the parser
therefore we have not been able to measure its influence in preparing for a particular new text processing task
a more powerful approach is to allow patterns or rules to form the basis for this pre tagging
the test corpus contains NUM NUM oov words composed as follows NUM NUM oov common words NUM NUM NUM NUM oov proper names NUM NUM NUM NUM oov composite words NUM NUM
these developments have focused even greater attention on the bottleneck of acquiring reliable manually tagged training data
this is especially true if the annotations are to be used for subsequent manual or automatic training procedures
in this paper we have concentrated on the named entity task as a generic case of corpus annotation
of course there are many different ways in which corpora are being annotated for many different tasks
figure NUM shows an example sequence of rules that could be composed for pre tagging a corpus with person tags
in addition the granularity is perhaps still too coarse to measure the incremental influences of pre tagging rules
in addition making these corrections removes both a precision and a recall error at the same time
these initial experiments involved a single expert annotator on a single tagging task muc6 named entity
this is the case of the adjectives triste and ingdnieuz which will get respectively the three and two senses as illustrated in NUM to NUM in NUM triste and inggnieu have the head on the formal role and the adjectives have a stative sense in 9c and 10c on the telic they have a manifestation sense
cet hoinme est triste en jouant an piano this man is sad at playing piano another property exhibited by these adjectives is that of multiple semantic selection that is they are able to predicate of different semantic types examples NUM to NUM namely nouns denoting individuals the a examples objects b and events c
the state cl is encoded in the ottmal an event encoded in the agentive role e2 denotes the cause or origin of the state i.e. the experiencing event encoded in the telic role e3 it denotes then the manifestation of the state i.e.
apply if c is a category s is a set of types and t is a tree bank then apply c s t yields a tree bank t by indexing each instance of category c in t such that the c constituent is of semantic type t e s with a unique index i
dead ends in the tree are places where further computation is blocked by the no alternating skip rule
b ich glaube dab du nicht tgten sollst
integrating syntactic and prosodic information for the efficient detection of empty categories
table NUM p ecall precision and error for the
our overall recognition rate of NUM NUM for the s3 classifier cf
for the experiments mlps with NUM NUM nodes in the first second hidden layer showed best results
since cases where the verb trace is not located at the end of a sentence i.e.
as the fgures in table NUM demonstrate the answer to this question is yes
i believe you shall not kill i believe you should not kill
as should be evident the search tree can be quite large even if the words being aligned are fairly short
a prototype implementation has been built in prolog and tested on a corpus of NUM known cognate pairs from various languages
an alignment can be viewed as a way of stepping through two words concurrently consuming all the segments of each
as a first NUM traditionally the problem is formulated in terms of operations to turn one string into the other
the apply edp algorithm takes four arguments the exposition node of the edp that will be applied a newly created exposition node which will become the root of the explanation plan that will be constructed the verbosity specification and the loop variable bindings
the first of these views will be used to produce the sentence during embryo sac formation the embryo sac is formed from the megaspore mother cell and the second will produce the sentence embryo sac formation occurs in the ovule
third to ensure that a knowledge base is not tailored to the purposes of explanation generation we can enter into a contractual agreement with knowledge engineers this eliminates all requests for representational modifications that would skew the representation to the task of explanation generation
combining hierarchically structured discourse objects with embedded pro null lester and porter robust explanation generators cedural constructs edps have been used to represent discourse knowledge about physical objects and processes and they have been tested in the generation of hundreds of explanations of biological concepts
the distinction between elaboration nodes and topic nodes is maintained only as a conceptual aid to discourse knowledge engineers it stands as a reminder that topic nodes are used to specify the primary content of explanations and elaboration nodes are used to specify supplementary content
as the plan is constructed the explanation planner updates the user model to reflect the contextual changes that will result from explaining the views in the explanation plan attends to the verbosity specification and invokes kb accessors to extract information from the knowledge base
just as prototypical programming languages offer conditionals iterative control structures and procedural abstraction edps offer discourse knowledge engineers counterparts of these constructs that are precisely customized for explanation planning NUM moreover each edp names multiple kb accessors which are invoked at explanation planning time
although this ability to perform local content determination is essential it is insufficient given a query posed by a user the generator must be able to choose multiple kb accessors provide the appropriate arguments to these accessors and organize the resulting views
also the tokenizer is responsible for maintaining the mapping between these tw o tokenizations so that the output of tools which use different tokenization schemes can be combined
a trivia l error in this system caused two of the NUM test messages to be garbled sufficiently that the scorer detected virtually n o correct coreference in them
we did not subject this component to rigorous testing but did examine its output for approximately NUM blind test sentences and found that only one error was made
the model used binary valued features of the word to which the putative end of sentence marker was conjoined as well as binary valued features of the precedin g and following words
in this data structure enhancements to the input data such as tokenization part of speech tags or parse trees are stored in separate aligned files
if one of the ancestors is the entry male for example it may be concluded that th e word itself typically denotes an entity which is male
a more sophisticated approach would involve using word sense disambiguation techniques to guess the correct sense of the word and then only quer y wordnet about that particular sense
these variant references include references to people by first name only last name only last name and an honorific and references whic h omit middle names
fun loving guy a spokesman says in addition patterns were implemented to identify phrases containing monetary figures in which alternate representations of the amount are present
note that the parser incorporates punctuation into the statistical model so a comma between two noun phrases is seen as a strong indication of an appositive relationship
we abbreviate m for mother r for russ and whoisgoing for who s going
cohesion endexpr t itadake masu ka desu ocohesion endexpr itadake masu ka desu NUM cohesion endexpr masu ka desu y NUM cohesion endexpr ka desu
cohesion endexpr endexpr i endexpr x ocohesion endexpr endexpr i end xptl i z NUM pq cohesion endexpr endexpr i l p m l q endexpr
where speech act is the speech act type verb is the ma n verb in the utterance and nouns is a set of nouns in the utterance e.g. a subject noun and an object noun for the main verb
u NUM speech act NUM verb1 nouns1 u NUM speech act2 verb NUM nouns2 u i speech act i verbi nounsi uj speech actj verbj nounsj local
cohesion local u u l j lcohesion speechact speechactr speecti a cr rcb j 2cohesion verb verb r verb j 3cohesion noun nounsr nounsj NUM where and c lcb NUM l are NUM NUM NUM
according to the information our method is composed of two processes NUM identifying the expressions which indicate a speech act type called speech act expressions in an utterance and NUM calculating the plausibility of the speech act patterns by using the dialogue corpus annotated with local cohesion
for example itadake masu ka requirement and desu response are interpolated by the relation of the speech act types not the speech act expressions i.e. requirement response as follows cohesion endexpr itadake masu ka desu
state calculus 1deg and thus be directly composed with other transducers which encode tag correction rules and or perform further steps of text analysis
null section NUM below outlines the nature of lrs in our approach and their status in the computational process
udrs where all the discourse referents are properly bound
this may lead to structures outside the first order udrt fragment
table NUM also shows the precision of each method
consider jacques chirac e st le president de la france bill clinton is the president of usa nevertheless the upper case does not totally satisfy our semantic intuition since we also observe nouns with the upper case such as president or president which certainly do not designate one particular person here we encounter the fundamental problem of the definition of the term proper
the following example shows a part of a noun therefore in the automatic analyses of texts written in korean we intend to consider the definition problem of proper nouns from a different view point whatever the given definition of proper nouns is once a complete list of them is available we presumably do not need any longer this particular distinction between proper and common nouns
bag ga will not be recognized as one of these cases even though there exists a simple noun bag pumpkin in the dictionary of common nouns since the postposition required by this noun is not NUM ga but ol r
kim has studied in the u s a during NUM years while a given name alone hardly appears with pts b NUM NUM t NUM z minu bagsa neun migug eise NUM nyengan gongbuha essda dr
for example nouns such as sun earth or moon semantically appropriate to the definition of proper nouns such as mars or jupiter do not have to be written with the upper case initial hence they are not considered as proper nouns
if the string containing ga is not found in these dictionaries then the f mal syllable ga might be a verb a nominative postposition attached to a noun or an inflectional suffix attached to a verb or else it is an in ga
if we want to recognize automatically pns in a given text in order to construct an electronic lexicon of pns recall that is the ratio of pns retrieved for a given grammar over the number of pns in the text should certainly be higher than precision
if it can then abe will be examined to see if can be a word by searching the NUM chara q er suffix linked list pointed at by ab
throughout the experiments below n is always chosen to be NUM so that the NUM confidence interval i.e. t NUM NUM of t z is about k0 NUM
whether tim sole chm acter omt osing shouhl not NUM combined with th suffix of t to form another word instea d
it is well known that an n gram statistics language model is just as effective as t ut nmch more eificient than a syntactk semantic analyser in determining the correct word sequence
tile vocabulary increases from NUM words to NUM words
a tirst step towmds buihting a language model based on n gram statistics is to de vek p an etiment lexical analyser to id ntify all the words in the corpus
thc sill ix will bc stored in the bin table for l harac t r words again wilh clear indication of its suffix status
both approaches ln ve serious liinitat ions
lexical analysis is a basic process of analyzing and understanding a language
the searched word is then converted into phonetics and retrieved with its information if the word is in the phonetic index
code could of course be added in the dictionary modules providing information on how to form the plurals or conjugations
a lexical entry in a dictionary without syntactic and semantic information is in essence a context free letter to sound rule
this transcription program has also been used to create a phonetic index and retrieve a word without knowing how to write it
a word is scanned left to right and on syllables that fall under the category of stress refusers a flag is set
these rules are very well known among linguists and need to be formalized in the same way as were the grapheme to phoneme rules
these range from the trivial in languages like spanish or swahili to extremely complex in languages such as english and french
in databases phonetics can help retrieve a word or a proper name without the user needing to know the correct spelling
in addition the lexical entry need not contain phonetics especially if the entry in question is adequately handled by rule
rules with ab as ls are tested in the order in which they are written NUM NUM
the symbols in these sequences are taken from the set r of dependency relations
the feature function of model NUM is for adjusting errors in word level
the cp3 zu reparieren is interpreted as sentential object of versuchen while das fahrrad is regarded as uninterpreted again and thus is transferred to the cp3 where it is interpreted as the direct object of reparieren
unlike modals zcm verbs are analyzed as taking an infinitival cp as complement and assign accusative or dative ease to the subject of the infinitival clause e.g. the accusative case to ihr in NUM
the task of identifying the grammatical function of an argument is complicated by the large number of possible word orders which results from the interaction of three syntactic processes verb second scrambling and extraposition
if so the new structure is completed and for each argument that is not in its base position a chain is created to hnk the argument with that position in which a trace is inserted
when the infinitival complement zu k tssen is attached this uninterpreted argument is treated as a new argument of the infinitival verb and interpreted as its direct object resulting in the structure NUM
the aim of the ais is to match the arguments with the subcategorization properties argument structure of the predicate and thus to establish an interpretation which corresponds to the assignment of the thematic roles
in this case a final interpretation of the arguments is established immediately at the moment of attachment the arguments are inserted into the definitive argument table of the clause and interpreted by being matched with the argument structure of the verb theta assignment if more than one interpretation is possible different hypotheses are considered in parallel
the argument structure of a verb predicate is provided by the lexicon and specifies the number and type of arguments that the predicate can take while there can be more than one argument structure for a verb at the lexical level there is only one argument table for a verb node at the syntactic level which contains the arguments of the clause
from a structural point of view this reordering can be analyzed as verb raising vr the verbs which whould precede the uppermost final auxiliary without vr are attached to the right of the auxiliary head right adjoined position forming head chains with their base positions as represented in NUM
dips is a practical system under development which uses a large sized lexicon over NUM NUM entries and which at present covers a large range of grammatical constructions such as simple and complex sentences finite and non finite clauses active and passive voice wh constructions topicalization extraposition scrambling long distance dependencies and verb raising
the contents of each barrel increased by NUM pickles to a total of NUM pickles per barrel
we have hcompare the price increased by five percent to a total of NUM NUM dollars per share
last year the supreme court defined when companies such as military contractors may defend themselves
NUM NUM llsu0jly i lcb r lcb over lcb NUM
in all the above cases except for sentence NUM the complement can be unambiguously recovered
this has resulted in the enrichment and improvement of the original comlex syntax dictionary
comlgx had a frame group which classifie l together a number of wh comt lements
task of tagging cxmnl rcb lcs in a lcb lcb ri rcb us
unless otherwise specified dmsc exmnples are all from the h i plls
null the forms you to suru be going to and kakeru be about to take up the occurrence of events
c whew after burrowing and swimming out of alcatraz amid nearby shots and searchlights that was narrow
since an analogical system relies on a database of pre translated example pairs it results in high translation quality
since each property of an object is associated with an eventuality argument we can assign a level of salience for that eventuality
integrating reiter and dale s prioritization of these considerations with spud s other considerations leads to the following ranking of criteria for comparison
we have found this to be useful in practice
in this paper we introduce three new techniques for statistical language models extension modeling nonmonotonic contexts and the divergence heuristic
we note that our frequencies are incorrect when used in an extension model that contains contexts that are proper suffixes of each other
in addition to rule NUM bfp utilize rule NUM in making predictions for pronominal reference
b yesterday was a beautiful day and he was excited about trying out his new sailboat
such examples suggest that more is involved in pronoun interpretation than simply reasoning about semantic plausibility
three intersentential relationships between a pair of utterances u and un l are defined
the assignment of he tony is ruled out by a syntactic constraint violation
what remains at issue is the manner in which salience is utilized by the pronoun interpreter
he arrived just as the store was closing for the day
he was sick and furious at being woken up so early
dr u3 are those departure times
at this point the decoder knows all contexts which are not proper suffixes of other contexts ie d ld
u alternately flashing one and seven
however the features that determine appropriateness of conventional attributions are better modelled as properties of objects in an evolving model of discourse
u alternately displaying one and seven
as formulated the predictions these rules make about the preferred referents of pronouns are fairly limited
if we ignore this violation the resulting transition is again a rough shift the lowest ranked relation
correspondence that line up in rows and columns
each threshold is used to filter candidate chains
several key terms will help to explain simr
bitext maps are bijective functions in bitext spaces
our initial observation is that dg can not use binary precedence constraints as psg does
each word is associated with a category which functions like the non terminals in cfg
employing a ransiation system including for example the cost of manual post editing
besides the classification nametag also assigns unique identifiers to those names that refer to the same entity such as international business machines and ibm
for each text unit the analyzer compared the succession egraphs computed the similarity metric value an d selected the maximal matching egraph that exceeded the similarity threshold
in addition to these core modules hasten includes a tokenizer a document structure facility a lexical data facility and an object oriented template scoring program
in order to use the egraph for matching other text the structural element must be generalized into word classes grammatical constituents or arbitrarily defined word sequences
during this time no effort was made to actually generate the template slots vacancy reason and on the job and hasten generated a default fill of unclear and no respectively
since extraction examples are the core knowledge source for hasten s extraction capability it is worthwhil e to explore the relationship between the number of examples and extraction performance
figure NUM egraph subset test results figure NUM shows eight experimental runs on the final test data using extraction biases to partition the egraphs i n several ways
sra used the combination of two systems for the muc NUM tasks nametag a commercial software product that recognizes proper names and other key phrases in text and hasten an experimental text extraction system
the no names configuration eliminates this error but causes nametag to miss the mentions of coke and coca cola which were also contained in its static list of names
as illustrated in figure NUM the user annotates examples of what to extract labeling the important region s of text with their relationship e g the successee to the expressed concept e.g.
the strength of the present work is that it captures a number of phenomena discussed elsewhere separately and does so within the unified framework of description
moreover we can determine which tree to use by looking at each tree once even when the same tree is associated with multiple lexical items
the universal sigma star language all possible strings of any length including the empty string
the distribution of the auxiliary brackets is controlled by NUM NUM and NUM
if upper describes the null set as in the lower part is irrelevant because there is no replacement
for this paper we need eventualities as abstract representations of spatiotemporal scope and information states to abstract the scope of modal operators like possibility and belief
for convenience we represent identity pairs by a single symbol for example we write a a as a
null the table below describes the types of expressions and special symbols that are used to define the replacement operators
for the sake of convenience we also equate a language consisting of a single string with the string itself
because we regard the identity relation on a as equivalent to a we write a a as just a
the two level model also shares our pure relational view of replacement as it is not concerned about the application procedure
the figures in this paper correspond exactly to the output of the regular expression compiler in the xerox finite state calculus
in this paper we have presented some novel applications of ebl technique to parsing ltag
the description of the tuple components is given in table NUM
however the speedup when compared to the xtag system is a factor of about NUM
experiment NUM c the setup for this experiment is shown in figure NUM
NUM show v me n the d fiights n from p boston n to p philadelphia n
this type of generalization is not possible in other ebl approaches
NUM show me the flights from boston to philadelphia
for example if the test sentence tagged appropriately is
support for the hypothesis that name searching can lead to retrieval performance improvement was provided by simulating name searching using a proximity operator which required that queiy multiple word name terms occur within two non stopwords of each other in the text of a document the name frequency analyses show that names occur frequently enough in case law to merit special handling
the trees a5 and a6 are also no longer distinct so we denote them by a
experiment NUM b this experiment is similar to experiment l a
the main conclusions of this study are NUM that name recognition in text can be done effectively NUM that names occur frequently enough in both texts and queries of legal and news databases to make their recognition worthwhile and NUM that name searching can lead to improved retrieval for queries with personal names
the numbers of parameters of the tag bigram model the tag hmm and the pair hmm are approximated by the equations nt NUM nd ns NUM ns nt nd and ns NUM ns nd respectively where nt is the number of tags ns is the number of states of the hmm and nd is the number of entries in the dictionary
the problem of stochastic tagging is formulated in the next section NUM and the extended reestimation method in section NUM a way of determining the credit factor based on a rule based tagger is described in section NUM experiments which evaluate the proposed method are reported in section NUM
we have shown that ccg gtrc as formulated above is weakly equivalent to ccg std
although one experiment fig NUM did n t use the credit factor assignment function it is regarded as using a special function of the credit factor that returns NUM or NUM that is a step function with a cost threshold of NUM
if the language model can be estimated from untagged corpora and the dictionary of a target application then the above two problems would be resolved because large amounts of untagged corpora could be easily used and untagged corpora are neutral toward any applications
although the total model estimated from an untagged corpus is worse than that from a model using a tagged corpus a part of the model using the untagged corpus may be better because estimations from untagged corpora can use very extensive training material
we discuss these issues in turn
any derived category is a combination of lexical categories
the second problem of overgeneration calls for another step
that is no movement of arguments across the functor is allowed
the finite lexicon with finite extension assures the termination of the process
here pc g corresponds to the prior probability that n random data are classified in to a set of groups o
the method tries to select and merge the group pair that brings about the maximum value of the posterior probability p gic
both methods rely on an enriched set of input features compared to our previous work
recursively unify each constituent with the lexicon
there are two problems with option NUM during syntactic realization
second level index relations by name
finally because unification is the governing process constraints are bidirectional
examples of such patterns of interaction are given in the following sections
however there are certain limits to performing aggregation
these monolingual models are reversible in the sense they can be used for analysis or generation
table NUM names and abbreviations in NUM document
in comparison to statistical techniques which have also been used successflllly on large corpora it is our understanding that simple recurrent networks may be particularly suitable tbr domains where only smaller corpora are awdlable or where class liter on data is hard to got as it is the case lcb or pragmatic dialog acts
in terms of the current framework both of them add the telicity to the verb which does not inherently contain the telicity
now a reverse process on trees delivers an analysis for the prototype as illustrated in figure NUM
in the sequel we will apply the principle of analogy not on words anymore but on sentences
NUM NUM effect of words with multiple functions
ahvays verilles tile first two distance equa tions lcb dist u t u v
we first make a very strong but necessary assumption about the nature of the solution of an analogy
nnd may have never been uttered before by the speaker nor heard before by the list crier NUM
in our attempt to discover a mathematical explanation of analogy we were long hindered by the notation itself
we may see that the topic is more specialized when the threshold is high
with this inclusion relation the clusters form a hierarchy figure NUM
the less the number of anchor branch is the looser the constraint is
recall that our algorithm unlike many other pronunciation algorithms is likely to remain silent
thus a co occurrence graph can be constructed by co occurrence relations in a corpus
figure NUM shows a map of m n transitive graphs
in order to complete the ormmisation dist re mains to be defined l dition distances which have been proposed in many works levenshtein NUM wagner fischer NUM selkow NUM etc are a good atl lidate
the three trees in the right and bottom corners are the corresponding analyses of the sentences of figure NUM
also we represent contextual information that is important for other verbmobil components as e.g.
the basis olprocessing is a training corpus annotated with the speech acts of the utterances
currently confidence values returned from the decision tree are employed when it is desired that a single antecedent be selected for a given anaphor
anaphora resolution it is hard to evaluate how their anaphora resolution capability compares with ours since it is not a separate module
we have evaluated this algorithm on two different pronunciation tasks
we have plans to use cross validation across the training set as a method of determining error rates by which to prefer one predicted antecedent over another
an orderer ranks hypotheses in a preference order if there is more than one hypothesis left in the set after applying all the applicable filters
information related to dates like months weeks days and updates the thematic structure of the dialogue history
this device scans the input for a small set of predetermined words which are characteristic for certain stages of the dialogue
according to the third criteria of statistical efficiency the best model is one that achieves the lowest test message entropy for a given model order
according to the second criteria of statistical efficiency the best model is the one that achieves the lowest test message entropy using the fewest parameters
our extension selection rule s e x e d is defined implicitly by the set e of extensions currently in the model
current approaches to automatic speech and handwriting transcription demand a strong language model with a small number of states and an even smaller number of parameters
this is a form of parameter tying that increases the accuracy of the model s predictions while reducing the number of free parameters in the model
the divergence heuristic allows our models to generalize from the training corpus to the testing corpus even for nonstationary sources such as the brown corpus
now the profitability of the better context deg NUM is reduced and the divergence heuristic may therefore not include it in the model
it is also worthwhile to interpret the parameters of the extension model estimated from the brown corpus to better understand the interaction between our model class and our heuristic model selection algorithm
next in section NUM NUM we use our mdl codelengths to derive a practical model selection algorithm with which to find a good model in the vast class of all extension models
the perplexity v is related to the log likelihood by v e n where n is the total number of words processed
at the root of our smoothing procedure however lies not a unigram model but an aggregate markov model with c NUM classes
in section NUM we examine another sort of intermediate model one that arises from combinations of non adjacent words
the following example illustrates a general view of coreference
their removal risks elimination of crucial words in a query and adversely affect retrieval especially when the queries are short
the document collection is usually huge of gigabyte size and both queries and documents are domain unrestricted and unpredictable
retrievals using lexicons of four different sizes with long and short versions of the trec NUM queries were performed and evaluated
the major rule based stopword removal is rule NUM while others have minor effects because they occur much less often
pircs is an automatic learning based ir system that is conceptualized as a NUM layer network and operates via activation spreading
it is seen that the larger lexicon improves average precision by about NUM from around NUM NUM to about NUM NUM
our lexicon based stopwords consists of NUM entries in our list tagged as NUM
short queries of a few words perform more than NUM worse than paragraph size queries
we believe the rules we use for step b though simple are useful
one or two words on the other hand often do not supply sufficient clues to a retrieval engine
currently terminal matching is performed left to right
and visual c NUM NUM is our programming language
it reflects the degree of a word belonging to classes approximately
these questions constitute the problem of finding a word classification
comparing with brill e s method schutze h
the kullback leibler distance from p to q is defined as
then we repeat this process until certain step is reached
in this case the hearer in the spirit of collaboration must accept the judgment and so also adopt the belief that the plan is in error even if he initially found the plan adequate
given that the move is understood both conversants by way of the rules given in section NUM NUM will believe that it is mutually believed that the speaker believes the current plan to be in error
the first rule is that if it is mutually believed that the speaker intends to achieve goal by means of plan then it will be mutually believed that the speaker has goal as one of her goals
since the action schemas are used for both constructing the plans of the system and inferring the plans of the user it is sometimes necessary to refer to the speaker or hearer in a general way
using the planning paradigm has several advantages it allows both tasks to be captured in a single paradigm that is used for modeling general intelligent behavior it allows more of the content of an utterance to be accounted for by a uniform process and only a single knowledge source for referring expressions is needed instead of having this knowledge embedded in special algorithms for each task
in our work we have taken clark and wilkes gibbs s descriptive model and recast it into a computational one thus demonstrating the computational feasibility of their work and its compatibility with current practices in artificial intelligence
this body of work uses plan construction techniques to generate explanations and uses the computational linguistics volume NUM number NUM constructed plan as a basis for recovery strategies if the user does n t understand the explanation
peter a heeman and graeme hirst collaborating on referring expressions NUM comparisons to related work in providing a computational model of how agents collaborate upon referring expressions we have touched on several different areas of research
i if a n ablative ease marked oblique object deuoting m edible entity ix present then there should not be any direct el jeer a d tile verb w m s to eat a NUM ieec oj the edible oblique od eet l or NUM if the abla tive
that is when a partially specified ease rame such as unifies successfully with the given constraint above the unspecified portion will be properly instantiated with the experieneer being coindexed with the subject in the arguments
NUM NUM since we assume that the agents have mutual knowledge of the action schemas and that agents can execute surface speech actions we do not consider beliefs about generation or about the executability of primitive actions
finally by using clark and wilkes gibbs s model as a basis for our work we aim not only to add support to their model but also to gain a much richer understanding of the subject
we plan to develop a graphical user interface to all of the c scorers so that all of th e useful emacslisp features translated and so that we can provide more features without noticeable impact on th e performance
he reports that his program outperforms all other non word based statistical parsers grammars on this corpus
these elements include a set of premodifiers e g the an a a set of postmodifiers e g period question mark double quote and a set of corporate designators e g company co inc ltd
for example if we flip a fair coin once the resulting empirical distribution over h t is either NUM NUM or NUM NUM not the fair coin distribution NUM NUM NUM NUM
table NUM shows ql p the ratio ql x x and the weighted point divergence x ln x ql x
the degree to which a given set of weights accounts for a training corpus is measured by the similarity between the distribution q x determined by the weights fl and the distribution of trees x in the training corpus
finally d llq is equal to the cross entropy of q less the entropy of and the entropy of is constant with respect to q hence minimizing cross entropy maximizing likelihood is equivalent to minimizing divergence
optimal mapping for a hierarchical template or for relational level objects is an unsolved problem in compute r science
however we do not consider the absolute degree of overrepresentation but rather the degree of overrepresentation relative to x if y and xn are equally overrepresented there is no reason to reject y in favor of xn
for example the expected rule frequencies fl and f2 of rules with left hand side s already sum to NUM so they are adopted without change as fll and f12
we do not simply add y to the sample that would give us a sample computational linguistics volume NUM number NUM from p but rather we make a stochastic decision whether to accept the proposal y or reject it
the scorer parses the data from the input data files and creates structured internal representations of th e information
r respects f structure reentrancies indicated in terms of identical tag annotations without fllrther stipulation
it can e asily be recast in l erlns of hierarchical sets finite functions directed graphs etc
the extension is straightforward but messy to state in full generality and for reasons of space not given here
we provide a reverse mapping from qlfs to f structures and establish isomorphic subsets of the qlf and lfg formalism
the two tinm clmlses cow r non subcategorizmfle granmml ical fulmtions aim what we call alomic attrilmtewdue pairs
ditferent languages express grammatical flmctions such as subject or object in a variety of ways e.g.
a more satisfactory translation into qlf complicates the treatment of nominal inodification as abstracted qlf forms
it comes as no surprise that we can eliminate r and provide a direct underspecified interpretation for f structures
the output is then determined by extrapolation from the k nearest neighbors i.e. the output is chosen that has the highest relative frequency among the nearest neighbors
we can use this s attribution transformation for case assignment to the subject nominative case or structural case
this analysis mostly concerns the first algorithm nlab which is constituted of a series of binary choices
the ll compilation method yields results similar to those of the lr compilation although less clear cut
so for the ll table the co occurrence of lexical categories does not play a filtering role
however ordinary context free rules do not encode many other types of lexical information also used in parsing
finally the choice of rule NUM or rule NUM depends on the actual verb in the string
in general it is not necessary for a parser to implement the principles of the grammar directly
this same parser includes a module for the computation of long distance dependencies which works by generate and test
in addition most english words are either derived themselves or serve as bases of at least one derivational affix
this unreliability is due in part to the inherent exceptionality of lexical generalization and thus can be improved only partially
a different sort of non conformity is produced when the morphological analyzer finds a spurious parse
automated acquisition of this information would thus increase the robustness and portability of nlp systems
word sense disambiguation for the bases and derived forms that could not be resolved using part of speech tags was not performed
are used to collect tokens from the corpus that were likely to have been derived using ize
empty categories are licensed in two computational steps structural licensing by an appropriate head and feature instantiation
the licensing of subjects in the phrase marker done by predication must occur in the specifier head configuration
these sentences of course were not seen at the training phase of our model
consequently if ws has a high y value everywhere then the cosine measure between any wt and this ws would be high
when all vnknown words are represented in worms a matching function is needed to find the best worm pairs as bilingual lexicon entries
this means that for any liged forest l the elements of the form tip q t do not a need to be computed in the and relations since they will never produce a useful non terminal
a strength of our discourse processor is that because it was designed to take a languageindependent meaning representation interlingua as its input it runs without modification on either english or spanish input
this subset contains dictionary entries which occur at mid range frequency in the corpus so that they are more likely to be content words
furthermore the frequent nature of the seed words led to our choice of the euclidean distance instead of the cosine measure
figure NUM shows the ranking of the true translations among all the candidates for all NUM cases for the purpose of a translator aid
if a pair of bilingual words are supposed to be translations of each other they should share the most significant y values
xi a lcb except when the production form num d bet NUM is used
if we assume that the cf backbone g is non cyclic the extraction of a parse is linear in n
one way to solve this problem is to unfold the shared parse forest and to extract individual parse trees
our vision for the parsing of a string x with a lig l can be summarized in few lines
a b c and x if fif2 x0 and a production r NUM the number of relation symbols is a constant therefore the number of such productions seems to be of fourth degree in the number of non terminals and linear in the number of productions
the numbers i refer to definition NUM we can easily checked that this grammar is reduced
it is likes that determines syntactic fea null tures of beans and which provides a semantic role for it
this can informally be seen by noting that the worst case complexity is due to the completion rules NUM NUM NUM NUM because they apply to a pair of states rather than just one state
the search space of possible alternatives is so large that it is not practical to find an optimal ltig however by means of simple heuristics and hill climbing significant reductions in the number of elementary trees can be obtained
the ltig lexicalization procedure presented in section NUM produces grammars that have no left auxiliary trees and are left anchored ones where for each elementary tree the first element that must be matched against the input is a lexical item
this does not change the worst case complexity but is a dramatic improvement in typical situations because it has the effect of dramatically reducing the size of the grammar that has to be considered when parsing a particular input string
because the system is adaptive it can be focused on especially difficult cases and combined with existing systems to achieve still better error rates as shown in section NUM
if there is only one way to derive a given tree in g the mappings between derivations in g and g are one to one and there is therefore only one way to derive a given tree in g
then g generates exactly the same trees as g further if there is only one way to generate each tree generated by g then there is only one way to generate each tree generated by g
i j if x c vfoot vx to take advantage of equivalent states during parsing one skips directly from the first to the last state in a set of equivalent states
taken as a group the two trees t and u along with the substitution operation between them can be replaced by the appropriate new tree t e t t that was added in the construction of g
further suppose that if t is an initial tree x y let t be the set of NUM this is true even if bar hillel s categorial grammars are augmented with composition joshi personal communication
both authors feel indebted to the other members of the mikrokosmos team
we also call this step terminalmatching
NUM semantic and computational treatment of adjectives old and
hence they can legitimately replace unigrams as the base model in the smoothing procedure
to tackle this problem we built two maximum entropy models
in m my languages adjectives and adverbs are the sanle
the docuverse interface presents the user with an array of nodes not unlike the array of nodes depicted in figure NUM
the constraint solving equation NUM is then correspondingly adjusted as
even if one couhl imagine sl oring all he inll0 l ed fornls of a hmguag e such its french the inl orma tion associated with l hose forms is awdlmfl l oda y only from analysis sofl ware
nevertheless it would be interesting to see exactly how this relationship plays out for aggregate and mixed order markov models
this is done by interposing these models between different order n grams in the smoothing procedure
the main motivation is to have both a robust and a computational efficient natural language system
l istributed by tile linguistic data consortium
c4 NUM is a tdidt top down induction of decision trees decision tree learning algorithm which constructs a decision tree on the basis of a set of examples tit
this is subject tbr fllrther resem ch
the following is one of these clustering results
table NUM allomorphic variation in dutch diminu
lattice when adding new atomic nodes
this can be the node itself
for a conditional model kullback leibler divergence is computed as
it is essentially a combination ofinstantiated atomic features too
i variables with certain instantiated behavior variables
this is best asthis important difference was pointed out by one of the anonymous reviewers whom i thank
the lexical entries containing r are lcb clvc vc3 rcb and lcb ktb rcb respectively
the past few years have witnessed an increased interest in applying finite state methods to language and speech problems
this in turn generated interest in devising algorithms for compiling rules which describe regular languages relations into finite state automata
this lexicon can be substantially reduced by intersecting it with proj ect l NUM
0p a k the parentheses are ignored if there is only one argument
the following syriac example is used here with the infamous semitic root lcb ktb rcb notion of writing
restrict r substitute r w o NUM insert lcb rcb o a b
the material was a NUM word section taken from a fiction passage in the brown corpus
the number of all possible classes is NUM h
it remains only to remove all instances of p from the final machine determinize and minimize it
it integrates top down and bottom up idea in word classification
participants were asked to analyze their system s performance on that article and comment on it in their presentations and papers
but it can be applied in multiple language too
we use four subcorpora mentioned above as test sets
the numbers on the edges are the weights of the sub trees starting at the pointed node
here we will trace the execution of the example NUM utterance subdialog given in section NUM and thereby illustrate the theory of operation in detail
consequently the usefulness of the expectation is for selecting between grammatical utterances derived from the perceived voice signal that have minimal utterance cost
in the current example assume the machine has previously output there should be a wire from terminal NUM to terminal NUM
further recurrences on zmodsubdialog discover usercan adjust knob x and undertake vocalize adjust knob NUM
infer that the user has intensional knowledge about a physical state if the user has knowledge on how to observe or achieve the physical state
unless the user explicitly needs some type of clarification the computer will select its response solely according to its next goal for the task
if it is not then it may be related to a nearby active subdialog or with less probability a more remote one
as with the sra experiment the only differences in performance between the two bbn configurations are with the organization type
since voice dialog is always tied to proving a given subgoal the set of all interactions related to that goal comprise a subdialog
as an example the system might have the goal of determining the position up or down of a certain switch swl
note that nearly NUM of the tags were enamex and that almost half of those were subcategofized as organization names
information extraction is a relatively new application of natural language processing techniques in which basic information and relationships are found and extracted from text
the handling of conjunction followed that of the treebank annotators as to whether to show separate basenps or a single basenp spanning the conjunction NUM possessives were treated as a special case viewing the possessive marker as the first word of a new basenp thus flattening the recursive structure in a useful way
running church s program on test material however reveals that the definition of np embodied in church s program is quite simplified in that it does not include for example structures or words conjoined within np by either explicit conjunctions like and and or or implicitly by commas
table NUM the perplexity of psts for the batch mode
the following coding interpretation may help to understand the issue
as a result the labeling algorithm of figure NUM is forced to generalize over many levels with a consequent loss of information
these verbs usually produce noise because of wordnet ambiguity and of the spurious for the category input examples fed to ciaula
semantic similarity is strongly suggested by the observation of verb configurations in which words of the same conceptual type play the same roles
there are instead clusters with a low overlap score that seem very appropriate if one looks at the usage patterns in the corpus
we also discuss several implementation issues
the results for these runs in tables NUM and NUM suggest that the lexical rules improve performance on the basenp chunk task by about NUM roughly NUM of the overall error reduction and on the partitioning chunk task by about NUM roughly NUM of the error reduction
if this session was in declarative mode they were told the system would act like an assistant so that they could control the dialog and they were given an example of a short interaction so that they could observe the kind of control that can be achieved
the theory revolves around a prolog style theorem proving model with a variety of special features to accommodate the needs of a dialog system
this can be done efficiently if we can quickly approximate how the probability of the training data changes when a move is applied
in this section we describe how the parameters of our grammar the probabilities associated with each grammar rule are set
in fact the odes t1 l NUM NUM NUM are not part of the canonical class code mapping associated with class NUM NUM
when we parsed the NUM example sentences in part two of levin s book including the negative examples these sentences reduce to NUM unique patterns
the remainder of this section describes the assignrnent of signatures to semantic cbusses and the two experiments for determining the relation of syntactic information to semantic cbtsses
the value n NUM n ti is the number of different ways a symbol expands under the lari and young methodology
instead we grossly approximate the optimal values by deterministically setting parameters based on the viterbi parse of the training data parsed so far
in the inside outside algorithm the gradient descent search discovers the nearest local minimum in the search landscape to the initial grammar
the first pass row refers to the main grammar induction phase of our algorithm and the post pass row refers to the inside outside post pass
for the third domain we took english text and reduced the size of the vocabulary by mapping each word to its part of speech tag
because of the computational demands of our algorithm it is currently impractical to apply it to large vocabulary or large training set problems
the architecture was developed by the contractors architecture working group cawg over the past two years
aspect denotes certain general verb classes binnick
the cost c of selecting a given expectation is a function of two parameters NUM the utterance cost u which measures the distance of the perceived voice signal from a grammatical utterance as defined by the system grammar and NUM the expectation cost e which measures the degree of locality of the selected expectation with respect to computational linguistics volume NUM number NUM the current subdialog
in using the talk system we have found that keeping the partner informed in some way is reassuring and helpful
but there were also NUM cases NUM NUM
even more serious problems arise on the phonological level
another fuge has to be inserted after allee
finally we discuss areas for future work
the name specific system significantly outperforms the generic system
this list was then sorted and made unique
there are two ways of interpreting the results
in a study analyzing the quality of the content of talk aided conversations compared with conversations on the same topic carried out by natural speakers the content of the computer aided conversations was rated significantly higher than that of the unaided samples p NUM
NUM the c authority y j will be c accountable t to the c c f6 j financial secretary o
that is a aa and aa a generate the same two output strings which are also generated by aaa and similarly with a aa and aa a which can also be generated by aaa
null moreover this data disproves b uerle s explanation of NUM clarifies smith s definition of a viewpoint and motivates the need for a neutral viewpoint in german
a heuristic to deal with this is to specify for each of the two languages whether prepositions or postpositions are more common where preposition here is meant not in the usual part of speech sense but rather in a broad sense of the tendency of function words to attach left or right
then yi generates e i NUM e n c n c i NUM for all computational linguistics volume NUM number NUM NUM i n NUM so the new production a b1y1 also generates e c
aside from the bilingual orientation three major features distinguish the formalism from the finite state transducers more traditionally found in computational linguistics it skips directly to a context free rather than finite state base it permits a minimal extra degree of ordering flexibility and its probabilistic formulation admits an efficient maximum likelihood bilingual parsing algorithm
for the two singleton productions which permit any word in either sentence to be unmatched a small c constant can be chosen for the probabilities bit and bq so that the optimal bracketing resorts to these productions only when it is computational linguistics volume NUM number NUM otherwise impossible to match the singletons
a reduction of the ambiguity level of an ambiguous word w with k morphological analyses a1 ak occurs when it is possible to select from a1 ak a proper subset of i analyses NUM g NUM k such that the right analysis of w is one of these NUM analyses
applying this algorithm to the sets and the counters extracted from the corpus our previous example yields the following probabilities NUM hqph NUM NUM h qph NUM NUM NUM because of the finite nature of our algorithm we assign non zero probabilities even to events that do not occur in the training corpus
computational linguistics volume NUM number NUM both p and r are indexed by the nonterminals in the grammar
even if the matrix p is sparse the matrix inversion can be prohibitive for large numbers of nonterminals n
as in the forward pass simple reverse completion would not terminate in the presence of cyclic unit productions
whenever we wish to apply our method to some other language that has a similar NUM this assumption does not hold for a small number of verbs that take as a subject only animate nouns with a specific gender such as yldh n f she gave birth
before proceeding we can convince ourselves that this is indeed the only case we have to worry about
computational linguistics volume NUM number NUM form i k x
the main difference is the updating of rule probabilities for which the e expansion probabilities are again needed
this factor is just the product NUM i NUM eyk where j is the dot position
the following modifications to the probabilistic earley parser implement the for null ward phase of the viterbi computation
after that the m step consists of a simple normalization of these counts to yield the new production probabilities
NUM refers to the direct object
let us consider a NUM NUM matching
this step constructs the initial asm
brih ftp ftp cs jhu edu pub brill
his method involves the following two problems
each pst node s is has a corresponding prediction
when applied to the five to ten most frequent words this pairing can reduce processing time during translation by dramatically reducing the amount of data which must be read from the index file for example there might be NUM NUM occurrences of a word pair instead of NUM NUM NUM occurrences of one of the words and NUM NUM of the other word and thus the number of adjacency comparisons which must be made
if the output from the core analysis is correct the related nodes will then be used to fil l in the appropriate templates or slots for example the subject of a sacking event would be used to fill the succession org slot
nun l rcb er m l qualil y of i rmlsl tl ions lcb legra les gradually as the size and lualil lcb rcb the i ilingual diction try aim synonym list leerease
ebmt is essentially translation by analogy given a source language passage s and a collection of aligned source target text pairs lind the best match for s in the source language half of the text collection and accept the target language half of that match as the translation
iven thc NUM hree re lcb luire i knowledge s rcb llp lcb es o e lcb rplls lcb li lcb l ionary and word root list panei lcb mt can begin pro lcb h
panebmt was first put to the test during an internm evaluation in august NUM whi lcb h w ts similar in design i o l he ari a mt ewdual ions white o kmnell NUM lcb NUM
task oriented dialogue such as that of our slds is a paradigm case of shared goal dialogue
finally we evaluate the model on several corpora
less flattering of course the test thereby revealed several deficiencies in our coopera live dialogue design
NUM background and phonetic motivation phonological and phonetic notations have been developed by linguists primarily as descriptive tools using rewrite rules operating on highly abstracted basic units derived from articulatory phonetics
a novice user however will need to listen to the system s introdnction to itself
the time varying sequential properties of speech which are difficult for neural nets to handle can thus be modeled as a spatial pattern in an accessible and straightforward manner
take into account possible and possibly erroneous user inferences by analogy fi om related task domains
the algorithm was run with iterations threshold range feature filtering and frequency square root feature strength
we propose ihat NUM and NUM are both vl eci k
third as argued above gpll is presupposed by maxims gpi gp2 and gp5 gp9
these reductions are possible even where the input pattern space may be only sparsely populated yielding a flexible encoding with not too many degrees of freedom
NUM may appear trivial as supportive of the design of usable information service systems
these vectors would occupy little storage space and might be passed as input to a further som layer to try to cluster similar sounding words
it seems obvious that it can not he because ihese aspects arc absent from human huntan spoken dialogue
that the results should be similar is to be expected as this data is essentially spectral and bears little resemblance to real vocal tract data
the computation for byblos will increase
the corer feature is coref if no np in ficui corefers with an np in the ficui i the infer feature is infer if no np in ficui is inferentially linked to an np in ficui i the global pro
the som coding replaces the linguistic description and leads to direct access of waveform values for a given diphone which then become default values for the next stage to operate on
although the high standard deviations show that the tuned algorithm is not well fitted to each narrative it is likely that it is over specialized to the training sample in the sense that test narratives are likely to exhibit further variation
by using average human performance as a baseline against which to evaluate algorithms we are asking whether algorithms perform in a manner that reflects an abstraction over a population of humans rather than whether they perform like a typical human
the results obtained via machine learning are also better than the results obtained using error analysis ea in table NUM primarily NUM the actual tree branches on every value of word1 the figure merges these branches for clarity
we are testing whether characterizing the use of referring expressions certain pronouns in terms of relative knowledge about segments whether the current referent was already mentioned in the current segment is useful for classifying the current boundary site
and indicate falling versus rising sentence final intonational contours indicates phrase final but not sentence final intonation x indicates a pause lasting x seconds measured to an accuracy of about NUM
experiments are currently being carried out to determine whether these maps or those based on formants will work better as part of h prototype speech synthesis system
hence the approach described here might also be useful in the design of a hybrid neural symbolic system to operate in the speech synthesis domain
carrier NUM in distance miles NUM NUM NUM NUM
the former module see section NUM NUM is template driven canned text interspersed with slots
an example see figure NUM gives an idea of how the system works
the templates account for the flexibility including the linguistic variation of the messages
null for each argument the intonation module calculates a piecewise linear intonation contour based on slot specific intonation models
the transformation of mu NUM into carrier NUM is straightforward no specific condition
the general purpose model is only used if no more specific model is available
it consists of a cascade of different duration models each having a decreasing specificity
it consists of two components the mts generation and the mts prosodic integration parts
finally the integrated ept is fed into the tts synthesis module pts
currently about NUM of vanf rules are derived from fastus
the obvious choice was smart tokenization which included named entity detection
the original vanf development tool NUM NUM
training and rules modificatio n all rules development was done by hand
NUM the walk through exampl e NUM NUM
knight ridder information participated in the ne tas k only
the only difference is the input and output formats
p p enamex type organization mccann enamex still handles promotions and media buying for coke
vanf used a cascaded non deterministic state machines approach and is based on fastus
if one expects to evaluate human computer spoken language interaction one will need a system that can give the quick responses that people normally expect in spoken in null teraction
care must be taken in any training not to overly bias the type of linguistic behavior that users will exhibit if claims of general capability and robustness are to be validated
on the other hand they were using the system in a data collection mode at that point rather than in a formal experimental evaluation of the system
in general linguistic coverage of snlds in the past has been limited and to the extent that limitations will exist in the next generation of snlds such limitations need to be measured and described
for the future expect measurements of speech recognition performance and basic utterance understanding to remain important but there should also be more emphasis on measuring robustness and measuring the utility of domain independent knowledge about dialog
in other circumstances system failure might be cast as user error because the user did not follow the allowed syntax or else spoke a word that was not in the recognizers vocabulary
furthermore we should expect real time response from evaluated systems a sharp reduction in the amount of specialized training for using systems and the use of longitudinal studies to see how user behavior evolves
weighted models can also contribute to efficiency because dynamic programming can be used to eliminate suboptimal derivations
however the number of target positions for transductions is not constrained by these efficiency considerations
we found many interactions between the different sources of evidence
the hypothetical user satisfaction ratings shown in table NUM range from a high of NUM to a low of NUM
in previous work researchers have usually made distinctions based on their intuition
a predicted category must be a lexical category that lies somewhere between the extreme positions
moreover it uses memorization and it ensures that the predicate succeeds at most once
it uses a precompiled version of the grammar in which no empty productions are present
the experiments were performed on a 125mhz hp ux NUM machine with NUM megabytes of memory
the appropriateness of the semantic representation given the dialogue context the bigram score
in the second phase the robustness component selects a sequence of such maximal projections
a path in the input graph can be constructed by taking steps of two types
in the application for which the head corner parser was developed robust processing is essential
thus we obtain items for all possible maximal projections anywhere in the input graph
our experiments with morphology support our atgument about distinguishing homonymy and polysemy
cases like stone wall stonewall or bottle neck bottleneck are infrequent
profit programs are compiled into prolog programs so that no meta interpreter is needed for their execution
as h b where h is an atom the head and b is a list of atoms the negative literals
for simplicity we have not provided a semantics here but it is easy to add a semantic interpretation as a fourth argument in the usual manner
returning to the categorial grammar example above the control rule and selection rule are specified by the prolog code below which can be informally described as follows
the add adjuncts NUM and division NUM predicates formalize the lexical rules a which adds adjuncts to verbs and d the division rule
division xo yo i z y z division io yo i y
in the categorial grammar example only x NUM goals are memoized and thus only these goals incur the cost of table management
as is well known memoizing parsers do not suffer from this deficiency and we present a memoizing interpreter below which does terminate
item NUM is generated by the initial goal its sole negative literal is selected for program resolution producing items NUM NUM corresponding to three program clauses for x NUM
note that the application rules are left recursive so a top down parser will in general fail to terminate with such a grammar
third the task exposed some of us to research areas with which we only had passing familiarity
most of these tests are described in NUM but some additional test s were added to increase coverage
the biggest NUM NUM difficulty is to prevent bride of cogniac from marking too many things as coreferent
bride of cogniac attempts to determine whether fuzzy string matches such as the unions and unions indicate coreference
in the rare event that all three taggers disagree the system uses the tag assigned by the maximum entropy tagger
given a maximal noun phrase we find the head non recursive noun phrase through a left recursive descent of the parse tree
nouns ending in man which do not end in woman tend to denote male humans
for convenience we define any sequence of white space separated tokens to be a word while discussing this stage of processing
timing describes the rate at which work was completed
this was extended to include patterns of the form verb npl to be np2
it also employs the pleonastic it filter described above and a quoted speech component not present i n cogniac
although the verbs of the categories NUM and NUM do n t contain telicity the arguments of the verbs or some kinds of adverbs can set up the endpoint of the process as discussed later
in order to do this sthe syntax for finite domain terms is terra domain
to evaluate the result of the experiment we will examine the meaning of teiru which is one of the most fundamental aspectual forms since the classification itself is difficult to evaluate objectively
the most general sort is top and all other sorts must be subsorts of top
profit mlows the programmer or grammar developer to declare an inheritance hierarchy features and templates
note that expansion of the templates yields the usual definition of the member relation in prolog
to this point the definition of a tig is essentially identical to the definition of a tag
of course this variant is not very efficient because it requires us to find and use i n NUM
more formally we want i to be the projection of the candidate set n a onto just the f and x tiers
NUM number the tiers such that f and x are numbered NUM and all other tiers have distinct positive numbers
the components are drawn from a smaller alphabet a lcb l rcb
i max voi or parse voi vo i voi underlying voicing features surface
thus ci r NUM if the ci transducer outputs the string llll on input r tesar NUM
formalizing ot is necessary not only to flesh it out as a linguistic theory but also for the sake of computational phonology
otherwise it is said to violate that constraint where the value of c r specifies the degree of violation
in diagrams of timelines such as 4b and 5b the intent is that only horizontal order matters
after morphological trans null extreme results would have been obtained if same and different orientation conjunction types were equally distributed
when the lemma is accepted or typed entirely if the predictor fails the system offers the suffixes that are correct for this lemma ordered by frequencies
gradual change indicators express the progress of change of state such as dandan gradually sukosizutu little by little jojom gradually dondon constantly
if one can acquire aspectual properties of verbs properly and know how the other constituents in a sentence operate on them then the aspectual meaning of the whole sentence will be determined monotonically
typically this formal language will be hand crafted to enhance performance on some task specific domain
we employ a trivial decoder and language model since our emphasis is on evaluating the performance of different translation models
each clump is then generated from a single formal language word using a translation model
some trained translation probabilities are shown for the unigram and headword models in table NUM
the translation models were trained with NUM context independent atis sentences and smoothed with NUM sentences
in these models the alignment provides all of the structure in the translation model
then both the alignment and the clumping are hidden structures which must be summed over to train the models
the word part is ambiguous between a noun and a verb singular third person and it is disambiguated incorrectly
hence we avoid extra operations related to feeding and updating the cache
this multiplicity and the on going growth of the number of different entities cf
retain r rough shift rs c u
these principles allow for a major extension of the original centering algorithm
in order to accommodate to these empirical results divergent solutions are proposed
the cp s ui NUM is taken over for ui
the evaluation of lift succeeds with respect to two levels of embedding
the text is first tagged manually without using the disambiguators and the output of the tagger is then compared to the hand tagged result
the centered segmentation analysis of the sample text is given in table NUM
and the linear distance they have to their corresponding antecedents
from the table we observe that the tree based methods perform considerably worse than frequency significant at any conceivable level even when cross validation is employed
in order to select the nodes to shrink we normally need to use new data that has not been used for the construction of the original tree
it is given in a schematic form in table NUM
for morphological productivity test NUM we measure several variables related to the freedom of the word to receive affixes and to participate in compounds
this mild dependence will increase somewhat the probabilities under the true null distribution but we can be confident that probabilities such as NUM NUM will remain significant
we have presented a quantitative analysis of the performance of measurable linguistic tests for the selection of the semantically unmarked term out of a pair of antonymous adjectives
finally the variables for the pairs are computed as the differences between the corresponding variables for the adjectives in each pair
in addition while adjectives tend to have prevalent markedness and polarity values in the language at large frequently these values are negated in specific domains or contexts
we use the length of a word in characters which is a reasonable indirect index of morphological complexity for tests NUM and NUM
the number of syllables in a word is another reasonable indicator of morphological complexity that we consider although it is much harder to compute automatically than word length
em o be a witnessing derivable sequent i.e. for NUM i 3m bi e l wi
the invariant now states that for any primitive b the b count of the rhs and the lhs of any derivable sequent are the same
manfred stede and birte schmitz initiate the proceedings with a close look at discourse particles the little words which can carry so much meaning especially in terms of the overall dialogue structure
NUM improved performance in spoken natural language dialog systems
given a word in a particular context the context would activate some clusters in the dendrogram based on its similarity with the contexts of the words in the clusters then the correct sense of the word could be determined by comparing its definitions with those of the words in the clusters
our proposed mgoril h u is asked to do t lc sa nm tiring over the test lnta and retri wd perl or imuwes ou NUM he two ttitf reut oul om s m munlly imlexed and aul olual ically iudexed al stracl s are omparec
l liey are based oit the infor t l t lcb io l ofillverse octlltteiit fre tuency iscriliiht tl ion wthi a nd l rol abilisi ic vmue sail on NUM lcb NUM
since the d ictionary making method gives a list of candidate nouns we only need to ctaeck if a candidate is a coml ound noun and judge if the eompotl mts el the candidate compound noun e re consistent with the content of the deemneat
our proposed method to handle compound nouns with a goal to increase the recall while preserving the precision computes the relevance of the component nouns of a compound noun to the document content by comparing the document sets that are supported by the component nouns and the terms of the document
llverse o ltittelll t reqttelley ltiethod is also shown to work with little t erl orrnanc varbtl ion a cross l ercnl onl tins
all ilet cont lilt fers from the other two be cause of lhe constraint cp u cb un in l he lug pain
speech acts is given in fig
each triple in the list of triples represents a local tree
rather name searching was simulated by probabilistic searching with a proximity operator for multiple word names
a dialogue memory is constructed incrementally
table NUM also shows the relative sizes of the various types of instructions in the corpus as well as the number of examples from this sample that came from each type
we then use two copies of the resulting decision tree represented by the diamond shaped nodes marked with mform to test the accuracy of the testing and the training sets
note that although this level of accuracy is better than NUM NUM the score achieved by simply selecting dont in all cases there is still more work to be done
given the corpus analysis and the learned system networks discussed above we will present an example of how preventative expressions can be delivered in drafter an implemented text generation application
NUM there is no meta functional distinction in the network but rather all the features regardless of their semantic type are included in the same tree
table NUM contains the results for the system on the nmsu data
in addition the results show a high precision of NUM
NUM if your plans call for replacing the wood base molding with vinyl cove molding be careful not to damage the walls as you remove the wood base
when the probe returned more than NUM examples for a grammatical form we randomly selected around NUM of those returned as shown in line NUM of table NUM labeled raw sample
functional features include the semantic features of the message being expressed the pragmatic features of the context of communication and the features of the surrounding text being generated
it may be admitted that the notation used for the dotted rules was partly motivated by the possibility of immediately testing the algorithm using the finite state calculus in prolog the regular expressions listed above can be evaluated directly using the wildcard capabilities of the finite state calculus
formula NUM expresses the restriction that a dotted rule of the form NUM which represents starting to parse the right hand side of a rule may be preceded only by nothing the start of the string or by a dotted rule that is not of the form z which would represent the end of parsing the right hand side of a rule
the finite state grammar derived in this way can not in general recognize the same language as the more powerful grammar used for analysis but since it is being used as a front end or filter one would like it not to reject any string that is accepted by the analysis grammar so we are primarily interested in sound approximations or approximations from above
because nonpronominals contribute discourse content pitch accented nonpronominals are mainly interpreted with respect to the mutual beliefs that is for their propositional content
by analogy the remaining pitch accents seem to either weaken or strengthen the current center s cb status but do not force a reordering
and therefore the attentionai effect of pitch ac null cents can be formally expressed as an effect on the order of items in cf
i investigate this via a phenomenon that by the strictest interpretation of either centering or intonation theories should not occur the case of pitch accented pronominals
therefore while contrastive stress may be mandated when grammatical features select the wrong cospecifier the accenting is only felicitous when there is an alternate referent available
the cb loses both its centered status and rank null ing in the current utterance as attention shifts to a new center
attentional salience measures the degree to which an item is salient expressible as a partial ordering e.g. its ranking in cf
cf is a partially ordered list of centering candidates NUM the cb at the head of cf is the current center of attention
these hypotheses arise from the following chain of assumptions NUM to analyze the effects of pitch accents on pronominals it is necessary to distinguish between attentional and propositional salience
null subje l s are nol surprisingly the m isl frequenl ly us d expression NUM for c ntinuf s NUM tie difference tmlwec n
although incorporating the credit factor into the hmm improved the results they remained at a level similar to that of the tag bigram model with the credit factor
in the precision evaluation the correct morpheme was defined as that matching the segmentation tag and spelling of the base form of the hand tagged morpheme
the most exciting is that near human performance is within the state of the art for mixed cas e english
parameters in plum many aspects of plum s behavior can be controlled by simply varying the values of system parameters
it applies finite state patterns to the input which consists of word tokens with part of speech and semantic concept information
the rule based fragment interpreter applies semanti c rules to each fragment produced by fpp in a bottom up compositional fashion
the template generator must address any arbitrary constraints a s well as deal with the basic details of formatting
the scaled backward probabilities are defined in the same way using the scaling factors obtained in the calculation of the forward probabilities
te was slightly different as the training data from the dry run was still valid
the discourse component creates two primary structures a discourse predicate database and the ddos
in other words the training data extracted from an untagged corpus using only a dictionary are by nature too noisy to build a reliable model
yet we believe that we are only beginning to understand techniques for learning domain independent knowledg e and domain dependent knowledge
the class based bigram model predicts that word wl is followed by word w2 with probability
we examine smoothing procedures in which these models are interposed between different order n grams
for example the words smile smiled smiles smiling and smilingly all from the corpus are reduced to the root smile and treated equally
the improvement provided by lsa averaged over all confusion sets is about NUM and for the sets with the same part of speech the average improvement is NUM
the similarity between this text passage vector and the confusion word vectors can be used to predict the most likely word given the context or text in which it will appear
the inserted confusion word is then removed from the sentence but not the bigrams of which it is a part because its presence biases the comparison which oc curs later
lsa attempts to eliminate the noise from the data by first representing the texts in a high dimensional space and then reducing the dimensionality of the space to only the most important dimensions
examination of table NUM reveals that it is difficult to make a direct comparison between the results of lsa and tribayes due to the differences in the partitioning of the brown corpus
a better baseline is the performance that would be expected by simply adopting the most likely configuration without regard to lexical items
space does not permit a discussion of all the ways lexical sensitivity can be introduced into the tig parser
in this paper we propose an unsupervised reestimation approach and a two class classification method to extract embedded words from a large unsegmented chinese text and assign possible parts of speech to each word with a similar reestimation method
that is they work seeing each word in the dictionary as a whole to be guessed without taking into account the morphosyntactical information inherent to the languages
it should be noted that if a tig makes use of adjoining constraints then the conversion of the tig to a tag deriving the same trees can become more complex or even impossible depending on the details of exactly how the adjoining constraints are allowed to act in the tig and tag
for instance if a word is tagged with the parts of speech n v a by the system and it has the parts of speech n adv in the standard dictionary then the per word recall will be NUM NUM for this word and the per word precision will be NUM NUM
to simplify the the design of the classifier we use a simple linear discrimination function for classification g where xs is the feature vector or score vector and ws is a set of weights acquired from the seed corpus for the various components of the score vector
because the annotated tags for a word is usually considered as a whole when constructing a dictionary entry it may be desirable to define a per word precision and per word recall to measure how good the tags for a word is annotated and then properly associate a weight to each word to evaluate the performance for the whole system
firstly the number of word tag pairs common to the extracted word tag list and the word tag dictionary divided by the number of pairs in the extracted list is defined as the raw precision rate the raw recall rate is defined similarly as the number of common word tag pairs divided by the number of word tag pairs in the word tag dictionary
a viterbi reestimation process as outlined below could be used both for the word segmentation and pos tagging tasks to optimize the tagging patterns including segmentation patters and pos tagging patterns to a reasonable way
since the word dictionary and word tag dictionary which are used for comparison with the extracted dictionary are constructed independently of the corpus from which the lexicon entries are extracted the reported performances could be greatly underestimated
where p x and p y are the prior probabilities of the individual characters and p x y is the joint probability for the two characters to appear in the same NUM gram
the path i.e. the segmentation pattern with the highest score as evaluated according to the initial set of parameters i.e. word probabilities is then marked as the best path for the current iteration
first our approach is inherently lossy in that not all the information in the input ad may be analyzed into the schema
out of these the able rule seems to be the most homogeneous and error proof
symbolic case based reasoning techniques are used to quantify the extent to which users queries match database objects allowing the ranking of query results
the next time the system needs to refer to the same event it can omit some information that it has already shown to the user e.g. the fact that talcahuano is in chile and can instead focus on information that has not been included previously
there is no need to train the tagger on our text type because the actual tags do not matter as long as tagging is consistent
accordingly they extensively used the inside outside algorithm to reestimate the grnmmnr and have the same problem of structural data sparseness
and because there can be only one head of a tree weos must be head of only one word
to increase the correctness of the learned grammar marcken proposed to include lexical information to the phrase structure grammar
once lrs are defined in a computational scenario a decision is required about the time of application of those rules
every word in the sentence must have its head except the word which is the head of the sentence
this means that the lack of lexical information in phrase structure grammar is a major weak point for syntactic disambiguation
here we define the non constituent objects complete link and complete sequence which are used in pdg reestimation and bfp algorithms
salesperson sold the dog buiscuits figure NUM shows a dependency tree as a hierarchical representation and a link representation respectively
thus the time complexity of the best frrst parsing algorithm is o ns
the lexicon for which our lrs are introduced is intended to support the computational specification and use of text meaning representations
between two nouns nl and n2 in the bunruigoihyo thesaurus len nl n2 and their similarity szm nl n2
unlike the former approach the required memory space can be restricted to o n because only a list of semantic codes for each word is required
given an input the system identifies the verb sense on the basis of the similarity between the input and examples for each verb sense contained in the database
because of the high degree of morphological ambiguity in hebrew some of the words in the sw sets may also be ambiguous
we assume that this is the case in the following examples and will return to this issue in the next two sections
we then established for each anaphor in the test data whether a zero anaphor in this position would violate these syntactic constraints or not and obtained a new classification tree as shown in figure NUM the matched rate of rule NUM is NUM as shown in the same figure
since this is the only information we extract from the corpus our algorithm needs only this hash table and is therefore very efficient
note that although the new material in rule NUM was motivated by the prior work of chen and others the exact form of the new constraint was formulated after considering the distribution of anaphora in the data which means that an improvement on this data was almost inevitable
NUM examples several aspects of the algorithm described in the previous section can be better understood by looking at some clarifying examples
as long as the other possible analyses of such a word are not too frequent it only slightly affects the final probabilities
approximated probabilities given an ambiguous word the approximated probabilities of the word are the probabilities calculated using the method described in this paper
motivated by the way we use the morpho lexical probabilities for morphological disambiguation we can divide the probability of an analysis into three categories
our method may also yield an incorrect approximation for analyses where the similarity assumption we use between the frequency of an moshe levinger et al
for this reason NUM the approximated probability for this analysis NUM NUM is substantially lower than its test corpus probability NUM NUM
a robust and efficient three layered dialogue component for a speech to speech translation system
keywords were proposed in NUM sentence location was proposed in NUM sentence type was proposed in NUM NUM etc and rhetorica relations were proposed in studies using rhetorical structures sud as a
it seems that some lexical adjuncts may play an important role in the definition of domain specific senses of verbs
because of the relatively sparse examples the over generality of wordnet and the over specificity of ciaula produced limited interactions
in figure NUM we show two basic level categories obtained with different values of the model parameters for the rsd
second we must balance the effect of verbs that have more than one synset pointing to the same hi
figure NUM shows an excerpt of the ciaula clusters of table NUM with the prototypical description of each cluster
class NUM NUM was generated in a different run in which we imposed a tighter similarity among the cluster members
in wordnet there are limited definitions of the conceptual labels used or hyperonims
if we consider the prototypical descriptions of clusters globally we observe recurrent patterns of use of the clustered verbs
in this sense it is preferable to integrate the translation model within a conventional csr system to carry out a simultaneous search for the recognized sentence and its corresponding translation
in exploring multiple pp attachment it seems natural to investigate the effects of the distance of the pp from the verb
verbs are vessels for human creativity in language communication and so much is left to further studies
in some cases the problem is the overambiguity and the very fine grained concept labels adopted in wordnet
we used a pruning frequency k of NUM and a pruning threshold of NUM in some of our experiments
the probability of the next word is then the ratio of two consecutive likelihood values returned at the root
let us also assume that all the trees have maximal depth d then po t a NUM a NUM where n is the number of leaves of t of depth less than the maximal depth and n2 is the number of internal nodes of t
given a prefix wl wk generated so far the context node used for prediction is found by starting from the root of the tree and taking branches corresponding to wk wk NUM until a leaf is reached or the next son does not exist in the tree
whenever an entirely new word is observed first case it is necessary to first send an indication of a novel event and then transfer the identity of that word using a lower level coder for instance a pst over the alphabet e in which the words in u are written
step NUM for each verb in pairs add up the frequency of the co occurrence with the adverbs contained in the array adverbs
it is sitnply a change of v jtte elf tile eardinality value
or x y create a lattice by branching on x y and
our approach is to assign word lattices to each fragment of the input in a bottom up compositional fashion
in terms of their interactions with the rest of the sentence these manifestations of the adjunct are identical
large scale natural language generation requires the integration of vast mounts of knowledge lexical grammatical and conceptual
suppose that according to our knowledge bases input i may be rendered as sentence a or sentence b if we had a device that could invoke new easily obtainable knowledge to score the input output pair i a against i b we could then choose a over b or vice versa
if for some reason the analysis of beisha fails to resolve these ambiguities the generator can pass them along in the lattice it builds e.g. the american shrine in this case the statistical model has a strong preference for the american company which is nearly always the correct translation
this distinction is based on the singular plural opposition
in our grammar defaults include singular noun phrases the definite article nominal direct objects in versus on active voice that versus who the alphabetically first synonym for open class words etc statistical a sentence extractor based on word bigram probabilities as described in sections NUM and NUM
this concern prompted us to develop an automatically generated thumbnail sketch of the current gui window which appears in the upper left corner of the help window in a java applet along with the table of contents and contains hyperlinks for each of the widgets on the window these hyperlinks display the corresponding help topics on the right hand side
one of these error types is almost exclusively in the realm of automatic disambiguation
the functives of an individual object act as relations between objects
the correctness of the tagging must be judged relative to some norm
while striving to design highly sophisticated fully automatic systems has undoubtedly led to a deeper understanding of the text generation process it has had the unfortunate effect to date of limiting the use of techniques pioneered in the nlg community to just a few niches where high knowledge acquisition costs stand a chance of being balanced by substantial volume of needed texts cf
the suc has been annotated by a process that combines automatic and manual steps
cohesion local u r uj cohesion speechact speechact r speech act NUM
more specifically we can automatically acquire discourse knowledge from an annotated corpus with local cohesion
although the statistical approach is easy to implement it has a major problem i.e. sparse data problem
j j j smoothing method NUM interpolate the plausibility by using the speech act types themselves
the results of the experiment were able to segment a dialogue into several subdialogues
we have extracted about one hundred fixed expressions from the database by the extraction method
hesion in this paper we approximate an utterance in a dialogue to a three tuple
we plan to apply the method to speech recognition in a speech to speech machine translation system
humans are not infallible if anyone thought so NUM NUM of the errors are man made
thus u3 to u6 are built up as one structure and form a subdialogue with the topic date
y ocohesionendexpr itadake mas u ka desu y lcohesionspeechacttype req uirement response
instead it is the function words that get mixed up in all their different uses
the most frequent derivational pattern for swedish adverbs makes them identical to neutral singular indefinite adjectives
swedish nouns are inflected according to five different declensions one of which has zero plural
p d corresponds to the full np and thus its position in cf is determined by the np s grammatical flmction as regards p my working heuristics is to rank it as iinmediately preceding
also NUM of x n plniie q are encoded by moans of poss ssive np s and vice versa NUM of possessive np s are used for lcb x ntinue s
shared entities entities which the speaker believes are known to the hearer can be referred to using identifiable reference e.g. definite deixis e.g. the president and naming e.g. ronald reagan
NUM 2since the fillers of the speaker and hearer roles are ideational units they can be extensively specified for user modelling purposes including the place of origin social class social roles etc of the participant
the participant roles do not need to be included in every sentence specification but may be in some for the following reasons pronominalisation if the filler of the speaker or hearer role happens to play some role in the ideational specification then an appropriate pronoun will be used in the generated string e.g. t you
say is the name of the lisp function which evaluates the speech act specification calling the generator dialog NUM is the name of this particular speech act each speech act is given a unique identifier its unit id
NUM while constructing a sentence the sentence generator refers to this list at various points to see if a particular semantic role is relevant and on the basis of this chooses one syntactic structure over another
it also indicates which of the roles of each entity are relevant for expression and are thus expressed if possible and which entities are identifiable in context and can thus be referred to by name
to this end wag s input specification allows a field relevant roles which records the roles of each entity which are currently relevant for expression e.g. as was used in the examples of section NUM NUM NUM
an elicit move indicates that the speaker requires some contentfull response while a propose move may require changes of state of belief in the hearer support moves indicate the speaker s acceptance of the prior speaker s proposition
textual semantics concerns the role of the text and its components as a message while creating a text whether a single utterance or a whole book we have a certain amount of content we wish to encode
other information in the input specification helps tailor the expression of the content such as an indicator of which kb element to use as the head of the generated form which is theme which elements are recoverable and identifiable
this move simply introduces some extra arbitrary bias as a basis for distinguishing proofs
providing very accurate automated data extraction capability for this type of application is th e objective of the dx project
in addition the singular possessive marking s is also removed and a possessive attribute set
the inserted tokens can now be recognized as a measure by a rules such as the following sot
the fifth feature expansion is very useful when the chars attribute is a mixture of letters numbers symbols
the first is unabashedly a brute force method and is to identify by look up for those data classes like person this conversion activity is part of the navy program to develop the support technologies for producing and using interactive electroni c technical manuals ietm for new and existing systems as governed by the mil m NUM standard
the last two examples both have the knowledge classification features meas time date bu t christmas is also categorized as a holiday and is both capitalized and a name the phrase pai d holiday is neither capitalized nor a name but fills the semantic role of referring to a holiday entity
things that worked wel l the knowledge classification feature hierarchy works termendously well in supportin g identification and discrimination of data classes e g people having the main branches of hument person while cities have the main branches of place center city and signal words associate d with time references have the branches of meas time date hour
and we had endless problems with the fact that the knowledge bank is incomplete the fact that it has so very many entries usually meant that problems were with our rules not with the knowledge bank but there were many times when expected entries were simply not there or had been miscoded in some way
that for title pref for example specifies that the chars value should have an initial capitalized letter that the type of the title ought to be pref for prefix and that the value of cl the second hierarchical knowledge classification value c0 is the first should be person
for example the following rule expands all tokens of type mix type mix vi expand vl the result of expanding a token whose chars are 451bs sq in is to replace this token in the token stream with the following ones sot
do we need to parse indeed
retrieval conferences trec NUM and trec NUM the
lexemes which are in the lexicon are in uppercase those that are not are in lowercase
the row labeled length refers to the length of the output string in words
currently realpro supports ascii html and rtf output
for example we may accept language natural and processing language from natural language processing as correct however case trading would make a mediocre term when extracted from insider trading case
recall that not all words in the input representation need be in the lexicon
the original prise has been modified to allow handling of multi word phrases differential term weighting schemes automatic query expansion index partitioning and rank merging as well as dealing with complex documents
full details of tip parser have been described in the trec NUM report NUM as well as in other works NUM NUM NUM NUM NUM NUM
this tree matching is in the general case exponential in n
we thus decided to introduce only minimum number of changes to our indexing and search processes and even roll back some of the trec NUM extensions which dealt with longer and somewhat redundant queries
the head in such a pair is a central element of a phrase main verb main noun etc while the modifier is one of the adjunct arguments of the head
for each permutation it was determined how many words c had been shifted to positions different from those in the original german matrix
the simulation was conducted by randomly permuting the word order of the german matrix and then computing the similarity s to the english matrix
the suffix of w of length i is denoted suffi w for o i iwl
in these matrices the entries belonging to those pairs of words that in texts co occur more frequently than expected have been marked with a dot
however in this paper it is further assumed that the co occurrence patterns in original texts axe not fundamentally different from those in translated texts
the method proposed is based on the assumption that there is a correlation between the patterns of word co occurrences in texts of different languages
an algorithm currently under consttuction therefore searches for many local minima and tries to find out what word correspondences axe the most reliable ones
as shown in figure NUM the control flow now switches to the middle part of the diagram where the user identifies the corpus to be visualized
afler experimenting with various paradigms such as color or icons we ve concluded that the size or radius of the nodes best conveys this information
as part of hnc s involvement in the arpa sponsored tipster program hnc has developed a neural network technique that can learn word level relationships from free text
a neural network based learning algorithm is designed to adjust word vectors such that terms that are used in a similar context will have vectors that point in similar directions
alternatively rather than using a node for a query the user can type free text into a window and submit the free text as a query
the list is presented in a window with the document id the value of the dot product and the first line of text in the document
the value of the parameter s for updating neighbor nodes is determined by a gaussian function based on the nearness of the neighbor node to the winning node
however these engines suffer fi om the fact that they require the user to specify a query of limited length and they offer no visual interface for browsing
the key technical feature of context vector technology is the representation of terms documents and queries by high dimensional vectors consisting of real valued numbers or components
this vector representation of information content can thus be used for document retrieval routing document clustering self organizing subject index and other text processing
in this paper we describe a method of classifying japanese text documents using domain specific kanji charactcrs
NUM specialties corresponded to the domains of the ndc automatically and the rest was aligned manually
tame NUM division of the nippon decimal chtssification technology engineering lass
it is simpler to extract ka iji characters than to extract japanese words
so the rnaximuln number of dimensiolis of the training space is about NUM NUM
in our approach we extracted domain specific kanji characters for document classification by the x NUM metho i
we would also like to study the relation between tile quality of the classification result and the size of the documents
table NUM a classification result of a book artiticial intelligm ce and lluman l eil g
NUM two or more themes in one document many articles of tensei jingo contain two or more themes
in other words there is no guarantee that we can extract the appropriate domain specific kanji characters from every domain
this state ensures that values for these mandatory fields are obtained from the user before issuing a cgi query
for eight judges ranging k between NUM and NUM corresponded to a precision score range of NUM to NUM meaning that there were relatively few words NUM of those found by the automatic segmenter on which all judges agreed whereas most of the words found by the segmenter were such that one human judge agreed
however there is again local grammatical information that should favor the split in the case of la both ma3 horse and ma3 lu4 are nouns but only ma3 is consistent with the classifier pil the classifier sproat shih gale and chang word segmentation for chinese for horses
two independence assumptions axe required to make this model computationally feasible
for example it is well known that one can build a finite state bigram word model by simply assigning a state si to each word w in the vocabulary and having word arcs leaving that state weighted such that for each wj and corresponding arc aj leaving si the cost on aj is the bigram cost of wiwj
NUM where p ts is the probability of one unseen hanzi in class cls e n ts is the expected number of hanzi in cls seen once n is the total number of hanzi and e n t is the expected number of unseen hanzi in class cls
finally quite a few hanzi are homographs meaning that they may be pronounced in several different ways and in extreme cases apparently represent different morphemes the prenominal modification marker ft deo is presumably a different morpheme from the second morpheme of i l mu4 di4 even though they are written the same way
for the seen word generals there is an c nc transduction from to the node preceding t this arc has cost cost cost unseen lcb so that the cost of the whole path is the desired cost
the shallow analysis module returns a shallow syntactic parse tree with various lexical and syntactic features
the system then returns the target lan guage portion of the best example ms output
in an example based translation architecture pairs of bilingual expressions are stored in the example database
other classes handled by the current system are discussed in section NUM the morphological analysis itself can be handled using well known techniques from finite state morphol9 the initial estimates are derived from the frequencies in the corpus of the strings of hanzi making up each word in the lexicon whether or not each string is actually an instance of the word in question
the method returns a set of normalized i
only its words and part of speech tags were utilized
the precision statistics for the individual lexical semantic features discussed above are presented in table NUM and table NUM lexical semantic information was collected for NUM words bases and derived forms
step i amounts to doing a semantic analysis of a number of affixes the goal of which is to find semantic generalizations for an affix that hold for a large percentage of its instances
for example in a situation where the father says kim is throwing the ball and points at kim who is throwing the ball a child might be able learn what throw and ball mean
a system that utilizes a lexicon so constructed is interested primarily in the overall precision of the information contained within and thus the results presented in the next section conflate these two types of false positives
for re the result state of the derived form is the same as that of the base rstate eq base rstate e.g. the result of reactivating something is the same as activating it
since the suffix ful applies to nominal bases only a noun reading is possible as the stem of stressful and thus one would attach the lexical semantics cued by ful to the noun sense
in particular i explore the issue of what is a good parsing technique to apply to principle based theories of grammar
chains consist of the word that undergoes movement and all the positions this word occupies in the course of a derivation
in nlab where there is no restriction on the number of active chains the growth rate is n k
for nlab and nlab m the formula is na k where na is the number of active chains
s attribution could not be performed however in parsing a language with all the characteristics given in NUM
a more principled treatment comes from recent developments in the theory that have changed somewhat the representation used for adverbs
the approach shares frank s intuition that linguistic principles have a form which can be exploited in structuring the parser
the precompilation of these conditions would require computing all the possible combinations without any reduction of the space of analysis
referential information q anaphor q pronominal indices this qualitative classification forms a partitioning into natural classes based on information content
in fact the algorithm i propose appears to be interestingly correlated to a gap in the typology of natural languages
the syntax of theorist is an extension of the predicate calculus
NUM see figure NUM for how mother might interpret this turn
the linguistic intentions of informref are compatible with the reconstruction
the relationship between try and shouldtry and their possible explanations
it distinguishes two types of formulae facts and defaults
challenges display understanding of an utterance while denying its appropriateness
in the input we represent this dialogue as the following sequence
in this simulation t1 was explained as an intentional pretelling
the reasoner also attributed to her the linguistic intentions of pretelling
it requires only a small lexicon which can be less than NUM NUM words and a training corpus of NUM NUM sentences
this is often true of applications that invoke cgi scripts on the web
for example the american airlines web site only permits a query NUM
the training and cross validation sets could thus be constructed in a few minutes and the resulting system error rate was very low
one advantage of decision tree induction is that the al null gorithm clearly indicates which of the input attributes are most important
tm we constructed a training text of NUM items from the sfiddeutsche zeitung corpus and a cross validation text of NUM items
languages involves obtaining or building a small lexicon containing the necessary part of speech data and constructing small training and cross validation texts
any token beginning with a period exclamation point or question mark is assigned a possible end of sentence punctuation tag
the essence of the satz system lies in how machine learning is used rather than in which particular method is used
the context vectors which we call descriptor arrays are input to a machine learning algorithm trained to disambiguate sentence boundaries
computational linguistics volume NUM number NUM forward neural network to disambiguate periods and achieve an error rate averaging NUM
in closing we wish to mention that the sublanguage technique we have described is a general approach to enhancing a statistical language model and is therefore applicable to tasks besides speech recognition such as optical haracter recognition and machine translation
the absolute improvement looks tiny however the rejative improvement excluding mne NUM NUM is quite impressive becmlse there are several types of error which can not be corrected by the sublanguage model as was explained before
this formula can be motivated by the fact that die sublanguage score will be combilm t linearly with general language nlo m s ores whml mainly consist of the logarithm of the tri gram NUM robabilides
the relative weights of the eight scores are determined by an optimization procedure on a training data set which was produced under the same conditions as our evaluation data set trot has no overlap with tile evaluation data set
the nmnerat r is a pure sublanguage score and it works to ad l tim s or of the sublanguage mo m to the ther s ores
we combine these scores with the score produced by our sublanguage omponent an NUM our cache inodel score and then select the hypothesis with the highest combined score as the output of our system
to date we have tuned them by manual optimization using a relatively small mnnt er of trials and a very small training set the NUM articles for which we have n best transcrit dons
we lid not use all the words in tile previous ul teran e but rather filtered out several types of words in order to retain only lopi relate NUM words
ltere we exehtde flmetion wol ds as we did for keyword selection NUM eta use flm don words are generally coinilloll throughout ti thrent sul languages
the s ore or ea h sentenc is eal ulated by a unmlating the score of 1he sele te l wor ls ill the hyt othesis tlere l sco rc h is the sut language score of hypothesis h
building an sdrs invoh es computing a rhetorical relation between the representation of the current clause and the sdrs built so far
if it ends in an obstruent the allomorph is jc
we discuss the relevance of our method for linguistics attd language technology
a supervised rule induction algorithm is used to learn to predict the
b apply the procedure recursively to subsets created this way
the fi equency distribution of the different categories is given in table NUM
meurer u sauerland m schrattenholzer and v
where discourse is represented as a recursive set of drss representing the clauses linked together with rhetorical relations such as elaboration and contrast
from source to target cfg skeletons to build a ta rget
the final xttmsion of tra nsla tion
ordering of pa tterns described in the previous section
mt system consists of about NUM defa ult tra nsla tion
therefore we have to account for highly ungrammaticm constructions
use the analysis to see if there is any scope for the prediction or automatic insertion of utterances during a conversation
the idea is that it is not necessary to fully store a case as a path when only a few feature values of the case make its classification unique
also it does n t provide information about the nature of the attitude moi
due to the favorable complexity properties of igtrees lookup time in igtrees is independent on number of cases both tagger generation and tagging are extremely fast
the parser then searches for paths through the graph that match as closely as possible grammatical inputs
a lexicon is extracted from t by computing for each word in t the number of times it occurs with each category
the automatic component of the tool employs a stochastic tagging model induced from previously annotated sentences
the annotator always has the option of altering already assigned tags
this first automation step has considerably increased the efficiency of annotation
automatic completion and error check on user input are supported
they are stored together with the corpus which permits easy modification when needed
to back this claim the representation of structures from the suzanne corpus cf
most errors are due to wrong identification of the subject and different kinds of objects in s s and vp s
the largest part of the window contains the graphical representation of the structure being annotated
for NUM where the verb denotes an activity the simple aspect supports the 4of
simple combinations are usually taken to be things like function application and set union or intersection
remeinber that the mp for live sws nothing about the start and end points of the specilied state
eating denotes an activity with a definite final state where what was eaten ends up inside the eater s stomach
they are onstellations of acts which serve to sl rllctlll
in the case of NUM this is compatible with the possibility of there being exactly one such event
we therefore need a single mp for the simple aspect which enables us to conclude different things tbr the two cases
what concerns me here is the apparent change in the contribution of the present participle marker in NUM
for the moment is that cat denotes ii extended relic action and hiccup denotes an instantaneous evenl
the status of tile lower verb projections in NUM is still underspecified
tim is partitions oc and bg are here also treated as semantic objects
the representation developed here is relatively independent of the underlying semantic theory of focus
this becomes clear when we regard the following alternative prosodic marking of NUM
while non f marked constituents have to be given f marked constituents need not necessarily be new
the graph produced by the linking constrnints is the one in NUM
an ich weifi daft hans otto ein buch gab
b f marking of an internal argument of a head licenses the f marking of the head
we discuss extensions to our plan based discourse processor in order to make this possible
penalties or preferences for all graded constraints in the inference chain are summed together
then we discuss our discourse processor focusing on those characteristics needed to generate predictions lbr disambiguation
the main modules of our system include speech recognition parsing discourse processing and generation
an NUM i is a frame based language independent meaning reprcsen ration of a sentence
they act together each specializing in different types of cases to constrain the final result
by introducing graded constraints we avoid expanding the search space among the plan operators
l tble NUM disanfliiguation if all hnllliguous s ntencos
originmly our discourse processor took as its input the single best parse returned by the parser
the third issue is very important for successful confl ination of predictions from different knowledge sources
clearly the work could and should be extended to consider all possible combinations of the principles in all possible orders
english translations appear later in this paper
others are onomatopoetic and difficult to translate
this divides our problem into five sub problems
a first name last name model would rank richard bryan more highly than richard brian
for example nancy kerrigan should be preferred over nancy care again
in section NUM i compare the head corner parser with the other parsers implemented in the programme for the ovis application and show that the head corner parser operates much faster than implementations of a bottom up earley parser and related chart based parsers
NUM it should be clear that the results to be presented should not be taken as a formal evaluation but are presented solely to give an impression of the practical feasibility of the parser at least for its present purpose
again in many cases if arg is instantiated as a determiner or preposition for instance this search is doomed to fail as a vp subcategorizing for a category arg may simply not be derivable by the grammar
typically such a table makes it possible to predict that in order to parse a finite sentence the parser should start with a finite verb to parse a singular noun phrase the parser should start with a singular noun etc
finally in the dutch ovis grammar in which verb second is implemented by gap threading no hidden head recursion occurs as long as the head corner table includes information about the feature vslash which encodes whether or not a v gap is expected
the ovis grammar for dutch contains about NUM NUM lexical entries many of which are station and city names and NUM rules a substantial fraction of which are concerned with time and date expressions including NUM epsilon rules
i argue in favor of such a memorization strategy with goal weakening in comparison with ordinary chart parsers because such a strategy can be applied selectively and therefore enormously reduces the space requirements of the parser while no practical loss in time efficiency is observed
this is because it may be possible that an empty category is predicted as the head after which trying to construct a larger projection of this head gives rise to a parse goal for which a similar empty category is a possible candidate head
and it s more specific then mark it do this for all such items the implementation uses a faster implementation of memorizating in which both goal items and result items are indexed by the functor of the category and the string positions
what is the target japanese sound inventory
a principled approach to adjective disambiguation using nouns there
the rules in table NUM can be easily implemented
more complex are the two instances of old wine
another widely relevant class of indicators are body parts
this statement is formalized in the appendix
coverage increased from NUM to NUM instances
katz principled disambiguation harness and cruiser
unlike a pure translation memory however pan i iimt does not require all exact match with a memorized translation
rules associated with the same number are unordered relative to one another
what are the sources of these errors
panebmt consists of approximately NUM NUM lines of code including the code for a glossary mode which will not be described here
of these NUM were missing from an online NUM NUM entry bilingual dictionary
these partial translations are then combined with the results of other translation en gines to form the final translation produced by the pangloss system
index n rcb l required i rcb ul im l rcb roves speed al run time
secondly the observations of coordination variants also suggest that the coordinating conjunction can be preceded by an optional comma and followed by an optional adverb e.g.
the significant improvement to precision is due to the elimination of all spurious egraph matches since egraph key s are by definition relevant
we present a method for inferring transformations from a corpus in the purpose of developing a gram null mar of syntactic transformations for term variants
in cle explicit intermediate levels of linguistic representation are used in the different phases of the analysis
yet in turkish the surface subject of a passive sentence can only be the direct object of the active form
for instance in figure NUM the range of the terminal node positions of vp overlaps with those of the subject b and the finite verb hd
an asterix stands for a wildcard in a pattern
using l instead of for rule scoring favors higher estimates NUM obtained over larger samples n
to do that we eliminate all the rules with frequency f less than a certain threshold NUM which usually is set quite low NUM NUM
then it tries to segment an affix by subtracting the shorter word wj without the mutative ending from the longer word wi
this rule will for instance correctly classify the word unscrewed if the word screwed is listed in the lexicon as vbd vbn
every word from the two lexicons was guessed by a rule set and the results were compared with the information the word had in the lexicon
the brill tagger showed some better results error rate mean NUM NUM NUM with the standard error deb o
and the third type of mistagging occurred when the word pos guesser assigned the correct pos class to a word but the tagger still disambiguated this class incorrectly
setting the threshold 0s at a certain level we include in the working rule sets only those rules whose scores are higher than the threshold
the contrast between NUM and NUM illustrates this restriction NUM whoi did you see a picture of i in the newspaper
step c select a sense s of w that maximizes the similarity between w and selectors
we represented the phrases using a proximity operator and tried several experiments to include the related form when it was found in the corpus
there were NUM such words but only NUM were clearly unrelated in meaning to the presumed root i.e. cases like policy police
we also found it was very difficult to improve retrieval performance over the performance of the porter stemmer which does not use a lexicon
the preceding following word is tagged z and the word two before after is tagged w
ingly NUM the semantic class label NUM NUM above is taken from levin s NUM book verbs of contiguous location i.e. the class to which the verb touch has been assigned
this distinction is characterized by different positionings of the marker in the lexical entries produced by NUM NUM and NUM are used in place of the ultimate fillers such as john and house
as described above the lcs representation is used as the basis of matching routines for assessing students answers to free response questions about a short foreign language passage
our acquisition program lexicall takes as input the result of previous work on verb classification and thematic grid tagging and outputs lcs representations for different
lexicall locates the appropriate template in the lcs database using the class grid pairing as an in null holder that points to the root cause of the overall lexicaj entry
the missing and extra output is internal to the nlp component of the tutor i.e. this is not the final response displayed to the student
in fact we have used the same acquisition program without modification for building our spanish and arabic lcs based lexicons each of size comparable to our english lcs based lexicon
NUM three inputs are required for acquisition of verb entries a semantic class a thematic grid and a lexeme which we will henceforth abbreviate as class grid lexeme
since this matches a sub component of the lcs template the program recognizes this as a match against the grid and the marker is placed in the template at the level of with
NUM cause thing NUM go ident thing NUM toward ident thing NUM at ident thing NUM adorned NUM
below we list three parameters which highlight the possible differences among approaches to lrs
this work has been supported in part by departmerit of defense under contract number mda NUM c NUM
we show that though the use of lrs is justified they do not come costfree
typically involving a shift in syntactic category these lrs are often less productive than inflection oriented ones
semi automatic output checking is required even with blocking and preemtion procedures built in
from submit to submitted via submission on lexical rules in large scale lexicon acquisition
verbs like kill relate or necessitate do not form such adjectives comfortably or at all
even the most seemingly regular processes do not typically go through in NUM of all cases
it should be made equally clear however that the use of lrs is not cost free
nevertheless large scope lrs are justified because they facilitate the unavoidable process of large scale semi automatic lexical acquisition
sjur ncrstebc moshagen computing centre for the humanities
red in jon painted the house red
however the nature and number of these abstract syntactic classes are not very clear and it seems difficult to come up with a sound method for how to decide on such classes
used together with rule NUM c on pa n i it introduces an xcomp element that des ribes the resulting state of the surface being painted
NUM jon painted the house red
the sign model sm gives a theoretical foundation for structuring lexical information along semantic lines it prescribes a strong semantic basis and suggests various kinds of expansion rules for generating complete word entries
specifying source and limit as coloring means that the painter s use of force involves some observable actions that identifies him as painting and that the surface being painted is recognizable from the same force
then we expand the conceptual structure into a completed description completed using a rule called completed and apply the rule in figure NUM c to add a third argument
ilandling the unknown word problem by increasing the size of the lexicon is not that straightforward given that most unknown words are open class items such as nouns verbs adjectives and adverbs
tagging statistics q efore training are based on the lexicon an t rules acquire l fl om tile brown corpus and the wai l street jour nai corpus
for instance the so called nominative case marker is realized as i when the preceding morpheme ends with a consonant and as ka when the preceding morpheme ends with a vowel as illustrated below
dane concludes that the distinction between given information and theme is justified while the distinction between new information and rheme is not
in sentence 2b two nominal anaphors occur akku accumulator and rechner computer
following this line of argumentation we here propose to classify all occurrences of centering transition pairs with respect to the costs they imply
we also make a second even more general methodological claim for which we have gathered some preliminary though still not conclusive evidence
we were also interested in finding out whether the functional ordering we propose possibly includes the grammatical role based criteria discussed so far
it consists of an equally balanced treatment of intersentential pro nominal anaphora and textual ellipsis also called functional or partial anaphora
german the object language we deal with is also a free word order language like japanese possibly even more constrained
they consider the role of expressive means in japanese to indicate topic status and the speaker s perspective thus introducing functional notions viz
thus we arrive at a trichotomy between given information theme and rheme the latter being equivalent to new information
because of this new type of accumulator is the computer for approximately NUM hours with power provided
at each point it finds the threshold to change that gives the most bang for the buck
since we do n t have time to parse with one million parameter combinations we need a better search algorithm
however this previous work differed significantly from ours both in the techniques used and in the parameters optimized
finally we hoped to find an algorithm that was somewhat less heuristic in nature
global thresholding is performed in a bottom up chart parser immediately after each length is completed
in some grammars such as pcfgs probabilities are associated with the grammar rules
furthermore their algorithm requires time o n a to run just once
once we have computed the preceding arrays computing maxl ne l p l is straightforward
in an idealized multiple pass speech recognizer we first run a simple pass computing the forward and backward probabilities
although our results require further analysis we do not believe that this makes a significant difference in the fea if the trigger model performs poorly relative to the trigram model in the following sentence this feature roughly speaking boosts the probability of a segment at this location by a factor of NUM NUM
for our domain we have developed segmentation rules which allow the system to split turns into utterances
since they occur relatively rarely in our spoken utterances we have chosen not to incorporate structural disambigualion
each word is associated with NUM its most plausible basic syntactic category e.g.
between input and output layer there are the hidden layer and the context layer
the network was not able to learn correct assignments due to the little training data
therefbre we just focus on the description of the network with the best generalization performance
l ue to space restrictions it is not possible to describe all these comparisons
alter each incoming word the segmentation parsing and dialog act processing analyze the current input
therefore we consider this new approach as very promising for learning dialog act processing
such decisions are difficult and ad null ditional knowledge like prosody might help here
this paper presents a computational strategy for detecting conflicts regarding proposed beliefs and for engaging in collaborative negotiation to resolve the conflicts that warrant resolution
furthermore by capturing the negotiation process in a recursive propose evaluate modify cycle of actions our model can successfully handle embedded negotiation subdialogues
if justification is predicted to be necessary the system will first construct the justification chains that could be used to support bel
after the evaluation of the di ogue model in figure NUM modify proposal is invoked because the top level proposed belief is not accepted
figure NUM shows the problem solving recipes NUM for correct node and its subaction modify node that is responsible for the actual modification of the proposal
the system believes that being on sabbatical implies a faculty member is not teaching any courses thus the proposed evidential relationship will be accepted
for the sake of the present discussion this example shows us that users are not always able to correct errors on the contrary we have seen above that the percentage of users errors is high
on the contrary misunderstandings are more difficult to detect and solve because usually the dialogue system may get an interpretation of the user s utterance but that interpretation is not the one intended by the speaker
in collaborative consultation dialogues the consultant and the executing agent collaborate on developing a plan to achieve the executing agent s domain goal
in t2 u the arrival city is recognized and understood as a generic city the dialogue strategy does not reject this information but it enters a clarification subdialogue in order to solve the ambiguity t3 s and t4 u
if the expected information is not found in the semantic representation of the current user s utterance the dialogue system hypothesizes that something went wrong in the previous analysis and it interprets that situation as an occurrence of non understanding
the speaker independent recognizers designed for such applications assure the coverage of a great number of speakers but some aspects of the speech modality of some users can induce the recognizer to make mistakes especially in recognizing long sentences
as described above we take grammar induction to be the search for the grammar g that optimizes the objective function p olg p g
we have tested the word scoring module with the incorporated filtering on errors produced by two existing asr systems from sri and cambridge university
the objective function is taken to be some measure dependent on the training data one generally wants to find a grammar that in some sense accurately models the training data
using our initial hypothesis grammar we parse the first sentence of the training data and search for the optimal grammar over just that one sentence using the described search framework
we use the resulting grammar to parse the second sentence and then search for the optimal grammar over the first two sentences using the last grammar as the starting point
a long range language model such as that described in section NUM NUM uses selected words from the past ten twenty or more sentences to inform its decision on the possible identity of the next word
first order rates of change of this measure are then calculated to decide the placement of boundaries between blocks which are then adjusted to coincide with the paragraph segmentation provided as input to the algorithm
null to illustrate and to show that our approach is in no way restricted to text consider the task of partitioning a stream of multimedia data containing audio text and video
roughly speaking after seeing an s word the empirical probability of witnessing the corresponding t in the next n words is boosted by the factor in the third column
after calculating the gain of each candidate feature the one with the largest gain is chosen to be added to the model and all of the model s parameters are then adjusted using iterative scaling
furthermore hearst s approach segments at the paragraph level which may be too coarse for applications like information retrieval on transcribed or automatically recognized spoken documents in which paragraph boundaries are not known
a commonly cited flaw of the precision recall figures is their complementary nature hypothesizing more boundaries raises precision at the expense of recall allowing an algorithm designer to tweak parameters to trade precision for recall
in addition to the estimate of topicality that relevance features provide we included features pertaining to the identity of words before and after potential segment boundaries as candidates in our exponential model
if we observe the behavior of r as a function of the position of the word within a segment we find that on average r slowly increases from below zero to well above zero
neighborhood of given random variable is defined by the set of random variables that directly atfect the given random variable
let x be a t olysemous noun and a sentence x be
our method for classification of articles uses the results of dismnbiguation method
at the beginning of section NUM we sketched how to generate dags from an av grammar g via nondeterministic derivations
an automatic clustering of articles using dictionary definitions
NUM NUM linking nouns with their semantically similar nouns
table NUM topic and category name
table NUM the results of stage five
the modified approach so neatly moves indexation requirements off into the constraint equation domain that we shall henceforth drop all consideration of them assuming them to be appropriately managed in the background
NUM a set of features fl fn with weights fll fin to define the field distribution
in order to determine the size of corpus needed we experimented with a frequency list of the NUM NUM most frequent word forms
instead the rules are designed to recognize organization names in almost complet e absence of any information about particular organization names with the sole exception of a few acronyms such as ibm gm etc
included are personal honorifics dr ms military an d religious titles vicar sgt corporate posts ceo chairman and profession words analyst spokesperson
the magnitude of the ne error is mitigated by the fact that identical mentions of incorrectly tagged named entities are merged for th e sake of te template generation and thus do not all individually spawn spurious templates
we were aware of these mis features in our handling of person and organization name templates but had left these problems unaddressed since they seemed to have only minimal impact on the forma l training data
person james person num NUM num years old is stepping down as post chief executiv e officer post on date july NUM date and will retire as post chairman post at the end of the year
none james none num NUM num years old is stepping down as post chief executive officer post o n date july NUM date and will retire as post chairman post at the end of the year
the rule that maps these propositions to a succession event is job out pers ttl org holds job pers job x job ttl org job retired ttl ttl when applied to the above propositions this rule yields job out pers NUM ttl NUM org NUM j o NUM
these rules generate so called corpnp phrases that is noun phrases that are headed by an organizational common noun such a s agency maker and of course company
walter thompson punctoker lost j NUM inc type NUM inc text table NUM ne errors on walkthrough message nature of the problem problem cases resulting errors repercussions of ne errors walter thompson fallon mcelligott NUM spu pers NUM mis org
the punctoker wraps lex tags around text where necessary to indicate its decisions as in the following singapore l ex pos jj based lex as this example suggests in some cases the punctoker guides subsequent part of speech tagging by addin g a part of speech attribute to the lex tags that it emits
she knows a lot about wine
he has been acting quite odd
i took him to the vet the other day
computational linguistics volume NUM number NUM
her husband is kind to her
this work utilized these earlier drafts
centering given a particular attentional state
it is worth pointing out that neither the som nor the lvq algorithm handles raw data such as waveform values or image intensity values but each operates on data such as spectral components or lpc coefficients that are themselves the output of a significant processing stage and can justifiably be called features
these codings are both closer to the acoustic domain and capable of greater flexibility than the standard phonetic notations
evidence that such feedback affects speech is the degradation seen in the speech of persons with acquired deafness
formant data can be used to introduce a perceptual measure of similarity see section NUM below
if a speech synthesis system has a phonetic interface or level of operation it is then possible to introduce learning techniques for subsequent modules e.g. those which calculate durations or an intonation contour and to have an idea of what is happening in phonetic terms when things go wrong and therefore how the training program or learning method may be adjusted
k open mid and closed which can not be encoded by a binary bit rcb in this case the point is not to do feature extraction since the features are already known but to provide a statistical clustering in 2d that can indicate whether the features chosen provide a good basis for analysis
among the simplifying assumptions remaining from this approach are that transitions into and out of a consonant are identical and that the same transition may be used in each cv combination regardless of the larger phonetic environment
an example of the a parse tree b nf1 and c normal form
among them a lot of errors occur in assigning the case for the first noun of a compound noun
in such a way the system can be easily scaled up and well trained based on the well established theories
accordingly several models encoding structure information in the semantic score formulation are proposed for case identification and word sense discrimination
the normal form used here is quite generalized and flexible therefore it is also applicable in other tasks
formally regarding the nf1 alternatives the semantic score sse m ni can be expressed as follows
a word sequence would in general correspond to more than one part of speech sequence
please refer to NUM for the details of the case set
it is found that a very large portion of error come from the syntactic ambiguity
afterwards different syntactic structures that are semantically equivalent are normalized to the desired normal form nf structure
when a node receives an item it attempts to form new items by combining it with items from other nodes
the structure above represents the relative order observed in korean i.e. the head final parameter setting ii
yet exactly the same rules are used in x5 and x6 as are used in xl and x2
we present a general framework for parsing by message passing and describe our implementation of gb principles as attribute value constraints
once a sentence has been parsed the corresponding parse trees are retrieved from a parse forest one by one
certain dominance links in the network are designated as barrier links indicated in figure NUM by solid rectangles
in addition adjuncts are associated with the xbar level by means of an adjunct dominance link in the grammar network
rather these examples are intended to illustrate that the parser is able to handle translationally contrastive sentences equally efficiently
we have automated the process of grammarnetwork construction and have demonstrated that the system handles well known translationally divergent sentences
tomita s parser runs about NUM times faster than principar lin and goebel s runs about twice as fast
we adopt lexical conceptual structure lcs and use a parameter setting approach to handle well known translationally divergent sentences
the possible feature values were dont for the do not and don forms discussed above never for imperatives containing never and neg tc for take care make sure be careful and be sure expressions with negative arguments
the upper stream processes the training set and contains a type module which marks the main syntactic form i.e. dont never or neg tc as the variable to be predicted and the awareness safety and intentionality features as the inputs
it confirms our intuitions that never imperatives are used when personal safety may be endangered coded as safety badp and that neg tc forms are used when the reader is expected to be aware of the danger that may arise cf
this sub network was not based on a corpus study of french preventatives but rather was implemented by taking the leamed english decision tree modifying it in accordance with the intuitions of a french speaker and automatically constructing french systems from that modified decision tree
we broke the corpus texts into expressions using a simple sentence breaking algorithm and then collected the negative imperatives by probing for expressions that contain the grammatical forms we were interested in i.e. expressions containing phrases such as don NUM never and take care
if authors wish to prevent the reader from disconnecting the ground connection and they decide that the reader is unaware of this danger that the action would be unconsciously performed and that the consequences are indeed life threatening drafter produces the following text never disconnect the ground
NUM more precisely the topic proper refers to one of those items that at the given time point are most salient in the stock of knowledge shared by the speaker and according to the speaker s assumption by the hearer
this usage occasionally recommended by manuals and textbooks concerning for example the stylistics of czech or russian makes it possible to read such a text aloud without paying much attention to the choice of the placement of the intonation center
topic focus identification these pairs of sentences as is known from previous discussions show that tfa is relevant not only for a possible placement of the sentence in a context but also for its semantic interpretation even for its truth conditions
the dichotomy of topic and focus based in the praguean functional generative description on the scale of communicative dynamism is relevant not only for a possible placement of the sentence in a context but also for its semantic interpretation
the following rule holds rule NUM if a sentence part a precedes another one b under so and both a and b are in the focus of a sentence s then a precedes b in the word order of s
above all this concerns the following points in which a more general procedure could be formulated i the procedure should also take into account deeper embedded sentence parts embedded verb clauses modifiers in noun groups etc
iii if a semantic comparison of lexical items with those present in the preceding utterances of the discourse is made possible see point i then the cases of ambiguity resulting from the procedure could be considerably reduced
in NUM all of the cb words belong to the topic according to i or with our NUM to ii then iii determines the adjunct of chemistry as the focus of NUM
for example in NUM a the group to few girls is in such an ambiguous position in some of the readings the boundary between topic and focus precedes this group in others the boundary follows it
the performance of the method in this sense is much more interesting and important since it examines more accurately the quality of the probabilities as data for other more sophisticated systems that use higher levels of information
another class involves transformations such as you are surprised to something surprised you which makes explicit the object doing the surprising
however we believe these performance levels to be quite competitive and promising
altered a heuristic which caused categorisation problems for sentence initial words which was originall y too strong it had been reducing morphological possibilities to a single item
any modifications to rule bases in the system s core must be carried out with a careful view to their effect on al l aspects of the core
lexical information actual words are represented in the net and their properties are stored in the net as opposed to having a separate lexicon
a summary template for example can consist of the slots personal names organizations numeric expression s etc found in the source article
it is possible for the subsequent analysis to reject suggested trees and try the next best but this option is not used in our muc system
it is heavily used in most stages of analysis and the results of analysis are added to it as a disambiguated logical representation of the input
unfortunately this processing occurs before the document is stored in the semnet and as a result additiona l quotation marks are added to the final output
as a result it has consumed a significant part of the development effort and has also contributed to a significant proportion of the error s in core analysis
and localion noun np gjge location noun nc g ge
figure NUM examples of parse output cont d
in november NUM a redesigned muc NUM has held in which each participant could choose to be evaluated in one or more of the following tasks a named entity task a template element task a scenario template task and a co reference task
the impact of the tipster text program metric based evaluations can be readily seen from the single statistic that over NUM institutions have already participated in either a tipster text program internal evaluation or one or more of the muc met and trec evaluation programs
NUM in the spring of NUM the tipster text program was nominated by the community management staff as a reinvention laboratory in recognition of its teamwork its customer focus and the fact that it has broken down exiting bureaucratic barriers
muc NUM coincided with the tipster phase NUM month evaluation and consisted of the same information extraction tasks that had been assigned to the phase i participants formation of business joint ventures and microelectronic chip fabrication each domain in two languages english and japanese
unfortunately a detailed response to this question is beyond the scope of this paper since the full answers to this question lies in the collective papers contained in the proceedings of the tipster text program phase NUM the proceedings for each of the recent message understanding conferences muc and for each of the text retrieval conferences trec and the rest of this proceedings for phase ii
we found that it is very effective to augment the initial word list with automatically extracted words using character type heuristics
the thirteen different formal metric based evaluations conducted variously under the banners of the tipster text program phase i NUM muc NUM met NUM and trec NUM could not have been executed without sufficient quantities of training and testing data
this infusion of funds helped raised the tipster program to a higher level insured that its extensive program to collect and prepare sufficient quantities of training and testing data could be completed as planned and at the desired level of quality and provided the impetus for the tipster text program to undertake the development of its first operational prototype system based upon tipster technology i.e. the hookah project at the drug enforcement administration
the drop in the number of word types indicates the removal of infrequent words and unzeliable word hypotheses from the dictionary
to a first approximation a point in the text where character type changes is likely to be a word boundary
t NUM NUM NUM and the rule features cat verb measure pa el
in every case the automata produced by the compiler were manually checked for correctness and the machines were executed in generation mode to ensure that they did not over generate
thus the performance of a real anaphor generation algorithm based on the rules proposed here may be different from the experimental results we obtained
since the formalism allows values to be variables drawn from a predefined finite set of possible values variables entered by the user are replaced by a disjunction over all the possible values
in other words in order for a a to be sanctioned it must be followed by the sequence 13as to how this is done is a matter of implementation
let r represent the center of the rule with the correct lexical expressions and the incorrect surface expressions with respect to r
for example although r5 is devoid of context expressions the rule is composite indicating that if the root measure is pa el then gemination must occur and vice versa
rn NUM ik NUM t albl ri ce note that in order to remain within finite state power both the attributes and the values in feature structures must be atomic
the syntactic constraints approach which is an extension of the short context approach was found to be useful and reliable but its applicability based on the proportion of ambiguous words that were fully disambiguated was very poor
a projective tree would require beans to be connected to either i or know none of which is conceptually directly related to beans
in this way the nationality information in japanese giant nec becomes associated with the canonical nam e nippon electric corp
we mentioned above that the inferential architecture that we have adopted here is in good part motivate d by a desire to simplify template generation
even when using error correcting parsing and dialog expectations the circuit fix it shop misunderstood NUM NUM of user utterances during the experimental testing
it demonstrates that an exemplar based learning approach is suitable for the wsd task achieving high disambiguation accuracy
what constitutes best is determined by a cost matrix for the possible words in the vocabulary and the given grammar
obs meas des val observing a measurement described by des where val is the value
the author also wishes to express his thanks to steven a gordon and robert d hoggard for their suggestions concerning this work and an earlier draft of this paper
and decides when sequences of punctuation and alphabeti c characters are to be broken up into several lexemes e.g. singapore based
for muc NUM we combined these rules with NUM hand crafted contextual rules that correct residual tagging errors that were especially detrimental to our ne performance
the interpretation encoding the overall apposition ends up in the modifiers slot an approach adopted from the standard linguistic account o f phrase modification
though the full details of this inference process are of legitimate interest i n their own right we will only note some highlights here
to begin with the tractability of forward inference in this framework is guaranteed just in case the inference axioms meet a certain syntactic requirement
note that this process of combining a job out and successor fact effectively achieves what is ofte n accomplished in data extraction systems by template merging
we have thus pulled these rules out of the main fray of inference and apply them only after all other forward chaining is complete
name coreference in te of all three tasks te is actually the one that explicitly requires most idiosyncratic processing beyon d phrasing and inference
hence not having the ability to identify ambiguous words in the sw sets has a meaningful effect on the quality of the probabilities only in cases where some similar word is ambiguous and its other analysis is frequent in the language
we classify verbs into six categories by means of the aspectual features which are defined on the basis of the possibility of co occurrence with aspectual forms and adverbs
since teiru can not include a time instant at which a state is drastically changed it must denote one of the intervals depicted below the lines
table NUM the aspectual forms used in the experiment forms followable verb categories you to suru be going to kakeru be about to
there is only one kind of internal argument in terms of thematic roles that does provide an event terminus and that is a goal
table NUM shows the results obtained from running the process of table NUM on NUM sentences containing teiru which are randomly selected from the edr japanese corpus
when the form is tekuru or teiku put it on record only if the verb is modified by gradual change indicators g
NUM and NUM to a consequent state NUM NUM and NUM to a progressive state
this classification of adverbs is used not only for determining the aspectual categories of verbs but also for examining the meaning of teiru as mentioned later
for english with its relatively simple inflectional spelling changes this works well
for this reason complement unification is not actually carried out at compile time
in generation the process works in reverse starting from indexes on the lexical affix characters
they are too complex morphologically to yield easily to the simpler techniques that can work for english
the same compiled forms are used for analysis and generation but are indexed differently
members of the surface and lexieal strings may be characters or classes of single characters
one category is for the root form and one for the inflected form
the allowed finite set of values of each feature must be prespecified
the parser is augmented with an algorithm which computes working memory load during an analysis e.g.
the overall wml NUM is higher than for the left branching derivation NUM
tree NUM which is also pointed to by the spelling pattern describes the feminine forms of nouns analogously
this research was partly funded by the defense research agency malvern uk under strategic research project m2ybt44x
heritance hierarchy provides a partial ordering on parameters which is exploited in the learning procedure
however his comparative study is done on only one word using a data set of NUM NUM examples
in particular the definition of atomic categories needs extending to deal with featural variation e.g.
equivalent derivations for kim loves sandy become available figure NUM shows the non conventional left branching one
step NUM check for inconsistencies in the data distribution null the numbers in the boxes of table NUM were tallied and analyzed for internal consistency and non conformity to our original expectations that is to show that the hypothesis was not invalidated
this scanning process identified a small number of data subtypes which were individually describable in terms of the meaning forms or distributions of names and which collectively seemed to comprise a large percentage of all names extracted NUM
to count the number of tagged vip person names for example presupposes somebody s interpretation of whether vip includes only chiefs of state or chiefs of state and cabinet ministers or these plus nobel prize winning scientists novelists peace activists etc
for example there has been no attempt to score the extraction of transliterated foreign person names or of short form aliases of corporation names or of julian dates as opposed to gregorian dates as opposed to dates of the chinese lunar calendar
note that an appendix on vip names or country and capital city names is more likely to appear in a desk top dictionary than a list called ethnic surnames in their native scripts and common anglicized english renderings
however when the approach involves labor intensive pattern development based on linguistic structures future language analytic development could be focused by applying in advance something like the foregoing procedure supported by tailored versions of concordance tools and other on line analytic aids
for met we were of course primarily concerned with the foundational name tagging task many downstream modules of the system were left unused
the multilingual entity task a descriptive analysis of enamex in spanish
in other words any pp that is not an agent phrase simply threads the value of this feature unchanged
from this point of view the proposed technique is a word alignment method that imposes a more realistic distortion penalty
furthermore pebls with automatically selected k using NUM fold cross validation gives slightly higher performance compared with naive bayes
assuming a head driven bigram model as before there are only three possibile anlayses of this sequence which we write by listing the pairs of words that enter into predictive relationships to map back into traditional phrase structure grammars linking two heads x y is the same as specifying that there is some phrase xp headed by x which is a sibling to some phrase yp headed by y
in example NUM it can be observed that the discourse marker is within the rr of a restart
the heart of exemplar based learning is a measure of the similarity or distance between two examples
a document collection may be selected a detection need created or selected from a library and these items sent to the detection engine of choice hnc software or umass each of which use entirely different algorithms for detection
thus annotations with extracted information or linguistic information may easily be passed between components that need the information and a common viewer may be used to examine the annotations on a document regardless of which component did the extraction
this processing necessitates linking e.g. person p with company x and showing that company x has entered into a joint venture agreement with company y to manufacture a product r through x s subsidiary company z
the basic object of the program was to showcase the way that different vendor components could work together under the architecture
after examining the results from the query process the detection need may be modified for a new run
in the routing case the corpus is passed against a group of detection needs
the results of a detection need may be viewed as a list of relevant documents
there can also be other filled pauses which are rare such as eh and oops
this is discussed in section NUM
the remaining productions are all lexical
this is discussed in section NUM
the form is called lexical normal form
figure NUM a simple constituent matching itg
figure NUM inversion transducer parse tree
trainable coarse bilingual grammars for parallel text bracketing
we are presently conducting more quantitative evaluations of the bracketing performance improvement
the relative movement of the log likelihood is what is important here
we will return to this topic in section NUM NUM
this is referred to as the dictionary completion process
attributes pos np pos vbd i pos dt pos nn name type person
in this latter case the dictionary is incomplete
concerning performance we wanted a system that would most of all be highly accurate less than NUM misses and false alarms ye t robust in the sense of failing seldom failing soft and restoring easily
we believe this worthy of reexamination
NUM NUM critical tokenization and the syntactic graph
the definitions above can not fulfill this completeness requirement
within this context the speech generator receives as input a partially ordered conceptual representation of information to be communicated
in order to do this lexical choice in both media must be coordinated
the speech micro planner is given as input a set of information that must be conveyed
implicitly this is a claim that the text is structured by the speaker s intentions and more specifically by the difference between the intention that the hearer adopt a belief or desire expressed in a text span and the intention that a span contribute to this adoption
in rst the internal structure of a span consists of a nucleus which we have characterized as expressing a belief or action the hearer is intended to adopt a satellite which is intended to facilitate that adoption and an intentional relation between the nucleus and satellite
once we recognize that an informational analysis is needed simultaneously with ils and that the informational analysis should be determined by domain relations without reference to how the relations are employed by the speaker exactly how to determine informational structure becomes an underconstrained question
this relation between intentions partially constrains the order of what is said and thus introduces a distinction between necessary order originating with a satisfaction precedence relation of the underlying intentions and artifactual order additional ordering that must be imposed to produce linearized text
each relation is defined in terms of a set of constraints on the nucleus the satellite and the nucleus satellite combination as well as a specification of the effect that the speaker is attempting to achieve on the bearer s beliefs or inclinations
the plan for achieving i0 is to first manifest i0 by expressing the action in a the core nucleus and then to contribute to the achievement of i0 by providing the motivation in b c the embedded segment satellite
the nucleus is defined as the element that is more essential to the speaker s purpose while the satellite is functionally dependent on the nucleus and could be replaced with a different satellite without changing the function of the schema
there are five schema patterns each consisting of two or more spans a specification of each span as either nucleus or satellite and a specification of the rst relation s that exist between these spans
NUM association for computational linguistics computational linguistics volume NUM number NUM prior research has established that recognition of intentional structure and therefore appropriate generation of cues to such structure is crucial for many discourse processing tasks
edward accepts input from two devices ke yboard and mouse device
by simultaneously pressing the multiple selection key multiple objects can be selected
in section NUM NUM we present an overview of these user generated referring expressions
in edward we have adopted alshawi s notion of cfs and elaborated it
the instances passing the filter are represented by icons that depict their class
the nodes in the network represent classes and instances of entities and relations
in the present paper most attention will be given to spatial deixis
basically the speech texical chooser must check what attributes the text generator includes in its reference and omit those
examples of the tasks the subjects were to perform are the following
deixis and anaphora as we expected different subjects performed the tasks differently
speech can also clarify graphical conventions without requiring the user to look away from the graphics to read an associated text
redistribution is represented in our system by pairing arguments and functions and not in terms of movement so the proposed hierarchy of syntactic descriptions for the family anchored by a verb comprises the three following dimensions 3we talk about some causative constructions analyzed as complex predicates with co anchors in french as in jean a fait s assoir les enfants
language independent information such as the domain knowledge the analytic top concepts and information on instances can be stored only once and can be made available to all the language specific modules via the inter lingual relations
the transfer and generation modules can use the discourse function to decide whether a lexical correspondent should be produced in the target language and if so which one and at what position of the utterance
table NUM complex equivalence relations for mismatching meanings
default inheritance does not seem to be justified to represent tree schemata from the linguistic point of view
this makes them less typical examples of cause relations
while english often uses verbs for these purposes german also offers a range of particles for speakers to convey a positive example gem negative example leider or in different example ruhig attitude towards the propositional content in their utterance or towards the last utterance of the dialogue partner
multilingual design of eurowordnet piek vossen university of amsterdam
table i shows the time consumed by our program to align sentences under different conditions
the ill record object is linked to the top concept object
algorithm discussed al ove and a th nthmn
this allows for some constrained flexibility
the models evaluated here are a term document model where each term is a vector in document vector space two term term models where each term is a vector in term space and the coordinates are either the cooccurrence or the mutual information between terms
in the dominance preserving algorithm we relax the requirement of h a preserw tion
this problem is very difficult for a human being to resolve when he is not an expert in the field to which the term belongs and one can hope that an automated classification process would be of great help
this preparatory processing allows the document to be represented as a set of terms candidate terms and key words a classification method is then implemented to classify a subset of NUM NUM terms in the NUM themes and NUM semantic fields that make up the thesaurus
word sense disambiguation uses a single context generally a window of a few words around the word to be disambiguated as input to predict its sense among a few possible senses generally less than ten
the local density of an expression is the mean of the cosines between documents which contain the given expression
i lcb esearch over the past few years using parallel senten ealigned bilingual corpora sugg lcb sts
more plausibly dialogue partner asymmelry is ahsent from prototypical cases of human hunmn dialogue background knowledge is so perwmive as lo he easily ignored and grice explicitly was not concerned with dialogue failure pure and simple
an example is presented below of a piece of dialogue fi om the user test in which two system cooperativity problems occur s means system and u means user s NUM do you want retnrn tickets
each identified problem in the dialogue corpus was categorised according to which principle had been violated and described in terms of the sympton s a diagnosis d and a cure c
if the user expects some topic to come tip early in the dialogue that topic s non occurrence at its expected place may cause a clarification sub lialogue which the systeln c innc t understand
being limitcd in its task capabilities and intended for walk up and use application our slds needs to protect itself from unmanageahlc dialogue contributions by providing users with an upq ront mental model of what it can and can not do
NUM the principles were observed in the design of the system s dialogue behavior we assunled this would serve to reduce the occurrence of user diah gue behavior that lhe sysiem had not been designed to handle
thus the main dil ference between grice s work and ours is that the maxims were developed to account for cooperalivity in hulllall hulnal dialogue whereas our principles were developed to aceoulll or cooperativity in hunlan nmchhle dialogue
p1 i appears to be presupposed by maxinm gpi gp2 and g155 NUM NUM in the sense that it is not possible to adhc e to any of ihese maxims without adhering to gpii
this provides col ptis l ased coilfiiilllit oil i liluxilils gpi NUM and cii NUM p9 i.e. of ttleir staling basic principles of cooperative task oriented hulliall illachine dialogue
if such a lexical match was found the procedure did not attempt to match v with any other target node
we have also devised on the basis of the markov chain monte carlo mcmc technique e.g.
we would like to express special appreciation to the six acl anonymous reviewers who have provided many valuable comments and criticisms
for example when NUM NUM NUM ball will not be assigned into any cluster
in their implementation for estimating p kj ici however both of them suffer from computational intractability
in this paper we refer to this approach as the word based method hereafter referred to as wbm
letting NUM NUM NUM NUM be a predetermined threshold value if the following inequality holds
the primary contribution of this research is that we have proposed the use of the finite mixture model in document classification
NUM is it better to conduct soft clustering fmm than to do hard clustering hcm
for example an error may cause the system to believe that the departure city and the arrival city in a flights arrival departure application are the same
the purpose of this paper is to describe an sd system in particular the dialogue manager that is being developed with these objectives in mind
if a word is assigned only to one cluster we view its total frequency as its frequency within that cluster
some domain specific interactions are required once the dialogue is in one of these higher level states and these can be described by a different set of states
NUM initial this is the state in which each dialogue starts and reverts to after a query made by the user has been completely processed
while we have attempted to provide an upper layer that covers most ia tasks the lower layer states given here axe just examples of some possible states
as a result the system accepts the corrected value provided by the user assuming that this new value is correctly recognized and provides appropriate feedback
in the second case it informs the user that the word plane is unknown to the system and requests him her to rephrase the query
was found to produce the most accurate models
suppose our training sample has n sense tagged sentences
theorem NUM states that no critical tokenization can be produced by further tokenizing words in other tokenizations
these two test groups are of a different nature
there are four pos feature variables representing the pos of the two words immediately preceding l1 l2 and following r1 r2 the ambiguous word
however we recommend fss for sparse data nlp data is typically sparse since it reduces the impact of very high degrees of freedom and the resultant unreliable parameter estimates on model selection
we observe the related problem for fss bic
the average complexity of the naive bayes models is also lower than the average complexity of the models resulting from any combination of the search strategies and evaluation criteria except bss bic and fss bic
where g and dof are defined above
using the nautilus system in the interface to a virtual environment ve is a natural extension of this work
electrical computer engineering recinto universitario de mayaguez upr mayaguez pr NUM mperez c exodo upr
a spoken language interface to a virtual reality system video
this allows us to take advantage of certain powerful linguistic properties as described below
thus this gives us a property separating gb theories of movement that license strongly context free languages from those that potentially do n t if we can establish a fixed bound on the number of chains that can overlap then the definition we sketch here will suffice to capture the theory in l NUM and consequently the k p theory licenses only strongly context free languages
since we can interpret any bounded number of indices simply as distinct labels there is no difficulty in identifying the members of referential chains in l NUM on these and similar grounds we can extend k p these accounts to identify adjacent members of referential chains and at least in the case of english of chains of head movement and of rightward movement
this may affect the probabilities calculated for this analysis
as a resuit although the treatment in gkp s is actually declarative this fact is far from obvious
we discuss l2 p a monadic second order logical framework for such an approach to syntax that has the distinctive virtue of being superficially expressive supporting direct statement of most linguistically significant syntactic properties but having well defined strong generative capacity languages are definable in l2k p iff they are strongly context free
by focusing on the structural properties of languages rather than on mechanisms for generating or checking structures that exhibit those properties this model theoretic approach can offer simpler and significantly clearer expression of theories and can potentially provide a uniform formalization allowing disparate theories to be compared on the basis of those properties
the injectivity of bitext maps enables a method for recovering some of the rejected valid chains
the context free languages are probably best characterized by finite state tree automata these correspond to recognition by a collection of processes each with a fixed amount of memory where the number of processes is linear in the length of the input and all communication between processes is completed at the time they are spawned
these automata theoretic characterizations determine along one axis the types of resources required to generate or recognize the lan2whether there are theories that can not be captured at least without explicitly encoding the derivations is an open question of considerable theoretical interest as is the question of what empirical consequences such an essential dynamic character might have
l2k p is the monadic second order language over the signature including a set of individual constants k a set of monadic predicates p and binary predicates for immediate domination domination linear precedence and equality
fast training is achieved by reading all the noun phrase instances into memory
this means that the penalty for a parsing error may not be significant
we found that the parameters may easily be biased owing to data sparseness
9an alternative way would be to keep the corpus in the disk
the results are evaluated using the standard measures of recall and precision
in other words every step of parameter update increases the likelihood
the parameters estimated on each subcorpus are then merged averaged
table NUM effects of phrases with feedback and trec NUM topics
morphological ambiguity is a severe problem in modern hebrew
on the other hand at least NUM NUM of all words retain the correct morphological analysis
the lexicon employs NUM tags mainly for part of speech inflection and derivation for instance
in section NUM this rule based system is tested against a NUM NUM word corpus of previously unseen text
this corpus based information typically concerns sequences of NUM NUM tags or words with some well known exceptions e.g.
members in nonfinite clauses are indicated with lower case tags the rest with upper case
the distributions of adverbs and certain other categories overlaps this may explain this error type
linguistic techniques have done well at related levels morphology syntax but not here
in short like morphology and syntax parts of speech seem to be a rule governed phenomenon
currently it accounts for all major syntactic structures of english but in a somewhat underspecific fashion
since td fundsand lcb funds and fund sand rcb i.e. td fundsand l NUM NUM the character string fundsand has tokenization ambiguity
the poset td fundsand lcb funds and fund sand rcb has both funds and and fund sand as its minimal elements but has no least element
in the literature however the general practice is not to formally define and classify ambiguities but to apply various terms to them such as overlapping ambiguity and combinational ambiguity in their intuitive and normally fuzzy senses
we will prove that for any character string on a complete tokenization dictionary its critical points are all and only unambiguous token boundaries and its critical fragments are the longest substrings with all inner positions ambiguous
the author would like to thank ho chung lui for his supervision and kok wee gan zhibiao wu zhendong dong paul horng jyh wu kim teng lua chunyu kit and teow hin ngair for many helpful discussions
the principle argument is while research is by nature trial anderror and different knowledge sources contribute to different facets of the solution it is nonetheless more crucial and productive to understand where the core of the problem really lies
in this area our primary claim is that critical tokenization is an excellent intermediate representation that offers much assistance both in the development of effective tokenization knowledge and heuristics and in the improvement and implementation of efficient tokenization algorithms
also topic shift is a key method of sharing in the control of the conversational direction
table NUM the dimension of morphological ambiguity in hebrew
in the above expression the first term is for the construction of larger st i h from the combination of sr i j and its adjacent lr j h
likewise in figure NUM the outside probability of lt in the bold box is computed by slimming all the products of the inside probabilities under the white headed arrow and the outside probabilities under the black headed arrow each
whatever the direction is a complete link for wij is always constructed with a dependency link between wi and wj a rightward complete sequence from i to ra and a leftward complete sequence from j to m NUM for an m between i and j NUM rightward complete sequence is always composed with a combination of a rightward complete sequence and a rightward complete link
complete sequence inside probabilities r s inside probability of complete sequence is the probability that word sequence wio is generated when there is st i j or s i j
a it raises the practical specter of huge constant factors NUM 4deg for real grammars
in our work best first parsing is done by inscribing the four entries with maximum probabilities lt i j l i j st i j and sr i j to each chart positions in bottom up left to right manner without any extra condition checking
the rest of this paper is organized as follows
NUM head f projection principle the o sem value of a phrase is s linked to the o sem value of its head daughter
a context that wouhl enforce the latter in schwarzschild s theory tlrodu es it in the underspecitied account
for instance two of the twelve f markings for i are indistinguishable it and iii
once we know that all salient context has been considered a rule of i bcus closure is at plied
one could as well argue that since a sentence should be s informative as possible given constituents should be avoided
hans has to otto a book given sentence 25t produces the following graph hans hat otto ein buch gegeben
to illustrate resolution in tile graph representation take the following example in context NUM a amta hat otto fotografiert
learning morpho lexical probabilities hqp h NUM NUM
this is to account for data like the following where ihn in 17b is given NUM a wen hat peters mutter gelobt
her visit visitors even if functional elements are ignored the rules in NUM produce nine alternative f markings that have to be checked against the context for givenness
length of match constraints can be encoded with fewer diacritics
the directed replacement operators have many useful applications
the expression d a a NUM NUM compiles into a transducer that inserts brackets around maximal instances of the noun phrase pattern
with the current definition of the directed replacement we have now been able to compute similar tokenizers for several other languages french spanish italian portuguese dutch german
posite transducer to the string dannvaan
such transducers have a wide range of applications
it is a composition of many auxiliary relations
we permit carets to appear freely while matching
NUM we consider this issue in more detail
similarly upper accepts any nonfinal brackets
finally the proportions are normalized to obtain probabilities
the nodes of the graph represent points in time and an edge between two nodes represents a word that may have been uttered between the corresponding points in time
and some particles indicate surprise at an utterance made by the partner example oh
the shift from computational linguistics to language engineering NUM is indicative of new trends in nlp
we start with the masutaazutoonamento problem from section NUM
a small library of application objects is provided with the architecture
portability is ensured through the use of the java programming language
the corelli document architecture is currently used as the integration layer for the corelli machine translation system
an application can define its own classes for documents and collections
what remains is for the server code implementing the idl operational interface to be developed
if the component has a java api it can be encapsulated directly in the server
a particular nlp architecture embodies design choices related to how components can talk to each other
are transparently transferred by the orb to a document services implementation object for invocation
while these distinctions are relevant semantically i.e. in certain cases they may lead to slightly different updates of an information state they often can be ignored by a dialogue manager
the right daughter of the node labeled evidence is as in example 2b an elementary tree expecting the consequence of the supposition you need money
this paper presents a new approach to bitext correspondence problem bcp of noisy bilingual corpora based on image processing ip techniques
that is to be expected since the translation is not literal and the mutual information estimate based on an outside source might not be relevant
to avoid any confnsion with the term hidden in comparison with speech recognition we observe that the model states as such representing words are not hidden but the actual alignments i.e. the sequence of position index pairs j
for each of the transformation steps described above all probability models were trained anew i e the lexicon probabilities p fle the alignment probabilities p ili NUM and the bigram language probabilities p ele
null to describe these word by word alignments we introduce the mapping j o j which assigns a position j with source word fj to the position i aj with target word ei
when aligning the words in parallel texts for indo european language pairs like spanish english german english halian german we typically observe a strong localization effect figure NUM illustrates this effect for the language pair spanish to english
using the same basic principles we can rewrite the probability by introducing the hidden aligmnents a a l aj aa for a sentence pair f c
to show usefulness of the robust parser proposed in this paper we made some experiments
i was a last minute read intedopin9 attendee at a french journalism convention
to enhance the performance of the robust parser for extragrainmatical sentences we proposed several heuristics
table NUM shows the results of the robust parser on wsj
the error value e of an edge is calculated as follows
an extragrammatical sentence is what a normal parser fails to analyze
this paper is organized as follows we first review a general algorithm for least errors recognition
to upgrade this robust parser we proposed heuristics through the analysis on the penn treebank corpus
the objective of this algorithm is to parse input string with the least number of errors
subcategorization frames the most notable syntactic phenomenon of japanese is so called scrambling
it takes as input the dependency structures generated for the sentence by a dependency grammar finds all triple of modifier particle and modificant relations calculates mutual information of each relation and chooses the structure for which the product of the mutual information of its relations is the highest
accordingly the occurrences for the modificants in these relations o o and c 7o are extracted from cod obtaining a list of modifier concept identifiers with the number of their occurrences
null person wom n mother drive each person f ctory wife f ct worker NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM next the taxonomic hierarchy for each particle modificant is built and the mutual information calculated for each concept identifier
n tional investig tion based cause pro s our objective is to develop a method to automatically select the correct dependency structures accurately or at least those which have the highest probability of being correct
so are all the case roles in e.g.
these frames should reflect linguistic facts of lexical information being concentrated on verbs
the other kind of description is overlapping of deep cases
NUM NUM corresponds to a different s block from the others except for e.g.
NUM association for computational linguistics computational linguistics volume NUM number NUM quired
finite state transducers can be composed intersected merged with the union operation and sometimes determinized
the program stops when all states have been inspected and when no additional state is created
on line NUM the program goes to the construction of the transitions of state NUM
second the proof of completeness the algorithm terminates in the case of subsequential functions
the following lemma states an invariant that holds for each state s built within the algorithm
the contents of each corpus is a sequence of elements drawn from a collection of terminal elements markers for the left and right structural delimiters lsd and rsd respectively and possibly other markup irrelevant to the content of the text or its structural analysis
we achieved this result by representing the rules acquired for brill s tagger as nondeterministic finite state transducers
in fact y does n t contain thus the number of resp
the first substitution would ensure that there was no term j for the second substitution to replace
for kamp strict identity involves copying the discourse referent of the antecedent and identifying it with that of the ellided pronoun
we plan to explore ways of processing unknown words in future work either by initially assigning them all open class tags or devising an unsupervised version of the rule based unknown
more precisely let w wl w l denote an increasing function of the similarity between wl and w and let wl denote the set of words most similar to wl
we found that in contrast to back off smoothing where such events are often discarded from training with little discernible effect similarity based smoothing methods suffer noticeable performance degradation when singletons events that occur exactly once are omitted
a similarity based language model consists of three parts a scheme for deciding which word pairs require a similarity based estimate a method for combining information from similar words and of course a function measuring the similarity between words
we evaluated the similarity measures listed above on a word sense disambiguation task in which each method is presented with a noun and two verbs and decides which verb is more likely to have the noun as a direct object
the results for the mle ol case are depicted in figure NUM again we see the similarity based methods achieving far lower error rates than the mle back off and rand methods and again a always performed the best
sloppy identity copies the conditions on the antecedent discourse referent and applies them to the discourse referent of the ellided pronoun
to conclude a few further arguments for this view that are independent of any particular proposals for dealing with ellipsis
later we show experiments with up to six thresholds
this paper is one of a series that discuss how this may
prevalence of thematized purposes m certain types of instructions
these rules are not realization rules
finally section NUM provides some conclusive remarks
lexical discrimination with the italian version of wordnet
for lexical discrimination with italian wottdnet
figure NUM the italian wordnet interface
NUM selectional restrictions obtained from the whole
a final manual check is performed for all the data automatically acquired
data acquisition has been mostly manual with the help of a graphical interface
some of the nouns used in the experiment are shown in figure NUM
in the second hypothesis selectional restrictions are taken from the whole noun hierarchy
the latter are necessarily highly sensitive to sense distinctions and have developed a facility to retrieve and distinguish the multiple meanings of a word more easily than naive languge users who may have a less rich representation of word meanings at their fingertips
thus bgh can be considered as four trees each of which has NUM levels in depth see figure NUM with each leaf as a set of words
this paper focuses on classifying only nouns in terms of a class code based on the first NUM digits namely up to the fifth level of the noun tree
assuming conditional independence between c and v v given w that is p clw v p clv v we
in our experiments the NUM NUM nouns described in section NUM were classified in terms of the core thesaurus bgh using the three search strategies described in the previous section
in this paper all these approaches are evaluated for word classification in which a target document corresponds to a target word and a document category corresponds to a thesaurus class code
the second problem with handcrafted thesauri is that their classification is based on the intuition of lexicographers with their classification criteria not always being clear
each noun in the training data was categorized according to its bgh NUM digit class code generating NUM category clusters see table NUM
in our case however since we assume a core thesaurus there is room for argument as to whether we should consider this claim
the general format of the expansion rules is as follows
figure NUM generated entry for resultative use of paintedv
the change of course is that the surface is gradually covered by some paint
it states that if a verb is used inflectional rule
in general the criterial factors affect the implicitation of arguments in syntactic expressions e.g.
two problematic issues in most lexicon systems today are their size and restricted domain of use
quence like that we first have to indicate which morpho syntactic rule to use
the sign expansion approach is now used as a basis for the troll lexicon project in trondheim
argument NUM in ion painted and the introduction of new ones e.g.
another problem is related to the notions and structures adopted in the lexicon systems
the evaluation consisted of compiling a list of criteria for self evaluation and three experiments with external volunteers mostly students from a local interpreter school
especially derivation seems a useful module for mt systems since the meaning shift in derivation is relatively predictable and therefore the derivation process can be recreated in the target language in most cases
personal translator only allows to look at an entry and its translation options but not at its subject marker and systran does not allow any access to the built in lexicon
again the user can do three things but now we may assume that the user will not challenge this proposed candidate as it corresponds with the actual input from the user
the advantage of such a routine is that it applies to all inputs from the user in a uniform way and does not put a too heavy burden on the user s attention
the other from the point of view of this paper more interesting source of messages received by the dm are mostly the result of a user initiative the user has said something
seldom the source word is a compound part of which is unknown and inserted into the translation the warm heartedness das warme heartedness
also the dm sends a message to the speech recognition using the select sr grammar function to activate a new set of sub grammars corresponding to the new state of the system and the ongoing dialogue
whenever the user indicates that s he wants to say something by pressing the ptt button the dm is notified of this ptt event and waits for the actual results of speech recognition
in this module the lexical items are mapped to tasks found in the task lexicon and related to a context containing information about the current situation including application and dialogue
we have described some aspects of the design of the dm module within the vodis project with special attention to the methods the dm can employ to compensate for the limitations of speech recognition
thanks to bernhard schrsder and three anonymous reviewers for their valuable comments
in studying all the occurrences in all their contexts of the oov words of a corpus we aim to automatically obtain new lexicons which represent the corpus studied
this tagging system is based on a NUM class probabilistic language model which has been trained on a corpus of NUM million words contained in articles of the french newspaper le monde
i would like to thank dominic a merz for his help in performing the evaluation and for many helpful suggestions on earlier versions of the paper
the modules described here take into account all the simple oov words which are those composed with only alphabetical characters no space hyphen digits or special characters
by using statistical language models we show how to automatically assign one or several categories to the oov words which are found in our corpora
the actual performance of automatic speech treatment systems often limits their application to smaller subject areas of language medical texts economic articles etc
they use the derivational prefix re to translate english reorient as german orientieren wieder which is not correct but can be regarded as sense preserving
then we add this lexicon to our general lexicon and we use the syntactic labels given to each word to constrain the grapheme to phoneme transcription rules as well as the liaisongeneration rules
in fact when you have to decide whether an oov word is a family name or a town name the word context of the oov word is more useful than its syntactic class context
the context analysis of oov words permits the choice from all the possible categories proposed by the devin of the one which best fits with the context of the oov word
then by taking into account all the occurences of each oov word we are able to automatically extract a new lexicon of oov word with reliable labels associated to each word
the problem has arisen in this case through confilsion of sentential and top categories in the grammar
fhe fi equency of this pattern in the corpus is an artifact of its journalistic mmlre
s np s inappropriate because the mother category should really be np
the more nonterminals there are in the shorter cells the more combinations of nonterminals the parser must consider
we wanted to extend the work of rayner et al to general pcfgs including those that were recursive
the dash interpolation is the first punctuation mark for which generalisation becomes slightly complicated
for any sort of quotation marks excluding so called victorian quotation
beam thresholding typically allows tighter thresholds since there are fewer approximations but does not benefit from global information
we confess that our real interest is in more complicated grammars such as those that use head words
experimental comparisons of these techniques show that they lead to considerable speedups over traditional thresholding when used separately
a common phrase such as parce que because which is typically two syllables in normal speech parsko becomes three syllables in very slow or emphatic speech parsoko
these rules are generic rules and sometimes may not apply in unusual cases such as in very slow speech where each word is pronounced or in poetry which often has its own set of rules different from normal speech
ca is replaced by ge if the left context of the output buffer between angle brackets is an element of c3 preceded by g and the left context of the input buffer is h NUM NUM NUM
considering the complexity of the problems presented above it was quickly understood that letter to sound rules had to be treated like an expert system with a rule set developed by an expert a linguist and an interpreter to interpret the rules
today most speech synthesizers do not include such a large dictionary which in any case must be complemented by a set of rules just in case the word or the proper name is not in the dictionary
further it is not always possible to resolve the ambiguity from part of speech in i read books the pronunciation of read ri d or red is ambiguous
the latest version is used in different products from text to speech synthesizers both hardware and software assistive devices and games and will soon be used in proper name retrieval both on computer systems and over the telephone
in the current implementation we employ three degrees of reliability which are separated by two thresholds NUM and NUM
petit animal or between such information is available during the translation process
NUM NUM vingt digit NUM NUM trois NUM is rewritten cent if followed right context by two digits and a space etc abbreviations
the structure of a sample sentence is shown in figure NUM figure NUM shows those parts of the markov models for sentences s and verb phrases vp that represent the correct paths for the example
for instance lloce lists the following cross references for the topic of eb food jg shopkeepers and shops selling food most of which are systematic inter sense relations similar to those described in above mentioned work
this paper describes a heuristic algorithm capable of automatically assigning a label to each of the senses in a machine readable dictionary mrd for the purpose of acquiring a computational semantic lexicon for treatment of lexical ambiguity
our discussion also entails the possible implication of the labels to such problems as acquisition of a lexicon capable of providing broad coverage systematic word sense shifts lexical underspecification and acquisition of zero derivatives at the sense level
our discussion entails how the availability of these labels provides the means for treating such problems as acquisition of a lexicon capable of providing broad coverage systematic word sense shifts lexical underspecification and acquisition of zero derivatives
sim d s wherekeyd the set of pos keyword pairs in definition d the overall relevancy of cross references to a topic wk NUM the degree of ambiguity of the keyword k in a b NUM when a b in a b NUM when a b
for instance the sense of duck label with topic ad can be coerced into an eb sense when necessary with the availability of the lexical rule stipulating a sense shift from ad and eb
ltag elementary trees abstract the combinatorial properties of words in a linguistically appealing way
hand constructed topic based classes of words coupled with lexical rules as common topic and cross references of topics prove to be highly affecfive both in coverage and precision for wsd admittedly for sense definitions a somehow restricted type of text
a definition scheme begins with a genus term that is conceptual parent or ancestor of the sense followed by the so called differentia that consists of words semanficauy related to the sense to provide specifics about the sense
s trees specify the main verb and the number and position of its arguments
text tokenization and segmentatio n louella uses sequential processing which simplifies the text with each phase
the pattern matcher uses a knowledge base of pattern action rules grouped in rule packages
as training progresses the rules are generalized to cover more and more pos sible constructs
this rule contains a variable i n which is bound when the rule is matched
in the postprocessing stage we apply any heuristics learned during the course of system development
first our method of generalizing had not reached fruition by the time of the evaluation
of course the best performance occurs when louella recognizes all of the organization s present
since location information is often found in the descriptor phrase these three slots are somewhat related
one additional ne system feature used by the te system is a company rename function
only people and named companies found by the ne system will be processed by the te system
we then define a boolean feature value type for the feature subcat as follows lcb npl pl ppl rcb lcb np2 p2 pp2 rcb lcb np3 p3 pp3 rcb we encode the subcategorization possibilities in the obvious way using these new symbols
if the models in the set are represented as lists of atoms then a single atom as feature value holds of is true in a model if it is a member of the list representing the model a conjunction of atoms holds if both conjuncts hold etc
the word sender for example would be nine ways ambiguous given a rule like n lcb rcb v lcb rcb affix lcb lex er rcb with just one entry for send this problem goes away
for example an english np rule like np lcb rcb det lcb rcb adjp lcb rcb nbar lcb rcb actually makes it impossible to capture nbar co ordination unless it is treated as ellipsis
suite NUM miller s yard mill lane cambridge cb2 1rq uk NUM association for computational linguistics computational linguistics volume NUM number NUM it would therefore be desirable to combine the efficiency of pure unification based systems with the availability of richer grammatical formalisms
vp lcb rcb v lcb subcat np rcb np lcb rcb vp lcb rcb v lcb subcat np pp rcb np lcb rcb pp lcb rcb
if syntactic composition is performed after morphological composition we would get compositions such as iyi okumu 6ocuk or iyi okurnu ocuk which yield ill formed semantics for this utterance
these properties include agreement features such as person number and possessive and selectional re null a special feature value called none is used for imposing certain morphotactic constraints and to make sure that the stem is not inflected with the same feature more than once
the NUM tuple direction morpheme type process type indicates direction NUM left right unspecified morpheme type free bound and the type of morphological or syntactic attachment e.g. affix clitic syntactic concatenation reduplication
to account for both cases the suffix lu must be allowed to modify the head it is attached to e.g. lb in figure NUM or a compound head encompassing the word boundaries e.g. NUM in figure NUM
the second heuristic requires that we record all cases where two words occur in simplex nps and compare the number of times the words occur as a strictly adjacent pair with the number of times they are separated
for instance the translation engine is based on the its NUM interactive model cf
this is the case for implicit nodes of t and for some nodes of t that have been newly created
temporal adjuncts relate to some context e.g.
figure NUM overview of the system tg NUM
any of these effects are subject to backtracking
is first translated into gil before being processed
figure NUM an excerpt of tgl syntax
larger grammars may become difficult to maintain unless special care is taken by the grammar writer to preserve a global structure of rules both by defining suitable categories and by documenting the rules
arguments and in part adjuncts are specified for their role for cardinality for quantificational force under c0ntent qforce and further details such as name strings and natural gender
when the expert sense was the first on the list taggers working in the random order condition selected the expert sense less frequently than the taggers working in the frequency order condition
these are denoted by a in the error column
thus taking the correct five readings however our method has advantages in its being more general and better motivated
it can serve as a basis for many more developments
the algorithm then proceeds to the next quadruple i.e.
we wish to investigate how the methods developed in nlp research can be used to improve the effectiveness of pictalk in supporting the conversations of these users without increasing its complexity
since the root of t is not labeled s t is not required for any purpose other than substitution
first lexicalized grammars can not derive the empty string because every structure introduces at least one lexical item
depending on the amount of sharing present in a grammar an exponential decrease in the grammar size is possible
the compounding of substitutions in the ltig procedure causes there to be a large amount of sharing between the elementary trees
one consequence of the simultaneous nature of the rosenkrantz procedure is that one need not select an order of the nonterminals
however it is also needed to remove chain rules which would otherwise lead to nonlexicalized rules in the output
according to property f every am rooted initial tree is left anchored because there are no higher indexed nonterminals
mark all the interior nodes in all the initial trees created by lemma NUM as nodes where adjunction can not occur
the second category of characteristics relates to the form of reference used in the expression from which t was created specifically whether it was de3one could imagine a variety of more detailed and informative characteristics of context than those used here
this will then allow all of the mutual belief constraints that were postponed to be evaluated since they will now have only a single solution
this appears to be more adequate since both their semantic and syntactic properties differ substantially from auto semantic words
hierarchy is defined then a more careful selection would be possible to obtained different degrees of type abstraction and to achieve a more domain sensitive determination of the subgrammars
as shown later a systematic use of type information leads to a very compact representation of the extracted data and supports an elegant but efficient generalization step
using their typed feature structure notation figure NUM displays a possible mrs of the string sandy gives a chair to kim abbreviated where convenient
in our current system these upper bounds are directly used as the supertypes to be considered during the type abstraction step
however before we describe how partial matching is realized we will demonstrate in more detail the exact matching strategy using the example mrs shown in figure NUM
however we hope to increase the efficiency of this step by using head oriented strategies since this might help to re solve disjunctive constraints as early as possible
5if we would allow for an exhaustive partial match see below then the strings book and kim would additionally be generated
we compare word class based n gram language model with typical n gram language model using perplexity
note that it memorizes not only the rule application structure of a successful process but also the way the grammar mutumly relates the compositional parts of the input mrs
a template together with its corresponding index describes all sentences of the language that share the same derivation and whose mrs are consistent with that of the index
a dialogue model based on speech acts seems to be an appropriate approach also from the point of view of machine translation and of transfer in particular while in written discourse sentences can be considered the basic units of transfer this assumption is not valid for spoken dialogues
our top level goal schedule meeting see below is decomposed into three subgoals each of which is responsible for the treatment of one dialogue segment the in null troductory phase greet introduce topic the negotiation phase negotiate and the closing phase finish
it is an intrinsic feature of the dialogue planner that it is able to process any input even dialogues which do not the least coincide with our expectations of a valid dialogue and that it proceeds properly if the parts processed by verbmobil contain gaps
one example is the translation of the german geht es bei ihnen which can be translated as does it suit you or how about your place depending on whether the dialogue partners discussed a time or a place before
a robust and efficient three layered dialogue component for a speech to speech translation system jan alexandersson and elisabeth maier and norbert reithinger dfki gmbh stuhlsatzenhausweg NUM d NUM saarbrficken germany lcb alexanders son maier reithinger rcb dfki uni sb
the university of durham reported that they had intended to use gazetteer and company name lists but did n t because they found that the lists did not have much effect on their system s performance
the first scenario was used as an example of the general design of the st task the second was used for the muc NUM dry run evaluation and the third was used for the formal evauation
it also facilitates information extraction since some of the information in the extraction templates is in the form of literal text strings which some systems have in the past had difficulty reproducing in their output
as indicated in table NUM all systems performed better on identifying person names than on identifying organization or location names and all but a few systems performed better on location names than on organization names
in addition a number of errors identifying entity names were made some of those errors also showed up as errors on the template element task and are described in a later section of this paper
discussion of the results for each task is organized generally under the following topics results on task as whole results on some aspects of task performance on walkthrough article
although the management scenario contained only five domain specific slots disregarding slots containing pointers to other objects it nonetheless reflected an interest in capturing as complete a representation of the basic event as possible
analysis of sentence structures to identify grammatical relations such as predicate nominals is needed in order to relate those same pieces of information in creative artists agency is a big talent agency based in hollywood
bbn system outputs for walkthrough article despite the fact that data to fill those slots was often present over half the in and out objects in the answer key contain data for those two slots
finally the current notation presents a set of issues such as its inability to represent multiple antecedents as in conjoined nps or alternate antecedents as in the case of referential ambiguity
before we present the action schemas for referring expressions we need to introduce the notation that we use
table NUM comparison of generalization accuracy of
figure NUM contains an illustration of a piece of slalom
user with feedback on his or her writing without involving a human teacher
because any sufficiently big sense tagged corpus does not exist we also propose a new unsupervised context based word sense disambiguation algorithm which amends the training corpus for the pp attachment by word sense tags
that s can not be discourse segment purposes or another mechanism must be proposed to account for the shift in focus which occurs within the single segment
in this paper we have demonstrated one way in which tst is not adequate for describing the structure of discourses with multiple threads in a perspicuous manner
while this is acceptable for the purposes of speech act recognition and while it is better than failing completely it is not the correct discourse structure
these examples contain the sorts of phenomena we have found in our corpus but have been been simplified for the how does your schedule look for next week
because spoken language is imperfect to begin with and because the parsing process is imperfect as well the input to the discourse processor was far from ideal
it is commonly impossible to tell out of context which speech act might be performed by some utterances since without the disambiguating context they could perform multiple speech acts
similarly sentence NUM chains up to an instantiation of the response operator from an instantiation of the accept operator sentence NUM is assigned the speech act accept
these are recognized in cases where the plan inferencer chooses not to attach the current inference chain to the previous plan tree
this would seem to leave all of the deliberation over meeting times within a single monolithic discourse segment leaving the vast majority of the dialogue with no segmentation
the hope is that this will get the students to write more
the student does n t know that such agreement exists in the language
aside from content the generated response should have several other characteristics
thus for example monday correctly resolved to monday 19th of august but incorrectly treated as a starting rather than an ending time contributes NUM errors of omission and NUM errors of commission and no credit is given for recognizing the date
such a system must have several components
there are several reasons why a model of second language acquisition is necessary
NUM the student is mistaken about the syntactic form the agreement takes
thus the placement algorithm must take into account both of these writing strategies
once this information is recorded the next sentence will be analyzed
during systoni inll lenlenlalion one constantly worries about truth and evidence
NUM then is a specific principle subsumed by gpi2
NUM s gives priority to discount over time without proper reason
red discount can be oblained on the departures at x y and z
these maxilns are capable of perforii illg the same task in guiding dialogue design
take into account legitimate partner expectalions as to your own background knowlcdse
as in i graph deviations indicated potential dialogue design problems
we then justify the comparison between principles and maxires section NUM
since i is reasonably likely in the context blish but completely unlikely in the context euestablish we may well wonder why the model does not include the negative extension i in addition to e or even instead of e
that is the parameters p yk xl x2 xm NUM yk c v are tied if the associated events satisfy the following conditions c x1 xm l yi qa and c xl xm l yk qn yk e v NUM yiff v where qn is currently set to NUM
next c alq w w is estimated as c alw c w for the previously seen symbols c e q w and ac r NUM w w is estimated uniformly as NUM NUM w for the novel symbols r NUM w
provide feedback oil e lch piece of infor ination provided by lhe riser
ale w e a ale w e u lcb a rcb ale w e alt w a alt w u lcb a rcb alt w let us now use these incremental cost benefit formulas to design a simple heuristic estimation algorithm for the extension model
we refer to the vector of left neighbors of a word as its left contezt vector and to the vector of right neighbors as its right contezt vector
the decision to consider only immediate neighbors is responsible for this type of error since taking a wider context into account would disambiguate the parts of speech in question
punctuation marks special symbols interjections foreign words and tags with fewer than NUM instances were excluded from the evaluation
recall is the number of correct tokens divided by the total number of tokens of t in the first column
the last column gives van rijsbergen s f measure which computes an aggregate score from precision and recall van rijsbergen
this need motivates research on fully automatic text processing that may rely on general principles of linguistics and computation but does not depend on knowledge about individual words
i am grateful for helpful comments to steve finch jan pedersen and two anonymous reviewers from acl and eacl
for example for certain has both senses of certain particular and sure
this basic idea of measuring distributional similarity in terms of shared neighbors must be modified because of the sparseness of the data
for example if we write then we are specifying a network of three nodes a b and c and two predicates boolean valued attributes coded as datr paths a and b with c inheriting a value for a from a and for b from b the network is orthogonal since a and b represent distinct sets of predicates
our objective has been a language that i has an explicit theory of inference ii has an explicit declarative semantics iii can be readily and efficiently implemented iv has the necessary expressive power to encode the lexical entries presupposed by work in the unification grammar tradition and v can express all the evident generalizations and subgeneralizations about such entries
however its most obvious application is to bootstrap lexical access in language processing systems that make direct use of an on line lexicon given a surface form in analysis or a semantic form in generation we need to identify a lexical entry associated with that form by reverse query and then access other lexical information associated with the entry by conventional inference
the word pairs in table NUM have been selected randomly from the test set with the criterion that they scored significantly ie NUM NUM on at least one of the three measures d1 d2 and d3
thus we have staff and reporter influencing each other almost equally while the asymmetric influence on in from its right context addition is also detected by the dim s
what is needed here is a directed ie one sided influence measure dim something that serves as a measure of influence of one word on another rather than as a simple symmetric co existence probability of two words
each derivation in the generative statistical model produces an ordered dependency tree that is a tree in which nodes dominate ordered sequences of left and right subtrees and in which the nodes have labels taken from the vocabulary v and the arcs have labels taken from a set r of relation symbols
head automata mono lingual language models consist of a lexicon in which each entry is a pair w m of a word w from a vocabulary v and a head automaton m defined below and a parameter table giving an assignment of costs to events in a generative process involving the automata
initialization for each word w in the input between positions i and j the lattice is initialized with phrases lcb w lcb rcb i j m q c for any lexical entry w m and any final state q of the automaton m in the entry
mean distance model in the mean distance model we make use of some measure of goodness of a solution ts for some input s by comparing it against an ideal solution is for s with a distance metric h h t i d in which d is a non negative real number
one important advantage of using representations that are close to natural language itself is that it reduces the degrees of freedom in specifying language and task models making these models easier to ac null quire automatically
the average time taken for translation of sentences of unrestricted length from the atis corpus was around NUM NUM seconds with approximately NUM NUM seconds being taken by the analysis algorithm and NUM NUM seconds by the transfer algorithm
with the same assumptions we made for the mean distance model let eh c be the average of h t ts for solutions derived from sequences of choices including the context c
head automata can also accept some non regular languages requiring coordination of the left and right sequences for example the language anb requiring two states and the language of palindromes over a finite alphabet
when making a transition with relation ri in the path select a graph edge with label ri from w to some previously unvisited node wi with finite dependency cost c wilw ri
the number in the tuple associated with each word is a signed number if a complement dependency is being expressed and is an unsigned number if a modifier dependency is being expressed s
if a lexical item corresponds to a potential recursive structure then it will be necessary to encode this information by making the result part of the functor to be x x
the generalization over recursive structures in a categorial grammar however will require further annotations of the proof trees in order to identify the anchor of a recursive structure
the combining operation is indicated by the nature of the arcs broken line for substitution and bold line for adjunction while the address of the operation is indicated as part of the node label
the information tipster i extracts resembles a completed form
the complete document is displayed in yet another window
figure NUM the annotation list window with annotation text
a limited view of the headword list is provided
the system also contained a prototype translation memory tool
the first prototype focused on spanish and english text
this is particularly true in the case of oleada
participatory prototyping again focuses on users and their tasks
most advanced language analysis tools e.g.
NUM NUM tipster and the computing research laboratory
we modify our language model assuming that nonterminals exhibit the distributional properties of their heads
however this simple class of models only uses phrases to capture relations between adjacent words
in a mono sl eaker discourse text the general situation seems to be tim assertion of a negative fact simply rases the positive one of course this erasing is virtual when the positive fact is only l resul posed
this means of course that coherence in natural language discourses is quite diffecent from tim consistency in a mat w matit al theory which has lo be consistent in each o its sets of i rol ositions
device for word order variation which is difficult to reconcile with the descriptivity requirement
the intension contains those objects whose representation is supposed valid for speakers and situations related to discourse enunciation aitd to the application dolnain there exists a consensus between the speakers of the discom se about these objects which reflects general background knowledge NUM e dog is a stupid and spitej u animal the intensional objects are then kinds of logical concepts in their world
un ier his restriction the knowledge stored in the base is positive when the discern s asserts a negatiw fact do is arc not slupid this presttpl oscs that the positive corresponding fiu t doqs arc slupid has ah e dy been asserted exl licitly or implicitly nnd that a eonl radiction may arise
n some rises the he gallon of a isingr it suggests the ingredi mcc of the object a to another type j such that there exists a type NUM which is greater than NUM and NUM in the lattice of types the spoke wheel is not a part of a ear it zs part of a bike
figure NUM NUM the dynamical properties of the inside outside algorithm
figure NUM NUM graphically depicts the evolution of this dynamical system
there are only three words here and therefore three heads
let us look one final time at the sequence v p n
the parser is given the part of speech of each word
this is important given the computational complexity of estimating long distance word pair probabilities from unbracketed corpora
the NUM parts of speech in the treebank were collapsed to NUM resulting in NUM grammar rules
whether this succeeds depends on the information conveyed by the sentence which information has been conveyed earlier and whether the sentence can find a place in a natural grouping of sentences in paragraphs
the assumption seems to be that the closest antecedent is the correct antecedent
our experiments with the confidence factor parameter indicates the trade off between recall and precision
this paper compared our automated and manual acquisition of anaphora resolution strategies and reported optimistic results for the former
although a part of its task is to merge multiple referents when they corefer i.e.
the six mlrs using decision trees with different parameter combinations are described in table NUM
it uses discourse knowledge sources ks s which are manually selected and ordered
thus the system performance evaluated against all the anaphora in texts could be different
the same training texts used by the mlrs served as development data for the mdr
figure NUM autoslog ts flowchart stage NUM generating concept nodes
roughly NUM of the texts are classified as relevant
however we see a trade off at higher recall levels
figure NUM shows the size of the resulting dictionaries after filtering
in this paper we first describe one approach we are taking to build an automatically trainable anaphora resolution system
we have employed different training methods using three parameters anaphoric chains anaphoric type identification and confidence factors
our approach uses a combination of domain independent linguistic rules and statistics
patterns of type a should have high relevancy rates
while hmms also use hidden variables to represent word classes the dynamics are fundamentally different
third the user may initiate dialog on a new subgoal in ways that will be discussed below
it also allows intermediate levels of control where one participant may gently rather than strongly lead the interactions
the most important aspect of dialog control is the ability to select the next subdialog to be entered
the dialog controller manages a number of generic rules related to dialog and can provide the associated expectations
the networks and their probabilities are created automatically from grammatical rules and text samples input by the designer
their semantics is built directly into the parse trees and translated into sql for access to a database
many of the points discussed above are illustrated by the following excerpts from repair dialogs using our system
different stages of processing place different requirements on the classification system so customised tagsets have been developed
the tagset used at this stage mode NUM has NUM classes not distinguished for number
there must be at least one word beyond the end of the subject and before the end of sentence mark
it addresses the representation of language for connectionist processing and describes methods of constraining the problem size
this is conventionally a single layer net since there is one layer of processing nodes
the node determiner would occur often in both correct and incorrect strings
we need larger tagsets to capture more linguistic information but smaller ones to constrain the computational load
a functional approach is taken to tagging words are allocated to classes depending on their syntactic role
where ai p i jk li bi x y p i x yli w1 and w2 are the vocabulary sizes of the two languages and n is the number of nonterrninal categories
we can accomodate the fact that no chinese part of speech lexicon is available with noninformative distributions as follows NUM
this applies to both straight and inverted orientations an example with inverted orientation is shown in figure NUM
however at present bracketed corpora for chinese are unknown as is the case for many other languages
it is important to stress at the outset that aparallel bracketed corpus is different from a bracketed parallel corpus
we begin by considering for each nonterminal the probability of its use in a derivation of the observed sentence pair
our second strategy is to apply an unsupervised training algorithm to tune the probabilistic parameters of the sitg
this procedure can be done recursively until the demanded number of classes is reached
both types of models are trained by expectation maximization em algorithms for maximum likelihood estimation
given the body of texts the selective extraction should be sensitive to the different observed information
this always results in c4 NUM selecting a few features from NUM to NUM for the final tree
namely we pick the simplest tree with trib pos as the root if one exists otherwise the simplest tree
as part of this presentation we apply these techniques to the results of a large number of classifiers developed using the methodology presented in NUM NUM NUM NUM NUM and NUM which tag words according to their meanings i.e. that perform word sense disambiguation
note that the brackets on the two tiers are ordered with respect to each other
sthe trees that c4 NUM generates are right branching so this description is fairly adequate
error reduction is NUM for core2 and only NUM for impl core
the best tree was obtained partly by informed choice partly by trial and error
the probabilistie model defines for each class and each ambiguous object the probability that the object belongs to that class given the values of the non classification variables
moreover it is hard to see how the best combination of individual features could be found by manual inspection
both clauses describe actions with the first action description embedded in a matriz you should
each segment constituent both core and contributors may itself be a segment with a core contributor structure
in section NUM we use aggregate and mixed order models to improve the probability estimates from n grams
crossing rules where zt is found in the direction opposite of that of y are labeled with x
the use of an even larger number of naive subjects might yield a finer grained set of segments cf
the largest and the most statistically significant difference is the higher precision of the condition NUM automated algorithm
the performance of the learned decision trees averaged over the NUM test narratives is shown in table NUM
most b like previous research shows that pauses preceding boundaries have average longer durations
a third person definite pronoun provides a referential link if its index occurs anywhere in the current segment
the first is the wide variation in the number of boundaries that subjects used as discussed above
the NUM training narratives range in length from NUM to NUM phrases avg NUM NUM
each model uniquely defines a classifier
in addition preliminary data from a third coder provides good evidence that coref can be coded reliably
as with the pause features the cue phrase features were motivated by previous results in the literature
category x bird that encodes the attribute
figure l b plots classification accuracy versus number of words examined instead of those selected
the proof is based on the idea that any instance of such a grammar can be simulated by a standard ccg
the variable constraint states that NUM variables are limited to the defined positions in gtrcs
we thus decided to focus only on restrictive modifiers the best candidates to bring terminological i.e.
however the size of the intermediate networks resulting from the intersection of the initial sentence network with the sets of constraints raises serious efficiency issues
this may sound like a severe disadvantage of the approach as deciding on the order of the transducers relies mostly on the grammarian s intuition
nevertheless as this principle leads to a significant number of incorrect attachments in the case of more free style texts the vc expansion network is optionally applied depending on the input text
segmentation consists of bracketing and labeling adjacent constituents that belong to a same partial construction e.g. a nominal or a verbal phrase or a more primitive partial syntactic chain if necessary
here we use the basic idea described in the np marking temporary beginnings tbeginvc and ends tendvc of vc are first marked
it may include words or segments nps pps aps or other vcs that are possibly linked as arguments or adjuncts to the verb
the process of tagging words and segments with syntactic functions is a good example of the non monotonic nature of the parser and its hybrid constructive reductionnist approach
we first mark infinitives and present participle segments as they are simpler than finite verb phrases they are not recursive they can not contain other vcs
we next ranked the sentence positions by their average yield to produce the optimal position policy opp for topic positions for the genre
selecting NUM sentences is NUM of the average length of a ziff text the precision is NUM NUM and the recall NUM NUM
but this assumption does not support goal oriented topic search in which one wants to know whether a text pertains to some particular prespecified topics
transducers at the top of the sequence are ranked higher in the sense that they apply first thus blocking the application of similar constructions at a later stage in the sequence
later in the sequence other transducers allow for subject inversion thus violating the constraint on subject verb order especially in some specific contexts where inversion is likely to occur e.g.
the contributions of precision p and recall r from each m word window alone can be approximated by
for a goal oriented perspective one has to develop a different method to derive an opp this remains the topic of future work
cqp provides a query language which is a conservative extension of familia unix regular expression facilities NUM xkwic is a user interface tuned for corpus search
although a large set of dictionaries have been exploited as lexicm resources the most widely used monolingual mrd for nlp is ldoce which was designed for learners of english
in order to apply conceptual distance wordnet was chosen as the hierarchical knowledge base and bilingual dictionaries were used to link spanish and french words to the english concepts
thus usually a dictionary definition is written to employ a genus term combined with differentia which distinguishes the word being defined from other words with the same genus term NUM
although most of the techniques for word sense resolution have been presented as stand alone it is our belief that full fledged lexical ambiguity resolution should combine several information sources and techniques
al say there is no reason why there could not be an implementation of lt nsl which read sgml elements from a database rather than from files
because both lppl and dgile are poorly semantically coded we decided to enrich the dictionary assigning automatically a semantic tag to each dictionary sense see section NUM NUM for more details
however the heuristics that depend only on the size of the data NUM NUM perform poorly on lppl while they are powerful methods for dgile
in general the results obtained for each heuristic seem to be poor but always over the random choice baseline also shown in tables NUM and NUM
based on them most ungrammatical constituents can be eliminated
in chinese there arc a group of verbs with especial synlactic functions
the constituents inside them can not have syntactic relationship with the outside ones
table NUM distribution of the simple and complex sentences in the training and test sets
the difficulty to parse nati ral language sentences is their high ambiguities
table NUM results on the training set and test set
with NUM NUM or g NUM crossing brackets respectively
although the experimental results are encouraging there are many possibilities for improvement of the algorithm
examples are word doubling and omission of a common function word
either could be a valuable aid in choosing the correct correction
for comparison a variety of other data has been collected
one word in this purview is in focus at a time
there was a reduction of over half the transitions on the trie formed from the alvey lexicon
its intermediate syntactic stages involve phrasal parsing followed by full syntactic analysis top down left corner
which deal with illformed input usually step directly from spelling considerations to a full scale sentence parse
there is a phase to be added after detection of a tag or partial parsing error
the phrasal phase and partial parsing have been extracted and are being adapted to the present purpose
to achieve this the ends of the wordstring delimited by the purview need to be treated differently
by the above definition of well definedness on sdrss the compound is coherent only if we can resoh e rc to a particular relation via the sdrt update function which in turn is determined by dice
similar results hold for similar experiments on the brown corpus
symbol accuracy was NUM NUM recall was NUM NUM
a word is now both a character sequence and a set of symbols
we take this as a vindication of the perturbation of compositions representation
the reason is that hidden structure is largely a compile time phenomena
for the chinese word recall was NUM NUM and crossing brackets was NUM NUM
naturally many patterns in text and speech reflect interesting properties of language
for words to be added to the lexicon two things are needed
for scratching her nose the perturbation may just be frequency
similar questions could be asked of subword units like syllables
content the extent to which the information is adequate and focused
during embryo sac generation the embryo sac is generated from the megaspore
core connection process finds the connection between process and a core process
figure NUM depicts the biology knowledge base s representation of embryo sac formation
for example the location for embryo sac formation is the concept ovule
explanation generation is typically decomposed into two subtasks explanation planning and realization
our work focuses on content determination and organization and de emphasizes issues in realization
to fulfill this function they provide constructs known as content specification expressions
content specification expressions reside in content specification nodes as in figure NUM
in the two tree banks we tested on there are many subtrees that differ in semantic type hut otherwise share the same syntactic semantic structure
but instead of computing the most likely interpretation of a string it computes the interpretation of the most likely combination of semantically annotated subtrees
if a corpus with semantically annotated sentences is used the same approach can also generate the most probable semantic interpretation of an input sentence
now an analysis for the sentence a woman whistles can for instance be generated in the way shown in figure NUM
for the semantic dop model the semantic type of an expression c is a pair of types tz t2
the annotation method is robust and flexible as we are dealing with real spoken data containing a lot of clearly ungrammatical utterances
ignoring semantic types will result in loss of accuracy but distinguishing all different semantic types will result in loss of coverage and generalizing power
a derivation of a string is a tuple of subtrees such that their composition results in a tree whose yield is the string
for instance it is possible that the specific features of the student s native language l1 will affect the rate or order of acquisition of the second language l2
secondly given the current inaccuracies of speech recognition how can developers implement domain independent strategies for limiting the damage caused by misrecognition while at the same time mainraining an apparently natural conversational flow between system and user
for example s he may click on a particular error name to get a currently canned explanation or s he may ask the system to mark the occurrences of a particular error only
this knowledge can then be utilized to assess the user s second language ability and other user characteristics and to evaluate the success or failure of the correction techniques employed thus far
notice that there are also relationships among the hierarchies
consider the following example found in one of our writing samples my brother like to go this sentence appears to most of us to have a problem in subject verb agreement
thus in addition to providing feedback on the student s writing a tutoring system should be capable of offering sample understandable input using constructions that the student is currently attempting to master
in total the samples represent about NUM NUM words
figure NUM icicle overall system design
provide strong clues as to the sense of a word
both approaches hinge on the belief that surrounding noun
comparable performance with the two human judges is also achieved
table NUM shows the accuracies attained by the human judges
the flow of our approach is illustrated in figure NUM
their responses are then tallied with the seusetagged test corpus
unlike existing methods the approach does not require annotated corpora
at present work is being undertaken to examine how well a simple competitive neural network can perform on such a task
firstly as in the human case learning proceeds on line without any need for a separate stage of statistical analysis
this is not to say that non neural network approaches could not permit a word to belong to more than one cluster e.g.
each of the methods described here represents each target word in the same manner regardless of the syntactic or semantic designation which might conventionally be assigned to it
approaches to semantic clustering a number of analyses were carried out on text corpora to examine the sorts of semantic groupings that can be achieved using simple statistical methods
initial analyses were carried out on the lund and trollope corpora using a short window length of only one word position either side of the target word
secondly it is straightforward to allow any given word to be clustered into as many separate clusters as the system dictates subject to the maximum number of output units available
under the tokenization operation every character string can be tokenized into a set of different tokenizations
null critical ambiguity and hidden ambiguity in tokenization constitute our third group of findings
the em algorithm is used to produce a maximum likelihood estimate of the model parameters taking into account all possible alignments and clumpings
instead both the alignment and the clumping are viewed as hidden quantities for which all values are possible with some probability
the goal of a natural language understanding nlu system is to interpret a user s request and respond with an appropriate action
now with renaissance technologies stonybrook ny usa sdella rentec tom meps roukos tward c watson
the poisson and general fertility models show a NUM gain in performance over the basic clump model when using the partially annotated corpus
this gives fractional counts for each of the NUM alignments which are then used to update the the general fertility model parameters
if one assumes that the fertility is modeled by the poisson distribution with mean fertility e xt tf n p n
to avoid ill formedness in sentence tokenization we now introduce the concept of a complete tokenization dictionary
by lemma NUM it would be the same as saying that x is a supertokenization of y
table NUM in section NUM u1 to ua illustrate the constant theme while u7 to u10 illustrate the linear thematization of rhemes
we also introduce as a notational convention that a discourse segment is identified by its index s and its opening and closing utterance viz
uz simply continues the same segment since the textual ellipsis seite page refers to handbuch manual
vertical lines show the extension of a segment its end is fixed by an assignment to ds s end
the identification of the preferred center with the theme implies that it is of major relevance for determining the thematic progression of a text
on the other hand centered segmentation may replace the cache model entirely since both are competing models of the attentional state
since g x g y there must be ixkl lykl
consequently either x or y is unable to fulfill condition NUM of definition NUM
manual correction was performed by experienced native analysts for each language separately
five taggers have been realized and tested using bi pos and tri pos transition probabilities
which is a nth order markovian chain for the language model mlm
the most important one is the presence of errors in the training text
the optimal solution is estimated by the well known viterbi algorithm
where m is the number of words in the sentence w
in the english text the chi square distance between the tag
two modules consume the majority of the tagger computational time
in other words the ct tokenization a bc d is left out in all the other three sets
even though the correspondence between forward backward and inside outside probabilities is very close there are important differences between speech recognition hmms and natural language processing pcfgs
we are now costructing a japanese dependency parser that employes mistake driven mixture of decision trees
on the other hand the performance of the bi gram mixture was not satisfactory
we investigate the effects of lexicon size and stopwords on chinese information retrieval using our method of short word segmentation based on simple language usage rules and statistics
null table NUM summarizes the algorithm construct htree that constructs the hierarchical tag context tree
by using the trade off the optimal level of b is se lected
we first automatically segmented and tagged these sentences and then revised them by hand
the algorithm first construct a hierarchical context tree by using the current weight vector
nara NUM NUM japan mat su c is aist nara ac
more precisely the algorithm reduces the weight of examples that are correctly handled
it is straightforward to extend this binary tree to a basic tag context tree
we did not implement tribayes nor did we use the same partitioning of the brown corpus as tribayes
that is the initial data processing steps and lsa space construction parameters have all been the same
furthermore terms which did not appear in more than one sentence in the training corpus were removed
we wanted to fix this variable for all confusion sets and this number gives a good average performance
baseline performance is the percentage of correct predictions made by choosing the given most frequent word
in all but the case of lcb amount number rcb lsa improves upon the baseline performance
from the final column containing words of the same part of speech and those which have different parts of speech
figure NUM results of reducing the t s and d matrices produced by svd to rank k
the t matrix is a representation of the original term vectors as vectors of derived orthogonal factor values
note that even if one statically stores possible word similarity combinations o n NUM space is required
our process is based on the following four steps a to d a facts lookup on a manually created NUM entry lexicon called l0
figure NUM trie and suffix tree for string w accbacac
the construction of the wflue for the cmnposed tenses results fl om a complex interaction between the ilowever the fiat f structure in NUM providc s no room for a statement of selectional requirements allowing massive overgeneration e.g.
figure NUM the graphical representation of the parse structures of a big man slipped on the ice and the
in a grouping process a single nonterminai label is assigned to each group of brackets which are similar
in this paper we oncentrate on two issues within the broader perspective of i ai lcb gii am the treatment of auxiliaries and the transi arent representation of multilfle genitive nps in german
this restriction must be encoded at some lew NUM but does not follow from tile distiction between gen and ien2 wtfich are flmctions that do not bear any semantic content on the ir own
a s a rtgmt ni s i llci lli o illf i lllil ioll exical sthiidaii ics is ii oded
in section NUM we introduce the concept of differential entropy as the termination condition used in step NUM
note hat the level of elnlmdtting in the f structure exacl ly mirrors t he c st ructure each verbm cnwnt takes a c mpl m n
relative entropy d pilip2 is a measure of the amount of extra information beyond p2 needed to describe pl
otherwise an appropriate pattern feature has to be produced to ensure the realization of the argument at the args level
when the whole set of equations was provided the accuracy became about NUM
the growing interest in the dependency concept which roughly corresponds to the o roles of gb subcategorization in hpsg and the so called domain of locality of tag again raises the issue whether non lexical categories are necessary for linguistic analysis
after reviewing several proposals in this section we argue in the next section that word order the description of which is the most prominent difference between psgs and dgs can adequately be described without reference to non lexical categories
thus no current variant of dg not even tesni re s original formulation is compatible with gaifman s conception which seems to be motivated by formal considerations only viz the proof of equivalence
the set c of word classes is hierarchically ordered by a subclass relation NUM isacccxc a word w of class c inherits the valencies and domain sequence from c which are accessed by NUM w valencies
for the trec NUM chinese collection of documents and queries it is found that a small NUM lexicon coupled with some simple linguistic rules is sufficient to provide indexing features for good retrieval results
by a case suffix which determines ease and number the resulting noun form
ge eral ion of a phrase sial is by realizing i lcb s head dtr
acting as the hearer the system performs plan inference on each set of actions that it observes and then applies any rules that it can
the smart group crn ae used many fewer terms because of their new algorithms
they also used passage retrieval as in trec NUM but found it detrimental in trec NUM
it could also discuss the privatization of amtrak as an alternative to continuing government subsidies
the new adhoc topics used in trec NUM reflect a slight change in direction
for several years some participants have been concerned about the definition of the routing task
task NUM was to retrieve as many relevant docnments as possible within a certain timeframe
another factor in topic expansion is the number of terms being added to the topics
again for more details on the various runs and procedures see the cited papers in the trec NUM proceedings
for example the english sentence NUM NUM the book is red has a corresponding japanese equivalent NUM NUM hon wa akai desu book top red is if we mirror the japanese bracketing structure in english to form the initial tncb we obtain book the red is
the performance for city university where similar algorithms were used in trec NUM and trec NUM dropped by NUM
the reduction in theoretical complexity is achieved by placing constraints on the power of the target grammar when operating on instantiated signs and by using a more restrictive data structure than a bag which we call a target language normalised commutative bracketing tncb
the complexity of this algorithm is o n because all permutations n for an input of size n may have to be explored to find the correct answer and indeed must be explored in order to verify that there is no answer
in figure NUM it can be seen how the maximal tncb composed of nodes NUM NUM and NUM is conjoined with the maximal tncb composed of nodes NUM NUM and NUM giving the tncb made up of nodes NUM to NUM the new node NUM is
for example consider the bag of signs that have been derived through the shake and bake process which represent the phrase NUM the big brown dog now since the determiner and adjectives all modify the same noun most grammars will allow us to construct the phrases NUM the dog NUM the big dog NUM the brown dog as well as the correct one
that paper also discuases some of the possible difficulties in integrating the sfm and rst
at the point where rst and the sfm meet how is this structure generated
as you will see the features here are precisely those of the rhetorical relations of rst
these in turn have constituents called moves and each move consists of one or more acts
the second is dialogue generation the aim of which is to produce co operative interactive discourse
figure NUM a system network and its realization rules that integrates the systemic flowchart
in principle the two are separate but there is little point in multiplying terms unnecessarily
in this section we review the approach based oll monolingual head automata together with transfer mapping
we have only first order type dependencies such that the local probabilities or costs when using the negative logarithms of the probabilities depend on i q on the arcs or transitions in the lattice
for example all the maintenance documents must be divided into tasks and subtasks
transfer in this model is a mapping between unordered dependency trees
hence the requirement for additional states in the bilingual transducer versions
the pair elc is referred to as a choice
one difference being that the head automata derivations are always trees
the head transducers were built by modifying the english head acceptors defined for the transfer system
the cost function used in the experiments is computed as
selection of a dependent word and acceptor given a head word and a dependency relation
for unseen choices we replace the context c and event e with larger equivalence classes
it contains NUM NUM entries words with NUM NUM pronunciations
learning phonological rule probabilities from speech corpora with exploratory computational phonology
erate phone sequences from word orthography as an additional source of pronunciations
figure NUM pronunciation models for of and the
a decision tree is learned for each underlying phoneme specifying its surface
typically we use NUM NUM hidden units
the maximum path at each point is extended non maximal paths are pruned
the size of each dot indicates the magnitude of the local phone likelihood
now a single iteration of the rule probability algorithm must perform the following computation
pron be the set of pronunciations derived from the forced viterbi output
thus robust analysis and translation are also required
both interpretations are plausible with la being the most likely in the absence of a long pause after the first adjective
only for move a we need an extra predicate to accomplish a movement if there is a possible movement to the node that has just been created
the main idea of head driven parsing is as was stated before that heads contain relevant information for the parsing process and that they therefore should be parsed before their sisters
for example if the verb is intransitive we know that v does not require a complement sister and we know that we do not need an agrop on top of vp
the fact that v contains lexical information and functional heads like agro and agrs do not could be used as a justification for the fact that the latter are not head corners
if we follow NUM in combination with the tree in figure NUM we establish the fact that the parser searches its way down to the verb as soon as possible
sets of items generated in recognizing i saw a tall old man in the park with a telescope
in the minimalist framework lexical information belonging to a chain is available from the moment that the first position of the chain is created because that is the moment when the lexicon is consulted
the top down prediction step moves from thegoal agrsp to agrs to agrop to agro to vp to v and finally to the lexical head corner v where the bottom up process starts as the minimalist program requires
a is the head of b if there is a rule with b as left hand side lhs and a as the head daughter on the right hand side rhs
like in earley s parser there are three phases completer predictor and scanner
e t graphcat returns the set of the edges of the graph t graphca t
the algorithm has been implemented in common lisp and runs under the unix operating system
the paper describes an improved earley type recognizer with a complexity o igl2n3
category cat lsull c ti graph at
inadequacies in the methods for selecting the best parse
dialogue a model of dialogue has been implemented
other links between concepts include synonym and antonym
this textref handling is completely invisible to the semantic rules
each link has an arc and a set of targets
the heap limit has been mentioned as one resource limit
hence how the core performs is of prime interest
which act as references int o the document
some concept nodes may have more than one related textref
r r r request p p p promise i i i inform etc parameter values u user and s system
1sol these NUM sentence pairs NUM were exact matches in terms of the way they were tagged
in the vector space model each word w is represented by a vector comprising statistical factors of co occurrence
the speaker chooses the topic proper the least dynamic element among the items assumed to be most salient in the hearer s memory
for some relations the boundaries of the model world are searched for e.g. the bottom most file for others the area in the relatum s vicinity e.g. the file left of donald report or the area of the most salient objects e.g. the file on the left if the directory containing that file is very salient are searched for
both edward s graphics processes and its syntactic semantic and pragmatic interpretation processes operate on line i.e. interpretation starts directly and goes on while the user enters the remaining of his utterance incrementally i.e. the interpretation is built up piece by piece from left to right and in parallel i.e. more than one interpretation process can be handled at every moment
the types of referring expressions of table NUM do not exactly match the four types mentioned in table NUM unimodal graphical deixis was not encouraged in the experiment and therefore did not occur reference by name occurred frequently but this type of reference is not considered to be deictic or anaphoric and their interpretation is therefore less interesting from a computational linguistics point of view
let us take figure NUM in which the number of subsets is given as two without loss of generality
we allow developers to alias messages to promote text reuse as well as to facilitate equality checking
first every template is linearized that is an order of appearance its predicate and case roles is established
the final step of the creation of the text plan tree is to test this tree for complexity and depth
the sorting function used for ordering is based on heuristics such as the statement which describes more NUM
in the description below the template for which linking is attempted is referred to as current
zone NUM contains a frequency ordered list of linearized cooccurrences of the verb with a particular subset of case roles
the realization stage linearizes the plan and takes care of the ellipsis conjoined structures punctuation and morphological forms
sublanguage of patent claims n2 n3 jv n4 n10 n4 n3 n11 n1 numl n5 n6 n5 n7 n8 n9
the appropriate list is presented to the user for selecting the most appropriate realization of the content to be conveyed
if no match is possible with conceptual schema node labels the procedure matches NUM
subject keywords interactive automatic generation conceptual schema template patent claim
as seen above in the procedures for defining mcca categories addition of lexical semantic information in the form of derivational and morphological relations and semantic components common across part of speech boundaries information now lacking in wordnet synsets would facilitate the development of concept grammars
we describe the unique characteristics of mcca how its categories relate to wordnet synsets the analysis methods used in mcca to provide quantitative information about texts what implications this has for the use of wordnet in tagging and how these techniques may contribute to lexical semantic tagging
these examples of words in the sanction and normaave categories repeated in other categories indicates a need to define categories not only in terms of supercategories using the hearst sch tze model but also with additional lexical semantic information not present in wordnet or mcca categories
the words in this category include academic artist biologist creator critic hiffidrian instructor observer philosopher sin general we have found that assignment of only about NUM to NUM percent of the words in a category is questionable
the system achieves its current performance without using linguistic tools such as a part of speech tagger syntactic parser pronoun resoultion algorithm or discourse analyzer
it is obvious that using the concept counting technique we have suggested so far a concept higher in the hierarchy tends to be more general
hence we feel that the concept counting paradigm is a robust method which can serve as a basis upon which to build an automated text summarization system
for example if a noun concept is selected we can find its accompanying verb if verb is selected we find its subject noun
underlying the use of word frequency is the assumption that the more a word is used in a text the more important it is in that text
as the amount of text available online keeps growing it becomes increasingly difficult for people to keep track of and locate the information of interest to them
the problem is that word counting method misses the important concepts behind those words vegetables fruit etc relates to groceries at the deeper level of semantics
and if in addition the text also mentions workstation and mainframe it is reasonable to say that the topic of the text is related to digital computer
we define the branch ratio threshold t t to serve as a cutoff point for the determination of interestingness i.e. the degree of generalization
NUM the weight of a sentence is equal to the sum of weights of parent concepts of words in the sentence
althonpsh many similarity measures have been studied two of them seem to have gained popularity in the recent literature the cosine and tf idf measures
the second is to select the words which are in the highest so many on each fist and not in the highest so many on the other fist
comparing these routing results to the classification results the question may be raised why the probability that a set is from a class needs to be calculated
in the example below we assume a multinomial distribution for our dice and fred the largest conditional probability of getting a certain output given a certain input
although we anticipate improvements to all of the methods through the use of phrases feedback term expansion and clustering these have not yet been implemented
note that this problem is the same problem that document retrieval systems have with doeuments of varying lengths longer documents are ranked lower than they should be
the weights for all of the distinguishing terms in a set are combined into a single value called the set weight
elementary statistics tells us that a maximum likelihood ratio test is the best way to calculate the probability that a set of outcomes was produced by a given input
for this example let us choose the words that are in the top NUM on each list and not in the top NUM on the other fist
future work with trec data will determine whether these are repeatable results or whether the small test data was particularly well tuned to the multinomial distribution method
each translated utterance is judged in three versions by different judges
in what follows we will often identify a pst node with its label
its english version was used for the named entity task ne in muc NUM NUM
nametag combines dynamic pattern recognition with static lexical look up to achieve high recall and precision at high speed
thus capitalization clue was not as relevant in spanish met texts as english wsj texts in recognizing proper names
thus the system can utilize the results of name recognition in the subsequent segmentation process and increase segmentation accuracy
the met final blind tests were conducted using NUM kyodo articles for japanese and NUM afp articles for spanish
in these cases the system often mistags the whole string as a person and misses the organization name
in these cases not generating aliases results in missing names i.e. loss in recall
this was advantageous for japanese because any entity recognized in the main body was utilized in segmentation of headlines
but it does not currently generate an alias which is a character subsequence of its full name like nikkou for n ihonkoukuu since aliases are by definition already recognized as names in a given article they often appear in contexts where patterns do not apply
the japanese system currently performs a limited organization alias recognition such as i d llke to thank mila ramos santacruz and misa miyaclfi who assisted me with manual tagging of development texts and lexical development for spanish and japanese respectively and kevin hausman who is the principal developer of the c nametag engine
this differs from sense induction using distributional similarity to partition word instances into clusters that may have no relation to standard sense partitions
the work reported here is the first to take advantage of this regularity in conjunction with separate models of local context for each word
the decision list algorithm resolves any conflicts by using only the single most reliable piece of evidence not a combination of all matching collocations
in a large corpus identify all examples of the given polysemous word storing their contexts as lines in an initially untagged training set
divide life into plant and animal kingdom close up studies of plant life and natural nissan car and truck plant in japan is
plant and animal llfe plant virus life cycle plant and animal kingdom plant and animal life plant life are delicately plant life
use a single defining collocate for each class remarkably good performance may be achieved by identifying a single defining collocate for each class e.g.
using the salient words of a dictionary definition as seeds increases the coverage of the concept space improving accuracy NUM NUM
by doing so he loses many important distinctions such as collocational distance word sequence and the existence of predicate argument relationships between words
sri is developing an architecture for learning rules by example where in a highly developed grammar a constrained version of the rule or metarule is generated from a user s annotations for relatively undeveloped grammars simple s v o patterns are recognized in annotations and corresponding variant patterns are generated
on both the named entity and coreference resolution tasks the fastus system was one of the top performers
the sri tipster phase ii program focused on supporting the development of an integrated architecture by helping to define the tipster architecture and improving the portability of data extraction applications by enabling users to define and tailor their own information needs
their contributions concerned input on the nature of basic entities such as documents and text segments and ways of communicating information from extraction modules to other modules in order to allow extraction and detection modules to work together
subsequent to this design work sri made their fastus information extraction system compliant with the tipster architecture and integrated it with the new mexico state university implementation of the architecture that was demonstrated at the tipster 12month workshop in may NUM
istic to distinguish them from each other becomes the rules used in the components and how these rules are implemented
we adopted the concept of discourse segment structure in gs86 to build up the constraint at segment beginning
the average matching rates for all test texts are NUM NUM and NUM
this indicates that the better rules seem to disagree with the speakers no more than the speakers disagree among themselves
in this paper we equipped each system with such a possible anaphor generation rule
in the following we use the above rule names to represent the systems
in brief the more sophisticated constraints a rule contains the better it performs
this shows the difference of concepts of salience used between the speakers and tr3
athe use of these rules enables us to investigate the effectiveness of individual constraints
a parameter is provided to specify the appearance of that expression in terms of slots that are allowed to be filled
the input to the system is a small set of seed words for a category and a representative text corpus
we collected only nouns under the assumption that most if not all true category members would be nouns3
in this dialog person a makes an initial presentation in line NUM
it aligned the joint venture frames and other frames from each system and then used various heuristics to combine the outputs frame by frame
it is not required that both participants mutually believe there is an error
would be viewed as a request for the clerk to clarify that plan
if a proposal is understood it is incorporated into the current plan
the fourth step calls the plan constructor to complete the partial plan
people are goal oriented and can plan courses of actions to achieve their goals
these speech actions are the building blocks that referring expressions are made from
bel agt prop agt believes that prop is true
similarly the transducer learned for word final stop devoicing would fail to perform devoicing when a word ends in two voiced stops as it too returns to its state NUM upon seeing a second voiced stop rather than staying in state NUM
therefore choosing the feature with the maximum information content can be done in o fk time where f is the number of features and the entire decision tree can be learned in o k NUM time
setting r deletion aside for the present a data set was constructed by applying the t insertion rule in NUM the t deletion rule in NUM and the flapping rule already seen in NUM one after another
two arcs are considered to have the same behavior if the same phonological features have changed between the input segment and the output segment that corresponds to it and if the preceding and following output segments of the two arcs are identical
in order to implement such a faithfulness bias in ostia our algorithm guesses the most probable segment to segment alignment between the input and output strings and uses this information to distribute the output symbols among the arcs of the initial tree transducer
to resolve conflicts between the output symbols for a given arc symbols may NUM no matter what alignment is used we are guaranteed that at least the correspondence learned will be some generalization that preserves the behavior of the training set
as a final example if the ostia algorithm is trained on cases of flapping in which the preceding environment is every stressed vowel but one the algorithm has no way of knowing that it can generalize the environment to all stressed vowels
suppose vo is the root oft for any vet we use v l andv r to denote its two sub nodes let tc be the set of all the nodes corresponding with the sense clusters we can get tc by clustering vo nlff calling the following procedure
table NUM estimating the parameters of g2 using the erf method
x1 x2 x3 x4 NUM NUM NUM NUM NUM NUM NUM NUM NUM
it should be the case that most values for disl cluw w will be smaller than a threshold but some will be bigger even close to NUM this is because most contexts in which the mono sense words occur would contain meaningful words for the senses while other contexts contain much noise and less words even no words in the contexts are meaningful for the senses
these are methods that have been developed for random fields
figure NUM illustrates how a dag is generated from g2
by parameter estimation we mean determining values for the weights ft
renormalization is necessary because of the failed derivations
but it can be taken as a useful first approximation
product of zero terms and hence has value NUM
because of lack of space here we can not consider non subcategorizable grammatical functions
ud undecidable agreed undecidable design error classification
however precision is no t a good yardstick for evaluating the performance of the induction process because it measures the outcome against a flawed lexicon the induced features because of the data driven nature of the process are more precise when measured against the real world of the sublanguage domain than the hand built entries that are the product mostly of introspection and anecdotal evidence
NUM given the fact that the presence of a feature is the result of a positive decision action by a linguist whereas the absence may be an oversight there should be it slight bias in favor of the former the sensitivity threshold can bc adjusted by shifting the point at which the weight of evidence is considered sufficient to decide in favor of adopting the feature
our theory uses the notions of structural closeness and textual closeness and takes both of these factors into account for argumentative discourse
when proverb opens a new attentional space the reader will be given information to post an open goal and the corresponding premises
instead of splitting presentation goals into subgoals they follow the local derivation relation to find a proof step to be presented next
in fact alternative classifications were only made in NUM cases
taken together the two ai proa hes rei resent a step owards providing uniformly al plical le treatments for differing languages thus lightening the burden for machine translation
both types mainly occur post verbally and often at the very end of a graphic sentence where it may be difficult to decide whether the concerned word is a predicative adjective or an adverb
neutralisation both analyses were regarded as equivalent
below are three quotations dealing with this matter
it is a carefully composed balanced corpus
linguistic indeterminacy as a source of errors in tagging
the evaluation of the results is far from trivial
in particular are the problems with undecidable cases treated
no spoken language material is included in the corpus
as has been pointed out for english material cf
in many languages like fr nch or japalmse the infornmtioll arried by will future or have perfect is realized mort hologically rather than i eriphrastically
the central innovation in the system is its approach to syntactic analysis which is now performed through a sequence of phrase finding rules that are processed by a simple interpreter
as such the work t resented here can NUM e seen a s a sma ll t ut necessary ste i towards th re alization of a road coverage grammar
although gorrell proposes a general principle to guide initial attachment decisions simplicity no vacuous structure building and specifies the conditions under which unconscious reanalysis may occur the model leaves unspecified the problem of how the system may be implemented
based on this segmentation pro verb makes reference decisions according to a discourse theory adapted from reichman for this special application
reasons that may still remain in the focus of attention at the current point from the structural perspective are considered as structurally close
n gram language modeling so that many of the NUM f NUM terms can be omitted beforehand
we would like to thank peter berck and anders green for their help with software for the experiments
some are more or less arguable e.g. the prepositional reading is preferred after a noun
the eventual result was that NUM NUM of the words in the corpus were tagged correctly
we can compute statistics about the relevance of features by looking at which features are good predictors of the class labels
note that no abstractions such as grammatical rules stochastic automata or decision trees are extracted from the examples
this is particularly the case with the ambiguity between past par null ticiples and adjectives
the size of corpus would have to be limited because it should be also checked
words not found in the lexicon are analyzed by a separate finite state transducer the guesser
getting a correct result for a particular sentence does not necessarily increase the overall success rate
we also investigated how the errors compare between the two taggers
sometimes a little bit of extra lexical information is required
for the constraint based tagger we set one month time limit for writing the constraints by hand
some of the constraints are almost NUM accurate some of them just describe tendencies
t s v to indicate substitution of s for v in t which specifically does not act to avoid accidental binding
one consequence of this difference is to allow them a more standard treatment of word order not requiring an enriched term labeling algebra
however the precise character of either chart or proof net based methods for parsing hybrid system grammars is a topic requiring further research
an analogy is often drawn between cg and dg based on equating categorial functors with heads whereby the arguments sought by a functor are seen as its dependents
the full processing model can then be either serial exploring the most highly ranked transitions first but allowing backtracking if the semantic plausibility of the current interpretation drops too low or ranked parallel exploring just the n paths ranked highest according to the transition probabilities and semantic plausibility
one possibility corresponds to the prediction of an s s modifier a second to the prediction of an np s np s modifier i.e. a verb phrase toodiffer a third to there being a function which takes the subject and the verb as separate arguments and the fourth corresponds to there being a function which requires an s np argument
the most important practical difference is that the differing directions of natural movement will tend to foster very different linguistic accounts
if wh arguments need to be treated specially anyway to deal with non peripheral extraction and if composition as a general rule is problematic this suggests we should perhaps return to grammars which use just application as a general operation but have a special treatment for wh arguments
however state prediction can also apply and can be instantiated in four ways these correspond to different ways of cutting up the left and right subcategorisation lists of the lexical entry likes i.e. as np NUM or NUM np
the general processing model therefore consists of transitions of the form syntactic type i syntactic typei NUM semantic repi semantic repi NUM 3this might turn out to be similar to one view of tree adjoining grammar where adjunction adds into a pre existing well formed tree structure
most parsers which work left to right along an input string can be described in terms of state transitions i.e. by rules which say how the current parsing state e.g. a stack of categories or a chart can be transformed by the next word into a new state
the domain of our system is restricted to points of interest to a traveling business person such as names and directions of business districts conference centers hotels money exchange restaurants
another problem with multilinguality is mixed language recognition
consequently the search result is less accurate
the same microphone was used to record both languages
note that although successor is normally a two place relation its valence here is one by virtue of the phraser not finding a named person as a subject to the clause
dealing with multilinguality in a spoken language query translator
null NUM how to deal with mixed language recognition
linear logic is an example of a resource sensitive logic requiring that each assumption resource is used precisely once in any deduction
of greater importance however is the fact that inference rules are the mechanism by which we instantiat e domain specific constraints and set up the particulars required for scenario level templates
these results provide for an efficient incremental linear deduction method that can be used with various labeling disciplines as a basis for parsing a range of type logical formalisms
the big NUM auto maker is attempting to regain market share
etc which appears immediately before the name alias in the text
used a larger lexicon than the basic configuration gershwin baseline
NUM as a single expression in accordance with the task guidelines
a scenario template st task captures domain and task specific information
system configuration of the sra system produced the same output as chopin base
figure NUM overall information extraction recall and precision on the st tas k
the in and out object contains st specific information that relates the event with the persons
udurham s use of a world model and sometimes not cf
performance on the vacancy reason and on the job slots was better for nearly all systems
rayner et al used this insight for a hierarchical non recursive grammar and only used their technique to prune after the first level of the grammar
NUM define square x x x NUM define square cont x cont x x NUM square display NUM thus whereas result values in a non cps program flow upwards in the procedure call tree in a cps program result values flow downwards in the procedure call tree
in fact my own observations suggest that with minor modifications such as the use of integers rather than lists to indicate string positions and vectors indexed by string positions rather than lists in the memoization routines an extremely efficient chart parser can be obtained from the code presented here
of course we could try to weaken this identity requirement e.g. by only requiring that fx and memo f x are identical when the reduction of the former terminates but it is not clear how to do this systematically
the expression recognize words is true iff words is a list of words that can be analyzed as an s i.e. if the empty string is a one of right string positions of an s whose left string position is the whole string to be recognized
poe eq car poe word continuation cdr poe NUM thus this formaliza on makes use of mutability to return final results and so can not be expressed in a purely func onal language
the apparent circularity in the definition of the functions corresponding to left recursive categories suggests that it may be worthwhile reformulating the recognition problem in such a way that the string position results are produced incrementally rather than in one fell swoop as in the formalization just described
for example epsilon recognizes the empty string i.e. it maps every string position NUM into the singleton set lcb NUM rcb opt fa recognizes an optional constituent and k f o recognizes zero or more occurrences of the substrings recognized by fa
the continuation passed to cps fn checks to see if each result of this evaluation is subsumed by some other result already produced for this entry if it is not it is pushed onto the results component of this entry and finally passed to each caller continuation associated with this entry
procedurally speaking it seems as if memoization is applying too late in the left recursive cases reasoning by analogy with earley deduction we need to construct an entry in the memo table when such a function is called not when the result of its evaluation is known
NUM thus rather than constructing a set of all the right string positions as in the previous encoding this encoding exploits the ability of the cps approach to return a value zero one or more times corresponding to the number of right string positions
the basic role of the edr corpus is first to identify the sentence constituents of sentences and then to indicate how the constituents combine to form the morphological syntactic and semantic structure of the sentence using a large number of actual examples
distributional tagging of an occurrence of a word w proceeds then by retrieving the four relevant context vectors right context vector of previous word left context vector of following word both context vectors of w concatenating them to one NUM component vector mapping this vector to NUM dimensions computing the correlations with the NUM cluster centroids and finally assigning the occurrence to the closest cluster
the concept classification dictionary contains the set of pairs of concepts that have super sub is a relation
they are now being utilized at many sites for both academic and commercial purposes table NUM
this chapter describes the roles of the major subdictionaryies of the edr electronic dictionary and shows some examples
finally we will give the outline of the new r d project which edr will launch in fiscal NUM
in the concept dictionary each concept is uniquely identified by a concept identifier which is a hexadecimal number
the sub concepts of school are elementary school university and so forth
every word dictionary record has a concci t identifier to link the word dictionary and the concept dictionary
information on the syntactic level includes parts of speech as well as surface case information and other grammatical attributes
if a first pass state at some time is unlikely then the analogous second pass state is probably also unlikely so we can threshold it out
NUM i old formula lcb i rcb xo y lcb j rcb new formula x c o y t r constraints lcb c lcb i rcb rr lcb j rcb c 7r rcb if
of course for our second pass to be more accurate it will probably be more complicated typically containing an increased number of nonterminals and productions
in the first case if we are currently above the goal entropy then we loosen our thresholds leading to slower speed and lower entropy
simr s chain recognition heuristic accepts non monotonic chains
later we will use p and n to distinguish between pronouns and nominal anaphora
we tested both algorithms on two test sets from this corpus
distributional as well syntactic knowledge is a crucial source of information for large scale similarity estimation among detected terms
lexicon perplexity indicates how sure a translation lexicon is about its contents
the large mrbd resulted in the most useful filter for this pair of languages
most lexicon entries are improved by just one or two filters after which more filtering gives
the only remaining question was what minimum lcsr value should indicate that two words are cognates
i used an appro mate string matching algorithm to capture a more general notion of cognateness
fortunately bible provided an objective criterion for tag set design and a fast evaluation method
otherwise correct translation pairs would be filtered out because of superficial differences like tense and capitalization
the evaluation was performed objectively and automatically using bitext based lexicon evaluation bible described below
all translation lexicons discussed in this paper were created and evaluated using the procedure in figure NUM
the other three knowledge sources have not previously been used for the task of inducing translation lexicons
on our NUM pc the systems differ greatly in speed
null c correct the translation is correct
second we ran the tests on all systems
but there are hardly any reports on comparative evaluations
our evaluation consisted of three steps
this also varies a great deal
personal translator has NUM subject areas
in our present study the aim of which is the comparison between the current and new versions of profet a test design similar to the one described in the two evaluation studies above will be used
the word lexicons unigrams and bigrams were created with the new lexicon creation algorithm from a union corpus of the NUM NUM word subset of the stockholm ume corpus suc NUM while awaiting the forthcoming NUM million word final version and a NUM million word conglomerate of electronic texts NUM including running text from newspapers legal documents novels adolescent literature and cookbooks
first of all a study conducted by a speech pathologist with a number of subjects will be presented
rather the goal was to promote coherent thinking in the writing process by demoting semantically incongruous word choices
in the testing of the spanish vaess version of profet savings were NUM NUM NUM NUM for texts with lengths of NUM NUM characters and the number of prediction suggestions set to NUM with the number of suggestions set to NUM the savings were NUM NUM NUM NUM
for instance the lower keystroke savings in swedish compared to english might be explained in part by the fact that compounding the formation of a new word i.e. string through the concatenation of two or more words is a highly productive word creation strategy in swedish but not in english
the cross language variations in the results could stem from several factors one undoubtedly being an unfortunate non reversible character conversion error for c which for danish resulted in predictions with the letter o and for norwegian no predictions for words with this character
the results are presented in table NUM where preds is the number of suggestions presented in the prediction window chars the number of characters in the text keys the number of keystrokes required with word prediction and saved the keystroke savings expressed as a percentage of the number of keystrokes that would have been required had word prediction not been used
NUM because the filled pauses such as urn un and er do not occur frequently in the spoken corpus the effects of filled pauses are not demonstrated in this paper
how many syllables are repeated in the repetition repairs is an interesting problem in cognition table NUM lists the distribution of length of the repeated syllable strings in the repetition repairs
that is some repeated syllable strings that do not satisfy the criteria of unfilled pause and glottal stop but they are usually repetition repairs
if two consecutive utterances are equal repetition repairs usually do not occur within and between them when the length of the utterances is long enough
in contrast to type i cue patterns another kind of patterns type ii cue patterns are also considered to increase the recall rate
addition replacement repairs have NUM NUM NUM NUM and NUM NUM NUM NUM in conversations l and NUM respectively
in human conversation most of the repetition repairs occur within an utterance or between two consecutive utterances of one speaker without interrupting by other speakers
the basic idea is to distinguish between language production activities that effect the global shift of attention and language production activities that involve only local attentional movement
therefore we conclude that the phrase structure for extraposition can not involve a hierarchiathese examples are less acceptable to speakers of northern variants of german
and since we failed to make adverbs off limits as new first names in this stage it decides that while mccann and one mccann note the capitalization are distinct persons
as expected the use of lexical categories had a major impact on the learning algorithm
we had to go back to original text to include those portions of the article header whic h were not processed and to recover from cases where the tokenizer had dropped characters despite our modifications
despite that couple hours estimate we would have to say that our greatest limiting factor wa s time time to test more thoroughly and isolate the causes of the biggest problems
this knowledge engineering error led to the worst recall or precision number on our overall ne results a precision o n timex of NUM avoiding that error would have raised it to NUM
at the sense cide guideword level with an average NUM senses per word the sense tagger was correct NUM of the time
lcb corresponds to the tactical component of a general natural language generation system nlg
figure NUM the generalized mrs of the string sandy gives a chair to kim
smoothening also especially in german particles often help to create an overall appropriate intonation contour and at the same time can serve to express cooperativity and politeness examples denn doch
this paper describes a working sense tagger which attempts to automatically link each word in a text corpus to its corresponding sense in a machinereadable dictionary
in many cases it would not be difficult to replace the corrected portion of an utterance with the portion that overwrites it thereby sparing the hearer from reworking the correction herself
in our current system an efficient chart based bidirectional parser is used for performing the training phase
six special intermediate tags have been created to reduce the number of tag pairs that need to be listed and to add partial parsing to the process
in that case templ is not inserted and the recursion stops at that branch
in this companion paper we show that f structures are just as easily interpretable as udrss
we pointed out that the particles investigated here have at least one reading in which the discourse usage is central to their usage and not semantic contribution to propositional content
there are three main processes involved in refining the tagger s performance refining the lexicographic data or indeed adding whole new categories of lexicographic data e.g.
for example we reason with date expressions to determine whether one date is a specification of another or a separate one which is sometimes important for disambiguation
in the next section we will show how to overcome even this kind of restriction
as for routine formulas they first of all cause the standard problems of idiomatic phrases they need to be recognized as a single unit of meaning so that they can be translated en bloc
lexical choice is a computationally complex task requiring a generation system to consider a potentially large number of mappings between concepts and words
for this reason its calculation is delayed until the last phase of generation when all information are gathered at the lexical node
in the verbmobil prototype that was completed last year a number of particles are considered ambiguous between scopal modal focusing adverb on the one hand and pragmatic adverb on the other
this presupposes lexical representations that adequately describe the possible variants of the expression e.g. whether additional modifiers may be inserted into a phrase etc
again literal translations should give way to conventionalized english formulas hence x no i wanted to say y is less felicitous than x no i meant y
in the example it specifies the two complements possessor and pos sessed each of which will eventually be realized as an np
in all these cases and many others the literal compositional meaning is not the point of using the phrase and they typically can not be translated word by word
it uses information automatically extracted from the mrd to find matches between the dictionary and the corpus sentences and combines different types of information by simple additive scores with manually set weightings
all the words in the dictionary covering the NUM sentences were manually tagged with lsp semantic word class labels
only the option option lines of the first choice box need to be adapted
in the future html files for an unrestricted and varying number of pdss will have to be produced
specific sql queries can then return the id number of the sentence with the relevant information instead of the information itself
the words marked belonging to the selected semantic sublanguage word class are displayed in boldface
semantic word class labels which were originally not foreseen in the dutch lexicon had to be added
on the other hand no advantage can then be taken from the sublanguage co occurrence patterns for semantic disambiguation
the fly conversion of the html file dynamic html code as presented in section NUM NUM
as he pxa ml le shows he morl hologi al
in general NUM of the dialogues in directive mode have no unusual transitions where we define unusual as a transition not described by our model
for example the percentage of all transitions out of the diagnosis phase that went to the assessment phase is NUM NUM in directive mode and NUM NUM in declarative mode
for example when one subject said the circuit is smith and gordon human computer dialogue working the speech recognizer returned the words faster it is working
we conducted a paired t test on the paired differences NUM in the average number of utterances spoken per dialogue between the two modes as a function of the problem number
computing this test statistic for the two subdialogue phases in the domain where we would expect additional experience to have the most effect assessment and diagnosis yields the following results
consequently we do not find that the order in which a subject was given the initiative has a significant effect on the number of utterances spoken in a given subdialogue phase
normally implemented dialogue systems tend to be based on processing models that are rich in domain information but are deficient in one or more areas of knowledge about dialogue
extending the model to describe dialogue structure at the more abstract level of task phases would allow the system to track the excessive and unusual subdialogue transitions observed in this study
the primary cause of these misunderstandings was the misrecognition of the words spoken by the user only NUM of the user s utterances were correctly recognized word for word
the net effect should be that user task control in declarative mode will lead to more frequent linguistic control shifts although the computer will still have overall control of most utterances
these differences can bring noise into categorization because training relies on similarity between training and test documents
a spin off of information retrieval known as text categorization shares a similar research interest
topic identification concerns a problem of predicting terms in text which indicate its subject or theme
let d e 9v d and h be a title associated with the document d
we divided each news article into a set of NUM word segments ordered according to their appearance
the test set was then divided into nine subsets of news articles according to the length
as a consequence an increase in text length results in a larger set of potential topics
one possible way out is to choose categories not from outside of the documents but from within
a text structure is identified by measuring the similarity between segments comprising the text and its title
now to adapt text categorization for use in topic identification requires a slight change in the former
it is a hard task to adapt wordnet subsets to pre existing categories especially when they are domain dependent
the features we use throughout the experiments are single words at the lemma level for nouns and verbs only with minimal frequency of NUM occurrences in the corpus
in both cases however whenever there is a need to update the weights all the weights are being updated actually n out of the 2n
documents that have been categorized by humans are usually used as training data for a text categorization system later on the trained system is used to categorize new documents
on one hand there are cases where a feature is more indicative to the relevance of the document to a category when it appears several times in a document
on the other hand in any long document there may be some random feature that is not significantly indicative to the current category although it repeats many times
thus the frequency of a feature throughout the data set for example can not be taken into account and we take into account only the if term
theoretical analyses of the winnow family of algorithms have predicted an exceptional ability to deal with large numbers of features and to adapt to new trends not seen during training
due to the bursty nature of term occurrence in documents as well as the variation in document length a feature may occur in a document more than once
the ipsim system receives as input goals to be proven and commands to start stop and furnish information
the algorithm is designed to guarantee that false information that may be entered into the tree will be eventually found
the phones were rep null resented by context independent continuousdensity hmms
we believe that the benefits will flow in the other direction as well and that a concomitant increase in system performance will follow as one applies the same mixed initiative development environment to the problem of domain specific tailoring of the language processing system
recent developments in the area of corpus based language processing systems indicate that the successful application of any system to a new task depends to a very large extent on the careful and frequent evaluation of the evolving system against training and test corpora
this paper describes a new set of integrated tools collectively called the alembic workbench that uses a mixed initiative approach to bootstrapping the manual tagging process with the goal of reducing the overhead associated with corpus development
these decisions are handled primarily by hand coded consolidation routines wrap up a trainabl e discourse analyzer designed to establish relational links between referents and resolve a trainable coreference analyzer
earlier work has shown that with the training dat obtained in the course of only a couple of hours of text annotation an information extraction system can be induced purely automatically that achieves a very competitive level of performance
as can be seen in these experiments there is a clear increase in the productivity as a function of both the user interface second column and the application of pre tagging rules third and fourth columns
derived pre tagging heuristics in the previous section we presented our approach to mixed initiative corpus development and tagging heuristics without assuming any sophistication on the part of the human user beyond a clear understanding of the information extraction task being addressed
when our system does not extract a new status due to low recall by domain specific cns this ca n cause wrap up to discard relevant persons and organizations further lowering recall
trainable technologies are valuable in the battle against the knowledge engineering bottleneck but we feel that it is important to provide adequate levels of training in order to realize their potential
the version of resolve used for te and st relied on a subset of its domain independent feature set rather than the larger domain enhanced featur e set used for the co task
unfortunately crystal s cn definitions offer little help with noun phrase analysis since they operate at a relatively coarse leve l of granularity with respect to a complex noun phrase
the knowledge resolve uses in order to learn to classify coreferent phrases is based on the same shallow knowledge used by ou r other system components
coreference co resolve is a coreference resolution system that uses machine learning techniques to determine coreferen t relationships among relevant phrases in a text
if the patterns used in this feature extractor were expanded to include for example is stepping down as then i t might have been more useful
here is a rundown of how the features at the top of resolve s decision tree were operating during th e complete walkthrough text alias yes was used for NUM instances
the right part of the diagram gives a schematic representation of the use of this transducer
the recognition rate of the speech recognizer on the spontaneous speech was NUM NUM
in general they include potential questions and statements about subtasks of the current task
in the interpretative process still others will be eliminated because of irrelevance to the situation
with regard to the extra dtrs pps have to precede sentences or relative clauses as stated in 36b
parallel examples exist for english null 1degnote that this is possible as the pmc is valid only for headed structures
another common assumption is that extraposition is not subject to the islands constraints that hold for extraction to the left
this is confirmed by the observation that fronted elements can be involved in multiple extraposition as in NUM
NUM nobody must live here who is earning more than twenty pounds a week
our account allows to explains the interaction of extraposition with fronting and coordination and predicts constraints on multiple extraposition
this ensures that extraposed elements originating from the same phrase axe sisters and hence can be ordered by lpcs
f our analysis predicts the asymmetry between extraposition from subjects and objects as found e.g. in coordination data
three of the four coders asked for clarification of the overview distinction which turned out to be a major source of unreliability there were no other queries
in both align and check moves the speaker tends to have an answer in mind and it is more natural to formulate them as yes no questions
in english phrases can be extraposed i.e. dislocated to the right boundary of a sentence
zcf keller NUM where we posited the s node as a fixed site for the binding of extraposed elements
for each category a simple sst was built its category sst csst
given a label l NUM picks out the transitive closure over sentential complements and their dependents
these systems accept speech and text input and are trained using an example based approach
the aligned candidates with the sequence are too light allow to select only one multi anchor tree whom be n1 chosen by
another issue this kind of utterance raises is whether or not a speculated ending time of the interval should be filled in using knowledge of how long meetings usually last
the word about was deleted so that neither of the first passes can span the entire sentence
in the first stage of the algorithm only english words which are tagged as nouns or proper nouns are used to match words in the chinese text
figure NUM analysis of give me more information the company recovery from an omission
its contribution to the unit production relation p x yi will then be p x y1 yi lyiyi l yj iieyk from the resulting revised pu matrix we compute the closure ru as usual
ihrntaking exchanges can be to initiate respond follow up or conduct a simultaneous response with initiation e.g.
a similar problem held true for the org alias slot
they were easy to hand craft and adapt to the muc NUM requirements
as more and more of the input is revealed the set of possible derivations each of which corresponds to a parse can either expand as new choices are introduced or shrink as a result of resolved ambiguities
we also redesigned the preprocess fro m the ground up
these rules operate principally by inspecting the morphology of words
these facts are literally just read out of the database
these cases are marked with double daggers tt
virtually all of the organizations found by alembic are recognized from first principles
as the result the process is halted up at the 45th step and NUM groups are obtained
pruning is formally straightforward in earley parsers in each state set rank states according to their values then remove those states with small probabilities compared to the current best candidate or simply those whose rank exceeds a given limit
we replaced our categorial grammar pseudo parser as suggested above
a model of temporal reference resolution in scheduling dialogs was presented which supports linear recency and has very good coverage and an algorithm based on the model was described
the former nuance communication recognizer system is constrained by a context free grammar
however our results appear at least comparable with those previously reported for an adult english vocabulary
words occurring only once are extremely hard to translate although our algorithm was able to find some pairs which occurred only once
similarly determiner choice will depend to some extent on the verb governing the noun phrase
for example a request template might have slots for requested action and for the requestee
it seems to be very difficult to achieve keystrokes savings much above NUM
however for our purposes we suspect that it is unnecessary to distinguish these uses
dempster s rule combines two mass distributions m NUM and m NUM to form a third distribution m NUM that represents the consensus of the original two distributions the new mass distribution in effect leans toward the areas of agreement between the original distributions and away from points of conflict
so we can deduce that r j should be a single chinese word corresponding to legislative council
traasfer to make fine grained distinctions between alternatives in cases where the semantic representations of source mid target lmlguage do not match up exactly
unknown strings are added to the database thus allowing them to be predicted subsequently
of course in this artificial language the z s actually carry no information
return lcb when merge upper tult tu certainty eel rcb rule a3 starting time case of anaphoric relation NUM
the robust parser needs con null fidence scoring module to point out inserted and substituted elements
the choices of segment size seed words and euclidean distance measure are all direct consequences of the atypical nature of the english english pilot test set
premise is ambiguous between an wide scope and a narrow scope reading of the indefinite np
since em based word alignment algorithms using random initialization can fall into local maxima our output can also be used to provide a better initializing basis for em methods
our new algorithm takes it one step further by backtracking to reconstruct the dtw paths and then automatically choosing the best points on these dtw paths as anchor points
between the two sets of data out of NUM anaphoric references there are fewer than NUM for which the immediately preceding time is not an appropriate antecedent
translation rules are applied to part of representations
the parser will select the right matching along the syntactico semantic operations thanks to expectations of substitution sites
figure NUM shows the overall architecture of profile and the two interfaces to it a user interface on the world wide web and an interface to a natural language generation system
for instance we would not label properly noun phrases such as rice university as it contains the word rice which can be categorized as a food
these representations are fed to crep which extracts noun phrases on either side of the entity either pre modifiers or appositions from the news corpus
in this paper we describe a system called profile that tracks prior references to a given entity by extracting descriptions for later use in summarization
we present a prototype system called profile which uses a client server architecture to extract noun phrase descriptions of entities such as people places and organizations
table NUM shows some examples of descriptions and the concepts under which they are classified based on the wordnet hypernyms for some trigger words
one of the more important current goals is to increase coverage of the system by providing interfaces to a large number of on line sources of news
the result is a system that can combine descriptions from articles appearing only a few minutes before the ones being summarized with descriptions from past news in a permanent record for future use
the content planner of a language generation system that needs to present an entity to the user that he has not seen previously might want to include some background information about it
the deeper representation allows for grammatical transformations such as aggregation e.g. president yeltsin president clinton can be generated as presidents yeltsin and clinton
for organization objects the challenge is greater requiring extraction of location description and identification of the type of organization
figure NUM the translation relation concerning give
to avoid a heavy combinatorial search directly operations to combine two adjacent jokers are not attempted
the negation works as negation by failure
consequently it should come as no surprise to see various kinds of theoretical generalization or summarization work in the literature
as the tokenization a bc d is not a subtokenization of any other possible tokenizations it fulfills the principle of maximum tokenization
what is unique here is our attempt to model sentence tokenization as the inverse problem of sentence generation
they both stop at merely representing possible tokenizations as a single large finite state diagram word graph
for the character string s fundsand there is bo fundsand lcb fund sand rcb
for the character string s theblueprint there is bd s lcb the blueprint rcb
for the character string s theblueprint there is sd s lcb the blueprint rcb
this algorithm is a special version of the greedy type implementation of the forward maximum tokenization and is still in active use
what needs to be undertaken now is to substitute something more precise for the principle of maximum tokenization
simr s chain recognition heuristic exploits these properties to decide which chains in the search rectangle might be tpc chains
in this manner simr circumvents the difficult problem of word identification in these languages
the smooth injective map recognizer simr bitext mapping algorithm advances the state of the art on several frontiers
groups of tpcs with a roughly linear arrangement in the bitext space are called chains
i am unable to determine whether suspectl6 is the murderer of lord dunsmore
NUM match pairs of positional difference vectors giving scores
although the paradigm of exploratory computational phonology is only in its infancy we believe our rule probability estimation algorithm to be a new and useful instance of the use of probabilistic techniques and spoken language corpora in computational linguistics
our algorithm is based on a multi layer perceptron mlp which is trained to compute the conditional probability of a phone given an acoustic feature vector for one frame together with NUM ms of surrounding context
is it the case that suspect16 is the murderer of lord dunsmore
NUM the final category of characteristics relates to the distance in the text between the expressions from which s and t were created which we categorize as being in one of five equivalence classes very close close mid distance far away and very far away
the probability of coreference between templates situated similarly to a and d may be NUM NUM with respect to all contexts in the training data however it is almost certainly not this high with respect to the subset of cases in which a template similar to c is similarly situated
in the near future nametag will continue to improve its coverage accuracy and speed
nametag also classified creative artists agency as government due to the organizational head noun agency
this section reports on various other test results that fall outside of the official muc NUM tests
the next three runs use one half three quarters and all of the egraphs respectively
one possibility is to automatically derive other egraphs from thos e that have been encoded manually
currently sra is focusing on the issues of speed robustness portability and trainability
in figure NUM the extracted concept is called succession representing a management succession event
the semantic labels org in and post are attached to the appropriate structural elements
post chief executive officer multiple references to the same named entity e.g.
generation the generator applies an output script to the collector representations to produce the data templates
assume again that e lcb NUM NUM rcb
cycles might emerge to treat unknown sequences of words i.e.
so as we can see the surface speech actions for clarifications operate on components of the plan that is being built namely the surface speech actions of referring expression plans
finally the fifth step is the surface speech action s actions which is used to inform the hearer of the surface speech actions that are being added to the referring expression plan
a context free grarnxrmr is represented as a definite clause specification as follows
the predicate side effect is used to construct the parse forest grammar
figure NUM a parse tree extracted from the parse forest grammar
clearly there are pcp s that do not have a solution
peter a heeman and graeme hirst collaborating on referring expressions goal system bel user bel system error p1 p22 NUM
the last part of the condition states that if the speaker s referring expression was successful from the beginning no collaboration is NUM all variables mentioned in the rules are existentially quantified
third we have accounted for collaborative activity by proposing that agents are in a certain mental state that includes a goal a plan that they are currently considering and intentions
thus the techniques for robust processing that give rise to such cycles can not be used
when a speaker produces an utterance as long as the hearer finds it coherent he can add a belief that the speaker has made the utterance to accomplish some communicative goal
for the refashioning plans we propose that there is a single surface speech action s actions that is used for both replacing a part of a plan and expanding it
after a plan has been contributed to the conversation by way of its surface speech actions the speaker and hearer update their beliefs to reflect the contribution that has been made
since both conversants expect the other to behave in this way each judgment and refashioning so long as they are understood results in the judgment or refashioning being mutually believed
we investigate performing temporal reference resolution directly without also attempting to recognize discourse structure or intentions
thus tu2 can not be simply thrown away or ignored once we are done interpreting tus
therefore two tasks were performed to aid in developing the analysis presented in section NUM
the rightmost column shows that there is a small amount of error in the input representation
so the time complexity of the algorithm is o ns
figure NUM depicts rightward complete sequence for an m
we also compute the feasible feature structures of sublexicon i to be
r1 and r2 sanction the first and third consonants respectively
finally section NUM provides an evaluation and some concluding remarks
for each a k we compute the new left context
this is achieved by modifying eq NUM as follows
the methods of automatically identifying and extracting uninterrupted and interrupted collocations from very large corpora has been proposed
this method has made it possible to automatically and quickly extract and tabulate substrings of any length used in source texts
but when a single sentence includes other sentences the extraction of the combinations in units of sentences poses complications
in contrast in the method proposed in this paper these numbers have reduced to NUM and NUM respectively
then all of the combinations of k ness for every sentence are written down into a file and sorted
NUM all fields other than the four sentence numbers esn nsc and rn are deleted
the records of pt NUM are sorted in the order of corresponding string words to obtain spt o sorted pointer table NUM
in this method combinations of uninterrupted collocational substrings which collocate at different positions within a sentence are extracted and counted
phrase translations or pattern translations based on phrase or pattern dictionaries are considered very useful for the translations of these expressions
but should such substring NUM be located in a separate or overlap position it is to be extracted
this data run is called the experimental run
as mentioned in weischedel et al NUM the best performing system at the second message understanding conference muc NUM simply halted parsing when an unknown word was encountered
their definition is non standard for instance all prepositional phrases whether complement or not are left unattached
finally we have no special rule to prohibit articles and possessives from appearing in the same noun phrase but the bigram the his is so awful that the null article is always selected in the presence of a possessive pronoun
note that the sample sentences shown for the random extraction model are not of the quality that would normally be expected from a knowledge based generator because of the high degree of ambiguity unspecified features in our semantic input
this incompleteness can be in turn attributed in part to the lack of such information in japanese source text and in part to our own desire to find out how much of the ambiguity can be automatically resolved with our statistical model
subsidiary on an japan s of perkin elmer co s hold a stocks s majority and as for a beginnings productia of an stepper and an dry etching devices which were applied for an constructia of microcircuit microchip was planed
the latter smoothing operation not only optimally regresses the probabilities of seen n grams but also assigns a non zero probability to all unseen n grams which depends on how likely their component m grams m n i.e. words and bigrams are
these features were relevant to the semantic representation but their values were not extractable from the japanese sentence and thus each of their combinations corresponded to a particular interpretation among the many possible in the presence of incompleteness in the semantic input
the alternative of randomized decisions offers increased paraphrasing power but also the risk of producing some non fluent expressions we could generate sentences like the dog chased a cat and a dog will chase the cat but also an earth circles a sun
for example the selection of a word from a pair of frequently co occurring adjacent words will automatically create a strong bias for the selection of the other member of the pair if the latter is compatible with the semantic concept being lexicalized
in contrast in bottom up parsing and in our generation model a special data structure a chart or a lattice respectively is used to efficiently encode multiple analyses and to allow structure sharing between many alternatives eliminating repeated search
NUM how to identify such islands is an important problem in nlg grammatical rules e.g. agreement may help group words together and collocational knowledge can also mark the boundaries of some lexical islands e.g. nominal compounds
shallow parsing techniques are used to collect training and test data from a text corpus
we have shown that morphological recognition the distinction between closed class and open class words and syntactic knowledge are powerful tools in hz ndling unknown words especially when we use a post mortem method of determining the probable lexical classes of words
such goals or argumentative intent are used by the content planner in reasoning about what information to include
the only remaining option is to position the lexical choice module between the content planner and the syntactic realization module
NUM both the input and the output of a fuf program are feature structures called functional descriptions fds
while syntagmafic decisions may seem to be more syntactic in nature they are directly intertwined with various lexical choices
note NUM in figure NUM and the syntactic structure of a clause is constructed
the left hand side shows the conceptual structure that is the input to the lexical chooser
this may lead to the decision to refer to the same situation as a glass half full or half empty
the lexicon the choice of one word can constrain the choice of other words in a sentence
oanalyze relationships among segments and fragments
the analyst interface process csci displays a summary list of the named entities associated with the selected document
otherwise anew record containing the information is added to the relations and connected to the named entity
extraction and reference resolution are the nltoolset functions that glue all individual pieces together to create the entities
additionally the process operates on address earlties and number entities and connects these to the named entities
the annotations for this document are placed into relational records in the canis server sql server
the document manager conforms to the concepts and specifications of the tipster phase ii architecture design document version NUM NUM
the canis application is pc based and using the microsoft visual c compiler and visual basic on the pc
the nltoolset currently runs on sun microsystem s un x based platforms and pcs using microsoft windows nt
null NUM increase the weights of the labels more compat null ible with the context support greater than NUM and decrease those of the less compatible labels support less than NUM NUM using the updating function null
labels are proposed intuitively or by trial and error
one text is from an article about aids another concerns brainwashing techniques the third describes guerilla warfare tactics the fourth addresses the assassination of j f kennedy the last is an extract from a speech by noam chomsky
resolution of this ambiguity will be crucial for translation
once relations between prosody and speech acts have been extracted from corpora labeled with speech act information researchers can attempt to supply natural prosody for synthesized utterances according to the specified speech acts
this experiment tested the reliability of assigning grammatical functions given the category of the phrase and the daughter nodes supplied by the annotator
cas were first identified according to monolingual criteria
the semantic tokens are obtained from standard on line thesaura
two versions of the morpheme network for the estimations were used one limited by a cost width of NUM fig NUM and the other by a cost width of NUM figs
the applicability semantics can be viewed as an evaluation of the semantic instruction associated with the top syntactic node in the tree description
i and ii thus allow for the input to come from a module that need not have linguistic knowledge
the syntactic theory also affects the processing we have augmented the syntactic operations to account for the integration of the semantics
the syntactic coverage of the generator is influenced by the xtag system the first version of protector in fact used tags
in the area of grammar development tag has been the basis of one of the largest grammars developed for english NUM
uppersem can be the minimum information that necessarily has to be conveyed in order for the generator to achieve the initial communicative intentions
we build the corresponding semantics of the generated sentence and aim for it to be as close as possible to the input semantics
an utterance path is the sequence of nodes and arcs that are traversed in the process of mapping a graph to a sentence
the quantifier a is chosen for c and the candidate set lcb rl r22 rcb is returned
posed d tree NUM results from the addition to j3 of v as a new leftmost or rightmost sub d tree below NUM
the generator never produces sentences with semantics which is more specific than the lower semantic bound which gives some degree of coherence
the algorithm also handles models containing more relationships than model i to generate sentences of the form
the mapping from partitioned dependency f mction to quantifiers is non deterministic as NUM shows
the partitions 12a c are among the possible partitions of dependency function i NUM
when ur is high it is natural that the topic becomes inestimable
the estimation of topic becomes clltticult with two factors cs and ur
our question is to extract such subgraphs of topics from a co occurrence graph
english words referred as examples will be w itten in this font
prefer redeem redemption repay tidewater the threshold should be lower for this topic
the reason is that the two words in different topics do not co occur
they are merged into cluster NUM when the threshold is lowered to NUM NUM
an output subgraph of higher threshold is included as that of lower threshold
however even f the lattice is large only a fraction of the nodes will be relevant as possible constraints for a particular joint distributions NUM x y since most of the nodes will have zero or very small feature count
because of the decompositional nature of the maximum entropy model it can act as a back off model too overlapping simpler features naturally coexist with more complex ones and the weights of the complex features are just the excess on which they different from their constituent simpler features
the axis of ordinates describes the number of anchor branches m
the former gives clusters by decomposing the input graph by detecting duplicate branches
NUM sl has mistaken an instance of act aintended as an instance of act aobserved NUM a reconstruction of the discourse is possible NUM sl would expect to do ar ply in this reconstruction and NUM s may perform a fourth turn repair
NUM sl wants speaker s2 to do action a2 NUM sl would expect a2 to follow an action al and NUM sl may adopt the plan of performing al to trigger a2 i.e. the linguistic intentions of al are compatible with ts
intend m knowref r wholsgoing the linguistic intentions of inform not knowref are and not knowref m whoisgoing intend m knowif r not knowref m whoisgoing
t1 m pretell m r whoisgoing t2 r askref r m whoisgoing t3 m inform m r not knowref m whoisgoing t4 r informref r m whoisgoing
although t2 might also be explained by abducing that russ misunderstood t1 as an attempted pretelling we see that she considers this explanation to be less likely because otherwise she would have been more inclined to make t3 a third turn repair no i m asking you
a default can be given either by default p d or default p d w where p is a priority value d is an atomic formula with only free variables as arguments and w is a wff
if NUM s2 has apparently mistaken an instance of act aintended for act aobserved NUM sl would expect arepty to follow aintended and NUM sl may perform a third turn repair i.e. it would be reasonable and compatible for s2 to perform areply
NUM the function not is distinct from the boolean connective NUM we use it to capture the supposition expressed by an agent who says something negative e.g. i do not want to go which might be represented as inform s h not wanttogo rcb
that given a discourse context ts NUM has performed the discourse level act a discourse level acts are related to surface level acts by the following default default NUM pickform sl s2 asurfaceform a ts NUM decomp asurfaceform a a try s1 s2 a ts d utter s1 s2 asurfaceform ts
NUM another category is associated with the existence of the diaeresis mark on a vowel of either a candidate diphthong or an excessive diphthong
the automatic identification of these instances would be based on a morphological analysis of words a process beyond the scope of the present analysis
x2 shows an unusual way of bracketing basically mr james NUM years old is produced instead of the expected mr james NUM years old
since the ne and co scorers take notice of exact position s within the text they were confused by these additional symbols and many of the markups after the firs t occurrence of these additional quotes were scored as incorrect
furthermore this process is used on titles the grammar of article titles is quite different from that for normal text so we avoided full analysis of titles and joined title textrefs to nodes when a surface match was found
using the general template facility the organization template and the person template ar e defined as event based templates since it is possible to find a clear underlying concept person or organization from which to produce a template
although it might help to produce named entity results directly from parser output it woul d not help with other tasks and applications in which it is important that the named entities are treate d correctly by the whole analysis
specialisation links a set to one possible subset for example in figure NUM chairman u represents the set of all possible chairmen and old chairman u the set of all possible old chairmen
events can have other arcs such as those indicating temporal information the status of the information e g known fact hypothesis etc or arcs that indicate the source of the information
tense information about the phrase is added from the features fs and an internal event built from th e roles collected this links gottesman with the concept of the individual who is an analyst with painewebber
a similar stage was added to help unify certain occurrences of proper names cases such as faa and federal aviation authority and abbreviated forms such as panam and pan american
as is to be expected in a system of lolita s size and complexity we see the effects of several small bugs in the analysis which obscure the potential scores witness our recent improvement in the walk through article
in similar fashion in an application environment integrating annotation modules from different suppliers it would be desirable to record the source of particular annotations using an annotator attribute
for example the implementation could define a file segment as a portion of a file with start and end positions and support operations for creating a bytesequence from a file segment
the minimal requirement for an implementation of the architecture is to be able to obtain the length of a bytesequence and to convert between a bytesequence and a string
NUM as noted earlier new sources of data will need to be converted by the application into collections of documents before they can be processed within the tipster architecture
a complementary operation readsgml reads a sgml document which conforms to this format with all attributes and end tags explicit and creates a document with annotations
fuzzy match means that a document may be returned if it lacks one or more arguments but the document is presumably ranked lower than documents that match all arguments
furthermore if that value is true the entry may also have one or more annotations of type relevant section whose spans indicate the relevant sections of the document
this operation is shown as retrieve documents NUM in the figure below the documentcollectionlndex input is not shown and produces a collection
the extractionneed would serve as the starting point for customization which would be performed by the analyst using an interactive customization tool and drawing upon the template object library
however it may be desirable to record this structure directly through a constituents attribute whose value is a sequence of annotations representing the immediate constituents of the initial annotation
we assume the existence of two corpora c l and c rl
therefore and unsurprisingly the bulk of disagreement lies somewhere in between
each document to he classified is processed the same as the training sets are up to the selection of distinguishing terms the header information is removed remaining words are separated at blank spaces onto individual flues and stemming is performed to remove embedded sgml syntax possessives punctuation and many suffixes
we can see that now the rankings are as we expect set NUM is the output most likely to have been created with the fair die and set NUM the least and set NUM is the output most likely to have been created with the loaded die and set NUM the least
the first type is nonterminal transition in which state identification is pushed into the stack
the re estimation algorithm presented in this paper may be seen as another version for general cfg
network used to model and process context free languages in stochastic parameters
figure NUM shows the network configuration in computing the outside probability
probability of output the most likely die to produce each output is the one with the maximum probability
the information of valid categories is useful when the chart is used in computing outside probabilities
states at which pop transitions are defined are called pop states
layer s returns the layer state s belongs to
in general the grammar expressed in prtn consists of layers
there have been attempts to associate probabilities with context free grammar formalisms
the top eight adjectives in the table say very little about the nouns that they might modify
in contrast the two languages have different rules for comas and colons especially around quotations
it applies these rules to unknown words to tag them with the appropriate part of speech information
the verb with the highest semantic entropy by far is the functional verb place holder do
in contrast pattern matching systems assemble structure bottomup and only in the face of compelling syntactic or semantic evidence in a nearly deterministic manner
see the previous section for more discussion about weaker forms of speech acts
the ldoce defining text has roughly half a million words in its NUM entries which is half the size of the brown corpus used in the current experiment
however modelling the dependency between different concepts in different contexts will lead to an explosion of the complexity of the model
the first stages deal primarily with name recognition people s names organization names geographic names and names of executive positions executive vice president for recall and precision
it would require us to organize the grammar in such a way that limited additions could be made by non specialists without having to understand the entire grammar again not a simple task
when such a pattern is matched a corresponding event structure is generated recording the type of event for this scenario hiring or firing and the people and companies involved
after such a move the conversants will believe it mutually believed that the speaker has a replacement newplan for the current plan plan
the next stage of processing is reference resolution
in many cases semantic coherence information is not adequate to select the correct sense and knowledge about local constraints is needed
each conceptual set corresponds to a sense in the entry and contains all the defining concepts which occur in the definition of the sense
contains a subject and a verb and in writing begins with a capital letter and ends with one of the marks
since the training data is not sense tagged the data collected will contain noise due to spurious senses of polysemous words
secondly and more importantly the two corpora which are also the test corpora are very different in genre
if the headword of an entry is a defining concept dc the conceptual expansion is given as lcb lcb dc rcb rcb
figure NUM sample discourse structure NUM but the other
the experimental results show that the precision rate of NUM NUM and the recall rate of NUM NUM can be achieved
in table NUM the repetition repairs form the majority NUM the speech repairs discussed in this paper are all self repairs
ds NUM NUM tuesday i have a class from NUM NUM NUM NUM
typical examples are pronouns such as wo3 i and g ni3 you
after the unfilled pause information is added to the baseline model the experimental results for two conversations are listed below
because repairs introduce much noise direct application of this method without repair processing is expected to have worse performance s
at the same time NUM of errors in the repairing segment can be reduced for the chinese homophone disambiguation
thus chinese homophone disambiguation is difficult but important in a chinese phonetic input method and a chinese speech recognition system
argmax p cis c p sic p c argmax c p s
the paper gives a description of the dialog theory presents examples of its capabilities and includes a detailed trace of one of those examples showing all significant mechanisms
in this case it looks for so called missing axioms which would help complete the proof and it engages in dialog to try to acquire them
the theory of user modeling thus is simply to specify user capabilities in the prolog style rules and let the natural execution of ipsim select what to say or not say
since all interactions occur in the context of a current subdialog the user s input is far more predictable than would be indicated by a general grammar for english
he or she may respond with a request for a clarifcation such as where is the switch or with an unanticipated comment such as there is no wire connected to terminal NUM
standard tst achieved NUM NUM accuracy while extended tst achieved NUM NUM
this leads to the missing axiom theory we describe for processing discourse and some rather simple mechanisms for employing the user model for managing multiple subdialogs and for creating and using expectation
this technique has been encoded into our refashioning plans and so can be used for both constructing repairs and inferring how another agent has repaired a plan
this time the algorithm precisely located NUM of the boundaries
extraction systems are still best extended and modified by the system developers themselves or by individuals who have received significant training
technique for performing volumetric measurements is a type NUM variant of measurement technique
reducing the ambiguity of part of speech tags eliminates ambiguity in local parsing
furthermore part of speech ambiguity resolution permits construction of correct derivational links
so the effect of the refashioning plans is that the hearer will believe that the speaker wants the new referring expression plan to replace the current one
awe use unix regular expression symbols for rules and transformations
kidney function is a type NUM variant of renal function
the syntactic structure of the term is also modified e.g.
the compatibility between each of the linguistic intentions of a proposed action and each of the active suppositions in a context is captured by the predicate lintentionsok which is true if and only if none of the incompatibilities described in section NUM NUM NUM hold
in order to balance completeness and accuracy expansions are limited
in this case russ finds that the former type of explanation is possible using the metaplan for plan adoption to explain shouldtry m r pretell m r whoisgoing ts NUM
the last rule involves an inference that is not shared
note that the consequent has an unbound variable newplan
for the examples considered here any model of belief would suffice for simplicity we chose to include beliefs and goals explicitly in the initial background theory and allow agents to make assumptions about each other s beliefs and goals by default
in the coreference set containing templates a b c and d system knowledge external to the probabilistic model indicates that the type ammunition in template c is not compatible with the type rail in a and b therefore these are taken a priori to be non coreferential
conversely when an agent says something that is inconsistent with another s expectations then the other agent may change her interpretation of an earlier turn and direct her response to the reinterpretation accomplishing what is known as a fourth turn repair
hearer agt agt is the current hearer
neither is controlling the dialog they are simply collaborating
in section NUM we chose not to consider functional heads as head corners
this process is organized into three steps linguistic extraction
every utterance is numbered and labeled the labels indicate speakers
gt builds the vp before other projections
this is a very time consuming task that should be automated
artificial communication languages have been designed for human discourse e.g.
the server must analyze human generated text and verbalize machine initiated goals
the former extracts appointment related information from users input texts
further development of cos ma into an industrial prototype is envisaged
we will get NUM possible tag sequences solely for seg NUM in the sentence NUM fig NUM
otherwise the clarification subdialogue goes on along the same lines
zu folgenden zeiten geht es bei mir am NUM
branches are associated with rules for combining the semantics of the subtrees
for each category the first row shows the percentage of phrases belongi lg to a specific category according to manual zsignment and the percentage of correct assignments
whereas local statistics to those derived from the article in which the input sentence stands like a chche
the preliminary open tests show that the segmentation precision of cseg tagl NUM is about NUM NUM NUM NUM pos tagging precision about NUM NUM NUM NUM and the recall and precision for unknown words are ranging from NUM NUM to NUM NUM and from NUM NUM to NUM NUM respectively
based on the per word precision and recall we define the average precision resp
it is also desirable to know how the neighboring characters for an n gram is distributed
word identification performance for the vtw vtt and vtw tcc vtt topologies seed NUM sentences
the performance of the postfiltering model is shown in columns NUM NUM of table NUM
note that the words in the seed corpus are always included in the candidate list
the re estimated probabilities are acquired from both the seed corpus and the highest scored tagging results
the tagset used in this dictionary contains NUM tags including two punctuation tags
therefore such missing tags will introduce some tag extracting errors in the training processes
similarly the second statement delays the principles on phrase until the subcat information is known
we therefore allow the user to name a principle and supply it with a specific delay
delay principlel or even delay deterministic principlel if that is appropriate
we showed how such an architecture facilitates the modular and compact encoding of principle based grammars
we feel that this improves on earlier purely definite clause based approaches
we will start out by illustrating our architecture with an example
sations can be expressed in a more compact and modular way
speculative computation may thus be reduced to a necessary minimum
in the minimalist program movement occurs to check features
another example is the english to chinese head transducer for noun phrase dependency relations shown in figure NUM typical target positions for transitions corresponding to noun phrase modification noun phrases are head final in chinese are as follows the position for transitions emitting the chinese particle pronounced de may be either NUM NUM or NUM depending on the transducer states for the transition
in this paper we describe the head transducer model used for translation in an experimental english to mandarin speech translation system
since e is meant to be treated as a generic placeholder for any arbitrary z of the proper type c must not appear in any terms instantiated for logic variables during the proof of c z g
o w o w shown below
distance d x y is used to measure such effects
the implementation of coordination crucially uses the capability of aprolog for universal quantification in the goal of a clause pi is the meta level operator for v and vz m is written as pi x l
lob corpus are separated into two parts whose volume ratio is NUM NUM
figure NUM shows correct rate error rate and undecidable rate
thus the formulae idf and anv ann are complementary
some of them are listed below in descending order by the strength
the present state of the gospel is the result of an accident prone history
the topics in this example are problem and dislocation
the distance is measured by the difference between cardinal numbers of two words
the postulation NUM could be also observed from the above example
these are the basic metrics calculated for all tasks
the coreference scorer is still in emacslisp
any legitimate bnf can be handled
missing the number of possible fills for which there was no response
a key object may be similar to more than one response object
with extra linguistic information the quality of translation will be improved and the problem of translation mismatches can be solved
as described in section NUM NUM the natural course of transition from subdialogue to subdialogue is described by the following regular expression i a d r t nf where n represents the number of individual repairs in the problem i.e. number of missing wires in our domain
different languages realize the same concept using varying numbers of words for example a single english word may surface as a compound in french
this complicates the problem of matching the words between a sentence pair since it means that compounds or collocations must sometimes be treated as lexical units
for more complex examples the transcripts of the dialogues collected during the experiment are available by anonymous ftp
table NUM shows the classification into the various subdialogues of the utterances from the sample dialogues of figure NUM
her attempts to yield the initiative to users still led to statements that guided users step by step through the task
in the directive mode dialogue the subject is performing task goals under the close guidance of the computer
how will this change when the computer operates in declarative mode and control is given back to the user
to see how itgs maintain needed flexibility consider figure NUM which shows all NUM possible complete matchings between two constituents of length four each
we have introduced a new formalism the inversion transduction grammar and surveyed a variety of its applications to extracting linguistic information from parallel corpora
inspection revealed that performance was greatly hampered by our noisy translation lexicon which was automatically learned it could be manually post edited to reduce errors
in this paper we introduce a system
it is a representation like that produced by a procedural planner containing the procedural hierarchy of the process being expressed as well as some information about the lexical items used to express each action and its arguments
imagene s system network is built in a similar manner but because it constructs text structures rather than sentences its realization statements have a flavor significantly different from their counterparts in the grammar developed for penman
the representation makes use of five relations purpose precondition result sequence and concurrent which are used as abstractions to identify the lexical and grammatical manifestations of the procedural relations inherent in the process
the fact that the global purpose feature is required for entry to the purpose tnf system as well as the input conditions represented normally in the figure is indicated with an arrow pointing to the additional input conditions
the second to place calls on the other hand is expressed in final position as a to infinitive with its sub action stated as an imperative return to seat
the problem to be addressed by the corpus analysis in step NUM is to determine the contextual features used to choose these forms as opposed to the alternate forms that could have expressed the same basic information
such networks are traversed based on the appropriate features of the communicative context and as a side effect of this traversal linguistic structures are constructed by realization statements that are associated with each feature of the network
its construction required a considerable amount of analysis of sample texts but unfortunately very little is said about how this analysis was actually performed and how well the text produced by pauline matches the text in the corpus
the first is stated as an imperative remove the phone with the sub actions expressed in participial form within a by prepositional phrase by firmly grasping top of handset and pulling out
furthermore any attempted solutions to these problems must be capable of operating at a speed close enough to real time that users are not faced with unacceptable delays
spelling variation is clearest in cases where an english word like swiieh shows up transliterated variously c NUM c x NUM NUM in different dictionaries
other forms of ellipsis other forms of eb lipsis besides vp ellipsis can be handled substitutionally
neither kamp nor kehler extend their copying substitution mechanism to anything besides pronouns as we have done
the only criterion that we have at the present time is a qualitative one that is the usefulness of the results of the clustering methods for a ke building a conceptual model
we represent the elliptical sentence again abbreviated as a partially resolved qlf
the reason that substitutions are not applied immediately upon ellipsis resolution is as follows
a simple uninteresting example to fix some notation null NUM john slept
this is reflected in the order sensitive interleaving of scope and ellipsis resolution in dsp s account
but the ellipsis substitution overrides this substituting a new term and index fa
a partially instantiated qlf therefore effectively specifies a set of possible evaluations or semantic compositions
as this example illustrates tense and aspect on ellipsis and antecedent do not have to agree
we performed NUM runs of the learning program each using NUM of the NUM training narratives for that run s training set for learning the tree and the remaining narrative for testing
the learned semantic categorization of the adjectives can also be used in the reverse direction to help in interpreting the conjunctions they participate
we compute the average frequency of the words in each group expecting the group with higher average frequency to contain the positive terms
we constructed this set by taking all adjectives appearing in our corpus NUM times or more then removing adjectives that have no orientation
after presenting our results and evaluation we discuss simulation experiments that show how our method performs under different conditions of sparseness of data
thus the simulation measures only how well the system separates positive from negative adjectives not how well it determines which is which
we next validate our hypothesis that conjunctions constrain the orientation of conjoined adjectives and then describe the remaining three steps of the algorithm
however we can improve performance by noting that conjunctions using but exhibit the opposite pattern usually involving adjectives of different orientations
we have also provided for constituent order and stylistic variations within noun phrases based on certain emphasis and formality features
as reported in passonneau and litman to appear we also evaluated a simple additive method for combining algorithms in which a boundary is proposed if each separate algorithm proposes a boundary
each algorithm np a cue a pause a was designed to replicate the subjects segmentation task break up a narrative into contiguous segments with segment breaks falling between prosodic phrases
though these cover diverse text sorts viz
passonneau suggests that the centering data structures need to be modified appropriately while walker concludes that the local centering data should be left as they are and further be complemented by a cache mechanism
in the latter case the theme changes in each utterance from handbuch manual via inhaltsverzeichnis table of contents to kapitel chapter etc
if these utterances contained the expression NUM itself the algorithm would have built a different discourse structure and therefore NUM in u10 were reachable for the anaphor in ulz
no wonder beneath the table of contents one finds the terse instruction one should oneself the pages of this section please from disk print out impertinence
if ui NUM and ui indicate the end of a sequence in which a series of thematizations of rhemes have occurred all embedded segments are lifted by the function lift to a higher level s
though we fed the centered segmentation algorithm with rather long texts up to NUM utterances the antecedents of only two anaphoric expressions had to bridge a hierarchical distance of more than NUM levels
sg4 provide clear and comprehensible communication of what the system can and can not do
intuitively this is not surprising we designed the experiment to yield a predominance of domain specific terms by means of the mrd and hansards filters
sable scalable architecture for bilingual lexicography is a turn key system for producing clean broad coverage translation lexicons from raw unaligned parallel texts bitexts
on the 2nd plateau or higher NUM entries passed both the collins and the hansard filters NUM remained on or above the 3rd plateau
neither of the translation lexicon construction modules pay any attention to word order so they work equally well for language pairs with different word order
with the exception of NUM these values of n indicate that the reliability of the judgments is generally reasonable albeit not entirely beyond debate
comparison with a manual short word segmentation of the set of NUM trec NUM queries shows that we achieve NUM NUM recall and NUM precision on average
the iterative filtering module then alternates between estimating the most likely translations among word tokens in the bitext and estimating the most likely translations between word types
for example to have protection in english is often translated as tre prot6g6 in canadian parliamentary proceedings so for that domain the pair protection prot6g6 would be marked p
its design is modular and minimizes the need for language specific components with no dependence on genre or word order similarity nor sentence boundaries or other anchors in the input
we try to find the best representations of these features and the best ways to match them
in our new algorithm we use a similar positional difference vector representation and dtw matching techniques
for example if positional difference vectors for the word governor and its translation in chinese
note that the two signals are shifted and warped versions of each other with some minor noise
however our previous algorithm only used the dtw score for finding the most correlated word pairs
each filter is based on a particular knowledge source and can be placed into the cascade independently of the others
the knowledge sources are cast as filters so that any subset of them can be cascaded in a uniform framework
bible assigned a score for each model and these scores were used to compare the effectiveness of various filter cascades
this is a liberal estimate of the upper bound on the internal consistency of bible test sets
after filtering we get points such as shown in the right hand side of figure NUM
however this euclidean distance filtering greatly improved the speed of this stage of bilingual lexicon compilation
the standard trailslation of schlecht to bad is blocked for verbs like suit that presuppose a positive attitude adverbial
the different verbmobil semanti construction components use variants of udrs as their semas tic formalisms el
more liked in 9a is t ranslated by the verb prefer in 9t
the handling of such rule interactions is known to be one of the major problems in scaling up mt systems
they are usually based on individual lexical items but might also involve partial phrases for treating idioms and other collocations e.g.
the compiled program includes the selection of rules the control of rule applications and calls to external processes if necessary
the recursive embedding is expressed via additional subordination constraints on labels which occur as arguments of su h olmrators
the current scenario is restricted to the task of appointment scheduling and the languages involved are english german and japanese
tile main differences between out approach and theirs are the use of flat semantic representations and tile non recursive transfer rules
io l suppore s li l2 experioncer s x
the interpretation of a number of syntactic constructions depends on recognizing parallelism including those cited in table NUM
once the ikrs has heuristically recovered clusters of widgets likely to form a functional group these clusters as well as any explicitly represented groups e.g.
word parsing is obviously a much more difficult task than part of speech parsing even if all words are known
for example if there is a mismatch in the rule used by the system it may be necessary to modify the end of an accepted proposal
it could be also possible to add some morphological information in the dictionary to propose the words with the most appropriate morphological characteristics gender number
as there is a huge variety of inflected languages let us concentrate on the particular characteristics of the basque language customising to this case the operational way
NUM application of mentioned word in this section the use of previously reviewed word prediction methods for non inflected languages is studied and their suitability for inflected languages is discussed
after the atomic features have been selected we using the iterative scaling compile a fully saturated model for the maximal constraint space and then start to eliminate the most specific constraints
given that g g e dgr a dependency tree covering the whole input exists and the algorithm will be able to guess the dependents of every head correctly
several additional criteria were used to filter out unsuitable sentence pairs
the constituent alignment includes a word alignment as a by product
in a region of the scatterplot containing n points there will be only n k l such subsequences of length k
what makes this a localized filter is that only points within the search rectangle count towards each other s ambiguity level
the time complexity remains the same as the normal form case
for example a broad coverage english bracketer may be available
the set of transductions generated by g is denoted t g
this observation holds assuming that the translation lexicon s coverage is reasonably good
the lower left corner of the rectangle is the origin of the bitext space and represents the two texts beginnings
if more than one chain is found simr accepts the chain whose points are least dispersed around its least squares line
translation lexicons can be extracted from machinereadable bilingual dictionaries mrbds in the rare cases where mrbds are available
for each such point the matching predicate must decide whether the e and f are likely to be mutual translations
chains that are too big will span too long a segment of the tbm to be well approximated by a line
the constant slope property suggests another constraint simr should consider only chains that are roughly parallel to the main diagonal
all simr needs is a place to start the trace and a good place to start is at the beginning
subsequent search rectangles are anchored at the top right corner of the previously found chain as shown in figure NUM
figure NUM a uvg dl for deriving semantic repre sentations such as NUM
several different transduction systems have been used in the past by the computational and theoretical linguistics communities
the links are indicated as boxed numbers to the right of the nonterminal to which they apply
put differently all productions from a given vector must be used the same number of times
in figures NUM and NUM the synchronous productions are designated by a bold italic left hand side symbol
f i i figure NUM synchuvg dl derivation steps NUM and NUM
a parse forest in g is a directed acyclic graph which is ordered and bipartite
NUM eine reihe von staaten suchen gesch iftliche a series from states seek business kontakte zu der region
the joint venture task required NUM templates with a total of NUM slots for the output double tile number of slots defined for muc NUM and the task documentation was over NUM pages long
the old style muc information extraction task based on a description of a particular lass of events a scenario was called the scenario template task
language understanding technology might develop in ways very diiii rent from those imagined by the committee and these internal evaluations might turn ollt t t e irrelevant distractions
the dry run ix ok t l u e in april NUM wil h a s enario iuvolving labor union n
this format proved awkward when an event had several participmlts e.g. several victims of a terrorist attack and one wanted to record a set of facts about each participant
the second goal was NUM o focus on portability in the inibrmation extraction task the ability to rapidly retarget a system to extract information about a different class of events
slots are tilh d only if inforlnation is explicitly given in the text or ill the ease of the country can be indrred doln an explicit locale
the results of this muc provide valuable positive testimony on behalf of information extra tion but further improvement in both portability and performance is needed tbr many applications
for each muc participating groups have been given sample messages and instructions on the type of information to be extracted and have developed a system to process such messages
one receives a description of a class of events to be identiffed in the text for each of these events one must fill a template with information about the event
the sl l oiigly colineclx i c oinponeill s
coronarj en l clrconrzexe montre de de artere
in other words it is not always possible to resort to statistical methods
i igure NUM parse tree for stenose serre de le hone commun gauche
5a is split in different parts in the similarity graph fig
the corresponding software syci ai e has been developped by the tirst author
2changing passive into active sentences using a verb instead of a nominalization and so on
initially the queue contains just no NUM NUM NUM where no is the start node and possibly no NUM NUM a if no is also a final state with acoustic score a
where su is the total number of semantic units in the translated corpus annotation and sus sut and sup are the number of substitutions insertions and deletions that are necessary to make the translated grammar update equivalent to the translation of the corpus update
however since the main task of the linguistic component is to analyze utterances semantically an equally important measure is concept accuracy i.e. the extent to which semantic analysis corresponds with the meaning of the utterance that was actually produced by the user
furthermore we calculate precision the number of correct semantic units divided by the number of semantic units which were produced and recall the number of correct semantic units divided by the number of semantic units of the annotation
even if we take into account these reservations it seems that we can conclude that the robustness component adequately extracts useful information even in cases where no full parse is possible concept accuracy is luckily much higher than sentence accuracy
robust processing of the output of the speech recognizer extensive coverage of locative phrases and temporal expressions and the construction of fine grained semantic representations with the long term goal of developing a general computational grammar which covers all the major constructions of dutch
almost any parsing technique such as left corner parsing lr parsing etc can be adapted so that the first constraint above is satisfied the second constraint is achieved by structuring the grammar such that the top category directly generates a number of grammatical categories
although the added benefit of grammatical analysis over concept spotting is not clear for our relatively simple application the grammatical approach may become essential as soon as the application is extended in such a way that mor complicated grammatical constructions need to be recognized
all other information associated with the rule concerning the matching of head features the instantiation of features used to code long distance dependencies and the semantic effect of the rule follows from the fact that the rules are instances of the class head complement structure
in this case only the information provided by training data for the noun in the test tuple is used
old formula lcb j rcb z new formula z lcb j rcb constraints NUM NUM ac b av a b b a a bllv the previous inference rule NUM modifies to NUM which is simpler since indexation constraints are now handled by the separate constraint equations
from earlier discussion it should be clear that an incremental analysis is one in which any dependency to be established is established as soon as possible in terms of the order of delivery of assumptions
NUM NUM correcting misparses by lexicalizing verbs
we assume that proof assumptions explicitly record order of delivery information marked by a natural number and so take the form n x n further we require the ordering to go beyond simple order of delivery in relatively ordering first order assumptions that derive from the same original higher order formula
if the composition operator makes use of context then the representation extends naturally to a more powerful form of context free grammars where composition is tree insertion
although it is obviously a naive simplification many of the interesting properties of the compositional representation surface even when meanings are treating as sets of arbitrary symbols
after NUM iterations of training without meaning and then a further NUM iterations with the text sequences were parsed again without access to the true meaning
although the phoneme model is extremely poor many words are recognizable and this is the first significant lexicon learned directly from spoken speech without supervision
at the same time it leaves open the possibility that idiosyncratic information will be attached to the whole as with the meaning of kicking the bucket
the length of the description of a word is a measure of its linguistic plausibility and can serve as a buffer against learning unnatural coincidences
tnation al foot ball a gue
national football league o football league
furthermore because the compressed text is stored in terms of linguistic units like words it can be searched indexed and parsed without decompression
if it is assumed that no other word counts change these assumptions allow one to predict the counts and probabilities of all words after the change
in this system the machine itself explains how to use it
one anlt entry covers two comlex entries given the different treatment of the relevant complements but the classifier keeps them distinct
however the distinction between arguments and adjuncts is expressed following x bar theory e.g.
our system already gathers head lemmas in patterns so any of these approaches could be applied in principle
nevertheless this experiment demonstrates that lexicalizing a grammar parser with subcategorization frequencies can appreciably improve the accuracy of parse ranking
however recognizing same similar arguments requires considerable quantities of lexical data or the ability to back off to lexical semantic classes
this comparison illustrates the problem of errors of omission common to computational lexicons constructed manually and also from machine readable dictionaries
the geig measures for the lexicalized parser show a NUM improvement in the crossing bracket score figure NUM
further evaluation of the results for these seven verbs reveals that the filtering phase is the weak link in the systerri
NUM a he attributed his failure he said to no blank one buying his books
if we classify pattern x by looking at its nearest neighbors we are in fact estimating the probability p classlx by looking at the relative frequency of the class in the set defined by simk x where slink x is a function from x to the set of most similar patterns present in the training data NUM
although in most applications this leads to a higher accuracy because it rejects schemata which do not match the most important features sometimes this constraint needs 4unless two schemata are exactly tied in their ig values
this is desirable when i there are a number of schemata which are almost equally relevant ii the top ranked schema selects too few cases to make a reliable estimate or iii the chance that the few items instantiating the schema are mislabeled in the training material is high
the left and right rules are as for necessity in NUM
if information gain weights are used in combination with the overlap metric individual schemata instead of buckets become the steps of the back off sequence NUM the ordering becomes slightly more complicated now as it depends on the number of wildcards and on the magnitude of the weights attached to those wildcards
this paper proposes a new method for learning a context sensitive conditional probability context free grammar from an unlabeled bracketed corpus based on clustering analysis and describes a natural language parsing model which uses a probability based scoring function of the grammar to rank parses of a sentence
during learning a lagt can reset genuine param null NUM generate cost NUM gc NUM parse cost
the british national corpus in order to provide more useful results in a substantial proportion of the residual words which can not be successfully tagged we have introduced portmanteau tags
if we choose the top NUM sentence positions according to the opp figure NUM tells us that these NUM sentences extracts e the average length of an abstract cover NUM of a in which NUM derives solely from one word matches NUM two words NUM three words and NUM four words
the problem in this data set is to disambiguate whether a pp attaches to the verb as in i ate pizza with a fork or to the noun as in i ate pizza with cheese
the wml of a sentence type can be used to determine whether it can function as a trigger at a particular stage in learning
second the place where games end or are abandoned is marked
does the response mean yes no or something more complex
therefore in english all wh questions tend to be categorized as query w
for simplicity and because of time limitations we opted not to retrai n the noun phrase detector
these three taggers which were trained on the penn treebank wall street journal corpus tag pre tokenized text
coders generally become more consistent with experience
system performance prior to running bride of cogniac the last component which posits coreference in table NUM
part of speech tags follow words and a slash and are specified using the penn treebank tagset
we did not have time to empirically verify this hypothesis but intend to do so in the future
for example fred bloggs president of acme who was elected yesterday would be reduced to fred bloggs
it took approximately NUM minutes to process an average length article when processing was done in batch mode
in the other version hyphenated words are split into multipl e tokens based on the above criteria
on the other hand only one of the thirteen senses for end has person as its ancestor
reproducibility can be tested by training several coders and comparing their results
only NUM of the NUM possible boundary sites are classified as boundary
we computed the yield of each sentence position in each text essentially by counting NUM how many topic keywords would be taken over verbatim from the texts as opposed to generated paraphrastically by the human extractor was a question for empirical determination the answer provides an upper bound for the power of the position method
since u2 satisfies a knowledge precondition related to answering c l u2 contributes to the dr goal and is tagged as such
second because our success measure n takes into account the complexity of the task comparisons can be made across dialogue tasks
finally to our knowledge we are the first to propose using user satisfaction to determine weights on factors related to performance
regression on the table NUM data for both sets of users tests which factors utt rep most strongly predicts us
all antecedent formulas are associated with variables
the equivalent column sums in both tables reflects that users of both agents were assumed to have performed the same scenarios
the relation head corner is the reflexive and transitive closure of the head relation
note that a rule is triggered only with a fully instantiated head daughter
in figure NUM the motivation for this technique is illustrated with an example
null in order to explain the parser i first introduce some terminology
however if we pick the first solution then a cyclic term results
coming back to the example in the previous subsection if our first goal
smaller equal eo qo smaller equal q e
now we can continue by picking up a solution from the second table
to parse np the category n from NUM to NUM is predicted
section NUM contains the procedure for compiling the secondary lexicon
on the contrary in principle the user is able to detect and correct such errors and often she does it immediately or in subsequent turns
the training set was collected in previous experimentationsof the system users responses to specific system dialog act were classified for training different language models
in particular by selecting words before a syntactic tree has been constructed the lexical syntactic features associated with alternate lexical choices can constrain the high level structure of the final tree which is a key feature to handling floating constraints
while there are clearly many aspects in which our current approach requires further work we may claim that speech is a viable interface if we provide spoken systems with robust dialogue management
in our application domain misconceptions i.e. errors in the prior knowledge of a participant usumly concern the expression of departure dates as in the dialogue excerpt shown in figure NUM
the meaifing of sequences of sentences is seen as strongly connected with their inferential behavior
second the lexical entries are sufficiently general for reflecting similarities between single lexemes
a new joined representation format is developed which is exemplified by analyses of german verbs
corresponding to these requirements we exploit the specific strengths of two distinct semantic theories
each pair consists of tim generalized case information and the eorresl onding the
this mapt ing offers two starting points for an integration of drt and set
the axioms have in common that they involve the concept ciiange slc n cf
m d jin c NUM the assertion
we can then derive systematically which are the suitable grammatical realizations of each role
these then describe partial lexical fields like e.g. to give or to take
NUM compute the positional difference vector of each word
new anchor point finding and noise elimination techniques are introduced
we will demonstrate that the implementation of our approach outperforms an implementation based on the strict tree structure approach
NUM they had not seen before at one num pron of the busiest times of the school year
the shallow structure of verb chains is also given the tag set distinguishes between auxiliaries and main verbs finite and nonfinite
in this paper we proposed a method of applying clustering aaalysis to learn a context sensitive probab flistic grammar from an unlabeled bracketed corpus
particularly when p ejcz is zero we can not calculate the divergence of two probability distributions because the denomi ator becomes zero
by waiting until content planning is complete lexical and syntactic constraints can be represented explicitly and independently of one another instead of being embedded into full phrases allowing for a more economical and flexible word based lexicon that incorporates phrasal constraints
further in the experiment described in this paper the model was trained with data obtained by an unsupervised procedure which performs with an accuracy of approximately NUM for training data
a high inflation rate expects the economist the economist expects a high inflation rate NUM die okonomen erwarten eine hohe inflationsrate
this explanation is also consistent with the decrease in tagger expert matches along with increasing polysemy
our expectation is still that set NUM should be ranked in the middle between sets NUM and NUM for each die
if the two characters prior to the ing are the same and riot s remove the second one
the nominal phrases preceding and following the verb in NUM are both ambiguous with respect to case they may be nominative or accusative
these make use of unambiguous examples provided by a treebank or a learning procedure in order to train a model to decide the attachment of ambiguous constructs
the verb erwarren to expect takes in one reading a nominative np as its subject and an accusative np as its object
NUM die gesellschaft erwartet in diesem jahr the society expects in this year in siidostasien einen umsatz in southeast asia a turnover von NUM millionen dm
when a finite separable prefix main verb occupies the second position in the clause its prefix takes the last position in the clause core
personal and institutional or company names were tagged in an sgml like manner
this is possible but the nature of document and query text is quite different
from these vectors we estimate the density of the distribution of the scores for each method figure NUM gives these densities for the frequency test and the log linear model with smoothing splines on the most difficult case the morphologically unrelated adjectives
the log linear regression model generalizes this setting to binomial sampling where the response variable follows a bernoulli distribution corresponding to a two category outcome note that the variance of the error term is not independent of the mean of y any more
both the standard and smoothed log linear models outperform the frequency test on the morphologically unrelated adjectives significant at the NUM and NUM NUM levels respectively while the log linear model s performance is comparable to the frequency test s on the morphologically related adjectives
the method proceeds recursively by selecting a new variable possibly the same as in the parent node and a new cutting point for each subtree until all the cases in one subtree belong to the same category or the data becomes too sparse
starting from the root the variable x which better discriminates among the possible outcomes is selected and a test of the form x consiant is as null to or better than the observed one is listed in the p value column for each test
some of the tests that have been proposed in the linguistics literature notably tests that are based on the formal complexity and differentiation properties of the words fail to give any useful information at all at least with the approximations we used for them section NUM
this is all the more remarkable in the case of the morphologically related adjectives where frequency outperforms length of the words recall that the latter directly encodes the formal markedness relationship so frequency is able to correctly classify some of the cases where formal and semantic markedness values disagree
if principles are precompiled in the form of grammar rules the size of the grammar increases
however the icmh is also confirmed by the other pairwise comparisons and by the global results
as indicated above markables include names o f organizations persons and locations and direct mentions of dates times currency values and percentages
note that the number of instances of percentages in the test set is so small that a singl e mistake could result in an error of NUM
configuration and the difference in scores is largely due to the fact that the system overgenerated to a greater extent on the body than on th e headline
the slot that most systems performed best on is new status the lowest error score posted on that slo t is NUM median of NUM
using a simple counting scheme the algorithm obtains recall and precision scores by determining the minimal perturbations required to align the equivalence classes in the key and response
nearly half the sites chose to participate in all four tasks and all but one site participated in a t least one sgml task and one extraction task
however we still have full sentenc e parsing e g usheffield udurham umanitoba we sometimes have expectations of deep understanding cf
the variety of high frequency phenomen a covered by the task is partially represented in the following hypothetical example where all bracketed tex t segments are considered coreferential
some work on expanding the scope of the ne task has been carried out in the context of a foreign languag e ne evaluation conducted in the spring of NUM
the association of shortened forms of the name with the full name depends on techniques that could be used for ne and co a s well as for te
so why not apply good turing directly to the structural units of a stochastic grammar
jelinek NUM katz NUM church gale NUM
the use of the good tufing method in natural language technology is far from new
it only assumes that the sample is obtained at random from the total population
in this paper i present ongoing work on the data oriented parsing dop model
second the icmh predicts that long distance dependencies represented as chains are computed in steps
as in the previous section we will take only the subtrees upto depth three
the probability definitions of derivation and parse in dop3 are the same as in dop1
we assume that there are no unknown subtrees that depend on an unknown syntactic structure
dictionary resources in oleada are indexed alphabetically by word like their paper counterparts
our system takes two types of goals
after another translation trigger d is obtained with missing subject filled by some default word
consider this passage from the corpus translated into english preceding time thursday NUM august sl NUM on thursday i can only meet after two pm
first let s suppose kare ga is assumed to be the subject of the relative clause by the system
these values were derived by disabling all the rules and just evaluating the input as is after performing normalization so the evaluation software could be applied
these results show that the system is performing with NUM accuracy overall which is significantly better than the lower bound defined below of NUM
return lcb when merge tutemp tu certainty cf rcb figure NUM main temporal resolution rules
there are subdialogs in the nmsu data but none in the cmu data for which our recency algorithm fails because it lacks a mechanism for recognizing subdialogs
detailed results for the test sets are presented next starting with results for the cmu data see table NUM
as mentioned in section NUM the main results are based on comparisons against human annotation of the held out test data
assoc list ml assoc list m u lcb m assoc list m2 assoc list m u m rcb
suppose a node in a tree dominates a frontier which has the substring aiaj to the left of the foot node and akat to the right of the footnode
also every initial tree is labeled at the root by the start symbol s and has leaf nodes labeled with symbols from NUM u lcb e rcb
figure NUM translation of a simple sentence ronbun paper noun typical word thesis noun for degree essay noun general dissertation noun for degree figure NUM alternatives window for ronbun
figure NUM situation where we can not infer the adjunction if we simply get rid of the mid NUM NUM a tree
let c contain an internal node m labeled x and let fl be the auxiliary tree with root node also labeled x
the first polynomial time algorithm for tal parsing was proposed in NUM and had a run time of o n6
the most important requirement will be translation quality because the reader is usually different from the mt user
null the recognizer implemented two different algorithms for matrix multiplication namely the trivial cubic time algorithm and an algorithm that exploits the sparsity of the matrices
in this paper we have presented an o m n2 time algorithm for parsing tals n being the length of the input string
the user should choose the area so that it contains necessary and sufficient words to be one meaningful expression
for instance we might notice that the capitalization when it is seen particlll rly with words of length NUM has a different distribution from its general one
sra has als o developed graphical annotation tools e g nametool discourse tagging tool to support the creation of trainin g data for automated acquisition
hasten required approximately NUM person weeks for its development and muc NUM required NUM person weeks o f effort including the interim test formal test and final report
the extended reestimation algorithm can approximately maximize the modified likelihood and improve the model accuracy
the credit factor improved the upper bound of the estimation accuracy from an untagged corpus
a credit factor can improve the reliability of the model estimated from an untagged corpus
if a model is correctly estimated then a larger cost width will improve precision
the tagger chose the best morpheme sequence from the network by each stochastic model
using this property the reestimation formulae can be replaced with the scaled versions
it is applied to model estimation for a tagger from a untagged corpus
the in credit u means the beginning of text indicator
in fact we experienced the underflow problem in preliminary experiments with the edr corpus
in all evaluation tasks ne te and st plum was run over all messages to detect and correct any causes o f system breaks
since the new scoring software did not suppor t visualization of the differences between system output and the answer key we wrote a visualization tool to do so
we started over and performance now is very high
as described earlier the egraphs originating from a particular text unit are withheld and not used for extraction on that unit
the lightweight procedures in identifinder are sgml recognition hidde n markov models finite state pattern recognition and sgml output
sometimes understanding the relatio n more fully is of no consequence since the information does not contribute to the template filling task
recent statistical full parsers e.g. bbn s ibm s and upenn s have such quantitatively better performance that they are qualitatively better
dooner NUM plum s discourse component creates a meaning for the whole message from the meaning of each sentence
once all the semantic forms have been processed heuristic rules are applied to fill any empty slots from the text surrounding the forms that triggered a given ddo
james NUM years old is stepping down as chief executive officer on july NUM and will retire as chairman at the end of the year
this allowed us to focus development on those portions of the data which wer e directly relevant to the task without having to always read through the irrelevant portions
for example plum has parameters to control aspects of tagging parsing pattern matching event merging and slo t filling by discourse and template filling
fourth due to the task specification some of the scenario extraction output may not come directly from the egraph matches
hasten extracted the principal succession event involving james and dooner but failed to detect both management posts
the template element task required NUM personegraphs one for an untitled personal name and one for a titled persona l name
the scoring program employs a top down comparison algorithm that produces performance measures as well as a side by side display as illustrated below
even though the muc NUM extraction task focused on one scenario sra did not want to produce a single extractio n result
a disjunction indicates that there are multiple but mutually exclusive ways of expressing a certain semantic fact
it also provides a way for verifying that they do not overlap and express certain facts superfluously
we propose a coarser notion of equivalence in order to let more phrases to be folded together
null this example begins to show how the various components of the representation control the generation process
these propositional variables compose into larger boolean expressions that define derivations of larger structures
this way the sentence john moved into the room quickly is realized
these annotations consist of logical expressions that identify particular realizations encoded in the chart
and atta chments that have been observed in online experiments of language production and comprehension can now be put in relation with the frequency of these alterna tives m la rger amounts of texts
fig NUM shows the representation of the sentence NUM sie wurde van preuliischen truppen besetzt site was by prussiaa troops occupied und NUM dem preutlischen staat angegliedert and NUM to the prussia n
as the annotation scheme does not distinguish different bar levels or any similar intermediate categories only a small set of node labels is needed currently NUM tags s np ap
daran wird ihn anna erkennen tss er weint such a word order independent representation has the advantage of all structural ini orrrlation being encoded in a single data structure
accuracy of the unreliable NUM of assignments is NUM i.e. the annotator has to alter the choice in NUM of NUM cases when asked ibr confirmation
special thanks go to oliver plaehn who implemented the annotation tool and to our fearless annotators roland hendriks kerstin k15ckner thomas schulz and bernd paul simon
the diff rence between the particular nk s lies in the positional and part of speech information which is also sufficient to recover theory specific structures frorn our underspecified representations
turn out to be promemarie mainly due to the partial idealised character of competence grammars which often margmalise or ignore such important phenolnena as deficient e.g.
the results obtained via machine learning are also somewhat better than the results obtained using hand tuning particularly with respect to precision condition NUM in table NUM and are a great improvement over the original np results condition NUM in table NUM
we selected the dialogue acts by examining the verbmobil corpus which consists of transliterated spoken dialogues german and english for appointment scheduling
the networks serve as the basis for the implementation of a parser which determines whether an incoming dialogue act is compatible with the dialogue model
we currently integrate the speaker direction into the prediction process which results in a gain of up to NUM in the prediction hit rate
this is especially important because the input given to the dialogue component is unreliable when dialogue act information is computed via the keyword spotter
however their set of dialogue acts or the equivalents called illocutionary force types does not include dialogue acts to handle deviations
in discourse interpretation all we usually know about an entity is the small set of properties presented explicitly in the text itself
in a limited domain however it appears quite feasible to construct a domain specific form based questionnaire designed to test a subject s understanding of a given utterance
the first deals with the linguistic form of the enquiry for example whether it is a command imperative a yes no question or a wh question
the third section lists some NUM constraints on the object explicitly mentioned in the enquiry like one way from new york to boston on sunday
the intention is that the source text version of the utterance should act as a baseline with which the source and target speech versions can respectively be compared
one option would be to position lexical choice as part of syntactic realization as just a very specific type of syntactic decision i.e. option NUM in figure NUM
judging was done by subjects who had not participated in system development were native speakers of the target language and were fluent in the source language
this strategy is not felicitous when dealing with multiple relations as illustrated by the two examples whose inputs and corresponding alternative outputs are shown in figure NUM and figure NUM respectively
due to the wide variety of constraints on word selection that we consider lexicalization is positioned after the content of the generated text has been determined and before syntactic realization takes place
in the student advising domain argumentative intent or the desire of the speaker to cause the hearer to evaluate the information provided in a particular light plays an important role
note however that during this stage of phrase planning neither the syntactic category nor the lexical head of the constituent are yet chosen
this means that multiple content units can be realized by a single surface element and conversely that a single content unit can be realized by a variety of surface elements
thus it should be possible for a conceptual structure to be realized by a clause a nominalization a noun noun modifier a predicative adjective or a prepositional phrase
functional unification is based on two principles information is encoded in simple and regular structures called functional descriptions fds and fds can be manipulated through the operation of unification
this is in contrast to sentences NUM and NUM in the same figure where perspective switches from class assignt to assignt type with the focus being the same
this subset of constructions has been chosen because it constitutes the crucial test set for principle based parsers it involves complex interactions of principles over large portions of the tree
they stiffer from serious shortcomings when faced with ambiguous input as they do not have enough global knowledge of the possible structures in the language to recover from erroneous parses
moreover chains divide into two types NUM it should be noted that although phrase structure rules are reduced to the bare bones they can not be eliminated altogether
experimentation with different amounts of precompilation shows that off line precompilation speeds up parsing only up to a certain point and that too much precompilation slows down the parser again
since it paola merlo modularity and information content classes is desirable for the parser to maintain the level of explanatory power of the theory it must maintain its modularity
moreover if one looks at grammar NUM which is smaller than grammar NUM one can see that the average number of conflicts decreases quite a bit
grammar NUM has a higher number of average conflicts than grammar NUM but it is smaller both by rules and lr entries so it is more compact
it is important to use both measures because one can build a high recall low precision system or a low recall high precision system neither of which may be appropriate in certain situations
name anaphora in japanese are different from those in english in that any combination of characters in an antecedent can be name anaphora as long as the character order is preserved e.g.
if the parameter is off a binary decision tree is trained to answer just yes or no and does not have to answer the types of anaphora
to build mlrs we first trained decision trees with NUM anaphora NUM of which NUM were name org NUM qzpro org NUM dnp org NUM zpro org in NUM training texts
one approach is to use a particular bias say in preferring the antecedent closest to the anaphor among those with the highest confidence as in the results reported here
the underbars are nmrely placeholders for bound variables in our case
there are a variety of methods in the ai literature for learning from exarnples
thus starting dom the first element of the above sibling list viz
in our case tile first positiw example would be this thick book
regularizing the representation has the positive effect of making the transfer rules simpler in the limiting case a fully interlingual system they become trivial
when this parameter is on we select a set of positive training examples and a set of negative training examples for each anaphor in a text in the following way
while some such template sets exist such as those assembled for the message understanding conferences collecting such large amounts of training data for each new domain may be impractical
it is possible that the component characters of a word are fre such as shut and gu6 of the word q shu gu6 fruit which mean water and fruit respectively
this is an important feature of the system the context dependent activation of nodes which enables the system to dynamically decide what is relevant at a given point in time and influences what actions to take through the posting of top down codelets
the computational temperature is an approximate measure of the amount of coherency in the system s interpretation of a sentence the value at a given time is a function of the amount and quality of linguistic structures that have been built in the workspace
again by activation spreading to the chunk node codelets gan palmer and lua a statistically emergent approach building chunk objects will be posted which will further lead to the posting of codelets that determine how chunk objects can be related
in this run the strength of the proposed affinity relation between the characters ps r n and sh ng is NUM while that of the existing affinity relation between the characters b n and
we will use a sample run of the program on sentence NUM to illustrate many central features of the model including the selection of a codelet the selection of competing alternatives the interaction between the workspace and the conceptual network etc
several systems posted scores under NUM error for locations but none was able to do so for oganizations
summary ne scores on primary metrics for the top NUM out of NUM systems tested in order of
no analysis has been done of the relative difficulty of the muc NUM st task compared to previous extraction evaluation tasks
for percentages about half the systems had NUM error which reflects the simplicity of that particular subtask
the issues with respect to the st task relate primarily to the ambitiousness of the scenario templates defined for muc NUM
coreference many aspects of the co task are in definite need of review for reasons of either theory or practice
the scenario is designed around the management post rather than around the succession act itself
participants were invited to enter their systems in as many as four different task oriented evaluations
named entity the primary subject for review in the ne evaluation is its limited scope
all the participating sites also submitted systems for evaluation on the te and ne tasks
constraint NUM states that the sum of the probabilities of the extensions e w available in in a given context w can not sum to more than unity
accordingly the incremental cost of adding the set e of extensions to the context w is defined as NUM while the incremental benefit is defined as NUM
in contrast an extension model includes a selection rule s e x e d whose inputs include the past history and the symbol to be predicted
the incremental cost and benefit of adding a single parameter to a given context can not be accurately approximated in isolation from any other parameters that might be added to that context
c lw c w w if c q w ae alw w otherwise
next in section NUM we demonstrate the efficacy of our techniques on the brown corpus an eclectic collection of english prose containing approximately one million words of text
the addition of a single parameter w r to the model c will immediately change a alw by definition of the model class
the first term encodes iti with an elias code and the second term recursively partitions c w into c w for every context w
we follow dsp in claiming that this example has five readings in which the jjjb reading shown in NUM is missing
results of NUM NUM words only and NUM NUM words and classes are reported
the affective level is less explicit expressed through nonverbal cues and tone of voice addressing a less conscious aspect of the learner
a related issue is sense granularity
what determines whether the senses are related
i am grateful for their support
nec research institute NUM independence way
the next section will discuss our experiments with morphology
table NUM distribution of zero affix morphology within dictionary definitions
the effectiveness of capturing related senses via word overlap
this is not satisfactory for two reasons
we will discuss this in section NUM NUM NUM
instead lexical units may be decomposed int o a set of seinantic entities e.g. in the case of deriwztions arid fi r a nmre line grained lexical semantics
this rule will be tried first by calling the external domain model r r testing whe ther the sort assigned to x is not suhsumed by the sort letup point
labels are also useflll for grouping sets of conditions e.g. for i artitions whidt belong to the restriction of a qmmtifier or which are part of a specific sub drs
furthermore because the recursive rule application is not part of tile rules themselves our approach solves problems with discontinuous translation equivalences which tile former approach can not handle well
the ability to underspecify quantifier and operator scope together with certain lexical ambiguities is impof rant for a practical machine translation system like verbmobil because it supports ambiguity preserving translations
the appropriate transfer rule looks like 6a which can be reduced to 6b because no argument switching takes place and we can use the metarule again
whereas in the underlying rule application scheme assumed here the more general rule in 6b will be blocked by the more specific rule in NUM
for example the word funny is segmented into two word hypotheses rare and strange
thus the process of initiating the achievement of the new intention has the result that some and perhaps all of the items currently in the cache are replaced with items having to do with the new intention
thus utterance 22a but as far as the certificates are concerned has the effect that the focus space related to the discussion of retirement investments corresponding to utterances NUM to NUM is popped from the stack
a probe just after a pronoun and before the verb in a return pop could determine whether the pronoun alone is an adequate retrieval cue or whether selectional information from the verb is required or simply speeds processing
so by the stack model this segment is handled by the same focus stack popping mechanism as we saw for dialogue a however in dialogue b utterance 8a is more difficult if not impossible to interpret
it should be possible to test how long it takes to resolve anaphors in return pops and under what conditions it can be done considering the data presented here on competing referents irus explicit closing and selectional restrictions
however according to the cache model irus make information accessible that is not accessible by virtue of hierarchical recency so that processes of content based inferences inference of discourse relations and interpretation of anaphors can take place with less effort
although translation of written and spoken language have much in common there is no evading the fact that text and speech are in some ways fundamentally different modalities
since utterance NUM clearly indicates completion of the interrupting segment the focus space for the interruption in NUM to NUM is popped from the stack after utterance NUM leaving the focus space for utterances NUM to NUM on the top of the stack
figure NUM word length distribution of the ed corpus figure NUM shows the actual and estimated word length distzibutious in the corpus we used in
this is achieved by the expression NUM k inserto p k ci c k r NUM the intersection with a c k r ensures that the first feature structure to appear to the right of v is ck zero or more feasible tuples followed by ck followed by zero or more feasible tuples or feature structures
an additional problem is that many of these particles are ambiguous in that they also have an interpretation not related to discourse structure
the three papers in this section explore some aspects of the slt problem which highlight the differences between translation of written and spoken text
keiko horiguchi and alexander franz describe another piece of work aimed at counteracting the problems involved in taking translation input from a speech recognizer
we assume that word length probability p lc obeys a poisson distribution whose parameter is the average word length a in the training corpus
agents act to make their goals true
we can get a set of initial estimates of the word frequencies by segmenting the training corpus using a heuristic non stochastic dictionary based word segmenter
as the diagram shows there are four requirements to our approach
the use of a hierarchy of access screens keeps each one simple
during the evaluation several areas needing improvement in the nlu system emerged
consider the example below where the source text is presented first
te requires recognition of organizations persons and properties of them
a second goal is to explore the architecture for such a system
an audio signal is received from radio television or telephone
NUM a b evaluates to NUM if the job code arguments differ on the first digit NUM NUM if they differ on the second digit and so on
a further proviso is added that the maximum difference between any two parameter values must be NUM which ensures that all parameters have an equivalent maximal difference NUM
the second rule is for refashioning moves
there are currently very many interact sites where jobs are advertised and indeed using information retrieval 1tree is language engineering project le NUM of the european commission s fourth framework programme
but no other application as far as we can discover offers the opportunity of searching and of getting summaries of job ads in languages other than that of the original announcement
since both searchable job ad information and query data are represented in a language independent format matches will be made regardless of the language in which the data was entered
moderate german required vs tall german required while other text portions serve a dual purpose for example when the name of the employer also indicates the location
in this paper we have described an approach that summarizes ads into a base schema and then generates output in the desired language in a principled though restricted way
those parameters for which no value has been specified will exactly match every possible parameter value and as such the database search is only constrained by those values which users enter
the query engine uses symbolic case based reasoning techniques while the generation module integrates canned text templates and grammar rules to produce texts and hypertexts in a simple way
notice that very different kinds of content would be required to effectively correct the above error depending on the actual reason for making it
for the rule to be triggered cat verb of the rule must match with cat verb of the lexical entry lcb clvc2vc3 rcb and measure pa el of the rule must match with measure p al pa el of the lexical entry lcb ktb rcb
figure NUM the portion of the constraint structure ibr a portion of the the turkish verb ye
we present a constraint based case flame lexicon arctfitecture fbr bi directionm mapping between a syntactic case dame and a semantic dame
where is a edible is a constraint of the t orrrt iiead i sem edible
as a second example consider tile default sense of ye corresponding to cat somcthi z q
in this p q er we present a unification based apl ro wh to constraint be seal
economy of re NUM reselltation is achieved via sharing of eon straints across many verb se nse definitions
the system has i ecn inll hmt nte l using the tfs sysrein
are for possi my idiomatic senses which explicitly require w rious voice na rkings
this section presents action schemas for clarifications
for these reasons we ran a second set of experiments with neural networks which generally do well with a high number of variables because they protect against overfitting
as the text databases available to users become larger and more heterogeneous genre becomes increasingly important for computational linguistics as a complement to topical and structural principles of classification
finally by decomposing genres into facets we can concentrate on whatever generic aspect is important in a particular application e.g. narrativity for one looking for accounts of the storming of the bastille
we are particularly interested in applications to information retrieval where users are often looking for texts with particular quite narrow generic properties authoritatively written documents opinion pieces scientific articles and so on
we suspect that the indifferent performance in scitech and legal texts may simply reflect the fact that these genre levels are fairly infrequent in the brown corpus and hence in our training set
in a machine that chooses randomly performance would be NUM and all of the numbers in the table would be significantly better than chance p NUM
such features have not been used in previous work on genre recognition but we believe they have an important role to play being at once significant and very frequent
we propose a theory of genres as bundles of facets which correlate with various surface cues and argue that genre detection based on surface cues is as successful as detection based on deeper structural properties
lexter also extracts phraseological units pu which are informative collocations of the candidate terms
let w be the last word to be attached into the tree
NUM i know the man who believes the countess killed herself
NUM i know the man who believes the countess killed himself
the search for the lowering site is of particular importance
let s be the subtree projection of the new word
let n be a node in the current tree description
they are equivalent to abney s attach l and attach respectively
NUM i saw the man with the telescope
we call this set the subtree projection of that lexical category
this behavior can not be captured whether we adopt a bottom up or a top down search for tree lowering
the above discussion makes it clear that morphological cueing provides only a partial solution to the problem of acquiring lexical semantic information
when hearers identify an apparent inconsistency they can reinterpret an earlier utterance and respond to it anew
graphical representations of the same results are on figures NUM NUM and NUM
for instance whether an adverbial expression appears in pre or postverbal position depends on subtle semantic differences
similarly the derived forms of ment entail that an event took place and refer either to this event the proposition that the event occurred or something resulting from the event refers to e or prop oi result e.g. a restatement entails that a restating occurred and refers either to this event the proposition that the event occurred or to the actual utterance or written document resulting from the restating event
however this approach is hindered by the need for a large amount of initial lexical semantic information and the need for a robust natural language understanding system that produces semantic representations as output since producing this output requires precisely the lexical semantic information the system is trying to acquire
such reasoning is clearly nonmonotonic here we suggest that it can be characterized quite naturally as abduction
errors are corrected by replacing or deleting parts of the problematic utterance so that it makes sense
it is not an intrinsic property of a word but a constraint by dominating or governing element
agents are able to detect and repair their own misunderstandings as well as those of others
however current formalizations do not seem to account for the context sensitivity of speakers beliefs
where there is more than one expected act a condition is used to distinguish them
2s two speech acts are ambiguous whenever they can be performed with the same surface level form
the third component of the model is t a speaker s theory of communicative interaction
as a placeholder for such a theory there is a compatibility relation for expressed suppositions
the translation module performs translation of the specified region obeying user specification passed by the interface module
translation equivalent for the component words of an idiomatic expression changes synchronously when one of them is altered
then whole sentence is assumed to be the translation region and d is obtained finally
after dictionary lookup the user can invoke syntactic transformation in terms of grammatical information in the dictionary
syntactic transformation proceeds step by step in a bottom up manner combining smaller translation components into larger ones
next we turn to more complex examples and show how more than one translation units are combined
litman also adds a new general heuristic stop chaining if an ambiguity can not be resolved
conversation acts include traditional speech act types as well as what traum and hinkelman call grounding acts
figure NUM this figure shows an excerpt from a search for the type disease with a distance four to the
to increase a precision of knowledge extraction in some cases it is quite important to resolve references of pronominal anaphora
this surface lexical structure corresponds to semantic relations between concepts represented by these terms
figure NUM this figure shows a correspondence of many phrases to one lexico semantic pattern
person v head suffer have sustain devel nc head quot infarction quot lcb on in rcb date
a conceptual structure which corresponds to the pattern adds more implicit information to the pattern
an example of a correspondence of many phrases to one lexico semantic pattern is shown in figure NUM
other phrases are decomposed into constituents for recalculation of saved phrase weights as described in mikheev NUM
next we cluster pure adjectival modifiers into groups using synonym antonym information available in wordnet
kupiec NUM a specialized partial robust parser and a case attachment module
in table NUM under l01 we repeat the same experiments using our larger lexicon which is derived from the collection using l0 as the basis
for instance construction of the high voltage line is a pu built with the candidate term high voltage line
NUM if lll dal asel as the tesl prot mid the remaining NUM NUM as training parl
NUM the orreslionding dimitmtive allomorph abbreviated to e etjc t tie
although several decision tree and rule induction variants have been proposed we chose this program because it is widely available and reasonably well tested
the example shows that this computationally simple approach also succeeds in discovering categories in an unsupervised way on tile basis of data for supervised learning
table NUM outline of bunruigoihy3 bgh
pattern matching techniques will still have a crucia l role for domain specific details but we believe they can be greatly improved by deeper understanding
simultaneous compilation allows a factoring out of horizontal structure from vertical structure within the sublinear space in such a way that the partial information of word order can drive computation of hierarchical structure for the categorial parsing problem in the presence of non associativity
relative to a model each type a has an interpretation as a subset d a of l given that primitive types are interpreted as some such subsets complex types receive their denotations by residualion as follows cf
although the one way term unification for groupoid compilation of the non associative calculus is very fast we want to get round the fact that a hierarchical binary structure on the input string needs to be posited before inference begins
taking linear validity as the highest common factor of sublinear categorial calculi we have been able to show a strategy based on resolution in which the flow of information is such that one term in unification is always ground
it happens that left occurrences of product are not motivated in grammar but more critically sequent proof normalisation leaves the non determinism of partitioning and offers no general method for multimodal extensions which may have complex and interacting structural properties
another source of derivational equivalence is that a complex id axiom instance such as n s n s can be proved either by a direct matching against the axiom scheme or by two rule applications
novice pc user should ha re
the posmon status gen in state of affairs indicates that the system is unsure of whethe r the status should be in or in acting
as with ne many groups performed at a level higher than any previous template fill task i n muc NUM NUM or NUM
theorem NUM a character string s over an alphabet has tokenization ambiguity on a tokenization dictionary d if and only if s has either critical ambiguity in tokenization or hidden ambiguity in tokenization
by merging all word strings produced together with word strings in co s lcb abc d ab cd a bcd rcb the entire tokenization set to s is reclaimed
in the literature usually a poset is graphically presented in a hasse diagram which is a digraph with vertices representing poset elements and arcs representing direct partial order relations between poset elements
the poset td abcd lcb a b c d a b cd a bc d a bcd ab c d ab cd abc d rcb has three minimal elements abc d ab cd a bcd
NUM note as a widely adopted convention in case k NUM wl wk NUM represents the empty word string v and cl ck NUM represents the empty character string e
that is for any tokenization y y e to s there exists critical tokenization x x e co s such that x is a supertokenization of y
i or an example see section v NUM ex
finally notice that the equation that specifies passive morphology appears on the passive verb node
in the previous section we referred to individual lines in datr definitions as statements
in addition both global and local contexts are updated to the new settings
similarly all the mor present forms inherit from verb except for the explicitly cited mor present tense sing three
NUM undeclared variables are similarly assumed to range over the full set of atoms
and the appropriate place to do this is in the verb node thus
verb syn form syn tense syn number syn person
inheritance is an issue central to the design of any formalism for representing inheritance networks
from the perspective of a standard untyped dag encoding language like patr this is strange
a very few words in english have alternative morphological forms for the same syntactic specification
the ais has to deal with two types of difficulties first the predicate with its argument structure is not always available at the time the argument is attached second the large number of possible word orders in german makes the argument s grammatical function difficult to determine
in control constructions the subject is a null pronoun pro which can be coreferential with controlled by the subject example 7a or the object example tb of the upper clause according to the lexicai property of the main verb
the verb second constraint requires that the tensed verb occupies the second position of the main clause for the first position however a large number of constituents xp is possible such as the subject an object an adjunct an empty operator
thus the x schema is parameterized in german as follows the complement compl precedes the head x deg for the categories v a i whereas it follows the head for the categories c d p n adv NUM as the specifier spec is always on the left the x schema has the structure given in NUM
when the infinitival clause zu schlagen is interpreted as the sentential complement of scheint the uninterpreted arguments ihn and die frau are transferred to this clause in order to be interpreted with respect to the verb schlagen die frau is taken as the subject of schlagen with the thematic role agent and ihn as the direct object of the same verb with the thematic role patient
in order to generalize and simplify our maximum entropy model we unconstrain the most specific features compute a new simplified maximum entropy model and if it still predicts well we repeat the process
univ aix fr proj ect s multext
section NUM makes some concluding remarks
the creoleisation took approximately two person months
the resulting system has all the functionality of the original lasie system
various implementations of tipster systems are available including one in gate
minimized markup texts are converted to a normal form before processing
finally we look at a system that falls outside our three categories
tagset to tagset mapping and in some cases by extra processing e.g.
in this regard lasie was probably typical of existing le systems and modules
to obviate the need to deal with some difficult types of sgml e.g.
the number of parameters or transition probabilities scales as v n where v is the vocabulary size
NUM r dp i would like it a room con ip vp with shower and vl with view pp on the garden several of the components used by itsvox have been described elsewhere
the generality of the approach is evident from the open test s comparably high coverage and precision rates
a succession event is identified if the event has an action that can be generalised to a set of predefined succession actions e.g. to dismiss to fire etc or can be itself identified as a succession event e g appointment promotion
we consider the use of language models whose size and accuracy are intermediate between different order n gram models
table NUM shows the final perplexities after thirty two iterations of em for various aggregate markov models
a lexical lookup using the phonetic trie representation described in the next section will produce a lexical chart
our models were trained by thirty two iterations of em allowing for nearly complete convergence in the log likelihood
in this regime aggregate models can be relied upon to compute the probabilities of unseen word combinations
while some cases of polysemy can be disambiguated relatively easily for instance on the basis of a gender distinction in the source sentence as in NUM other cases such as the much simplified one in NUM are obviously much harder to handle unless additional information is included in the bilingual dictionary
the itsvox system consists i of a signal processing module based on the standard n best approach cf
in the statistical language modelling we however are often concerned with the conditional probabilities what will be the probability of y to take a certain value y if we see a feature configuration x
it is also at this level that our decision to restrict dialogues to the source language is the most challenging
NUM ivan likes hisi mother and his father and jamesj does too
see especially their section NUM NUM NUM
now people think that no one can
last night at bob sj party she asked himj out
NUM NUM arguments on the basis of parallelism in coordinate structures
the complexity of a parsing algorithm is a composite function of the length of the input and the size of the grammar
this includes facts like the status of books in the library
with the fight formalism constructing pragmatics semantics and syntax simultaneously is easier and better
since we are the library the lexical item we is chosen
this new classification however significantly increased the training time and eliminated only NUM of the NUM errors NUM NUM
the tree set for have includes unmarked ld and top trees
we consult the knowledge base for further information about book19
tree types are shared between lexical items figure NUM
our assumption seems to hold for the experiments in this study demonstrate that the method provides broad coverage alignment with almost no loss in precision
quantitatively the results which are very suggestive in the lr compilation are less clear in the other two methods
this paper has shown an algorithm for data preparation and open compound extraction
however the robustness of the satz system allows it to still produce a relatively high accuracy without relying on extensive abbreviation lists
this convergence may be one reason for tile bunching of scores for this task most systems fell in a rather narrow range in both recall and precision
was solue overlap b lween t hc arli les assigned s t hal we could iiio s lll
consequently the scores were appreciably lower ranging across most systems from NUM to NUM in recall and from NUM to NUM in precision
there seemed general agreement that having prepared code for template elements in advance did make it easier to port a system to a new seenario in a few weeks
most participants worked on the tasks for NUM months a few the tipster contractors had been at work on the tasks tbr consi lerably longer
to address these goals the meeting formulated an ambitious menu of tasks for muc NUM with the idea that individual participants could choose a subset of these tasks
therefore we have to include the direction to the string observation
the mucs are notable however in that they in large part have shaped the research program in information extraction and brought it to its current state rcb
results of testing will show whether either of the approaches are better on their own and how they perform when they are combnined and will hopefully show an improvement in performance over the ad hoc methods used previously
NUM the show s distributor viacom inc is giving an ultimatum either sign new long term commitments to buy future episodes or risk losing cosby to a competitor
two approaches to finding the syntactic function of punctuation marks are discussed and procedures are described by which the results from these approaches can be tested and evaluated both against each other as well as against other work
for instance consider the following example that has an ambiguous pp attaclmaent problem we had a lot of interests in common
attachment of punctuation to the non head daughter only seems to be legal when mother and head daughter are of the same bar level and indeed more often than not they are identical categories regardless of what that bar level is
since these corpora are almost all hand produced some errors and idiosyncrasies are inevitable one important part of the analysis is therefore to identify possible instances of these and if they are cleat to remove them from the results
NUM rather rigid schemes of text generation and predictable semantic relations are used to define senses in mrds such as ldoce
for instance land earth mass slope and sand are the genus terms that are categorically related to bank
therefore these definitions can be disambiguated very effectively on the base of similarity between the defining keywords and the words lists in lloce
step NUM assign to d the label s with the maximum value of sire d s over a threshold
in this study we have evaluated our method using all senses for NUM words that have been studied in wsd literature
NUM a slope made at oends in a road or race track so that they are safer for cars to go round
in the second stage we select the label which is associated with word lists most similar to the definition as the result
there is an obvious simple way to compute a conditional model by computing a joint model x y for every value of the behavior variable separately and then the conditional model is computed as
the new class as z figure NUM i endrogram construction
classes in the text ms an o jective hmction
figure NUM size and recall press report
in the maximization phase each sentence translation pair in the corpus is aligned by maximizing the translation probability pr s i t
recursive structures are specified via the auxiliary trees
figure NUM flowchart of the xtag system with
in this experiment the almost parse resulting from the ebl lookup is input to the stapler that generates all possible modifier attachments and performs term unification thus generating all the derivation trees
the idea is to annotate each of the finite state arcs of the regular expression matcher with the elementary tree associated with that pos and also indicate which elementary tree it would be adjoined or substituted into
given that the index of a test sentence matches one of the indices from the training phase the generalized parse retrieved will be a parse of the test sentence modulo the modifiers
to accommodate the additional modifiers that may be present in the test sentences we need to pro null vide a mechanism that assigns the additional modifiers and their arguments the following NUM
the novel aspects are a immediate generalization of parses in the training set b generalization over recursive structures and c representation of generalized parses as finite state transducers
then the index of this sentence is NUM vndn pn since the two prepositions in the parse of this sentence would anchor the same auxiliary trees
however not all mental adjectives will be able to project the two types they denote i.e.
a portmanteau tag is used ill a situation where there is insufficient evidence for claws to make a clear distinction between two tags
is generally higher among function words than among content words which of course leads to more situations where errors can occur
the obtained hierarchical clusters are ewdua ted
it needs only less than twice size of fiction domain corpus to achieve the performance of the baseline grammar
theorem NUM synchuvg dl has the language preservation property
figure NUM vector derivation tree for derivation of
appendix a NUM presents the additional object classes which would be needed to support such customization
each slot value pair in the template object is represented as an attribute value pair on the annotation
these pose a problem for synchuvg dl for the same reason that they pose a problem for other local synchronous systems the syntactic dependency structures represented by the two derivations are different
it is clear that content words here nouns verbs adjectives participles proper nouns are seldom involved in errors
each tipster system will be provided with a number of annotators procedures for generating annotations
the construction of 7r consists of two stages
as a practicing corpus tagger NUM know that this unorthodox method can sometimes be the best way out of problematic situations
the required adaptation to the basic branch and bound algorithm is not discussed here
the prototype presently under construction departs from the use of concept spotting
these are linguistically motivated domainindependent representations of the meaning of utterances
null in this paper we argue that existing synchronous systems can not handle in a computationally attrac null tive way a standard problem in syntax semantics translation namely quantifier scoping
grammatical analysis in the ovis spoken dialogue system
table NUM summarizes the results of these two experiments
in the symmetric approach learning is conducted using both languages simultaneously thus removing any donor language biases
when unilieacion ac the urren level is com lece i.e. noching furl her can i e added to the int ut sl ruccure every substructure of the input rei resencing acat egory is recursively unified wil h i he gral h nar
also adjunction is st ecili m lexically t he ad iuncc is seen as the semanl ic head which selects t he kiu l of signs i modifies it modified sign relnains lit synca t ic head f the rcsulcing phrase
among NUM test sentences only NUM sentences can be parsed owing to two following reasons NUM our algorithm considers rules which occur more than NUM times in the corpus NUM test sentences have different characteristics from training sentences
in fig NUM the htps representation of the german verb gem walks and it s representation in t uf is shown exemplifying the following mappings of hps onto fuf the subtyl ing of the iieai is represented i y the cat feature of fuf
for the integration of x2morf into i uf the unification engine used in x2morf was replaced by i uf itself and the existing word grammar and norph h xieon were reformulated in the fuf forlaalism and tim word form g neration l ask is now NUM erfontwd l y fui itself
an example is larleu with plural g irlen fhe i iterface i el we en synl act ic and wor tew t roeessing is provided hy the emma lexicon
i inearizacion traverses the ere ex ra cs the m rings foun l in che lex eacnre o the leaves and lai cens gl is scruclure a ccording co NUM pattern directives fen u l
fhe elrlll la lexicon l asses i hese lhatures to the morl hoh gica level and l he wom level gr utth art akes ca re of sele l ing the al propri at e allixes
the ata NUM specification can indeed be considered as a sociolectal system or standard i.e. a system that rules the communicative usage inside a restrained community of persons
as stated above our initial aim in this corpora study was the building of a corpus based annotated test set to be used for the evaluation of mt systems
tm the model also represents the alternative interpretations that the hearer has considered as a result of repair
because the maintenance manuals are submitted to annual updates the document contains also a large number of factual information such as dates version numbers aircraft type reference page numbers etc
from a communicative point of view the ata specification defines the types of discursive genres illocutionary force of the utterances the writer has to adopt according to a pre defined document structure
indeed for us only the directive illocutionary value of utterances could explain the choice of translating the french infinitive verbs in english imperative verbs
the contrastive study led us to identify some recurrent in our corpus translational consequences due to the pragmatic value preservation from french to english
this part of the study resulted also in the identification of some phenomena that ffave to be strictly formalised if we want them to be correctly handled by a machine translation system
NUM underlying syntactic behaviors the second step of the corpus study consisted in the observation of the formal structures of the utterances previously labeled from a pragmatic point of view
the keeping of the pragmatic value from french to english can also lead to the restitution in english of some missing elements in french rotation des roues rotate the wheels
the result obtained for the following sequence pair this format though a bit complex for an human eye has the advantage to clearly separate annotations from the original textual data
in this way prides supports the most modern lnternet access technology
each user may have any number of profiles covering different topics
in accordance with prides requirements the inquery product was modified to optimize retrieval algorithms
shell interprets a simplitied sql stylc query language and provides editing completion and command and query result history
categorially and semantically ambiguous words are avoided where possible and only included when ambiguity is explicitly tested for
thus the precise identification of the coverage of a system and of its defteieneies is rendered easier
deletion e.g. of an obligatory complement german der mana qer hiilt den vortrag
the seamless coupling between the test suite and the nl system allows running flflly automated retrieve process and compare cycles in the continuous progress evaluation of the grammar and software such that after making changes to the system the irnpact on coverage and performance can be determined in an overnight batch job
the methodology also addresses the specific goals of tsnlp to produce multi purpose multi user and multilingual test suites
for NUM he tsnia proj t wore the lack of gone ra guidelines for the t sl suite construction of adeqmvte a nd compreh nsiv test mnterial and of al prol rial tools
learner is presented a set of examples the experience of the system
the three terms in the expression are depicted in figure NUM
NUM if t is empty a category has to be found on the basis of other information e.g.
when all three thresholding methods are used together they yield very significant speedups over traditional beam thresholding while achieving the same level of performance
thus what we want is a thresholding technique that uses some global information for thresholding rather than just using information in a single cell
the first of these is essentially beam thresholding for each rule p l r if nonterminal l in left cell if nonterminal r in right cell add p to parent cell
unfortunately there is no way to do this efficiently as part of the intermediate computation of a bottom up chart parser NUM we will approximate p l as follows
there are o n NUM nodes in the chart and each node is examined exactly three times so the run time of this algorithm is o n2
the first section of the algorithm works forwards computing for each i f i which contains the score of the best sequence covering terminals tl ti NUM
in the first technique we tried productioninstance thresholding we remove from consideration in the second pass the descendants of all production instances whose combined inside outside probability falls below a threshold
for each nonterminal l in left cell for each nonterminal r in right cell for each rule p l r add p to parent cell without a prior
find out who is the boss of the nici followed by find out who is the secretary of the nici
this statistical approach reached a performance of NUM correctness on the training and test set compared to the NUM and NUM of our dialog network
in multi word expressions some words also have categories that may not appear anywhere else
the intension of the personal pronouns ik i and jij you is represented using the following predicates
furthermore inductive learning algorithms work in a data driven mode and have the ability to extract gradual regularities in a robust manner
we had chosen simple recurrent networks since these networks can represent the previous context in an utterante in their recurrent context layer
an utterance was counted as classified in the correct dialog act class if the majority of the outputs of the dialog act network corresponded with the desired dialog act
we have presented two taggers for french a statistical one and a constraint based one
the paper is structured as follows first we will outline the domain and task and we will illustrate the dialog act categories
then the dialog act with the highest averaged plausibility vector for a complete utterance would be taken as the computed dialog act
for a further evaluation of our trmned network architecture we compared our results with a statistical approach based on the same data
the dialog act network provides the currently recognized dialog act for the current fiat fl ame representation of the utterance part
am going from milano to rome
si da milano a roma di sera
t4 u was a confirmation turn of the user
the lexicon specifies morphophonological and syntactic features of words and contains links between words and the knowledge base carla huls et al
in the application domain of dialogos the goal of the interaction is to collect all the parameters that are necessary to access the railway database for retrieving the information that satisfy user s needs
we will show the positive effects of robust dialogue management and dialogue state dependent language modeling by taking into account both the recognition and understanding performance and the success rate of dialogue transactions
the last turn of the system is a dialogue act that fulfill two communicative goals that is the implicit confirmation of the arrival city and the request of the departure city
in the example on the left the letter t stands for turn the letters u and s stand for user and system respectively
edward s context model performed well on all NUM test expressions but cataphora will also be misinterpreted
yes from milano to roma in the evening
when do you want to travel
focusing is used to limit the information that must be considered in identifying the referents of certain classes of definite nps
moreover we did not implement any flooring constraints NUM on the probabilities p clwl or p w21c
so even with a more complex syntactic structure than the translation examples above the changes can be described by composing mappings between elementary trees or just in the transfer lexicon
the node np1 of the left hand tree of figure NUM can then be described by the string nppnppsrpnil an associated mnemonic nickname might be t1 subjnp
this sort of link is unnecessary when stags are used in mt as the trees are lexicalised and the information is shared in the transfer lexicon
related to this is that at least between english and french extensive syntactic mismatch is unusual much of the difficulty in translation coming from lexical idiosyncrasies
tree adjoining grammars tags cover the first part as a formalism for describing the syntactic aspects of text they have a number of desirable features
so let variables x and y stand for any string of argument types acceptable in tree names for example x could be nxlnx2 and y nl
the situation is more complex in paraphrasing by definition the mappings are between units of text with differing syntactic properties
in the right hand tree of the figure NUM pairing the subject nps of both sentences are linked to np1 of the left hand tree this is a statement that both resulting sentences have the same subject
a paraphrase representation can be thought of as comprising two parts a representation for each of the source and target texts and a representation for mapping between them
in following definitions f is for inside probability and a is for outside probability
the weight for each factor is determined by using held out data
the quantiative results for the wsj and tdt models are collected in tables NUM and NUM respectively
this is because of the fact that the data is tokenized for speech processing whence c
finally for many purposes it is useful to have a metric that is a single number
the operator is the xnor function both or neither on its two operands
this feature makes sense in light of the sign off conventions that news reporters and anchors follow
although they have snuck into the literature in this disguise we believe they are unwelcome guests
which if true contributes a factor of e x NUM NUM to the exponential model
the second was built on the topic detection and tracking corpus allan to appear
model b was trained on the first two million words of the tdt corpus
both models were tested on the last NUM NUM million words of the tdt corpus
an age limit mentions the law not the law does not mention an age limit
rights blames the public authorities the mexican association for human rights blames the public authorities
one problem with this approach is that it is usually not available for a broad coverage system
the back off estimate computes the probability of a word given the n NUM preceding words
in this sentence it must be determined which nominal phrase is the subject of the verb
a high inflation rate expects the economist the economist expects a high inflation rate
this paper describes a procedure to determine the subject and object in ambiguous german constructs automatically
the economists expect a high inflation rate the economists expect a high inflation rate
further development of the morphology component and grammar definition should lead to improved results
the candidate set is computed by finding the subset of the objects in the world that the speaker believes could be referred to by the head noun the objects that the speaker and hearer have an appropriate mutual belief about
each component in the plan the action headers constraints steps and effects are referred to as nodes of the plan and are given names so as to distinguish two nodes that have the same content
the constraints and decompositions of the discourse actions encode the conditions under which they can be applied how the referring expression derivations can be refashioned and how the speaker s beliefs can be communicated to the hearer
this plan is in the common ground of the participants and we propose rules that are sanctioned by the mental state both for accepting plans that clarify the current plan and for adopting goals to do likewise
their system acting as the clerk can not interpret this as a clarification of the take train trip plan since the utterance can not be seen as a step of that plan p
however it requires that the error in the evaluation occurred in an action that does not decompose into any primitive actions which for referring expressions will be the instance of modifiers that terminates the addition of modifiers
finally plan construction is the process of finding a plan derivation whose yield will achieve a given effect and plan inference is the process of finding a plan derivation whose yield is a set of observed primitive actions
a benchmark for such future work could be dialog NUM NUM below from the london lund corpus svartvik and quirk NUM s NUM NUM a NUM NUM which is the basis of the example used in section NUM
since these goals can not be directly achieved by a plan of action the speaker must instead plan actions that will achieve them indirectly for instance by planning an utterance that results in the hearer recognizing her goal
the stapler assigns this using the structure of 3in a complement auxiliary tree the anchor subcategorizes for the foot node which is not the case for a modifier auxiliaxy tree
local descriptors alter just the local memory while global descriptors alter both the local and global settings
in this paper a different application of name searching is considered using name recognition and matching to support ranked retrieval of flee text documents
name matching assumes that two character strings have been identified which are names and the question is only whether they are instances of the same name
other names acronyms and abbreviations were also lagged including geographic product facility and court case names
for this reason we concentrated only on noun phrases analysis as the main source of terminologic information NUM a term is obtained by applying several mechanisms that add to a source word generally a noun a set of further specifications as additional constraints of semantic nature
the following criteria is defined to capture singleton terms if exists at least one document where a noun i is required as index because it is relevant for that document and selective with respect to other documents then such a noun denotes a relevant domain term i.e.
the final row shows the eleven point averages and the numbers in parentheses are the percentage improvement of the proximity based approach over the baseline
in order to approach the problem of terminological induction we thus need NUM to extract surface forms that are possible candidates as concept markers NUM to decide which of those candidates are actu null ally concepts within a given knowledge domain identified by the set of analyzed texts
the prospect of adaptation for information retrieval of the name recognition and matching techniques developed for these applications seems promising however
the third part of the study analyzes the frequency of occurrence of personal and company names in legal and newspaper text collections and queries
examples in this section are not from the corpus but similar examt les were fo md the meeting took NUM hours
for example in isolation agree may not occur intransitively unless it has a plural subject com lex intrans recip class
however the data is rife with examples of intransitive agree occurring with a singular subjeer as seen by the following examlfles
verbs like increase decrease and expand take complement groups which require a noun with the subclass nunit
although it is called nunitp to underline the fact that ordinarily the nouns that occur are nunits or at
wc have added a separate frame group with the name nunitp to i lcb ange which includes the conq leinents mentioned abow
class intrans ellipsis for these cases and since we feel that the coinplement is underlyingly t resent the tagger is able
often been sked why we did not m u llin t g instead of painstakingly hand tagging
NUM nunitp to tag or not to tag another lass of noun t hrases caused us great soul searching
nadvp dir in general these verbs do not take regular np complenlents at least not with the same meaning
the cb 19c is thus again house
vl NUM he is required to be at least NUM years old
there appear to be strong constraints on the kinds of transitions that are allowed however
this conjecture derives from a belief that this approach will most effectively limit the inferences required
c susie prefers the green plastic tugboat to the teddy bear
the basic constraint on center movement is given by rule NUM
these rules can explain a range of variations in local coherence
sequences NUM and NUM are typical cases
in particular not only the value free interpretation but also various loadings may be contributed
this generalization is important for capturing certain regularities in the use of definite descriptions and pronouns
the tree shown in figure NUM represents a hierarchy of directories depicted as bookcases and files e.g. reports papers e mail messages and books
bridgestone sports has so far been entrusting production of golf club parts
a sample muc NUM message and template is shown in figure NUM
we now had to reduce these proposal s to detailed specifications
demonstrative expressions e.g. dit bestand deze this file this one in combination with the realization of an appropriate pointing gesture are common examples of multimodal referring expressions
for example the class person contains two subordinate classes man and woman and the concept of sending an object to someone is represented by a generic relation called send
mediated perhap s by a relational level object
we believe that the fact that these systems use two separate mechanisms for modeling linguistic and perceptual context is a disadvantage over the use of only one mechanism for referent resolution
spatial deixis involves demonstratives or other referring expressions that are produced in combination with a pointing gesture e.g. thiss file in which NUM represents the pointing gesture
these tasks were supposed to induce inferential anaphors NUM plural referring expressions NUM spatial deixis NUM and multimodal referring expressions NUM
NUM interpreting deictic and anaphoric expressions in edward edward is able to interpret the two kinds of referring expressions distinguished in the introduction viz deictic and anaphoric expressions
in addition to the notion of linguistic expectations which exist in any situation the model incorporates a cognitive belief about the future notion of expectation
generation corresponds to the following problem in theorist explain shouldtry sl s2 aa ts a decomp as aa
NUM because russ s previous utterance had not been the first part of an adjacency pair he can not explain her utterance as acceptance or challenge
as a result t3 can not be attributed to any expected act and must be attributed to a misunderstanding either by russ or by mother
first utterances can make only a part of the speaker s goals explicit to the hearer so hearers must reason abductively to account for them
in plan adoption speakers simply choose an action that can be expected to achieve a desired illocutionary goal given social norms and the discourse context
NUM NUM the need for both intentional and social information the problem of interpreting an utterance involves deciding what actions the speaker is doing or trying to do
participants normally rely on their expectations to determine whether the conversation is proceeding smoothly if nothing unusual is detected then understanding is presumed to occur
there is a danger in treating compatibility as a default in that one might miss some intuitively incompatible cases and hence some misunderstandings might not be detectable
our approach is to make compatibility a default and define axioms to exclude clearly incompatible cases such as these the suppositions q and not q
the query language of ims cwb is an elegant and orthogonal design which we believe it would be appropriate to adopt or adapt as a standard for corpus search
katakana phrases are the largest source of text phrases that do not appear in bilingual dictionaries or training corpora a k a
in this paper we have concentrated on the repair of mis understanding
we adopted a simple unigram scoring method that multiplies the scores of the known words and phrases in a sequence
only mappings with conditional probabilities greater than NUM are shown so tile figures may not sum to NUM
np a used three features while cue a and pause a each made use of a single feature
table NUM shows the average human performance for both the training and test sets of narratives
no algorithm or combination of algorithms performed as well as humans
the remaining NUM narratives are reserved for future research
if the value is sentence final contour
performance of individual speakers varies widely as shown by the high standard deviations in our tables
our results lend further support to the hypothesis that linguistic devices correlate with discourse structure cf
note that quantitatively the machine learning results are slightly better than the hand tuning results
while this method does not make sense for humans computers can truly ignore previous iterations
word2 is assigned the second lexical item if cue2 is true na otherwise
h p h distancer i NUM NUM
it could ease system maintenance through welldefined well document internal interfaces between modules
we would like to incorporate the spatter parser which parses far more accurately than fp p does and would look to add to our domain independent semantic lexicon so as to improve merging of entit y descriptions
next the slots in those objects are filled usin g information in the ddo the discourse predicate database other sources of information such as the message heade r e g document number or from heuristics e g the type of an organization object is most likely to be company
the new software developments employed in muc NUM are a stand alone c based name spotter identifinder t a rewrite of the initial components of plum a more robust message reader a revised discourse component and output generator to more cleanly separate discourse structures from th e final template structure and a semantic inference component
in bbn s part of speech tagger post NUM a bi gram probability model frequency models for known words derived from large corpora and probabilities based on word endings for unknown words ar e employed to assign part of speech to the highly ambiguous words and unknown words of the corpus
a score of NUM indicates that the filler was found either directly by the semantics or by a sentence level pattern NUM that it was found in the same fragment as a trigger form NUM in the same sentence NUM in the the template generator takes the ddos produced by discourse processing and fills out the application specifi c templates
during the last week we opened the blind material which had been released in early september
and cc concentrate vb on in his pp duties nns as in rear jj commodore nn at in the dt new york np yacht np club np
it performs reference resolution for pronouns and anaphoric definite nps set and member type references may be treated
figure NUM ne system architecture rectangles represent domain independent language independen t algorithms ovals represent knowledge base s
the notion of sharing is defined based on the similarity as in equation NUM
whether coupling these two methods would increase overall effectivity is an empirical matter requiring further exploration
the system progressively collects examples by selecting those with greatest utility
ipal also contains example case fillers as shown in figure NUM
we further evaluated the system s performance in the following way
this is the issue we turn to in this section
let us look again at figure i in section NUM
in his remarks the vice president lauded the tipster program s teamwork for spanning the intelligence community and partnering with the private sector and leading universities
in met the participants performed the muc NUM named entity task in one or more of the following foreign languages spanish chinese and japanese
over the past seven years a unique bonding chemistry has developed among the large number of government personnel who have had an active hand in the tipster program
the darpa program manager was a strong proponent and advocate of an evaluation driven research paradigm that he was following in the speech component of his r d program
this paper argues that this success has been made possible in large part by tipster s early adoption of and continuing adherence to an evaluation driven research paradigm
throughout its seven year history tipster has achieved many exciting and important research results but listing them here is beyond the intended purpose of this paper
this ccd factor is taken into account when computing the score of a verb s sense
the interpretation candidates for v are derived from the database as sl NUM and s3
the feature NUM strictly speaking it must also provide a rescuing procedure
aop is licensed by the same conditions that license an intermediate a trace
up NUM the question at np a closed door meeting k mart is scheduled to hold today with analysts will be why are n t we seeing better improvement in sales
on the contrary adding categorical information multiplies nondeterminism by adding structural configurations
in fact ambiguities caused by empty categories occur according to structural partitions
some qualitative observations might help clarify the sources of ambiguity in the tables
the same three grammars were compiled in left corner lc tables
this is precisely what i propose to compile in the lexical co occurrence table
frank s parser is augmented by a parse stack to parse head final languages
multiple chains occurring in the same sentence can either be disjoint or intersected
NUM a whoi did maryi seem tj to like ti b
but it is more informative to report performance with both accuracy rate and selection power
in general the measures of accuracy rate and the selection power are highly correlated
it is deleted from the surface
the following convention has been adopted
in addition parameters are usually estimated poorly when the training data is sparse
finally a parameter tying scheme is proposed to reduce the number of parameters
figure NUM the decomposition of a given syntactic tree x into different phrase levels
both turing s and the back off procedures perform better than the maximum likelihood procedure
first the number of parameters can be reduced by using the tying scheme
the reduction of the number of parameters is over NUM for each language model
in the baseline model the parameters are estimated by using the maximum likelihood method
this result indicates that the context free assumption adopted by most stochastic parsers might not hold
that object must be capable of being defined by one or more parameters with the further requirement that comparison operations upon any two parameter values must yield a numeric value reflecting the semantic difference between the values
let us modify the above rule as follows s e pnlx name x c advert ises as vacant a position as chef
traditionally the process of generation is divided into two steps generation of message structure from database records what to say and generation of sentences from message structures how to say it
there is of course no easy solution to these problems from the language engineering point of view our service must simply advise users to check that the job description in the target country corresponds to their understanding
the main aim of the schema is to represent in a consistent way the information which the analysis module extracts from the job ads which the query module searches and from which the generation module produces text
an example of standardized vocabulary in our domain is words like fulltime part time which have an agreed meaning or adjectives like essential as applied to requirements such as experience or a driving licence
second the format of the output can be controlled and customised by the user which means again that the output text is a summary or digest not necessarily presented in the same order as the original text
this is confirmed by the following coordination data which shows that an element which is extraposed form the subject can not occur at vp level NUM is nobody must live here and benefit from income support who is earning more than twenty pounds a week
an example of some job titles is shown in figure NUM the hierarchical nature of the titles and also the existence of some synonyms is suggested by the numbering scheme and is more or less self explanatory
also there is a significant workforce for which foreign language skills are not a prerequisite for working abroad and which furthermore has traditionally been one of the most mobile seasonal semi and unskilled workers
as the binding of extraposed elements is only possible at the right periphery of a phrase the head extra schema specifies its head daughter as pea right and marks its mother node as pea extra the latter is needed for the treatment of adjuncts el
the system must use the established dialog context in order to properly interpret every user utterance as follows
this example would have a relatively low parse cost indicating the system has high confidence in its interpretation
of the results of applying the functions terminal location and tree location to the relevant information above
or done and NUM assertion that the learn fact learning a fact
although it significantly reduces the under verification rate this strategy clearly leads to an ex null cessive number of over verifications
are there any possibilities for further reductions in the under verifications without a substantial increase in the over verification rate
as the threshold was raised more utterances are verified resulting in fewer under verifications but more over verifications
other researchers who contributed to the development of the experimental system include alan w biermann robert d rodman ruth s
be led a displaying nothing NUM c what is the switch at when the led is off
in section NUM we will see how these features assist in deciding when to engage the user in a verification subdialog
furthermore verification of all utterances can be needlessly wearisome to the user especially if the system is working well
where a b z and w are variables over the set of parts of speech
j where tj c context vector of target i after update old context vector of target i before update NUM adjustment step size n context vector for neighborj of target i a j desired context vector dot product for
in the case of prides cgis provide access to all prides services and data subject to user access privileges
this provides prides to the maximum possible user community while simultaneously eliminating the need for prides specific client side software
during pilot operations an extensive evaluation program will gather quantitative and qualitative data about how users work with prides
again the user can customize the display format to suit their personal work style
the user has a variety of options for sorting and segmenting the folder contents list
save folders can also be downloaded to the user s local disk for additional processing
this information is available in hard copy on a daily basis or on cd rom on a quarterly basis
if the document scores above the threshold for the profile it is added to the user s mail folder
the web browser software may be mosaic netscape navigator or any other browser which can process html forms
prides is a pilot effort which will serve users operationally for six months between july NUM and january NUM
for example if a researcher has a new concept for extracting data he may be able to use existing document management capabilities persistent knowledge bases matching functions and defined user interface to test the idea quickly and cost effectively
these tools lie outside the architecture but use information about document relevancy relationships between documents phrase lists name lists and relational or object data base records which has been exported by the functionality residing within the tipster architecture
NUM NUM architecture goals provide application program interfaces apis so that document detection and data extraction can either jointly or singly import process and export data allow the building of applications to handle one or more human languages
it describes procedures for obtaining the current version of the icd submitting icd specifications for a new tipster module proposing modifications to existing icd specifications obtaining certification for a tipster compliant application obtaining information about previously developed tipster compliant modules
templatic morphology is best exemplified in semitic root and pattern morphology
typically at the procurement stage and during the early stages of development more technologically oriented input from the technology transfer officer is required as the project develops and is deployed the end user provides more input regarding needs
for the application developer the architecture will provide most of the same support that is available for the cotr in identifying appropriate tipster technology identifying teaming partners monitoring the project making risk assessment and measuring performance
as a related service the tipster program will also maintain a catalog of other sharable resources such as lexicons gazetteers data dictionaries and query libraries as well as tools to assist in building compliant applications
lcb ktb rcb notion of writing is the root it may occur in all measures apart from measure NUM NUM lcb ui rcb is the perfective passive vocalism
pattern measure i NUM NUM root measure NUM NUM NUM NUM NUM vocalism tense perf voice pass
in ppc the domain of the operation is tile kernel b ij this type is denoted by o and is defined in 5a
if specifying the task of the parser what the parser is supposed to do turns out to be so problematic one could even question the rationality of natural language parser design as a whole
NUM they were circulating a letter expressing concern that pron rel cs it would give the developing countries a blank cheque to demand money from donors to finance sustainable develop null ment
after joint examination y was agreed to be the correct alternative in all cases but NUM where x and y were regarded as equally possible
NUM that pron dem cs there was no outburst of protest over the new policy suggests that public anxiety over genetic engineering has ebbed in recent years
the results are then automatically compared and the differences are jointly examined by these linguists to see whether the differences are due to inattention or whether they are intentional i.e.
for the analysis of unrecognised words we used a rule based heuristic component that assigns morphological analyses one or more to each word not represented in the lexicon of the system
at the level of syntax most of the initial differences were identified as obvious mistakes e.g. he was pmainv fa uxv addressing his hosts
the corpora consisted of continuous text rather than isolated sentences this made the use of textual knowledge possible in the selection of the correct alternative
evaluating a parser s output as well as designing a computational lexicon and grammar presupposes a predefined parser independent specification of the grammatical representation
the only substan null tial difference seems to be that somewhat more effort for documenting the individual solution is needed at the level of syntax
for one successful modeling of coreference relationships is hampered by the crudeness of the representations used
the style displayed in this example is fairly typical although in some cases the sentence struc
we focus on the four mentions of depots in the text which are highlighted with italics
obviously this can not be done with a set of probabilities that add up to NUM
we would expect further gains from encoding additional training data and modeling more informative characteristics of context
therefore we used all such pairs in each training set to train the maximum entropy model
for reasons described below we trained separate pairwise probability models for each of the two approaches
no alternatives were included that were a priori known to be impossible due to incompatibilities
templates in context but by virtue of the existence of conflicting models derived from those templates
significantly larger coreference sets can lead to an enormous number of possible coreference configurations
as one might expect this move only sweeps the problem under the rug
we wish to establish that each semantic equivalence class contains exactly one nf parse
self organizing dialogue structure makes it more portable
indeed one might implement NUM by modifying ccg s phrase structure grammar
the essence of this method is limiting search space utilizing distinguished word classes characteristic to idiomatic expressions revealed by an intensive analysis of these expressions
the most important requirement for the translation module is robustness in the sense that it does n t drop a word even when specifications are contradictory
we presented an interactive machine aided translation method to support writing in a foreign language which is a combination of dictionary lookup and interactive machine translation
null our stepwise conversion scheme in which conversion proceeds from smaller structures to larger ones is a natural conclusion of our try and error based conversion approach
we remark that this restriction is effective only on default decision of which node to pause at and does not restrict operations by the user
most important characteristics of this interactive translation method is that the japanese input is converted to english in several steps allowing user interaction at each step
the interface module is in charge of user interaction morphological analysis and predicting translation equivalent and region as well as function as a front end
to cope with this problem we extended the translation equivalent selection interface so that translation equivalents can be specified as a set for these expressions
just as the translation equivalent selection can be freely changed the area can be changed by dragging the left or right edge of the underline
this expectation is natural and realistic in a country like japan where all high school graduates are supposed to have completed six year course in english
let us instead use a narrower intensional definition of spurious ambiguity
does b following this reasoning we are led into statistical language modeling
consequently the selection of the preposition can be postponed until the very end
NUM NUM NUM the new companies plan the launching in february
when long distance features control grammaticality we can not rely on the statistical model
our approach differs from traditional top down generation in the same way that top down and bottom up parsing differ
this is a subtle interaction is planned a main verb or an adjective
if the selections are correct there is no need to refine the grammar
a new company will have in mind that it is establishing it on february
in general detecting violations will not hurt performance by more than a constant factor
when all alternatives have been processed results are collected into a new e structure
unfortunately it forces us to restate the same syntactic constraint in many places
re quir ments of the current ina rket efficiency scala bility
ic h po ndencies as agreement and sulma tegoriza tion in NUM atterns
within the pair of trees nodes may be linked
the source sentence is parsed according to the source grammar
a higher priority is given to aat g structure over flat g
canonical ordering of the arguments of transitive verbs in korean is sov
all the other nodes are mapped to each relevant node except s
the translation process consists of three steps
second the source derivation tree is transferred to a target derivation
fibre NUM the k e transfer lexicon
each elementary tree is given a priority
it should be noted that our rule packages employ variable bindings to collect information during the pattern matching process
when these scenarios went un null recognized by the system the names were tagged as separate entities
in this manner the system is able to link descriptive phrases that are found in the following forms
appositives prenominals and name modified head nouns are directly associated with their respective named entities during name recognition
usually these were stories in which corporate officers were transferring from one part of a company to another
american express the large financial institution also known as amex will open an office in peking
identifying variations of a person name or organization name is a basic form of coreference that underlies other strategies
the following are examples of some un named organitations the clinton administration federal appeals court mucster s coreference research group a new york consultancy its banking unit an arm of the mucster unit those phrases that have not already been associated with a named entity through context cues must then be associated by reference if possible
this is believed to be due to their predominance within the set of noun phrases found by our system
hence the returned constraint correctly represents the semantic representation for all readings
the main idea to make ka from medium sized corpora a feasible and efficient task is to perform a robust syntactic analysis using lexter see section NUM followed by a semi automatic semantic analysis where automatic clustering techniques are used interactively by the knowledge engineer see sections NUM and NUM
NUM there are about NUM NUM kanji char tcte s that are considered neccssary h r general literacy
in previous researches texts were represented by significarlt woms and a word was regarded as a minimmn semantic unit
in these articles it is usual that the introductory part has little relation to the main theme
figure NUM a procedure ibr the l ocllliient lassilication ushlg i olliain sl ecilic kanji characters
further appendix shows tim meanings of each domain specific kanji character of library science domain
each code is dcvided into t0 items which in turn have details using one or two digits
then the value x NUM ofkanji i x is expressed by the equations as follows
there are two considerations about the extraction of the domain specific kanji characters using the x NUM method
we have applied this idea to the japanese text representation where a kanji character is a morpheme
a post processing stage could add this detail to the parser output but we give two reasons for making the distinction while parsing first identifying complements is complex enough to warrant a probabilistic treatment
in extraction cases the penn treebank annotation co indexes a trace with the whnp head of the sbar so it is straightforward to add this information to trees in training data
the addition of lexical heads leads to an enormous number of potential rules making direct estimation of rhs lcb lhs infeasible because of sparse data problems
our models use much less sophisticated n gram estimation methods and might well benefit from methods such as decision tree estimation which could condition on richer history than just surface distance
p h ln in ni ll h h rl rl rm rm NUM
here the head initially decides to take a single np c subject to its left and no complements a rnultiset or bag is a set which may contain duplicate non terminal labels
the patterns of graylevel are meant to provide a compact summary of which passages of the document matched which topics of the query
however these methods do not use subtopic structure to guide their choices focusing more on the beginning and ending of the document and on position within paragraphs
this is especially important in expository text in which the subject matter tends to structure the discourse more so than characters setting and so on
we should expect to see in grouping together paragraph sized units instead of utterances a decrease in the complexity of the feature set and algorithm needed
much of the current work in empirical discourse processing makes use of hierarchical discourse models and several prominent theories of discourse assume a hierarchical segmentation model
but often one or another will change considerably while others will change less radically and all kinds of varied interactions between these several factors are possible
they conclude that segmenting the text by means of multiple windows can be very helpful if readers are familiar with the mechanisms supplied for manipulating the display
to solve shallow parsing with the relaxation labeling algorithm we model each word in the sentence as a variable and each of its possible readings as a label for that variable
since we are dealing with a set of constraints and want to find a solution which optimally satisfies them m1 we can use a standard constraint satisfaction algorithm to solve that problem
x sij vvi where p is the weight for label j in variable vi and sij the support received by the same combination
the training corpus is partly ambiguous so the bi trigram information acquired will be slightly noisy but accurate enough to provide an almost supervised statistical model
the approach can use linguistic rules and corpus based statistics so the strengths of both linguistic and statistical approaches to nlp can be combined in a single framework
1a weighted labeling is a weight assignment for each label of each variable such that the weights for the labels of the same variable add up to one
if the algorithm should apply the constraints in a more strict way we can introduce an influence threshold under which a constraint does not have enough influence i.e. is not applied
since constraints are used to decide how compatible a tag is with its context they have to assess the compatibility of a combination of readings
the rules are contextual constraints for resolving syntactic ambiguities expressed as alternative tags and the statistical language model consists of corpus based n grams of syntactic tags
each stage of the algorithm is described in more detail below
in some cases the system also needs to know which constraints axe ne gotiable
success if none of the previous states were found a query is issued to the system
if a word is assigned to several different clusters we distribute its total frequency among those clusters in proportion to the frequency with which the word appears in each of their respective related categories
the states in the dm are shown in figure NUM and are described in detail in this section
the existence of one of the first nine states listed below may be determined without a database query
simply becomes the standard em algorithm and 6we have confirmed in our preliminary experiment that mcmc performs slightly better than em in document classification but we omit the details here due to space limitations
testing for the example in tab NUM we can calculate according to eq NUM the likelihood values of the two categories with respect to the document in fig NUM tab
if that happens the user is notified of the inconsistency so that the error may be rectified
the em algorithm first arbitrarily sets the initial value of NUM which we denote as NUM NUM and then successively calculates the values of NUM on the basis of its most recent values
fig NUM shows the best result for each method for the category corn in the first data set and fig NUM that for grain in the second data set
NUM p wlkj o w k where ikjl denotes the number of elements belonging to kj then we will get the same classification result as in hcm
NUM distinction between ba flights which it knows about and other flights which it does not know about but for which users are referred to airport help desks sometimes by being given the phone numbers of those desks
the table shows that NUM cases were reclassified re that the two cases of alternative classifications involved gg1 and gg7 and that an agreed classification involved a debate on a component issue id c deb
the analysers agreed that the system should always offer the phone number of an alternative information service when it was not itself able to provide the desired information instead of merely telling users to ring that alternative service
we are investigating what it takes to make him an expert in using det by having him analyze the same sundial sub corpus as was reported on above and we hope that he will participate in the planned second sundial sub corpus exercise
in the test case objectivity of detection will have to be based on the empirical fact if it is a fact that developers who are well versed in using the tool actually do detect the same problems
ifdet works well under circumstances NUM we shall know more on how to use it for the analysis of corpora produced without scenarios such as in field tests or without the scenarios being available
the transcriptions came with a header which identifies each dialogue markup of user and system utterances consecutive numbering of the lines in each dialogue transcription and markup of pauses ahs hmms and coughs
similarly ggi say enough and gg5 be relevant may on occasion be two faces of the same coin if you do n t say enough what you actually do say may be irrelevant
turning now to the objectivity or intersubjectivity of the performed analysis we mentioned earlier that this raises two issues wrt the sundial corpus a to which extent do the analysers identify the same cases types of guideline violation
of the NUM claimed guideline violations NUM were agreed upon as constituting actual guideline violations comprising the status descriptors identity complementarity consequence violations user symptoms altematives reclassification re and reclassification rce
lasie module interfaces were not standardised when originally produced and its cre oleization gives a good indication of the ease of integrating other le tools into gate
aside from the host of fundamental theoretical problems that remain to be answered in nlp language engineering faces a variety of problems of its own
after execution the results of completed modules are available for viewing by clicking again on the module box and are displayed using an appropriate annotation viewer as described above
typically a creole object will be a wrapperaround a pre existing le module or database a tagger or parser a lexicon or ngram index for example
in this graph the boxes denoting modules are active buttons clicking on them will if conditions are right cause the module to be executed
these annotations can be viewed either in raw form using a generic annotation viewer or in an annotation specific way if special annotation viewers are available
the ggi is a graphical tool that encapsulates the gdm and creole resources in a fashion suitable for interactive building and testing of le components and systems
gdm imposes constraints on the i o format of creole objects namely that all information must be associated with byte offsets and conform to the annotations model of the tipster architecture
note that the viewers are general for particular types of annotation so for example the same procedure is used for any pos tag set named entity markup etc
centering required to determine that the pronoun does not refer to a member of the current forward looking centers and to identify the context back to which attention is shifting
the various discourse segments are all directed at providing the system with relevant constraints for the database query
there are different types of trees for extraction
the tree schemata are generated grouped in families
we see several possible applications of the tool
it includes cases of reduction of arguments e.g.
such a lexicalized formalism needs a practical organization
so a verb anchored tree has a sentential root
in the conjunction of two descriptions the identification of two nodes known to be the same either by inference or because they have the same constant requires the unification of such meta features
dimension NUM the syntactic realizations of the functions it expresses the way the different syntactic functions are positioned at the phrase structure level in canonical position or in cliticized or extracted position
well formedness of elementary trees is also expressed through the form of the hierarchy itself the content of the classes the inheritance links the inheritance modes for the different slots
in practice they very accurately translate correct input sentences but also accept and translate incorrect sentences producing meaningless results
at each point in the sentence r looks up the combination of the best partial word segmentation hypothesis ending at the point and all word hypotheses starting at the point
each piece of evidence contains a belief beli and an evidential relationship supports beli bel
it contains the applicabifity conditions for performing an action the subactions comprising the body of an action etc
the goal of the modification process is to resolve the agents conflicts regarding the unaccepted top level proposed beliefs
year supports the belief that he is not going on sabbatical next year supports visitor smith ibm next year on sabbatical smith next year perhaps because dr smith has expressed his desire to spend his sabbatical only at ibm
however the system will not accept the top level proposed belief teaches smith a since the system has a prior belief to the contrary as expressed in utterance NUM and the only evidence provided by the user was an implication whose antecedent was not accepted
the correct node specialization of modify proposal will be invoked since the focus of modification is a belief and in order to satisfy the precondition of modify node figure NUM mb s u on sabbatical smith next year will be posted as a mutual belief to be achieved
this model views coll tive planning as agent a proposing a set of actions and beliefs to be i ted into the plan being developed agent b evaluating the proposal to determine whether or not he accepts the proposal and ff not agent b proposing a set of modifications to a s original proposal
this makes the schema neutral with respect to analysis query and generation languages
first it integrates canned text templates and grammar rules into a single grammar formalism
a heuristic to deal with this is to specify for each of the two languages whether prepositions or postpositions more common where preposition here is meant not in the usual part of speech sense but rather in a broad sense of the tendency of function words to attach left or right
v due NUM rcb eeause multit le engines cml i r rcb lcb luce e luiva enl arcs which are ombine i in the lcb ha rl wil h both engil cs lcb redited for the arc
the ebmt engine described here is a completely new implementation ill c replacing an earlier lisp version
figure NUM bilingual dictionary entries american health organization and prior project evaluations NUM indexed as described above
additionally candidate chunks are omitted if the alignment was successfifl but the scoring function indicates a poor match
the text is tokenized prior to indexing so that words in any of the equivalence classes detined in the ebmt contiguration tile such as month names countries or measuring units as well as the predetined equiwdence class nuntlmr are indexed under the equivalence class rather than their own names
next the sentence pairs containing tile chunks retold in the lirst phase are read from disk and alignment is performed on each in order to determine the translation of the chunk unless the match is against the entire col pus entry in which case the entire target language sentence is taken as the translation
annotation is viewed as an interactive process where manual and automatic processing alternate
two different possibilities for defining selectional restrictions are considered NUM selectional restrictions obtained from the frames currently provided by wordnet
a backward sequential search bss begins by designating the saturated model as the current model
g NUM x NUM is the most consistent of the evaluation criteria in feature selection
the significance tests especially the exact conditional are more affected by the search strategy
this preference order reflects the presumed inference load put on the hearer or speaker to coherently decode or encode a discourse
bss bic and the naive bayes find the most accurate model for NUM of NUM words
let this research was supported by the office of naval research under grant number n00014 NUM NUM NUM
sequential model selection is a viable means of choosing a probabilistic model to perform word sense disambiguation
it states the conditions for the comprehensive ordering of items on c x and y denote lexical heads
the preprocessing stage for definition of word interest includes part of speech pos tagging and stop word removal thereby yielding the following result
functional ante express constraints generate are the same as those generated by walker et al including these model extensions
nevertheless it is evident that in comparison our algorithm is simpler requires less preprocessing and does not rely on information idiosyncratic to ldoce
algorithm i sense division for a head word h step NUM given a head word h read its definition defh from ldoce
for instance chicken is listed under both topics eb and topic ad while duck is listed under ad but not eb
the sets of completely connected nodes i.e. cliques correspond to sets of interdependent variables
fi and oi be the frequency and probability of observing the i th feature vector respectively
to make hay is listed among the NUM most frequent verbs
step NUM can be executed in time o nn as discussed in section NUM since the size of tx and t is o nn all other steps can be easily executed in linear time
since we require a weighting scheme that is decreasing in l we set w wl w NUM n wl w l fl with fl again free
all the similarity functions we describe below depend just on the base language model p i not the discounted model NUM from section NUM NUM above
in the usual word sense disambiguation problem the method to be tested is presented with an ambiguous word in some context and is asked to identify the correct sense of the word from the context
where c wl w2 is the frequency of wl w2 in the training corpus and c wl is the frequency of wt
thus we do not measure the absolute quality of the assignment of probabilities as would be the case in a perplexity evaluation but rather the relative quality
given the smoothed denominator distribution we set l v wl w lo d wlllw l where NUM is a free parameter
the similarity based methods also do much better than rand which indicates that it is not enough to simply combine information from other words arbitrarily it is quite important to take word similarity into account
suppose that attachments represented by cfg rules and lengths are extracted from the correct syntactic trees in training data and the frequency of each kind of attachment is obtained as f ll NUM NUM l r1 r2
thus our problem involves the following three subproblems a resolving structural ambiguities based on lpr in terms of probabilistic representations b resolving structural ambiguities based on rap and alpp in terms of probabilistic representations and c combining the two
the number i accuracy obtained was NUM NUM table NUM represents this result as lex3 lex2 syn where the number n accuracy is defined as the fraction of the test sentences whose preferred interpretation is successfully ranked in the first n candidates
ptex3 denotes the lexical likelihood value of an interpretation calculated as the geometric mean of three word probabilities pte NUM the lexical likelihood value of an interpretation calculated as the geometric mean of two word probabilities and psyn the syntactic likelihood value of an interpretation
the realization of such a method would make it possible to a save the cost of defining knowledge by hand b do away with the subjectivity inherent in human definition c make it easier to adapt a natural language analysis system to a new domain
assuming that NUM is known to be NUM if h is a verb phrase and m is a prepositional phrase the preference value is likely to be high but if h is a noun phrase and m is a prepositional phrase it is likely to be low
he points to several campaigns with pride including the taster s choice commercials that are like a running soap opera
we then employed the maximum likelihood estimator to estimate length probabilities using the selected syntactic trees e.g. if cfg rule np np pp is applied x times and among the attachments obtained by applying this rule xi of them have the lengths of NUM and NUM then the length probability p NUM NUM np np pp is estimated as
table NUM shows tagging accuracy depending on the three different levels of reliability
in order to explore this idea further we selected the confusion set lcb amount number rcb as a testbed for performance tuning to a particular confusion set
what we really want is an approximation of the original space that eliminates the majority of the noise and captures the most important ideas or semantics of the texts
the goal of lsa then is to take the evidence i.e. words presented and uncover the underlying semantics of the text passage
the average sentence length in the corpus is NUM words so this step has the effect of reducing the size of the data to approximately half the original
a sentence from the test corpus is selected and the location of the confusion word in the sentence is treated as an unknown word which must be predicted
similarly the sentences used to test the lsa space s predictions were those extracted from the test corpus which contained words from the confusion set being examined
while the results of this experiment look very nice they still do n t tell us anything about how useful the technique is when applied to unedited text
we ve shown that lsa can be used to attack the problem of identifying contextual misuses of words particularly when those words are the same part of speech
as a result lsa gives them a higher weight and lsa almost always predicts amount when the confusion word in the test sentence appears in this context
both systems randomly split the data such that roughly NUM is allocated to the training corpus and the remaining NUM is reserved for the test corpus
section NUM shows how italian wottdnet has been coupled with the parser both for describing lexical senses and as a repository for selectional restrictions
the concept somebody includes not only the synset person but also all the synsets denoting group of people that could hold the agent thematic role
there is insufficient information in the node labels to disambiguate the grammatical function
section NUM reports a number of experiments that has been performed to individuate the methodology design with the best trade off between disambiguation rate and precision
preliminary versions exist for five more pairs swedish french french english english danish french d
however even dlough this is a compile time algorithm we should be con erned about its eflio ciency since it has ext onential comph xity
the main source of complexity is that we inight have to check very pair of sul sets of disjun tions fl oin the group
the modularizatioil algorithm performs both of these steps repeatedly until either a pmr of indepe ndent ease r rms is found or until all possible pmrs have been checked
the alternatives are the conjunction al c NUM a a2 NUM and the cases are the disjunction al v a2
groups then involves determining that cases can be split into a conjunction of two smaller cases a v a a a v a
since ill many current nlp systems a signiticant amount of tilne is spent performing unification optimizing feature structures for unillcatioll shouhl increase the tmrtbrmance of these syst ems
since the performance of an algorithm ibr processing constraints with dependent disjmmtions is highly deterxnined by its input the transformatioll presented in this paper should prove beneficial for all such algorithms
taking this into account we decided to keep the output of the initial sst and to include there the information necessary for removing the category labels
table NUM shows tagging accuracy depending on the three different levels of reliability
the rule in figure NUM c shows how a resultative construction like jon painted the wall red is supported by a minimal sign like paint
after all analyses are completed the text along with the error results and annotations from the error rules will be passe d to the response generator
the figure indicates that while the s plural ending is being acquired so too are both proper and regular nouns and one and two word sentences
the authors are site coordinators the project has been conducted by them and other members including mariana damova duco dokter margit langemets auke van slooten petra smit maria stambolieva tarmo vaino and ulle viks
a demonstration in unix for applied natural language processing emphasizes components put to novel technical uses in intelligent computer assisted morphological analysis icall including disambiguated morphological analysis and lemmatized indexing for an aligned bilingual corpus of word examples
during phase ii tipster became primary sponsor for both the message understanding conferences and the text retrieval conferences based on the belief that these forums for evaluation of text processing technologies are essential to continued success in tipster research and development
while continuing its traditional focus on advanced research and metrics based evaluation the need for a supporting architecture was recognized along with the realization that many of the techniques developed were now sufficiently stable to be applied in an operational environment as demonstration projects
conclusion of phase h the NUM month workshop which concluded tipster phase ii brought together a large number of researchers and developers to discuss their results and describe their progress since NUM and to present their findings to a variety of potential customers
the use of these technologies will undoubtedly expand well beyond the prototypes and operational systems built during tipster phase ii for a small number of government agencies as the world at large recognizes the need for document detection and information extraction
both the message understanding conferences muc s which preceded tipster and the text retrieval conferences trec s evaluated the state of the art and provided a major additional benefit in promoting text processing research and development outside of the tipster text contracts since they were organized by nrad and nist and advertised to a wider community
by regularly providing a forum for discussion the program has also fostered cooperation among an ever expanding group of academic institutions and industry vendors who have shared ideas and resources while pursuing different approaches to the problems of text processing
comparing these figures with related works is very diflqcult due to the differences in the underlying semantic type systems and mainly to the variety of information used by the different methods
first a set of thematic verb instances from source sentences are collected for each given semantic class so that social verbs are taken separate from change or cognition verbs
this is reasonable for nouns only NUM unique beginners but it seems still inappropriate for verbs that have hundreds of unique beginners about NUM
he was n t ready to face the day
NUM this work has been partially supported by the esprit lre project n NUM ecran modeling semantic information is much more corpus and domain dependent than pos or syntactic tagging
one possible answer to this problem is the use of generic and modula r
thus cite inl ut st rue cure never ontmns all motions
i atterns need not spe ify an absolut e ordering
first it replaces the contimmtion class mechanism with a feature based word grammar and lexicon
figure NUM lexical entry h r gem in hpsg and in fuf
sentence initial position of the finite verb is encountered in imperative clauses and yes no questions
ong dis ance del endcncies a r
sel or h mimmce s heniai a and princillles
after converting the texts to an upper case only format with all capital letters and retraining the network on the texts in this format the system was able to correctly label all but NUM NUM
we present results of testing the satz system with a neural network including investigations of the effects of varying network parameters such as hidden layer size threshold values and amount of training data
mark wasson and colleagues invested nine staff months developing a system that recognizes special tokens e.g. nondictionary terms such as proper names legal statute citations etc as well as sentence boundaries
in an evaluation on a sample of NUM documents the developers of the program found it to incorrectly classify sentence boundaries NUM times out of NUM possible an error rate of NUM NUM
the probabilities were actually estimated for the beginning and end of paragraphs rather than for all sentences since paragraph boundaries were explicitly marked in the ap corpus while the sentence boundaries were not
the strength may be used only to indicate the presence or absence of f in the document in which case it takes on only the values NUM or NUM it may be equal to n f d or it can take other values to reflect also the size of the document
we decided to see if satz could be applied in such a way that it improved the results on the hard cases on which the hand written rules were unable to perform as well as desired
the style program produced an error rate of NUM NUM over the ocr texts the satz system using a neural network trained on mixed case wsj texts produced an error rate of NUM NUM
in many languages the punctuation mark that indicates the end of sentence boundary is ambiguous thus the tokenizers of most nlp systems must be equipped with special sentence boundary recognition rules for every new text collection
the existence of punctuation in grammatical subsentences suggests the possibility of a further decomposition of the sentence boundary problem into types of sentence boundaries one of which would be embedded sentence boundary
quantification problem although not within drt
these ends can be achieved by using general purpose components for both speech and language processing and training them on domain specific speech and text corpora
applying rda to a tutor s explanation is exhaustive i.e. every word in the explanation belongs to exactly one element in the analysis
in order to devise an algorithm for cue selection and placement we must determine how cue usage is affected by combinations of these factors
the study is part of a project to improve the explanation component of a computer system that trains avionics technicians to troubleshoot complex electronic circuitry
we are grateful to erin glendening for her patient and careful coding and database entry and to maria gordin for her reliability coding
however this relation is embedded in the contributor of the relation between b and c which is cued by this is because
to determine how to build an automated explanation component we collected protocols of NUM human expert tutors providing explanations during the critiquing session
because the results reported in this paper depend only on the structural aspects of the analysis our reliability assessment is confined to these
null we hypothesized that cue selection for one relation constrains the cue selection for relations embedded in it to be a different cue
cues are words or phrases such as because first although and also that mark structural and semantic relationships between discourse entities
of course the authors are responsible for all remaining errors
in this section we discuss a novel thresholding technique multiple pass parsing
the second pass is more complicated and slower but also more accurate
the first and simplest technique we will examine is beam thresholding
we attempted to duplicate this technique but achieved only negligible performance improvements
NUM there are many possible examples of first and second pass combinations
first storing all the forward and backward probabilities can be expensive
that is we assume independence between the elements of a sequence
in this paper we will consider three different kinds of thresholding
any nodes with a probability below this threshold are pruned
to solve both these problems only states at word transitions are saved
in particular we have demonstrated NUM how variation in document length can be tolerated through either normalization or negative weights NUM the positive effect of applying a threshold range in training NUM alternatives in considering feature frequency and NUM the benefits of discarding irrelevant features as part of the training algorithm
in the basic versions the strength of the feature is taken to indicate only the presence or absence of f in the document that is it is either NUM or NUM the training algorithm was run iteratively on the training set until no mistakes were made on the training collection or until some upper bound NUM on the number of iterations was reached
motivated by this concern a discrimination oriented learning procedure is proposed in this paper to adjust the parameters iterafively such that the correct ranking orders can be achieved
the reduce action n quan nlm given the left contexts p n2 never occurred in the training set
on the contrary the results with robust learning for the test set as shown in table NUM b are much better in all cases
this is not surprising because the lr parsing table generator would merge certain states according to the context free grammar and the closure operations on the sets of items
with such an approach it is very easy to implement mildly context sensitive probabilistic parsing on existing lr parsers and the probabilities can be easily trained
in the above equation it is assumed that each phrase level is highly correlated with its immediately preceding phrase level but less correlated with other preceding phrase levels
according to the results of our experiments the first term is equal to one in most cases and it makes little contribution to discriminating different syntactic structures
the understanding system depicted in figure NUM derives the semantic frame representation directly from the parse tree
the algorithm updates the weights of active features only when a mistake is made as follows NUM in the promotion step following a mistake on a positive example the positive part of the weight is promoted w a w while the negative part of the weight is demoted wi ft
we used formula NUM to calculate the value of xp j xai2j and xd y where xp i xai and xd indicate which word i appears most frequently in t e context j of paragraph article and domain respectively
for simplicity the term cotr will be used in the following discussion
semantic grammar controversies of the NUM s
fred was the president of legal beagle inc
which would have led us to tag j
t barnum as a person and f
both active and passive verb forms are recognized
each token is looked up in our dictionaries
these patterns also serve to resolve some type ambiguities
noun phrase conjunction is also handled at this stage
james is vacating chairman and chief executive officer
nyu did relatively well on the scenario template task
if different lexical entries are assigned to the content words in the following sentence because they differ semantically but not syntactically then the sentence will have NUM parses NUM NUM for the attachment ambiguity to be disambiguated
the topmost vp which is a constituent of the s will unify the in and out values so that either the default agent meaning or an actual agent meaning is therefore transmitted eventually back to the verb
when fi ame simple time day of week wednesday tin e or day noruing a speech act multiple suggest accept who frame i frame free sentence type state
NUM considered hostile act this was considered to be a hostile act
such a failure will never happen in our encoding thus far since the entry for btm in each bitstring is NUM there will always be one adjacent argument pair unlinked and so unification will always succeed
value must be a list of categories we can also if required use a simple type of feature default to make the categories written by a grammarian more succinct default person NUM
construct a selector feature whose values will be of the form x x where the second member of the tuple is a tuple of the same length as that in the values feature
the parser may produce many ilfl s for a single sentencej sometimes as many as one hundred or lnore
the value of next is another instance of the kleene category which shares the value of the finish feature and where the value of the kcat feature is the adj category as it appeared on the original rule
figure NUM excerpt from the dialogos corpus
the grammarian presumably wanted the value of the sere feature to depend on the adjp actually present while wanting computational linguistics volume NUM number NUM the value of the agr feature to be set ultimately by the nbar
our goal in other words is to measure objectively the ability of subjects to understand the content of speech output
we have worked on a corpus of NUM route descriptions in french
for example the action of the type progression e.g.
a path is made up of transfers and relays
in our case the purpose of the intermediate representation is to extract from the linguistic description the information concerning the route with the aim of representing it in the form of a sketch
we have decomposed it into a path and landmarks
it seems that in some cases verbal side incompleteness problems might be solved thanks to some relevant linguistic markers as well as to the knowledge included in the conceptual model of the route
these latter are included in sketches in the form of verbal labels
then you turn right you come across a series of sign posts
to be precise for each class c we have listed the words for which c arg maxe p c w figure NUM shows a histogram of the winning assignment probabilities maxe p c w for these words
finally we needed a method for combining context based and non context based predictions in such a way as to reflect not only which factors are important but also to what extent they are important and under what circumstances
this disjunction is hard to handle computationally
it is interesting that another sense of international utilizes the owned by property noted above as in owned by a set of two or more countries and yet another combines location with event relatedness as in manufactured in a set of two or more eounlries
the disambiguation mnong such multiple senses is not a simple matter and in an unusual contraposition to the standard semantic problem of infinite polysemy a move up rather than down to the undifferentiated generic meaning of an adjective like international is recommended in case of disambiguation problems
in particular we demonstrate NUM how variation in document length can be tolerated by either normalizing feature weights or by using negative weights NUM the positive effect of applying a threshold range in training NUM alternatives in considering feature frequency and NUM the benefits of discarding features while training
thus it would be applicable to noun noun combinations adverb verb combinations and other mollification situations as illustrated in NUM the most challenging cases in all kinds of modification would be those where syntactic dependency does not i redeternfine semantic dependency
tmr is a fr une based language where frame names typically refer to instances of ontological concepts slot names are derived from a set of ontological properties and slot fillers are either elements of property value sets or pointers to concept instances
the first element is represented ill a set notation setl shows that varl belongs to the set whose typical member is denoted by a variable refseml in tile ease of authentic and nominal but not in file case of fake
the superentry for abuse includes at least three senses roughly abuse v1 insult verbally abuse v2 violate a law or a privilege and abuse v3 assault physically rod the adjective may be derived from any one of them
in NUM the linking attribute size is selected rather high in the hierarchy of attributes because in the ontology size attribute is the parent of such properties as length a tt r i but e wldtti aqtribute a r e a attribute weight attribute etc
the method is based on the discovery of a small number of basic types of adjectival lexical entries and its nse with minor modifications with a l wge number of specific lexical entries thus nmking tile acquisition of adjectives cognitively easier faster and cheaper
as colnputational semantics m wes to large scale syslcms serving non toy dom fins the need for large leticons with entries of all lexical categories is ireconfing increasingly acute and the attention is turning more towards such previously neglected or avoklcd categories as the adjectives
we have also discovered that this approach to adjectival me ruing is language independent what varies from language to language is the adjectival superentries i.e. the various combinations of different meanings of the same adjective as well as adjectival availability for a certain metaling
finally by tracing the path from the root node to a leaf node aud assigning a bit to each bra uch with zero or one representing a left or right branc b respectively we car assign a bit string word bits to each word in the vocabulary
this problem is best solved with a best first search
however both appear to sacrifice accuracy when compared to aic
figure the gray partitions indicate all the possible constructions of st m and all the possible constructions of sl m NUM j respectively
the first term in the above expression is for the combination of st v j from lt v i and st i j
thus the probability of a dependency tree is defined either by p lr bos eos or by p st NUM sos
sailboating sailing sailing sailing key class NUM mccann erickson the agency mccann erickson interpublic group s mccann erickson min mccann erickson mccann we our mccann their mccann the agency mccann we the agency the agency mccann mccann the agency mccann we one of the largest world wide agencies min one status opt the mccann family min family us mccann it
for any set of english temporal expressions their information content can be computed and compared which allows the system to compute answers to the yes no questions about various aspects of time answers to the when how long and how often queries of the resulting knowledge base and t o a limited extent temporal ordering of the events described in the documents
NUM in the full paper we give detailed examples showing the different neighborhoods induced by the different measures which we omit here for reasons of space
the uno system automatically computes relations between the following expressions many differences very many differences not very many differences reasoning with uncertainty and qualitative probabilistic reasoning without underlying numeric values just like the other systems participating in muc NUM our system often misses a high level relation betwee n entities described in a sentence because our parser does not attempt or fails to compute full sentential leve l parsing
in the first NUM test articles there were NUM or NUM problematic markings and NUM or NUM of unproblemati c markings with bits of text that was not there appearing and portions of teh existing text disappearing reti chairman at the end of the year
closely mimics representation and reasoning inherent in natural languag e the uno model closely mimics the representation and reasoning inherent in natural language because it s translation procedure data structures of the representation and inference engine are motivated by th e semantics and pragmatics of natural language
pn tpn rcb their left hand side type is the representation of a noun phrase the largest type or the name of a concept the right hand side is a two element set a property value p and tp a set of t p elements representing the fact that the property value p holds at a temporal interval t with the probability p
good performance on identifying locations stems from the combination of our rather complete critica l knowledge bases with the major named types and geographical information for all countries and automati c interpretation of locative expressions with a known geographical type such as the city of farmington hills and the isle of man and expressions of the form smaller region larger region such as poland new york
NUM asked why would choose to voluntarily exit while he still to so young m e ex type person james enamex says it is time to be a tad selfish about how h espends his d enamex type person james enamex who has a reputation sa asextraordinarily ton kmaster says that because he had a greattime
central claim of the current paper is that tile interactions between ineaning postulates can produce sut tle effects which you may miss if you simply label items as belonging to lasses or as being in relationships with one another and leave it at that if you simply say for instance that some event is progressive without spelling out the mps for progressive
several comments should be made with respect to the overall adhoc recall precision averages
but in NUM there is nothing to say that this sequence has more than one member and the fact that only on peach is involved suggests that it has exactly one member whereas in NUM the temporal properties of the conceptually instantaneous act of hiccupping mean that there must be more than one such event
the following meaning postulate says that the relationshit simple holds between an instant t and all event type p if there is an interval i which contains t and for any instant t in i there is some event e of the apt ropriate tyi e which starts before t and finishes after it
we an always onstrain a set of eve nts to t e a singleton if we need to so certainly nothing is lost by talking about sets rather than individuals
the tense of the core verb together with ally auxiliaries specify a relationship between the present time now all anaphoric reference time re and the time mentionea in this relationship
there for more than five years in total whereas it is atl but impossilfle to read NUM as saying anything other than that his residence in bray took no more or less than five years
cityal city university london okapi at trec NUM by s e
however additional care was taken not to overexpand the very short topics
in the case of NUM it is not possible for there to be a single event since the start and end points of a single hiccup are taken to occur with no intervening instant
and a few groups experimented in trec NUM with an alternative definition of routing
in trec the track was made official and NUM groups took part
the percentage of relevant documents found however has not changed much
cityal city university london okapi at trec NUM by s e
the pircsl system used both passage retrieval subdocuments and topic expansion
since it finds a referent file i only for dir NUM it can determine file l as referent and dir NUM as relatum
a particular linguistic expression describing a projective relation can be used in three different ways deictically intrinsically and extrinsically
projective relations convey information about the direction in which an object is located with respect to another object or to the world
we call definite nps referring to the only object of a certain type visible at that moment implicit spatial carla huls et al
if another related relation is added to the knowledge base e.g. live in NUM expressed by a subsequent koen woont in amsterdam
die them is considered to refer to the most salient instance satisfying the semantic restrictions in this case set NUM
at the bottom of the model world window a mouse documentation bar is presented the a screen dump of edward
using a mouse the user can manipulate the graphical representation of the domain objects by pointing clicking and dragging
in the context of the present paper we distinguish three types of deixis personal temporal and spatial deixis
one of the application domains involves a file system environment with documents authors a garbage container and so on
here we focus on just the attentionm level
according to our objectives to obtain subgraphs of topics words in class NUM is quite important to be duplicated
case is assigned structurally to a syntactic position governed by a case assigner
our formulation of this parametric distinction is as follows
computational linguistics volume NUM number NUM properties of the phrase containing it
the value barrier means that the movement has already crossed one barrier
squibs and discussions efficient parsing for korean and english a parameterized message passing approach
for example c precedes ip in the english network of figure NUM
figure NUM depicts portions of the grammar networks used for english and korean
we focus particularly on the problem of processing head final languages such as korean
in english the setting of this parameter is i
both cfg parsers use the grammar iii in tomita NUM pp
the system is a walk up and use prototype slds for over the phone ticket reservation for danish domestic flights
it is common for our system to find multiple possible parses of an input string where some parses may contain mal rules and others do not some may contain different mal rules than others etc deciding between these multiple parses corresponds to deciding which errors if any the student made in the given sentence
in this paper we introduce a computerassisted writing tool for deaf users of american sign language asl
knowing where the student is in acquiring the second language can help a system distinguish among the cases above
finally either case NUM or NUM is likely if subject verb agreement has already been acquired by the user
in table NUM we present results from small test corpora for the productive affixes handled by the current version of the system as with names the segmentation of morphologically derived words is generally either right or wrong
note that it is in precision that our overall performance would appear to be poorer than the reported performance of chang et al yet based on their published examples our system appears to be doing better precisionwise
fortunately we were able to obtain a copy of the full set of sentences from chang et al on which wang li and chang tested their system along with the output of their system
on the first of these the b set our system had NUM recall and NUM precision on the second the c set it had NUM recall and NUM precision
it can also be seen clearly in this plot that two of the taiwan speakers cluster very closely together and the third taiwan speaker is also close in the most significant dimension the x axis
interestingly chang et al report NUM NUM recall and NUM NUM precision on an NUM NUM word corpus seemingly our system finds as many names as their system but with four times as many false hits
the first probability is estimated from a name count in a text database and the rest of the probabilities sproat shih gale and chang word segmentation for chinese are estimated from a large list of personal names
NUM tts systems in general need to do more than simply compute the pronunciations of individual words they also need to compute intonational phrase boundaries in long utterances and assign relative prominence to words in those utterances
the difference between the two situations is that in the former the agent derives exactly one interpretation of an utterance and hence is initially unaware of any problem in the latter the agent derives either more than one interpretation with no way to choose between them or no interpretation at all and so the problem is immediately apparent
we sum up the generalizations that are captured by our analysis a relative clauses sentences and pps can be extraposed nouns and verbs can function as antecedents
only in fronting examples like NUM the vp does form a separate constituent and hence does exhibit the periphery marking needed for extraposition
b both extraposition from fronted phrases and fronting from extraposed elements axe accounted for by our head extra schema which is constrained by the pmc
the resulting document context vectors have the property that documents that discuss similar themes will have context vectors that point in similar directions
this has the effect of spreading out the context vectors hopefully driving the context vectors of non co occurring words closer to orthogonality
other vector space approaches to text retrieval exist but none embody the ability to learn word level relationships NUM NUM
sets of words text passages and queries and documents can also be represented by context vectors in the same information space
in order to avoid the requirement for multiple iterations hnc proposes to evaluate the performance of the following one step learning law
it is a basic tenet of the matchplus approach that words that are used in a similar context convey similar meaning
for this example it is assumed that the pair attack and ataque are a tie word pair
hnc has performed preliminary evaluation of an english spanish version of this system by examining stem trees for tie words and non tie words
NUM a NUM t j where t context vector for word stem i tj context vector for word stemj ate desired dot product for
neighbors for ataque are personas matado groupo and contra
the condition for binding the dependency is formulated relative to the antecedent of the extraposed phrase which entails that no fixed site for extraposition exists
an idle subscriber tl has a phonenumber NUM and has a phonenumber NUM and an idle subscriber t2 has a phonenumber NUM
grains tops or tips of things is the exception since this is a relation which is encoded in the language by means of constructions
the confusing point is that i imon in tile example rodaja di limon surfaces grammatically as substances usually do namely zerodelermined
this way the eoinlx sition of slice n np alld cake np will restlll iu au n slice of cake
namely each kind at pn call combine with cerlain relercatial llotllls but call 11o1 combine wilh others depending on ccrlain fcaltlrcs of tile refcrelllial tlouxl
in any case such relalion would exist between cake and slice of cake namely the part of relalion shouhl stand between slice and any sliceable thing
for the sake of unification in the lkb sem will be the conjunction of this predicate and the sem value of the sign deuoting the whole
therefl re the const role will be assigned to one of both i types i str true i str false
these fealures are nlostly linguistic type countabilily singular or plural bul also can depend on knowledge of the world physical slale etc
this paper describes a system of representation of nouns denoting portions segments and relative quantities of entities in order to account for this case of part whole relationship
our future work is to include NUM comparing the various methods over the entire reuters corpus and over other data bases NUM developing better ways of creating clusters
this was in keeping withthe principle that in this case speed of response was the key issue and if the phrase was not exactly what was required an approximation to the wording needed would in any case be sufficient
the current aggregation modules of the natural language generator of vinst is described and an improvement is proposed with one new aggregation rule and a bidirectional grammar
if the first word of a sentence is a typical beginning for sentential premodifying phrases e.g.
the hope is that the two sorts of prediction will prove complementary
in informal usability tests we have gathered much useful information about areas in which cogenthelp could be improved the most important of these being ease of authoring
to date we have gathered substantial feedback on cogenthelp functionality from our trial user group at raytheon especially on the need for authoring support and visual navigation aids
widgets contained within a panel of a window can be used as a basis for help layout as discussed below
one of the side effects of this approach is that the statistics provide feedback on which heuristics are most appropriate
patterns of type b are more difficult to identify but will occur with high frequency in relevant texts
the autoslog dictionary contains NUM unique concept node patterns NUM which were all deemed to be relevant by a person
one of the main differences between autoslog and previous lexical acquisition systems is that autoslog creates new definitions entirely from scratch
information extraction ie is a natural language processing task that involves extracting predefined types of information from text
subsequently we developed a system called autoslog that can build concept node dictionaries automatically using an annotated training corpus
a noun phrase that is incorrectly annotated often produces an undesirable extraction pattern or produces no extraction pattern at all
the document management component handles thc files
it may be reassembled decrypted decompressed
form NUM document the internal tipster document
this would include being able to access and change
the module interfaces are unambiguously defined in the icd
circus separates each sentence into clauses and identifies the subject verb direct object and prepositional phrases in each clause
the cotr must monitor and oversee the application design
the architecture will provide a standard for document markups
w is the total number of words in the text
this would be problematic for most source determined analyses because recovering this relation necessitates that bill be parallel to both john and mary a possible but unattractive prospect
table NUM overage and sentence alignability
representing syntactic boundary markers in the same way that tags are allocated to words or to punctuation marks they can represent the boundaries of syntactic constituents such as noun phrases and verb phrases
he showed that for languages at least as high in the chomsky hierarchy as cfgs inference from positive data alone is strictly less powerful than inference from both positive and negative data together
it is not feasible to detect one string out of this number if the classifier marked all strings incorrect the percentage wrongly classified would only be NUM NUM yet it would be quite useless
solutions with lse are not necessarily the same as minimizing the number of misclassifications and for certain types of data this second method of direct training may be appropriate
however with positive data alone a problem of over generalization arises the postulated grammar may be a superset of the real grammar and sentences that are outside the real grammar could be accepted
back propagation and some single layer training methods typically minimize a metric based on the least squared error lse between desired and actual activation of the output nodes
however for single layer nets we can choose to update weights directly the error at an output node can trigger weight updates on the connections that feed it
examples such as those above appear to demonstrate that a discourse determined theory may account for at least some cases of dependencies between anaphoric relationships in source and target clauses
the output from the net is a vector whose NUM elements or nodes represent correct and incorrect yes and no see figure NUM
what these examples do show is the need to refine source determined analyses in deriving the predicates that clauses make available in the discourse
initimly this is the set of runtime entries resulting from the lexicon matching phase
the position indicates whether word is the head or the modifier in depen null dency relation
when b NUM i s is the number of bits needed to encode s
the matrix is divided into k NUM x k NUM blocks
senses NUM sake benefit and NUM interestingness are merged
there is one exception to the above procedure for retrieving and aligning chunks
we present an algorithm that uses the same knowledge sources to disambiguate different words
assumption NUM similarity is independent of the unit used in the information measure
however some local contexts hardly provide any constraint on the meaning of a word
the text was parsed in NUM hours on a sparc ultra NUM NUM with 96mb of memory
we will specify left combination right combination is the mirror image of left combination
this correlation does not translate into any high performing algorithm based primarily on pause duration
min NUM and error rate is NUM NUM r NUM
word2 is assigned the second lexical item if cue2 is true na otherwise
figure NUM shows the subjects responses for the excerpt corresponding to figure NUM
we conclude each review by summarizing the differences between our study and previous work
however phrase NUM NUM is the onset of an ficu that continues through NUM NUM
the column headed np in figure NUM indicates boundaries assigned by the np algorithm
boundary data which had been collected but not analyzed was not available
the users can enter their appointment constraints via a graphical user interface and receive the results either by e mail or via their electronic calendar
in the latter case the user starts his agent by entering via a graphical interface the appointment constraints to be used in the negotiation
in rose s second stage interaction with the user the system generates a set of queries and then uses the user s answers to these queries to narrow down to a single best meaning representation hypothesis
the basic constraints include the time interval within which the appointment must be fixed the duration of the meeting and the participants
for demonstration purposes e mail is exchanged between different accounts on a local host which the server is running on as well
we would also like to give special acknowledgement to stuart shieber mckay professor of computer science at harvard university who endorsed and helped foster the completion of this the first phase of nymble s development
for example departamento department could often start an organization name and adjectival place names such as coreana korean could appear in locations and by convention are not capitalized
while the number of word states within each name class is equal to ivi this interior bigram language model is ergodic i.e. there is a probability associated with every one of the ivi NUM transitions
this paper presents a statistical learned approach to finding names and other nonrecursive entities in text as per the muc NUM definition of the ne task using a variant of the standard hidden markov model
it can be used as the first step in a chain of processors a next level of processing could relate two or more named entities or perhaps even give semantics to that relationship using a verb
the question arises how the system should deal with unknown words since there are three ways in which they can appear in a bigram as the current word as the previous word or as both
also most of the word features are used to distinguish types of numbers which are language independent NUM the rationale for having such features is clear in roman languages capitalization gives good evidence of names
for start of sentence every new language and every new class of new information to spot one has to write a new set of rules to cover the new language and to cover the new class of information
we have shown that using a fairly simple probabilistic model finding names and other numerical entities as specified by the muc tasks can be performed with near human performance often likened to an f of NUM or above
we then expanded the feature set to its current state in order to capture more subtleties related mostly to numbers due to increased performance although not entirely dramatic on every test we kept the enlarged feature set
NUM for example let c2 be the number of repair utterances
user no i want to leave from torino in the evening
a terminal node can be either non empty figure la corresponding to a basic discourse unit usually a clause or empty
in addition carrying out sentence level processing in parallel with discourse processing and allowing each to inform the other would allow co reference interpretation to follow from decisions about discourse relations and vice versa
other dialogue actions such as clarifications or justifications may intervene but there is a sense of an expectation being resolved when the suggestion is responded to
all left extraposed clauses in english raise expectations as in example NUM so all the subordinate conjunctions in knott s list would be included as well
the imperative verb suppose however signals a coherence relation of antecedent consequent a c with a consequence expected later in the discourse
because it is already expected the adverbial does not lead to the creation of a separate elementary tree but see the next example
here the interpretation of sentence NUM a would correspond to the degenerate case of a tree consisting of a single non empty node shown in figure 4b i
figure 4a iv shows the interpretation of sentence 2c you d see he s very difficult to find substituted at NUM satisfying that remaining expectation
the article consists of a title NUM plus two sentences NUM NUM
the paper also brings concepts from the rhetorical structure theory rst to the statistical analysis of a text structure
and lastly there are n2 NUM o m NUM c rules
a boolean matrix is a matrix with entries from the set lcb NUM NUM rcb
i also gratefully acknowledge partial support from an nsf graduate fellowship and an at t grpw alfp grant
we define c parsers in this way to make the class of c parsers as broad as possible
let us prove the only if direction first
we will describe a way to do this later
j g w answers queries in constant time
because this selection technique is time consuming we only apply it to a subset of the discriminations
our approach to nlp involves a hybrid use of corpus statistics supplemented by linguistic heuristics
in addition syntactic category analysis is also helpful in adjusting cutoff parameters for statistics
the noun phrase analysis techniques are also potentially useful for book indexing and automatic thesaurus extraction
often more is required as when spelling transcription or ocr errors occur
in practice we have used NUM NUM as the threshold for most processing phases
this assigns the lowest priority to those pairs filtered out by syntactic category analysis
recall improves slightly about NUM as shown in table NUM
there are about NUM million simplex nps in the corpus and about NUM NUM million complex nps
word pairs are given an association score s according to the following rules
lexical atoms may be found among proper names idioms and many noun noun compounds
because of its limited power of resolution the sentence and its limited method of identification ordinal positions in a text it has to be augmented by additional more precise techniques
the subjects were all native speakers of english since we used an englistl cortms and were
we further argue that coherence although certainly desirable is imi ossible without a large scale knowledge based NUM ext mldersl an ling syst em which would not only slow down dm l erformance signiticantly but necessarily could not be domain inde NUM endent
it can be argued that so far we have only dealt with short texts about a single topic
from these individual recall precision values the average was computed to yield a global measure for interhuinan precision recall
this is also rout it faster than a word stemming algorithm which has to perh rm a inorphological analysis
table NUM significance of sentence score correlation between human sul jeet s all NUM articles
this result indicates that there is a good inter subject agreement about the relative relevance of sentences in these texts
the system generates abstracts from newspaper articles by selecting the most relevant sentences and combining them in text order
the focus of this paper will be the description and evaluation of an abstracting system which avoids the disadvantages coming along with most of these traditional approaches while still being able to achieve a performance which matches closely the results of an identical abstracting task performed by human subjects in a comparative study
take an arl me fl om the corl us NUM and lmild a word weight matrix for all contellt words across all sentences l f idf omputal ion where the idf vahms ttte r trieved fl om a preconqmted file
if yes or no and even if we confine ourselves to a synchronic view of language what would be a working guideline which delineates the assumed good example drmk from the alleged not so good one figure NUM
inference begins with a call to theorist to explain the input utter m r surface request m r informif r m knowref r whoisgoing ts NUM this utterance must be explained by finding a discourse level speech act that it might accomplish and a metaplan or misunderstanding that would explain this act
we adopted a different strategy one that provided us with a large set of sentences in which target adjectives could be disambiguated automatically and with complete reliability
section NUM NUM addresses some of the contextual relations between adjectives and noun senses that sometimes resolve adjective sense when the intrinsic attributes of the noun sense do not
katz principled disambiguation the statistically significant indicators listed above with that of the nouns from the subcorpora that are not significant as indicators of target sense
the extent of applicability of such a procedure can be inferred from the coverage column which records the proportion of target adjectives that modify projected indicator nouns
we have found that a substantial proportion of adjectives can be disambiguated by the nouns they modify largely on the basis of general semantic attributes characterizing those nouns
the adjective however may apply to that individual a animate noun sense or to the role itself a animate noun sense
this assessment is based on an empirical study of five of the most frequent ambiguous adjectives in english hard light old right and short
radio broadcasts however now that even plain people could afford loud speakers on their sets held old fans to the major league races and attracted new ones
in most of these cases noun senses themselves supplied the attributes used by the rules of table NUM to disambiguate adjectives
we therefore extracted from the aphb corpus a random sample of NUM sentences containing adjectival instances of each target adjective for a total of NUM sentences in all
this paper presents the results of a comparative study of search strategies and evaluation criteria for measuring model fit
in both cases bic selects models whose complexity is too low and adversely affects accuracy when compared to aic
the text consists of every sentence from the acl dci wall street journal corpus that contains any of the nouns interest bill concern and drug any of the verbs close help agree and include or any of the adjectives chief public last and common
however it is more problematic to detect user s corrections if they happen some turns after the occurrence of the errors
the default classifier assigns every instance of an ambiguous word with its most frequent sense in the training sample
polite style for adult more informal style for classmate
the algorithm often fails in such cases for two reasons
four utterances that are available to open a conversation e.g.
there are two parts to the training process identification of the wordnet sense usage of headwords of interest and the building of specific rules
for the as kind of process description it computes a value for the local variable reference concept which returns the valuefemalegametophyteformation
functional description skeleton retriever charged with the task of selecting the correct functional description skeleton from the skeleton library
each of the four writers was given NUM concepts to explain and each concept was assigned to exactly one writer
although it is clear that coherence is of paramount importance for explanation generation there is no litmus test for it
he also developed a view retriever and a highly refined theory of explanation generation in which views play a significant role
it is preferable to construct i.e. extract views at runtime rather than encoding them in a knowledge base
the next structure comes under the heading partic this is where the thematic roles of the clause are specified
in a sense edps are schemata whose representation has been fine tuned to maximize ease of use on a large scale
explanation generation terminates when the realization component has translated all of the views in the explanation plan to natural language
by recursively invoking apply edp determine content causes the planner to traverse the elaboration branches of a content node
however the word unigram based segmenter consistently identifies it as a single word
this helps improve the purity of the training data
plant life growing on plant which is
are protected by plant parts remaining from
plant and animal tissue plant in japan is
yarowsky unagi ci s upenn edu
after initial experiments along these lines we decided to step back and build a generative model of the transliteration process which goes like this NUM
the following figure illustrates this sample initial state
there are many ways to write an english word like switch in katakana all equally valid but we do not have this flexibility in the reverse direction
all in all we are quite pleased with the performance of alembic in muc NUM
note that long japanese vowel sounds are written with two symbols a a instead of just one an
the first wfst simply merges long japanese vowel sounds into new symbols aa ii uu ee and oo
however as we shall see there are many spelling variations that complicate the mapping between japanese sounds and katakana writing
a portion of the wfsa looks like this los NUM NUM federal o o013 angele s month NUM NUM
plant life and natural plant life in water
this effect varies depending on the type of collocation
she asked betsy whether she liked the gift
the center may shift within a single segment
NUM a terry really goofs sometimes
NUM a terry really goofs sometimes
he had frequented the store for many years
discourses are more than mere sequences of utterances
he is required to be NUM years old
NUM a terry really goofs sometimes
rule NUM is satisfied throughout NUM
in his thesis tomita NUM mentions the impact of syntax on determining the part of speech of unknown words during parsing
test subdialogue the number of utterances is significantly reduced i.e. users who take the initiative can verify the circuit behavior without dialogue
then the terminal class inherits the relevant realization for each of the cited functions subject
productive use of derivations is limited by the predictability of the semantic relation of the stem to the affix
figure NUM shows the lexical rule in ale notation the rule is simplified for ease of exposition
since they are more accurate than ending guessing rules they are applied before ending guessing rules and improve the precision of the guessings by about NUM
these rules guess a pos class for a word just on the basis of its ending characters and without looking up its stem in the lexicon
if the guessed pos set is the same as the pos set stated in the lexicon we count it as success otherwise it is failure
the corpus results are better because the training technique explicitly targeted the rule sets to the most frequent cases of the corpus rather than the lexicon
the first part of the table shows that when the xerox guesser is applied before the e75 guesser we measure a drop in the performance
the grammar development environment ale had to be modified to allow run time evaluation of lexical rules
for a language with rich morphology lexical rules can be used for controlled generation of surface forms
NUM a ocuk kitab t oku du child nom book acc object read tense NUM sg
the latter causes exponential growth in the lexicon due to intensive use of inflections and derivations in turkish
words are distinguished from phrases by disallowing any kind of gapping below the word level in the tree
for instance the locative case suffix in turkish also marks an np as adjunct NUM
NUM is the original work on turkish that combines finite state morphotactics with morphophonemic alternations
if we wish to test an actual computational model for natural language processing its complexity demands the construction of a computer program to execute it
subcategorization this principle relates to the principles of well formedness of functional structures in lfg
we composed each of these nondeterministic transducers and turned the resulting transducer into a deterministic transducer
moreover the finite state tagger inherits from the rule based system its compactness compared with stochastic taggers
we have proven in this section that our techniques apply to the class of transformation based systems
the first step of the tagging process consists of looking up each word in a dictionary
issues related to the determinization of finite state transducers are discussed in the section following this one
the transducers obtained in the previous step still need to be applied one after the other
for example the transducer shown in figure NUM is not equivalent to any subsequential transducer
the first rule says to change tag vbn to vbd if the previous tag is np
the second rule says to change vbd to tag vbn if the next tag is by
finally we add a manner component to distinguish among verbs in a class such the motion verbs run walk and march
the feature dynamic encodes the distinction be null tween events dynamic and states 0dynamic
for instance while NUM words have at least NUM occurrences in the combined corpus only a subset of NUM words has at least NUM occurrences
the second test set wsj6 consists of NUM NUM occurrences of these NUM words that occur in NUM text files of the wall street journal corpus
out of this syntactic database and following principles of well formedness the generator creates elementary trees
it is also the case that the last NUM NUM of polysemous words in a corpus have only a small number of distinct senses on average
brill s tagger came pretrained on the brown corpus and had a corresponding guessing component
thus we decided not to count as an error the mismatch of the nn nnp tags
the ending guessing rules on the other hand do not use information about stems
we also use the actual frequencies of word usage collected from a raw corpus
word frequency distribution was estimated from the brown corpus which reflects multidomain language use
the most frequent open class tags of this tag set are shown in table NUM
the cascading guesser outperformed the other two guessers on every subcorpus of the brown corpus
d where t l c0 NUM is a coefficient of the t distribution
conclusions useful for speech recognition lexical disambiguation and topic boundary recognition
special credit is due to mark liberman for sharing his insights about zipf s law for drawing my attention to the simon mandelbrot controversy and for supplying various background material
which is deceptively similar to turing s formula eq NUM the only difference being that it x NUM assigns more relative frequency mass to frequency count x
the results demonstrate that turing s formula is qualitatively different from the various extensions to zipf s law and suggest that it smooths the frequency estimates towards a geometric distribution
the asymptotic behavior of the relative frequency as a function of rank implicit in one interpretation of turing s local reestimation formula was derived and compared with zipf s law
r NUM upon examining the frequency function n1 e we realize that we have an exponential NUM distribution with intensity parameter the probability of the most common species
instead semantic tagging should be a first step in the interpretation process by assigning each lexj cal
null open dots o connect systematically related types that are not normally interpreted simultaneously
for instance the noun evidence is of type communication psychological and the following representation is generated
traditionally a lot of research in lexical semantics has been occupied with the problem of ambiguity in homonyms
the discourse marker it refers back to an np that expresses more than one interpretation at the same time
corelex is implemented as a database of associative arrays which allows a fast lookup of this information in pattern matching
the first step in analyzing a new corpus involves tagging each noun that is in corelex with an underspecified semantic tag
the pattern matching is class sensitive in employing the assigned corelex tag to determine if the application of this pattern is appropriate
this context also has single negative extension lcb e rcb corresponding to the fact that the character e is still possible in the context euestablish but considerably less likely than in that context s maximal proper suffix blish
also to fig NUM in section NUM
fig NUM which shows a dependency tree for the instance of the vertex cover problem from fig NUM the two dependencies ul and u2 represent the complement of the vertex cover
it is this claim that we challenge here
d2 contains two precedence restrictions which require that know represented by self must follow the subject first precedence constraint and precede the object second precedence constraint
the necessity of non projective analyses in dg results from examples like beans NUM know john likes and the restriction to lexical nodes which prohibits gapthreading and other mechanisms tied to phrasal categories
extraction is analyzed by establishing another dependency visitor between the verb and the extractee which is required to precede the verb as in visitor of verb precedes it
if a nonzero anaphor is animate then it is pronominalized otherwise it is nominalized
if the nominal form is chosen then the preference rule is consulted to get a description
the development of datr has been guided by a number of concerns which we summarize here
gold standard sentences are those occurring m both author summary and source text m line with kupmcet al s gold standard gold standard b human judgement
collected mlght not generahze to other genres some land of automation seems desirable to assist a possible adaptation NUM location method
figure NUM summarizes the contribution of the in dividual methods NUM using the cue phrase method method NUM is clearly the strongest single heuris
doenment frequency tf ldf method loy ldegdeg n core w oo ct r
the authors would hke to thank chrm brew janet httzeman and two anonymous referees for comments on earher drafts of the paper the first author m supported by an epsrc studentshp
figure NUM thtrd experiment impact of training ma
companng our experiment to kuplec et al s the most obvlous dn erence m the dfiyerence m data our texts are likely to be more heterogeneous coming from areas of computational hngumtlcs with
figure NUM composition of gold standards for trmnmg sets
our breadth first search considers shorter contexts before longer ones and consequently the decision to add a profitable context y may significantly decrease the benefit of a more profitable context xy particularly when c xy c y
first of all it is clear that on the whole people s expectations of what mt will do for them are changing
we have extended the algorithm to precluded hypotheses that are inconsistent with such constraints by initializing those entries in the dp table corresponding to illegal sub hypotheses with zero probabilities these entries are blocked from recomputation during the dp phase
the prepositional bias has already correctly restricted the singleton tbe d to attach to the right but of course the does not belong outside the rest of the sentence but rather with authority
notice that in contrast the linguistic evaluation criterion is insensitive to whether the bracketings of the two sentences match each other in any semantic way as long as the monolingual bracketings in each sentence are correct
we often find the same concept realized using different numbers of words in the two languages creating potential difficulties for word alignment what is a single word in english may be realized as a compound in chinese
for example in the rewrite rule a b x y c z e the terminal symbols z and z are symbols of the language lx and are emitted on stream NUM while the terminal symbol y is a symbol of the language l2 and is emitted on stream NUM this rule implies that z y must be a valid entry in the translation lexicon
lemma2 for any inversion invariant transduction grammar g there exists an equivalent inversion invariant transduction gratrm r g where t g t g t g t g such that the right hand side of any production of g contains either a single terminal pair or a list of nonterminals
this improves parsing efficiency but requires overcommiunent since the algorithm is always forced to choose between a bc and ab c statures even when no choice is clearly better
tim operator performs the usual paitwise concatenation so that a b yields the string pair cx c2 where cx a1bx and NUM a2b2
as such markup is typically treated as external to structural annotations within susanne trees containing a sentence and sentence punctuation can not be a possible target for alignment across the two corpora
terminal and tree locations similarly separate programs may be invoked to provide tables of byte offsets of terminals and start and endpoints of trees
two trees in c t and c r are aligned if they share the same yield under the image of i.e.
that is the content is a collection of elements generally corresponding to words and punctuation and this will be roughly constant across the two corpora
each maximal tree containing a tree of greater than depth one in the treebank may also contain sentence punctuation which is treated within the structural markup
according to the first criteria of statistical efficiency the best model is the one that achieves the smallest total codelength l t c of the training corpus t and model c using the fewest parameters
such automatic checking will be useful both in the case of manual edits to a corpus and also in the case where automatic analysis is performed
we then describe an efficient implementation which is also modular in that the core of the implementation can be reused regardless of the format of markup used in the corpora
w w e subtrees c r and we i w t j e subtrees c t
the reason for using multiple analogs is twofold first it obviates the risk of being wrongly influenced by one very exceptional analog second it enables us to model conspiracy effects more accurately
nonetheless the results reported hereafter have been obtained using this breadth first strategy mainly because this search was associated with a more efficient procedure for reconstructing pronunciations see below
whose pronunciation was used in the inferential process
table NUM a comparatiw l valuation
the average values for these measures are reported hereafter
table NUM shows some of the different patterns retrieved
the complete pronunciation procedure is represented on figure NUM
table NUM suimnaries the performa uce
its f measure is less than half of louella s average performance
warning this message does not represent louella s typical performance
notice that all of the person objects have actually been extracted
these companies are usually in the act of announcing some event
with the addition of these two modifications our total ne f measure rose to NUM NUM
in addition however the slot may also contain a phrase describing an un named organization
this list also helps identify coke as referring to the coca cola company
louella threw out the variation because it was known in the gazetteer as a city name
the text organize r tries to assemble the extracted information into a lucid account of events
postprocessing postprocessing is the final review of the extracted information before the templates are gener ated
the ability to provide translations for collocations is important for three main reasons
various thresholds are used in champollion s algorithm to reduce the search space
the structure suggested by this rule has to be integrated in the skeletal structure
1degin lexicalised dtgs the main verbs would be already present in the initial trees
the two combination operations that dtg uses are subsertion and sister null subsertion
the generator tries to convert the partial syntactic structure into a complete syntactic tree
the main verb is generated using the terminal mapping rule NUM iii in figure NUM
thus the generator finds all possible solutions producing the best first
alternatively a notion of semantic distance NUM might be employed
hence node NUM NUM in the active tree has a tense link with node vo in the passive tree where tense is the attribute in common and a lex link with node i NUM in the passive tree where the lexeme is shared
in our system three agents cnagent tfnagent and cpnagent are set up to be responsible for finding candidates in input texts accordingly
as indicated in section NUM we meet a very serious difficulty without relevant knowledge even humanbeings will definitely fail to solve it
as shown in figure NUM unannotated text is first passed through the unsupervised initial state annotator where each word is assigned a list of all allowable tags
we begin our exploration providing the training algorithm with a minimal amount of initial knowledge namely knowing the allowable tags for each word and nothing else
there a peak accuracy of NUM NUM was attained using the lob corpus n using the penn treebank corpus a peak accuracy of NUM NUM resulted
next we show a method for combining unsupervised and supervised rule based training algorithms to create a highly accurate tagger using only a small amount of manually tagged text
with no information beyond the dictionary entry for the word can the best we can do is randomly guess between the possible tags for can in this context
scoring criterion when using supervised transformation based learning to train a part of speech tagger the scoring function is just the tagging accuracy that results from applying a transformation
we next explore unsupervised and weakly supervised training as a practical alternative when the necessary resources are not available for supervised training
the sentences in i are examples of ambiguity and the sentence NUM and NUM examples of unknown word
in addition we have shown that by combining unsupervised and supervised learning we can obtain a tagger that significantly outperforms a tagger trained using purely supervised learning
if a word has virtually no association with the category then it deserves a NUM
note that r NUM is a partial function from udrss to f structures
the translations below are obtained with the full definitions in the appendix
f structures the language of wff s well formed f structures is defined below
the definition given above uses textual representations of f structures
proof is by induction on the complexity of
zdeghere we need to drop the clause boundedness constraint
and dom o can easily be spelled out formally
7proper names are dealt with in the full definitions in the appendix
to evaluate performance the usual measures of recall and precision were used
second dynamic programming normally gives only one alignment for each pair of strings but comparative reconstruction may need the n best alternatives or all that meet some criterion
resnik s approach differs from the present one in three major ways
h3 the neighbor met the boy yesterday
a general procedure for determining tfa in such languages can then be based on the following points i all complementations preceding the verb are cb and thus belong to the topic
rather these correlates take the form of labels on edges see the syntactic units previously illustrated or of parts of complex labels on nodes such as values of morphological categories e.g.
in this sense the order of the relevant complementations arguments and free modifications in the a sentences may be understood as primary and that in b as secondary
for example only cb items can have the shape of weak pronouns or be deleted thus in he left the subject is cb whereas in he left it is nb
in sentences with items embedded more deeply than the immediate complementations of the main verb it is necessary to characterize the positions of individual word occurrences in the sentence in a more specific way
in the same vein example NUM documents that objective precedes origin see section NUM in which the relevance of the secondary position of the intonation center is discussed
topic focus identification focus proper as the most dynamic element carrying the intonation center s cd is semantically relevant for the scopes of quantifiers as illustrated by example NUM
on the preferred reading the quantifier belonging to the topic has a wide scope which is in agreement with the view according to which the focus is asserted about the topic
thus NUM a can answer two of questions NUM NUM whereas NUM b can answer just one of them
since log assumption NUM means that the func logbx logb b tion f must satisfy the following condition vc o f x y f cz cy assumption NUM similarity is additive with respect to commonality
in this method the japanese sentence and english translation within the japanese and english aligned sentence pairs are analyzed
in particular the subject and object are often omitted in japanese whereas they are normally obligatory in english
methods for extracting the most effective rules for resolving japanese zero pronouns from aligned sentence pairs will also be studied
so resolution rules must be made depencling on the target domain of the documents
then the pairs of japanese word phrase and their english equivalent word phrase are identified from each aligned sentence pair
this is not a imitation of proposed method but a imitation of the alignment algorithm
furthermore the types of zero pronouns change depending on the types of documents which must be analyzed
furthermore rules can only be made when slrnilar expressions to those containing the zero pronouns are found in the corpus
because of these problems a method to make resolution rules of zero pronouns effectively and efficiently is aeeded
analysts who make these resolution rules must be f rnl iar with the nlp system itself
that is the parser must search a large set of tagged word combinations in order to choose the fight one
our approach uses a feature collocation lattice and selects the atomic features without resorting to iterative scaling a fter the atomic features have been selected we using the iterative scaling compute a fully saturated model for the maximal constraint space and then start to eliminate the most specific constraints
we can exploit this fact to have the system automatically group the nodes into regions of similar themes
alternatively the user can peruse the list of region labels and select them directly from the list
these neighbor nodes are nodes that are close in the map space to the winning node
all operations in matchplus are based on geometry of these high dimensional spaces NUM
the hardware is a simd numerical array processor snap in essence a floating point parallel array processor
this biasing enforces an equiprobable winning distribution and results in a more useful clustering of the input information space
activated nodes influence what tasks the system will focus on subsequently through the posting of top down codelets
in general the number of codelets posted is a function of the length of a sentence
figure NUM summarizes the cycle number in which various types of structures were constructed during this run
gan palmer and lua a statistically emergent approach l affinit dormant affinity affinity affix figure NUM
given the sentential context of NUM however only the second alternation is correct
the workspace is initialized with nine character objects each corresponding to a character of the sentence
given the sentential context of NUM however only the second alternative is correct
the underlined fragment NUM yudn gongzud in NUM has overlap local ambiguity
the program adopts a holistic approach in which word identification forms an integral component of sentence analysis
uses dictionary entries as the smallest linguistic units that are combined to create more complex patterns
juman analyzes the input japanese string into a single best sequence of morphemes with morphological attributes
the difficulty is that even known organization person and location names are often ambiguous
there were numerous cases in met however where dictionary entries cut across name boundaries
their recognition relies on both internal name patterns and linguistic contexts
the muc NUM experience in the use of juman was also helpful
this approach makes ie dictionaries diverge from the off the shelf ones
name tagging in the output uses these text position values
the first japanese fastus was the muc NUM joint venture system developed in NUM
named entities can be recognized based on linguistic contexts in complex phrase patterns
thus for all tagsets and languages a larger training text is required in order to minimize the error rate
the second part of the table shows that the cascading application of the morphological rule sets together with the ending guessing rules increases the over null all precision of the guessing by a further NUM
NUM al anda2 and l NUM and NUM and scoring constraint r number of sets of events lcb a1 a b1 b rcb of types oq a NUM NUM respectively that all overlap on the timeline
the task of assigning a set of pos tags to a word is actually quite similar to the task of document categorisation where a document should be assigned with a set of descriptors which represent its contents
in this paper the word occurrence threshold has been set to one in all experiments
table NUM demonstrates some results of this experiment
as was pointed out above one of the requirements in many techniques for automatic learning of part of speech guessing rules is specially prepared training data a pre tagged training corpus training examples etc
the error rate depends strongly on the test text and language and the type and size of the tagset
furthermore we showed that a small training corpus is sufficient for good performance and we estimate that annotating enough data to achieve good performance would require only several hours of work in comparison to the many hours required to generate pos tag and lexical probabilities
we have pondered the lessons of previous mucs an d worried about the wisdom of a research agenda dominated by score reports
as can seen fl om the table performance degrades as the quantity of training data decreases but even with only NUM exalnple sentences performance is bet i lhan the baselines of NUM NUM if a sentence bound l is guessed at every potential site and NUM NUM k if only token final instances of sentence ending punctuation are assumed to be boundaries
it consi ts of the following steps
let be an alphabet d a dictionary over the alphabet and s a character string over the alphabet
using i he convincing structural interd t endencies thai liib89 shows for a subset of the german focus adverbs containing crsl the generalization of tire approach suggested here to other ambiguous ad verbs seems very promising
rhe example testifies the following three uses of crsl in the context NUM a the recipient understands the introduced event as the first of a sequence of events that he expects to be completed by the following text
we plotted the results after each NUM words stepping down the ranked list to see whether the words near the top of the list were more highly associated with the category than words farther down
or the i i a reading to l e acceptable at least for homogeneous descriptions the event descril tion tested at a situation must not subsume the previously tested evt att description
now we think that the epa reading interpretes tile asserted event which is backed by the described scenario as the lirst one that is indeed realized within the range of possible instantiations that the sequence of opportunities provides i.e.
we take it for granted that this difference is the reason why the decisive conflict that we mentioned fllrther abow only arises if the temporm location is introduced by modification i.e. in case it is introduced by an adjunct
allows for the r reading because the most natural analysis gives wide scope to the temporal adjunct i.e. the sentence is analyzed like NUM h where clearly the adjunct serves to localize the temporal perspective
whether there are other synta tic criteria that further lisam NUM iguate NUM etween the thre e readiugs mso del ends on tire structnral description assigned to th focus l article use
one might reasonably expect to see this at the root node since incompatible type s should certainly be strong evidence for non coreference
one could have a person verify that each word belongs to the target category before adding it to the seed word list but this would require human interaction at each iteration of the feedback cycle
with NUM c we encounter so to speak the symmetric picture with regard to the el a reading t no wing n employees entails the previous y tested knowin t n cmployccs
in NUM NUM the descrip tions of subsequent events states in this case of the presuppositional line are more gcnerm predicates than the description of the predecessors i e each such sequence collapses in its tirst element in essen e
first in lieu of step NUM the gnf procedure converts the input into chomsky normal form
schabes and waters tree insertion grammar in step NUM no change is necessary in the al initial tree
much work on efficient processing algorithms has been done in the logic grammar framework
these trees can be eliminated by repeated application of lemma NUM let t be an empty tree
it should also be possible to convert the resulting parse trees into parse trees in the original grammar
the fourth important difference between the ltig and gnf procedures is the way they handle left recursive rules
in the following discussion we assume a modified version of the gnf procedure that takes this approach
this eliminates infinite ambiguity and empty rules and puts the input grammar in a very specific form
clauses containing disjunctive terms are compiled to several clauses one for each consistent combination of disjuncts
the prolog representation of a sort is an instance of the prolog representation of its supersorts
templates are called by using the template name prefixed with c in a profit term
member element list member element rest list
empty rules the auxiliary assumption that g does not contain empty rules can be dispensed with
there must be some nonempty frontier element after the foot of t because g is not infinitely ambiguous
we illustrate these principles for compiling sorted feature terms into prolog terms with an example from apse
NUM declaration files that contain information for compilation derived from the declarations
the results show that a narrow window of training context t NUM words works best for this task and that at least second order co occurrence relations are necessary
the four groups from table NUM we find that both the distance between cvl and cv4 and that between cv2 and cv3 are very high which reflects the fact that they are not similar with each other
from a point of empirical view we suppose that each sense of a word is corresponded with a particular kind of context it appears and the similarity between word senses can be measured by their corresponding contexts
given a word in some context we suppose that some clusters in the space can be activated by the context which reflects the fact that the contexts of the clusters are similar with the given context
but due to the fact that the given context can suggest the correct sense of the word there should be clusters among all activated ones in which the senses are similar with the correct sense
obviously the disambiguation accuracy will be reduced if the cluster contains less words because less words in the cluster will lead to invalidity of its definition vectors in revealing the similar words included in their definitions
using speech output in combination with speech recognition helps to avoid the use of textual displays which can be difficult to read on immersire presentation equipment and which can interfere with the user s view and the reality of the virtual world
in order to make the interface flexible and easy to use the rules have been designed to allow the user to phrase an utterance in a variety of ways improving the naturalness of the interaction by taking advantage of the strong linguistic processing capabilities of nautilus
since the contributions of both the source target and bilingual components of the models are applied simultaneously when computing the costs of partial derivations there is no need to pass multiple alternatives forwards from source analysis to transfer to generation the translation ranked globally optimal is computed with a single admissible search
the parse tree was not used for analysis however
the instance arc connects a concept to an instance of that concept e.g. a particular chairman chairman1 i would b e linked to chairman u by an instance link
or make statements such as when i wrote pointing i was referring to brickwork it enables applications to produce output which is highly related to the original text
the automatic tagger is truly automatic in that it has not at all been adjusted to the specific task at hand
considering the large proportion of the number of running words that these major categories cover this is even more remarkable
i would like to thank ronald m kaplan martin kay andr4 kempe john maxwell and annie zaenen for helpful discussions at the beginning of the project as well as paula newman and kenneth NUM
it may well turn out that in all cases that are of practical interest the lower language is in fact a singleton or at least some finite set but it is not so by definition
this is called the textref system and has several uses it allows the core to analyze input which talks about surface components of the input text
it can run on many machines ie those for which there is a haskell compiler and is normally given a heap size o f NUM megabytes
lolita s analysis of the numex money and percent category is better than the other categories because of the relativ e simplicity of the grammars for these expressions
on it s own this technique currently produces a low recall largely because th e core analysis may not produce exactly the correct relationships between the relevant nodes
because we must start from the left and have to choose the longest match aba must be replaced ignoring the possible replacements for b ba and ab
this new node is then returned as the meaning of the anaphor later stages eg pragmatics will attempt to disambiguate the reference and wil l replace the new node with the chosen node
something similar happens with relative quantification
semantics of portions and partitive nouns for nlp
component whole links hand arm membercollection tree forest and so
li igure NUM modelled l ortions
specific sub types are shown in figs
they select ii entities either individuals or substances
i draught three cups is needed to do the task
particular relations are also consistent with particular hypotheses about the segmentation of a given sentence and the scores for particular relations can be incremented or decremented depending upon whether the segmentations with which they are consistent are popular or not
cascaded ellipsis the number of readings obtained for john revised his paper before the teacher did and then simon did was used as n benchmark by dsp
newezpr old subs returns new if old new e as the qlf becomes more instantiated the set of possible evaluations narrows towards a singleton
first replace j by m throughout NUM and then replace term j by term m
the scope of the term is indicated by the scope node i l j prefixing the formula sleep term j
it makes no sense to apply the substitutions before the antecedent is fully resolved though it does make sense to decide what the appropriate substitutions should be
in kehler s case it is hard to see how his role assignment functions can be extended to deal with non referential terms in the desired manner
scope parallelism may not be significant where proper names are concerned but is important when it comes to more obviously quantificational terms section NUM NUM
this makes semantic interpretation a highly orderdependent affair e.g. the order in which a functor is composed with its arguments can substantially affect the resulting meaning
words with incorrect approximation the words whose approximation is neither good nor reasonable
NUM prob upper threshold cat prob NUM prob lower threshold
as for the fifth word here the approximation we got is totally incorrect
for our experiment we picked from this small corpus two kinds of test groups
the aelr has to be restricted languagespecifically to account corectly for extraposition from vp english has a head initial vp therefore the right periphery of the vp can not be formed by the verb but is provided by vp adjuncts adverbs and pps
the contrast between NUM and NUM makes clear that subjects are boundaries for fronting but not for extraposition NUM with what color hair i did a man i come into the room
learning morpho lexical probabilities this might seem at first sight an impossible mission
wil smoking unhealthy is NUM well das argument i einen mann j aufgeregt hat daft rauchen ungesund ist i der das fest besuchte j wil
we observe that the serialization for multiple extraposed elements matters for pps but not for relative clauses NUM a man i j came in who was smiling j with blond hair i
we can therefore formulate the following lexical requirement NUM NUM inv per right all other lexical entries are marked per left and hence can not introduce a right periphery
hal NUM no one i puts things j in the sink that would block it j who wants to go on being a friend of mine
gue NUM no one i puts things j in the sink who wants to go on being a friend of mine i that would block it j
a masculine form of a number the feminine form of the same number
an indefinite form of a noun the definite form of the same noun
transduction models are weighted so the costs for translation derivations can be combined with those from acoustic processing
b john got to work late
he had eaten a big breakfast
b mary built a dog house
just after NUM mary pushed john
past event precede sx john fell
just after s1 mary stared at john
they used NUM tons of bricks
mary was seated behind the desk
and sometimes this ambiguity is unwarranted
this illustrates that function words do need not be included in the input dsynts and that syntactic issues such as subject verb and noun determiner agreement are handled automatically
the tree in figure NUM yields NUM NUM mary winning this competition means she can study in paris and can live with her aunt whom she adores
null the complexity of the generation algorithm derives primarily from the tree traversals which must be performed twice when passing from dsynts to ssynts and from ssynts to the dmorphs
if we add feature question to the verb and feature number pl to the node for boy then we get NUM NUM do these boys see mary
for example in order to generate saw rather than the default seed for the past tense of to see the following entry would be added to the
rthermore there is no non determinism in real pro the input to realpro fully determines the output though the input is a very abstract linguistic representation which is well suited for interfacing with knowledge based applications
realization is fairly well understood both from a linguistic and from a computational point of view and therefore most projects that use text generation do not include the realizer in the scope of their research
in this respect our tests have put power translator and telegraph at a disadvantage since we did not extend their lexicons with any add on lexicons
we tried to check if a lexicon entry that is marked with a subject area will still be found if no subject area is selected
such users would prefer to continue using their original language rather than to learn an alternative symbol system
during training naive bayes constructs the matrix p vjici and p ci is estimated from the distribution of training examples among the classes
these two violations of the exclusion rule are shown in figure NUM
table NUM global performance of different dictionaries
in addition as a side effect of evahlating a global inheritance descriptor the global context is updated
finally the new rule for path eztensions serves as a way of making any path extension explicit
let t be a datra theory defined with respect to the set of nodes node and the set of atoms atom
to axiomatise the ilew evaluation relation the datrc rules m e modified to incorporate the global context parameter
the rules leal ing with global node t adl pairs and global nodes work in a similar way
the author wishes to thank roger evans gerald gazdar and david weir for suggestions and comments relating to this work
a partieuiar t rot hnn on erns datr s notion of nonlocal or global inheritance
when each element of c is atomic then c is called a path and denoted p
this section provides an evaluation semantics for a default free variant of datr with both local and global inheritance datrg
depending on who is respected by speaker the honorification type is determined for example when a subject referent an ohject referent and addressee are honored by speaker subject honorification object honorification and addressee honorification occur respectively
clock acc present hon past dec r presemed a clock to m in the sentence 26a four persons are involved youngsoo sungmin the person r and the person m the order of their relative social status is as illustrated in NUM
NUM NUM template for a relation of social status when a subject referent or an object referent is respected by speaker the social status of the subject referent or the object referent is higher than that of both speaker and addressee as formalized in NUM
an honorific verbal ending is used when the social status of addressee is higher than that of speaker in this case speaker shows honor to addressee or when the social status of speaker is higher than that of addressee in this case speaker shows courtesy to addressee
NUM ind o indsp ind o indad ind o inds if a humble form of a verb is available but is not used the social status of speaker is equal to or higher than that of an object referent as illustrated in NUM
NUM sungmin heesoo sungmin m heesoo m sungmin youngsoo it is derived from sentence 26a that the social status of m is higher than that of sungmin as shown in NUM whereas it is derived from 26b that the social status of m is not higher than that of sungmin as illustrated in NUM
from the information supplied by the attribute s status we can infer that l l l g l and where and e stand for the relation higher than and not equal to respectively
this is determined during the dialogue where the user can indicate a preference for less or more elaborate monologues
together this should improve overall recognition merging and discrimination of all objects an d may be a key to accurately recognizing events relationships not detectable in a single sentence
the similarity between two given words is then computationally measured using two vectors representing those words
we also report on the effectivity of our framework in the task of word sense disambiguation
the precision is the ratio of the number of correct interpretations to the number of outputs
since it is based on mathematical methods this type of similarity measurement has been popular
thereafter we approximate the solution for x by averaging the solutions for x derived from each subset
s concerns the structure on which the rule is used and specifies which parts of this structure should be considered by the rule
for prediction without adaptation the same method is applied except that nodes are not added and counts are not updated
context modeling for language and speech generation
the basic insight of focus accent e.g.
this requires extensions of the ist formalism
you will now hear k NUM this composition
dyd s context model was designed to be such a context model
if not a different candidate sentence is subjected to examination etc
it might be viewed as a modest computationally feasible version of drt
drss contain both less and more than what is needed for language generation
if semantic factors would not intervene k NUM would carry an accent
this is not an absolute criterion in particular optional words falsely considered to be insertions by the filtering are not recovered
the threshold and minimum separation were determined on heldout data in order to maximize the probability p and turned out to be a NUM NUM and e NUM for the wsj model and ot NUM NUM and e NUM for the tdt models
t f w tlfl w t2f2 w i tnfn w the normalization constants zx w NUM e x f deg
emphasized words mark where a long range language model might reasonably be expected to outperform assign higher probabilities than a short range model some doctors are more skilled at doing the procedure than others so it s recommended that patients ask doctors about their track record
notice that in this domain many of the segments are quite short adding special difficulties for the segmentation problem figure NUM shows the performance of the tdt segmenter model b on five randomly chosen blocks of NUM sentences from the tdt test data
another model was constructed on the broadcast news corpus bn made up of approximately NUM million words four and a half years of transcripts of various news broadcasts including cnn news political roundtables npr broadcasts and interviews
by restricting the conditioning information to the previous two words the trigram model is making the simplifying assumption clearly false that the use of language one finds in television radio and newspaper can be modeled by a second order markov process
note also that with a modest extension of functionality it is possible to use the data architecture described here to implement patches e.g. to the tokenisation process
for efficiency reasons you would n t want to use long pipelines of tools if each tool had to reparse the sgml and deal with the full language
as described above it is quite possible for sgml to represent stand off annotation in a similar way to tipster
we call a transition pair cheap if the backward looking center of the current utterance is correctly predicted by the preferred center of the immediately preceding utterance i.e. cb ui gp ui l i NUM n
the natural language agent uses a parser implemented in prolog to parse strings that originate from the speech recognition agent and assign typed feature structures to them
location command line coordhst NUM NUM NUM NUM i NUM NUM figure NUM line feature structure
create unit type mlal object echelon platoon j nit xcoord NUM location xcoord NUM j poi t
furthermore using unimodal speech to indicate more com null plex spatial features such as routes and areas is practically infeasible if accuracy of shape is important
figure NUM prediction results for various menu sizes
one possible solution is to back off to broader categories so we investigated the use of part of speech pos transition probabilities extracted from existing tagged corpora penn treebank
the semantic categories are arranged in a hierarchy
given the great improvements that have been made in speech recognition by using hidden markov models hmms it is natural to expect that these techniques would be beneficial for word prediction
NUM NUM words of data represents around three months of input and much more data would be necessary to collect useful trigrams vocabulary size in the NUM NUM word sample is over NUM NUM
however the work reported here was prompted by the needs of an individual who has lost the ability to speak due to amyotrophic lateral sclerosis als or lou gehrig s disease
the initial version of this system incorporates word prediction as describec in the next section and also a small number of fixed text utterances accessible via dedicated keys or menus
giving the utterance the correct stress compounds vary in stress in a way that partly depends on meaning contrast cotton bag meaning bag made out of cotton and bag for cotton
keystrokes needed without prediction for example choosing table after inputting t a would give a score of NUM since a space is automatically output after the word
moreover the precision equals NUM from 1st 38nd steps indicating that the merging process is suitable
interactively corrected output of the tagger can be incrementally added to the case bases continually improving the performance of the overall system
we believe a global attentional hierarchy plays a crucial role in choosing reference expressions beyond this particular domain of application
furthermore it turned out to be also important for other generation decisions such as paragraph scoping and layout
finally the combination of hierarchical planning with local navigation needs more research as a topic in its own right
fourteen pcas are generated by the macroplanner of proverb for our example in figure NUM
as illustrated in fig r below processing is still divided int o three main steps a unix and c based preprocess a lisp based syntactic analysis and a lisp based inferenc e phase
one of the fortunate if unexpected consequences o f the phraser s semantic grammar is that maintaining these cross references is considerably simpler than was th e case in our more linguistically inspired categorial parser of old
we represent operators as elementary trees in an ltag and use tag operations to combine them we give the meaning of each tree as a formula in an ontologically promiscuous representation language and we model the praghmtics of operators by associating with each tree a set of discourse constraints describing when that operator can and should be used
the active attentional space is the innermost attentional space that contains the local focus
that is other things being equal spud should choose to incorporate at each stage the syntactic semantic pragmatic unit which refers to maximally salient entities and other things being equal spud should incorporate a basic level predicate
moreover by evaluating and selecting alternatives on the basis of their pragmatic semantic and syntactic contribution to the sentence as a whole the procedure uniformly handles a variety of interactions inside a sentence including collocations
on the one hand are arbitrary fixed undecomposable combinations like by and large on the other are locutions like override a veto whose preferred co occurrence derives from the specificity of the semantics of the components
for the examples we have considered what seems right is to coindex the information states of modifiers and their heads and to coindex the information state of a verb with all its arguments except the subject
the interpreter has somewhat less recognition power than a finite state machine and operates by successively relabeling the input according t o the rule actions more on this below
words that do not appear in th e lexicon are assigned a default tag of nn common noun or nnp proper noun depending on capitalization
the first argument of each predicate is the information state in which the various predications hold the second argument is the eventuality which witnesses the application of the predicate s indicates the salience ranking of the states
among the te errors not arising from ne processing errors note in particular those that occurred on the most difficult slots org descriptor org locale and org country
person pers NUM complex phrases those with embedded phrases are typically interpreted as conjuncts of simple r interpretations the exception being np coordination as in chairman and chief executive
rule sequences for mug6 for muc NUM alembic relies on three sequences of phraser rules divided roughly into rules for generatin g ne specific phrases those for finding te related phrases and those for st phrases
in either case the overall processing power derives as much from the fact that the rules are sequenced and feed each other in turn as it does from the expressiveness of the rule language
initial phrasing produces a number of phrase structures many o f which have the initial null labeling none while some have been assigned an initial label e g num
for example an unknown word ending in ly is now assumed to be a noun verb adverb adjective and modifier
all 0pen class variants if the sentence still fails to parse all possible open class parts of speech are given to all open class words in the sentence
with NUM of the open class dictionary missingmin other words using only closed class wordsmthere are NUM deletions an average of NUM NUM per sentence or NUM NUM of the total parses
the first run uses a variation tomita s method it assigns all possible open class parts of speech noun verb adjective adverb and modifier to unknown words
however morphological generation involves the construction of new word forms by applying rules of afiixation to base forms and so it is only indirectly helpful in the analysis of unknown words
since hierarchical planning operators embody explicit communicative norms they are given a higher priority
syntactic knowledge can be used to aid in the analysis of unknown words sentence structure can be a strong clue as to the possible part of speech of an unknown word
this paper will empirically investigate how well a dictionary of closed class words syntactic parsing rules and a morphological recognizer can parse sentences containing unknown words in natural language processing tasks
this experiment shows that the performance of the parser is enhanced greatly when morphological recognition is used in conjunction with syntactic rules to parse sentences containing unknown words from the timit corpus
performance of the experimental system in terms of insertions with NUM of the open class dictionary missing is comparable to the baseline performance with NUM of the open class dictionary removed
section NUM concerns related works and sections NUM concludes the paper
it is time consuming to compile fine grained word models for each language
bi grams of subdivision are too general to selectively detect exceptions
a hierarchical tag context tree is constructed by a two step methodology
define the resulting tree to be t t
let us look at the procedure from the information theoretical viewpoint
sophisticated word models largely depend on the target language
next they generalize the exceptions and refine the previous rules
the first option is to construct more sophisticated word models
we can only use simpler word models in these languages
this is also required to get a more natural prosodics in text to speech synthesizers
this is a new block of rules run after the grapheme phoneme conversion
it was enriched with probabilities estimated by parsing the same training data with the final model and using relative frequencies of use as probability estimates
this representation was chosen NUM the right to left match has already been described
NUM we used square brackets for the segmental output of the rules
for information retrieval open and closed phonemes are always considered identical
a limited parsing has been done using the same formalism as letter to sound
the ambiguity is often between a conjugated verb and another grammatical category
homographs are pairs of words that are orthographically identical but phonetically different
it would be an extremely difficult task to create such a list
however it is possible to achieve more generality if we apply a further abstraction step on a generalized mrs
during the application phase a new semantic input mrs t is used for the retrieval of the decision tree
at the sub sense level with an average NUM senses per word the sense tagger was correct NUM of the time
singular nouns being followed by the 3ps form of the present simple conjunctions tending to co ordinate the same grammatical tags
besides some simple tests for suffixes for unknown words capitalization register and frequency the main tagging processes are the following
if the argument pattern subject and objects fail to match a tag sequence this is considered a verb complementation pattern failure
mr is then used as a path description for traversing the decision tree
tdl allows the user to define hierarchicallyordered types consisting of type and feature constraints
figure NUM the mrs of the string sandy gives a chair to kim
thus viewed our approach is directed towards the automatic creation of application specific generation systems
in the first phase tm extracts and generalizes the derivation tree of fs called the template of fs
in the case of partial matching however the decision tree describes only possible prefixes for a new input
the extraction takes place by usi n 1l o fs l 2l NUM
adding the ambiguous word to all the sw sets allows the algorithm to take this fact into account
lexical forms e.g. morphemes in morphology appear in braces lcb rcb phonological segments in square brackets and elements of tuples in angle brackets
to be constructors for forward and backward slash
thus using the abstract syntax capabilities of prolog we can have a direct implementation of the underlying linguistic formalism in stark contrast to the first order simulation shown in figure NUM
we wo n t show the complete declaration of the reducer but the key clause is simply red app abe n n n n
instead it is a simple matter of using the meta level reduction to eliminate redexes to produce the final result abe x found i harry x
it should be stressed that this particular kind of lf is assumed here purely for the sake of illustration to make the point that composition at the level of derivation and lf are oneto one
NUM for example if x is a unary function then the semantic rule is ta and if the functions have two arguments then the rule is 7b
the built in a term manipulation is used as a meta language in which the object language of ccg logical forms is expressed and variables in the object language are mapped to variables in the meta language
however for some interesting cases such as a combinatory categorial grammar account of coordination constructs this can only be done by obscuring the underlying linguistic theory with the tricks needed for implementation
generally the error rate decreases when the training text is increased
a mean value of NUM bytes per word is allocated
in some languages a number of grammatical categories is not applicable
generally tagger speed increases when the training text is increased
generally tagger errors can be classified into three categories a
in this way the unknown words ambiguity is decreased significantly
strong dependencies on the language and the estimation accuracy of the model parameters influence this reduction
the quantization function approximates the computations producing theoretically differing solutions
a fully automatic training and tagging program has been implemented on an ibm pc compatible NUM based computer
each part contained NUM NUM words for the english text and NUM NUM words for the french text
also let c3 be the result label
figure NUM a part of the bracket grouping process
towards automatic grammar acquisition from a bracketed corpus thanaruk theeramunkong
figure NUM depicts an example of the grouping process
the detail of this technique is illustrated below
1a bracket corresponds to a node in figure NUM
for instance at a merge step h NUM NUM b n NUM a data collection c has been partitioned into a set of groups g
intuitively when the size of data is large the small number should be used as a in the experimental results in this paper we assigned a with a value of NUM NUM
NUM NUM entries from the japanese english online dictionary edict with occurrence frequencies between NUM and NUM are chosen as seed words
if we find the correct translation among the top NUM candidates we obtain a precision of around NUM
additionally the word relation matrix could be used in combination with other word siguature featur for non parallel corpora
for evaluation we need to select a test set of known technical term translations
the information can be used in language modeling in addition to the currently popular n gram models and word trigger pairs
test i tries to find the correct translation for each of the nineteen japanese terms among the nineteen engl sh terms
in all cases a translation is counted as correct if the top candidate is the rig ht one
to evaluate this we again chose the nineteen english japanese terms from the wsj nikkei non parallel corpus as a test set
the previous two evaluations show that the precision of best candidate translation using our algorithm is around NUM on average
these were used in the experiments described in section NUM
this constraint has one uninstantiated variable cand which has a unique non null solution namely the candidate set associated with the head noun
the plan constructor uses a computational linguistics volume NUM number NUM best first search strategy expanding the derivation with the fewest number of surface speech actions
all of the reasoning is done in the refer action and the intermediate actions so no constraints or effects are included in the surface speech actions
in this case predicate is a lambda expression of two variables one corresponding to entity and the other to otherentity for instance x
our approach is coursely grained and leaves much room for future development in every respect
so when the first response is processed it can attach to the first suggestion
these are speech acts such as suggest which are not responses to previous speech acts
our goals for the discourse processor include recognizing speech acts and resolving ellipsis and anaphora
the evaluation was conducted on a corpus of NUM previously unseen spontaneous english dialogues containing a total of NUM sentences
it can not simply be a matter of immediate focus since the week is never mentioned in NUM
however mctag lack expressive power since while syntactic relations are invariably subject to c command or dominance constraints there is no way to state that two trees from a set must be in a dominance relation in the derived tree
while the results are less than perfect they indicate that extended tst outperforms standard tst on spontaneous scheduling dialogues
furthermore there is an inconsistency in the directionality of the operations used for complementation in tag nominal complements are substituted into their governing verb s tree while the governing verb s tree is adjoined into its own clausal complement
incremental translation dienstags mn zehn ist bet mir nun wiederum schlecht tuesday at NUM is for me now again bad this syntactic and semantic category knowledge is used by the segmentation parser for two main purposes
furthermore dtg unlike tag can provide a uniform analysis for wh movement in english and kashmiri despite the fact that the wh element in kashmiri appears in sentence second position and not sentence initial position as in english
we have also described our extension to tst in terms of a practical application of it in our implemented discourse processor
for example if the anchor is a finite verb it will project to s indicating that an overt syntactic surface subject is required for agreement with it and perhaps case assignment
thus nodes separated by an i edge will remain in a mother daughter relationship throughout the derivation whereas nodes separated by an d edge can be equated or have a path of any length inserted between them during a derivation
while we could demonstrate that such a local strategy could assign correct dialog acts in many eases it might be interesting to explore to what extent knowledge about previous dialog acts in previous utterances could oven improve our resalts
pus are recursively broken up into two parts similarly to the candidate terms and the links are called h link and e link
the interactive conceptual analysis in the present article we only described the first step of the ka process the conceptual fields construction
in this case we can see that over NUM of the words found their translation in the top NUM candidates although it gives fewer words with translations in top NUM
as mentioned earlier many chinese words have multiple part of speech tags such as the chinese for declaration declare development developing adjourned adjournment or expenditure spend
given two corresponding clusters of words from the corpus context heterogeneity could be used to further divide and refine the clusters into few candidate translation words for a given word
the following basic intentions are covered in our model the presentation of a variable the comparison of variables or sets of variables the evolution of a variable along another one the correlation of variables and the distribution of a variable over another one
the topic of these debates varies though is to some extent confined to the same domain namely the political and social issues of hong kong
the nametag case insensitive mode was run on the upper case version of the test data
this generalization is represented as a structura l class and a set of constraints
the actual definition of the structural class is maintained in the auxiliary knowledge base
an incoming text with absolutely nothing in common with the egraph receives a NUM NUM
the reference resolver supports the analyzer by providing links from references to their referents
our search capability allows interactive information discovery methods
figure NUM documents containing bank of japan
figure NUM the NUM and NUM most frequent names
figure NUM top NUM entity names
the system is customizable in several ways
this also led to some error in the vector representation
our dtw algorithm with path reconstruction is as follows initialization
we keep pairs of words if their t NUM NUM where
t NUM NUM which shows that their correlation is reliable
we obtained the evaluations of three human judges el e3
our program also runs much faster than other lexicon based alignment methods
the positional vectors have different lengths which complicates the matching process
it indeed is the name for green paper a government document
actually pulling the cat s tail is cantonese slang for collusion
the system must first determine wbether justification for bel is needed by predicting whether or not merely informing the user of bel will be sufficient to convince him of bel
the candidate foci tree will be identical to the proposed belief tree in figure NUM since both the top level proposed belief and its proposed evidence were rejected during the evaluation process
easy methods for saving individual sentences or complete documents to new text files are provided
to accommodate this a tipster annotation and document management feature was added to the system
the system will predict that either piece of evidence combined with the proposed mutual belief is sufficient to change the user s belief thus the filtering heuristics are applied
searches are automatically expanded by a fuzzy matching scheme if the initial search fails
the dictionary tool s usability is also enhanced by its sophisticated lexical search capability
machines are excellent tools for quickly searching for retrieving and storing information
documents and collections are processed quickly and results can be re accessed though collection attributes
this paper describes the methodology used to develop oleada and the current system s capabilities
tipster developers work to provide a variety of specialized software subsystems that support tipster development
languages include those using latin characters chinese japanese arabic and russian
working translators use a variety of resources that lend themselves to electronic storage and retrieval
the talks resumed the metarule that implements the middle transformation is as follows
a relational company term such as unit can have another company as a complement
the following is a fragment of a grammar for verb groups in the basic phrase recognizer
this is obviously a place where the algorithm can be improved
but we have been surprised how strong a performance can be achieved just with these simple heuristics
we would then use the resulting coreference chains to increase the richness of concepts in the text
for the semantics of the specialized rule we encode the mapping the user has constructed
the first problem is to identify the phase in which the new rule should be defined
the use of indices like NUM allows us to access the attributes of terminal symbols
this says that when an organization resumes talks with an organization it is a significant event
however because objects are generally composed of multiple slots and because objects of the same typ e often have data in common this notion of similarity between a key and a response object can be complicated
they include a some sing some plur the both many at least four o monotone decreasing quantifiers are ones with an at most n interpretation
currently most of our NUM empirical work still treats the system as though it produced text output we describe this mode of evaluation in section NUM NUM
it is useful to note that a variable s candidate set is related to an unpartititioned dependency function in exactly the same way that its focus set is related to the focus of the partitioned function
it is thus a strategy used to introduce unshared entities into the discourse
what we need is a representation of the relevant relations in the kb
the um provides a generalised classification system of conceptual entities
once the entity is introduced some form of definite reference is appropriate
or are they supposed to complete the proposition in some way
the advantages of wag s input specification language are summarised below
note that wag serves in monologic as well as dialogic interactions
is the hearer supposed to accept the content as a fact
in this approach both processes have access to the kb
the sentence specifihation of figure NUM reflects this mode of generation
it is important to remember that essentially equal answer keys ar e being compared as key and response and the decision as to which key is the key and which the response is arbitrary and affects the humans scores
results on the repairing capacities according to the filtering behavior are presented in table NUM weakly recovered means that all the information is present in the semantic representation but part of it may be marked as uncertain with other parasite information see figure NUM for an example
this ranking is consistent with the intuition that punctuation and interjections have more semantic weight than function words but less than content words
in addition to the scoring categories described above in the scoring section the following metrics are als o displayed in all of the score reports recall the number of correct divided by the number of possible
section NUM describes the italian prototype of wordnet while section NUM shows how selectional restrictions has been added to verb senses
the correct alignment decision for a mandarin compound frequently involves more sue j ker and jason s chang word alignment
the classalign algorithm expands coverage almost twofold to over NUM while maintaining the same level of precision
in the expectation phase the parameters t s i t and d i i j l m in the smt model for all possible values of s t i j i and m are estimated from the sample of an aligned bilingual corpus
the proposed algorithm called classalign relies on an automatic procedure to acquire class based alignment rules it does not employ word by word translation probabilities nor does it use an iterative em algorithm for estimating such probabilities
statistical machine translation smt can be understood as a word by word model consisting of two submodels a language model for generating a source text segment s and a translation model for mapping s to its translation t
the rules describe conventional strategies for producing coherent utterances thereby displaying understanding and strategies for identifying misunderstanding
while our taxonomy might seem small most other acts appear to be specializations of those that we selected
expected s1 areply ts d shouldtry sl s2 areply ts
misunderstandings are classified according to which participant recognizes that the misunderstanding has occurred and whom she thinks has misunderstood
NUM if a discourse has just begun then any utterance that starts an adjacency pair will be coherent
NUM it is important to note that this is just one of the possible explanations available to russ
these rating heuristics are problematic because they conflate linguistic and pragmatic knowledge with knowledge about the search mechanism itself
to interpret an utterance the approach applies a set of context independent inference rules to identify all plausible plans
within the speech understanding community the word expectation has been used differently from our use here
there are two stages which we call semantic and pragmatic
the lolita system was entered in muc NUM mainly because of the importance of evaluation
lookups in the dictionary are done with the root forms suggested by affix stripping
selection of best parse tree subsequent analysis operates on a single tree
rather than cause a crash due to overrunning limits the parse is abandoned
an initial stage of this is done on the fly in semantics
this compares with formal evaluation scores of NUM recall and NUM precision
says that because he had a great time in advertising
work is also in progress to integrate the text based core with a speech recogniser
more detail on the architecture of lolita can be found in NUM
the wildcats then closed out the first half
this would result in the following probabilities
tf idf weighting on most likely words
this would produce the following fists
this would produce the following lists
set NUM contained editorials from vietnam
whether core and contributor are adjacent in linear order
each segment originates with an intention of the speaker
finally the best tree was obtained as follows
figure NUM decision tree for core2 occurrence
table NUM distributions of relations and cue occurrences
table NUM distributions of relations and cue occurrences
the goal is a system designed to be used over an extended period of time with the capacity to model the student s state of language proficiency and changes in that proficiency
italian wordnet has been coupled with a parser and a number of experiments have been performed to individuate the methodology with the best trade off between disambiguation rate and precision
although the generation of cgi queries is driven by the schema to database and user to database mappings files some degree of application specific work still needs to be performed
ii more deeply embedded items belong to the topic focus if their head words in the framework of dependency syntax belong there
the verb and the noun car both belong to the focus according to i and so does her according to ii
as we have just seen the b sentences rather than their a counterparts are restricted to one of the possible tfas
the function of this algorithm can be checked and its usefulness connected with that of the underlying framework may then be compared with other approaches
in english the surface word order is determined by grammatical rules to a large extent so that intonation plays a more decisive role than in the slavonic languages
to be able to reduce the ambiguity of the written shape of the sentence as much as possible it is necessary to take into account certain semantic clues
this means that it is included in the topic of sentence b on all its readings i.e. in all syntactic representations of the sentence
a cb item is always considered to be less dynamic than its head and than its nb sister nodes i.e. nodes depending on the same head
NUM we assume that at least one reading of the sentence has been assigned an f nb element by now
thus for instance NUM h corresponds to the following sentences NUM h1 the neighbor met the boy yesterday
this caused by the contemporary presence of the sense letter missive and of the proper noun john as respectively patient and beneficiary of the write verb sense
as a potential solution one may propose to have the preposition directly be integrated phonologically and in terms of synsem information into the np domain object corresponding to einen hund
on the other hand the attachment site of the preposition will have to be higher than the relative clause because clearly the relative clause modifies the nominal but not the pp
but this means that whether or not something can be extraposed has been rendered exempt from lexical variation in principle unlike in reape s system where extraposability is a matter of lexical selection
we propose a novel approach to extraposition in german within an alternative conception of syntax in which syntactic structure and linear order are mediated not via encodings of hierarchical relations but instead via order domains
NUM the latter is constrained to be the concatenation of the phonology values of the domain elements in the corresponding sign s order 2for expository convenience semantic information is systematically ignored in this paper
this means that we can have a stronger theory as constraints on extraposability will be result of general conditions on the syntactic licensing schema e.g. the right roof constraint in NUM
NUM in this sense extraposition is subject to a monotonicity condition to the effect that the element in question has to occur in the same linear relationship in the smaller and the larger domains viz
figure h derivation of v1 clause using order domains
figure NUM domain formation using compaction and shuffle
combining the outputs of the plum and shogun systems appeared promising because the two systems had different performance profiles in terms of precision and recall but similar f scores
this score represents an upper bound on how well we could possibly do if our combining strategies allowed us to choose exactly the best frames provided by each system
in the test conducted here vocabulary was successfully added and slot filling performance improved using only the nlu shell tools however the process could be improved
while the nlu shell users need detailed familiarity with the processes and knowledge needed for extracting formatted data from natural language they do not need to be programmers
an inference rule queried the semantic database to determine whether the job situation person was involved in multiple job situations if so it would add predicates for the job situation s
recent statistical full parsers e.g. bbn s ibm s and upenn s have such quantitatively better performance that they are qualitatively better
as summarized in table NUM and in figure NUM the effectiveness of information extraction on training material dropped from the high 80s to the mid 50s
reading these texts in an office environment had a NUM error rate which is better than the current best error rate of NUM on broadcast news
using the evaluation methodology developed under the darpa sponsored message understanding conferences muc we measured information extraction performance on the muc NUM template element te task
we would like to test the techniques on quite different languages such as chinese where performance for manually based systems is lagging behind systems in english and spanish
this means that while the parser disambiguates it also builds up a dependency forest that in turn is reduced by other disambiguation rules and a global pruning mechanism
in this section we present a set of rules and show how those rules can parse the sentence joan said whatever john likes to decide suits her
she pron pers fem gen sg3 gn she pron pers fem acc sg3 obj figure NUM a sentence after morphological analysis
for example it is not possible to reliably pick head modifier pairs from the parser output or collect arguments of verbs which was one of the tasks we originally were interested in
but it is also possible to declare the last item in a chmn of the links e.g. the verb chain would have been wanted using the keywords top and bottom
null our notation makes a difference between valency rule based and subcategorisation lexical the valency tells which arguments are expected the subcategorisation tells which combinations are legitimate
NUM the infinitives preceded by the infinitive marker to can be reliably linked to the verbs with the proper subcategorisation i.e. the verb belongs to both categories ptcl cohpl v and sv0
e the most typical case is that the context gives some evidence about the correct reading but we know that there are some rare instances when that reading is not correct
moreover there is no longer any need to remove these readings explicitly by rules because the global pruning removes readings which have not obtained any extra evidence
therefore top v ch mainvalways ends with the main verb in the verb chain whether this be a single finite verb like likes or a chain like would have been liked
for these reasons the short table NUM includes both the lowest and the highest semantic entropy values for english words in the hansards
in our prototype the dialogue manager and the text generator will collaborate to handle these situations
the overall sd system is responsible for taking user utterances as input processing them in a given context in an attempt to understand the user s query and satisfying his her request
we see that each step in the plan is acknowledged before the next one is given
the strategies described in the previous section will serve as an important system guideline to present information
this kind of encoding is useful to avoid the creation of choice points for the lexicon of languages where one inflectional form may correspond to different feature values
templates are defined by expressions of the form NUM where name and value can be arbitrary profit terms including variables and template calls
the clauses of a profit program can make use of the datatypes sorts features templates and finite domains that are introduced in the declarations
most large scale linguistic descriptions make use of sorted feature formalisms NUM but implementations of these formalisms are in general too slow for building practically usable nlp systems
the sort hierarchy must not contain any cycles i.e. there must be no sorts a and b such that a b anda b a
in the course of grammar development it is often necessary to change the location of a feature in order to get the right structuring of information
in the organization of linguistic knowledge feature structures are often deeply embedded due to the need to group together sets of features whose value can be structure shared
declarations and clauses can come in any order in a profit file so that the declarations can be written next to the clauses that make use of them
error handling is currently being improved to give informative and helpful warnings in case of undefined sorts features and templates or cyclic sort hierarchies or template definitions
as table NUM shows this is not the case in our system
table NUM knowledge provided by each heuristic overall results
thus adding new heuristics with different methodologies and different knowledge e.g.
nevertheless quality and size of the lexical knowledge resources are important
not all the heuristics are suitable to be applied to all definitions
including monosemous genus in the results c f
the results are provided in table NUM
from corpora as they become available will certainly improve the results
in order to obtain an unofficial score we had to edit our results which we did strictly according to th e trace
b no peter only likes mary
but is n t this restriction fairly idiosyncratic
formally this is captured as follows
null the equations resulting from the interpretation of
there are a number of practical differences between transformation based error driven learning and learning decision trees
the method used for initially tagging unknown words will be described in a later section
a transformation can make reference to any string of characters up to a bounded length
annotated text is necessary in training to measure the effect of transformations on tagging accuracy
the second transformation fixes a tagging such as might md vanish vbp vb
annotations of the test corpus were not used in any way to train the system
below we show a lexical entry for the word half in the transformation based tagger
the preceding word is tagged z and the following word is tagged w NUM
whether theorem NUM NUM nf per reading remains true depends on what set of rules is removed
we may therefore regard a syntax tree as a static recipe for combining word meanings into a phrase meaning
NUM overview of the parsing strategy as is well known general cfg parsing methods can be applied directly to ccg
NUM the following sections show how to obtain effectively the same result without doing any semantic interpretation or comparison at all
NUM shows the constituents that untrammeled ccg will find in the course of parsing john likes mary
in general our goal is to discover exactly one analysis for each substring meaning pair
generating all parses is inefficient and obscures whatever true semantic ambiguities are in the input
so NUM is meaningful and true even if a a are produced by a restricted ccg
a parse tree or subtree that satisfies NUM is said to be in normal form nf
for instance more natural pronunciations can be attempted for yn questions or for confirmation questions including tag questions in english as in the train goes east does n t it
a useful metric should mso be robust with respect to the scale words sentences paragraphs for instance at which boundaries are determined
an example of an improper legitimate query could be what time does my plane leave if the system expects the word flight but not plane
it can be expected that with such a method the quality of the results depends on the thematic comparability of the corpora but not on their degree of pazallelism
though the trigram prior was trained on approximately NUM million words the trigger parameters were trained on a NUM million word subset of the bn corpus
the fluctuating curve is the probability assigned by the exponential model con null the lower verticle lines indicate reference segmentations truth
two main tyl es of rule based bag generators have been proposed
as lnenl ione earlier these indices derivation
we distinguish variables by uniquely typing them
such a wfss will have been constructed during the bag generation process
the situation in string w is analogous to that in condition NUM
proposes handling ine ticiency NUM the expense of completeness
in compiling outer domains inner domains are used to facilitate computation
if extended appropriately these constraints could prune the search space even further
the results of the experiment are shown in table NUM
first the inner domains of the grammar are calculated
there are obvious practical reasons for this
some of the error situations may be regarded as truly undecidable
the number of part of speech tags used in the suc is NUM
the output was compared to the same texts with manual disambiguation
one such norm is the suc tagging manual ejerhed et al
the xpost algorithm has been transferred to other languages than english
the present article is based on a manual analysis of a large number of tagging errors
such situations are more common in automatic tagging but they occur in manual tagging as well
there are legal permissions allowing the corpus to be used and distributed for non commercial research purposes
in general differences in user behavior depending on the level of computer initiative were observed
note that virtually no utterances were ever spoken during the repair phase of declarative mode dialogues
approximate adaptation of p c i ai s to the smoothed version of the estimator is simple
such smoothing typically reduces slightly the estimates for values with positive counts and gives small positive estimates for values with a zero count
assertion the speaker has control unless the assertion was a response to a question
consequently sessions NUM and NUM are balanced for difficulty only through the first eight problems
dialogue NUM declarative mode NUM c this is the circuit fix it shop
this response selection process has been implemented as part of the previously mentioned circuit fix it shop
we define the selection probability as a linear function of vote entropy p gd where g is an entropy gain parameter
this is a serious knowledge engineering bottleneck when the goal is to develop a language and annotation independent tagger generator
we say that a subtree t with type information at every node is semantically determinate iff we can determine a unique correct semantic rule for every cfg rule r NUM occurring in t
sequential selection examines unlabeled examples as they are supplied one by one and measures the disagreement in their classification by the committee
another important area for future work is in developing sample selection methods which are independent of the eventual learning method to be applied
otherwise even if the current value of the parameter is very uncertain acquiring additional statistics will not change the resulting classifications
furthermore the model does not fully take into account different types of miscommunication and their repair
NUM the effectiveness of randomized committee based 5note that most other work on tagging has measured accuracy over all words not just ambiguous ones
the context blish is the maximal proper suffix of two other contexts in the model ouestablish and euestablish
when a new category is constructed for the antecedent any tense resolutions also need to be undone since the original ones may no longer be appropriate for the revised category
the more that context dependence enters into the interpretive mapping so that meanings are correspondingly more context independent the harder it is to maintain a principle of strict compositionality in interpretation
this is like set union except that where subs1 and subs both substitute for a particular item the substitution from subsl is retained and not that in subs
it is argued that the order independence results from viewing semantic interpretation as building a description of a semantic composition instead of the more common view of interpretation as actually performing the composition
the non deterministic nature of evaluation and the role of substitutions draws us to conclude that ellipsis substitutions operate on descriptions of the semantic compositions not the results of such compositions
the modification is to allow strict substitutions on terms not explicitly appearing in the ellipsis antecedent i.e. the implicit his paper in the second ellipsis when resolving the third ellipsis
note also that the term index substitution applies to the scope node so that j is replaced by in
the information about relative social status provides the context in which a sentence is felicitous
in this paper we have discussed a method to compute relative social status of the individuals involved in a dialogue
from the sentence 21a the order in NUM is drawn
y z NUM higher inf three k p i
the feattn e structure of a lexical sign is as shown in NUM
ni loc pont a object oscontent ii l lconx a structure ofcontextj
the plain verbal endings and honorific verbal endings are as illustrated in NUM
this fact is guaranteed by the contextual indices inheritance principle shown in NUM
on the other hand the value of the feature iiackgp is a set
as a device of linking sentences the conjunctive kuliko and is used
speakers first explained simple routes such as getting from one station to another on the subway and progressed gradually to the most complex task of planning a round trip journey from harvard square to several boston tourist sights
note that there were one third fewer consensus labeled phrases for text alone labelings than for text and speech see table NUM
table NUM shows that group s produced significantly more consensus boundaries for both read p NUM
two methods of discourse segmentation were employed by subjects who had expertise in the g s theory
this level of prosodic phrase served as our primary unit of analysis for measuring both speech and discourse properties
5flammia uses a flexible definition of segment match to calculate pairwise observed agreement roughly a segment in one segmentation is matched if both its sbeg and sf correspond to segment boundary locations in the other segmentation
the orthographic environment that characterizes the usage of the potential discourse marker
consider for example the rule a b prevbigram c c that changes tag a to tag b if the previous two tags are c
the transducer t9 locext t6 of figure NUM gives a more complete and slightly more complex example of this algorithm
we will show that paradise provides a useful methodology for evaluating dialog systems that integrates and enhances previous work
in this section we give a worst case upper bound of the size of the subsequential transducer in terms of the size of the input transducer
utterances can be either appropriate ap inappropriate ip or ambiguous am
user satisfaction a metric that attempts to captures user s perceptions about the usability of the system
the lexical tagger initially tags each word with its most likely tag estimated by examining a large tagged corpus without regard to context
a list of tagging errors with their counts is compiled by comparing the output of the lexical tagger to the correct part of speech assignment
the rhetorical parsing algorithm has been fully implemented in c
to achieve high speed for this procedure the dictionary is represented by a deterministic finite state automaton with both fast access and small storage space
its denotation is the set of all desks in the universe of discourse
figure NUM the sequence of calculations for approximating s a s b i e coded for the finite state calculus
for instance a surzounding context including the word church will indicate a strong support for the pastor sense of m i ter as opposed NUM o its other se ses
as for the complementations following the verb rule NUM may be stated rule NUM the boundary between topic to the left and focus to the right can be drawn between any two elements following the verb provided that those belonging to the focus are arranged in the surface word order in accordance with so see section NUM
for the information content algorithm a window size of NUM i.e. NUM nouns to the lefz and right was found to yield the best results w NUM t for the conceptual density algorithm the optimum window size was found to be NUM
some of the authors concentrate on focalizers and their scopes and or foci whereas we consider a sentence containing no focalizer to constitute the prototypical case it is open to discussion whether in this a covert focalizer such as the assertive modality of the main verb is present on some level of representation
n the actum implement ttion we only htwe to work on the bigr ml t lble instead of tim whole text
the symbol topic denotes here whether the given item belongs to the topic or to the focus touch stores the information if the complementation has been already determined sem is the semantic information about the verb general specific intermediate and itree and rtree are the left and right subtrees in the dependency tree
if this prediction disagrees with the actual value in the annotated data adjust bpa is invoked to alter the bpa s for the observed cues and reset current bpa is invoked to adjust the current bpa s to reflect the actual initiative holder step NUM
we have unfortunately found it impossible to perform comparison evaluations against other systems due to the unavailability of chinese parsers in general
for the first sequence the second matches the second vn NUM when it is compared to the earlierdefined value of vn NUM
the only initial edge to be added is pron since this edge is finished we add it to the agenda
these context conditions help cut the parser s search space by eliminating many possible parse trees increasing both parsing speed and accuracy
for example let a lcb l rcb b lcb r rcb andc d e lcb r rcb betwo production rules
it will be interesting to explore the relationships between our grammar and other context sensitive grammar formalisms a topic we are currently pursuing
we have found this approach to be very effective for constraining the types of ambiguities that arise from the compounding flexibility in chinese
the first is constant increment where each time a disagreement occurs the value for the actual initiative holder in the bpa is incremented by a constant a while 4bpa s are represented by functions whose names take the form of m b
these were the cases that were generally more difficult for the taggers as reflected in lower tagger expert agreement
this creates extraordinary difficulties for grammar writers since robust rules for such compound forms tend to also accept many undesirable forms
we observed that for low values of the threshold less than NUM of the valid translations are missed for example for the threshold value of NUM NUM we currently use the error rate is NUM NUM
for named entity our pattern set built on work done for previous mucs
NUM heuristic filtering of words with low local frequency may be more or less efficient depending on the word but a higher percentage of discarded words will come at the cost of inadvertently throwing out some valid words
however this version achieves only NUM NUM on the apte split compared with NUM NUM of balancedwinnow
walter thompson as a company i n the phrase hired from j
we have compared this version with a few other algorithms which have appeared in the literature on the complete reuters corpus
abcoude word graphs the average number of milliseconds per word graph and the maximum number of milliseconds for a word graph
two errors accounted for most of our incorrect slots on the scenario template task
these three alternatives examine the tradeoff between the positive and negative impacts of assigning a strength in proportion to feature frequency
NUM who have clearly and eloquently set forth the advantage s of this approach
in other words in our case x and y are highly asymmetric a NUM value and a NUM NUM match is much more informative than a NUM value or NUM NUM match
however in the merging decision case pairs that are far away will not be in the data set if there are coreferring expressions between them and thus the probability for coreference at long distances will be diminished
if not it attempted a parse covering the largest substring of the sentence which it could
we want to infer that fred left the cuban cigar corp
this causes the the correct parse to be chosen np vn relph np np we have only found this function useful for left and right contexts rather than the main body of production right hand sides
perhaps it was a result of including patterns beyond those found in the forma l training
null easyenglish combines features from both standard grammar checkers and controlled language cl compliance checkers with checks for structural ambiguity in a way that we believe is general enough to be useful for any writer not just tec hnical writers
if this is used as a verb they beef a lot it should be flagged as slang on the other hand if it is used as a noun he ate beef it should not be flagged
furthermore it can be helpful for the parser to take the tags into account especially quote and highlighting tags which may delimit complete phrases header tags can influence the parser to prefer noun phrase analyses over sentence analysis
NUM in order to distinguish this action from the primitive actions it has a step that is marked null
likewise if one agent proposes a referring expression through a refashioning the other must accept the refashioning
the third check gives the user the option to specify a controlled vocabulary all words that are not in the controlledvocabulary file or that are improperly used with respect to part of speech will be flagged should the user decide to turn this check on
both have the same moves available to them for either can judge the description and either can refashion it
if it was rejected or the decision postponed then one participant or the other would refashion the referring expression
there are two reasons why the evaluation could have failed either no objects match or more than one matches
the purpose of this subset is to inform the hearer of the surface speech actions that the speaker found problematic
because the explanation component we are building interacts with users via text and menus the student and human tutor were required to communicate in written form
care must therefore be taken in finding the right instantiation so that blame is attributed to the action at fault
due to this coordination the results of our analyses of good texts can be used as rules that are implemented in the generation system
some but not all of these are represented by senses in wordnet and none are identified as having this special function
the other schema is shown in figure NUM and is used for describing objects in terms of some other object
the third step substitutes the replacement into the referring expression plan undoing all variable instantiations in the old plan
the second step is to refashion the referring expression so that it identifies the candidate chosen in the first step
further we are able to formulate and verify more general patterns about the distribution of types of cues in the corpus
this is the case whenever we can encode the alphabet of the corpus in such a way that alignment is possible
the purpose of this segment is to inform the student that she made the strategy error of testing inside paxt3 too soon
as we understand lochbaum s theory for each factor distinguishing these alternatives the potential intentions are all discussed inside of a single discourse segment whose purpose is to explore the options so that the decision can be made
its focus space is pushed onto the stack and then popped off when the focus space for the response to the suggestion for tuesday in ds NUM is pushed NUM clearly this suggestion is not an interruption however
head transducers offer efficiency and robustness advantages to the speech translation application there is empirical evidence supporting this claim at least in the case of comparison with a transfer approach
where freq w k is the number of occurrences of w k in the reference corpus the precision precision c i is then defined as n n tdegt
furthermore certain techniques for robust parsing can be modelled as finite state transducers
as explained in the previous section sentences are generated by means of s templates
we experimentally observed that only h the ratio between lower and upper bound significantly modifies the resulting sets of categories ci we established that a good compromise is h NUM NUM
the obvious advantage of semantic tags is that words are clustered according to an intuitive principle they belong to the same concept rather than to some probabilistic measure
as we have seen an important part of the context model is a discourse model
we attempted slight changes in the definitions and computation of co dp and NUM a for example weighting words with their frequency but globally the behavior remain as those in figure NUM
that s can not has a major impact on the analysis of scheduling dialogues such as the one in figure NUM since the majority of the exchanges in scheduling dialogues are devoted to deliberating over which date and at which time to schedule a meeting
we can express the generality g ci as NUM dm ci being dm c i the average distance between the categories of c i and the wordnet topmost synsets
sense n NUM lineage line line of descent descent bloodline blood line blood pedigree ancestry origin parentage stock genealogy family tree
this process of recursive transduction of local trees is shown graphically in figure NUM in which the pair of words starting the entire derivation is w4 v4
the head is notionally at position NUM and the standard positions immediately to the left and right of the head are numbered as NUM and NUM respectively
in a first implementation rather than really computing solutions of analogies on trees we retrieve them from the tree bank using approximate matching
annotated corpora become available nowadays automatic knowledge acquisition from them becomes a new efficient approach and has been widely used in many natural language processing systems
a parsing algorithm to i this problem must deal with two important issues NUM how to produce the suitable syntactic trees from a u
they can directly modify a noun such as the verb xun an wain in the phrase xurd o n
a typical path through the state diagram is shown in bold this converts the english dependency sequence for statement sentences with the pattern actor head object temporal into the corresponding chinese sequence actor temporal head object
second the preference matching model constructs the syntactic trees through bracket matching operations and select a preference matched tree using probability score scheme as output figure NUM d
the aim of labeling approach is to eliminate the ungrammatical matched constituents and label the suitable syntactic tags for the reasonable constituents according to their internal structure and external context information
as expected this proposal renders an account of some important linguistic phenomena in particular prefixing suffixing and infixing
named entity the named entity performance was severely effected by a bug which virtually eliminated one entire response out of the set of thirty accordingly the difference in scores between official an d unofficial is most dramatic here
the te and st systems were developed using a range of tools for reference resolution for information extraction for simple discourse processing and for template generation as well as a variety of spotters for spotting entities
the caller is not able to interrupt when he does not comprehend the given information
in vios the travel plan is presented using templates filled with specific stations and times
each piece of information corresponds to a turn in the dialogue
only in this case with five utterances a whole travel plan is given at once
some researchers question the validity of the complete dictionary assumption
the departure and arrival times commonly serve as new information
repair sequences appear at different places in the information exchange
one two three and four information elements respectively
they may appear directly after the utterance to which they react
participants in natural social conversation further demonstrate their co operative involvement with frequent positive feedback while a partner is making an extended contribution to the conversation and by means of repair strategies when things go wrong threatening breakdown of the conversation
figure NUM representation for type actorelation
there are currently four slot types distinguished by how the slo t output is produced concept slot
ga mr is an homograph of several items
we present an lo based approach to recognizing of proper names in korean texts
simple nouns derived nouns and compound nouns must be available
however recall precision plots show that recall and precision arc inversely related
iss ess den ga be is past past interrogation the string NUM
these are also written with initial upper case differently from usual adjectives
some fts with their associated pntypes d senbai NUM NUM
we augmented this table with marginal totals arriving at NUM categories each of which represents a triplet of attribute values possibly with one or more do n t care elements
we can also eliminate the transitions lcb NUM q7 q13 NUM q9 q17 rcb because the lexical rule NUM requires the value of z to be empty list
one possibility for extending the theory is to introduce two subtypes of word i.e. simple word and derived word and define an additional feature in with appropriate value word for objects of type derived word
the definite clause encoding of the interaction predicates resulting from unfolding the frame predicates for the lexical entry of figure NUM with respect to the interaction predicate of figure NUM is given in figure NUM
during word class specialization though when the finite state automaton representing global lexical rule application is pruned with respect to a particular base lexical entry we know which subclass we are dealing with
text NUM similarly contains a single sentence but has three topic shifts in addition to topic chains within the sentence as shown in fig NUM since no discourse segment boundaries occur within the sentence the discourse segment boundary constraint in tr2 has no effect on this test text which means that both tr1 and tr2 produce the same output
since only base lexical entries that feed lexical rules are modified by the lexical rule compiler the covariation encoding naturally only results in space savings for those lexical entries to which lexical rules apply
finally all such treatments of lexical rules currently available presuppose a fully explicit notation of lexical rule specifications that transfer properties not changed by the lexical rules to the newly created lexical entry
for those appropriate paths not specified in the out specification one can then add path equalities between the in and the out specifications of the lexical rule to ensure framing of those path values
the slot fill in rules are predicates that check node controls or use the inference functions available in the core
in particular the following structures are handled in the order given reported speech paragraphs sentences and words
the basic idea was to make use of available acoustic information in order to point out a limited set of words to suspect especially inserted words and to exploit the potential of linguistic knowledge in order to repair the best sentence hypothesis
to simplify our implementation this is the only effect that is stated for the action schemas for referring expressions
a plan derivation is an instance of an action that has been recursively expanded into primitive actions its yield
thus we can model a collaborative dialog in terms of the changes that are being made to the plan derivation
second the constraints keep track of which objects could be believed to be the referent of the referring expression
although acts of reference have been incorporated into plan based models determining the content of referring expressions has n t been
the second tier accounts for the collaborative behavior of the agents how they adopt goals and coordinate their activity
an important consequence of our proposal is that the current plan need not allow the successful achievement of the goal
in order to address the problem that we have set out we have limited the scope of our work
finally although belief revision is an important part of how agents collaborate we do not explicitly address this
they conducted experiments in which participants had to refer to objects tangram patterns that are difficult to describe
a core link is established between the elided and antecedent events in the same way as for pronouns
the latter is true if corresponding to the possessor xl there is an x2 that is similar to xl
moreover if the bt tokenization exists it is a ct tokenization
for instance freeze x append x b z means to delay the ewfluation of append until x is instantinted
the obtained sub structures are unified with core structures when NUM the input edge covers a whole input or NUM the edge is a non head daughter edge of sonm other edge
that is sd s lcb abc d a bcd rcb
the rule schema in the exampie has an auxiliary term append NUM NUM NUM
note that this change does not affect the discussion on the correctness of our parsing method because the difference can be seen as only changes of order of unification
NUM the diajunetion of the following three conditions is satisfied where a n t n or b n t is a descendant of n
the line between the origin and the terminus is the main diagonal
the rectangle keeps expanding until at least one acceptable chain is found
a chain s size is simply the number of points it contains
simr was ported to spanish english while i was visiting sun microsystems laboratories
the results are presented in table NUM
note that in the dfss the already raised feature structures are eliminated and that the dfs of the transition arc t contains the frozen query as the goals
NUM NUM step NUM construct axis generators
NUM NUM step NUM construct matching predicate
next steps i NUM goals of queryhrl hr2 h h ri represents the goals which are to be solved in the steps following the i th step
in other words any character string at most has single ft tokenization
the chain recognition heuristic ignores points whose ambiguity level is too high
the chain recognition heuristic pays no attention to whether chains are monotonic
pre stored phrases are not generally considered suitable for the conduct of free flowing social conversation where it is assumed that subtle nuances of meaning need to be constructed as the conversation proceeds in directions that could not have been foreseen
thus fruits et agrumes tropicaux literally tropical citrus fruits or fruits is a coordination variant of the term fruits tropicaux tropical fruits
this leads to a conflict with condition NUM in the definition
the push back operation allows the two arcs to be combined into one and their destination states to be merged
since ostia can learn any subsequential relation in the limit why these difficulties with the phonological rule induction task
thus the transducer will fail to perform devoicing when two voiced stops occur at the end of a word
name matching in a database context is the process of comparing two character strings and determining whether or not the two strings designate the same entity in the applications borgman and siegfried considered the same person but more generally the same institutional geographical or other proper named entities as well
figure NUM shows the correct decision tree for flapping obtained by pruning the tree in figure NUM
our algorithm first learned an incorrect transducer whose decision tree for state NUM is shown in figure NUM
we automatically induced this decision tree from the arcs leaving state NUM in the machine of figure NUM
one class of errors in this transducer is caused by the input falling off the model
the minimum number of states for a subsequential transducer performing the composition of the three rules is five
then we give a brief analysis of available readings ss3 a generalization of the analysis ss4 and finally describe a computational implementation in prolog NUM
based on a critical analysis of readings that are available from these data the claim is that scope phenomena can be characterized by a combination of syntactic surface adjacency and semantic function argument relationship
it is generally assumed that sentences with multiple quantified nps are to be interpreted by one or more unambiguous logical forms in which the scope of traditional logical quantifiers determines the reading or readings
a 11the up operator in NUM takes a term of type t to a term of type e but a further description of ps is not relevant to the present discussion
the parser leaves the slot for the referent of it unspecified in its interpretation
the user response does not satisfy the missing axiom for completing the substep NUM
ccgs make use of a limited set of combinators type raising t function composition b and function substitution s with directionality of combination for syntactic grammaticality
NUM this suggests that the uvc may not be the only principle under which hobbs shieber s reading is excluded s the other four readings of a are self evidently available
their main utility is to provide expectation for error correction as we do in our system
theorem proving will often reach goals that can only be satisfied by interactions with the user
weaker control allows the partner to introduce minor but not major variations from the selected path
thus the query about the led would yield as expectations some possible descriptions for the led
mentioned facts are stored in the model as known to the user and are not repeated
in combination with derivations involving transitive verbs with subject and object nps such as ones in figure NUM this correctly accounts for four grammatical readings for NUM a
a pragmatic architecture for voice dialog machines aimed at the equipment repair problem has been implemented
f hipp wyrick company inc NUM maple cove lane charlotte n c
this process can continue ad infinitum
151st sentence NUM NUM fig NUM is an isometric view of the magazine taken from the machine side with one cartridge shown in the unprocessed position and two cartridges shown in the processed position
since most unified parses contained various errors such as incorrect modification patterns and incorrect parts of speech assigned to some words fewer errors generally resulted in better translations but incorrect parts of speech resulted in worse translations
to be precise the position and the part of speech of each instance of every lemma are stored along with the lemma s modifiee modifier relationships with other content words extracted from as you can see the help display provides additional information about the menu options ava lable as well as a list of related topics
our approach to handling ill formed sentences is fundamentally different from previous ones in that it reanalyzes the part of speech and modifiee modifier relationships of each word in an ill formed sentence by using information extracted from analyses of other sentences in the same text thus attempting to generate the analysis most appropriate to the discourse
since text NUM contained longer and more complex sentences thml text NUM our esg parser failed to generate unified parses more often in text NUM on the other hand the frequency of morphologically identical words and collocation patterns was higher in text NUM and our method was more effective in text NUM
partial parses are joined as follows first the possibility of joining the first two partim parses is examined then either the unification of the first two parses or the second parse is examined to determine whether it can be joined to the third parse then the examination moves to the next parse and so on
44th sentence therefore these two partial parses are restructured by changing the part of speech of the word side to noun and the modifiee of the noun operator to the noun side while at the same time changing the modifiee of the noun side to the verb take
since the discourse information consists of modification patterns extracted from complete parses it reflects the grammar rules of the parser and a matching pattern with a part of speech rather than an actual word on one side can be regarded as a relaxation rule in the sense that syntactic and semantic constraints are less restrictive than the corresponding grammar rule in the parser
moreover our method improves the translation accuracy especially for frequently repeated phrases which are usually considered to be important and leads to an improvement in the overall accuracy of the natural language processing system
first for a candidate modifier and modifiee an identical pattern containing the modifier word and the modifiee word in the same part of speech and in the same relationship is searched for in the discourse information
while the plan based component uses declarative knowledge albeit acquired automatically dialogue act predictions are based solely on the annotated verbmobil corpus
the thematic structure is mainly used to resolve this type of anaphoric expressions if requested by the semantic evaluation or the transfer module
instead we have selected a combination of several simple and efficient approaches which together form a robust and efficient processing platform
the central storage for dialogue information within the overall system is the dialogue module that exchanges data with NUM of the other modules
the statistical knowledge base for the prediction algorithm is trained on the vzrbmoml corpus that in its major parts contains well behaved dialogues
since no module in verbmobil must ever crash we had to apply various methods to get a high degree of robustness
the information about the relation between the date under consideration and the speaking time can be immediately computed from the thematic structure
prior to the selection of the dialogue acts we analyzed dialogues from verbmobil s corpus of spoken and transliterated scheduling dialogues
very good that suits me too we can make a note of that figure NUM an example dialogue
thus two left rules may be permutable n cn cn n s s can be proved by choosing to work on either connective first
it applies when the agenda goal is atomic and picks out antecedent types which yields that atom cf the eventual range condition of
in practice the interest is in computing semantic forms implicit in the structural descriptions which are themselves usually implicit in the history of a derivation recognizing well formedness of a string
labelled unfolding of categorial formulas has been invoked ill the references cited as a way of checking well formedness of proof nets for categorial calculi by unification of labels on linked formulas
on a first phase we use italian wordnet as a lexicon repository to carry on lexical analysis
what we show here is that such implementation can be realized systematically indeed by a mechanical compilation while grammars themselves are written in higher level categorial grammar formalism
there is a need for methods applying to whole classes of systems in ways which are principled and powerful enough to support the further generalisations that grammar development will demand
morphok gical level information helps tagger to determine the tag of the word more
tures that represent the constraint or information sources of givcu prol h m domain
events there are cases where the anchor of a bridging dd is not an np but a vp or a sentence
there exist error prone words in every ta gging systern
these in for mr ion sources are linearly cotnl ined by weighl cd
st the positivity of mrt is satislied
we will describe mrff model and its parameter estimation method later
NUM NUM NUM model NUM morphological inforntation indnded
the energy function u tjw is of this form
although riehemann s approach is very elegant it is not adequate for verb prefixes
the corpus was then randomly partitioned into a training set of NUM NUM sentences and a test set of the remaining NUM NUM sentences to eliminate possible systematic biases
but regrettably we are not in a position to undertake a study of how humans judge typical usage so we will turn instead to a less ideal source the authors of the wall street journal
we varied two parameters NUM the window size used during the construction of the network either narrow NUM NUM words medium NUM NUM words or wide NUM NUM words NUM the maximum order of co occurrence relation allowed NUM NUM or NUM
however since these thesauri were originally compiled for human use they are not always suitable for computer based natural language processing
therefore we annotated the boundaries between discourse segments in the test data and the hierarchical discourse structures by hand according to perceived discourse segment intentions
the topic is always either definite refers to something that the reader already knows about or generic refers to a class of entities
by using this normal form representation the senses of content words and the relationships among constituents in a sentence can be well specified
however similar to the situation in text NUM the speakers have varied agreement on the choice of anaphora for the topic shiftings in these two texts
we now describe an experiment comparing the anaphora generated by a hypothetical computer employing this rule and those occurring in real text to see how well it works
we sent some generated texts to a number of native speakers of chinese and compared human created results and computer generated text to investigate the quality of the generated anaphora
in addition this alignment method is implemented using lecdoce examples and their translations for training and testing while further examples from a technical manual in both english and chinese are used for an open test
to this end we draw on the two classification systems of words in longman lexicon of contemporary english lloce and tongyici cilin synonym forest cilin
the algorithm s performance could definitely be improved by enhancing the various modules of the algorithms e.g. morphological analyses bilingual dictionar monolingual thesauri and rule acquisition
this paper is motivated by the following observations first the above survey dearly reveals that word based methods offer only limited coverage even after they are trained with an extremely large bilingual corpus
for aligned corpora to be useful for nlp tasks such as machine translation and word sense disambiguation a coverage rate higher than NUM is desirable even at the expense of a slightly lower precision rate
mandarin verbs do have aspect morphemes including j le perfective guo experienced action and zhe durative
classalign achieves a degree of generality in the sense that a true connection can be identified even when it occurs only rarel rcb or not at all in the training corpus
english is the relative simplicity of word structure
it has determined that a particular knob should be set to NUM and then a voltage measurement should be made
the detectionneed parameter is required since each query must point to the original detectionneed this detectionneed may contain a narrative characterization of the query being created but no information from the detectionneed is used in creating the query the tipster architecture provides for two types of document detection operations retrieval and routing
the cond clause determines if the memoized function has been called with args before by checking if the continuations component of the table entry is nonempty
annotate document annotatorname string invokes annotation procedure annotatorname on the document see section NUM NUM
it may be desirable in future versions of the architecture to perform type checking based on such declarations
an annotation provides information about a portion of the document including possibly the entire document
this chapter elaborates the general structure of annotations noting some of the issues which arise at each stage
a particular extraction task will involve several kinds of template objects for events people organizations etc
this precedence list is used to determine the nesting of sgml tags if two annotations involve the same span
the present architecture treats extraction engines as modules which have been hand coded for specific tasks extraction scenarios
the overall type checking mechanism would be fairly complex and so has not been included in the current architecture
sentences that do not end normally are treated as incomplete sentences
the database is somewhat inconsistent in that information for some fields is occasionally missing more than one person is listed in the name field business information is added to the name field first names and street names are abbreviated
street names the component types collected from the city first name and street databases were integrated into a combined list of NUM NUM productive name components NUM from city names NUM from first names NUM NUM from street names
evidently there is a finite list of lexical items that almost unambiguously mark a name as a street name among these items are strafle weg platz gasse allee markt and probably a dozen more
morphology erroneous decomposition of substrings hyper correction over old system e.g. rim par strafle ri mpa instead of rimpar strafle rimpa
at the same time many current or envisioned applications such as reverse directory systems automated operator services catalog ordering or navigation systems to name just a few crucially depend upon an accurate and intelligible pronunciation of names
show me flights from boston on uh from denver on monday
the compiler as described in the last section has been fully implemented under quintus prolog
c else t is a simple type do nothing at all
the same is true for the control information which is also dealt with off line
the computationally oriented reader will now wonder how we expect to deal with non termination anyway
the weights were adjusted in such a way that for any token i.e. word or word form in the input text an immediate match in the lexicon is always favored over name analysis which in turn is prefered to unknown word analysis
definition hiding type the set of hiding types is the smallest set s t
secondly solutions in our approach have the so called subsumption monotonicity or persistence property
t rcb and the set of hiding types is lcb list ne list rcb
ideally we should have a mechanism for focus tracking to reduce the number of false positives sidner
the input conceptual graph is merely transformed into a semantic tree
this mapping is shown in the top half of figure NUM
the fuf default control regime develops this structure in breadth first order
the discourse model can also include general properties that describe the conversational situation as a whole for example it might specify the formality of the register in which the communication is being conducted
the relevant fragment of the lexicon is shown in figure NUM
this process is the generation counterpart of a compositional semantic interpretation
it must be able to apply constraints in a flexible order
departamento de informatica recife pe NUM NUM brazil
there are two barriers to such integration incompatability of NUM presentation of information about text and tile mechanisms for storage rctriewj and inter module communication of that information in ompatability of type of information used and produced by different modules
disjunctions encode the available choice points of a system and introduce backtracking in the unification process
it can also be realized by an adjective or a noun
we then ran the em mgorithm to determine symbol mapping garbling probabilities
only postnominal prepositional phrases introduced by one of these prepositions have been allowed for term expressions
hot bs NUM strube and hahn NUM
on the other hand examples 3a the barberi shaves himi
4c the barber who shaved the clienti told the elienti a story
example NUM however illustrates that further nonloeal coindexings are admissible
suggest antecedent candidates x y in the order determined in step NUM
13b pauli revises sam sj decision fi r himselfi
neither of the t ronouns is confined structurally to one of the intrasenttmtial antece lent
b the client i appreciates that the barber sttaves the clicnt i
rization the bulk of the vsm for information retrieval ir is representing naturallanguage expressions as term weight vectors
tit resumption would be cataphorie verify that x is definite
the original formulation of chomsky s binding theory proved to be unsuitable for immediate implementation
the modsaf movement task for a tank platoon is different from the one for an infantry platoon or the one for a tank company
for example it is possible with commandtalk to tell modsaf to center its map display on a point that is not currently visible
in order to keep these two grammars synchronized we have implemented a compiler that derives the recognition grammar automatically from the nl grammar
it provides a long narrow window running across the top of the screen the only visible indication that a modsaf is commandtalkenabled
the start it configuration is data driven so it is easy to add processes and command line arguments or change default values
the contextual interpretation ci agent accepts a logical form from the nl agent and produces one or more commands to modsaf
the principal agents used in commandtalk are speech recognition natural language contextual interpretation push to talk modsaf start it
to derive a recognition grammar with coverage equivalent to the nl grammar we must restrict the form of the nl grammar
for instance a conditional model can predict how likely that a word will be a verb if the previous word was a noun and the previous but one word was an adjective p wordi vb wordi NUM nn wordl NUM adj
after from a set of samples we have constructed a feature collocation lattice NUM c w which we will call the empirical lattice we try to estimate which atomic features contribute and which do not to the frequency distribution on the reference nodes
the feature frequency of a node similar to equation NUM will then be x ek sieo fek 8i which is the sum of all the configuration frequency counts to of the descendant nodes
for instance ff our our feature space is NUM gap stop NUM fstop gap fstop NUM cap fstop the constraint for the feature NUM will look like
for training we collected from the wsj corpus NUM NUM samples of the form y f f and n f f where y stands for the end of sentence n stands for otherwise and fs stand for the features of the model
we will require that the normalization constants z for each joint model x y ensure that all probabilities in a joint model sum up to the empirical marginal probability of the behavior variable y thus accounting only for the true proportions of the joint models
this function takes two values NUM if the constraint is active and NUM otherwise lagrange multiplier is the weight of the j th constraint xj z is the normalization constant which ensures that the probabilities for all configurations sum up to NUM
the comparison has been carried out throughout the different aligned sections
when two nodes or specifications are tied for being most suspicious finer grained criteria are used to break the tie
figure NUM all of the significant dictionary errors
there was no phonological noise and no cross word effects
naturally it is unable to parse the input
figure NUM presents some entries in the final dictionary and figure NUM presents all NUM NUM of the dictionary entries that might be reasonably considered mistakes
we describe the results of a single run of the algorithm trained on one exposure to each of the NUM utterances containing a total of NUM different stems
NUM words used most frequently in good parses
to fix such problems it is obvious more constraint on morpheme order must be incorporated into the parsing process perhaps in the form of a statistical grammar acquired simultaneously with the dictionary
we eliminate these words leaving NUM
figure NUM the most common semantically empty
to help a pattern defining an unambiguous context match several passes are made over the sentence during disambiguation
the grammar avoids risky predictions therefore NUM NUM of all words remain ambiguous an average NUM NUM NUM NUM
while introducing ambiguity is regarded as relatively straightforward disambiguation is known to be a difficult and controversial problem
each rule specifies one or more context patterns or constraints where the tag is illegitimate
a syntactic grammar appears to predict the distribution of parts of speech as a side effect
NUM computer what is the voltage between connector one two one and connector three four
however the best distributional account of parts of speech appears achievable by means of a syntactic grammar s
in addition heuristic rules can be used for ranking alternative analyses accepted by the strict rules
if the tag prep occurs in none of the specified contexts the sentence reading containing it is discarded
null indeed few contest the fact that reliable linguistic rules can be written for resolving some part of speech ambiguities
a type attribute accompanies each tag element an d identifies the subtype of each tagged string for enamex the type value can be organization person or location for timex the type value can be date or time and for numex the type value can be money or percent
in searching for a path the parser may delete or insert words to achieve a match
a conservative parser would perform a reduction only if there was strong usually local syntactic evidence or strong semantic support
NUM global parsing considerations sometimes led to local errors our system was designed to generate a full sentence parse if at all possible
this approach can be viewed as a form of conservative parsing although the high level structures which are created are not explicitly syntactic
null we considered carefully whether these difficulties might be readily overcome using an approach which was still based on a comprehensive syntactic grammar
at this stage pronouns and definite noun phrases which refer back to previously mentioned people or organizations are linked to these antecedents
all of these stages are basically scenarioindependent except for the recognition of executive positions which was added for this scenario
pie it determines that np sem c person is the subject vg c run is the verb and np sem c company is the object
clause syntax is now utilized in the metarules for defining patterns and in the rules which analyze example sentences to produce patterns
when all the sentences of an article have been analyzed a final stage of processing assembles the information and generates a template in the format required for muc
adding syntactic constructs needed for a new scenario was hard having a broad coverage linguistically principled grammar meant that relatively few additions were needed when moving to a new scenario
the process of numbering the terminal elements and computing the set of minimal differences will give rise to a normalized form of the two corpora something like the following where the two leftmost columns come from susanne the others from penn
it is necessary to distinguish between files which are storage units sgml documents which may be composed of a number of files by means of external entity references and hyper documents which are linked ensembles of documents using e.g.
it also provides functionality getnextqueryelement infile query subquery regexp outfile where query is an lt nsl query which allows one to specify particular elements on the basis of their position in the document structure and their attribute values
this means that all markup minimisation is expanded to its full form sgml entities are expanded into their value except for sdata entities and all sgml names of elements attributes etc are normalised
although the example above shows links to only one document it is possible to link to several documents e.g. to a word document and a lexicon document word source doc filel from id p4 wl lex doc lexl from id iex NUM word note that the architecture is recursive in that e.g.
for our example above this means that the connection between the phrase encoding document and the segmented document would be in two steps the phrase document would use a public identifier which the catalogue would map to the particular file
with these tools it is easy to select portions of text which are of interest using the query language and to convert them into either plain text or another text format such as NUM NUM tex or html
the original unix architecture allows the rapid construction of efficient pipelines of conceptually simple processes to carry out relatively complex tasks but is restricted to a simple model of streams as sequences of bytes lines or fields
the most important reason why we use sgml for all corpus linguistic annotation is that it forces us to formally describe the markup we will be using and provides software for checking that these markup invariants hold in an annotated corpus
two major issues in corpus based nlp are how best to deal with medium to large scale corpora often with complex linguistic annotations and what system architecture best supports the reuse of software components in a modular and interchangeable fashion
the processing for ne followed the identical sequence of step s lexical analysis and reduction as was followed for the te and st tasks then diverged to its ow n postprocessing component to write the ne file
our results show that this approach works well and the modularity of the patterns makes i t easy to add coverage as we discover additional clues such as those we discuss in the walkthrough with respect to organizations
after the lexical analysis the input string has been converted into a list of NUM sentences each sentenc e containing a list of tokens this list includes cap tokens inserted in front of every capitalized token
categories are made of wordnet terms which is not the general case of standard or user defined categories
john sent out a letter to mary
the impermanent nature of vocal communication makes speech an intrinsically more unreliable medium conversely a spoken utterance contains information that is only residually present in its text version such as prosody tone and accent
this system part of the counter drug intelligence system cdis was built around the nltoolset NUM which was originally developed by ge and is now being developed and supported b y lockheed martin
the reliance on case information meant that headlines were a bit of a problem despite giving them somewhat special treatment our error rate was higher there than elsewhere there was some lexicon work as well
then non boundary elaeif before sentence final contour
existing methods for this problem are data driven
NUM intermediate generation form the intermediate generation form igf will contain the type of sentences e.g. a fact or an assertion dcl a rule rule a yes no question ynq a what which or who question whq a noun phrase np and many more
the igf will contain a new aggregation rule the so called predicate grouping rule which will make the generated text easier to read further on is proposed to use a bidirectional grammar for the surface generation
in the first experiment a single context independent language model was used it was trained on a set of NUM NUM utterances produced by italian native speakers
i0 conclusions and future work we have in this paper shortly described the current nl generator of the vinst system
the natural language expression after being processed by the natural module has a lot of redundant noun phrases
the igf contains also two aggregation features subject and predicate grouping which makes the text nicer to read
many thanks to ed hovy at information sciences institute usc for advising me and for stimulating discussions via email and many thanks also to stefan preifelt and m ns engstedt at ellemtel telecommunication systems laboratory for beeing inspiring workmates during the vinst prototype implementation and also for introducing me to the telecom domain
some of the features are the same as the one qlf uses except for predcomp sg and pg
the igf needs to be stored together with the loxy expression until they are going to be used by the nl generator
we have to construct an intermediate generation form igf which will contain the suitable linguistic primitives
the syntax of the igf is described by showing the prolog predicate int genform NUM and its content
a turn like NUM can be composed out of several sentences and subsentential phrases free elements like the phrase ira april which do not stand in an obvious syntactic relationship with the surrounding material and which occur much more often in spontaneous speech than in other environments
if the correct position turns out to be consistently ranked among the positions with the highest NUM probability within a sentence then it might be preferable for the parsing module to consider the NUM positions in descending order rather than to introduce traces for all positions ranked above a threshold
it has to be added though that in many cases the correct verb trace position is at the end of the sentence which is often very reliably marked with a prosodic phrase boundary even if this sentence is uttered in a sequence together with other phrases or sentences
it turns out then that a fine grained prosodic classification of utterance turns based on correlations between syntactic and prosodic structure is not only of use to determine the segmentation of a turn but also to predict which positions are eligible for trace stipulation
tile sgml codes denote e g
documenl whict is submitted for trmlsladon
during source language analysis the sentences are assigned a surface syntactic structure
it has further to be considered that the recognition rate for perceptual labeling contained those cases where phrase boundaries have been recognized in positions which are impossible on syntactic grounds el the number of cases in table NUM where a NUM position was classified as b3 and vice versa
table NUM texts for tagging experiments
this constitutes one round of reshuffling
a new approach we adopted incorporates the following steps
we set the number c of classes to NUM
consequenlly a f dl sofl inechanism was inlroduce d
chemistry and patent document terms of a more legal nature
these questions are called basic questions
figure NUM example of an event
when the phrases are used only to supplement not replace the single words for indexing some parsing errors may be tolerable
the effectiveness of using syntactic phrases provided by the parser to supplement single words for indexing is evaluated with a NUM megabytes document collection
the fact that each kind of phrase can improve precision significantly when used separately shows that these phrases are indeed very useful for indexing
in the experiments reported in the paper nearly half of the total number of word pairs seen in the training chunk were dropped
a consequence of this is that markers in a corpus for empty elements may be retained and operated on even if such markers are additional to the original text and represent part of a hypothesis as to the text s linguistic organization
there are a few reasons why we feel confident that a certain degree of optimism is justified here
it has more NUM outccanes than would be expected with the fair die
as the explanation planner uses this knowledge to construct a response it can determine if the antecedent of the rule the user of the system is familiar with the object where the process occurs is satisfied by the current context if the antecedent is satisfied then the explanation planner can include in the explanation the subtopics associated with the rule s consequent
otherwise the compute inclusion algorithm must consider the topic s importance and the amount of detail requested and will include the topic in the following circumstances the verbosity is high the verbosity is low but the topic s centrality has been rated as high by the discourse knowledge engineer or the verbosity is medium and the topic s centrality has been rated as medium or high
although there were substantial differences between knight and writer NUM knight was somewhat closer to writer NUM it was very close to writer NUM and its performance actually exceeded that of writer NUM knight and writers NUM NUM and NUM did not differ significantly table NUM
this work has demonstrated that NUM separating out knowledge base access from explanation planning can enable the construction of a robust system that extracts coherent views from a semantically rich large scale knowledge base and NUM explanation design packages a hybrid representation of discourse knowledge that combines a frame based representation with procedural constructs facilitate the iterative refinement of discourse knowledge
in contrast if the system applies the participants accessor with photosynthesis as the concept of interest but with energy transduction as the reference process then it would extract information about the transducer chlorophyll the energy provider a photon the input energy form light and the output energy form chemical bond energy
an example is reported in figure NUM
syntactically a functional description is a set of attribute and value pairs a v collectively called a feature set where a is an attribute a feature and v is either an atomic value or a nested feature set is to illustrate figure NUM depicts a sample functional description
the applier must then weigh several factors in its decision about whether to include the topic in the explanation inclusion which is the inclusion condition associated with the topic centrality which is the centrality rating that the discourse knowledge engineer has assigned to the topic and verbosity which is the verbosity specification supplied by the user
although edps have inclusion conditions which are similar to the constraint attribute of rst based operators and they provide a centrality attribute which enables knight to reason about the inclusion of a topic if space is limited edps do not in general permit knight to reason about the goals fulfilled by particular text segments as do plan based systems
denote start and final states respectively
there are several reasons increase confidence in markup and determine areas of disagreement if two or more corpora agree on parts of an analysis one may trust that choice of grouping more than those groupings on which the corpora differ
nonterminal NUM returns a set of nonterminal edges in layer i
for classification the document is classified into the class which has the maximum score
but now consider the case in which set NUM was ten times larger as shown in the table below
the planner calls the discourse processor with a list of discourse actions such as the following NUM elicit the determinants of the erroneous variable NUM elicit the currently active determinant NUM elicit the relationship between the active determinant and the erroneous variable NUM elicit the correct value alternatives to elicit are to give a declarative explanation or a hint remember that
students are asked to predict the value of the seven parameters at three points in time the dr or direct response stage immediately atter the precipitating event the rr or reflex response stage after the nervous system responds and the ss or new steady state stage
figure NUM also shows the influence of the nervous system which plays an essential role in blood pressure this work was supported by the cognitive science program office of naval research under grant no n00014 NUM NUM NUM to illinois institute of technology
example of generated dialogue here is an excerpt from a conversation generated from the tutoring tutoring tactic illustrated above t remember that the direct response occurs immediately and produces changes in the system before any reflex is activated
for example in generators based on a chart NUM arser the hm tanmntal rule is applie NUM only when the edges to be ombined share no exical leaves in contrast to requiring that the two edges have source and target nodes in common
in the case of the vp brownl can not appear as a leaf either because expansions of the vp are restricted to np complements with NUM as their semantic index which in turn would also require adjectives within them to rcb lave this index
in the general case however tile size of the outer domains is o n2 where n is the number of distinct signs this number can be controlled by employing equivalence classes of different levels of specificity for pre terminal and non terminal signs
thus a more complex grammar would allow the man from the bag ex NUM lcb thel manl shaves l himselfl rcb even though himself has the same index as the iilan
then grammar g would allow disconnected strings to be generated contrary to assumption NUM NUM his is because d would not be able to rewrite NUM in such a way that both daughters were connected to leading to a disconnected string
the indices involved in determining connectivity arc specified as pa rameters for a pro ticul tr formalism l k r exanq le in tlpsg play a major role in preventing the generation of incorrect translations
this action causes the complete structure for the dog barked to be discarded and replaced with that for he brown tog barked which in turn is discarded and replaced by the big brown dog barked
test the big brown clog barked terminate null in this scqnence donble und rscorc indicates the starting position of a moved constituent the moved constituent itself is given in bold t ce the bracketing indicates analyzed constituents for expository purposes the algorithm has been oversimplified but the general idea remains the salne
such graphs can NUM e use l to elinlinate NUM he substrings in l 3xaml le NUM unh rtunately the technique exploits specilic asl ects of categorial grammars an l it is not lear how the y may he used with other formalisms
besides the mere storage of dialogue related data there are also inference mechanisms integrating the data in representations of different aspects of the dialogue
the one in the lower left corner is used for performing clarification dialogues and the other for visualization purposes see section NUM
what the parse results of dop3 do indicate is that for sentences without unknown words the parse accuracy for word strings is of the same order as the parse accuracy for p o s strings which was NUM at maximum depth NUM see section NUM NUM
for reasons of simplicity we will write in the following t o u o v as t o u deg v now the ambiguous sentence she displayed the dress on the table can be parsed in many ways by combining subtrees from the corpus
sys a call from berlin to hamburg at NUM costs NUM pfennig per minute
concluding remarks as we wdcome delegates to what we believe is the first major open meeting in europe devoted entirely to slt but surely not the last we signal yet another important milestone in the history of machine translation
these rules are a function of NUM these test to diagnosis transitions occur because after repairing one of the missing wires the test phase would show that the circuit is still not working due to the other missing wire causing a transition back to the diagnosis phase to discover the other problem
the author is a visiting researcher in the speech pro cessing group fz131 deutsche telekom
c sys the rate or the total cost of a call to frankfurt
with this dialogue act in the immediately preceding context the ambiguity is resolved as referring to a time and the correct translation is determined
abstraction means that we transform several similar goals into a new more abstract goal
however since not many utterances were spoken in the repair phase of the directive mode dialogues either the major source of the reduction in the absolute number of utterances spoken per dialogue occurred in the assessment diagnosis and test phases especially the test phase
the second and third sessions each consisted of NUM review work with the speech recognizer NUM a review of the instructions and NUM usage of the system on up to NUM problems depending on how rapidly the problems were solved
half the subjects used the system when it was operating in directive mode for session NUM while the other half used the system when it was operating in declarative mode for session NUM the mode was of course reversed for session NUM for both groups
based on the relative number of completed dialogues that required the repair of two missing wires NUM in directive mode NUM in declarative mode the expected percentage of transitions from test to diagnosis would be NUM NUM in directive mode and NUM NUM in declarative mode
in testing our theory of variable initiative dialogue there were two main types of phenomena we wished to examine NUM general aspects of task efficiency such as time to completion and number of utterances spoken and NUM the nature of the dialogue structure
conversely users will tend to give up trying to redirect the computer s attention when the computer has the initiative because the machine will proceed on its own line of reasoning ignoring what it perceives as user interrupts even when these interrupts are actually attempts at resolving previous miscommunications
the next step in extending the dialogue processing model is to incorporate the knowledge gained from this study in addressing two of the most significant unresolved problems in human computer dialogue NUM automatic switching of initiative during dialogue and NUM automatic detection and repair of miscommunication
although we originally expected little change in the number of utterances as a function of initiative for the diagnosis phase the large increase in the number of utterances spoken for that phase for problem NUM during directive mode interactions had a major impact on the overall averages
with respect to the organization and person objects there are issues such as rather fuzzy distinctions among the three organization subtypes and between the organization name and alias the extremely limited scope of the person title slot and the lack of a person descriptor slot
but since we assume that all syntactic structures have been seen we can derive the unknown subtrees that are needed for parsing a certain input sentence by allowing the unknown words and unknown category words of the sentence to mismatch with the lexical terminals of the training set subtrees
as mentioned above to contribute to the correctness of the overall system we perform different kinds of clarification dialogues with the user
notice that these studies are deeply related to the syntax of nouns especially that of human nouns
use of posthn appropriate to human nouns there are specific items appropriate to human nouns we name them posthn
therefore bag ga will be analyzed as a proper name bag family name alone followed by an inga
these nouns ns syntactically and semantically incomplete always require proper names to their left side
they actually occur in the positions of common nouns as shown in the following graph functions of the attached noun
ro NUM o r q geu namja josenin i bughan eui tongchija ida
with incomplete noun in notice that when we recognize incomplete nouns i.e.
but only the first occurrence of ga is an incomplete noun which accompanies a pn
this context helps to f md proper names but is not a sufficient condition to recognize them automatically
that is i when precision goes up recall typically goes down and vice versa
the word string this is his book covers the word string th is is his book but not vice versa
here in addition to traditional devices such as syntax and semantics he even employed principles of psychology and chemistry such as crystallization
entry in ldoce NUM an order given by a judge which fixes a punishment for a criminal found guilty in court NUM a group of words that forms a statement command exclamation or question usu
however the results provide a rough approximation of the upper bound of performance of such systems the human subject achieves an average accuracy of NUM over the twelve words which is NUM lower than our system
testing is performed on a set of unseen dialogues that were not used for developing the translation modules or training the speech recognizer
obviously there are still cases where the sentence does not provide enough contextual information even using conceptual co occurrence data such as when the sentence is too short and contextual information from a larger context has to be used
in salient word based approaches due to the problem of data sparseness many less frequently occurring words which are intuitively salient to a particular word sense will not be identified in practice unless an extremely large corpus is used
attachments can be divided into two different types of combination null NUM
b wenn das kind die alte frau h tte besuchen wollen
c siei sehien cp ti ihn gesehen zu haben
the projected node is then inserted into the chart as an edge
figure NUM structure of a german clause
arguments desperately seeking interpretation parsing german infinitives
das fahrrad can be nominative or accusative
scale interactive gb based NUM parsing system
np nadvp manner he put it firmly
in fact as argtled the maxims ai o able to do the better job because they i.e.
developed by j grimshaw and r jackendoff
the price increased to NUM dollars from i0 dollars
NUM NUM he agreed with her
this lcb lassiticadon is cerl ainly useful in mt lcb lersta n ling the argument stru iaue if dm v lcb l l s
NUM NUM that child always hits
he headed home east that way
frequent shifting leads to a lack of local coherence as was illustrated by the contrast between discourse NUM and discourse NUM in section NUM thus rule NUM provides a constraint on speakers and on natural language generation systems
because the information needed to compute a unique interpretation for an utterance is not always available at the time the utterance occurs in the discourse the ways in which a theory treats partial information affects its computational tractability as the basis for discourse interpretation
in this case cb un t is the most likely candidate for cb un NUM it continues to be cb in un l and continues to be likely to fill that role in un NUM
it remains an open question how long to retain these loading situations although those corresponding to elements of cf that are not carried forward either as the cb or as cfs of the subsequent utterance can obviously be dropped
however the preferred reading of the pronoun respectively she and her in utterance e of both sequences is susan who is realized in the subject position of the d utterances
more specifically the initial utterance a in each segment could begin a segment about an individual named john or one about john s favorite music store or one about the fact that john wants to buy a piano
it is only when one gets to the word sick that it is clear that it must be tony and not terry who is sick and hence that the pronoun in utterance e refers to tony not terry
the precise definition of u realizes c depends on the semantic theory one adopts s one feature that distinguishes centering from other treatments of related discourse phenomena is that the realization relation combines syntactic semantic discourse and intentional factors
the effect of factors such as word order especially fronting clausal subordination and lexical semantics as well as the interaction among these factors are areas of active investigation section NUM again provides references to such work
furthermore since the head is to a large extent identical to the mother category effective top down identification of a potential head should be possible
after a review of the motivation for head driven parsing strategies and head corner parsing in particular a nondeterministic version of the head corner parser is presented
indicating that we want to parse a sentence from position zero to twelve with category s sere a sentence with a semantic representation that is yet to be discovered
in prolog we can keep track of each goal that has already been searched and keep a list of the corresponding solution s
NUM the solution implemented in the head corner parser is to use for each pair of functors of categories the generalization of the head corner relation
it starts by locating a potential head of the phrase and then proceeds by parsing the daughters to the left and the right of the head
first even if the various sub domains are semantically distinct multiple sub domain grammars will likely contain some of the same rules
these responses were used to build the lexicon and the concept grammar rules
system therefore classified the response in better intervention crook counseling
NUM NUM does manual preprocessing of the data outweigh the benefits of automated scoring
for the f h item each examinee can give up to NUM responses
in this way lexical entries can be linked appropriately to text specific information
for this study suffixes were removed by hand from the parsed data
the second approach looked at similarity measures between responses based on lexical overlap
e crooks now have a decreased ability to purchase guns
concepts in the concept grammars were linked to the lexicon
the diagnostics are produced faster as well
as the maximum probability of any derivation from i that successfully parses both es t
as we shall argue below this restriction reduces matching flexibility in a desirable fashion
in general the problem is that both the straight and inverted concatenation operations are associative
the best matching constituent types between the two languages may not include the same core arguments
first word matchings can be overlooked simply due to deficiencies in our translation lexicon
note that the english parse tree already determines the split point s for breaking e0
a straightforward extension to the original algorithm inhibits hypotheses that are inconsistent with given constraints
the algorithm is modified to include the following computations and remains the same otherwise
also note that and NUM are simply constants written mnemonically
where iis the object described and j is the object with which i is connected by the functivc f
we defined unconditional and conditional replacement taking the unconditional obligatory replacement as the basic case
by starting with obligatory replacement we can easily define an optional version of the operator
null the upper side brackets are eliminated by the inverse replacement defined in NUM
the redundant brackets on the lower side are important for the other versions of the operation
a conditional replacement expression has four components upper lower left and right
this work is based on many years of productive collaboration with ronald m kaplan and martin kay
here upper lower left and right are placeholders for regular expressions of any complexity
this may seem a simple matter but it is not as kaplan and kay NUM show
NUM unconditional replacement to the regular expression language described above we add the new replacement operator
in effect whatever the size of the lexicon used one can always find oov words in texts
these rules operate on individual word tokens
NUM NUM NUM elimination of consonant sequences and loanword hyphenation
the prohibitive hyphenation rules regarding vowel splitting are as follows v1
we performed three ex null periments
the total number of errors was NUM
rules f1 f11 table NUM resolve the ambiguity of NUM NUM different patterns
the exact definition NUM of set cc is given in table NUM
such properties are not likely to be similarly expressed in a pattern based model
to decrease the complexity of the hyphenator we used only lowercase patterns
augmenting the dictionary yields a significant improvement in word segmentation accuracy
in general re estimation has little impact on word segmentation accuracy
re estimation subdivides an erroneous longest match if the frequencies of the shorter words are significantly large
likewise if one of the conversants proposes a replacement the other accepts it
then we use these joint models for the computing of a conditional model as described in section NUM NUM
we also devised a new method to estimate initial word frequencies
if the plan is not adequate then they must work together to refashion it
roman alphabet is also used for western origin words and acronyms
in this case there is only one and the system labels it pl
this method works suzprisingly well as shown in the experiment
a sample fragment of a bilingual lexicon is given in message templates are target language grammar rules corresponding to the input sentence expressions represented in the semantic frame for instance the word order constraint of the target language is specified in this module
the core of om translation system consists of two modules the understanding analysis module tina and the generation nlodule genesis NUM these modules are driven by a set of files which specify the source and target language grammars
a set of semantically significant vocabulary items could be tagged as imnmtable and all the words in tile sentence except these anchor words would be converted recall that we resolve the ambiguity problem by constraining the grammar with semantic categories
that is we put equal importance on recall and precision
the unknown words would be replaced in the sentence string with their corresponding part of speech tag and the semantk grammar woukl be auginented to handle generic adjectives nouns verbs etc intermixed in the rules at appropriate positions
a potential solution to the unknown word problem is to do part of speeeh tagging and replace unknown words with their parts of speech and bootstrap the parts of speech instead of the actual words to the analysis grammar
second we feel that their definition of a partial shared plan is too restrictive
these discourse actions are meta actions that take as a parameter a referring expression plan
following kn i assume that the prefix is the head of complex affix words but like riehemann i do not assume a binary structure
the semantics of the complex word is composed at the head and then structure shared with the whole word in accordance with the semantics principle
lvlost hpsg work on german affixation focuses on the suffix bar which can combine with verbs most of them transitive to form an adjective
for example the entry for durch can be derived from fig NUM by deleting all information specific to the complement eilen
in their model bar is of sort bar surf and subcategorizes for verbs of sort bar verb to form adjectives of sort bar comp adj
the schema for postpone plan shown in figure NUM is similar to rejectplan
the semantic framework chosen here is lexical conceptual structure which has been applied successfidly to the interface between morphology and lexical semantics by e.g.
the internal structure of a complex derived word is given in fig NUM morphological information is given at the feature morpii
in keeping with the i ipsg semant ics principle the semantics of the complex word is structure shared with the semantics of the head
interlingual representations also tend to bc less portable to new domains since they if they are to be truly interlingual they normally need to be based on domain concepts which have to be redefined for each new domain a task that involves considerable human intervention much of it at an expert level
NUM the bill was then sent back to the house to resolve the question of how to address budget limits on credit allocations for the federal housing administration
thus we add to the lattice all sub configurations of a newly added configuration which are the intersections with the other nodes
the ambiguity in the ellipsis results from copying each possibility
whereas previous models of dialogue tend to represent discourse meaning from some global perspective make use of either purely structural or purely intentional information and give minimal attention to repair in our model each agent has his or her own model of the discourse
the information elements that the system has gathered during the query phase will have to serve as the given information while the new information that the system has received from the database query will have to function as the new information
the choice for a certain scenario will depend on two types of information NUM the information elements that the system has gathered during the query phase and NUM the information that the system has received from the database query
nevertheless the table shows that speakers may violate these habits since they may utter reconfirmations after the whole presentation NUM although it seemed that they had understood and accepted it and wh questions and checks directly after an informing utterance or the acknowledgement of that utterance
human human ovr dialogues a study of a sample of NUM information dialogues out of a corpus of over NUM dialogues shows that the presentation of a travel plan in a human human dialogue involves more than just a monologue that presents the entire plan at once
NUM naive subjects took part in the experiment they were equally distributed by sex the age range was from NUM to NUM years
h that will be u h depart from hilversum at eight hours nineteen c eight hours yes h and then change amsterdam c u h yes i departure amsterdam eight fifty five c eight fifty five h will arrive at amsterdam lelylaan at nine o three c okay so depart at eight hours nineteen okay thank you very much human operator
all the calls were performed over the public telephone network and in different environments house office street and car
by excluding from the corpus a set of dialogues that failed for users errors we obtained a ts result of NUM
additionally the answer key contains optional objects which are included in the scoring calculations only ff they have been mapped to a response object
it may be improved by expanding the falter to include semantic categories via a facility like wordnet or through our internal conceptual hierarchy
examination of our system s performance in associating descriptive phrases to a referent entity brought us to several conclusions regarding our system s techniques
it was found that NUM originated in appositives prenominals and post modifiers and NUM originated in other descriptive noun phrases
ranked selection favoring appositives is run without this heuristic an increase of four recall and three precision points is achieved
the muc6 template element task is typical of what our applications often require it encapsulates information about one entity within the template element
since we have a way to evaluate our performance on this task via the muc6 data we used it to conduct our experiments
the scoring set had previously been held blind but it has been released for the purposes of a thorough evaluation of our metheds
this change has a deleterious effect on the scores for the descriptor slot and confirins our hypothesis that the context associated descriptors are more reliable
this may account for our system s superior performance in identifying locale country information our scores were the highest of the muc6 participants
we shall call the schema NUM the agreement schema for the function g projected by the verb
for pp2 perfordlance is again high recalling that the algorithm is discriminating five possible attachment configurations and the baseline expectation was only NUM NUM
for this the agreement schema must be handled in a different semantics of locate with respect to NUM
an anonymous slot is created in the scope off and n is made to point to it
the word tokens in test group2 better represent the typical ambiguous word in the language but their test corpus probabilities were calculated from a relatively small sample of tagged words
the linguistic intentions of the pretelling are and knowref m whoisgoing and knowsbetterref m r whoisgoing and intend m do m informref m r whoisgoing
in our case ctog c is actually a finite disjunction gil v gi2 v of functions
both kinds of interaction are possible in the non layered architecture we propose
this use of context free algorithms is not possible if the number of possible source positions for transduct ions is increased so that incomplete transducer source sequences are no longer simple segments
finally as shown in d an analysis and a qlf based translation are found for the whole sentence allowing the inadequate word for word translation of could you show me as pourriez vous montrez moi to be improved to a more grammatical pourriezvous m indiquer
the plots are based on samples consisting of all and only those words occurring in max havelaar upper left and trouw upper right that belong to the morphological category of heid ignoring all other words and preserving their order of appearance in the original texts
observed vocabulary size v n dotted lines and expected vocabulary size e v n solid lines for three novels left hand panels and the corresponding overstimation errors e v n v n dotted lines and their sentence randomized versions lines see section NUM NUM right hand panels
within successive issues of a newspaper in which a given topic is often discussed on several pages within the same newspaper and in which a topic may reappear in subsequent issues strands of inter textual cohesion may still contribute significantly to the large divergence between the observed and expected vocabulary size
in what follows i will first present an attempt to understand the differences in the error scores e v n v n shown in figure NUM as a function of differences in the use of key words at the discourse level
av mk v mk v mk NUM denote the number of new types observed in text slice k and let avu mk vu mk vu mk NUM
unfortunately the assumptions underlying NUM are overly simplistic and seriously call into question the reliability of p as a measure of lexical specialization and the same holds for the explanatory value of this model for the inaccuracy of e v n
note that in addition to positive difference scores which should be present given that e v mk v mk for most or as in alice in wonderland for all values of k we also have negative difference scores
fields of temporal units are partially ordered as in figure NUM from least to most specific
the upper left panel of figure NUM shows that it was possible to select the parameters of sichel s model such that the observed frequencies of the first NUM frequency ranks v n f f NUM NUM do not differ significantly from their model dependent interpolation and extrapolation from sample the first half of the trouw data to population the complete trouw data
lagts which have a history of mutually successful interaction and high fitness can reproduce
to see that consider for example the case where we look for all the texts with the word hqph encirclement
it is not unreasonable to regard it as a stopword in both english and chinese
the latter has a slight edge in average precision NUM NUM vs NUM NUM
these are our new short words that are data mined from the corpus itself
one needs only retain high and low frequency thresholds to screen out frequency based statistical stopwords
examples NUM to NUM show counter examples with even number of characters that do not obey this rule
it is also interesting to see that the small lexicon is sufficient to yield this good result
on average close to NUM NUM out of the first NUM retrieved documents are relevant
they average to a few short words and we hope to see more pronounced effects
our methods and feature sets were found to be most successful in disambiguating nouns rather than adjectives or verbs
first it is computationally expensive and convergence can be slow for problems with large numbers of model parameters
also included in figure NUM is the percentage of each sample that is composed of the majority sense
extremely skewed distributions pose a challenging learning problem since the sample contains precious little information regarding minority classes
in future work we will investigate procedures for feature selection that are more sensitive to minority classes
for the verbs there was no significant difference between the three feature sets when using mcquitty s method
implicit in ward s method is the assumption that the sample comes from a mixture of normal distributions
in these experiments we use NUM unrestricted collocation features ul2 ul1 ur1 and ur2
the proposed tying approach after being combined with the robust learning procedure significantly reduces the error rate compared with the baseline NUM NUM error reduction is achieved from NUM NUM to NUM NUM
moving vertically a change in canonical pattern occurs everything else remains invariant
since the probability p n quan nlm i n quan reduced has a large value NUM NUM the probability p n quan nlm i n quan are reduced l2 p li n2 is accordingly large also
for boolean retrieval systems one approach would be to put the burden of query name recognition on the user by requiring that the user tag a query term as being a personal company or other name
before embarking on the rest of the rules an illustrated example seems in order
in addition to enable the system to take into account information associated with long distance dependency we plan to modify the syntactic model so that it can evaluate structural dependency across various subtrees in the parse history
NUM the average speech rate by subjects was NUM NUM sentences per minute the average task completion times for successful dialogs were NUM NUM minutes
as will be seen in section NUM this difference in scores is an important component in deciding when to engage in verification subdialogs
for example if we hear the word the we expect the next word to be either an adjective or noun
such verification is effective at reducing errors that result from word misrecognitions but does nothing to reduce misunderstandings that result from other causes
key to response scores obtained in an interannotator test are probably a truer measure of human performance than key to key scoring provides
the verification is accomplished through the use of a verification subdialog a short sequence of utterances intended to confirm or reject the hypothesized interpretation
pereira and schabes do not report the sentence accuracy nor the parse accuracy of their system
the highest accuracy of NUM is achieved by using all subtrees from the training set
consider the imaginary extremely simple corpus in figure NUM which consists of only two trees
as the reader may easily ascertain a different derivation may yield a different parse tree
the muc NUM scoring method is based on a two step process of mapping an item generated by a system under evaluation the response to the corresponding item in the human generated answer key and then scoring th e mapped items
it is elegant in its way of determining the minimal number of changes in the linkage s required to make the response the same as the key to calculate recall and to make the key the same as the response t o calculate precision
a lexicon lez maps words from an alphabet e to word classes which in turn are associated with valencies and domain sequences
as dgs are restricted to lexical nodes one can not e.g. describe the so called unbounded dependencies without giving up projectivity
the head the word of category x must occur between the i th and the i NUM th modifier
since dg analyses are hierarchically flatter binary precedence constraints result in inconsistencies as the analyses of word grammar and lexicase illustrate
the subordinated word is assigned the class r while the governing word is assigned the subclass of o denoting the node it represents
x is the marginal empirical probability of the factor variables
therefore these nodes are obsolete and can be safely removed from the lattice
with a suitable choice of z for each joint model
for every ei e one word f ei is assigned class hi and governed by s in valency hi
initially we constrain all the nodes which satisfy the above requirement
the rules acquired in this way also have the characteristic that the y allow one to readily mix hand crafted and machine learned elements
this simple idea unfortunately runs into complications due to the presence of higher order functions
accuracy figures of the last four rows are all based on only seven collocation features as described earlier in this section
the user may ignore suggestions by following his or her own preferred dialog paths as occurs in statements NUM and NUM
when this occurs ipsim can halt and pass control to the dialog controller indicating an opportunity to engage in dialog
this leads to a clarification subdialog explaining the position of the knob and a response from the user okay
so control is returned to the previously active subdialog where the goal was to put the knob to one zero
the selected meaning of the incoming utterance will be the least cost match between an output for the translation grammar and the expectations
in this project it was assumed that the speech recognizer would provide a graph of alternate guesses of the current input
the reopening may be initiated either by the system because of a change in priorities in its agenda or by the user
an example of one of the specifications for the led is as follows observation observe the behavior of the led
by representing text chunking as a kind of tagging problem it becomes possible to easily apply transformation based learning
a similar list of the first ten rules for the chunk task can be seen in table NUM
current tag current tag and tag to left current tag and tag to right two tags to left two tags to right
this section discusses how text chunking can be encoded as a tagging problem that can be conveniently addressed using transformational learning
efficient dialog requires that each participant understand the purpose of the interaction and have the necessary prerequisites to cooperate in its achievement
these chunks were extracted from the treebank parses basically by selecting nps that contained no nested nps NUM
such an index allows the process of applying rules to be performed without having to search through the corpus
one such direction is to expand the template set by adding templates that are sensitive to the chunk structure
the lexicon contains NUM words representing NUM NUM of all the occurences of oov proper names in our corpus
then formulae may combine only if they are adjacent and in the appropriate left right order
out acting the person is no longer going to be an acting officer
in this paper we also hold the same assumption but start from a different point
note that each object defined by the template bnf contains a comment slot
we design a merging procedure to establish the dendrogram structure of the space and give an heuristic algorithm to find the nodes sense clusters corresponding with sets of similar senses in the dendrogram
are not considered jobs employment at that company
so future work also includes how to make an appropriate decision on the length of the contexts to be considered meanwhile make out the meaningful information carried by the words outside the considered contexts
furthermore we suppose that the nodes immediately below the level correspond with the clusters of similar senses
in our implementation we first build the semantic space based on the contexts of the mono sense words and structure the senses in the space as a dendrogram which we call structured semantic space
these predictions conditioned on only a single previous word in the sentence are inherently weaker than those conditioned on all k previous words
when estimating the parameter di for the chinese semantic space we let n NUM i.e. we only take NUM words to the left or the right of a word as its context
to do this we construct a random graph by randomly assigning NUM nodes to the two possible orientations
we move this adjective and proceed with the next iteration until no movements can improve the objective function
therefore by combining the aspectual categories of verbs and those that are defined in terms of their surface argument structures we can obtain an elaborate classification based on semantic types of verbs
on the other hand the forms owaru cease and oeru finish can follow the verbs which are telic t and takes up the end point of the process
aspect refers to the internal temporal structure of events and is distinguished from tense which has a deictic element in it of reference to a point of time anchored by the speaker s utterance
we can arbitrarily increase the probability of finding the globally optimal solution by repeatedly running the algorithm with different starting partitions
to extract conjunctions between adjectives we used a two level finite state grammar which covers complex modification patterns and noun adjective apposition
NUM the average frequencies in each group are compared and the group with the higher frequency is labeled as positive
positive adequate central clever famous intelligent remarkable reputed sensitive slender thriving negative contagious drunken ignorant lanky and negative orientations
moreover in all cases the ratio of the two group frequencies correctly identified the positive subgroup
we thank ken church and the at t bell laboratories for making the parts part of speech tagger available to us
however throughout this paper both the expressions most preferred syntactic structure and correct syntactic structure refer to the syntactic structure most preferred by our linguistic experts
in addition to simplify the computation we approximate the full context lcb b f g rcb with a window of finite length around lcb f g rcb
for such learning procedures the distance between the correct candidate and other competitive ones may be too small to cover the possible statistical variations between the training corpus and the real application
the judge is now asked to classify the quality of the translation along a seven point scale the points on the scale have been chosen to reflect the distinctions judges most frequently have been observed to make in practice
in this case p xm m NUM substituted by the smoothed value of the m NUM gram probability p xm x2n NUM
nevertheless higher complexity may be expected mainly due to the existence of lemmas and suffixes
most of the grammars may have an implicit recursion which may be shown by rules
as there are no probability tables there is no problem related to their extension
thus two dictionaries one for lemmas and other for suffixes are used
the declension of the dictionary entry mendi which means mountain can be seen in table NUM
in some cases it could be possible for the system to give proposals before entering the beginning of a word
adaptation to the user s lexicon is possible because there is no need to increase the size of the table
it treats the beginning of the sentence like the first approach using statistical information
in this approach each word has some associated semantic categories while in the previous one categories were purely syntactic
in this paper some word prediction techniques are reviewed and the difficulties to apply them to inflected languages are studied
the controlled vocabulary provided by longman is a list of all the words used in the definitions but in its crude form it does not suit our purpose
by choosing a particular continuation of the dialogue a dialogue participant is pursuing a certain goal
however with the results of the error analysis as a starting point we feel that a definition of the factor set is now more feasible
one reason is that it is difficult to construct or select a database if the set of factors that influence name pronunciation is at least partially unknown
trec NUM provided an opportunity for complex experimentation
pronunciation rules holes in the general null purpose pronunciation rule set were revealed by orthographic substrings that do not occur in the regular lexicon
names in this section we will present a compositional model of street names that is based on a morphological word model and also includes a phonetic syllable model
finally the completely analyzed word is tagged as a name and a word boundary is appended on the way to the final state end
the quality of the other alternative model for referent resolution the grosz and sidner model seems to compare to the quality of edward s context model
let us assume that the referent of the report about gr2 has a sv of NUM just before sentence 2a or 2b is interpreted
another significant point our experiments have shown is that the sentence can also provide enough contextual information for semantic coherence based NUM the result is less than conclusive since only one human subject is tested
the context of a word is defined to be the current sentence the system processes the corpus sentence by sentence and collects conceptual co occurrence data for each defining concept which occurs in the sentence
however a crucial difference between the two problems is that in the n gram task the words wl to wn are sequentim giving a natural order in which backing off takes place from p wn wl w NUM wn l to NUM wniw2 w3 wn NUM to NUM w w3 w4 wn l and so on
counts of lower order tuples can also be made for example f NUM p from is the number of times p from is seen with noun attachment in training data f v is n2 research is the number of times v is n2 research is seen with either attachment and any value of n1 and p
note that the estimation of NUM wn w w2 wn NUM is analogous to the estimation of NUM NUM v nl p n2 and the above method can therefore also be applied to the pp attachment problem
since users generally can resolve these ambiguities it seems reasonable to incorporate facilities for interactive disambiguation into speech translation systems especially those aiming for broad coverage
starting the search process at the most salient instance saves computational costs
in this section we will describe the knowledge base and context model
the graphics analyzer always immediately updates the selected cf of the demonstratum
we would like to thank dale gerdemann paul john king and two anonymous referees for helpful discussion and comments
an indicated referent cf finally causes a referent that is indicated by either the system or the user to be very salient for a short time
although some results were promising the method s performance strongly depended on the domain of the texts and the dictionary entries
the criteria used to select the path involve preferences for sequences that have been encountered in a target language corpus for the use of more sophisticated transfer methods over less sophisticated and for larger over smaller chunks
note that our evaluation is more strict than the conventional one especially for difficult texts because they contain more complex matches
our basic algorithm is an iterative adjustment of the anchor matrix am using the alignable sentence matrix asm
for all possible sentence correspondences jsentencei and esentencej any pair in asm the following operations are applied in order
as a result texts of various length and of various genres in structurally different languages can be aligned with high precision
in this section we report the result of experiments on aligning sentences in bilingual texts and on statistically acquired word correspondences
after NUM seconds for full preprocessing the first iteration took NUM seconds with tto NUM NUM and izow NUM NUM
however there are two major differences both deriving form the fact that carpenter uses an open world interpretation
in both of the turkish translations the surface subject is program whereas the surface subject changes in the english inputs
obviously sufficient mutual information between nouns and verbs adjectives and determiners would force the global optimum to include multiple expansions of the noun p category but it seems likely given the characteristics of the inside outside algorithm that before such mutual information could be inferred from text the inside outside algorithm would enter a local optimum that does not pass the noun feature out
there are many possible explanations for this result but the two we prefer are that either the inside outside algorithm as might be expected given our arguments failed to find a grammar that propagated head features optimally or that there was insufficient mutual information in the small corpus for our enhancement to traditional scfgs to have much impact
word head features were created by assigning numbers a common feature other words found in any case variation in the celex english language database were given a feature particular to their lemma thus mapping car and cars to the same feature and all other case sensitve words received their own unique feature
for example in the runs initialized with NUM unset learning malagasy vos adults and NUM default svo learning vos adults the learning procedure which dominated the population was a variant vos default learner in which the value for subjdir was reversed to reflect the position of the subject in this language
the unset learner was initialized with all unset whilst the default learner had default settings for the parameters gendir and subjdir and argorder which specify a minimal svo right branching grammar as well as default off settings for comp and perm which determine the availability of composition and permutation respectively
english with permutation has a lower mean wml than english without permutation though they are stringset identical whilst a hypothetical mixture of japanese sov clausal order with english phrasal syntax has a mean wml which is NUM worse than that for english
first the unit production relation has to be extended to allow for unit production chains due to null productions
there are cases however when that cost should be minimized e.g. when rule probabilities are iteratively reestimated
the total string probability is thus 2p3q NUM the computed c and NUM values for the final state
first replace the old pl relation by the one that takes into account null productions as sketched above
the nonprobabilistic earley parser can just stop here but as in prediction this would lead to truncated probabilities
consequently the example trace shows the factor p NUM being introduced into the forward probability terms in the prediction steps
dummy states enter the chart only as a result of initialization as opposed to being derived from grammar productions
rl can be computed once for each grammar and used for table lookup in the following modified prediction step
they also state mistakenly that a and c together are a sufficient condition for b
NUM NUM overall viterbi constitency corresponds then to NUM NUM individual success rate which is optimistic
in the remaining cases the system would be unable to analyze the input or no output would be correct
in the first pair constituent structures international telephone services is represented by a complete subtree
a representation r is ambiguous if it is multiple or f eper r contains an underspecified p
in the second pair dependency structures the representing subtrees are not complete subtrees of the whole tree
a proper representation p is underst ecified if it is undefined with respect to some necessaryinformation
there are two cases the intbrmation is specified but its value is unknown or it is nfissing altogether
we have proposed a technique for labeling ambiguities in texts and in dialogue transcriptions and experimented it on multilingual data
in the third part we propose a format for ambiguity labeling and illustrate it examples from a transcribed dialogue
it tries to segment an affix by leftmost string subtraction for suffixes and rightmost string subtraction for prefixes
to extract such rules a special operator v is applied to every pair of words from the lexicon
such filtering reduces the rule sets more than tenfold and does not leave clearly coincidental cases among the rules
to do that we eliminate all the rules with the frequency f less than a certain threshold NUM
this makes the accuracy drop caused by the cascading guesser to be less than NUM in general
the best results on unknown words were again obtained on the cascading guesser NUM NUM NUM
i would also like to thank chris brew for helpful discussions on the issues related to this paper
another important conclusion from the evaluation experiments is that the morphological guessing rules do improve the guessing performance
the notation lcb clmnprstv a rcb denotes a set of possible consonants represented by the variable a which also occurs on the right hand side of the rule indicating that the same selection must be made for both occurrences
words unknown to the lexicon present a substantial problem to part of speech pos tagging of real world texts
the best of these methods are reported to achieve NUM NUM of tagging accuracy on unknown words e.g.
the np features reflect passonneau s hypotheses that adjacent utterances are more likely to contain expressions that corefer or that are inferentially linked if they occur within the same segment and that a definite pronoun is more likely than a full np to refer to an entity that was mentioned in the current segment if not in the previous utterance
for n NUM and above the slope of the curve suddenly becomes linear and much less steep corresponding to a much more gradual decrease in frequency as values of n go to NUM that there should be any cases where six or seven subjects identify the same boundary is highly improbable but on average this happens NUM NUM times per narrative
the results enlighten the repairing capacities of a couple filtering module robust parsing module
moreover spurious hypothesis generated along the passes are still hard to eliminate
NUM of the testset is out of the nuance context free grammar
what we also discuss here which has not been presented in previous work is a preliminary evaluation of the reliability of our method where we give a conservative lower bound suggesting that the method is reliable
the re evaluation of a word score will derive from this word alignment value
the latter are represented by a single digit n in the string and an item n classname in the classes list multiple occurrences of the same n in a single rule must all match the same character in a given application
however two factors prevent this performance from being closer to ideal e.g. recall and precision NUM elsewhere we have discussed problems with the use of ir metrics given that segmentation is a fuzzy phenomenon
the first input to c4 NUM specifies the names of the classes to be learned boundary and nonboundary and the names and potential values of a fixed set of coding features figure NUM
then each sequence together with any associated restrictions on orthographic features undergoes analysis by the compiled spelling rules section NUM NUM with the surface sequence and the root part of the lexical sequence initially uninstantiated
this has led us to develop a tailoring environment which focuses all of the available knowledge on accelerating the corpus development process
using the same skilled annotator we inlroduced a completely new corpus for which named entity tagging happened to be needed within our company
one way of doing this is to apply tags to any and all strings in a document that match a given string
training data can not be generated without this NUM p r or f measure is a weighted combination of recall and precision
recent advances in corpus based manual and automatic training methods have shown promise in reducing the time and cost of this porting process
current support includes the latin NUM languages japanese jis chinese gb1232 russian greek and thai
one clear effect of increasing training set size is a reduction in the sensitivity of the learning procedure to particular training sets
a set of rule schemata defining a set of possible rule instances determines the rule space that the learning procedure explores
the parallel tag file ptf format used by the workbench provides another means by which a translator could be written
figure NUM shows the f measure performance smoothed by averaging neighboring data points to get a clearer picture of the general tendency
the latter could then be used by a modified version of the current system as the basis for efficient lookup of spelling patterns which as in the current system would allow possible lexical roots to be calculated
this results in typical compilation times of about a minute and has allowed a reasonably full feature based description of french inflectional morphology to be developed in about a month by a linguist new to the system
this work was completed within the dialogue group of the human communication research centre
editorial comments that help to establish the dialogue context are given in square brackets
if the transferee has acknowledged the information clearly enough an align move may not be necessary
surprisingly non hcrc coders appeared to be able to distinguish the clarify move better than in house coders
these sites were all conversational move boundaries except those between ready moves and the moves following them
for NUM out of NUM boundaries marked by at least two coders the category was agreed
example NUM g go right round ehm until you get to just above them
example NUM g we re going to start above th directly above the telephone kiosk
f alright well i ll need to go below i ve got a blacksmith marked
an align move checks the partner s attention agreement or readiness for the next move
e g NUM NUM a john pounded the metal flat
NUM the decision point could be even delayed into the generation phase of mt
the analyzer follows the permutation procedure described above for the second auxiliary verb
to discover the families of term variants we first consider a notion of collocation which is less restrictive than variation
the same is done for all possible ends of np tendnp i.e. nouns numerals pronouns etc
instead of viewing these distributions as constraints on the underlying probabilistic model we view them as sources of evidence
however after backing off to three elements we abandon the standard backed off estimation technique
the pseudo code for procedure b3 is shown below simplified for reasons of space
null in this paper we consider the problem of prepositional phrase pp ambiguity
in their work the top NUM transformations learned are primarily based on specific prepositions
this is clearly an aspect of the task where better knowledge representation would improv e system performance
overall official results are shown in table NUM overall unofficial results are shown in table NUM
s abbreviates sentence np mean s noun phrase and vp stands for verb phrase
for example apple computer inc might be referred to as apple apple inc etc
this results in both the male weight and the female weight being set to non zero values
nonetheless identifying instances of pleonastic it which do not corefer is still significant
only wordnet the xtag morphological analyzer and the gazetteer were used in the final system
for polysemous words wordnet may give conflicting evidence because of the word s multiple senses
this substantially increases the sparse data problems when compared with the single pp attachment case
in july the students proposed to the faculty that we formally participate in the coreference task
we may also improve upon the voting model by incorporating information regarding which tagger proposed each tag
the treatment of anaphora in this paper is as a relationship between a temporal unit representing a time evoked in the current utterance and one representing a time evoked in a previous utterance
distributional generalisations are manually coded as a grammar a system of constraint rules used for discarding contextually illegitimate analyses
the following example illustrates a case of antecedent containment that is not recognized by the filter as currently formulated
in tables NUM NUM and NUM we present results on each major subpart of the program
thus if there are three vps preceding the vpe we have NUM NUM NUM NUM NUM NUM
the vp headed by tells is modified by the adverbial phrase labeled advp containing the vpe
in what follows we first present background on the data set and the coding of that data
this allows the matrix antecedent know what the law of averages is to be correctly selected
NUM all the generals who held important commands in world war NUM did not write books
however we also examined clause rel in combination with the syntactic filter because of their close connection
it is selected because it has a would auxiliary which is the same category as the vpe
after validation the documentalists must position the selected terms in the thesaurus
of morphological including part of speech analysis i.e. the syntax tags were ignored
a linguistic and statistical extractor proposes candidate terms for validation by the documentalists
a part of speech tagger s task is often illustrated with a noun verb ambiguous word directly preceded by an unambiguous determiner e.g.
differences between smaller and larger domains include a higher out of vocabulary rate a higher rate of ambiguity and generally the existence of a much wider range of expressions and expressive devices in dialogues which make the semantic grammar approach which worked well in the narrower domain problematic
r contains o n w rules and o n2 NUM o m NUM NUM s rules
the method is completely automatic and produces accurate results at NUM of the cases
the simple frequency test was also evaluated in each testing set for comparison purposes
when each new element is tied into already learned data and is presented so that pieces of new knowledge introduced together are related conceptually the learning process gains a more significant meaning and new material is assimilate more quickly and entirely
for each of the variables we measured how many pairs in each group it classified correctly
whether to separate words derived through affixation from compounds or combine these types of morphological relationships
naturally the validity of our results depends on the quality of our measurements
when the difference is zero the variable selects neither the first or second adjective as unmarked
the intercept is dropped because the prior probabilities of the two outcomes are known to be equal
all this information except the number of syllables can also be automatically extracted from the corpus
tailored toward the writing style of fluent native english speakers they do not catch many errors that are common in the writing of people who are deaf and at the same time they flag many constructions that are not errors
linguistic and statistical terminological filtering are used
we have investigated the possibility of doing a search on the parse trees of correct sentences in the writing sample in order to find those that most closely fit a desired template perhaps based on a sentence the learner has written incorrectly elsewhere
once errors have been detected the system must determine which errors to focus on in the correction what basic content to include in the corrective response our model of second language acquisition is crucial for these tasks as well
second these syntactically ambiguous points are critical in efficiently resolving ambiguity
guo critical tokenization NUM NUM string generation and tokenization versus language derivation and parsing
that is any tokenization can be reproduced from a critical tokenization
given the character string s abcd
by definition s has tokenization ambiguity
this is also known as multi combinational ambiguity
we believe the additional step is worthwhile
the least element is another important concept
with some care it is possible to draw each ci with two or fewer states and with a number of arcs proportional to the number of tiers mentioned by the constraint
thus a timetine bears not only autosegmental fe tures like nasal gestures inasi and prosodic onstituents such as syllables o
in ot a phonological grammar for a language consists of vector c1 c2 c of soft constraints drawn from a universal fixed set con
when confronted with this pathological case the finite state methods respond essentially by enumerating all possible permutations of v g though with sharing of prefixes
this repeated pruning is already an improvement over ellison s original algorithm which saves pruning till the end and so continues to grow exponentially with every new constraint
note that to preserve the form of the predicates in NUM and keep the automaton deterministic we need to split some of the arcs above into multiple arcs
the claim for o t as universal grammar is not substantive or falsifiable without formal definitions of the putative universal grammar objects repns con and gen see below
one avenue for future improvements is a new finite state notion factored automata where regular languages are represented compactly via formal intersections n iai of fsas
after all of the sentences have been parsed in this way the current system displays the text with colored highlighting over all error containing sentences different colors are used for different classes of error again as identified from the real rules which were used
there exist many obstacles to this process some which are shared with other native language populations and some which are unique such as the absence of the opportunity to have english input tailored to the personal level of acquisition and understanding of the learner
NUM NUM from string positions to state names
we can employ context free transduction grammars in simple attempts at generative models for bilingual sentence pairs
but first i describe the motivations for this approach
the first consideration does not deserve much further attention
such information is represented in the table as well
the same technique is applied for the lex head link relation
items in the first table are called goal items
items in this second table are called result items
if it is the newer result is ignored
to get an overall agreement of greater than NUM NUM would require reducing the set of speakers from NUM to a carefully selected NUM
neither specified for anaphoric antecedents in ui not an issue here nor for anaphoric antecedents beyond ui NUM
table NUM anaphoric antecedent in utterance u table NUM and table NUM give the success rate of the
if none of the above cases applies then for utterance ui a new embedded segment is opened
she thus intends to extend the scope of centering in accordance with cognitively plausible limits of the attentional span
the fourth column depicts the levels of discourse segments which are computed by the algorithm in table NUM
in the course of processing the following utterances this decision may be retracted by the function lift
note however that our model does not require any prefixed size corresponding to the limited attention constraint
the anaphor handbuch manual in u6 co specifies the cv NUM us
we will refer to this case representation as ddfat d for disambiguated f for focus a for ambiguous and t for target
the first element of the c i the preferred center cp is marked by bold font
there are several types of information which can be stored in the case base for each word ranging from the words themselves to intricate lexical representations
the models we consider here are non deterministic models where the two languages role is symmetric
memory based learning is an expensive algorithm of each test item all feature values must be compared to the corresponding feature values of all training items
a maximum entropy approach to identifying sentence boundaries
then the variable vp in NUM will be incorrectly interpreted as unbound
note that inc2 NUM q r s t n
we can essentially use the previous technique of reducing to boolean matrix multiplication
NUM define seq a b lambda p
t figure NUM adjunction operation k t
m spans a subtree yielding e then
for step 4a there are two cases to take care of
and proceeds to find the transitive closure b of this matrix
the operation involved is called adjunction
this grammar is shown in figure NUM
NUM do a composition operation i.e.
the word error rate is reduced from NUM NUM lcb rcb t o NUM NUM lcb NUM the sentence error rate is reduced from NUM NUM to NUM NUM
to draw the analogy with speech recognition we have to identify the states along the vertical axis with the positions i of the target words ei and the time along the horizont al
as norvig notes more specialized data structures such as hash tables can improve performance
to study the effect of the language model we tested a zerogram a unigram and a bigram language model using the standard set of NUM NUM training sentences
it can be seen that the semantic meaning of the sentence in the source language may be preserved even if there are three word errors according t o our performance criterion
i.e. going along the alignment paths for all sentence pairs perform maximum likelihood estimation of the model parameters for modelfree distributions these estimates result in rela tive
the problem now is to find the unknown mapping j aj ca which defines a path through a network with a uniform trellis structure
since we have only first order dependencies in our model it is easy to see that the auxiliary quantity nmst satisfy the following dp recursion equation q i j e p fjle
d eatment of pot favor the word pot favor is always moved to the end of the sentence and replaced by the one word token pot favor
for instance speech recognition as it moves through an utterance should be able to benefit from preliminary analysis results for segments earlier in the utterance
training the acoustic models with vocal tract normalization vtln speaker normalization reduced the word error rate even further to NUM NUM
in his approach the theme corresponds to the cb and the theme rheme hierarchy can be derived from those elements of c u i that are realized in in
we motivated our proposal by the constraints which hold for a free word order language such as german and derived our results from data intensive empirical studies of real world expository texts
suits while the naive and the canonical approaches work reasonably well for the literary text but exhibit a poor performance for the texts from the it domain and the news magazine
the evaluation was carried out manually in order to circumvent error chaining NUM table NUM summarizes the total numbers of anaphors textual ellipses utterances and words in the test set
the resolution of text level nominal and pronominal anaphora contributes to the construction of referentially valid text knowledge bases while the resolution of textual ellipsis yields referentially coherent text knowledge bases
given information vs new information constituting the information structure of utterances on the one hand and theme vs rheme on the other constituting the thematic structure of utterances cf
we here subscribe to these considerations too and will return in section NUM to these notions in order to rephrase them more explicitly by using the terminology of the centering model
the cb u the most highly ranked element of c t un i realized in in corresponds to the element which represents the given information
this prediction can be used to constrain speech recognition
NUM part of speech tagging is a good application to test the computational linguistics volume NUM number NUM learner for several reasons
based on a re evaluation of empirical arguments discussed in the literature on centering we stipulate that exchanging grammatical by functional criteria is also a reasonable strategy for fixed word order languages
as much as possible it was important that the rules represented the relationship between multiple concepts within a phrasal constituent
as is discussed later in the paper our scoring systems must be able to deal with such near match responses
the ultimate goal is to develop a scoring system which can reliably analyze response content
NUM sample correct responses to the police item a better cadet training programs b
the terms had to be identified as metonyms in order to classify the responses accurately
each single relevant word or NUM NUM word term was linked to a concept entry
examinee responses do not have to be in complete sentences and can be up to NUM words in length
the phrasal constituent itself that is whether it was an np or a vp did not seem relevant
the improvement which occurred by augmenting the lexicon further supports our procedure for classifying responses
currently we are developing a program to automate the generation of the concept grammars
his goal was to generate turkish sentences of varying complexity from input semantic representations in penman s sentence planning language spl
bug lcb in evden okula otobiisle today home abl school dat bus with it was ahmet who went from home to
the paper is organized as follows the next section presents relevant aspects of constituent order in turkish sentences and factors that determine it
null conclusions we have presented the highlights of our work on tactical generation in turkish a free constituent order language with agglutinative word structures
her generator also uses relevant features of the information structure of the input and can handle word order variations within embedded clauses
our grammar uses a right linear rule backbone which implements a recursive finite state machine for dealing with alternative word orders
there are also certain constraints at sentence level when explicit case marking is not used e.g. with indefinite direct objects
we have used a recursively structured finite state machine for handling the changes in constituent order implemented as a right linear grammar backbone
these two sentences are used to describe the same event i.e. have the same logical form but they are used in different discourse situations
in turkish the information which links the sentence to the previous context the topic is in the first position
we prove here that for a fixed set of primitive queries any binary decision tree can be converted into a transformation list
next the tranformations from l1 will be applied if x is true since s is the initial state label for l1
table NUM distribution of indexes headed by attivit5
since context carries information about terms it should be involved in the procedure for their extraction
also dr t sharpe from the medical school of the university of manchester for the eye pathology corpus
its context adjectives nouns and verbs that surround it are extracted from the corpus
when the aim is the extraction of single word terms domain dependent linguistic information i.e.
the length of the string is incorporated in the c value measure resulting to c value
at this point a list of context words together with their weights has been created
these characteristics are combined in the following way to assign a weight to the context word
we incorporate context information in the form of weights constructed in a fully automatic way
the corpus is tagged and a linguistic filter will only accept specific part of speech sequencies
this situation can be an event a relation or state in addition to a process in its most common aspectually restricted sense
note that in order to choose between these sentences the lexical chooser needs information other than just content encoded in the domain representation
this content representation does not indicate which relations should appear as the head element in the linguistic structure and which should appear as dependents
since any approach must deal with a combinatorial explosion of possible mappings and ordering of constraints computational efficiency is in general an issue
furthermore interaction between constraints is multidirectional making it difficult to determine a systematic ordering in which constraints should be taken into account
in the previous example the elements are generated by changing the contents of the gender attribute in the morphological analysis while keeping all the other attributes unchanged
our corpus analysis of the basketball domain for example indicates that historical knowledge is floating whereas game result information is structural
typically a generator has two modules each corresponding to one of these two tasks a content planner and a linguistic realizer
the reason for testing the method only on a relatively small set of test texts is that no tagged hebrew corpus is currently available for a more powerful evaluation
the algorithm uses two thresholds an upper threshold and a lower threshold which serve to choose the right analysis or to rule out wrong analyses respectively
for the problem in hebrew a set of ten rules NUM was sufficient for the generation of sw sets for all the possible morphological analyses in hebrew
it may even have a significance from a psycholinguistic point of view by suggesting that these kind of probabilities are also used by a human reader of hebrew
at the end of this section we shall identify some cases for which our method fails to find a reasonable approximation for the morpho lexical probabilities of an ambiguous word
the rich morphology of the language and the fact that many particles are attached to the word forming a single string further contribute to the morphological ambiguity
the approximated probability for this analysis is calculated by looking at the frequency of the similar word h h vdn the hour
from these tables we can see that our method yielded incorrect approximation for only NUM words out of the NUM words in the test groups NUM NUM
after that we have calculated averages of those eleven values in order to get single figures for comparison
when the system performs k per doe assignment the value of k is ranged from NUM to a reasonable maximum
we have then computed recall and precision for eleven levels of threshold both macro and micro averaging
semantic closeness between documents and queries is computed by the cosine of the anglebetween document and query vectors
we have selected reuters because it has been used in other work facilitating the comparison of resuits
this results in a lack of comparability among the approaches forcing to replicate experiments from other researchers
pasha ii agents are easily adapted to the owner s preferences
the possible semantic links correspond to the cen semantic links
surgical deed clause limit s synlactic tag
d a database with prepositions and their ftmctions
e filling in the slots ex
frames are chosen for the implementation of the model
surgical deed subtype and part of speech cat
each slot can specify conditions its filler must meet
case grammar gives an analysis of a sentence centered around lhe verb
in a later phase we intend to correct these cases of overgeneration
each of the other combinations finds the most most accurate model for NUM of NUM words except for fss exact conditional which never finds the most accurate model
in cases where no statistical solution is possible plan based repair is used
figure NUM a dialogue model for the description of appointment scheduling dialogs
whether the dialogue participants know each other
that looks bad figure NUM part of an example dialogue
figure NUM hit rates for NUM dialogues using NUM predic tions
figure NUM example of statistical repair
figure NUM predictions and hit rates
utilizing statistical dialogue act processing in verbmobil
this situation frequently occurs in speech and handwriting recognition systems or in machine translation
for batch training we partitioned randomly the data into training and testing sets
the special string represents a wild card that can be matched with any observed word
for the purposes of this paper however we will use psts as our starting point
once a node is chosen a word is picked randomly by the node s prediction function
context dependent priors and trees with wildcards can be obtained by a simple extension of the present derivation
li a given observation sequence matches a unique path from the root to a leaf
our learning scheme and data structures favor instead any method that is based only on word counts
tion ac purposes mt require cg satstlites ob in low earth lo orbits sa supplemented ct by geostation
a methodology aiming to support semantic bootstrapping in a nlp application is defined
some of these methods rely on semantic classes in order to improve robustness
for example in the rsd corpus the verb catalogue appears NUM times
x t c x s c NUM
semantically typed contexts to derive a variety of lexical information e.g.
a second test has been carried out on wordnet unambiguous verbs e.g.
where explicit semantic selectional restrictions ob for syntactic arguments e.g.
it is questionalble how expressive is the resuiting tag system
figure NUM dependency tree link representations
and the structural data sparseness problem still remains
figure NUM best first parsing entries
figure NUM abstract complete link and complete sequence
section NUM describes the reestimation algorithm
a particularly interesting dichotomy arises for the two forms a and an of the indefinite article the latter because it always precedes a word that begins with a vowel is inherently more predictive
note that NUM in the label n od NUM is the homograph counter number generated by terminologyframework
it follows that if such a constellation occurs and the premises are valid the concepts t and x should be merged
thus af cl is NUM NUM
to prevent the human annotator from missing errors the tagger for grammatical functions is equipped with a measure for the reliability of its output
this examination will be directed by v3 and v4 prohibitive rules
otherwise a token of 2v or vc type is extracted
some two vowel orthographic combinations are phonetically equivalent to a vowel consonant sound
they do not apply to substrings containing initial or final consonants
consider the case where a consonant or consonant sequence precedes v2
diaeresis marks were used as a discriminating factor for additional candidates
the issue to examine is vowel splitting independent of consonants
however complete automatic hyphenation is a rather complex task
formal definitions of all categories are given in table NUM
all maximal mergings of the pailts are created
basic validation includes formal redundancy and consistency checks
hence the count based models are interpolated in a way that is consistent with their eventual use
each word is perhaps ambiguously assigned a category and lf and when the syntactical operations assign a new category to a constituent the corresponding semantic operations produce a new lf for that constituent as well
then after the meta level reduction using the new scoped constant c the following goal is called coord s app c john app c bill ii
for example the sentence mary gave every dog a bone and some policeman a flower results in the lf NUM 12this is a case in which the paxticulax lf assumed here fails to yield another available scoping
this technique is also used in the fl reducer briefly mentioned at the end of the previous section and a similar technique will be used here to implement coordination by recursively descending through the two arguments to be coordinated
coord fs b abs it abs s abs t pi x coord b x s x t x
abe obj abe sub found sub obj m m abe x app abs sub found sub x harry
whereas svd finds a global minimum in its error measure however em only finds a local one
slot names are shown in capitals fillers in quote marks are stored as strings other fillers are coded
then linking would be nothing but connecting nodes
as expected this fraction decreases monotonically with the number of bigrams that are mixed into each prediction
assuming that there exist entities which have aspects of a life form as well as aspects of not being a life form we may be interested whether wordnet refleets this non boolean logic view
NUM it is worth noting that only the em algorithm directly optimizes the log likelihood in eq
an aggregate markov model with c NUM classes was used as the base model in the smoothing procedure
as an example of a semantic grammax based approach
for example the example data ba se
if appropriate distributional information for words is available
NUM NUM exa r ples vs interlingua
bhiagusl example database figure NUM example based translation architecture
figure NUM viewing input a distorted exanlples
a formal basis for spoken language translation by analogy
unfortunately the pure analogical approach lacks scalability
similarly we assume that the probability of e.g.
this distance is defined by the following recurrence
we compared this to a trigram model that backed off to the m NUM model in table NUM
the most interesting issues arise in the treatment of tiller gap dependencies which are represented as chains
the first case arises when the node n is a lexical wh word which starts a wh chain
in grammar NUM the number of conflicts is multiplied by the number of categories
for the same sentence there are more relevant links if the collapsed feature set is larger
therefore gpsg is in principle more amenable to being processed by known parsing techniques
users implement an application driver that invokes the engine and configures the processing
when some grammatical constraint is newly introduced on an already translated expression and if it requires structural deformation the system looks for the registered structure and generates it again so that it meets the new constraint
however their method is limited to syntactic dependency disambiguation by explicitly specifying the words in the dependency relation and it is difficult to expand the method to handle the types of ambiguity discussed in this paper
the user can denwa wo kakeru make verb make a phone call telephone verb telephone call verb call up go back to the idiomatic interpretation by choosing the alternative denwa wo kakeru at the last line of these alternatives windows
the usacom project is part of the larger
nametag can either generate a document that has the names annotated with sgml standardized generalized markup language or provide a table of the names with indices offsets to the text annotation mode
iii project plans this project will integrate with two communitydeveloped reference architectures
this mixed representation level of the source and target language expression serves as a playground for all subsequent interactions
consider that the speaker intends to express an underlying message s but speech errors certain speech properties misrecognitions and other factors interfere resulting in the actual utterance i which forms the input to the translation system figure NUM
each point i j on this graph is on the dtw path vl v2 where vl is from english words in the lexicon and v2 is from the chinese words in the lexicon
five of them are singled out due to the type of mistake which is shown below in figure NUM a hyponym link has been coded where a meronymic one would have been correct
we know point i is matched to point j and point u to point v the texts between these two points are matched but we do not make any assumption about how this segment of texts are matched
quick e can be expressed by satisfying the condition qt pl r2 which means choosing the left
facts can be expressed only under certain conditions and it needs to be verified that the conditions are honored in a mutually consistent way
to avoid expressing it twice a further negative constraint is placed on node NUM which requires pl r2 to be false
we still use the semantic variables as indices but we do not let the bit arrays be part of the identification of edges
the next example shows a chart with semantic arrays and exemplifies how the conditions appearing in their slots control realizations of the input
the method we propose in this paper can be deployed as an infrastructure for solving certain other problems in generation and translation
when edges combine to form a larger constituent their arrays anion together and checked to verify that no fact is duplicated
the method allows simultaneous generation from multiple interpretations without hindering the generation process or causing any work to be superfluously duplicated
it has shown effectiveness in computing such a lexicon from texts with no sentence boundary information and with noise fine grain sentence alignment is not necessary for lexicon compilation as long as we have highly reliable anchor points
given the position vector of a word p i where the values of this vector are the positions at which this word occurs in the corpus one can compute a positional difference vector v i NUM where vii NUM p i p i NUM
we convert this position vector into a binary vector v1 of NUM dimensions where vi i NUM if prosperity occured within the ith segment vi i NUM otherwise
figure NUM bilingual lexicon compilation results rithm found a handful of these such as fung g wong poon hui iam cyc tam etc
the operator is analogous to in situation semantics it indicates among other things that a formula describes an event
a portion of this work was performed at the university of rochester computer science department and supported by onr arpa research grant number n00014 NUM j NUM
however as mentioned in section NUM there are many types of surface cues which correspond to a variety of lexical semantic information
in addition working with the brown corpus NUM NUM million words and NUM affixes provided such information for over NUM words
some exemplars are centralize formalize categorize colonize brighten stiffen falsify intensify mummify and glorify
in fact if one reactivates something then it is also being activated the derived form entails the base entails base
similarly one could guess that filp means something like eat upon hearing i filped the delicious sandwich and now i m full
semantic information many derivational affixes only apply to bases with certain semantic characteristics and only produce derived forms with certain semantic characteristics
german ich mschte ein ruhiges zimmer mit teiefon und fernseher his iibermorgen reservieren
this set is defined as follows null NUM distortmax rnaxoi tor t p distortie i we solve this problem with a dynamic programming algorithm that finds a set of distortion operators with maximal probability
early on we identified key sentences those sentences directly responsible for the generation of some importan t entity such as a succession
half of the training data was set aside for blind test until the last week of the evaluation the remaining half was for development
a nd h xica l sema ntics
clearly much of this process is governed by the specific requirements of the application considerations which have little to do with linguistic processing
the flat structure of the job situation object makes merging much simpler as well as making the case frame and semantic pattern s easier to define
set of source cfg skeletons in t
a set t of transla tion
one problem with ocr texts is that periods in the original text may be scanned as commas or dropped from the text completely
we therefore applied a simple filter to the raw ocr data to locate areas of high noise and remove them from the text
the licensing of the empty category pro also requires the inflectional head of the sentence to bear the feature strong agr and it occurs in the specifier head configuration
the spirit of the hypothesis is that linguistic theory is formed by heterogeneous types of information and that the representation used to describe them is a derived concept
moreover empty categories are moved up so that they are encountered as high in the tree as possible
the lexicon may contain as many as several hundred very specific tags which we first need to map into more general categories
to reduce the effects of overfitting the c4 NUM learning algorithm prunes the tree after the entire decision tree has been constructed
in the satz system we use a sigrnoidal squashing function on all hidden nodes and the single output node of the neural network
as a matter of fact one of the interesting features of this implementation is that it offers a unified treatment of all of the chain types presented above
from these data we concluded that a NUM token context NUM preceding the punctuation mark and NUM following produces the best results
the result of the compilation are shown in table NUM and the distribution of the conflicts is shown in table NUM
notice that adding information subcategory selection etc has a filtering effect and the resulting grammar is smaller
additional topics for further work include the estimation of lexical probabilities from bilingual corpora improving the integration between the speech recognition and the translation components in order to increase recognition accuracy and translation robustness and extending the system to additional languages
thus one might write c x y c to express that c and c are alike except for the discourse entities x and y
this paper outlines and evaluates a new part of speech tagger
like a rule about syntactic functions
nothing hinges on our choic e of NUM NUM as a rule induction mechanism
only informatioil about the last syllable is re levant in predicting the correct allomorph
it contains gr2 report and qbgc
after selection of the file icon labeled donald report
table NUM comparison of accuracy between hand crafl ed and induced rules
the algorithm for the construction of a c4 NUM decision tree can be easily stated
NUM information about the last syllable without stress and onset nc corpus
the generalization error is given for each allomorph for the four different training corpora
the language generation component uses it as well
the indicated object henceforward referred to as the demonstratum e.g. a file icon directory tree or screen position is marked using reverse video and becomes selected
the object will be in the scope of the visibility cf and if no other object of this type is in context the visible object thus will be selected as the referent
does koen live in nijmegen now is not included by the time interval of live in NUM the relation no longer holds and the system would respond negatively
the application domain is military tactical air control
the central notion in this model is salience
subject referent cfs model the carla huls et al
is thistthe e mail to lou which one
the rule reads x is legitimate only if it occurs in context lc1 rc1 or in context lc2 rc2 or in context lcn rcn
the main problem with this approach seems to be that resolving part of speech ambiguities on a large scale without introducing a considerable error margin is very difficult at best
from this nearly unambiguous combined output the success of the hybrid was measured by automatically comparing it with a benchmark version of the test corpus at the level
hat that clb cs that det central dem sg that adv that pron dem sg that rel pron sg pl
this kind of representation seeks to be i sufficiently expressive for stating grammatical genermisations in an economical and transparent fashion and ii sufficiently underspecific to make for a structurally resolvable grammatical representation
the tagger consists of the following sequential components tokeniser engcg morphological analyser lexicon morphological heuristics engcg morphological disambiguator lookup of alternative syntactic tags finite state syntactic disambiguator
the hybrid approach first uses a smoothing technique to estimate the initial parameters
the remaining spurious ambiguity is avoided by a particular way of constructing the parse trees described in what follows
we call this mapping function the descendants function
this paper discusses a different application improving information retrieval through name recognition
these grammars were stripped of their arguments in order to convert them into context free grammars
we give a new treatment of tabular lr parsing which is an alternative to tomita s generalized lr algorithm
this does not affect the power of these devices since states can be encoded within stack symbols and transitions
c2i r g q2lr p2i rt where the rules in p2lr are given by
we have a certain nonterminal ainit which is initially inserted in u0 NUM in order to start the recognition process
th grammars used here have this property
we repeat this process until we can no longer find a modification that improves the current hypothesis grammar
initially the set of nonterminal symbols consists of a different nonterminal symbol expanding to each terminal symbol
in addition it allows us to train parameters that were fixed during the initial grammar induction phase
a conspicuous shortcoming in our search framework is that the grammars in our search space are fairly unexpressive
for smoothing a particular n gram model we took a linear combination of all lower order n gram models
however solomonoff does not specify a concrete search algorithm and only makes suggestions as to its nature
we compare the performance of our algorithm to n gram models and the inside outside algorithm in three language modeling tasks
the algorithm employs a greedy heuristic search within a bayesian framework and a post pass using the inside outside algorithm
we satisfy the goal of favoring smaller grammars by choosing a prior that assigns higher probabilities to such grammars
in addition n gram models are extremely large thus making them difficult to implement efficiently in memory constrained applications
this run is used as a control since all words in the corpus are defined in the lexicon
an agent will answer noopinion if it m not answer or if i does not m derstand the question it will confirm the hyi othesis if it obtains a positive evaluation of it and it will withdraw it in case of negative evaluation
closed class parts of speech are such that all the words with that part of speech can be enumerated completely
for example during the morphological and syntactical analysis of the sentence i y want v the d c mail f address f or v interactions between morph and synt are useful
the fed collection consists of NUM NUM federal case law documents
in the systetn an agent willing to send a message will use the following message format sender receiver s performative for content
in the example based approach the patterns are listed in the form of model examples
input entered by the user must be encoded using the same method adopted by the analysis module
identified jobs can then be ranked according to how closely they resemble the user s ideal job
co operative efforts are required to maintain flow with the orderly development of topic being managed by means of topic shifts that are small enough to maintain continuity with occasional larger shifts to establish new directions
pn x company x name name
pn x name x name name
for social goals on the other hand described by brown and yule as listener oriented precision of conversational content may sometimes be less important than aspects of delivery especially timing
it is important to incorporate features within an aac design which by modelling such aspects of natural conversation will help users to pursue their social goals more effectively than has been possible so far
null NUM remove from m the set of runtime entries for nodes in s
the model is general enough to cover the common translation problems discussed in the literature e.g.
at the same time lexically centered views of language have continued to increase in popularity
the second element an event e is an equivalence class of states after transitions
figure h head automaton m scans left and right sequences of relations ri for dependents wi of w
we have used backed off costs in the translation application for the various cost functions described below
we have built an experimental translation system using the monolingual and translation models described in this paper
NUM if r is empty add the configuration to the set of subtree solutions
let fi and fj be the mapping functions for en null tries ei and ej
we conclude with a discussion of the adequacy of annotated linguistic strings as representations for machine translation
this means that neither the hmm tagger nor the cascading guesser had been trained on the texts and words used for evaluation
all the hapax words and capitalized words with frequency less than NUM were not seen at the training of the cascading guesser
this made the improvement over the baseline xerox guesser NUM in precision and NUM in coverage on the test sample
n grams provide such predictions only at very short ranges
for example an ending guessing rule can predict that a word is a gerund or an adjective if it ends with ing
for every rule acquired we need to estimate whether it is an effective rule worth retaining in the working rule set
the method for finding the optimal threshold is based on empirical evaluations of the rule sets and is described in section NUM NUM
the ending guessing rules naturally include some proper english suffixes but mostly they are simply highly predictive ending segments of words
this has three advantages the size of the training lexicon is large and does not depend on the size or even the existence of the annotated corpus
indeed the error rate on the proper nouns was much smaller than on the rest of the unknown words which means that they are much easier to guess
the proximity searches treated non name terms in same way the baseline searches did
it is a complex state denoting john s habit
nissim francez computer science department the technion
when john saw her she crossed the street
in this drs n denotes the utterance time
logistics experts can use their natural voice to ask for different types of information or to control the display of the logistics system
joe NUM woods occurred in NUM documents and had an idf of NUM NUM
the users all army personnel also thought that speech would reduce training time because it was so easy to use
non tipster modules may be made tipster compliant by the use of wrappers which provide tipster compliant interfaces
interfaces vs internals the tipster icd defines interfaces inputs and outputs between modules
in addition this design meets the requirements of a number of us government agencies
in addition user interface gui requirements are not covered by the architecture
a clustering component is a component that groups similar documents together without user specified detection criteria
using bbn valad speech at the logistics anchor desk
for some projects the cotr and the technology transfer officer are the same person
for some projects the technology transfer officer and the cotr are the same person
valad was developed by bbn under the sponsorship of darpa the defense advanced research projects agency to demonstrate the applicability of advanced speech recognition and language understanding technology to realistic data base tasks
many people are involved in many different ways in working with a text processing application
by following those specifications the developer is assured that his modules will be tipstercompliant
an itg can accommodate a wider range of ordering variation between the lan null computational linguistics volume NUM number NUM where is the secretary of finance when needed
c NUM association for computational linguistics computational linguistics volume NUM number NUM fung and church NUM wu and xia NUM fung and mckeown NUM
however it turns out that the itg restriction of allowing only matchings with straight or inverted orientation effectively cuts the combinatorial growth while still maintaining flexibility where needed
productions in the form of c however are not permitted by the normal form we use in which each bracket can only hold two constituents
however the identification of subsentential nested phrasal translations within the parallel texts remains a nontrivial problem due to the added complexity of dealing with constituent structure
for each example e in b a draw k models randomly from p mis b classify e by each model giving classifications lcb ci rcb c measure the disagreement de for e over lcb ei rcb NUM select for annotation the m examples from b with the highest de NUM update s by the statistics of the selected exam null ples
for this research we define a word by its parts of speech and a small set of features
by themselves word alignments are of little use but they provide potential anchor points for other applications or for subsequent learning stages to acquire more interesting structures
of course this assumption over simplistically NUM direct comparison with word align should be avoided however since it is intended to work on corpora whose sentences are not aligned
various tasks such as segmentation word alignment and bracket annotation are naturally incorporated as subproblems and a high degree of compatibility with conventional monolingual methods is retained
a question expressed in nl it is difficult to express questions in vl is translated to a loxy expression that the theorem prover tries to prove
NUM improvements on architecture the present natural language generator of vinst is difficult to control because there are only two control features natural and compact available
fact bases can be paraphrased into natural language either after that an event is executed with the interpreter or as an answer to a question to the theorem prover
when generating a text from a fact base in vinst the text becomes very tedious to read since the text is very redundant and does not feel correct conceptually
third the representations should be amenable to efficient computer processing
which class of representation systems do we consider in our labeling
r is multiple if and onlzi n m per
it is denotexl hem by proa er r
in the case of utterances the same remarks apply
there are often less possible utterances than all possible combinations
k a v pl p2 pn
trees decorated with various types of structures are very popular
because he sees it on his screen and steps in
because we do n t have frequency and morphology information on these abstract nodes we can not predict whether two nodes are of the same or different orientation
nevertheless we expect the trends to be similar to the ones shown in figure NUM and the results of table NUM on real data support this expectation
however chart initialization presents some technical problems
in order to assist the experimenter in determining when a misrecognition occurred the experimenter monitored the file where automatic logging occurred
these results indicate there are differences in user behavior and dialogue structure as a function of the computer s level of initiative
the results indicate there are differences in user behavior and dialogue structure as a function of the computer s level of initiative
note subject then added the missing wire and manually performed all necessary checks to verify the circuit was functioning properly
null they had demonstrated problem solving skills by having successfully completed one computer science course and had taken or were taking another
in both cases the user s assertions continue the topic introduced by the computer and do not cause a change of control
in contrast while the computer was operating in declarative mode the user attempted to correct NUM of the misunderstandings
as discussed in section NUM NUM however not all dialogues followed this model due to user initiative and dialogue miscommunication
the evaluation subcorpus was designed to have approximately equal numbers of all represented combinations of facet levels
two informal pilot studies indicated that it gave better results than linear discrimination and linear regression
for example the mainstream american press is classified as middle and tabloid newspapers as popular
while the corpus contains NUM samples many of the samples contain several texts
apart from giving us a theoretical framework for understanding genres facets offer two practical advantages
is genre a single property or attribute that can be neatly laid out in some hierarchical structure
examples of ones that we use are terms of address e.g. mr ms
examples include counts of question marks exclamations marks capitalized and hyphenated words and acronyms
sorting search results according to genre will gain importance as the typical data base becomes increasingly heterogeneous
we evaluated the opp method m el discourse segmentation darcu NUM various ways
parsing sgml in its full generality and providing validation and adequate error detection is indeed rather hard
the sorts of mappings that abeill deals with are lexically idiosyncratic the english sentences kim likes dale and kim misses dale while syntactically parallel and semantically fairly dose are translated to different syntactic structures in french see figure NUM
in addition to chinese and thai we also performed segmentation experiments using a large corpus of english in which all the spaces had been removed from the texts
in paraphrasing the tree notation thus becomes fairly clumsy as well as consuming a large amount of space given the large derived trees it fails to reflect the generality provided by the summary links
proposed here has a string comprising node labels with relations between them signifying a relationship taken from the set lcb parent child left sibling right sibling rcb abbreviated lcb p c ls rs rcb
since with paraphrasing the transfer lexicon does not play such a role the shared information is represented by this new type of link between the trees where the links are labeled according to the information shared
in tree structure terms we have
a problem with the stag formalism in this situation is that it does n t capture the generality of the mapping between 2a and 2b separate tree pairings will have to be made for verbs in the matrix clause which have complementation patterns different from that of the above examples the same is true for verbs in the subordinate clause
then for example the tree for 2a can be defined as the adjunction of a fln0nx0vx tree generic relative clause tree standing for e.g. n0nx0vnxlnx2 into an an0vy tree the tree for 2b can be defined as a conjoined s tree having a parent sm node and NUM child nodes an0vx and an0vy
naturally this can only be defined for pairs of nodes which have the same structure NUM that is in the context of paraphrasing it is effectively a statement that the paired subtrees are identical
as such generally applicable paraphrases are appropriate so syntactic paraphrases paraphrases that can be represented in terms of a mapping between syntax trees describing each of the paraphrase alternatives have been chosen for their general applicability
the darker the tile the more frequent the term white indicates NUM black indicates NUM or more hits the frequencies of all the terms within a term set are added together
texttiling assumes that a set of lexical items is in use during the course of a given subtopic discussion and when that subtopic changes a significant proportion of the vocabulary changes as well
for example if the user decides that medical terms should be better represented the min hits or min lack of rnagtnat lon stalls op dcai disk appiications o
the implication is that the proper unit is the one that groups together the information that performs some communicative function in most cases this unit will range from one to several paragraphs
if we can characterize this marking of topic shift then we shall have found a structural basis for dividing up stretches of discourse into a series of smaller units each on a separate topic
automated multi paragraph segmentation should help with the first step of this process and is more important than ever now that pre existing documents are being put up for display on the world wide web
more specifically texttiling is meant to apply to expository text that is not heavily stylized or structured and for simplicity does not make use of headings or other kinds of orthographic information
one obvious way to use segmentation information is to have the system display the passages with the closest similarity to the query and to display a passage based summary of the documents contents
and we gave three dialogue modes to every subjects as shown in below mode a using only speech input and output our conventional system mode b using speech input and multi modal output graphical output on display and speech output mode c using multi modal input and output input speech and using touch screen output speech and graphic on display users used three systems on line mode at the computer room
when the exponential model outperforms the trigram model r NUM
does the word appear up to NUM word in the future
there are several sanity checks that validate the use of our metric
we then build a family of conditional exponential models of the general form
are there blank video frames nearby
the second feature uses the relevance statistic t
NUM NUM language model relevance features
our proposed metric satisfies the listed desiderata
figure NUM near the beginning of a segment an adap
figure NUM data flow in training the exponential seg mentation model
for example if a user s query does n t have enough conditions information to answer the question by sysytem or if there is much retrieved information from the knowledge database for user s question the dialogue manager queries the user to get necessary conditions or to select the candidate respectively
modex has been fielded at a software engineering lab at raytheon inc
expression for example on figure NUM
figure NUM description used for documentation
figure NUM text plan configuration interface
figure NUM the university ooo diagram
the main requests are the following
frank belfo d and sue jones
how can this information best be communicated
we are thankful to k benner m
figure NUM shows the modex architecture
a single expert in using the tool would then find NUM NUM NUM or NUM of the guideline violation types found by two analysers together
then by the above definition of well definedness on sdrss the discourse is incoherent and we have presupposition failure
over the past two years we have been developing
smoothing is necessary to avoid giving a non zero probability for possible senses which are not found in a particular corpus
since this updated sdrs is welldefined prefer frequent senses ensures that it s true
then we assume that exactly one of update z a
then this content can be added to the constituents in a constrained manner through a process known as sdrs update
the rule in NUM is therefore theoretically inadequate because it predicts that all noun noun compounds are acceptable
in another subcategory of compounds the head provides the predicate e.g. dog catcher bottle crusher
figure NUM pomons of the signatures of several concepts
further if the problem solver ca n t retrieve any information related to the user s question the problem solver proposes an alternative plan information by changing a part of conditions of usr s query and send it back to dialgoue manager
in addition we have adopted a simple convention for those cases in which context information is insufficient f r total disaml iguat ion the highest possible attachment site is chosen
despite this we believe that in the comparison considered in this paper it is reasonable to make an overall assessment that the head transducer system is more effective that the transfer based system
null a replace demonstrative word with adequate words registered for a demonstrative word database b unify different semantic networks using default knowledges which are considered to be semantically equivalent to each other processing for semantically omissions
we gave a task of making some plans of mt fuji sightseeing to NUM users a j NUM users where evaluation of language processing part who did not know about this system novises in advance
NUM quinlan j r discovering rules by induction from large collections of examples in expert systems in the micro electronic age ed michie d edinburgh university press NUM
the main drawback with the system is that it does n t make maximal use of the training data in so far that with small training samples one word may be sufficient to make a decision
however given that we are processing new texts there are many occasions where an end or a beginning is identified but the corresponding beginning or end is not
component units recognized by the system are cities provinces countries company prefixes an d suffixes company beginning and ending words club association etc
new sub collections are formed with elements containing one value which contributed both to positive or negative outcomes are collected and the tree building process is repeated for each of these new collections
at this point a heuristic is applied which for every un matched bracket in the text work s forward or backward until some appropriate point is reached
the sum of the approximate information contents for each column is calculated and the colum n with the highest value is chosen as the primary decision
the major problem here is that the system has not learned a rule which uses mr to identify the word previous to a name
each tuple was marked as having the start or end of a specifi c type of proper name at the middle word of the tuple
the data used in the basic system is derived from public domain source university phone lists the tipster gazetteer and government data bases of company names
rcb are replaced by only two sentence boundary markers
excessive nodes for identifying the various bar levels in the phrase structure grammar are also deleted or compacted
afterwards to reduce the estimation error caused by the maximum likelihood estimation the good turing s smoothing method is applied
to reduce the estimation error from maximum likelihood estimation the good tufing s smoothing method is also applied
the average number of words per sentence for the training set and the testing set are NUM NUM and NUM NUM respectively
as an example the parse tree nf1 and the desired normal form structure are illustrated in figure NUM
in such a way the semantic score can be defined in terms of the case subtrees and the nf1 subtrees
like the standard trigram tagging procedures the lexical score s x t k is expressed as follows
in the nf representation the tense modal voice and type information of a sentence are extracted as features
alignment of shared forests for bilingual corpora
figure NUM there is no lea preserving alignment be
the pairing with the maximum score is then selected
o1 lit a lizulll ml t t ol l mi a u t i lit rodu gy
table NUM changes in accuracy due to l ex match ileuristie
optimization variables we experimented with variants of thc
we then removed those examples that although they contained the desired lexical string did not constitute negative imperatives e.g. if you don like the colors of the file use binder to change them
safety this feature captures whether or not the author believes that the agent s safety is put at risk by performing a badp is used when the agent s safety is put at risk by performing a
this data can be used to find correlations between the two types of features correlations which in text generation are typically implemented as decision trees or rule sets mapping from function features to forms
if p a is the proportion of times the coders agree and p e is the proportion of times that coders are expected to agree by chance k is computed as follows
the conversion routine takes the following inputs the applicable language s c4 NUM produces its decision trees based on examples from a particular language and kpml is capable of being conditionalised for particular languages
awareness aw neg tc awareness unaw intention con dont intention unc safety badp never NUM safety not dont
this gives rise to three distinctly non systemic features of these learned networks only the systems are shown in the kpml dump given in figure NUM the realisation statements choosers ii quiries and inquiry implementations are not shown
to date our domain of application has been manuals for software user interfaces but because this domain does not commonly contain preventative expressions see table NUM we have extended drafter s domain model to include coverage for do it yourself applications
the second is to use in intermediate stages of the calculation additional auxiliary symbols which do not appear in the final result
moreover if no nonterminal for which there is an e production is used more than once in the grammar then l0 l1 NUM
furthermore if factor ua with a e e is an implicit node of t such that h u but not h ua are implicit nodes of t we create node u in t if u was an implicit node and establish an a link from u to implicit node h u of t
algorithm NUM let t and t be the suffix trees for strings w and w respectively bl l gl d move link down root of t and the established a links at each iteration of algorithm NUM when constructing the suffix tree aligmnent in figure NUM to denote a links we use the same integer numbers as in figure NUM
for each implicit node of t that dominates some node in f p and that is the target of some a link from some source node of tx we record the sum of the counts of the dominated nodes in fl p
then at step NUM integer is added to e since no condition has been imposed above on string x and on suffix y x y we conclude that the final value ofe must be the positive evidence of transformation u7 v
to each type of sentence above there is a set of features e.g.
the discussion above holds for both versions of winnow studied here positivewinnow and balancedwinnow
we hope however that the procedure outlined in the present paper can serve as one of the starting points both for a comparison of the views on tfa based on dependency and on other syntactic theories and for achieving a relatively complete algorithmic analysis of tfa as that dimension of the sentence structure which permits a characterization of the sentence in its fundamental interactive nature
cle uses quasi logical form qlf as linguistic representation for the parsed nl string
NUM neighbor act he obj meet pret t yesterday time most of our symbols for indefinite preterite actor addressee objective directional should be self explanatory gener al relationship is the free modification typical for an adjectival modifier of a noun
furthermore it is not economical to enlarge the number of nodes beyond necessity adding special nodes for prepositions or articles which can accompany only their nouns or for conjunctions and auxiliary verbs which can accompany only lexical verbs and which do not accept any other arguments or modifications of their own
topic focus identification iii if the verb and all its immediate complementations in other words all elements of the center of the sentence are cb then only the nb item s embedded under the most dynamic element of the center constitutes the focus with the rest of the sentence belonging to its topic
ambiguity is denoted here in an abbreviated way so that t f means t in some readings and f in others in combination with the values of other words in the sentence and t f means obtaining f only in case there is no other f in the sentence
the verbs are accompanied by their valency frames grids which also include data on the surface shape of the individual kinds of complementations so that it is easy to reconstruct the original sentence at the end of the procedure NUM basis of our procedure is founded on dependency syntax and covers just the simple shapes of english sentences
nonterminal functions in addition a second extension is the introduction of a variety of nonterminal functions that may be attached to any nonterminal or terminal symbol3 these functions are de1the term nonterminal unctions was chosen for mnemonic purposes it is actually a misnomer since they can be apphed to terminal symbols as well
we find the extended edge is finished so we add the relph to the agenda then pop it creating a new edge np reiph np an entry subtree np which spans ij is already in the chart when the last edge is created
but j can not be parsed as a locative phrase with the leader since its domain is not ge instead it is parsed as the modifier of at which point the parser will further check whether i plus j can be parsed as a locative phrase
the sentence and the grammar we use here are oversimplified but show how a right context is handled
the difference from standard earley parsing aside from the rule transformation mentioned above lies is in match
the difference between the two phrases is that although and are both location nouns not all nps following a can be formed into locative phrase only if the head noun of the np is a location noun can it can be parsed as a locative phrase
2for this and all subsequent examples a is the chinese written form b is its pronunciation c is its word gloss means there is no directly corresponding word in engfish and d is its approximate english translation
the easyenglish system can be viewed as a grammar checker in that standard grammar checking facilities such as spell checking word count sentence length and detection of passive constructions are available in addition to the checks for ambiguity
the emphasis of a cl compliance checker is on ensuring that the input text document conforms to the restrictions imposed by the definition of the cl whereas the emphasis of a standard grammar checker is on ensuring that the text is not ungrammatical
if different system users a possible rephrasing would be by using the same application program if different objects a possible rephrasing would be different objects that use the same application program
using a full parse easyenglish is able to spot a variety of punctuation errors including but not limited to missing commas ill conjoined clauses and noun 7this sentence is actually ambiguous in many ways here we shall not address the other ambiguities
however we have found that a number of checks can be implemented successfully including but not limited to checks for lack of parallelism in coordination and in list elements passives double negatives long sentences incomplete sentences wrong pronoun case and long noun strings
furthermore the addition of a new language will ask for the addition of new equivalence relations to all the other languages with all the possible consequences
first we describe the backbone of tile system the eurotra research project and prototype
in the accusative however y would be properly estimated to belong to the set of l s due to the mutual independence of the two accusative case filler sets even though examples did not fully cover each of the ranges of l s and NUM s
consider figure NUM where each symbol denotes an example in s with symbols x belonging to x and symbols e belonging to t the curved lines delimit the semantic vicinities extents of the two e s i.e. sense NUM and sense NUM respectively NUM
move the word from its current class to another class if that movement increases the ami most among all the possible movements
intuitively preference should be given to cases displaying case fillers which are classified in semantic categories of greater independence
in order to build an operational system the following problems have to be taken into account NUM
we introduce a utility function tuf x which computes the training utility figure for an example x
the semantic similarity between two sentences is graphically portrayed by the physical distance between the two symbols representing them
the ideal bitext mapping algorithm should be fast and accurate use little memory and degrade gracefully when faced with translation irregularities like omissions and in
for instance if several identical constructs e.g. x a are allowed in a recursive structure x and the input contains a y followed by three or more consecutive xs e.g. yxxx then the reduction of the second and third xs will return to the same state after the same rule is applied at that state
patrans also has a limited strategy for lranslaling ompounds composil ionally
since the philosophies of performance improvement for these two algorithms are different one from the estimation point of view and the other from the discrimination point of view it is interesting to combine these two algorithms and investigate the effect of the robust learning procedure on the smoothed parameters
the simulation results compared with the results obtained by using the discriminative learning procedure are shown in table NUM table NUM a shows that performances with robust learning in the training set are a little worse than those with discrimination learning for the l1 syntactic language models
if y v c xl xm l y q where qa is a present threshold it is assumed that the estimated value of p xm x n l is reliable and no action is required in this situation
it was found that a very large portion of errors result from attachment problems including prepositional phrase pp attachment and modification scope for adverbial phrases adjective phrases and relative clauses while less than NUM of the errors arise because of incorrect part of speech tagging
with this tying scheme the number of parameters is reduced by a factor of NUM NUM from NUM NUM x NUM to NUM NUM x los and the accuracy rate for parse tree selection is improved up to NUM NUM when the robust learning procedure is applied on the tied parameters
by using turing s formula robust learning hybrid approach for the lex l2 syn l2 model the accuracy rate for parse tree selection is improved to NUM NUM which corresponds to a NUM NUM error reduction compared with the baseline of NUM NUM accuracy
in the icicle system the input feedback cycle begins when the student enters a portion of text into the computer
NUM these errors are exemplified by the following once the situation changes they different people
also the asl marking is generally not empty of informational content
figure NUM language complexity in slalom NUM figure NUM contains an illustration of a piece of
sic statement yes no question command
within each hierarchy the intention is to capture an ordering on the feature acquisition
essentially we will need to place a particular user in the model
he admits though that this may be due in part to teacher talk a related communication phenomenon
there are a large number of issues that must be dealt with in determining an appropriate response for a student
table NUM n best rescoring performance n NUM
unfortunately scheme is deficient in that it does not allow mutually recursive functional definitions of the kind in 2a or 2b
note that this is not a question of reduction strategy e.g. normal order versus applicative order but an issue about the syntactic scope of variables
a general way of doing this is by defining a higher order procedure memo that takes a function as an argument and returns a memoized version of it
in either case the caller continuation needs to be stored in the continuations component of the table entry so that it can receive any additional results produced by the unmemoized procedure
if these three functions definitions replace the earlier definitions given in NUM NUM and NUM the fragment in figure i defines a cps recognizer
a work around is to add a vacuous lambda abstraction and application as in 11a in effect delaying the evaluation of function definition
in this fragment there are no partially specified arguments or results such as would be involved if the fragment used feature structures so the subsumption relation is in fact equality
the prolog is used for the nl system and the supercard for the vl part and for the user interaction of the system
for example a cps function recognizing the terminal item will arguably a future auxiliary in a class of its own could be written as in NUM
NUM define s seq np vp computational linguistics volume NUM number NUM further suppose NUM precedes 2a textually in the program
a corresponding linguistically segmented test set was also made available NUM
the top ranking hypothesis was then considered as the recognized output
unfortunately it is an order of magnitude slower than the prolog version
in practice this is not a very tight limit since simple noun phrases with more than six words are quite rare
efficient construction of underspecified semantics under massive ambiguity
fig NUM for an example NUM
let us now state the method informally
pn sj inpi can be computed as st p np st j
figure NUM packed semantics construction algorithm
NUM every name used has a unique definition
heuristics NUM inserted phrases between commas or parentheses most of inserted phrases are surrounded by commas or parentheses
it is impossible to construct complete grammar rules in the real parsing system to succeed in analyzing every real sentence
in this paper we have presented the robust parser with the extended least errors recognition algorithm as the recovery mechanism
our short term goal is to propose an automatic method that can learn parameter values of heuristics by analyzing the corpus
all refrigerators whether they are defrosted manually or not need to be cleaned
the algorithm works as follows a procedure scan is carried out for each state in s i
in figure NUM np denotes the final state whose rule has np as its lhs
so we assume some phrases for example noun phrases as fiducial nonterminals which means error free nonterminals
multiple linear regression produces a set of coefficients weights describing the relative contribution of each predictor factor in accounting for the variance in a predicted factor
although our work focuses on the design construction and evaluation of explanation planners by constructing a full scale natural language generator it becomes possible to conduct a pure empirical evaluation of explanation planners
for example the subdialogue about the attribute arrival city sa consists of utterances a6 and u6 its cost cl sa is NUM
dsp s analysis strictly speaking generates all six readings however they appeal to anaphor antecedent linking relationships to eliminate the jjjb reading
subsequently it constructs the three word elements of p s containing one of
in this step champollion examines all members of the set p of possible translations
using the dice coefficient NUM were correctly translated and NUM were incorrectly translated
champollion assumes that the target collocation is a combination of some subset of these words
most sentences neatly corresponded to translations in the paired corpus with few extraneous sentences
the ability to automatically acquire collocation translations is thus a definite advantage for sublanguage translation
his current address is netpatrol consulting tel maneh NUM haifa NUM israel
for example the english collocation to demonstrate support is translated as prouver son adhdsion
each new sentence in the corpus provides a new independent sample for every variable xg
this process is repeated until no more highly correlated combinations of words can be found
on the other hand it is important to extract trading insider to be able to match documents containing phrases insider trading sanctions act or insider trading activity
the inverse is also true finding a concept where it really is n t makes an irrelevant document more likely to be highly ranked than with single word based representation
for example joint venture is an important term in the wall street journal wsj henceforth database while neither joint nor venture is important by itself
in trec NUM where this weighing scheme was fully deployed for the first time it proved very useful for sharpening the focus of long frequently convoluted queries
this indicates a higher than average concentration of relevant documents in the first NUM NUM documents retrieved which can leverage further gains in performance via an automatic feedback process
certain highly ambiguous usually single word terms may be dropped provided that they also occur as elements in some compound terms
subsequently certain types of phrases are extracted from the parse trees and used as compound indexing terms in addition to single word terms
unfortunately thesaurus based query expansion is usually quite uneffective unless the subject domain is sufficiently narrow and the thesaurus sufficiently domain specific
massive query expansion has been implemented as an automatic feedback mode using known relevance judgements for these topics with respect trec NUM database
for example the term natural language may be considered to subsume a term denoting a specific human language e.g. english
we handle these dialogue acts by means of repair in order to make the planning process more efficient since these dialogue acts can occur at any point in the dialogue the plan recognizer in the worst case has to test for every new utterance whether it is one of the dialogue acts which indicates a deviation
one is the dimensionality of the vectors
this process is repeated until no region combining occurs
this intensity depends upon three factors
this will draw the region outline on the map
initially each node is given its own region
in pseudo code for each node vector it j
the regions are outlines drawn around sets of nodes
the remainder of this paper is organized as follows
these are stored in a document context vector database
put this file heres and this7 one theret
multimodal deictic referring expressions combine referring linguistic expressions with simulated pointing gestures
edward s context model determined the right referent in all NUM referring expressions
alice mailed thisse mail to wim thiste mail to koen and thiste mail to carla
however the spatial description the file below the directory is unambiguous
they are the role fillers of the relation expressed by the main clause
NUM NUM NUM NUM NUM the article is about his wife
profit is an extension of standard prolog with features inheritance and templates
the matcher selects the example with the smallest distance to the input and assembles the target language portions of the selected example pairs to form a complete translation in the target language
when applied to spoken language the central step in analogical translation is a robust matching step that compares the output of the speech recognition component with the contents of the example database
in an analogical system it is possible to incrementally improve the translation quality by adding more examples to the example database and by effecting corresponding improvements in the matching function by e.g.
the prior probability distribution over the exampie database p examples is used to penalize highly specialized example pairs that should be used less often
the second step of source language analysis is carried out using an augmented context free grammar for the nlyacc parser ishii ohta and saito
in practice defining maintaining and extending such a formalism for multiple not closely related languages has proved to be a major challenge
iw rcb the result of this is the optimal alignment between the input and the example as well as the minimum distance between them
sentence NUM is the antecedent clause for example NUM one of the more problematic examples in the literature
the hnc matchplus system was developed as part of the arpa sponsored tipster text program
at the same time since the example databa e must be adapted for every new domain it is important to minimize the amount of manual effort
this asymmetry is motivated by a tradeoff between model complexity and search efficiency
sorted feature terms have several advantages over prolog terms as a representation langauge
training texts test texts and held out texts are all sequences of word tag pairs
in the training phase a set of events are extracted from the training texts
such a transducer marks in a left to right fashion the maximal instances of a by adding the bracketing strings t1 and t2
in particular segments chunks are defined by constraints rather than patterns in order to ensure broader coverage
however at each step linguistic contraints may eliminate or correct some of the previously added information
we expand vcs to include segments and to consider them as arguments or adjuncts of the vc head
a vc verb chunk is a sequence containing at least one verb the head
although the vocabulary used in this experiment is slightly different from the other comprehensive
b repeat merging until all the elements in v1 are put in a single class
then we will extend the paradigm of clustering from word based clustering to compound based clustering
in the initial stage each word is assigned to its own distinct class
furthermore clustering is much more useful if the clusters are of variable granularity
to that extent the obtained hierarchical clusters are considered to be portable across domains
so initially the c NUM most frequent words are in the merging region
we think here in particular of the questions whether there is a significant stretch of path between two elements of environment landmarks or a turn and a landmark mentioned in the text immediately one after the other
for example since every route segment contains one relay which may be a turn and one transfer the information concerning the fragment of the route expressed by tournez k gauche et puis droite turn to the left and then to the right must be completed by adding a transfer between the two turns
the same question holds for the right turn puis tu tournes droite and the sign posts panneaux d informations should the posts be represented as immediately following the turning point as expressed in the text or should there be a path between them
then we will describe our general model for an automatic translator and some aspects of the underlying knowledge representation
in everyday communication situations verbal rds are often accompanied by sketches thus participating in a 2mode representation
this kind of ambiguity is not really perceived unless we want to derive a graphic representation of the route
during the understanding process the linguistic meaning has to be represented before the conceptual representation can be created
we have thus specified several categories of route description sequences the main ones being action prescriptions e.g.
various linguistic and statistical methods to handle this problem are coming to light
another inkoortant question is how to position a term in an existing thesaurus
these new terms are used in the various representation models described below
it s a difficult exercise because of the wide variety of fields
for each sort one must declare which features are introduced by it
thus applications downstream from the indexing depend on these terminological resources
puis quand ou then when or which mark the relationships between sequences or groups of sequences have been categorized according to their functions e.g.
tu continues tout droit en longeant les terrains de tennis et tu tombes sur le bdtiment a NUM in the description here above we can observe some ambiguities or incompleteness of information which may be a problem for a graphic depiction
the exact value of n is calculated separately for each distinct word using the following formula
a system with bad results from speech recognition makes it impossible to satisfy many of the commandments how could a user judge a system as flexible consistent adaptive simple etc if s he is often misunderstood by the system
as a consequence the branching factor the number of available keywords at a given point is always significantly less than the total number of key words which further decreases the chance of speech recognition errors NUM nevertheless sr errors can not be precluded
this makes it possible to partition the language by means of a number of sub grammars roughly speaking there is a default set of always active keywords and each mode is associated with its own grammar thus one could speak of a hifisubgrammar a navigation subgrammar etc
more concretely the project aims at developing a vocal interface to an existing driver information system namely the berlin rcm303a of robert bosch gmbh which integrates a tuner an amplifier a cd changer a cassette player a navigation computer and a gsm telephone
the system can use any odbc open database connectivity compliant database and form based boolean queries from the client module similar to those seen in any web search engine are translated into standard sql queries automatically
table NUM smaltimento in different dictionaries
each section thus includes NUM NUM terms
from time to time however the construction of different types of structures is interleaved
NUM descriptions of word objects include their categorial information and sense
NUM a bound character can not occur independently as a word
he bend down body he bends down his body
therefore when this fragment appears in sentence 8a
the relations the system builds represent its interpretation of the sentence
NUM NUM grammatical descriptions of terms in italian
a linguistic structure is not built by a single codelet
there is no global controller deciding which processes to run next
an obvious area for improvement is the time of day handling
bss exact conditional removes only a few interactions while fss exact conditional adds many interactions and in both cases the resulting models have poor accuracy
any two nodes that are not directly connected by an edge are conditionally independent given the values of the nodes on the path that connects them
si will be observed in a training sample where each observation is represented by the feature variables f1 f f3 s
as an overview of the database content the client module lets the user browse the top NUM and NUM most frequent entity person and location names and s t terms in the database cf figure NUM
the search stops when either NUM every hypothesized model results in an unacceptably small increase in fit or NUM the current model is saturated
these are the parameters of the fully saturated model the model in which the value of each variable directly affects the values of all the other variables
NUM the three binary collocation specific features c1 c2 ca indicate ifa particular word occurs in a sentence with an ambiguous word
the difference between bss and fss is clearly il4the percentage of ambiguous words in a held out test sample that are disambiguated correctly or not
aic and bic eliminate interactions that have high dof s and thus have large numbers of parameters much earlier in bss than the significance tests
NUM the pos features have one of NUM possible pos tags derived from the first letter of the tags in the acl dci wsj corpus
NUM NUM a vlsnal lliap of lcb oiicop lcb s luld
in addition to its basic capability of allowing a user to send boolean queries in english against english and japanese documents and to view the results in semi and fully translated forms the system has many innovative capabilities
ul elligcnce a r icicltc NUM ni lcb s
whether the intended reading is also the preferred one depends on selectional restrictions preference criteria and morphological features
according to this peter and the speaker writer i for the distinguished i i lcb f for the self share the attitud of having a plan for realizing
in NUM the parentheses mark the argument of crst the brackets an notated by f the focus elen ent fi om which the semantic focus constituent is deveh pe l
we retain the following criterion temporal location criterion the r reading is acceptable only if the focus constituent of the scope of erst does not contribute a temporal localization by modification of a basic event type
in the following representation of l wc consider the case where the numeral ix focused only not the np containing the numeral or the entire event description in the scope of crm
this can not truly he called a sequence
null tim cliques i ring ill sitia ll
the alternative contexts a e determine the meaning of the first sentence of NUM according to this paper describes research done within the sonderforschunsbereich NUM NUM at ims
tile order of the oppertunities is inherited from the order of the ps o which conforms to the intrinsic order of the set of mternatives of the focused element i.e.
what about temporal locations in the background part
compare the following examl les to this end
implication rules that are of the form
the tagger has two rule components
part of speech tags are provided for each word
x covers y or x has a cover relation to y denoted x y if for any substring xs of x there exists substring ys of y such that ixsl iysl and g xs g ys
in section NUM we define the word string cover relation and prove it to be a partial order define critical tokenization as the set of minimal elements of the tokenization partially ordered set and illustrate the relationship between critical tokeniz ition and string tokenization
among them there are lcb abe d rcb lcb ab c d a bc d rcb lcb ab cd rcb lcb ab e d a b ed rcb lcb a bed rcb lcb a be d a b ed rcb and lcb ab c d a be d a b ed rcb lcb a b c d rcb
moreover the tokenization funds and has critical ambiguity in tokenization since there exists another possible tokenization fund sand such that both funds and fund sand and fund sand funds and do not hold
given a typical english dictionary and the character string s fundsand the position after the middle character s is ambiguous in tokenization or is an ambiguous token boundary since it is a token boundary in tokenization funds and but not in another tokenization fund sand
the information is displayed on a map or appears in tabular form as is appropriate
a formal experiment with valad conducted by major t bowman at the command and general staff college at ft leavenworth concluded that users could perform routine tasks in NUM NUM the time using speech compared to using the keyboard and mouse
by enabling users to specify information in the database in natural english valad supports applications such as military planning processes and enhances decision support environments
bbn has developed a system called valad voice activated logistics anchor desk which provides a spoken language interface to a logistics information system
the longest match constraint is the identity relation on a certain set of strings
the transducer is displayed in figure NUM
secondly there may be alternative replacements with the same starting point
we define directed replacement by means of a composition of regular relations
the percent sign y is used as an escape character
this property turns out to be important for a number of applications
we label the major components in accordance with the outline in figure NUM
figure NUM a positive filter figure NUM application of an np vp parser
this section presents our algorithm for temporal reference resolution
we introduce an original data structure and efficient algorithms that learn some families of transformations that are relevant for part of speech tagging and phonological rule systems
portability to new languages involving the acquisition of both monolingual and cross linguistic information should also be as straightforward as possible
going back an additional time covers the remaining cases
the basic data object is the document
figure NUM document annotations as a centralized
annotations are attributed objects that contain application objects
the naming service binds a name to an object
it presents a software architecture which is geared to support the development of a variety of large scale nlp applications information retrieval corpus processing multilingual mt and integration of speech components
named collections and documents are persistent
NUM NUM porting of the temple machine translation system
client server communication is supported by the java door orb
a variety of solutions are possible as illustrated below
table NUM the test material the four grammars and
the 2lk automaton associated with g can now be introduced
we are now ready to give a formal specification of the tabular algorithm
this set contains a single parse tree consisting of a single node labeled a
the elements that are used in the filtering process are counted individually
the parser is derived from the 2lr automata introduced in the previous section
computation of the entries of u is moderated by a filtering process
one element in qlr or q2lr from one or two other elements
therefore it would be very surprising if such errors did not appear in the test sample taken from a different source
some general suggestions can be drawn in order to individuate a trade of between the effort necessary for describing selectional restrictions and the lexical disambiguation obtained
errors are reported at the word level and at the sentence level word leveh insertions ins
o he hecho la reserva de una habitacidn con televisidn y t el fono a hombre del sefior morales
to this purpose we introduce the auxiliary quantity q i j e probability of the best
in this section we introduce a monotoue hmm based alignment and an associated dp based search algorithm for translation
a multli level approach that allows a small say NUM number of large forward and backward tran null sitions
in the second example the personal pronoun me was placed at the end of the source sentence
each of which consists of two steps posilion alignm th given the model paramet ers
deletions del and total lmmber of word errors ver
transformation step original corpora categorization por2favor word splitting translation errors
therefore the probability of alignment aj for position j should have a dependence on the previous alignment position o j l
under this view the challenge is how to exploit context in performing a task rather than how to map natural language phrases to expressions of a formalism for coding meaning independently of context or intended use
for simplicity we consider a feature structure with instantiated values to be an atomic object of length one which can be a label of a transition in a fsa NUM hence the above lexical forms become abcd kfl efbf
the heuristics assign the error values to each error hypothesis edge and edges which has less error values axe processed first
but this algorithm can handle only the errors of terminal symbols because it does n t consider the errors of nonterminal nodes
by taking this viewpoint we seem to be ignoring the intuition that most interesting natural language processing tasks translation summarization interfaces are semantic in nature
for the second measure meaning preservation grammatical errors were allowed if they did not interfere with meaning in the sense of misleading the hearer
in this method our search process translates a source sentence s to ts in the target language and then translates t back to a source language sentence
probabilistic model in this model we assume a probability distribution on the possible events for a context that is e p elc NUM
however this would result in an exponential search specifically a search tree with a branching factor of the order of the number of matching entries per input word
lexical parameters can control the saturation of a lexical item for example a verb that is both transitive and intransitive by starting the same automaton in different states
note that our algorithm is for analysis in the sense of finding the best derivation which in general is a higher time complexity problem than recognition
unlike the head automata monolingual models the transfer model operates with unordered dependency trees that is it treats the dependents of a word as an unordered bag
the speaker playing the travel agent has information about hotels transportation etc on which to base answers to the traveller s questions
one type of coherent knowledge packet is a view
three steps can be taken to promote better evaluation
the final version of the explain process explanation design
it consists of megasporogenesis and embryo sac generation
moreover knight currently employs very rudimentary pronominalization techniques
knight scored within half a grade of the biologists
knight s edps are much more schema like than plan like
research on knight suggests several directions for future work
NUM formation of two panels of domain experts
we did the same for the explanations of processes
subdomains should be chosen to be semantically distinct so that sentences may be easily classified into sub domains by both humans and machine
then the user can choose the word in the list he or she wanted to enter or continue typing if it is not in the list
while most of these methods can be modified to be used in other languages with similar structures they may not be directly adapted to inflected languages
when the number of different forms of a word is small it is possible to include all of them in the dictionary used in word prediction
we are grateful to tomoyuki fujita of nec for his constant encouragement
the scheduling domain grammars which have been under development for three years achieve about NUM acceptable translations on unseen transcribed input
as we have seen in the previous section it is very difficult to predict complete words in inflected languages because of the variations a word may have
the same thing happens with the semantic approach which has as it has been said before the same procedural characteristics as the second syntactic one
these methods are not very used because their results are similar to those of the syntactic approaches but the increase in complex ty is great
on a test set of six unseen dialogues we achieve about NUM acceptable translation of transcribed sdus in the travel domain
however they may also occur after the acknowledgement of the utterance to which they react and at the end of the complete presentation and acceptance of the travel plan
therefore the only parameters we have to re estimate in the language model are the word frequencies
we filter features that are irrelevant for the category based on the weights they were assigned in the first few training rounds
the search is for a linear separator that best separates documents that are relevant to the category from those that are not
the function of the ontology is to supply world knowledge to lexical syntactic and semantic processes ibid
the latter pattern presents the noun bound to varl in the subject position and the adjective in the predicative position
it is pleasant and challengeable for a linguist to think that each pragmatic relation is worth discovering and activating in lexical acquisition
many deverbal adjectives in the corpus derive their lexical entries from verbs which are not morphologically related to them NUM
the review is conducted from the perspective of highly desirable full automation vs the reality and unavoidability of exceptions requiring manual treatment
in the former there are two subcategorization patterns marked NUM and NUM listed in syn struc of NUM
each triple specifies how often word occurred in lc and the likelihood ratio of lc and word
let lcw denote the set of local contexts of all occurrences of w in the input text
whether such an anchor is a property object or process concept defines the adjective as truly scalar relative denominal or deverbal respectively
merical values like these correspond to the feature of gradability which extends beyond scalarity all scalar adjectives are gradable but not all gradable adjectives are true scalars
we defined a statistical word model to assign a reasonable word probability to an arbitrary substring in the input sentence
the study described in section NUM NUM was used to determined the version that performs best out of those we have experimented with
the figures are presented in table NUM we expect to be able to present a more detailed discussion of their ignificance by the time of the workshop
the task of a training algorithm for a linear text classifier is to find a weight vector which best classifies new text documents
the judge has to decide whether the contents of each field in the form are compatible between the two versions
note that unlike the evidential approach the probability of the pair d and a coreferring does not come into play given that coreference between d and b and between b and a has been factored in
on the other hand the entry for NUM in the pp to iiif rs frame for appear represents a spurious entry appear does not occur in the NUM meaning of become visible with a to prepositional phrase and a subject controlled infinitive
the results part of which is shown in table NUM indicate that some senses are much more strongly connected with the other words in the group and so probably predominate in the corpus that was used to induce the group
for nlg purposes we will have to investigate how the communicative goals to be realized within one utterance are ranked how the speech act of an utterance is determined how the abstraction step alters the content to be expressed and how different kinds of aggregation rules are realized linguistically
on recognizing features of the local syntactic context of a verb occurrence the look up method uses the local syntactic context to identify the likely subcategorization pattern while the automatic classification method uses the local syntactic context to extract marker words
alternations include syntactic transformations such as there insertion e.g. a ship appeared on the horizon there appeared a ship on the horizon and locative inversion e.g. on the horizon there appeared a ship
part b of figure NUM lists no alternations that are applicable to this subcategorization frame while part c shows only two wordnet synsets where appear takes an adjectival complement senses NUM and NUM
while in the general case it is possible to have multiple links between word forms corresponding to different sense pairings typically each si will contain only one sense and their union will contain a few elements
the automatic classification method could be extended to classify sense sets using as its input corpus the output of the syntactic constraints look up method where verb tokens have been tagged with a subset of the full collection of senses
mapping the more precise syntactic information in comlex to the verb frames of wordnet allows the construction of a more detailed syntactic entry for each word sense and enables the association of alternation constraints with the senses in wordnet
to make ageru and give interchangeable a rule such as ageru e give e is suffice where e is an event
since we can not model coreference involving objects that have resulted from previous hypothetical merges the appropriate feature values for distance and form of referring expression would become unclear we make the following approximation
however manually segmented corpora are not always available in a particular target domain and manual segmentation is very expensive
when NUM NUM some conflict between the distributions exists dempster s rule has the effect of focusing on the agreement between the distributions by eliminating the conflicting portions and normalizing what remains
the first component in eq NUM becomes a u o vtr NUM the expression allows to appear in the left and right contexts of v however at the left of v the expression tr to rc puts the restriction that the first tuple at the left end must be in a not in c
category and infection stem from ingn realization is a combination of the values in paint and ingn and semantics is the minimal sign s conceptual structure expanded into a complex argument indexed as NUM instead of stor ing two entries for paintv and paintingn that partly contain the same information we derive the entries dynamically from a single paint entry
the realizational part is the string t aint whereas the semantic part denotes a situation with two arguments indexed as NUM and NUM the aspectual value punctuao describes the situation as durative whereas the selectional restriction dim states that argument NUM is to serve as some two dimensional surface
paintn paint sub i paint subj obj rcb paint t subj j obj j xcomp paint sub j obl rcb
except tbr l hc control and duration requirement l he conc ep tua NUM structure must also contain a criterially anchored argument i.e. mt argument that includes at least one semantic role that is not noneritcrial
the work in the troll project is now concentrated on the construction of a complete lexicon for norwegian and this work is also to serve as an evaluation of both the lexicon structures and the sign model
the fi ames combine with lexical expansion rules to create dynamic entries of actual words with morphological and syntactic properties as illustrated by the lfg representations in assumed since the theory is intended to fit into any syntactic theory
it posits an abstract level of sign representation that is not associated with any word classes and establishes a framework within which word relationships as well as relationships between different kinds of linguistic properties can be described in a systematic way
the stored lexical entries are sign frames rather than actual words and a whole system of expansion rules and consistency rules are used to generate dynamic entries of words that contain all the necessary semantic syntactic and morphological information
at each iteration the algorithm approximates the gain in the model s predictiveness that would result from imposing the constraints corresponding to each of the existing inactive features and selects the one with the highest anticipated payoff
these modules are language specific and external to the core generation system
this paper describes our work in progress in automatic english to korean text translation
letter end 0f token inserts a special mark e.g. a newtine at the end of a letter sequence
for the solution sketched al ove we have evmuated the
table NUM training data data for system develop
in the first training set the NUM coreference sets gave rise to characteristics of context for NUM pairs of templates in the second the NUM coreference sets gave rise to characteristics for NUM pairs of templates
if two word segmentation hypotheses divers in the number of words the one with fewer words is almost always selected
accordingly the probabilitiy for generating the first word of a name class is factored into two parts
an example taken from an ibm manual different system users may operate on different objects using the same application program
more generally how does performance vary as the training set size is increased or decreased
as little as NUM NUM words of training data produces performance nearly comparable to handcrafted systems
also name finding can be directly employed for link analysis and other information retrieval problems
f f12 NUM rp NUM NUM 2r p
within each of the name class states we use a statistical bigram language model with the usual one word per state emission
with any learning technique one of the important questions is how much training data is required to get acceptable performance
the first eight features arise from the need to distinguish and annotate monetary amounts percentages times and dates
a positive value was learned for the feature modeling cases in which templates s and t had at least two identical non nil slot values as well as for the feature modeling an exact match of complex name values
it is certainly the case that preprocessing a document with the same parser that is used for source analysis improves the mt results
however it is not necessary for the parse to reflect this since this can be reflected in the easyenglish rule instead
we describe the authoring tool easyenglish which is part of ibm s internal sgml editing environment information development workbench
easyenglish helps writers produce clearer and simpler english by pointing out ambiguity and complexity as well as performing some standard grammar checking
summarising where are we should we go
finally to simulate link inheritance in derivations of gs each asynchronous production in v and v is transferred to v either without any change or by composing with some nonterminal c both its left hand side and the heir nonterminal in its right hand side
future work will include a more extensive reliability study one that includes the intentional and informational relations
the synchronization is non local in the sense that once links are introduced during a derivation by a synchronized pair of grammar rules they need not continue to impinge on the nodes that introduced them the links may be reassigned to a newly introduced nonterminal when an original node is rewritten
the question is whether the choice between the same and an alternate cue correlates with the embeddedness of the two relations
our ultimate goal is to provide a text generation component that can be used in a variety of application systems
the constituent that expresses the purpose in this case b is the core of the segment
in factor based retrieval the occurrence of a combination of descriptive factor values is the criteria for retrieving the accompanying cues
there are several kinds of judgements made in an rda analysis and all of them are possible sources of disagreement
the contributor in c NUM provides a reason for this susceptibility i.e. that part2 is moved frequently
that is two relations one embedded in the other should be signaled by different cues
based on these results our text generation algorithm will use embeddedness as a factor in cue selection
potentially wrong modification okay if i is a baboon who grew up mild in the jungle
using specialised text corpus to automatically enhance a general lexicon is the aim of this study
it is not possible to simply make a morphological module which allows us to process proper names
then we labeled again the same corpus this time using the devin
a specific module dedicated to composite words is currently being developed
table NUM details for the common words lexicon the results obtained on the correct words
using this idea we trained a statistical model with all the words from our dictionary
automatic lexicon enhancement by means of corpus tagging
then we keep for each word only the most frequent label
in the first stage we labeled this corpus without using the devin
this allows us to specify the pragmatic constraints associated with the tree type once and for all regardless of which verb selects it
NUM NUM examples of dependency relation head transducers
a lower window size increases the resolving power but decreases the accuracy of the algorithm
the process is k then iterated a fixed number of times
a newspaper is an easy test for such an algorithm though
the formula now takes the average of the biased ratios
figure NUM calculation of number of nearest neigh bours
koen lives in amsterdam the open ended time interval of the first live in relation is closed ending at the current machine time at the time of interpretation of the second relation
for example such constructions are used with place names such as frankfurt germany or smith barney harris upham co
but we prefer noun phrase NUM s noun phrase NUM since it is more appropriate for further processing steps
we did no t independently measure the performance of their tool using this modified rule set but may do so in the future
a weight of NUM NUM is assigned to the person feature for uncle whereas only NUM NUM is assigned to this feature in end
disjunctions are indicated using a vertical bar i and optional elements are surrounded by brackets
on the first it builds coreference chains containin g alternate forms of corporate and person names as identified by the name extraction module
second it ranks the relative salience of an anaphor s candidate antecedents in a partial order rather than a total order
as is the case with appositives indefinites are filtered from the final output but are marked and used in later processing
as yet no extensive performance tests have been made but both recall and precision on labele d edges is over NUM
this model was built quickly using a general maximum entropy modeling tool which will be discussed in a forthcoming paper NUM
in other words over NUM of the original parses are produced
for the first run the full dictionary found in dictl0 is used
the issue of disambiguation is not explicitly dealt with in this experiment
typically new verbs in a language are not coined as irregulax verbs
by using additional information a morphological recognizer could circumvent this problem
figure NUM shows that the insertion rate in the baseline data is enormous
it is implemented in common lisp
table NUM deletion and insertion data module used in this work
the current version of the tool supports viewing annotating and analyzing documents in NUM bit NUM bit and NUM byte character sets
each synset in the monolingual wordnets will have at least one equivalence relanon with a record in this ili
in order to bc able to ewfluate the quality of tim abstracts t rodueed by our system we conducted an experiment where we asked NUM human subjects to choose the most relevant NUM NUM sentences from the six articles dom the test set
we need to refine the algorithm to handle cases that are currently problematic
tiffs paper can i e el rained kern t im author s heine NUM age whose url is ht tp www h l cmu e lu zechner klaus htnfl
the traditional ir approach is that the user inputs a boolean query possibly in a natural language like formulation and the system responds by presenting to the user the texts that are a best match to his query
e.g. the anes system described i y tries to i lenl iily l hese texts beforehand to NUM e ex luded fl om abstracl ing
the basic system consists of a pipeline of unix process
NUM applying mark up to potential components of complete names
this data can be easily generated from the training articles
the text is then processed in groups of five words
some further experiments are also planned with the autolearn system
in addition we plan to improve our collection of patterns
background crl submitted two systems for the named entity task
crl nmsu description of the crl nmsu systems used for muc NUM
other forms of descriptions such as relative clauses are the focus of ongoing implementation
no specific code was inserted to handle numbers or dates
the current version of the system is being made generally available
we believe that such an approach to information extraction can be classified as a collaborative database
table NUM shows the precision recall values for the tf idf method described in section and for a default method that selects just the first n senfences fi om the beginning of each artme lead method
the hearer or reader not only infers that the cb has not changed even though no pronoun has been used but also recognizes that the description holds of the old cb
in this case the noun phrase her husband contributes two individuals the husband and the lover to cf 32a and cf 33a
in the first clause of both utterances 13d and 14d the direct object is pronominalized the pronoun it refers to the green plastic tugboat
each leads to a different type of inference load on the hearer both of which we believe relate to rule NUM however neither constitutes a violation of rule NUM
we conjecture that the form of expression in a discourse substantially affects the resource demands made upon a hearer in discourse processing and through this influences the perceived coherence of the discourse
NUM association for computational linguistics computational linguistics volume NUM number NUM the original motivations for centering the basic definitions underlying the centering framework and the original theoretical claims
it is not merely that utterances themselves contain only partial information but that it may only be subsequent to an utterance that sufficient information is available for computing a unique interpretation
in utterance 20c john continues as the cb but in utterance 20d he is only retained mike has become the most highly ranked element of the cf
rule NUM if any element of cf un is realized by a pronoun in unq NUM then the cb un l must be realized by a pronoun also
webber NUM as well as cases in which the center is functionally dependent on or otherwise implicitly focused by an element of the cf of the previous utterance cf
the consolidated guidelines were then tested as a tool for the diagnostic evaluation of a corpus of NUM dialogues collected during a scenario based controlled user test of the implemented system
the fact that we had the scenarios meant that problems of dialogue interaction could be objectively detected through comparison between expected according to the scenario and actual user system exchanges
computational linguistics volume NUM number NUM the resulting best path in general consists of a number of maximal projections
this could be formalized by a scoring function in which insertion into analysis result is cheaper than deletion
the predicates for the maintenance of the goal table are defined in NUM
the memorized version of the parse predicate can be defined as in NUM
moreover and more importantly the head corner relation will typically become much less predictive
this problem is sometimes called hidden left recursion in the context of left corner parsers
the second more important objection is that empty categories are hypothesized essentially everywhere
in the preceding paragraphs we have said nothing about empty productions epsilon rules
the top category start symbol of the ovis grammar is defined as the category max gem
the robustness procedure consists of a best first search from the beginning of the graph to the end of the graph
he considers neither the extraction of a specialized grammar for supporting controlled language generation nor strong integration with the normal generator
the value of the feature liszt is actually treated like a set i.e. the relative order of the elements is immaterial
the instantiated phrasal templates are then combined by the tactical component to produce larger units if possible see below
in this way the user can control which sort of structural ambiguities should be avoided because they are known to cause misunderstandings
i would like to thank the hpsg people from csli stanford for their kind support and for providing the hpsg based english grammar
however it will not be able to generate a sentence like a man gives a book to kim since the retrieval
NUM the tdl grammar formalism is very powerful supporting distributed disjunction full negation as well as full boolean type logic
we already have implemented a similar ebl method for parsing which supports on line learning as well as statistical based management of extracted data
instead of providing immediate feedback on each piece of information provided by the user the system waits until the user has provided the information necessary for executing a query to its database
the chosen input mrs element is then used for performing lexical lookup where lexical elements are indexed by their relation name
training phase the training module tm starts right after the resulting feature structure fs for the input mrs mrs has been computed
line NUM is an example for insufficient information
for reasons we will explain soon traversal is directed by type
a transition labeled stands if the input symbol say a can be processed at the initial state of t7 one does n t know yet whether a will be the beginning of a word that can be transformed e.g.
analysis of discourse structure is needed in order to identify long distance relationships
for instance the rule a b previor2tag c which changes a into b if the previous tag or the one before is c must be applied twice on c a a resulting in the output c b b
per name or per alias instead of organization org name or org alias
the first reason for inefficiency is the fact that an individual rule is compared at each token of the input regardless of the fact that some of the current tokens may have been previously examined when matching the same rule at a previous position
the dictionary lookup and the tagging of unknown words take roughly the same amount of time but since the second procedure only applies on unknown words around NUM in our experiments the percentage of time it takes is much smaller
NUM this construction is similar to the transduction built within the proof of eilenberg s cross section local extension of f denoted rmlocext f is the composition of a right minimal y decomposition mdy with ida
soundness and completeness are a consequence of the main proposition which states that if a transducer t represents a subsequential function f then the algorithm determinizetransducer described in the previous section applied on t computes a subsequential transducer representing the same function
for instance a state lcb ql wl q2 w2 rcb means that this state corresponds to a path that leads to q and q2 in the original transducer and that the emission of wl resp
in the loop do lcb rcb while q n one builds the transitions of each state one after the other if the transition points to a state not already built a new state is added thus incrementing n
possible branch of a goal g that it knows
in order to characterize the test sets somewhat further table NUM lists the word and sentence accuracy both of the best path through the word graph using acoustic scores only the best possible path through the word graph and a combination of the acoustic score and a bigram language model
besides s p and a other factors can be taken into account as well such as the semantic score which is obtained by comparing the updates corresponding to maximal projections with the meaning of the question generated by the system prior to the user utterance
in figure NUM this subset consists of million due convertible subordinated etc
few attempts have been made to explore non parallel corpora of monolingual texts in the same domain
all correlation measures use the above likelihood scores in different formulations
meanwhile we have found that the correct translation is often among the top NUM candidates
meanwhile precisions for translating less polysemous content words are higher
we use online dictionaries to provide the it seed word lists
the dimensionality of worm vectors we have chosen is not optimal
figure NUM evaluation results of worm in NUM NUM wall street journal
for example we obtained NUM NUM entries from the japanese english online dictionary edict using these criteria
therefore it favors word pairs which share the most number of closely related seed words
the optimal tree can then be found with a standard dynamic programming chart parser for weighted cfgs
how to induce a lexicon and a phonology like NUM for a particular language
this paper introduces primitive optimality theory otp a linguistically motivated formalization of ot
the constraints also score other criteria such as how easy the material is to pronounce
to represent imp otp uses not the autosegmentai representation in 4a igoldsmith
perhaps the filtering operation of any ga constraint can be simulated with a system of finite state constraints
the previous section gave a useful trick for speeding up ellison s algorithm in the typical case
NUM gives an expression for all strings that correctly describe the single tier shown
the remaining constraints are satisfied only as well as they can be given this set of survivors
the ith singular value can be interpreted as indicating the strength of the ith principal component of c to and do are orthonormal matrices that approximate the rows and columns of c respectively
semantic understanding is necessary to distinguish between the states described by phrases of the form to be adjective and the processes described by phrases of the form to be past participle
the left context neighborhood of onto reflects the fact that prepositional phrases are used in the same position as adverbs like away and together thus making their left context similar
present participles and gerunds are difficult because they exhibit both verbal and nominal properties and occur in a wide variety of different contexts whereas other parts of speech have a few typical and frequent contexts
therefore we will measure the similarity between two words with respect to their syntactic behavior to say their left side by the degree to which they share the same neighbors on the left
however since the number of tags with better and worse performance is about the same NUM and NUM one can not conclude with certainty that generalized context vectors induce tags of higher quality
for example seemed and would have similar left contexts and they characterize the right contexts of he and the firefighter as potentially containing an inflected verb form
subj i sg ni NUM subj NUM sg u s2 subj NUM sg a NUM subj i pl tu NUM subj NUM pl m NUM subj NUM pl wa NUM
however when either the node or the path is omitted from a global inheritance descriptor rather than using the node or path of the left hand side of the statement that contains it the local context of the definition the values of a global context are used instead
in practice however we need not be too concerned about the distinction descriptions are written as definitional statements queries are read off as extensional statementsj NUM the declarative interpretation of global inheritance suggests an alternative procedural characterization to the one already discussed which we outline here
second defined here means defined by a definitional statement that is a statement local inheritance operates entirely with definitional statements implicitly introducing new ones for nodel path1 on the basis of those defined for node2 path2
obj i sg ni s4 obj NUM sg ku NUM obj NUM sg m s4 obj NUM pl tu NUM obj NUM pl wa NUM obj NUM pl wa NUM
present tense sing one love present tense sing two love present tense sing three love s present tense plur love present participle love ing past tense sing one love ed past tense sing two love ed past tense sing three love ed past tense plur love ed past participle love ed passive participle love ed
all of these are freely available on request as is an extensive archive of over one hundred example fragments some of which illustrate formal techniques and others of which are applications of datr to the lexical phonology morphology syntax or semantics of a wide variety of different languages
determining whether b and d actually correspond is a question of historical reconstruction not of alignment
a skip is what happens when it consumes a segment from one word while leaving the other word alone
on closely similar languages such as spanish italian or german danish the aligner would have performed much better
in all of these cases eliminating the inflectional endings would have resulted in correct or nearly correct alignments
excessively narrow phonetic transcriptions do not help they introduce too many subtle mismatches that should have been ignored
here as elsewhere hyphens in either string correspond to skipped segments in the other NUM the aligner is not allowed to perform in succession a skip on one string and then a skip on the other because the result would be equivalent to a match of possibly dissimilar segments
the alignment algorithm is simply a depth first search of this tree beginning at the top of figure NUM
fortunately the aligner can greatly narrow the search by putting the evaluation metric to use as it works
a match is what happens when the aligner consumes a segment from each of the two words in a single step thereby aligning the two segments with each other whether or not they are phonologically similar
that is at each position in the pair of input strings the aligner tries first a match then a skip on the first word then a skip on the second and computes all the consequences of each
to contrast the engcg morphological description with the well known brown corpus tags engcg is more distinctive in that a part of speech distinction is spelled out in the description of i determiner pronoun ii prepositionconjunction iii determiner adverb pronoun and iv subjunctive imperative infinitive prese tense homographs
this work is supported by national science council taiwan under contracts nsc NUM NUM e007 NUM and nsc NUM NUM e007 NUM
resolve was designed to find coreferent relationships among references to people and organizations since the muc NUM co training material included annotations for entities other than people and organizations a special interface was used to mark NUM relevan t texts from the st task for person and organization references NUM hours of work
these branch points indicate that anytime w e had two references to the same type of object neither phrase is a pronoun the second phrase is not a proper name both are in the same sentence and the first phrase is a proper name then resolve classified the two references a s coreferent
if the person was not from the same noun phrase as a status cn the tree returns negative
p2 is computed as the number correct for a type divided by the objects reported as having that type
some of the features used in these representations are domain independent and some are created for specific domains
the only time it erred was when resolve was handed badly trimmed phrases or phrases with incorrect semantic features
i i most recent compatible subject no i i i same type no NUM NUM
decision points are more reliable when a large number of trainin g instances are examined for a given condition
it is also a mistake to rely on a trainable system componen t that is not given enough training
had same type been more reliable it probably would have found its way to the top of the tree
a dependency tree for a v e k is constructed by NUM
figure NUM encoding a solution to the vertex cover prob lem from fig NUM
fig NUM for one solution this encodes a solution of the vertex cover problem
fig NUM gives a simple example where lcb c d rcb is a vertex cover
we will show that adding a little linguistically required flexibility might well render recognition a p complete
what is of interest here is that there is no mentioning of order in tesnisre s work
using morphosyntactic features with special interpretations a word defines abstract positions into which modifiers are mapped
we now sketch a minimal dg that incorporates only word classes and word order as descriptional dimensions
figure NUM word order domains in beans i know john likes
the dependents of s in valencies hl are from the set v vo
these codelength formulas will be used to match the complexity of the model to the complexity of the data
in the first state the source generates the alphabet e lcb NUM NUM NUM rcb uniformly
by means of the following experiments we hope to demonstrate the utility of our context modeling techniques
the third term represents an upper bound on the cost of encoding c w
the fourth term represents the cost of encoding c iw for e w
let us now estimate the conditional probabilities a required for an extension model
both authors are partially supported by young investigator award iri0258517 to the first author from the national science foundation
message entropy in bits symbol is for the testing corpus only as per traditional model validation methodology
so it seems unlikely that this structure would be learned using our scheme
using our new formalism the head properties of a look like
in particular variable tagsets and label collections should be allowed
the degree of automation increases with the amount of data available
treebanks of the tbrmat described ill tile m ove section have been designed tbr english
tllereff re the solutions they offer are not always optirnal for other language types
the tagger rates NUM of all assignments as reliable and carries them out fully automatically
structure is not represented directly but can be recovered from the tree and trace filler ammtations
sentences NUM words or longer were skipped
simplifying the search space reaps additional benefits
additional assumptions in order to account for such important phenomena as complex norninal np components cf
moreover the so called dp analysis views the article der as the head of the phrase
let us look at a particular example
but it also hypothesizes words like edby
why later does the algorithm not move towards a global optimum
sthis is why we can safely ignore recursive rules in this discussion
these alternations are represented on figure NUM
this aspect definitely needs to be improved
we finally explain how these patterns are used in the pronunciation procedure
graphcmic domain phonemic domain figure NUM the pronunciation of an unknown word
this suggests that the words which were not pronounced are not randomly distributed
figure NUM shows that the coverage and precision of the ltp estimate is not very high
this expands the set of possible analogs which is accordingly reordered etc
for two strings x and y having a non empty common prefix resp
the second search strategy has been devised precisely to cope with this problem
and we would like to thank ling ling wang and jyh shing jang for their valuable comments and suggestions
generating annotated corpora is time consuming and sometimes difficult though the payoffs are often significant
domain specific text annotations however require a domain expert and have much narrower applicability
statistics are then used to sift through the myriad of patterns that it produces
however the number of concept nodes drops off rapidly at higher relevancy rates
figure NUM shows a sample sentence and the instantiated concept node produced by circus
for text classification autoslog ts requires no manual effort beyond the preclassified training corpus
the second stage involves collecting statistics to determine which concept nodes represent domain specific expressions
after processing the training texts we have a huge collection of concept nodes
for each text we kept track of the concept nodes that were activated
section NUM NUM presents experiments with concept node filtering techniques for the information extraction task
figure NUM figure NUM gives some examples of the different mis
the following table shows the results of seven experiments for different maximum depths of the subtrees
finally we save space by storing the language independent information only once
a variety of document collections in english spanish and arabic are available under the document manager to support the various components
bbn and lm both provide template viewers for displaying the extraction of relationships that are comprised of multiple annotations each of which can then be examined in detail
each of the detection engines uses entirely different methods to obtain their output but both can accept the same detection need and both produce tipster compatible output
annotations are the mechanism for collecting extracted information from a document including the actual data the type of data and any relationships for the data
transparent to the user the query runs in sql against a hand crafted oracle database of joint venture articles and retrieves relevant documents
information extraction ie is the identification and output of specific types of data elements and relationships from free text
to write a word like golf bag in katakana some compromises must be made
this method uses a generative model incorporating several distinct stages in the transliteration process
we do no pruning so the final wfsa contains every solution however unlikely
we can test each engine independently and be confident that their results are combined correctly
the specific document text may also be viewed and in some cases the text that contributed to the document being selected as relevant can be highlighted
a unique capability of specifying the detection need is the ability to accept sample documents that represent the relevant documents or sample documents which represent the non relevant documents so called query by example
finally not all katakana phrases can be sounded out by back transliteration
relevant to speech recognition situations in which the speaker has a heavy foreign accent
in lb this end point is denied by the second sentence
er fuhr nach hause zu der zeit
b der angeklagte fuhr nach hause
there he drank a glass of trollinger
NUM the neutral viewpoint contains the initial point of the situation
we have therefore to define the initial point and the first stage
figure NUM the a imperfective b perfective and c
default information given by the situation aspect may be overridden by the context
a discourse grammar developed for english can not easily be applied to german
smith NUM NUM NUM the defendant had an accident
we give a novel method for parsing these words by estimating the probabilities of unknown subtrees
thus one can seriously put into question the merits of sophisticated training and learning algorithms
to drive this process we take description as the paradigm for sentence planning
we review these claims briefly in order to establish that they are consistent
the informational structure would contain an accompanying informational relation for each intentional relation
these constraints guarantee that a correct rst analysis will form a tree structure
schemas are basic structural units or patterns in the application of rst relations
this occurs when the multiple satellites bear different relations to their nucleus
speaker intentions corresponding to the g s relation of dominance among intentions
that is nuclearity rightly belongs in the definitions of intentional relations
in g s intentions may be related by satisfaction precedence in addition to dominance
we show that such models can intervene between different order n grams in the smoothing procedure
aggregate markov models are class based bigram models in which the mapping from words to classes is probabilistic
a purpose in satisfaction precedes another purpose im when in must be satisfied first
the second implicit rst claim about ils is a refinement of the first
the features before and after depend on the final punctuation of the phrases pi and pi l respectively
we evaluate each algorithm by examining its performance in segmenting an initial test set of NUM of our NUM narratives
note that it is conservative also because it is based on the proportion of identical matches between two data sets
or somewhat less than NUM meaning that a small proportion of the observed disagreement would have arisen by chance
where nl is the total number of l s and no is the total number of lsx4 NUM
recall that significance of a boundary increases exponentially with the number of subjects who agree on a boundary
it is only recently that attempts have been made to quantitatively evaluate how utterance features correlate with independently justified segmentations
utterance like units referred to as moves were identified in the transcripts and subjects were asked to identify transaction boundaries
to evaluate hierarchical aspects of segmentation flammia and zue also developed a new measure derived from the kappa coefficient
then in section NUM we present the linguistic underpinnings of our work
in section NUM we describe our algorithm and its operation on an example
this of course is so only if the localizing predicate allows for multiple periodic instantiation
l he four criteria of the last sectiou can be used in order to exclude readings f crsl
such resolutions may be available by an inference component that operates over richer contextual knowledge
consider the following examples NUM peter zeigte erst auf die vierte gliickszahl
this latter heuristics is expensive howew r in that it checks extra sententim information
new is the information about the progress of the instantiation of the presupposed event concepts
times of day allow for this also adjuncts like after lunch etc
pensive strategy that in addition not always comes up with a clear preference for one of the alternatives
without fllrther comment we thiuk that the criterion is cent trot x by the data
had to communicate to solve the task
this research has been partially funded by cicyt tic96 NUM c03 NUM item project and the european comission le NUM eurowordnet project
again the best method differs from one dictionary to the other each one prefers the method used in the previous section
several approaches have been proposed for attaching the correct sense from a set of prescribed ones of a word in context
furthermore even those heuristics with apparently poor results provide knowledge to the final result not provided by the rest of heuristics
this information is later used to posit coreference between words or phrases such as today tomorrow this week this year and dates such as november NUM NUM
first is the issue of filtering term lists this has been dealt with by constraints on processing and by post processing overgenerated lists
variants were extracted on the eci corpus through this transformation the following observations and changes have been made
the third allows there to be a verb phrase modifier
consider the following trees where the np s node is empty
the nature of the semantic links between a word and its derivational forms is not checked and all allomorphic alternants are generated
these results indicate a very high level of accuracy NUM NUM of the variants extracted by the system are correct ones
for example genic expressions genes were expressed expression of this gene etc are extracted as variants of gene expression
the second role can be captured by the parser constructing semantic representations directly
applicative categorial grammars allow categories to have arguments which are themselves functions e.g.
the only real difference is that bar hillel allowed arguments to themselves be functions
it requires an np to the right to form an np s
finally it is worth noting why it is necessary to use h lists
a successful parse is achieved if the final state expects no more arguments
in this section we define a grammar similar to bar hillel s first grammar
the simplified tree structures for NUM and NUM are given in NUM and NUM
therefore we restrict the aelr to nouns on the input side which disallows adjunct extraposition from vp and hence avoids spurious ambiguities
puzzled maria that smoking unh is that the cat and nonloc value of each conjunct daughter is identical to that of the mother
this guarantees that nei9a similar rule has to be formulated for verbs with separable prefixes where the prefix marks the right periphery
in case a no material exists to the right of the extraposed element which could intervene between it and an antecedent
cr we take both constraints as evidence that extraposition is different from fronting and should be handled using a separate nonlocal feature
we present english and german data partly taken from corpora and provide an analysis using a nonlocal dependency and lexical rules
note that the semantic contribution of the adjunct standardly dealt with by the semantics principle is incorporated into this lexical rule
note that the specification inheriextra lcb rcb requires all members of extra to be bound at the same level
the ltag formalism does not dictate particular syntactic analyses ours follow basic gb conventions
as a first step the first equation is decomposed to r pf ipf c t ex pf c ipf ia where c is a new constant since r pf is a variable we are in a flex rigid situation and have the possibilities of projection and imitation
in contrast to traditional uncoloured substitutions a colored substitution a is a pair at at where the term substitution a t maps colored variables i.e. the pair xc of a variable x and the color c to formulae of the appropriate type and the color substitution a c maps color variables to colors
however there are cases where the dual 4note that this equation falls out of our formal system in that it is untyped and thus can not be solved by the algorithm described in section NUM as the solutions will show we have to allow for fsv and f to have different types
j3 reduces to c giving the trivial equation c t c which can be deleted by the decomposition rules similarly in the second equation the projection binding azw z for h NUM solves the equation while the second projection clashes and the imitation binding kzw ipf is not pf monochrome
this framework uses a variant of the simply typed a calculus where symbol occurrences can be annotated with so called colors and substitutions must obey the following constraint given a labeling of occurrences as either primary or secondary the por excludes of the set of linguistically valid solutions any solution which contains a primary occurrence
as usual in a calculus the set wff of well formed formulae consists of colored NUM constants ca runs runsa possibly uncoloured variables x xa yb function applications of the form mn and a abstractions of the form ax m
always the equations constraining the interpretation an of this anaphor are an al always tom take x to al s mother tom take sue to al s mother an jo always fsv tom take sue to jo s mother consider the first equation
to capture this data government and binding analyses postulate first that the antecedent is raised by quantifier raising and second that pronouns that are c commanded and preceded by their antecedent are represented either as a a bound variable or as a constant whereas other pronouns can only be represented by a constant cf
in particular this forces any occurrences of of to appear as a bound variable in the value assigned to r pf whereas in can appear either as i 0f a color variable unifies with any color constant or as a bound variable this in effect models the sloppy strict ambiguity
retrieval systems address this problem by expanding the query words using related words from a thesaurus salton and mc gill NUM
currently the system has a vocabulary of NUM words
an z open agent architecture is a trademark of sri international
the recognizers use an hmm based continuous speakerindependent speech recognition technology for pc s under windows NUM nt
creation of area features with unimodal speech would be more complex still if not infeasible
it is also installed at nrad s command center of the future
the system was used by the us army s 82nd airborne corps
figure NUM quickset running on a wireless handheld pc
it produces a single most likely interpretation of an utterance
the english and turkish case frames for clauses sentences are generally similar to each other with differences seen in the sentence s mood and the verb s aspect and modality
morphology and syntax of turkish are very different from english therefore the formalism used to represent english texts has to be altered significantly for turkish text representation
lastly the generator maps the turkish case frame into the turkish sentence which is then post edited by a human translator to get an intelligible and accurate translation
the turkish sentence output by the generator is post edited by a human translator to ensure accuracy and intelligibility of the target sentence
the structural transfer method which uses the recursively embedded case frames as intermediate representation proved to be very suitable in the application of english to turkish machine translation
the map interface agent provides the usual pan and zoom capabilities multiple overlays icons etc
the order of the words in the output sentences are determined by the topic and focus features of the target case frame which are mapped during the transfer phase
another problem encountered in the transfer module is complex lexical transfer with category changes such as the example given below NUM john gave a weak cough
table NUM shows the NUM most frequent errors NUM which constitute NUM of all errors NUM errors occurred during NUM test runs
in comparing a distribution q to the empirical distribution we shall actually measure dissimilarity rather than similarity
tree type x appears in the corpus and NUM is the empirical distribution defined as
instead i concentrate on parameter estimation which for attribute value grammars can not be accomplished by standard techniques
probabilistic analogs of regular and context free grammars are well known in computational linguistics and currently the subject of intensive research
algorithmically we compute the expectation of each rule s frequency and normalize among rules with the same left hand side
the weight fli is obtained from p fi by normalizing among rules with the same left hand side
if we generate twelve trees at random from ql it would not be too surprising if we got the corpus in
otherwise writing b x for the field weight ii fl lxl
intuitively we need only use as many features as are necessary to distinguish among trees that have different empirical probabilities
is the kullback leibler kl divergence defined as d pllq in x q x
if no such candidates exist the word does not belong to any of the semantic classes in the hierarchy and is usually labeled as the entity class
our approach reconciles the domain specific hierarchy with this ast network and exploits wordnet to uncover semantic ci s es without the need of an otated corpus
as a result non local mc tag dl multi component tag with dominance link was proposed as a way of handling scrambling NUM
for both algorithmg the NUM sentence test set is randomly partitioned into a NUM training set and a NUM testing set in proportion with the overall class distribution
the task of the semantic distance module is to reflect accurately the notion of closeness between the chosen concept node of the word and the semantic class nodes
our approach in effect allows domain specific s ic class dis mbiguation to latch onto the improvements in the active research area of word sense disambiguation
NUM generate the first word inside that name class conditioning on the current and previous nameclasses
we used the surrounding nouns of a word in free vmn g text as the norm grouping and followed his algorit r without modifications
if however in line with most current theories categories are taken to be bundles of features and crucially if one of these featflres has the value of a stack of categories then this hierarchical structure can indeed be represented
a more distant goal is to ascertain whether the performance of the model can improve after parsing new texts and processing the data therein even without hand correction of the parses and if so what the limits are to such self improvement
although the results at present are extremely modest it should be borne in mind both that the amount of data the system has to work on is very small and that the smoothing of transition probabilities is still far from optimal
the advantage would be re that tained however that the system is still fine grained fido enough to reflect the idiosyncratic patterns of in had dividual words and could override this paradig chewed matic information if sufficient data were available
for example in a bigram model trained on sufficient data the probability of the bigram dog barked could be expected to be significantly higher than cat barked and this slice of world knowledge is something our model lacks
if we represent a slashed category x with the lower case x and use the notation a b for a category a carrying a feature b then the topicalized sentence NUM this bone the man gave the puppy
it has been assumed so far that we are using a right linear indexed grammar but such a rule is expressly disallowed in an indexed grammar and so allowing transitions of this kind ends the formalism s weak equivalence to the context free grammars
at the expense of unlimited growth in the length of states as the model is to be trained from real data transitions involving long states as in NUM will have an ever smaller and eventually effectively nil probability
figure NUM gcg derivation for kim loves sandy
in this class of grammars every rule has the form a NUM a b a b e lcb non terminals rcb a e lcb terminals rcb and all trees have the simple structure a a a b c d b b
the main claim it makes is that effective language processing requires a consideration of both the structural and statistical as NUM pects of language whereas traditional competence grammars rely only on the former and standard statistical techniques such as n gram models only on the latter
this function helps to eliminate user uneasiness
the corresponding transducer locates the instances of upper in the input string under the left to right longest match regimen just described
but instead of replacing the matched strings the transducer just copies them inserting the specified prefix and suffix
we use the term directed replacement to describe a replacement relation that is constrained by directionality and length of match
kaplan and kay suggest that additional restrictions such as longestmatch could be imposed to further constrain rule application
but it helps to understand the logic to see where the auxiliary marks would be in the hypothetical intermediate results
it can be applied to texts where the interesting and uninteresting regions are defined by any kind of regular pattern
without the left to right longest match constraints the tokenizing transducer would not produce deterministic output
figure NUM composition of an np and a vp spotter figure NUM shows the effect of applying this com
in bigram tagging each example consists of a sequence of several words
and then sequentially examined the following examples in the corpus for possible labeling
the classifier then assigns the example to the class with the highest score
if e is selected get its correct label and update s accordingly
therefore reducing annotation cost is an important research goal for statistical nlp
this paper extends our previous work on committee based sample selection for probabilistic classifiers
our experimental study of variants of the selection method suggests several practical conclusions
annotating large textual corpora for training natural language models is a costly process
two member selection seems to have a clear though small advantage
we thank yoav freund and yishay mansour for helpful discussions
some of the low frequency items required extensive dictionary look up to verify the decision
unfortunately the documentation does not tell us about the effects of such a selection
our sentence list for english nouns thus looked like NUM NUM
lexicon size is an important selling argument for print dictionaries and for mt systems
an example is technology with its subfields space technology food technoloy technical norms etc
german assistant contains a wide variety of readings although it scored badly in our tests
if we omit the wrong form tags from the positive count i.e.
but there are differences of up to NUM for the low frequency class
since our test does not distinguish these variants we took only one of these stems
table NUM gives the number of incorrect determiner selections over all frequency classes
we use the symbol pr to denote general based on bayes decision rule
we use a bigram language model which is given in terms of the condit ional
experiments on the eutrans corpus produced a word error rate of NUM NUM
r i have made a reservation for a room with tv and telephone for mr morales
j is a sort of partial probability as in t ime alignment for speech recognit ion
r l would like you to wake us up tomorrow at a quarter past two
a i have made a reservation for a room with tv and telephone for mr morales
the search procedure i.e. an algorithm to perform the argmax operation in an efficient way
who left on tnesdayf and on wednesday requires substitutions for form constructions in qlf not described here representing prepositional phrases
kehler adopts an analysis where referential arguments to verbs are represented as related to a davidsonian event via thematic role functions e.g.
the pronoun has been resolved to have a contextual restriction rft j that co indexes it with the subject term
the ellipsis should follow modulo the substitutions the same composition as the antecedent whatever that composition is eventually determined to be
this is so the antecedent and ellipsis categories are both used to determine what fozm should be substituted for the antecedent form
this leads to an uninterpretable cyclic qlf in much the same way that dsp obtain a violation of the occurs check on sound unification
that is we are looking for a predicate that when applied to the subject term of the ellipsis antecedent returns the antecedent
for reasons that will be explained shortly it is important that resolution does not actually carry out the application of the substitutions
the other two readings only scope the quantifier after resolution and differ in giving the pronoun a strict or a sloppy interpretation
the texan expression has four arguments an index a determiner quantifier an explicit restriction and an additional contextually derived restriction
these oov words generally occur several times in the corpus and a number of these occurrences can be important
this will have to be tested in a future exercise
we have evaluated NUM mt systems which translate between english and german and which are all positioned in the low price market under us NUM
these steps had to be done for both translation directions german to english and vice versa but here we concentrate on english to german
if we look at compounds that form an orthographic unit like vestryman waterbird we can only find evidence for segmentations by langenscheidts t1 and german assistant
while annotating sentences with the tags we observed that verbs obtained many wrong form judgements NUM and more for the low frequency class
the translated sentences were annotated with one of the following tags u unknown word the source word is unknown and is inserted into the translation
while we can not determine the absolute lexicon size with a black box test we can determine the relative lexical coverage of systems dealing with the same language pair
given these predictions about the relative contribution of different factors to performance it is then possible to return to the problem first introduced in section NUM given potentially conflicting performance criteria such as robustness and efficiency how can the performance of agent a and agent b be compared
this avm consists of four attributes abbreviations for each attribute name are also shown NUM in table NUM these attribute value pairs are annotated with the direction of information flow to represent who acquires the information although this information is not used for evaluation
let us now apply the determinization algorithm of figure NUM on the finite state transducer t4 of figure NUM and show how it builds the subsequential transducer t10 of figure NUM
we then prove the correctness of the general algorithm for determinizing whenever possible finite state transducers and we successfully apply this algorithm to the previously obtained nondeterministic transducer
one possible exception is task based success measures such as transaction success task completion or quality of solution metrics which can be either an objective or a subjective measure depending on whether the users goals are well defined at the beginning of the dialogue
elementary attentional spaces are often composed of multiple pcas produced by consecutive navigation steps such as u5 and u6
do not make your contribution more informative than is required
reference resolution reference resolution links the he in the second sentence to the most recent previously mentioned person in this case mr
we obtain a lexicon of NUM items representing NUM of all the occurences of oov common words in our corpus
the mean position of the expert choice for all parts of speech in the frequency order was NUM NUM in the random condition the mean position of the expert choice was NUM NUM
the fact that dictionaries differ frequently with respect to the number of senses for polysemous words points to the difficulty of representing different meanings of a word as discrete and non overlapping sense distinctions
whereas the meanings of nouns tend to be stable in the presence of different verbs verbs can show subtle meaning variations depending on the kinds of noun arguments with which they co occur
higher NUM when the agreed upon sense was the first choice rather than a subsequent one NUM on the list of alternative senses in the dictionary
during sequential tagging each content word in a running text is tagged so the meanings of highly polysemous adjectives often become clear as the tagger looks to the head noun
group NUM contalned words with NUM senses group NUM words with NUM NUM senses the words in group NUM had NUM NUM and in group NUM NUM or more senses
the groups were created so that each contained approximately NUM of the words from each part of speech i.e. the groups were similar in size for each syntactic category
inter tagger agreement followed the same pattern as tagger expert matches agreement decreased with increasing polysemy agreement rates were highest for nouns and lowest for verbs and adjectives in both conditions
apart from salience it is also shown that referring expressions are strongly influenced by other aspects of human preference
in particular word recognition and syntactic analysis of phrases sentences and utterances should have a lot to say to each other the probability of a word should depend on its place in the top down context of surrounding words just as the probability of a phrase or larger syntactic unit should depend on the bottom up information of the words which it contains
NUM of the oov words had been correctly tagged by the proper names language model
the simplest one conveying the derivation of a new intermediate conclusion is illustrated in the introduction
this paper describes the way in which proverb refers to previouslyderived results while verbalizing machine found proofs
another factor to be considered in distinguishing the two cues is the embeddedness discussed in the next section
the local navigation operators simulate the unplanned part of proof presentation
for many applications these two techniques are a complementary pair
for example in the sentences the dna segment NUM b xp dna fragment
which looks for matches between csrs and or subsets of csrs and concept grammar rules in rubric categories associated with each essay part
electricity elechical potential charge negatively charged fragments rate size smaller fragments move faster calibration
words in the phrasal node which do not match a lexical concept are not included in the set of extracted phrasal nodes
table NUM shows the results of using the automatic scoring prototype to score NUM excellent test essays and NUM poor test essays
therefore vocabulary which occurs in the test data but not in the training data was ignored during the process of concept extraction
this work is also applicable for other types of assessment as well such as for employee training courses in corporate and government settings
the word piece is a metonym for the concept fragment so these two words may be used interchangably
we have implemented a concept extraction program for preprocessing of essay data that outputs conceptual information as it exists in the structure of a sentence
given our preliminary successes with test questions that elicit multiple responses from examinees similar scoring methods were applied for scoring ap biology essay
we assume a reader will build up a partial proof tree as his model of the ongoing discourse
every sense of words in artmes which should be lustered is automatically disambiguated in advance
second thanks to the lexical recovery from word candidates in the n best hypothesis the spoken input can be decoded further
they are all high frequency function words that play many different syntactic roles depending on their context
while a hierarchical planner recursively breaks generation tasks into subtasks local organization navigates the domain object following the local focus of
the words identified as inserted or as substituted are marked but the decision is laid upon the robust parsing or subsequent linguistic processes
thus since in our case elaboration has been chosen on the mother if ith satdilite a then n NUM for n prefer act nucleus act for n re enter at discourse unit
it is that dialogues regularly contain monologues but monologues w hile they can be interrupted by mini dialogues to clear up misunderstandings and to challenge etc occur either explicitly or implicitly within a dialogue
as we have seen rst relations can be fully integrated with the richer sfm framework by restating the sister dependency relations of rst as occurring via their mother unit i.e. in an element and unit model
whether the utterance is a repair ldeg for example let c2 be the number of repair utterances
fawcett and davies concluded we do not expect a seamless join between the two models to occur effortlessly there are bound to be difficulties of many kinds as we explore possible ways of bringing the two models together
move com plexity our choice is with satellite m lack of space prevents us from describing the effects of choosing simplem or co ordinatedm but the rules given cover the former
as it enters the network in figure NUM it knows that it has the goal of filling the respond element hence r of an exchange and that it is to be uttered by ivy i.e. the system
thus the network and realization rules together provide for the recursive embedding of acts with further acts at either n or s or at both in this way then we do indeed move virtually seamlessly from the sfm structure to rst relations
as you will see if you look at the realization rules the selection of move inserts first the unit move into the structure and then locates the element nucleus n at place NUM in that move s structure
for example if the concept money has been observed then the lexical item bank has the meaning closest to money in the network savings institution rather than edge of river etc thus disambiguation becomes a second possible application of co oc s results beyond the abovementioned primary use for constraining speech recognition
stop stop in state q at which point the se null quences are considered complete
by distinguishing between hierarchical planning and focus guided navigation proverb achieves a natural segmentation of context into an attentional hierarchy
the least accuracy is achieved for sentences the best for prepositional phrases
this can be fixed by introducing an extra tag for nouns denoting numericals
translation word error rate is defined as the number of words in the source which are judged to have been mistranslated
there was a strong systematic relationship between the structure of the models used in the two systems in the following sense
there are many aspects to the effectiveness of the translation component of a speech translator making comparisons between systems difficult
as can be seen from table NUM the transducer system has a lower model complexity according to all three measures
we define a cost function f as a real valued function taking two arguments a event e and a context c
the processing times reported above are averages over the same NUM test utterances used in the accuracy evaluation
right transition write a symbol r onto the left end of the right sequence and enter state qi l
in other words they are the dependency relations between w and the dependents of w to its left and right
evaluator e1 is a native cantonese speaker e2 a mandarin speaker and e3 a speaker of both languages
the upper layer is completely domain independent while the lower layer has dialogue states that constitute domain specific subdialogues
the required variety is achieved by having many different templates for the same information and by having a flexible mechanism for combining the generated sentences into texts
we will compare the dedicated context models that have been proposed in theoretical and computational linguistics with the more general models proposed in artificial intelligence
only local conditions on the context model and the properties of the current s template determine whether a sentence is appropriate at a certain point in the text
the system can for instance start mentioning the date of composition or information could be added that contrasts this composition with a previous one
c a leaf is marked a if it is lexically marked as unfit to carry an accent that is due to informational status
this context model with all its diverse components may not be as elegant as some of the context models discussed in the present section
the appropriateness of a referring expression depends among other things on the existence and kinds of references to the referred object in previous sentences
since s templates are structured objects conditions guaranteeing the appropriate choice for the variable parts of the templates can refer to information contained in these structures
secondly a selection has to be made from all s templates in such a way that the text generated conveys all and only the required information
the way in which users can indicate their areas of interest will not be discussed in this paper which focuses on language and speech generation
NUM everything that informs of inconsistency is motivated if not already known
figure NUM context model after the user contribution NUM need a car
figure NUM information flow in the cdm system
b commmficative act expressive and evocative attitudes
some communicative obligations are listed in fig NUM
the paper is organised as follows
we will not go into detailed evaluation of the approaches see e.g.
partner is able to deal with
finally note that r and r NUM are recursive they allow for arbitrary embeddings of e.g.
it can easily be recast in terms of hierarchical sets finite functions directed graphs etc
the effect of the synonyms on this filter was negligible presumably since synonyms are often hyponyms of hypernyms
we report on our results of disambiguating the verbs in the semantic filters by adding wordnet sense annotations
we would like to thank julie dahmer charles lin and david woodard for their help in annotating the verbs
NUM we then checked whether the unknown verbs those not used to construct proportions of levin verbs
in this case there are NUM assignments known to be wrong out of a total of NUM assignments
we considered the best candidate to be the one that matched the greatest proportion of the verbs for that class
when we add the semantic filter the number of assignments drops to NUM NUM of the unfiltered assignments
we take as our starting point NUM ldoce verbs approximately NUM of which do not occur in levin s classes
to create a semantic filter we take a semantic class from levin and extend it with related verbs from wordnet
a replaced word which gives the highest value of the strength of word chain will be a solution of an implicit spelling error
tion of the NUM in ling restrictions
the phenomena and its implications translate directly to german
binding principle b may bc violat d
she had invited him on sunday
interdependency collisions did not happen too frequent
not bound in its binding category
the reasoning behind this is stylistic and syntactic as claim texts must be both legible and syntactically unambiguous
instead the different clues from syntax semantics prosodb i and world knowledge are typically weak and have to be weighted against each other in the light of the complete utterance
for example if the synonym set for lexical selection is as follows engage hold attach lock join clamp fasten the system will present this list to the user in the descending order of frequencies with the idea that the user would prefer to select the first applicable word on the list
therefore at the lexical selection stage of the generation of a patent claim the system must be able to choose that member of the synonym set of candidates whose meaning is the broadest for our system we determined the breadth of meaning of word senses by calculating the relative occurrence frequencies of every word sense the training corpus
as a result of morphological analysis the interactive input specification knowledge elicitation stage provides information about the boundaries of case role values which makes it possible to use special simplifying morphological rules thus for instance if the filler of the theme case role consists of a single word it must be a noun
a cassette for holding excess lengths of light waveguides in a splice area comprising a cover part and a pot shaped bottom part having a bottom disk and a rim extending perpendicular to said bottom disk said cover and bottom parts are superimposed to enclose jointly an area forming a magazine for excess lengths of waveguides said cover part being rotatable in said bottom part
NUM case roles are labeled in the lexicon entry for a particular predicate and the correspondence between the la null bel and the rank for this word is established there see description of lexicon entry below
two guide slots formed in said cover part said slots being approximately radially directed guide members disposed on said cover part a splice holder mounted on said cover part to form a rotatable splice holder
the output of the content specification stage and input into the generation stage consists of a list of filled templates in which the templates with the title of the invention in their subject slot are marked
predicates finite mount is mounted forms of verbs and mounting participles in predicative uses all nouns except device assembly cam ne see below shaft cage adjectives partici horizontal each
our algorithm consistently outperformed the inside outside algorithm in these experiments
if the rule is wrong most of the times it is a bad rule which should not be included into the final rule set
we also smooth NUM so as not to have zeros in positive or negative outcome probabilities NUM i
furthermore the distinction between those two kinds of realization is generally delegated to the underlying semantic theory
given a set of topoi t a set of strength markers f the set d lcb positive negative rcb of directions and the set v lcb id pc opp rcb of polyphonic values we define the set c txfxdxv of argumentative cells the topos its direction the strength and the polyphonic commitment
remaining difficulties lay in the linguistic theories themselves mainly combining modifiers and cataloguing topoi the signification of tss s which should be compositional and the integration of argumentative semantics with informative and illocutionary elements
we describe and compute the signification of such sentences by specifying how the key words in italics constrain the argumentative power of the terminal sub sentences tss i was robbed yesterday and i had money
we wish to design structures so that they may be used for two additional tasks that may require a top down process NUM accept tss descriptions containing free variables and produce the sets of constraints on them that lead to a solution NUM provide the interpretation process with a way of generating unusual significations of tss s required by the global effect of the structure
connective but the signification of p1 but p2 is computed from the significations of p1 and p2 with the following modifications generate alternatives according to a partition of topoi of p1 and p2 whose cells have free commitment variables with the opposite relation which holds in t in each alternative commit the corresponding cells with the value pc for p1 and id for p2
in NUM and NUM the robbery is considered good while in NUM money is normally considered good too and in NUM the oddest it is considered bad imagine a speaker who this research is supported by sncf direction de la recherche d6partement rp NUM rue de londres NUM paris cedex NUM france
derivations denominal verbs deverbal nouns and part of speech changes can be modelled respectively by adding subcategorization frames discharging subcategorization frames and type coercions via lexical rules
when the xerox tagger was equipped with our cascading guesser its accuracy on unknown words increased by almost NUM upto NUM NUM
for our experiment we perform two data runs
again only the closed class lexicon is consulted
however the components do not access this whiteboa d
i am very grateful for the support and stimulation i received there
solomonoff s induction framework is not restricted to probabilistic context free grammars
we hypothesize that the additional information will provide greater sensitivity for characterizing the concepts and themes
comments on issues relating to this paper and its initial draft
content analysis provides distributional methods for analyzing characteristics of textual material
absolute absolutely consequence consequently correct correctly
but of particular interest here are the adverb forms
we will propagate these meaning components to the lexical items
mctavish has also used the heuristic reasoning for this category
this is accomplished by visually inspecting the mds graphical output
dimensional context vectors provides an initial characterization of the texts
the results are thus strictly determined by the number of matching taggings with no ambiguous coding allowed
each adjective is coded with the possible clnss es of the nouns which it may modify
table h dp based search algorithm for the monotone translation model
nonetheless these results especially at sub sense level compare favourably with other research in the area
such methods are far more appropriate for work in restricted contexts where representative training corpora can be more easily derived
our part of speech tagger is based on a series of rules listing valid transition pair sequences of grammatical tags
for each word the sense with the highest score is assumed to be the sense meant in the context
thus all valid sequences can be given a score by adding up the relevant transition pair scores
chess programmers tend to prefer additive weightings because they ate far simpler to program and also more efficient
at the end of all these processes each sense of each word will have a particular score
the p and p are a particularly powerful feature which enable intermediate phrases to be ignored
the increased homogeneity makes it suitable for investigating our hypothesis of predominant verb senses
a flexible set of facilities co oc has been implemented in common lisp to aid collection of such discourserange co occurrence information and to provide quick access to the statistics for on line use
in section NUM NUM we introduce some of the key features of dtg and explain how they are intended to address the problems that we have identified with tag
whextraction in kashmiri proceeds as in english except that the wh word ends up in sentence second position with a topic from the matrix clause in sentence initial position
we add slcs to ensure that the projections are respected by components of other d trees that may be inserted during a derivation
we have given a reasonably precise definition of sa trees since they play such an important role in the motivation for this work
in addition seem depends on claim as does its nominal argument he and adore depends on seem
clausal complementation could not be handled uniformly by substitution because of the existence of syntactic phenomena such as long distance wh movement in english
for each internal node either all of its daughters are linked by i edges or it has a single daughter that is linked to it by a d edge
before discussion these operations further we consider a second problem with tag that has implications for the design of these new composition operations in particular subsertion
the first problem discussed in section NUM NUM is that the tag operations of substitution and adjunction do not map cleanly onto the relations of complementation and modification
we are also grateful to tilman becker gerald gazdar aravind joshi bob kasper bill keller tony kroch klans netter and the acl95 referees
also if the statistic is updated during normal operation it can adapt itself to the dialogue patterns of the verbmobil user leading to a higher prediction accuracy
the output of the dialogue module is delivered to any module that needs information about the dialogue pursued so far as for example the transfer module and the semantic construction evaluation module
the components are a statistic module the task of the statistic module is the prediction of the following speech act using knowledge about speech act frequencies in our training corpus
vorschlag deo06 i pause i propose from tuesday the fifth deo06 NUM pause no tuesday the fourth to saturday the eighth pause those five days
vorschlag if we trace the processing with the finite state machine and the statistics component allowing two predictions we get the following results lation provided by verbmobil and el the english speaker
then negotiation begins where the discourse participants repeatedly offer possible time frames make counter offers refine the time frames reject offers and request other possibilities
also when the finite state machine detectss an error the planner must activate plan operators which are specialized for recovering the dialogue state in order not to fail
while the statistical component completely relies on numerical information and is able to provide scored predictions in a fast and efficient way the planner handles time intensive tasks exploiting various knowledge sources in particular linguistic information
in the subplan 0ffer 0perator for example which is responsible for planning a speech act of the type vorschlag the action retrieve theme filters the information relevant for the progress of the negotiation e.g.
in addition we show the transformation based algorithm to be effective in improving the output of several existing word segmentation algorithms in three different languages
this rule is then applied to all applicable sentences and the process is repeated until no rule improves the score of the training data
experiments however show that if the positive bias between thc word senses of the training set and the testing quadruples is removed the accuracy of the pp attachment falls substantially
our method performs better on the more polysemous words which axe the most difficult to prune
si l filteri l si c si since in otp the input is a partial order of edge brackets and sn is a set of one or more total orders timelines a natural approach is to successively refine a partial order
thus at any time the ith tier may be beginning or ending a constituent or both at once i or it may be in a steady state in the interior or exterior of a constituent
nospreadright voi voi NUM vo i underlying voicing may not spread rightward as in NUM h nondegenerate f every foot must cross at least one morn boundary m
note that other forms such as those in NUM can be decomposed into a sequence of two or the formalism is complicated slightly by the possibility of deleting segments syncope or inserting segments epenthesis as illustrated by the candidates be null low
c in order to mlow adjacency of the surface consonants in i as expected by assimilation processes and encouraged by a high ranked constraint note that the underlying vowel must be allowed to have zero width an option available to to input but not output constituents
NUM NUM c NUM and a and NUM or NUM or scoring constraint r number of sets of events lcb a1 a2 rcb of types l a respectively that all overlap on the timeline and whose intersection does not overlap any event of type NUM NUM
in some cases the transformations were able to recover some of the word but were rarely able to produce the full desired output
while the alphabetic system is obviously harder to segment we still see a significant reduction in the segmenter error rate using the transformation based algorithm
the other two tasks template element and scenario template were information extraction tasks that followed on from the muc evaluations conducted in previous years
the document section results show NUM error on document date and dateline NUM error on headline and NUM error on text
restriction of the corpus to wall street journal articles resulted in a limited variety of markables and in reliance on capitalization to identify candidates for annotation
for person objects this challenge is small since the only additional bit of information required is the person s title mr
since the development time for the muc NUM task was extremely short it could be expected that the test would result in only modest performance levels
however there were at least three factors that might lead one to expect higher levels of performance than seen in previous muc evaluations NUM
it appears that there is a wide variety of sources of error that impose limits on system effectiveness whatever the techniques employed by the system
the reverse is not the case i.e. org country may be filled even if org locale is not but this situation is relatively rare
the variety of high frequency phenomena covered by the task is partially represented in the following hypothetical example where all bracketed text segments are considered coreferential
the manually filled templates were created with the aid of tabula rasa a software tool developed for the tipster text program by new mexico state university
computer connect the end of the black wire with the large plug to connector one two one
thus the and of two rows will represent the set of lower bounds they have in common
unification formalisms like ours are intended to be capable of encoding semantic as well as syntactic descriptions
at this point the reader might well wonder why kleene operators were wanted in the first place
we will write such declarations as category np lcb person number rcb
p lcb lex for semvalues for benefactive for time period rcb
for each position representing an excluded combination we unify the variable arguments on each side of it
finally there is also knowledge about the user that is acquired during the course of the dialog
this is done by allowing the iterations counter on each specification to reduce its effective level of suspicion
so the system looks for expectations of other subdialogs that either have been invoked or might be invoked
after a response meaning has been resolved it is entered into the database with all its presuppositions
entry of zmodsubdialog with vocalize adjust knob NUM sends control down its second path
timing considerations by the controller may dictate the halting of a given proof and resorting to other action
it is also possible that the user has previously found the switch but not recently reported its position
if the proof succeeds using internally available knowledge the dialog terminates without any interaction with the user
in hybrid analogical translation the use of a morphological and syntactic module for shallow analysis to derive a linguistic representation with syntactic and lexical features allows us to handle phenomena such as inflections transformations and languagespecific phenomena such as the english determiner system and certain japanese constructions that encode politeness information in a linguistically efficient manner
we assume that the probability of echoing a word depends only on the word itself so that the following holds NUM p echo word ew ewx p eh q
the framework of interlingua based translation rests on the presupposition that there can be a universal unaxnbiguous language neutral and practically if not formally sound knowledge representation forrealism to mediate between source and target languages
captures translation correspondences in a natural way by means of corresponding natural language expressions in the souce and target language other less natural means of knowledge representation would require significantly more effort to acquire and maintain
the figures below show the sample size for the various tag elements and type values
the scenario is designed around the management post rather than around the succession act itself
these two slots caused problems for the annotators as well as for the systems
systems scored approximately NUM NUM points lower f measure on st than on te
the algorithm compares the equivalence classes defined by the coreference links in the manually generated answer key and the system generated response
this capability has other useful applications as well e g it enables text highlighting in a browser
named entity the primary subject for review in the ne evaluation is its limited scope
the real challenge of te comes from associating other bits of information with the entity
summary ne scores on primary metrics for the top NUM out of NUM systems tested in order of
the satie nonames configuration resulted in a three point decrease in recal l and one point decrease in precision
an initial score for each word in the best sentence is taken either from the word acoustic score or from the sentence score distributed uniformly on the words
enamex type quot person dooner enamex has big challenge that will be his top priority
interestingly no value was learned for template s being a possible but non preferred referent but a small positive value was learned for it not being on the list at all presumably this covers cases in which the coreference module fails to identify an existing referent
however a key challenge for such interfaces is to couple successfully automatic speech recognition asr and natural language processing modules nlp given their limits
the japanese english bilingual dictionary contains NUM NUM words and the english japanese bilingual dictionary contains NUM NUM words
morphological information relates to headword morpheme mid intbrmation on the connectivity of roof phemes
example NUM let us consider the bangla simple sentence below in which the nps have been under
the leadconcept dictionary describes information on the concepts themselves
table NUM number of user sites of the edr electronic dictionary
the concept explication is an explanation which expresses the meaning of the concept
an overview of the edr electronic dictianary and the current status of its utilization
the co occurrence dictionm y describes collocational information in the form of binm y relations
the concept classification dictionary describes the super sub relations among the NUM NUM concc pt
the headconcept is a word whose meaning is close to the content meaning of the concept
the japanese cooccmtence dictionary contains NUM NUM phrases and the english co occurrence dictionary contains NUM NUM phrases
in NUM the number of matching nodes has been used to rate different matches which is similar to finding maximal reductions in NUM
we assume that NUM our example is based on iordanskaja e al s notion of maximal reductions of a semantic net see NUM page NUM
thus at the point when all internal generation goals of the first skeletal mapping rule have been exhausted the generator knows how much of the initial graph remains to be expressed
also note that the arcs to from the conceptual relations do not reflect any directionality of the processing they can be traversed accessed from any of the nodes they connect
similar conditions hold when in the phase of covering the remaining emantics the applicability semantics of a mapping rule is matched against the initial semantics
in those processes parts of the semantics are mapped onto partial syntactic structures which are integrated and the result is still a partial syntactic structure
realisability it should be possible to incorporate the partial syntactic structure of the mapping rule into the current syntactic structure being built by the generator
if lexicalised dtgis used as the base syntactic formalism at this stage the mapping rule will introduce the head of the sentence structure the main verb
our generator uses a particular syntactic theory d tree grammar dig which we briefly introduce because the generation strategy is influenced by the linguistic structures and the operations on them
in order to constrain the way in which the non substituted components can be interspersed dtg uses subsertion insertion constraints which explicitly specify what components from what trees can appear within a certain d links
subsequent treatments of non peripheral extraction based on the lambek calculus where standard composition is built in it is a rule which can be proven from the calculus have either introduced an alternative to the forward and backward slashes i.e. and for normal args
firstly there is nothing equivalent to a stack mechanism at all times the state is characterised by a single syntactic type and a single semantic value not by some stack of semantic values or syntax trees which are waiting to be connected together
if a grammar is augmented with operations which are powerful enough to make most initial fragments constituents then there may be unwanted interactions with the rest of the grammar examples of this in the case of ccg and the lambek calculus are given in section NUM
most cgs either choose the third of these to give a vp structure or include a rule of associativity which means that the types are interchangeable in the lambek calculus associativity is a consequence of the calculus rather than being specified separately
the two rules specified so far need to be further generalised to allow for the case where a lexical item has more than one argument e.g. if we replace likes by a di transitive such as gives or a tri transitive such as bets
what we obtain is the single rule of state application which corresponds to application when the list of arguments r1 is empty to function composition when r1 is of length one and to n ary composition when r1 is of length n
while our formal presentation does not discuss abstraction since it can be implemented in terms of constraint selection as just described because our implementation uses the underlying prolog s unification mechanism to solve equality constraints over terms it provides an explicit abstraction operation
for example the first reading of the dutch sentence NUM frits opzettelijk marie lijkt te ontwijken deliberately seems avoid fritz deliberately seems to avoid marie fritz seems to deliberately avoid marie is obtained by the analysis depicted in figure NUM
x clause operator x backward combinator x modal operator b x x left right forward application x x y left mid x y mid right
bn can explain a number of puzzling scope phenomena by proposing that heads specifically verbs subcategorize for adjuncts as well as arguments rather than allowing adjuncts to subcategorize for the arguments they modify as is standard in categorial grammar
thus it is infeasible to enumerate all of the categories that could be associated with a verb when it is retrieved from the lexicon so following bn we treat the predicates add adjlmcts NUM and division NUM as coroutined constraints which are only resolved when their second arguments become sufficiently instantiated
just as in item NUM the second argument of the single literal in item NUM is not sufficiently instantiated so item NUM is tagged solution and the unresolved literal is inherited by item NUM item NUM contains the partially resolved analysis of the verb complex
we assume that the agreement schema for a function g may select the structure that satisfies the constraints
re estimation usually improves a sequence of grammatical function words written in hiragana at the sentence final predicate phrase if the initial segmentation and the correct segmentation have the same number of words
however an original property of our lexicaiized tree grammar is to integrate a set of semantic operations which lay down additional constraints
for example in recognizing spoken japanese if we can predict the relative probability that the current utterance is a yn question as opposed to an inform we may be able to differentiate utterance final ka a question particle and utterance final ga a conjunction or politeness particle which are often very similar phonetically
new words are classified on the basis of relative probabilities of a
we choose the notation g ql vl for one agreement schemata for the function g
when k becomes bigger however the category based approach becomes superior
a joker tree is an overspecified tree that cumulates semantic features from different candidates whose elementary tree share the same structure NUM
as we can see there are about NUM of the words are ambiguous with regards to the t s they take
the last possibility d is to link through a non structured list of concepts which forms the superset of all concepts encountered in the different languages involved
the parsing performance is saturated at very small size of training corpus
figure NUM clustering result two conditions
you can find it in the figures
figure NUM parsing accuracy for individual section
we use non overlapping and average distance clustering
we acquire two nonterminal grammars from corpus
in this section the parsing experiments are described
these are generally worse than the trees completely parsed
this paper has presented satz a sentence boundary disambiguation system that possesses the following desirable characteristics it is robust and does not require a hand built grammar or specialized rules that depend heavily on capitalization multiple spaces between sentences etc
a large percentage of the errors made by the alembic module fell into the second category described in section NUM NUM where one of the five abbreviations co corp ltd inc or u s occurred at the end of a sentence
in our case punctuation marks remaining ambiguous after processing by satz can be treated as soft boundaries while unambiguous punctuation marks as well as paragraph boundaries can be treated as hard boundaries thus allowing the alignment program greater flexibility
training of the weights is not performed on this text the cross validation text is instead used to increase the generalization of the training such that when the total training error over the cross validation text reaches a minimum training is halted
the boundary disambiguation module is part of a comprehensive preprocess pipeline that utilizes a list of NUM abbreviations and a series of over NUM hand crafted rules to identify sentence boundaries as well as titles date and time expressions and abbreviations
the texts of the two domains are parsed with several grammars e.g.
the size of the training corpus is an interesting and important issue
it is not comparably high enough to be selected
he utilized characteristics of japanese character classes
we observe the variation of frequency of the sorted n gram data and extract the strings that experience a significant hange in frequency of oc urrence when their length is extended
NUM recent years a large amount of text corpora haw become available and it is now becoming possible to conduct more rigorous experiments on text corpora
the automatic extraction of open compounds from text corpora
this paper describes a new method for extracting open compounds uninterrupted sequences of words from text corpora of languages such as thai japanese and korea that exhibit unexplicit word segmentation
figure NUM result of extraction of convention for
we call this three character unit a cluster
NUM produce n gram strings following thai spelling rules
figure NUM result of extraction of thai revenue
p figure NUM hasten s extraction vision
the number of his test cases was NUM ours is NUM
air tight armor suit which might serve air noise and chemical air and water and general air noise water and wastes
context heterogeneity measures how productive the context of a word is in a given domain independent of its absolute occurrence frequency in the text
we propose a novel context heterogeneity similarity measure between words and their translations in helping to compile bilingual lexicon entries from a non parallel english chinese corpus
i wish to thank kathleen mckeown and ken church for their advice and support and at t bell laboratories for use of software and equipments
to test the discriminative ability of this feature we choose two clusters of known english and chinese word pairs debate
figure NUM scenario template interim results
figure NUM extraction performance time line
figure NUM scenario template test results
when a substring of an unknown word coincides with other word in the dictionary it is very likely to be broken down into the dictionary word and the remaining substring
we begin with the two apos in a conjunction below containing a common apo f
run on a sun sparcstation NUM
at the same time NUM of errors in the repairing segments can be reduced for chinese homophone disambiguation
although the repetition repairs have the simple surface form correcting such a kind of speech repairs is not trivial
this is because the matched string usually denotes an emphasis when it is long enough
each character is pronounced as a syllable and many syllables are shared by several characters
thus chinese homophone disambiguation can be regarded as a process of conversion of syllable to character
that is NUM NUM errors are recovered by the repair processing
these patterns called type i cue patterns are used to increase the precision rate
this is because this kind of repairs is the most common form of repairs
table NUM lists the frequency distribution of each type of repairs in two conversations
in total this corpus contains NUM NUM utterances NUM NUM words and NUM NUM turns
the inside outside algorithm is a probabilistic parameter reestimation algorithm for phrase structure grammars in cnf and thus can not be directly used for reestimation of probabilistic dependency grammnrs
this process is applied until no merges can be done to the rules that scored poorly
we again use the equation for the derivative of the rank eq NUM but now
in other words one may prove a by showing that the negatton of a is false but the point is that the negation of a is another object than a i.e. the object to be proved has changed and indeed it can not reasonably be maintained that to negate a is a special way to prove a the two violations of the exclusion rule figure NUM
however in many cases the mapping from case marker to gf is not one to one
there are several romanization schemes for katakana writing we have already been using one in our examples
and transliteration is clearly an information losing operation aisukuriimu loses the distinction between ice cream and i scream
like most problems in computational linguistics this one requires full world knowledge for a NUM solution
the final stage contains all back transliterations suggested by the models and we finally extract the best one
other errors are due to unigram training problems or more rarely incorrect or brittle phonetic models
we have presented a method for automatic back transliteration which while far from perfect is highly competitive
n y e is a rare sound sequence but is written when it occurs
if we know from context that the transliterated phrase is a personal name this model is more precise
for example we can not drop the t in switch nor can we write arture when we mean archer
the differences between e v n and v n are substantially reduced and may remain slightly negative as in alice in wonderland or slightly positive as for moby dick or they may fluctuate around zero in an unpredictable way as in max havelaar
any japanese nlp application requ res word segmentation as the first stage because there are phonological and semantic units whose pronunciation and meaning is not trivially derivable from that of the individual characters
although the theoretical or expected vocabulary size e v n generally is of the same order of magnitude as the observed vocabulary size the lack of precision one observes time and again casts serious doubt on the reliability of a number of measures in word frequency statistics
zipf s law prescribes the asymptotic behavior of the relative frequencies of species as a function of their rank
the mean of ms f m and mct f m appears to approximate the population relative class frequencies mp f reasonably well as shown in table NUM for the trouw data as well as for alice in wonderland moby dick and max havelaar
in section NUM therefore i consider a number of possible sources for the misfit in greater detail nonrandomness at the sentence level due to syntactic structure nonrandomness due to the discourse structure of the text as a whole and nonrandomness due to thematic cohesion in restricted sequences of sentences paragraphs
samples of words generally contain often small subsets of all the different types available in the population
this may be due to the relatively small number of words that emerge as significantly underdispersed for this corpus
such as black and the other is for relative modifiers such as larger
relative entropy d pc pc2 is a measure of the amount of extra information beyond p c needed to describe pc2 the divergence between poe and pc NUM is defined as d pc lpc d pc lpcz and is a measure of how di icult it is to distinguish between the two distributions
cosma has understood the following time expression which is not consistent monday nov NUM NUM
cosma hat die folgende zeitangabe verstanden die nicht konsistent ist montag den NUM NUM NUM
it may inherit from different classes of operation contexts whose definitions are determined by the underlying domains of application
since b rejects the proposed date NUM a new loop is started by h NUM
most utterances concerning the domain of appointment scheduling are incomplete at least in two respects
note that managers can still be shared by virtual systems and they behavior can vary from one system to another
appointment denotes the interval of the appointment proper e.g. in NUM
duration on the contrary encodes the duration of the appointment expressed in minutes
however an nl system in a realistic application should not fail on unexpected input
the pasha ii interaction mechanism includes besides communication via tcp ip protocols e mail interaction
NUM john revised john s paper before the teacher revised the teacher s paper and bill revised john s bill s paper before the teacher revised the teacher s paper
based on a more comprehensive inquiry performed on the lexical database wordnet tm NUM NUM this paper presents a selection of pertinent checking rules and the results of their application to wordnet NUM NUM
terpretations of user utterances in the light of specialist knowledge brought to bear by the appropriate domain expert the discourse state which records the current status confirmed assumed etc of the parameters that apply to the dialogue objects and the request template which when fully populated is used by the handle transaction class a database driver to make a database access
according to our experience the potential of this methodology has not yet been fully exploited due to lack of understanding of applicable formal rules or due to inflexibility of available software tools
figure NUM word alignment for a german english sentence pair
the argmax operation denotes the search problem
q argmax lcb pr ejt l r f
NUM NUM alignment with mixture distri mtion
the notational convention will be as follows
assuming a tmifornl flignment prol ability
more significantly though it is our intention to use our suite of classes in implementations that support highly complex interactions with the user a single dialogue may range over several business domains each of which may use several distinct skill sets
note the similarity between equations NUM and NUM
front the first and the third since conf case lcb NUM rcb a v a anti co c ca e
NUM lemma NUM contexted constraints c NUM v c NUM is satisfialtle if t c NUM a NUM c NUM is satisfiable where p is a new propositional variable
the grammar used is the one trained by the algorithm described in section NUM a dynamic programming algorithm is used if there are two proposed constituents which span the same set of words and have the same isbel then the lower probability constituent can be safely discarded
the database contained only NUM short cuts with respect to the hyponym hypernym hierarchy for noun concepts and NUM short cuts with respect to the troponym troponymof hierarchy for verb concepts
we also know that case and case have no disjunets in common because they have no alternative variables in colnmon so icasc r case l icase l x icasc l
this phenomena is also observed in the vector model
the mean being NUM NUM NUM NUM
NUM NUM keywords experiment effectiveness of the method
figure NUM the sample of the article
table NUM the location of key paragraphs
figure NUM the structure of newspaper articles
table NUM the sample results of clustering
evaluation is performed by three human judges
table NUM the results of keyword experiment
to date our translation system for the scheduling domain has achieved performance levels on unseen data of over NUM acceptable translations on transcribed input and over NUM acceptable translations on speech input recognized with a NUM NUM word accuracy depending on the language
as well as providing the standard keyword in context facilities and giving access to the query language it gives the user sophisticated tools for managing the query history manipulating the display and storing search results
if one is creating an annotated corpus for public distribution then sgml is probably the format of choice and thus an sgml based nlp system such as lt nsl will be appropriate
the typical working style if you are concerned with syntax is to search for sequences of attributes which you believe to be highly correlated with particular syntactic structures
there are tools which interpret sgml elements in the corpus text as offsets into files of audio data allowing very flexible retrieval and output of audio information using queries defined over the corpus text and its annotations
what is required for markup correction are specialised editors which only allow a specific subset of the markup to be edited and which provide an optimised user interface for this limited set of edit operations
although tipster lets one define annotations and their associated attributes in the present version and presumably also in gate these definitions are treated only as documentation and are not validated by the system
it is not clear what level of evidence the performance of manning s system is based on but the system was applied to NUM NUM million words of text c f
null in figure NUM we estimate the accuracy with which our system ranks true positive classes against the correct ranking for the seven verbs whose corpus input was manually analyzed
the system consists of the following six components which are applied in sequence to sentences containing a specific predicate in order to retrieve a set of subcategorization classes for that predicate NUM
they report that for NUM verbs their system correctly predicts the most frequent class and for NUM verbs it correctly predicts the second most frequent class if there was one
the grammar consists of NUM phrase structure rule schemata in the format accepted by the parser a syntactic variant of a definite clause grammar with iterative kleene operators
a patternsets evaluator which evaluates sets of patternsets gathered for a single predicate constructing putative subcategorization entries and filtering the latter on the basis of their reliability and likelihood
we expect that a more sophisticated smoothing technique a larger acquisition corpus and extensions to the system to deal with nominal and adjectival predicates would improve accuracy still further
we then parsed the test set with each verb subcategorization possibility weighted by its raw frequency score and using the naive add one smoothing technique to allow for omitted possibilities
in particular we wanted to establish whether the subcategorization frequency information for individual verbs could be used to improve the accuracy of a parser that uses statistical techniques to rank analyses
for example building entries for attribute and given that one of the sentences in our data was la the tagger and lemmatizer return lb
finally the error identification module is responsible for updating any discourse information tracked by the system e.g. focus information
NUM vs NUM we restrict the head extra schema as follows for english we assume that both inherislash and inher extra have to be empty for all elements of extra dtrs
to implement the binding of extraposed elements we introduce an additional immediate dominance schema which draws on a new subtype of headstruc called head extra struc bearing the feature extra dtrs taking a list of sign
hal NUM iti struck a grammarian j last month that this clause is grammatical who analyzed it j
NUM ein buch j i hat er i geschrieben das a book has he written which ihn weltberiihmt gemacht hat j
german has a head final vp which entails that a verb in final position can form the right periphery of a phrase making extraposition of vp adjuncts and complements possible
NUM a paper i j just came out which you might be interested in which talks about extraposition i
NUM a paper i j just came out which talks about extraposition i which you might be interested in j
ll NUM extensive and intensive enquiries have been made into whether this fear of this penalty in fact deters people from murdering
generating an ltag out of a principle based hierarchical representation
because of this generative device we do not need to introduce lexico syntactic rules and thus we do not have to face the problems of ordering and bounding their application
dimension NUM syntactic realizations of functions it expresses the way the different syntactic functions are positioned at the phrase structure level in canonical cliticized extracted position
their corresponding terminal classes are created first by associating a canonical subcat dimension NUM with a compatible redistribution including the case of no redistribution dimension NUM
a terminal class is translated into its corresponding elementary tree s by taking the minimal satisfying tree s of the partial description of the class NUM
out of about NUM hand written classes the tool generates NUM trees for the NUM families for verbs without sentential complements NUM NUM of which were present in the preexisting grammar
network and partial descriptions of trees the generation process described above is quite powerful in the context of ltags because it carries out automatically all the relevant crossings of linguistic phenomena
identity of nodes is stated in our system by naming both nodes in the same way since in descriptions of trees nodes are referred to by constants
in the hierarchy of syntactic descriptions we propose the partial description associated with a class is the unification of the own description of the class with all inherited partial descriptions
then we trained decision trees in the mlr NUM configuration with varied numbers of training texts namely NUM NUM NUM NUM and NUM texts
with privative features other sentential constituents can add to features provided by the verb but not remove them
the lcs is assumed to be t unless one of these telicizing components is present
our approach is different from theirs in that their decision tree identifies which of the two possible antecedents for a given anaphor is better
null we draw upon these insights in revising our lcs lexicon in order to encode the aspectual features of verbs
a single algorithm may therefore be used to determine lexical aspect classes and features at both verbal and sentence levels
be loc NUM on loc NUM thing NUM
lexical aspect refers to the 0ype of situation denoted by the verb alone or combined with other sentential constituents
NUM i leave thing NUM toward away from by
with telic added by the np or pp yielding an accomplishment interpretation
NUM NUM experimental results on data set test
figure NUM parse tree with correct pp attachment
where the separate precision and recal l scores were both officially reported to be NUM
as a second step names that are designated as aliases are recorded as such
in the former case the users of the agent systems usually are not involved in the negotiation
as a consequence the dialogues modeled within the server represent only part of the complete multi participant negotiation
equality reasoning much of the strength of this inferential framework derives from its equality mechanism
note that event individuals are by definition only associated with relations not unary predicates
james and then exploiting any job change facts that held about thi s antecedent
facts that formerly held of only one individual are then copied to its co designating siblings
we then exploit inference to instantiate domai n constraints and resolve restricted classes of coreference
yesterday none mccann none made official what had been widely anticipated ttl mr ttl
the lexico n maps words to their most frequently occurring tag in the training corpus
has been identified as a title and assigned the part of speech nnp proper noun
this pre loading process may be very slow but it can be carried out whenever the user has time to do it and under circumstances when there will certainly be much less time pressure than during actual conversation
classes of words which occur with similar transitions
NUM factor out elements on the stack which are merely carried over from state to state which was done earlier in looking at the correspondence of state transitions to categorial types
one where categories are supplemented with a stack of unbounded length as above if restricted to right linear trees also as above is equivalent to a context free grammar
will have the analysis although there is no space in this paper to go into greater detail further constructions involving unbounded dependency and complement control phenomena can be captured in similar ways
taking as an example heavy np shift suppose that the corpus contained two distinct transitions for the word threw with the particle out both before and after the object
the only transition in 4a that differs from that of the corresponding word in the core variant 3a is that of dog which has the respective transitions null
to find the most probable parse for a sentence we simply find the path from word to word which maximizes the product of the state transitions as we have a first order markov process
firstly for each word type in the corpus we can collect the transitions with which it occurs and calculate its probability distribution over all possible transitions an infinite number of which will be zero
similar schemas are being investigated to characterize gapping constructions
if the summarization system can find the needed information in other online sources then it can produce an improved summary by merging information from multiple sources with information extracted from the input articles
the method can be applied in a semiinteractive process in which the system selects several new examples for annotation at a time and updates its statistics after receiving their labels from the user
also the average count is lower in a model constructed by selective training than in a fully trained model suggesting that the selection method avoids using examples which increase the counts for already known parameters
the maximum likelihood estimate for each of the multinomial s distribution parameters ai is i in practice this estimator is usually smoothed in n some way to compensate for data sparseness
in this work we assumed a uniform prior distribution for each model parameter we have not addressed the question of how to best choose a prior for this problem
for example sufficient statistics may yield an accurate NUM NUM probability estimate for a class c in a given example making it certain that c is the appropriate classification
we investigate the committee based method where the learning algorithm evaluates an example by giving it to a committee containing several variant models all consistent with the training data seen so far
for models with multiple parameters parame null ter estimates for different committee members differ more when they are based on low training counts and they agree more when based on high counts
we compare the amount of training required by different selection methods to achieve a given tagging accuracy on the test set where both the amount of training and tagging accuracy are measured over ambiguous words
an important application that we are considering is applying the technology to text available using other protocols such as smtp for electronic mail and retrieve descriptions for entities mentioned in such messages
concerning the performance of the taggers in unknown words we present in figure NUM as an example the hmm ts2 error rate for the tagset of the main grammatical categories which is also the worst case for this set of grammatical categories
the probability distribution of the tags of unknown words is significantly different from the distribution for known words while it is very close to the probability distribution of the tags of the less probable known words both in the english and french text
although it may seem trivial to the string tokenization problem the critical tokenization is in fact absolutely crucial
in the main category of tagset experiments the model parameters for the mlm systems are estimated accurately when the training text exceeds NUM NUM NUM NUM words unknown word error rate for the hmm ts2 tagger and the set of main grammatical categories
multiple chi square experiments were carried out by transferring successively a portion of NUM NUM words from the open testing text to the training text and by modifying the word occurrence threshold from NUM to NUM in order to determine the experimentally optimal threshold
we will also show that any tokenized string can be reproduced from a critically tokenized word string but not vice versa
binary search maximizes the searching speed of the first module while the following three transformation techniques decrease the computing time of the second module avoid underflow or overflow phenomena and use the faster and low cost fixed point arithmetic system
let g be an alphabet d a dictionary on the alphabet and s a character strings over the alphabet
NUM critical and hidden ambiguities this section clarifies the relationship between critical tokenization and various types of tokenization ambiguities
pn dn wn in which pi di and wi represent part of speech subdivision and word respectively
the stochastic approach generally attains NUM to NUM accuracy and replaces the labor intensive compilation of linguistics rules by using an automated learning algorithm
to derive a new stochastic tagger we have two options since stochastic taggers generally comprise two components word model and tag model
the area is a tangled thicket of examples in which readings are mysteriously missing and small changes reverse judgments
the generality of the approach makes it directly applicable to a variety of other types of ellipsis and reference
whereas one might expect there to be as many as six readings for this sentence dalrymple et ai
every korean sentence indicates whether honorification occurs in it
thus in computing social status speaker should be available
what is wrong with this counter example
NUM indsp indad NUM NUM inference in a coherent dialogue by a coherent dialogue we mean that there is no conflicting inference of social status from the sentences occurring in the dialogue
NUM indsp indad if a plain verbal ending is used the social status of speaker is equal to or higher than that of addressee as shown in NUM
thus dialogue NUM is incoherent in that the relative order of social status between the person m whose index is m and the person sungmin whose index is sm is not consistent
the diagrmn in NUM provides the contextual information that speaker shows honor to a subject referent and that the social status of the subject referent is higher than that of speaker and addressee
honorific forms of compound verbals in korean
nom ace meet past dec soonchul met minyoung
should we incorporate a language identifier in parallel to the recognizers or should we accept tile loss in recognition rate but enjoy the flexibility of a mixed language recognizer
the problem is to work out how to combine the meanings contribute d by each of the modes in order to determine what the user actually intends to communicate
the multimodal integration agent determines and ranks potential unifications of spoken and gestural input and issues complete commands to the bridge agent
units objectives and lines can also be generated using unimodal gestures by drawing their map symbols in the desired location
integration of spoken and gestural input is driven by unification of typed feature structures representing the semantic contributions of the different modes
we will demonstrate in our system multimodal integration allows speech input to compensate for errors in gesture recognition and vice versa
the initial application of our multimodal interface architecture has been in the development of the quickset system an interface for setting up and interacting with distributed interactive simulations
it should not be difficult to recognize bitext sections that consist of non linguistic text
finally if simr does get lost the resulting bitext map will contain telltale discontinuities
however many existing translators tools and machine translation strategies are based on aligned sentences
the algorithm s first step is to perform a transitive closure over the input correspondence relation
the chain recognition heuristic involves two threshold parameters maximum point dispersal and maximum angle deviation
then simr selects another region of the bitext space to search for the next chain
sorting the points by their displacement is the most computationally expensive step in the recognition process
all variants share however the rejection of phrasal nodes although phrasal features are sometimes allowed and the introduction of edge labels to distinguish different dependency relations
there are two methods for interpreting transducers
tt intuitively the automata is built by three approximations as follows
in traditional rewrite formalism such rules will be contradicting each other
i.e. r in any context minus r in all valid contexts
sc cr u coerce v NUM t
we shall describe our handling of rule features with a two level example
the above analysis is repeated below with the feature structures incorporated into p
NUM for an algorithm to decide the vc problem consider a data structure representing the vertices of the graph e.g. a set
nominal anaphora do not have unique forms as their zero and pronominal counterparts do
nametag is a side effect of the effort to create a fast robust and portable multilingual preprocessor called turbotag
first not all occurrences of the extraction concept can be encoded with an egraph
the egraphs required NUM syntactic categories NUM semantic classes and NUM lexical properties
hasten is implemented in allegro common lisp including a development environment written in clim
define the generator output script which maps the collector concepts to the template format
the muc NUM interim scenario task of labor negotiations provides another good application for hasten
this figure clearly illustrates that hasten has the ability to trade recall for precision
kevin hausman implemented all of the c code for the nametag engine
at the heart of this approach is that once well formed constituents can only grow they can never be dismantled
NUM when we adjoin a maximal tncb within an null other tncb nodes dominating the new well formed node are disrupted
there are always exactly n NUM non leaf nodes so the complexity of the test phase is o n
structural transfer can be incorporated to improve the efficiency of generation but it is never necessary for correctness or even tractability
even if generation ultimately fails maximal well formed fragments will have been built the latter may be presented to the user allowing graceful degradation of output quality
we take advantage of this by recording the constituents that have combined within the tncb which is designed to allow further constituents to be incorporated with minimal recomputation
for instance precedence monotonicity requires that the status of a clause strictly its lexical head as main or subordinate has to be transferred into german
considering the algorithm described above we note that the number of rewrites necessary to repair the initial guess is no more than the number of ill formed tncbs
in figure NUM the tncb composed of nodes NUM NUM and NUM is inserted inside the tncb composed of nodes NUM NUM and NUM
the former strategy includes feature based grammars with weak unification
the substring linking function the substring linking function is denoted a i
NUM the average run time per sentence
this creates too many possible parses per sentence
these functions are list in the following sections
in the previous example if we change rule
we refine the rule with our excluded category function
3the character is an aspect particle
encouraging experimental results with our current grammar are described
maintain the initiative if the response is expected follow up new new request take the initiative if the response is non expected somethingelse new indir request
our work differs from this in that we study general requirements of communication rather than rhetorical relations and their augmentation with speaker intentions to determine propriate responses
a competent agent relates the topic of her contribution to what has been discussed previously or marks an awkward topic shift appropriately otherwise the agent risks being understood
she also has the right to expect the partner to collaborate or at least not prevent the agent from achieving her goal
the result is a communicative goal c goal a set of communicative intentions instantiated according to the current task and role
it advocates a view point where the system s fnnctionality is iml roved by relating the dialogue situation to communication in general
response specifies the system s communicative goal up to the semantic representation using the same subtasks as analysis but in a reverse order
in cdm joint purpose represents the communicative strategy that an agent has chosen in a particular situation to collaborate with her partner
comparing the structural complexity of the two models is somewhat more difficult but we can make a graph theoretic abstraction and count the number of edges in model components
elc log n el c n elc log n ele
the improvement in word error rates of the transducer system was achieved without the benefit of the additional counts from unsupervised training mentioned above with NUM NUM utterances
we write the value of the function as f elc borrowing notation from the special case of conditional probabilities
such a transducer m is associated with a pair of words a source word w and a target word t
matching the nodes and arcs of the source fragment of an entry against a local subgraph including a node labeled by w
furthermore in this approach vectors require o n NUM memory space given that n is the number of words and therefore large data sizes can prove prohibitive
icm nc l for some y e i ncl rcb
for example proper names for companies become common nouns denoting their products
NUM NUM carol has two anxieties her job and her children
the pairs of features ct and pl impose semantic conditions
in addition each concrete particular can be seen as a minimal aggregate
another restriction is that inflected verbs agree in grammatical number with their subjects
NUM NUM the wiring and the piping is in the storeroom
semanticists have uniformly opted for specification whereas the facts call for non specification
the systematic connection between english mass and count nouns has long been known
a subject domain for the sentence is created by looking at the subject codes of each likely from the tests so far sense of every word in the sentence and at any document information available about the subject domain of the article e.g. a sports page
else NUM swap roles of speaker and hearer rat speaker mt hearer raa speaker ma hearer rat hearer rat speaker rad hearer raa speaker cue set cues in new data goto step NUM figure l training algorithm for determining bpx s
expecting to be followed by a verb p and p around noun phrases acting as objects p and p around adverbial or prepositional phrases or sub clauses thus for example a determiner may only be preceded by p or p or a pre determiner
lf monsieur le pr6sident notre gouvernement a prouv6 son adh6sion ces importants principes en prenant des mesures pour appliquer plus syst6matiquement les pr6ceptes de la charte figure NUM example pair of matched sentences from the hansards corpus
the best single word translation is thus officielles the best pair officielles langues the best translation with NUM words suivantes doug ddposer lewis pdtitions honneur officielles langues
for instance in responding to agent xs proposal of sending a boxcar to coming via dansville agent b may take over the dialogue initiative but not the task initiative by saying we ca n t go by dansville because we ve got engine i going on that track
we show that a set of cues which can be recognized based on linguistic and domain knowledge alone can be utilized by a model for tracking initiative to predict the task and dialogue initiative holders with NUM NUM and NUM NUM accuracies respectively in collaborative planning dialogues
in the second stage xtract identifies combinations of word pairs from stage one with other words and phrases producing compounds and idiomatic templates i.e. phrases with one or more holes to be filled by specific syntactic types
in this way the number of erroneous decisions made when si is used at the final pass is a lower bound on the number of errors that would have been made if si had also been used in the intermediate stages
our method in comparison takes o n log n time to sort n candidates by their local frequency fxy but it retrieves the frequency fy and computes the dice coefficient for a much smaller percentage of them
at this point we know the global frequency of the source collocation fx and the local frequency of the candidate translation word fxy but not the global frequency of the candidate word fy
in other words is there a possibility that a group of words has high similarity with the source collocation above the threshold and at the same time one or more of its subgroups have similarity below the threshold
we first measured the percentage of missed valid translations when either a or b or both do not pass the threshold but ab should for different values of the threshold parameter solid line in figure NUM
while each of these tools is based on simple statistics and tackles elementary tasks we have demonstrated with our work on champollion that by combining them one can reach new levels of complexity in the automatic treatment of natural languages
we instead choose to look more at the immediate context around a word by dividing collocation match weightings by the distance between the pair of collocating words expecting subject domain tagging see section NUM NUM to deal with more long range effects
if a multi word unit is found it is given an initial additional score a headstart over the words treated separately proportional to the number of words in the unit minus NUM but this can easily be canceled out by other scores
these issue s are hardly new they have been well known at least since the syntactic grammar vs
although such terms do not necessarily figure in recognized terminological thesauri it is obvious that some structure can be imposed on these terms for example to enable a user who is looking for a job in an eating establishment to be presented with jobs in a variety of such places
grammar rules are interpreted and formally expressed in terms of regular expressions of word substrings and exact hyphenation rules are derived
study the question of whether all sequences presented in the rules of table NUM exist within acceptable modern greek words arises
list mathematics mathematical dist physics physical dist mathematics physics dist mathematical physical
lists of exceptions seem to be obligatory in such an approach because their lack would lead to the generation of impermissible hyphens
it seems that in all cases it has to do with some weakest links along the pair of strings considered minimisation of a sum of distances
nevertheless the exact mathematical properties and especially the necessary and sufficient conditions on strings under which the above mentioned properties hold remain for the large part to be studied
the phthong sequences that modern greek permits are however restricted by principles of grammar that are assumed to be universal
NUM before proceeding to the presentation and analysis of these rules some terms that will be used need to be defined
the lolita system handles these by creating a series of sentences each of which is enclosed in quotation marks
another way of looking at parsing performance is examining the sequences of textref that get attached t o net nodes
once an event is disambiguated the system attempts to establish plausible connections between it an d the previously processed discourse
in general the cheape r heuristics are applied first before using the more powerful but more expensive deep heuristics
parsing can sometimes fail on very large forests decoding these requires a lot of resources time memory
the result of this stage is a parse forest a directed acyclic graph which indicates all possible parses
finally possible syntacti c categories for a word are determined from the lexical and sometimes semantic node information
the laboratory for natural language engineering lnle at the university of durham is focussed o n developing this core
these include patterns which recognize larger noun phrase structures than simple noun groups and patterns which recognize clausa l structures
the noun group patterns are essentially a direct transcription of that portion of our english grammar int o our pattern language
when an sgml form is generated from an annotated document rules must be applied to realize each type of annotation as a sequence of characters
writesgml document annotationset annotationprecedence sequence of string string converts a document together with a set of annotations into sgml format
as an illustration of this approach consider the result of annotating a document consisting of the sentence the kgb kidnapped arpa program manager umay b funded
this has been done for one of the slots in the example below the role slot of personnel but could have done it for others
if the architecture is operating in a networked environment this name will presumably consist of a host name and a unique name on that host
a central goal in creating the tipster architecture is for different modules to be able to share information about a document through the use of annotations
a system may ignore operators that it does not implement or it may map them to the nearest reasonable alternative in that system s query language
the capability to annotate sentences and tokens will be obligatory for a tipster system since so many other properties may be expected to assume their existence
instead of having the value of the attribute corresponding to that slot be a string it would be a reference to an annotation of type string annotation
and as we shall see more of the grammar crept in a s our effort progressed
model NUM is a hid den markov model hmm of the target language whose states correspond to source text tokens see figure l with the addition of one special null state to account for target text words that have no strong direct correlation to any word in the source text
the aim of this paper ix to explore the feasibility of this target tezt mediated style of imt in one parti ularly simph form a word onq h tion system which ai temltts to fill in the sutlixes of target text words from manually typed prefixes t
null a second small set identifies possessive forms involving either common nouns or names as just identified
another interesting characteristic of the data is the discrepancy between the number of correctly anticipated characters and those in completed suffixes
figure NUM a plausible state sequence by which the hmm corresponding to the english sentence i have other
j ai d autres cxcmplcs d autres pays cxamples from many other countries might generate the french sentence shown
the output probabilities vertical arrows depend on the words involved eg p d i lcb from NUM rcb p d i from matrices for these models they have the property that unlike hmm s in general they generate target language words independently
model NUM is similar to model NUM except that states are attgmented with a target token t osition cotnponent attd transition probabilities depend on both source and target token positions NUM with the topographical constraint that a state s target token t ositioll component must always match the current actual position
as each target text character is tylted a t rot osed omttletion for tit currenl word is tisttlayed if this is orreet the translator ntay a cept it att l l cgin typing the next word
to capitalize on this our system s french vocabulary is divided into two parts a small active component whose contents are always used for generation and a much larger passive part which comes into play only when the active vocabulary contains no extensions to the urrent t refix
as our methods of associating noun phrases by reference improves our ability to associate location information may improve as well
o wrong analyses the results are shown in table NUM
one such problem is sense disambiguation
figure NUM cover part bottom bottom part
the latter results in the production of a draft claim
second the order of templates in the output string is established
a full set of heuristics see in sheremetyeva et al NUM
it is easier for users to manipulate natural and not artificial language
the values of these case roles are supplied by the user l
a claim must be composed so as to make patent infringement difficult
every concrete invention is represented as an instance of the general schema
the result of linearization for our example is illustrated in figure NUM
an algorithm for clustering can be top down or bottom up
this also blurs the topic focus of a cluster
this feature is developed into an algorithm for clustering
our approach is different it is graph theoretical
chapter NUM proposes and discusses an algorithm for clustering
chapter NUM shows the relationships between clustering and transitivity
lexical databases have been employed recently in word sense disarnbiguation
they clustered verbs using the gravity of multivariate analysis
null a maximal subgraph c ot be missed
in general we have linked wordnet weight vectors to training weigth vectors
partial subcategorization frames sl s are judged as independent if for every subset sil si of j of these partial subcategorization frames NUM z the following inequalities hold
wefocus on recall being the discussion analogous for precismn
the influence of low precision training has produced this effect
their mechanism uses the blackboard organization which displays at a global level all pertinent information and has subroutines with specialty functions to update the blackboard
in addition if the physical state is a property then infer that the user knows how to locate the object that has the property
as the system proceeds on a given subdialog it should always be ready to drop it abruptly if some other subdialog suddenly seems more appropriate
the primary required facilities are a problem solver that can deduce the necessary action sequences and a set of subsystems capable of carrying out those sequences
one of the main tasks of this system is to supply the dialog machine with debugging queries such as what is the led showing
the dialog controller also provides a broader class of expectations called task related expectations which are based on general principles about the performance of actions
infer that the user has knowledge on how to observe a physical state if he or she has knowledge on how to achieve the physical state
makeanference proofnum mentalstate user int know action how to do goalaction true infer meaning
each meaning is matched with its corresponding dialog expectation and its expectation and utterance costs are combined into a total cost by an expectation function
figure NUM presents a zero level model of the main processor which illustrates the system principles of operation without burdening the reader with too much detail
NUM bilingual lexicon compilation without sentence alignment automatically compiling a bilingual lexicon of nouns and proper nouns can contribute significantly to breaking the bottleneck in machine translation and machine aided translation systems
inputs for this process were a susanne file and the corresponding combined file from the treebank i.e.
this can be conveniently represented as the stretch of terminal elements included within a pair of structural delimiters i.e.
an outside probability is the result of computing many inside probabilities
figure NUM is the pictorial view of the inside probability
figure NUM illustrates the steps of the revised outside computation
the mathematical model used here is to represent each category as a multinomial distribution
let a word sequence of length n be denoted by
last l returns the last state of layer i
many insides that are missed in the table are compositions of smaller insides
many of the high frequency words were technological or were japanese locations and companies
bout NUM returns the states from which layer NUM branches out
these were fbis documents from june and july of NUM on four different topics
the algorithm incrementally fixes each preposition into the configuration and the more informative pp1 training data is exploited to settle the competition for possible attachments for each subsequent preposition
the simplest explanation of this fact is that more inherently noun attaching prepositions must be occurring in 2nd and 3rd positions
NUM sears officials insist they do n t intend to abandon the everyday pricing approach in the face of the poor results
similarly the test set for pp3 is a subset of the pp2 test set of approximately NUM NUM NUM
although only NUM defining concepts are used the set of all possible combinations a power set of the defining concepts is so huge that it is very unlikely two word senses will have the same combination of defining concepts unless they are almost identical in meaning
the principle adopted in collecting co occurrence data is that every pair of content words which co occur in a sentence should have equal contribution to the conceptual co occurrence data regardless of the number of definitions senses of the words and the lengths of the definitions
conceptual expansion lcb lcb order judge punish crime criminal fred guilt court rcb lcb group word form statement command question contain subject verb write begin capital letter end
for example in a legal trial context the correct sense of sentence in the clause she was asked to repeat the last word of her previous sentence will be its word sense rather than its legal sense which would have been selected if a larger context is used instead
in the experiment a human subject is asked to perform the same disambiguation task as our system given the same contextual information NUM since our system only uses semantic coherence information and has no deeper understanding of the meaning of the text the human subject is asked to disambiguate the target word given a list of all the content words in the context sentence of the target word in random order
the equivalence classes are the models of the identity equivalence coreference relation
work on all six of the issues discussed here began at atr interpreting telecommunications laboratories in kyoto japan
the all objects recall and precision scores are shown in figure NUM
specifically we show that a privative model of aspect provides an appropriate diagnostic for revising exical representations aspectual interpretations that arise only in the presence of other constituents may be removed from the lexicon and derived compositionally
text filtering recall and precision for scenario test sets with approximately NUM richness
the evaluations are performed by one or more independent graders
the in and out object contains stspecific information that relates the event with the persons
nonetheless performance is much lower on this slot than on others
as mentioned parsability of spontaneous utterances can be enhanced by filtering hesitation expressions from them in preprocessing
figure NUM overall information extraction recall and precision on the st task NUM
instead of failing on unexpected input shallow parsing methods always yield results although they may not capture all of the meaning intended by the user
thus associating interface objects with ccms provides a flexible way of realizing distributed processing performed by components implemented in different languages and running on different machines
the core of the system consists of a tokenizer which scans the input using a set of regular expressions to identify the fragment patterns e.g.
the heart of an agent is the cooperative planning layer in which negotiation strategies are represented as programs and executed by a language interpreter
it includes the list of misspelled words found in the input message which may give the partner a clue for understanding the source of the error
the semantic representation of the input sines structure is explored by a set of rules in such a way that all information relevant for the appointment domain is captured
a scenario template st task captures domain and task specific information
once a clarification is provided the server attempts to build an il expression by merging and or replacing the information already available with the newly extracted one cf
peter a heeman and graeme hirst collaborating on referring expressions table NUM predicates and actions
achieve plan goal executing plan will cause goal to be true
it provides the link between the mental state of the agent and the planning processes
for this we use the propositions speaker speaker and hearer hearer
fourth it would specify how collaborative activity could be embedded in or embed other types of interactions
below are two excerpts from clark and wilkes gibbs s experiments that illustrate the acceptance process
NUM although our model does not account for the questioning intonation it could be a manifestation of the s postpone
only once it has been accepted will it be moved into the shared space also in mutual belief
the only goal is NUM which is to inform the user of the error in the plan
to achieve this goal the plan constructor builds an instance of expand plan previously shown in figure NUM
john said he called his teacher an idiot and bill said he insulted his teacher too
this as a by product tells us that the top is the top of the ladder
examples like NUM test the adequacy of an analysis at a fine grained level of detail
the student who revised his paper did better than the student who handed it in as is
however features of syntax that are not manifested in logical form are not taken into account
other cases in the literature indicate that the situation is more complicated than might initially be evident
in contrast the similar sentence given in NUM appears to have all four readings
nomena in table NUM we briefly discuss the algorithm s handling of lazy pronoun cases
while inheriting all principles to the most specific types and transforming the resulting constraints to a disjunctive normal form can significantly slow down compile times the advantage is that no inheritance needs to be done on line
figure NUM solution to the query wfs arthur sleeps h word zero bar subcat ne listj figure NUM solution for the query worda subcat ne list
constraints NUM NUM encode a simple version of x bar theory constraint NUM ensures the propagation of categorial information along the head path and constraint NUM ensures that complements obey the subcategorization requirements of the heads
the result in fig NUM shows that our x bar principles have applied bar level two requires that the subcat list must be empty and bar level one can only appear on phrases
as the grammar constraints combine sub strings in a non concatenative fashion we use a preprocessor that chunks the input string into linearisation domains which are then fed to the constraint solver
we assume that the grammar writer guarantees that each type in the grammar is consistent for a grammar g and every type t there is a model of g that satisfies t
figure NUM phrase structure rules and the lexicon
now consider the principles defined in fig NUM
but now all the items that fall under anon will be encoded as the same term
on each category where a type feature specification is present add the selector feature also
this rule just says that an x is a valid parse
i described this technique as expressing a limited notion of default
this feature sends the default value something as the out value
the compilation technique given here assumes that the types are atomic
on the categories of the rule introducing the constraint coindex the feature specifications as follows
it will of course generalize to any other area having the same structural properties
we then introduce rules expanding xcomp as the real category corresponding to that feature
the store of the x is the out value of the y
in the processing of spontaneous language the need for predictions at the morphological or lexical level is clear
but in none of these cases would subsequent errors result if upon exiting the subdialog the offending information were popped off a discourse stack or otherwise made inaccessible
we assume that each intermediate conclusion is put into high focus when it is presented as a newly derived conclusion or cited as a reason supporting the derivation of another intermediate result
as zipf s law would predict there is a long tail of word types which occur too infrequently to permit gathering useful statistics
the hypothetical user satisfaction ratings shown in table NUM range from a high of NUM to a low of NUM
svd infers context similarities between words that may not be apparent in the original co occurrence matrix due to the natural randomness in any corpus sample
and secondly what constitutes wheat and chaff is different for each domain so this dieting must bc repeated lot every port
but all rules have exceptions and often it turns out these exception s are not isolated or random so tile rule is finetuned
NUM word types tagged as verbs occurred frequently enough 10x in the training corpus to warrant constructing a vector or context digest
we are completing the named entity translation into c and expect to do the same wit h coreference at a later date
we were forced to divide the development and test articles and their answer keys into subsets making the scoring process even longer
translation from emacslisp into c the semi automated scoring program was originally developed for muc NUM in emacslisp by general electri c using saic specifications
we will show how critical the mapping algorithm is in scoring and the problems inherent in deciding what the best mapping should be
the scores for a particular slot fill are tallied as follows possible the number of fills provided in the key
the best key set is selected according to a pre determined metric and the tallie s for the remaining key sets are ignored
over the years we have moved to complete automation of the scoring and have tried out several differen t approaches to mapping
however named entity input could still be internally represented as a low level template no pointers object with slot fills
similarly the total slot score i s the total of all the slots for all the objects across all the documents
this change to c also allows us to implement all of the old features and add features that were put on hold because of the limitation s of emacslisp
NUM reasons in closed attentional spaces a reasons that are the root of a closed attentional space immediate subordinate to the active attentional space are structurally close
NUM a the authority will be accountable to the financial secretary NUM
NUM the e authority t will be accountable t to the c financial l secretary j
let s examine the interpretation of compounds
output rrecords that can never be created
it is queried frequently by the discourse processor
several systems performed at NUM or above
the scoring mechanism suffers from the linchpin phenomenon
the results are graphed below in figure NUM
further work would make it even more effective
there is one consistency checker for every factbase
instructional texts do not simply consist of lists of imperatives instructions may also describe eulogise inform and explain
NUM dial the numbers of the mercury authorisa null tion code by pressing the appropriate numbers on the keypad
for portuguese enablement two relations are preferred purpose and sequence with a strong preference for the latter
in this way we can encode language specific pragmatic principles into tools that support the process of multilingual document production
imperative ho n i nominal imperative nominal npc e
there was a high degree of agreement m what constituted all example of each
ilow then are generation and enablement realized in the three languages of study
NUM avant l emploi faites tremper le boyau dans de l eau ti6de
s in french discourse markers do not accompany all expressions of generation
although omit and implicit forms lead to the same surface structure the existence of an implicit hint in the other part of the verbalization affects a reader s understanding
this distribution is weighted to allow for productivit3 differences between schemata
interannotator scoring showed that one annotator missed tagging one instance of coke as an optional organization and the other annotator misse d one date expression september
the task documentation appendix e includes definition of an artifact entity but that entity type was not used i n muc NUM for either the dry run or the formal run
other sources of excitement are the spinoff efforts that the ne and co tasks have inspired that bring these tasks and their potential applications to th e attention of new research groups and new customer groups
any participant in a future muc evaluation faces the challenge of providing a named entity identificatio n capability that would score in the 90th percentile on the f measure on a task such as the muc NUM one
another set of issues is semantic i n nature and includes fundamental questions such as the validity of including type coreference in the task and th e legitimacy of the implied definition of coreference versus reference
table NUM contains a paraphrased summary of the output that was to be generated for each of these events along with a summary of the output that was actually generated by systems evaluated for muc NUM
the organization template element objects are presen t at the lowest level along with the person objects and they are pointed to not only by the in and ou t object but also by the succession event object
one of the innovations of muc NUM was to formaliz e the general structure of event templates and all three scenarios defined in the course of muc NUM conformed to that general structure appendix e
an example can be found in press and hold the mouse button while you move the mouse
the two sgml based tasks required innovations to tie system internal data structures to the original text s o that the annotations could be inserted by the system without altering the original text in any other way
even the simplest of the tasks named entity occasionally requires in depth processing e g to determine whether NUM pounds is an expression of weight or of monetary value
NUM shows possible interpretations for cotton bag with associated probabilities
the maximum entropy framework naturally combines the good sides of the two methods and at the same time it accounts for the interactions between features
as the linear interpolation the back off method does not account for possible interactions between different knowledge sources which can lead to overestimation of some events
they must first be discharged by the scoping rule which substitutes the terms and indices by a bound variables
note though that this order dependence applies at the level of evaluating qlfs not constructing and resolving them
imagine that in the example on figure NUM most of the times when we saw the feature b we saw the feature c as well
we need estimates of NUM wl and of NUM w0 the probability of observing a new word w0 at node s
in the table below each adjacency category is accompanied by an example
we constr our features to their reference probabilities p xk p xk and using equation NUM obtain
however some changes need to be made in order to accommodate new material introduced by the ellipsis
ellipsis resolution thus amounts to selecting an antecedent and determining a set of substitutions to apply to it
but this view emerges naturally from our treatment of substitutions and is arguably a more natural characterisation of the phenomena
sloppy identity corresponds to copying the function but applying it to the event of the ellided clause
this is now illustrated with a more interesting example adapted from hirshbfihler as cited by dsp
in our account the choice of strict or sloppy substitutions for secondary terms can constrain permissible quantifier scopings
the interpretation of the ellipsis is then given by applying this predicate to the subject of the ellipsis
however we extend the notion of strict and sloppy identity to deal with more than just pronouns
entry cons continuation car entry define push result
then we observe the occurrence of both the antecedent and anaphor in the topic position to see the effect of topic on zero anaphora
after these definitions have been loaded an expression such as the one in NUM can be evaluated
to determine the applicability of the new constraint to each anaphor we had to access the discourse segment structures of the test data
NUM define terminal word lambda continuation poe if and pair
if the memoized procedure has been called with args before the results associated with this table entry can be reused
only two speakers agree with one another with a kappa value of more than NUM NUM none with a value of greater than NUM NUM
since all systems produce the same result on text NUM unsurprisingly they all have the same matching rate as shown in table NUM
NUM this problem can arise even if syntactic constructions specifically designed to express mutual recursion are used such as letrec
for example suppose s is defined as in NUM and vp is defined as in 2a
figure NUM screen dump of the annotation tool
we simulate this computer by hand and note down the difference between the anaphora generated by the computer and those in the test data
for example the black dog and the brown dog are distractors to each other because they are of the same category dog
to this end we applied the igtree formalism to the task
memory based learning is a form of supervised learning based on similarity based reasoning
the NUM adjacency relationships used by the disambiguator are listed below
the current word error rate of the esst recognizer is about NUM
reduction in the number of senses with a corresponding precision of NUM that indicate a good agreement between wordnet and the system classifications many classes are pruned out lower recall but most of the remaining ones axe among the initial ones
this feature enables users to make notes or annotations to a document on line
users can match any string with any part of a word or phrase
since these exceptions are english specific they can not be explained via pragmatics
in fact she had an f measure of NUM NUM for that slot
this attitude may be less appropriate when there is less of an overlap between the vocabulary of the mrbd and the vocabulary of the training bitext as when dealing with technical text or with a very small mrbd
unfortunately this criterion produces false negatives for pairs like government and gouvernement and false positives for words with a great difference in length like conseil and conservative
if word t in a target sentence is the translation of word s in the corresponding source sentence then words occurring before s in the source sentence will likely correspond to words occurring before t in the target sentence
measuring precision is much more difficult because it is unclear what a correct lexicon entry is different translations are appropriate for different contexts and in most cases more than one translation is correct
candidate word pairs are drawn from a corpus of aligned sentences s t is a candidate iff t appears in the translation of a sentence containing s in the simplest case the decision procedure considers m1 candidates for inclusion in the lexicon but the new framework allows a cascade of non statistical filters to remove inappropriate pairs fl om consideration
this statistic actually represents the percentage of words in the target test corpus that would be correctly translated from the source if the lexicon were used as a simple map therefore if the lexicon is to be used as part of a machine assisted translation system then the percent correct score will be inversely proportional to the required post editing time
as shown in figure NUM the only independent variable in the framework is the cascade of filters used on the translation candidates generated by each sentence pair while the only dependent variable is a numerical score
in this study the average is computed by type and not by token because translations for the most frequent words are easy to estimate using any reasonable statistical decision procedure even without any extra information
although there was some overlap an average of NUM of the words in each sentence were paired up with a cognate or with a translation found in the mrbd leaving few candidate translations for the remaining NUM
each of these nouns or proper nouns is converted from their positions in the text into a vector
all vectors from english and chinese are matched against each other by dynamic time warping dtw
NUM one main advantage of having such conditions is the preservation of the modularity of transfer equivalences because we do not have to specify the translation of the particular verb which only triggers the specific translation of the adverbial
in a purely monotonic system without overriding it would be possible to apply the transfer rule in 6b to sentence NUM in addition to the rule in NUM leading to a wrong translation
t on tuesday in may at three at e lster file class deiinitions in lla arid llc cluster together those prepositions which can be used t o express a temporal location
for instance in the specific t anslation of schlecht to not good as defined in 3c without conditions one would have to add tile verb passen into the bag to test for such a specific context
semantic operators like negation modals or intensitier adverbials such as really take extra label arguments fi r referring to other elements in the flat list which m e in the relative scope of these operators a
we are currently working on identifying full noun phrases and compound words from noisy parallel corpora with statistical and linguistic information
proper names in hong kong there is a specific system for the transliteration of chinese family names into english
the main focus then was accurate alignment but the procedure produced a small number of word translations as a by product
word frequency and position information for high and low frequency words are represented in two different vector forms for pattern matching
word pairs when the sentence alignments for the corpus are unknown standard techniques for extracting bilingual lexicons can not apply
they were not in the first lexicon because their frequencies were too low to be well represented by positional difference vectors
here for prosperity m NUM NUM which shows that these two words are indeed highly correlated
in a more general perspective tile notion of similarity between linguistic objects plays a central role in many corpus based natural language processing applications
grieg grieg a difficulty with the descriptor slot is its mixed role
they need to know for instance that a longer than usual silence is not signalling the termination of the interaction and this can be achieved through the inclusion of such utterances as hang on
without this print out looks one in vain for a hint why the auto continue function in the postscript emulation does not work
the solution we propose starts from the observation that additional constraints on valid antecedents are placed by the global discourse structure previous utterances are embedded in
the theme of un is represented by the preferred center cp un the most highly ranked element of c un
hence the centered discourse segmentation procedure works in an incremental way and revises only locally relevant yet globally irrelevant segmentation decisions on the fly
the segment counter s is incremented and a new segment at level NUM is opened setting the beginning and the ending to NUM
im betrieb macht e gr durch ein kr iftiges arbeitsger usch auf sich aufmerksam das auch im stand by modus noch gut vemehmbar ist
except for one meager page containing the table of contents for the hp mode seeks one in vain for further information
limitations of working memory are modelled in the parser by associating a cost with each stack cell occupied during each step of a derivation and recency and depth of processing effects are modelled by resetting this cost each time a reduction occurs the working memory load wml algorithm is given in figure NUM figure NUM gives the right branching derivation for kim loves sandy found by the parser utilising a grammar without permutation
the correct pronunciation of names is one of the biggest challenges for text to speech tts conversion systems
our training material is based on publically available data extracted from a phone and address directory of germany
together with the basic street name markers these components were used to construct a name analysis module
the finite state transducer that this grammar is compiled into is far too complex to be usefully diagrammed here
the second set henceforth test data was extracted from the databases of the cities frankfurt am main and dresden
on the training data the new system outperforms the old one in NUM of the NUM cases NUM NUM
one obvious area for improvement is to add a name specific set of pronunciation rules to the general purpose one
third onomastica developed name specific grapheme to phoneme rule sets whereas we did not augment the general purpose pronunciation rules
in the transition between hecke and allee a fuge n has to be inserted
after some editing and data clean up the final list of linguistically motivated street name morphemes contained NUM NUM entries
paraphrasing and aggregating argumentative text using text structure
a word or a word collocation like operating room can occur in any number of synsets with each synset reflecting a different sense of the word
figure NUM the realization class for derive
for a comparison see sec NUM NUM
conjunction between two finite verb forms
due to its high frequency of occurrence
the sampling was as follows all texts shorter than NUM NUM words were excluded and all others were truncated at NUM NUM words
needed for the detection of the particular error type as for error typology developed for the purpose of the error detection techniques used in the l roject of
more experience is also required to formulate strategies to choose among alternatives
ill particular this means that measures are to be tbund which would allow for sl litting the input sentence into clauses by purely superficial criteria
constraints of the whole input could not have i een perforlned on the original st ring which would have hindered the error messa ge
of course tile wordforms of the verb must be mmmbiguously identifia me as such i.e. such tbrms as eu j d u lral lm
rule prescribing that there alwa ys
the paper describes several possibilities of using finite state automata a s means for speeding up the performance of a grammar and parsing based as opposed to patternmatching based grammar checker able to detect errors from a predefined set
second text structure provides us stronger means to specify textual operations
the lflst argtuncnt indicates tile type of composition fl r complement incorporation
t answers to the questions are of various types boolean categorical integer sets of integers
the only other piece of work the author has found which aims to measure similarity between corpus most resemble each other
this in turn may trigger the execution of a cue word rule
does it matter whether lexicographers use this corpora or that or are they similar enough for it to make no difference
this measure i s quite close to our evaluation f measure of NUM NUM
because each of the child s responses is known to the system and is predictably limited to those available in pictalk it might be possible for the computer to converse fairly naturally with the child
these grades are used for both in domain and out of domain sentences
in the travel domain a number like twelve fifteen
the recall was NUM all the labels concerned were flagged
another possibility is the creation of views or masks
dutch sublanguage semantic tagging combined with mark up technology
be lcb nhan sager cs nyu
NUM operative procedure quintuple coronary bypass
luckily this process can be automated
NUM pre operative diagnosis coronary sclerosis
the first page is conceived as a menu window
the node number NUM only has the label h ttchir
NUM NUM r ecycling the results of robust
here let the i th similarity in b be vsm a b and let path a b denote the path between words a and b in the thesaurus
it is an obvious fact that the czech tagset is totally different from the english tagset
a serious advantage of the described approach is that in mbl the back off sequence is specified by the used similarity metric without manual intervention or the estimation of smoothing parameters on held out data and requires only one parameter for each feature instead of an exponential number of parameters
we present a filtering method and a repairing parsing strategy which fit in a complete system architecture
computer simply acknowledges processing the last user utterance
figure NUM sample dialogues directive and declarative
the robust parsing runs in real time on an sgi indigos2 impact r4400 NUM mhz
table NUM shows the actual breakdown in percentages
subjects participated in the experiment in three sessions
NUM recent empirical studies relevant to human computer mixed initiative
smith and gordon human computer dialogue NUM NUM summary of results
for example in situations where the repair subdialogue was not explicitly verbalized it was not clear whether subsequent descriptions of the circuit behavior indicated that the current subdialogue was test or assessment
for our domain we expect a reduction in the number of utterances spoken in declarative mode since the repair process adding a wire is similar across the different problem types
otherwise the two sets of experiments are qualitatively similar
the distance between two probabilities for the best and second best alternative pbest and psec ond is measured by their quotient
information on learnability and user response can be elicited via a subject survey and through comparison to alternative forms of user interface for completing the same task e.g. discrete speech versus continuous speech keyboard vs speech input and speech vs multimodal input
it is hoped that system failure will disappear and be replaced by system robustness that is a measure of how well a system responds in error situations either because of misunderstandings by itself or because of misstatements by the human user
when the algorithm was applied to the NUM rule grammar shown in figure NUM it was not possible to complete the calculations for any ordering of the rules even with the improvement mentioned in the previous section as the automata became too large for the finite state calculus on the computer that was being used
since approximately the mid NUM s technology has been adequate if not ideal for researchers to construct spoken natural language dialog systems snlds in order to test theories of natural language processing and to see what machines were capable of based on current technological limits
comparing the speed at which someone can obtain information over the telephone by using a speech based interface as opposed to the ubiquitous touch tone interface with exhausting menu hierarchies that most businesses have this seems to be true of businesses in the united states might be very illuminating indeed
for example some inputs to the system will be contextually self contained e.g. the red switch is in the off position when there is only one red switch in the domain while other inputs require the use of dialog knowledge to be understood
theoretically this simplification could be applied to a variety of vowel sequences but examination shows that acceptable words containing such sequences do not always exist and not all sequences split
in other words the maximal consonant prefix of a word is always hyphenated with the following vowel and the maximal consonant suffix of a word is always hyphenated with the preceding vowel
the apparent advantage of top down parsing is thus lost when llpsgs are to be parsed
furthermore the analysis fails in those cases where tile correct position is rated lower titan this value i e
table NUM shows the correspondence between the NUM and b3 labels not taking turn final labels into account
a lexical rule guarantees that the selector shares all relevant information with the dsb value of the selected verbal projection
it is a well known fact that these two phenomena often are orthogonal to each other
with this method in theory the mlp estimates posteriori probabilities for the classes under consideration
the methods described in this paper have been implemented as part of the ibm synsem
yesterday fixed he the car yesterday he fixed the car
in a v2 clause the scope of the verb is determined with respect to the empty verbal head only
1t is preserved in la although the verb shows up in a different position
on each trial the learner first makes a prediction and then receives feedback which may be used to update the current hypothesis the vector of weights
in particular we study mistake driven learning algorithms that are based on the winnow family and investigate ways to apply them in domains with the above characteristics
while we have chosen in this study to use a fairly simple set of features it is straight forward to plug in instead a richer set of features
the treatment described there addresses at the same time at least two different concerns length variation of documents and feature repetition
now algorithm which incorporates the NUM range modification a square root of occurrences as the feature strength and the discard features modification balancedwinnow in table NUM
continuing this line of research we present different algorithms and focus on adjusting them to the unique characteristics of the domain yielding good performance on the categorization task
in addition a training algorithm may give also advice on the issue of feature selection by reducing the weight of non important features and thus effectively discarding them
formally let w be an auxiliary symbol not in the grammar s alphabet and let w ida w be a place holder representing r
on the other hand rimon and herz s next looks beyond the empty constituent in a way that conditions NUM NUM do not so initial approximation r s s o s s z a
while there is truth to this the rest of this paper describes a representation of language that bypasses many of the apparent difficulties
succinctly leaving the cost of pointers to component words as the dominant cost of both the lexicon and the representation of the input
it is difficult to imagine any induction algorithm learning kicking the bucket from this corpus without also mistakenly learning scratching her nose
this paper discusses the problem of learning language from unprocessed text and speech signals concentrating on the problem of learning a lexicon
both problems can be solved if linguistic units for now words in the lexicon are built by composition of other units
work we have performed other experiments using this representation and search algorithm on tasks in unsupervised learning from speech and grammar induction
seem to imply that any unsupervised language learning program that returns only one segmentation of the input is bound to make many mistakes
finally since add and delete cycles can compensate for initial mistakes inexact heuristics can be used for adding and deleting words
thus we avoid explicit explanatory statements about the complex interrelation between word order and syntactic structure in free word order languages
as for the second condition we use an auxiliary symbol w as a place holder representing r introduce p freely then substitute r in place of w
however it is not yet possible to draw general conclusions about the relative efficiency of the two procedures
in our work the sense of an ambiguous word is represented by a feature whose value is missing
pereira and wright s algorithm gives an intermediate unfolded recogniser of size exponential in n for these right linear grammars
it might be possible to subdivide query w into theoretically interesting categories rather than using it as a wastebasket but in the map task such queries are rare enough that subdivision is not worthwhile
finally gg6 avoid obscurity and gg7 do n t be ambiguous may on occasion be difficult to distinguish obscure utterances sometimes lend themselves to a variety of interpretations
the difficulties lie not only in the initial conception of for instance a new tool or in tool drafting and early in house testing
they also sometimes engage in subdialogues not relevant to any segment of the route sometimes about the experimental setup but often nothing at all to do with the task
the abandoned game subcode turned out to be so scarce in the cross coding study that it was not possible to calculate agreement for it but agreement is probably poor
in addition there were a few cases where coders were allowed to place a boundary on either side of a discourse marker but the coders did not agree
roe reclassification to already identified case agreed reclassification of a design error case as being identical to one that had already been identified
in addition to the initiation and response moves the coding scheme identifies ready moves as moves that occur after the close of a dialogue game and prepare the conversation for a new game to be initiated
verbal acknowledgments do not have to appear even after substantial explanations and instructions since acknowledgment can be given nonverbally especially in face to face settings and because the partner may not wait for one to occur
the purpose of the most common type of align move is for the transferer to know that the information has been successfully transferred so that they can close that part of the dialogue and move on
the auxiliary symbols not involved in those rules could then be removed before the application of NUM NUM
r a s s i z s s i l s s i s s i NUM a
consequently lexical distinctions correlate with non equiwflent sets of associated inference s
tile occurence of a variable preceded by t is blocked
a detailed lexical representation of leihe n will be given in section NUM
however our central goal is an integration of d113 and set
the field as a whole is characterized by a bsf
figure NUM the posmon of employer in the net does not reflect tts gloss
again as described in section NUM we calculate probabilities for alternative candidates in order to get reliability estimates
with a feature weighting metric such as information gain mbl is particularly at an advantage for nlp tasks where conditioning events are complex where they consist of the fusion of different information sources or when the data is noisy
the choice made would not effect the correctness of the compilation
NUM NUM of sentences can be parsed with NUM recall NUM NUM precision aud NUM NUM crossings per sentence
thus simk x specifies a ordering in a collins 8z brooks style back off sequence where each bucket is a step in the sequence and each schema is a term in the estimation formula at that step
in our prp iminary experiments we also found out that the information are potential for characterizing constituents in a sentence
during iteratively merging the most slm l r labels all labels will finally be gathered to a single group
basically divergence as well as relative entropy is not exactly s nnilarity measure instead it indicates distributional dissimilarity
sims at to most probabilistic models and our clustering process there is a problem of low frequency events in this model
the experiment is done by selecting the top jv of contexts and use it instead of all contexts in the clustering process
one common problem was the simple failure to recognize hire as an indicator of a succession
since japanese sentences have no delimiters e.g. spaces between words a morphological analyzer tagger must decide word segmentation in addition to part of speech assignment
another example is the following NUM in the past customers had to vp go to ibm when they vp outgrew the vax
an antecedent that also occurs within quoted material
q NUM association for computational linguistics computational linguistics volume NUM number NUM syntactic configurations and then ranks remaining candidates using preference factors involving recency parallelism clausal relations and quotation structure
NUM since the blind test examples are all taken from the wall street journal corpus it is most appropriate to compare the blind test results directly to the results on the wall street journal corpus
NUM while the precise formulation of principle b remains controversial it is generally agreed to rule out for example the binding of a pronoun in object position by an np in subject position
each semantic dialogue unit is analyzed into an interlingua representation
he asked between wheezes of laughter
row NUM shows the results of applying our baseline prediction method to the various corpora
predicts initiative shifts based on the current initiative holders and and the effects that observed cues have on changing them
there is no difficulty in computing a lattice from spotted phones given information regarding the maximum gap and overlap of phones
adjust bpa adjusts the bpa s for the observed cues in favor of the actual initiative holder
they usually merely require a direct response and thus typically do not result in an initiative shift
the numbers shown are correct predictions in each instance with the corresponding percentages shown in parentheses
this paper discussed a model for tracking initiative between participants in mixed initiative dialogue interactions
the reasons for selecting the dempster shafer theory as the basis for our model are twofold
previous work on mixed initiative dialogues focused on tracking a single thread of control among participants
in the rthis is the value that yields the optimal results figure NUM
if this query results in a successful match then the dialogue is in this state
the average length of esst push to talk utterances is NUM NUM words
null quantity regulators measure out events such as gokiro aruku walk 5kin
they play a role of framing the interval on which the focus should be brought
table NUM a part of the array modified verb adverb class labels pigicl c2 aiqie
NUM but different from it in scale and determinability of the categories
NUM non gradual process verbs are those that express only processes and not changes of state
the task for korean texts requires extra efforts due to the complexity of inflections
they uses three features dynamic telic and atomic
however these constraints on the inherent features of verbs are only concerned with a single event
because each principle is formulated to be as general as possible the logical abstraction of each principle from the others causes a lot of overgeneration of structure and consequently a very large search space
in cross talk dialogues participants can speak freely and their speech can overlap
however the discrimination of the categories needs negative evidence which we can not use by definition
in step NUM we classify adverbs on the basis of the discussion in the previous section
NUM calculate the similarity of every pair of the derived labels
its extension is subject to fllrther investigations
we show that the two approaches are closely related and we argue that feature weighting methods in the memory based paradigm can offer the advantage of automatically specifying a suitable domainspecific hierarchy between most specific and most general conditioning information without the need for a large number of parameters
word classification can solve the problems of data sparseness and have far fewer parameters
they corr spond to words t l NUM NUM NUM and more than NUM cha ra ters with group sizes equal t NUM NUM NUM NUM and NUM rt stmctively
a language model as a t ost processor is esse ntial to a recognizer of speech or characters in order to determine the approi riate word se que n e and henc e the semantics of an ini ut line of text or utterance
all tut li ate entries are coral trier1 i.e. if is a word as well as a suflix tilt two entries arc combined into one with a n indication that it can serve as a word as well as a suffix
tllc t rot os d algorithm of this t al r makes use t a f rward ma ximmn matching st ra t gy to i hultify w r s in this r sl l this algorithm is a structural atll roa h
the greedy model was defined such that the result of the greedy merging strategy is assigned the appropriate probability pk with the remainder of the probability mass NUM p rcb distributed uniformly among the remaining possible alternatives
in the first case some general tutoring should be given explaining that agreement exists in the language the circumstances in which the agreement needs to be marked and the iform the agreement should take
NUM NUM encoding the vertex cover problem in discontinuous dg
the hard constraints correspond to paragraph boundaries
since our scheme permits crossing edges visualisa tion
figure NUM shows a screen dump of the tool
sets of correspondence points to alignments
what can simr do for them
it has a highly deviant slope
a geometric approach to mapping bitext correspondence
noise filters would be more effective
table NUM comparison of alignment algorithms
each cell in the grid represents the product
NUM tile tagger suggests partial or cornplete parses
then we proceed showing that the equivalence claim holds
the tree resembles traditional constituent structures
however this resulted in only about NUM NUM improvement
part of speech is annotated at word level
since we have been unable to find a polynomial time algorithm to train the general fertility model we use the poisson model to expose the hidden alignments
separable verb prefixes are labeled svp
a pa ttern br a la rger doma in of loca lity tends to give a shorter deriva tion
using a set of parameters the matcher computes the similarity between them
hasten represents the extraction examples using a data structure called an egraph
july NUM and will retire as chairman at the en of the year
the collector collects and merges the semantic information according to concep t specifications
the NUM test documents had a size of NUM NUM characters
nametag can process text without the use of these names
walter thompson last september as vice chairman chief strategy officer world wide
furthermore the order o f encoding the examples may also effect performance
an example of an ending time case that is not handled is the utterance let smeet until thursday under the meaning that they should meet from today through thursday
we can also distinguish local and head fea tures a s postula ted in i i sg
table NUM shows the processing time for the three officia l
hasten performed reasonably well achieving a recall precision of NUM NUM
ich la noch trainieren bin because i there still train am the segmentation parser is able to segment NUM of the NUM turns with NUM utterances correctly
the fundamental difference between a context model and an extension model lies in the inputs to the context selection rule not its outputs
for the base case n NUM and the statement is true by the definition of validity constraints 4a and 4c
copy area would be handled using the lexical function sioc which returns the name of the location associated with an activity
this criteria measures the statistical efficiency of a model class according to traditional model validation methodology tempered by a healthy concern for overfitting
nodes are labeled as supplying information about a particular entity or nbbout NUM l x
the modifier of h in dependency d is accessed by
by this criteria the extension model class is better than the context model class and both are significantly better than the ngram
the negative evidence of r w r t
for example suppose spud has the goal of describing the part of the library where copying takes place location e30
thus the following fsa defining the regular language l aa b i.e. an even number of a s followed by at least one b is given as start qo final q2
let w be some non null string
as suggested by an acl reviewer one could also try to model haplology phenomena such as the s in english sentences like the chef at joe s hat where joe s is the name of a restaurant using a finite state transducer
a complexity analysis of algorithm NUM is straightforward
we schematically specify our first learning algorithm below
stop as soon as a symbol is not matched
a klvdp iool nil n h dovmlall
indeed the experience of the information retrieval community has indicated that idf is a very useful quantity
it is this task that we address here
supported by a grant from the esrc
three structuring strategies are being investigated
this is harder than it might at first seem
figure NUM comparison of scores for a NUM clause grid
since broad coverage parsers for german especially robust parsers that assign predicate argument structure and allow crossing branches are not available or require an annotated traing corpus cf
we also examine the usefulness of various heuristics in forming these segmentations
as such it only operates on clauses that are not sentenceinitial
much better fits are obtained by introducing a second parameter such as inverse document frequency idf
the approach can not restrict discontinuities properly however
i conclude that it is hard to estimate the japanese model from only an untagged corpus and a dictionary
several systems performed at NUM or above
all other sgml tag pairs are read but ignored
compared to plum s previous performance in muc NUM NUM and NUM our progress was muc h more rapid and our official score was higher than in any previous template fill task
a typical te level pattern would seek to attach descriptions to organizations while a st level rule would find a potential succession an d attach the person organization and post information related to it
bbn particularly would like to investigate ho w statistical algorithms over large unmarked corpora can effectively extrapolate from a few training examples such a s in st in muc NUM to provide greater coverage
it will be interesting to see if this general template task is broadly useful and whethe r performance is at a level high enough to warrant deployment in some real task s
in this example stepping down in the first fragment and succeeded in the second fragment are judged by the discourse processor to be referring t o the same succession
together that should reduce overhead for participants further
below are the part of speech tag s produced by post for the following sentence from the walkthrough message and concentrate on his duties as rear commodore at the new york yacht club
for example here is the lexical semantic entry for step in figure NUM we show the semantic representation that is built for the sentence he will be succeeded by mr
let us consider figure NUM where the basic notation is the same as in figure NUM and one possible problem caused by case filler ambiguity is illustrated
it should also be noted that since bunruigoihyo is a relatively small sized thesaurus and does not enumerate many word senses this problem is not critical in our case
their method selects those examples that the system classifies in this case matching a text category with minimum certainty
lewis et al proposed the notion of uncertain example sampling for the training of statistics based text classifiers NUM
figure NUM shows the result of the experiment with several values of in which the optimal value seems to be in the range NUM NUM to NUM NUM
however as all these methods are implemented for statistics based models there is a need to explore how to formalize and map these concepts into the example based approach
during this phase a human expert provides the correct interpretation of the samples so that the system can then be trained for the execution of the remaining data
the task of the system is to interpret the verbs occurring in the input text i.e. to choose one sense from among a set of candidates
we at first estimated the system s performance by its precision that is the ratio of the number of correct outputs compared to the number of inputs
in other words prg k can be expressed as a convolution of poissons with a density function
where oaw are the absolute occurrences of w in the corpus
the first set of experiments experiments l a through NUM c are intended to measure the coverage of the fst representation of the parses of sentences from a range of corpora atis ibm manual and alvey
modifier attachment the ebl lookup is not guaranteed to output all possible modifier null head dependencies for a give input since the modifier generalization assigns the same modifier head link as was in the training example to all the additional modifiers
as a result the index of a generalized parse of a sentence with modifiers is no longer a string but a regular expression pattern on the pos sequence and retrieval of a generalized parse involves regular expression pattern matching on the indices
given this context the training phase of the ebl process involves generalizing the derivation trees generated by xtag for a training sentence and storing these generalized parses in the generalized parse 2there axe some differences between derivation trees and conventional dependency trees
if the ebl lookup fails to retrieve a parse which happens for NUM of the sentences then the tree assignment ambiguity is not reduced and the full parser parses with all the trees for the words of the sentence
the fst was tested for retrieval of a generalized parse for each of the test sentences that were pretagged with the correct pos sequence in experiment NUM we make use of the pos tagger to do the tagging
however in our approach there is another generalization that falls out of the ltag representation which allows for flexible matching of the index to allow the system to parse sentences that are not necessarily of the same length as any sentence in the training corpus
the initial trees as and auxiliary trees 3s for the sentence show me the flights from boston to philadelphia are shown in figure NUM due to the limited space we have shown only the features on the al tree
adjective phrases ap and noun phrases np are confused by the tagger line NUM in table NUM since almost all ap s can be np s
grammatical functions are assigned using standard statistical part of speech tagging methods cf
the following features of our fornlmisrn a re then of particular importance simpler i.e.
for example a test instance might be the sentence fragment robbed the bank the disambiguation method must decide whether bank refers to a river bank a savings bank or perhaps some other alternative
however this conjecture should be tested empirically
given a language l and a word w e l we can find manually or automatically by a morphological analyzer for l all the possible morphological analyses of the word w
in order to evaluate the quality of the approximation we got by our method we should compare the approximated probabilities for the words in these test groups with the test corpus probabilities we found
for the second test group test group2 we randomly picked a short text from the corpus from which we extracted all the ambiguous word tokens appearing at least NUM times in the small corpus
when macro averaging we have used the category oriented definition of recall and precision
since the morpho lexical probabilities we use are calculated from a large hebrew corpus representing a certain hebrew sublanguage these NUM texts were randomly selected from texts belonging to the same sublanguage
in english most such function words are unambiguous
such systems must handle the morphological ambiguity problem
quality control is provided by an independent group of analysts who review the extraction and database update
for project hookah user involvement has been essential for getting the information extraction application done correctly
once the subject is identified the data extracted from the document is retyped into naddis
thousands of documents are processed per week by these personnel a substantial volume of data
another lesson learned from the user interface pertains to the tradeoff between recall and precision
this module also transmits updates to naddis and handles certain database em3r conditions
an audit trail mechanism will track the status of each dea NUM in the system
there is also unformatted text where much of the useful information is found
project hookah will augment an existing work flow that depends on substantial manual data extraction
currently dea personnel read hardcopy dea 6s and other forms to manually identify extractable data
the left and right hand sides of the rule are delimited by the rule name my01 NUM in this case and each constituent has a set of features that are associated with it
in an interpretation phase the mete interpreter will parse a natural language expression outputting an lp rules space approriate representation whereas in the instance selection phase the 1though indeed this is the usual way of looking at the task sometimes we may need to start with some lp rules already known the program we shml describe supports both regimes
given a particular hnmediate dominance grammar and hierarchies of feature values potentially rel evant for linearization the systelu s bias the leanler generates appropriate naturm language expressions to be ewduated as positive or negative by a teacher and produces as output ianear precedence rules which can be directly used NUM y the gralnmar
the first column gives the dialog with the teacher the second the program s internal representation of the l p rules space and the third those ules a re expressed in their nnore familiar and final form that can be utilized directly by the NUM gra nmmr
the grammar will generate simple declarative and interrogative sentences like the jonses read this thick book the 3onses read these thick books do the jonses smile etc as well as all their ungranuimtical permutations read this thick book the lo lscs NUM he jonses read thick this book do ct c
our program solves this problem by exploiting the fact peculiar to our application that the nodes in a grammar are hierarchically structured therefore we may try to linearize a set of nodes a and b higher up in a tree o lly after all lower level nodcs dominated by both a and ft have already been ordered
that is just one of the mlowing three situations is valid for the ordering of any two nodes a and NUM either a b a precedes b or d b a follows b or a NUM a occurs in either position with respect to b the last
the current g sel does not cover it so it relnains the sanle the g set is specialized as little as possible to exchlde tile negative which yields g sot det num adj s set adj the last example is positive
the lp rules space will be an unordered set whose elements are pairs ot nodes connected by one of the relations or e a lp set a b b l e c
from an information theoretic perspective prediction is dual to compression and statistical modeling
test set was NUM and on the second NUM
where po t is the prior probability of the pst t
these mixtures have provably and practically better performance than almost any single model
the perplexity of the model decreases significantly as a function of the depth
the pst was trained on the brown corpus with maximal depth of five
the main obstacle to those applications is the space required for the pst
several sentences that appeared in the test data were corrupted in different ways
these weights are used for tracking a mixture of psts
the prediction probability distribution 7s is estimated from empirical counts
then we will describe the overall architecture of the dialog component in the screen system symbolic connectionist robust enterprise for natural language consisting of the segmentation parser and the dialog act network
formally we define the notion of a paradigmatic relationship as follows
the full text is very rich in information but
NUM NUM x NUM without the null hypothesis
this reveals a bald obvious fact about language
section NUM presents the case for using word frequencies
there are two recurring themes amongst the noes
the experiemtus described below all use same size corpora
let us look closer at why this occurs
words are not selected at random
this also makes the rule for 3e superfluous since it uses an interlingua predicate for the anaphor in german and english
furthermore the ambiguity of additional sequences can be resolved without proposing additional hyphens by using the rule stating that stress can not be applied to a syllable beyond the antepenultimate position
in the previous sections hyphenation issues were examined as they pertain to modern greek with the goal of achieving machine hyphenation that is both accurate and complete to the highest degree possible
NUM repeat NUM until a termination condition is detected
these are included within the context of the definition of various vowel combinations but are rarely explicitly included within the set of standard hyphenation rules
many ambiguities caused by circular definitions of the prohibitive rule vowel sequences are detected an overwhelming majority of which are resolved within the present framework
the purpose of this paper is to formally examine hyphenation as it pertains to modern greek with the aim of achieving accurate and thorough machine hyphenation
existing hyphenator programs meet the first requirement either by decreasing the number of proposed hyphens or by establishing stop lists containing the appropriately hyphenated exceptional words
NUM hyphenation patterns of consonant sequences table NUM are unchanged because consonants do not take stress marks and moreover the vowels contained in these patterns are independent of stress
overall it was feasible to make an analytical examination of the hyphenating system mainly because most of the known hyphenation properties were expressed or could be expressed in terms of orthographic representation
for double vowel blends and the elements of vc which are digrams by definition the impermissible hyphen point falls between its two constituent vowels or
finally diphthongs and excessive diphthongs NUM are vowel sequences consisting of two parts each part can comprise either a vowel or a double vowel blend
the value of an attribute may be inter alia a reference to a collection document annotation or attribute
the tipster architecture assumes a name space of persistent objects each persistent object is assigned a unique name a string
another package associated with an extraction system would represent the annotation types corresponding to the template objects created by that system
if constraint is the empty sequence no constraint is placed on the attributes all annotations of the given type are selected
in the current architecture annotation type declarations serve only as documentation they are not processed by any component of the architecture
for the annotations of type parse the constituents are either nonterminals other annotations in the parse group or tokens
type is the type of progress update provided to the function returns false to terminate the search returns true to continue the search
each such template object provides information about a portion of a document and is therefore represented in the tipster architecture by an annotation
both of these are illustrated in our second example which is an elaboration of our first example to include parse information
removeannotation document or annotationset id string removes the annotation with the specified id from the document or annotationset
as shown in section NUM these models assign finite probability to all word combinations even those that are not observed in the training set
the results in table NUM were ob we used a backoff procedure instead of interpolation to avoid the estimation of trigram smoothing parameters
second we adjust the predictions of the skip k transition matrices by em so that they match the contexts in which they are invoked
starting with the previous word we toss a coin with bias ai wt i to see if this word has high predictive value
we consider specifically the skip k transition matrices m wt k wt whose predictions are conditioned on the kth previous word in the sentence
the training data consisted of approximately NUM million words three million sentences the test data NUM million words one half million sentences
the em algorithm also handles probabilistic constraints in a natural way allowing words to belong to more than one class if this increases the overall likelihood
it should be possible to test psychologically using reaction time methods whether and under what conditions irus function to
here e has been telling h about how her money is invested and then poses a question in c NUM
however it may not be possible to retain the relevant material in the cache
of the NUM tokens with competing antecedents NUM tokens have no competing antecedents if selectional restrictions are also applied
computational linguistics volume NUM number NUM below their reintroduction suggests that in fact they are not accessible
all conversants in a dialogue have their own cache and some conversational processes are devoted to keeping these caches synchronized
the state of the stack when returning from an interruption is identical for interruptions of various lengths and depths of embedding
in dialogue b the interruption is too long and the working set for the interruption uses all of the cache
in many cases only sentence fragments are uttered which often are grammatically incomplete or even incorrect
it could be argued that the latter at least could be achieved by hooking a commercial machine translation mt system up to an internet employment service
this simple approach has certain advantages over a more complex approach based on traditional phrase structure parsing especially since we are not particularly interested in phrase structure as such
then a hybrid architecture for the dialogue component and its embedding into the verbmobil prototype are discussed
the result is a set of possible matches linked to correctly filled schemas so that even previously unseen words can normally be correctly assigned to the appropriate slot
the total distance between any two job instances is simply a measure of the distances between indi null vidual parameter distances and is given by NUM
thus even though one can map the names of job categories from one language to another it is not necessarily true that they mean the same thing
hypertext is generated by means of rules that are very similar to the grammar rules described above but are formulated on a meta level with respect to sentence text rules
thus a spanish bar can or could until recently advertise for pretty girls wanted as bar staff and men wanted to work in the kitchen
the only task of the planner therefore is the construction of the dialogue memory
since information on job ads is represented in a language independent format a search profile in one language will retrieve job ad information entered in any of languages supported
future plans include increasing the number of fields over which the search can be conducted and permitting users to specify the relative importance of each parameter to the search
the state machine is extended to allow for phenomena that might appear anywhere in a dialogue e.g.
also a planning only approach is inappropriate when the dialogue is processed only partially
the four best predictions and their scores are akzeptanz NUM NUM
they are of particular importance since the system has to work under real time constraints
NUM to follow the dialogue when v rbmobil is off line
the traditional literature on genre is rich with classificatory schemes and systems some of which might in retrospect be analyzed as simple attribute systems
table NUM shows how often each of the binary machines correctly determined whether a text did or did not fall in a particular facet level
variation measures capture the amount of variation of a certain count cue in a text e.g. the standard deviation in sentence length
because of space constraints we present this amount of detail only for the six genre levels with logistic regression on selected surface variables
editorials might best be treated in future experiments as a subtype of nonfiction perhaps distinguished by separate facets such as opinion and institutional authorship
globally failure to use position results in NUM NUM of correct configurations while use of position results in NUM correct attachments
examples are given in table NUM
table NUM shows these similarity measures
ab nc NUM NUM abc jj NUM NUM cd vb NUM NUM d nc NUM NUM
NUM NUM a sample segmentation using only dictionary words
evaluation of the segmentation as a whole
furthermore there are four words represented in the dictionary
a non optimal analysis is shown with dotted lines in the bottom frame
to date we have not done a separate evaluation of foreign name recognition
among these are words derived by various productive processes including
it turned out that in combination with the morphological features a context of one disambiguated tag of the word to the left of the unknown word and one ambiguous category of the word to the right gives good results
in our memory based approach feature weighting rather than feature selection for determining the relevance of features is integrated more smoothly with the similarity metric and our results are based on experiments with a larger corpus NUM million cases
input the root node n of a subtree start value top node of a complete igtree an unlabeled case i with information gain ordered feature values fi fn start value fl f
the window size used by the algorithm will also dynamically change depending on the information present in the context for the disambiguation of a particular focus symbol see schfitze et al NUM and pereira et al NUM with that category
this implies that feature values that do not contribute to the disambiguation of the case classification i.e. the values of the features with lower feature relevance values than the the lowest value of the disambiguating features are not stored in the tree
because of the lack of word senses the semantics assigned to a particular word is only considered correct if it holds for all senses occurring in the relevant derived word tokens NUM for example the axiom above must hold for all senses of centralize occurring in the corpus in order for the centralize change of state pair to be correct
the igtree formalism provides automatic nonparametric estimation of classifications for low frequency contexts it is similar in this respect to backed off training but avoids non optimal estimation due to false intuitions or non convergence of the gradient descent procedure used in some versions of backedoff training
in practice when using our tagger this is of course not the case because the disambiguated tags in the left context of the current word to be tagged are the result of a previous decision of the tagger which may be a mistake
as a consequence current nlp systems have only small lexicons and thus can only operate in restricted domains
in order to produce the results presented in the next section the above steps were performed as follows
it should also be noted that the semantic judgements require that the semantics be expressed in a precise way
the main advantage of the surface cueing approach is that the input required is currently available there is an ever increasing supply of online text which can be automatically part of speech tagged assigned shallow syntactic structure by robust partial parsing systems and morphologically analyzed all without any prior lexical semantics
in the second sort the non conformity arises because a cue has been found where one does not exist
of the bases NUM percent conform to the ize dependent axiom which will be discussed in the next section
some natural language processing nlp tasks can be performed with only coarse grained semantic information about individual words
this paper describes an acquisition method which makes use of fixed correspondences between derivational affixes and lexical semantic information
in one case cooccurring words determines translations of one another even though their meaning can be understood compositionally
a prominent feature is added in this implementation it works as a language conversion front end to an arbitrary application
an interactive easy touse translation support facility targeted for non professional translators is desirable
these idiomatic expressions are another major source that violates the compositionaiity assumption of the method
also the user can go back to any former steps by canceling former translations
we present an interactive translation method to support non professional users to write an original document
an example is grammatical relation ambiguity between a case element and a verb when the case marker is hidden
one is inherited attributes corresponding to constraints posed by higher nodes to lower ones
in other words our system divides the translation step into smaller pieces and allows post editing at every step
by combining this idiom dictionary and translation function the user can obtain a useful skeleton for target language expression
after revising his understanding of turn NUM russ performs a surface informref that displays his acceptance of the revised interpretation
below we will repeat this rule and then sketch the proof considering each of the premises in the default
soruetimes verbs require diilhre nt co nbin ttions of o rglll lellts or explicitly require tha t certa iu
they also formalize unification and generalization operators between tin featm e structures along with defining well formedness notion that we use in our system
NUM constraints on argument co occurrence that express obligatory argument co occurrence constraints along with constraints that indicate when certain arguments should not occur in order resolve a sense
in this case the sense resoluliou of the embedded case frame is also pe jbrmed concurrently with the ease flame resolution of the lop level frame
this allows us to specify a preference for expected analyses when there is an ambiguity
in the system this is implemented as a special predicate inconsistentli
each path from the root to a leaf represents a single interpretation of the dialogue
in this section we present nn overview of stru ture of lexicon entries m d the nature of the consire juts
we thank ray reiter for his suggestion that we use abduction and james allen for his advice about temporal logics
in human dialogues both the producer and the recipient of an utterance have a say in determining its interpretation
these expectations are determined both by an agent s understanding of typical behavior and by his or her mental state
the sacrifice here is a loss of generality the mechanisms for recognizing goals are specific to carberry s implementation
to influence this trade off the user can instruct the system to hide a disjunctive principle in an auxiliary relation in order to keep it from being multiplied out with the other constraints
the interpretation of the resulting program is lazy in the sense that we do not enumerate fully specific solutions but compute more general answers for which a grammatical instantiation is guaranteed to exist
however we believe that the addition of delaying and an interpretation strategy as described in this paper would add to the attractiveness of ale as a constraint based grammar development platform
it should be pointed out that parsing with such a grammar would be difficult with any system as it does neither have nor allow the addition of a context free backbone
interpretation as a guiding principle the interpreter follows the ideas of the andorra model NUM in that it always executes deterministic goals before non deterministic ones
it thus becomes possible to develop and test hpsg grammars in a computational system without having to recode them as phrase structure or definite clause grammars
ale provides relations and type constraints i.e. only types as antecedents but their unfolding is neither lazy nor can it be controlled by the user in any way
the first order terms of prolog and dcgs were replaced by feature structures in patr style systems NUM and in recent years systems using typed feature structures have been developed
internally this corresponds to introducing an auxiliary relation under the name supplied by the user and delaying it accordingly so that the choice points introduced by the principle are hidden
next zero pronouns within a japanese sentence are identified by using the syntactic and semantic structure of the japanese sentence and their antecedents within the english sentence are identified by using the characteristics of anaphoric and deictic expressions in english
in NUM out of NUM cases the antecedents of the zero pronouns were not explicitly translated in english by using the passive voice but humans can easily understand that the antecedents of the zero pronouns are in the same sentence
in fact a significant majority of these institutions have participated at least twice and many have participated with even greater frequency
the authors would like to thank nirit kadmon and uwe reyle for reading a preliminary version of this paper
the location time is an interval used to temporally locate eventualities in accordance with their aspectual classification
further event clauses in the discourse introduce a new event which is included within the then current reference time
clause this event introduces a new reference time just after the event time of the main clause
the embedding conditions for the whole construction are just like those for a regular if or every clause i.e. the sentence is true if every proper embedding of the antecedent box can be extended to a proper embedding of the combination of the antecedent and the consequent boxes
when it is simple present the location time coincides with the utterance time NUM temporal adverbials restrict the location time temporal adverbs introduce a drs condition on the location time while temporal subordinate clauses introduce a relation between the event time s of the subordinate clause and the location time of the main clause
temporal connectives are viewed as relations tc between two sets of events NUM lcb el e2 i el e2 e tc rcb the quantificational structure of such sentences can be analyzed either by an iteration of monadic quantifiers or as a single dyadic quantifier of type NUM NUM NUM
the temporal clause may be processed before the main clause since t the location time of e which replaces rl the reference time of partee s analysis as the temporal index of the eventuality in the the main clause arises from processing the main clause not updating the reference time of the subordinate clause
it is not immediately obvious how the generative capacity of sdl grammars relate to lambek grammars or nondirectional lambek grammars based on calculus lp
hence the lhs of any sequent in the proof must be a subsequence of u with some additional b types and c types interspersed
our claim now is that a given NUM partition problem f is solvable if and only if v wl w3m is in l gr
null in semidirectional lambek calculus we add as additional connective the p implication but equip it only with a right rule
notice that this natural deduction style proof in the type logic corresponds very closely to the phrasestructure tree one would like to adopt in an analysis with traces
the cut free system enjoys as usual for lambek like logics the subformula property in any proof only subformulae of the goal sequent may appear
categorial grammar cg and in particular lambek categorial grammar lcg have their well known benefits for the formal treatment of natural language syntax and semantics
we thus can derive bill misses as an s from the hypothesis that there is a phantom np in the place of the trace
we can show cut elimination for this calculus by a straight forward adaptation of the cut elimination proof for l we omit the proof for reasons of space
this excludes many context free or even regular languages but includes some context sensitive ones e.g. the permutation closure of a n b n c n
a variety of steps can be taken to reduce the number of elementary trees produced by the ltig procedure
to start with the choice of an ordering lcb a1 am rcb for the nonterminals is significant
further since there are no x rooted trees in a a n t lcb rcb
since t can not be substituted into itself this generates only a finite number of additional trees
since t can not be substituted into this only generates a finite number of additional trees
nonuniqueness indicates that a subtree or a supertree appears at several different places in the grammar
this avoids going through the normal rule application process and has the effect of reducing the grammar size
after that the left recursive a2 rules are converted into right recursive rules utilizing a new nonterminal z2
the final step of the gnf procedure lexicalizes the z2 rules as shown at the bottom of figure NUM
and the column of text input is the perforamnce of our system when system inputted a transcription of user s utterances that the recognition rate of the speech recognizer was assumed as NUM
then the dialogue manager decides a flow of dialogue using the intention that is sent back from the intention analyzer and acquires available information from dialogue history as contextual information
usually the reference distribution is simply the empirical distribution of our features in the configuration space
the spoken language has looser restriction of the grammar than the written language and has ambiguous phenomena such as interjections ellipses inversions repairs unknown words and so on
secondly to get intention for managing dialogues the dialogue manager passes semantic network to intention m analyzer which extracts a dialogue intention and conditions information of a user s query
l he head features of the argumen are made availal le t o the functor via the arghead feature thus aim ling the funcl or o subcateg rize for its argumenl e.g. by re luiriug a certain inflection ara digm
input speech is analyzed by the following and regression coefficients acep the acoustic models consist of NUM syllable based hmms which have NUM states NUM gaussian densities and NUM discrete duration distributions
once we have realized that communication takes place in a specific context with a specific goal and have accepted that sentence by sentence linguistically correct translation is not a necessary condition for successful multilingual communication we can start exploiting the full potential of spoken dialogues in human human and human machine interaction the basic structure of dialogues the ways to control dialogue flow the possibility for repair
complicating details which result fl om the gll claim to mfiversality may t e emil ted
we have routinely classified actions expressed with to infinitive constructions as parent nodes rather than as sibling nodes
for example the instruct is marked as passive indicating that the agentless passive should be used
the linker and tense markers are also used to mark the appropriate linker and tense of the expression
when you are instructed remove the phone by grasping the top of the handset and pulling it
this way the extensive lexicon that would have been necessary for the surface realization was not required
imagene currently includes a domain model and lexical entries for cordless telephones and a few other specific examples
computational linguistics volume NUM number NUM 9a follow the steps in the illustration below for desk installation
the goal of the verb in this case the busy numbers metonymically refers to the action as a whole
a quite strict constraint requires the pronoun to agree with its antecedent in person number and gender
use the vol lo hi NUM switch to adjust volume to your preferred listening level
the adjustment nominalization in 10b was apparently not used because it required more than one argument
we only consider one dimension neighbor
the computational cost fs very high
the morphemes produce the following underlying form NUM a e kateb c v c v c j i i k t b katteb is derived then by the gemination implying causative of the middle consonant t NUM
the path mor root is not defined at wordl but inherits the value love from love
in adding these new specifications we have added a little extra structure as well
most are just like regular lexemes except that they deviate in one or two characteristics
since mor form is not defined at wordl it will inherit from verb via love
our use of the local global terminology always refers to the outermost descriptor of an expression
abbreviates and is entirely equivalent to node path1 defl
for example in sentence NUM NUM fisu nj de f ming y y zh6ngda computer struc invention implication profound the invention of the computer has profound implications the first three characters are identified as the word t j fisu nfi computer because it is the longest matched substring found in a word dictionary
we got a complete list of terms simple nouns as well as complex nominals to be used as a test set rt
this capability has other useful applications as well e.g. it enables text highlighting in a browser
slot level performance on enamex follows a different pattern for most systems from slot level performance on numex and timex
the election of these categories was done while keeping with the example based nature of the project
the os is windows nt NUM NUM
instead of destroying the affinity relation between the characters b n and
the urgency of a codelet is a number assigned at the time of its creation to represent the importance of the task that it is supposed to carry out this is an integer between NUM to NUM with NUM as the least urgent and NUM as the most urgent
we have shown that this approach is able to automatically induce a chunking model from supervised training that achieves recall and precision of NUM for basenp chunks and NUM for partitioning n and v chunks
s NUM a john hoped mary would ask him out but bill actually knew that she would
mother kris c love ivan mother ivan p ivan d
however as discussed in section NUM its elliptical counterpart has no such reading
b john s coach thinks hei has a chance and jamesj does too
as such they do not contribute to our understanding of the possible range of meanings of elliptical verb phrases
NUM every boy in mrs smith s class hoped she would pass him
NUM john told mary to hand in his paper before bill does
artificial intelligence center NUM ravenswood avenue menlo park ca NUM
the full imagene architecture as depicted in figure NUM consists of a system network and a sentence building routine and is built on top of penman
in figure NUM for example the remove action grasp action and pull action nodes refer to the actions of removing grasping and pulling the handset
building the rst structure between nodes structure unlink imagene uses structure to create hierarchical text structure links between nodes and unlink to remove them
in figure NUM the remove phone node for example is marked as an imperative and the grasp action as an ing form with the linker by
the network having the basic high level structure shown in figure NUM performs the two basic functions of content and rhetorical status selection and grammatical form selection
as a side effect of traversing the network imagene s realization statements automatically realize these consequences in a text structure also shown in figure NUM
as can be seen in figure NUM the global feature of the scope system contains five realization statements all making changes to the evolving text structure
he claimed that purposes should always be fronted because this facilitates the top lown construction of a procedural plan by readers as they progress through the text
this paper has addressed the problem of determining the precise lexical and grammatical forms for expressing procedural relations between actions in the context of instructional text generation
trl allows the text structure to include a representation of the hierarchical structure of the text in terms of rst including both nucleus satellite and multi nuclear schemata
for example the verb roll appears in two subclasses of manner of motion verbs that are distinguished on the basis of whether the grammatical subject is animate or inanimate
as machine readable resources i.e. online dictionaries thesauri and other knowledge sources become readily available to nlp researchers automated acquisition has become increasingly more attractive
the best result using both positive and negative evidence to identify semantic classes gives NUM NUM of the verbs having perfect overlaps relating semantic classes to syntactic signatures
the closest synonyms are then selected fl om these sets by comparing the ldoce grammar codes of tire unknown word with those associated with each synonym candidate
we have developed a relation between ldoce codes and levin classes in mnch the same way that we associated syntactic signatures with the semantic classes in the earlier experiments
to return to the change of state verbs we now consider the syntactic signature of the verb break rather than the signature of the semantic class as a unit
here mutual information is used to measure how strongly two characters are associated
the problem of difficulty in learning interactive operations is also avoided since our interactions are essentially those of simple kana kanji conversion operations
we found that NUM verbs were classifed correctly sthe spanish english dictionary was built at the university of maryland the arabic english dictionary was produced by alpnet a company in utah that develops translation aids
phe basic idea is to first determine tire most likely candidates for semantic classification of a verb by examining the verb s synonym sets many of which intersect directly with the verbs classified by leviu
in these approaches the system asks a set of questions to the user to resolve ambiguities not solvable by itself
an agreed upon and specifically tailored metric and evaluation methodology for periodically measuring progress towards accomplishing each of the chosen tasks
a wfst is a wfsa with a pair of symbols on each transition one input and one output
lastly we assume that he has linguistic expectations regarding pretell askref and askif as in section NUM NUM
as can be seen these rules can be expanded to np noun prep np
in these approaches the current sentence is being parsed using a grammar to get the most probable categories
later on the system may require some help from the user to verify if the categorisation was correct
each entry in the dictionary will associate a word with its syntactic category and its frequency of apparition
finally conclusions about word prediction for inflected languages are extracted from the experience with the basque language
these methods are going to be presented by increasing complexity from the simplest to the most complex
the simplest word prediction method is to built a dictionary containing words and their relative frequencies of apparition
the lemmas and the suffies are still treated separately but syntactical information is included in the system
tables another possibility is to use the relative probability of appearance of a word depending on the previous one
several word prediction methods to help the communication of people with disabilities can be found in the recent literature
however since regular hebrew texts are not written in this script they first must be transcribed to phonemic texts
the remainder of this paper will present an explanation of the syntactic and semantic behavior of these adjectives within the framework of generative lexicon theory henceforth gl
un livre triste a sad book e un examen triste a sad exam NUM a un homme furieux an angry man b
in terms of temporal relations the qualia encode specific constraints on the relative temporal ordering of the wducs of tile qumia
in this case it is the properties inherited through the formal and not the lexical semantics of the word that suggests how it can be experienced
we differentiate type a and b constituents representing subtrees whose roots have straight and inverted orientation respectively
the theoretical cross linguistic hypothesis here is that the core arguments of frames tend to stay together over different languages
as predicted they will not be able to modify an event or an object as illustrated in NUM
we are now able to show how the head distinction is relevant to classify emotional states adjectives and explain their semantic selection
a random sample of the phrasal translation pairs was then drawn giving a precision estimate of NUM NUM
we focus here on the coordination of syntagmatic categories as opposite of lexical categories
section NUM includes examples of such cases and shows that our proposal can manage them adequately
peter i ask for a bike and marie for a fishing rod
the bq distribution actually encodes the english chinese translation lexicon with degrees of probability on each potential word translation
note that modelizing coordination of different categories as the unification i.e.
the second is a default argument d argj as it need not to be present at the syntactic level as shown in exampies NUM
a more realistic in between scenario occurs when partial parse information is available for one or both of the languages
for each source node v with label l v the procedure using this heuristic would first attempt to find a target node v with a bel l v such that l v translated as l v in the bilingual dictionary a perfcct lexical match
we focus on the methodological aspects of ka from texts
our relative standing on these tasks for the most part accorded with the effort we invested in the task s over the last few months
we expect that as the size of our dictionary increases the lex match optimization will have a greater effect
l ex match optimization may be an itldication that we should raise the score for lexical matches of node labels
a noun with a possessive pronoun the same noun with all the other possessive pronouns with the same person attribute
for each noun a huge number of compound forms will be generated
the resulting telic is a process result lcp as shown in NUM
this is possible because lemon is a subtype of fruit
this work also has important consequences for applications in multilingual natural language processing
in section NUM we describe the grammar for ovis and in section NUM we describe the output of the nlp module
this class itself is defined in terms of general principles such as the head feature valence filler and semantics principle
conclusion we have argued in this paper that sophisticated grammatical analysis in combination with a robust parser can be applied successfully as an ingredient of a spoken dialogue system
the grammar for ovis is in turn written in a way to allow an easy translation to pure dcgs NUM the structure of this paper is as follows
other rules are defined in terms of the classes head adjunct and head filler structure which in thrn inherit from a subset of the general principles mentioned above
the robust parsing algorithm is described in section NUM section NUM reports test results showing that grammatical analysis allows fast and accurate processing of spoken input
for instance the grammar currently contains several head complement rules which allow a verb preposition or determiner to combine with one or more complements
in that case simple concept spotting may not be able to correctly process all constructions whereas the capabilities of the grammatical approach extend much further
dutch in developing the ovis grammar we have tried to combine the short term goal of developing a grammar which meets the requirements imposed by the application i.e.
note that dep employs the procedures gov which traces the relevant argument back to its source assumption the head and fun which finds the functionally dominant assumption within the argument subproof the dependent
looking to individual utterances the greater the number of acts which can be performed the more complex or perplex the language model becomes
in lhis process ihe principles achieved their current lornl as shown in table NUM
snote that this is not to say that the system is unable to combine these two types e.g. a combination so s np so so np is derivable with appropriate compilation
figure NUM table of claimed violations of ggi
in order to compare qes the topology form of lateral connections and adaptation functions must be the same since the amount of lateral interaction determines the self organizing power of the network
the process of proof construction is nondeterministic in the order of selection of dependencies for incorporation and so a single set of dependences can yield multiple distinct but equivalent proofs as we would expect
any complex term is recursively broken up into two parts head e.g.
generation of an intonation contour though this has been implemented with neural nets is probably best handled with rules as it is almost purely a prosodic i.e. sentence level matter
the primary aim of these notations is explanation and understanding and there are difficulties in incorporating them into systems with a practical aim such as deriving speech from text which tend to be data driven
therefore there is a need to find ways of incorporating other sources of variability into synthetic speech including for example the feedback a talker receives from the perception of their own voice
additional sources of variability such as stress and emotional quality could also be accounted for with this kind of trajectory in a low dimensional space rather than attempting to derive a speaker independent symbolic notation
thanks to the laboratory of computer and information science helsinki university of technology for making available their som3 ak software used here with minor modifications
the notations derived here are based on an ordered pattern space that can be dealt with more easily by neural networks and by systems involving a neural and symbolic component
in the simplest case of competitive learning the neighborhood contains only one unit so a minimal qe may be achieved but in this case there is no self organizing effect
the notation o zn o zl indicates a sequence of n arguments where n may be zero e.g. the case NUM NUM corresponds precisely to the rule NUM
it is important to have specialised lexicons which cover these smaller subject areas in order to optimise the synthesis or recognition applications
a practical message to speech strategy for dialogue systems p spyns NUM f deprez NUM l van tichelen NUM and b van coile NUM NUM NUM lernout hauspie speech products sint krispijnstraat NUM b NUM ieper belgium tel NUM NUM NUM NUM NUM fax NUM NUM NUM NUM NUM NUM e l i s university of gent sint pietersnieuwstraat NUM b NUM gent belgium
the slot specific information provided by the carrier that can be taken into account is among others the begin pitch the end pitch the declination rate and the intonation context final fall continuation rise etc of the argument
examples of mus and carriers are given in figure NUM NUM the mts generation part basically tries to procure a method that ensures the variability of a piece of information and takes the related linguistic variations into account selection of the correct variant
e.g. the dictionary entry for the determiner is an on vo i a nb sg a is the default an is used before nouns with a vocalic onset and both forms are singular
our prosody transplantation tool see section NUM exploits this idea for the fixed parts of a message it allows to overrule prosody generated by general models as is done by tts with specific prosody copied from natural speech
the transformation of a mu into one or more carri null ers is guided by a two fold mechanism argument dependent carrier selection the carrier is selected in function of a characteristic of an argument
a carrier is a template containing the enriched phonetic transcription of canned text transplanted from an appropriate donor message see above together with the prosodic information for the free slot parts see below
the specific enriched phonetic transcription ept obtained in this manner can be fed to a tts system whereby the normal linguistic and prosodic modules based on general models are by passed phonetics to speech pts
variations on the carrier level combine both a message unit can be expressed by other carriers possibly implying other paradigmatic combinations and or different syntagmatic variations e g x replaces y y is substituted with x variations on the level of the message units can be semantic new combinations of message units lead to the creation of new messages
figure NUM error rates for each test set where the
since the first sense usually represents the most inclusive meaning of the word taggers daunted by the task of examining a large number of closely related senses or unsure about certain sense distinctions may simply chose the first sense rather than continue searching for further subdifferentiations
for example this prohibits a b t tkc ct a b c cl where the underlined argument can be unboundedly large
the tdt models were trained on 2m words and tested on NUM NUM m words of previously unseen tdt data
this error metric in common use in speech recognition can be achieved by a similar viterbi search
the in specification of this lexical rule makes use of an append relation to constrain the valence attribute of the auxiliaries serving as its input
in general any lexical rule can apply to the output of another lexical rule which is sometimes referred to as free application
does the word appear up to NUM sentences in the future but not NUM sentences in the past
for the tdt experiments a larger vocabulary and roughly NUM NUM candidate features were available to the induction program
this probability distribution arises by incrementally building a log linear model that weighs different features of the data
in this section and the next we describe these language models and explain their utility in identifying segments
this makes it possible to eliminate some of the transitions that seem to be possible when judging on the basis of the follow relation alone
the traditional first i rest list notation is used and the operator stands for the append relation in the usual way
summing up we distinguish the lexical rule predicates encoding the specification of the linguist from the frame predicates taking care of the frame specification
to obtain afinite automaton such a repetition is encoded as a transition cycling back to a state in the lexical rule sequence preceding it
to eliminate the frame predicates completely we can successively unfold the frame predicates and the lexical rule predicates with respect to the interaction predicates
the final representa24 in general there is not always enough information available to determine whether two sequences of lexical rule applications produce identical entries
even though one can prune certain transitions even in such cyclic cases it is possible that certain inapplicable transitions remain in the pruned automaton
speech recognition is gradually becoming robust place and because of this many companies are realizing the value of a spoken interface to their products and services
comparing the performance of the novice taggers with that of experienced lexicographers we find that the degree of polysemy part of speech and the position within the wordnet entry of the target words played a role in the taggers choices
applying standard parsing algorithms to such grammars is unsatisfactory for a number of reasons
in this paper i discuss in full detail the implementation of the head corner parser
therefore it seems that head corner parsing is unsuitable for such robust parsing techniques
as appears to be the case for some of the rule schemata used in hpsg
prolog provides a built in backtrack search procedure memorization can be applied selectively
basically this entails augmenting the translation model with terms of the form p nlf where n is the number of clumps generated by the formal language word f
the difference is that instead of replacing one of the instances of modifier it replaces the terminal instance of modifiers by a modifiers subplan that distinguishes one of the objects from the others that match thus effecting an expansion of the surface speech actions
the system will take the role of person b and we will give it the belief that there are two objects that are weird a television antenna which is on the television and a fern plant which is in the corner
in this form it is particularly simple to explicitly sum over all alignments a to obtain p e c f by repeated application of the distributive law
similarly we denote the formal language by f a tuple of order g f whose individual elements are denoted by fi
plan repair substitute plan node newaction newplan undo all variable bindings in plan except those in primitive actions and then substitute the action header newaction into plan at node resulting in the partial plan newplan
rule NUM goal system bel user bel system error plan node cstate system user plan goal bel system error plan node
after the above beliefs have been added there are a number of inferences that the agents can make and in fact can believe will be made by the other participant as well and so these inferences can be mutually believed
bmb system user goal user knowref system user entityl NUM bject bel system bel user achieve pl knowref system user entityl NUM bject
rule NUM goal system bel user bel system replace plan newplan cstate system user plan goal bmb system user error plan node
again in the spirit of collaboration the hearer must accept this replacement and since both expect each other to behave this way both adopt the belief that it is mutually believed that the new referring expression plan replaces the old one
person b postpones his decision in line NUM by voicing a tentative okay and then proceeds to refashion the referring expression the result being the guy reading holding his book to the left kind of standing up
a tree is locally complete as soon as all arguments which it licenses and which are not licensed elsewhere are realized
we have described how hpsg specifications can be compiled into tag in a manner that is faithful to both frameworks
while slash introduction is based on the standard filler head schema slash percolation is essentially constrained to the head spine
the rule schemata include rules for complementation including head subject and head complement relations head adjunct and filler head relations
we present an implemented compilation algorithm that translates hpsg into lexicalized feature based tag relating concepts of the two theories
we therefore have to generalize over different types of daughters in hpsg and define a general notion of a functor
for example the root of t2 specifies the subs to be a non empty list
being a non trunk node it will either be a foot or a substitution node
in either case it will eventually be unified with some node in another tree
we have also developed a scheme to effectively organize the trees associated with lexical items
been generated with respect to our gramrn r by trained tmrn n c but whose skills at parsing with the grammar were not as good as those of our three team members
these conditions are intended to permit only useful applications of a given rule and reflect experience gained by parsing millions of words with the grammar and crucially by generalizing this experience in ways believed appropriate
how lexical generalizations help prediction in our parser is conditioned not only on questions about feature values of words and non terminal nodes but also on questions about raw words wordstrings and whole sentences
in tagging for instance there tend not to be any finite verbs in these contexts and this fact helps with the task of differentiating say preterit forms from past participles functioning adjectivally e.g.
using bayes rule one would write p aif as p a f p f a p a i the model for p fia uses only the parallel corpus but the model for p a makes full use of the data in the atr treebank
since NUM of running words in our test data have NUM or more correct tags potential differences in performance evaluation are large vis a vis traditional metrics s similarly in the case of the parser we evaluate performance against a special gold standard test set which lists every correct parse with respect to the grammar for each test sentence
but other sources of multiple correct parses exist as well and range from say several equally good attachment sites within a parse for a given modifier even given full document context to cases where the grammar itself provides several equally good parses for a sentence through the presence of normally independent rules whose function nonetheless overlaps to some degree
the source treebank to atr conversion model was built using the same system described in sections NUM and NUM the sole difference being that the question l nguage was extended to allow for questions about the source treebank
if not the parser has erred by omrn sion rather than by commission it has ommitted the correct parse from consideration but not because it seemed mj lrely
automatic extraction of aspectual information from a monolingual corpus
it describes the experiential fact of an event
though not represented in figure NUM a consequent state can be taken up with the verbs of categories NUM and NUM if the endpoints of the processes are set up by explicit expressions
the word meanings are senses with denotational meanings man while the instances are senses with referential meanings john smith
pus NUM word forms cover NUM of all the ambiguity and two thirds of the ambiguity is covered by NUM word forms
the precision on the whole is NUM
because the linguistic information is needed we decided to encode the information in a more straightforward way as explicit linguistic disambiguation rules
in this phase statistics is crucial to control the relevance of linguistically plausible forms of all the guessed terms
a term is thus characterized by a general commitment about it and this has some effects on its usage
it has been argued that statistical taggers are superior to rulebased hand coded ones because of better accuracy and better adaptability easy to train
again in table NUM NUM of the fifteen indexes found by the ti method are complex nominals
both these dictionaries as well as the automatically generated dictionary td have been compared with the reference rt
note that the notion of terminological legal expression here is not equivalent to that of legal noun phrases
the set of potential well formed noun phrases are selected according to two parsers working with different np hood heuristics
this improves the overall ability of the linguistic processor and supports term oriented rather than lemma oriented lexical acquisition
for example a sequence of determiner noun noun verb preposition is frequently disambiguated in the wrong way e.g.
this paper presents a method for incorporating rule features in the resulting automata
this means that the tagger errs only when a rare reading should be chosen in a context where the most common reading is still acceptable
these examples introduce a number of research issues concerning the representation and management of the cb and cf discourse entities
NUM a john has been having a lot of trouble arranging his vacation
for example that would be the case if the introductory adverbials were left off the b utterances
in these examples both value free and value loaded interpretations are shown to stem from the same full definite noun phrase
NUM a have you seen the new toys the kids got this weekend
we assume here that the door ranks above the house in cf 19b
two particular ways in which such situations may hold have been noticed in previous research
in the most pronounced cases the wrong choice will mislead a hearer and force backtracking to a correct interpretation
in section NUM we discuss a number of factors that may affect the ordering on the elements of ct
two tasks were involved international joint ventures and electronic circuit fabrication in two languages english and japanese
the committee felt that it was important to demonstrate that useful extraction systems could be created in a few weeks
there was some overlap between the articles assigned so that we could measure the consistency of annotatio n between sites
in part this reflected a feeling that the problem s with the coreference specification were the most amenable to solution
the sample template in figure NUM illustrates several of the other features which added to the complexity of the muc NUM task
a sample article and correspondin g template for the muc NUM english joint venture task are shown in figures NUM and NUM
there were evaluations for four tasks named entity coreference template element and scenario template
the person and organizatio n objects are the template element objects which are invariant across scenarios
appendix sample scenario templat e shown below is a sample filled template for the muc NUM scenario template task
for annotation purposes we wanted to use texts which could be redistributed to other sites with minimal encumbrances
it derives the meaning of the sentence out of available elements and furthermore predicts the missing elements required to meet the constraints
we also show how to use a bayesian approach based on recursive priors over all possible psts to efficiently maintain tree mixtures
one of the most important features of the present algorithm that it can work in a fully online adaptive mode
where t is the set of trees of maximal depth d and e is the null context the root node
moreover the interpolation weights between the different prediction contexts are automatically determined by the performance of each model on past observations
it remains to describe how the probabilities p wls 7s w are estimated from empirical counts
we describe analyze and experimentally evaluate a new probabilistic model for word sequence prediction in natural languages based on prediction suffi v trees psts
we plan to investigate how the present work may be usefully combined with models of those phenomena especially local finite state syntactic models and distributional models of semantic relations
for those nodes the update is simply multiplication by the node s prediction for wn for the rest of the nodes the likelihood values do not change
since d is usually very small most currently used word n grams models are trigrams the update and prediction times are essentially linear in the text length
in the second case it is only necessary to transfer the identity of the word by referring to the shorter context in which the word has already appeared
specificity tu returns the specificity of the most specific field in tu
a focus list keeps track of what has been discussed so far in the dialog
a system that produces extraneous values is more problematic than one that leaves entries unspecified
step NUM the ailt chosen is the one with the highest certainty factor
the different findings might be due to the fact that different problems are being addressed
for instance rule na2 covers only the starting time case of non anaphoric relation NUM
these backup de s might be available in case the rule applications fail on the most recent de
this figure is NUM minus the precision of the input representation after normalization
the attachment module has been implemented at NUM
note that the m NUM mixed order model corresponds to a ml bigram model
one of the drawbacks of n gram models is that their size grows rapidly with their order
the e step is to compute for each bigram wlw NUM in the training set the posterior
smoothing plays an essential role in language models where ml predictions are unreliable for rare events
this could be extended beyond adjective noun modification to verbs that belong to the candidate string s context
this paper presents the incorporation of external information derived from the context of the candidate string
we compare our results to a baseline trigram model that backs off to bigram and unigram models
it is partly motivated by the observation that translators must paraphrase when the target language has no obvious equivalent for some word or syntactic construct in the source text
null the translational inconsistency of a source word s is proportional to the entropy h t s of its translational pdf p tis
this heuristic produces undesired results however since it implies that the transla null tion of a word which is never linked to anything is perfectly consistent
ideally a measure of translational inconsistency should be sensitive to which null links represent the same sense of a given source word and which ones represent different senses
when a pattern is matched a semantic form is assigned by the pattern
each of these can be further categorized as known unknown and referential
for muc NUM some high performing groups invested a small number of person years
the texts were examined for words or collocations which needed to be added to the domain dependent lexicon
figure NUM shows how performance on the st task improved over time on our blind test set
the template definitions for the objects which are common to both te and st are almost identical
each filler found in the text is assigned a confidence score base d on distance from trigger
post tags each word with one of NUM possible tags with NUM accuracy for known words
first a quick review of what is new is provided then a walkthrough of system components
the semantic interpreter contains two sub components a rule based fragment interpreter and a pattern base d sentence interpreter
figure i block diagram for the self organizing japanese word segmenter
the word identification method collected a list of NUM word hypotheses from trainingol
the latter is expected to be close to the distribution of unknown words
this happens frequently at the sequence of grammatical function words vrritten in hiragana
it is a corpus of NUM NUM million words NUM thousand sentences
other asian languages such as chinese and thai have the same problem
the goal of our research is unsupervised learning of japanese word segmentation
let the input japanese character sequence be c clc2 cm
the major contribution of this paper is its treatment of unseen words
figure NUM shows a fragment of bunruigoihyo including some of the nouns in both figure NUM and the example sentence above with each word corresponding to a leaf in the structure of the thesaurus
given an input in our case a simple sentence the system identifies the verb sense on the basis of the scored similarity between the input and the examples given for each verb sense
this makes the reasoning nonmonotonic because the addition of a new fact or overriding default may make less preferable hypotheses underivable
when suppositions are not simple we check their compatibility by verifying that each of the conjuncts of each supposition is compatible
these misunderstandings are also difficult to prevent because they can result from many common sources including intra sentential ambiguity and mishearing
NUM communication can occur despite such differences because speakers with similar linguistic experiences presumably will develop similar expectations about how discourse works
mcroy and hirst the repair of speech act misunderstandings he might assume that one of the two types of misunderstanding has occurred
the explanation contains the metaplanning assumption that mother was pretelling as part of a plan to get russ to ask a question
here we will consider a general model of dialogue that also accounts for the detection and repair of speech act misunderstandings
NUM the architecture of the model our model characterizes a participant in a dialogue alternately acting as speaker and hearer
NUM note that this choice allows for a speaker feigning the occurrence of a misunderstanding in order to achieve some social goal
we specify for each discourse act the acts that might be expected if the hearer has understood the speaker correctly
this information would be stored in the rules themselves in ordinary context free rules
to support this claim two features of an implemented parser are discussed
the examples in sections NUM NUM NUM and NUM NUM NUM
the symbol yp stands for any maximal projection admitted by linguistic theory
practically there is reason to think that it is more efficient
we will only discuss chains of the types of NUM
the effects discussed above could be an artifact of the compilation technique
this is a crucial feature of the algorithms for chain formation proposed here
such a theory is clearly not modular although highly general and abstract
the compilation is done in such a way that the overgeneration is wellbehaved
lexical features in v funct c selected c
the parameters of the scoring function are then learned from the training corpus using the discriminative learning algorithm
this happens because in general to reduce modeling errors a model accounting for more contextual information is desired
while originally introduced as a means of establishing language theoretic complexity results for constraint based theories this language has much to recommend it as a general framework for theories of syntax in its own right
figure NUM showed one such attempt where f was transformed into a predicted idf by introducing a poisson assumption i f log2 l e deg with NUM fw
under a poisson the NUM instances of somewhat should be found in approximately d NUM n NUM NUM d NUM n NUM NUM NUM NUM documents
significant improvement of NUM NUM error reduction rate is attained when we apply the robust learning procedure on the smoothed parameters
take the sentence a stack ofpinfeed paper three inches high may be placed underneath it as an example
a mechanism with a smaller sp value is more likely to include the most preferred candidate for some given n best hypotheses
the effects of parameter smoothing for null events with turing s formula and the back off method are investigated in this paper
therefore different scores should be assigned different weights to account for both the contribution in discrimination and the dynamic ranges
the average number of words per sentence for the training set and the test set were NUM NUM and NUM NUM respectively
rameters for an m gram model i.e. the conditional probability of a word given the m l preceding words
note that the words near the top of the list tend to be more appropriate for use in an information retrieval system than the words toward the bottom of the list
the features are a subset of those described in section NUM
segmentation has played a significant role in much work on discourse
rather than evaluating examples individually for their informativeness a large batch of examples is examined and the m best are selected for annotation
the systems in our experiment perhaps produce too similar results
the value is sentence final contour
cue phrase features cue1 true false
the major difference from human performance is relatively poorer precision
the second is the inherently fuzzy nature of boundary location
combined feature cue prosody complex true false
agreement among subjects on boundaries was significant at below the NUM
noun phrase features coref coref corer na
effective use of expectation is necessary for constraining the search for interpretations and achieving efficient processing of nl inputs
utterance NUM to determine what the user needs help with i.e. adding the wire
a robust dialogue system with spontaneous speech understanding and cooperative response
an under verification is defined as the event where the system generates a meaning that is incorrect but not verified
after testing system prototypes with a few volunteers eight subjects used the system during the formal experimental phase
the particular circuit being used causes the light emitting diode led to alternately display a one and seven
to be more specific for each possible analysis lexical entry the morphological information we define a set of words that we call similar words sw
nonetheless the fact that ambiguous words in the sw sets can not be automatically identified does not affect the quality of the probabilities obtained by our method for most ambiguous words
for each analysis we compute its average number of occurrences by summing up all the counters for each word in the sw set and dividing this sum by the sw size
the choice of which words should be included in the sw set of a given analysis is determined by a set of pre defined rules based on the intuition of a native speaker
unfortunately the similarity assumption does not hold in this case since the indefinite form of h is much more frequent in hebrew than the definite form of the word
because of the fact that hebrew corpora untagged and tagged as well are not available in the public domain we had to build a hebrew corpus especially for this project
the notions of head and dependent
the above normalisation approach is somewhat nonstandard
the suggested method depends primarily on the following property a lexical entry in hebrew may have many different word forms some of which are ambiguous and some of which are not
thus finding methods to reduce the morphological ambiguity in the language is a great challenge for researchers in the field and for people who wish to develop natural language applications for hebrew
also entities described with bare plurals are commonly found to be coreferential with other entities in addition to cases in which they have their more standard generic meanings
however using this method impossible combinations e.g. a c b b c c ac c c will also receive positive probability mass
we have a characteristic representing whether the potential antecedent is the preferred antecedent NUM a non preferred but possible antecedent or not on the list of possible antecedents
for instance the pairwise probability of coreference between c and d was originally NUM NUM which might be reasonable if those were the only two templates generated from the text
alternatively in cases in which all of the templates in a coreference set are pairwise compatible the greedy method will produce the configuration in which they are all coreferential
data for the merging decision model unlike the evidential model the merging decision model does not always utilize all of the palrwise probabilities between pairs in a coreference set
therefore the training set for the maximum entropy algorithm was pared down to only contain those pairs that the merger would have considered in deriving the correct coreference configurations
we have performed an informal study of fastus s processing of a set of texts which indicates that the merging phase is where most of the ambiguities as well as most of the errors lie
for instance one could add a characteristic indicating when a template is created from a phrase in a subject line or table as many cases of coreference with subsequent indefinite phrases occur in this circumstance
it also increased precision for spanish because only lexicon and alias lookups were applied to all upper case headlines avoiding spurious or erroneous names generated by patterns
despite the differences in types of articles and the additional requirements the nametag japanese and spanish systems achieved high performance in both recall and precision
second sra s japanese segmenter which uses a large lexicon morphological analysis and heuristic based rules to segment a sentence into words is fully integrated into the nametag engine
another chicken and egg problem was encountered in constructions where a person name and an organization name appear next to each other in a sentence and there is no delimiter between the two e.g.
for example the spanish system can recognize pefia as an alias for josd francisco pefia gdmez by generating a paternal name alone
good name recognition requires good segmentation as name recognition patterns rely on properties of words segmented by the segmenter such as part of speech and other linguistic attributes
the nametag japanese system currently generates aliases like silicongraphics for silicon graphicscorp by stripping off certain corporate designators at the end of names
at a de q level at least we pose the hypothesis that a text is generally coherent
but if we consider shorter contexts the meaningful information for word sense disambiguation may be lost
in the met spanish articles the capitalization convention was rather unpredictable e.g. oficina de lucha contra la droga puerto cubano de mariel
in addition the met guidelines had additional requirements not found in muc6 such as tagging of relative dates and tagging organizations as locations when they are used as facilities
lesson two is start early on the mucs
thus both consuela washington and ms
quantifications are either measurements or mathematical or logical expressions
this rule would match instances such as dr
these examples suffice to illustrate the rich set of syntactic and semantic knowledge ban k features that are available for use in the dxl rules to identify and discriminate among even very simila r data classes NUM
NUM this would not actually be a good way of approaching recognition of person name instances but it does illustrate what additiona l attributes would be added as a result of consulting the knowledge bank
also not all relevant attributes are shown
the knowledge bank actually has a much different representation
the infer n NUM rules about negations are valid only it the intensional universe
section NUM describes the current implementation as a front end to an arbitrary application
the second line is highlighted to show that it is the current selection
the training set consisted of the first million words in the corpus with sentence ordering randomized to compensate for inhomogeneity in corpus composition
a much simplified view of the dx system is given in figure NUM
this study shows that negation is very sehlom at the origin of incoherct c
the feature rest is our mechanism for allowing partial matchings between rules and semantic inputs
fortunately long distance features like agreement are among the first that go into any symbolic generator
a bigram model will happily select a sentence like i only hires men who is good pilots
of course this requires extremely efficient handling of the disjunctions inherent in large word lattices
a robust generator must be able to operate well even when pieces of knowledge axe missing
the generation lexicon does not contain much collocational knowledge e.g. on the field vs
notice that the sixpath lattice has more states and is more complex than the eight path one
our generator operates on lexical islands which do not interact with other words or concepts
we then used penman to generate NUM NUM english sentences corresponding to the NUM NUM NUM
so we can get away with treating possessive pronouns like regular adjectives greatly simplifying our grammar
this is implemented by fixin g a time limit on the process resource usage being proportional to time we refer to expiry of the time limi t as a timeout
x1 is parsed as some kind of np but the associate d surface text is not understandable as such clearly a parsing error
as previously noted even when a parse is produced it may have missed easy named entities becaus e of the full parsing strategy
for example query NUM is transformed from indicators of economic and business relations between mexico and asian countries such as japan china and korea
the results were then evaluated in trec by hand translating the trec spanish monolingual queries into english and applying the automatic query translation methods to produce new spanish queries
with translated queries a query translation system that produces spanish queries from hand translated english versions of original spanish queries can then be compared against the original queries
NUM on two queries performance of the ep approach was as good as the reference queries although they tended to have better precision at higher recall
it is possible to translate every document at index time for example but the resource costs are substantially higher than translating the query at retrieval time
NUM on at least two queries performance of the lexical methods was as good or better than the reference queries
it is based on the identification of the sublanguage specific co occurrence properties of words in the syntactic relations in which they occur in the texts
some formatting codes from the un documents have been eliminated in some of the queries reducing the count to below NUM terms in those queries
previously translated document corpora can be made available for exploiting domain specific terminology by direct comparison of the retrieval results for the query and target document languages
in section NUM i compare a number of dimensions of the cache and stack models
shared knowledge of the task structure creates expectations that determine what is in the cache
an examination of the data shows that irus occur in NUM of the NUM return pops
this squib has discussed the role of limited attention in a computational model of discourse processing
these processes make items salient that have not been explicitly mentioned
however linear recency ignores the effects of retention and retrieval
items in the cache can also be stored to main memory
the cache represents working memory and main memory represents long term memory
i use the term information status to refer to the status of information as either shared or unshared
if so that information will be preferentially retained in the cache because it is being used
on returning after an interruption the conversants must initiate a cued retrieval of beliefs and intentions
him and his wife ii is not clear how to deal with lhese constructions within lhe entering framework and thus i have left lih m unanalyzed for the time t eing
to uncover term inclusion the system scans the term bank and replaces each entry of a term which currently in focus with its number
for infrequent words where the empirical method for finding semantic class ca n t be applied the wordnet technique described above is used
assuming that i and ii hold for s and w then for each a e let NUM s r a the algorithm line NUM is such that
this shows that co occurrence measure can give similar clusters of words in different languages from non parallel texts
since the tokenizer is not perfect the word translation extraction process is affected by this preprocessing
there is little correlation between such statistics of a word and its translation in non parallel corpora
in the above cases the context heterogeneity values of the chinese translation is not reliable
there are very few function words in chinese compared to other languages especially to english
based on this information we derive statistics of bilingual word pairs from a non parallel corpus
c b fight heterogeneity y c a number of different types of tokens
hong kong chinese use many terms borrowed from classical chinese which tend to be more concise
a logical way to cope with such sparse data problem is to use larger non parallel corpora
and in english they frequently accuse each other of paying lip service to various issues
the three rightmost columns present the svs after the interpretation of the utterance in the left column
for our data analysis we suppose a new discourse segment purpose for each new sentence
their initial significance weight is NUM NUM nested term referents are the referents expressed by np modifiers
temporal deixis relates the time of speech to the relation s expressed by the utterance
in research laboratories a couple of systems capable of interpreting deictic expressions recently have been developed
in a worst case scenario associative cfs interfere with the referent resolution of normal anaphoric expressions
in the bottom left area of the screen is the nl interaction window labeled dialoog dialogue
the salience of a referent is influenced by both linguistic and per null carla huls et al
the phrase the ball in front of the car for example can have three interpretations
these metrics assume that ideal behavior would be to identify all and only the target boundaries the values for b and c in figure NUM would thus both equal NUM representing no errors u the ideal values for recall precision fallout and error are NUM NUM NUM and NUM while the worst values are NUM NUM NUM and NUM
we intend to port this system to the yellow pages information access application in the near future
NUM unknown query in most applications the user may query for different types of information
in such a case the likelihood value for each category ci becomes
for simplicity suppose that there are only two categories cl and c2
furthermore it can economize on the space necessary for storing knowledge
where w denotes a random variable representing any word in the vocabulary
that is we view a document as a sequence of words
again we did not conduct stemming or use stop words
we omit an explanation of this work here due to space limitations
let us conclude this paper with the following remarks null NUM
lie between the dashed boundaries count as coocctlr rence8
notice that a word pair could make sense in both
figure NUM summary of filtered translation lexicon domain specificity statistics
press select and drag the folder onto the workspace background
the invalid category captures the system s mistakes on this dimension
melamed also acknowledges grants arpa n00014 NUM j NUM and arpa n6600194c NUM
table NUM random sample of sable output on soft ware manuals
NUM then choose one or both of the following specific
all summary statistics are reported in terms of the group annotation
the following is a brief description of sable s main components
the expression hydraulic oil filter lends itself to two different bracketings corresponding to filter for hydraulic oil and hydraulic filter for oil
as the following example demonstrates the explicit encoding of conditions in which each fact is expressed provides a powerful way of controlling the realizations of the various paraphrases
in patr if v agr per has value NUM then neither v agr nor v agr per num can have atomic values
then when the results of the generation are to be enmnerated the logical structure of the input reappears and affects the interpretation of tile boolean array
each disjunctive edge in the chart is annotated with a set of such variables each of which mutually exclusively defines a particular alternative derivation of that edge
however to avoid expressing the same facts more than once a further constraint is required to guarantee that only one of the disjuncts eventually get chosen
the two conditions taken together mean that a complete expression of the semantic input is conditioned on both pl and q2 being the choices in the relevant disjunctions
with this extra definition we no longer need a mor form definition in wordl so it becomes wordl
overall NUM NUM of all words remained ambiguous due to the failure of the finite state parser c f section NUM
tested against a benchmark corpus of NUM NUM words of previously unseen text this syntax based system reaches an accuracy of above NUM
a constraint grammar can be viewed as a collection NUM of pattern action rules no more than one for each ambiguity forming tag
interestingly no significant improvement beyond the NUM barrier by means of purely data driven systems has been reported so far
in terms of the accuracy of known systems the data driven approach seems then to provide the best model of part of speech distribution
next a new system with the following properties is outlined and evaluated the tagger uses only linguistic distributional rules
morphological syntactic and clause boundary descriptors are introduced as ambiguities with simple mappings these ambiguities are then resolved in parallel
this should appear a little curious because very competitive results have been achieved using the linguistic approach at related levels of description
table NUM shows for each algorithm the times in seconds for the overall construction and the number of states and arcs of the output transducers
in other words we tested twenty two rules where the left context or the right context is varied in length from zero to ten occurrences of c
for our experiments we used the alphabet of a realistic application the text analyzer for the bell laboratories german text to speech system consisting of NUM labels
weighted rewrite rules can be compiled into weighted finite state transducers namely transducers generalized by providing transitions with a weighted output under the same context condition
in some applications such as those related to speech processing one needs to use weighted rewrite rules namely rewrite rules to which weights are associated
in addition to their formal language theory interest systems such as those of aristid lindenmayer provide rich mathematical models for biological development rozenberg and sa omaa NUM
in contrast the construction of the right context machine in our algorithm involves only the single determinization of the automaton representing p and thus is much less expensive
the subtypes of t have different appropriate features the values of which have to be preserved
the frame predicate for lexical rule NUM is defined by the two clauses displayed in figure NUM
automatic outputs can be used not only to revise them but also to aid ambiguity resolution an essential problem in natural language processing
gs2 in figure NUM is the clusters obtained under the loosest constraint m is the maximum and n is the ni um
we are apt to pay attention only to the words of class NUM but that of class NUM plays an important role in clustering
stage two clustering method given a vector representation of paragraphs p1 p as in formula NUM a similarity between two paragraphs pi pj in an article would be obtained by using formula NUM
recall and precision in table NUM are as follows number of correct keywords recall number of keywords which are selected by human number of correct keywords precision number of keywords which are selected in our method recall and precision in table NUM show the means in each paragraph
after obtaining tree s from the partial description the generator translates the node constants into the concatenation of syntactic category and index if it exists
in the hierarchy of syntactic descriptions we propose the partial description associated with a class is the unification of the own description of the class with all inherited partial descriptions
in the french grammar there are also separate trees for cleft sentences with gaps in the clause while the corresponding it clefts are handled as relative clauses in the english grammar
when the grammar is used for parsing for instance the words of the sentence to be parsed are associated with the relevant tree schemata to form complete lexicalized trees
two nodes in two conjunct descriptions referred to by the same constant are the same node and two nodes referred to by different constants can either be equal or different
though they could be a further step of factorization it seemed interesting to get the whole picture of the grammar within the hierarchy and not only the base trees
with the approach of an automatic generation of tag trees we have found necessary to explicit these rules which are defined using the notions of argument and syntactic function
in the following the link relations are expressed verbally
these three principles establish direct or indirect links towards foc
the principles just presented compose the representation introduced in sec
why shmdd a tree with less f marking be pragmatically preferred
karl hat ein buclt gelesen f
unibrtunatcly the formal nature of integration is still ill understood
paula has a red rose photographed was hat sie davor getan
the best known and most widely available are our own brighton sussex which is written in prolog and runs on most unix platforms gibbon s bielefeld ddatr scheme and node sicstus prolog implementations and kilbury s duesseldorf qdatr prolog implementation which runs in compiled form on pcs and on sicstus prolog under unix
these axioms then give rise to theorems such as these spell love love love s loves i o v e e d i o v e d i o v e e r NUM o v e r i o v e i y NUM o v e NUM y NUM for clarity this fst does not exploit default inheritance to capture the NUM overlap between the subject and object pronoun paradigms
NUM datr does not permit a theorem set such as the following to be derived from a consistent of course as far as datr is concerned lcb hoof s hooves rcb is just a sequence encode the regular and subregular polysemy associated with the crop fiber yarn fabric and garment senses of words like cotton and silk
the reverse query task again presupposes that we have a description available to us but instead of starting with a known query we start instead with a known value love ed say and the task is to infer what queries would lead to that value love mor past participle love mor past tense sing one etc
one obvious approach is to use nonmonotonicity and inheritance machinery to capture such lexical irregularity and subregularity and much recent research into the design of representation languages for natural language lexicons has thus made use of nonmonotonic inheritance networks or semantic nets as originally developed for more general representation purposes in artificial intelligence
computational linguistics volume NUM number NUM the problem is that the inheritance mechanism we have been using is local in the sense that it can only be used to inherit either from a specifically named node and or path or relative to the local context of the node and or path at which it is defined
it is not altered when local inheritance descriptors are followed it remembers where we started from but when a global descriptor is encountered it is the global context that is used to fill in any missing node or path components in the descriptor and hence to decide where to pass control to
among other things this means that implementations of datr can either treat apparent violations of functionality as syntactic errors and require the user to eliminate them or more commonly in existing implementations treat them as intentional corrections and silently erase earlier statements for the node and path for which a violation has been detected
heuristics NUM NUM NUM and NUM or extracted from the whole dictionary as a unique lexical knowledge resource e.g.
as explained earlier both case bases are implemented as igtrees
when modules are developed specifically for gate they can embed tipster calls throughout their code and dispense with the wrapper intermediary
these parameters can be changed from the interface at run time e.g. to tell a parser to use a different lexicon
ice might well form a useful backbone for an nlp infrastructure and could operate in any of the three paradigms
these problems include information extraction text summarisation document generation machine translation second language learning amongst others
the recent completion of this work means a full assessment of the strengths and weaknesses of gate is not yet possible
alep while in principle open is primarily an advanced system for developing and manipulating feature structure knowledge bases under unification
NUM at first thought texts may appear to be one null dimensional consisting of a sequence of characters
this does not mean however that flatfile sgml is an appropriate format for an architecture for le systems
the complete vie system comprises ten modules each of which is a creole object integrated into gate
we discuss five here then note the possibility of compli null mentary inter operation of the two
this derivation process involves the actions selection of a pair of words wo e v1 and vo e v2 and a head transducer NUM NUM to start the entire derivation
average accuracy provides a reliable estimate of the generalization accuracy
more specifically memory based tagging with igtrees has the following advantages
note that we are adding the results of eight different heuristics with eight different performances improving the individual performance of each one
recall from section NUM that vp ellipsis do it do that do so and related constructions form a natural class of expressions
to summarize thus far a purely discourse determined analysis predicts that a sentence with ellipsis should display the same readings in a given context that the unelided form would in the same context
in the equational account the dependency between anaphoric relationships in source and target follows immediately from the mechanism used for constructing and solving the equations
does it arise directly through some uniform relation between the two clauses or does it follow indirectly from independently motivated discourse principles governing pronominal reference
of course the approach of dsp does not require syntactic parallelism in setting up the equation for resolving ellipsis many examples of nonsyntactic parallelisms are provided in that work
by recognizing the full parallelism as manifested in the equation askout mary john p ohn mary the equational analysis straightforwardly generates the sloppy reading
specifically like phrases such as regarding john as for john and with respect to john the phrase in john s case crucially depends on context for its interpretation
in each case the unification based integration strategy compensates for errors in gesture recognition through type constraints on the values of features
we have also developed a java based quickset agent that provides a portal to the simulation over the world wide web
though the issue should not be prejudged it would be reasonable to disallow such accent as an elided vp by its very nature has no possibility for exhibiting accent
we would then have to require as hardt s account in fact does that the discourse principles be applied as if no strong accent or deictic gestures were applied
panebmt is merely one of the translation engines used by pangloss the others are transfer engines dictionaries and glossaries and a knowledge based machine translation engine figure NUM
by selecting the last occurrences of each word sequence one effectively gives the most recent additions to the corpus the highest weight precisely what is needed for a translation meanory
the oc urrence list for each word is comp tred tgainst the occurrence list for the prior word and against the list of chunks extending to the prior word
the final selection of the correct cover for the input is left for the statistical language model as is the case for all of the other translation engines in pangloss
not all words will be associated one to one however the current implementation requires that at least one such unique association be found in order to provide an anchor for the alignment protess
since the corpus used in the experiments described here was based almost entirely on the un proceedings rather than newswire text panebmt did not find many long chunks during the evaluation
to limit the size of the index file a long list of tile most frequent words were omitted from the index as were punctuation marks
further allowance was made for the m indexed frequent words by permitring any sequence of frequent words between two indexed words producing many erroneous matches
as currently implemented the ebmt engine is unable to properly deal with translations that do not involve one for one correspondences between source and target words e.g.
the newer implementation fully indexes the corpus anti thus examines only exact matches with the input ensuring that only good matches are actually processed
the cross references in lloce are primarily between topics
directly comparing methods is often difficult
these labels represent gaps in the lloce
the algorithm is divided into two stages
the author suggested that the method can also apply to dictionary definitions
this includes a finite state part of speech tagger a derivational morphological processor for analysis and generation and a unification based shallow level parser using transformational rules over syntactic patterns
where fi and ei are the observed and expected counts of the i th feature vector respectively
similarly a notion of type NUM collocation is defined based on the co occurence of wl and w2 including their derivational relatives
this paper briefly overviews a project whose long term goal is the development of a writing tutor for deaf people who use american sign language asl
the highest accuracy for each word is in bold type while any accuracies less than the default classifier are italicized
the major difference among the criteria in figures NUM and NUM is that the exact conditional test adds many more interactions
the search strategies presented in this paper are backward sequential search bss and forward sequential search fss
this is also illustrated by the fact that the average accuracy between bss aic and fss aic only differs by NUM
even large training sets tend to misrepresent low probability events since rare events may not appear in the training corpus at all
we can apply it to the conditional distribution p wl induced by wl on words in v2
we also investigated katz s claim that one can discard singletons in the training data resulting in a more compact language model without significant loss of performance
the last column indicates whether the weight w wl w associated with each similarity function depends on a parameter that needs to be tuned experimentally
furthermore a scheme based on the total divergence of empirical dis null tributions to their average NUM yielded statistically significant improvement in error rate over cooccurrence smoothing
it is similarly restricted if before is replaced with just before or ten minutes before
the subordinate clause triggers the introduction of an event marker e with its event time marker t
state clauses introduce new states which include the current reference time and do not update it
in this approach each of the relevant temporal markers resides in its appropriate box yielding the correct quantificational structure
the exact temporal relation denoted by a temporal connective depends on the aspectual classes of the eventualities related by it NUM
this concept of reference time is no longer an instant of time but rather an interval
in narrative sequences event clauses seem to advance the narrative time while states block its progression
dependent disjunctions can be represented by alternative cast forms as shown in definition NUM below
aln i he case of infinite h xica detinitc clauses arc
the last section showed all efl ective algoritlnn for modularizing groups of dependent disjunet iolls
many of the groups of disjunctions in their feature structures can be made more efficient via modularization
they are independence if their free combination is equal to the original ease tbrm case
the process of splitting a group of dependent disjunctions into smallel groups is called modularization
however this is unnecessary since we want to permit both solutions to be simultaneously true
in addition an aggregation is defined to be a set of aggregates with the requirements that their join yield the greatest aggregate that is the unit aggregate and that it be minimal in the sense that no aggregate in the set is a proper sub aggregate of any other aggregate in the set
this can be formalized as follows where no denotes a count noun cm denotes the morphological operation of conversion from a count noun to a mass noun r is a function which associates with an object its parts NUM and NUM i denotes the interpretation function
on the other hand pronouns and count nouns evince the alternation between singular and plural even if the alternation is sometimes not morphologically realized as is the case with many nouns for wildlife e.g. sheep deer whereas proper names and mass nouns do not evince such an alternation
above i presented a semantic syntactic and morphological account of the mass count distinction and i have shown how that account can be extended to accommodate the fact that mass nouns can be converted into count nouns and count nouns into mass nouns with concomitant shifts in the meanings of the nouns
a predicate is evaluated not with respect to the denotation of a demonstrative noun phrase which is its argument hut with respect to the elements in an aggregation constructed from the demonstrative noun phrase s denotation where the choice of aggregation is determined by one s knowledge of the world and one s context
next if the quantifier is universal then the predicate must be true of each aggregate in the aggregation to which the quantifier is restricted and if it is existential then the predicate must be true of at least one aggregate in the aggregation to which the quantifier is restricted
thus chocolates beers and hamburgers may denote servings of the denotation of the underlying mass noun
as remarked above a mass noun is unspecified as to whether or not its denotation has minimal parts
notice that this is analogous to the constraint imposed by these features on the denotation of demonstrative noun phrases
NUM and NUM we union all subsequences from the principal model s with all those subsequences from the auxiliary model n that are not in s
finally we generate the completed s n typc transducer from the joint unions of subsequences usi and us n as decribed above eq
sregular expression operators used in this section are explained in the annex zero of ambiguous classes ca and ends with the following unambiguous class c
this transformation is especially advantageous for part of speech tagging because the resulting transducer can be composed with other transducers that encode correction rules for the most frequent tagging errors
NUM and all extended middle subsequences c n ranging from any unambiguous class cu in the sentence to the following unambiguous class eq
when coming from the initial state and c2 t21 the most likely pair of class c2 when coming from the state of c3 t31
there is a large variation in speed between ssince n0 type and nl type transducers have deterministic states only a particular fast matching algorithm can be used for them
the joint probability of an extended middle class subsequence c of length s together with a tag subsequence tr can be estimated by
oaw depends obviously on the corpus
when arguments are assigned with the same tags e.g.
table h rsd verbs with the highest initial polysemy
learning methods are usually search algorithms through concept spaces
a recall of NUM NUM has been obtained
exhaustive experimental data on nouns are not yet available
mrd senses pos tags corpus contexts
pp disambiguation rules with a direct semantic interpretation
classes are derived by pure collocational analysis of corpora
figure NUM declarative main clause in fuf
significant enhancements to lcs based tutoring could be achieved by combining this representation with a mechanism for handling issues related to discourse and pragmatics
the thematic roles ag th and goal correspond to code numbers NUM NUM and NUM respectively
the english sentence differs structurally from the spanish in that the noun phrase the house corresponds to a prepositional phrase a la casa
NUM the in the lcs template acts as a wildcard it will be filled by a lexeme i.e. a root form of the verb
the main advantage of using the lcs is that it allows the author to type in an answer that is general enough to match any number of additional answers
this has been obtained interliving the syntactic and semantic processes as soon as the semantic test fails the constituent is rejected
during the semantic analysis italian wordnet is used as a kind of knowledge base kb exploiting the structural relationships among synsets
in a sign extmnsion le xi on system we must dis tinguish between stored lexical entries and generated lexical entries
as shown by the lexical entry of walk in figure NUM naturally intransitive verbs are rooted in minimal signs with only one conceptual argument
the generation of the lexical entry in figure NUM thus can be written as the following derivational sequence ated entry for paintingn
the syntactic functions are added by the rule in figure NUM d plus two rules that we here can call subjl and objl
the filture work also includes establishing proper interfaces to various syntactic theories so that the system can be integrated with existing parsers and generators
we examine the value of these techniques when used separately and when combined
it thus runs n times during the course of parsing a sentence of length n
our transfer model involves a bilingual lexicon specifying paired source target fragments of dependency trees
in particular we have found that it is more important to threshold productions than nonterminals
the use of any one of these techniques does not exclude the use of the others
also we need to do checks that the denominator when computing ratio is n t too small
as can be seen the inside score was by far the most nearly strictly increasing metric
right transition write a symbol rl onto the left end of r1 write a symbol r to position a in the target sequences and enter state qi t
the rest of the mapping proceeds in a straightforward fashion until all of the information in the source case frame is mapped onto the target case frame
in combining multiple techniques we need to find optimal combinations of thresholding parameters
to remedy this problem we introduce a novel thresholding technique global thresholding
the case markings enables turkish to have a relatively free wordorder property where every variation in the word order in a sentence results in a different meaning
the decision made by the automatic segmenter is shown as a verticle line above the horzontal line at the appropriate position
in addition the statistical variations between the training corpus and real tasks are usually not taken into consideration in the estimation procedure
although the context sensitive model in the above equation provides the ability to deal with intra level context sensitivity it fails to catch inter level correlation
moreover the formulation in equation NUM provides a way to consider both intra level context sensitivity and inter level correlation of the underlying context free grammar
however because of the implicit encoding of the parsing history a state may fail to distinguish some left contextual environments correctly
mle however frequently suffers from the large estimation error caused by the lack of sufficient training data in many statistical approaches
the total probability estimate using turing s formula for all the events that actually occurred in the sample space is equal to
according to turing s formula the probability mass nl n is then equally distributed over the events that never occur in the sample
jing shin chang has given valuable suggestions for writing this paper in particular for the comparison with briscoe and carroll s approach
a bootstrapping procedure for parameter estimation with respect to a very large corpus therefore will be applied in future research
where fl is a weighting factor between NUM and NUM
this paper presents a novel multimodal system applied to the setup and control of distributed interactive simulations
the transfer module also attacks problelns related to sentential transformation such as the ones required in the example below NUM there are programs in the disk
the version presented here derives from a more general approach to the implementation of lattice operations by ait kaci et al NUM which shows how to implement not only efficient unification of terms greatest lower bound glb in a type lattice but also of generalization least upper bound lub and complement
given such a feature the following rules and lexical entries are sufficient to account for the data above where in a more traditional feature based approach we would have had multiple entries for the determiners and two rules for the determiner less nps one for the case of a mass noun the other for the plurals
pp lcb agent something npsem if vsem vlf vsem rcb p lcb rcb np lcb if npsem rcb an agentive pp replaces the default agent value with that of the agentive np and passes up the daughter vp meaning
x lcb in in out out rcb x lcb in in out nxt rcb y lcb in nxt out out rcb this is like a subcat schema which combines an x projection with a y complement threading the appropriate information
NUM kleene lcb finish f next f rcb the third rule which is general and so only need occur once in the compiled grammar terminates the iteration by extending the daughters of rule i by the sequence of categories that appeared in the original rule
whereas in the lattice we are using for illustration some choices for a b and c do have this property e.g. a agent b person c living other choices e.g. a person b plant c computer do not
the purpose of this division is simply to identify one daughter whose responsibility it is to enforce ordering relations among its sisters and to transmit constraints on ordering elsewhere within the domain both downwards to relevant subconstituents and upwards to constituents containing this one but still within the domain within which the ordering must be enforced
vp lcb subcat rest rcb vp lcb subcat next irest rcb next vp lcb lex send subcat lcb cat np rcb lcb cat p rcb lcb cat pp rcb rcb
the first feature asks if the letter c appears in the previous five words if so the probability of a segment boundary is boosted by a factor of NUM NUM
it captures the notion of nearness in a principled way gently penalizing algorithms that hypothesize boundaries that are n t quite right and scaling down with the algorithm s degradation
qualitative assessment as well as the evaluation of our algorithm with this new metric demonstrates its effectiveness in two very different domains wall street journal articles and broadcast news transcripts
we urge them to be evaluated by their family physicians and this can be done by a very simple procedure simply by having them test with a stethoscope for symptoms of blockage
for he NUM example the figure NUM NUM represents the question does the word he appear in the next five words which is assigned a weight of NUM NUM
we take a unique approach to incorporating the information inherent in various features using the statistical framework of exponential models to choose the best features and combine them in a principled manner
to aid its search the system consults a set of simple lexical hints it has learned to associate with the presence of boundaries through inspection of a large corpus of annotated data
kozima generalizes lexical cohesiveness to apply to a window of text and plots the cohesiveness of successive text windows in a document identifying the valleys in the measure as segment boundaries
our research aim in developing this generic template is to investigate a new approach to the evaluation of dialogue management systems
we also propose a new probabilistically motivated error metric for use by the natural language processing and information retrieval communities intended to supersede precision and recall for appraising segmentation algorithms
where h w n w n l w x is the word history the n words preceding w in the text and z h is the normalization constant
the second experiment which we label class based implicitly takes word sense distinctions into account by considering each occurrence of a verb individually and assigning it a single syntactic signature according to class membership
there were three ways of treating prepositions i mark the pp with the preposition ii ignore the preposition and iii keel only the prepositions
we argue that these algorithms which categorize documents by learning a linear separator in the feature space have a few properties that make them ideal for this domain
typically such cases arise when an additional particle needs to be generated on the target side for example the yes no question particle in chinese
the search algorithm used in our implementation is a head outwards dynamic programming algorithm similar to the parsing algorithm for monolingual head acceptors described in alshawi 1996a
while word segmentation for linguistic analysis may aim at the longest string that carry a specific semantic content this may not be ideal for ir because one then has to deal with the problem of partial string matching when a query term matches only part of a document term or vice versa
quently when a trace is created before so the features belonging to that trace are unknown
the first dimension describes the number of readings available for an entry
ever on the lookout for additional evaluation measures the committee decided to make the creation o f template elements for all the people and organizations in a text a separate muc task
for each muc participatin g groups have been given sample messages and instructions on the type of information to be extracted an d have developed a system to process such messages
not everyone would share these presumptions but participants in the next muc would be free to enter the informatio n extraction evaluation and skip some or all of these internal evaluations
the first step was to do some manual text annotation for the four tasks named entity and the semeval triad which were quite different from what had been tried before
the new company based in kaohsiung southern taiwan is owned NUM pct by bridgestone sports NUM pct by union precision casting co
it may also have an attribute of the form ref n which indicates that this phrase is coreferential with the phrase with id n
it also reflected a conviction that coreference identification had been and would remain critical to success in information extraction and so i t was important to encourage advances in coreference
round NUM dry run once the task specifications seemed reasonably stable nrad organized a dry run a full scale rehearsal for muc NUM but with all results reported anonymously
in earlier mucs each event had been represented as a single template in effect a single record in a data base with a large numbe r of attributes
first this method allows us to measure the statistics based word similarity while retaining the optimal required memory space o n
in addition the identifiers used in the pseudo code version are explained in table NUM the variables and in table NUM the functions
according to our desiderata the new algorithm interfaces two major external modules whose precise functionality is outside the scope of this paper next property and insert unify
generating referential descriptions i requires selecting a set of descriptors according to criteria which reflect humans preferences and verbalizing these descriptors while meeting natural language constraints
the third deficit concerns the lack of control that these algorithms suffer from when assessing the structural complexity of a certain description is required which certainly influences its communicative adequacy too
this notion signifies a referring expression that serves the purpose of letting the hearer identify a particular object out of a set of objects assumed to be in the current focus of attention
NUM the descriptor is an attribute and it does not further reduce the set of potential distractors c38 s NUM
simple cases are not problematic for instance when two descriptors achieve unique identification and can be expressed by a simple noun phrase consisting of a head noun and an adjective
in order to pursue the identification goal the perception facilities preferably look for salient places in the vicinity of the object to be identified rather than to distant places
while the precise form of this restriction is technically motivated it guarantees that a description built this way is always connected we believe that it is also cognitively plausible
recall no of matched case trees specified by the model total no of case trees specified by the linguistic experts no of matched case treesspecified by the model precision total no of case trees specified by the model where a case tree specified by the model is said to match with the correct one if the corresponding cases of the case tree are fully identical to those of the correct case tree
with no smoothing or pruning of any kind and with no more than NUM features induced from the candidate set of several hundred thousand
figure NUM first several features induced for the tdt corpus presented in order of selection with e fac tors underneath
the decision tree algorithm like ours chooses from a space of candidate features some of which are similar to our vocabulary questions
the idea is to construct a model which assigns to each position in the data stream a probability that a boundary belongs at that position
one type of morphographemic error is that consonant substitution may not take place before appending a suffix
handling of various semitic error problems is illustrated with reference to arabic and syriac examples
we then evaluate our method by way of an experiment in section NUM and applied this method to the task of word sense disambiguation in section NUM
the morphological analysis proceeds by selecting rules that hypothesise lexical strings for a given surface string
the error rules are then considered when ordinary morphological rules fail
t supported by a benefactor studentship from st john s college
in examples of grammars variables begin with a capital letter
a vowel is considered shifted if the same vowel has been omitted earlier in the word
the partition contextual tuples consist of rule name surf lex
the pattern type of the vocalism clashes with the broken plural pattern that the root expects
the module win determine the sense of this wor in this example the correct sense of plane is sense NUM i.e. the sense of an aeroplane
the principles have been validated in three ways
take users relevant background knowledge into account
NUM there is no departure at NUM NUM
NUM are you particularly interested in discotmt
each specific principle is subsumed by a generic principle
initiate clarification recta communication in case of inconsistent user input
hfitiate clarification recta communication in case of ambifzuous user input
the algorithm has been implemented for the prolog or dcg constraint system i.e. constraints are equations over first order terms
note that any f depends only on xv and can be thought of as a unary predicate
the remainder constraints in this case are the residual substitutions al and a2 that transform t into t1 or t2 respectively
a critical assumption for the method has been that semantic rules never fail i.e. no search is involved in semantics construction
hence what we need is an algorithm that factores out common parts of the constraints on the logical level pushing disjunctions down
in the first we consider only the structure of the parse forest however ignore the content of rule or leaf constraints
the constraint cr v xv xvl x is called the rule constraint for
using the same variable for ul uk is unproblematic because no two of these nodes can ever occur in a tree reading
the terminal yield as well as the label of two and nodes are identical if and only if they both are children of one or node
discrimination based learning procedures in general tend to overtune the training set performance unless the number of available data is several times larger computational linguistics volume NUM number NUM than the number of parameters based on our experience
for example a NUM NUM accuracy rate is attained for the lex l2 syn l2 model while the accuracy rate is NUM NUM for the lex l2 syn l1 model
after examining the estimated parameters by using these two smoothing procedures we found that some syntactic parameters for null events were assigned very large values by the back off procedure while they were assigned small probabilities by turing s formula
from the estimation point of view the parameters for null events may be assigned better estimated values by using the back off method however these parameters do not necessarily guarantee that the discrimination power will be better improved
tying procedure consider the m gram events lcb xl xm NUM yi rcb myi c v which have the same m NUM gram history lcb xl xm NUM rcb
the ratio of the syntactic weight to the lexical weight i.e. wsyn wlex finally turns out to be NUM NUM for the lex l2 syn l2 model after the discriminative learning procedure is applied
however the performance improvement for the test set is far less than that for the training set since the statistical variations between the training set and the test set are not taken into consideration in the learning procedure
subsequentiality of transformation based systems the proof of correctness of the determinization algorithm and the fact that the algorithm terminates on the transducer encoding brill s tagger show that the final function is subsequential and equivalent to brill s original tagger
for people who are unable to speak most high tech
the research was partially supported by nsf sger iri NUM
the rightmost column contains test message entropy in bits symbol
only the second and fourth terms are signficant
the extension model class is the overwhelming winner
our results are summarized in the following table
we use the first NUM of each file in the corpus to estimate our models and then use the remaining NUM of each file in the corpus to evaluate the models
cally although not necessarily related to both
by the definition of NUM ry
section NUM discusses possible improvements to the model class
that is the word string fund sand is the only bt tokenization
the correspondence between surface and lexical strings for an entire word is licensed if there is a partitioning of both so that each partition pair of corresponding surface and lexica targets is licensed by a rule and no partition breaks an obligatory rule
development compilation and run time efficiency are quite acceptable and the use of rules containing complex feature augmented categories allows morphotactic behaviors and non segmentm spelling constraints to be specified in a way that is perspicuous to linguists leading to rapid development of descriptions adequate for full nlp
it is not necessary for the surface target to contain exactly one character for the blocking effect to apply because the semantics of obligatoriness is that the lezicaltarget and all contexts taken together make the specified surface target of whatever length obligatory for that partition
for case NUM the spelling rules may be applied directly just as in rule compilation to a specified surface or lexical character sequence as if no surface b o j e lexical b NUM j e rule def
the values of n required are those for which for some spelling rule there are k characters in the target lexical string and n k from the beginning of the right context up to but not including a boundary symbol
run time speeds are quite adequate for full nlp and reflect the fact that the system is implemented in prolog rather than say c and that full syntactico semantic analyses of sentences rather than just morpheme sequences or acceptability judgments are produced
a spelling pattern consists of partially specified surface and lexical root character sequences fully specified surface and lexical affix sequences orthographic feature constraints associated with the spelling rules and affixes used and a pair of syntactic category specifications derived from the production rules used
if an obligatory rule specifies that lexical x must be realized as surface y when certain contextual and feature conditions hold then a partitioning where x is realized as something other than y is only allowed if one or more of those conditions is unsatisfied
because the probabilistic chunker proposed in this paper is based on syntactic tags parts of speech a part of speech tagger is needed
this has not been necessary for the descriptions developed so far but its implementation is not expected to lead to any great decrease in run time performance because the nondeterminism it induces in the lookup process is no different in kind from that arising from alternations at root affix boundaries
a similar approach has been also successfully applied in the tsnlp database cf
it should be noted that while exploring ways to create ltigs with small numbers of elementary trees is interesting it may not be of practical significance because the number of elementary trees is not a good measure of the size of a tig
NUM heureuz anzieuz malheut euz honteu soucieuz etc
thanks also to laurence danlos and graham russell for their conunents
in this section we explained the polyvalency of mental adjectives
because the grammar is coarse while the lexicon is fine the approach retains the previous approach s high sensitivity to lexical matching constraints
each syntactic production occurs in both straight and inverted orientations to model ignorance of the ordering tendencies of the corresponding chinese constituents
the queries are then available to any natural language text retrieval system
the results were as follows NUM
the effect of this is to transfer knowledge of english syntactic constraints or more precisely probabilistic preferences to the bilingual task
aside from linguistic motivations stemming from the compositionality principle this constraint is important for computational reasons to avoid exponential bilingual matching times
thus a parser with this grammar can build a bilingual parse tree for any possible itg matching on a pair of input sentences
grammatical information is far less easily available for chinese than for english however with respect to part of speech lexicons as well as grammars
the mutation strategy applied between one and ten modification operations to each of the NUM queries per generation and collected only the best NUM of the queries to propagate into the next generation
in this effort we applied a qr decomposition technique to reduce the complexity of calculating the singular value decomposition resulting in query translation that took only a matter of seconds on a sparc NUM
the first two productions are sufficient to generate all possible matchings of itg expressiveness this follows from the normal form theorem
the second approach generalizes the inside outside algorithm to adjust the grammar parameters so as to improve the likelihood of a training corpus
NUM the other methods performed even more poorly
figure NUM c diagrams the process
peter sells a bike and gives a fishing rod to mary
it is now clear that while the principle of maximum tokenization is very useful in sentence tokenization it lacks precise understanding in the literature
i give back the bill and someone to pay
our approach to micro planning integrates a variety of different types of operators for aggregation information within a single sentence
jean dances the waltz and pierre the tango
2c je sais ps marie et qu elle est venue ici
out of the set of propositions given as input the micro planner selects one proposition to start with
recent work in computational linguistics which makes use of statisticm methods to cluster words into groups which reflect their meaning is attractive in this context as it potentially provides a means for developing conceptual structure without supervision without giving any prior information about the language to the system and without making a priori distinctions between concrete and abstract words
these criticisms arise largely because cluster analysis is a purely descriptive statistical method and strongly suggest that alternative methods must be found which can provide a more objective measure of the success of the technique being used
the dendrograms resulting from these analyses did not show any marked improvement over those obtained from the earlier analyses and even when the window length was increased to NUM words each side of the target word clear differences were not easy to detect from the dendrograms although the sorts of groupings noted earlier were still identifiable
that is target words were represented by vectors whose components reflected the bigram statistics of occurrence of context words at the word position immediately preceding the target want wanted tried went derided think thousht hope believe knew feel felt expect wish forget
this situation suggests that the learning of the meanings of many words and their relation to the meanings of other words may be achieved in an unsupervised fashion and that our ability to develop a categorization for words may be driven at least in part by structure latent in the language being learned
this is to weaken the effects of syntax although the analyses described here do not make use of this facility
the members of some of these groups were selected following inspection of the relevant dendrograms and are listed in table NUM
looking groupings revealed in a dendrogram whilst ignoring what may be a very large number of less attractive ones
the micro planner uses information from the lexicon to determine how to combine the propositions together while satisfying grammatical and lexical constraints
magic exploits the extensive online data avail null ter cpmc as its source of content for its briefing
you would just have to ask him for it
figure NUM d will be explained shortly
there are approximately 54k sentences in the brown corpus
on the one hand john is very generous
for example suppose you needed some money
null example NUM a john is very generous
NUM words has yielded some interesting results
discourse processing subsumes several distinguishable but interlinked processes
we will continue to monitor for such examples
our results thus suggest that accurately predicting discourse segmentation involves far more than directly using known linguistic differences between discourse boundaries and nonboundaries is here we analyze some of the likely reasons for our computational linguistics volume NUM number NUM results to motivate the methodologies for algorithm improvement presented in the next section
NUM hcm can not make the best use of information about the differences among the frequencies of words assigned to an individual cluster
it then classifies d into cs as log s l dlcs is larger than log s l dlc NUM
we also show by example that subjects segmentations reflect the presumed episode structure of the narrative
the assumption of independence is important for motivating statistical analyses of how probable the observed distributions are
as a first data set we used a subset of the reuters newswire data prepared by lewis called reuters21578 distribution NUM NUM
fig s NUM and NUM show precision recall curves for the first data set and those for the second data set respectively
to create this data we repeatedly performed the following experiment and randomly selected one result
in section NUM we discuss the significance of our results and briefly highlight our current directions
prompts repetitions and summaries rather than cue words more often signaled control based discourse segment boundaries
the complex equivalence relations are needed to help the relation assignment during the development process when there is a lexical gap in one language or when meanings do not exactly fit
the observed count fi is simply the frequency in the training sample
this makes it possible to precisely establish the specific equivalence relation across pairs of languages but it also multiplies the work by the number of languages to be linked
sequential model selection for word sense disambiguation
and NUM the value of NUM
members of this family are distinguished by their different values of
event NUM generalisation event NUM rank universal happen definition subject dooner NUM rank named individual family inanimate manmade action succeed NUM despite the lack of any analysis of the stepping down sentence the system scored NUM recall and NUM precision on the formal evaluation of the walk through article
these plots illustrate that bss bic selects models of too low complexity
rules derived manually automatically and through a combination of efforts have been applied successfully in a variety of languages including english spanish portuguese japanese and chinese
NUM mismatches and language specific semantic configurations within the eurowordnet database the wordnets can be compared with respect to the language internal relations their lexical semantic configuration and in terms of their equivalence relations
naturally this observation suggests tokenization disambiguation strategies notably different from the mainstream best path finding strategy
b linking through an structured artificial language c linking through one of the languages d linking through an non structured index the first option a is to pair wise link the languages involved
the first heuristic is implemented by requiring the frequency of the pair to be higher than the frequency of any other pair that is formed by either word with other words in common contexts within a simplex noun phrase
short phrases often nominal compounds are preferred over long complex phrases because short phrases have better chances for matching short phrases in queries and will still match longer phrases owing to the short phrases they have in common
virtually all commercial ir systems with the exception of the clarit system index only on words since the identification of words in texts is typically easier and more efficient than the identification of more complex structures
in practice the process effectively nominates phrases that are true atomic concepts in a particular domain of discourse or are being used so consistently as unit concepts that they can be safely taken to be lexical atoms
for example if the phrase computer aided design occurs frequently in a corpus aided design may be judged a good association pair even though computer aided might be a better pair
the preference score ps for a pair is determined by the ratio of its local dominance count ldc the total number of cases in which the pair is locally dominant to its frequency
but once a pair is determined to be a lexical atom it will behave exactly like a single word in subsequent processing so in later phases atoms with more than two words can be detected
the multilingual eurowordnet database thus consists of separate language internal modules separate language external modules and an inter lingual module which has the following advantages it will be possible to use the database for multilingual retrieval
for example in an np with the sequence x y g we compare s x y with s y g whichever is higher is locally dominant
if more than one association is possible above theshold in a particular np we make all possible associations but in order of ps the first grouping goes to the pair with highest ps and so on
two checkpoints are required tion map in contour view
this suggests using variable length windows sizing according to maximal match
subject object pp obj can be defined with a high degree of accuracy
it is thus possible to reassign syntactic markings at a later stage of the sequence
the output annotated string is identical to the input string and the construction is bypassed
marking each transducer defines syntactic constructions using two major operations segmentation and syntactic marking
step NUM the same is done with the tbeginvcs inserted at the beginning of a sentence
the other syntactic functions such as object pp obj verb modifier etc are tagged using similar steps
previous work in finite state parsing at sentence level falls into two categories the constructive approach or the reductionist approach
figure NUM cumulative dhit per topic for the top NUM
iwei total number of windows of size m in e
figure NUM vol NUM optimal position policy determina
what data is most appropriate for determining the optimal position
this makes the results somewhat weaker than they could be
we determined the optimal position for topic occurrence as follows
the system serves two purposes as an information extraction tool it allows users to search for textual descriptions of entities as a utility to generate functional descriptions fd it is used in a functional unification based generation system
we are interested in implementing existing algorithms or designing our own that will match different instances of the same entity appearing in different syntactic forms e.g. to establish that plo is an alias for the palestine liberation organization
e.g. if an ontology contains information about the word president as being a realization of the concept head of state then under certain conditions the description can be replaced by one referring to head of state
if we have retrieved prime minister as a description for silvio berlusconi and later we obtain knowledge that someone else has become italy s primer minister then we can generate former prime minister using a transformation of the old fd
users can select an entity such as 3ohn major specify what semantic classes of descriptions they want to retrieve e.g. age posi null tion nationality as well as the maximal number of queries that they want
we keep information about the surface string that is used to describe the entity in newswire e.g. addis ababa the source of the description and the date that the entry has been made in the database e.g. reuters95 NUM NUM
among the factors that will influence the selection and ordering of descriptions we can note the user s interests his knowledge of the entity the focus of the summary e.g. democratic presidential candidate for bill clinton vs u s president
one impediment to end users composing their own rules is the particular syntax of alembic s phraser rules so we anticipate exploring other simpler rule languages that will encourage end user participation
remove the nodes which are partial heads this prevents linking of cars in the np red cars and blu e cars but has to allow a link between sugar in i like sugar manufacturers because i like suga r
further in the specific case NUM in which m n
tion l o log ojpj wt
creating clusters there are any number of ways to create clusters on a given set of words
on the other hand the number of parameters in fmm is larger than that in hcm
we adopted the lewis split in the corpus to obtain the training data and the test data
the use of fmm is also appropriate from the viewpoint of number of parameters
will a wordclustering based method fmm outperform a word based method wbm here
work in computational phonology has demonstrated that given certain conditions such rewrite rules can be represented as finite state transducers fsts
the comparison just discussed involves a rather artificim ruleset but the differences in performance that we have highlighted show up in real applications
in this paper we argue for a linguistically principled approach to disambiguation in which relevant contextual clues are narrowly defined in syntactic and semantic terms and in which only highly reliable clues are exploited
in our data locational senses of side always involve the directional sense of right in right side of unless there is decisive evidence to the contrary in the same sentence or in the immediately surrounding discourse
verb senses can relate systematically to adjective senses because adjectives often designate attributes pertinent to the application of the verbal action to by the referent of the modified object subject noun
it is a cultural and thus semantic fact that wines and other nonanimate entities that undergo developmental changes and pass through maturational stages are treated as living things
for example a human noun indicates the not tall sense of short a concrete noun indicates a not long sense of short
the two inch layer of fat that is attached to the inside of the seal s skin is left intact and finally the whole hide is turned right side out
finite state transducers under the condition that no rule be allowed to apply any more than a finite number of times to its own output
antonyms most often co occur in direct comparisons or in contrastive opposition directly reflecting both the identity of the attribute to which they pertain and the contrast in its value
statistical methods play a definite role in this work helping to organize and analyze data but the disambiguation method itself does not employ statistical data or decision criteria
this approach results in improved understanding of the disambiguation problem both in general and on a word specific basis and leads to broadly applicable and nearly errorless clues to word sense
u v ewp rtpi 6np sj es 8kesnpi6np where wp is the set of all possible word pairs
parsing is much faster taking less than NUM hour to parse all noun phrases in the corpus of a NUM megabyte text
the parsing speed can be scaled up to gigabytes of text even when the parser needs to be re trained over the noun phrases in the whole corpus
smith hipp and biermann an architecture for voice dialog systems NUM suggestive
in order to evaluate other corpus based methods we wanted to establish a baseline for queries formed from these moderate frequency term sets
again as with functional perplexity the perplexity of a language in this sense is difficult to measure but it is helpful to look to the extent that the system attempts to constrain the user s language for performing a task
rather than attempting to measure accuracy speed of output we propose principles for the evaluation of the underlying theoretical linguistic model of dialogue management in a given system in terms of how well it fits our generic template for dialogue management systems
bos zos NUM bos eos NUM eos NUM the reesthnation algorithm computes the inside probabilities r r and t inscribing them into the chart in bottom up and left to right
these restrictions on composition of complete sequence is for the ease of description of algorithm the basic complete link and complete sequence are also shown in the figure NUM following notations are used to represent the four kinds of objects for a word sequence wij and for an m from i to j NUM
the second term me n the construction of larger lr i h from the combination of sr i j st j NUM h and the dependency link from wi to wh
the word order in these languages serves to structure the information being conveyed to the hearer e.g.
the same composition rules allow two verbs to compose together to handle complex sentences with embedded clauses
these categories are a shorthand forthe many syntactic and semantic features associated with each lexical item
the identity rule allows two constituents with the same discourse function often variables to combine
in generation the same topic found in the database query is maintained in the answer
as indicated by the translations word order variation in complex sentences also affects the interpretation
in multiset ccg NUM we capture the syntax of free
the syntactic category for verbs provides no hierarchical or precedence information
the derivation for this answer is seen in figure NUM
i define topic and focus according to their informational status
for every word in the corpus whose environment matches the triggering environment if the word has tag x and x is the correct tag then making this transformation will result in an additional tagging error so we increment the number of errors caused when making the transformation given the part of speech tag of the previous word lines NUM and NUM
it has been shown however that some supervised training prior to the unsupervised phase is often beneficial
the number of frequency counts NUM plotted y axis versus classification accuracy achieved x axis
modelling inflections derivations and the corresponding phonological alternations via lexical rules amounts to the lexicalization of morphology
in the second case the entity is marked indefinite and all scrambling is blocked by the lexical rule
in terms of performance run time execution of the rules seems to be a far better alternative than pre compilation
NUM an alternative is to use a lexical rule for differentiating predicate and term reading of the lexical entry
in this study a lexical inheritance hierarchy is used in conjunction with the lexical rules to obtain type constraints and feature structures for free forms words bound forms are not part of the lexicon
if morphology is treated almost like syntax lexical knowledge should contain richer morphological information including a semantic representation for bound forms affixes information about boundedness freeness of morphemes and the type of attachment e.g. affixation cliticization syntactic concatenation NUM NUM
the in the update means that the information between the square brackets representing the focus of the user utterance must be retracted while the denotes the corrected information
then for unknown words p w i t p unknown word i t p capitalize feature i t p suffixes hyphenation i t using this equation for unknown word emit probabilities within the stochastic tagger an accuracy of NUM was obtained on the wall street journal corpus
several assumptions and approximations on the probabilities p w t and p t lead to good comprises concerning memory and computational complexity
the unknown words are tagged using an experimentally proven stochastic hypothesis that links the stochastic behavior of the unknown words with that of the less probable known words
the corpus ambiguity was measured by the mean number of possible tags for each word of the corpus for both sets of grammatical tags table NUM
a the typhoon reason has broken a part the city block
results from the definition of the expected correct tagging rate the hmm ts model maximizes the correctly tagged sentences while the hmm t model maximizes the correctly tagged words
in this case in order to use the forward backward and the viterbi algorithm we must estimate the unknown word s conditional probabilities p w i t
measurements of error rate time response and memory requirements have shown that the taggers performance is satisfactory even though a small training text is available
b a monster agent has broken a part the city block
c NUM association for computational linguistics computational linguistics volume NUM number NUM along with great research advances the infrastructure is in place for this line of research to grow even stronger with on line corpora the grist of the corpus based natural language processing grindstone getting bigger and better and becoming more readily available
fine tuning csrs csrs were generated for all sentences in an essay
the computer rubric recall that a rubric is a scoring key
essentially the essay can be treated as a sequence of short answer responses
examinees typically partitioned the essay into sections that corresponded to the scoring guide
csrs were automatically generated for all sentences in each essay
each lexical entry contained a superordinate concept and an associated list of metonyms
no further reproduction is permitted without written permission of ets
it takes approximately NUM minutes to automatically generate the rules
the concept grammar rules are described later in the paper
rubric categories are the criteria that determine a correct response
both hierarchies will enable a user to customize the database with semantic features without having to access the language internal relations of each wordnet
figure NUM shows the planned robust system architectur with the part of speech tagger and the word fl r word translator integrated into the core understanding generation systein
due to the highly telegraphic nature of the muc ii data genermizing tile grammar will increase the ambiguity of an input sentence greatly of
note that all of the entries in a message template are optional so that a statement need not contain a suhject or a verb phrase
i am unable to determine whether suspectl6 had access to the poison
our model concentrates on two specific dialogue cues questions and answers
this methodology allows investigators to test different aspects of a dialogue theory
in this paper we examine mechanisms for automatic dialogue initiative setting
thus an agent may have initiative over one goal but not another
i am unable to determine whether suspectl0 is the murderer of lord dunsmore
rather they provide a means for exploring proposed dialogue initiative schemes
is it the case that suspectl0 had a motive to murder lord dunsmore
is it the case that suspectl0 had an opportunity to administer the poison
such a pair always exists since if not thus a
this proves that mdy has bounded variations and therefore that it is subsequential
given a set of rules the tagger is constructed in four steps
bca this factor is transformed into its image bc resp
given a function fl that transforms say a into b i.e.
let us show that dom iti c dom iti
the inclusion of such particles often depended on additional distinctions not present in the original english automata
at the other end of the scale we would probably not use genre to describe the class of sermons by john donne since that class while it has distinctive formal characteristics is not extensible
the proposed relationships between aac design approaches pragmatic aspects of natural conversation and short and long term conversational goals are illustrated in figure NUM
rap prefers an interpretation with a smaller d
figure NUM rap alpp and length
in general the level of focus of an object is established when it is activated and decreases with the flow of discourse
although finer differentiation may be needed our theory only distinguishes between reasons residing in attentional spaces that are structurally close or structurally distant
in this section we illustrate the attentional hierarchy with the help of an example which will be used to discuss reference choices later
although developed for a specific application we believe the main rationales behind of our system architecture are useful for natural language generation in general
concretely such references must be made for previously derived conclusions used as premises and for the inference method used in the current step
NUM if a reason is structurally distant but textually close first try to find an implicit form if impossible omit it
the corresponding discourse model after the completion of the presentation of the proof in figure NUM is a proof tree shown in figure NUM
each such derivation is realized in proverb by a proof communicative act pea following the viewpoint that language utterances are actions
in all discourse based theories the update of the focus status is tightly coupled to the factoring of the flux of text into segments
null NUM the omit form in this case a word such as thus or therefore will be used
however the kitc also has weight
tr3 performs better than tr2 for texts with simple discourse segment structure
f youxie shui hui liu chulai
g i j reduced j reduced h i i
the experiment is executed in three steps
yeh and mellish an empirical study on anaphora
signals a boundary of a discourse segment
finally section NUM presents the conclusions
we believe that for the most part during normal dialogue the minimal effects of any speech act are all that are required
they can lead to an update of the state representation of the system or to an interruption e.g. in the case of an incoming phone call
a controller module provides a software interface to the berlin system it can modify the state of the berlin system and it can retrieve information from it
when the top element of this list is different from the user s actual utterance we are facing a sr error
it tries to extract as much information as possible from the received input simply ignoring input it can not parse e.g. interjections and false starts
e.g. different voices can be used as the auditive counterparts of different active windows in a windows based operating system
the dm module described in this paper will be part of the first vodis prototype to be completed in fall NUM
the best way to follow commandments is to take them as a source of inspiration and not follow them to the letter
the major experiments in trec NUM included the development of automatic query expansion techniques the use of passages or subdocuments to increase the precision of retrieval results and the use of the training information to select only the best terms for routing queries
although the collection sizes are roughly equivalent in megabytes there is a range of document lengths across collections from very short documents doe to very long fir also the range of document lengths within a collection varies
the top NUM runs cityal 1nqi01 and crnlea have excellent performance see figure NUM in the middle recall range NUM to NUM with this performance likely coming from the query expansion
jones m m hancock beaulieu and m gatford used a probabilistic term weighting scheme similar to that used in trec NUM but expanded the topics by up to NUM terms average around NUM automatically selected from the top NUM documents retrieved
inqi02 the manually modified version of 1nq101 had a NUM improvement in average precision over inqi01 and NUM topics that were superior in performance for the manual system as opposed to only NUM for the automatic system
assctv1 mead data central inc query expansion reduction and its impact on retrieval effectiveness by x allan lu and robert b keefer is also a manual expansion of queries using an associative thesaurus built from the trec data
in particular the concepts field has been removed because it was felt that real adhoc questions would not contain this field and because inclusion of the field discouraged research into techniques for expansion of too short user need expressions
table NUM gives the average number of terms in the title description narrative and concept fields all three fields for trec NUM and trec NUM no concept field in trec NUM and only a description field in trec NUM
this lack of system differentiation comes from the very wide performance variation across topics the cross topic variance is much greater than the cross system variance and points to the need for more research into how to statistically characterize the trec results
the dominant new themes in the automatic adhoc runs are the use of some type of term expansion beyond the terms contained in the less rich trec NUM topics and some form of passage or subdocument retrieval element
thirty five different subcategorization frames are used for all verbs in wordnet and the frames supplied are partial
during this process a detailed entry for the word is formed containing both syntactic and semantic information
similarly the words question and dispute are also connected but through a different subset of senses
finally the union of the sets s contains the pre null dominant sense of x
2in this domain the two adjectives are complementaxies describing the two types of issued stock shares
table NUM reduction in ambiguity and sense tagging error rate for the cluster based method as measured for
this part of the corpus is more homogeneous and contains a larger number of articles NUM
we anticipate that the use of multiple methods to investigate sense pruning will lead to more robust results
our method for using detailed knowledge about verb subcategorizations and alternations to prune verb senses is domain independent
the muc NUM tasks i n particular had been quite complex and a great effort had been invested by the government in preparing th e training and test data and by the participants in adapting their systems for these tasks
for example the first row shows that of the NUM texts that really are of the reportage genre level NUM were correctly classified as re portage NUM were misclassified as editorial and NUM as nonfiction
formula NUM for s a s b r a s s NUM z s s NUM o s s NUM s s NUM NUM
branches are added to the tree until the decision tree can classify all items in the training set
computational linguistics volume NUM number NUM appropriately and the same input data is presented multiple times
when the aim is to develop an aid for social conversation there seem to be good reasons for adopting a basically phrase storage approach
the sz corpus did have a lower bound of NUM NUM which was similar to the NUM NUM
we trained satz on NUM of the problematic examples described above taken from the wsj training corpus
we have successfully adapted the satz system to german and french and the results are described below
the initial capital in saturday does not necessarily indicate that saturday is the first word in the sentence
the transferability of the system from english to other languages is also demonstrated on french and german text
two adjustable sensitivity thresholds to and tl are used to classify the results of the disambiguation
the method currently widely used for determining sentence boundaries is a regular grammar usually with limited lookahead
this is because some systems insert the source word into the target sentence if the source word and its translation is not in the lexicon
louella s scenario is about changes in corporate management
other variables are also present within the macro definitions
typically these tools are sequentially applied to text
these marked phrases are then reduced into single tokens
verdi dvorak wagner mahler chopin n
since it is conceivable that mr
both models are base d on the scenario specifications
the following sections show the extraction process taking place
reference resolution is ongoing throughout processing
f measure to 49r 60p with a NUM NUM
the goal of the generator is to produce a sentence whose corresponding semantics is as close as possible to the input semantics i.e. the realisation adds as little as possible extra material and misses as little as possible of the original input
dtgs try to overcome the problems associated with tags while remaining faithful to what is seen as the key advantages of tags NUM the extended domain of locality over which syntactic dependencies are stated and function argument structure is captured
in the same spirit after the generator has consumed expressed a concept in the input semantics the system checks that the lexical semantics of the generated word is more specific than the corresponding concept if there is one in the upper semantic bound
an alternative paraphrase like fred hurried with a limp ll can be generated using a lexical mapping rule for the verb hurry which groups imovementi and together and a another mapping rule expressing limping as a pp
among those that have looked at generation with conceptual graphs are generation using lexical conceptual grammar NUM and generating from cgs using categorial grammar in the domain of technical documentation NUM
our generation technique provides flexibility to address cases where the entire input can not be expressed in a single sentence by first generating a best match sentence and allowing the remaining semantics to be generated in a follow up sentence
it is possible for components above the substituted node to drift arbitrarily far up the d tree and distribute themselves within domination links or above the root in any way that is compatible with the domination relationships present in the substituted d tree
other work addressing surface realisation from semantic networks includes generation using meaning text theory NUM generation using the sneps representation formalism NUM generation from conceptual dependency graphs NUM
note NUM with lexical items specified for each linguistic constituent cf
after the word lists had been prepared we constructed a simple sentence with every word since some systems can not translate lists with single word units
the work of improving the performance of the system is still ongoing
iclclai icidiclclai i c id lii iii ii iiiil iclciai iclclai NUM NUM figure NUM partial matches of a b prevbigram c c on the input c d c c a NUM
the function star takes into account these cases the repeat loop accounts for the case when the first symbol of NUM is starred too
the number of possible segmentations for some sentences may be rather large
NUM NUM global statistics local statistics
table NUM unsupervised training test set accuracy
NUM line NUM says that the current state within the loop is q and that this state for all the diagonal pairs a a s t a is not an input symbol on any outgoing arc from this state
the paper is a first attempt to fill a gap in the dependency literature by providing a mathematical result on the complexity of recognition with a dependency grammar
the items including a non null depcat are just passive receptors waiting to be reactivated later when and ii the recognition of the hypothesized substructure has successfully completed
the first of the category cat is the set of categories that appear as leftmost node of a subtree headed by cat
the initial set of states consists of a single state so that contains all the possible strings a
NUM supervised training is feasible when one has access to a large manually tagged training corpus from the same domain as that to which the trained tagger will be applied
1degthe graphs are choppy because after each transformation is applied correctness for words not yet fully disambiguated is judged after randomly selecting from the possible tags for that word
a dependency relation is an asymmetric relation between a word callexl head governor parent and a word called modifier dependent daughter
the processing speed is about NUM characters per second on pentium NUM pc
thus even though the dialogue strategies in figures NUM and NUM are radically different the avm task representation for these dialogues is identical and the performance of the system for the same task can thus be assessed on the basis of the avm representation
despite the advantages of this approach the parallel file data structure had some drawbacks
note that the semantics has been accumulated incrementally and straightforwardly in parallel with the syntax
in the query phase of this dialogue the speakers have established the day a global arrival time the arrival place and the departure place
again the knowledge base is consulted to select np lemmas that truly describe book19
we have built a first implementation which shows to be of great utility in accelerating the construction of tree banks and improving their consistency
the algorithm identifies the combinations of words and trees that satisfy the most communicate goals and eliminate the most distractors
in NUM NUM the semantic difference concerns the distribution of the scopes of quantifiers
operational criteria to distinguish between these two values again may be found in the question test and in similar procedures
NUM a john made a canoe out of every log
in the present paper it is possible only to characterize these notions briefly and informally
often this is what was referred to by the focus proper of the preceding utterance
NUM a everybody in this room knows at least two languages
in either case the other complementations are handled according to cb below
in fgd the sentence structure is understood as based on the relation of syntactic dependency and is thus extremely flat
different surface means are used to express the differences in tfa in the english examples
their acceptability often depends on the lexical setting of the sentence and on pragmatic factors
cogniac was designed to perform pronominal resolution in highly ambiguou s contexts and is distinguished from other approaches to pronominal resolution in the following ways
the fact that modules further along in the pipeline do not alter the outpu t of earlier components means that output files can be read only
we combined the output of the following three pos tagger s using a simple voting scheme eric brill s rule based tagger version NUM NUM
unfortunately the wordnet taxonomy is more like a tree than a lattice so that many useful multipl e inheritance links do not exist
in august we were given permission from yael ravin of ibm s information retrieval group to use the ibm name extraction module NUM
however the method employed in the current system is able to discriminat e reliably on a coarse level between cases like end and uncle
the wordnet semantic concordance provides frequency information from a fraction of the brown corpus for senses of end and other words in the noun database
a list of these verbs including serve work continue and resign was compiled and these patterns were used as well
the third step looks for upper case string matches which are not variant name references or which do not contai n corporate designators or honorifics
implementation of finite state transducers once the final finite state transducer is computed applying it to an input is straightforward it consists of following the unique sequence of transitions whose left labels correspond to the input
a quite different solution which is often applied for the same problem if a left corner parser is used is to compile the grammar into an equivalent grammar without gaps
it equates markables which share a common head noun using various metrics of similarity
for example suppose that a verb noun collocation e is given as in the formula NUM in section NUM NUM NUM
however the two models are different in the orders of the first NUM selected features with more than one cases
NUM we described the results of the experiment on learning the models of subcategorization preference from the edr japanese bracketed corpus
sense restriction c1 cz of case marked argument adjunct nouns are represented by classes at arbitrary levels of the thesaurus
when considering NUM we have to decide which superordinate class generates each observed leaf class in the verb noun
the work concentrated on the extraction of declarative representation of case frames and did not consider their performance in sentence parsing
the agent proposes the first slot in the interval that is available according to its calendar
in turkish there is a significant amount of interaction between morphology and syntax
i would like to thank my advisor cem bozsahin for sharing his ideas with me
we use to denote the combination of categories x and y giving the result z
entries in the eategorial lexicon have tactical constraints grammatical and semantic features and phonological representation
for instance the causative suffix dhr has eight different realizations but only one lexical entry
figure NUM scope ambiguity of a nominal bound mor pheme
as an implementation we have been working on the modelling of turkish causatives using this framework
in this paper we address the problem of modelling interactions between different levels of language analysis
for instance in turkish word formation is based on suffixation of derivational and inflectional morphemes
we now assume a first order dependence on the alignments aj only vr fj aslf lcb j a i el where in addition we have assmned that tile translation probability del ends only oil aj and not oil aj l
the model is based on a decomposition of the joint probability br l into a product over the probabilities for each word j a j l whefe fo norll la iz i tion NUM solls the 8elltc ce length probability p j l has been included
for lppl the match among lemmas proved most useful while dgile yielded better results when matching the first four characters of words
those operations are theoretically equivalent to tree adjoining grammar operations
it is computed in the same time as word confidence scores
where is a linear slope that ensures a minimal decrease
NUM NUM NUM NUM NUM NUM NUM NUM winning assignment probability probabilities maxc p clw for the three hundred most commonly occurring words
the n squared problem while using an interlingual representation would seem to be the obvious way to avoid the n squared problem translating between n languages involves order n NUM transfer pairs we are sceptical about interlinguas for the following reasons
this is an area that deserves more attention than it has received to date indeed it is not obvious how best to perform such an evaluation so as to measure meaningfully the performance both of the overall system and of each of its components
translation our intuitive impression based on many evaluation runs in several different language pairs is that the fine grained style of speech to text evaluation described in the preceding section gives a much more informative picture of the system s performance than the simple acceptable unacceptable dichotomy
the target language user may interrupt processing before the more global methods have finished if the translation assuming it can be viewed on a screen is adequate or the system itself may abandon a sentence and present its current best translation if a specified time has elapsed
qlf bascd transfer is used for the first time and the transfer rule in figure NUM is used to translate early as de bonne heure which because it is a pp is placed after vol flight by the french grammar
to make matters worse the corpus might contain chunks of texts which appear in one language but not in its translation NUM suggesting a discontinuous mapping between some parallel texts
figure NUM the top ranked words for each category
while more work needs to be done to refine this procedure and characterize the types of categories it can handle we believe that this is a promising approach for corpus based semantic knowledge acquisition
we ll use the category animal as an example
since the instructions allowed the users to assign a zero to a word if they did not know what it meant we manually removed the zeros and assigned ratings that we thought were appropriate
our approach is designed for domain specific text processing so the text corpus should be a representative sample of texts for the domain and the categories should be semantic classes associated with the domain
because the backoff models are only consulted for unseen word combinations the perplexity on these word combinations serves as a reasonable figure of merit
the context windows do not cut across sentence boundaries
for mug NUM it took slightly under half a minute to process a typical ws j article in the development set
the knowledge representation module consists of the boolean algebras module knowledg e base interpreter and the inference engine module
enamex type quot person quot james enamex says it is time to bee tad selfish about bow h e spends his deys
our very good performance on the task of identifying temporal expressions was sligthly improved wit h handling numbers
we briefly comment on the scope importance and quantity of the tasks we had decided to do
another piece of code we had to develop were functions for chosing markables and outputting sgml tagged text
the reader module contains functions for breaking input text into documents paragraphs sentences and words
the uno system does not only identify explicit temporal expressions but also automatically reasons wit h them
even this unofficial score does not reflect well our real performance only shows that we did something
the parser produces both syntactic parse trees and the uno semantic representation of the natural languag e input
non co occurring word stem context vectors are adjusted by subtracting the mean context vector at the end of each update iteration
rosenfeld reported a test set perplexity of NUM a NUM reduction from the NUM perplexity of a baseline trigram backoff model
basic and law are both matched to NUM so we know the correct translation for 2g is basic law which is a compound noun
l rcb te cenl er will shift but ill ilet null cont sudi prediction is not fultilled
when applying entering to real text one realizes that lnany issues have llot been solved yet
an np of type possessive refers to two entities the possessor po and the possessed p d
hiterpreting referential expressions is importatlt for any large coverage nl system while such systems do exist for italian e.g.
centering theory has q pealing traits from t oth cognitive and comi ulational points of view
this model appears to produce very good results although the terms added are occasionally disconcerting for the user since they represent parts of words or characters from two different words that commonly appear together in a phrase
while there is a great deal of grassroots support in the unix world for display of chinese and japanese kterm cxterm documentation and stability are unreliable and they do not support sophisticated pointer driven or menu based interaction
by ending analysis we mean evidence of a word s part of speech given its spelling e.g. the probability that a word is a noun given it ends in tion
in bbn s part of speech tagger post a bi gram probability model and frequency models for known words derived from large corpora are employed to assign a part of speech to all words of the sentence in context
since neither capitalization nor ending analysis are available in chinese the only alternative to reducing the error rate in chinese newswire is reducing the number of unknown words e.g. by developing a list of words plus parts of speech
a given system s performance will be reported in terms of recall and precision recall indicates what percentage of all the relevant documents were retrieved at a given point precision indicates what percentage of the documents retrieved were relevant
indicates pressing or release of the button that activates verbmobil
this information is built up by the planner during dialogue processing
these goals have to be fulfilled in the specified order
in the case of verbmobil these units are speech acts
the keyword iterate specifies that negotiation phases can occur repeatedly
reason figure NUM a dialogue model for the description of
the rest of the dialogue can only be followed by a keyword spotter
ablehnung NUM NUM and aufforderung stellung NUM NUM
this requirement is solved partially by using a statistics based speech act prediction component
nevertheless we will refer to them as speech acts throughout this paper
our method achieves state of theart performance in both domains and allows the easy integration of diverse information sources such as rich lexical representations
her system produces text using a technique she called local organization
comparing a statistical and a constraint based method jean pierre chanod and pasi tapanainen rank xerox research centre grenoble laboratory NUM chemin de maupertuis NUM meylan france jean
there are as many steps as features and there are a total of NUM f terms divided over all the steps
it should however be noted that the computation of ig weights is many orders of magnitude faster than the laborious evaluation of terms on held out data
however these works differ in their focus from our analysis in that the emphasis is put on similarity between values of a feature e.g.
we can assume for simplicity s sake that the ax do not depend on the value of x i but only on i
for example assuming that vbn is the most likely tag for the word killed and vbd for shot the lexical tagger might assign the following part of speech tags NUM since the lexical tagger does not use any contextual information many words can be tagged incorrectly
there are two ways to train the statistical tagger from a tagged corpus or using a selforganising method that does not need a tagged corpus
some errors are common to both taggers the constraint based tagger generally being more accurate often with a ratio of i to NUM
one possible explanation for the superior performance of the final non contextual rules is that they are meant to apply after the previous rules failed to disambiguate the word
without human assistance in the training the result was not impressive and we had to spend much time tuning the tagger and guiding the learning process
the first one says that a singular noun is not likely to be followed by a noun this is not always true but we could call this a tendency
in addition to these there are syntactic parameters that must be programmed into the message passing mechanism itself not just into the grammar network
it can even be the case that discarding the rare readings would not induce a detectable loss in accuracy e.g. in the conflict between cela as a pronoun and as a verb
in such a case we restrict the use of the rarest categories to contexts where the most frequent reading is not at all possible otherwise the most frequent reading is preferred
to determine word order a normalised proof term is first transformed to give a yield term in which its orderable elements are structured in accordance with their original manner of combination e.g.
if we simplify the resulting proof term using for a and left right juxtaposition for application we get the familiar composition term az x yz
note that this formulation includes a system of term
the system of term labeling has the following features
NUM reasons in precontrol attentional spaces are structurally distant
sion distributions per hmm since the states were tied in pairs the first with the second the third with the fourth and the fifth with the sixth
note that due to the condition of determinism there can be no more than one valid path and hence at most one translation for a given input string
centering cb un l because cf un is only partially ordered additional factors may constrain the choice
the tutoring provided by the system would then be hand tailored toward the individual user and his her level of acquisition of written english
classrooms benefit from both forms but the deaf learner has limited to no exposure to correct forms so responses that encourage inductive learning may be particularly useful
we are currently performing statistical analysis on our growing body of hand corrected samples to see what error classes co occur with statistical significance
we anticipate that slalom when fully developed will initially outline the typical steps in acquiring english as a second language
this solution was not applicable in cases where all the similar words in a given sw set were misleading words
the results of this comparison for the two test groups we used are shown in table NUM and table NUM
the algorithm runs essentially in time and space linear in the size of the training data so larger domains are within our reach
we are indebted to stuart shieber for his suggestions and guidance as well as his invaluable comments on earlier drafts of this paper
the accuracy can even be enhanced if the native speaker is told from which sublanguage the ambiguous word was taken
the difference in performance is due to different evaluation methods different tag sets and different corpora
thus the disambiguation of a given word can be achieved using other word forms of the same lexical entry
since this paper suggests a method for morphological disambiguation using probabilities the notion of morpho lexical probabilities is also required
finally in section NUM a simple strategy for morphological disambiguation in hebrew using morpho lexical probabilities will be described
if there are k rules in the grammar and thus k parameters then the search takes place in a fixed k dimensional space ir
given this the morphological analyzer was only used in order to obtain the input files for the disambiguation project
in such cases the analysis that this word belongs to is assigned a higher probability than its real morpho lexical probability
cattle but as said before we re not commited here to discuss individuation of collections
there are pns which clearly specify shape roi aja lunzp while others underspecify il fragment
of the several different types of word level associations lexical and lexico semantic associations are among the most significant local associations
the lexicon used was a simple 30kword superset of the vocabulary of the training corpus
select b entities iherefore individuated and countable the tip of the tongue
their values cml be as discussed above either absolute or relative depending on tile kind of portion
let s consider t oi a ia i e i imon round slice of lemon
slice qf cake do bern the combinatorial possibilities of nouns while those of pns are distinct and specific
table NUM illustrates how a dim can be effective in detecting lexical and lexico semantic associations
table NUM asymmetry in co occurrence relationships word pairs with significant influence in either
indeed comprehension would be utterly impossible without the extensive application of contextual information
at the time of the experiment no further annotated corpus material was available to us
table NUM mlr NUM configuration with varied training data sizes
table NUM indicates that with even NUM training texts
figure NUM text tagged with discourse information using sgml
example of training features are shown in table NUM
the only evaluation result provided is their extraction result
table NUM examples of training features
grammatical analysis is thereby shown to be a viable alternative to techniques such as concept spotting
we utilized off the shelf components whenever possible
such transitions represent periods of time when the speech recognizer hypothesizes that no words are uttered
the circles denote nodes which are first this proof which are omitted
the reason why is this in evolving the text planning rule base it makes sense to localize decisions as much as possible however to handle rule interactions in a single pass one is forced to centralize these decisions which can become cumbersome
drafter is considerably more ambitious in aiming to automate the production of multilingual task oriented help at the same time however drafter is more limited in that it is not evolution oriented aiming only to generate satisfactory initial drafts whence its name
the current authoring interface shown in figure NUM uses cogenthelp s existing architecture together with the http forms protocol to allow the user to edit the text snippets for each widget in substantially the same context they would inhabit in generated help topics
in the case at hand we have chosen to use the group and cluster structure combined with a top down left to right spatial sort while such a spatial sort alone is insufficient as we saw above a spatial sort which respects functional groups turns out to work well
when the text planner detects via the ikrs that a phrase sized message such as t0 enable is the same for a group of widgets it generates a description that applies to the whole group rather than repeating the same description several times in close proximity
in the upper right frame of the page note that there is the following description of how to enable all of the four buttons below the operators list boxes rather than a repetition of the same message four times in close proximity these commands are currently disabled
the types of snippets in current use include a one sentence short description of the relevant gui component a paragraph sized elaboration on the short description various phrase sized messages concerning the conditions under which it is visible or enabled if appropriate and a list of references to other topics
we would also like to thank our sponsors at the department of defense
for back transliteration NUM they are more useful for english to japanese forward transliteration
to show the validity of these heuristics we compare the result of the robust parser using heuristics with one not using heuristics
s p vp each final state means the recognition of a nonterminal
when the original parsing algorithm terminates unsuccessfully the algorithm begins to assume errors of insertion deletion and mutation of a word
at the cost of the complete robustness however this algorithm degrades the efficiency of parsing and generates many intermediate edges
null NUM p elw pronounces english word sequences
we can show that our robust parser can compensate for lack of rules using only NUM rules with the recovery mechanism and heuristics
null NUM and NUM are weights for the error of terminal nodes and NUM is a weight for the error of nonterminal nodes
for example if people tend to write sentences with inserted phrases then the parameter fli sert on must increase
in our extended algorithm the same scan as that of the original algorithm is used while completer is modified and extended
however his experiment was not based on the errors in running texts but on artificial ones which were randomly generated by human
the stored troponym links have to be removed
many corpus based methods for natural language processing nlp are based on supervised training acquiring information from a manually annotated corpus
in a language like french the type of attachment is crucial to determine whether a liaison between a word ending with a latent consonant and a word starting with a vowel is obligatory possible impossible NUM
each multinomial random variable corresponds to a conditioning event and its values are given by the corresponding set of conditioned events
the total number of morphemes is about NUM million
the function check can be realized by phrases like sehe ich das richtig
integration of hand crafted and statistical resources in measuring word similarity
figure NUM approximation of the statistics based length
as a result tsukau is interpreted as to operate
NUM NUM resolution of the simultaneous equa
figure NUM a fragment of the thesaurus
the similarity of the two distributions implies how consistently the two term sets will behave given a query at retriewd time
it is by itself the only critical fragment of this character string
degthanks to mark steedman beryl hoffman anoop sarkar and the reviewers
as compared with all other tokenizations no effort can be saved
although lexical type raising involving variables can be introduced to derive such a constituent
NUM his is the practice that has never been tried in korean document indexing but has some important merits
for const const the same rules as in ccg std are applicable
for structural similarity coarse dependency based nlp methods do not account for fine structural relations involved in type NUM variants
corpus based methods for natural language processing often use supervised training requiring expensive manual annotation of training corpora
the first element deletes all such features on the surface
we find that all variants achieve a significant reduction in annotation cost though their computational efficiency differs
the lexical argument categories e.g. a are underspecified with respect to the feature
a z stand for nonterminals and a z for complex constant categories
eau et de l dvaporation de surface coor
table NUM evaluation of simple vs rich indexing
for example the noun phrase expansion rule is4
productive stripping and concatenation rules are applied on lemmas
both indexings have been manually checked
we focus here on the multi word task
the action of the system is twofold
tipster ii is a joint effort among many sites to develop working systems that integrate information retrieval and information extraction
the statistical and neural network methods perform the best on this particular problem and we discuss a potential reason for this observed difference
introduction recent research in empirical corpus based natural language processing has explored a number of different methods for learning from data
function words for example determiners auxiliary verbs etc support and coordinate the combination of content words into meaningful sentences
this formula will yield a significance score that lies within the range NUM to high significance to low significance
for example when calculating the signif null icance of the least frequent words only two nearest neighbors are considered
however it should be noted that these extra breaks were usually denoted by smaller minima and on inspection the vast majority of them were in sensible places
when comparing wordnets one specific language can be taken as a starting point
next the language specific configuration of the reference wordnet can be generated bottom up
some intermediate nodes have been removed all had value no
an additional NUM task specific cn definitions were learned from the NUM st training texts
semantic type NUM synonyms are found in the variant the structure may be modified e.g.
the omit form where a reason is not mentioned at all
we have rewritten all of our software in order to enhance cross platform portability
the following graph shows the learning curve for cn definitions that identified organization names
for the muc NUM evaluation we used the c4 NUM decision tree induction system NUM
or do they inadvertently create a wedge between basi c research and applied research
resolve succeeds here by examining pairwise combinations of nou n phrases nps
the location organization and people specialists rely on dictionaries for their recognition
wrap up is responsible for all the relational links that describe successions and in and out instances
gm is an alias of general motors corp
tag to each matched constituent through the following sequential processing steps a set the syntactic tags according to the statistical reduction rule if it can be searched in syntactic tag reduction data s2 using the constituent structure string as a keyword
as the widely accepted pos tagging is based on the following premises a most constituent boundaries in a chinese sentence can be predicted according to their local word and pos information b the parsing complexi be reduced based on constituent boundary prediction
indicates that the combination frequency of the noun phrase np with preposition p under the local context p np vp is NUM and with verb phrase vp is NUM they will be helpful in preference matching model
the aim of the parser is to take a correctly segmented and pos tagged chinese sentence as input for example figure NUM a and produce a phrase structure ee as output figure NUM b
b a candidate parse tree the correct one represented by its bracketed and labeled form c a constituent boundary prediction representation of a d a preference matched tree of c
NUM syntactic tag reduction data s2 this group of data records the possibilities for the constituent structures to be reduced as different syntactic tags represented by a set of statistical rules constituent structure lcb syntactic tag reduction probability rcb
therefore a basic matching algorithm can be built as follows starting from the preprocessed sentence s w t b we first use the simple matching operation then the expanded matching operation so as to fred every possible matched constituent in the sentence
NUM constituent preference data s4 this group of data records the preference for a constituent to be combined with its left adjacent constituent or the right adjacent one under local context counted by the frequencies of different constituent combination cases in treebank see figure NUM which are represented as lcb constituent combination case left combination frequency right combination frequency rcb for example lcb p nf4 vp NUM NUM
then a simple preference based approach can be added into the basic matching algorithm to improve the parsing efficiency if p b c p a b ct then the matching ol eration a b will be discarded
pet operates over a shifting window of text it can be attached simply and asynchronously to the emacs editor
it would be sensible to keep a reference to the wider context i.e. be able to refer to earlier detections corrections
this morphological lookup operates over a character trie which has been compressed into a directed graph
previous spelling programs unless restricted to a very small set of words have operated as post processors
the right hand side represents the error surface and the left hand side the surface with error removed
shallow processing is also interesting because it should be cheaper and faster than a complete analysis of the whole sentence
if all of these categories eventually fail analysis backtracking to alternative correction candidates different graphemes will occur
apart from anything else representative is hard to decide spectrum of errors or distribution of errors
addition of a space is trickier because of the focus on the word as a processing unit e.g.
secondly any corpus of text usually contains only those errors that were left undetected in the text
state of the workspace at cycle NUM
first the field of explanation generation has experienced a dearth of raw materials
however because its inclusion condition is not satisfied this branch of the traversal halts
it then instantiates the content specification template on as kind of process description which it then evaluates
the kb accessor library currently uses more than NUM different error codes to report error conditions
the relationship between the knight evaluation and those of its predecessors is summarized in table NUM
NUM the purpose of this constraint is to promote immediate nondeliberative reactions from the judges
we then requested knight to generate explanations about the NUM concepts that passed through these filters
in addition we ruled out concepts that were too abstract e.g. object
type checking they employ a type checking system that exploits the knowledge base s taxonomy
the ig weights for the four features v n p n were respectively NUM NUM NUM NUM NUM NUM NUM NUM
hasten s modular design will also facilitate the integration of other supporting software modules such as syntactic parsers and discourse modules
preferences among sequences of center transitions rule NUM discussed in section NUM hypothesizes a preference among types of transitions
research in second language acquisition and education indicates that as a learner is mastering a subject there is a certain subset of the material that is currently within his grasp
this paper concerns relationships among focus of attention choice of referring expression and perceived coherence of utterances within a discourse segment
we argued that the coherence of discourse was affected by the compatibility between centering properties of an utterance and choice of referring expression
the intervening utterance c here provides for a shift in center from john to mike making the full sequence coherent
independent of the grammatical position of the c b and also demonstrates that rule NUM operates independently of the type of centering transition
the novel aspect of this system under development is that it views the task faced by these writers as one of second language acquisition
the fact that being in subject position contributes in and of itself to the likelihood an entity will be the highest ranked cf i.e.
this situation typically holds when an utterance directly realizes an entity implicitly focused by an element of the cf of the previous utterance
rule NUM does not preclude using a proper name or definite description for the cb if there are no pronouns in an utterance
this model will then be tailored to the needs of individual students via a series of filters one for each user characteristic that might alter the initial generic model
if on the other hand it is currently within the zpd i.e. currently being acquired by the user then case NUM is the most likely situation
since the ordering of the terminal yield is given by the template it is also possible to follow other selection strategies e.g. a semantic head driven strategy which could lead to more efficient terminal matching because the head element is supposed to provide selectional restriction information for its dependents
by using these filters it is possible to restrict the range of structural properties of candidate phrasal templates e.g. extract only saturated nps or subtrees having at least two daughters or subtrees which have no immediate recursive structures
the result of the tactical generator is a feature structure or a set of such structures in the case of multiple paraphrases containing among others the input logical form the computed string and a representation of the derivation
the main reasons are that NUM lexical lookup often returns several lexical readings for an mrs element which introduces lexical non determinism and NUM the lexical elements introduce most of the disjunctive constraints which makes unification very complex
informally ebl can be considered as an intelligent storage unit of example based generalized parts of the grammatical search space determined via training by the tactical generator3 processing of similar new input is then reduced to simple lookup and matching operations which circumvent re computation of this already known search space
the main advantage for the proposed new method for nlg is that the complexity of the grammatical decision making process during nlg can be vastly reduced because the ebl method supports the adaption of a nlg system to a particular use of a language
perhaps less so in the case of abuse than in the case of abandon directly below it is reasonable to suggest that in many cases of dictionary polysemy it is the single sense of the verb modified by different types of nouns that can fill its case slots
certain classes of nouns he asserts offer specific properties for good to work on it he respects in which evaluations of things c an be made differ with differences in the other semantic features of the words that refer to those things
as computational semantics moves to large scale systems serving non toy domains the need for large lexicons with entries of all lexical categories in them is becoming increasingly acute and the attention of computational semanticists and lexicographers is turning more towards such previously neglected or avoided categories as the adjectives
the event related scalars actually gradables see fn NUM do not really differ from true scalars in terms of their gradability NUM the event related non scalars can acquire grad null ability at the cost of a meaning shift or marginal acceptability NUM
the transition formulae from a noun lexical entry 3i to that of a denominal adjective 3ii or from a verb lexical entry NUM to those of deverbal adjectives NUM NUM are examples of lrs in which we are interested here
the least trivial relationship to establish involved constrains on the modified noun thus recognizing an adjective like well endowed as characterizing the size of a certain part of human male anatomy a different part of human female anatomy or the amount of money available to an institution
clearly a productive semantic process a shift takes place here probably along the lines of NUM and therefore a dynamic rule exists which creates adjective entries for these predicating pseudoscalar pseudo qualitative senses of the seemingly perfectly relative adjectives
ym there is ixsl iysl and g xs g ys
we are not convinced of the effectiveness and necessity of both of the schools of tokenization research
let i be the first indice s t
computational linguistics volume NUM number NUM NUM NUM worst case complexity
moreover as emphasized in this paper the tokenization set has some very good mathematical properties
there is no postponed emission at the initial state
it must be added however that the two are largely used interchangeably in this paper
this can be explain in equation where peo0 is the estimated probability for xbased on some count c so to estimate the probability of w appear after w w l
to deal with such structural problems an island driven parsing style might well be preferable
this notion is now defined precisely
a head transducer is a transduction version of the finite state head acceptors employed in the transfer model
the word strings funds and and fund sand do not cover each other
in a hasse diagram all connections implied by the partial order s transitive property are eliminated
that is the cover relation is a reflexive partial order
in addition let computational linguistics volume NUM number NUM single word x
the theme in this paper is therefore to develop such a mathematical description
without such definitions of well formedness any rigorous formal study would be impossible
in other words critical tokenization is the most compact representation of tokenization
the objective in this paper is to develop its mathematical description and understanding
in this case by definition s has hidden ambiguity in tokenization
the second part of the theorem is from the definition of critical tokenization
NUM if yes what type of ambiguity does it have
nevertheless efforts do exist to rigorously assign them precise formal meanings
gate provides a solution to the first two of these based on the work of the tipster architecture group
based on these figures i estimate that a sense tagged corpus of NUM NUM words is sufficient to build a broad coverage high accuracy wsd program capable of significantly outperforming the most frequent sense classifier on average over all content words appearing in an arbitrary unrestricted english text
in this paper i argue that a large human sense tagged corpus is also critical as well as necessary to achieve broad coverage high accuracy word sense disambiguation where the sense distinction is at the level of a good desk top dictionary such as word net
however in the absence of well accepted guidelines for making an appropriate level of sense distinction using the sense classification given in woi i net an on line publicly available dictionary seems a natural choice
from the combination of brown corpus NUM million words and wall street journal corpus NUM NUM million words up to NUM NUM sentences each containing an occurrence of the word w are extracted from the combined corpus with each sentence containing a sense tagged occurrence of w
i believe it is of particular importance to investigate this issue in the context of word sense disambiguation as the payoff is high given that a large sense tagged corpus is currently not available and remains one of the most critical bottlenecks in achieving wide coverage high accuracy wsd
the position of a phrase depends on the position of its descendants
NUM and higher if we only consider the reliable cases
this procedure is captured by the planning operator below
pcas are the primitive actions planned by the macroplanner of proverb like speech acts they can be defined in terms of the communicative goals they fulfill as well as their possible verbalizations
based on empirical data reichman argues that the choice of referring expressions is constrained both by the status of the discourse space and by the object s level of focus within this space
this paper was written while the author was a visitor at dept of cs univ of toronto using facilities supported by a grant from the natural sciences and engineering research council of canada
consider for instance the following definite clause grammar dcg rule s sem arg vp arg sem
the microplanner and the realizer of proverb finally produces proof let f be a group u be a subgroup of f NUM and 1u be unit elements of f and u respectively
in contrast if a reason is in a closed subproof but is not its conclusion it is likely that the reason has already been moved out of the reader s focus of attention
introduction this position paper reviews some aspects of my research in speech translation since NUM
i would like to thank the anonymous referees for helpful comments on an earlier draft of this paper
thus after all these transformations we obtained a lexicon of NUM NUM entries for training and the test lexicon of NUM NUM entries
only NUM of the original NUM in the test set were included in the study due to illegibility or use of diagrams instead of text to respond to the question
the results show NUM agreement for exact scores between human rater and computer scores and NUM agreement for exact or adjacent scores between human rater and computer scores
table NUM shows the distribution of the workload and the tagging accuracy among the different rule sets of the cascading guesser
the default assignment of the nn tag to unguessed words performed very poorly having the error rate of NUM
after a successful application of the operator the resulting general rule is substituted for the two merged ones
for example lexicon entry and guesser s categorization for developed jj vbd vbn
the corresponding corpus should also be large enough to obtain reliable estimates of word frequency distribution for at least NUM NUM NUM NUM words
in this paper we describe a novel fully automatic technique for the induction of pos class guessing rules for unknown words
at the same time it does not require additional annotation since that annotation already exists regardless of the rule induction task
the results reported in the paper show NUM agreement on exact or adjacent scores between human rater scores and computer hased scores for NUM test essays
in a manually built up semantic net in which not the concept definitions automatically determine the position of the concepts in the net but rather the links coded by the lexicographers the formal properties of the encoded attributes and relations provide necessary but not sufficient conditions to support maintenance of internal consistency and avoidance of redundancy
the more relations introduced into a manually built up net the more dependencies are created which hold each other in check once they are explicated formulated as guidelines and implemented they can be used to support internal consistency a necessary but not sufficient condition for the correctness of a semantic net
another paraphrase of this rule is that binary antonymy is a structure preserving or homomorphic mapping with respect to synonymy
there is a simple checking rule for this because the rule is equivalent to maximal cardinality NUM of antosemy
these cases have not yet been evaluated by a native speaker but we suggest that they deserve revision
in either case the descriptor and locale information if any is inserted into slots o f the organization mtoken
more exploration is needed on this especially in light of the fact that both the recall and precision rates were low
concerning segmentation of discourse a natural segmentation can be easily achieved if we could distinguish between language generation activities affecting global structure of attention and those only moving the local focus
the first successful match occurred in the headline resulting in the extraction of the succession event but not the post input marketing media advertising john dooner will succeed james at helm of mccann erickson example NUM NUM b similarity NUM NUM
therefore as an additional experiment sra devised the very minimal or micro muc template specification to represent the management succession event as shown below this template specification completely eliminates the in and out object the set fill slots and the distinction of an acting post
the reduction steps taken to identify portions of text for marking in ne also filled the slots with the appropriate text for the te task
currently there are three mutation methods cross over replace some structural elements of one egraph with elements from another trim eliminate structural elements from the ends of the egraph merge combine the structural elements of multiple egraphs the mutation module compares every egraph with each other and for each pair of significantly similar egraphs applies the three methods
slowness of the system was a problem but not a major one as it took only a minute or two per article
it only pays attention to the final reduction except in the case of locations inside money wher e brackets are inserted for both
the very first reduction stage is a junk reduction to delete tables so they are not seen by subsequent reduction stages
finally the postprocessing step writes each expectation to the te result file making final adjustments to the slot fillers as needed
p a l NUM a p a NUM NUM a p b NUM NUM b p b NUM NUM b
checking whether the intersection is empty or not is then usually very simple as well only in the latter case will the parser terminate succesfully
most existing constraint based parsing algorithms will terminate for grammars that exhibit the property that for each string there is only a finite number of possible derivations
therefore we are interested in methods that only generate a small subset of this e.g. if the intersection is empty we want an empty parse forest grammar
note that we allow the input to be a full fsa possibly including cycles etc since some of the above mentioned techniques indeed result in cycles
wordnet makes fairly fine grained distinctions roughly comparable to a collegiate dictionary
they worked more scrupulously which is reflected in the higher confidence ratings
we now turn to the sense choices that were made by most taggers
the target words were classified into four groups depending on their polysemy count
they finished the task within NUM NUM hours
this was generally true for words from all polysemy groups and pos
increasing polysemy of the target words produced less tagger expert and inter tagger agreement
we therefore expected less overall agreement for verbs tags than for nouns
we expected the semantic flexibility of verbs to create additional difbculties for tagging
by this reasoning we expected nouns to present fewer difficulties to taggers
the numbers associated with nodes are the corresponding line numbers in figure NUM children of nodes are given in the order they have been presented
both domains are described by NUM where the domain sequence is represented as
the dependency tree rooted in s covers the whole input a v e k
this sums up to iei ivl potential dependents which is the number of terminals in a besides s
assumptions about unexpected actions or interpretations i.e. adoptplan challenge done selfmisunderstanding and othermisunderstanding are given as very weak defaults so that axioms can be written to express a preference for expected analyses when there is an ambiguity
we have discussed the limitations of the e ciency of prediction and introduced the idea of cogeneration which combines free text entry with fixed text associated with templates
we could not improve on this with the taggers we tried possibly because of the small size of our training sample and the very short length of most of the utterances
the combinations contained in v1 v2 v3 and v4 are distinguished in terms of their constituent elements
a type can always be replaced by a disjunction of its most specific subtypes and the appropriate features a sample lexical entry
the testing documents for the computer issues were documents from the intemet plus part of the ziff couection
three generic categories of query construction were defined based on the mount and kind of manual intervention used
a spanish collection has been built and used during trec NUM and trec NUM with a total of NUM topics
the NUM routing topics for testing are a specific subset of the training topics selected by nist
for more details on the various runs and procedures please see the cited paper in the trec NUM proceedings
the highest NUM terms were chosen with an average of NUM of those not in the original topic
in this paper we describe an alternative generation component which has polynomial time complexity
the lexicalist approach to machine translation offers significant advantages in the development of linguistic descriptions
the generator cycles through two phases a test phase and a rewrite phase
if the tncb evaluates successfully the orthography of its value is the desired result
however the target derivation information itself is not used to assist the algorithm
in the case of the adhoc task including most of the track runs also there is a slight increase in the percentage of unique documents found probably caused by the wider variety of expansion terms used by the systems to compensate for the lack of a narrative section in the topic
the deletion of a maximal tncb removes two ill formed nodes figure NUM
in order to verify whether this structure is valid we evaluate the tncb
the first three are used to define the fourth transformation move which improves ill formed tncbs
after the test phase we discover that every single interior node is ill formed
the algorithm reconstructs the dtw paths of these positional vector pairs giving us a set of word position points which are filtered to yield anchor points
since most of these terms are nouns proper nouns or noun phrases compiling a bilingual lexicon of these word groups is an important first step
we treat the bilingual lexicon compilation problem as a pattern matching problem each word shares some common features with its counterpart in the translated text
as ultimately we will be interested in finding domain specific terms we can concentrate our effort on those words which are nouns or proper nouns first
the NUM anchor points NUM NUM NUM NUM NUM NUM divide the two texts into NUM nonlinear segments
we carried out two sets of evaluations first counting only the best matched pairs then counting top three chinese translations for an english word
we present a pattern matching method for compiling a bilingual lexicon of nouns and proper nouns from unaligned noisy parallel texts of asian indo european language pairs
a wildcard character is one that can signify any letter and is represented by an asterisk
to accommodate this oleada has a bookmark feature that keeps a list of lexical items previously found
the second prototype contained more resources had improved document management and was changed to a client server architecture
a process of iterative refinement user observation participativeprototyping and formative evaluations ultimately shapes useful systems
dictionary entries can be retrieved for one of these words by clicking on the word in the list
more mono and bilingual dictionaries were added as was the cia chiefs of state database and a world gazetteer
the result is that language translators and learners can use their existing knowledge of how to use these dictionaries
oleada has morphological analysis component that enables the system to return information on morphological variants of a search term
translation subsystems that support retrieval of documents in many languages based on a query in one language
annotated text in figure NUM appears as colored highlights on the computer screen or as grayed text here
in this context we believe that our model which is precisely capable of detecting local similarities in lexicons and to 16erform on the basis of these sinailarities a global inferential transfer of knowledge is especially well suited for a large range of nlp tasks
if the first lines of table NUM are indeed true phonemic correlates of the derivation corresponding to various classes of adjectives a careful examination of the last lines reveals that the extraction procedure is easily fooled by accidental pairs like imp imply on only or earearly
the nettalk dataset contains plurisyllabic words complex derivatives loan words etc and allows to test the ability of our model to learn complex morphophonological phenomenas notably vocalic alternations and other kinds of phonologically conditioned root a llomorphy that are very difficult to learn
for example the strength of word chain see the example l in lc higher than la while the probabilities of the sequence of parts of speech of la and ic are equal
the first experiment consists in infering the pronunciation of the NUM pseudo words originally used in glushko s experiments which have been used as a test bed for various other pronunciation algorithms and allow for a fair head tohead comparison between the paradigmatic cascades model and other analogy based procedures
the search procedure is stopped as soon an analog is found in l a or else when the distance between x and the topmost element of the stack which monotonously decreases vi pi NUM falls below a pre defined theshold
we have evaluated two different search strategies which implement various ways to alternate between expansion stages the stack is expanded by generating the derivatives of the topmost element and matching stages elements in the stack are looked for in the lexicon
the basic idea is to generate a x defined as lcb ai x forai e NUM x e domain ai rcb which contains all the words that can be derived from x using a function in NUM
in this approach the position of an analog in the stack is assessed a s a function of the distance between the original word x and the analog y a a a x according to
an example consider the following model rep rl
for r in 12a the appropriate call is therefore
in terms of diffculty swedish danish is clearly the easiest language pair and swedish french is clearly the hardest
a novel method which evaluates the system s actual spoken output is currently undergoing initial testing and is described in section NUM NUM
according to process pas the following restrictions must be processed first
thes are called r s outer and inner quantifiers respectively
is done by breaking it down into substructures NUM which are processed almost independently
after the speech is decoded into text the translator converts one language to another
thus h indicates instantiation of the pronominal s cospecifier as the cb while l fails to instantiate it as the cb the partially ordered set salient scale invoked by l h is cf the inference path evoked by h l is for attentional purposes a traversal of cf
if there is any explicit error then the second step the spell checking process will be called to give a suggestion with a set of most likely word kaw9fi a
from table NUM we can see that smaller distances between the senses and the activated clusters mean higher accuracy of disambiguation
we are currently implementing such a system with cantonese and english as the main languages
thanks are also due to patcharee varasai supapas kurntanode thitipom tharapoome and mukda suktarajam for their helpful on the preparation of the training corpus puchong uthayopat and amarin deemagam for their helpful to complete this paper
in both cases a tree is built up in a bottom up way by starting with a head lexical head corner in the parsing algorithm target in the structure building operations and creating the sister of the head recursively etc NUM by treating only lexical heads as head corners we achieved that our parsing algorithm completely represents gt
if this mother is a head corner of the goal and the mother and the goal are not equal the whole process is repeated by selecting a rule with the new head corner i.e. the mother of the first head corner on its rhs
indian languages are mostly nonconfigurational and highly inflectional
in section NUM it is assumed that movement is invariably leftward and that gt and move a are bottom up mechanisms
this solution is compatible with the minimalist program in the sense that in this way the tree is built up in an absolute bottom up way i.e. starting from v so that a position that should be filled by movement is always created after the position from which the moved element comes
then the head agrs that should be filled with an adjoined verb by movement from agro in a transitive sentence or v in an intransitive sentence is created before agro and v to avoid moving constituents from a part of the tree that has not been built yet the head corner table for the minimalist head corner parser is not constructed completely according to x theory see NUM
these two assumptions in combination with the fact that gt and move a are bottom up operations effect that the moved phrase marker has to be contained in the tree that was built so far NUM the tree in figure NUM illustrates different kinds of movement
our method is based on a delayed evaluation of syntactic encoding schema
the technique may also be useful for other languages having similar properties
it should be pointed out that cseg tagl NUM is just the result of the first round of our investigation
lolita is written mostly in haskell a non strict functional programming language NUM
unfortunately the walk through article containe d several features which highlighted some bugs in the core analysis
finally the subject of the event is returned as the meaning of that subtree
we define the preliminary open tests show that for cseg tagl NUM
however neither was used in the formal evaluation due to their limited effect on performance
more details about the formalism used in the net can be found in NUM
which is significantly higher than the scores for the walk through article
many noises will be unnecessarily introduced as cn2 and cn3 in 5a
a candidate can be regarded as a guess with a value of belief
this makes correspondences which were not picked up during the semantic analysis of individua l sentences
for example if the user has marked the string the union resumed talks with the company and placed the union in one slot and the company in another then phase n is the complex phrase recognizer since it provides those noun groups as independent objects
moreover we are only handling the case where the new rule to be induced is a specialization of an already existing rule in the sense that location based is a specialization of noun past participle in general the problem of rule induction is very hard
one way to achieve this would be to have automatic learning of patterns from examples provided by the user
ples the fastspec language and the compile time transformations make it easier for linguists and computer scientists to define patterns
once the rule is hypothesized it will be presented to the user in some form for feedback and validation
such a system is first of all a convenient text editor for filling data bases from text by hand
we experimented with an algorithm for bare noun groups but it hurt precision more than it helped recall
we will be developing a sophisticated general version of the system as part of our tipster iii research
in the atomic approach the system recognizes entities of a certain highly restricted type and assumes that they play a particular role in a particular event based on that type then after event merging it is determined whether enough information as been accumulated for this to he an event of interest
on the other hand if the string is the union s resumption of talks with the company then the complex phrase recognizer will not do since it combines at least the union and possibly the company into the same complex noun group as resumption
the results of our preliminary experiments show that accent differences cause recognizer performance to degrade
in short the items for state f indicate the possible combinations of sentence segments inclusive of the given fragment ws t because the chart contains items of all the valid sentence segments that were generated through the layer c e
a more e cient use of data would be to build two models one to estimate the likelihood of an atr parse p a given raw text the other to estimate p fia
when the immediate transition is of terminal type the transition probability aq and the probability of the s th word at the transition b j ws are multiplied together with the inside probability of the rest of the sequence ws l t
where u last layer j v c bin layer j layer i layer v layer j layer u and uv is a pop transition
the fact that both edward s graphics processes and its syntactic semantic and pragmatic interpretation processes operate on line incrementally and in parallel implies that the context effects of a pointing gesture can immediately be taken into account by the reference analysis process
in totum pro parte pointing objects are selected either by enclosing the icons in a mouse driven rectangle or by pointing to an icon that is part of a compound object typically the root of a directory tree and pressing the select compound object mouse button
suppose there are two directory icons and two file icons positioned as schematically indicated in figure NUM suppose all objects have a sv of NUM and no other files and directories are in context i.e. have a sv greater than NUM
it maintains the context model the knowledge base and the lexicon in addition it decides which individual instances stored in the knowledge base must be represented on the graphics display and it makes sure that the display is always up to date
next after completion of the phrase the salience of each referent is retrieved by adding the significance weights of all cfs that have this individual instance in their scope s the most salient individual instance is taken to be the referent of the phrase
the first involves the incorporation of associative cfs that create some salience for associates of individual instances just mentioned e.g. upon mentioning of the nici creating associative cfs for the institute s secretary its director its hosting university etc
referents are presented by the name of the concept class they belong to followed by the number sign and a unique number enclosed in angle brackets e.g. directory NUM and spin report NUM spin reports are a special kind of project reports
the role filler class restrictions then specify for example that the fillers of the agent and recipient roles must be either persons or institutions and that the filler of the goal role the object that is sent must be concrete and excludes persons
though our empirical and analytical studies were only small and provide no firm basis for drawing conclusions we do find indications that the quality of edward s context model compares to a large extent to the quality of the more complex grosz and sidner model
as soon as the graphical representations icons of the referents in the scope of a visible referent cf become invisible e.g. as a result of a scroll action the weight drops to NUM and the cf will be discarded
we use hmm based isolated word recognition system as the recognition engine and a statistical translator for the translation engine
since recognition and analysis using such models may be computationally expensive for applications such as speech processing in which speed is important finite state models are often preferred
NUM structure of semantic space due to the similarity dissimilarity relation between word senses those in the semantic space can not be distributed in an uniform way
s therefore extraposability should be tied to the linear properties of the constituent in question not to its grammatical function
we attribute this fact to a general constraint against extraposed nps in clauses except for adverbial accusative nps denoting time intervals
this misses the generalization that extraposability of some element is tied directly to the final occurrence within the constituent it is dislocated from
however this would violate an implicit assumption made in order domain based approaches to linearization to the effect that domain objects are inalterable
our approach also makes the correct prediction that extraposition is only possible if the extraposed element is already final in the extraposition source
NUM eine dame ist an der tiir a lady is at the door die sie sprechen will
this means that as an alternative linearization of NUM we can also have the extrapositionless analysis in figure NUM
we therefore propose an impoverished data structure for elements of order domains which only consists of categorial and semantic information viz
we evaluated our parser against the selected dependencies in the test samples
then we proceed to describe the central ideas of our new parser
in the sub tree we mark each non preliminary node with the distance between the two merged subnodes which we also refer to as the weight of the node
figure NUM not only part of speech tags
the rule thus is of the form
whatever is ambiguous in NUM ways
figure NUM percentages of heads correctly attached
figure NUM benchmark used in the evaluation
it seems the parser leaves some amount of the words unlinked e.g.
the subcategorisation valency information is not printed here
the measure is in words excluding punctuation
user put the knob to one zero
here is an example from the prolog code
all conjunctions of adjectives are extracted from the corpus along with relevant morphological relations
the movement to subdialogs is indicated by indentation
NUM user the circuit is working
NUM computer this is the circuit fixit shop
next a set of expected responses is compiled
thus it completes a dialog of length zero
we will examine first the voice input system
these assertions are entered into the user model
we produce a function fix to model the distribution based on commonly used smoothing tools and locate its inflection point by settingf x NUM
we begin with an overview of techniques which have been used for pp attachment disambiguation and then consider how one of the most successful of these the backed off estimation technique can be applied to the general problem of multiple pp attachment
ambiguity is the most specific feature of natural languages which sets them aside from programming languages and which is at the root of the difficulty of the parsing enterprise pervading languages at all levels lexical morphological syntactic semantic and pragmatic
the competitive backed off estimate procedure presented below operates by initially fixing the configuration of the first preposition to either the vp or the direct object np and then considers how the second preposition would be optimally attached into the configuration
table NUM the sum is able to correctly disambiguate NUM of the genus in dgile NUM improvement over sense ordering and NUM of the genus in lppl NUM improvement
in fact we use unsupervised techniques i.e. those that do not require hand coding of any kind that draw knowledge from a variety of sources the source dictionaries bilingual dictionaries and wordnet in diverse ways
we then determine c which indicates the preference for p2 to attach to the vp or to n2 and c which is the preference for p2 to attach to the vp or to nl
in the final instance step NUM where the c indicates a preference for n2 attachment and c indicates a preference for nl attachment a tie break is necessary to determine which noun to attach to
the weights each heuristic assigns to the rivaling senses of one genus are normalized to the interval between NUM best weight and NUM formula NUM shows the normalized value a given heuristic will give to sense e of the genus according to the weight assigned to the heuristic to sense e and the maximum weight of all the sense of the genus ei
to compute the distance between any two words wl w2 all the corresponding concepts in wordnet el e2j are searched via a bilingual dictionary and the minimum of the summatory for each concept in the path between each possible combination of c1 and c2 is returned as shown below
formulas NUM and NUM proved the most suitable of several other possibilities for this task including those which included full definitions in NUM or those using other conceptual distance formulas c f
as dictionaries are special texts whose subject matter is a language or a pair of languages in the case of bilingual dictionaries they provide a wide range of information about words by giving definitions of senses of words and doing that supplying knowledge not just about language but about the world itself
in this case given an hyponym o and a set of possible hypernyms we select the candidate hzypernym e which yields maximum similarity among semantic vectors sv o e sim vo NUM where sim can be the dot product cosine or euclidean distance as before
with the combination of the heuristics sum we obtained an improvement over sense ordering heuristic NUM of NUM from NUM to NUM in dgile and of NUM from NUM to NUM in lppl maintaining in both cases a coverage of NUM
a second challenge was that we had very little effort to devote to the manual system in spanish in fact after a certain point there was insufficient effort available to track the evolving set of guidelines for spanish
however part of speech labeling in chinese is more of a challenge than in the other languages because of two factors chinese has very little inflection and no capitalization thereby offering less evidence to predict the category of an unknown word
one strength in the effort was that the presence of lower case words in spanish names and the generally unreliable use of capitalization in the names was straightforwardly handled by the patterns and did not pose a difficulty as we would have anticipated
the understanding module uses the model developed from training to predict the met categories in new input sentences
for instance locations often mark the start of an organization name and persons may start an organization name
figure NUM identifinder system architecture rectangles represent domain independent language independent algorithms ovals represent knowledge bases
the components common to both languages are the message reader which dealt with the input format and sgml conventions via a declarative format description the part of speech tagger bbn post a lexical pattern matcher driven by knowledge bases of patterns and lexicons specific to each language and the sgml annotation generator
whilst some understood the points system to indicate the order of importance of each feature others such as NUM considered the points to be an indication of how important the feature was to their system
prospective users hoping to re use a dms should first decide what they want from one if they can frame their requirements in terms of our generic template they can eliminate candidate systems which do not focus on the required features
research is also ongoing to examine and quantify the benefits of multimodal interaction in general and our architecture in particular
in particular it was discovered in this research that multimodal interaction generates simpler language than unimodal spoken commands to maps
architecturally quickset uses distributed agent technologies based on the open agent architecture for interoperation information brokering and distribution
further development of quickset s spoken gestural and multimodal integration capabilites are continuing
it posts queries for updates to the state of the simulation via java code that interacts with the blackboard and facilitator
figure NUM a blackboard is used by a facilitator agent who routes queries to appropriate agents for solution
quickset runs on both desktop and hand held pc s communicating over wired and wireless lan s or modem links
web display agent the web display agent can be used to create entities points lines and areas
this mutually compensatory interpretation process is capable of analyzing multimodal constructions as well as speech only and pen only constructions when they occur
keywords multimodal interfaces agent architecture gesture recognition speech recognition natural language processing distributed interactive simulation
it places as many words of same orientation as possible into the same subset
this may never actually occur depending on how accurate the contextual restrictions are
null finally there are errors that are specific to the constraint based tagger
they are often related to errors that could be corrected with some extra work
for instance a construction like preposition clitic finite verb was not forbidden
for instance the contextual rules define various contexts where the preposition tag for des is preferred
we select the tag proposed by the statistical disambiguator if it is not removed during step NUM
after all the transducers have been applied each word in the sentence has only one analysis
this paper has presented innovative approaches for two particular syntactic phenomena auxiliaries and multiple genitive nps
the approach adopted here is a fiat analysis of auxiliaries at f structure NUM
sullj or ob i and that the level of embedding ranges from zero to infinite
the length constraint forces us always to choose the longest or the shortest replacement whenever there are multiple candidate strings starting at a given location
thus instead of having to hunt for evidence this approach is able to exploit the expertise of seasoned linguists who constructed the initial lexicon which was intentionally designed to be broad coverage
such a strategy not only avoids having to distinguish good cues from irrelevant triggers but is capable of inducing some features like assertion for which there is no marker that would indicate its presence
verbal subcatcgorization frames like transitivity or the ability to take a that complement or to infinitive can be induced for new words based on a composite of features associated with similar verbs that are
the algorithm of both authors basically inw lves a pattern matcher that scans the input for a verb and once an anchor is found its right context is searched for cues lot subcategorization fi ames
out of the box pundit returned NUM parses for the NUM sentences in the training corpus some of which were false positives versus NUM successful parses using the attuned lexicon
manning s triggers on the other hand are more sophisticated but because they are less dependable he must rely on heavy statistical filtering to reduce the noise
the trmitional gramnmr knowledgebase is the product of a never ending attempt by linguists to impose order on something that refuses to be pinned down because it is a living thing
markov models for example predict the pos of a word based on the tags of the two or three words preceding it bigrams and trigrams respectively
this paper shows how thc process of fitting a lexicalizcd grammar to a domain can be automated to a great extent by using a hybrid system that combines traditimml knowledge based techniques with a corpus based approach
synchuvg dl can be used to synchronize a syntactic grammar for these languages either with a semantic grammar or with the syntactic grammar of another language for machine translation applications
two vectors are synchronized by specifying a bijective synchronization mapping as in local synchronization between the non heir right hand side occurrences of nonterminals in the productions of the two vectors
thus while in synchuvg dl there is link inheritance as in non local synchronization link inheritance is only possible with those productions that themselves are not subject to the synchronization requirement
the derivation structure of a uvg dl is just the derivation structure of the same derivation in the underlying context free grammar the cfg obtained by forming the union of all vectors
while synchronous systems are becoming more and more popular surprisingly little is known about the formal characteristics of these systems with the exception of the finite state devices
the variety of tasks designed for muc NUM reflects the interests of both participants and sponsors in assessin g and furthering research that can satisfy some urgent text processing needs in the very near term and can lead t o solutions to more challenging text understanding problems in the longer term
using the scoring method in which one annotator s draft key serves as the key and th e other annotator s draft key serves as the response the overall consistency score was NUM NUM on the f measure with NUM recall and NUM precision
six of the seven sites that participated in the coreferenc e evaluation also participated in the muc NUM information extraction evaluation and five of the six made use of the results of the processing that produced their coreference output in the processing that produced their informatio n extraction output
a few of the evaluation sites reported that good name alias recognition alone would buy a system a lot o f recall and precision points on this task perhaps about NUM recall since proper names constituted a large minorit y of the annotations and NUM precision
then the two synchronous productions in v and v are composed into a single production in v by composing the two left hand sides in a compound symbol and by concatenating the two right hand sides
in the case of the management succession scenario a proposal was made to eliminate the three slots discussed above and more including the relational object itself an d to put the personnel information in the event object see the sra paper in this volume
the inclusion of four different tasks in the evaluation implicitly encouraged sites to design general purpos e architectures that allow the production of a variety of types of output from a single internal representation in orde r to allow use of the full range of analysis techniques for all tasks
with respect to the organization and person objects there are issues such as rather fuzzy distinctions among the three organization subtypes an d between the organization name and alias the extremely limited scope of the person title slot and the lack of a person descriptor slot
however the organization portion of the te task is not limited to recognizing th e referential identity between full and shortened names it requires the use of text analysis techniques at all levels of text structure to associate the descriptive and locative information with the appropriate entity
there was a large number of factors that contributed to the NUM disagreement including overlooking coreferential nps using differen t interpretations of vague portions of the guidelines and making different subjective decisions when the text of a n article was ambiguous sloppy etc
the current architecture does not make any specific provision for the modification of the original text
this would then be translated to produce a customizedextractionsystem
escape all tokens until escape are query terms not operators
query language operators are represented within the detectionneed using sgml style tags
NUM coreference tagging as is being defined for muc NUM
an extraction engine adds annotations describing the events and their participants
consequently theorem NUM is augmented NUM diphthongs and excessive diphthongs will be defined operationally in the next pages
word hyphenation depends strictly on the target natural language and many of the problems encountered are language specific
NUM examination of excessive diphthong candidates showed that NUM are immediately eliminated i.e. always split
detailed examination of the candidates of this category led to the conclusion that the candidates always split during hyphenation
for the subset not covered no general rule was formulated but particular instances that always split were identified
it should be noted that additional rules covering additional vowel sequences under specific contexts have been found and examined
thus the input word is divided into substrings and the corresponding rules are applied to the substrings
grammar rules c1 c2 and c3 determine the hyphenation of word substrings comprising embedded consonants between vowels
however as lemma NUM c explicitly acknowledges hyphenation is restricted by diphthongs and excessive diphthongs
all categories found are explained below and representative hyphenated examples along with ipa transcriptions and translations are given
we illustrate our approach in terms of a simple example inference
the indices are our book keeping devices for label and variable management
tags are used to represent reentrancies and will often appear vacuously
scope of indefinites indefinites labeled li may take arbitrarily wide scope in the representation
then we calculate the overall heterogeneity oh of all these subsets as a weighted sum of their expected information
the side condition in the second clause ensures that only identical substructures can have identical tags
so far the testing set has not been expanded to include examples on which to test these so that until and concurrent expressions
remove phone here the removal of the phone must not be attempted until after the device has instructed the user to do so
the current study has provided a characterization of certain aspects of instructional text that has been effectively applied to the generation of instructional text in general
if no nominalization is available tnf arguments will produce the to infinitive tnf unless the infinitive form requires the expression of redundant arguments
this paper is based on earlier work done with susanna cumming whose approach to language study has inspired much of what we have done here
if so either a by purpose or an adjoined purpose expression is used depending upon the complexity of the resulting sentence as determined by sentence complexity
as was the case in our corpus imagene expresses purposes that involve five or more propositions using the adjoined form and otherwise with the by form
it is built in imagene s process representation language prl which is also implemented in loom and will also be discussed in section NUM NUM
for this experiment only we discarded those sentences which could not be parsed with the specified setting of the threshold rather than retrying with looser thresholds
this makes it hard for a probabilistic model to estimate all parameters
figure NUM generating an analysis for a woman whistles
here f denotes the frequency with which a particular tuple occurs
it is an iterative greedy algorithm
we now define the probability of an interpretation of an input string
first let us consider what subtrees the corpus makes available now
figure NUM another derivation generating the same
however van den berg et ai
NUM NUM the statistical model of data oriented semantic interpretation
figure NUM different derivation generating the same
these segments and the embedding relationships between them form the lin guistic structure
t tests again were one tailed unless marked by t and significance levels were NUM
with these word lists we computed NUM
these measures are time consuming and difficult to apply
examples are automotive aviation space chemistry
table NUM table NUM comparison of weighted average
the difference between NUM and NUM stems mostly from the fact that the mt systems regard unknown words as compounds split them up into known units and translate these units
shorter longer shorter t shorter t shorter
cda NUM NUM at harvard university and by at t bell laboratories
results reported are significant at the NUM
we can then eliminate from consideration in our later passes all nodes for which the probability of being in the correct parse was too small in the first pass
the most important difference between global thresholding and beam thresholding is that global thresholding is global any node in the chart can help prune out any other node
come up with must be considered a single idiom partly to avoid a literal interpretation that would change the meaning of the sentence as described in criterion NUM and it also has the meaning locate which further qualifies this sentence as an idiom according to criterion NUM
this causes english premier to move up to second position
this is why evaluation of translation has eluded automation efforts until now
languages with a similar syntax tend to express ideas in similar order
of course identical words can mean different things in different languages
this in turn depends partly on the size of the mrbd
bible also helped to select the optimum tag set for the pos filter
the better filter cashade produce lexicons whose precision comes close to this mark
it does not however directly measure the quality of those contents
this yields n average cumulative hit rates for the lexicon as a whole
i thank my supervisors dr s ananiadou and prof j tsujii
currently the measures contain the parameters in a flat way
note that at this point lexical lookup has replaced the surface representation of coke and ceo wit h their canonical forms
NUM could the idiom be replaced with a single verb which has the same meaning
due to inadequate restrictions on our use of capitalization the system als o decided while mccann and one mccann were distinct persons
spanish for instance has both a significantly larger verb inflection paradigm and a freer word order than english
james in th e headline is found because it follows succeed mccann is found because of mccann family
on the latter not having a pattern to cover things like chief executive officer of mccann erickson wa s an omission on our part
the subscript on c is used simply to make clear that c has NUM possible values
the results on the walkthrough article see table NUM compared to our overall results show that this wa s indeed a relatively difficult article
in this sense the concept of a cl checker may be too broad
this generates the following message ambiguous pronoun reference 2hem
a similar point has been made for caterpillar technical english hayes ct al
it is then up to the user to decide which interpretation is intended
this includes morphological analysis which was useful primarily for determinin g the root form of nationalities such as canadian canada
passive construction was not included by the header file
the disambiguation rules explore the network to spot ambiguous and potentially ambiguous constructions
while the nltoolset also provides a parser afte r some initial development we abandoned it on ats and did not use it on muc NUM
the vocabulary checks rely on two things the parser and user dictionaries
these include misspelled or unknown words duplicated words and the like
the implemented system called godot general purpose ontology disambignation and tuning has two main components a classifier c godot that tunes wordnet to a given domain and wsd godot that locally disambignates and tags the source corpus contexts
wordnet s entry for this sense of get around includes as synonyms avoid and bypass which if used in place of the idiom do not change the meaning of the sentence
similarly the german wenn ich da real nachsehe should not be translated preserving the conditionality hence if i look this up but by the common phrase let me see
in step NUM a words verb or noun is assigned to a class according to the contexts in which it appears collective contexts are used contemporarily as what matters here is domain specific class membership and not contextual sense disambiguation
null the method proposed in this paper suggests and provides evidences that processing a corpus first to tune a general purpose taxonomy to the underlying domain and then sense disambiguating word occurrences according to the derived semantic classification is feasible
the translation framework of verbmobil is strongly lexeme based thus for any particle in the german source utterance the transfer component seeks a corresponding english word on the basis of the reading determined
other kind of transitive verbs such as taberu eat may not let the source be added because the agent here is not the source position of the patient in the event action
that is all the case roles that appear in e g NUM NUM a b and c are assigned different deep cases from one another lcb john agent door patient key instrument rcb
this is to be done by identifying superficially different case patterns with an idea of alternative case markers and semantic roles and by largely extending the notion and the formulation of voice conversion for japanese auxiliary verbs and equivalents
if a lexicographer is asked to fill in the deep cases as usual in the s blocks of e g NUM NUM a and b he or she will assign patient on window in e g NUM NUM a and on paint in e g NUM NUM b
in the above example however the permutation commands of causative b results in generating two identical case markers wo violating the unique surface case principle as is shown in e g NUM NUM so the pelxnutation is blocked
these phenomena seem to have imposed difficulties upon the design of the lexicon so that no list of japanese verb classes and types comparable to grishman94 levin93 or hornby75 for english was readily available when our project started NUM
however in order to develop a working nlp system even these recent researches may presuppose the use of exhaustive coding of verb subcategorization frame knowledge to let the new lexical features be automatically extracted and fully functional in their systems
as is briefly mentioned in the previous sections the entry in our lexicon is composed of three blocks m morphology block s syntax block and c concept block
ellipses appear to be a much more serious problem with japanese than with english because all the supposedly obligatory case elements are virtually free to be dropped or to be placed anywhere in the sentence except for the predicate position at the end
so instead of assigning only one deep case onto the deep case slot and create bunch of whole subcategorization frames we introduced an ambiguity marker such as airm agent instrument reason means to be assigned on the case slot
it should be noted that the morphological ambiguity in hebrew makes even simple applications as is often considered when dealing with other languages complicated
the roles point to the appropriate arguments of the class assignt again note the dotted lines to the nodes ai and ai assignt
pred player NUM spec a ffl j m which turns out to be the translation image under r of the f structure i associated with the conclusion la summarizing we have that indeed rr lil which given that NUM is correct does come as too much of a surprise
in contrast our model for lexical choice accommodates floating constraints resulting in a system with a high degree of paraphrasing power
this fact has been used to simplify the state diagrams by treating this combination as a single terminal symbol dan hence the approximations are drawn with NUM and NUM states respectively
in this revised version we automatically identified similar words as misleading words by looking at the counters of all the similar words in a given sw set
learning morpho lexical probabilities the need to add the original ambiguous word to all the sw sets of its analyses can be made clear by the following example
define goals and tasks for muc NUM NUM
a cause of the problem appears to be an assumption we made that s d is equal to w d that is that every noun in the text counts as a potential topic of text
the use of text structure in information retrieval was motivated by the need for dealing with large documents whose breadth of vocabulary may easily mislead the retrieval system into making a wrong judgement about their relevancy to the query
interestingly enough the situation turns around when l is large and j is small thus at j NUM there is a NUM increase for NUM l NUM but a NUM decrease for NUM l NUM
ntft d is a normalized term frequency of t in d which is given by tfta ntft d max t f d where tftd denotes a frequency of the term t in d and max tfd the frequency of the most frequent term in d
fc t is the frequency of an index t in de token f dc is the total count of word tokens in dc and similarly for token f d and token f d
dc is a collection of texts in d whose title contains a term c doc f d is the count of texts in d similarly doc f dc refers to the count of texts which have a term c in the title
since in text categorization categories are determined beforehand in such a way as to meet the user s specific tastes or needs they may not serve as a topic or a theme in that they need not have a semantic relevance to the contents of documents
the fixed length approach uses the first i words of the text i being constant across texts whereas the proportional length approach uses the first j of words contained in the text so that the actual length of segment is proportional to that of the whole text
note that sense NUM of have is an appropriate wordnet sense for this occurrence apple ii owners for example had have to verbjnt NUM to use their television sets as screens and stored data on audiocassettes
these are frequently common words with no capitalization to indicate whether the word is being used as a name or not
sense NUM carties the idea of ownership which should be applied to the object papers while sense NUM has the meaning o experience or receive which should be applied to the object sales
first a direct approach in which only the categoriesthemselves are the terms used in representation has been tested
thereare currently several tc test collections from which a training subset and a test subset can be obtained
we believe that our results are very encouraging
in part because there are leaves synsets that converge into a single category of the set in part because there are leaves synsets of a word that do not reach any of these categories
the a ci is measured as
this later definition is more coherent with the task but theformer allows to identify the most problematic categories
it may well be the case that merging two senses in a single category is a reasonable thing to do if the senses do not draw interesting for the domain distinctions
the following performance factors have been selected to express the scoring function generality in principle we would like to represent the semantics of the domain using the highest possible level of generalisation
sct c i is the set of smaller wordnet categories with p s lb that do not belong to the ci set see next section
this paper approached the problem of domain appropriate semantic tagging
we have performed this task manually becausethe small number of categories in the test collection made it affordable
in our approach such a normalization problem is avoided by considering a group of highly correlated phrase levels as a single phrase level and evaluating the sequence of transitions for such phrase levels between the shift actions
when categories consist of several synonyms like zron steel all of them are used in the representation
again higher order functions can be used to simplify the definitions of the cps functions corresponding to categories
for spontaneous examples however performance was unsatisfactory because of the gaps repairs and other noise common in spontaneous speech
however odds of that happening are slim since word from coke headquarters in atlanta is that
however this would also be intractable due to the undirectedness of the search through the vast number of possibilities
the documents were distributed on cd roms with about NUM gigabyte of data on each compressed to fit
details of specific system approaches are in the proceedings of the trec NUM conference NUM
the ccg rules shown in figure NUM are implemented in the system described 1in the genera sense not specifically the ccg rule for function composition
the second set of NUM routing topics was selected to build a subeollection in the domain of computers
generating a unique response may be one way of doing this but it is by no means the only strategy that is effective in natural conversation
this investigation resulted in a completely new term weighting similarity strategy that performs well for all lengths of documents
because of the different sets of topics involved the exact amount of improvement can not be computed
all four trecs have used the pooling method NUM to assemble the relevance assessments
def2 extracts an organization from any syntactic buffer as long as th e organization string specialist identified an organization in that buffer
so we must conclude that crystal and indeed all coarse grained sentence analysis was totally irrelevant to the te task
inductive learning algorithms eventually flatten out with enough training but performance tends t o increase steadily up until that plateau is reached
the one month time frame for st was not a problem for us as far as crystal wrap up and resolve were concerned
text claimed by the wrong specialist say an organization name marked as a location i s counted as an incorrect organization type
named entities ne the ne task was handled by four independent string specialists designed and implemented during our muc NUM preparations
the main criterion for relevance is whether a person is involved in a management succession event which is reflected i n this tree
as much as we try to exploit trainable technologies there are nevertheless places where some amount of manua l coding is still needed
over the years we have come to appreciate the significant difficulties of software evaluation and the uniqu e problems associated with language processing evaluations
an argument can be made that the approach taken here relies on a formalism that entails implementation issues that are more difficult than for the other solutions and inherently not as efficient
in fact in that subdialog this response corresponds to a request for clarification
first the beginning of new games is coded naming the game s purpose according to the game s initiating move
the following moves are used within games after an initiation and serve to fulfill the expectations set up within the game
in the map task this usually involves the route giver telling the route follower how to navigate part of the route
example NUM g if you come in a wee bit so that you re about an inch away from both edges
therefore the best analysis considers how well coders agree on where games start and for agreed starts where they end
there were not enough instances of abandoned games marked to test formally but she did not appear to use the coding consistently
note that such move boundaries form a set of independently derived units which can be used to calculate agreement on transaction segmentation
each subject was given the coding instructions and a sample dialogue extract and pair of maps to take away and examine at leisure
note that agenda based pcfg parsers in general require more than o n NUM run time because when better derivations are discovered they may be forced to propagate improvements to productions that they have previously considered
if the information were elicited the move would be a response such as a reply to a question
it is possible that transactions are simply too large for the participants to remember how to pick up where they left off
this reduces the number of nondeterministic choices related to lexical lookup and more importantly allows syntactic information to be used to ensure termination of the covariation encoding of lexical rules
NUM immediate dominance schemata and lexical rules however is that immediate dominance schemata are fully specified in the linguistic theory and can thus be directly interpreted as a relation on objects
for analyses proposing infinite lexica though a definite clause encoding of disjunctive possibilities is still necessary and constraint propagation is indispensable for efficient processing
substantial computational expertise is required to provide restrictions on the instantiation status of a goal which must be fulfilled before the goal can be executed
during word class specialization the compiler then deals with such specifications by pruning the corresponding transitions in the finite state automaton representing global lexical rule interaction for the particular lexical entry under consideration
however while speech rates in normal conversation are around NUM NUM words per minute wpm and skilled typists can achieve rates of NUM NUM wpm NUM conditions which impair physical ability to speak usually cause more general loss of motor function and typically speech prosthesis users can only output at best NUM NUM wpm using a keyboard with much lower rates if direct letter selection is not possible
NUM note that in the case of transitions belonging to a cycle only those transitions can be removed that are useless at the first visit and after any traversal of the cycle
as a result the arcs NUM q7 q2 and NUM q9 q3 which are marked with gray dots in figure NUM can be removed
there are however both left linear and right linear grammars for which the number of states in the final automaton is not bounded by any polynomial function of the size of the grammar
we then only obtain seven of the clauses of figure NUM those calling lex rule l or lex rule NUM as well as the unit clauses for q l q NUM q3 and q NUM
the covariation approach builds on this proposal and extends it in three ways first the approach shows how to detect and encode the interaction of a set of lexical rules
the dialogues were analyzed by hand sit should be noted that we do not claim to have solved the problem of discourse processing of spontaneous dialogues
for example the object of get can practically be anything
this section describes what we perceive to be the main advantages of the hybrid analogical approach to speech translation
NUM finding low frequency bilingual word pairs many nouns and proper nouns were not translated in the previous stages of our algorithm
then let d p n be the distance between the example and the input
a threshold is applied to the dtw score of each pair selecting the most correlated pairs as the first bilingual lexicon
this means it is easier to port it to different domains and to apply it to new languages
let s consider again the word facility in NUM
NUM assign a unique label to each node of which lower nodes are assigned labels
is applied as a balancing weight between the observed distribution and the iform distribution
these two methods ufflized a corpus which includes both lexical categories and nontermi al categories
the second method adopted information theoretic methods from speech recognition
based on its entropy or perplexity where the accuracy of parsing is not taken into account
it follows that it is worth trying to infer a grammar from corpora without nontermlnal labels
this process is performed throughout all parse trees in the corpus
for exantpie there axe translation example pairs at the clause level phrase level and word level
an earley parser generates state i kx a if and only if there is a partial derivation s xo k lxly xo k l i g xo k lxk i l l deriving a prefix xo i NUM of the input
we compute the segment vector for all english nouns and proper nouns not found in the first lexicon and whose frequency is above two
type v incomplete noun postposition e the incomplete noun n ssi mr miss mrs
figure NUM homograph types of ga
likewise in the following sentence the noun franqais and american started with the upper case can not be considered as proper names whatever the definition of proper name is dr
nevertheless as mentioned above serious ambiguity problems appear in the distinction of ns from their homographs we here propose two complex local grammars in order to increase the ratio of identification of ns
therefore a local grammar recognizing pts such as figure NUM will reduce numerous mismatchings between the strings like 2b and the combination of the items found in a dictionary
so for word pairs whose euclidean distance is below the threshold we still need to use dtw matching to find the best translation
intuitively the forward probability oq kx is the probability of an earley generator producing the prefix of the input up to position i NUM while passing through state kx at position i
when completing a state x y and moving the dot to get x y additional states have to be added obtained by moving the dot further over any nonterminals in that have nonzero e expansion probability
although clearly not a perfect model of natural language stochastic context free grammars scfgs are superior to nonprobabilistic cfgs with probability theory providing a sound theoretical basis for ranking and pruning of parses as well as for integration with models for nonsyntactic aspects of language
NUM note the difference between complete and completed states complete states those with the dot to the right of the entire rhs are the result of a completion or scanning step but completion also produces states that are not yet complete
path reconstruction in our algorithm we reconstruct the dtw path and obtain the points on the path for later use
the probability of the surrounding of y fl is the probability of the surrounding of x fl plus the choice of the rule of production for x and the expansion of the partial lhs a which are together given by
however for fully parameterized grammars in cnf we can verify the scaling of the algorithm in terms of the number of nonterminals n and verify that it has the same o n NUM time and space requirements as the inside outside i o and lri algorithms
tile best and worst performances were NUM NUM number of mixture NUM and NUM NUM number of mixture NUM respectively
the second and third questions are resolved by introducing hierarchical tag context trees and mistake driven mixture method that are respectively described in section NUM and NUM
in the rest of the paper we assume all tagging methods share the word model p wilti and differ only in the tag model
for example bi gram and tri gram models approximate their tag probability as p tilti NUM and p tilti l ti respectively
because the maximal likelihood estimator mle emphasizes the most frequent connections an exceptional connection is placed in the same class as a frequent connection
however this approach has a clear limitation the exceptional connections that do not occur so often can not be detected by the single tree model
the best and worst performances were NUM NUM number of mixture NUM and NUM NUM number of mixture NUM respectively
for non identical vectors dtw traces the correspondences between all points in v1 and v2 with no penalty for deletions or insertions
where wl w are all words of the test set constructed by listing the sentences of the test set end to end separated by a sentence boundary marker
for each node z visited increment the component count c x t by weight t
if their euclidean distance is higher than a certain threshold we filter the pair out and do not use dtw matching on them
for words like those in section NUM NUM homosexuel h t rosexuel tol sioge entresol tournesol a class is defined with the prefixes ending with a vowel
for instance in the newspaper name ouest france wsst fr s an epenthetic NUM vowel is often inserted w st3 fras
mute e is eliminated before a vowel phoneme and after a consonant phoneme followed by a vowel phoneme as in emploiera plwaora which becomes plwara
several blocks of rules can be defined corresponding to different scans from left to right of the string the output string replacing the input text at the end of a block of rules
NUM b b b is eliminated if the left context in the output buffer is already a phoneme b
speech recognition algorithms must know all the phonetic variations of the words in the vocabulary to be recognized so the output should be a set of phonetic strings corresponding to the input word
both open o and closed o are also acceptable in many words e.g. automobile a rodrome augmenter autonome austral ozone
the maximum point dispersal parameter limits the width of accepted chains but nothing limits their length
if the two were not scaled then position i in v1 would correspond to position j in v2 where j i is a constant
the first two alternatives would minimize the error with respect to only one language or the other
axis generators need to be built only once per language rather than once per language pair
thus church s method is only applicable to language pairs with similar alphabets
the heuristic considers only chains of exactly the specified size whose points are injective
assumption NUM the function f x y is continuous
however to correctly select the right syntactic structure in this example p quan quan i quan reduced l2 p li n2 should be greater than p n quan nlm i n quan reduced l2 p li n2
any sort of mdl criterion applied to a system of rewrite rules would prefer a rule such as NUM t dx v v to a rule such as NUM t dx NUM v NUM v which is the equivalent of the transducer learned from the training data
many thanks to jerry feldman for advice and encouragement to isabel galiano ronda for her help with the ostia algorithm and to eric fosler sharon inkelas lauri karttunen jos60ncina orhan orgun ronitt rubinfeld stuart russell andreas stolcke gary tajchman four anonymous coli reviewers and an anonymous reviewer for acl NUM
it is worth noting that conflicts in the input to the id3 algorithm where the same path to a leaf covers examples that behave differently are impossible no two phonemes agree in every feature and because our transducers are deterministic there is at most one arc leaving a state labeled with a given input phoneme
on a typical run on NUM NUM german words with final stop devoicing applied using a sparc NUM calculating alignment information rewriting each output string in variable notation and building the initial tree transducer took NUM seconds the state merging took NUM seconds inducing the decision trees took under i second and the pruning took NUM minutes and NUM second
NUM r o vocalic consonantal NUM o t ls NUM t o n vdegcalic stress in our second experiment we applied our learning algorithm to a more difficult problem inducing multiple rules at once
NUM showroom sh owl r uh2 m sh owl r a second class of errors is caused by an incorrect transition with NUM for example the transducer incorrectly fails to flap after oy2 because upon seeing oy2 in state NUM the machine stays in state NUM rather than making the transition to state NUM
computational linguistics volume NUM number NUM one example of an unnatural induction is shown in figure NUM the final transducer induced by ostia on the three word training set of figure NUM ostia has a tendency to produce overly clumped transducers as illustrated by the arcs with output b ae and n d in figure NUM or even figure NUM
we then augmented ostia with three kinds of learning biases which are specific to natural language phonology and are assumed explicitly or implicitly by every theory of phonology faithfulness underlying segments tend to be realized similarly on the surface community similar segments behave similarly and context phonological rules need access to variables in their context
we then augmented ostia with three kinds of learning biases that are specific to natural language phonology and that are assumed explicitly or implicitly by every theory of phonology faithfulness underlying segments tend to be realized similarly on the surface community similar segments behave similarly and context phonological rules need access to variables in their context
for ir where the purpose is to detect documents with high probability of relevance rather than exact matching of meaning and is a more forgiving environment it may be adequate
c frequency filter after a first pass through the test corpus via steps a and b a list of candidate short words will be generated with their frequency of occurrence
as expected retrieval effectiveness decreases substantially over NUM compared to the full length queries from around NUM NUM to NUM NUM exptyp NUM lll tables NUM NUM
the removal of stopwords for lll but the peril of accidentally removing a crucial word remains leading again to about NUM drop in effectiveness exptyp NUM vs NUM l11
since the presence of stopwords has been shown to have a benign effect on chinese retrieval it appears advisable to keep them as indexing terms to guard against such unexpected results
there is incremental improvements in average precision by using the larger lexicon e.g. for exptyp NUM from NUM NUM l0 to NUM NUM lll about NUM
c does not have such an intervening operator
a ccg implementation is described and compared to other approaches
this generalization captures the earlier observations about availability of readings
his suggestion is adopted with various subsequent revisions cited earlier
NUM the question is whether our theory should predict this possibility as well
the dialog also illustrates the effects of the user model
z the rest of the paper is organized as follows
here we give a description of the estimation method that we implemented and evaluated
the user may not always respond with the desired information
here the response is parsed against expected meanings without success
NUM computer i am familiar with that circuit
NUM computer glad to have been of assistance
there is also general task knowledge about completing locative descriptions
the output from this system is a gadl meaning representation
it contains much of the information about the application domain
it uses the rules in the knowledge base described below
second the results of reductions can be used to provide additional context for later reductions for example person reduction is done after organization so a reduced organization can help the patter n matcher recognize a person as in the token sequence artie mcdonald org s president where org is the mtoken produced by the earlier reduction
a reduction can also involve multiple previously reduced mtokens filling the slots of one with information from another for example the reduction of the token sequence org a loc based manufacturer includes filling th e org descriptor org locale and org country slots of org with the descriptive phrase and th e information from loc
the nltoolset provides a merging tool which merges expectations of the same type person organization etc as long as the fillers of their corresponding slots do not conflict a conflict occurs if both have a filler the fillers are different and the slot is not allowed to have multiple fillers
this included entries for all the countries with alternat e phrases such as west germany for federal republic of germany and irregular derivations such a s dutch for netherlands and entries for major cities and geographical regions with their countr y information included
the lookup on atlanta has provided the information that it is a city and that its country is the us figure NUM after entity reduction s the initial reduction stages take care of money percent date time and location then secondary references to location
the only things worth noting here are the yesterday errors already discussed that the system decided NUM pounds was a reference to money and that the information in the lexica l entry for atlanta was used to fill the slots of the loc mtoken
for organizations we limited it to a few dozen major ones that have no reliabl e internal clues and often occur without any contextual clues such as white house fannie mae bi g board coca cola and coke macy s exxon etc
we therefore devised a separate stack mechanism which keeps track of the org mtokens for each sentence when an or g descriptor is reduced in the final te reduction stage the stack is searched starting at the current sentence to find the closest suitable referent that precedes the descriptor and to add the descriptor text to th e mtoken for that referent
org type company org name ammirati purls organization NUM NUM org type other org name new york yacht club organization NUM NUM org type company org name new york times organization NUM NUM org type company org name coca cola org alias coke organization NUM NUM
it also carefully fills slots such as org type and a few others added just for this purpose so as to prevent improper merges for example it reduces the token sequence the org unit to two org mtokens one old and one new with slots filled so that they could not merge wit h each other
in these cases we overcome both the problems with tag considered in section NUM
in the above discussion substitutability played a central role in ruling out the derivation
we will follow the dependency literature in referring to complementation and modification as syntactic dependency
we now describe informally a structure that can be used to encode a dtg derivation
the sa tree for this derivation corresponds to the dependency tree given previously in figure NUM
in this section we have discussed examples where the elementary objects have been obtained by projecting from lexical items
we observe in passing that the sic associated to the d edge in the seems d tree also rules out this derivation
this is illustrated in 2a for a simple clause and in 2b for a complex clause
in the composed d tree NUM the component a NUM is substituted at a substitution node in NUM
the value of NUM is the next word probability function for the given context s
in fact heart is the only body part that beats
NUM NUM a semantic tag system for nouns and
in the following slmsum ts explmned first at the macro level of system archrecture and system components then the descnptlon narrows down to the xmcro level of processmg after a demonstration of the text representation two exemplary relevance agents are discussed
null more details on the lower bounds for the test data sets are shown next see table NUM
and fellowship NUM NUM from the swiss nsf to the first author
we present the results of initial experiments with several approaches to estimating such distributions in an application using fastus
v b m r z output the probability p qlx produced by the mlp for each frame is first converted to the likelihood p xlq by dividing by the prior p q according to bayes rule we ignore p z since it is constant here
sra combined the results of nametag and some additional processing by hasten to perform the template element task
walter thompson as a person rather than a organization since it looks like a personal name
walter thompson errors described in the named entity system walkthrough caused a spurious organization and person object
the reference resolver attempted to resolve organizational references to the names in order to extract additional non local information
recall also improves mainly because unusual examples that fail to match other egraph s are now matched by their own egraph
if all extraction examples fail to exceed the threshold the extractor does not extract anything from the incoming text unit
in the walkthrough document the collector combines the semantic representations from the headline and sentence NUM into the following representation
these requirements motivate hasten s vision of extraction which is to use the simplest input from users extraction examples
each analysis phase can access the results of previous phases thus enablin g complex embedded semantic representations to be created
hence the intersection question is undecidable too
observe that the dcg is off line parsable
nal actions defined in curly braces
fsa of course generalizes such word lattices
in adopting this belief the system updates the cstate by replacing the current plan with the new plan and adding beliefs that capture the utterance of newplan as outlined in section NUM NUM above
a yes no problem is undecidable cf
this results in a parse forest grammar
this is the gpsg and f tag approach
in order to model an agent s participation in a dialog we need to model how the mental state of the agent changes as a result of the contributions that are made to the dialog
on the basis of NUM NUM and NUM the system is able to apply rule NUM and so adopts the goal of accepting the plan
also it adds the beliefs that capture the utterance of the refashioned plan that the system intends it as a means to achieve the referring action and that it does achieve this goal
given that the move has been understood each conversant will believe it is mutually believed that the speaker believes that the current plan will achieve the goal second condition of the rule
an action schema consists of a header constraints a decomposition and an effect and it encodes the constraints under which an effect can be achieved by performing the steps in the decomposition
third the constraints ensure that a sufficient number of surface speech actions are added so that the set of candidates associated with the entire referring expression consists of only a single object the referent
if cand contains more than one object then this constraint will fail pinning the blame on the terminating instance of modifiers for there not being enough descriptors to allow the referent to be identified
robust parsing based on discourse information completing partial parses of ill formed sentences on the basis of discourse information
for the pos av av of each word pn pp phrases what information is available about the appears in sentences NUM
in the second partial parse the word side is analyzed as a verb
in this section we describe an algorithm for completing incomplete parses by using this information
i would like to thank michael mcdonald for invaluable help in proofreading this paper
in a consistent text many words and phrases are repeatedly used in more than one sentence
to examine the possibility of modification discourse information is applied at three different levels
table NUM discourse information on modifiees and modifiers of a noun cursor
a lower score NUM NUM is awarded for each ambiguous modifiee modifier relationship since such relationships are less reliable
the results are shown in ing how well the output japanese sentence conveyed the meaning of the input english sentence
but what are the correct responses
the best score for each set is in boldface
first as mentioned above our method of evaluation is not ideal it may make our results just seem poor
this can be explained perhaps by the fact that differences between near synonyms often involve differences in short distance collocations with neighboring words e.g. face the task
we compared the correctness of the choices made by our program to the baseline of always choosing the most frequent synonym according to the training corpus
comnnmieation language leveloped by sad sian because it is adapted to the eomlnulfication neetts of the system
to evaluate the lexical choice program we selected several sets of near synonyms shown in table NUM that have low polysemy in the corpus and that occur with similar frequencies
for each set we collected all sentences from the yetunseen NUM wall street journal part of speech tagged that contained any of the members of the set ignoring word sense
on node we find the hiding type list
the implementation was trained and evaluated on a large corpus and results show that the inclusion of second order co occurrence relations improves the performance of our implemented lexical choice program
in this paper we describe an unsupervised learning algorithm for automatically training a rule based part of speech tagger without using a manually tagged corpus
closed contains all other closed class words
this data run is called the baseline run
the cosine measure will give the highest value to vector pairs which share the most non zero y values
second the morphological recognition system is detailed
in the case of uninterrupted collocations with string length of NUM or mere characters and frequency of appearance NUM or more times there were NUM NUM millions types of expressions total frequency of NUM NUM millions times extracted by the n gram method
in the case of interrupted collocational substring extraction combining the substring with frequency of NUM times or more extracted by the first method NUM NUM thousand types of pairs of substrings with the total frequency of NUM NUM thousands were extracted
NUM n gram method and the problem involved NUM conditions for collocational substring extradtion in order to extract uninterrupted collocation without omission and to minimize extraction of fractional substrings we will introduce the following three conditions
unfortunately this does not satisfy condition NUM at the time when extracted substring list has been compiled information regarding mutual inter relationship between the extracted substrings within the original text has been lost rendering calculations impossible
NUM necessity of validity check for string words when one substring is extracted in order not to extract the absorbed string from the same part of sotlrce text where the substring was already extracted case NUM related records of spt need to be checked if the record is valid or not before extracting the next substring
in the case of interrupted collocational substring extraction combining the substring with frequency of NUM times or more extracted by the first method NUM NUM thousand types of pairs of substrinks with the total frequency of NUM NUM thousands were extracted
from these results it can be said that viewed from the point of extraction of collocational expressions as units of syntactic and semantic expressions substrings obtained by conventional methods include a voluminous amount of fractional substrings
NUM extraction of uninterrupted collocation NUM NUM invaliditafion of extracted substfings NUM co relations between extracted substrings in order to satisfy the requirement of condition NUM consider the extraction of n gram substring after extracting m gram substring
in the case of substrings of NUM or more characters these number reduced to NUM most of substrings extracted by the proposed method forms expressions as syntactic or semantic units and there are few fractional substrings
NUM if a stopping convergence criterion NUM is satisfied stop otherwise go to to step NUM
each constraint c e cs states a compatibility value cr for a combination of pairs variable label
though not global maximum of the log likelihood
synt will immediatly reject the address verb solution in this sentence thanks to its knowledges
recall is the percentage of words that get the correct tag among the tags proposed by the system
to improve the objectivity of the evaluation the benchmark corpus as well as parser outputs have
the benchmark corpus contains NUM words mainly ing forms and nonfinite ed forms with two correct syntactic analyses
of about two bigrams and three trigrams NUM a single linguistic constraint might have to override five statistical constraints
the texts totaling NUM NUM words were copied from the gutenberg e text archive and they represent present day american english
relaxation labeling is a generic name for a family of iterative algorithms which perform function optimisation based on local information
the traces starting with s indicate the line on which the applied rule is in the grammar file
thus susanne tag iw is mapped to lob tag in
to simplify definition NUM definitions NUM and NUM are formulated
this is because proper name forms the bare subject or object
for applications involving large documents the implementation may wish to provide the ability to directly access portions of the document
note that the ditto tags are not considered in this experiment
some of these words are characteristic words such as ytl NUM
thus susanne tag nnlux is mapped to lob tag nn
the output of the tagger is the input of the chunker
generate a system specific detectionquery from an analysis of the detectionneed the detectionquery has the same externalld as the detectionneed
if the next token is escape then it is a query term and not the end of the escape
the tags that can not be located at the end of chunks are listed as follows
thus a text body may consist of paragraphs a paragraph of sentences a sentence of tokens etc
information extraction the extraction from a document of information concerning particular classes of events is a form of document annotation
any text between a left bracket and a right bracket is considered a comment
each document in this collection should have an attribute relevant with the value true or false
if intervaltype is percent then interval NUM means provide status when each NUM of the documents are processed
parts of the library as we shall see below are best distinguished by the special services they provide e.g. the reference desk
finally we extend spud s evaluation of alternatives so that it describes the most salient entities possible and uses basic level terms wherever possible
table NUM perplexities of two smoothed trigram mod
at any point salience assigns each entity a position in a partial order that indicates how accessible it is for reference in the current context
however unlike systemic networks our system derives its functional choices dynamically using a simple declarative specification of function that correlates well with recent linguistic work
second by treating different conventional combinations as mere paraphrases of one another researchers complicate the statement of when and why to use conventional forms
the modifications achieved by lexical functions are parallel as with narrow in NUM NUM a they made a narrow escape
elementary trees without foot nodes are called initial trees and can only substitute trees with foot nodes are called auxiliary trees and must adjoin
adequate models of human language for syntactic analysis and semantic interpretation are typically of context free complexity or beyond
when n NUM this reduces to next t the set of terminals that may follow the terminal t
e mail elhadad cs bgu ac il t computer science department new york ny NUM usa
it must avoid encoding assumptions about the mapping between domain concepts and lexical structure
experiments with bottom up island driven chart parsing from charts initialized with phones are anticipated
first fuf allows the representation of constraints on lexical choice in a declarative and compositional manner
in this case the lexical chooser decides to map a semantic relation to a simple clause
unify top level input with the lexicon i.e. the single unification step described just above
however the perspective is a relation in the conceptual network whereas the focus is an entity
NUM fds are often called feature structures or attribute value matrices in the literature
the output fd results from the unification of this fug with the input fd
in general the lexical chooser needs information about discourse and about speaker intent
this makes it difficult to determine a systematic ordering in which to apply constraints
under a word based approach frequent words with a consistent translation can be aligned at a high rate of precision
for these applications we must go one step further from sentence alignment and identify alignment at the word level
these morphological differences result in a difference in the number of words in an english sentence and its mandarin translation
adding and deleting words is commonplace sometimes resulting in a paraphrased or free translation
the noun phrase the people she is speaking to in e25 is paraphrased as audience
other methods for aligning english and mandarin texts in the literature also fall prey to the problem of mandarin compounds
the first three experiments were designed to demonstrate the effectiveness of the naive dictalign algorithm based on a bilingual mrd
by this notion english is an svo language in which the verb typically follows the subject and precedes the object
we are grateful to keh yih su for his suggestions and comments at the early stage in the development of this work
the extraction process csci sends the collection identifier and document identifier to the analyst upon initialization the extraction process csci spawns the nltoolset server object which ties all the nltoolset s data resources together into a single object and loads into it the nltoolset system specification file
the options available to an analyst here are a name lookup b index review data records for this entity c create links between entity names and reviewed records and d add and modify information associated with the entity ie
the nltoolset combines artificial intelligence ad methods especially nl processing knowledge based systems and information retrieval techniques with simpler methods such as finite state machines lexical analysis and word based text search to provide broad functionality without sacrificing robustness and speed
when a document is selected for display by an analyst the analyst interface process csci passes the collection identifier and document identifier for the document to the document manager process csci which retrieves and returns the document and its relational records
as a result no speech act set has yet become standard
a string transformation is a rewriting rule denoted as u v where u and v are strings such that u ivt
compare such lexical prediction with the communicative act based predictions discussed above
co oc can support various statistical smoothing measures
should adjectives nouns and verbs all be considered to carry the same amount of information or should they be assigned different weights NUM the investigation of the assignment of weights on the parameters used for the measures
both commercial and academic groups participated
the scoring results are then tallied and reported
when human baseline data are juxtaposed with the system scores it is clear that the systems are approaching human accuracy with a much higher speed offering further support for readiness for application
the nuance recognizer is customized in two ways for use in commandtalk
commandtalk is implemented as a set of agents communicating as described above
NUM NUM start it start it is a graphical processing spawning agent
the complete regular expression defining a is then
oaa also provides an agent library to simplify turning independent components into agents
this means that no feature values are structures that can grow arbitrarily large
the modsaf agent consists of a thin layer on top of modsaf
the push to talk ptt agent manages the interactions with the user
the clue lies in the observation that a pragmatic restriction is governing the instantiation of the implicit local subject in exmnples NUM but not in examples NUM in NUM duc to the obvious conclusioil that someone who accepts an action is not the conscious actor of it paul is pragmati ally ruled out as the local subject of the decision domain
the gemini grammar must not contain any indirect recursion
this agent provides two mechanisms for the user to initiate a spoken command
they are captured by the following theorist rules default NUM expectedreply pao pcondition do sl areplv ts active pdo ts lexpectation p ao pcondition do s1 areply k believe sl pcondition NUM expected s1 arepl ts
our research supports the argument that it is important to distinguish homonymy and polysemy
we conducted two sets of experiments one concerned with homonymy and one concerned with polysemy
conflating these forms has the effect of grouping unrelated concepts and thus increases the net ambiguity
however we also found improvements even aexcluding closed class words such as of and for
in addition the requirement for phrases imposes a significant burden on the user
the second experiment involved quantifying the degree of ambiguity found in the test collections
the research described in this paper is one of the largest studies ever done
the most basic issue is one of identity what is a word sense
the best counter example to the correlation between semantic entropy and log frequency is the period which is the most frequent token in the english hansards and has a semantic entropy of zero
for instance the most frequent pronoun in table NUM is eleventh from the bottom of the list of thirty seven because t has a very consistent meaning
the entropy of english light verbs would likely remain relatively high if english chinese bitexts were used instead of english french because the lexicalization patterns involving light verbs in english are particular to english
the semantic entropy of a word can be interpreted as its semantic ambiguity and is inversely proportional to the word s informatio n content semantic weight and consistency in translation
adjectives in the middle of the table are more typical but they are less specific than the adjectives in the bottom third of the table
reliance on this property of translated texts is a double edged sword however due to the converse possibility that two languages share an unusual syntactic construct or an unusual bit of ontology
we call this method most frequent in table NUM
for each word NUM random trials were conducted and the accuracy figures were averaged over the i0 trials
since probabilities are always between zero and one their logarithms are always negative the minus sign in the formula ensures that entropies are always positive
now only the s node of the seems tree which is its maximal projection is substitutable
we will elaborate on each step in the following sections
information extracted above that level included the individual the position they were taking on if any the corporation in which the position is held the position they were leaving if reported the corporation of the former position the reason for the change e.g. retirement whether the holder was acting in that position or not etc
this different hit measure dhit played an important role since the opp should be tuned to sentence positions that bear as many different topic keywords as possible instead of positions with very high appearances of just a few topic keywords
this paper provides a quick summary of the following topics enhancements to the plum information extraction engine what we learned from muc NUM the sixth message understanding conference the results of an experiment on merging templates from two different information extraction engines a learning technique for named entity recognition and towards information extraction from speech
though the technique has clearly resulted in systems with very high performance and very high speed and has also led to commercial products we believe that the technology based on learning is highly desirable for the following reasons NUM freeing a group s best people from manually writing such rules and maintaining them is a better use of the time of highly gifted people
the purposes of our study are to clarify these contradictions to test the abovementioned intuitions and results and to verify the hypothesis that the importance of a sentence in a text is indeed related to its ordinal position
as a preliminary experiment we provided the single best transcription to plum configured for st the muc NUM domain on succession of corporate officers in order to determine the kinds of problems that speech input would pose
part of speech editor pose maintains the part of speech models that are used to determine word categories in the face of ambiguity e.g. is hit a verb or a noun
furthermore there is a need to constrain the way in which the non substituted components can be interspersed NUM
nothing about our approach is restricted to the named entity task other kinds of data could be spotted with similar techniques such as product names addresses core noun phrases verb groups and military units
since the components are the same in the te and st system configurations and since the knowledge bases of te are inherited by st we have represented this in figure NUM via making te the next innermost circle
we collected facts about the training corpus including the average number of paragraphs per text ppt the average number of sentences per paragraph spp and the average number of sentences per human made summary sps
though we could have used topic keywords for both training and evaluation we decided that the abstracts would provide a more interesting and practical measure for output since the opp method extracts from the text full sentences instead of topic phrases
to guard against very long documents which can lead to outlier in frequency estimates these are divided into subdocuments of about NUM characters in size ending on a paragraph boundary
we describe the head transducer model used in an experimental english to mandarin speech translation system
basically the parsing framework combines the constraint grammar framework removing ambiguous readings with a mechanism that adds dependencies between readings or tags
however the set of similarity values outnumbers the variables
this is done by having surface speech actions for each component of a description plus a surface speech action that expresses a speaker s intention to refer
it is this mental state that sanctions the adoption of goals and the acceptance of inferred plans and so acts as a link between understanding and generation
third we deal with objects that both the speaker and hearer know of though they might have different beliefs about what propositions hold for these objects
the referring expression that results from this is then judged and the process continues until the referring expression is acceptable enough to the participants for current purposes
we conducted experiments on noun entries in the bunruigoihyo thesaurus
as we mentioned the action for referring called refer is mapped to the surface speech actions through the use of intermediate actions and plan decomposition
the plan derivation expresses beliefs of the speaker how actions contribute to the achievement of the goal and what constraints hold that will allow successful identification
this rule works in many cases but we believe that our list may be too long and many words that have content such as ex NUM NUM are stopped
this is exactly where our word similarity measurement can be applied
we say that the recall of version NUM of the utterance with respect to version i is the proportion of the fields filled in version NUM that are filled in compatibly in version NUM conversely the precision is the proportion of the fields filled in in version NUM that are filled in compatibly in version i
tl is a subscriber and tl is idle and tl has NUM and NUM is a phonenumber and tl has NUM and NUM is a phonenumber and t2 is a subscriber and t2 is idle and t2 has NUM and NUM is a phonenumber
here follows two examples on how the paraphrasing would look like with the new architecture upon paraphrasing a loxy fact base to nl not yet implemented the only thing which changes between the two examples is the content of the igf
our aggregation rule says if two or more identical and hence redundant noun phrases are repeated consecutive then remove all the noun phrases except the first one this operation will remove the repetitive generation of the noun phrase and the text becomes concise
in the specification part to paraphrase the rules expressed in formal language to paraphrase automata further on to paraphrase questions asked to the theorem prover and to paraphrase the executed events and the newly created fact base
to solve the problem of the not naturalness of the loxy formulas and make them more natural the following two modules have been constructed the natural and compact modules and finally the surface grammar
we have found it too inflexible and the generated text too tedious to read therefore is suggested a new nl architecture where the user and the context of the user interaction is used to extract an intermediate generation form igf
NUM ul the t arser tries to translate ompom ds which are not code t in the dictionarie s
the success of the system is mainly based on this fundamental lninciple of tailoring it to a specific text type and sub jeer field
this is one of the new features of patrans compared to the eur tra model and will be discussed in detail below
but patrans is also a project which combines academic research and practical applications and which has shown that mt is viable in limited domains
the word was not seen in the training corpus or NUM the word was seen tagged with y at least once in the training corpus
for each transformation application all triggering environments are first found in the corpus and then the transformation triggered by each triggering environment is carried out
segmentation finally tile text is separated into units for translation i.e. sentences for which various recognition patterns haw been set up
to match the NUM NUM accuracy achieved by the stochastic tagger when it was trained on one million words only the first NUM transformations are needed
p in rb jj is greater than p in in jj but the outcome will be highly dependent
below we show how a transformation based approach can be taken for tagging unknown words by automatically learning cues to predict the most likely tag for words not seen in the training corpus
these textrefs are then combined with the entity type and finally adde d to the sgml tree
for instance in information retrieval the incorrect segmentation for the fragment p i in la and lb will definitely cause improper access to the texts involving it
to study the effects of lexicons on short queries we further perform retrievals using only the first sentence of each query that belongs to the title section of an original topic
hyper templates are structures whose slots can refer to other templates thus creating a graph of templates
the head corner table in NUM illustrates that functional heads like agro and agrs are not processed as heads
to ensure the integrity of the evaluation results a central stipulation of the methodology is that the following condition be maintained throughout the study computer blindness none of the participants can be aware that some texts are machine generated or for that matter that a computer is in any way involved in the study
the use of the core to improve determination of prosody in synthetic speech is also being investigated
computational linguistics volume NUM number NUM second although edps were employed successfully in the generation of hundreds of explanations the fact that they have more in common with schemata than with the operators of top down planners is indicative of a fundamental limitation the intentional structure of the discourse is unavailable for inference
to determine the content of the information associated with elaboration nodes determine content invokes the apply edp algorithm
if the inclusion condition evaluates to false the topic should be excluded regardless of the other two factors
at this point we had a pool of NUM explanations NUM of these pertained to objects NUM written by biologists and NUM by computational linguistics volume NUM number NUM knight and the other NUM pertained to processes also NUM written by biologists and NUM by knight
the refined parameters are then used to furtherget a better tagging result
this representation incorporates two assumptions that must be relaxed in any model that accounts for the negotiation of meaning first that hearers are always credulous about what the speaker says and second that neither participant makes mistakes
this small seed corpus contains NUM NUM words about 42k bytes
an electronic dictionary with parts of speech information can thus be acquired automatically
the following sections describe two such possibilities
the configuration is shown in figure NUM
recalls divided by the number of words in the word list
the extension could be made to other n grams in a similar way
recall of the system as the sum of per word precisions resp
NUM also when we consider possible evidence against mother adopting this plan namely whether the linguistic intentions of pretelling were incompatible with those that have been expressed it would be consistent to assume that mother is intending this plan
plan based accounts interpret speech acts by chaining from subaction to action from actions to effects of other actions and from preconditions to actions to identify a plan i.e. a set of actions that includes the observed act
when parsing a sentence the lexicon is not by definition consulted at the beginning of the chain
figure NUM n best list and translation results for could you show me an early flight please
m structure schema are projected by the main verb of a sentence
to allow annotations to modify the text and in particular to insert characters in such a way that subsequent accesses to the text see the modified text in place of the original text it is necessary to require a representation of positions in a document which allows for insertions e.g. by using integers above the length of the original string to refer to inserted elements
NUM evaluating a speech to speech system as though it were a speech to text system introduces a certain measure of distortion
the rank value of previous topics do obviously increase
the distance can be defined to be
correct rate NUM of NUM paragraphs
adds all the documents in collection to the documentcollectionindex retrievedocuments sequence of documentcollectionlndex retrievalquery numbertoretrieve integer monitor or nil collection returns a collection of documents of maximal length numbertoretrieve which are most closely related to the detectionneed from which the retrieval query is derived
table NUM shows the statistics of the training corpus
symbols tx and c denotes mean and standard deviation
the assumed topics are assigned by a linguist
row NUM reflects an interesting phenomenon
each produc j e px is the production ri e p up to some tion r i non terminal renaming
of rating each prediction between NUM and NUM
these probabilities are estimated from separate training data
table NUM validation of our conjunction hypothesis
morphological relationships between adjectives also play a role
same and different orientation links between adjectives form a graph
figure NUM shows randomly selected terms from this set
figure NUM simulation results obtained on NUM nodes
NUM table NUM shows the results of these analyses
the p value is the probability that similar
in naturally occuring dialogue the structure of the surface interaction differs from the underlying dialogue history insofar as certain communicative goals are jointly expressed in one utterance others may even be omitted
we extended this notion to allow aggregation of communicative goals depending on the common feature we defined four strategies for condensing dialogue interaction abbreviation abstraction omission and dominance
in the tables below we summarize how continuations states of the nodes in task description and the system s beliefs about the state of the nodes are mapped onto specific communicative goals
we call the condensing of information abbreviation when a number of continuations that would become adjoining parts of the parse tree and furthermore represent the same communicative goal are expressed in one utterance
analyses of human human dialogues are a good basis for an initial task model and a lexicon but it is difficult to determine which aspects of these analyses will generalize to human computer dialogues and which ones will not
authors are in opposite alphabetical order this time
we modelled this behavior for mixed initiative dialogue
figure NUM structures that can be abbreviated
that is at some point during diagnosis either the computer or the user becomes suspicious of the initial problem assessment and consequently moves back to assessment to be sure that the erroneous circuit behavior is properly understood
during our formal experiment the system was able to find the correct meaning for NUM NUM of the more than NUM NUM input utterances even though only NUM of these inputs were correctly recognized word for word
most prior work on natural language dialogue has either focused on individual subproblems such as quantification presuppositions ellipsis anaphoric reference and user modeling or else focused on dialogue processing issues in database query applications
since it is well known that users adapt to the system it will be unclear how the results from a particular set of human computer dialogues generalize to a model of interaction based on a different dialogue model
we do not find this result surprising because some expertise was gained during the preliminary training session so some subjects were ready to be given initiative in session NUM in fact the two subjects who struggled with using declarative mode in session NUM only contribute NUM of the NUM declarative mode data points used in computing the averages
the dilemma of researchers is nicely summarized by fraser and gilbert the designer is caught in a vicious circle it is necessary to know the characteristics of dialogues between people and automata in order to be able to build the system but it is impossible to know what such dialogues would be like until such a system has been built p
NUM the first session consisted of NUM the primary speech training lasting approximately NUM to NUM minutes NUM approximately NUM minutes of instruction on using the system and NUM practice using the system by attempting to solve four warmup problems with the system operating in directive mode the mode where the computer has maximal control
computational linguistics volume NUM number NUM the classification of the linguistic goal of the current utterance assertion command question or prompt and reflect the status of initiative after the utterance was made
the possible judgement categories are the following the headings are those used in tables NUM and NUM below
the robust learning procedure achieves more than NUM improvement compared with the discriminative learning procedure for all language models
the dm keeps track of the information provided by the user by maintaining an information state or form
table NUM compares the top NUM words in table NUM labeled better keywords with the bottom NUM words in table NUM labeled worse keywords
here we argue that even in the strictest linguistic sense there exists no single character that can not be used as a single character word in sentences
a tokenization w c to s has tokenization ambiguity if there exists another tokenization w e to s w w
we conjecture that the best keywords tend to be found toward the middle of the frequency range where there are relatively large deviations from poisson as illustrated in figure NUM
as this tokenization differs from the above mentioned tokenization of the string of single character words the critical fragment has at least two different tokenizations and thus has tokenization ambiguity
but the single tokenization does not fulfill condition NUM in the definition above for k NUM because the longer word abc exists in the dictionary
moreover we will confirm that some important maximum tokenization variations such as forward and backward maximum matching and shortest tokenization are all subclasses of critical tokenization
on the other hand if all the characters are words in the dictionary any character string can at least be tokenized as a string of single character words
besides providing a framework to better understand previous wor k as has been attempted here a good formalization should also lead to new questions and insights
the theme in this paper is to study the problem of sentence tokenization in the framework of formal languages a direction that has recently attracted some attention
clearly the st tokenization ab c de which fulfills the principle of maximum tokenization and is the desired tokenization in some cases is neither ft nor bt tokenization
this confirms that the tuned algorithm is over calibrated to the training set
abcoude one important property of this representation is that it allows encoding of speech act information
duration is assigned x if pause is true NUM otherwise
we tested all pairwise combinations and the combination of all three algorithms
ag fin tile order of the pi and tile corresponding ei is inferred from the implicit or ler of the alternatives of the focused element l rom this setting and the assertion of an occurrence of peter pointing to the fourth lucky number at the temporal perspective t the representation entails remizations of those events of the presupposition line that precede the counterpart of e in the presupposed sequence
but then stating that an event of the corresponding antecedent type indeed was realized the assertional impact of the r reading and stating that it occurred at a time as was expected consequence of the specific description of antecedent and anaphor and simultaneously insinuating that it could have been realized earlier presuppositional structure of the r reading supported by intplicature results in a contradiction
l he r reading i resul poses a sequence of events concel tumized as a plan or an expectation about the ongoing of the world and it assumes that fl om the t erspective of the contextual l ersl ecl ive time a part of the sequence is remized at cording to the ordering of the plan or expectation
d cus backgr und criterion if the asstllnption of s ction NUM NUM is true thai it the i i a sccnario the ackground event type is tested for specitic remizations it is natural to think of this scenario to be reasonably con eptualizt d ouly if the i ackg rouud eveut type merits testing
the cue phrase features are also obtained by automatic analysis of the transcripts
extraposition is the occurrence of prepositional or sentential complements or adjuncts after the verb in its base position v deg
the interpretation module works as follows the first step is to check whether there are arguments to be interpreted
consider example NUM the pronoun sie and the nominative dp der professor are attached to the main clause
as a consequence arguments and adjuncts attached to the upper clause can be interpreted with respect to the infinitival clause
if the child the old woman would have visit want if the child had wanted to visit the old woman
NUM the current constituent is attached to a con null stituent of the left context right attachment
this argument table is matched with the corresponding argument structures which has the effect of filtering the inappropriate argument structures
since rtiemand is non ambiguously interpreted as the subject of versucht the subject reading for das fahrrad fails
cp yesterday has her the professor tried to kiss yesterday the professor tried to kiss her
then boundary elself after sentence final contour
of particular concern are choices among NUM continuation of the center from one utterance not only to the next but also to subsequent utterances NUM re tention of the center from one utterance to the next NUM shifting the center if it is neither retained nor continued s
computational linguistics volume NUM number NUM purposes we will use the following schematic to refer to the centers of utterances in a sequence for un cb un a cf un el e2 ep a ek for some k
the avm consists of the information that must be exchanged between the agent and the user during the dialogue represented as a set of ordered pairs of attributes and their possible values
in particular a pair continuations across un and across un l represented as cont un u NUM and cont un t u NUM respectively is preferred over a pair of retentions ret un u NUM and ret u t un NUM
in particular this constraint stipulates that no element in an utterance can be realized as a pronoun unless the backward looking center of the utterance is realized as a pronoun also is rule NUM represents one function of pronominal reference the use of a pronoun to realize the cb signals the hearer that the speaker is continuing to talk about the same thing
mean length of non trivial utterances objective metrics can be calculated without recourse to human judgement and in many cases can be calculated automatically by the spoken dialogue system
but cb 25b is the value free interpretation of the noun phrase the vice president as in the vice president of the united states is the president s key man in negotiations with congress whereas cb 26b is the value loaded interpretation as in the person who now is vice president of the united states
the two most important features of situation semantics from the standpoint of the theory of discourse interpretation we wished to develop were NUM that it allows for the partial interpretation of utterances as they occur in discourse and NUM that it provides a framework in which a rich theory of the dependence of interpretation on abstract features of context may be elaborated
thus as discussed in section NUM to support centering a semantic theory must support the construction of partial interpretations in particular for elements of cf locality of cb un the choice of a backward looking center for an utterance un is from the set of forward looking centers of the previous utterance un in this sense the cb is strictly local
1deg centering is controlled by a combination of discourse factors center determination is not solely a syntactic semantic or pragmatic process NUM factors governing centering before we can examine the linguistic features that contribute to an entity s being the backward looking center of an utterance it is necessary to provide support for the claim that there is only a single backward looking center
aside from this there may be a subtle processing difference between him in variant 6e2 and with tony in variant 6e3
for example when a user follows a hyperlink united states it takes the user to a collection of documents which contains the english term united states and its aliases e.g. us u s a etc and the japanese translations of united states and their aliases
the subtype column in the screen indicates more detailed types of the entity e.g. organization company facility etc from this screen the user can go to a list of all english and japanese documents which mention for example bank of japan by clicking the link cf figure NUM
for example among the errors we have encountered an mt system failed to recognize a person name mori hanae in kanji characters segmented it into three words mori hana and e and translated them into forest england and blessing respectively
once the user selects a particular document for viewing the client sends the document to an appropriate i.e. english or japanese indexing server for creating hyperlinks for the indexed terms and in the case of a japanese document sends the indexed terms to the term translation module to translate the japanese terms into english
center retaining cb un4 NUM cb un but cb un l NUM t cp un l
this strategy would predict no garden path effect for follow on 6el since it assigns terry as the referent of he and sticks with it
the author thanks barbara grosz david israel megumi kameyama christine nakatani gregory ward and four anonymous reviewers for helpful comments and discussions
to illustrate we consider a modification to passage NUM shown in passage NUM with three possible follow ons 6el e3
however each passage shares sentences 7a c and therefore cp utc and cb u7c are the same for each follow on
computational linguistics volume NUM number NUM the new cp and an object pronoun the referent of which will be the new cb
vis a vis thh az forfre ident ointo at for ut at you c gpreu that it agreed b thefact thtt the prenmt middle easter n peace prvcm which expands pale inian pr ional autonomy b firmlymaintained in the filmre it includes thettart of syl i an
the standard method of evaluating bitext mapping algorithms is to compare their output to a hand constructed reference set of tpcs
another source of local slope variation is non linguistic text such as white space or tables of numbers
a bitext map can be derived from a set of correspondence points by linear interpolation
the only complication is that linear interpolation is not well defined for non monotonic sets of points
the following subsections describe but two of the more interesting extensions in the current implementation
more precise input would also make a big difference gsa s performance will improve whenever simr s performance improves
groups of tpcs with a roughly linear arrangement in the bitext space are called chains
simr exploits these properties to decide which chains in the scatterplot might be tpc chains
reducing this source of noise makes it much easier for simr to stay on track
however a frequent token type can be rare in some parts of the text
in this section we consider how to make predictions based on a convex combination of pairwise correlations
a state is useless if it is not contained in any valid path
finally the useless states that may appear during this construction are removed
the first phase that is needed is the application of a parsing algorithm which is such that NUM grammaticality is investigated for all paths not only for the complete paths from the first to a final node in the word graph and NUM grammaticality of those paths is investigated for each category from a fixed set
in sections NUM to NUM NUM we establish the rules for the generation of anaphora in chinese
thus for figure NUM tlsubjnp tfleftsubjnp where t21eftsubjnp is nppsrpsmpnil for the right hand tree
the proof NUM which omits lambda terms illustrates that hypothetical reasoning in proofs i.e. the use of additional assumptions that are later discharged or canceled such as z here is driven by the presence of higher order formulae such as xo yc z here
let us next consider the degree of incrementality that the above system allows and the sense in which 6to prove strong normalisation it is sufficient to give a metric which assigns to each proof a finite non negative integer score and under which every contraction reduces a proof s score by a non zero amount
the NUM test narratives range in length from NUM to NUM phrases avg s7 NUM or from NUM to NUM clauses avg NUM NUM
ideal behavior would be to identify all and only the target boundaries the values for b and c in fig NUM would thus both equal o representing no errors
NUM thus we chose not to use articles for descriptions of nominal anaphora in our system
all perplexity figures given in the paper are computed by combining sentence probabilities the probability of sentence wow1 w wn l is given by yin lp wilwo wi NUM where w0 and wn l are i NUM the start and end of sentence markers respectively
the inference is marked as m n where m is the argument position of the functor always the lefthand premise that is involved in the combination and n indicates the number of arguments inherited from the argument righthand premise
in proving xo yo z yo w wo z x compilation of the premise formulae yields the indexed formulae that form the assumptions of NUM where formulae i and iv both derive from xo yo z
several researchers note that principle based parsers allowing no grammar precompilation are inefficient
rather only when some rule asks whether the token has the attribute values associated with an organizational title will the knowledge bank be checked for these
language was available and training begun but because of personnel unavailability serious use of i t did not begin until september
outputs include both a tagging of the target in the input and th e inclusion of the target characters in a list of other such person targets
the life cycle of the dx project is thus independent of the muc which has been a very useful i f not the best timed experience
apply dxl rules for more complex interacting rule sets in the followin g order placel place2 orgl org2 personl person2 person3
also an efficient earley style parser can be constructed as discussed below for grammars of this form
i introduction in documentation terminologies thesauri and other terminological lists are reference systems which can be used for manual or automatic indexing
here the term provides a means for accessing information through its standardising effect on the query and on the text to be found
in particular the disciplines of information processing computers etc and biology or genetics are characterised today by an extraordinary terminological activity
for example in the edf thesaurus the syntactic structures of terms are distributed as follows thus term extraction is initially syntactical
description of the corpus the corpus studied is a set of i0 NUM scientific and technical doc unents in french NUM NUM NUM words
that question can itself be subdivided into several questions that concern the role of the standard relationships in a thesaurus synonymy hyperonymy etc
the question studied in this experiment concerns the positioning or classification of a term in a subject field or semantic field of a thesaurus
unfortunately the abundance of electronic corpora and the relative maturity of natural language processing techniques have induced a shortage of updated terminological data
indeed it seems that a small number of words usually very general uniterms are very frequent but are not terms
controlled indexing a supplementary way of characterising a document s contents is by recognizing controlled terms in the document that belong to a thesaurus
firstly building new linguistic structures for new sentences is delinitely made faster
only sentences which a re substrings of other strings may be coded
this exl lanal ion a ccoutjts for prefixing suffix
by convention NUM lcb el with g being the empty string
i rench rdaction r 6actionnauv r rcssion x
but the corresponding mathematical properties appear not to hold ill the general case
after conducting user appreciation studies it was clear that the next version of vios should act more human like
this is the reason why we will take the human operator as the source of inspiration for improvement
then we will describe in detail how information is presented in vios and in the ova corpus
ovr provides information about dutch public transport systems ranging from local bus services to long distance trains
a clarification sequence consists of a wh question of the caller and an appropriate answer by the information service
a checking sequence consists of a check by the caller and an appropriate answer by the information service
a reconfirmation sequence consists of a reconfirm by the caller and an appropriate answer by the information service
the caller will apply a clarification sequence if he wants extra information about the current plan
table NUM shows the frequency of the caller s repair sequences compalred with the presence of positive acknowledgements
in the english only matchplus system this is a straight forward process
in the following we will use an example sentence to demonstrate how the algorithm works
we will begin with the named entity scorer but because there is so much in common with the scenario template and template element scorers these wil l be easier to modify
examples of the chinese output still give by far the most important indication of parsing quality
figure NUM accuracy corpus size dependency curve
if the set t is still heterogeneous and there are no more attribute values to divide with the tree is terminated and the leaf is marked by the majority class of the node
in the following example figure NUM we
the same two quadruples fall below the sdt for nouns as dqn q3 q2 dqv q3 q4 ffi NUM NUM NUM NUM NUM NUM NUM NUM
verbs which tend to be more polysemons and can change their meanings depending on the kind of the object they take are formed into NUM groups and have altogether NUM possible roots
in the ease of verbs the situation is even more complex because many verbs do not share the same hierarchy and therefore there is no direct path between the concepts they represent
as shown in the following table our algorithm appears to have the fact that many words in both the training and the testing sets were not found in wordnet caused a reduction in the accuracy
also we believe that a bigger training corpus would increase performance in the case of less frequent prepositions which do not have enough training examples to allow for induction of a reliable decision tree
table NUM hypothetical performance data from users of
figure NUM presents one dialogue from this domain
figure NUM task defined discourse structure of agent a
paradise a framework for evaluating spoken dialogue agents
for this reason the example database was designed in such a way that it is possible to acquire new examples by a semi automatic method consisting of an automatic extraction step from a bilingual corpus see
table NUM attribute value matrix simplified train timetable
the table below shows the two configurations and their frequencies in the corpus
kay also presents a head corner parser
an efficient implementation of the head corner parser
lex figure NUM the head corner parser
perhaps surprisingly the possibility of specifying a workable grammatical representation is a matter of controversy even at lower levels of analysis e.g.
in the context of linguistic descriptions the types concerned are often categories i.e. non atomic entities
NUM NUM NUM c scheduled not stated e deb offer give phone no
as we go v inf y pres to inf null apparent intention to follow suit are grievous blows
to complete the picture we also need to account for the fact that the conversants are collaborating
this paper presents a computational model of how a conversational participant collaborates in making a referring action successful
thus the mental state together with the rules provides the link between these two processes
in the case of referring this will be the plan derivation that corresponds to the referring expression
we are left with the question of to what extent repeated use of words within relatively short sequences of sentences henceforth for ease of reference paragraphs affects the accuracy of e v n
the first tier is the planning component which accounts for how utterances are both understood and generated
the action schemas make use of a number of predicates and these are defined in table NUM
rather this action has an associated procedure that determines a description that satisfies the preconditions of describe
we experimented with different numbers of seed words but were surprised to find that only NUM seed words per category worked quite well
while the ranked lists are far from perfect one can see that there are many category members near the top of each list
they found that typically the participant trying to describe a tangram pattern would present an initial referring expression
in particular the variable category will be instantiated from the co referential variable in the surface speech action
NUM a ladder weighs NUM lb with its center of gravity NUM ft from the foot and a NUM lb man is NUM ft from the top
because the second clause will only have the sloppy derivation received from the first the strict derivation that the third clause requires from the second will not be present
while he does not discuss the contrast between this case and sentence NUM we do not see any reason why his framework could not accommodate our solution
for instance strict sloppy ambiguities are not restricted to vp ellipsis but are common to a wide range of constructions that rely on parallelism between two eventualities some of which are listed in table NUM
sloppy readings with events sentence NUM has a sloppy reading in which the second main clause means i will kiss you even if you do n t want me to kiss you
because of the ellipsis e22 must stand in a parallel relation to some previous eventuality here the only candidate is john s revising his paper e12
our account of parallelism applies twice in handling this example once in creating a complex antecedent from recognizing the parallelism between the first two clauses and again in resolving the ellipsis against this antecedent
the purpose of the shallow analysis component is to identify clauses and phrases to identify modifying relations as long as they are unambiguous deriving a canonical interpretation in ambiguous cases and to convert some surface variations into features
table NUM and NUM indicate that a sense tagged corpus collected for NUM NUM words will cover at least NUM of all content word occurrences in the brown corpus and at least NUM of all content word occurrences in the wall street journal corpus
d e1 NUM jtransition d e2 NUM jtransltion formal hold el constitutive lcb glass rcb telic walk through act e agentive make act
in what follows we take s and t to be arbitrary templates where the natural language expression from which t was created appears later in the text than the expression from which s was created
ordinarily the input of such a system is a sequence of words
the notion of compatibility refers to dialogue acts which have closely related meanings or which can be easily realized in one utterance
according to this hierarchy if we find iaptop and hand held computer in a text we can infer that the text is about portable computers which is their parent concept
once the head clause structure has been built it is passed to the rest of the lexical chooser which determines which syntactic forms can be selected for each modifier when appropriate lexical resources are found
saw an empty operator is postulated by analogy to the man whom that i saw
second the stack of an lr parser encodes the notion of c command implicitly
in formalism other than gb theory gaps are encoded directly into the rules
simple precompilation is not a solution to the inefficiency of principle based parsing however
the precision measure plays a central role in the text summarization problem the higher the precision score the higher probability that the algorithm would identify the true topics of a text
we only outline a proof of this property here by focusing on step NUM to execute this step we visit tr in post order
in the notation of a links we then use a subscript indicating the suffix tree of the target node in order to distinguish among different linkings
here we introduce a decision problem associated with the optimization problem of learning the transformations with the highest score and outline an np completeness proof
let e lcb a b rcb we construct an instance of the ts problem l k rcb over e as follows
to show np hardness we consider the clique decision problem for undirected simple connected graphs and transform such a problem to the ts problem
specifically it a defines a vocabulary as a set of words w and defines as clusters its subsets kl k n satisfying t3 xk j w and ki fq kj NUM i j i.e. each word is assigned only to a single cluster and b treats uniformly all the words assigned to the same cluster
it is not difficult to see that the remaining transformations denoted by implicit nodes of tx do not have score greater than the one above
the main aim of the enquiry is not to produce an error report on wordnet NUM NUM but to develop a methodology of redundancy and consistency checks for re use
figure NUM shows a particular NUM clause grid scored against all other possible NUM clause grids where the grid at the top is the intended correct one and the scores reflect degrees of similarity in order to evaluate these computer generated grids a set of manually derived grids is needed
the training set trees were converted into subtrees together with their substitution probabilities
since the raw penn treebank data contains many inconsistencies in its annotations cf
our first question is concerned with the performance of dop1 on unedited data
in addition the dialogue module is faced with incomplete and incorrect input and sometimes even gaps
parse accuracy for word strings from the atis corpus by dop4 against dop3
the system is fully integrated in the verbmobil system and has been tested on several thousands of utterances
it is therefore evident that we will get impractical processing times with dop3
we will restrict ourselves to subtrees whose unknownness depends only on unknown terminals
for sentences with unknown category words the method appeared to be completely inadequate
these data are again stored in the context memories shown above and are accessed by the other verbmobil modules
for the deep analysis side to the right the turn is segmented into four utterances guten tag u klein wit m ssen noch einen terrain ausmachen flit die mitarbeiterbesprechung for which the semantic evaluation component has assigned the dialogue acts greet introduce name init date and sug gest support date respectively
the high frequency presence of appositives with person names provide one powerful source of semanti c context resolution
and the brute force look up approach handles over NUM of our instance recognition and is easily extensible
the user can 1slanted words show romaji transcription of respective japanese words
for any particular rule the whole document is searched from the first token to the last
prefix titles like bank are characterized in the knowledge bank by a number o f
the learned system and model we used proved to be highly portable to a new language
the first type of entry mostly common words facilitates sentence parsing when needed
r taenttty r property ascription tntenstve NUM
pattern NUM chaining NUM others NUM
since this is treated in another module we define our aggregation at the semantic level
this body of knowledge must be refined to further improve the quality of the text produced
but of course nominations for the library can only come with experience whic h is only now maturing
if the second alternative is chosen it is interpreted as honorific
to generate a summary for case a we should simply choose apple as the main idea instead of its parent concept since it is by far the most mentioned
into so form f gf sigma pred start subj obj
r defines a simple homomorphic embedding of f structures into qlfs
if the model complexity does not match the data complexity then both the total codelength of the past observations and the predictive error increase
since we are not concerned with the generation of descriptions for different levels of users we look only at the former group of work which aims at generating descriptions for a subsequent reference to distinguish it from the set of entities with which it might be confused
we compare our proposal with approaches discussed in the literature
qlf is one of information packaging rather than mgthing else
s 6proof induction on the complexity of y
another approach to the problem of incomplete knowledge is the following
such definitions are useflfl in showing basic results such as preservation of truth
insights into the dialogue processing of verbmobil
as shown in the table by using the preference rule in addition to the fact that the majority of the nominal anaphora using full descriptions are matched a considerable number of reduced descriptions are matched as well giving an overall match of NUM
moreover our approach treats apparent conflicts with expectations as meaningful for example if an utterance is inconsistent with expectations then the reasoner will try to explain the inconsistency
the typical way to model interpretations has been to represent the discourse as a partially completed plan corresponding to the actual beliefs perhaps even mutual beliefs of the participants cf
this approach has worked well for us but as one reviewer remarked it is an interesting issue as to whether they are also a function of the locutionary level
as a result of this interpretation not knowref m whoisgoing is added to the discourse model as the fact expressednot knowref m whoisgoing
summary speaker sl should do action areply in discourse ts when NUM sl expects areply to occur next and NUM sl may accept the interpretation corresponding to ts
these acts differ from our own meta plans in that they are organized into a finite state grammar and do not account for grounding acts that would violate a receiver s expectations
this approach can not handle more than a few relationships between utterances and plans and can not handle any utterances that do not relate to the domain plan in a direct manner
our model achieved NUM NUM accuracy which is the highest quoted score on this test set known to the authors
then the constraint removing algorithm boiled the constraint space down to NUM NUM in two hours
where wi is the frequency count of the i th configuration over the total number of observed configurations
this method selects samples based on the training utility factor of the examples i.e. the informativity of the data with respect to future training
this method is based on the assumption that there is no need for teaching the system the correct answer when it answered with high certainty
although many researchers have considered the problem of avoiding misunderstanding e.g. by correcting misconceptions previously none has addressed the problem of identifying and repairing misunderstandings once they occurred
where the ca approach is weakest is in its explanation of how the recipient of an utterance is able to understand an utterance that is the first part of an adjacency pair
if the sum is greater than NUM store the verb in a list called verbs
the ability of aspectual forms to follow verbs is constrained by the inherent features of verbs
adjective pairs with no connecting links are assigned the neutral dissimilarity NUM NUM
figure NUM shows some of the adjectives in set a4 and their classifications
aspectual distinctions correspond to how parts of the time line are delineated
she reports the results obtained from running the program on NUM sentences of the lob corpus
we checked the cases in table NUM downward from the top
we define an objective unction scoring each possible
set of adjectives with predetermined orientation labels
or different orientation with NUM accuracy
NUM except implicitly in the form of definitions and usage examples
we implemented a morphological analyzer which matches adjectives related in this manner
figure NUM randomly selected adjectives with positive
partition NUM of the adjectives into two subgroups c1 and c2 as
whenever this node is traversed the constraint needs to be honored
note that the negative constraints are not composed into the boolean expressions
a conjunction defines a part of a certain path in the forest
in the generation scheme developed here this is much more complicated
the meaning of combined conditions is the following
generation of paraphrases from ambiguous logical forms
it indicates that the third semantic fact is expressible in condition pl
this constraint is the negation of the conjunction of the two conditions
figure NUM shows the generation forest that encodes these renditions of the input
this path corresponds to the sentence john rushed into the room
these two smooth null tung hui chiang et al
robust learning smoothing and parameter tying table NUM
if we see plenty of output like this then grammatical work on agreement is needed
compared with turing s formula the probability for an m gram that does not occur in the sample is backed off to refer to its corresponding m NUM gram probability
the experimental results are shown in table NUM
robust learning smoothing and parameter tying table NUM
robust learning smoothing and parameter tying table NUM
the relation between syntactic dependencies and surface order can nontheless be inferred from the data
a specific section includes terms like smaltimento dei rifiuti smaltimento di materiale tossico smaitimento di gas di scarieo
figure NUM shows an example of the ranked list
we predict what is going to happen next
an array of map nodes for self organization
the second corpus sole24oore is an excerpt of financial news from the sole NUM ore economic newspaper of about NUM NUM NUM
by mousing on a node i.e.
for each non empty temporal unit tuft from focuslist starting with most recent if specificity tu t specificity tu and not empty merge upper tuft tu then
in the set of documents from NUM to NUM lto20 column these allow to discriminate between attivit6 and attivitd antropica
the different classes are characterized by the following semantic patterns as shown in figure NUM basili pazienza and velardi verb classification table NUM excerpt of ciaula clusters for cognition verbs in the rsd
frequently the object of a cognition verb is a physical property or a natural object and the analysis is performed with some instrumentality ins cloud parameters are derived from satellite
the purpose of one such experiment which we describe in this paper is to find some points of contact between psychologically motivated models as wordnet and data driven models as ciaula NUM
a bad choice in the same domain is the class indicate establish foresee determine that received the overly general label create make
in general lists of words or phrases are used in place of a single label so that the reader may have an idea of what is really meant
especially with large ciaula clusters the number of synsets becomes too large and the algorithm does not gain enough evidence of any significantly promising pattern in the hierarchy
as proposed in this paper an appropriate process of lexical tuning can significantly reduce the overgenerality excessive ambiguity and underspecificity weak constraints on verb argument structures that is typical of general purpose resources
hereafter ld is the class evaluate regulate assign determine examine resolve maintain that received the label judge form an opinion of pass judgement on
given a set of examples of verb uses we can more or less easily tell whether a conceptual definition in terms of thematic structures as provided by ciaula is appropriate or not
the cut off frequencies o c2 are thresholds determining whether to back off or not at each level counts lower than ci at stage i are deemed to be too low to give an accurate estimate so in this case backing off continues
finally the updated query can be used to retrieve a new set of documents shown as retrieve documents NUM at the bottom of the figure
given the restriction that all features must allow only a finite number of values it would be trivial to transform all unification rules into rules over atomic categories by generating all possible full feature instantiations of every rule and making up an atomic name for each combination of category and feature values that occur in these fully instantiated rules
it should be noted that although we are not using the full power of the gemini grammar formalism we still gain considerable benefit from gemini because the feature constraints let us write the grammar much more compactly gemini s morphology component simplifies maintaining the vocabulary and gemini s unification based semantic rules let us specify the translation from word strings into logical forms easily and systematically
when dealing with some new construction we first rather mindlessly overgenerate providing the grammar with many ways to express the same thing
when no explicit information is present we can resort to treating single words as lexical islands essentially adopting a view of maximum compositionality
two strategies have been used in lexical choice when knowledge gaps exist selection of a default NUM and random choice among alternatives
as we discussed in section NUM a number of generation difficulties can be traced to the existence of constraints between words and phrases
twenty or thirty choice points typically multiply into millions or billions of potential sentences and it is infeasible to generate them all independently
an e structure consists of a list of distinct syntactic categories paired with english word lattices syn lat syn lat
the type of the lexical islands and the manner by which they have been identified do not affect the way our generator processes them
a prime example of this in the commandtalk grammar is the rule coordinate hums digit f digit f digit f digit f which says that a set of coordinate numbers can be a sequence of four digits
there are many possible formats for specifying a finite state grammar and the one used by the nuance recognition system specifies a single definition for each atomic nonterminal symbol as a regular expression over vocabulary words and other nonterminals such that there is no direct or indirect recursion in the set of definitions
that is no rule subsets are allowed with patterns such as a bc c ad immediately recursive rules are allowed but only if the recursive category is leftmost or rightmost in the list of daughters so that there is no form of center embedding
since the ci agent is immediately notified whenever the user creates an object through the modsaf gui the ci can note the salience of such objects and make them available for pronominal reference just as objects created by speech are leading to smoother interoperation between speech and the gui
commandtalk combines a number of separate components integrated through the use of the open agent architecture including the nuance speech recognition system the gemini natural language parsing and interpretation system a contextual interpretation modhle a push to talk agent the modsaf battlefield simulator and start it a graphical processing spawning agent
f w2 w3 wn f w w NUM wn NUM f w3 w4 ten f w3 w4 w the idea here is to use mle estimates based on lower order n grams if counts are not high enough to make an accurate estimate at the current level
from this table it may be reasonable to conclude that progress has been made since the muc NUM performance level is at least as high as for three of the four muc NUM tasks and since that performance level was reached after a much shorter time
then critics are applied to the resulting ailt lowering the certainty factor if the information is judged to be incompatible with the dialog state
a sample run of the prototyl e system is shown in figure NUM
gua ra ntee a monotonic improve nwnt in mt a ccuracy
it is a concept as it requires a larger number of constraints on the information to be searched for in texts
rns ha sad oli 2otltoxti tl free gramma l
patterns is said to accept an input s iff there is a deriva tion
syntactic depend umi s hi na tura l
pa tterns as d ffault tra nsla ti m
coml ute the m scst choice of tra rtsla tion
obviously the artificial pseudo semantic representations make the problem much easier we experiment with them as a first step somewhere between learning language from a radio and providing an unambiguous textual transcription as might be used for training a speech recognition system
for the time being we approximate the problem as induction from phone sequences rather than acoustic pressure and assume that learning takes place in an environment where simple semantic representations of the speech intent are available to the acquisition mechanism
the training corpus contains more than NUM NUM chinese words
when presented with an utterance the algorithm goes through the following sequence of actions it attempts to cover parse the utterance phones and semantic symbols with a sequence of words from the dictionary each word offset a certain distance into the phone sequence with words potentially overlapping
occasionally the program removes rarely used words from the dictionary and removes words which can themselves be parsed
input lcb boat a in rabbit the be rcb the rabbit s in a boat
to better investigate more realistic formulations of the acquisition problem we are extending our coverage to actual phonetic transcriptions of speech by allowing for various phonological processes and noise and by building in probabilistic models of morphology and syntax
later in the acquisition process it encounters the sentence you kicked off he sock when the dictionary contains among other words yu lcb you rcb a lcb the rcb and rsuk lcb on this basis it adds kikt f lcb kick off rcb and sak lcb sock rcb to the dictionary
removing the restriction on empty semantics and also setting the semantics of the function words a an the that and of to lcb rcb the most common empty words learned are given in figure NUM the ring problem surfaces among other words learned are now k lcb car rcb and br lcb bri ig rcb
in section NUM we compare our approach with other mt approaches
in section NUM we present a summary of the implementation aspects
the bilingual equivalences are described on the basis of semantic representations
approxinmtely NUM researchers in NUM public aad industrial institutions are involved
whereas our approach does not preclude the use of interlingua predicates
this condition is necessary because of the polysemy of those prepositions
NUM a das paflt echt scmecht bei mir
for a description of type definitions see NUM below
the result is a hilly transpaxent and modulax interface for filtering the applicability of transfer rules
the compiled trazlsfcr program is embcdded in the incremental and parallel axchitecture of the verbmobil prototype
the best analysis of the corpus is taken to be the true analysis the frequencies are re estimated and the algorithm is repeated until it converges
the former is used primarily for grammatical function words such as particles and inflectional endings while the latter is used primarily to transliterate western origin words
in a departure from the algorithm the system uses simple heuristic for ignoring subdialogs a time is ignored if the utterance evoking it is in the simple past or past perfect
such a theory would do the following
one of the most frequent undesirable effects of re estimation is subdividing an infrequent word into highly frequent words or a frequent word and an unknown word
our work is based on the observation that category members are often surrounded by other category members in text for example in conjunctions lions and tigers and bears lists lions tigers bears appositives the stallion a white arabian and nominal compounds arabian stallion tuna fish
two main work lines are open first we have to conduct new series of experiments to check the lexical database and the combined approaches with other more sophisticated training approaches second we will extend the multiple resource technique to other text classification tasks like text routing or relevance feedback in text retrieval
the ambiguity of the greek corpus is more than three times greater than the next one the german corpus
as we said before one of the major virtues of re estimation is its ability to remove inappropriate word hypotheses generated by the initial word identification procedure
we use three surface speech actions
NUM NUM rules for updating the mental state
this is captured by rule NUM
we face two immediate problems NUM
word horse would be a NUM because one of its mean ings refers to an animal
the training module and recognition module were first tested on english in early march
the one we believe most likely to succeed is vastly increasing the vocabulary size
plum and other information extraction systems seem well poised to deal with such problems
for example given a context free grammar and an associated tree structure p for u the part of p representing a substring v of u is the smallest subtree q containing all leaves corresponding to v q is not necessarily the whole subtree of p rooted at the root of q conversely for each part q of p we suppose that we know how to define the fragment v of u represented by q
by contrast several groups this year achieved an f in the 50s in NUM calendar days
NUM NUM NUM entering into a collaborative activity
we added a general parser for sgml to simplify text descriptions of documents that contain sgml markup
first the fill rule editor was used to attempt to improve performance on an existing application
a reasonable parser is a parser such as its size and time complexity are tractable over the class of intended utterances assumptions about its ultimate capabilities especially about its disambiguation capabilities are realistic given the state of the art
as for now they comprise the disambiguation scope how far does the solution of the ambiguity kernel carry over in the subsequent utterances and the multimodality what kind of cues could be used to help solve the ambiguity in a multimodal setting
theory and practice of ambiguity labeling with a view to interactive disambiguation in text and speech mt christian boitet geta clips imag ujf cnrs NUM rue de la chimie bp NUM NUM grenoble cedex NUM france christian
it also clear that another sequence such as important husiness addresses presents the same sort of ambiguity or ambiguity type in analogous contexts here ambiguity of attachment or structural ambiguity
file underlilmd fragment has an ambiguity ot attachment because it has two different skeleton NUM representations international telephone services international telephone services as a title this sequence presents the same ambiguity
we lex i pro cat pronoun person i number plur read lex read v cat verb person i number plur
even if we want to label ambiguities independently of any specific analyzer we must have in mind a certain class of possible representation systems for analysis results and to be clear about what an ambiguous representation is and about what counts as an ambiguity etc
keywords interactive disambiguation ambiguity labeling ambiguity occurrence ambiguity kernel introduction we are interested in improving the quality of mt systems for monolinguals where the input can be text or speech no revision is possible and the controlled language approach is not usable
NUM repeat NUM NUM until all nodes in the corpus are assigned labels
such a grammar may be further elaborated as follows
alignments across different languages are kept whenever possible
NUM NUM canned text templates or grammar
additionally this approach at least when applied after acquisition time does not allow explicit ordering of word senses a practice preferred by many lexicographers to indicate relative frequency or salience this sort of information can be captured by other mechanisms e.g. using frequency of occurrence statistics
for instance beber can be derived into beb lcb e dero bebe e dor beb i do beb i da volver into vuelto and communiear into telecommunicac on etc
provide the user with a boolean choice
NUM very good knowledge of english
a multilingual internet based employment advertisement system is described
the most relevant information is obviously job types
generative power of ccgs with generalized type raised categories
this subsection shows some results of our preliminary experiments to confirm effectiveness of the proposed grammar acquisition techniques
a mixed order markov model combines the information in these matrices for different values of k
the system starts with the s reject action
in particular for each input sequence of ilts it produces a sequence of ailts and then chooses the best sequence for the corresponding utterances
the first problem lies in the fact that everything that goes into a leq relation to one hole can not be in a leq relation to the other hole of the same discourse relation predicate because of its partitioning character
especially when one of the two is determined as anaphoric that is sentence external the scope of this discourse relation seems to be wider than the others noda in fig NUM is an example for this
the use of noda is dif crent from the norreal use of the copula in that it takes a temporalized sentence as a complement and at the same time lacks the argument of the copular predication
when every arc of this kind has been expanded we have the ex null panded usst
gogo nara yamada ga i ru noda afternoon cond pn nom be pres aux pres among discourse relations with sentence external anaphoric binding there are two types those whose antecedent part is bound sentence externally and those whose conclusion part is bound sentence externally
additionally it is assumed that we have a syntactic strategy in which the topic phrase is dealt with as an adjunct modification which should be interpreted in the discourse structure with respect to the main predicate of a sentence
getsuyoubi wa gogo nara daijoubu da monday top afternoon cond okay coppres as or monday it is ok i it is in the a ternoon gogo nara getsuyoubi wa daijoubu da aftcrnoon cond monday top okay cop prcs i it is in the afternoon the monday is okay discourse relations can in contrast all be of the type whose antecedent part or conclusion part is bound sentence externally
as for the semantic construction it is aimed at that semantic analyses of japanese as well as german should be done in the same formalism which is especially challenging taking differences of the two languages into account compared to languages like german and english peculiarities of japanese such as the absence of definite articles seem to invite common semantic analyses based on underspecification
a discourse relation is represented in lud as a predicate with three arguments the first one is a term for the type of the concerning discourse relation the second one is an underspecified scope domain of the antecedent part and the last one is another underspecified scope domain for the conclusion part
we have also stated an interesting semantic constraint on the resolution of multiple discourse relations which seems to prevail over the syntactic c command constraint discourse relations should be scopally compared with each other on the criteria whether the restriction antecedent part or to the scope conclusion part of a discourse relation has an anaphoric force
we assume that an agent will maintain a record of these expressed attitudes represented as a turn sequence
the antecedents of these axioms refer to ambiguities and inconsistencies with expressed linguistic intentions as well as expectations
thus as in our own model two copies of the system can converse with each other negotiating referents of referring expressions that are not understood by trying to recognize the referring plans of the other repairing them where necessary and presenting the new referring plan to the other for approval
in the map task these questions are most often about what the partner has on the map
does the response contribute task domain information or does it only show evidence that communication has been successful
moves are the building blocks for conversational game structure which reflects the goal structure of the dialogue
in addition natural dialogue participants often fail to make clear to their partners what their goals are
the distinctions used to classify moves are summarized in computational linguistics volume NUM number NUM the action
all of these response moves help to fulfill the goals proposed by the initiating moves that they follow
since games nest it is not possible to analyze game segmentation in the same way as was done for moves
dialogue openings and closings were omitted since they are well understood but do not correspond to categories in the classification scheme
some coders have commented that the coding practice was unstructured enough that it was easy to forget to use the subcode
there was also confusion about whether a game with an agreed beginning was embedded or not k NUM
as far as disambiguated tags for left context words are used these are of course not obtained by retrieval from the lexicon which provides ambiguous categories but by using the previous decisions of the tagger
a new sentence is tagged by selecting for each word in the sentence and its context the most similar case s in memory and extrapolating the category of the word from these nearest neighbors
when a word is not found in the lexicon its lexical representation is computed on the basis of its form its context is determined and the resulting pattern is looked up in the unknown words case base
in most taggers some form of morphological analysis is performed on unknown words in an attempt to relate the unknown word to a known combination of known morphemes thereby allowing its association with one or more possible categories
without context man is more probably a noun than a verb and its contextual probability e.g. after a pronoun man is more probably a verb than a noun as in they man the boats
as the chance of an unknown word being a function word is small and cases representing function words may interfere with correct classification of open class words only open class words are used during construction of the unknown words case base
we will call this case representation pdassst NUM three suffix letters s one prefix letter p one left disambiguated context words d and one ambiguous right context word a
the architecture takes the form of a tagger generator given a corpus tagged with the desired tag set a pos tagger is generated which maps the words of new text to tags in this tag set according to the same systematicity
finally the cost of building the tree on the basis of a set of cases is proportional to n log v f in the worst case compare o n for training in ib1
a theory t describing his or her linguistic knowledge including principles of interaction and facts relating linguistic acts
a alternatives alternative classifications of the same design error case by the two annotators
more sophisticated linguistic information comes in several forms all of which may need to be represented if performance in an automatic acquisition of lexical regularities is to be improved
we use sgml as a way of exchanging information between modules in a knowledge acquisition system and of storing that information in persistent store when it has been processed
identified by the robust partial parser noun and verb groups which include domain semantic categories elicited at the precategorization phase are collected together with frequencies of their appearance in the corpus
it matches in the text patterns which represent hypotheses of the knowledge engineer groups together and generalizes the found cases and presents them to the knowledge engineer for a final decision
this knowledge about domain in the form it is extracted is not quite suitable to be included into the knowledge base and require a post processing of the linguistically trained knowledge engineer
it shows a subcluster of drugs top left disease based nouns top right body part adjectives lower left and condition modifying adjectives lower right
to be powerful enough for our purposes this pattern language should be quite complex and it is important to provide an easy way for specification of such patterns with a question guided process
in the simpliest case the knowledge engineer can examine a context for occurrences for a word or a type provided that the type exists in the term bank as represented in figure NUM
both of these stages complement each other the discovery of semantic categories allows the system to look for patterns and discovered patterns serve as diagnostic units for further extraction of these categories
the textual semantics represents the various strategies for structuring the message
morptiilevel specifies separability NUM unseparable NUM separable
wag thus allows elements of the semantic specification to take a default value if not specified
as the environments in which systems are tested become more challenging the ability to handle partially understood utterances will be important
this measure is used to describe the amount of search that a speech recognition component must do in translating the input signal
in the future we hope that such restrictions will not be necessary or at the very least be greatly lessened
theme specification in wag is identical to that used in penman
the main dimensions which they evaluated were NUM learnability NUM correctness NUM timing and NUM user response
nevertheless if our main research focus is on our theories of natural language processing we would like to justify our theory by showing how well it performs
consequently when reporting evaluations a variety of measures will be needed in order to allow ones colleagues to gain an idea of the effectiveness of the system
depending on the nature of the task the amount of training required will be varied and still needs to be reported
each of these spaces can be though of as a pattern stated over the ideation base
one way to capture this would be the development of measures that show the utility of domain independent dialog knowledge as compared to domain specific information which a system contains
another way in which this is used is in comparing the efficiency of natural language interaction to other modes of communication that could be used for the given task
here the segment between b NUM b NUM is also an embedded segment
this recursion may be used as a way to indicate the recursion of the concatenation of the suffixes because they can express the syntactic role of a word in a sentence as it was noted in the introduction
the system s answer violates two guidelines sg2 and gg7 as indicated in the markup
for example user can utter is here while positioning the location or menu
we explained this dialogue system to them and asked them to speak to the system freely and spontaneously
if one of them is satisfied by some words in the sentence it is accepted as the corresponding semantic network
when no network is generated at this stage the interpreter checks the sentence using keyword based method NUM
null b incorrect case if there are some heuristics for correcting apply them to the semantic representation
NUM keyword analysis later mentioned is per null formed by using a partial result of the analysis
d syntax and semantics analysis for sentence including invalid misrecognized post positions and inversion of word order
the grammar used in our speech recognizer is represented by a context free grammar which describes the syntactic and semantic information
c syntax and semantics analysis for sentence including omission of post positions and inversion of word order
the system fills slots in different frames in parallel using a form of dynamic programming beam search
while these are just some of the very primitive findings they are nevertheless promising and motivate us to rigorously formalize the tokenization problem and to carefully explore logical consequences
the rest of the paper is organized as follows in section NUM we formally define the string generation and tokenization operations that form the basis of our framework
section NUM discusses the relationship between critical tokenization and various types of tokenization ambiguities while section NUM addresses the relationship between critical tokenization and various types of maximum tokenizations
as long as the principle of maximum tokenization is accepted the resolution of critical ambiguity in tokenization is the only problem requiring knowledge and heuristics beyond the existing dictionary
for instance a lemma might select the transitive family ruling out the passive trees
features structures are associated with the trees that are combined with substitution and adjunction
a partial description is a set of constraints that characterizes a set of trees
for the french grammar the average number of equations per tree is NUM
this is done by taking the least tree s satisfying the description
figure NUM tree for french for the full passive of a strict transitive
in the partial descriptions shown the constants naming the nodes start with
they are given with their specific properties and with their inherited properties as well
the tree schemata selected by predicative items are grouped into families and collectively selected
but the automatic triggering ordering and bounding of the lexical rules is not discussed
kehler centering for pronoun interpretation correctly predicts that he and him in sentence 3d refer to terry and tony respectively since this assignment results in a continue relation whereas the tony terry assignment results in a less preferred retain relation
the following rules are proposed in gjw rule NUM if any element of cf un is realized by a pronoun in utterance un l then cb un l must be realized as a pronoun also
under such a strategy one could assume that cb un NUM is computed incrementally using the assumption that no additional elements will appear in un l that are more highly ranked in cf un
NUM that is the initial preference for the subject pronominal he in sentence NUM the author and several informants prefer the subject pronoun to refer to tony initially causing a garden path effect in each case
this difference does not appear to be reflected in the actual judgements for these two examples in both cases we find a similar garden path effect although experimental evidence would be required to confirm these judgements
retains often result in an ambiguity based on whether a subsequent subject pronoun refers to cb u resulting in a continue or to c u resulting in a smooth shift
in gjw s centering theory each utterance un in a discourse has exactly one backward looking center denoted co u z and a partially ordered set of forward looking centers denoted cf un
then garden paths would be predicted when this assumption does not hold and the assignment of cb un NUM must be changed in addition to those caused by semantic influences such as in sentence 3e
in fact the example illustrates a general property of the bfp algorithm that the preferred assignment for a pronoun in such examples even in subject position can not be determined until the entire sentence has been processed
certain approaches to robust parsing require purely bottom up processing
the third consideration is relevant only for robust parsing
these structures serve as input either to the orthographic display component or to the speech synthesis component
in sicstus prolog this results in a crash
often a distinction is made between recognition and parsing
NUM lexical analysis NUM NUM noun
this lexical analysis o l noun
there are categories leftds from qo to q s t
figure NUM definite clause specification of the head corner parser
NUM NUM practical relevance of head corner parsing efficiency and robustness
moreover the space requirements are far more modest
if this conjecture is true we can now suggest a simple strategy for automatic tagging of hebrew texts for each ambiguous word find the morpho lexical probabilities of each possible analysis
to sketch the translation process and the interaction of the four components consider the following example
first at the lexicographie level if an input sentence contains unknown words
NUM for instance liaison is obligatory between a prenominal adjective and a noun e.g.
to test this we took two corpora which were indisputably of the same language type each was a random subset of the bnc
NUM je voudrais une chambre avec douche et avec rue sur le jardin
itsvox is interactive in the sense that it can request on line information from the user
one advantage of this smoothing procedure is that it is straightforward to assess the performance of different backoff models
porting the lexicon to a new domain is as simple as bootstrapping another category space
note that since there is only one symbol from the input namely ar and because an auxiliary tree has at least one label from thus checking for one adjunction is sufficient as there can be at most one adjunction
thus in order to represent a node we need to use a matrix of higher dimension namely dimension NUM to characterize the substring that appears to the left of the footnode and the substring that appears to the right of the footnode
c ak ai aj NUM j instead of finding the transitive closure by the customary method based on recursively splitting into disjoint parts a more complex procedure based on splitting with overlaps is used
n is a set of nonterminals lcb a1 a2 ak rcb is a finite set of terminals p is a finite set of productions a1 is the start symbol is assumed to be in the chomsky normal form
but if the last operation to create i j k NUM was an adjunction as shown in figure 5c we can concentrate on the tree il j k NUM initially spanned by node m
hence the distributional information is generalized by means of a matrix manipulation method called singular value decomposition svd
its elliptical counterpart 9b however can not be taken as having the meaning of 9a NUM kehler and shieber anaphoric dependencies in ellipsis b mike tyson will always be considered one of the greats of professional boxing
on the other hand if the pronoun refers intrasententially to ivan so that the source clause is taken to mean that ivan loves his own mother as in example 3a then the target clause is ambiguous between two readings
if there is only one way to derive a given tree in g the mappings between derivations in g and g are one to one and there is therefore only one way to derive a given tree in gl after an application of lemmas NUM NUM a tig may no longer be in reduced form however it can be brought back to reduced form by discarding any unnecessary elementary trees
some care must be taken in clearly defining what is meant by corresponding unelided form in particular with respect to whether or not any of the deleted elements in the elided form can receive accent in the unelided form
if his refers extrasententially to some third person say kris that is if the source clause is taken to mean that ivan loves kris s mother then the target clause must mean that james also loves kris s mother
for sentence 3a this identity is captured by equation 4c which under suitable assumptions has two solutions for the meaning p of the elided vp namely those in 4d and 4e
although these forms differ with respect to their syntactic and some of their referential properties all have one property in common their meaning depends on information that is given in and therefore recoverable from the existing discourse state
as noted by dsp the equational analysis applies not only to vp ellipsis but also to the recovery of predicates for interpreting other forms such as do it and do so anaphora gapping stripping and related constructions
making this quite reasonable assumption discourse determined analyses are to be seen as reducing vp ellipsis not to general discourse principles for pronominal reference as they generally have been presented but to a more specific construction
imagene s realizations correctly match all four lexical and grammatical issues in NUM of the expressions in the training set and NUM in the testing set
as can be seen in all of the charts the level of match is better for the training set but still good for the testing set
examples similar to 12a were found in the corpus whereas those similar to the alternative to infinitive expression 12b were not
there are however a number of other lexical and phrasal differences including the lexical items chosen for the object references and the use of determiners
the first problem that must be addressed in any rst analysis is the segmentation of the text into spans that will serve as the atomic units of description
the spans themselves can be expressed as single propositional units or as more complex spans the latter being the case with the two spans in this sequence
meteer does not address what to do if after using her constraints to remove unacceptable forms of expression there are a number of remaining acceptable forms
it was developed as a framework for describing text structure viewed in terms of the semantic and pragmatic relations that hold between text spans at all levels
in the current corpus this set is non empty which leads to the conclusion that there are other factors at work in the question of purpose slot
example 3c uses a for preposition with a noun phrase that refers to the object or goal of the corresponding action as the complement
note that this test only determines the status of the entity in context we ensure separately that the sentence includes enough content to distinguish inite articles a an and NUM are used for entities that are not uniquely identifiable
recent years have seen a dramatic increase in the availability of on line text collections which are useful in many areas of computational linguistics research
using the lexicon and information about have27 available from figure NUM b spud determines that of lemmas that truthfully and appropriately describe have27 as an s have has the most specific licensing conditions
different constructions make different assumptions about the status of entities and propositions in the discourse which we model by including in each tree a specification of the contextual conditions under which use of the tree is pragmatically licensed
suppose our system is given the task of answering the following question NUM do you have the books for syntax NUM and pragmatics NUM figure NUM shows part of the discourse model after processing the question
NUM sentences as referring expressions our proposal is to treat the realization of sentences as parallel to the construction of referring expressions and thereby bring to bear modern discourse oriented theories of semantics and the idea that language use is inten tional action
NUM the satz system represents the context surrounding a punctuation mark as a sequence of vectors where the vector constructed for each context word represents an estimate of the part of speech distribution for the word obtained from a lexicon containing part of speech frequency data
NUM the training error is the least mean squares error one half the sum of the squares of all the errors where the error of a particular item is the difference between the desired output and the actual output of the neural net
in fact we find that these lists are both domain and object dependent
see section NUM for more details
only a single problematic case remains viz
this work has been funded by lgfg baden wiirttemberg m
they are organized into three layers NUM at the top level sb denotes the basic relation for the overall ranking of information structure is patterns
der status des akkus wird dem anwender angezeigt
he distinguishes between two crucial dichotomies viz
elliptical antecedents are ranked higher than elliptical expressions
transition pairs hold for two immediately successive utterances
t NUM can be an abbreviation t NUM is a comma or semicolon t NUM can be a sentence ending punctuation mark t NUM can be a pronoun t NUM is capitalized t NUM can be a conjunction
the lowest error rate obtained using a larger training set to induce the decision tree NUM NUM however is better than the lowest error rate NUM NUM for the neural network trained on a larger set
NUM the fifth issue recognizes the importance of speech act identification in dialogue translation and considers how a defensible and usable classification may be found
sentences in the original corpus that yield these underlying rule patterns the majority of them can be eliminated
this information would be recorded by annotations of type monolingualtextsegment which each have language and characterset attributes
NUM a text segment may consist of text in one or more languages and character codes
since a major concern of the treebank is to avoid requiring annotators to make arbitrary decisions we allow words to be associated with more than one pos tag
an important characteristic of this corpus is that it is written and used in compliance with the so called ata NUM NUM specification
to structure our annotated test set we defined a so called annotation scheme and we adopted the descriptive language sgml
these schemes are actually generic representations that allow us to characterise all the textual sequences of our corpus using only NUM scheme labels
to really get a structured file of annotated test data we chose to build it in compliance with the sgml iso standard
indeed using the annotations the evaluator can easily link the mistakes of a machine translation system with the concerned linguistic units
in this article we will describe how we built a reusable annotated test set through the study of a bilingual corpus
in order to build the test set it appeared necessary to structure this information so that it could be exploited by the industrial evaluator
as we can notice in the above examples the pragmatic value of the utterances has a very clear incidence on their morpho syntactic structure
any task to be performed should be described as a succession of subtasks which are explained in the form of a succession of orders
in this article the linguistic constraints correspond to the linguistic characteristics of the corpora to be treated by the machine translation system
it should be noted that human performance on this task was also relatively low but it is unclear whether the degree of disagreement can be accounted for primarily by the reasons given above o r whether the disagreement is attributable to the fact that the guidelines for that slot had not been finalized at the time when the annotators created their version of the keys
in most cases this is due to the fact that the concept type of the terms is not known for example ex
the mui titai e system consists of NUM modules NUM a syntactic tagger and lcmmatizer for l utch medical language
a sample synchuvg dl grammar is shown in figure NUM
the guessing module is an important help tbr the attgmentation of the concept lexicon and consequently an important part of the multitale system when tagging unknown texts
to be able to make a choice for one of them the constraints are connected with priority numbers obtained by corpus observation ex
the type lexicon gives general information for the surgical deed subtype the surgical deed lexicon gives information for the individual token the individual surgical deed concept
if the lexicon does not contain a medical term the tagger can not assign a semantic link to this unknown term and another one in the sentence
multitale has been devised therefore primarily with the aim to make explicit semantic information in medical texts which should lead to more refined information retrieval results
it is still a consolation to see that human annotators are seven times as good as computers when it comes to disambiguation
the constraints cc pathology cc colnbi and cc anatomy see frame br verwijderen of the cc slot are considered as good candidates
given syntactical chuncks verbs nps pps within a certain space ehmse mui 1tfale tries to assign eolmepts to them
we briefly discuss a sample derivation
trained context vectors result in a concept space where similarity of direction corresponds to similarity of meaning
for the present however an extractionneed is a the operation which generates templates from documents
machine translation efforts have been partially successful but these techniques frequently ignore subtleties in the translation process
among the ten most common error types for either automatic or manual disambiguation there are actually only two that involve content words
conversely stems that never appear in a similar context will have context vectors that are approximately orthogonal
it is important to keep a clear borderline between situations that could be solved in principle and such that are truly undecidable
it is important to note that systems utilizing compositional apparatus for the analysis of complex nominals need not treat all compounds compositionally
when an appropriate schema is identified it is instantiated with lexical items from the italian lexicon in order to generate the italian translation
translation from english to italian is substantially more difficult given the difference in explicitness regarding the semantic relation between the head and modifier
the italian forms are accounted for by a schema like NUM except that the preposition is di and the linkage is to the agentive qualia role
in particular it provides the foundations for machine translation of complex nominals between english and italian and can be readily applied in multi lingual generation and multi lingual information extraction
given the range of different semantic relations that can hold between the elements of a complex nominal they are frequently ambiguous
a human editor can then select the appropriate interpretation from the candidate set and add have the compound added to the lexicon
conversely words that are never used in a similar context will have vectors that are approximately orthogonal
wordnet and the pos tagged source corpus axe used to select relevant words in each semantic class
first semantic tags allow to cluster togheter source syntactic collocations according to similar classifications
many contexts may cooperate to trigger a given class and several classifications may arise when different contexts suggest independent classes
the derived clusters are very interesting but are not amenable for a direct linguistic analysis
some vrbs nouns are no longer ambiguous in the domain their unique tag is retained
difficulties in interpreting data derived from numerical cluster analysis emerge also in other studies e.g.
availability of explicit semantic tags like ob allows to derive semantic selectional constraints as in NUM
these last are very frequent in a language like italian where prepositional phrases play a role similar to english compounds
in both the word sold is the head of the sentence
a dependency tree of a n word sentence is always composed of n NUM dependency links
standard grammar checkers seem to have even worse problems with this check on the order of a precision of less than NUM percent
in l wo level m rphoh gy mor NUM hol honology is treated by means of rules hal the process of recasting the original itps structures in the fuf tbrmalism can best be described by exalnples
c eneracion scari s fl om an undersl e ified input fe al urc struc ure li uf unifies the grammar inlo the input scrucl ure i.e. enricln s and furl her
the defaull strategy olleccs all subsi rucl ures of chc current level having a cat feature l xplicit specification o subconsf il uents is also l ossible via the special feai ure
in order to a c ounl for linear ordering of l tlo resulting i ree shaped feature structure fuf performs a linearizal ion l oc s8 a NUM e t
cat phrase head dtr cat lex cat percolate arguments args lcb head dtr args rcb t ecursion only on head daughter cset head dtr
handled in ill s not in terms of nlovcment imc via structure sharing of the values of a slash i ea cure er olacing the moving consl icuenl
at the phrasal level the argument which has to try to fill slash by unification be extracted e.g. in wh questions the constituent asked for has to be specified as tire slash feature of the args
however the algorithm ensures that the second vowel v or will be associated with the first
thus the expression can be written as vlcl c2c cg v2
hyphenator programs in modern typesetting systems are necessary to eliminate excess space between adjacent words in texts
in addition greek vowels are sometimes accented so ambiguity resolution concerns thousands of vowel sequences
the output of a hyphenator program is a set of permissible hyphen points within the input word
according to the definition of a hyphen these words have exactly n NUM hyphen points
the process of developing a similarly performing hyphenator for such languages would be different
vowel tokens are further classified according to the nearby resident vowel and consonant tokens
the hyphenator program comprises two parts the lexical analyzer and the actual hyphenator
in order to make the observation easier we clustered the domains based on the cross entropy data
in this paper we describe two observations and an experiment which suggest an answer to the questions
for example air pollution would be translated into pollution d air
in this experiment grammars are acquired from the corpus of a single domain or from some combination of domains
in order to avoid the unknown word problem we used a general dictionary to supplement the dictionary acquired from corpus
figure NUM and figure NUM shows recall and precision of the parsing result for the romance and love story
in other words all rules can only have either s or np as their left hand side symbol
the same text is parsed with NUM different types of grammars of several variations of training corpus size
the results for the press reportage is not so obvious but the same tendencies can be observed
but how about the difference between press report and romance and love story
figure NUM and figure NUM shows recall and precision of the parsing result for the press reportage text
words which occur very few times also have unreliable context heterogeneity
1in generative phonology NUM is usually written as u
the cd roms are easy to search and process but not timely
this heuristic greatly increased the context heterogeneity values of many nouns
in additi m one of the main conclusions of a study of existing t esl s suil es colmuctcd during the first slage of the project esl iva l
depending on the type of the incoming dialogue act specialized repair operators are used
this principle not only ensures systernaticity during the test data onstruction but also allows test data users to apply the test data in a progressive order obtained from the special attribute presupposition in the phenomena classification
lm ger tho ll lly cxist ing gollcral lest suites n u l i purl osc mid iliu i i user lesl suites fin three i hlropea n
the x axis represents the different dimogues while the y axis gives the hit rate for three predictions
the translations stick to the german words as close as possible and are not provided by verbmobil
the treatment of these cases is the task of the dialogue plan recognizer of the dialogue component
even more extreme is the second pair with hit rates of approximately NUM vs NUM
when an unexpected dialogue act occurs a plan operator is activated which distinguishes various types of repair
the simplest case covers dialogue acts which can appear at any point of the dialogue as e.g.
instead it comes after the init of the topic to be negotiated and after a deliberate
the latter case can be found in our example init gets an additional reading of sugeest
NUM NUM lower and upper bounds on performance
in the second technique node thresholding we remove from consideration the descendants of all nodes whose inside outside probability falls below a threshold
in our pilot experiments we found that in some cases one technique works slightly better and in some cases the other does
we then wish to get as much entropy reduction as possible per time increase that is we want the steepest slope possible
the central construct in this framework is that of context factor cf
to the best of our knowledge using the prior probability in beam thresholding is new although not particularly insightful on our part
it is defined recursively as follows
the second knowledge source edward uses to analyze referring expressions is the context model
these parsers try to do best first parsing with some function akin to a thresholding function determining what is best
furthermore subdialogues do not interfere with the referent resolution of the main dialogue
discourse intentions can provide clues for the beginning and ending of dialogues and subdialogues
of all focused elements the backward looking center is the one that is central in that utterance
a combination of syntactic semantic and discourse information is used to identify the backward looking center
a stack is created in which the focus spaces corresponding to the discourse segment purposes are stored
a second mechanism called centering or immediate focusing is used for pronoun resolution
the error rate reduction between the two experiments is NUM NUM of wa and NUM NUM of su
a conjunct confirmation of departure hour and arrival city was asked and the user confirmed both of them
the recognition errors seem to cause a cognitive overload in the users that influence their degree of co operativeness
however the need for confirmations may result in a lack 1this version of dialogos only considers the first best solution
null most current task oriented applications of telephone human machine dialogue are developed for being used by a large population of potential users
this implies that the timing of their subject s pointing gestures would satisfy the restriction mentioned above
in dialogos there are four classes of dialogue acts request confirmation clarification and request plus confirmation
the wa and su results on the global utterance corpus were NUM for wa and NUM for su
guided by the language interpreter the dialogue manager then decides which of the referents was intended
since it has no principled way to decide between them it initiates the clarification subdialogue of t3 s
the dialogue module makes use of pragmatic based expectations about the semantic content of the next user s utterance
the resulting hyphen points are given in terms of the absolute starting position in the word of the first or the second token of the sequence currently being examined
for e n ts then we substitute a smooth s against the number of class elements
the question is how to normalize the probabilities in such a way that smaller groupings have a better shot at winning
the family name set is restricted there are a few hundred single hanzi family names and about ten double hanzi ones
nator the n ts can be measured well by counting and we replace the expectation by the observation
one class comprises words derived by productive morphological processes such as plural noun formation using the suffix tt meno
in any event to date we have not compared different methods for deriving the set of initial frequency estimates
this flexibility along with the simplicity of implementation and expansion makes this framework an attractive base for continued research
in the case of adverbial reduplication illustrated in 3b an adjective of the form ab is reduplicated as aabb
in this figure w s denote words and x s denote the statistics based length sbl for short of each branch i
the percentage scores on the axis labels represent the amount of variation in the data explained by the dimension in question
despite these limitations a purely finite state approach to chinese word segmentation enjoys a number of strong advantages
we address the following problems in such a way that they are able to be solved by the way of statistical methods
a consequence for machine translation is that much of the synchronising of tags is between elementary trees
a special interface has been developed to compare the semantic configurations across languages and to track down differences
one example of the rest NUM concepts is shown in figure NUM
the pragmatic design of the database makes it possible to gather empirical evidence for a common cross linguistic ontology
the columns labeled as different and categ
individual phones are represented by means of hidden markov models hmms
spanish zme pueden dar las llaves de la habitacisn por favor
figure NUM next to the language internal relations there are also six different types of inter lingual relations
in persian the clause boundary is overtly marked by the suffix i on the antecedent
the parser has clear limitations due to the fact that it was developed mainly for exploratory purposes
in this case an argument chain a chain is started as in passives
if chains compose they do not have intersecting elements but they create a new link
for instance it deals only with very simple nominal phrases and it does not treat adjunction
future research must lead in a direction that enables us to define more precisely this basic intuition
as table NUM shows the distribution of the conflicts in grammar NUM presents some gaps
similarly the obligatoriness of ip and vp as complements of co and i0 is lost
first of all the parser must decide whether to start a new chain or not
the interpretation of the lc table derived from grammar NUM poses a problem for the icmh
we can a lso extend tra nsta tion
sequence for ea ch input sul string
rules a re lifferentia t d
if one assumes that lex lives under the path synsem instead of synsemiloc than the problem turns into a non issue
so in NUM wird is coinbined with a trace or a lexical rule is applied to it
the account argued for in this paper can describe the fronting phenomena without the assumption of an infinite lexicon
to introdnce a nonlocal dependency for a verbal complex this schema requires an additional licensing condition to be met
NUM a bus will karl fahren
due to space limitations the figures show a tree for a flat head complement structure
this list of argmnents however is not instantiatcd in the resulting sign
therefore the hpsg principles admit any kind of combination of totally unrelated signs
these fields include date fields which support date ranging
sentences like 5a are ruled out because wird selects a complement in bse form that has a vcomp value none
then ncorrect recall nkey null 2there were however a number of individual rescm eh efforts in information extraction underway be bre the first muc including the work on information formatting of mediem narrative by sager at new york university the formatting of naval equipment failure reports by marsh at the naval research laboratory and the dbg work by logieon for radc
e for predicate argumealt sl ru l llr pracl ically every new coiis ill l NUM y li l simple clauses and noun l hrases r tise l new issues which had i o t e toilet lively r solve l
name l eni ity was inl mdcd to b a siml h task on whi h syst ems coul t lernoustrat e a high level of NUM rforumn e high enough for imme lim e use
for each executive post one generates a succession event template which contains refl rences to the organization template for the organization involved and the in and out template for the activity involving that post if an article describes a person leaving and a person start ing the same job there will be two in and out templates
seemed r asonably stm l b nlbd rg ufiz l a lry run a full s al r hearsal for muc NUM i ul with all result s r NUM ori ed a nouymously
in keeping with he hierarchical template structure introduced in muc NUM it was envisioned hat the inini muc would have an eventlevel template pointing to templates representing he partieitmnts in the event people orgmfizations products e tc me liated perhaps by a relational level template
a higher value for the threshold has two advantages first it offers higher selectivity allowing fewer false positives proposed translations that are not considered NUM note that the number of sentences that do not contain any of x a or b does not enter any of the dice coefficients computed by champollion and consequently does not affect the algorithm s decisions
synchronous tags stags comprise a pair of trees plus links between nodes of the trees
as mentioned above the ill should be the super set of all concepts occurring in the separate wordnets
t hall all abstraction over a proposition about ewmts
we therefore need to develop a collection of meanin q
NUM he keeps his boat tied up by the bank
i will now look in some detail at aspect and aktionsart
in NUM iie keeps his money tied up in the bank
the details of these relationships are spelt out via mps
meaning NUM osl ulates are NUM ilece ssary
in both cases the sentence reports a sequence of events
this sort of information could be much more easily recorded in the hierarchical structure introduced for muc NUM in which there wa s a single object for an event which pointed to a list of objects one for each participant in the event
then shortly before the conference participants are given a set of test messages to be run through their system without making any changes to the system the output of each participant s system is then evaluated against a manually prepared answer key
although it is difficult to meaningfully compare results on different scenarios the scores obtained by most systems after a few weeks NUM to NUM recall NUM to NUM precision were comparable to the best scores obtained in prio r mucs
the knowledge base stores the permanent generic and specific world knowledge of the system whereas the context model temporarily memorizes which individual instances from the knowledge base have been referred to in the dialogue
in place of a single template the joint venture task employe d NUM object types with a total of NUM slots for the output double the number of slots defined for muc NUM an d the task documentation also doubled in size to over NUM pages in length
the tie up relationship also pointed to an ownership object which specified the total capitalization using standard codes for different currencies and the percentage ownershi p of the various participants in the joint venture which may involve some calculation as in the example show n here
furthermore the addition of new cfs which would require explicit detailed changes in grosz and sidner s rules will be easier because the procedures that use the salience information can stay exactly the same
as we understand the grosz and sidner model it processed NUM referring expressions correctly but this may be inaccurate since we do not have an implemented version of the model at our disposal
nested term referent cfs have an initial significance weight of NUM relation cfs are created for all the relations expressed by a sentence e.g. by the main clause or by nps modifying prepositional phrases
notice that multimodal expressions with a redundant pointing gesture e.g. gr2 reportz if there is just one object named gr2 report in the context are solved the same way
so if the user points to an icon the salience of its referent increases immediately making it the most likely candidate referent of the phrase at hand
edward can not determine which object s area the user referred to unless this pointing action is part of a multimodal expression such as dit7 boek thisz book
we implemented this simplistic model and provided edward with a switch to determine whether sentences should be processed either with the original context model or with this alternative simplistic model
the initial word identification procedwe is as follows
this is an example of the zipf law
error analysis of parser disambiguation output shows that the c ia lcb parser handles well ambiguities which are not strongly dependent upon the context for a reasonable interpretation labr ex ample the sl anish word uua can mean either ouc or a as an indefinite reference
the ambiguous text was then manually disambiguated
it is calculated by f f12 x p r NUM where p is precision r is recall and fl is the relative importance given to recall over precision
instead of trying to learn a general function for combining various information sources we could decide which source of information to trust in a particular case and classify the type of ambiguity at ti md with the best ap1 ro tch for thl s mil iguity
to prefer segmentation hypothesis c11c2 over czc2 the following relation must if two word segmentation hypotheses have the same number of words the one with larger product of word frequencies is selected
the word segmentation task can be defined as finding a word segmentation l r that maximizes the joint probability of word sequence given character sequence ill p w c
ic or xaml le if l h sl eakers are trying to estal lish a dab when they can meet then the sccoud to the jourlh is t hc most lid ly itd erl retatiotj
the statistical parse disambiguation method makes use of the three non context based scores described in section NUM the two context based approaches combine the three non context based scores as well as the three context based scores namely the focusing flag the focusing score and the graded constraint score
to relax these assumptions the hearer s model distinguishes the beliefs that speakers claim or act as if they have during the dialogue from those that the hearer actually believes they have
her model introduces a new set of discourse level goals such as seek confirmation that are recognized on the basis of the current properties of the dialogue model and the mutual beliefs of the participants
NUM NUM a related concern is how an agent s beliefs might change after an utterance has been understood as an act of a particular type
to keep track of the current interpretation of the dialogue we introduce the notion of activation of a supposition with respect to a turn sequence
our rule based algorithm learned a sequence of NUM transformations which improved the score from NUM NUM to NUM NUM a NUM NUM error reduction
NUM other misunderstandings are possible for example there can be disagreement about what object a speaker is trying to identify with a referring expression cf
fact qintentionsok a ts d expectedreply pao pcondition do s a ts
the lexpectation relation captures the notion of linguistic expectation discussed in section NUM NUM relating each act to the acts that might be expected to follow
mcroy and hirst the repair of speech act misunderstandings the decomp relation links surface level forms to the discourse level forms that they might accomplish in different contexts
the theorem prover may assume ground instances of any of these predicates if they are consistent with all facts and with any defaults having higher priority
figure NUM the hy brid data structure that represents the suffix tree and the prediction func tions at each node
as shown in the table the performance of the map model is consistently worse than the performance of the mixture of psts
one of the goals of this work is to describe algorithmic and data structure changes that support the construction of psts over unbounded vocabularies
therefore at each node we keep a data structure to track of the number of times each word appeared in that context
in comparison a trigram backoff model built form the same training set has perplexity of NUM NUM on the second test set
we may need to add new nodes with new entries in the data structure for the first appearance of a word
one can easily verify that every standard n gram model can be represented by a pst but the opposite is not true
the negative log likelihood and the posterior probability assuming that the listed sentences are all the possible alternatives are provided
the problem of sequence prediction appears more difficult when the sequence elements are words rather than characters from a small fixed alphabet
a pst t can be used to generate a stream of words or to compute prefix probabilities over a given stream
however a naive approach to finding direct correspondences between english letters and katakana symbols suffers from a number of problems
figure NUM two examples where the assumption that modifiers are generated independently of each
the e0 s on the rule number line indicate where the vowel shift rule was applied to replace an error surface vowel with NUM
although the error probably results from a different fault a deleted long vowel can be treated in the same way as a deleted consonant
with current transcription practice long vowels are commonly written as two characters they are possibly better represented as a single distinct character
when designing tabular methods that simulate nondeterministic computations of 4lr two main difficulties are encountered a reduce transition in alrt is an elementary operation that removes from the stack a number of elements bounded by the size of the underlying grammar
by manually tagging all the relevant sentences we found that the first analysis but was the right analysis NUM times and the second analysis a hall was the right analysis only NUM times
note that in the case of a reduce reduce conflict with two grammar rules sharing some suffix in the right hand side the gathering steps of a2lrt will treat both rules simultaneously until the parts of the right hand sides are reached where the two rules differ
in words q is found in entry ui j if and only if at input position j the automaton would push some element q on top of some lower part of the stack that remains unaffected while the input from i to j is being read
our treatment of tabular lr parsing has two important advantages over the one by tomita it is conceptually simpler because we make use of simple concepts such as a grammar transformation and the well understood cyk algorithm instead of a complicated mechanism working on graph structured stacks
kutb for kutib to distinguish it from katab and iii voealised texts incorporate full vocalisation e.g.
to speed data entry the user usually enters the base characters say a paragraph and then goes back and enters the diacritics
this is not just because most users are working with phrase construction systems which do not impose restrictions on precision except those due to time constraints
the second reason for an integrated treatment of traces is to improve the parameterisation of the model
NUM NUM NUM NUM table NUM wall street journal like artificial grammar
our methodology is derived from that described by NUM
this post pass allows us to express dependencies between adjacent symbols
however searching for optimal parameter values is extremely expensive computationally
to minimize these effects we process the training data incrementally
the symmetric approach is based upon the use of a unified hash table
hnc has developed an approach to the mir problem that leverages the context vector technology
to address this issue we use an inside outside algorithm post pass
the stem in question is fed to the hashing function and the index is produced
for the text case these sets of symbols are paragraphs documents and queries
notice how the transliteration is more phonetic than orthographic the letter h in johnson does not produce any katakana
where the coordinates i0 jo are such that
the symbol separates the grammar body from a set of conditions on the database
most job ads express the location of the work either explicitly or implicitly in the contact address
for both these reasons our system can not be descibed as a translation system
it is not hard to see that the two rules above form the beginning of a grammar
in a sense templates are just generalized canned texts and grammars are just generalized templates
the fact that it is a processor gives a transformation based learner greater than the classifier based decision tree
residuals that occur in at least two out of four cities NUM NUM were then added to the list of NUM NUM morphemes
these are just a few of the many recent applications of corpus based techniques in natural language processing
the cost of the following morph boundary is higher NUM NUM than usual in order to favor components that do not require infixation
some names of the latter type may actually refer to persons names but the origin is not transparent to the native speaker
the system was implemented in the framework of finite state transducer technology using linguistic criteria as well as frequency distributions derived from a database
fst technology enables the dynamic combination and recombination of lexical and morphological substrings which can not be achieved by a static pronunciation dictionary
in analogy to the procedure for city names these morphemes were used in a recall test on the original street name component type list
in this paper we will describe a simple rule based approach to automated learning of linguistic knowledge
the reasoning behind this is that there are component types that occur exactly once in a given city but do occur in virtually every city
below we describe a new approach to corpus based natural language processing called transformation based error driven learning
in the above example only c and d are the meaningful sentences
when training contextual probabilities on one million words an accuracy of NUM NUM was achieved
baseline accuracy when the words that are unambiguous in our lexicon are not considered is NUM NUM
an unannotated text can be used to check the conditions in all of the above transformation templates
the mgorithm has the additional advantages of being conceptually simple and computationmly inexpensive to implement
however the measure given is at the core of the algorithm
at each stage there is a sharp difference in accuracy between tuples with and without a preposition
recent work has considered corpus based or statistical approaches to the problem of prepositional phrase attachment ambiguity
section NUM NUM describes experiments which show that tuples containing the preposition are much better indicators of attachment
the data consisted of training and test files of NUM and NUM quintuples respectively
a particularly surprising result is the significance of low count events in training data
note that this method effectively gives more weight to tuples with high overall counts
the following method of combining the counts was found to work best in practice
in some cases it may also provide feedback by updating a displayed image
the ith expression refers to symbols on the ith tape
tile remaining morphemes represent the affixes for measures NUM NUM and NUM
the paper provides a new computational analysis of root and pattern morphology based on prosody
the special symbol indicates an empty context which is always satisfied
the remaining measures involve infixation and are discussed in the next section
eaktab aim pu ktib measure NUM
lcb a a a rcb is tile base template
the purpose of this architecture concept is to serve as an introduction to the tipster architecture for potential users
care should be taken to distinguish the tipster architecture from an application which is built in compliance with it
it uses a template to string rules file that contains rules for all possible types of interactions
note that under this assumption information conveyed through a combination of text and non text may be partially exploited
a form NUM document may contain markups e.g. the results of a non tipster document retrieval application
anything done to it between receipt by the end user s site and input into the tipster architecture
from a user s point of view the point of view taken here the distinction blurs
as for application developers the architecture will aid the researcher in identifying collaborators to fill their technology gaps
this provides a basis from which to estimate the cost and staffing required for a similar new component
a tipster module is a module whose input and output specifications are defined in the tipster interface control document
additional definitions may be found in the glossary of the tipster architecture design and in the architecture requirements document
c nf leftchild seqno a nf rightchild seqno
the tree nf a may not be a legal parse under the restricted grammar
karttunen s version takes worst case exponential time for each redundancy check see footnote ss3
for example s s s s may form a constituent by either blx or blx
even after leaving aside less frequent spontaneity usage and indirect passivization there are still at least three general interpretations direct passivization possibility and honorific
hereafter the word idiomatic expression is used in a rather broad sense if translation of a combination of words is not predictable from their individual behavior we call it an idiomatic expression
please note that the underlined part in b is replaced by its equivalent english expression the book he bought and the whole sentence is underlined now
these idiomatic expressions either of source language or target language are hard to translate since they do not allow literal translation and difficult to find in other dictionaries
when there is more than one possible translation the different possibilities are shown in an alternatives window similar to figure NUM allowing the user to change the choice
their reading skill and grammar knowledge is usually enough to judge the quality of current mt systems but they may need help from mt systems when browsing the internet
its competitor NUM is not nor is any larger tree containing 8b
in this scheme an attribute of a syntax tree node is calculated from that of the children nodes by a semantic rule associated with the syntax rule used to build the node from children
since word order and functional words carrying grammatical functions are unchanged the user can easily recognize the skeleton of the sentence and clearly grasp the correspondence between the original word and its translation equivalent
measure of how well z illatc hes lcb
figure NUM canonicalizing ccg parser that handles arbitrary restrictions on the rule set
b the anaphora procedure skips the resolution of a given anaphor when this anaphor is preceded by an unattached preposition
fields of fl NUM and other constituents already in c subtrees are also nf trees
these rules are applied to the conceptual representation and their output is a set of candidate antecedents
paper is independent of the used approaches in both anaphora and attachment modules
NUM if there are still unattached pps apply the attachment procedure again
the resolution of the anaphor is then postponed to the second phase of anaphora resolution
this is because the resolution rules may have an empty role as a parameter due to this unattached preposition
NUM the sale of credito was first proposed last august and that of bci late last lear
b when the anaphor occurs after one or several unattached prepositions it could be an intrasententiai anaphor i.e.
presently we are working on the extension of the anaphora module particularly to deal also with the anaphoric definite noun phrases
boundary markers can be considered invisible tags or hypertags which have probabilistic relationships with adjacent tags in the same way that words do
there were arbitrary limits of a maximum of NUM words in the pre subject and NUM words within the subject for the initial work described here
these are adjacent tags which are not allowed such as determiner verb or start of subject verb
if both positive and negative data is used counter examples will reduce the postulated grammar so that it is nearer the real grammar
our system is data driven as far as possible the rules are invoked if they are needed to make the problem computationally tractable
the sequential order of the input is captured here partially by taking adjacent tags pairs and triples as the feature elements
by the rules of the template element task the old name should become the alias of the new name
furthermore the scoring program results can vary depending on how the mapping between respouse and answer key is done
ntis sometimes causes a fluctuation in the number of possible correct answers as reported by the scoring program
rules which allow the automatic system to take greater advantage of context cues will be developed for such specialized areas
when it reaches smith jewelers it will compare the falter against a faltered version of the name
the following is a score of the original configuration using the ranked selection system
there were only four aliases missed because they were not generated from the full name
a careful examination of the name alias results provides insight into the success of this technique
null a surprising result of this experiment is that the percentage of descriptors associated by context is still so high
performance on the name alias task our system had the second highest score in organization alias identification in the muc6 evaluation
this indicates why feedback messages should be phrased in a consistent style i and if
an adequate balance between both strategies together with implicit validation would greatly improve the situation
notice that this compromise puts extra emphasis on the marking of the current interaction context cf
thus this choice might lead to a reduction in user friendliness of the system
the user can challenge the prompt by explicitly saying no or abort
in the current example the user can be expected to go for the second option
lea comes to a list of seven cardinal rules that partially overlaps our NUM commandments
this means that special attention should be paid to effective methods to compensate for speech recognition errors
with regard to iv a several techniques are used within vodis to prevent sr errors
recognition good results from speech recognition is a conditio sine qua non for any spoken dialogue system
NUM as a first illustrative example consider a simplification of the train timetable domain of dialogues NUM and NUM where the timetable only contains information about rush hour trains between four cities as shown in table NUM
we have given a general account of parallelism in discourse and applied it to the special case of resolving possible readings for instances of vp ellipsis
this short paper focuesses on the phenomenon of these expectations in discourse and their expression in a discourse level ltag
figure 4c iii shows the interpretation of clause 3c substituted at NUM satisfying that expectation
first reviewing the NUM constructions that knott has identified as potential cue phrases in the brown corpus NUM one finds NUM adverbial phrases such as initially at first to start with etc whose presence in a clause would lead to an expectation being raised
his return for the period january NUM to june NUM NUM is due april NUM NUM
in our approach the foot node of an auxiliary tree must be its leftmost terminal because all adjoining operations take place on a suitably defined right frontier i.e. the path from the root of a tree to its rightmost leaf node such that all newly introduced material lies to the right of the adjunction site
in the following variation of example NUM the fact that clause b participates in elaborating the interpretation of clause a rather than in satisfying the expectation it raises which it does in example NUM may not be unambiguously clear until the discourse marker for example in clause c is processed
in the figures presented here non terminal nodes in a discourse structure are labeled with coherence relations merely to indicate the functions that project appropriate content beliefs and other side effects into the recipient s discourse model
one reason for this rf restriction is to maintain a strict correspondence between a left to right reading of the terminal nodes of a discourse structure and the text it analyses i.e. principle of sequentiality a left to right reading of the terminal frontier of the tree associated with a discourse must correspond to the span of text it analyses in that same left to right order
next figure 4c ii shows the auxiliary tree with substitution site NUM corresponding to clause 3b being adjoined as a sister to the interpretation of clause 3a as evidence for the claim made there
while we do not have answers to all these questions a very preliminary analysis of the brown corpus a corpus of approximately NUM email messages and a short romanian text by t vianu approx
given the ubiquity of strict sloppy ambiguities one would expect these to be a by product of general discourse resolution mechanisms and not mechanisms specific to vp ellipsis
the overall shape of the implementation is shown in figure NUM
in fact though there is no contrast and psv should be normally deaccented due to givenness
NUM a in the 11th minute ajax took the lead through a goal by kluivert
the proposed approach is domain specific in that it relies heavily on the data structures that form the input from generation
since goal event and card event are different types they are not expected to be contrastible
this means that although theoretically appealing the hou approach to contrastive accent is less attractive from a computational viewpoint
on the other hand if it is too broad then anything will be predicted to contrast with anything
an open question which still remains is at which level data structures should be compared
also implementation of higher order unification can be quite inefficient
if the definition is too strict not all cases of contrast will be accounted for
this shows that the presence of an alternative item is not sufficient to trigger contrast accent
this requirement is fulfilled and removed from the subcat list when a trace or a modifier non terminal which has the gap feature is generated
swith the exception of the top rule in the tree which has the form top h h
of one or more non terminals or lexical where lhs is a part of speech tag and rhs is a word
allowing the model to learn a preference for rightbranching structures NUM does the string contain a verb
the assumption that complements are generated independently of each other often leads to incorrect parses see figure NUM for further explanation
all words occurring less than NUM times in training data and words in test data which rexcept cases l2 and r2 which have NUM levels so that
i would like to thank mitch marcus jason eisner dan melamed and adwait ratnaparkhi for many useful discussions and comments on earlier versions of this paper
the connective strengths of the topics in the previous paragraph with the nouns and the verbs in the current paragraph are computed and compared with the topics in the current paragraph
this can be extended to include other organizational events such as corporate joint ventures
in addition we intend to port modex to at least two new oo modeling environments in the near future
this belief is reflected by the large number of graphical oo modeling tools currently in research labs and on the market
a representation corresponding to the text plan of figure NUM is shown in figure NUM
conjunctions and in description on figure NUM or introduce cue words between constituents cf
this editing can be done via links labeled edit which appear in figure NUM
current work on modex is supported by the trp road cooperative agreement f30602 NUM NUM NUM with the sponsorship of darpa and rome laboratory
however this belief is not accurate as some recent empirical studies show
finally a formatter takes the final text structure to produce an html document
figure NUM shows an example of a description generated by modex for the university model
in the subsequent algorithms c5 and c14 are used to indicate the larger sets of configurations
while the approach will correctly predict the lack of reading NUM for sentence NUM it does so for the wrong reason
in particular for highly inflectional languages such as greek these word lists would have to be extremely extensive in order to include all possible inflectional and derivational word forms
for choosing delete rules we have experimented with two approaches
consider the example fragment bir masa dlr
we will motivate this using a simple example from turkish
using some very basic noun phrase agreement constraints in turkish
null our results are summarized in the following set of tables
thus we have used very tight and conservative rules in hand crafting
this state allows statistics to be collected over unambiguous contexts
the second approach that we have used is considerably simpler
one may wish to get the arrival departure information for a given flight verify if a particular book is available at a library find the stock price for any fund access yellow page information on line check maintain voice mail remotely get schedules for entertainment events perform remote banking transactions get used car prices and the list goes on and on
morphological disambiguation of previously unseen text proceeds as follows NUM
the applied to the current text on which disambiguation is performed
NUM meta query the dialogue reaches this state when the user either explicitly asks for help e.g. please help me what can i say etc or asks for some meta level information about the system s capabilities e.g. what cities do you know about
the author wishes to thank jack godfrey for several useful discussions and his comments on an earlier draft of this paper charles hemphiu for his comments and for developing and providing the dagger speech recognizer and the anonymous reviewers for their valuable suggestions that helped improve the final version of this paper
generation of proper user feedback requires us to also examine the source page of the result of the query
a special hack had to be built into the query generator to assign an appropriate value to this field
because any initial discourse representation effort must by necessity be considered only a beginning the next step was to incrementally revise the edps
a promising line of future work is to construct a large corpus of computational linguistics volume NUM number NUM parsed discourse through a formal analysis
the edp formalism has been implemented in the km frame based knowledge representation language which is the same representational language used in the biology knowledge base
hence they represent information not only about a large number of concepts but also about a large number of relationships that hold between the concepts
however the question to insure coherence how should the content of individual portions of an explanation be selected is equally important
a representation should be sufficiently expressive that it can be used to encode the kinds of discourse knowledge discussed above and it should be applicable to
while this work was essential for gaining insights about biological texts it was a sketchy and preliminary effort to informally characterize their content and organization
each time a discourse knowledge engineer creates a local variable he or she creates an expression for computing the value of the local variable at runtime
just as other programming languages provide local variables e.g. the binding list of a let statement in lisp so do content specification nodes
another important aspect of representing discourse knowledge is the ability to encode the conditions under which a group of propositions should be included in an explanation
in his disambiguation experiments schiitze used post hoc alignment of clusters to word senses
when the training parameters are held constant the algorithm will converge on a stable residual set
it is much stronger for words in a predicate argument relationship than for arbitrary associations at equivalent distance
yet to date the full power of this property has not been exploited for sense disambiguation
unlike many previous bootstrapping approaches the present algorithm can escape from initial misclassification
however certain strong collocates may become entrenched as indicators for the wrong class
at the end of step NUM this property is used for error correction
some example grammar rules instantiating these ideas are given in NUM
figure NUM parse tree based on the mix of word and part of speech sequence
the question is how much semantic and syntactic information is necessary
in this section we report two types of experimental results
table NUM test data evaluation results on the three types
table NUM test data evaluation results on the three
these drawbacks are reflected in the performance evaluation of our machine translation system
a corpus based approach for building semantic lexicons
models describing these types of dependencies are referred to as alignment models
as referring to april NUM th
we would like to incorporate the following into the current model lists of organizations person names and locations an aliasing algorithm which dynamically updates the model where e.g.
subsequent search rectangles are anchored at the top right corner of the previously found chain as shown in figure NUM
each time simr accepts a chain it selects another region of the bitext space to search for the next chain
in this case the unknown word error rate increases about NUM NUM percent for all the languages except the greek language
a matching predicate is a heuristic for deciding whether a given pair of tokens are likely to be mutual translations
this paper was much improved by helpful comments from mitch marcus adwait ratnaparkhi bonnie webber and three anonymous reviewers
translation lexicons can be extracted from machine readable bilingual dictionaries mrbds in the rare cases where mrbds are available
for example english adjective noun pairs usually correspond to french noun adjective pairs
number of error range fraction of test points in characters test points both of these algorithms use sentence boundary information
the lower left corner of the rectangle is the origin of the bitext space and represents the two texts beginnings
what makes this a localized filter is that only points within the search rectangle count toward each other s ambiguity level
a NUM alignment with hmm we now propose all hmm based alignment model
disambiguation although there is agreement in general about the utility of wsd within the nlp community i will briefly address some objections to wsd in this section
in our approach we decided to reuse the data which come naturally with a tagger viz
in the other evaluation experiment we measured the performance of the guessing rules against the training corpus
first in the extraction of the morphological rules we did not attempt to model non concatenative cases
this is where word pos guessers take their place they employ the analysis of word features e.g.
one can notice a slight difference in the results obtained over the lexicon and the corpus
both of the taggers come with data and word guessing components pre trained on the brown corpus NUM
after a successful application of the merging the resulting rule substitutes the two merged ones
then we multiplied these results by the corpus frequency of this particular word and averaged them
a more appealing approach is an empirical automatic acquisition of such rules using available lexical resources
morphological word guessing rules describe how one word can be guessed given that another word is known
for the preventative expression subnetwork this turned out to be a relatively simple matter
the average accuracy of the learned decision trees on the testing sets was NUM NUM
in addition part of speech taggers are often being coupled with a syntactic analysis module
the resulting rule based tagger performs as well as state of the art taggers based upon probabilistic models
this renewal of interest is due to the speed and compactness of finite state representations
however current implementations of the rule based tagger run more slowly than previous approaches
the tagger runs nearly ten times faster than the fastest of the other systems
this tree takes the three function features and predicts the dont never and neg tc forms
we have not yet analyzed such expressions and thus do not support them in drafter
this node may be expressed in any number of different grammatical forms depending upon context
one run of the system for example gave the following decision tree
this greatly simplifies the tasks of building and testing text planning resources for new domains
semi fixed phrases are not identified as such nor are there any explicit linguistic rules
while this al l roach reduces the size of the search sl ace it does not prune it sulllciently for cert in classes of rood tiers
some of the phrases found in typical job ads serve to signal specific slots e.g.
pl s have been ana lysed
this can be approached either by means of pre storing complete phrases ready for retrieval and output in a subsequent interaction or by constructing phrases at the time they are required during an interaction
in order to do so instantiation rules must be applied to a bsf in a recursive way
also there are reusable phrases which serve to move the conversation forward and which although suitable as responses to many different things a partner might say need to be selected specifically
we think however that our approach is more general because our use of a broad coverage geaeral english grammar NUM allows us to go beyond the concept of cl to look for more general types of ambiguities
it is also useful to be able to identify tables and displays thereby allowing differential treatment of them
when there then is a mistake in subject verb agreement it becomes very hard to produce a reliable parse
this sentence generates two messages for the passives one without a rephrasing and one with a rephrasing passive construction is defined in the file which was not included by the header file
an earlier version of easyenglish was written in prolog however the current version is written in pure ansi c and hence the question of platform is mainly a matter of supplying an appropriate editor interface
the fixed point transformation converts the floating point logarithmic additions into an equal number of fixed point additions
a simplistic view of this rule would be if a present participial clause modifies the object suggest two rephrasings one that forces the attachment to the subject and one that forces the attachment to the object
formatting tags are of great help in the segmentation process and may be enlisted for identifying conditions such as missing periods or other sentence delimiters and lack of parallelism in lists both of which are handled by easyenglish
let v be a verb with n senses NUM NUM 8n and let pssi c be the set of example case fillers for the case c associated with the sense si
in the training phase the system stores samples of manually disambiguated verb senses simply checked or appropriately corrected by a human in the database to be later used in a new execution phase
following table NUM we assign NUM and NUM to be the maximum and the minimum of the interpretation score and therefore cma NUM disregarding the value of in equation NUM
in this experiment it can be seen that the performance of random sampling was again surpassed by our training utility sampling method and the size of the database can be reduced without degrading the system s performance
second there is a problem as to when to stop the training that is as mentioned in section NUM it is not reasonable to manually analyze large corpora as they can provide virtually infinite input
formally this is expressed by equation NUM where s s is the score of the sense s of the input verb and sim nc gs c is the maximum similarity degree between the input complement nc and the corresponding complements in the database example pss c equation NUM
a greater accuracy of performance of the system will lead to a greater in equation NUM cmax is the maximum value of the interpretation certainty which can be derived by substituting the maximum and the mimimum interpretation score for si x and s2 x respectively in equation NUM
however in the situation as in figure ll b since a the task of distinction between the verb senses NUM and NUM is easier and b instances where the sense ambiguity of case fillers corresponds to distinct verb senses will be rare training using either xl or x2 will be less effective than as in figure ll a
the column of of sentences denotes the number of sentences in the corpus of senses denotes the number of verb senses based on ipal and lower bound denotes the precision gained by using a naive method where the system systematically chooses the most frequently appearing interpretation in the training data NUM
consider p xample NUM ib eliminate the wfss the dog from further consideration a connected graph of lexical signs is constructed before generation is started figure NUM
in order to make the cluster contain more words we must make use of ambiguous words
when the comment is signed the head position brows and gaze change
in fact sentences containing these types of errors were common in our samples
so far our description of the learner s generation process takes only the first language into account
the first model component captures the influence of the first language on the acquisition of the second
the level of the user s current ability in the second language is designated i
note that we have collected writing samples with some user information for the authors of each sample
the response generator must also select an appropriate instructional strategy from the tutoring module
seem approp ate to include tutorial dialogue expinning verb morphology in both cases
consider the following simple example my brother like to go
finally the system must have the ability to generate appropriate corrective tutorial messages
moreover we are dealing with language for specific purpose texts and not with general texts
at the end of this operation the candidate terms appearing in a conceptual field are validated
the links around a np within a pu are also interesting
struction which can be applied to the object line
figure NUM a partial description of the concept line
for instance the pus length of the line and nominal
and nominal power of the object line the pu
the other candidate terms are not kept because they are considered as non relevant by the ke
the columns are labeled by the expansions nominal or adjectival of the nps being clustered
the papers summarizing the efforts sponsored by the tipster program are included in this volume
the tipster program began in june NUM just after the conclusion of the second message understanding conference muc NUM
NUM to see a concrete example consider the word r h nt NUM and one of its analyses the verb to see masculine singular third person past tense
as shown in the classification trees of rule NUM in figure NUM pronouns are in the minority of the nonzeros in the test data and indeed this is clearly the case in the language in general
however this is not so in the case of conversational speech as is clear from the examples above
non sentence elements are words or phrases which are inserted in an utterance disrupting the flow of the sentence
a nominal anaphor is referred to as a reduced form or a reduction of the initial reference if its head noun is the same as the initial reference and its modification part is a strict subset of the optional part in the initial reference otherwise if it is identical to the initial reference th6n it is a full description
when we tried to make a quantitative comparison using statistical methods we found that for many analyses papp looks like a good approximation for ptest but from a statistical point of view the approximation is not satisfying
individual agencies issued separate requests for proposals for each such project
needs for architecture and algorithm improvements or additional research were fed back to the r d projects
the main reason for this is that the words in the sw set of a given analysis can be considered similar in their frequency to the analysis only from a qualitative point of view and not from a quantitative one
scenarios were developed to indicate the variety of actual applications of the systems to be developed
review of phase i the tipster program officially began at a kickoffworkshop held in september of NUM
a broad agency announcement soliciting proposals for participation in phase ii r d was issued in august NUM
several major concepts were also outlined and accepted as central to tipster s progress
the initial set of results suggests that most anaphora including zero anaphora and full and reduced descriptions for nominal anaphora can be effectively generated by a rule using simple syntactic semantic and discourse constraints
figure NUM lexical entries for nouns
the parser controls the overall processing
the paper is structured as follow
tion between the correspondent selectional restrictions holds too
figure NUM an example of sentence
figure NUM reports the quantitative results of the ex
something is defined as he complement of somebody
figure NUM quantitative results obtained on NUM sentences
the architecture is easily extendable to other languages
given the choice to express an action rhetorically as a purpose imagene is capable of producing seven grammatical forms for its expression most of which can be either fronted or not fronted
a text by text comparison of the results of the legxbdry criterion very bad medtocre gooc very good m the fan protocol and the results of the quahty of the abstract incomprehensible not very clear
m order to adjust the procedure to the above requ ments for the seraphin assessment we set up a jury of four readers a qualified french language teacher specmllsed m teaching abstract and synthesis techmques a documentary researcher working in a documentary umt two users wrh different training backgrounds for the first stage of the assessment we gave each member of the jury seven texts then
table NUM quahty of the abstract m terms of fields or natures of the source toxt
two initial results from the corpus study were reported
our results show that this heuristic is over constraining
all but one NUM NUM occurrences of sn ce accompanied relations in contributor core order while all NUM NUM occurrences of because accompanied relations in core contributor order NUM
figure NUM embeddedness correlates with choice be
the other constituents help to achieve the segment purpose
since and because are always placed with a contributor
see testing the new version of profet below
events are included in their location time recorded in the drs as e c t on the respective markers while states temporally overlap their location time recorded as s o t
for example in the following sentence NUM the event triggers the introduction of an event marker e and location time marker t into the drs with the drs condition e c t
NUM the following were neither barred nor suspended stephanie veselich enright
loll expansions seem only to occur in descriptive contexts
consider the following sentence pair from our corpus NUM a
NUM we like climbing up rock trees and clift
NUM the cat lay there quietly relaxed and warm
the issue now arises of the best way to integrate punctuation into a nl grammar
this paper describes the first step towards the construction of a theoretically motivated account of punctuation
in the next sections we try to generalise these rule patterns and discuss their possible implementation
my regards to the international academic and research comnmnity in the field of computational linguistics thank you and good bye
additional examples are shown in figure NUM
we implement this with another postprocessing stage
figure NUM the singleton rebalancing schema
among these it seems plausible that we have a better chance of doing well on the unseen test data if we choose a linear separator that separates the positive and negative training examples as widely as possible
for the remainder of this section we denote a training document with rn active features by d sil si si where sij stands for the strength of the ij feature
this issue is a potential problem for a linear classifier which scores a document by summing the weights of all its active features a long document may have a better chance of exceeding the threshold merely by its length
we find that while a vanilla version of these algorithms performs rather well a quantum leap in performance is achieved when we modify the algorithms to better address some of the specific characteristics we identify in textual domains
these algorithms use the training data where each document is labeled by zero or more categories to learn a weight vector which is used later on in the test phase to classify new text documents
NUM in the demotion step following a mistake on a negative example the coefficient ofsij in eq NUM is decreased the positive part of the weight is demoted w j3
however the major difference between the two algorithms one using only positive weights and the other allowing also negative weights plays a significant role when applied in the current domain as discussed in section NUM
as in the standard ir solution we suggest to modify s f d the strength of the feature f in d by using a quantity that is normalized with respect to the document size
second prompts should fit in with the ongoing dialogue
this information is orthogonal to that extracted from conjunctions only NUM of the NUM morphologically related pairs have been observed in conjunctions in our corpus
the relative fitness of a lagt is a function of the proportion of its linguistic interactions which have been successful the expressivity of the language s spoken and optionally of the mean wml for parsing during a cycle of interactions
this state is no longer an atomic eventuality
though simulation can expose likely evolutionary pathways under varying conditions these might have been blocked by accidental factors such as genetic drift or bottlenecks causing premature fixation of alleles in the genotype roughly corresponding to certain p setting values
in some of these runs the entire population evolved a default subjdir right setting though some lagts always retained unset settings for the other two ordering parameters gendir and argo as is illustrated in figure NUM
NUM mary has met the president
prom the perspective of the parameters framework the bioprogram hypothesis claims that children are endowed genetically with a ug which by default specifies the stereotypical core creole grammar with right branching syntax and subject verb object order as in saramaccan
the distinctions between absolute default or unset specifications also form part of the encoding a d figure NUM shows several equivalent and equally correct sequential encodings of the fragment of the english type system outlined above
to test whether it was memory limitations during learning or during parsing which were affecting the results another series of runs for english was performed with either memory limitations during learning but not parsing enabled or vice versa
this fact is even more remarkable because of the necessity to write specialized code to handle the peculiarities of this task
the bottom line scot es for named entity performance follow along with the task subcategorization scores for the complete response
for example judy jones president of exxon has been hired as ce o of ge
during development generic patterns were instantiated to extract organization s which would likely be involved in a succession event
when it reaches smith jewelers it will compare the filter against a filtered version of the name
by examining a training set of articles for the sentences which report the events of interest rules are developed
it is louella s chance to apply any heuristics which may seem helpful to an accurate re porting of information
matches othernp rule NUM ppnoun lcb NUM rcb he will be succeeded by dooner NUM
official bottom line and unofficial total slo t scores are louella had very high recall in the template element task
weight value that is is NUM pounds of currency significant enough to outweigh NUM pounds of weight loss
first two daughter signs are substituted to the head dtp position and nor head dti position of the rewriting rule rule r
a characteristic of hpsg is in the flexibility of principles which demands complex operations such as append or subtraction of list value feature structures
we also NUM elieve that by pursuing this direction for optimizing ttl sg parsers we can reach the point whe re
the d is used to represe nt the dependency NUM etween the nn ther sign and the daughters through structure sharings
we could not measure the execution time for a totally naive algorithm which t uilds parse trees without las because of uwashing
the next stages deal with noun and verb groups
in each learning iteration the learner searches for the transformation which maximizes this function
in addition the learned rules can be converted into a deterministic finite state transducer
the generation of elementary trees from more abstract data needs the characterization of what is a well formed elementary tree in the framework of tag
in each case a NUM NUM word untagged corpus was used for initial unsupervised training
the current word is w and the preceding following word is tagged z
one method is to manually alter the tagging model based on human error analysis
a tagged corpus can also be used to improve the accuracy of unsupervised transformation based learning
using these equivalence classes greatly reduces the number of parameters that need to be estimated
when training on NUM NUM words test set accuracy was NUM NUM excluding punctuation
NUM one of the two preceding following words is tagged z NUM
the current word is w and the preceding following word is x NUM
this is a two steps process it first creates some terminal classes with inherited properties only they are totally defined by their list of super classes
the early development phase focused on one slds with one particular dialogue structure in one particular domain of application designed for a particular type of task i.e. reservation in one particular development phase i.e. evaluation of an implemented system and in circumstances of controlled user testing where we had available the scenarios used by the subjects as well as the full design specification of the system
using this already existing hierarchy and the implemented principles of well formedness will lead to a grammar for another language compatible with the french grammar
the similarity of pi and pj is measured by the inner product of their normalised vectors and is defined as follows nj does not appear in pi nj is a keyword and appears in pi nj is not a keyword and appears
it insures transparency and coherence in syntactic descriptions and allows the generation of the elementary trees of an ltag with systematic crossing of linguistic phenomena
disc aims to develop a first detailed and integrated set of development and evaluation methods and procedures guidelines checklists heuristics for best practice in the field of dialogue engineering as well as a range of much needed dialogue engineering support concepts and software tools
the knowledge exploited here about dependencies between words at arbitrary distance can operate particularly efficiently with an n gram driven recognizer
in this paper we have examined three issues concerning the robustness of multilingual speech interfaces for spoken language translation systems accent differences mixed language input and the use of common feature sets for hmm based speech recognizers for english and cantonese
vabcd a b c d missing vabcd a b d c d y too many ds computational linguistics volume NUM number NUM here are the categories for simplicity regarded as lexical that can appear on subcat lists
if we require the in value of the agent phrase to be the default value for an agent i.e. a meaning like something then this threading analysis has the incidental advantage that only one agent phrase can be present
nevertheless practical experience has shown that with care and some experimentation it is possible to develop linguistic descriptions that are succinct and relatively elegant while still lending themselves to efficient and most importantly bidirectional processing
this ensures that only the features mentioned on the kleene category will be identically instantiated across all occurrences enabling the semantic problem mentioned earlier to be solved at least in principle the current illustration does not do so
if the type feature is instantiated then the selector feature will be of the form indicated by the conditional constraint that is the x in the second component of the tuple will be in the position corresponding to the value
if we know that the first and last arguments are always incompatible then an attempt to link up all the positions will result in something that will be trying to unify NUM and NUM and this will fail as required
note that one can not get round the ambiguity problem by just restricting the agent nominalization rule to one or two types of subcategorization many different types of verbal complement may be involved sleeper designer thinker etc
if on the other hand we simply omit btm from the list of types then when two types have no lower bound the result of anding together their corresponding bitstring will be a bitstring consisting entirely of zeros
however since having a glb of btm is usually meant to signal that two types are incomparable and thus do not have a glb it would be more useful if we could contrive that unification would fail for such cases
for both wsj texts and atr corpus the tagging error rate dropped by more than NUM when using word bits information extracted from the 5mw text and increasing the clustering text size further decreases the error rate
the sizes of the texts are NUM million words mw 10mw 20mw and 50mw
random word bits are expected to give no class information to the tagger except for the identity of words
the first two lines show questions about identity of words around the current word and tags for previous words
in the following sections we will first describe a method of creating a binary tree representation of the vocabulary and present results of evaluating and comparing the quality of the clusters obtained from texts of very different sizes
one notable point in this result is that introducing word bits constructed from wsj texts is as effective for tagging attt texts as it is for tagging ws3 texts even though these texts are from very different domains
l analyze de la variance generic relationship i statistique n on param6trique
this consists of producing two types of indexes candidate terms and descriptors
controlled indexing of the corpus supplied NUM NUM terms of NUM NUM in the thesaurus
each document describes the objectives and stages of a research project on a particular subject
the current work attempts to repair misrecognitions by mobilising available acoustic cues and by using linguistic abstraction and syntactico semantic predictions
the improvement from the chart re estimation algorithm is measured in the number of actual inside and outside computations done to estimate the parameters
the basic implementation of an inside outside algorithm assumes tables for insides and outsides so that identical insides and outsides need not be recomputed
t layer j w i t i figure NUM illustration of inside probability
the suggested method focuses mainly on reducing the computational overhead of outside computation which is a major portion of a re estimation
the insides that participate in generating the input sentence can be identified by running the inside algorithm one more time top down
the inside outside algorithm provides a formal basis for estimating parameters of context free languages so that the probabilities of the word sequences sample sentences may be maximized
a valid sequence can begin only at state thus to be strict p1 NUM has an additional product p
this is because the inside algorithm works on a network from han and choi a chart re estimation algorithm left to right and one transition at a time
the complexity of the outside algorithm we present is o n4g NUM where n is the input size and g is the number of states
if texture is defined as a property of piiysical objecf or material and silk is a descendent of either of them then the v duc carried in the lexicon entry for smooth will be inserted by the analyzer as the texture property value for file instance of silk in tim tmr
lexicon entries for most open class lexieal items represent word and plu ase senses which c m be either directly mapped into ontological concepts or derived by locally that is in the lexicon entry itself modifying constraints on property wducs of concepts used to specify the meaning of the given lexical item
this discussion illustrates an important point in our approach namely that syntactic modification does not necessarily imply semantic modification
is our approach to adjectival moditication of holms applicable to adverhi d mcxlilication of ved s
the literature on adjectives shows a scarcity of systematic semantic analyses or lexicx graplfic descriptions of adjectives
there has been a sporadic interest in the adjective fake see iw mska NUM cf
n v ufl cime visit6 n v guardia di finanza visit6 v n visit6 aeroporto di fiumicino
2in lexical acquisition the role of other syntactic categories e.g.
all these methods deal with the problem of np recognition
the terminology extraction have been run over both tile corpora
the reference document set was a collection of NUM documents
the experts compiled a set of NUM terms organized in NUM sections i.e.
recall and precision of the syntactic analysis is consequently higher
the significant improvement against this standard sources is very successful
syntactic relations between complex nominals and other sentence segments
the keywords which will be used in retrieving similar articles are selected from previously dictated sentences
it corre sponds to 10a0 if tlm possit le word error improvement
the two scores are linearly combined with sri s six scores
with regard to the size of sublanguagc set a constant size may not be optimal
the second pass aims at recovering from would be deleted words by re inserting expected co occurring words
the denominator of the log in formula NUM is the unigram probability of wor t w
so one fllture direction is to look for a more suitable retrieval method for our purpose
next we will present the strategies an asp system has to follow if such a system is to present information in a sensible manner
we can apply a set of head transducers recursively to derive a pair of source target ordered dependency trees
head transducer models consist of collections of weighted finite state transducers associated with pairs of lexical items in a bilingual lexicon
the use of relation symbols here is a result of the historical development of the system from an earlier transfer model
in practice we restrict the selection of such pairs to those provided by a bilingual lexicon for the two languages
it should also be noted that the above positions do not completely define the order of modifiers in the transduction
in the model described in this paper the symbols written are dependency relation symbols or the empty symbol e
children who have no speech or whose speech is so impaired that they can not be readily understood may not have had opportunities to develop a familiarity with the structure and pragmatics of conversational interactions nor the skills required to maintain them
null the third pass can chose to ignore joker trees introduced in the first pass
head transduction is a translation method in which weighted finite state transducers are associated with source target word pairs
the version we used for english to chinese translation allows additional target positions as explained in section NUM
interestingly the ability of our model to process data which are not aligned makes it directly applicable to the reverse problem i.e. phonemeto grapheme conversion
table NUM lists the distribution of the smallest distances between the word senses and the activated clusters and the accuracy of the disambiguation
unfortunately the complexity of determining such a level is exponential to the edges in the dendrogram which demonstrates that the problem is hard
so the definitions of all the words in the clusters contain strong and meaningful information about the very sense of the word in the context
the time referred to in for example from NUM to NUM on wednesday the 19th of august is represented as august 19th wednesday NUM pm august 19th wednesday NUM pm thus the information from multiple noun phrases is often merged into a single representation of the underlying interval evoked by the utterance
to produce one ilt the parser maps the main event and its participants into one of a small set of case frames for example a meet frame or an is busy frame and produces a surface representation of any temporal information which is faithful to the input utterance
number of activated number of collocations of activated clusters occurrences of the word in the corpus and implement our algorithm on them respectively
in this paper we propose a formal resource of language structured semantic space as a foundation for word sense disambiguation tasks
where the numerator is the mean weight of the nodes in sub t while the denominator is the mean weight of the nodes in t sub t
the toolkit provides an editor for editing and managing these rules
these include editors for lexical semantic and knowledgebase work
these rules are written in prolog
it required approximately two person months to develop
applications can run in either windows nt or unix
nl assistant a toolkit for developing natural language applications
these include comlex and wordnet as well as additional internally developed resources
it runs in a windows nt environment
the toolkit is designed for both speech and text based interface applications
further the number of screens that can be accessed by a user using the organisational framework is restricted by the need to limit the complexity of the system to that suitable for someone who is either very young or who has learning difficulties
contextually disallowable semantic representations are registered as filters
after dusting apply two coats of vinyl paint
before use soak the tube in warm water
rcb go change the tag candidate of one word selected to tninilnize the energy t iinction in k th step froni t k to j k i a n l l t e t this process until there is tlo change
ivvc ilscd line tr inl erl olat ion ine hod l rot a s6 jclin sg and assigning c luel cy NUM for uilknowll word weisctig for sliioothilig in ii m m
lique function is proportional to energy fun tion and it represents the unstability of current state of randoni varia bles in clique or it has high value when the state of mrf is bad low value when the st te of mi lcb f is nero to solution
so it is iscd in the l rol leni which has slliall sea rch space NUM NUM NUM ilsed ill i1m m m rf model call ilse both viterbi algoril hni and siinulated anealing but it is not nowtl io ilse sinitllated allne aling ill fimm
h mniersley liflbrd thcorein NUM he probability dish ib ulio n i NUM is tibbs dish ibulion if and only if random wzriable NUM is llarkov random field for givcn ncigborhood syslc n n i
we assume tha t every prob d lity value el tag se luenee is larger l hml zero bee rose ungraluiuat ic d sellt ellces fill tl pem in htlllhill l tligll ge lis ge including meaningless sequence of characters
second i extend my previous analysis by also discussing the centering functions of full nps in subject position and some occurrences of pronotuis tuiaccounted for by centering
tering yet b prose that describes situations involving several animate refe rents bee ause strong t ronouns can refer only to animate referents
there is one runtime entry for each successful match and possibly a null entry for the node if the word label for i is included in successful matches for other entries
this pruning condition is effective at curbing a combinatorial explosion arising from for example prepositional phrase attachment ambiguities coded in the alternative trees t and t
in a dependency tree crossing links are not allowed
NUM partition these solutions into subsets with the same word label for the node fl n and select the solution with lowest cost c from each subset
the images gi li form a partition of the nodes of s where li is the set of labeled source nodes in the source fragment hi of ei
normalized distance model the mean distance model does not use the constraint that a particular choice faced by a process is always a choice between events with the same context
the methods make use of a supervised training set and an unsupervised training set both sets being chosen at random from the NUM NUM or so atis sentences available to us
we can then estimate the probabilistic model costs with c elc ln n c ln n elc
the generic and specific principles of cooperativity in dialogue
s no departure at NUM NUM
some generic principles subsume specific principles
grice s ulaxims are all generic
NUM NUM principles which are reducible to maxims
NUM NUM prindples hicldng equivalenls lililolil4 the maxims
section NUM compares principles and maxims
am lustnauer tor hatte er einen schweren unfall und musste ins krankenhaus eingeliefert werden
a neutral view on a situation gives only a confirmation of the initial point
we showed that smith s notion of a neutral viewpoint is crucial for german
every inference regarding the ending of a situation is due to the context or our world knowledge
at the lustnauer tower he had a serious accident and had to be admitted to the hospital
the canis prototype will show the customer a new way of doing business
the nltoolset is portable extensible robust genetic and language independent
entities together through relation links in the sql data null base
for each of the details available about a name ie
the abstracting and indexing process is a time consuming and laborious task
the majority of the effort is the indexing part of the process
tion addressees and subject line associated with the current document
this server acts as a communication driven pipe to the canis prototype
evaluation will be performed by analysts at their site using real data
logs generated by each of the canis system processes in read only mode
to gauge how well knight generates explanations about objects as opposed to processes we computed means and standard errors for both knight s explanations of objects and the biologists explanations of objects
conducting a formal study with a generator has posed difficulties for at least three reasons the absence of large scale knowledge bases the problem of robustness and the subjective nature of the task
NUM accessing semantically rich large scale knowledge bases to perform well an explanation system must select from a knowledge base precisely that information needed to answer users questions with coherent and complete explanations
although edps are more schema like than plan based approaches and consequently do not permit an explanation system to reason about the goals fulfilled by particular text segments NUM they have proven enormously successful for discourse knowledge engineering
to develop explanation systems for a broad range of domains tasks and question types discourse knowledge engineers must be able to create and efficiently debug the discourse knowledge that drives the systems behavior
the principal advantage of top down planners over schema based generators is their ability to reason about the structure content and goals of explanations as opposed to merely instantiating pre existing plans embodied by schemata
pauline s texts were not formally analyzed by a panel of judges and it did not produce texts on a wide range of topics it generated texts on only three different events
these kinds of systems must be able to address issues of animated content determination and organization as well as determining at runtime which media should be used to realize the concepts to be communicated
despite these drawbacks edps have proven to be very useful as a discourse knowledge engineering tool a result that can be attributed in large part to their combining a frame based representation with procedural constructs
the specialists that we used for ne consist of separate routines designed to handle locations organizations people dates money and percentages
the goals of we would like to thank alex lascarides and massimo poesio for comments on an earlier draft
it consists of two parts the constraint based portion and the preference based portion NUM
figure NUM multimodal integration architecture figure NUM presents the main agents involved in the
he let it ring or two minutes in which such elaboration is possible
the mgorithm used for calculating the temporal structure of a discourse can be summarised as follows
but then the resulting temporal structures will be highly ambiguous even in small discourses
the wireless hand held unit is a NUM 1b fujitsu stylistic NUM figure NUM
this corresponds to temporal centering s preference to continue the current thread
but an event can only elaborate another event as in NUM
this is because a well formed text should not leave threads dangling or unfinished
the algorithm is part of an hpsgstyle discourse grammar implemented in carpenter s ale formalism
to retrieve run time information cogenthelp uses expersoft s powerbroker orb for inter process communication in comparison to the neuron data connection other ipc methods could be more easily substituted
in section NUM we highlight the nlg techniques used in support of the software engineering goals identified in section NUM in section NUM we describe cogenthelp s authoring interface
cogenthelp is a prototype tool for authoring dynamically generated on line help for applications with graphical user interfaces embodying the evolution friendly properties of tools in the literate programming tradition
cogenthelp is also unusual in that it is to date one of the few tools to bring nlg techniques to bear on the problem of authoring dynamically generated documents cf
in evolving the design of cogenthelp we have employed a rapid prototyping approach in working with our trp road NUM consortium partners at raytheon who are serving as a trial user group
also by project end we hope to port cogenthelp to a more affordable java based gui builder in order to make it useful to a much broader community of developers
while space precludes a detailed description it is worth noting that exemplars for java supports abstraction specialization and content based revisions all of which have proved useful in the present effort
we are optimistic that this group will find cogenthelp suitable for actual use in developing a production quality help system by the end of our rome laboratorysponsored software documentation sbir project in mid NUM
these html trees are then linearized into an ascii stream by a separate formatter so that they can be displayed in a web browser cf
as will be explained below grouping widgets by type was considered inadequate because doing so would obscure functional relationships between widgets of different types
furthermore we only applied our algorithm to nouns
the blocks on the diagonal are all 0s
where per refers to proper names recognized as persons
in our experiments NUM NUM NUM was used
they are called selectors of w
separate classifiers have to be trained for different words
the similarity between the concepts hill and coast is
figure NUM a fragment of wordnet
therefore we relaxed the correctness criterion
table NUM compares results for proximity based to the baseline
speech or gesture marked as partial needs to be integrated with another mode in order to derive an executable command
units and objectives can be laid down on the map by speaking their name and gesturing on the desired location
the realization of this argument as an adjective like red comes from the fact that the new argument is of dimension coloring
for example the similarity between depth first search and leather sofa is neither higher nor lower than the similarity between rectangle and interest rate
NUM senses NUM fixed charge for borrowing money NUM a right or legal share of something and NUM financial interest in something are merged
the local contexts of the two nouns boy and dog in NUM are as follows the dependency relations between nouns and their determiners are ignored
the database entry corresponding to table NUM is as follows c c org NUM NUM NUM plant NUM NUM NUM
the polysemous words in the input text are disambiguated in the following steps step a parse the input text and extract local contexts of each word
general purpose lexical resources such as word net longman dictionary of contemporary english ldoce and roget s thesaurus strive to achieve completeness
dog adjn brown rood compl chase head using a broad coverage parser to parse a corpus we construct a local context database
if we substitute this abbreviation by the full form of the word in en ra engineer gem we get the following results the sentence is processed NUM NUM s the number of variants decreases by four NUM and the number of derived items is of course also smaller i NUM
the original sentence s can then act as the ideal solution of the overall process
here we can easily solve the problem where the nominal group ends on the right hand side in general we need to parse the whole sentence in order to get this information but in some specific cases we can rely only on the surface word order
it would of course be possible to use only one dictionary containing morphosyntactic information about particular words lemmas but for the sake of an easier update of information during the development of the system we have decided to keep morphemic and syntactic data in separate files
figures inadequately of course in this case also as a righthand attribute to the word pithartem instr as it is shown in the following screenshots for the sake of simplicity we demonstrate only the relevant part of derivation trees
the core idea of this approach is the following syntactic constructions which even in free word order languages may be parsed locally certain adjectival or prepositional phrases etc should be parsed first in order to avoid their mutual unnecessary from the point of view of grammar checking
this means that the grammar should be divided into certain layers of rules not necessarily disjunctive which will be applied one atter the other in principle they may be applied even in cycles but this options is not used in our implementation
another important point is that the results of parsing in layers provides only positive information i.e. it is able to sort out sentences which are certainly correct but the failure of parsing in layers does not necessarily mean that the sentence is incorrect
this approach makes the grammar more complicated than it is necessary and it may also influence the quality of results an error on the left hand side of a verb may also prevent an attachment of the items fi om the right hand side of the verb
if we do not put any additional restriction on the order of application of rules then the rule filling the subcategorization slots for subject and object may be applied in two ways either first filling the slot for the subject and then the object or vice versa
some although not all speakers find the sloppy reading in which bill hands in his own paper to be acceptable
the cost function specifies the model parameters the other components are the model structure
in this case a second regression yields the predictive equation
the results also show n is significant at p NUM
this problem will become more serious when the values l and m in word clustering are large which renders the clustering itself relatively meaningless
NUM the translation s atistics for the training data are shown in table NUM
there are any number of ways to create clusters in hard clustering but the method employed is crucial to the accuracy of document classification
if elaborate translations are required we increase the number of semantic frame categories
NUM shows the break even point for each method for the first data set and tab NUM shows that for the second data set
in addition it provides for easy access to the meaning of domain specific expressions
all clause level categories including statements infinitives etc are mapped onto clause
in section NUM we suimnarize the characteristics of our source language text comprised of naval operational umssages
the overall goal of our translation work is automatic text and speech translation for limited domain multilingual applications
regarding the unknown word problem an obvious solution is to expand the lexicon
current proposals for dialog evaluation metrics are both objective and subjective
similarly af cl for user NUM is NUM NUM
the actual implementation of such a scheme requires multiple passes over the corpus to generate phrases
to achieve more complete and accurate phrase based indexing we propose to use the following
our future work will explore such applications of the techniques we have described in this paper
in general we see improvement in both recall and precision
precision measures how many of the retrieved documents are indeed relevant
by definition all two word nips score their pairs as locally dominant
so initiall only two word lexical atoms can be detected
however recognition of lexical atoms in free text is difficult
first it can focus the use of statistics by helping to eliminate irrelevant structures from consideration
word frequency e.g. NUM in the actual experiment figure NUM formulas for scoring
independently of the order of the rules the rules having the longest match will be first tested
the first sentence in our japanese to english je translation snapshot figure NUM for exam
therefore the results in both languages were comparable
pr nc i w NUM NUM
computational linguistics volume NUM number NUM such as le the and de of
smoothing technique such as deleted interpolation
set sizes on performance in english
NUM NUM interest in problem and potential applications
null payment topicalized bank transferobjective wait for polite modest a a
dmt has been proposed as a general technique for spoken language translation
NUM NUM name finding as an information theoretic problem
figure NUM NUM pictorial representation of conceptual model
a translation success rate of about NUM has been demonstrated in a jacknife test sumita92 a
if instantaneous response is required the rest dominant process is retrieval of the closest translation patterns from bulk collection
in that case the same words is counted as many times as it appears in the text
in table NUM for instance the effectiveness falls from NUM to NUM at i NUM
the feature selection process is iterated until the approximate gain for all the remaining inactive features is negligible
much previous work in this area has relied on ad hoc methods
each core contributor pair is also analyzed for its informational relation
figure NUM an example tutor explanation
NUM NUM effect of segment embeddedness on cue selection
getting the message across in rst based text
factor based retrievals provide information about cues that is unique to this study
finally the data is not spoken as in these other studies
from unsystematic analyses of naturally occurring data
NUM is more susceptible to damage
figure NUM effect of number of training examples on wsd accuracy averaged over NUM words with at least NUM
information retrieval ir is a practical nlp task where wsd has brought about improvement in accuracy
current efforts aim at improving the performance of the tagger
the other levels are identified at near baseline performance
involved or narrative vs non narrative
when different descriptions are automatically marked for semantics profile can prefer to generate one over another based on semantic features
we have made an attempt to reuse the descriptions retrieved by the system in more than a trivial way
the cnn web site and to all local news delivered via nntp to our local news domain
we would ideally want to build a comprehensive and shareable database of profiles that can be queried over the world wide web
an example of a finite state description of the entity yasser arafat is shown in figure NUM
we have described a system that allows users to retrieve descriptions of entities using a web based search engine
these consist of all sequences of words that were tagged as proper nouns np by pos
there exist many live sources of newswire on the internet that can be used for this second case
it collects and merges information from distributed sources thus allowing for a more complete record of information
table NUM gives the results of the experiments
in theory there should be cooperation between the different branches of government
and so cotton bag means bag made from cotton in this context
NUM all graphical models have a graphical representation such that each variable in the model is mapped to a node in the graph and there is an undirected edge between each pair of nodes corresponding to interdependent variables
a sentence with an ambiguous word is represented by a feature set with three types of contextual feature variables NUM NUM the morphological feature e indicates if an ambiguous noun is plural or not
however using mrd as the knowledge source for sense division and disambiguation encounters certain problems
in particular we believe that an analysis of the pragmatics of children s conversations will provide a basis for predicting from a corpus of plausible utterances those utterances that will be most useful for a particular child
on the other hand words in the differentia such as river lake field
how well a model characterizes the training sample is determined by measuring the fit of the model to the sample i.e. how well the distribution defined by the model matches the distribution observed in the training sample
in n NUM th step where n is the number of word senses in s a final node is produced
the agents systems use a calendar management system for displaying to their owners the results of the appointment negotiations
is directly derived from the parse tree andbecomes the
no of misparsed sentences NUM NUM NUM NUM
table NUM test data evaluation results on the lexicalized
the repair utterance in figure NUM is u2 but note that according to the avm task tagging u2 simultaneously addresses the information goals for arrival city and depart range
misparsing due to the omission of a preposition e.g.
generation can then proceed as usual
NUM NUM experimental results on data set test
fourteen sets of training data were generated using the NUM development articles supplied for muc NUM
for the name recognition problem the training data was converted in tuple of five words
for example a surname may have been seen previously but not the attached forename
the decision trees thus formed can be output in a readable if somewhat lengthy form
the basic system had approximatel y six man months of work in its original development
walk through article performance here was recall NUM and precision NUM
source code and data files can be ftp ed from crl mai l cable crl nmsu edu
a web interface to the tagger is als o available from crl s home page
these rules are specific to the training articles and they are generalized so that they can be run on other articles from the domain
most of the errors that occur are due to failures of the bracket insertion heuristic
the only data source used for the autolearn system was the NUM muc NUM training texts
the substantive content of these fragments may be input with a particular interaction and a specific conver sational partner in mind or it may be more general for use with any of a number of potential partners
a wide range of conversation attributes could be used to predict the most appropriate utterance e.g. the anticipated conversation partner phrases selected for similar conversation partners content related to the last phrase selected
after some semantic polishing to improve the content for linguistic purposes the realization component translates the views in the explanation plan to sentences
NUM the apply edp algorithm figure NUM and the algorithms it invokes traverse the hierarchical structure of the edp to build an explanation plan
the issue of the size of the grammar is not addressed in this experiment
all these files were created randomly and independently using the words in dictl0
if the sentence fails to parse in this pass the parser moves on to pass four
the following approach is used to allow our parser to cope with unknown words in a sentence
obviously there is a trade off between the deletion rate and the insertion rate
we would like to compare a computer generated morphological recognition module with the hand generated corpora
thus if the word is encountered again later it now would be in the lexicon
this would allow the parser to parse sentences containing unknown words in a robust and autonomous fashion
charniak s paper NUM outlines the use of statistical equations in part of speech tagging
these attempts have followed several different methodologies and have focused on various aspects of the unknown words
the computation of merging process is only equal to the splitting calculation in one level in the tree
but when we do the process of right left the left branch has less words than the right
therefore we should modify the slogan do not compute things twice to do not compute expensive things twice
furpvacingoal iime l f up p vstat goal goal head head f4 goal
usually recognizers can be adapted to be able to recover the possible parse trees of that sentence if any
clearly this is not a more specific goal than we solved before so we need to solve this goal afresh
a goal category can be parsed if a predicted lexical category can be shown to be a head corner of that goal
a goal weakening technique is introduced which greatly improves average case efficiency both in terms of speed and space requirements
in the case of constraint based grammars however the cost associated with maintaining such a chart should not be underestimated
in each of the experiments discussed in section NUM the use of selective memorization with goal weakening outperforms standard chart parsers
it will be clear that this category adjp should end in position NUM but can never start before position NUM
however there is a problem it will be unclear what the heads of the newly created rules will be
because of the central role played by discourse knowledge engineers a representation of discourse knowledge should be designed to minimize the effort required to understand modify and represent new discourse knowledge
the utterances are intended to support the goal of maintaining conversational flow when a suitable specific response to something a partner says is not available and fulfil general pragmatic functions such as initiation to get attention e.g.
for ea h t olysemous nmm we selected the tirst top NUM definitions in the lictionary
however the results of freq figure NUM shows that they are classified incorre tly
output a number5 of major airlinesl adopted continental2 airlines2 in tal le i underline signifies polysenmus nolln
we s qeeted the first top NUM definitions in the dictionary for each noun and used theln in the cxperilnent
we selected different NUM artmes from NUM NUM wsj and applied to stage one four
in figure NUM the u ticles tre judged to l tssify into eight categories
as shown in table NUM there arc NUM nets which could not NUM e clustered correctly in our method
the limit of the first n terms of a converging infinite series as tends to infinity
it requires that the first feature structure to appear to the right of v is ck
we can now define c o ce to be the comprehensibility of the source speech with respect to the source text and ct get to be the comprehensibility of the target speech with respect to the source text
grammatical aspect takes these situation types and presents them as impeffective john was winning the race loving his job or perfective john had won loved his job
NUM a major source of the difficulty in assigning lexical aspect features to verbs is the ability of verbs to appear in sentences denoting situations of multiple aspectual types
we define the unary operator a which produces a set of ending guessing computational linguistics volume NUM number NUM rules from a word in the lexicon w c i
for instance the entry for the word book which can be a noun nn or a verb vb would look like book nn vb
such rules account for many cases of the irregular suffixation as for instance try y ied tried prefix prefix morphological rules with no mutative segments NUM
to perform such rule merging over a rule set the rules that have not been included into the working rule set are first sorted by their score and the rules with the best scores are merged first
we put all the hapax words from the brown corpus that were found in the cnlex derived lexicon into the test collection test lexicon and all other words from the celex derived lexicon into the training lexicon
since a word can take on several different pos tags in the lexicon it can be represented as a string pos class record where the pos class is a set of one or more pos tags
in this paper as a first step of our grammar acquisition we focus on step NUM NUM that is how to group nodes of which lower nodes are lexical categories
as for the factor of a well known estimate ris89 is applied and it is reduced pc g to a constant value a NUM regardless of the merged pair
let s denote a posterior probability with p gic where c is a collection of data i.e. in figure NUM c lcb c1 e2 cn rcb and g is a set of groups clusters i.e. g lcb gz g2 rcb
so g u g sim g g sc g sc g sc g i p elg ceg smaximizing p gic is a generalization of mac mush l cel hood estimation
in this paper we propose a method to group brackets in a bracketed corpus with lexical tags according to their local contextual information as a first step towards the automatic acquisition of a context free grammar
in figure NUM there are three unique labels derived el adj noun cc2 art noun and cs pron noujv
as for brill s schemata that checks the presence of a particular character in an unknown word we capture a similar feature by collecting the ending guessing rules for proper nouns and hyphenated words separately
we also believe that general purpose lexicons contain less erroneous information than those derived from annotated corpora NUM xerox s technique is not documented and can be determined only by inspection of the source code
table NUM gives the number of possible alignments for words of various lengths when both words are of length n there are about NUM NUM alignments not counting dead ends
the segments of two words may be misaligned because of affixes living or fossilized reduplication and sound changes that alter the number of segments such as elision or monophthongization
an algorithm to align words for historical comparison
finding the right alignment may require searching
this alignment step is not necessarily trivial
i call this restriction the no alternating skips rule
the algorithm consists of an evaluation metric and a guided search procedure
exact formulas are given in the appendix
we feel this indicates that the models are performing well in scoring the parses
looking through the various tables in this paper you may have noticed that words with higher entropy tend to have higher frequency
not the word ordering but case postpositions mark the case of these nps in relation to the main verb
this measurement technique has produced entropy rankings that correspond well with intuitions about the relative semantic import of various words and word classes
the most frequent of the thirty seven pronouns in the corpus i is eleventh from the bottom of the list
we present a new synchronous rewriting system and argue that it can handle certain phenomena that are not covered by existing synchronous systems
thus to improve accuracy we should reduce the specificity of the bracketing s commitment in such cases
we introduce inversion invariant transduction grammars which serve as generative models for parallel bilingual sentences with weak order constraints
i would like to thank xuanyin xia eva wai man foug pascale fung and derick wood
rectionality and flipping eommutativity equivalences see lemma NUM are also applied whenever they render the associativity equivalences applicable
each a corresponds to a level of bracketing and can be thought of as demarcating some unspecified kind of syntactic category
as a spe aal case the null symbol e in either language means that no output token is generated
if the goal is just to obtain for monolingual sentences the extra brackets can be discarded aft parsing
the problem is that singletons have no discriminative power between alternative bracket matchings they only contribute to the ambiguity
this simple swategcm is effective because the majority of unmatched singletons are function words that counterparts in the other language
moreover conventional compounds are frequently and unlmxlictably missing from translation lexicons and this can furllu degrade perforinane
we also wish to thank robert maclntyre and ann taylor for valualde discussions on the penn treebank annotation
what is striking in this figure is that the inside outside algorithm is so attracted to grammars whose terminals concentrate probability on small numbers of rules that it is incapable of performing real search
most errors are due to wrong identification of the subject and different kinds of objects in sentences and vps
the largest part of the window contains the graphical representation of tim structure being annot ate t
in the first phase the ma in flmctionality for buihling and displaying unordered trees is supplied
the data drivenness of this approach presents a clear advantage over tile traditional idealised notion of competence grammar
acquiring linguistically plausible phrase structure grammars from ordinary text has proven difficult for standard induction techniques and researchers have turned to supervised training from bracketed corpora
for instance the first determiner among the nk s can be treated as the specifier of the phrase
thus the estimated probability of a rule after the first pass is directly proportional to how many of these parse trees the rule features in
they are equally probable because they use the same number of expansions NUM and because word bigrams are uniform at the start of the parsing process
dort trank er ein glas trollinger
he was driving home at this time
examples sam owned three peach orchards
semelfactive mr ramsey reached the lighthouse
achievement viewpoints smith postulates three different viewpoints
activity mrs ramsey wrote a letter
accomplishment lily knocked at the door
edinburgh eh8 9lw scotland u k
state lily swam in the pond
semantic entropy as measured here actually correlates quite well with the logarithm of word frequency p NUM NUM
so we can discard either NUM q2 q7 or NUM q3 q9 and eliminate the arcs from states that then become unreachable
however finding bilingual corpora can be problematic in some domains
champouion can not make any distinction between domain specific and general collocations
this happens for all settings of c in figure NUM
smadja mckeown and hatzivassiloglou translating collocations for bilingual lexicons
this operation is described in detail in section NUM NUM below
stage NUM step NUM scoring of possible translations
at the translation phase only the indices are accessed
for si x y in particular we have
e mail kathy cs columbia edu vh cs columbia edu
in this summarization paradigm problems arise when information needed for the summary is either missing from the input article s or not extracted by the information extraction system
it is high on the list because of its frequent pleonastic function it is necessary to
since the need for a description may arise at a later time than when the entity was found and may require searching new text the description finder must first locate these expressions in the text
in our work stored phrases would be used to provide content that can identify a person or place for a reader in addition to providing the actual phrasing
at this stage search is limited to the database of retrieved descriptions only thus reducing search time as no connections will be made to external news sources at the time of the query
in contrast with previous work on information extraction our work has the following features it builds a database of profiles for entities by storing descriptions from a collected corpus of past news
thus we had to carry out conversions of the original data into the format presented above which resulted in the so called czech modified corpus with the following features v te used the complete modified corpus NUM tokens in the experiments no NUM no NUM no NUM and a small part of this corpus in the experiment no NUM as indicated in table NUM NUM
these results show not surprisingly of course that the more data the better results experiments of no NUM vs no NUM but in order to get better results for a trigram tag prediction model we would need far more data
v e used a small 100k tokens part of wsj in the experiment no NUM and the complete corpus 1m tokens in the experiments no NUM no NUM and no NUM table NUM NUM contains the basic characteristics of the training data
c w argmaxtpr t w argmaxtpr tlw pr w argrnaxtpr w t argmaxtpr w t pr t
for comparison we have applied the same code and settings to tag an english text another four experiments using the same size of training and test data in the experiments in order to avoid any doubt concerning the validity of the comparison
sthe transformation based tagger is available through anonymous ftp to ftp cs jhu edu in pub brill programs
transformations are used differently in the unsupervised learner than in the supervised learner
figure NUM shows test set tagging accuracy as a function of transformation number
transformations will then be learned to fix errors made by the unsupervised learner
these results are significantly lower than the results achieved using unsupervised transformation based learning
after learning NUM NUM transformations training set accuracy increases to NUM NUM
the unsupervised rule learning algorithm is based on the following simple idea
it could support system upgrading to take advantage of improved technology by making it much easier to replace an older module with a newer and hopefully better performing module
the tipster architecture a standardized interface for providing document management document retrieval and information extraction services is one of the major products of phase ii of the tipster program
completeness in some sense the architecture will never be complete there will always be requests to standardize additional services or resources associated with text analysis
the architecture was specified in terms of a hierarchy of object classes with operations associated with each class and inheriting operations from classes above it in the hierarchy
a number of specialized object types had to be added for retrieval and routing including different types of queries and indexes for document and query collections
and by making it easier to combine language analysis modules in novel ways for example combining extraction and detection it could enhance system performance
the government originally hoped that an architecture could be completed in six months and that we could then all go back to doing research and writing systems in conformance with the architecture
in addition it was recognized that real success for the architecture lay in making it attractive enough in both design and availability of components to be widely and voluntarily adopted
the architecture as yet makes no special provision for operation in a multi process environment we need to include such mechanisms as read and write locks and transaction control which are typical of data base systems
this demo provided the first albeit limited demonstration of plug and play and the first demonstration of detection and extraction systems interacting through annotated documents
thus adjoining is always limited to a rf the presence of a substitution site just changes what node that rf is rooted at
figure NUM it shows that this would introduce a non empty node uk above and to the right of the substitution sites
the idealized analysis presented above could lead to a simple deterministic incremental algorithm if there were no uncertainty due to local or global ambiguity
this is adjoined to the single node tree in figure 4b i yielding the tree shown in figure 4b iv
we currently use two kinds of substitution structures non empty nodes figure la and elementary trees with substitution sites figure lb
a node is empty only in not having an associated discourse unit or relation it can still have an associated feature structure
so the frequency with which a particular word gets linked to nothing is an important factor in estimating its semantic entropy
a less frequent term typically provides evidence for asmaller number of categories
default NUM birdsfly b bird b d fly b
most bitexts contain a number of word tokens in each text for which there is no obvious counterpart in the other text
we have presented a portable wide coverage approach to domain specific semantic d disambiguation which performs comparably with human judges
priorities enable one to specify that one default is stronger than another perhaps because it represents an exception
the samples being rather small only the most common dependencies are evaluated subject object and predicative
NUM personal pronouns have morphological ambiguity between nominative nom and accusative acc readings
the evaluation was done using small excerpts of data not used in the development of the system
to solve the problems we developed a more powerful rule formalism which utilises an explicit dependency representation
in the best case we are sure that some reading is correct in the current context
linking as the global pruning section NUM later extracts dependency links that form consistent trees
projectivity or adjacency NUM was not an issue for tesni re NUM ch
one of the crude measures to evaluate dependencies is to count how many times the correct head is found
this is done by declaring the heads and the dependents complement or modifier in the context tests
a turn sequence represents the interpretations of the discourse that a participant has considered up to a particular time
we skip the complete explanatory argument here and just say that grosso mode the function of the copula construction is to synchronize calendar knowledge also information about different calendars r reading of NUM NUM with the actnm available perspective times whereas the function of the temporal adjunct is to relate the descibed event to some predefined time
some code for handling of finite domains was adapted from a program by gertjan van noord
features that are not instantiated can be omitted there is no need for anonymous variables
the rel orted event refers to the event of the presul l o sition line that marks the boundary l wcen the instantiated nnd tire nol instantiated event coucepts and it does this in right the satlle way as delinite descriptions do with resl ect to their mltecedents
as a further possibly optionm constrain the l reading introduces the implicature that a noi further specified person or grout cx null petted h r the perst ective time t that the plamtcd or expected sequence of events shouhl be realized to a greater degree
in the literature the representation of the fo using use of ersl and corresponding uses of noch and schon often comprises the information that tile reported remization of the event is earlier or later depending on the reading and the adverb than the speaker writer and or the recipient or even a third person would have expected
a pt lt l i l NUM in contrast to the epa reading we assume that in the r reading the predicates pi that we obtain from the information structure of the erstargument are not related to a sequence of opportunities for doing something but describe events ei of an expectation about the ongoing of the world or a plan e
the syntax of feature declarations is given in NUM
a feature must be introduced only at one most general sort
two intensional terms are identical only if they have been unified
the immediate subsorts of top can be declared to be extensional
where st is the temperature regulated strength s is the original strength and t is the temperature
this human capability highlights the fact that there is a continual interaction between word identification and sentence interpretation
the workspace is meant to be the region where the system does the parsing and construction required to understand a sentence
figure NUM shows an example of a possible state of the workspace when the system is processing sentence NUM
in addition there are word and chunk codelet types which are responsible for the construction of words and chunks
it differs from NUM in that the sentence in which it appears has two plausible interpretations
the method constantly selects a set of categories at the medium high level of generality different for each domain
a right level of abstraction so as to mediate at best between overambiguity and overgenerality
as a consequence in most on line thesaura words are extremely ambiguous with very subtle distinctions among senses
it is well known that statistically based approaches to lexical knowledge acquisition are faced with the problem of low counts
each iterative step that creates a c i also creates a set of underpopulated categories sct ci
function composition enables a function to be applied to its argument even if that argument is incomplete e.g.
for each set of categories c i generated by the algorithm in section NUM we computed on the reference corpus the following two l rformance figures precision for each ci let w c i be the set of words in the reference corpus covered by the met c i
for example it gives a type to john thinks mary but not to john thinks each
the lexicon is identical to that for a standard aacg except for having h lists which are always set to empty
the transition on encountering john is deterministic state application can not apply and state prediction can only be instantiated one way
h ar g rl r ll figure h transition rules
null however there are problems with having just composition the most basic of the non applicative operations
but the trouble of building a good tag handling system is well rewarded
at this experiment we obtained similar metrics apart from the coverage which dropped about NUM NUM for ending NUM and xerox rule sets and NUM for the suffix NUM rule set
important implications and practical applications of critical tokenization in effective ambiguity resolution and in efficient tokenization implementation are also carefully examined
just counting the number of such word strings will provide the answer to whether or not the character string is ambiguous
in our view this rarely provides a reasonable iephrasing
in addition easyenglish has a flexible system for using dictionaries as it does its analysis
the second criterion that of the divisivity of reference suggested by cheng NUM pp
see figure NUM input sentence is a stream of characters without explicit delimiters
in tagging a rule such as change the tag of the current word to x and of the previous word to y if z holds can easily be handled in the processor based system whereas it would be difficult to handle in a classification system
a graph of accuracy as a function of transformation number on the test set for lexicalized rules is shown in figure NUM before applying any transformations test set accuracy is NUM NUM so the transformations reduce the error rate by NUM over the baseline
the sentences shown in example NUM lc and ld are meaningful sentences
for a decision tree to take advantage of this information any word whose outcome is dependent upon the tagging of to would need the entire decision tree structure for the proper classification of each occurrence of to built into its decision tree path
better unknown word accuracy may be possible by training and using two sets of contextual rules one maximizing known word accuracy and the other maximizing unknown word accuracy and then applying the appropriate transformations to a word when tagging depending upon whether the word appears in the lexicon
in a stochastic n gram tagger the information about words that follow modals would be hidden deeply in the thousands or tens of thousands of contextual probabilities p tagi i zagi lzagi NUM and the result of multiplying different combinations of these probabilities together
from the NUM NUM word training corpus NUM NUM words were used to learn rules for tagging unknown words and NUM NUM words were used to learn contextual rules NUM rules were learned for tagging unknown words and NUM contextual tagging rules were learned
the experiment has shown that word filtering can eliminate most of the alternative word sequences
vf however if we change 29b to force the value loaded interpretation as in NUM then only the value loaded interpretation NUM is possible
it also can help the hearer adapting to the acoustic properties of the speaker s utterance without losing information
consider every feature that might be added to field mt and choose the best one
this time there are no missing dags to account for the missing probability mass
for that matter how can it be that ql is never greater than fi
that is if fl are the erf weights for a given grammar
it should be observed that the resulting weights are precisely the weights of model m1
random fields can be seen as a generalization of markov chains and stochastic branching processes
the length of the window used can be varied
this has several features which appear to be desirable
of these word sense disambiguation is attractive
to achieve this an unsupervised competitive neural network is being used
figure NUM design of the moving window
edinburgh eh8 9jz scotland u k
to verify that the syntactic likelihood is indeed useful we conducted the following additional experiment
the basic outline of the moving window used is shown in figure NUM
this work is motivated by psychological as well as by computational issues
colours are used to indicate the status of each component with respect to the current document collection dark red components have already been run and their results are available for viewing light green components have all their required inputs available and are ready to run and gray amber components require a currently unavailable input before they can become runnable
multext compatibility multext NUM NUM NUM was an eu project to produce tools for multilingual corpus annotation and sample corpora marked up according to the same standards used to drive the tool development
some of the objects in vie are freely available software e.g. the brill partof speech tagger NUM while others are derived from sheffield s muc NUM entry lasie NUM
in a gate based system the real work of processing texts analysis summarisation translation etc
and as increasing numbers of creole modules and databases become available through collaboration with sites able to provide single le components e.g. from the multext tools we expect gate and therefore the tipster architec NUM ture to become widely used in the le research community
as we built sheffield s muc NUM entry lasie NUM it was often the case that we were unsure of the implications for system performance of using tagger x instead of tagger y or gazetteer a instead of pattern matcher b in the ggi interface substitution of components is a point and click operation
working with gate the researcher will from the outset reuse existing components the overhead for doing so being much lower than is conventionally the NUM case instead of learning new methods for each module reused the common apis of gdm and creole mean that only one integration mechanism must be NUM learned
the authors would like to thank malcolm crawford of ilash university of sheffield for presenting a version of this paper at the april NUM tipster workshop and for extensive comments during the preparation of this paper
a creole object may be a wrapper around a pre existing le module or database e.g. a tagger or parser a lexicon or n gram index or may be developed from scratch to conform to the tipster architecture
this way we can generalize over semantic types and exploit relevant type information in the parsing process at the same time
thus the question of how we parse telegraphic messages accurately and efficiently becomes a critical issue in machine translation
the semantic types are classified into sets that can be distinguished on the basis of their behavior in the tree bank
with these observations in mind we decided to group the types and relax the constraints on semantic unification
it incrementally builds a probabilistic model of corrected annotations allowing it to quickly suggest alternative semantic analyses to the annotator
and it is clear how such lexicalized rules with the semantic categories reduce the syntactic ambiguity of the input text
the most probable analysis of a new sentence is constructed by combining fragments from the corpus in the most probable way
as an example figure NUM shows one of the decompositions of the annotated corpus sentence a man whistles
in the present paper we discuss a computationally effective version of that method and an implemented system that uses it
to summarize the grammar which combines syntactic rules and lexicalized semantic rules fares better than the syntactic lgrcal mm
these experimental results are also compared with the parsing results with respect to the lexicalized semantic grammar discussed in section NUM
the substructure go itself has two arguments thing NUM and toward ident and a modifier with poss NUM
we have to allow all possible words e of the target vocabulary
models describing these types of dependencies are referred to as alignrnen t
whereas pr j lale lcb is the string translation model
axis with the positions j of the source words j
in addition to all preprocessing steps we removed the punct uation
and NUM NUM when all transformation steps as described below had been applied
r explain the bill for room number three two four for me
the system parses and semantically analyzes the author s response into a corresponding lcs representation which is then prestored in a database of possible responses
it is commonly supposed that the imperfective viewpoint which refers to the middle of a situation omitting the initial as well as the final point can be used for describing a background within a discourse cf
2ain ertion otdeletion ofmutation lees st strictly NUM in lyon s ori l p per in fact there axe cases that an inserted phrase can not be constructed to form a nonterminal node
by these heuristics our robust parser can process only plausible edges first inste i of processing all generated edges at the same time so that we can enhance the performance of the robust parser and result in the great reduction in the number of resultant trees
one of the main results of this work is the definition of a relat ion between broad semantic classes and lcs meaniug components
improving the ejficiency of human machine dialogue a computational model of variable initiative and negotiation in collaborative problem
NUM c there is supposed to be a wire between connector nine nine and connector one zero zero
this author argues that computer computer simulations are one layer in the multi layer process of building human computer dialogue systems
the initiative will now belong to whomever the initiative is for the goal on top of the stackj
table NUM presents results computed from NUM collaborations where the agents who is the murderer of lord dunsmore
computercomputer simulations allow us to evaluate our computational models and explore issues that can not be resolved analytically
unlike previous research in dialogue initiative however we attach an initiative level to each goal in the task tree
there is a tradeoff in that negotiation is expensive both in terms of time and computational complexity
NUM c there is supposed to be a wire between connector nine eight and connector one zero two
to narrow the search the doctor will try to find a pathology that accounts for these symptoms
evaluating a dialogue management system is a difficult and often subjective experience
given the above discussion the immediate objection that one can raise is that discourse markers are doubly ambiguous in some cases their use is only sentential i.e. they make a semantic contribution to the interpretation of a clause and even in the cases where markers have a discourse usage they are ambiguous with respect to the rhetorical relations that they mark and the sizes of the textual spans that they connect
this is a measure of genericness or applicationindependence of a given system which can be used to moderate accuracy speed scores in comparisons of very unlike dmss serving different domains
for applications where more than one task is to be performed in a single dialogue the dialogue manager needs to be able to identify when the user switches from one task to another
g over informativeness there are two interpretations of over informativeness system and user orientated system orientated over informativeness allows the dialogue manager to present more information to the user than was actually explicitly requested
as an example consider the following dialogue between the system and user where the user responds with a reply which is overinformative null user i d like to make an appointment
a anaphora anaphora frequently occurs in dialogue
different applications require different dms features
the purpose of computing discourse functions in analysis is twofold it supports disambiguation not only of the discourse particles but also of the surrounding words and computation of the dialogue act underlying the utterance and it helps in segmentation i.e. breaking an utterance into portions that serve as complete units for further processing
however one area which we will examine in slightly more detail because of its relevance to the work on word prediction discussed above is the proposed use of semantic categories in the grammar and in the statistical component
somebody person v people v people multitude v social group a society v subculture v political system v moiety v clan
composition also allows alternative non conventional semantically equivalent leftbranching derivations
type constraints of this kind serve to greatly reduce the degree of ambiguity in a given complex nominal but it will still generally be the case that more than one interpretation is predicted for a given form
ffi parts of qualia ffi telic purl ose of c agentive how ol is brought about given this model of lexical representation a noun such as knife has the entry in NUM
in these cases it can be expected that the cumulative contribution of the weights and in particular those that are not indicative to the current category does not count towards exceeding the threshold but rather averages out to NUM indeed as we found out no special normalization is required when using these algorithms
we argue that the development of a practical model of compound interpretation crucially depends on issues of lexicon design
the assignment of a complex structure to an individual quale is coherent with the general interpretation of qualia structure
the modifier hunting is a process nominal and provides hunt as the telic within the telic of the compound
the resulting model would provide the probability that a given complex nominal involves a particular kind of modification relation
NUM lemon juice typestr arg1 e liquid d arg1 i lemon argstr d e1 transition qualia formal agentive squeeze act
NUM NUM short cuts in the generic hierarchy
NUM NUM are short cuts in the meronymic
whenever it tries to build a partially recognized constituent it incrementally verifies the admissibility of the semantic part of such a constituent using the wordnet hierarchy
in the section following that we present our complementary method a technique utilizing verb clusters automatically computed from corpus data
this information on predominant senses for each word form in a given corpus can be computed automatically but remains implicit
a program consuits the three databases and the mapping tables and for each word occurrence constructs a list of the senses that are compatible with the syntactic constraints
we note that while words like question and ask are ultimately connected in wordnet the actual connections are only between some of the senses of the two words
for all NUM uses of appear in the corpus the average number of possible senses predicted by our method is NUM NUM
we have performed preliminary evaluation tests of our method for tagging verb occurrences with pruned word sense tags using the brown corpus
as we see from this experiment the integration of the two methods can improve the reduction rate of ambiguity but may slightly increase the error rate
syntactic clues a given word may have n distinct senses and appear within m different syntactic contexts but typically not all n x m combinations are valid
in english it is usually but not always the right daughter of a mother node that is strong
in order to maximize the speed of response these phrases were spoken when the button was activated without recourse to a menu of possible choices
the commonly used method for probabilistic classification the bayesian classifier chooses a class for a pattern x by picking the class that has the maximum conditional probability p classlx
we only used co occurrence data including the wo relation accusative case
the svmv model formalizes the probability p clw as follows
situation aspects smith introduces three so called conceptual features of situation aspects which have binary values NUM namely stative duratire and telic
this results in a back off sequence in which the terms at each step in the sequence are weighted with respect to each other but without the introduction of any additional weighting parameters
the significant difference between uramoto and our research can be summarized as follows
the dyd system produces spoken monologues derived from information stored in a general purpose database about w a mozart s instrumental compositions
the algorithm applies rules to pronunciations recursively when a context matches the left hand side of a phonological rule rule two pronunciations are produced one unchanged by the rule marked rule and one with the rule applied marked rule
by computing the weighted average seconds per phone for male and female speakers we found that females had an average of NUM ms phone while males had an average of NUM ms phone a difference of about NUM quite correlated with the similar differences in reduction and flapping
columns show the maximum number of class codes assigned to each target word
each noun is represented by a set of verbs co occurring with that noun
each edge is associated with an acoustic score representing a measure of confidence that the word perceived there is the word that was actually uttered
if further belief nestings are required then they can be derived using belief ascription techniques as required
the latter approach speech act processing based on speech act theory involves viewing dialogue in planning terms
finally in section NUM we discuss some implications and future directions of our work NUM speech acts and mental attitudes it is clear that any understanding of an utterance must involve reference to the attitudes of the speaker
dcgs can be translated directly into prolog for which interpreters and compilers exist that are fast enough to handle real time processing of spoken input
by an appropriate parsing algorithm one thus combines the robustness that can be achieved using concept spotting with the flexibility of a sophisticated language model
in section NUM we describe how viewgen represents mental attitudes and computes nested structures by a process of ascrip null tion and in section NUM show how such techniques can be used to represent speech acts for use in planning and plan recognition
an update is a logical formula which can be evaluated against an information state and which gives rise to a new updated information state
this is due to the fact that the test sentences contain many speech act verbs such as syuchousuru insist setumeisuru explain hyoumeisuru declare etc
as for the rest NUM verbs NUM verbs were identified in the step NUM NUM as the category which was not included in the set of categories outputted by the step NUM NUM
the number of model parameters is small and they have more reliable estimated values
since we are interested in recovering the name class state sequence we pursue eight theories at every given step of the algorithm
on the other hand the result also shows that merely annotating more data will not yield dramatic improvement in the performance
NUM the agent has unfnlfilled goals and the initiative adopt the partner s goal if the response is thematically related backto subquestion persist with the own goal if unrelated repeat new object
in cdm these aspects form the basis of the system s functionality dialogues are regarded as collaborative activities planned locally in the changed context as reactions to the previous contributions and governed by the rationality principles of ideal cooperation
unlike a traditional hmm the probability of generating a particular word is NUM for each word state inside each of the name class states
the rest of the features distinguish types of capitalization and all other words such as punctuation marks which are separate tokens
this section describes the model formally discussing the transition probabilities to the wordstates which generate the words of each name class
we chose to use a bigram language model because while less semantically appealing such n gram language models work remarkably well in practice
the task division and information flow in a cdm system NUM is shown in fig NUM the dialogue manager operates on the context model which is a dynamic knowledge base containing facts about the agents goals expressive and evocative attitudes central concepts topic and new information
expression may differ fl om evocation irony indirectness aud the evoked response fi om the evocative intentions the agent requests ilfformation that the partner can not or does not want to disclose the agent fa ils to fi ighten the partner becmme this has guessed the agent s malicious intentions
when the agent has the right to take the initiative a previously unfulfilled goal can be taken up
task goals are planned to complete a real workt task rent a car book flight repair a pump but because of uneven distribution of knowledge the agents usually need to collaborate to achieve the goal and thus formulate com nunicative goals to obtain missing information el
the system responses NUM and NUM NUM are based on the same strategy baclcto the system goes back to adopt the user s previous unfulfilled goal and tries to satisfy this in the updated context
ideal cooperation does not mean that the agents always react in the way the partner intended to evoke but rather it sets the normality assumptions for the way the agents would behave if no disturbing factors were present
null NUM generate all subsequent words inside the current name class where each subsequent word is conditioned on its immediate predecessor
communicative principles fimetion on the following levels NUM determination of the joint purpose reasoning about a communicative strategy in the context expectations initiatives unflflfilled goals thematic coherence NUM selection of the communicative goal filtering the joint purpose with respect to the agent s role and task
the forms tutuaru be in progress tekuru come into state and teiku go into state focus on the gradual process of change
here automatic annotation and human supervision are combined interactively whereby annotators are asked to confirm the local
instead of keeping track of the best path only of
almost reliable NUM pbes t NUM psecond
most of these errors were eliminated by comparing two independent annotations and cleaning up the data
they differ in that sentences s contain finite verbs and verb phrases vp contain non finite verbs
this provides a promising way of handling free word order phenomena
the fastest annotators cover up to das NUM startende bonusprogramm for vielflieger
the probabilities of alternative assignments are within some small specified distance
the exact definition of the anchor is based on linguistic knowledge
arguably the degree of polysemy of a word is related to the degree of difficulty of the tagging process
in our analysis for german we therefore highlighted the following two properties which can be stipulated regarding the neutral viewpoint the end point of a situation is beyond the focus of this viewpoint
kuwawaru join tutomeru be employed tomonau accompany tazuneru visit rainitisuru eome to japan uwamawaru be more than hokoru boast
the second step means that beliefs and intentions are dealt with by reasoning about the utterance context and communicative constraints instead of speech act types
an important strategy is to acknowledge in the instructions the wem nesses of the task definition and the dit iculties the tagger is likely to face
all other criteria exhibit more variation between fss and bss in feature set selection
maritally related and the system has the initiative and unfulfilled goals at least one based on the original task to provide information
step NUM for each adverb in pairs give an adverb class label the initial letter of the class name on the basis of the discussion in sec
b a leaf x is marked a if there is a recent occurrence of an expression y which is semantically subsumed by x
the conclusion seems unavoidable language generation requires a specific kind of context models which are suitable to formalize the notion of a linguistic context
it is well known that some of the most important issues in the design of a dialogue system involve the modeling of linguistic context
thus it is important for the system to maintain a record showing which information has been expressed and when it has been expressed
our experience has been that less strict contexts e.g. just alc or rc generate very useful delete rules which basically weed out what can almost never happen as it is certainly not very feasible to formulate hand crafted rules that specify what sequences of features are not possible
the system uses rules of the sort if lc and rc then choose parse or if lc and rc then delete parse where lc and rc are feature constraints on unambiguous left and right contexts of a given token and parse is a feature constraint on the parse s that is are chosen or deleted in that context if they are subsumed by that constraint
the feature names are as follows cat major category type minor category r00t main root form agr number and person agreement poss possessive agreement case surface case conv conversion to the category following with a certain suffix indicated by the argument after that taml tense aspect mood marker NUM sense verbal polarity des desire mood imp imperative mood 0ptoptative mood cond conditional
our results indicate that by combining these hand crafted statistical and learned information sources we can attain a recall of NUM to NUM with a corresponding precision of NUM to NUM and ambiguity of NUM NUM to NUM NUM parses per token on test texts however the impact of the rules that are learned is not significant as hand crafted rules do most of the easy work at the initial stages
to deal with this we have made the assumption that all unknown words have nominal roots and built a second morphological analyzer whose nominal root lexicon recognizes s where s is the turkish surface alphabet in the two level morphology sense but then tries to interpret an arbitrary postfix of the unknown word as a sequence of turkish suffixes subject to all morphographemic constraints
the specific problem tested involves disambiguating six senses of the word line using the words in the current and proceeding sentence as context
this paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context
akkasuru get worse tuyornaru get strong takarnaru become raised sinkoukasuru get more acute seityousuru grow up kappatukasuru make active
before it can be executed it needs a location feature indicating where to create the unit which is provided by the user s gesturing on the screen
by the time this tree is applied mr
tree NUM a portion of the person filter tree
it emerged from the discourse examples that a crucial function of the viewpoint is the commitment the speaker gives as to whether the end point has been reached or not
te is a test of fine grained information extraction
after the official evaluation we retrained resolve based on a prepruning procedure in which only the easiest subset of the positive training instances pairs of coreferent phrases are used
earlier experiments with resove in the muc NUM ejv domain showed that our unpruned c4 NUM decision trees used fo r coreference resolution tend to get higher recall and lower precision than unpruned decision trees
competent analysis of coreferent relationships among all noun phrases requires a much mor e refined knowledge base and much deeper linguistic analysis than we employ in any of our current informatio n extraction components
tree NUM a portion of the person status links tree
an instance i s formed for mr
most recent compatible subject yes was used for NUM instances
same string yes was used for NUM instances
the situation changes for one of the antect dent options of the still unresolved l ronoun is no longer available
the strategy of considering the more plausible antecedent choices first does not eliminate interdependency collisions in general and moreover NUM
c global sorting sort the anaphors v according to decreasing i lausibility of their individual best antecedent candidate
as the following examl les demonstrate it iv insufficient to know merely about the existence of a h cal domain
moreover by showing that t ragmatic inferences may t e necessary the limits of syntactic restrictions are ehleidated
this explanation is confirmed by the following data 13a paull revises sarn sj decision for h imi
starting with a recapitulation of current work on anaphor resolution it was argued for an approach which bases on syntactic restrictions
at current the kontext text analysis system employs a processing model according to which parsing is performed prior to anaphor resolution
this gives rise to a weak version of binding constraint verification the usage of which is of vital importance to the fimctioning of the interdependency test step 3b
because of the interdependency between parsing and anaphor resolution however these two problem lasses should be handled at one stage of processing rather than sequentially
if the spoken command had instead been barbed wire it would have been assigned the feature structure in figure NUM
however the existing analyst interface relies on character based dumb terminals for those analysts who are not familiar with window based systems the transition to using a mouse rather than cursor movement commands may be a more significant change than using an information extraction system
the task can be divided into an annotation and a comparison stage
table NUM match between the results of test systems and native speakers
it checks whether an anaphor is at the beginning of a discourse segment
then the following rule is used if a nominal form is decided on
figure NUM occurrence of anaphors in the test texts
the first one uses locality syntactic constraints and animacy
NUM when looking at a specific set of lexical rules though one can be more specific as to which sequences of lexical rule applications are possible
then the essential character null the chinese natural language system
the follow relation obtained for the set of four lexical rules is shown in figure NUM where follow lr listoflrs specifies compilation process
on each sheet is a text generated by our generation system
encoding the disjunctive possibilities for lexical rule application in this way instead of with definite clause attachments makes all relevant lexical information available at lexical lookup
they describe the use of a dataflow analysis for an off line improvement of grammars that determines automatically when a particular goal in a clause can best be executed
NUM as it stands our encoding of lexical rules and their application as covariation in lexical entries does not yet support the application of lexical rules on the fly
instead what we do is factor out the information common to all definitions of the called interaction predicate by computing the most specific generalization of these definitions
intuitively understood each defining clause of a frame predicate corresponds to a subclass of the class of lexical entries to which a lexical rule can be applied
we want our compiler to produce an encoding of lexical rules that allows us to execute lexical rules on the fly i.e. at some time after lexical lookup
continuous adverbs are those that can modify both states verbs d and process verbs p such as zutto for a long time itumademo forever etc
mds does not presume that a NUM climensional representation displays the distances between texts
this distance is used directly or in exploration of the differences between texts
since the mcca results seem more robust than tagging with wordnet synsets q v
this category includes the following along with various inflectional forms
the two sets of scores are used for computing the distance among texts
distributions can be analyzed using non agglomerative clustering to characterize the concepts and themes
desiderata for tagging with wordnet synsets or mcca categories
other synsets under expert and alyri iority do not fall into this category
percent and a range fi om NUM to NUM
these figures are much smaller than the number of simple combinations NUM NUM NUM
auxiliary verbs lexicon what we call the surface case permutation frame code scpf code
points with similar displacement can be grouped together by sorting as illustrated in figure NUM
during the merging process there are some sharp pealr indicating the rapid fluctuation of entropy
the duration of the situation can be drawn in two different ways as an unstructured and a structured phase which has internal stages
we denote the entropy of the sentence after thresholding by et
for example null the borrowing to raise these funds would be paid pay off verb i off as assets of sick thrifts are sold
if this sentence were given a literal reading perhaps by an automatic tagger with another winner might be identified as an acceptable prepositional phrase
finally it is helpful to the human tagger for the preprocessor to target these distinguished classes for which relatively high accuracy automatic solutions are possible
NUM does the word following the verb cease to have any of its usual or literal meanings as supplied by wordnet when used with that verb
verb NUM up the present situation the economies of these countries would be totally restructured to be able to almost sustain growth by themselves
word lemma wordnet pos wordnet sense numb for example the sacramento based s l had have verb NUM assets of NUM NUM billion at the end of september
a problem found several times in the corpus occurred when a single verb is used in a sentence that has two objects and each object suggests a different sense of the verb
we have made some suggestions for consistently identifying certain uses of verbs and for representing tags and have shared some guidelines from our annotation instructions for identifying idioms in the corpus
to maintain separate nnotations and also tie the constituents of an idiom together we suggest the format below or an analogous one which is generated by the preprocessor
the rule also illustrates that r can be a sequence of tuples
the tagger translates these grammar codes into sequences of grammatical tags and super segmental tags representing the possible sequences that may follow the verb and then integrates these with the selectional preference patterns
annotation efficiency increased by NUM namely from an average annotation time of NUM minutes to NUM minutes per sentence NUM to NUM words per hour
thus assuming no other taggers intervene the sense tagger will make the best possible assignment for these two admittedly rather ambiguous examples
each of the NUM words was manually assigned with just one sense tag and the tagging program likewise assigned precisely one sense tag to each word
it thus secrns sensible especially noting wilks and stevenson s analysis mentioned above to first run a sentence through a traditional part of speech tagger before trying to disambiguate the senses
from the test samples we collected the frequencies of all our features and included into the initial atomic feature set only those features which appeared more than NUM times in the positive training samples
although certain restrictions are applied to reduce the number of sequences to a manageable size e.g. a limit on the number of nested brackets
it is these resulting patterns that the pattern tagger uses to test the syntactic and semantic veracity of the tag sequences produced by the part of speech tagger
the main weightings currently in use which may be of interest to other researchers trying to combine different tests are shown in the table
local grammars lgs are constructed by examining specific syntactic contexts of lexical elements given that the general syntactic rules independent from lexical items can not provide accurate analyses
we would rather add a smaller factor b that has the same effect in that NUM NUM b NUM NUM b b will look something like the original c but with some paths missing some states split and some cycles unrolled
observe that each state of b has the form i x c for some i e i and c e c we form b from b by re merging states i x c and i x c where possible using an approach similar to dfa minimization
if c1 in a given language is violated by just the forms with coda consonants then filterl gen input includes only coda free candidates regardless of their other demerits such as discrepancies from input or unusual syllable structure
the consequence for zero width consituents is that even if a zero width NUM overlaps at the edge say with a surface v the latter can not claim on this basis alone to satisfy fill v v
filtering gen input through constraints 23a d we are left with just those candidates where stem bears n disjoint constituents of type s each coextensive with a constituent bearing a different label v e v g
the interior of a constituent is the open interval that excludes its edges thus lab is linked to both consonants c in 4b but the two consonants are not linked to each other because their interiors do not overlap
valid timelines those in repns also require that edge brackets come in matching pairs that constituents have positive width and that constituents of the same type do not overlap i.e. two constituents on the same tier may not be linked
however this ignores the bestpaths step we wish to keep just the best paths in r x that are compatible with a such paths might be long and include cycles in f x
a different use of expectation appears in sentence NUM where local expectations were related to the connection of a wire
NUM computer there is supposed to be a wire between connector one zero four and connector one zero two
figure NUM shows the relevant derivation for the fragment investigate two dialects of discussed at end of previous section
NUM every dealer shows most customers at most three cars but most mechanics every car
although it is beyond the scope of the present paper to discuss further details of intensionality it is clear that de re interpretations of nps are strongly related to referential np semantics in the sense that the de re reading of a is about a referred individual and not about an arbitrary such individual
one payoff of this close correspondence to natural languag e is the capability of automatically creating and querring knowledge bases from textual documents
while handling punctuation is good in general specifically for muc NUM it is needed fo r processing numbers and facilitating identification of apposites
in every such reading however the truth of NUM b depends upon finding appropriate individuals or the group for f such that each of those individuals or the group itself gets associated with appropriate individuals or a group of individuals for r via the relation visil ed
they attribute the reason to the logical structure of english as in NUM as it is considered unable to afford an unbound variable a constraint known as the unbound variable constraint uvc
for example as one of the referees pointed out the uvc is required to explain why in a below every professor must outscope a friend so as to bind the pronoun his
the evaluation metrics used for ne are essentially the same as those used for the two template filling tasks template element and scenario template
this discussion shows the operation of all parts of the zmodsubdialog model and illustrates the mechanisms used in our dialog machine
this makes it possible to reopen any subdialog at a later time to clarify revise or continue that interaction
consequently there is a successful exit of the current subdialog and achievement of the set knob NUM goal
bu t the bragging rights to coke s ubiquitous advertising belongs t o creative artists agency the big enamex type location quot hollywood enamex talent agency
the system generated outputs are from three different systems since no one system did better than all other systems on all three events
note that this event type does not have to be instantiated with a situation of this type it will therefore not be introduced like a discourse referent in a discourse representation structure
when the number of the resulting classes is larger than the pre defined number we use the merging technique presented above to reduce the number until it is equal to the pre defined number
this collection called a factored automaton serves as a compact representation of ha
to reflect the different influence of left neighbor and right neighbor of the word we introduce the probability for each word w to every class
however there are many pairs or triples of clusters that should be collapsed into one on linguistic grounds
tag induction fails for cardinals for the reasons mentioned above and for ing forms
in the experiment NUM NUM natural contexts were randomly selected processed by the svd and clustered into set
our tag set was then induced by clustering the reduced vectors of the NUM NUM selected occurrences into NUM classes
the only properties of context that we consider are the right context vector of the preceding word and the left context vector of the following word because they seem to represent the contextual information most important for the categorization of w
the left and right context vectors are the basis for four different tag induction experiments which are described in detail below induction based on word type only induction based on word type and context induction based on word type and context restricted to natural contexts induction based on word type and context using generalized left and right context vec
word side nearest neighbors onto left onto right seemed left seemed right into toward away off together against beside around down reduce among regarding against towards plus toward using unlike appeared might would remained had became could must should seem seems wanted want going meant tried expect likely table h words with most similar left and right neighbors for onto and seemed
by restricting the matrices to so and do to their first m k columns principal components one obtains the matrices t s and d their product c is the best least square approximation of c by a matrix of rank m c tsd
the results of the four experiments were evaluated by forming NUM classes of tags from the penn treebank as shown in table NUM preliminary experiments showed that distributional methods distinguish adnominal and predicative uses of adjectives e.g.
contexts with rare words less than ten occurrences were also excluded for similar reasons if a word only occurs nine or fewer times its left and right context vectors capture little information for syntactic categorization
this work improves on existing generation approaches in the following respects i unlike the majority of generators this one takes a non hierarchical logically well defined semantic representation as its input
while in the stage of fleshing out the skeleton sentence structure section NUM NUM the syntactic integration involves subsertion in the stage of covering the remaining semantics it is sister adjunction that is used
this way of matching allows the generator to convey only the information in the original semantics and what the language forces one to convey even though more information might be known about the particular situation
the algorithm has to be checked against more linguistic data and we intend to do more work on additional control mechanisms and also using alternative generation strategies using knowledge sources free from control information
we try to generate sentences whose semantics is as close as possible to the input in the sense that they introduce little extra material and leave uncovered a small part of the input semantics
under the second approach that of incremental consumption generation is done by gradually relating consuming pieces of the input semantics to linguistic structure NUM NUM
some researchers NUM are looking at finding an appropriate sequence of expansions of concepts and reductions of subparts of the semantic network until all concepts have realisations in the language
however all reasoning is done at the level of the clarification actions and so the surface actions do not include any constraints or effects
in the last two sections we discussed how initial referring expressions judgments and refashionings can be generated and understood in our plan based model
these candidates satisfy 23a c but violate 23d n times
for instance in appelt s model concept activations can be achieved by the action describe which is a primitive not further decomposed
but since the speaker and the hearer will inevitably have different beliefs about the world the hearer might not be able to identify the object
NUM and the acceptance of these contributions depends on whether the hearer believes he is understanding well enough for current purposes p
we have found that this simple approach can find the instantiation for valid plans and can find the action that is in error for the others
for the judgment plans we have the surface speech actions s accept s reject and s postpone corresponding to the three possibilities in their model
in the first case the referring expression is overconstrained and the evaluation would have failed on an action that decomposes into surface speech actions
each line represents a np an individual in statistical terms there is a NUM when the term built with the np and the expansion exists e.g.
in the information phase the information service uses these information elements to compose her presentation
23e says that a chain of abutting constituents uiviw
given the individuals variables matrix above a similarity measure between the individuals is calculated NUM and a hierarchical clustering method is performed with as input a similarity matrix
this kind of methods gives as a result a classification tree or dendrogram which has to be cut at a given level in order to produce clusters
such a predicate allows any symbol from a on the tiers it does not mention
it is easy to observe that usage of the word bank is different between the economic document domain and the geographic domain
the string could be wrongly selected if we do not observe its behavior ill the leftward sorted string table to determine tit correct left boundary
n a NUM n a and NUM NUM NUM
these are grammars acquired from the same size corpus of the same domain all domains non fiction domains and fiction domains
in the romance and love story domain the wide variety grammar in particular the fiction domain grammar quickly catch up to the performance of the baseline grammar
among the several types of linguistic knowledge we are interested in parsing the essential component of many nlp systems and hence domain dependencies of syntactic knowledge
in contrast the performance on some fiction domain texts k and l with the non fiction grammar is not so different from that of the same domain
thus a high threshold will result in less extracted semantics than a low threshold
the cornpetitive selection and unified selection of rightward and leftward sorted strings play an important role in improving accuracy of the extraction
it is not easy to argue why for some domains the result is better with the grammar of the same class rather than the same domain
these differences play a major role in the expression of inter variable phenomena such as comparison and correlation
consequently postgraphe must be able to automatically compute the relational keys it needs from the data
the properties have a variable number of parameters which can be used to further specify their function
we will now describe the major steps followed by the system in the generation of a report
one has to consider the writer s goals the data itself and the reader s interpretation
heuristics are used by postgraphe to trim the number of solutions down to a usable level
for example correlation is better preceived on a point graph than on a line graph
in order to show a comparison a few structural changes have to NUM be made
NUM good news otp usefully restricts the space of grammars to be learned
we have described an implemented generation system with an interactive content specification stage which operates in a conceptually and stylistically constrained environment
the surface text generation is handled by a modified version of pr4texte NUM
the fastest mode performs the minimum analysis at the greatest speed with the lowest performance
first we must apply some normalization in order to reduce the noise caused by the more ambiguous verbs
in the browsing mode the client module allows the user to browse the information in the database in various ways
the system consists of the indexing module the client module the term translation module and the web crawler
in this section we describe how we have incorporated this technology to improve multilingual information access in several innovative ways
first this module is unique in that it creates on the fly english translations of hiragana names and personal names
this translation module is sensitive to the semantic types of terms it is translating to resolve translation ambiguity
null indexing of names is particularly useful in the japanese case as it can improve overall segmentation and thus indexing accuracy
our multilingual capability enables the merging of possibly complementary data from both english and japanese sources and enriching the available information
he recognize that person is who he recognized who that person is
a document which the user is browsing can be translated on the fly by clicking the translate button
the client module then retrieves documents which match either the original english query or the translated japanese query
we then progressively add extra tests to the rule based on independently motivated but simple linguistic principles
the verb is selected by recursively unifying the process description including its newly assigned semr feature cf
NUM as discussed below this is a domain specific decision that only applies to the particular relation class assignt
thus two cross cutting constituent structures thematic and syntactic can be represented in the same fd
the semantic networks corresponding to examples NUM NUM and NUM are shown graphically in figure NUM
pragmatics information about speaker intent hearer background or previous discourse plays a role
which content units are floating and which are structural depends on the domain and the particular target sublanguage
lexical choice is performed automatically by unifying the lexicon or lexical fug with the conceptual input
but the phrase planning component has already determined to use the main verb to realize the input relation
first the head constituent of the linguistic structure is built from the description of the class assignt relation
the domain the same words are used to refer to different concepts in different domain sublanguages
as for the approximated probability of this analysis its sw set contains a single word hmwnh n lr n the counter the definite form of the same noun
what concerns us here is the interrelationship between the forms of referring expressions and the discourse segment structures
each phase of partial parsing is completed by concatenating those most reliable modification pairs together to form a single unit
in example NUM the left to right annotation has not been strictly observed since ber bermuda appears as a restart with repair within the repair of another restart
while it is difficult to judge overall accuracy some of the phases are onomatopoetic and others are simply too hard even for good human translators it is easier to identify system weaknesses and most of these lie in the p w model
according to discourse theories in linguistics given information tends to occur in the beginning of a sentence where the topic is established whereas new information tends to occur at the end the comment on the topic
let us consider an example text of the sort that we encounter in our application NUM 1the texts in our application are messages consisting of free text possibly interspersed with formatted tables or charts which themselves may contain natural language fragments that require analysis
the crayons that are sticking up it will be the headboard each sequence of words consisting of only continuers or assessments expressions such as uh huh right yeah oh really is also coded as a sentence as in examples NUM and NUM
in general we can convert this to a percentage by dividing by the total number of anaphora
taggers based on the hmm technique compensate for some serious training problems inherent in the mlm approach
given the sw set of each analysis we can now find in the corpus how many times each word appears calculate the expected frequency of each analysis and get the desired probabilities by normalizing the frequency distribution
indeed all possible phrases or paraphrases of actual content in a document are potentially valuable in indexing
in the remaining types of sentences the topic can be found at the beginning of the sentence
the second step is to check if these as forms are actually indepen lent from each other with respect to the original one
in the above exmnple t br instance NUM occurs twice in the first disjunction and occurs twi e in the second disjunction
however in case the extracted information does n t contain a handy description the system can use some descriptions retrieved by profile
another long term goal of our research is the generation of evolving summaries that continuously update the user on a given topic of interest
precision improves at various returned document levels as well as shown in table NUM
the result can be divided into three subtasks we allow for other components to store and re null trieve context information
in the first case the correct date will be translated in the latter the user is asked to repeat the whole turn
the sequence memory in figure NUM shows in addi null tion to the actual recognized dialogue act also the predictions for the following utterance
in particular we assume a procedure according to which the antecedent of an anaphoric temporal expression is first looked up in the il expressions of the text already parsed with a preference for the most recent expressions if no one is found the discourse memory is consulted to retrieve from previous parts of the if the client is not satisfied with such an expression backtracking will pass the next bcst structure etc
the first class includes probably any nl time expression even a simple expression such as NUM requires some extralinguistic knowledge to be understood in its proper contextual meaning in NUM the working day interval of the respective day must be known
the four main modules include the basic tcp ip connection to the server a parser of semantic representations of the server s analysis results which yields pasha ii structures an instantiation mechanism for semantic generation templates and a control regime that keeps track of the current dialogue
however human dialogue behavior differs from interaction between machine agents considerably as will be discussed in section NUM a human machine interface to existing appointment scheduling agent systems should comply to the following requirements human utterances must be analyzed to correspond closely to agent actions
for instance cosma clients can parameterize tg NUM so as to refer to their owner by a first person pronoun or by a full name or to use formal or informal form of addressing the human hearer or to prefer deictic time descriptions over anaphorical ones
lexical atoms help us by obviating the possibility of extraneous word matches that have nothing to do with true relevance
however reducing the size of the sample space by morphological processing of compound nouns should be considered in order to increase coverage
the detection of lexical atoms like the parsing of simplex noun phrases is also done in multiple phases
NUM er weist die kritik der prinzessin seine he rejects the criticism the princess his ohren seien zu grofl zurfick
for instance in sentence NUM the verb suchen to seek is erroneously in the third person plural
the decision algorithm determines for a given test tuple nl v n2 which noun is the subject of the verb v
the number of coded features and their interactions makes the manual construction of rules that predict cue occurrence and placement an intractable task
our results largely confirm the suggestions from the hterature and clarify them by highhghting the most influential features for a particular task
this is often the case with the whole tutor s explanation since its purpose is to answer the student s explicit question
ato make the example more intelligible we replaced references to parts of the circuit with the labels partl part2 and part3
this study clearly indicates that segment structure most notably the ordering of core and contributor is crucial for determining cuc occurrence
first the issue of placement arises only in the case of core for core1 cues only occur on the contributor
we constructed three subsets by always including the eight features that do not concern segment structure and adding one of those that does
NUM subsets built out of the NUM to NUM attributes appearing highest in the tree obtained by running c4 NUM on all features
joints are segments comprising more than one core but no contributor clusters are multiunit structures with no recognizable core contributor relation
this work has also benefited greatly from suggestions and advice from scott miller
table NUM shows the results for models NUM NUM and NUM
each set of patterns involves one left to right scan of the sentence
this paper proposes three new parsing models
taking the compound noun shipping materials as an example the corresponding cases for the words shipping and materials are both annotated as the heal3 case in the corpus as shown in figure
for example t h estimation interpolates
to a top down derivation of the tree
as complements are generated they are removed from the appropriate subcat multiset
this paper has proposed a generative lexicalised probabilistic parsing model
the second step is to calculate the degree of word in paragraph xp article xa and domain xd
in other cases such as morte da annegamento death from drowning and bruciatura da sole sun burn the preposition is da
we defined the degree of word i in paragraph article and domain as the deviation value of k contexts in paragraph article and domain respectively
this is because to disambiguate word senses in articles might affect the accuracy of context dependent domain specific key paragraphs retrieval since the meaning of a word characterises the domain in which it is used
most of these plans NUM concern earlier or later connections
according to table NUM in human judgement NUM out of NUM articles key paragraphs are located in the first parts and the ratio attained NUM NUM
the interpretation of the compound form hunting rifle can be glossed as follows a rifle which is used in its typical capacity i.e.
stk stock market NUM ret retailing NUM aro aerospace NUM env environment NUM pcs stones gold NUM cmd farm products NUM there are NUM NUM different nouns in NUM articles
in the examples of telic qualia modification considered so far NUM a b the modifying noun was always of type individual
the basic pattern established so far is that modification of telic agentive and constitutive involves da di and a respectively
the analysis of nominal compound constructions has proven to be a recalcitrant problem for linguistic semantics and poses serious challenges for natural language processing systems
alternatively version control can be provided by means of hyperlinking
any input file whose dtd satisfied this constraint could be tagged
does this mean the lt nsl architecture is application neutral
this is of benefit when working with corpora which may change
lt nsl provides the hyperlinking semantics to interpret this sgml
lt nsl is not specific to particular applications or dtds
annotations are linked to texts by means of character offsets NUM
the sample back end tools distributed with lt nsl reflect this fact
processes requiring sequential access to large text corpora are well supported
the result is a format easily readable by humans and programs
some of the methods we could use for assessing experimentally the accomplishment of these criteria would be introspection a lexicographer checks if the srs accomplish the criteria a and b above e.g. the manual diagnosis in table NUM
suit advocate buyer carrier client group proper name proper name administration government government leadership administration leadership provision concern leadership provision science NUM evaluation of the appropriateness of the candidates by means of a statistical measure
noun frequencies in the global weighting technique presented in equation NUM very polysemous nouns provide the same amount of evidence to every sense as nonambiguous nouns do while less ambiguous nouns could be more informative about the correct classes as long as they do not carry ambiguity
on the one hand from the point of view of lexicography the goal of evaluation would be to measure the quality of the srs induced i.e. how well the resulting classes correspond to the nouns as they were used in the corpus
the statistical measures used to detect associations on the distribution defined by two random variables x and y work by measuring the deviation of the conditional distribution p xjy from the expected distribution if both variables were considered independent i.e. the marginal distribution p x
as far as lexicography quality is concerned we think the main criteria srs acquired from corpora should meet are a correct categorization inferred classes should correspond to the correct senses of the words that are being generalized b appropriate generalization level and c good coverage the majority of the noun occurrences in the corpus should be successfully generalized by the induced srs
furthermore since most thesauri aim at a general word hierarchy the similarity between words used in specific domains technical terms can not be measured to the desired level of accuracy
resolution of the first equation yields the value ax l x spf for r pe
thus x d can be instantiated with any colored formula that does not contain the color d
the hocu algorithm is augmented with suitable rules for boolean constraint satisfaction for color equations
first the definition of colored substitutions ensures that the term assigned to r 0f is pf monochrome
to account for these differences some machinery is needed which turns dsp s intuitive idea into a fullyblown theory
collecting the bindings we arrive at the unique solution r f ayx ex pf x
t ia which can uniquely be solved by the color substitution pf a
we would like to thank david bean jeff lorenzen and kiri wagstaff for their help in judging our category lists
NUM NUM normal form parsing is safe complete
standard constituents are allowed when necessary
then a and a t are not semantically equivalent
the parser is therefore conservative and keeps both parses
it is convenient to begin with a special case
two parsing algorithms have been presented for practical use
both algorithms are safe complete and efficient
this presupposition and the predicate functions co occurrence principle are fulfilled by organizing the hierarchy along the three following dimensions dimension NUM canonical subcategorization frame this dimension defines the types of canonical subcategorization
and in addition to the practical problem of grammar storage redundancy makes it hard to get a clear vision of the theoretical and practical choices on which the grammar is based
as mentioned above the parser is implemented as a sequence of finite state networks
further as was mentioned in section NUM lexical idiosyncrasies are handled in the syntactic lexicon and not in the set of tree schemata
this is a two steps process it first creates some terminal classes with inherited properties only they are totally defined by their list of super classes
dimension NUM redistribution of syntactic functions this dimension defines the types of redistribution of functions including the case of no redistribution at all
this is simply achieved by fixing the canonical subcat frame dimension NUM at the development stage generation can also be done following other criterions
these links are obviously useful to underspecify a relation between two nodes at a general level that will be specified at an either lower or lateral level
then it translates these terminal classes into the relevant elementary tree schemata in the xtag NUM format so that they can be used for parsing
so the principle should be a principle of predicate functions co occurrence the trees for a predicative item contain positions for all the functions of its actual subcategorization
there are three classes of accessors those that are applicable to all concepts as kind of and functional those that are applicable to objects partonomic connection and substructural and those that are applicable to processes auxiliary process which includes accessing and translating a view of photosynthesis
we describe the data structure that helps controlling to whether or not a referent is identified and which the potential distractors are
knight is also the only system to have been evaluated in a kind of restricted turing test in which the quality of its text was evaluated by humans in a head to head comparison against the text produced by humans domain experts in response to the same set of questions
to eliminate all requests for representational modifications that would skew the knowledge bas e to the task of explanation generation the authors entered into this agreement they could request representational changes only if knowledge was incon null lester and porter robust explanation generators sistent or missing
because the operators explicitly record the rhetorical effects achieved and because the system records alternative operators it could have chosen as well as assumptions it made about the user the reactive planner can respond to follow up questions even if they are ambiguous in a principled manner
the view constructed from temporal information will produce the sentence embryo sac formation is a step of angiosperm sexual reproduction and the process details will result in the generation of descriptions of the steps of embryo sac formation namely megasporogenesis and embryo sac generation
we developed this methodology which involves two pan null computational linguistics volume NUM number NUM els of domain experts to combat the inherent subjectivity of nlg although multiple judges will rarely reach a consensus their collective opinion provides persuasive evidence about the quality of explanations
of course an insignificant difference does not indicate that knight s performance and the biologists performance was equivalent an even larger sample size might have shown a significant difference however it serves as an indicator that knight s performance approaches that of the biologists on these three dimensions
to effectively express what content should be included in explanations a representation of discourse knowledge should enable discourse knowledge engineers to encode specifications about how to choose propositions about particular topics the importance of those topics and ufider what conditions the propositions associated with the topics should be included
for example the subtopics of a process description might include NUM a categorical description of the process describing taxonomically what kind of process it is NUM how the actors of the process interact and NUM the location of the process
table NUM privative featural identification of aspectual classes
in the first case the special path component
our modified lcs lexicon theu allows aspect features to be determined algorithmically both from the verbal lexicon and from composed structures built from verbs and other sentence constituents using uniform processes and representations
however further work is required before we can demonstrate this in particular to validate or revise the formulae in ss3 and to further develop the compound schemata
deriving verbal and compositional lexical aspect for nlp applications
atelic verbs lack an inherent end though
the soldier marched the length of the field
this process involves the following actions in addition to the acceptor actions above selection of a word and acceptor to start an entire derivation
although the new syntactic formalism differs much from the constraint grammar s formalisms the basic rule types of the older formalism have been preserved among the new ones
the syntactic description in the english constraint grammar eng cg is implicitly dependency oriented it contains tags for heads and modifiers but not explicit links between them see figure NUM
we are concerned with surface syntactic parsing of running text
research unit for multilingual language technology p o
semantic features were assigned to those same terms and also to those terms of interest to the st tas k that appeared in the formal training set NUM texts
while practical this approach can lead to alternative wdid sentences not being gen r t i
to be able to exploit connectivity during generation inner and outer domains contain only triples in which binds has at least one element
i uring computation the set of binds is monotonically increased as difdreut ways of directly connecting sign and lexeme arc found
finally a new arc is added to the graph between the uew node and every other node lying in its outer domain
the approach introduced here compiles the relevant information of line fi om the grammar and uses it to check for connectivity during bag generation
the implementation was in prolog on a sun spares at on NUM the generation timings do not include garbage collection time
testing the algorithm on a range of sentences shows reductions in the generation time and the nmnber of edges constru cl cd
one disadvantage with the above generators is that they construct a nnnd er of strnctures which need dot have been computed at all
now consider reducing this string using the production rule d NUM to give the string w o
most of those who expound a theory of textual dislocation take it for granted that the gospel was written entirely by one author before the disturbance took place but a few leave it open to suppose that the original book had been revised even before the upheaval
ncs n m cs n o NUM ncs no o ncs no NUM ncs no cs no o NUM
the test data are selected from the first text of the files lobt di lobt f1 lobt g1 lobt h1 lobt ki lobt m1 and lobt n1 of horizontal version of lob tagged corpus for inside test hereafter we will use d01 f01 g01 h01 k01 m01 and n01 to represent these texts respectively
the two words are more strongly related to the verbs explain fell placing and suppose and nouns theories explanations roll codex disorder order disturbance and upheaval
where ncs represents the net connective strength o k denotes the cardinal number of the k th occurrence of the same n such that c noo c no NUM c no NUM c no k l c no k
for example with so many problems NUM to solve2 it would be a great help3 to select NUM some one problem NUM which might be the key NUM to all the others and begin NUM there
these categories include reportage editorial reviews religion skills trades popular lore belles lettres biography essays learned and scientific writings fictions humor adventure and western fiction love story etc
in addition uvg dl has dominance links
a sample derivation is in figure NUM
furthermore every tree in t can be obtained in this way
the vector derivation tree for either component derivation is obtained as follows
these cases remain an open research issue
figure NUM synchuvg dl derivation step NUM
consider two synchronized uvg dlderivations in a synchuvg dl
our reduction crucially relies on link inheritance
this enforces the requirement of simultaneous application of synchronous productions to linked nonterminals
we will refer to this system as non local synchronous tag nisynchtag
the module must suspend operation until all information required is available
if replacement is needed it will have to be removed
null NUM move a portion of a pre spl expression
if this happens it will have to be removed
not shown in figure i is the history of choices made in the course of processing
the outputs of parallel modules are unified and the unified pre spl expression becomes the working copy
as the graphs show the sample selection method achieves the same accuracy as complete training with fewer lexical and bigram counts
this behavior has the practical advantage of reducing the size of the model significantly by a factor of three here
denoting the number of committee members assigning c to e by v c e the vote entropy is
this procedure is repeated sequentially for successive batches of n examples returning to the start of the corpus at the end
apparently we do need some of the information present in the types of semantic expressions
thus it seems that a refined choice of the selection method is not crucial for achieving large reductions in annotation cost
we describe a family of methods for committee based sample selection and report experimental results for the task of stochastic part of speech tagging
this section presents the framework and terminology assumed for probabilistic classification as well as its instantiation for stochastic bigram part of speech tagging
a probabilistic classifier classifies input examples e by classes c e c where c is a known set of possible classes
in stochastic part of speech tagging the model assumed is a hidden markov model hmm and input examples are sentences
table NUM test data evaluation results on the syntactic
table NUM test data evaluation results on the mixed
b aircraft which were launched at NUM z
figure NUM misparse due to incorrect verb subcategorization
4muc ii stands for the second message understanding conference
NUM aircraft launched at NUM z
the semantic frame is an intermediate meaning representation which
ambiguity resolution for machine translation of telegraphic messages i
table NUM test data evaluation results on syntactic
figure NUM integration of the rule based part of speech tag
for most markers this procedure makes disjunctive hypotheses of the kind shown in NUM above
the first process will try to filter out any incorrect word boundary and any unsuitable tag
paratactic relations are those that hold between spans of equal importance
figure NUM the discourse tree of text NUM
the result from the experiment shows that the assigned values
NUM rhetorical coherence and cohesive relations hold between textual units of various sizes
the method procedure and results of our corpus analysis are discussed in section NUM
a thai word can have more than one part of speech
a back end algorithm that uses dot a preprocessor for drawing directed graphs
one way is to compare the automatically derived trees with trees that have been built manually
the substitutions are represented using the notation i lcb oldlnew rcb
applying this value for p in the ellipsis NUM we
note that the substitutions are not applied in the conventional order viz
the antecedents have been determined it appears to offer no special problems
the qlfs shown above omitted category information present in terms and orms
in addition it cures a slight overgeneration problem in dsp s account
with slight modification it gets a fifth reading of marginal plausibility
and cashing out the results of their application though omitting scope
in addition composition is only sensitive to the meanings of its components
again prior to resolution this scope node would be an uniustantiated mete variable
they are a static part of the language
unc is used to code situations in which the agent does n t realize that there is a choice involved cf
note that there is no overlap with direct troponymy but with indirect or inferable troponymy
morphological reconstruction research relies on the presence of stern
taking wordnet NUM NUM as an example we illustrate the development and application of such rules
resuit the database included NUM redundant direct entailments one is shown in figure NUM
another unfortunate decision would be to create two polysems NUM
it has been pointed out in this subsection that a troponym t of x also entails x
instead we show an example for inheritance via troponymy see figure NUM
with few exceptions they rely on intuitive analyses of topic structure operational definitions of discourse level properties e.g. interpreting paragraph breaks as discourse segment boundaries or theory neutral discourse segmentations where subjects are given instructions to simply mark changes in topic
note that increase means that there is a significantly greater increase in f0 rms or rate from prior to current phrase for category NUM than for category NUM of the comparison and decrease means that there is a significantly greater decrease
we examined the following acoustic and prosodic features of sbeg scont and sf consensus labeled phrases f0 maximum and f0 average NUM rms energy maximum and rms average speaking rate measured in syllables per second and duration of preceding and subsequent silent pauses
df l than did group t further it is not the case that text alone segmenters simply chose to place fewer boundaries in the discourse if this were so then we would expect a high percentage of scont consensus labels where no sbegs or sfs were identified
it will operate without any human interaction
either spoken or typed linguistic expressions are the driving force of interpretation
more precisely they are verbal language driven
i koons et al NUM describe two different systems
figure NUM the quickset user interface
figure NUM example symbols and gestures
we present an approach to multimodal integration which overcomes these limiting factors
figure NUM pen drawings of routes and areas
figure NUM typical pen input from real users
figure NUM feature structure for barbed wire
figure NUM feature structure for m 1a1 platoon
for instance in the verbalization below since u is an element in u u 1u u by the definition of unit the first reason of the pca in section NUM since 1v is the unit element of u is hinted at by the inference method which reads by the definition of unit
depending on the discourse history the following are two of the possible verbalizations NUM inference method omitted since NUM is the unit element of u and u is an element of u u lu u NUM reasons omitted according to the definition of unit element
hierarchical planning operators represent communicative norms concerning how a proof is to be presented can be split into subproofs how the subproofs can be mapped onto some linear order and how primitive subproofs should be conveyed by pcas
null NUM the implicit form by an implicit form we mean that although a reason is not verbalized directly a hint is given in the verbalization of either the inference method or of the conclusion
let s examine the reference choices in the second last sentence because u e f NUM is a solution of the equation which is actually line NUM in figure NUM and node NUM in figure NUM
although logically any proof node that uses the local focus as a premise could be chosen for the next step usually the one with the greatest semantic overlap with the focal centers is preferred in other words if one has proved a property about some semantic objects one will tend to continue to talk about these particular objects before turning to new objects
since a natual segmentation of discourse into attentional spaces is needed to carry out this task this paper first proposes an architecture for natural language generation that combines hierarchical planning and focus guided navigation a work in its own right
for instance creates two attentional spaces with a and b as the assumptions and formula as the goal by producing the verbalization to prove formula let us consider the two cases by assuming a and b
kenmore being a supervised algorithm relies on an annotated corpus of domain specific classes
a parse forest for the sentence is generated
thus the cardinality of the antonym set of trust NUM is NUM
these axe the poin worth noting null NUM corrtpu e concephtaldens b
the NUM NUM probability metric is our variant of the conceptual distance metric
formal redundancy and consistency checking rules for the lexical database wordnet tm NUM NUM dietrich h fischer gmd ipsi
we do not present examples from the adjectives and adverbs which are also contained in the database
it is assumed that the string being tested consists of ground terms so no unification is performed just matching
neither of the approximations is better than the other their intersection with NUM states is a better approximation than either
here the grammar has size o n NUM and the final approximation has NUM n l NUM states
where rhs x m n is the nth symbol on the right hand side of the ruth production for x
algorithms used to train text categorization systems in information retrieval ir are often ad hoc and poorly understood
the same method can be used with the finite state calculus approach define the relation NUM over nonterminals of the grammar s t
the strength of the feature f in the document d is denoted by s f d
the terms need not be ground so the prolog variable symbol is used instead of the wildcard symbol in the description of the algorithm
unknown tokens containing a digit NUM NUM are assumed to be numbers
a finite state calculus or finite automata toolkit is a set of programs for manipulating finite state automata and the regular languages and transducers that they describe
it is also possible to estimate part of speech data for new or unknown words
however it provides excellent support for modern software engineering such as modularity constrained polymorphism a strong but flexible type system
in this case it should simply move the corresponding textrefs from one concept to another but in some instances it was copying them
each concept node represents one slot fill and the generator o r the textref system is used to express them in english
most relevant to our work are non parametric methods which seem to yield better results than parametric techniques
null currently three classes of template exist event based templates where one clearly identifiable event is the subject of the article
in this example c NUM NUM means a forward probability of the 7th morpheme at the 1st state of the hmm
the sentence is a standard textual unit in natural language processing applications
the lexical level nodes are indexed via a simple dictionary ie a mapping from root words to all the senses of that word
all examples with scores in the range NUM NUM are considered mistakes
feature errors and unlikely pieces of grammar involve a cost the aim of the search is to extract the set of lowest cost trees
lexiclass is a clustering tool written using c language and specialised data analysis functions from splus tm software
tutuaru be in progress takes up it as a kind of state tekuru come into state views it from the end state of change while teiku go into state from the initial state of change
the form teiru indicates zoom in operation it is a function that takes an event as its input and returns a type of states which refers to unbounded regions i.e. a part of the time line with no distinct boundaries
by using these data we identify the aspectual categories of verbs in the step NUM since the categories can not be uniquely identified by aspectual forms only we use adverbs which can modify the only restricted set of verbs as shown in table NUM
NUM stauvo verbs t t l NUM NUM NUM atomic verbs ps NUM NUM resultatlve verbs
these derivative meanings are conditioned syntactically or contextually that is they are stipulated as derivative by explicit linguistic expressions such as adverbials etc while not concerned with the inherent features of verbs they can appear with most of verbs regardless of their aspectual categories
iv is a state where a speaker has an experience of the event described by a verb and corresponds to the intervals NUM NUM NUM NUM NUM NUM in figure3
although the classification has been done by hand it is much easier than that of verbs since adverbs are fewer than verbs in number NUM NUM vs NUM NUM in the corpus and have higher iconicity the isomorphism between form and meaning than verbs
adverb i label aikawarazu as usual c aegiaegi gasping p akaakato brightly p akuseku busily rcb p atafuta in a hurry rcb p atafutato in a hurry rcb p attoiuma in an instance a ikiiki vividly p
this class includes reduplicative onomatopoeia such as gasagasa batabata suisui sesseto butubutu etc which are expressing sound or manner of directed motion and rate adverbs such as yukkuri slowly tebayaku quickly etc which express the speed of motions
in the bigram model we can weight each probability of a pair of tags in both models estimated from tagged or untagged corpora
NUM armed with the current lexicon greedily link each word token with its most likely translation in each pair of aligned segments
figure NUM is an example of the network trellis that is generated from the morpheme network example given above fig NUM
it is clear that increasing robustness for example by providing backup strategies when the main analysis fails is a good idea
we are working towards such a level of robustness but our muc NUM results make it clear that we are not there yet
it is nothing more than whether or not one the denotation must be atomic
finally there are many cases where the shift in meaning is almost imperceptible
it is well known that proper names can undergo lexical conversion to become common nouns
NUM NUM mary gave jill a suggestion and john gave her one too
are there cases where the grammatical number is plural but the denotation is one
among these are such words as potato turnip carrot and rutabaga
moreover there is evidence that the choice of interpretation is relativized to context
nouns have a denotation of one but have the grammatical number of plural
the choice of aggregation is partially constrained by the features t pl
every pair of strings in the relation corresponds to a path from the initial NUM state of the transducer to a final state
thus denotes a literal ampersand as opposed to the intersection operator NUM is the ordinary zero symbol
expressions that contain the crossproduct x or the composition o operator describe regular relations rather than regular languages
the defi ion describes a regular relation whose members contain any number including zero of iterations of upper
our replacement operators are close relatives of the rewrite operator defined in kaplan and kay NUM but they are not identical to it
for that reason we prefer to define the replacement operator in relational terms without relying on an uncertain intuition about a particular procedure
the basic case for us is unconditional obligatory replacement defined in a purely relational way without any consideration of how it might be applied
x b we call the first member a the upper language and the second member b the lower language
in each case we give a regular expression that precisely defines the component followed by an english sentence describing the same language or relation
we construct a mathematical model of the events we want to correlate namely the appearance of any word or group of words in the sentences of our corpus as follows to each group of words g in either the source or the target language we map a binary random variable xc that takes the value NUM if g appears in a particular sentence and NUM if not
when given official languages as input the first iteration of step NUM simply sets p to p the second iteration selects NUM word pairs out of the possible NUM candidates the third iteration selects NUM word triplets and so on until the final ninth iteration when none of the three elements of p passes the threshold ta and thus p has no elements
we need all these three quantities to compute the dice coefficient but while fx is computed once for all y and it is very efficient to compute fxy at the same time as the set of sentences matching x is smadja mckeown and hatzivassiloglou translating collocations for bilingual lexicons identified it is more costly to find fy even if a special access structure is maintained
furthermore as the marginal probabilities of the two word groups become very small si x y tends to infinity independently of the distribution of matches including NUM NUM and NUM NUM ones and mismatches as long as the joint probability of NUM NUM matches is not zero
when given official languages as input see figure NUM this step produces a set s with the following eleven words suivantes doug d poser supr matie lewis p titions honneur program mixte officielles and langues
as is evident from the above equation the dice coefficient depends only on the conditional probabilities of seeing a NUM for one of the variables after seeing a NUM for the other variable and not on the marginal probabilities of l s for the two variables
but in the context of translation exchanging the l s and NUM s is equivalent to considering a word or word group to be present when it was absent and vice versa thus converting all NUM NUM matches to NUM NUM matches and all NUM NUM matches to NUM NUM matches
we eliminated the NUM function words and proper nouns and the NUM monosemons content words
the preference for the first among the available senses was even more pronounced in the inter tagger agreement
null in the frequency order condition the overall agreement was significantly p NUM NUM
this allows us to simultaneously represent and test the search space of all possible transformations
return the pair of nodes in the lastly created a link mong with the length of the successfully matched prefix of u
a well established corollary to zipf s law holds that a minority of words account for a majority of tokens in text
this allows us to maintain a large dictionary containing over NUM NUM forms entirely in memory using about 475k bytes
letters are not normally followed by either spaces or punctuation
as shown in table NUM completion performance improves substantially as a result
we assume the system can detect these and automatically suppress the character used to effect the completion
throughout the paper we take the dominance relation between nodes to be reflexive unless we write proper dominance
this strategy reduced the nmnber of parameters in the inodels by about NUM
of the three imt is the most ambitious and theoretically the most powerflfl
we introduced a simple enhancement to the ibm models designed to extend their coverage and make them more compact
we took a number of steps in art effort to achieve a good compromise
from the dcg we take its context free skeleton
the transformed variables now indicate that out of NUM sentences the two word groups appear together NUM times while each appears by itself three times and there are two sentences that contain none of the groups
this shows that the time used by all executions of shift link is NUM iwl iw l
of course the ideal case is when the prototype is already present in the tree bank which means that the analysis is tbnnd there too
we conjecture that by using statistical techniques to translate a particular type of construction known to be easily observable in language we can achieve better results than by applying the same technique to all constructions uniformly
ten distinct context types were defined for english
our system is devoted to patents about apparatuses
the labels are used so that we can operate with words and phrases irrespective of the actual inflectional form in which they appear in the user supplied input or will appear in the final text
this draft while not yet an english text is a list of proposition level structures templates specifying the proposition head and case role values filled by pos tagged word strings
lexical choice is interactively carried out during content specification with the system offering the user several kinds of aid in the choice of terminological entities and the lexical realization of relations among them
every question in the knowledge elicitation scenario is connected to one of the NUM synonym sets of english predicates arranged in the decreasing order of their frequency of occurrence in the training corpus
as can be seen from the figure composing claims can be a difficult task even for a patent expert let alone an inventor who is typically an engineer and not a technical writer
if either the nesting depth or the number of potentially conjoined structures becomes excessive the procedure reorders the subtrees of the text plan tree to produce te of acceptable length and complexity output text chunks
patent claims are the subject of legal protection
figure NUM labeling the conceptual schema tree
speech translation has special requirements for efficiency and robustness
one can see a reaetualisation of this principle in the ex unl te based nppro ch to machine l r mslation
we contend that it is important for the agents to take into account signals for such initiative shifts for two reasons
smith hipp and biermann an architecture for voice dialog systems a NUM computer add a wire between the v omega a hole on the voltmeter and connector NUM
consider the following subdialog taken from usage of the implemented system NUM computer what is the voltage between connector NUM and connector NUM NUM user i need help
if this interpretation of the utterance can be matched to the linguistic expectation the value computational linguistics volume NUM number NUM for it is filled with the value provided by the expectation
consequently the user can interrupt to any desired subdialog at any time but the computer is free to mention relevant though not required facts as a response to the user s statements
there are both additions and deletions smith hipp and biermann an architecture for voice dialog systems occurring after each utterance and this figure gives the average increase in assertions per user utterance
a description of the inferences is given below if the input indicates that the user has a goal to learn some information then conclude that the user does not know about the information
in this example smith hipp and biermann an architecture for voice dialog systems which knob is an expected response for the first utterance so control returns to that subdialog
the second session computational linguistics volume NUM number NUM was a data gathering test in which the subject could attempt up to ten problems with the dialog system locked in either directive or declarative mode
the specifications have three parts giving the observation to be made the conditions to be satisfied before making the observation and the actions to be taken depending on what is observed
for example the current topic could be the location of a connector which could be a subtopic of connecting a voltmeter wire which could be a subtopic of performing a voltage measurement
as a result he may initiate a subdialogue to resolve the problem resulting in a shift in task dialogue initiatives
in NUM of the cases the callers ask for another travel plan for instance for a connection from the station where the previous trip ended or another connection from the same departure place
ovr dialogues proceed in a specific way first greetings are exchanged then the client formulates his query next the operator gives the desired information and finally both parties say goodbye to each other
departure from delft at twenty hours forty two arrival at rotterdam cs at twenty hours fifty six there change to utrecht cs departure at twenty one hours seven arrival in utrecht cs at twenty one hours forty three
in case the caller does not notice problems himself the information service may infer from the caller s responses that the caller did not process her utterances as intended
the information transfer in an ovr dialogue consists of three phases NUM a query phase NUM a search phase and NUM an information phase
ten documents randomly chosen from each class were used as training
introduction of linguistic cues improves the performance of a statistical semantic knowledge acquisition system in the context of word grouping
the result is that many of our list of NUM defining concepts actually stand for a number of distinct concepts
in this paper we describe an approach which overcomes this problem using dictionary definitions
in the current experiment this conceptual co occurrence data is collected from the brown corpus
in order to acquire more reliable results we are currently seeking a few more subjects to repeat the experiment
a small proportion of test samples can not be disambiguated within the given context and are excluded from the experiment
core analysis of textual input starts from a lolita specific sgml representation of the input calle d an sgml tree
applications can then read the results of analysis from the semnet and generally interrogate the content s of the semnet
now both restrictions have been processed and pas NUM is processed with the candidate sets lcb rl r2 rcb and lcb sa s2 rcb
NUM if variable r is in narrow scope position in 17b then qr must be the quantifier v but is not generated in the final output
dependency functions partitions and focus sets each variable in a pas has a candidate set which is defined by its restriction and the model under consideration
for example 13ai is true in NUM assuming a means at least one but is not very informative
s the subject has not read through the whole corpus
negative mutual information is unreliable due to the smaller number of data points
embedded quantifiers the preceding discussion concentrated on simp e linguistic structures like NUM NUM which contain one main verb and noun phrases with no recursive structure
where fmax the focus maximum nc i candidate set and the q dec NUM relation is defined along the following lines
this kind of conceptual relationship is not always reflected at the lexical level
our scoring method is based on a probabilistic model at the conceptual level
the entry of sentence n in ldoce and its corresponding conceptual expansion
NUM the noise only leads to incorrect distribution of the occurrence probability
for each entry in ldoce we construct its corresponding conceptual expansion
for instance in legal reports the statistical data is domain dependent
people make use of syntactic semantic and pragmatic knowledge in sense disambiguation
for this purpose we tagged the english part of the corpus by a modified pos tagger and apply our algorithm to find the translations for words which are tagged as nouns plural nouns or proper nouns only
a maximum likelihood method would use the training data to give the following estimation for the conditional probability l l v nl p n2 f NUM v nl p n2 f v nl p n2 unfortunately sparse data problems make this estimate useless
NUM additionally it should be easier for the generator to determine an appropriate surface form
the evaluation of the referring expression plan indicates whether the referring action was successful or not
by viewing language as action the planning paradigm can be applied to natural language processing
we avoid this ambiguity by choosing an arbitrary ordering of the modifiers for each such plan
the actions reject plan and postpone plan decompose into the surface speech actions s reject and s postpone respectively
error p lan errorno de figure NUM reject plan schema
in the cases of cawsey and carletta both use meta actions to encode the plan repair techniques
this difference has an important ramification for it results in different interpretations of the discourse structure
how long is it from dansville to coming go ahead and fill up e1 with bananas
performance is considerably better than both chance and the naive baseline technique
edinburgh uk eh8 9lw mwc c cogsci ed
our formalism does not so far include boolean combinations of feature values
the feature flatconj stops spurious nestings if they are not wanted
assume that takes precedence over a unless parentheses indicate otherwise
values values selector selected values psem selected
set valued features are often used in conjunction with a membership test
and a NUM for the person column
consider the following problem as an illustration
as mentioned earlier there are some limitations associated with this technique
the general features of the implementation of this technique are as follows
note that this is the only possible derivation involving these three d trees modulo order of operations
before subserting its nominal arguments we sister adjoin the two adjectival trees to the tree for hotdogs
we end this section by briefly commenting on the other dtg operation of sister adjunction
for example consider the d trees a and NUM shown in figure NUM
tag however has two limitations which provide the motivation for this work
in figure NUM we give a dtg that generates sentence NUM
a sac gives a complete specification of what can be sister adjoined at a node
figure NUM d trees for 2b figure NUM shows the matrix and embedded clauses
we insert the subject component of fo adore above the anchor component of seems
definition NUM res f res f rs is a ma ireal feature structure such that each node n in f satisfies the following conditions
application features can be used to control the application of rules to particular lexical items where the applicability can not be deduced from spellings alone
the rule formalism and compiler described here work well for european languages with reasonably complex orthographic changes but a limited range of possible affix combinations
to simulate the challenge of speech input one speaker read wall street journal texts from the muc NUM corpora and automatically transcribed those texts via byblos
independent language independent algorithms ovals represent knowledge bases heavyweight processing includes procedures that depend on global evidence involve deeper understanding and are research oriented
example lightweight techniques are for sgml recognition hidden markov models finite state pattern recognition text classifiers text indexing and retrieval and sgml output
the success of a data extraction system is judged by how well its results fit a hand coded target set the key of structured template objects
the task in pnnciple is domain independent though in muc NUM it was evaluated only on documents obtained by a query for documents about change in corporate officers
by varying the acceptance threshold we hoped to find a level at which we would get enough increase in precision to offset the decrease in recall
this graph refers to three measures NUM undergeneration ug NUM over generation og and NUM error err
NUM t dx r v that the optimal transducer shown in figure NUM has only NUM states and would have no error on the test set of synthetic data
for example given one training set of examples of english flapping the algorithm induced a transducer that realizes an underlying t as dx either in the environment qr v or after a sequence of six consonants
for example in american english an underlying t is realized as a flap a tap of the tongue on the alveolar ridge after a stressed vowel and zero or more r s and before an unstressed vowel
our idea is that abstract biases from the domain of phonology whether innate i.e. part of ug or merely learned prior to the learning of rules can be used to guide a domain independent empirical induction algorithm
each time a branch is pruned one of the children s outcomes is picked arbitrarily for the new leaf and the entire training set of transductions is tested to see if the new transducer still produces the right output
for example the decision tree pruning algorithm discussed in section NUM NUM NUM which successfully generalized about the importance of stressed vowels to the flapping rule would have functioned identically with any feature set capable of distinguishing stressed from unstressed vowels
because our biases were applied to the learning of very simple spe style rules and to a nonprobabilistic theory of purely deterministic transducers we do not expect that our model as implemented has any practical use as a phonological learning device
if any errors are found testing is repeated using the outcome of the pruned node s other child e.g. the leaf with the positive rather than negative value for the feature being tested at the pruned node
at each step the longest common prefix of the outputs on all the arcs leaving one state is removed from the output strings of all the arcs leaving the state and suffixed to the single arc entering the state
even though english does not mark the two parts of the generation relation explicitly by means of discourse markers the combination of ordering syntax and rhetorical relation results in all but one c e in an unambiguous interpretation
there are only NUM hypertags the opening and closing brackets marking the possible location s of the syntactic constituent in question
for each cue type we grouped the errors based on whether or not a shift occurred in the actual dialogue
figure NUM organization and person object recall and precision on the te task figure NUM indicates the relative amount of error contributed by each of the slots in the organizatio n object
te performance of all systems on the walkthrough article was not as good as performance on the test set as a whole but the difference is small for about half the systems
restriction of the corpus to wall street journal articles resulted in a limited variety of markables and in reliance on capitalization to identify candidates for annotation
two of the slots vacancy reason and on the job had to be filled on the basis of inference fro m subtle linguistic cues in many cases
further simplification may be advisable in order to focus on core information elements and exclude somewha t idiosyncratic ones such as the three slots described above
the article contains about NUM words and approximately NUM coreference links of which all but about a dozen are reference s to individual persons or individual organizations see appendix a
an analysis by the participating sites of their system s performance on the walkthrough article provide s some insight into performance on aspects of the coreference task that were dominant in that article
the two versions of the independently prepared manual annotations of NUM articles were score d against each other using the scoring program in the normal key to response scoring mode
at the far end of the spectrum are bare common nouns such as the prenominal company in the example whose statu s as a referring expression may be questionable
sra ran an experiment on an upper case version of th e test set that showed NUM recall and NUM precision overall with identification of organization names presenting the greatest problem
we will propose this method for estimating the probabilities of unknown subtrees as well as of known subtrees
the problem is how to estimate the population probability of a subtree on the basis of the observed sample
in order to carefully study the experimental merits of dop3 we distinguished two classes of test sentences NUM test sentences containing both unknown and unknown category words NUM test sentences containing only unknown category words note that all NUM test sentences contained at least one potential unknown category word verb or noun
looking more carefully to the parse results of test sentences with only known words we discover a striking result for many of these sentences no parse could be generated at all not because a word was unknown but because an ambiguous word required a lexical category which it did n t have in the training set
this probability can be estimated as the number of occurrences of a subtree ti divided by the total number of occurrences of subtrees t with the same root node label as ti ti t root t root t
our calculation of NUM the total number of distinct np subtrees and NUM no can now be accomplished as follows NUM the total number of np subtrees that can be the result of the mismatch method is calculated by attaching in all possible ways NUM dummies to the tags of the unlexicalized np subtrees from the training set
the table shows a considerable increase in parse accuracy when enlarging the maximum depth of the subtrees from NUM to NUM the accuracy keeps increasing at a slower rate when the depth is enlarged further
although he only uses corpus subtrees smaller than depth NUM which in our experience constitutes a less than optimal version of the dop method see table NUM NUM charniak applies exactly the same statistics without ignoring low count events
however the fact that the chosen data determine the termination condition for the development means that the rules could be overfitting the chosen data
though rule NUM improves its predecessor s performance the result still discourages us from using it for the generation of zero anaphora in chinese
two entities are said to be distractors to yeh and mellish an empirical study on anaphora each other if they are of the same category
howeve it is possible to construct a similiar formalization in the purely functional subset of scheme by passing around an additional result argument here the last argument
the decision tree and classification tree can then be obtained from figure NUM by changing all nonzeroes nzs into nominals ns
the constraints on discourse segment beginnings in tr2 and tr3 and the salience constraint in tr3 would therefore have some effects on the output texts
anaphora of types c to f are not as salient as types a and b thus we group types c to f as nonsalient
although these variables are closed over their values are not applied when the defining expressions are evaluated so such definitions should not be problematic for an applicative order evaluator
NUM second backtracking parsers typically involve a significant amount of redundant computation and parsing time is exponential in the length of the input string in the worst case
zhege liliang i haoxiang wuxing de shou i q5 k ba xian j xiangxi zhuai j xianj jiu la bu zhi le
further tuples with nouns appearing in the bunruigoihyo thesaurus were selected
this property results in the rolmstness mid the ada ptability o l tl lliodol aud niakcs m t lcb f uiodcl stronger in data sl arscncss probhml
the locality of mrf is consistent with the as slliliptioii o i a ggiilg t roblein in that the tag of given word ca it be deterinined by the local context
using this fact we can use m NUM in paranieter estimation in m ii f we can derive NUM to be used in pglralneter estiniation fi om training data
experimental results show that the performance of the tagger gets improved as we add more statistical information and that mt lcb f based tagging model is better than ttmm based tagging model in data sparseness problem
l o solve the solution of it a numerical analysis mt thod is enerlaized iterative scaling was suggested l arroch72
the gemwal method used it ilmm is linear iul erl olatiot l which is l he w gght ed sutnmal ion of all prol alfilil y infornlat ion
the lex NUM literal in item NUM is resolved with the appropriate program clause producing item NUM
items NUM NUM analyze the empty string notice that there are no solution items for table NUM
subgoals can be selected in any order earley deduction always selects goals in left to right order
as noted above this kind of constraint coroutining is built in to a number of prolog implementations
such a form of abstraction can not be implemented using the selection rule alone
tags are associated with clauses by a user specified control rule as described below
here the xl are vectors of variables and the t are vectors of terms
this justifies speaking of the unique lemma table in t for a goal g
the optimized lattice assigns the probability to a node in the empirical lattice equal to that of its most specific sub node t from the optimized lattice
e tony was sick and furious at being woken up so early
efor instance it is impossible to see the configuration dr c without seeing the configuration dr NUM g
in the first case such features are usually present randomly in the reference nodes of the empirical lattice and therefore they do not have any discriminative power
this allows for using the most informative method first and back off to a less informative method if there is not enough information for a more informative one
rosenfeld NUM evaluates in detail a maximum entropy model which combines unigrams bigrams trigrams and long distance trigger words for the prediction of the next word
NUM defines a framework for the selection of the best performing constraint set and for the estimation of the weights as for these constraints
slndeed the feature space and the mapped configuration space can be identical if we want to memorize in our model all and only seen cases
concluding remarks are made in section NUM
the algorithm proposed herein attempts to broaden coverage by exploiting lexicographic resources
the smt model is then tested for the task of machine translation
classalign often fails under such circumstances
NUM NUM contrastive analysis of english and mandarin chinese
the algorithm s performance was evaluated using the two sets of data
in any case deriving the mbo51 to fc04 mapping would be an overgeneralization
informally memoized cps top down parsers terminate in the face of left recursion because they ensure that no unmemoized procedure is ever called twice with the same arguments
thus the segmentation experiments tried to answer three important issues s represents the sentence segment boundary
NUM shows how this definition could be used to compute and display using the scheme builtin display the square of the number NUM
symbol denotes that all the words can not be found in lob corpus
the third step is to find the corresponding lob tag for each susanne tag
this is because the mapping for ditto tags can be obtained by human easily
thus two heuristic rules are introduced to reduce the number of multiple tags
the well formed partially bracketed corpus is a milestone in the development of a treebank
however the work to build a large scale treebank is laborious and tedious
finally the performance evaluation model reports the evaluation results according to c and t
to map a susanne tag into a lob tag manually is a tedious work
the cps versions of the terminal se and alt functions are given as NUM NUM and NUM respectively
we examine the tag mapping for the preceding and subsequent three tags of 1w
by analyzing the error chunked results we find that many errors result from conjunctions
if the memoized procedure has not been called with args before it is necessary to call the unmemoized procedure cps fn to produce the result values for args
the goal of this paper is to discover why this is the case and present a functional formalization of memoized top down parsing for which this is not so
the function reduce is defined such that an expression of the form reduce haves as a depth first top down recognizer in which nondeterminism is simulated by backtracking
the text plan tree is traversed in a top down depth first fashion
the final report describes the performance of grapheme to phoneme rule sets developed for each language
however in NUM out of these NUM cases NUM NUM both systems were wrong
table NUM gives the numbers corresponding to the steps of the procedure just described
in other words roughly one out of eight names will be pronounced incorrectly
despite our large raw corpus we lack the type of database resources required by these methods
in the tts system it is also applied to the morphological analysis of compounds and unknown words
pronunciation performance was evaluated on the symbolic level by manually checking the correctness of the resulting transcriptions
the morphological information provided by the name analysis component is exploited by the phonological or pronunciation rules
the textual materials used in the evaluation experiments consisted of two sets of data
however for the reasons detailed above this approach is not feasible for names
so we ask about the length of the overall sentence in all models
our discourse model contains information on the shared knowledge of the speaker and hearer private knowledge of the speaker and a specification of entities and their discourse status
the lexical concept is input to a process called lexical selection
the speed of accessing this code is word frequency dependent NUM
the paper concentrates on experimental reaction time evidence in support of the theory
the generation of words in speech involves a number of processing stages
a selected lemma spreads its activation to the word s phonological code
these predictions find solid experimental support NUM
a theory of lexical access in speech production
but this approach has disadvantages in terms of development and maintenance
in phonetic encoding an articulatory gesture is computed for each phonological syllable as it comes available
articulation can be initiated as soon as all of a word s syllabic gestures have been prepared
one receives a description of a class of events to be identified in the text for each of these events one must fill a template with information about the event
first because it does not include rules for english expressions not relevant to the application the grammar runs faster and finds few grammatical ambiguities
m1 platoon tank platoon or charlie NUM NUM could all refer to the same entity in the simulation
it sends messages that keep the ci agent informed of the current state of the simulation and executes commands that it receives from the ci agent
this can not be done with the gui because there is no way to select a point that is not currently displayed on the map
since modsaf itself is the source of situational information about the simulation the interaction between the ci agent and modsaf is not a simple one direction pipeline
we simply look for sequences of the form x x or x x and replace them with x
for instance in the above example the greedy method produces the configuration a b c d because a is compatible with b c is not compatible with either and d is compatible with c with which merging would be attempted before the earlier evoked templates b and a
although on the side of the lsp mlp some new sublanguage semantic co occurrence patterns had to be defined the co occurrence patterns are highly language independent
thanks to the co occurrence patterns for the medical sublanguage only the label that is valid in this context h ttchir is ultimately selected
an example see figure NUM shows the output of the parse tree transducer that reshapes the dmlp tree into the required lsp mlp format
the file with the pseudo html codes see figure NUM could easily have been generated by the morphological component of the dmlp as well
the pds page is the bottom right part of the figure and partly overlaps the menupage which shows the selected pds and labels NUM
recall hits hits misses precision hits hits mistakes
NUM the weight of a sentence is the sum of weights of words in the sentence
some morphological processing may help but pronominalization and other forms of coreferentiality defeat simple word counting
we can use the ratio to find all the possible interesting concepts in a hierarchical concept taxonomy
4this threshold and the starting depth are determined by running the system through different parameter setting
we also implemented a simple plain word counting algorithm and a random selection algorithm for comparision
NUM similar to one but counts only one concept in null stance per sentence
the appropriate da is determined by experimenting with different values and choosing the best one
figure NUM a sample hierarchy for computer concept frequency ratio and starting depth
the average result of NUM input texts with branch ratio threshold NUM NUM NUM and starting depth NUM
the noun xwd vd i r a month sw1 lcb xwd NUM hxwd NUM nnn the month rcb the verb xwd masculine singular third person past tense he it was resumed
the specificity ordering of transfer rules is primarily defined in terms of the cardinality of matching subsets and by the subsumption order on terms
termin i2 n n il NUM vorschlag il where n n denotes a generic noun noun relation
for instance in the first training set we describe below p2 NUM
we can use a the standard transformation of an arbitrary cfr
the parsing problem for sd however we have shown to be np complete
otherwise there would be a simpler proof without this abstraction
d bk 3m k 3m for w3m
the sequent marked with is easily seen to be derivable without abstractions
we start by defining the lambek calculus and extend it to obtain sdl
proposition NUM cut elimination each sdl derivable sequent has a cut free proof
we do not care about other words that might be generated by gr
in order to examine where the performance comes from we also compared our method to the method which wsd and linking method are not applied
in figure NUM one day s newspaper articles consist of several different topics such as economic news international news etc
the first experiment keywords experiment is concerned with the keywords extracting technique and with verifying the effect of our method which introduces context dependency
in order to show the applicability of our method we applied zechner s key sentences method to key paragraphs extraction and compared it with our method
in the test data which consists of NUM NUM different nouns NUM NUM nouns appeared in only one article and the frequency of each of them is one
for a set of paragraphs p1 pm of an article we calculate the semantic similarity value of all possible pairs of paragraphs
the clustering algorithm is applied to the sets and produces a set of semantic clusters which are ordered in the descending order of their semantic similarity values
in table NUM z in z y of paragraph shows the number of paragraphs in an article y shows the number of articles
however in our method NUM articles are not extracted correctly while the key paragraph is located in the first parts of these articles
and when adding to this lexicon appropriate variations in a n organization name are included so that they would be recognized if they occured for example when such a secondary organization reference is reduced the text is put in the org alias slot the full form is pulled from the lexicon and put in the org name slot to ensure proper merging see below of the two referents
the applications of reduction patterns are done in sequence rather than all at once for a number o f reasons first some references to a person organization or location may not be recognizable b y themselves but other references to the same thing may be easier to spot
the overall results see table NUM were obtained in NUM person weeks of effort lifting some pattern and code ideas from the ats which worked on a very different set of message types and wasting a few day s on the st task and on filling in date templates
simulation experiments establish that very high levels of performance can be obtained with a modest number of links per word even when the links themselves are not always correctly classified
in the first pass lexical items are first matched with their corresponding elementary tree in the lexicon
the word is which is marked as a possible insertion is confirmed in its status and definitely eliminated
indeed the co occurrences captured by an n gram model suffer from a limited scope and an adjacency condition
when the suspect words have been spotted it remains to be decided whether they are substitutions or insertions
it is set on line as the maximum score that different typical cases of words to be eliminated could reach
another advantage for real applications is to be aware of the expected performances of the asr systems
the scoring module goal is to provide word acoustic confidence scores to help the robust parser in its task
they make use of the knowledge embedded in the lexical grammar and of candidates present in the n best hypothesis
we have studied its capacities to detect and predict missing elements and to select syntactically and semantically well formed sentences
the first deficit restricts feasible architectures of a generation system in which such an algorithm can reasonably be embedded because flexibility and incrementality of the descriptor selection task are limited
an example alembic role sequence that NUM
screen dump of a typical alembic workbench session
we hypothesize that this effect is partly indicative of the generalization behavior on which the learning procedure is based which amplifies the effects of choosing more or less representative training sentences by chance
the bootstrapping effect tends to increase over time
we present performance details in the user in a corpus development cycle making use of pre tagging facilities analysis facilities and the automatic generation of pre tagging rule sets through machine learning
i the named entity task from muc6 consists of adding tags to indicate expressions of type person location organization date time and money see NUM
the x axis indicates what kind of corpus development utilities were used NUM sgml mode of emacs text editor NUM workbench awb manual interface only NUM awb rule learning
for the named entity task in muc6 approximately NUM NUM words were provided as annotated training data by the conference organizers formal training and dryrun data sets
the first observation we make is that there is a clear and obvious direction of improvement by the time NUM documents have been tagged the annotation rate on group NUM has increased considerably
since the learning process is not merely memorizing phrases but generating contextual rules to try to predict phrase types and extents the rules are very sensitive to extremely small selections of training sentences
we created a key by analyzing the texts and entering the correct coreference relationships
recurs no less than NUM times
enote that the relations defining the original position of np2 i.e.
let l be the set of local relations in which n participates
petence and performance in the human sentence
let d be the current tree description
in our terms this means that assuming a binary right branching clause structure with the verb in its right corner the node selected for lowering must be as high as possible
on the assumption that saw selects for a pp instrumental argument we can derive this preference in the present model via the preference to attach as an argument as opposed to an adjunct
the sundial woz corpus comprises approx
it should be clear that while simple left and right attachment will suffice for attaching arguments without reanalysis it will not allow us to derive the reanalysis required in example NUM
one way of looking at what is happening here is to see the subject np john ga as being dissociated from the clause in which it is originally attached and reattached into the main clause
the structure of the main clause presented in NUM can be justifed on several grounds
a dsl dependency is bound if the verbal projection is selected by a verb in second position
the output of the parser is the semantic representation for the best string hypothesis in the lattice
module and the fau erlangen lmu munich prosody module in the mt project vl lthmomi of
table NUM recognition rates for NUM labels in for NUM and b3 classifiers
tim rt sponslbility for the contents of this study lies with the aa thors
the full set of features could not be used due to the lack of sufficient training data
for both NUM and b3 labels we obtained overall recognition rates of over NUM cf
for classification the document is classified into the class wmch has the maximum score
NUM syntax tree for gestern reparierte er den wagen
still a purely perceptual labeling of the phrase boundaries under consideration seems problematic
the words the and to were eliminated from each list
the multinomial equation is shown below for the case of NUM possible outcomes
i specifies the types of transitions and represents a stack
the class score is calculated by the following equation NUM
the class score is calculated by the following equation NUM
many of the high frequency words were location military or negoriarion related
the entire process of computing the above output completes in approximately fifty minutes on an unloaded sun sparcstation NUM
these training documents were then eliminated from the set of documents to be classified
terminal NUM returns a set of terminal edges in layer NUM
ramshaw and marcus noun phrase detector is based on eri c brill s work on learning transformational rules for part of speech tagging
unfortunately only one of the three components which posited coreference emerged as being highly precise the proper name matching component
we then began an intensive effort with full tim e participation from baldwin and reynar and part time efforts from the other authors
we intend to further refine this component and subject i t to automatic testing against a sentence detected corpus in the near future
when no data is available from the semantic concordance for some senses of a word the gaps in frequency are smoothed
this supplants the missin g inheritance link which would be needed in a complete semantic taxonomy between male and kinsman
for example the entry for man is linked to daughter nodes which include the entries bachelor boyfriend eunuch etc
it was trained using a section of the tagged and parsed treebank wall street journal corpus disjoint from the muc NUM test data
for example coreference is not posited if the number of words in the antecedent noun phrase is less than the number of words in the anaphor
it uses several knowledge sources including the ibm name extraction module and a simple unification system to produce coreference chains
the gui is being developed in visual basic
similarly for pp3 our performance of NUM NUM accuracy discriminating fourteen configurations far out strips the baseline of NUM NUM
having constructed the algorithm to determine the best configuration for NUM pps we can similarly generalize the algorithm to handle three
that is we favor the bias for which there is more evidence though whether this is optimal remains an empirical question
one of the most interesting topics of debate at the moment is the use of frequency information for automatic syntactic disambiguation
the thematic structure representing temporal expressions is displayed in the lower right corner
the challenge however is to be able to parse gigabytes of text in practically feasible time and as accurately as possible
sponsors ffm is jointly sponsored by the intelligence systems support office isso of oasd c3i the national drug intelligence center ndic and the office of research and development ord
ssingular nps such as a company are not helpful to this task since their denotations do not involve multiple individuals which explicitly induce this functional dependency
quantifying in is designed to make any possibly embedded np take the matrix scope by leaving a scoped variable in the argument position of the original np
we are using different lexical items for instance q every and e every for every in order to signify their semantic differences
in particular NUM does not have those two readings in which every dealer intercalates most customers and three cars
we have shown that the range of grammatical readings allowed by sentences with multiple quantified nps can be characterized by abstraction at function argument structure constrained by syntactic adjacency
this paper examines english constructions that allow multiple occurrences of quantified nps np modifications transitive or ditransitive verbs that complements and coordinate structures
for example a below has two readings de re and de dicto depending on the relativity of the existence of such an individual
notice that the generalization is not at work for the fragment of at least three companies touched in c since the conjunct is syntactically ungrammatical
thus keyboard mistyping increases the way of implicit misspelling which can not be dete ed easily using the dictionary based approach
besides these two ambiguities spelling errors in thai called implicit spelling errors also cause a lot more work for the parser
in example NUM we can also can find the most likely sequence of parts of speech by considering the previous part of speech
then a new set of words which are generated according to the causes of error will be replaced to flint detected word one by one
from the results of the experiment shown below word filter can eliminate many of alternative word sequences and corr t the unplicit error
o news lines was acquired rom china
chinese word segmentation based on maximum matching and word binding force
the cpu time used for segmenting a text of NUM NUM NUM characters is NUM NUM seconds on an
usually all but one of these alternatives arc syntactically and or semantically incorrect
this process will stop when i levels off over several generations
each prefix points to a link d list of associated suffixes
the word sequence with the highest fi equency product is accepted a s correct
the proposed algorithm provides a highly accurate and highly efficient way for word segmentation of chinese texts
for certain other languages such as german where word compounding is quite common morpheme decomposition algorithms tend to be much more complex
a less frequently examined category but one that is crucial to more natural speech synthesis is what we will refer to as functor homographs
moreover to compound the problem the pronunciation of proper names outside of the foreign speech community is often different from their original pronunciation
part of the educational process for a child is learning to read and educational literature is filled with disparate pedagogical approaches to this problem
in english the scan is done right to left to strip the suffixes of a word in sequence as shown in example NUM below
the system requires a computer specialist the rules require an expert in the domain to be processed in this case a linguist
since the rule set contains an unconstrained rule for each grapheme the matcher will always find a rule and will always make progress
a two tiered architecture compiler and interpreter has been designed to easily define and modify the rule set in our implementation of grapheme to phoneme rules
sthe initiative indices are represented as bpa s
it consists of four cue types
NUM NUM an analysis of the trains91 dialogues
normal style petit pti small recommencer rk3mase to begin again
for example there are a number of essential morphonemic rules in english that perform various tasks such as plural and past tense formation
the result is an alignment relation
much of this explosion has been fueled by the spectacular growth of the internet and especially the world wide web
figure NUM part of a typical scatterplot in bitext
the fixed chain size parameter also plays a role
on the contrary they often produce spurious matches
figure NUM the points of correspondence are num
the most universal knowledge source is a translation lexicon
figure NUM simr s expanding rectangle search
figure NUM frequent token types cause false points of
therefore scalability of the algorithm depends on the number of processors that can be used to compute a solution
2vq e only use the grammar switching feature of dag ger but it offers the ability to load completely new grammars dynamically if such a need arises
with corpora comprised of periodically released information it might be useful to visualize the information in the time domain
in table NUM we give a summary of the results and compare the three phrase combination runs with the corresponding baseline run
this tool provides the user with a tool to quickly and easily locate the most relevant portions of any document
if so it is further checked whether the main verb is available with the argument structures
in this paper we will present the argument interpretation strategy thanks to scott fergusson for comments
in almost the same way this strategy applies to the arguments of the infinitival clause in raising constructions
thus the list of argument structures is filtered and a list of new argument tables is returned
b pro ins theater zu gehen rlaubte cp die mutter ihrer tochteri nicht
in addition the cp3 is scrambled out of cp2 which is extraposed after the finite main verb
if the verb selecting the ipp is in its base position the order of the verbs differs from the usual one auxiliaries that would be at the right of the ipp immediately precede the final predicates as illustrated in example 4b where h te precedes besuchen wollen
the price increased sy from NUM a share
whereas verbs like eat can not he ate to from NUM pickles
w also found occurrences of hat itual intransit ives in the text
as comlex does not sense disambiguate the semantic difference does not af feet the dictionary entry
the price increased from NUM a share to NUM a share
lathe nunitp is s dollars in the examples of verb occurs almost exclusively with this type of np
NUM the nunitps are not syntacticly distinguished other nouns occur with similar structures
nps are not formally distinguished as such in the notation of the fl ame group
enamex type quot person quot james enamex sitting in his pluck office filled wit h photographs of sailing es well as huge models of among other tkings a dutch tugboat
the led is supposed to be displaying alternately flashing one and seven
rb NUM u led is displaying only a flashing seven
ferent cost measures in order to determine their relative contribution to performance
then using the performance equation above predicted performance for ra is
we hope that this framework will be broadly applied in future dialogue research
similarly a cl for user NUM is NUM NUM
the attribute names emphasize information exchange while the subtask names emphasize function
also performance can be calculated for subdialogues as well as whole dialogues
the attribute names at the end of each utterance will be explained below
there are NUM words sentence av
the remainder of the test period was spent on improving the nam e recognition which impacts all three tasks but resulted in very little improvement on the scenario template task
for each collector concept the analyzer processes the text and creates a link from the text to the semantic representations similar to the reference links created by the reference resolver
to demonstrate the portion of hasten s performance comes from nametag a third unofficial configuration was run that disabled reference resolution and the extraction of locations nationalities and descriptors
figure NUM shows the performance results for these three configurations on the final test and training data as well as the base configuration performance on the interim test and training data
many systems including ones from bbn sri lockheed martin university o f massachusetts and new mexico state university have made significant contributions in these areas
the factors represent how well the elements match how well they are ordered how well the adjacent elements are joined and how much semanti c content was bound
since the test data was manually tagged in mixed case and the muc NUM task specification includes case sensitive tagging rules the case insensitive performance would actually be slightl y higher
nametag demonstrated high performance at high speed for the named entity task as well as advanced capabilitie s that provided the majority of the performance for the template element task
the regular expression NUM a b i c x same as a b c x describes a relation consisting of an infinite set of pairs such as
we develop several versions of conditional replacement that allow the operation to be constrained by context o introduction linguistic descriptions in phonology morphology and syntax typically make use of an operation that replaces some symbol or sequence of symbols by another sequence or symbol
elementary trees are of two kinds a initial trees and b aux iliary trees
we present experimental results using ebl for different corpora and architectures to show the effectiveness of our approach
3there are also backward rules that are analogous to forward rules
for the induction step we consider each case of rule application in NUM
using this index a set of generalized parses is retrieved from the generalized parse database created in the training phase
this implies that the pos sequence covered by the auxiliary tree and its arguments can be repeated zero or more times
this of course means that we may not get all the possible attachments of the modifiers at this time
as part of the future work we will extend our approach to corpora with fewer repetitive sentence patterns
the speed up shown in the third row of table NUM is entirely due to this ambiguity reduction
experiment l a the details of the experiment with the atis corpus are as follows
after generalization the trees h and f12 are no longer distinct so we denote them by ft
the input sentence is subjected to morphological analysis and is parts of speech tagged before being sent to the parser
in the calculation of forward backward probabilities under flow sometimes occurs if the dictionaxy for making the morpheme network is large and or the length of the input sentence is long because the forward backward algorithm multiplies many small transition and output probabilities together
in the experiments described in the next section we approximated the results from this example see table NUM by the formula NUM a cost b where a was NUM NUM and b NUM NUM
the system can estimate three kinds of models the pairohmm formula NUM with output symbols as pairs of words and tags the tag bigram model formula NUM where n NUM and tag hmm formula NUM with output symbols as tags and p w t
the notation st i is used to denote the unscaled forward probabilities of the s th morpheme on the syncronous point l sl i to denote the scaled forward probabilities and l i to denote the local version of c before scaling cl is the scaling factor of synchronous point i
however in order to build an accurate stochastic language model large amounts of tagged text are needed and a tagged corpus may not always match a target application because of for example differences between the tag systems
we use map maximum a l osteriori estiniation method
hence the set of rules are language dependent but not domain dependent
note that a single morphological analysis may correspond to several senses
the output is shown in figure NUM
NUM NUM lexical choice within a generation system architecture
the li ltte function of model NUM is delined as
NUM an interactive program for manually tagging hebrew texts
try for the domain relation class assignt
the word awlm appeared NUM times in the small corpus
the input for our project is supplied by this module
given below is the latin hebrew transliteration used throughout the paper
NUM first the verb to have is selected cf
NUM we refer to this decision as perspective selection
no of remaining analyses fallout no of incorrect assignments no
mrf model uses me principle in combining information sources and parameter estimation
the resulting bpa s are then used to predict the task dialogue initiative holders for the next turn step NUM
defines a lexicographic order on that we also denote
these assisted in maintaining speed giving appropriate feedback to another speaker and being able to effect repairs
this can be explained that NUM t the graph does not have the ambiguity
here transitivity does not hold in a b d because there is no branch be b b tween a d
we spent NUM weeks on a system component that could reliably identify phrases from badger that wer e relevant candidates for the co task which in our view were phrases referring to people and organizations
its word segmentation information was never used to ensure that tr ing was unsupervised
the question arose what must be taken into account to decompose the co occurrence graph
word co occurrences form a graph regarding words as nodes and co occurrence relations as branches
the input in this paper is the word co occurrence graph obta ued from corpus
note that figure NUM NUM is a complete graph of NUM nodes
ice in cluster NUM means ice for cooling beverage whereas that in cluster NUM means ice to skate on
first we make the input graph from a 30m bytes of wall street journal
in gs2 all ambiguity of a branch and nodes at its ends are resolved
NUM inferable relations such as the transitive closure of a hierarchical relation or semantic relations induced by lexical ones need to be taken into account when checking real relations i.e. directly stored relations
null these pragmatic aspects of natural conversation contribute in different degrees to the various short and long term social goals of participants
abstracting the rule postulated for binary antonymy is for each set of synonyms s the set of antonyms of the elements of s must again be a set of synonyms or it is empty
therefore based on this idea we improve the previous rules for generation of zero anaphora to make rule NUM as shown in figure NUM
a period can act as the end of sentence or be a part of an abbreviation but when an abbreviation is the last word in a sentence the period denotes the end of sentence as well
however the divergence heuristic will first determine that the context NUM is profitable relative to the empty context and add it to the model
in section NUM we formally define the class of extension models and present a heuristic model selection algorithm for that model class based on the divergence criterion
while the conditional probability ih elh c of a single symbol r after the history h is defined as NUM
the fourth term encodes the magnitude of d as an integer bounded by the number n of internal vertices in the suffix tree
instantiated atomic features from t we will call wi a configuration from con figuration space w the configuration space w includes not only observed configurations w but rather all possible in the domain configurations many of which might have not ever been observed x1 is a constraint from the constraint space x imposed to the model
we call the model an nl type model the resulting fst an nl type transducer and the algorithm leading from the hmm to this transducer an nl type approximation of a 1st order hmm
comparing the evaluation results on the mixed grammar with those on the lexicalized semantic grammar discussed in section NUM the parsing coverage of the mixed grammar is much higher NUM than that of the semantic grammar NUM NUM
it shows about a NUM drop in average precision as well as in relevants retrieved compared with exptyp NUM
query NUM asks for documents on project hope and the chinese query is shown below
provided with the trec NUM collection are NUM very long and rich chinese topics mostly on current affairs
our method of segmentation is certainly too approximate for other applications such as linguistic analysis textto speech etc
examples NUM to NUM below show chunks that are even being surrounded by punctuation signs or stopwords
exptyp NUM l0 in table NUM is included as a demonstration of the perils associated with stopword removal
in the case ofa 1st order hmm unambiguous classes containing one tag only plus the sentence beginning and end positions constitute barriers to the propagation of hmm probabilities
table NUM effect of lexicon based and rule based stopwords on short query retrieval using l00 l01 l1
with a frequency threshold value in step c of NUM a final lexicon size of NUM NUM called l01 was obtained
the semantics of a leaf node is hence given as a constraint c x called a leaf constraint
one of her main assumptions is that a change in subject is accompanied by a change in vocabulary
this paper investigates one problem related to the tension between building linguistically based parsers and building efficient ones
in both cases the relation between the underlying position and the surface string is expressed by chains
includes some subcategorization information such as transitive intransitive raising and some co occurrence restrictions and functional selection
using syntactic features to compute empty categories reduces the search space complex chains can be computed efficiently
if the user changes the area the system changes the analysis according to the new constraint
this sentence has a dependency ambiguity so we also show how to resolve it through interaction
automatic disambiguation requires detailed semantic information especially when some case elements are missing or hidden
this software is currently available either as a package software or a pre installed software on personal computers
as soon as a is entered the dictionary look up function is invoked automatically
here because is assumed to be the first alternative as translation equivalent for node
interactivity is integrated with this model by allowing interactive operation when attribute is calculated at each node
they help the user to quickly grasp the content of web pages by providing rough translation
figure NUM is a snapshot of alternatives window for kakeru in the idiomatic interpretation
this is idiomatic because the correspondence between kakeru and make is peculiar to this interpretation
we express constraints on the arguments in the case frame of a verb via a NUM tier constraint hierarchy sharing constraints among the specification of other constraints and sense definitions whenever possible np s that have no case marking in turkish
bmb system user goal system bel user bel system error p l p22 NUM then by rule NUM which captures the cooperativity of the agents in communicative goals it adds the belief that it is mutually believed that the system believes there is an error
we generate the axe frame of each sense hy uni yiug a set of co oeeurrelme morphological synt tctic semantic md lexieal constraints on vert s their trguments
the effect has been formulated in this way because we are assuming that when a speaker has a communicative goal she plans to achieve the goal by making the hearer recognize it the effect will be achieved by the hearer inferring the speaker s plan regardless of whether or not the hearer is able to determine the actual referent
the order of lexieal rules in this figure reflects the reverse order of voice markers in purkish verbal morphology a so a given case frame m y have to go through three lexical rules until it finds a unifying entry in the lexicon
as described in section NUM NUM the system adds the following beliefs to capture the results of the plan inference process that it is mutually believed that the user has the goal of knowref and has adopted pl as a means to achieve it and that pl has an error on the terminating instance of modifiers node p22
our current implementation does not deal with multiple cansatiw w ice rnarkings which turkish allows or with the rather tricky surface case change of the object of causation depending on the transitivity of lit causativized verb
this ligure describes how a given case fi ame with its syntactic constituents is processed by a sequence of lexical rules each stripping off a certain voice marker and then attempting unification wii h t he lexicon for any possible sense resohttion
NUM lexieal constraints that indicate any specific constraints on the heads of the arguments in order to convey a certain sense and usually constrain the stem of the head noun to be a certain lexical form or one of a small set of lexical forms
as a more cornplicated exainl le employing nested clauses we presenl below the case frame for the last example in secl ion NUM where the verb rut catch is used with a clausal subjecl for a very specilic idiomatic usage
although clark and schaefer use the term contribution with respect to the discourse rather than the collaborative effort of referring their proposal is still relevant here judgments and refashionings are contributions to the collaborative effort and are subjected to an acceptance process with the result being that once they are accepted the state of the collaborative activity is updated
the algorithm illustrates how the collaborative activity progresses by the participants judging and refashioning the previously proposed referring expression NUM in fact we can see that the state of the process is characterized by the current referring expression re and the judgment of it judgment and that this state must be part of the common ground of the participants
the former requirement forces us to pick case c
this is exactly the right result
but syntax plays an implicit role
interpretations should seek to maximize similarity
there are three ways the recursion can bottom out
vp ellipsis is exemplified in sentence NUM
john revised his paper and bill did so too
john revised his paper more quickly than bill
similarity is a matter of degree
in step NUM of the algorithm we do not consider every conceivable labeled subdag but only the atomic i.e. single node subdags and those complex subdags that can be constructed by combining features already in the field or by combining a feature in the field with some atomic feature
computational linguistics volume NUM number NUM the language l g2 is the set of dags produced by successful derivations as shown in figure NUM the edges of the dags should actually be labeled with l s and NUM s but i the language generated by g2
the third column contains ql x x rather than x ql x so that one can see at a glance whether ql x is too large NUM or too small NUM
intuitively a is better than b because a permits us to distinguish the set lcb xl x3 rcb from the set lcb x2 x4 rcb the empirical probability of the former is NUM NUM NUM NUM NUM NUM whereas the empirical probability of the latter is NUM NUM
on the other hand the expected rule frequencies fs and f6 for rules with left hand side b sum to NUM NUM not NUM so they are doubled to yield weights t55 and t56
the overall divergence between and q is the average divergence where the averaging is over tree tokens in the corpus i.e. point divergences in x q x are weighted by x and summed
that is we generate dags at random in such a way that the relative frequency of dag x is qold x in the limit and we count how often the feature of interest appears in dags in our generated mini corpus
the evolution of a random branching process describes a tree in which a finite state process may spawn multiple child processes at the next time step but the number of processes and their states depend only on the state of the unique parent process at the preceding time step
for example the weight c NUM xl assigned to tree xl of observe that c NUM xa fllfl NUM which is to say fl l x fl x moreover since fl0 NUM NUM NUM it does not hurt to include additional factors fl xl for those i where y xl NUM
modifiers are represente NUM as extra arguments in the body of the form and take the form index of dm restriction as one of their argmnents NUM x scp form r gf np re str pred subs idiar y p p x form a gf am pred new
l eh w we detine a language of wj s well formed f struct tm s a family of translation function s r fi om lcb stru tures to unresolved qlfs and an inverse flmction r dom uin esolved qlfs hack to f structures r and r determine isolnorphic subsets of the qlf and lfg formalism
every inner word of wij must have its head and thus a link from the head
for example a language model with NUM accuracy rate for a task with an average of NUM NUM alternative syntactic structures per sentence which corresponds to the performance of random selection is by no means better than the language model that attains NUM accuracy rate when there are an average of NUM alternative syntactic structures per sentence
some examples are appointee deporlee blower campaigner assailant and claimant
the suffix less marks its derived forms with the analogous feature ful antonym
these experiments provided a clear indication of the potential of word meanings to improve the performance of a retrieval system
this lexicon was derived from a machine readable dictionary but contains no semantic information
tagging the corpus is necessary to make word sense disambiguation and morphological analysis easier
however increasing recall by looking at all open class categories would probably decrease precision
formalize something it must be non formal to begin with and will be formal after
the ending ate cues a change of state verb and le an activity verb
this definition of correct was constructed in part to make relatively quick human judgements possible
in addition the cues might not be present for the words of interest
thus it is possible to use un as a cue for telicity
when a symbol NUM is pushed on a stack at a given index at some place this very symbol must be popped some place else and we know that such recursive pairing is the essence of context freeness
hence it follows from equation NUM that the ith syntactic parameter compox t l a i corresponding to the correct candidate syn in the t NUM th iteration nent syn would be adjusted according to the following equation
our particular deriva null tion strategy is such that this distinguished child will always be derived after the secondary object and its descendants whether this secondary object lays to its left or to its right
equivalently if a0 an is g g a rightmost derivation where the relation symbol is overlined by the production used at each step we say that rl rn is a rightmost ao a derivation
in the first above element we say that the object b a a is the distinguished child of a a a and if f1f2 c0 c0 is the secondary object
however in the general case this resulting grammar is not a shared parse forest for the initial lig in the sense that the computation of stack of symbols along spines are not guaranteed to be consistent
we note that in l the key part is played by the middle c introduced by production rs0 and that this grammar is non ambiguous while in g the symbol c introduced by the last production t c is only a separator between w and w and that this grammar is ambiguous any occurrence of c may be this separator
since g is in binary form we know that the shared parse forest g x can be build in o n NUM time and the number of its productions is also in o n3
as so called function words they don t carry much inherent semantic meaning so the tense information of will is transferred to th e features of the main verb and the copula function of as is transformed into a syntactic construct
we altered the size of the english lexicon used in training and testing by removing large sections of the original lexicon and obtained the results in table NUM
these data demonstrate that a larger lexicon provides faster training and a lower error rate although the performance with the smaller lexica was still almost as accurate
these heuristics can be easily modified and adapted to the specific needs of a new language NUM although we obtained low error rates without changing the heuristics
this training procedure is often iterated many times in order to allow the weights to adjust NUM when to tl no punctuation mark is left ambiguous
one such class of texts are those that are the output of optical character recognition ocr typically these texts contain many extraneous or incorrect characters
the ambiguity of these punctuation marks is illustrated in the following difficult cases NUM the group included dr j m freeman and t boone pickens jr
we trained satz again this time using the decision tree learning method in order to see what types of rules were acquired for the problematic sentences
the combined robustness and accuracy of the system surpasses existing techniques consistently producing an error rate less than NUM NUM on a range of corpora and languages
but the probability of p n quan nlm n quan reduced l2 p li n2 is finally replaced by the probability of p n quan nlm i in quan reduced in the back off estimation procedure
similarly the definition of a sentence boundary is not necessarily absolute as large parts of texts may be incorrectly or incompletely scanned by the ocr program
at the same time however items that had been correctly labeled also fell between the thresholds and these are shown in the were correct column
an elementary tig tree is left anchored if the first nonempty frontier element other than NUM using a simple case by case analysis one can show that given a tig it is not possible to create a wrapping auxiliary tree
a deg a i s deg gax no in dotted layer productions the greek letters fl and NUM are used to represent sequences of zero or more nodes
for instance if a left auxiliary tree t has structure to the right of its spine this structure ends up on the left rather than the right of the node adjoined on in g
the right adjunction rules NUM NUM are analogous to the left adjunction rules but are triggered by states of the form a c o i j
since tigs do not treat the roots of initial trees in any special way there is no problem converting any operation applied to the root of u into an operation on the corresponding interior node of t
as illustrated in section NUM NUM the opportunities for sharing between the elementary trees in the ltigs created by the ltig procedure is so high that the grammars produced are often smaller than alternatives that have many fewer elementary trees
when the label is aj we generate new initial trees using lemma NUM these new rules are all left anchored because by the induction hypothesis all the trees u substituted by lemma NUM are left anchored
the tree t must either i ii be left anchored i.e. have a terminal as its first nonempty frontier node or have a first nonempty frontier node labeled aj where i j
the simultaneous adjunction can be replaced with a substitution chain combining the corresponding trees in t with u substituted into the tree at the bottom of the chain and the top of the chain used however u was used
NUM relations between cfg tig and tag in this section we briefly compare cfg tig and tag noting that tig shares a number of properties with cfg on one hand and tag on the other
again participants have to be able to cope with the unexpected
NUM NUM some practical differences between decision trees and transformation lists
these examples consist of an inimt outtmt association in our case e.g. a representation of a llotln as input and the corresponding dimilmtive sul ix as output
decision trees can be easily and automatically transformed into sets of if then rules production rules which are in general easier to understand by domain experts linguists in our case
the success rate of an algoril hm is obtained i y cah ulat ing ihc av ua r acclll lcy llllltll l
as far as the different encodings of the last syllable are concerned however the learnability experiment coroborates trommelen s claim that stress and onset are not necessary to predict the correct diminutive allomorph
the exp rim mts show thai the diminutive li marion NUM roblem is learnmfle in a data ri ml l way i.e.
the following is the knowledge derived by c4 NUM t rofll the flfll corpus with all information about the three last syllables the NUM syll corpus
the system produces rules which are comparable to rules proposed by linguists l slrthermore in the process of learning this morphological task the phonemes used are grouped into phonologically relevant categories
in this paper we argue that machine learning techniques can also assist in linguistic theory for visiting fl llow at nias netherlands instituee for advanced studies wassenaar the
the substitutional treatment of ellipsis presented here has broadly the same coverage as dsp s higher order unification treatment but has the computational advantages of i not requiring order sensitive interleaving of different resolution operations and ii not requiring greater than second order matching for dealing with quantifiers
the equation NUM is solved by setting p to something that takes a term t as an argument and substitutes t for tema j and the index of t for j throughout the ellipsis antecedent the rhs of NUM
the three readings of NUM are illustrated below listing substitutions to be applied to the antecedent sthis is true of the non paraliel tera fh in example NUM but this added complication does not affect the basic account of scope parallelism given earlier
here fit is a function that when applied to an entity denoting expression e.g. a variable or constant returns the property of being identical to that entity when it applies to a term index it returns an e type property contextually linked to the term
the alternative proposed here is to view seman null tic interpretation as a process of building a possibly partial description of the intended semantic composition i.e. partial descriptions of what the meanings of various constituents are and how they should be composed together while the order in which composition operations are performed can radically affect the outcome the order in which descriptions are built up is unimportant
a simplified qlf for NUM is NUM si and s2 hang term c b ter h v NUM where the indices c a and h are mnemonic for canadian flag american flag and house
the numbers between the tapes represent the rules in some grammar which allow the given lexical surface mappings
this section shows how feature structures which are associated with rules and lexical entries can be incorporated into fsas
in the system we fielded for muc NUM we ended up running entirely with hand crafted sequences as they outperformed the automatically learned rules
14to make the lexicon describe equal length relations a special symbol say NUM is inserted throughout
dooner is on the prowl for more creative talen t and is interested in acquiring a hot agency lex lex NUM
lalblcldlbls le fl ls lg hli f ll ic t NUM NUM NUM alblcldlo o e flololg hlilo oisuqace as indicated earlier in order to remain within finite state power all values in a feature structure must be instantiated
rule features play a role in the semantics of rules a states that if the contexts and rule features are satisfied the rule is triggered a c states that if the contexts lexical expressions and rule features are satisfied then the rule is applied
the igtree mechanism when applied to the known words case base automatically decides on the optimal context size for disambiguation of focus words
pebls then determines the k training examples with the shortest distance to the test example
here theorem proving would succeed with the first goal find swl but fail to find reportposition swl
moving to the second goal adjust knob NUM again it might have occurred that the user has just achieved this also
this includes the following general knowledge about the performance of actions and goals NUM knowledge about the decompositions of actions into substeps
session NUM introduced the subjects to the voice equipment and required that they speak at least two examples of each of the NUM vocabulary words
its correct behavior was to alternately display a NUM and a NUM on the led with the rate of alternation being adjustable by the potentiometer
firstly the use of a system which aims to be general purpose rather than generic means that it is not possible to start from a clean slate and populate the system with a set of rules ideally suited to just the muc evaluation
it is necessary to style outputs to the user to account for these variations and the prolog theorem proving tree easily adapts to this requirement
however their expectation is primarily syntax based while ours uses structure from all levels subdialog or focus based semantic and syntactic
as can be seen that ideal structured semantic space should be homogeneous i.e. the clusters in it should be well distributed neither too dense nor too sparse
so the task of disambiguating a word in a particular context is to locate an appropriate point in the space based on the context
ga s barber vp himself 2a gb client vp sthat barber vp him 3b 9c s barber srel who vp client vp client vp story 4c
as a consequence wrong results may be obtained e.g. in case of example lla as there is no t ossessive modifier paul will not be considered to be an mttecedent candidate for him
the central part of the binding theory develops the notion of local domain to which binding principles a b and c refer as binding category definition NUM binding category node x is binding category of node y if and only if x is the next node which dominates y and which contains a subject that e commands y
as a starting point word order in the two matrices was chosen such that word n in the german matrix was the translation of word n in the english matrix
depending on the tyl e of anaphor to be resolved preferenc s are applied coinprising the rather superficial and selfexf lanatory criteria of recen y cataphor penalty and sul ject preference
this work demonstrates that solomonoff s elegant framework deserves much further consideration
we need to modify our algorithm to more aggressively model n gram information
we assume a simple greedy search strategy
similar research includes work by cook et al
an appealing alternative is grammar based language models
probabilities for each rule are in parentheses
the notion of subject however is a more general one applying also to some kinds of nominal phrase attributes in particular certain variations of genitives and possessives NUM peter listens to sam si story about himself
these choices were necessary to make the search tractable
table NUM parameters and training time
as the number of overlapping verbs divided by the average of the number of verbs in the semantic class and the number of verbs in the syntactic signature
because the kontext text analysis system is based on a dependency grammar a mapping process generates the required representation from a dependency trees which is not suitable for a structural verification of the binding principles because vital details are not structurally visible
we note that the use of negative examples i.e. plausible uses of the verb in contexts which are disallowed was a key component of this experiment
the parse then for the sentence tony broke the crystal vase is simply the syntactic pattern np v np
the verb break belongs not only to the change of state class but also four other classes NUM NUM cheat NUM NUM split NUM NUM NUM hurl and NUM NUM NUM
we have conducted two experiments with the intent of addressing the issue of word sense ambiguity in extraction from machine readable resources for the construe tion of large scale knowledge sources
although this evidence is useful it is not available in dictionaries corpora or other convenient resources that could be used to extend levin s classification
since there are far more syntactic signatures than the NUM semantic classes it is clear that the mapping between signatures and semantic classes is not direct
verbs break chip crack crash crush fracture rip shatter slnash snap sl linter split tear
figures 4a and 4b figure 4b
NUM intentional linguistic structure in rst in contrast to its explicitness in g s ils is only implicit in rst
note that this synthesis encompasses the ils claims of both theories regarding the example discourse in figure NUM
a schema application describes the structure of a larger span of text in terms of multiple constituent spans
its use for a more delicate task aimed at the general public especially a public which is not necessarily highly educated is certainly out of the question for well known reasons which we need not explore here
accordingly job classification terms are classified coded i.e. a distinct code identifying the term is associated with each term and a list of standard names as well as recognized synonyms is associated with them
however the integration of all these functions is from a methodological point of view a good example of how a variety of techniques can be combined into a real application with a real use in the real world
the terminology module has been designed with the general aim of supporting all the common functionalities shared by the analysis generation and query modules and of supporting a language independent term bank to permit multilingual handling of the schema database contents
these are words which we often find in job ads associated with specific slots which we would like to translate if possible but which do not have the status of terms since they are neither structured nor standardized
the process of generating from a set of grammar rules given a particular job database entry will simply involve picking the rules the conditions of which best match the entry and using them to generate a document
the fillers for the slots may be coded language independent references to the terminological database source language strings which can nevertheless be translated on demand with reference to the lexicon or literal strings which will not be translated at all
of the three structures it is the effect of intentional structure on linguistic structure that concerns us in this paper
such interpolated markov sources are considerably more powerful than traditional n grams but contain even more parameters
NUM the expansion factor NUM h ensures that NUM h
conversely a low efficiency indicates that the model class does not adequately describe the observed data
in short the asm model is not a complete interpretation of the principle of maximum tokenization
we can minimize the impact by moving singletons as deep as possible closer to the individual word they precede or succeed or in other words we can widen the scope of the brackets immediately following the singleton
this is particularly striking when the number of parameters is plotted on a linear scale
then the following properties hold for the and operators where the relation means that the same two output strings are generated and the matching of the symbols is preserved
we stress again that the primary purpose of itgs is to maximize robustness for parallel corpus analysis rather than to verify grammaticality and therefore writing grammars is made much easier since the grammars can be minimal and very leaky
recasting this issue in terms of the general class of context free syntax directed transduction grammars the number of possible subtree matchings for a single constituent grows combinatorially with the number of symbols on a production s right hand side
parallel bilingual corpora have been shown to provide a rich source of constraints for statistical analysis brown et al NUM gale and church NUM gale church and yarowsky NUM church NUM brown et al NUM dagan church and gale NUM department of computer science university of science and technology clear water bay hong kong
nonetheless most related previous parallel corpus analysis models share certain conceptual approaches with ours loosely based on cross linguistic theories related to constituency case frames or thematic roles as well as computational feasibility needs
both document writers and users are supposed to be familiar with this specification
although word correspondences acquired by this step are sometimes false translations of each other they play a crucial role mainly in the final iterations phase
in the most general case initial anchors are only the first and final sentence pairs of both texts as depicted in figure NUM possible sentence correspondences are determined from the anchors
second due to rhetorical difference the number of multiple match i.e. NUM NUM NUM NUM NUM NUM and so on is more than that among european languages
because it is impossible in general to cover key words in all domains it is inevitable that statistics and hand crafted bilingual dictionaries must be used at the same time
the simple feature based approaches do n t work in flexible translations for structurally different languages such as japanese and english mainly for the following two reasons
for example p2 is the set comprising jsentence2 esentence2 and esentencej which means jsentence2 has the possibility of aligning with esentence2 or esentencej
in contrast if the two are not good translations of each other a should be small and b and c should be large
t prob wjpn weng prob wjpn prob weng prob wjpn weng
quite the opposite is the case in carpenter s approach where solutions are not guaranteed to have more specific extensions
these word correspondences greatly improved the performance for text NUM thus the statistical method well captures the domain specific keywords that are not included in general use bilingual dictionaries
an advantage of this approach is that it avoids discarding possible chunks merely because they are not part of the optimal cover for the input instead selecting the input coverage by how well the translations fit together to form a complete translation
the weights call be changed for ditdring lengths of the source chunk in order to adapt to varying impacts of the tests with varying numl ers of words in the chunk as well as varyit g impacts as some or all of the
in an internal evaluation panebmt achieved NUM NUM coverage of unrestricted spanish news wire text despite a simplistic subsententia alignment algorithm a subop ritual dictionary and a corpus dora a different domain than the evalual ion texts
integrating the hand crafted glossaries from pangloss into the corpus thus adding NUM NUM effectively pre aligned phrases to the corpus improved the matches against the corpus from NUM NUM to NUM NUM of the input and the coverage with good chunks to NUM NUM
since the more dequent word sequences an o cur hundreds of times in the eorl us the list of chunks is culled to eliminate all but the last tlve by default occurrences of any distinct word sequence
the lcb inal e olumn shows l he lcb o lcb m nund rcb er of source wor is covercd i rcb y at leasl lcb rcb lie 15rol osed r lcb
the engines lisl e i in t he l ables re glossaries haa lcb t raf lcb e t wor lcb i l hrase bilingum glossm ies
a source word is considered to be associated with a target language word whenever either the target word itself or any of the words in its root synonym list appear in the list of possible translations for the source word given by the dictionary
its 1this work as part of the l angloss project was supported i y tim u s i epartment of defense three main knowledge sources arc a sententiallyaligned parallel bilingual corpus a bilingual dictionary and a target language root synonym list
the context model is relatively simplistic
this way of organizing descriptions in definite clauses allows efficient processing techniques of logic programming to be used
edward uses the machine time as an anchoring point
the three preceding changes are made on the expression of NUM and the resulting transformation is given in the first line of table NUM changes are underlined
the area to be scanned depends on the context
NUM algorithmic identification of segment boundaries using linguistic cues as discussed in section NUM there has been little work on examining the use of linguistic cues for recognizing or generating segment boundaries NUM much less on evaluating the comparative utility of different types of information
consider a case where two subjects had NUM responses each j NUM each subject responded with NUM half the time nl no NUM and wherever one put a NUM the other did not m NUM
relatum and referent support each other in reference solution
the user can simulate pars pro toto and totum pro parte pointing gestures
each level of the tree specifies a test on a single feature with a branch for every possible outcome of NUM the manually derived segmentation algorithm evaluates boundary assignment incrementally i.e. utterance by utterance after computing the features for the current utterance or ficu
the first alternative model is a very simplistic one
she has a white rose photographed a s question leads to expect focus marking of the complete vp but intonational marking plus projection rules produce a narrow focus on weifle
since all acce nt on a word introduces just an arrow towards foc narrow focus on a word survives the check even in cases where the word is given
depending on the value of the argument resentation it must be stressed that a carrier is a very concise representation of a piece of recorded speech without segmental voice specific features
a message represents a complete sentence and is composed of one or more building blocks or message units mu which constitute the input of the mts system
these text and message generating systems are either resource intensive powerful cpu large storage and memory capacity or provide only limited flexibility which seriously hampers their integration in a dialogue system
it will be clear that the prosody of a carrier ept with slots although better than plain tts risks to be slightly inferior to that of an entire ept no slot
once the appropriate surface form is selected see section NUM NUM the resulting ept template with its arguments phonetically transcribed is integrated on the prosodic level see section NUM NUM
in addition as the language and task independent core engine is very strictly separated from the language dependent knowledge bases it is very easy to tailor the mts system to specific tasks
as an is associated with on vo vocalic onset the default case a is selected carrier de pendent argument realisation
a discussion concludes this paper see section4
each while loop probabilistic chunker based on definition NUM processes three parts of speech and concerns the dp NUM distribution between them
these phenomena can be handled by providing an obligatory rule for the case whether the letter changes but constraining the applicability of the rule with a feature and making the feature clash with that for roots where the change does not occur
because of the use of boolean vectors for both features and characters it is quite possible to constrain each partitioning by unifying it with the complement of one of the conditions of each applicable obligatory rule thereby preventing that rule from applying
i am grateful to manny rayner and anonymous european acl referees for commenting on earlier versions of this paper and to pierrette bouillion and malgorzata styg for comments and also fo providing me with their analyses of the french and polish examples respectively
compilation to a network may still make sense however and because these languages tend to exhibit few non eoncatenative morphophonological phenomena other than vowel harmony the continuation class mechanism may suffice to describe the allowed affix sequences at the surface level
as each of these is a sequence we can pick out elements of each by an index that is w will pick out the nth terminal element of the left corpus
first all legal sequences of morphemes are produced by top down nondeterministic application of the production rules section NUM NUM selecting affixes but keeping the root morpheme unspecified because as explained above the lexicon is undetermined at this stage
the first component above is a relation which accepts any surface symbol on its first tape and the lexicon on the remaining tapes
sc now accepts all and only the sequences of tupies described by the grammar but including the partitioning symbols p
as in that work a discrimination net of root forms would be required however this could be augmented independently of spelling pattern creation so that the flexibility resulting from not composing the lexicon with the spelling rules would not be lost
similarly some french verbs whose infinitives end in eler take a grave accent on the first e in the third person singular future modeler model becomes mod lera while others double the i instead e.g.
we also take this opportunity to transform the lsd and rsd used in the corpus into tokens used by the core processor that is lcb and rcb respectively
for instance in determining the probability of a coreference configuration a b c it does not consider the probability assigned to the pair a and c except to check that they are compatible
this dictated step wise development and required starting with solvable problems
the utterance cost u will be small if the voiced signal precisely matches a token sequence generated by the system grammar
the definition of a company was user oriented e.g.
still vanf should be considered a variation on the theme of fastus
each stage is responsible for specific kind of processing see below
a grammar defines a set of ndfsm and the sequence of their application
this part of the scanner is based on regular expressions lex
the approach is very similar to that used in fastus NUM
NUM all occurrences of coke were ignored
in addition enamex type person peter kim enamex was hired fro m
we can take the number of maximal trees of depth more than one within susanne as an indication of the number of trees within the treebank which are unalignable as a consequence of decisions about markup
ferred to i lcb i iii siill t NUM the saliency ordering on the cf list which is generally equated with grmnmatieal function for wes ern language s is subje t oilji t2 lb iect NUM oti1ers where otiteii s includes pret ositional phrases and adjuncts
it uses the context information to adapt outputs to their environment and sends the sequence to a dectalk system for voicing
in our model the lexicon consists of n j sublexica corresponding to the lexical elements in the formalism
it can not be excluded therefore that others might in the corpus have found types that demanded revision or extension of the guidelines
note that figure NUM does not include the cases and types that were either undecidable disagreed or rejected see figure NUM
violation ref quot sl NUM NUM quot guide it is not clear if the time provided is that of the timetable or the actual expected arrival time of the flight
by contrast with the danish dialogue system the sundial system being developed through the use of the analyzed corpus uses a delayed feedback strategy
though the in o rcb lerel f a text may resu t from a lot of heilolllella w restri t ourselves in this collll tllllicatio l o illcoh l ollce stelrllllillg from negations
rule features are satisfied if they match the feature structures of the lexical entries containing the lexical expressions in r respectively
finally the single undecidable case was one in which the non transcribed prosody of what the user said might have made the difference
as this was out first attempt at using the tool independently of one another we intend to repeat the exercise using the insights gained
if and when convincing generality and a satisfactory degree of objectivity in using det have been achieved a final transfer problem must be addressed
imagene embodies choices that are consistently made over a range of instructions and thus does not reflect isolated examples
example exact match NUM it is difficult if not impossible for anyone who has not pored over the thousands of pages of court pleadings and transcripts to have a worthwhile opinion on the underlying merits of the controversy
note also that imagene s realizations may even be better in some cases than the text in the corpus
note that the success criteria are increasingly strict if an example satisfies exact match it will also satisfy the other two criteria and if an example satisfies head match it will also satisfy head overlap
contained in an np argument of the containing vp as in figure NUM the parse tree for the following example NUM she was getting too old to take the pleasure from it that she used to
the system achieves a success rate of NUM NUM where success is defined as sharing of a head between the system choice and the coder choice while a baseline recency based scheme achieves a success rate o i NUM NUM
using the head overlap measure the system achieves a success rate of NUM NUM on a blind test of NUM wall street journal examples while the baseline recency scheme achieves a success rate of NUM NUM by this measure
coder NUM revoke payment whenever it chose coder NUM owned the bank and had the power to revoke payment whenever it chose in the following example the coders disagreed according to exact match although they agreed according to the other two success criteria NUM when bank financing for the buy out collapsed last week so did uaus stock
thus the original user model was incorrect where it included find knob and this assertion is deleted
n consider the following example NUM tells you what the characters are thinking and feeling advp far more precisely than intertitles or even words vpe would
the modification relation can also be a comparative relation as illustrated by the following example whose parse tree is given in figure NUM NUM all felt freer to discuss things than students had previously
the specific levels of match for the four most common rhetorical relations are detailed in figures NUM and NUM
this text is identical to the original text with respect to the four lexical and grammatical issues addressed here
imagene expresses this type of result as a future tense clause as seen in example 16a
these differences arise from the fact that the current study has not specifically addressed the issue of referring expressions
a temporary end of vc tendvc is then inserted on the right of any finite verb and the process of recognizing vcs consists of the following steps step NUM each certain tbeginvc1 is matched with a tendvc and the sequence is marked with vc and vc
evaluation of the word bits is carried out through the measurement of the error rate of the atr decision tree part of speech tagger
if that other node has a reducible slash value then we know that the reduction takes place in the other tree because the slash value must have been raised across the domination link where adjoining takes place
however it is only useful to produce a tree in which the susj value is not raised when the bottom of a domination link has both a one element list as value for subj and an empty comps list
therefore we have to identify which constituents in awe choose such a lexicalized approach because it will allow us to maintain a restriction that every tag tree resulting from the compilation must be rooted in a non emtpy lexical item
tree t1 provides such an example where a lexical item an equi verb triggers the reduction of an sf by taking a complement that is unsaturated for subj but never shares this value with one of its own sf values
above we noted that the preservation of some sfs along a path realized as a path from the root to the foot of an auxiliary tree does not imply that all sfs need to be preserved along that path
raising all sfs produces only fully saturated elementary trees and would require the root and foot of any auxiliary tree to share all sfs in order to be compatible with the sf values across any domination links where adjoining can take place
we will fill in the details below in the following order what information to raise across domination links where adjoining may take place how to determine auxiliary trees and foot nodes and when to terminate the projection
l cdegmps imp pp NUM from this lexical entry we can derive in the first phase a fully saturated initial tree by applying first the lexical slash termination rule and then the head complement head subject and filler headrule
while in tag all arguments related to a particular functor are represented in one elementary tree structure the functional application in hpsg is distributed over the phrasal schemata each of which can be viewed as a partial description of a local tree
however instead of systematically raising all possible subsets of sfs across domination links we can avoid producing a vast number of these partial projections by using auxiliary trees to provide guidance in determining when we need to raise only a particular subset of the sfs
the first is a poisson model which leads to appealing computational simplicity
the fertility models introduced below maintain these benefits while slightly improving performance
the models labeled clump use a basic clumped model without fertility
the language model is just the unsmoothed unigram probability distribution of the patterns
they learn automatically from a bilingual corpus of english and formal language sentences
they do not require linguistically knowledgeable experts to tediously annotate a training corpus
we have devised a grammar of french to serve as a basis for the creation of metarules for term variants
a type NUM collocation of a binary term is a text window containing its content words wl and w2 without consideration of the syntactic structure
second is exact syntactic match exact match with the bracket locations and rule names only
NUM we then add further questions which ask about the source treebank parse for the sentence being processed
we performed experiments using plain texts from six years of the wall street journal corpus to create clusters and word bits
these ranked lists were hand constructed and an effort was made to make them as difficult as possible to choose from
similarly we can ask about constituents that cross a given node of an atr parse
as a preliminary step to treeb k conversion we aligned the parallel and ati corpora
we will refer to the ibm lancaster treeb k version of this data as the parallel corpus
edward stubby smith is one of many that are associated with the semantic category nickname
each node contains values for NUM features and there are NUM values per feature on average
statistical models corresponding to each type of step provide estimates of the probability of each step s outcome
morphological heuristics is a rule based module for the analysis of those NUM NUM of input words
the context patterns can be local or global and they can refer to ambiguous or unambiguous analyses
our system engages in dialog only for the purpose of enabling theorem proving and voice interactions do not otherwise occur
there are two main methodologies for constructing the knowledge base of a natural language analyser the linguistic and the data driven
aligned subtrees we now offer the following definition
the english koskenniemi style lexicon contains over NUM NUM lexical entries each of which represents all inflected and some derived surface forms
the function resolved x s us cf
finally the function lift s i cf
table NUM lower box for the basic pattern
so the problem we consider is not a marginal one
we could not find an embedding of more than seven levels
f r standard instauationen kommt man gut ohne handbuch aus
this is trivially true for cases of a constant theme
it defines a model as a set of lagrange multipliers z NUM psn and has an exponential form
first it has been developed on the basis of a small corpus of written texts
movement this is a combination of a deletion with a subsequent conjunction or adjunction
as the result we have a fully specified maximum entropy model of the form z axo ax
we wish to thank judith klavans rebecca passonneau and the anonymous reviewers for providing us with useful comments on earlier versions of the paper
using automatically collected data the accuracy and applicability of each method is quantified and a statistical analysis of the significance of the results is performed
during prediction a path is traced from the root of the tree to a leaf and the category of the leaf is the category reported
performance relative to the simple frequency test equal to or larger than the observed one is listed in the p value column for each complex predictor
since all obtained measurements of accuracy were higher than NUM any rejection of the null hypothesis implies that the corresponding test is significantly better than chance
but they can predict the prevalent markedness value for each adjective in a given domain something which is impractical to do by hand separately for each domain
the methods we describe are based on the form of the words and their overall statistical properties and thus can not predict specific occurfences of markedness reversals
we present a corpus based study of methods that have been proposed in the linguistics literature for selecting the semantically unmarked term out of a pair of antonymous adjectives
one test while the frequency of the adjectives is the best single predictor we would expect to gain accuracy by combining the answers of several simple tests
within each hierarchy the intention so for example the model reflects the fact that the ing progressive form of verbs is generally acquired before the s plural form of nouns which is generally acquired before the s form of possessives etc
in other words a maximal tncb is a largest well formed component of a tncb
our approach is fully automatic but permits effective combination of available resources such as thesauri with language processing technology i.e. morphology part of speech tagging and syntactic analysis
ph90 catalogues six pitch accents all combinations of high h and low l pitch targets and structured as a main tone and an optional leading or trailing tone
NUM the specific attentional consequences of each pitch accent on pronominals can be extrapolated by analogy from the propositional interpretations in phgo by replacing mutual beliefs with cf as the salient set
for oral discourse however we must also consider the way intonation affects the interpretation of a sentence especially the cases in which it alters the predictions of centering theories
in addition while propositions can be excluded from the mutual beliefs because they fail to meet some inclusion criterion no lexical denotation is excluded from cf regardless of its propositional value
from these assumptions i derive the following attentional consequences for pitch accented pronominals only one pitch accent l h selects a cb other than that predicted by centering theory and thereby reorders cf
h predicates a proposition as mutually believed and proclaims its addition to the set of mutual beliefs l fails to predicate a proposition as mutually believed
the cb retains its centered status for the current utterance but its rank is lowered it no longer resides at the head of cf and therefore ceases to be the center in the next utterance
centering structures and operations to explain how speakers move an entity in and out of the center of mutual attention gjw89 formalizes attentional operations with two computational structures the forward looking
my goal here is to develop an analysis and a line of inquiry and to suggest that my derivative claims are plausible and even extensible to an attentional analysis of pitch accents on nonpronominals
to bestow an intonational marker of salience the pitch accent on a textual marker of salience the pronominal is unnecessarily redundant and especially when textual features correctly predict the focus of attention
for example if we had a model of what the student had already acquired what the student was currently acquiring and what the student was most likely to acquire next this could be used to select the most likely parse of the sentence in a principled fashion
NUM already at small data set sizes performance is relatively high
entry cons result cdr entry define result subsumed
cardie p c reports NUM NUM correct tagging for unknown words
computational linguistics volume NUM number NUM memoized cps top down recognizers do in fact correspond fairly closely to chart parsers
tagging speed in our current implementation is about NUM words per second
when fully developed its degree of detail will be variable from one to one equivalent with spl to an abstract form that contains only the deep semantic frame for a predication thereby being a notation in which for example commit suicide has suicide as head rather than commit
a fully automatic system has been developed and evaluated on unseen test data with good results
if a module is activated but is not able to make all the decisions it needs to or if it makes decisions that are known to be possibly incompatible with those made by other modules later on there are in general three options for how to proceed NUM
this means that their effects must be written as tree rewriting rules in the following general form see section NUM NUM pre spil pre spi2 naturally each module must know which tree transformation rule to apply to any given pre spi under any given conditions
since a condition that is represented by the roles domain and range is expressed in kpml as a hypotactic clause complex fragment c1 remains unchanged note that the aggregation module still has not run to make the changes for NUM
informally in a cps program an additional argument call it c is added to all functions and procedures
the task of the sp is to transform selected not necessarily consecutive plans which may vary in detail from text plans specifying only content and discourse organization to fine grained but incohesive sentence plans into completely specified specifications for the surface generator
NUM repeat step NUM starting from the most frequent word through the least frequent word
the value of ga c i is immaterial and therefore unspecified but see footnote NUM below
d1 disjunction domain w wearout undergoer i1 implant range d2 disjunction domain l loosen undergoer i1 range f fail undergoer i1 circumstance o occasional c1 condition domain r remove patient i1 mode necessity range d1
next timevalue rf returns the next timevalue that follows reference frame rf
pereira and wright s algorithm applied to the same problem gave an intermediate automaton the unfolded recogniser with NUM states and the final result after flattening and minimisation was a finite state approximation with NUM states
however in many other cases such as the grammar s a s a i b s b i e or the NUM rule grammar in the previous section the results are essentially different and neither of the approximations is better than the other
nevertheless the new algorithm seems to have the advantage of being open ended and adaptable in the previous section it was possible to complete a difficult calculation by relaxing the conditions of formulae NUM and it is easy to see how those conditions might also be strengthened
figure NUM shows the sequence of calculations that corresponds to applying the algorithm to the following grammar s asb s e with the following notational explanations it should be possible to understand the code and compare it with the description of the algorithm
together we believe these offer the best prospect for radically improved performance in the descriptor locale and country slots
figure NUM shows parse fragments for two sentences whic h generated the bulk of the succession information in the walkthrough message
two key design features of plum are statistical language modeling with the associated learning algorithms an d partial understanding
the specification of the input format is declarative allowing the system to be easily adapted to handle different message formats
since so little data was available we also created our own training data from wall street journal articles from NUM NUM
the entity name slot for all messages was used to quickly add names to the domain dependent lexicon for te and st
first the state of the art has progressed greatly in portability in the last fou r years
for example the generic predicate pp modifier indicates that two entities are connecte d via a certain preposition
entity descriptions typically arise from noun phrases events and states of affairs are often described in clauses
there are three basic types of semanti c forms entities events and states of affairs
investigation of these possibilities in the future is needed
two additional experiments have been planned
a re estimation method for stochastic language modeling from ambiguous observations
table NUM the precision on each cost of juman
the test sentences include about NUM japanese morphemes
the number of kinds of tags was NUM
new text passages can be projected into the space by computing a weighted average of the term vectors which correspond to the words in the new text
for each confusion set only those sentences in the training corpus which contained words in the confusion set were extracted for construction of an lsa space
seven of the NUM confusion sets contain words that are all the same part of speech and the remaining NUM contain words with different parts of speech
we explore the use of latent semantic analysis for correcting these incorrectly used words and the results are compared to earlier work based on a bayesian classifier
the difference between the baseline performance column and the training corpus frequency column gives some indication about how evenly distributed the words are between the two corpora
because the sentences in the brown corpus are not tagged with a markup language we identified individual sentences automatically based on a small set of heuristics
confusions sets whose words are different parts of speech are more effectively handled using a method which incorporates the word s part of speech as a feature
this situation leads lsa to believe that the bigrams the amount and amount of have more discrimination power than the corresponding bigrams which contain number
context reduction is a step in which the sentence is reduced in size to the confusion word plus the seven words on either side of the word or up to the sentence boundary
the local weight is a combination of the raw count of the particular term in the sentence and the term s proximity to the confusion word
a tool with a sharp point and euttjng edges for making holes in hard materials usually a motor driven tool
potential heads of terminological entries are selected according to their selective power in the corpus even very rare words of the corpus can be captured by NUM
NUM extend td also with those well formed restrictions cn l of any l e td according to the mutual information they exchange with i
the viterbi part of speech tag reestimation stage gives the figures of NUM NUM and NUM NUM weighted precision rates and NUM NUM and NUM NUM weighted recall rates for the NUM different configurations when using a seed corpus of NUM sentences
the per word precision for a word is defined as the number of tags commonly annotated in the dictionary entry and the extracted word tag list for the word divided by the number of tags in the extracted word tag entry for the word
since this approach adopts an unsupervised learning approach to construct the dictionary its performance in terms of precision and recall is less satisfactory than a supervised learning strategy where a large tagged corpus and dictionary are used
NUM automatic word identification viterbi training for words vtw to compile an electronic dictionary i.e. a word tag list in the current task we need to gather the word list within the corpus first
all the above patterns are related to the internal structure of the n grams our features and models however are more closely related to the intrinsic properties of the n gram itself or the contextual information with the other n grams
in fact however only about NUM of bigrams NUM of trigrams and NUM of NUM grams in the frequency filtered word candidates are recognized as words in a human constructed dictionary of more than 80k entries
apparently this was aimed at for all nouns but we saw a case where the hypernym relation implicitly was substituted by ts used for and this made up an or
the third column simply shows the initial precision and recall for the n grams which are more frequent than a frequency lower bound lb such word candidates are the base for evaluating the effects of the vtw and tcc modules
to relieve the problem the vtw module can be considered as a filter to the frequency filtered word candidates and we can further filter out inappropriate candidates by a tcc postfilter at the output end of the basic model
section NUM NUM which itself has practical import
the two authors coded independently and merged their results
this is most commonly due to over literal translation
note that although not all available features are used in the tree the included features represent NUM of the NUM general types of knowledge prosody cue phrases and noun phrases
figure NUM learned decision tree for segmentation
figure NUM features and their potential values
fig NUM for fallout and error
we have also shown how such an architecture can be modular and extensible and how its different components interact
we have implemented operators for superset addition contradiction refine ment change of perspective etc
summons figure NUM uses summarization operators to express various ways in which the templates that are to be generated are related 1the primary source e.g. an eyewitness and the sec ondary source e.g. a news agency are very important for producing accurate summaries to each other
the bold box is the position where the js is to be inscribed
it uses kqml subscription messages to learn in an asynchronous way about changes in the knowledge bases of other facilitators
planner it maintains contacts with the facilitators in order to keep the knowledge base of the summarizer up to date
the following example shows how the planner uses a kqml subscription message to subscribe to new messages related to e1 salvador
another instance of a database server is the facilitator connected to the node labeled ontology in figure NUM
dependency grammar defines a language as a set of dependency relations between any two words
the problems of structural data sparseness and lack of lexical information can be lessened with pdg
tions for future research currently our system can handle simple summaries consisting of NUM NUM sentence paragraph which are limited to the muc domain and to a few additional events for which we have manually created muc like templates
in the previous section we compared the approximated probabilities obtained by our method to the probabilities found by manually tagging a small corpus
all other probabilities an analysis with probability of this sort should not be selected as wrong right analysis solely according to its morpho lexical probability
for example the word awlm o 1b0 taken from test group1 has the following two morphological analyses NUM
we then manually tagged each ambiguous word and found for each one of its analyses how many times it was the right analysis
for each word in these test groups we extracted from the small corpus all the sentences in which the ambiguous word appears
u yet from the above counters we should be able to deduce that the first analysis has a very high morpho lexical probability
consider the word at t and its sets and counters as found in our training corpus NUM
a word was considered misleading if its counter was at least five times greater than that of any other word in the set
the key idea is to shift each of the analyses of an ambiguous word in such a way that they all become distinguishable
an element in this set is another word form of the same lexical entry that has similar morphological attributes to the given analysis
tree locations will therefore include any additional information within the corpus stored between the left and right delimiters
NUM there are six possible outputs for this algorithm
all remaining errors are my own
the use of slash features probably simplifies the computation
consider grammar i and grammar NUM in table NUM
the parsing algorithm is a generate and test backtracking regime
fong finds that this parsing approach is also inefficient
more than one chain can occur in a sentence
when building chains several problems must be solved
this occurs because certain groups of actions go together
maryi seemed t i to have been loved ti
it may also contain additional elements to represent for example the positing of orthographically null categories
the combination s that gives the highest value will be the most likely sequence s of tagged words
plan operators have been used to encode both the dialogue model and methods for recovery from erroneous dialogue states
predictions about what comes next are needed internally in the dialogue component and externally by other components in verb mobil
these constraints mostly address the context but they can also be used to check pragmatic features like e.g.
each plan operator represents a specific goal which it is able to fulfill in case specific constraints hold
we observed that by tuning the model parameters to obtain clusters of very similar instances a percentage varying between NUM and NUM of verbs depending upon the number of input observations belong to singletons that is are not similar enough to any other verb
for example in pattern ii a cognitive process rather than somebody is the agentive of to record e.g. the algorithm co records the changes and of the other members of the class labeled decide make up one s mind decide upon determine cluster NUM NUM
for japanese a similar problem arose because refinements to the guidelines over the course of met development were not reflected in the development data set
the chart in fig NUM shows the relative rankings of the four languages solid bars indicate training and shaded ones formal testing
to keep the derivation simple we assume here that the probabilities as are independent of s and that there are no wildcards that is f NUM c c for all s
in the case of a completely new word we need to multiply the probability of a novel event by an additional factor po wn interpreted as the prior probability of the word according to a lower level model
the system garnered commendable scores on all three languages despite its developers having at best passing linguistic fluency and in one case no language knowledge at all
in a rule sequence processor the object is to sequentially relabel a body of text according to an ordered rule set
therefore semantically driven nl interpreters may profitably be augmented with the information obtained by merging these different sources
on the other hand wordnet is often missing most of these precise technical uses of verbs
for each sense s in s v the set of wordnet hyperonims of s is defined
the purpose of this analysis is not to validate ciaula with wordnet nor to augment wordnet with ciaula
in fact a supertype could gain evidence only because several senses of the same verb point to it
every year public sentiment for conserving our rich natural heritage is growing but that heritage is shrinking even faster no joyride much of its contract if the present session of the cab driver in the early phases conspiracy but lacking money from commercial sponsors the stations have had to reduce its vacationing in online mode the advantage of psts with large maximal depth is clear
during a first experiment we ran the tagging algorithm using unrestricted sets of verbs first clustered by ciaula
kita et al extract expressions in the order of larger cost criteria values
table NUM shows examples of endexpr bigrams with local cohesion
in recognizing local cohesion our method uses these three coherence relations
different from global cohesion local cohesion does not have a hierarchy
indeed equation NUM gives very small values in some cases
u speech act verb nouns NUM
the first column represents global cohesion and the second column represents local cohesion
we can automatically acquire discourse knowledge from an annotated corpus with local cohesion
we automatically acquire these discourse knowledge from a annotated corpus with local cohesion
different semantic relations on the class of verb concepts two semantic relations are defined which are not logically independent troponymy i.e.
the assumption that trees within corpora are strictly nested represents an obvious limitation on the scope of the algorithm
if the theme plays a circumstantial role in the proposition it is usually realized as a sentence initial adjunct
in a system where relevance is represented as a list of entities we could not produce this sentence
the immediate discourse context includes entities introduced earlier in the discourse and also entities within the immediate physical context of the discourse e.g. the discourse participants speaker hearer or speaker hearer and those entities which the participants can point at for instance a nearby table or some person
are part of the immediate discourse context can be referred to using pronominalisation e.g. she them it this etc substitution e.g. i saw one or ellipsis the non mention of an entity e.g. going to the shop
the input for the coreference task was close in format to the named entity input and had the easier mapping conditions as well but the scoring was based on linkages rather than slot fills
wag thus integrates with more ease into a system intended for dialogic interaction such as a tutoring system
as we build a rhetorical structure tree the ideation which is necessary for each rhetorical relation is selected
figure NUM building a knowledge base NUM NUM NUM assertion of knowledge into kb figure NUM shows the forms which assert some
other speech functions cater to various alternative responses in dialogue for instance deny knowledge the speaker indicates that they are unable to answer a question due to lack of knowledge contradict the speaker indicates that they disagree with the prior speaker s proposition requestrepeat the speaker indicates that they did not fully hear the prior speaker s move
to rewrite these into new patterns requires use o f the dynamic variable assignment facility which takes whatever tokens were matched by the pattern and assigns them as a list to the variable indicated as shown b y
the third entry mr is also categorized as referring to a person and capitalized but is not a name rather a title o f type pref for prefix
the fourth entry militant again refers to a person but is neither a name nor a title nor capitalized but has a role value of either political or terrorist
things that did n t wor k aside from the fuzzy logic approach it took us a long time several weeks at least to learn that for pattern based rules simple is terribly much more effective than comprehensive
in this case the character string dr is associated with two types of titles one a prefix for person names and one a suffix for in city street references both values are returned for the attribute title typ
for locations and organizations the numbers are NUM NUM and NUM respectively the person rules are still in flux but will be i n this ballpark by the time of the conference
an equa l amount of time was spent in learning effective strategies for writing reliable rules especially learning to avoid the temptation of trying to recognize too many variations of data class instances with a singl e rule
person targets were often difficult but they tended to be the default case we had already done our best with our second set of place and organization rules and what remained was most likely a person
and organizations with and in contrast to as part of their name were difficult t o discriminate from conjoined organizations e g hollis and pergamon holdings ltd
the second major processing step is to check the knowledge bank to see whether the chars value of tokens are known as an instance of a data class of interest e g known locations or organizations
the comm process csci creates a document from the cable data and passes the document to the document manager process csci which stores this information in a document collection
it draws on the ssynt grammar which states rules of linear precedence according to arc labels
presumably because of deeper facts about language the grammar rules are quite small
also realpro does not map any sort of semantic labels to syntactic categories
in this technical note and demo we present a new off the shelf realizer realpro
we thus obtain de facto linear performance which is reflected in the numbers given above
pro pro gender fern figure NUM input structure for sentence NUM
thus notice that adjectives and nouns are conflated since complex nominal phrases have largely similar parse structures regardless of the difference between adjective and noun labels
this technical note presents realpro concentrating on its structure its coverage its interfaces and its performance
in 16b and 17b for example the complement directly saturates the event e2 or e3 as the qualia structures in NUM and NUM make explicit
what the architecture provides the tipster architecture provides functional descriptions for each of a set of modules for each included module the form and content of inputs to it and outputs from it
the architecture provides a means for handling many types of information within documents e.g. different types of formatted text vs free text text in different languages and text at different security levels
in particular parts in any natural language will be processable within the architecture provided that the language can be identified and indicated through markups and that architecturally compliant modules and components for the language exist
the architecture has a secondary purpose which is desirable but not of equal importance to the first namely to provide a convenient and efficient research framework for research in document detection and data extraction
two fundamental criteria must be met to do this costs must be controllable and minimized for development deployment and maintenance and functionality must be sufficient to handle a variety of analytical tasks
however modular interfaces have not yet been defined to the level of detail which will be required for the government to meet its goals of software reuse and modular upgrading i.e. they are underspecified
the erb will produce a tipster architecture conformance assessment document tacad detailing the ways in which the design complies with the architecture and those where it does not
first the tipster architecture committee will maintain a list of previously built tipster applications the contractors who built them and detailed information about each module in those applications
at these reviews any discrepancy or deviation from the tipster architecture must be documented and justified explained any new code or capabilities for the tipster application must be developed in accordance with the tipster architecture
to eliminate this undesirable effect desieno NUM has developed an improved competitive learning algorithm that makes use of the idea of conscience
the stem word vector that results in the highest dot product with the centroid region vector becomes the label for the region
and NUM NUM hapax legomena figure NUM reveals significant deviation in the first half of both texts
this suggests that the nonrandomness observed for words carries over to word based units such as digraphs and syllables
as we continue through the information age tools such as docuverse will no longer be considered luxuries but rather necessities
used in conjunction with flat maps the user would have a hierarchical som capable visualizing information at various levels of resolution
instead of showing all regions at once a user can select regions of interest from either the map or the label dialog
the corpus integral is very useful in that the user knows at a glance which themes are present in the corpus
searching for specific information is also supported so that the user can request in flee text form any desired information
as a global text characteristic it is probably insensitive to the strictly local constraints imposed by syntax
it is help l however if the training corpus is statistically representative of the corpora that will be visualized
unfortunately the central sections are not necessarily the ones characterized by the highest degree of lexical specialization
the rest of this paper is structured as follows in section NUM we present an overview of edward
the area occupying most of the screen is the graphics display a window called modelwereld model world
the end value may be now which is a dynamic value representing openendedness in a time interval
text documents are generally cb ssified by significant words keywords of the documents
copy all reports except for this one where the report named donald report is the demonstratum
all three models will try to locate the referent of he in the set of individual instances mentioned before
the bear icon at the bottom in the middle represents the system itself i.e. edward
the first live in relation can now be referred to in simple past tense the second in present tense
to determine the referent of a phrase first all individual instances satisfying the semantic restrictions of the phrase are listed
in this way tilt japanese text x is expressed as a point in the l
figure NUM shows two possible paths from the lattice of possible analyses of the input sentence NUM how do you say octopus in japanese previously shown in figure NUM
in 2a we want to split the two morphemes since the correct analysis is that we have the adverb cai2 just the modal verb neng2 be able and the main verb j ke4 fu2 overcome the competing analysis is of course that we have the noun yd
finally assuming a simple bigram backoff model we can derive the probability estimate for the particular unseen word i rcb as the product of the probability estimate for i and the probability estimate just derived for unseen plurals in p j r p p unseen
the wang li and chang system fails on fragment b because their system lacks the word youl youl soberly and misinterpreted the thus isolated first you1 as being the final hanzi of the preceding name similarly our system failed in fragment h since it is missing the abbreviation t tai2 du2 taiwan independence
that is given a choice between segmenting a sequence abc into abc and ab c the former will always be picked so long as its cost does not exceed the summed costs of ab and c while it is possible for abc to be so costly as to preclude the larger grouping this will certainly not usually be the case
this good turing estimate of p unseen can then be used in the normal way to define the probability of finding a novel instance of a construction in in a text p unseen lcb r p unseen r i p lcb r
NUM note that in chang et al s model the p rule NUM is estimated as the product of the probability of finding g1 in the first position of a two hanzi given name and the probability of finding g2 in the second position of a two hanzi given name and we use essentially the same estimate here with some modifications as described later on
mandarin exhibits several such processes including a not a question formation illustrated in 3a and adverbial reduplication illustrated in 3b in the particular form of a not a reduplication illustrated in 3a the first syllable of the verb is copied and the negative marker bu4 not is inserted between the copy and the full verb
let i denote the input expression consisting of a sequence of words along with certain features resulting from shallow parsing
our aim is to find the example that has the highest conditional probability of being appropriate to translate the given input
the first is an evaluation of the system s ability to mimic humans at the task of segmenting text into word sized units the second evaluates the proper name identification the third measures the performance on morphological analysis
for example hanzi containing the insect radical tend to denote insects and other crawling animals examples include wal frog fengl wasp and she2 snake
most natural language processing tasks require lexical semantic information
it is defined by the axiom below
only this reading is possible for stressful
above it was noted that un is a cue for telicity
the wfs predicate takes three arguments a difference list pair threading the string through the tree in the style of dcgs and level the syntactic category sign as functional style result argument s the analysis tree is encoded in two daughters features as part of the syntactic categories in the style of hpsg
of the sentences that parse particular problems were quotes and reported speech not withstanding the previously mentioned problems with reporte d speech the parser still had additional problems because the current grammar is quite restrictive abou t what can appear inside quotation marks
ment says that calls to wfs must be delayed until the first argument is instantiated to some list value
it lists the japanese prime minister and the peruvian president at the top as the japanese embassy hostage incident occurred recently
furthermore as we discussed earlier in section NUM NUM browsing documents by following hyperlinks allows a user to discover related information effectively
our system avoids these common but serious translation errors by taking advantage of the indexing module s ability to identify and disambiguate names
this can provide the user with a snapshot of what is in the database and what types of information are likely to be available
the user might see that nexgen and cyrix often occurs with intel and find out that they are competitors of intel in this field
this is especially useful in retrieving japanese documents because typically the user would not know various ways to say ibm in japanese
as described in section NUM NUM the indexing module automatically identifies aliases of names and keeps track of such alias links in the database
this ability to disambiguate an identified name is a first name a last name or a combination of first and last name
perlinking is based on the original or translated english terms the user can follow the links to both english and japanese documents transparently
the system also allows the user in their native language to browse and discover information buried in the database derived from the entire document collection
our goal is to create a program that after training on many such pairs can segment a new phonetic utterance into a sequence of morpheme identifiers
we will now consider the complete explanation in detail
for simplicity we represent these beliefs as facts
the askref would be expected see section NUM NUM NUM
the output for turn NUM from russ s perspective
NUM a statement about the underlying purpose of a
a statement about background knowledge that might be needed
metaplans encode strategies for selecting an appropriate act
the antecedents of these axioms refer to expectations
conflict with any stronger defaults that might apply
the decision tree shown in figure NUM is the result the performance of satz with this decision tree was nearly identical to the performance with the neural network
the authors would like to acknowledge valuable advice assistance and encouragement provided by manuel f ihndrich haym hirsh dan jurafsky and terry regier
a brief overview of the different components is presented in figure NUM
the rest of the dm was easily ported to this new application
short and long term goals may of course both be active with respect to the same conversation with short term goals contributing to longer term goals which in turn contribute to very long term goals such as quality of life and self fulfilment
the first text the training text contains NUM NUM test cases where a test case is an ambiguous punctuation mark
for example the brown corpus tags of present tense verb elements of the descriptor array assigned to each incoming token
the proportion of words falling into this category varies greatly depending on the style of the text and the uniformity of capitalization
this concludes the description of the various dialogue states
towards a pure spoken dialogue system for information access
system development is expected to be completed soon
the baseline system unix style achieved an error rate of NUM NUM on the sentence boundaries in the test set
it recursively examines each subtree to determine whether replacing it with a leaf or a branch would reduce the number of errors
the most obvious error visible in figure NUM is the suffix ing i which should be have an empty sememe set
a semantic dictionary was created by hand in which each root word from the utterances was mapped to a corresponding sememe
indeed such a word is properly hypothesized but a special mechanism prevents semantically empty words from being added to the dictionary
we derive an em algorithm to learn the mixing coefficients as well as the elements of the transition matrices
for instance in the case of two successive noun adjective ambiguities like le franc fort the strong franc or the frank fort we favor the noun adjective sequence except when the first word is a common prenominal adjective such as bon petit grand premier as in le petit fort the small fort or even le bon petit the good little one
the following definition specifies how outer domains are used definition4 a lexical sign lea is in the outer domain of sign if there is a triple sign lex binds in outer domains such that sign and lex unify with sign i and lez j respectively and there is at least one pair paths pathl e binds such that sign paths unifies with lezqpathl
the tra nsfer rule in NUM matches the decoinposilion of the comi at at ivt form lieber into its positive forin lieb atnt an additional comt arative predicate toget her with l he
at the current stage of automation the annotator determines the substructures to be grouped into a new phrase and assigns it a syntactic category
thus the format of the annotations is somewhat different from treebanks relying on a context free backbone augmented with trace filter annotations of non local dependencies
during comt ilation the sl class definition will be automatically expanded to the individual predicates whereas the tl class dclinition will be kept unexpanded such that the tat get gratnmar might be able to choose one of the idiosyncratic prepositions
the items in our lexicon contain information on the word form and lemma the inflection paradigm of verbs nouns and adjectives needed for both the wg and the tlr components and the blocking of rules by several classes of stems
this shows that catalan morphology can be more efficiently accounted for in a multi two level steps framework in which different tlr and wg rule sets are available depending on the type of word formation process to cover as depicted in figure NUM
due to the expressivity of the tlrs the wg can be very simple it is a dcg style grammar which builds a word out of the morphemes into which the surface string has been divided and provides the morphosyntactic information at the word level
the presentation focuses on the architecture of the annotation scheme and a number of techniques for increasing the efficiency and accuracy of annotation
in several cases the tagger has been able to abstract from annotation errors in training material which has proved very helpful in detecting inconsistencies and wrong structures
simply appealing to our everyday intert retation of tile term will not do
the analyses in this paper require a combination of truth functional operators and l abstraction
the mps for the core verb specify the temporal properties of the event type
compare now the following analysis of with the interpretation of NUM given earlier
mixed order models can not be used directly on the test set because they predict zero probability for unseen word combinations
you might for instance disagree with my decision to label the sleeper as the patient of the event
whi h is after all as much as you can infer fi om the simple aspect itself
the second experiment key paragraphs experiment shows how the extracted keywords can be used to extract key paragraphs
the greater the value of sim pi pi is the more similar these two paragraphs are
in order to get higher accuracy it is necessary to improve our wsd method
therefore our context dependency model contributes the extraction of key paragraphs although wsd and linking are still effective
the data we used consists of NUM NUM sentences par a paragraph and was not so many sentences within a paragraph
when the extracted ratio was NUM there were four articles whose correct ratio did not attained NUM
the last step to extract keywords is to calculate the context dependency of word i using formula NUM
like luhn s technique our method assumes that the words related to theme in an article appear throughout paragraphs
the keywords and their frequencies of appearance in paragraph NUM NUM and NUM are shown in table NUM
one possible cause of these results is that the clustering method might have a negative effect on extracting key paragraphs
14this formulation will be subject to changes once there is a clear concept of integration cf
again the representation will be fairly open as to which particular semantic formalism is chosen
the underspecified account dewfloped here recasts schwarzschild s ideas in a way that makes avoid f redundant
NUM die direktorin der firma muller begrusst the director of company miiller welcomes ihren besuch
8i onsider the status of this filter somewhat problematic
NUM a paula hat eine rote rose fotografiert
in computational applications a compact representation is a prerequisite for any successful treatment of is
existing theories capture this by rules that produce alternative focus structures
the phonological information is enriched by a feature prom prominence with values accented and unaccented
it is not clear whether it is a syntactic condition that constrains indirect f marking of a head
the top best hypothesis of the speaker s utterance is then passed to lhe parser
one possibility is to reassess and reestablish the context state when a conflict is detected between context and other predictions
the maximal score is assigned in the case that the inference chain does not attach
fragraent and the main semantic frame e.g. iree busy
our evaluation demonstrates the advantage of incorporating context based predictions into a purely non context based approach
or like elimination constraints they neither bind variables not eliminate any inferences
in general plan inference starts from the surface brms of sentences
our current efforts are aimed at solving the cumulative error problem in using discourse context
in developing our discourse processor for disambiguation we needed to address three major issues
inlk rmation makes il possible to choose the correct interprelal ion
by using the head of a rule to determine whether a rule is applicable the head driven shift reduce parser avoids the disadvantages of parsers in which either the left most or right most daughter is used to drive the selection of rules
parse with weakening cat p0 p e0 e NUM weaken cat weakenedcat parse weakenedcat p0 p e0 e cat weakenedcat
therefore the common use of logical forms as part of the categories will imply that you will hardly ever find two different analyses for a single category because two different analyses will also have two different logical forms
it is also often assumed that a bottom up parser is essential for such approaches to work parsers that use top down information such as the head corner parser may fail to recognize relevant subparses in the context of an ungrammaticality
therefore i will present the results of the parser for the current version of the ovis grammar in comparison with a number of other parsers that have been developed in the same project by my colleagues and myself
we present an approach to delayed lexical choice in generation based on subsumption within the sort hierarchy using a lexicon of nnderinstantiated signs which at derived fi om the normal lexicon by lexical rules
predict cat po p eo e small qo q small from qo q within eo e is a lexical category and possible head corner for cat from po p
the reason seems to be that the size of the second table is increased quite drastically because solutions may now be added to the table more than once for all goals that could give rise to that solution
leave the other to the old man
we do this by performing a noun based disambiguation experiment
both weight and color characterize all concrete entities
old men saw visions and young men dreamed dreams
similar results are found for semantically similar verbs
whereas the modified noun is of no help
it was n t hard to find marietta price
NUM this appearance however is spurious
in fact the average chunk was just over three words in length and less than three percent of the chunks were more than six words long
this quite naturally affects the quality of the final translation since many short pieces must be assembled into a translation rather than one or two long segments
for each distinct token the index contains a list of tile token s occurrences consisting of a sentence identifier and the word number within the sentence
lack of a one to one correspondence between source language and target language expressions can often cause the alignment to be incorrect or fail altogether under the current alignment algorithm
translation system reql iring essentially no knowledge of the structure of a language merely a large parallel corpus of example sentences atn a bilingual dictionary
in the context of the pangloss system such gaps are not a problem since one of the other engines can usually supply a translation covering each gap
thus in sentences john s justeson and slava m
the reason for this effect is easy to observe
texttiling segmenting text into multi paragraph subtopic passages
the problem remains then of how to detect subtopic shift
the first consists of using author supplied sectioning information
some summarization algorithms extract sentences directly from the text
they compare two methods of subdividing long texts
in example NUM the next two clauses do nothing to satisfy the expectation raised in clause a rather they give evidence for the claim made in a
another point to mention is that since we included the guessed words in the lexicon the brill tagger could use for the transformations all relevant pos tags for unknown words
servers lexical information is supplied by four external servers which are accessed by the natural language engine during processing
we merge together two rules if they scored below the threshold and have the same affix s mutative segment m and initial class i NUM
the score of the resulting rule will be higher than the scores of the individual rules since the number of positive observations increases and the number of the trials remains the same
this allows for the discrimination between rules that are no longer productive but have left their imprint on the basic lexicon and rules that are productive in real life texts
for example suppose that a word can take on one or more pos tags from the set of open class pos tags qj nn nns rb vb vbd vbg vbn vbz
for guessing rules to capture general language regularities the lexicon should be as general as possible i.e. should list all possible pos tags for a word and large
the induced morphological guessing rules turned out to consist mostly of the expected prefixes and suffixes of english and closely resemble the rules employed by the ispel unix spell checker
mistagging of the first type occurred when a guesser provided a broader pos class for an unknown word than a lexicon would and the tagger had difficulties with its disambiguation
the words in the sw set are only expected to appear approximately the same number of times as the analysis they represent
for example mouse in the plural is realized as mice deer in the plural is still realized as deer and means in the singular is still realized as means etc
the second sense as found in the paraphrase in NUM NUM would be salient in the situation in which a waiter at a restaurant is explaining to a customer why the price of a cup of coffee is found twice on a bill
the mistake of semanticists is to have confounded what it is for the denotation of a noun to be specified as having no minimal parts and what it is for the denotation of a noun to be unspecified for whether or not it has minimal parts
suppose further that the men form committees of various sizes including committees of one say rot mlra2ma mlm2m4 and m4msm6 and that the women too form committees say wlw w4ws and wl w3
in the next section i shall set out the widely recognized morphological and syntactic facts pertaining to common nouns and the noun phrases comprising them and set out an account of the morphology and syntax which honors a treatment of mass nouns in terms of non specification
the question arises are lexical rules for the conversion of mass nouns to count nouns and count nouns to mass nouns on the one hand and the assignment of the features t ct on the other redundant with respect to one another
the words facility has NUM something created to provide a particular service null NUM proficiency technique NUM adeptness deftness quickness NUM readiness effortlessness NUM toilet lavatory
thes four classes are easily distinguished on the basis of two criteria first whether or not the noun in question occurs equally freely in the singular and in the plural and second whether or not the noun in question tolerates the full range of determiner
the sense of the sentence in NUM NUM as found in the paraphrase in NUM NUM would be appropriate to the situation in which a sales clerk at a store which sells only coffee beans is explaining to a customer why a sales receipt shows two different prices
mass nouns through conversion can give rise to count nouns denoting instances of the denotation of the mass noun complexity detail discrepancy error effort instances of the exercise of work action activity exposure thought and shortage
for example NUM the condition in which the heart beats between NUM and NUM beats a minute the most frequent subjects of beat according to our local context database are the following NUM per badge bidder bunch challenger democrat dewey grass mummification pimp police return semi
a a lcb w rcb step c NUM modify the similarity matrix to remove the similarity values between other senses of w and senses of other words
a corollary of assumption NUM is that vy f NUM y f x o y f x y o which means that when there is no commonality between a and b their similarity is NUM no matter how different they are
the full range of feedback is not presented here
table NUM correspondence between nlp output and tutor feedback
each pair of adjectives has an associated dissimilarity value between NUM and NUM adjectives connected by same orientation links have low dissimilarities and conversely different orientation links result in high dissimilarities
language specific annotations such as the uarker in the lcs output are added to the templates by processing the components of thematic grid specifications as we will see in more detail next
once the question answering lesson is activated each of the student s responses is parsed and semantically analyzed into a lcs representation which is checked for a match against the corresponding prestored lcs representation
for example the instructor can program the tutor to notify the student about the omitted information in the form of a how question or it can choose to ignore it
if this information has not been hand added to the class grid lexeme specification as is the case with most of the verbs a default morphological process produces the appropriate form from tile lexeme
large scale acquisition of lcs based lexicons for foreign language tutoring
we have similar mappings defined in arabic and spanish
as realistic sounding as partially matched alternatives
any of the concepts in the ontology may be used singly or in combination in a lexical meaning representation
our lrs tend to be large scope rules which saves us a lot of time and effort on massive lexical acquisition
figure NUM below illustrates the overall process of generating new entries from a citation form by applying morpho semantic lrs
approach to lexical rules in this section we present a case study of lrs based on constructive derivational morphology
otherwise the lits will grossly overgenerate resulting in inappropriate entries computational inefficiency and degradation of accuracy
the nature of the links in the lexicon to the ontology is critical to the entire issue of lrs
representations of lexical meaning may be defined in terms of any number of ontological primitives called con cepts
we would like to thank margarita gonzales and jeff longwell for their help and implementation of the work reported here
ssss also of purdue university nlp lab w lafayette in NUM usa
lexical rules NUM were applied to NUM verb citation forms with NUM senses among them
it is necessary to locate the subject then identify the head and determine its number in order to translate the main verb correctly in sentences like NUM below
if the desired node does not win then the weight on connections to the desired node are incremented while the weights on connections to the unwanted node are decremented
if an input node is not already linked to the output node representing the desired response it will be connected and the weight on the connection will be initialised to NUM NUM
any single rule prohibiting a tuple of adjacent tags could be omitted and the neural network would handle it by linking the node representing that tuple to no only
if a cooler is fitted to the geaxl ox the pipe connections of the cooler must be regulaxly checked for corrosion
on the technical manuals the constraints of the grammatic framework put up to NUM of declarative sentences outside our system most commonly because the pre subject is too long
for instance frequently occurring modal verbs such as must are not dis null tinguished by number in english but they are in many other languages
for a minimal sign like walk see figure NUM which contains an argument the walker that monotonically moves along some one dimensional path the rule adds a new argument of dimensionality NUM dim
due to the complexity and wide coverage of lexical information full fledged lexicon systems easily grow undesirably big and must cope with intricate nets of dependencies among lexical items
due to speed concerns the stored entries and the expansion rules are in the troll lexicon supplemented with indexes that refer to well defined derivational sequences for complete word entries
the rule in figul c NUM e uses a suffix to create a noun hat re ers to some controlled durative activity
for keeping the speed of access at a satisfactory level lexical information is often repeated in different entries to reduce the number of consultations needed for a single user query
source means that this argument is the source of energy for the force involved in a painting process whereas controller indicates that the argument is in control of the process
a simple generated entry is the result of combining th minimal sign paint in figure NUM with the morpho syntactic auginen ation rule in figure NUM a
for a user requesting information fl om the lexicon l he stored entries m w be completely hidden and only the elaborate generated ones may be made available
hence as argument NUM of painthas a monotonic role the rule is able to add an argument that describes the resul rcb ing monotonic change of the surface being painted
the gradual covering of the surface with paint which is modeled by monotonic is also of the coloring type since we can verify the covering by looking at the surface
tagging statistics after training are divided into two categories both of which are based ou the rules acquired from training on data sets a b and c of the muc ii database
note that the systent design provides ti r verification of the system s understanding of each utterance to the originator in a paraphrase in the originator s language before transmission on the coalition network
the idea would be to include just enough semantic information to solve the ambiguity probleln effectively anchoring on words such as ship name that have high semantic relevance within the dommn
the intractat le ambiguities of natural language are overcome by restricting the domain and ire grammar rules which specify the semal tic co occurrence restrictions of head categories
da ngefs psn atready ftagite NUM i lcb ieal military balm ee between were fabriegted by stndyi ng a sentet ees ttm
bust parser to teal with this inadequacy tdeg despite this difficulty in designing a complete translation system an ideal translation system onght to be able to produce translations which are useflfl under auy circuinstances
clearly the system does not allow full word by word incrementality i.e.
thus in this paper the problem of suffixes will mainly be mentioned because our target language is the basque language
we apply our techniques to cky chart parsing one of the most commonly used parsing methods in natural language processing
we will refer to this as node njxk since it corresponds to a potential node in the final parse tree
the descendants function then appends the possible head words to the first pass nonterminals to get the second pass ones
if it is very small then our estimate may be unreliable and we do n t consider changing this parameter
the complexity of the model selected is shown in parenthesis
because of this difference we need to compute different ratios depending on which side of the goal we axe on
when we fail to parse a sentence because the thresholds are too tight we retry the parse with lower thresholds
however this does not give information about the probability of the node in the context of the full parse tree
using the prior can lead to a speedup of up to a factor of NUM at the same performance level
while other related work has focused on statistical learning we have explored the use of learning in simple recurrent networks
the output is the dialog act of the whole uttera m
the sm n of all values is NUM and each wdue is at leasl NUM NUM
sometimes the homograph problem can be solved by looking at the left and right context of the word but the general case requires a better understanding of the overall NUM the synthetic index is is w m where w is a word and m is a morpheme
in this paper we want to examine the potential of learning techniques at highcr pragmatic dialog levels of spoken language
for this domain we have developed a classification of dialog acts which is shown in table NUM together with examples
also friday the nineteenth is not possible reject but thursday afternoon is ok for mc nent in sci lcb een
l igure NUM architecture of dialog act component figure NUM gives an overview of our dialog compo
the segmentation parser and the dialog network already contain the robustness which is a precondition for dealing with real world speech input
after this initial distribution aualysis we now describe ore nel work architectur for learning dialog acts
we again require the ordering relation defined in NUM
figure NUM selecting the focus of modification
figure NUM shows our algorithm for this selection process
children in the candidate foci tree belx
we leave including appropriate iru s for future work
the model captures features specific to collaborative negotiation
if so no justification will be presented
the two approaches have some obvious differences
the applicability condition and precondition of mod fy node
it is identified by performing a depthfirst search on the proposed belief tree
we choose the set of terminals to be e lcb we l g 3n NUM rcb and choose the string to be parsed to be w wlw2
the approach is best explained by example
the nominative plural forln of masculine ani null mate nouns of the declension types pd and pi edse da is not ambiguous homonynmus with any other case forms apa rt
figure NUM and figure NUM show the precision recall graphs of window sizes NUM and NUM respectively
in other words the question whether there exist some errors for the recognition of which on the one hand a limited local context is msuf ficient i.e. it is necessa ry
we trained trigram models on two different corpora
does the word begin the preceding sentence
it has been estimated that nouns may mathematically have even NUM NUM inflected forms in basque language taking into account two levels of
knight is the only system to have been evaluated in the context of a semantically rich large scale knowledge base
we intend to mix simple audio and video features such as statistics from pauses black frames and color histograms with our lexical features in order to segment news broadcasts into component stories
model b was trained on the first 2m words of tdt corpus which is made up of a mix of cnn transcripts and reuters newswire and again the top NUM features were selected
our error metric p is simply the probability that two sentences drawn randomly from the corpus are correctly identified as belonging to the same document or not belonging to the same document
consulting table NUM we see that in the bn corpus the presence of vladimir will boost the probability of gennady by a factor of NUM NUM for the next n NUM words
table NUM quantitative results for tdt segmentation
table NUM quantitative results for wsj segmentation
however precision and recall are scale dependent quantities
this document is a tipster application conformance assessment document tacad
this problem is avoided via normalisation
from this wasson built a stand alone boundary recognizer in the form of a grammar converted into finite automata with NUM NUM states and NUM NUM transitions excluding the lexicon
c270 which was from the remaining NUM sentences of c2400 was set aside for testing
the information selected for stems are determined by the category of the stem itself recursively
NUM certain parses are deleted using context statistics on the corpus to be tagged
obviously in other similar cases it may be possible to resolve the ambiguity completely
on the other hand the form oya gives rise to the following parses
this research has been supported in part by a nato science for stability grant tu language
it is not clear how one would disambiguate this using just contextual or syntactic information
one simply can not choose among the first two using any amount of contextual information
harmony inapplicable on the graphemic representation though harmony is in effect in the pronunciation
NUM the motivation behind these rules is that they should improve precision without sacrificing recall
to cope with this problem we extend the original probability to one shown in the following formula
on the other hand in figure NUM ern and ce ar groul ed together corre tly
stage two representing every noun as a vector the goal of this stage is to rel resent every noun in a new article as a vector
according to table NUM there are NUM sets which could be lustered correctly in dis while NUM sets in freq
by accumulating the frequencies for these senses and then ordering the list of categories in terms of frequency the subject matter of the text can be identified
shows that ea h noun is rel laced l y a syml ol word which corresl onds to each sense of a word
for a given text each word is checked against the dictionary to determine the semantic codes associate l with it
tabh NUM the results of the wsd lnethod input a munber of major amines adopted continental amines
numl erl nuntl er5 are symbol words and show different senses of llunlber
like guthrie and yuasa s methods our approach adopts a vector representation i.e. every article is characterised by a vector
the only organization that will be provided to the ili is via two separate ontologies which are linked to ill records the top concept ontology which is a hierarchy of language independent concepts reflecting explicit opposition relations e.g.
note the default lexicon assignment of nnp to dooner and the rule based correction of more
note that this change is arguably erroneous depending on how one reads the scope of more
retired ttl ttl group ttl x grp retired grp
the alembic phrase finder or phraser for short performs the bulk of the system s syntactic analysis
this fact is all that is required for the template generator to subsequently issue the appropriate succession event templates
this rule yields the fact retired ttl ttl NUM and a similar rule yields retired ttl ttl NUM
timex forms introduced by the preprocessing date tagger must be preserved through the rest of the processing pipe
the most interesting form of domain inference is not compositional of course but based on discors e considerations
these NUM lines were so rare in the formal training data that we had simply no t noticed our omission
the skeleton is initialized with name and alias strings that were attached to the semantic individuals during name merging
she just gave betsy a wonderful bottle of wine
she told susan that she really liked the gift
on the other hand after retrieving the top k cases the algorithm does prefer those cases that match the test word when making its final predictions
this is especially true for definite noun phrase interpretation
i m reading the french lieutenant s woman
the mangy old beast always hates these visits
stuffed animals must really be out of fashion
c susan asked her whether she liked the gift
she reminded her that such hamsters were quite shy
the intentional structure comprises intentions and relations among them
he told terry to get lost and hung up
this paper looks briefly at the history of these conferences and then examine s the considerations which led to the structure of muc NUM NUM
we consider the three goals in the three section s below and describe the tasks which were developed to address each goal
again a stalwart grou p of volunteer annotators was assembled NUM each was provided with NUM articles from the wall street journal
the text shown is all upper case but for the first time the test materials contained mixed case text as well
a call for participation in the muc NUM formal evaluation was issued in june NUM the formal evaluation was held in september NUM
an algorithm developed by the mitre corporation for muc NUM was implemented by saic and used fo r scoring the coreference task NUM
the next step was the preparation of a substantial training corpus for the two novel tasks which remained named entity and coreference
organization NUM NUM org name coca cola org alias coke org type company org locale atlanta city org country united states
for several of these slots there are alternative correct answers only one of these answers is shown here
during iteratively merging the most similar labels all labels will finally be gathered to a single group
the examples discussed in the previous section demonstrate that our procedure avoids many of the deficits previous algorithms suffer from
therefore we have confined ourselves to a sketchy description of the algorithm s behavior in a moderately complex situation
in order to pursue these goals we state the following desiderata NUM
moreover the underlying assumption is unrealistic in cognitive as well as in technical terms
we conclude this presentation by explaining the pseudo code thereby pointing to the corresponding parts in the schematic overview
a component is interfaced which attempts to express the descriptors chosen on the lexical representation level to encounter expressibility problems
as shown the input word sequence is first tagged with the possible part of speech sequences
based on this formulation different models for case identification and word sense disambiguation are derived
figure NUM the decomposition of a given syntactic tree x into different phrase levels
in this system deep structure ambiguity is resolved with the proposed integrated score function
another important type of case error is to determine the class of a verb
is little indicative of the actual efficiency of more sophisticated implementations
for computation the integrated score function is further decomposed into the following equations
as expected the accuracy of parse tree selection is improved as the semantic interpreter is integrated
to resolve the ambiguity and uncertainty the related knowledge sources should be properly represented and integrated
such a rule based system is in general very expensive to construct and difficult to maintain
the ig values for the features are given in figure NUM
this small review will serve as a basis for coming sections in order to identify the key aspects that are involved in prediction
the most important advantages compared to current stochastic approaches are that i few training items a small tagged corpus are needed for relatively good performance ii the approach is incremental adding new cases does not require any recomputation of probabilities and iii it provides explanation capabilities and iv it requires no additional smoothing techniques to avoid zero probabilities the igtree takes care of that
in the following section we use examples from the walk through article to show how the four mu c tasks have been implemented on top of the lolita core
thus whilst our system has the external appearance of a pipeline architecture the evaluatio n of individual pieces of code need not occur in that strict order
unfortunately neither scorer reported an error and so the problem remained undetected until after the formal evaluation and indeed until after th e muc NUM conference itself
reported speech the walk through article contained a higher proportion of reported speech than most of the forma l training articles as well as some other uses of quotation marks
the algorithm works by examining the concepts created in the semnet following semantic analysis o f the article by the core system
as analysis progresses the system may change a decision abou t what concept a particular part of the text referred to
the bulk of the net NUM comes from wordnet a database containing lexical and semantic informatio n about word forms in english NUM
our grammar is written in a context free style using a simple feature system to parametrise pieces of grammar and contains some rules for handlin g non grammatical input
for example if no company is subject of a sacking event but on e is an object of such an event then this will be picked
in this technique appropriate concepts that are closely related to the event are considered as candidates and the closes t concept below a threshold value is picked
one such tool that we have been developing is a general purpose bracketer for unrestricted chinese text
null conclusion we have described an extension to context free grammars that admits a practical parsing algorithm
the generalized viterbi algorithm starts from the beginning of the input sentence and proceeds character by character
we chose the greedy algorithm because it is easy to implement and guaranteed to produce only one segmentation
figure NUM word segmentation accuracy the number of word tokens and word types at each re
want auxv b infl which means want to restrain
it consists of a word based statistical language model an initial estimation procedure and a re estimation procedure
word segmentation is an important problem for japanese because word boundaries are not marked in its writing system
the average word length a can be computed once the word frequencies in the texts are obtained
list to the original dictionary witli associated frequencies whether or not each string is actually a word
an asymptote is derived from turing s local reestimation formula for population frequencies and a local reestimation formula is derived from zipf s law for the asymptotic behavior of population frequencies
this implies a finite total population since the cumulative i e the sum or integral of the relative frequency over rank does not converge as rank approaches infinity
we are now prepared to derive the asymptotic behavior of the relative frequency f r of species as a function of their rank r implicit in eq NUM
turing s formula estimates locally what the frequency count of a species that occurred r times in a sample really would have been had the sample accurately reflected the underlying population distribution
parameterized by the real valued parameter o with the asymptotic behavior cr r NUM NUM NUM f r
universitpst des saarlandes fr NUM NUM computerlinguistik postfach NUM d NUM saarbriicken germany chr st er col i uni sb
where n wl w2 denotes the number of counts of wlw2 in the training set
partial path which ends in the grid point i j e
this work is partially supported by a tipster grant from the u s department of defense
we next describe two features of the system that were useful in the interpretation process NUM error correcting parsing and NUM dialog expectation
on the other hand example NUM requires a large number of costly insertions and deletions indicating a lack of confidence in the quality of the interpretation
what is important to note about these ne phraser rules is that they do not rely on a large database o f known company names
yesterday person mccann person made official what had been widely anticipated the post out phrase encodes the resignation of a person in a post
we conclude that the combination of considering both the local information of the parsing cost and the dialog context information about expectation provides the best strategy
an example would be asking did you mean to say the switch is up when that is what the user originally said
in contrast the new context dependent strategy strategy NUM achieves an over verification rate of NUM NUM but the under verification rate is only NUM NUM
utterance NUM to determine whether okay de null notes confirmation or comprehension i.e. confirmation that the wire has been obtained
for instance example NUM is transformed into a grammatical utterance by substituting the phonetically similar word six for fix and and for can
there were a variety of causes for the failure in the other NUM dialogs ranging from inadequate grammar coverage to subject error in connecting wires
unlike standard n gram models however the number of unseen word combinations actually decreases with the order of the model
however some sort of noise reduction technique such as the confidence intervals used by brent may be needed to detect the cue more accurately
by iteration the whole process of a collective event can be taken up regardless of the inherent features of verbs as mentioned above
the hidden variables in this process are the outcomes of the coin tosses which are unknown for each word wt k
to clarify further the use of the formalism and the operation of the mechanisms we now examine several further examples
the experimental results of our robust parser show high accuracy in recovery even though NUM of total rules are removed
the compilation of morphological information is motivated by the nature of the task and of the languages to be handled
figure NUM feature dependent dropping of accent chbre has root chef with pattern NUM and tree NUM
further it can not be assumed that the lexicon has been fully specified when the morphology rules are compiled
this paper describes a representation and associated compiler intended for two level morphological descriptions of the written forms of inflecting languages
the final e is a feminizing affix and can be seen as inducing the obligatory spelling change au ii
the v l r structure is always matched specially with a kleene star of the default spelling rule
gibson NUM hawkins NUM rambow and joshi NUM
the lattice defines intensionally the set of possible categories and rule schemata via type declarations on nodes
a few speakers also converged on vos n a more expressive but higher wml extension of vso n gwp comp
subset languages are represented by NUM NUM sentence types and full languages by NUM sentence types
w lereas they can provide even better solutions intrinsically they are usually adhoc and are lack of extensibility
this slows down run time performance a little but as we will see below the speed is still quite acceptable
the key design decision is to compose morphophonological and morphosyntactic information but not the lexicon when compiling the description
on the other hand a large value for al w indicates that the word w is highly predictive
for example we can drive a rule vp vb np comma rb comma pp from the following sentence
the effect of misunderstandings that are corrected during the course of the dialogue are reflected in the costs associated with the dialogue as will be discussed below
we created a test corpus of NUM sentences each NUM words long with a constant part of speech pattern abc
after training all but the NUM most probable rules were removed from the grammar and probabilities renormalized
every tenth sentence of the NUM sentences in the atis portion of the treebank was set aside for testing
not surprisingly the learning algorithm never converges to the recursive grammar during test runs on this corpus
instead it zeros in on the nearest such grammar only biased shghtly by its relative merits
the identification scheme the determination of a precise set of link labels is future work
the occurrence of NUM or more minority instances is not mainly a frequency effect these NUM adjective noun pairings are no more frequent on average than those that do not
paradise uses its avm representation to link the information goals of the task to any arbitrary dialogue behavior by tagging the dialogue with the attributes for the task
in english verbs and prepositions in configuration a are closely coupled semantically probably more closely than prepositions and nouns and we would expect that the mutual information between the verb and preposition would be greater than between the preposition and noun and greater still than between the verb and the noun
tuit has been fully tested in the oleada and temple demonstration projects for sunos NUM x and NUM x solaris
to demonstrate how extraction technology can be integrated with that of detection the lm template viewer also provides the means for designating any of the extraction output and generating automatically a query which incorporates the selected output as constituent elements of the query
tuit is a software library that can be used to construct multilingual tipster user interfaces for a set of common user tasks
the tipster architecture has been designed to enable a variety of different text applications to use a set of common text processing modules
crl developed tuit to support their work to integrate tipster modules for the NUM and NUM month tipster ii demonstrations as well as their oleada and temple demonstration projects
ease of incorporating and configuring new applications improve significantly with the tuit library with its own api as shown in figure NUM
however the computing research laboratory crl has constructed several tipster applications that use a common set of configurable graphical user interface gui functions
since user interfaces work best when customized for particular applications it is appropriator that no particular user interface styles or conventions are described in the tipster architecture specification
however this noun has two broad classes of meanings one refers to commitments on issues and is concrete the other refers to flanks and is concrete
section NUM showed that indicator nouns and in particular certain of their semantic features are quite reliable as bases for interpreting the meanings of the adjectives that modify them
in order to develop a performance function estimate that includes only significant factors and eliminates redundancies a second regression including only significant factors must then be done
for this purpose we therefore treated every noun from the co occurrence sentences as an indicator of the sense which that noun is projected to favor in the sample sentences
different part of speech nonterminals may generate the same words
probabilities on the grammar are placed as follows
the latter stage gives us the lexical translation probabilities
examples of the output are shown in figure NUM
the coarse bilingual grammar approach proposed here solves these problems by choosing the parse a of course this assumes that adequate grammars are available for both languages contrary to our present assumptions
clear water bay hong kong dekai cs ust
figure NUM perplexity on successive training iterations
more on the ordering flexibility will be said later
the subcorpora that were used in the work reported here consist of those sentences in which a target adjective and its antonym modify separate instances of the same noun or clause
the instructions were developed iteratively applying the current scheme and then revising it in light of dlt culties that arose
adaptation of the dictionary to the user s vocabulary is possible by updating the frequency and recency of the each word used
we give the taggers strategies for dealing with such problems such as asking themselves what is the most focal meaning component of the word in that particular context
one major difference between use in mt and paraphrase is in lexicalisation
for example consider the definition sentences for the first NUM senses of bank in ldoce NUM land along the side of a river lake etc
the need to detect and correct misunderstandings
consider the extracted np in beans i know john likes cf
figure NUM adjoining is constrained to nodes the inner rf indicated by the dashed arrow
the latter are one way by which a substitution site may be introduced into a tree
b on the other he is extremely difficult to find
c you d see that he s very difficult to find
NUM another term we use for auxiliary trees is adjoining structures
this is discussed in section NUM NUM in more detail
again none of the examples contains an embedded expectation
figure NUM analyses of examples NUM NUM and NUM
in NUM of these cases NUM NUM discourse units intervened before the raised expectation was satisfied
we conelude the paper with some thoughts on incremental discourse processing in light of these expectations
the whole discourse is a segment ds0 that attempts to realize i0 the speaker s intention for the hearer to adopt the intention of attending the ballet NUM as part of her plan to achieve i0 the speaker generates i1 the intention for the hearer to adopt the belief that the ballet will be very entertaining
for a particular instance of a cause effect in the domain it is equally plausible for a speaker to mention the effect to facilitate the hearer s adoption of belief in the cause as would be suggested by context i in figure NUM or to mention the cause to facilitate the hearer s adoption of belief in the effect as suggested by context ii
as a consequence strict application of the rst informational relations can result in a different structure than that imposed by the intentional relations and this is the source of the problem noted by moore and pollack
in addition note that this is preferable to adding surplus informational relations to allow either relatum to be the nucleus as was done in the volitional cause and volitional result case because NUM this obscures the fact that relations such as volitional cause and volitional result appeal to the same underlying domain relation and NUM the proliferation of relations weakens the restrictive power of the framework
further we argue that a synthesis of g s and rst is possible because the correspondence between dominance and nuclearity forms a great deal of common ground and because the remaining claims in the two theories are consistent
or because g s do not have the notion of core in their theory a more accurate characterization of the correspondence would be that the nucleus manifests a dominating intention while a satellite manifests a dominated intention
in the embedded span the nucleus b expresses a belief that the speaker intends the hearer to adopt and the satellite c is intended to facilitate this adoption by providing evidence for the belief
for each shallow structure s in the corpus containing one verbal and two nominative accusative nominal constituents let nl v n2 be such that v is the main verb in s and nl and n2 are the heads of the nominative accusative ncs in s such that nl precedes n2 in s
if the shallow structure consists of a verb second clause with an adverbial in the first position or of a verb final clause introduced by a conjunction or a complementizer then nl v n2 NUM is a training tuple see below for examples default rule
for instance the head of the nominal constituent nc der tennisspieler the tennis player is considered by the system to be the compound noun tennisspieler tennis player instead of its head noun spieler player
in the case where the counts are positive the numerator in the latter is the number of times the word wn followed the n gram w NUM in training data and in the former the number of times nl occurred as the subject with n2 as the object of v
this count is divided in the latter by the number of times the n gram w NUM was seen in training data and in the former by the number of times nl was seen as the subject or object of v with n2 as its object subject respectively
at the p1 level only the counts obtained for the verb are used in the estimate although for certain verbs some nouns may have definite preferences for appearing in the subject or object position this information was deemed on empirical grounds not to be appropriate for all verbs
deixis and anaphora table NUM example of salience value calculation
NUM NUM how the referent resolution models dealt with the test set
she continued as follows alice wrote him an e mail
similar problems occurred with this in task NUM
furthermore it will have problems with interpreting cataphora properly
the grosz and sidner model has a much broader scope
the general applicability of the technique adds to its beauty
this research was performed within the framework of the research program
deictic and anaphoric expressions frequently cause problems for nl analysis
table NUM presents an overview of the cfs edward uses
b ein mpsrchen erzpshlen wird er seiner tochter
b vortragen wird er es morgen
kill wollte die frau mit diesem messer
this is the case in pollards account
this solution makes use of a schema to introduce the nonlocal dependency
it therefore had to be a member of the comps list
the loc value of the saturated verbal complement is moved into slash
in this section we thoroughly analyze the labeling performed by the algorithm and in particular look into several uses that are made possible by the labels availability
segmentation also includes the identification of clause boundaries
NUM NUM an example of incremental description french subjects
when it comes to parsing no statement is fully accurate one may for instance find examples where even the subject and the verb do not agree in perfectly correct french sentences
step NUM any tbeginvc that was not matched
the incremental parser consists of a sequence of transducers
therefore the analysis is non monotonic and handles uncertainty
the remaining candidates are then considered as real subjects
the input to the parser is a tagged text
cautious segmentation prevents us from grouping syntactically independent segments
the meth proposed in this work takes advantages of a number of linguistic phenomena NUM division of senses is primarily along the line of subject and topic
in this paper we suggest consistently representing these as separate subclasses
this approach already compares favorably with the statistical average plansibility method produces a segmentation and dialog act assignment for all utteranccs in a robust manner and redaces knowledge engineering since it can be bootstrapped from rather small corpora
it therefore did not offer the discount option to this user nor did it correct the user s misunderstanding
the lnincil lcs cxanlincd in this section inlroducc a new aspect o1 dialogue cooperativity naniely parther asymmetry and speaker s consequent obligation to inform the partner s of non nornml speaker characteristics l ue to lhe latter the principles can not be subsumed by any olhcr l rinciplc or maxim
NUM is virtually equivalent to gp2 do not overdo inlormativeness and gp5 relevance
NUM may without any consequence other than improved clarity be replaced by gp2 and gp5
i rovide clear and sufficient instructions io users oil how interact with tile system
NUM adds an ilnportant clement to ihc analysis of dialogue coopcrativily by aiming at inlproving user coopcrativily
dialogue partner asynimctry occurs roughly when ollo or liloi e of iho dialogue partners is llot in a norlllal conditioll or situation leer installco a dialogue partner may have a hoalitlg deficiency or be located in a particularly noisy environnlcnt in such cases dialogue cooperativity depends oll the taking into accotlnt o that participant s special characteristics
the novel coot orativity aspoel they introduce is lhal they require the cooperative speaker to produce a specific dialogue contl il ution which explicitly expresses an intorprolalion of the intorlocuior s previous diah guo conlribulion s provided ihal the interlocutor has inado l dialogue contribution of a certain lypo such as a coninlitnlonl io book a flight
parser since user utterances could be ungrammatical in nature a partial parser has been implemented to parse the input utterance into its component phrases
table NUM calculating log likelihood values log l d cl
map finder is a simpler task and some of the upper layer states unknown query fewmatches and many matches never occur in this application
nnotice that words which are discarded in the dustering process should not to be counted in document size
for instance mast and colleagues report NUM for learning dialog act assignment with semantic classification trees and NUM for learning with pentagrams but they also used more categories than in our approach so that the approaches are not directly comparable
once in this state the user may start a new query ask for more information about the matched item or quit the system
in these graphs values given after fmm and hcm represent NUM in our clustering method e.g.
we would like our results to be true for the largest class of parsers possible
i thank joshua goodman rebecca hwa jon kleinberg and stuart shieber for many helpful comments and conversations
given that fast practical bmm algorithms are unlikely to exist we have established a limitation on practical cfg parsing
plpsusibility vectors for dialog acts represent the distribution of dialog acts for each word for the current corpus t owever for assigning a dialog act to a whole utterance all the words of this utterance have to be considered
however fss exact conditional has accuracy less than the default for NUM of NUM words and bss exact conditional has accuracy less than the default for NUM of NUM words
then we define the function i fl i f2 i by
claims NUM and NUM together prove that cil jl c derives w j2 NUM i2 as required
if feature selection is not in doubt i.e. it is fairly certain that all of the features are somehow relevant to classification then this is a reasonable approach
while n grams perform well in partof speech tagging and speech processing they require a fixed interdependency structure that is inappropriate for the broad class of contextual features used in word sense disambiguation
however during the early stages of fss the number of parameters in the models is very small and the differences between the information criteria and the significance tests are minimized
statistical models of word sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features
the expected count ei is calculated from the frequencies in the training data assuming that the hypothesized model i.e. the model generated in the search adequately fits the sample
the search stops when either NUM every hypothesized model results in an unacceptably high degradation in fit or NUM the current model has a complexity level of zero
the search stops when the ic values for all hypothesized models are greater than zero in the case of bss or less than zero in the case of fss
the user can also tell the system to hide or display individual objects show the thunderbird or sets of objects hide all the friendly aircraft that do n t have missiles
joysticks gloves and other manual input devices are useful for some types of control pointing manipulating objects but they are not well suited to more abstract input functions
we have experimented with different input knowledge only dialog act plausibility vectors additional abstract semantic plausibility vectors etc different architectures different numbers of context layers and different number of units in hidden layer ere
we now turn to our first application the definition of feature specification defaults fsds in gpsg
so while these may not be the right results they are not entirely the wrong kind of results
a grammatical theory expressed within such a framework is just the set of logical consequences of those axioms
NUM we offer this as an example of how re interpretations of this sort can inform the original theory
in gpsg this connection is made by the sequence of agreement relationships dictated by the foot feature principle
we look in particular at the definitions of a single aspect of each of gpsg and gb
the key thing to note about this treatment of fsds is its simplicity relative to the treatment of gkp s
the abstract properties of the mechanisms that might implement those theories however are not beyond our reach
armed with this definition we can identify individuals that are privileged wrt f simply as the mem
where n collection size and n the number of documents containing the term
recall measures how many of the relevant documents have actually been retrieved
NUM our goal is to generate different kinds of candidate syntactic phrases from the structure of a noun phrase so that the effectiveness of different combinations of phrases and single words can be tested
the experiment procedure is described by figure NUM
fast statistical parsing of noun phrases for document indexing
however the size of the collection used in
more specific indexing units are needed
these early experiments is relatively small
the basic idea is as follows
although schemata have been criticized because they lack flexibility they successfully capture many aspects of discourse structure
an alternate study would consist of judges consciously analyzing pairs of explanations to perform an explicit comparative analysis
NUM a separate study would be to evaluate knight on very short one sentence and two sentence explanations
to this end we combed the biology knowledge base for concepts that could furnish topics for questions
these algorithms have been used to generate explanations about hundreds of different concepts in the biology knowledge base
once this is accomplished the fd skeleton is passed along with the message specification to the fd skeleton processor
to encode organizational knowledge a representation of discourse knowledge should permit discourse knowledge engineers to encode topic subtopic relationships
NUM each of these considerations are discussed in turn followed by a representation that satisfies these criteria
participants process finds actor oriented view of process as reference viewed from the perspective of reference process
NUM to illustrate the participants accessor extracts information about the actors of the given process
low variance of slope the slope of a tpc chain is rarely much different from the bitext slope
functional representation of phrases and clauses has been introduced to facilitate expressing syntactic genermisations
parsing means intersecting the ambiguous sentence automaton with each rule automaton
a short introduction also here syntactic analysis means resolution of structural ambiguities
at the level of linguistic abstraction the grammar rules are essentially syntactic
when the differences were collectively examined it was agreed that virtually all were due to inattention
the benchmark corpus was created by first applying the preprocessor and morphological analyser to the test text
here is a realistic implication rule that partially defines the form of prepositional phrases
regarding the resolvability requirement certain kinds of structurmly unresolvable distinctions are never introduced
this section clarifies the relationship between critical tokenization ct and three other representative implementations of the principle of maximum tokenization i.e. forward maximum tokenization ft backward maximum tokenization bt and shortest to
as any single word in a word string is also its single word substring it can be concluded that for any word x in x there exists a substring ys of y such that x g ys
for instance there exists an optimal algorithm that can identify all and only critical points and thus all unambiguous token boundaries in time proportional to the input character string length but independent of the size of the tokenization dictionary
given a typical english dictionary and the character string s thisishisbook all three positions after character s are unambiguous in tokenization or are unambiguous token boundaries since all possible tokenizations must take these positions as token boundaries
finally we thank the anonymous reviewers for their useful comments
for the character string s abcd the word string a bcd is the only bt tokenization in to s lcb a b c d a b cd a bc d a bcd ab c d ab cd abc d rcb
the general process of phrase generation is illustrated in figure NUM
conceptually for any character string by checking every one of its possible substrings in a dictionary and then by enumerating all valid word concatenations all word strings with the character string as their generated character string can be produced
another interesting finding is that for those critical fragments with critical ambiguities by replacing the conventionally adopted meaning preservation criterion with the critical tokenization criterion disagreements among human judges on the acceptability of a tokenization basically become non existent
at each phase only two adjacent units are considered
for example japanese hai can be translated as yes if it is the response to a yn question but as all right if it is the response to an action request
section NUM shows the experimental results of reestimation algorithm on korean and finally section NUM concludes this paper
dependency grammar describes a language with a set of head dependent relations between any two words in the language
however in this paper we use the minimal definition of dependency grnmmar with head dependent relations only
subscripts of f and a are for the directionality r for rightward and l for leftward
double slashed links depict complete sequences which compose the lr together with the outermost dependency wi wj
the sub entries under the white headed arrow and the sub entries under the black headed arrow are merged into a larger entries
both of reestimation algorithm and best first parsing algorithm utilize a cyk style chart and the non constituent objects as chart entries
inside probability of a complete link is the sum of the probabilities of all the possible constructions of the complete link
performances of filtering when coupled with the robust parsing are indeed much more satisfactory
the sampling rate of acoustic models are NUM khz for nuance and NUM khz for abbot
our work addresses the integration of speech recognition and language processing for whole spoken dialogue systems
note that longest match constraint ignores any internal brackets
similarly the left to right constraint ignores any internal carets
the positive filter is composed of two transducers
the tokenization rules may be of several types
instances of a regular language without actually replacing them
figure NUM four factorizations of aba
in order to help pragmatics select between the multipie possible interpretations we utilise probabilities
NUM a mary sorted her clothes into various bags made from plastic
managers are not only mailmen but interpreters they translate between the reserved language of the whiteboard and the native languages of the components which are thus free to differ
the frequency information discussed in ss3 is insufficient on its own for disambiguating compounds
there also are other suffixes which are not shown in table NUM as those applied to a verb for subordinate sentences
however the axiom assume coherence below is derivable from the axioms given there
because supereoncept and subconcept are both designated by the same word thus creating homography they were detected by the check which relates to generic synecdoche
NUM note that this is more complex than drt s notion of update
however considering the requirements of nlp applications such as parsers or documents browsers two additional interfaces are provided since a set of annotations can be quite naturally interpreted as a chart a chart interface provides efficient access to annotations viewed as a directed graph following the classical model of the chart first presented in kay NUM
basic software engineering requirements a modular and scalable architecture enables the development of small and simple applications using a file based implementation such as a grammar checker as well as large and resource intensive applications information retrieval machine translation using a database back end with two levels of functionality allowing for a single user persistent store and a full size commercial database
an interv tree interface provides efficient ac null cess for efficient implementation of display functionalities
this task will be aided by two features first the temple system already utilizes the tipster document architecture for data exchange between components and second the temple system has a pipelined architecture which will allow modular encapsulation of translation stages e.g. dictionary lookup as corelli plug n play tools
this framework does not provide a model for controlling the interaction between the components of an application the designer of an nlp application can use a simple sequential model or more sophisticated blackboard models since this distributed model supports both the synchronous and the asynchronous types of communication between components it supports a large variety of control models
the idl to java compiler essentially produces three significant files one containing a java interface corresponding to the idl operational interface itself a second containing client side stub methods to invoke on remote object references along with code to handle orb communication overhead and a third containing server side skeleton methods to handle implementation object references
this paper reviews two domains of problems in natural language 1to use the name of two well known nlp journals
a portable implementation allows the development of small stand alone pc applications as well as large distributed unix applications
the data layer of the corelli architecture is derived from the tipster architecture and implements the requirements listed above
this architecture supports read only data e.g. data stored in a cd rom as well as writable data
a concept s label body takes its content from its first synset element which is also transformed on download into a term with this label body
on aj NUM to reduce the number of alignment parameters we assume that the hmm alignment probabilities p i i depend only on the jump width i i
seven non overlapping categories are used three categories for names surnames name and female names two categories for numbers regular numbers and room numbers and two categories for date and time of day
a key issue in modeling the string translation probability pr f le i is the question of how we define the correspondence between the words of the target sentence and the words of the source sentence
to counteract this phenomenon we split the verb into a verb part and pronoun part such as darnos dar nos and pienso yo pienso
to train the alignment and the lexicon model we use the maximum likelihood criterion in the so called maximum approximation i.e. the likelihood criterion covers only the most likely alignment rather than the set of all alignments
sentence level a sentence is counted as correct only if it is identical to the reference sentence
i e there is a word in the target string with no aligned word in the source string
this case is the regular one and we can use directly the probability of the bigram language model
to find the optimal alignment we use dynamic programming for which we have the following typical recursion formula
figure NUM the overview of the system
hence this parser is suitable for systems in real application areas
imagine that we adopt the following strategy to predict the word at time t
figure NUM shows the processing of extended completer
figure NUM shows how scan processes
a robust parser based on syntactic information
our robust parsing system is composed of two modules
an interactive or batch check may look for direct or indirect superconcept subeoncept pairs which have assigned terms with identical names thus giving rise to homographs
when reporting the percentage of utterances correctly understood it may be illuminating to report the cause of the utterances not understood is it because of a lack of domain knowledge a lack of vocabulary or a lack of ability at doing contextual interpretation
the probability that one of these branches is a successproducing branch is NUM l i NUM wi from equation NUM
the dialogue model outlined in this paper has been implemented and computer computer dialogues have been carried out to evaluate the model and judge the effectiveness of various dialogue initiative schemes
the x and y axis represent the amount of knowledge that each agent is given NUM and the z axis represents the percentage of branches explored from a single goal
an additional approach is to ask the collaborator for help if it is believed that the collaborator has a better chance of solving the goal or solving it more efficiently
assume that the agent does not know exactly what is in the collaborator s knowledge but does know the degree to which the collaborator knows about the factors related to a goal
by computing similar probabilities for each combination of factors the agent can compute the likelihood that the collaborator s first branch will be a successful branch and so on
the scarcity of such systems suggests that it is an extremely expensive process to build a functional human computer dialogue system and computer computer simulations can assist in reducing these costs
NUM however the local extensions of the functions we had to compute were subsequential function fa
the first decomposition leads to the output dbbbad and the second one to the output dabbbd
the techniques used for the construction of the finite state tagger are then formalized and mathematically proven correct
we conclude by proving that the method can be applied to the class of transformation based error driven systems
moreover the finite state tagger inherits from the rule based system its compactness compared with a stochastic tagger
in the times reported we included the time spent reading the input and writing the output
lines NUM NUM describe the fact that it is possible to start a transduction from any identity state
however as we will see in the next section brill s tagger is inherently slow
for a cheek of this rule the overlap of two virtual relations has to be determined antosemy and the transitive closure of hypernymy or troponymy
the performances of the four base language models are shown in table NUM mle NUM and mle ol both have error rates of exactly NUM because the test sets consist of unseen bigrams which are all assigned a probability of NUM by maximum likelihood estimates and thus are all ties for this method
for d wxhw l to be defined it must be the case that p w2 w l NUM whenever p w21wl NUM unfortunately this will not in general be the case for mles based on samples so we would need smoothed estimates of p w2 w that redistribute some probability mass to zerofrequency events
this last form makes it clear that NUM l wl w NUM with equality if and only if there are no words w2 such that both p w2lwj and p w2lw are strictly positive
unlike the measures described above wl may not necessarily be the closest word to itself that is there may exist a word w such that pc w l wl pc w wl
since we are concerned with the generation of system responses we ignore user goals for the time being
relevant sentences in essays were sentences identified in the scoring guide as containing information relevant to a rubric category
for the evaluation of the scoring method a small sample of poor essays were also scored to compare the results NUM human rater scoring of ap biology essays is based on a highly constrained scoring key called a rubric that specifies the criteria human raters use to assign scores to essays
typically csrs are generated with extraneous concepts that do not contribute to the core meaning of the response
for part b the categories were treatment i treatment NUM treatment NUM and treatment iv
it has been further pointed out by wilks et al NUM that word senses can be effectively captured on the basis of textual material the lexic on dwc lopcd for this study used an example based approach to compile a list of lexical items that characterized the content vocabulary used in the domain of the test question i.e. gel electrophoresis
to determine alternate words with similar meanings metonyms for words such as fragments and move were established in the lexicon so that the system could identify which words had similar meanings in the test item domain
these are one spot one band one inclusive line one probe one group one bond one segment one length of nucleotides one marking one strand one solid clump in one piece one bar one mass one stripe one bar and one blot
system training involved the following steps that are discussed in subsequent sections a manual lexicon development b automatic generation of concept structure representation csr c manual creation of a computer based rubric d manual csr fine tuning e automatic rule generation and f evaluation of training process
each component communicates with the coordinator and the whiteboard via a go between program called a manager which handles messages to and from the coordinator in a set of mailbox files
further blackboard systems are widely seen as difficult to debug since control is typically distributed with each component determining independently when to act and what actions to take
word for word translation cdp does not suppose cooperation with party of mister slfidek and it is n t true that chairman of christian democrats mister benda in telephone discussion with petr pithart enforced ing
our system can work with any text editor under windows that contains a macro language supporting the dde connection
figure NUM lexical entries for scriveze writs
we defined somebody using the following boolean combination of synsets
we excluded the possibility to capture these complex nominal phrases
italian wordnet has been used in two different phases of the linguistic analysis
founding the appropriate selectional restrictions revealed itself difficult and time consuming
we built selectional restrictions using the synsets of the noun hyerarchy
the next step is the definition of the sense subgategorization frame
a prototype has been realized which implements a multilingual lexical matrix
this phase discards the unplausible lectures pruning the search space by looking for compatibility semantic relations
in fact different selectional restrictions apply to different senses allowing to discriminate among different readings
the three tagsets word phrase and edge labels used by the annotation tool are variable
her work would therefore probably not be easily transportable to other corpora or languages
all information about this system is courtesy of a personal communication with mark wasson
to test this we prepared a small corpus of raw ocr data containing NUM NUM punctuation marks
removed a portion of the test data and incrementally added it to the training and cross validation sets
we constructed a training text of NUM test cases and a cross validation text of NUM test cases
we later experimented with a much smaller lexicon and these results are discussed in section NUM NUM
once learning mode is completed the parameters in the learning algorithm remain fixed
both make use of the words in the context found around the punctuation mark
the lowest error rate NUM NUM was obtained with a training set of NUM NUM items
it is efficient enough that it does not noticeably slow down text preprocessing
at the abstract level of representation one defines conceptual phonological fi ames that underly the actual words found in a language
in this paper we introduce a new approach to lexical organization that leads to more compact and flexible lexicons
in many cases it also clutters the lexicon structure so that important lexical relationships and generalizations are lost
the semantic part is a conceptual structure of the sign which is to capture all grammar relevant aspects of its meaning
figure NUM the stored frame paint is expanded into actual words with syntactic properties
x contains the information to be added and y the requirement for using the rule
morpho syntactic augmentation rules add a word category and an inflectional paradigm to a minimal sign
this ymds dm basic verb entry paintv which loes not contain any information abou syntactic realization
each semantic role is further characterized by means of a criterial factor that imposes certain role related observational properties on the argument
the medium must then describe a one dimensional path as for example to the school in jon walked to the school
efficiency reasons as well as the occasional need to generate comprehensible explanations to the classifications suggest that discarding irrelevant features is a desirable goal in ir applications
this commandment based on an old adage cf
iv thou shalt make thy system goof
how to obey the NUM commandments for spoken dialogue
the main advantage of adopting semantic categories is that we can easily specify the co occurrence restrictions of head categories e.g. the parse tree specifies that the category ships occurs with a small subset of nominal modifiers including uss which we call ship mod and therefore reduces the ambiguity of the input sentence
the core of our text translation system consists of an anal ysis module and a generation nlodule
we tackle the ambiguity problem t y incorporating syntactic and semantic categories in he analysis grammar
as is reflected in the parse tree both syntactic and semantic categories are utilized in our grammar specification
taken under fire by kirov with ssn NUM s are given in figure NUM and figure NUM respectively
focus on re null covering the error
NUM there are NUM rcb NUM messages for system development and NUM messages set aside for system evaluation
the words missing from a lexicon which we refer to here as out of vocabulary words or oov words represent a significant problem
this interesting idea of automatically enhancing specialised lexicons from a general lexicon and a big corpus is the aim of this paper
a constrained path starting with the initial state contains a sequence of states from state set NUM derived by repeated prediction followed by a single state from set NUM produced by scanning the first symbol followed by a sequence of states produced by completion followed by a sequence of predicted states followed by a state scanning the second symbol and so on
indeed the syntactic categories calculated by the devin were compared to those produced by the tagger when these words belonged to the lexicon
we classify these simple oov words in two categories the proper names and the commonwords which represent all the others
nevertheless the benefit of this technique is that it is automatic which allows us to test our module on an important corpus of tests
the column missing classes indicates the percentage of correct words which could have received more syntactic categories than those stored in the lexicon
we make the hypothesis that this model will correctly work on unknown words since these words should be governed by the same morphological principles
the resultant lexicons produced contain very few incorrect syntactic classes for each item which is represented in the corpus by a sufficient number of occurrences
such a view is attractive from a linguistic perspective if each vector represents a lexeme and its projection where the synchronous production is the basis of the lexical projection that the vector represents then the vector derivation tree is in fact the dependency tree of the sentence representing direct relations between lexemes such as grammatical function
we also present a new thresholding technique global thresholding which combined with the new beam thresholding gives an additional factor of two improvement and a novel technique multiple pass parsing that can be combined with the others to yield yet another NUM improvement
note that there are finitely many choices for the last step and each choice gives a different vector in g simulating the application of v and v to a set of occurrences of nonterminals in a particular link configuration in a sentential form of gs we now introduce a representation for sets of derivation trees in a uvg dl g
because we have already eliminated many nodes in our first pass the second pass can run much faster and despite the fact that we have to run two passes the added savings in the second pass can easily outweigh the cost of the first one
if our goal is to have the best performance we can while running in real time or to achieve a minimum acceptable performance level with as little time as necessary then a simple gradient descent function would n t work as well as our algorithm
if this is our goal then a normal gradient descent technique wo n t work since we ca n t use such a technique to optimize one function of a set of variables time as a function of thresholds while holding another one constant performance
for instance two nodes one an np and the other a fra g fragment may have equal inside probabilities but since there are far more nps than there are frag clauses the np node is more likely overall
assumption NUM the similarity between a pair of identical objects is NUM
our search engine is given a target performance level et to search for 3we could use gradient descent to minimize a weighted sum of time and performance but we would n t know at the beginning what performance we would have at the end
in general it would n t make sense to use a technique such as multiple pass parsing without other thresholding techniques our first pass would be overwhelmingly slow without some sort of threshwhile not thresholds e thresholdsset there are however some practical considerations
the closed e and open are also very much interchangeable in many words les baisser adolescent essai agressif blessant int ressant aigri biennal accession
if no rule is true the first character a to process is copied into the output buffer and the procedure starts again with the next character b the order in which rules are tried is NUM NUM NUM NUM
for example using the same words the default would be to ri fjulz and pro djuis rather than to refju s and prodjuls
trigram frequencies are computed from a large set of proper names whose ethnic group is known and used to classify a new proper name in terms of some language language group or language family the linguistic etymology of the name
for french open a and close NUM could be equivalent as would be o and o or el and c
the ai string in french words like bienfaisant con trefaisait faisait faisan satisfaisant etc is pronounced o but not in faisceau chauffais where the corresponding phoneme is an e
this corpus was chosen because it consists of complex polysyllabic forms NUM a sample taken from the brown corpus NUM NUM words which we felt to be sizable enough and representative enough to use to examine letter to sound accuracy
they can be applied in any order and need not be applied simultaneously or one right after the other
lastly when we factor in items that may not even be found in a dictionary such as proper nouns first names surnames place names names of corporations etc the necessity of a rule governed approach quickly becomes apparent
in a synchronous uvg dl synchuvg dl vectors from one uvg dl are synchronized with vectors from another uvg dl
theoretically this example could have as many as six readings paraphrased as follows NUM john revised john s paper before the teacher revised john s paper and bill revised john s bill s paper before the teacher revised john s bill s paper
the resulting sentence has two strict readings one in which both revised the same paper of john s generated by assuming coreference between the papers and one in which each revised a possibly different paper of john s generated by assuming coreference between the pronouns
whereas ellipsis resolution does not permit such readings in any circumstance in his account we claim that the lack of such readings for sentence NUM is due to constraints imposed by multiple parallelisms and not because of the correctness of identity of relations analyses
in proving similarity each pronoun can be taken to be coreferential with its parallel element cases a c and e or proven similar to it cases b d f and g
furthermore the tree to forest translation problem for our system can be solved in polynomial time that is given a derivation tree obtained according to one of the synchronized grammars we can construct the forest of all the translated derivation trees in the other grammar using a polynomial amount of time
given the state of the art in explanation generation the field is now well positioned to explore what may pose its greatest challenge and at the same time may result in its highest payoff generating explanations from semantically rich large scale knowledge bases
to make the second pair of arguments similar we can assume they are coreferential as a by product this tells us that the object the man s weight is acting on is the ladder and hence that the man is on the ladder
our assumption that similar words appear in identical context does not always hold
this reading results from the manner in which the strict reading for the first ellipsis is generated the final clause pronoun is resolved with the entity specified by the subject of the antecedent clause whereas our algorithm creates a dependency between the pronoun and its parallel element in the antecedent clause
the word lists associated with the label jell NUM is most similar to the key words of the definition
therefore the algorithm produces je112 as the label for a share in a company business etc
for instance the first NUM senses of issue are NUM the act of coming out
NUM only entries relevant to e test set m lloce are manually emered to e computer
analyses result not only illustrate the merits of these labels but also imply possible improvement of the algorithm
NUM sandbank a high underwater bank of sand in a river harbor etc
can appear with a family name alone a given name alone or a full name of
NUM in the markov model typically used for stochastic tagging state transition probabilities p tagi i tagi l tagi n express the likelihood of a tag immediately following n other tags and emit probabilities p wordj i tagi express the likelihood of a word given a tag
consequently the entire evaluation set was rerun with the changes made to improve the walk through message
the st system extracts information about complex events that involve template elements lik e organizations and people
it performs this assembly by using a model of the domain as delineated in the task specification
other widely known companies such as coca cola are identified through a list of known organizations
two inexperienced developers were then assigned to the ne task for the evaluation period
the bulk of the ne effort was directed toward perfecting the rules for recognition
a further step is to investigate the possibility of building a self trainin g system
this entailed extracting generic events which were disposable if not linked to task relevant events
each variation of a person or organization found is linked to the original name
we are currently looking into expanding the ne module to include a products package
at this stage a name will be recognized as being of a specific type person company government organization or other organization if it is defined i n the dictionary if it has a distinctive form or if it is an alias of a name of known type
our first run was mad e NUM days into the test period we reached NUM recall one week after the first run and NUM two weeks after th e NUM NUM first run our final run on the training corpus reached NUM recall curiously precision hovered close to NUM throughout the development period
noun group recognitio n the second stage of pattern matching recognizes noun groups nouns with their left modifier
the noun phrase patterns include noun phrase arguments such as president of general motors apposition
over the past five mucs new york university has clung faithfully to the idea that information extractio n should begin with a phase of full syntactic analysis followed by a semantic analysis of the syntactic structure
the name recognition stage generates enamex pnamex timex and numex annotations as a by product of the recognition process so named entity response generation only requires that the annotation s be converted to sgml
to see how these stages of processing work in concert to produce a template consider the crucial sentence s from the walkthrough article which produce two of the three succession events mr james NUM years old is stepping down as chief executive officer on july NUM and will retire as chairman at the end of the year
attributes when run this specifies a clause with a subject of class c person a verb of class c run which includes run and head and an object of class c company NUM
using defclausepattern reduced the number of patterns required and at the same time slightly irnproved coverage because when we had been expanding patterns by hand we had not included al l expansions in all cases
reduced relative clauses ibm headed by fred and conjoined ver b phrases and runs ibm and is run by fred
multiple applications of this schema use up subcategorized elements one at a time with a requirement that when the vp is combined with a subject to form a sentence the subcat list is empty or contains just one category unifiable with the subject depending on the approach taken
then on the category representing the domain within which all the members of the set are to be found we give no no no no as the value of in and a b c d as the value of out
this object is extensionally identical to the type living in decoding we can recover this fact by finding a bitstring which has a NUM in at least every position that the bitstring describing the disjunctive object has a NUM and as few as possible ls other than this
on the passive verb itself the features will look like this v lcb if see subj obj agent subj something subj obj rcb the surface subject is the semantic object passed down via the feature subj
for convenience and readability we shall also allow as feature values lists of values n tuples of values and prolog like terms lcb fl lcb f2 a rcb lcb f3 b rcb f4 c d e fs foo x y z rcb these constructs can be regarded as syntactic sugar for categories
iii if there is an agent phrase we need to put in the meaning of the np concerned in subject position iv if there is an agent phrase it may come at any distance from the verb in particular we can not be guaranteed that it will be either the lowest of computational linguistics volume NUM number NUM the highest of the vp modifiers in such sentences
the classes starting with NUM NUM NUM NUM and NUM are subordin te to abstract relations agents of human activities human activities products and natural objects and natural phenomena respectively
figure NUM d shows the changes of the precisions r rh and re as well as the case coverage of the test data during the training for the independent frame model the independence parameter NUM NUM
in online mode model structure and parameters counts are updated after each observation
suppose that after estimating parameters of subcategorization preference from the training corpus ps of verb noun collocations we obtain the set NUM of active features and the model ps ep v incorporating these features
in the similar way when considering a subcategorization frame which can generate a verb noun co location e there are several possibilities of the noun class generalization levels as the sense restrictions of the case marked nouns
as we described in the previous section there are several possibilities of the case dependencies in a verb noun collocation and this results in the differences of the subcategorization frames which can generate the given verb noun collocation
especially when the number of selected features are less than NUM rc is much higher when equals to NUM NUM than when equals to NUM NUM although the case coverage of the test data is much lower
then we define a part l subeate orization frame si of s as a subcategorization frame which has the same verb v as s as well as some of the case markers of s and their semantic classes
if the three cases in e are dependent on each other as in the generation of e in the formula NUM the generation of e is denoted as below in the case of the independent frame model
for the independent frame model we examined two different values of the independence parameter a i.e. c NUM NUM as a weak condition on independence judgment and NUM NUM as a strict condition on independence judgment
star dotted string set of strings lcb dotted string rcb rsw a t
the parse tables for the grammar g NUM are reported in fig NUM
subject object xcomplement label the dependency relations when the head is a verb
dependency syntax is an extremely lexicalized framework because the phrase structure component is totally absent
the first of a category x is computed by a simple procedure that we omit here
scanner scan state results in inserting a new item cat state i into the set si l
we describe an improved earley type recognizer for a projective dependency formalism
the function parse table computes the parse tables of the various categories
although this does not happen for our simple grammar g NUM
understanding an utterance means computing its meaning which may be formalized in different contexts such as speech acts or beliefs
examples of basic functions that operate on a set ofcells are the selection of cells with a given polyphonic value topos or direction
we describe the computation of this particular semantics based on the constraints that the superstructure impels to the argumentative power of terminal subsentences
the meaning is built from the context and from the signification of the sentence which lescribes all potential uses of the linguistic matter
in addition connectives and operators also specify the commitment of the speaker to semantic contents by means of the theory of polyphony
they may all be interpreted in a relevant context but hints for recognizing the need of an odd context are given
the relation between the speaker of a sentence and the utterer of a content defines the commitment of the speaker to such a semantic content
the strength is ruled by a subclass of operators called modifiers whose semantics is described precisely as modifying the strength of a selected topos
a topos is selected under one of its topical forms made up of a direction positive or negative and other parameters
modifier little the signification of little p changes the direction of the cells into the converse value anti orientation
train stations in other words the former concepts
criteria have been implemented for choosing a language for chosing between active and passive sentences for preferring paratactical over hypotactical style and for choice of formal vs informal wordings
test the lisp code under test is a boolean predicate usually about properties of the portion of input structure under investigation or about the state of some memory
it is of considerable practical benefit to keep the rule basis as independent as possible from external conditions such as changes to the output specification of the feeding system
the example shows the major language elements the top level consists of a speech act predicate and arguments for author addressee and theme the speechact proper
there is however the practical problem that the conditions on the criteria can only be fulfilled by 4note that this conclusion does not depend on the processing strategy chosen
what has been generated before or after it remains constant modulo some word forms that need to agree with new material and can thus be reused for subsequent solutions
the backtracking approach described is based on the assumption that any constraints introduced for some ego can be undone and recomputed on the basis of rules generating an alternative ego
given the input gil structure of figure NUM the vp sic am freitag treffen to meet you on friday could be genorated from this rule
from this information we can derive off line for any set of criteria which c rules have applied in the corpus and how often each c rule has applied within a derivation
these limits are due to a lack of look ahead information it is not known in general which decisions will have to be taken until all solutions have been generated
the first NUM word graphs of this set are semantically annotated
many of these words would be useful in a semantic dictionary for the category
this research was funded by nsf grant iri NUM and the university of utah research committee
for example words like ammunition or bullets are highly suggestive of a weapon
for example suppose a user wanted to build a dictionary of vehicle words
the output is a ranked list of words that are associated with the category
but the key question is whether the ranked list contains many true category members
the top NUM words NUM from each ranked list are shown in figure NUM
since this is a subjective question we set up an experiment involving human judges
for each category we began with the seed word lists shown in figure NUM
there are then two general ways in which the aligned wordnets can be accessed given a set of wm s in a source wordnet with their corresponding ilir s generate the same NUM only part of the available reformation is shown in this ilirs in the adjacent wordnet box with the corresponding wms in the reference wordnet
furthermore it can be applied to all dutch synsets related via the language internal relations to the dutch voorwerp
NUM a set of word meanings across languages have a simple equivalence relation and they have parallel language internal semantic relations
in example a the same economist position type acts as the context for organization or person names
these linguistic phases recognize increasingly complex expressions in the sentence recording syntactic and semantic attributes and producing template objects
the following are the main strengths of the system fastspec enables transparent rule definition of a complex finite state transducer
a prime example of such overlapping contexts is the positions held by persons within organizations as shown in figure NUM
the muc NUM japanese fastus gave us experience with NUM byte character input and juman a morphological analyzer developed at kyoto university
the name slot of a template entity has a name string value with its start and end positions in the document
these common nouns are complex morphemes parts of which can simultaneously belong to organization or location names
fastspec enables a fast cycle of rule specification compilation and testing during development NUM
in addition to the muc NUM fastus infrastructure past muc NUM and mimi experiences in general rule organization provided leverage
new fastus developments in the met system include new japanese grammars in fastspec new juman version NUM customized juman dictionary NUM byte adaptation of fastspec based fastus infrastructure and an sgml handler phase specified in fastspec NUM
the ili starts off as an unstructured list of wordnetl NUM synsets and will grow when new concepts will be added which are not present in wordnetl NUM note that the actual internal organization of the synsets by means of semantic relations can still be recovered from the wordnet database which is linked to the index as any of the other wordnets
if the entity does not exist the record is added to the relational database as a currently known index record
if a candidate template can be found and successfully instantiated the resulting feature structure fs mrd constitutes the generation result of mrs
the ebl method just described has been fully implemented and tested with a broad coverage hpsg based english grammar including more than NUM fully specified lexical entries
for interleaving the ebl application phase with normal processing a first pro6it is possible to parameterize our system to perform an exhaustive or a non exhaustive strategy
achieving more generality so far the application phase will only be able to re use templates for a semantic input which has the same semantic type information
it is possible to direct the subtree extraction process with the application of filters which are applied to the whole remaining subtree in each recursive step
the task of the retrieval operation in the case of a partial match is now to potentially find all subsequences of mrsg which lead to a template
generation of the extracted templates is performed solely by the ebl application phase i.e. we did not considered integration of ebl and chart generation
in the future we plan to combine ebl based generation and parsing to one uniform ebl approach usable for high level performance strategies which are based on a strict interleaving of parsing and generation cf
in this paper we assume that the strategic component of the nlg has already computed the mrs representation of the information of an underlying computer program
this module is sensitive to inconsistencies and therefore robustness and backup strategies are the most important features of this component
speech acts like begruessung and ve rabschiedung for example can be classified as dialogue flmctions controlling interaction management
in our hierarchy of plan operators the leaves i.e. the most specific operators correspond to the individual speech acts of the model as given in fig NUM their application is mainly controlled by pragmatic and contextual constraints
among these constraints are for example features related to the discourse participants acquaintance level of expertise and features related to the dialogue history e.g. the occurrence of a certain speech act in the preceding context
a finite state machine fsm the finite state machine describes the sequence of speech acts that are admissible in a standard appointment scheduling dialogue and checks the ongoing dialogue whether it follows these expectations see fig NUM
the plan based and the other two layers statistics and finite state machine interact in a number of ways in cases where gaps occur in the dialogue statistical rating can help to determine the speech acts which are most likely to miss
for the same reason the cosine measure is chosen as a matching function
it is useful as a baseline evaluation test set providing an estimate on performance
the wsj text contains 49m bytes of data and the nikkei 127m bytes
figure NUM part of the concordances of the word debenture in wsj1 and wsj2
segments are either sentences paragraphs or string groups delimited by anchor paints
conversely we need a larger segment size ff seed word frequency is low
second and less obvious predictive power may be improved
a variety of proper name types were excluded e.g.
he will be succeeded by mr dooner NUM
the changes occurred only in performance on identifying organizations
set is described at the beginning of this article
the person object contains 3the task documentation includes definition of an artifact entity but that entity type was not used in muc NUM for either the dry run or the formal run
however we still have full sentence parsing e.g.
this period comprised the evaluation epoch
there are miscellaneous outstanding problems with the te task
or can only referring expressions corefer
table NUM paraphrased summary of st
the prefix im in the word impossible and suffixes eg
this algorithm updates the dynamic knowledge bases of the un o system
be the set of all the semantic codes of its neighboring words which are given in the thesaurus for any c cr we define its salience with respect to w denoted as sal c w as NUM
and third short forms fit with our ongoing research on context
enamex type person quot llmes enamex is filled will thoughts of enjoying his three hobbies
all uno modules access the knowledge representation module and share its uniform representation
fig NUM demonstrates the distribution of the values of disl cluw w where x axle denotes the distance and y axle denotes the percent of the distances whose values are smaller than x NUM NUM among all distances
our system uniformly represents and reasons with taxonomic temporal and geographical knowledge
the grammar allows a limited context sensitivity via features on lexical categories and non terminals
finally in section NUM we will present an evaluation of the performance of our discourse processor with extended tst compared to its performance using standard tst
for each sentence if the correct speech act or either of two equally preferred best speech acts were recognized it was counted as correct
notice that the list of possible speech acts resulting from the pattern matching process are inserted in the a speech act slot a for ambiguous
we evaluated the effectiveness of our theory of discourse structure in the context of our implemented discourse processor which is part of the enthusiast speech translation system
since sentence NUM chains up to an instantiation of the response operator from an instantiation of the reject operator it is assigned the speech act reject
for example notice than in figure NUM because sentences NUM and NUM attach as responses they are assigned speech acts which are responses i.e.
otherwise the entity which the expression refers to would have already been popped from the stack by the time the reference would need to be resolved
currently we have only a limited version of this process implemented namely one which augments the time expressions between previous time expressions and current time expressions
that is why it is important to construct a discourse model which makes it possible to make use of contextual information for the purpose of disambiguating
in our approach a separate discourse segment is allocated for every potential plan discussed in the dialogue one corresponding to each parallel potential intention expressed
the fact that any so no event must be classified as such by the event e the event corresponding to the attitude report means that if we view e as a collection of infons we will have el ixnvo and by a result above ixnvol
it is expected that the use of sophisticated grammatical analysis allows for easier construction of linguistically more complex spoken dialogue systems
in house testing will inevitably be made on a limited number of systems and application domains and often is subject to other limitations of scope as well
the woz corpus analysis led to the identification of NUM guidelines of cooperative spoken human machine dialogue based on analysis of NUM examples of user system interaction problems
in order to evaluate the accuracy of the nlp component we used the same test set of NUM word graphs
as can be seen from this table this test set is considerably easier than the rest of this set
for this reason we also present results where applicable for a set of NUM arbitrarily selected word graphs
the user s query was first misunderstood but this part of the dialogue has been left out in the figure indicated as
the analysers disagreed however on whether the system should start by offering the phone number or provide the phone number right away of
NUM may be replaced by gp NUM and gp9 without significant loss
NUM has a role sitnihu to lhat of NUM
the principles of cooperative dialogue were made explicit based on the problems analysis
given an extension model c and a text corpus t iti t we define the total codelength l t c i i relative to the model class using a NUM part code
secondly we compared the principles with grice s maxims of cooperative human human dialogue
for every context w in d e w is the set of symbols available in the context w and a rlw is the conditional probability of the symbol c in the context w
the second term represents the incremental benefit in bits of using the direct estimate a a lw instead of the model probability NUM cr lw c in the context w
due to limited computational resources we set nmax NUM cmin NUM and restrict our our alphabet size to NUM ie all printing ascii characters ignoring case distinction
interlocutors may belong to different populations with correspondingly different needs of information in cooperative dialogue
separate whenever possible between the needs of novice and expert users user adaptive dialogue
in this case a t test shows that differences are only significant at the p NUM
categories that can be induced well those characterized by local dependencies could be input into procedures that learn phrase structure e.g.
finally the method fails if there are no local dependencies that could be used for categorization and only non local dependencies are informative
for example ties is used as a verb only NUM times out of NUM occurrences in the corpus
some exampies nouns in cluster NUM are heads of larger noun phrases whereas the nouns in cluster NUM are fullfledged nps
it is fairly good for prepositions determiners pronouns conjunctions the infinitive marker modals and the possessive marker
there were about a hundred word triplets whose four context vectors did not have non zero entries and could not be assigned a cluster
these two articles do not share any right neighbors since the former is only used before consonants and the latter only before vowels
table NUM shows the nearest neighbors of two words ordered according to closeness to the head word after the dimensionality reduction
for seemed left context neighbors are words that have similar types of noun phrases in subject position mainly auxiliaries
i ll try to show here ihat it is plausible to give a glob d account of such heterogeneous set of words since they bear a range of common and distinctive linguistic features and NUM NUM try to provide a representation feasible for nlp which account both for pns as a general class and for the homogeneous subclasses which wilhin them could be distinguished and defined NUM
what is more remarkable in mdld pns fig NUM it that the const type of the whole thelelore its value for const i lcb lts is iuherited by the portion e.g. if sugm is NUM and consists of grains a lump of sugar so if paper has no entailment about internal structure a sheet of paper has not either
therefore nouns such as bucket slice lump or grain are relalivc quantifiers in tile sense of ii an91 a relative quanttfier is so called because it specifies a quantity in relation to a rej rence mass in the de mlt case intejpretation this reference mass consists of the maximal instantiation of the pertinent categojy i.e. its ftdl extension in all conceivable worlds
provided all which has boon discussed up to here the general lex portion sign is defined as in fig NUM that is as selecting nps and resultiug in formal b entity denoting signs lherefore individuated and syntactically couutable where the only qualia feature which percolale from the whole is the telic role the rest of quales may be oveitiddeu by that of the pn
while the measure conveyed by cont elt and mi ld is absolute that of bound and dtcttl is relative a top of a box or a slice of bread will be bigger or smaller depending on tile magnitude of the box or the loaf of bread
we hypothesise that in general distinctions belween classes of pns contcerning selectional restrictions must be due to linguistic reasons while further specifications within each class would be due to properties of the referent l g it could be asstlmed that containers culrv baskets select bn items substances and plurals and more specifically cttps select liquids and baskets non liquids
bel hearer goal speaker knowref hearer speaker entity object figure NUM refer schema
in this case we use meta actions that encode how a plan derivation corresponding to a referring expression can be reasoned about and manipulated
the speaker attempts to achieve this goal by constructing a description of the object that she thinks will enable the hearer to identify it
fourth as the input and the output to our system we use representations of surface speech actions not natural language strings
activity progresses we need to account for the mutual beliefs that the agents adopt as a result of the utterances that are made
miscellaneous subset set lambda subset compute the subset subset of set that satisfies the lambda expression lambda
yield plan node actions the subplan rooted at node in plan has a yield of the primitive actions actions
bmb agtl agt2 prop agtl believes that it is mutually believed between himself and agt2 that prop is true
in conclusion i have described an approach in which different sublogics coexist and are interrelated within a single categorial system
i mary spoke and susan whispered to bill where the conjuncts are each analyzed as s pp
the proof below illustrates this formulation showing the composition of two implicationals a combination which requires associativity
consider next the implicational xo y which exhibits the interderivability xo y c y ox
a different abstraction and application operator is used for each implicational connective so that terms fully record the proof structure
earlier attempts to achieve this goal have employed modal operators called structural modalities whose use presents a number of problems
the following proofs are for the two transformations discussed in the previous section illustrating natural relations between levels
the tool lets a user define types of anaphora as necessary
we made distinction between qzpro and zpro when tagging zero pronouns
the transfer lexicon contained around NUM paired graph fragments most of which were used in both transfer directions
the subtree transfer search maintains a queue q of configurations corresponding to partial derivations for translating the subtree
NUM perform a left parent right traversal of the nodes of the resulting dependency tree yielding a target string
in this context recognition refers to checking that the input string can be generated from the grammar
the interpretation of this directed arc is that relation r holds between particular instances of w and w
the case of zero transitions will yield empty sequences corresponding to a leaf node of the dependency tree
the anaphoric chain parameter is used in selecting training examples
name anaphora are tagged when proper names are used anaphorically
thus finding antecedents of organizational anaphora is not straightforward
conversely too much pruning can also yield poorer results
mlr NUM shows the effect of not training on anaphoric chains
proposes the following ranking scheme to select antecedents of zero pronouns
the anaphoric type identification parameter is utilized in training decision trees
currently we use NUM features and they include lezical e.g.
the price increased NUM dollars a share
this indicates the translation is not literal and there are many deletions
it enables adult professionals all of whom use informational technology on the job to access pertinent and authentic materials perform motivated tasks and select a range of performance support tools
the design methodology used for developing ole ada and its precursor cibola is one of iterative design and the first step in this process is to understand the user through user protocol task analysis
although tipster technology does not immediately address these issues it does provide the basis for new systems that can support humans working with language and resources that can aid them in their work
examples include unix programs like grep editors like emacs that can support multilingual text and programming languages like perl and lisp and prolog that can be used to manipulate text data
in addition word frequencies in individual documents or smaller sub collections can be automatically compared to larger collections to identify distinctive words in the document that are significant with respect to the larger collection
like writing in the margin of a paper document oleada has an annotation list interface that can be used to associate other text either as input by the user or by linking the annotation
during task analysis it was noted that translators often use a variety of paper based tools and resources like mono and bilingual dictionaries specialized glossaries and thesauri to aid them in the translation task
fuzzy matching was added to aid users in searches
figure NUM shows the interface for this feature
will recognize iron a t his cry a sample constituent structure is given below
in the following paragraphs we give annotations for a number of such phenomena
the answer is that the inside outside algorithm is supremely unsuited to learning with this representation
any parse that involves one will have a bigger tree and be significantly less probable
a unifbrm representation of local and non local dependencies makes the structure more transparent NUM
learning a lexicon consists of finding a grammar that reduces the entropy of a training character sequence
this suggests that learning algorithms based on this representation are far less likely to encounter local maximums
olivier s learning algorithm soon creates rules such as w the and w tobe
for parts of speech y and z the rules we include in our base grammar are
the effectiveness of the class of models can only be verified by empirical tests
but the verb is likely to have a higher mutual information with the subject than inflection does
the government binding framework usually supposes that an inflection phrase is formed of inflection and the verb phrase
fig lb shows the result of processing an original trec query fig la after our lexicon lookup process
and could be easily reviewed by a person to separate the good definitions from the bad ones
we then ran circus over the same set of texts using the new concept node dictionary
all morphological variants for all open class words if the sentence fails its third parse attempt it is reparsed using the morphological recognizer on all open class words
the heuristics are divided into three categories depending upon where the targeted noun phrase is found
a person manually reviewed all NUM definitions and retained NUM of them for the final dictionary
lain theory autoslog ts should have generated all of the patterns that were generated by autoslog
the number of concept nodes drops off dramatically from NUM NUM to NUM NUM after frequency filtering alone
therefore the autoslog ts dictionary and statistics can be fed directly into the text classification algorithm
a good dictionary for information extraction should contain patterns that provide broad coverage of the domain
it may take days or even weeks for a domain expert to annotate several hundred texts
for example consider the sentence john smith was kidnapped by three armed men
the focus of our current research is to take advantage of the relative simplicity of head transducer models in working towards fully automatic model acquisition
in our system the four implemented levels of initiative follow these guidelines NUM directive mode
this is known elsewhere as the plan recognition problem and it has received much attention in recent years
connect the end of the black wire with the small plug to the minus corn hole on the voltmeter
for the technical manuals an average of NUM strings seldom more than NUM strings are left
these filters or rules differ fundamentally from generative rules that produce allowable strings in a language
next the subjects were told about the dialog system and its functions and capabilities in brief and simple terms
the circuit to be repaired was a multivibrator circuit constructed on a radio shack NUM in one project kit
information theoretic tools can be used to find the entropy of different tag sequence languages and support decisions on representation
the first step in constraining the problem size is to partition an unlimited vocabulary into a restricted number of partof speech tags
for example in sentence NUM above strings NUM NUM NUM NUM and NUM n can never be correct
NUM using negative information when parses are postulated for a sentence negative as well as positive examples are likely to occur
applying this particular rule to sentence NUM above would eliminate candidate strings NUM NUM and NUM NUM
these are elements of a sentence that should be separated as opposed to elements of constituents that cling together
candidate strings this system generates sets of tag strings for each sentence with the hypertags placed in all possible positions
it contains the list of semantic structures that have meaning for the current subdialog and for other active subdialogs
for example the relative order of target specifiers cardinals and ordinals will depend on the order of these modifiers in the source
given that the set of initial and auxiliary trees can have leaf nodes labeled with e we do some preprocessing on the tag g to obtain an association list assoc list for each node
the large increase in performance is a natural consequence of the fact that the categories help in reducing the total variability that can be found in the corpora although sentences do exhibit a great deal of variability the underlying syntactic structure is actually much less diverse
while rosenfeld s results and ours are not di null rectly comparable both demonstrate the utility of mixed order models
in this section we describe a criterion named differential entropy which is a measure of entropy perplexity fluctuation before and after merging a pair of labels let cl and c2 be the most similar pair of labels based on divergence or bayesia u posterior probability
table NUM summarizes the cardinality of these sets and the frequencies of cue occurrence
first the trees we obtained were extremely complex at least NUM nodes
we have presented the results of machine learning experiments concerning cue occurrence and placement
experiments show that cue occurrence in clusters depends only on informational and syntactic relations
the latter choice always results in trees overfitted to the data in our domain
we compute NUM confidence intervals for the two error rates using a t test
these features capture segment embedding core type and trib type qualitatively and a bore below quantitatively
infor mationalj structure similar to intentional structure but applied to informational relations
null coders analyze each explanation in the corpus and enter their analyses into a database
as our study shows individual features have no predictive power for cue occurrence
a value of NUM would indicate that the observed number of agreements is halfway between chance and perfect agreement
krippendorff s a reports to what degree the observed number of matches could be expected to arise by chance
we then present two methods for enhancing performance error analysis and machine learning section NUM NUM
an of NUM using two partitions of seven subjects would represent very good reproducibility with values above NUM
the results indicate that the observed distributions are highly significant i.e. unlikely to have arisen by chance
in the random distribution there are few bars of width NUM and none of any greater width
but even with this conservative evaluation reliability is fairly good on two narratives and promising on average
because we only have four subjects within each partition this necessarily produces fewer significant boundaries than our method
first we created seven hypothetical subjects each of whom assigns the same number of boundaries as one of the
the first part of our paper presents a method for empirically validating multiutterance units referred to as discourse segments
table NUM parsing accuracy using the wsj corpus
the precision and recall rates were respectively NUM NUM and NUM NUM in the first case NUM NUM and NUM NUM in the case of the newspaper articles
this notion of delayed assignment is crucial for robust parsing and requires that each statement in the sequence be linguistically cautious
at the same time segments are defined in a cautious way to ensure that clause boundaries and syntactic functions e.g.
the additional information provided at each stage of the sequence is instrumental in the definition of the later stages of the sequence
the main purpose of marking segments is therefore to constrain the particular linguistic space that determines the syntactic function of a word
thus the use of speech within magic models current practice
drips in protocol concentrations are nitroglycerin levophed dobu
our development of magic is very much an ongoing research project
there is considerable interest in producing fluent and concise sentences
it is after all a planned oneway presentation
tamine epinephrine and inocor figure NUM multimedia presentation generated by
smith is a NUM year old male patient of dr jordan undergoing cabg
the matching is applied iteratively on the input text to handle the case of embedded clauses arbitrarily bound to three iterations in the current implementations
for instance some networks identify segments for nps pps aps adjective phrases and verbs while others are dedicated to subject or object
the overall modular structure of the eurowordnet database can then be summed up as follows first there are the language modules containing the conceptual lexicons of each language involved
for that purpose the language specific wordnets will be stored as independent language internal systems in a central lexical database while the equivalent word meanings across the languages will be linked to each other
eurowordnet is an ec funded project le2 NUM that aims at building a multilingual database consisting of wordnets in several european languages english dutch italian and spanish
given the fact that we allow for a large number of language internal relations and six types of equivalence relations it may be clear that the different combinations of mismatches is exponential
by displaying the wordnets adjacently and by specifying the ill records separately for each synset in each tree the matching of the ili records can be indicated by drawing lines between the same ili records
so if one wordnet links dog to ammal and another wordnet links it to mammal and only via the latter to animal first these structures are not considered as serious mismatches
first of all this comparison gives us new hyperonyms that can be considered and secondly it gives us a new potential ill record fare l for the dutch wordnet
a drawback from a methodological point of view is that new words that are added in one of the languages might call for a revision of a part of the language independent network
for example intuitions appear to vary on causation or hyponymy as the relation between dutch pairs such as dzchttrekken close by pulling and dichtgaan become closed
it must be noted however that the ldoce homograph level is far more rough grained than the cide guideword level let alone the sub sense level and that wilks and stevenson s approach on its own would by its very nature not transfer down to more fine grained distinctions
mcroy and hirst the repair of speech act misunderstandings premise NUM a pretelling was active in ts NUM because of russ s interpretation of t1 NUM premises NUM a pretelling would be incompatible with an inform not knowref happening now
NUM cbs NUM cbs NUM cbs are the percentage of sentences
in addition for p r it is possible touse simple bigram approximation
NUM NUM statistical disambiguation model this section describes the way the best syntactic tree is selected
a constituent boundary parse of a sentence can be represented by a sequence of boundary tags
the other is complex sentence set in which every sentence has more than NUM words
NUM during the past few years we have developed a series of realization systems
this action extracts views from the knowledge base and attaches them to the explanation plan
the key to use mrr efficiently is to correctly identify the possible restriction regions in the sentences
syn tags np noun phrase mp numeral cla ssifier
the added possibility of an error rule
a recent microsoft product NUM keeps a record of personal habitual mistakes
these passages focused on explanations of the anatomy physiology and reproduction of plants
word transformation implies either implicit or explicit string comparison
splitting a word have not been implemented yet
computational linguistics volume NUM number NUM the supposition of the performance of some act that expresses via a linguistic intention any supposition that would be incompatible with another supposition of the agent s interpretation of the discourse
there are two significant difficulties with collecting test data
if the latter stage fails cle invokes partial parsing
under examination will be the number of errors missed caught and wrongly rightly corrected
this second method and its evaluation will be described in detail in this section
although our parser can not achieve good precision it is not so a serious problem because our parser tries to give more detail bracketing for a sentence them that given in the wsj corpus
currently the dialogue component processes more than NUM annotated dialogues from the verbmobil corpus
this is a real decomposition achieved by a program described in section NUM
figure NUM displays some of the lexicon learned from the brown corpus
lester and porter robust explanation generators table NUM comprehensive analysis
these in turn can be used to improve the edps
explanation planning is the task of determining the content and organization of explanations
by varying pragmatic information such as tone hovy enabled pauline to generate many different paragraphs on the same topic
knight s performance exceeded that of one of the biologists
next it creates the corresponding exposition node for the soon to be constructed explanation plan
this allows learning algorithms to fit detailed statistical properties of the data
in this way syntactic structure emerges in the internal representation of words
this is a very natural representation from the viewpoint of language
figure NUM presents a portion of an encoding of a hypothetical lexicon
again we measured the statistical significance of these differences
this phrase has the same structure as the idiom kicking the bucket
the words in the less frequent half are listed with their first level decomposition
this instantiation is easily tested on problems of text segmentation and compression
NUM ii enter thing NUM ed NUM iii pocket thing NUM NUM iv mine thing NUM NUM v create destroy thing NUM exist NUM by NUM
aspectual feature determination applies to the composed lcs by first assigning unspecified feature values atelic t non durative r and stative d and then monotonically setting these to positive values according to the presence of certain constituents
the privative model in table NUM allows states to become activities and accomplishments by adding dynamic and telic features but they may not become achievements since removal of the durative feature would be required
for example in tiw lcs states may be composed into an achievement or accomplishment structure because states are part 4since estar may be used with both relic lcb estar alto and atelic estar contento readings we analyze it as atelic to permit appropriate composition
we should note that while there are style checkers and grammar checkers on the market these programs do not satisfy the needs of the deaf
this primarily affected our numex and timex precision in the named entity task
table NUM slot by slot performance differences te task unrevised scores
the error identification phase must also look for semantic errors e.g. mixing of have and be and for discourse level errors e.g. np deletions
this user model would take into account a theory of second language acquisition which regards the process as a systematic revision of an internalized concept of the language to be acquired
in particular one would expect features shared in the l1 and l2 to be acquired more quickly than those which are not due to positive language transfer
where it differs from these systems is in being driven by rule sequences
the phrase parses as follows note the embedded post semantic phrase types
after all of the rules have been applied the phraser is done
the part of speech tagger first assigns initial parts of speech by consulting a large lexicon
this allows facts associated with one form to become propagated to the other
type determination t creative artists agency treated as gov NUM inc
other like rules distribute coordinated titles across the title holder and so forth
if one form is a shortening of the other as in mr
the main difference lies in how they determine the extent of support offered by the surrounding nouns
in our implementation of his approach we applied the method to general word sense disambiguation
the nearest candidate ej ss node is then chosen as the semautic class of the word
figure NUM muc NUM semantic class hierarchy as mapped onto wordnet
feedback from the udges reveal possible leverage for future improvements
l was shown to achieve high accuracy as compared to other word sense disambiguation algorithms
our approach harnesses wordnet ehectively to outperform supervised methods which rely on annots ed corpora
for both algorithm only the nouns of the same passage are incorporated into the context window
the set of mapped semantic cl ses in wordnet is shown in figure NUM s
besides those shown above a number of other types of groupings were evident which appeared to reflect syntactic rather than more specific semantic characteristics
unlike them though provision was made for presenting words along with context during the test phase as well as the training phase
further analyses were carried out in which the length of the context window was extended to NUM words either side of the target word
although they have received most attention from within computational linguistics such approaches are also of interest from the point of view of psychology
thus we are effectively using a combination of pos and word transition probabilities
window is more likely in this context than work
pereira et al NUM but rather that this is a very natural and attractive consequence of trsing the unsupervised neural network approach
since this information is not present the dendrograms resulting from the analysis show groupings of prepositions adjectives verbs and so on
we are in the process of developing and refining the semantic classification for nouns and verbs
effectively we regard wordnet as a source of information which we can exploit about word groups
the use of cluster analysis and related techniques has been popular for presenting the results of recent statistical language work within computational linguistics
this paper describes the use of statistical analyses of untagged corpora to detect similarities and differences in the meaning of words in text
it is possible to automatically analyze such pairs to gain enough knowledge to accurately map new katakana phrases that come along and learning approach travels well to other languages pairs
this string has two recognition errors ku for ta and c chps for NUM na
second table NUM shows the specificity of the trained bpa s with respect to application environments
the conditions of the metaplan are satisfiable because there is a plausible goal act that a pretelling would help mother to achieve and it is consistent for russ to assume that achieving this act was in fact her goal
the best results are obtained when values less than NUM are used
finally each tag conditional probability of the unknown word tags is normalized
on the other hand a time consuming training process has been reported
the main difference between the optimization criteria in NUM NUM NUM and that in NUM NUM NUM
similar results are achieved when the extended set of grammatical categories is tested
the taggers have been realized under ms dos using a NUM bit c compiler
errors due to the syntactical or grammatical style of the testing text
in mlm taggers these tags are equally weighted to the correct ones
in all languages the entries were tagged as they appeared in the text
tagger speed is closely related to the corpus ambiguity table NUM
in particular we can use two grammars one fast and simple and the other slower more complicated and more accurate
however it is difficult to compare these two in an absolute sense because both the evaluation data and code assignment scheme are different
consequently the transfer units remain small and independent of other elements thus the interdependencies between different rules are vastly reduced
figure NUM detailed pseudo code of the new algorithm the first part of the algorithm check success comprises the algorithm s termination criteria
another approach to disambiguation is to define a probability model and to rank interpretations on the basis of syntactic parsing
the reconstructed matrix defines a space that represents or predicts the frequency with which each term in the space would appear in a given document or text segment given an infinite sample of semantically similar texts lan null the reduced matrices gives x a least squares best fit reconstruction of the original matrix
for example there are many instances of the collocation the humber of in the training data
due to the random nature of this process however the corpora must differ between the two systems
the differences in the baseline predictor for each system are a result of different partitions of the brown corpus
the right half of table NUM shows the most frequent word in the training corpus from each confusion set
the premise of the lsa model is that an author begins with some idea or information to be communicated
figure NUM singular value decomposition svd of matrix x produces matrices t s and d
for a given confusion set an lsa space is constructed by treating each training sentence as a document
the original metropolis algorithm is also a special case of the metropolis hastings algorithm in which the proposal probability is symmetric that is g x y g y x
questionnaires are used to obtain a driver s license which is needed to drive a car which is needed to get to california then this same utterance could even be interpreted as an incomplete attempt to get to california
thanks to him jo calder and marc moens for guidance and advice throughout the project
l br exmnph a simple seni ence like would correspond l o he c structure and f sl rucl ur shown in NUM rod NUM respecdvt ly
judith ib rman mark johnson ll on kal l m marfa l iugenia nif o mtd annie zaenen for the many valuable discussi ns that served as input to this l at er
when karl reported the accident everyone had to laugh here the linking of the gen1 and en2 functions to the appropriate thematic rote in the german f structure driw s tile transfer of these functions to the sllllj and obj time lions of tile english f structure
this condition is written as NUM governs h d m a dg is thus characterized by NUM g lex c isac e the language l g includes any sequence of words for which a dependency tree can be constructed such that for each word h governing a word m in dependency d governs h d m holds
the necessity of this extension approved by most current dgs relates to the fact that dg must directly characterize dependencies which in psg are captured by a projective structure and additional processes such as coindexing or structure sharing most easily seen in treatments of so called unbounded dependencies
the instance s of s has iei valencies of class h k NUM e i valencies of class r and iwl k valencies of class u whose instances in turn have i ei NUM valencies of class r
in the isac hierarchy the classes ui share the superclass u the classes v the superclass r valencies are defined for the classes according to table NUM furthermore we define e dee lcb s rcb u lcb vii e NUM ivl rcb
NUM definition NUM dg recognition problem a possible instance of the dg recognition problem is a tuple g a where g lex c isac is a dependency grammar as defined in section NUM and a e e
a valency b d c describes a possible dependency relation by specifying a flag b indicating whether the dependency may be discontinuous the dependency name d a symbol and the word class c e c of the modifier
in anticipation of counter arguments such as that the presented dependency grammar was just too powerful we will present the proof using only one feature supplied by most dg formalisms namely the free order of modifiers with respect to their head
the latter word classes define a valency for words of class r for the other end point and a possibly discontinuous valency for another word of the identical class representing the end point of another edge which is included in the vertex cover
because is monophonous i.e. its relata originate from a single utterer while since can be polyphonous
our coding scheme for the exhaustive analysis of discourse allows a systematic evaluation and refinement of hypotheses concerning cues
altho a you know that part1 is good b you should eliminate part2 before troubleshooting in part3
these are found on the strength of titles like mr
the system missed both fallon mcelligott and mccann erickson
these relations describe how the situations referred to by the core and contributor are related in the domain
a second problem with wordnet is that it needs some important extensions to make it usable for effective parsing
now ne and te processing diverge
slots with pieces of the text matched
here the solutions are less obvious
person NUM NUM per title mr
the lexical analysis component has several subcomponents
the secondary stage picks up all remaining references to mccann
this called for some carefu l engineering
sterling software an nltoolset based system for muc NUM
in the adhoc task it is assumed that new questions are being asked against a static set of data
several groups tried these techniques in trec NUM and it was decided to form a track in this area for trec NUM
however the corneu group has run older systems those used in trec NUM and trec NUM against the trec NUM topics
several other experiments were made using manual modifications expansions of the topics and these are reported with the manual adhoc results
for some groups this means doing the routing and or adhoc task with the goal of achieving high retrieval effectiveness performance
initial number of relevant documents in addition to the completeness issue relevance judgments need to be checked for consistency
the more accurate results going from trec NUM to trec NUM mean that fewer nonrelevant documents are being found by the systems
the topics consist of natural language text describing a user s information need see section NUM NUM for details
once again the test phase fails to provide a well formed tncb so we repeat the rewrite phase this time finding dog to conjoin with the figure NUM shows the state just after the second pass through the test phase
even if there is not an exact isomorphism between the source and target commutative bracketings the first guess is still reasonable as long as the majority of child commutative bracketings in the target language are isomorphic with their equivalents in the source language
of these NUM used automatic construction of queries NUM used manual construction and NUM used interactive construction
in all cases readers are referred to the system papers in the trec NUM and trec NUM proceedings for more details
figure NUM the initial guess l t past bark brown tg figure NUM the tncb after past is moved to bark even though they did not have the correct linear precedence
a tncb is well formed iff its value is a sign ill formed iff its value is inconsistent undetermined and its value is undeter mined iff it has not been demonstrated whether it is well formed or ill formed
shake and bake translation assumes a source grammar a target grammar and a bilingual dictionary which relates translationally equivalent sets of lexical signs carrying across the semantic dependencies established by the source language analysis stage into the target language generation stage
for example one of the subcategorization frames of appear in part a adjp prkd r indicates a predicate adjective with subject raising as in he appeared confused
initial results indicate that verb senses can be pruned for highly polysemous verbs by up to NUM by the first method and by up to NUM by the second method
in the case of appear only NUM cells of the NUM x NUM matrix represent possible combinations of syntactic patterns with senses corresponding to a NUM NUM reduction in ambiguity
selecting a subset of almost synonymous verb senses is significantly harder than for example disambiguating bank between the edge of river and financial institution senses
thus we presume that the relevant sense of a given word form in a group is in the same lexical field as the senses of the other word forms in the same group
for example when the verb question has a that clause complement it can not have the sense of ask but rather must have the sense of challenge
we find a NUM NUM reduction of possible senses depending on whether we use the additional something somebody selectional constraints with only NUM NUM of the tags being incorrect
in principle this would make it possible to use the automatic classification method on a more heterogeneous corpus i.e. where the same verb occurs frequently with two distinct senses
by extrapolation we will assume that words appear in only one sense within a homogeneous corpus NUM except for certain high frequency verbs or for semantically empty support verbs
this set can be further reduced e.g. by giving more weight to senses supported by more than one of the s or by unambiguous y s
fortunately there are techniques for coordinating solutions to such sub problems and for using generative models in the reverse direction
dependency tree arcs are labeled with symbols taken from a set r of dependency rei iorss
we compare the effectiveness of two related machine translation models applied to the same limited domain task
moving to real words may give is crime the i corresponds to ai the s corresponds to su etc
the space figures give the average amount of memory allocated in processing each utterance
dunning NUM make use of both positive and negative instances of performing a task
the edge count for the transfer system includes the number of dependency graph edges in bilingual entries
the estimator consists of a simple three layer feed forward mlp trained with the back propagation algorithm see figure NUM
for efficiency reasons we actually compute the probabilities of all rules in parallel as shown in figure NUM
the algorithm takes a lexicon of underlying forms and applies phonological rules to produce a new lexicon of surface forms
we next attempted to judge the reliability of our automatic rule probability estimation algorithm by comparing it with hand transcribed pronunciations
we ran the estimation algorithm on NUM sea noes NUM NUM words read from the wall street journal
these completely automatic techniques requiring no hand written rules can allow a more fine grained analysis than our rule based algorithm
this paper presents an algorithm for learning the probabilities of optional phonological rules from corpora
these pronunciations are encoded in a hidden markov model hmm for each word
they are used to categorize documents by learning for each category a linear separator in the feature space
this is small consisting of commonly used words of NUM to NUM characters with some proper nouns of size NUM
input specification in the wag sentence generation system
t process quality figure NUM the upper model
another motivation to start with vp is that v contains information that is useful for the remainder of the structure building process
it can be concluded that the absolute bottom up approach for the building of trees is more useful for generation than for parsing
the next item v NUM NUM is produced by applying the completer to the item in s NUM v NUM NUM n
in spite of this we chose not to consider functional heads as heads in order to accomplish an absolute bottom up process
the features of the traces of a certain chain are known as soon as the so position is reached because all positions in a chain are linked
because we are dealing with parsing as opposed to generation in this paper there are certain discrepancies between the parser and the framework it is based on
the dependency formalism is translated into parse tables that determine the conditions of applicability of tile parser actions
these changes imply the restructuring of some parts of the recognizer with a plausible increment of the complexity
these items are inserted into si after having set to null the fourth element
in earley s terms this item corresponds to a dotted rule of the form cat z
this choice was made because it allows gt and move a to start constructing a vp before the projections to which constituents from vp are moved are constructed
i would like to thank gosse bouma john nerbonne gertjan van noord and jan wouter zwart for their helpful comments on earlier versions of this paper
the actual role structure allows for representation of semantic ambiguity
the original template element scores for the walk through article were p r NUM NUM
to some of these nodes will be attached a number of textref sequences
proper nouns and referents are at least recognized if not correctly interpreted
this is a robustnes s measure for when the core produces duplicated textrefs
this rule was non trivial to implement because the task definition was not clear
an important control is a node s rank this encodes quantification information
further work on the pre parser for named entities would have reduced this problem
unfortunately many of these are unimportant and frequently short sentences
this recommendation is reviewed by the tipster configuration control board
the parameters trained in such a way therefore provide a tolerance zone for the mismatch between the training and the testing sets
with these characteristics applications can comply with the architecture with relative ease but the interface elements do not have enough constraints to be precisely defined in an interface control document
this process will result in an enrichment of the architecture with the experience gained ffi om specific implementations as well as the beginnings of a library of information about what tipster compliant components exist throughout the government community
for the current architecture conformance is def med as follows designs of applications or products are submitted to a tipster engineering review board erb
in tipster phase ii the architecture is being tested by use in a number of applications under these circumstances conformance to the tipster architecture can not be rigidly def med
the architecture has been constructed with a high level of abstraction and flexibility
regarding those places within the design which do not comply the erb issues a recommendation that the architecture be changed that the design be changed or that the exception be allowed
in this paper a deep structure disambiguation system integrating a semantic interpreter a parser and a part of speech tagger is developed
theoretically a parse trees may be normalized into more than one nf1 structure however this happens seldom in our case
where i a and fsj denote the i th ancestor and the j th sibling of f m respectively
thus NUM b presupposes that they moved to chicago since this phrase belongs to the topic
our cb new nb neighbor cb has stolen nb my cb car nb
the fact that there are also cases in which different placements of the intonation center are suitable for the given context is not immediately relevant
the written shape of the sentence does not suffice here to determine tfa to such a degree as it does in czech for example
an algorithmic procedure has been formulated by h skoumalov completing the parsing of a written english sentence so as to identify its tfa
the readings without a focus are not valid representations of sentences since one of the basic assumptions is that every sentence contains a focus
NUM now we can see why the b examples in section NUM lack the ambiguity present in the a sentences
similarly with NUM there is a less probable pronunciation possible only in specific contexts with the pronoun him stressed
NUM as usual in computational linguistics it is impossible to handle all marginal and exceptional cases by a relatively simple general procedure
the total number of word tokens in these test texts was NUM NUM out of which nearly NUM were morphologically ambiguous
figure NUM ratio and degree of generalization
our next goal is generating a summary instead of just extracting sentences
when the system picks NUM sentences
we plan to improve the system s extraction resuits by incgrporating linguistic tools
in order to count concept frequency we employ a concept generalization taxonomy
a topic is a particular subject that we write about or discuss
wavefronts which one is the most appropriate for generation of topics
to represent and generalize concepts we use the hierarchical concept taxonomy wordnet
furthermore straightforward word counting can be misleading since it misses conceptual generalizations
one of the goals of the tipster phase h extraction project contract number NUM f133200 NUM has been to integrate extraction and detection technologies
set NUM which has a fairly uniform distribution is much more likely to have been created with the fair die than the loaded one
applying this approach to the document classification problem we may define the outcomes to be the sets of distinguish terms which dear me the classes
for the small test performed all of the methods produced about the same classification result and the multinomial distribution method produced the best routing result
note that most people can tell that the first passage below is about music even though the word music is not in the passage
a probabilistic method for classification was proposed by guthrle and walker NUM which assumed each class was distributed by the multinomial distribution
NUM use the high frequency words in each list which are not the high fiequency words in any other list by selecting the words which
z weight x log cotmt NUM document scoi e j z weigh02 xz log count l NUM
the expected probability for this set of words is NUM NUM minus the sum of the probabilities of all of the distinguishing terms in the iraining set
probability of output using these probabilities directly for ranking would place set NUM on the bottom of each list which does not agree with intuition
first for reasons of simplicity we base our model on simple segmental spe style rules it is not clear what the formal correspondence is of these rules to the more recent theoretical machinery of phonology e.g. optimality constraints
a central ingredient of the procedure which handles user s inputs sketched above is the system s validation feedback on the current sr candidate
NUM our modification proceeds in two stages first a dynamic programming method is used to compute a correspondence between input and output segments and second the alignment is used to distribute output symbols on the inital tree transducer
since the focus of our research is on adding prior knowledge to help guide an induction algorithm rather than the particular automaton approach chosen we expect our results to inform future work on the induction of other types of automata
NUM exploiting ehl k s p NUM oy2 t ih ng ehl k s p NUM oy2 t ih ng both of these problems are caused by insufficiently general labels on the transition arcs in figure NUM
when adding a new arc to the tree all the unused output segments up to and including those that map to the arc s input segment become the new arc s output and are now marked as having been used
for our experiments we used the celex pronunciations as the surface forms and generated underlying forms by revoicing the devoiced final stop for the appropriate forms those for which the word s orthography ends in a voiced stop
this use of positive only evidence is significant for both cognitive reasons children have been shown to make little use of negative evidence and practical ones positive examples but not negative examples are easily derived automatically from corpora
the application of machine learning techniques to reduce the customization time for data extraction systems the use of multilingual data extraction for targeted machine translation or gisting fusion of data extraction technology with other media speech video for multimedia fusion applications lii
sra participated in the multilingual entity task met in both japanese and spanish
figure NUM shows the result of processing the first NUM sentences from an edition of the times newspaper
the output from this stage is simply the sentence break numbers and their new smoothed correspondence values
a second focus of our phase iii research plans involve the intelligent summarization of texts
also incorporated into this demo were three processing modules of the generic fastus system name finder phrase finder and table recognizer
the above discussions show that the salience constraint in tr3 is sometimes effective in getting small improvements in the output texts
in each comparison we noted down the number of matches between the computer generated text and the human result
for each speaker the number for each test text is the average number of matches with the other eleven speakers
as for text NUM two speakers completely agree with tr2 while the others partly agree with tr2 and tr3
the salience constraint says that both the positions of an anaphor and its antecedent are the topics of their respective utterances
the locality constraint checks whether the anaphor in question occurs either in the immediately previous utterance or at a long distance
in this paper we evaluated the quality of anaphors in the texts generated by using various rules
such methods might include the insertion of conventional discourse markers in order to detect preferred breaking points e.g.
the grammatical structures of the machine created texts are simplified they are not as sophisticated as human texts
in this paper we present an evaluation of anaphors generated by a chinese natural language generation system
it is worth noting that the minima occurring within this article are not as pronounced as the actual article boundaries themselves
in order to distinguish the significant peaks and troughs from the many minor fluctuations a simple smoothing algorithm is used
figure NUM shows a graph for an expository text a NUM sentence psychology paper written by a fellow student
the result is that noise is flattened out while the larger peaks and troughs remain although slightly smaller
the addition of the significance measure represents an improvement on hearst s algorithm implemented by the berkeley digital library project
agent based architecture was chosen to support this application because it offers easy connection to legacy applications and the ability to run the same set of software components in a variety of hardware configurations ranging from stand alone on the handheld pc to distributed operation across numerous workstations and pcs
again it should be mentioned that the algorithm found more breaks than were immediately obvious to a human judge
NUM we collect small context windows surrounding each occurrence of a seed word as a head noun in the corpus
restricting the seed words to be head nouns ensures that the seed word is the main concept of the noun phrase
the system then updates its beliefs
NUM NUM understanding the weird creature
this implies that more category words would likely have been found if the users had reviewed more than NUM words
c NUM NUM NUM to montreal
they need to be proved just like constraints
the category score of a word w for category c is defined as c corefw c NUM reg
so these plans can check for these conditions
now we consider the effect of these refashioning plans
we presented the words in random order so that the user had no idea how our system had ranked the words
it looks like a hat that s upside down
there are a couple of ways of dealing with the problem
the kullback leibler divergence d llq is NUM NUM
instead dd l use gibbs sampling to estimate the needed expectations
to illustrate let us consider corpus NUM NUM again
computational linguistics volume NUM number NUM side sum to one
have suppressed the edge labels for the sake of perspicuity
let us consider what happens when we use the erf method
these terms are shown for each tree in table NUM
impossible for the resulting empirical distribution to match the distribution ql
although the we then ran the following experiment using NUM NUM million words of the penn treebank tagged wall street journal corpus
this rule states change a tag from plural common noun to singular common noun if the word has suffix ss
this can make it difficult to analyze understand and improve the ability of these approaches to model underlying linguistic behavior
in the nonlexicalized tagger the transformation templates we use are change tag a to tag b when NUM
change the tag NUM from in to rb if the word two positions to the right is as
they cite results making the closed vocabulary assumption that all possible tags for all words in the test set are known
we are currently exploring the possibility of incorporating word classes into the rule based learner in hopes of overcoming this problem
NUM this resulted in an accuracy of NUM NUM with an average of NUM NUM tags per word
figure NUM shows the first NUM transformations learned for tagging unknown words in the wall street journal corpus
porting a natural language processing nlp system to a new donmin renmins one of the bottlenecks in syntactic parsing because of the amount of effort required to fix gaps in the lexicon and to attune the existing grammar to the idiosyncracics of the new sublanguage
if the features are treated as boolean values present not present it will most certainly happen in neighborhoods with liberal cutoff points that there will be some disagreement for individual options so a heuristic must negotiate these eonllicts and settle for the best abstraction
when the core grammar is augmented to acconnnodate all these idiosyncracies the danger is not that an ungrammatical sentence might slip through but that perfectly legitimate input receives an incorrect analysis that is sanctioned by some peripheral grammar rule that does n t apply to the domain under investigation
since the carly 90s there has been a surge of intcrest in corpus based nlp rescarch some researchers have tackled the grammar proper making it a probabilistic system or doing away with a rule based system altogethcr and inducing a customizcd grammar from scratch using stochastic methods
NUM category space of context digests the category space described in this paper uses a very different approach to induce subcategorization frames instead of starting fi om scratch the existing rich lexicon is exploited and features are assigned to new words based on their paradigmatic relatedness to known words
how are bidirectional error patterns like the one above to be treated
the reasons for a situation being undecidable can however vary
global ambiguity the sentence was agreed to be globally ambiguous
below different inflections of the same verb can get mixed up
the errors occurring in the material can be classified according to type
it is this latter version that has been used in the experiment
the training is performed on ambiguity classes and not on individual word tokens
a characteristic tbature of the suc is its high number of different tags
these type definitions will be supplemented by fill rules in the form of comments
we guess what the most probable tag is in the remaining ambiguities
let us now consider what kind of errors the constraint based tagger produced
we need more rules for cases that the principled rules do not disambiguate
er identifies nouns as well as verbs due to possible borrowings from english
the transition biases describe the likelihood of various tag pairs occurring in succession
this goes beyond the scope of both the statistical and the constraint based taggers
for evaluation we used a corpus totally unrelated to the development corpus
we can also restrict the tagger to using only the most reliable rules
problems of the latter type are relatively rare but this sample was exceptional
this caused NUM errors which could easily be avoided by writing more rules
furthermore since we have also xt y xo y such non associative lexical specification is still compatible with the treatment of extraction described above
with only the rules NUM NUM NUM we would have a system where different substructural levels coexist but without interrelation
the converse transitions are not derivable since the converse substitution of brackets under is not allowed
is suitable for use in the general case of extraction where a np extraction site may occur non peripherally within a clause
for a term x y the permutativity of r suggests that both orderings of x and y are possible
cut inferences are interpreted via substitution with a b v representing the substitution of b for v in a
for english the number of hmm states is deduced from spectrograms
clear water bay hong kong lcb pascale eebert rcb tee
both ends of the system have a speech synthesizer for output speech
this is therefore a potentially interesting problem
however this is not generally true
this translator is a symmetrical query response system
for cantonese it is deduced from phoneme numbers for each word
the same initialization procedure was used to initialize the recognizer for both languages
an obvious solution seems to train the model on different accents
sometimes it is difficult to distinguish them from names of other types especially from person names
all but one of the development teams udurham had members who were veterans of muc NUM
however performance o f the systems as a group is better on the muc NUM test set
the highest st f measure score wa s NUM NUM NUM recall NUM precision
finally a change in administration of the muc evaluations is occurring that will bring fresh ideas
most human errors pertained to definite descriptions and bare nominals no t to names and pronouns
ne document subsection scores err metric in order of decreasing overall f measure p r
NUM separately rather than tagging fiscal NUM s second quarter ended aug
while this work was motivated by a need to pass probabilistic output to a downstream data fusion system these methods can be applied system internally also to supplant existing algorithms for merging in ie settings that do not allow for probabilistic output
successful interpretation of three sentences from the walkthrough article is necessary for high performance on these events
one set o f issues concerns the range of syntactically governed coreference phenomena that are considered markable
for example a frequent combination such as act or result can be seen as incompatible and therefore have to be split into different synsets whereas object or arnfact are very common combinations
where all occurrences of ab and c are mapped to x interspersed with unchanging pairings
represents here the identity pairs of symbols that are not explicitly present in the network
similarly on re entry to fill s they are reset to act and satellite act
however in performing these experiments we are interested in how far we can get with a fairly simple strategy that will port relatively easily to new domains rather than relying heavily on information that is specific to our current domain
for the animacy constraint we have had to determine by hand whether each individual object in our domain is likely to be treated as animate or not
so NUM denotes the probability that word w did n t belong to class i that is in the binary tree pc denotes the probability of the other branch class corresponding to
n is the total times of word w occurring in the corpus v w is the total times of words couple wtw NUM occurring in the corpus within the distance d
for example the word prosperity occurred seven times in the english text
in the case of the testing set many of the NUM testing quadruples were also handicapped by having no entry in wordnet
outline of the approach the number of coreference configurations over which a distribution is to be assigned depends on the number of templates in the coreference set and the set of a priori constraints against coreference between some of its members
figure NUM the large mrbd resulted in the most useful filter for this pair of languages
ex dialog date is mon 19th aug how about wednesday at NUM interpreted as NUM pm wed NUM aug the cases of anaphora considered NUM
figure NUM one of the heuristics used in the word alignment filter crossing partitions are
cognates are more common and therefore more useful in languages which are more closely related
only the most likely translation and the fourth most likely translation in the baseline lexicon are appropriate
two more examples of the benefits of different filter cascades are given in tables NUM and NUM
table NUM lexicon entries for french parti in NUM best lexicons generated with different filters
table NUM lexicon entries for french grand in NUM best lexicons generated with different filters
i hand aligned each pair of translations by paragraph most paragraphs contained between one and four sentences
bible evaluation is quite harsh because many translations are not word for word in real bitexts
this structure would definitely pose a difficult problem for our algorithm but there are no realizations in terms of our model of this structure in the data we analyzed
this filter did not however significantly improve the performance for any of the other data suggesting that the targeted kinds of subdialogs do not occur in the unseen data
return lcb when merge tult tu certainty cf rcb rule a2 all cases of anaphoric relation NUM
start month start date start day of week start hourszminute start time of day end month end date end day of week end hour minute end time of day figure NUM temporal units
it contains approximately NUM NUM inflected items along with their root forms and inflectional information such as case number and tense
we extracted a geographic name database from a publicly available version of the gazetteer which we downloaded from the center for lexical research
data extracted from a corpus of one particular domain is usually not very useful for processing text of another domain
the philosophy behind this methodology was that high precision components could be linked together serially to build an easily extensible modular system
the ease of incorporating new components was demonstrated by the addition of a ful l syntactic parser two weeks prior to the evaluation
probing the lexicon in evaluating commercial mt systems
first we prepared the word lists
they do this to a different extent
for instance general colin powell could be referred to as general powell colin powell mr powell and so forth
to translate vs to ferry across
all of these examples are hyphenated compounds
only their built in lexicons were evaluated here
for example apple in the phrase apple stock prices would be annotated if there were other references to apple in the article
for example end is judged as potentially compatible with a human referent because an end is a type of football player
the length ranges from NUM to NUM thus when a string of syllables repeats and the length of this string is greater than NUM we do not regard it as a repetition repair
for evaluating the effects of repair processing in this application we count how many syllables in the repairing segments are wrongly converted and how many wrongly converted syllables are recovered after the repair processing
since the simple pattern matching mechanism can not solve this problem properly two additional cues are firstly considered in the baseline model the length of the repeated syllable string and the number of interutterances
that is a glottal stop may occur between the repaired segment and the repairing segment for the repetition repairs whereas actual repeated characters usually do not have such a marker between them
according to the heuristic rule when more than NUM utterances pronounced by other speakers interrupt the speech of a speaker we do not check whether there is a repetition repair or not
that is there are NUM false alarms
some syllables correspond to even more than NUM characters
columns NUM and NUM then indicate the performance changes
the third rule is used to adopt the goal of communicating the system s acceptance of the current plan
the first goal adoption rule is for informing the hearer that there is an error in the current plan
the adoption of this belief will cause the retraction of any beliefs that the plan is adequate
thus the current plan through all of its refashionings remains in the common ground of the participants
content plan node content the node named by node in plan has content content
each segmei t constituent both core and contributors may itself be a segment with a core contributor structure or may be a simpler functional element
2to know what the suitability for the next shown approaches can he let us show a special case for basque verbs mainly auxiliary verbs
the first is s refer entity which is used to express the speaker s intention to refer
note that the context NUM plays no sl ecial role here but is simply carried unchanged from premises to conclusion
it permits inferences to be made about the values associated with node path pairs provided that the theory t contains the appropriate definitional sentences
the evaluation semantics presented in this paper constitutes the first fully worked out formal system of inference for datr theories
the formal definition of v for datrl is provided by just four rules of inference as shown in figure NUM
it establishes formally that the node path pair dog plur does indeed evaluate to dog s given the datrl theory above
a simple default mechanism provides for concise descriptions while allowing for particular exceptions to inherited information to be stated in a natural way
the rule is applieatfle just in case the theory t contains a detinitional sentence n t l eft
the premise states that n c evahmtes to fl in the glohal context n a
suppose that we wish to determine the value of dog sing in the exalnt le datrc theory
this paper presents a computational model of how conversational participants collaborate in order to make a referring action successful
first we look at referring expressions in isolation rather than as part of a larger speech act
the corpus study is intended to enable us to gather this information and is therefore conducted directly in terms of the factors thought responsible for cue selection and placement
statistical methods are most commonly used in part of speech tagging
we compared naive back off estimation and mbl with two sets of features pdass the first letter of the unknown word p the tag of the word to the left of the unknown word d a tag representing the set of possible lexical categories of the word to the right of the unknown word a and the two last letters s
third the usefulness of syntactic knowledge is explained
the words are put in random order because the system does not make use of syntactic information of the sentence either
the positive evidence of v w r t
the first property above can be proved as follows
NUM jean dlt g marie que son livre se vend bien
we now describe how transformations can be automatically learned
in our present study we evaluated pebls and naive bayes on a much larger corpus containing sense tagged occurrences of NUM nouns and NUM verbs
as in the i ictionary window it is also ossible to select another rench word in the corl ora outl tit a iid push the search i uttolr
for example in the sentence ijon donnc moi un baiscr good give me a kiss the disambiguator should return a tag for the word baiser indicating masculine uoun and in the sentence iine pent pas baiser tie can kiss the word baiser should be assigned with a tag indicating verb infinitive
la personne l umaine voutue par diet et plac6e au cmttre du dispositif de la soci6t est bafc e iortque le r gne de rargent e conjugue avec l agrc ivit d un r gime ot lla pr6dminence de la race ou de la classe remplace le souei d une politique au crvice de tous
the experimenter had a copy of the raw data form for the session a copy of the word list and a guide describing the allowed experimenter interaction with the subject
while the NUM incomplete dialogues also contain interesting phenomena we chose to focus the analysis on the completed dialogues as they represent the linguistic record of successful interactions with the system
by allowing the user to arbitrarily change subdialogues the computer is able to provide relevant assistance when a potential problem is reported without requiring language interaction for the task goals already completed
results about utterance classification into subdialogues frequency of user initiated subdialogue transitions regularity of subdialogue transitions frequency of linguistic control shifts and frequency of user initiated error corrections are presen ted
unfortunately we can also have difficulties generalizing from analyses of human computer dialogues because parameters of the particular system with which the dialogues were collected may have significantly affected the resulting dialogues
note subject put the switch up and set the knob to one zero observed the led display and noted the potential problem without requiring any assistance from the computer
thus the value NUM NUM for problem i means that the difference in the average number of utterances between control shifts was greater by NUM NUM utterances in directive mode over declarative mode
guinn has implemented the model and run extensive simulations of computer computer dialogues in order to explore the dynamic computational linguistics volume NUM number NUM setting of initiative as the dialogue ensues
sense n NUM stock caudex stalk stem
first an iterative method is used to create alternative sets of balanced categories
the performance of the selected set of categories is evaluated in terms of effective reduction of overambiguity
sense n NUM stock handle grip hold
the obvious strategy to reduce this problem is to generalise word patterns according to some clustering techniques
second a scoring function is applied to alternative sets to identify the best set
in figure 4a the not normalised precision and gramb are plotted for the test corpus
in most cases the noise introduced by overambiguity almost overrides the positive effect of semantic clustering
however available on line taxonomies are rather entangled and introduce an unnecessary level of ambiguity
in figure NUM the NUM oest selected categories for nouns are listed
frequency differences are also found between senses of derived forms including morphological derivation zero derivation and compounding
transforming the rule into a formula would help to better see that the rule is a kind of law of commutativity between antonymy and synonymy
sentences sections paragraphs and other semantically or rhetorically motivated textual units
as mentioned above topic identification is a two part process estimating and assigning
in text categorization a set of indices is said to represent a text
finally fd t is the frequency of an index t in d
the results are reported for two splits of the complete reuters corpus as explained in section NUM NUM
these figures were obtained using all features including part of speech and morphological form surrounding words local collocations and verb object syntactic relation
typically classifiers produced by the rocchio algorithm are restricted to having nonnegative weights
in this section we present the basic versions of the learning algorithms we use
when incorporating semantic structures the semantic head has to be preserved for example when sister adjoining the d tree for an adverbial construction the semantic head of the top syntactic node has to be the same as the semantic head of the node at which sister adjunction is done
in this paper we report recent improvements to the exemplar based learning approach for word sense disambiguation that have achieved higher disambiguation accuracy
any chosen rule based tagger will impart its own characteristic errors to credit factors it has been used to assign
the input to the generator is inputsem lowersem uppersem and a mixed structure partial which contains a syntactic part usually just one node but possibly something more complex and a semantic part which takes the form of semantic annotations on the syntactic nodes in the syntactic part
after NUM training texts it was getting half the or g names but appears from the slope of the learning curve that it was not reaching saturation yet
given the nature of trainable decision tree technology it is safe to assume that resolve s performance woul d improve for both recall and precision with additional training
james NUM years old is stepping down as chief executive office r on july NUM and will retire as chairman at the end of the year
it does depend on cn definitions that ar e appropriate for a given domain task but different cn dictionaries can be plugged into badger as fully portable dictionary files
for example we needed to write string manipulation functions to trim output strings in a n effort to generate slot fills consistent with the muc NUM slot fill guidelines
if resolve merges entities too aggressively recall will fall and if resolve is too passive in its merging decisions precision will suffer due to spurious entities
yet it appears to be effective on real data which is precisely why machine learning may be more effective in the long ru n than manual knowledge engineering
we therefore expected that our recall would be lower than other systems that attempted to find coreferent relationships among the full set of noun phrases defined by the muc
these big questions are easier to ask than answer but we have always found the muc evaluations to b e valuable for our own system development efforts
in the lcs durativity may be identified by the presence of act be go ext cause and let primitives as in NUM these are lacking in the achievement template shown in NUM
a morpheme is associated with the synchronous points which are located in the flow of characters of the morpheme
note that the precision of any model will never exceed the recall of juman see table NUM
it was also observed in the experiments that the accuracy of tagging was degraded by excessive iterations of reestimation
the coders recommended adding a new move category specifically for when one conversant completes or echoes an utterance begun by another conversant
coders could also reliably classify agreed responses as acknowledge clarify or one of the reply categories k NUM
the first is an identification of the game s purpose in this case the purpose is identified simply by the name of the game s initiating move
because this study was relatively small problems were diagnosed by looking at coding mismatches directly rather than by using statistical techniques
despite these warnings kappa has clear advantages over simpler metrics and can be interpreted as long as appropriate care is used
accuracy can be tested by comparing the codings produced by these same coders to the standard if such a standard exists
however in general the agreement on segmentation reached was very good and certainly provides a solid enough foundation for more classification
in the first kappa is used to assess agreement on whether or not transcribed word boundaries are also move segment boundaries
just move round the crashed spaceship so that you ve you reach the finish which should be left just left of the the chestnut tree
the first step in applying the comparative method to a pair of words suspected of being cognate is to align the segments of each word that appear to correspond
robust learning smoothing and parameter tying where wtex and wsyn stand for the lexical and syntactic weights respectively they are set to NUM NUM initially q j k corresponds to a transformation of the original vector q j k and is represented as the following equation
cases were constructed from these sentences by recording the features verb head noun of the first noun phrase preposition and head noun of the noun phrase contained in the pp
he length of a selltetl which contains a NUM ol yxemons n mn and the h ngth of a sentence of di tionary defilfition are maximuln NUM words
in the present study we used seven features in the representation of an example which are the local collocations of the surrounding NUM words
this terminology is inappropriate for historical linguistics since the ultimate goal is to derive the two strings from a common ancestor
skips in string NUM and string NUM are called deletions and insertions respectively and matches of dissimilar segments are called substitutions
for example latin do i give lines up with the middle do in greek didomi not the initial di
this can be realized by equation NUM where p x s is the probability that x is used in training with the sense s
this does not affect the validity of the example because t and s are certainly in corresponding positions regardless of their phonological history
first the strings being aligned are relatively short so the efficiency of dynamic programming on long strings is not needed
how the phraser works the phraser process operates in several steps
NUM even so lex lex lex pos nnp ttl whole mr lex
note how punctuation has bee n tokenized and mr
the tagger operates on text that has been lexicalized through pre processing
only the inferential back end of the system is largely unchanged
lcb aberdeen john clay lynette parann mbv mitre org
rule sequences now underlie all the major processing steps in alembic
there are several reasons for keeping this rule languag e simple
the inference component is central to all processing beyond phrase identification
facts enter the propositional database as the result of phrase interpretation
context free forward probabilities include all available probabilistic information subject to assumptions implicit in the scfg formalism available from an input prefix whereas the usual inside probabilities do not take into account the nonterminal prior probabilities that result from the top down relation to the start state
the generation phase begins in a small rectangular region of the bitext space whose diagonal is parallel to the main diagonal
because the current position dot also refers to the same input index in all states all nonterminals x x2 xc have been expanded into the same substring of the input between kl and the current position
state set NUM NUM NUM NUM NUM NUM it is easy to see that earley parser operations are correct in the sense that each chain of transitions predictions scanning steps completions corresponds to a possible partial derivation
proof p converging to NUM implies that the magnitude of pl s largest eigenvalue its spectral radius is NUM which in turn implies that the series y NUM p converges similarly for pt
during the prediction step we can ignore incoming states whose rhs nonterminal following the dot can not have the current input as a left corner and then eliminate from the remaining predictions all those whose lhs can not produce the current input as a left corner
certainly p x y is bounded above by the probability that the entire derivation starting at x terminates after n steps since a derivation could n t terminate without expanding the left most symbol to a terminal as opposed to a nonterminal
each bitext space contains a number of true points of correspondence tpcs other than the origin and the terminus
the significance of the matrix rl for the earley algorithm is that its elements are the sums of the probabilities of the potentially infinitely many prediction paths leading from a state kx a z to a predicted state iy
in many cases slots will be optional or have default fillers constructed according to context and previous inputs
we assume that instantiation of the slots can be aided by word and phrase prediction conditioned on slot choice
the work described in this paper concerns the communication needs of people who can not speak because of motor disabilities
the rest of the improvement for the enhanced method is due to the use of part of speech bigrams NUM
it contains NUM sentences NUM words randomly selected from a corpus of economic reports
we believe that such flexibility is necessary to maximize the chances of nlp research having practical utility for aac systems
suppose the user input open kitchen window into the requested action slot and that the requestee slot defaulted to you
the system might plausibly generate please could you open the kitchen window using fixed text associated with the request template
NUM to make the comparison between entropy and prediction we have to consider the information contributed by each input
in particular most are dedicated to speech output and can not be used to aid writing text or email
informally the resulting function recognizes substrings that are the concatenation of a substring recognized by fa and a substring recognized by f
however cognates of this last kind are usually too sparse to suffice by themselves
this phonetic lookup can be used to retrieve a misspelled word in a dictionary or a database or in a text editor to suggest corrections
they traditionally distinguish between the system which is as independent as possible from the application and the expert rules which are application dependent
first if we include derived forms and technical jargon there are well over three quarters of a million words in the english or french language
the longest match between the left string is of the rules in the block and the input string to be processed is searched
is pronounced NUM in bon but not in abandonner bonheur or bonne where the rule for o applies
thus far in the area of speech synthesis at least not much has been done to modify segmental phonology according to speech rate
must be declared i.e. the grapheme codes upper and lower case letters numbers punctuation diacritics and the phoneme codes
the search for each chain alternates between a generation phase and a recognition phase
the paper begins by laying down simr s geometric foundations and describing the algorithm
these consist mostly of functors abbreviations homographs and unassimilated loanwords such as adobe bayou cello coyote and the like
nevertheless for some words the distinction has to be made bol bd1 vs rose roz
the last step in the porting process is to re optimize simr s numerical parameters
in practice the chain recognition heuristic often accepts chains that span several sentences
none of these test bitexts were used anywhere in the training or porting procedures
3multi word expressions in the translation lexicon are treated just like any other character string
NUM solving the remaining ambiguities by running the final non contextual rules of the constraint based tagger
while terminal seq and alt suffice to define epsilon free context free grammars we can easily define other useful higher order functions
however significant differences exist with respect to processing strategies including blackboard management and conflict resolution the assignment of different subtasks to modules the organization of the modules and the organization of knowledge resources
in which the intermediate roles role1 and function are removed as soon as the process type and therefore also the name of the agent role are determined and replaced by appropriate information
we briefly discuss conflict resolution strategies in section NUM in general though we define a partial default sequence for the modules the discourse structuring and sentence structuring modules run first and in parallel
mechanism such as feature networks and a continuously transformable internal representation from tsl input to spl output using tree transformation operators to be a promising avenue of research given the complexity of the problems facing text generators
this occurs when it has been decided that the predicate y e.g. move is to be expressed as the verb to move rather than as the support verb construction make a move now move must be promoted to be the head of x in the emerging spl
NUM the level of sophistication of the knowledge within a module must not be ccnstrained by the sp architecture so that the modules might be crude initially but then can incrementally be refined without impeding throughput
in our example redundancy is apparent in pre spl fragments w l and f since their only internal difference is their type as well as the repetition of the whole d1 in fragment ci
NUM internal clause structuring the internal clause structure is predetermined by among other means the valency schema of the lexical unit that is chosen to serve as a head of a clause or phrase
in our example the spl under construction undergoes the following changes it has been determined that the first spl will contain a paratactic clause complex and that there will be a disjunction marker between the l and f fragments
all components of the system assume no prior domain knowledge and are therefore portable to many domains such as sports entertainment and business
this work was partially supported by nsf grant ger NUM NUM and by a grant from columbia university s strategic initiative fund sponsored by the provost s office
such inversions result in tpcs arranged like the middle two points in the previous chain of figure NUM simr has no problem accepting the inverted points
from this the following rule could be learned change the tag of a word from modal or noun or verb to noun if the previous word is the
we close with our current directions describing what parameters can influence a strategy for generating a sequence of anaphoric references to the same entity over time
its utility lies in its potential for representing entities present in one article with descriptions found in other articles possibly coming from another source
newswire to seed the database with an initial set of descriptions we used a NUM NUM mb corpus containing reuters newswire from march to june of NUM
our manual analysis showed that out of a total of NUM entities recovered in this way NUM NUM NUM are not names of entities
we can then use the existing indices of all web documents mentioning a given entity as a news corpus on which to perform the extraction of descriptions
in the second scenario the evolving summary we have to generate a sequence of descriptions which might possibly view the entity from different perspectives
the longest description retrieved by the system was NUM lexical items long maurizio gucci the former head of italy s gucci fashion dynasty
in reading NUM the same saxophonist was admired and detested at the same time
4without losing generality therefore we will consider only individual denoting nps in this paper
gives rise to a natural generalization of available readings as summarized below
a most students talked to a friend of every professor about his work
NUM a two representatives of three companies saw most samples
NUM 2in this simplistic notation we gloss over tense analysis among others
crucially samples seen by representatives of different companies were not necessarily the same
we clash that competence grammax makes even fewer readings available in the first place
we claim that this explicit functional dependency can be utilized to test availability of readings
a variable r inside an opaque operator bel hence the name for the technique
spoken language translation with the itsvox system
the table shows that the results of the partial parse method are disappointingly bad
to fully define the learner we must specify the three components of the learner the initial state annotator the set of transformation templates and the scoring criterion
certain elements in a poset are of special importance for many of the properties and applications of posets
in other words the critical tokenization operation maps any character string to its set of critical tokenizations
note that the tokenization the blue print is not critical not a critical tokenization
this section explores some helpful implications of critical tokenization in effective tokenization disambiguation and in efficient tokenization implementation
computational linguistics volume NUM number NUM these syntactically ambiguous points are critical in at least two senses
in other words any syntactic or semantics development should be guided by ambiguity resolution at these points
if a semantic enhancement does not interact with any of these points the enhancement is considered ineffective
since the scorer s mapping was intended to maximize overall f score the alignment it produced was well suited to our purposes
three nontrivial problems of thai morphological processing are word boundary ambiguity tagging ambiguity and implicit spelling errors
after clarifying both sentence generation and tokenization operations we undertake next to further clarify sentence tokenization ambiguities
let g be an alphabet d a dictionary and s a character string over the alphabet
where n o i is the total times of words which are in the vocabulary occurring in the corpus
p unknown word ti p ti unknown word p unknown word p ti p ti less probable word p unknown word NUM p ti
when the taggers are trained using the NUM NUM words of the english newspaper corpus a greater number of lexicon entries and a greater number of transition probabilities figure NUM is measured than in the case of the eec law corpus 100k words training text
NUM association for computational linguistics computational linguistics volume NUM number NUM and mancini NUM meteer schwartz and weischedel NUM merialdo NUM pelillo moro and refice NUM weischedel et al NUM wothke et al NUM
a number of closed and functional grammatical classes has very low probability for both unknown and words occurring only once e.g. the tags article determiner conjunction pronoun miscellaneous in english text and article determiner conjunction pronoun interjection and miscellaneous in french text
inference one other class of bridging dds includes cases based on a relation of reason cause consequence or set members between an anchor previous np and the dd as in republicans democratics the two sides and last week s earthquake the suffering people are going through
for example it seems likely that for our application it is much less problematic to miss information than to hallucinate
more specifically each history item is a triple consisting of a result item reference a rule name and a list of triples
in this figure the labels of the interior nodes are rule names and the labels of the leaves are references to result items
computing the first solution of average length sentences NUM NUM words takes between one and three seconds on a sun ss NUM
however if some utterance only prefers one interpretation in a given context but allows the other then the subsequent utterance may pick up on either one
it is commonly accepted and is a hypothesis under which our work on centering proceeds that a hearer s determination of noun phrase reference involves some process of inference
they are mostly used for word inflection otherwise for german every inflectional variant would have to be encoded as a rule
it has become ever more clear that it would be useful to have a definitive statement of division of applied sciences harvard university cambridge ma
each discourse segment exhibits both local coherence i.e. coherence among the utterances in that segment and global coherence i.e. coherence with other segments in the discourse
we describe our method for modeling probabilities between pairs of templates in the next section and describe two methods for deriving a distribution over the coreference configurations in section NUM we report on an evaluation and comparison of the approaches in section NUM
rule NUM reflects our intuition that continuation of the center and the use of retentions when possible to produce smooth transitions to a new center provides a basis for local coherence
the erroneous succession representation from sentence NUM is incompatible with this structure and is maintained separately
the semantic label anchor is attached to the main element or head of the example
sra has also investigated these issues and has renewed its effort with the development of hasten
the hasten system has a simple architecture consisting of four main modules shown in figure NUM
the metric computes the percent of the egraph that matched using a weighted sum of factors
due to the complexity of the task these steps may be repeated to adjust the definitions
nametag is a high speed software program consisting of a c engine and name recognition data
the base configuration performs the maximum analysis achieves the best results but is the slowest
however in upper case text group would presumably be included in the tag
they were ammirati puri s painewebber and mccann erickson
compromi completeness completeness in this context means the parse forest grammar contains all possible parses
a solution to this instance of pcp is the sequence NUM NUM NUM NUM obtaining the sequence 10111ul0
limit the dcg another approach is to limit the size of the categories that are being employed
after each derivation has been evaluated if there is just one valid derivation an instantiated derivation whose constraints all hold then the hearer will believe that he has understood
in order to be consistent with clark and wilkes gibbs s model we can see that if one agent finds the current referring expression problematic the other must accept that judgment
such techniques might be of use both in the case of written and spoken language input
our work attempts a plan based formalization of what linguistic collaboration is both in terms of the goals and intentions that underlie it and the surface speech acts that result from it
our approach to treating referring as a plan in which surface speech actions correspond to the components of the description allows us to capture how participants collaborate in building a referring expression
but sometimes they might lack the knowledge needed to formulate a plan of action or some of the actions that they plan might depend on coordinating their activity with other agents
first although much work has been done on how agents request clarifications or respond to such requests little attention has been paid to the collaborative aspects of clarification discourse
so it is interpreted instead as a request for a clarification of the clerk s gate NUM response implicitly assuming that gate NUM was not accepted
by answering these questions we will not only have a better model to base natural language interfaces on but we will also have a better understanding of how people interact
rule NUM bel system bel user achieve plan goal bmb system user plan user plan goal
figure NUM shows the derivation arrows represent decomposition and for brevity constraints and mental actions have been omitted and the parameters only of the surface speech actions are shown
on the other hand the discourse segment structure constrains the interpretation of linguistic expressions
pos cj pn v v pn v v pp aj n pp v pp what information is available about the as NUM system
the basic idea of our method is to improve the accuracy of sentence analysis simply by maintaining consistency in the usage of morphologically identical words within the same text
since sentences with incomplete parses are usually quite long and contain complicated structures it is hard to obtain a perfect analysis for those sentences
in both texts the discourse information provided enough information to unify partial parses of an incomplete parse in more than half of the cases
when the discourse information did not provide enough information to unify partial parses with the application of our method the heuristic rules were applied
these matching conditions at different levels are applied in such a manner that partial parses are joined through the most preferable nodes
then except for sentences with incomplete parses and multiple parses the results of each parse are stored as discourse information
the initial data used in the completion procedure are a set of partial parses generated by a bottom up parser as an incomplete parse tree
n rcb processed position n figure NUM example of a completed parse
a sentence without a topic is used to introduce a new entity into the discourse
for example the input string is c c2cs and the dictionary includes four words e c cs e e2c3
this indicates that the better rules seem to disagree with the speakers no more than the speakers disagree among themselves
the result of using rule NUM on the test data is shown in figure NUM
in this paper we treat the first and second person pronouns as nominal anaphora
for a given compound rule the set of relations in which r is invalid is
consider the expression a al ua2u uan and the de morgan s law equivalent
we define here a number of operations which will be used in our compilation process
note that in a compound rule each set of contexts is associated with a feature structure of its own
the arrows correspond to context restriction cr surface coercion sc and composite rules respectively
spoken language technologies are being viewed as one of the most important next steps towards truly natural interactive systems which are able to communicate with humans the same way that humans communicate with each other
additionally this version of rule feature matching does not cater for rules whose r span over two lexical forms
the two p s surrounding r ensure that coercion applies on at least one center of the rule
results of computational complexity exist for a wide range of phrase structure based grammar formalisms while there is an apparent lack of such results for dependency based formalisms
this contradi tion can NUM e based at least on the dill create betwec n
we believe that although prediction techniques are robust and flexible by themselves they can not offer the improvement in text input speed necessary to allow natural conversation using a text tospeech system
and the number of the same combination of nes are counted
thus the substring list of interrupted collocations can be obtained
this paper used japanese character chains to examine the algorithm
NUM NUM interrupted collocational substrings NUM characteristics of extracted substrings
an example of the algorithm is shown in fig NUM
and the total frequency of these amount to NUM times
the new methods are applied to japanese newspaper articles involving NUM NUM million characters
the positioning would be one of the three cases shown in fig NUM
here k is the number of substrings which compose interrupted colocational expressions
yet this algorithm can be applied to arbitrary symbol chains
we do not have space here to give details of all these aspects of the cogeneration system and it would be inappropriate given that development is in its early stages
our paper has a very different starting point
if upper is the empty set as in a b the expression compiles to a transducer that freely inserts as and bs in the input string
in the course of exploring the issues kaplan and kay developed a more abstract notion of rewrite rules which we exploit here but their NUM paper retains the procedural point of view
in our regular expression language we have to prefix the auxiliary context markers with the escape symbol to distinguish them from other the unconditional replacement of upper by lower ignoring irrelevant brackets
we provide a simple declarative definition for it easily expressed in terms of the other regular expression operators and extend it to the conditional case providing four ways to constrain replacement by a context
an fst pair a b can be thought of as the crossproduct of a and b the minimal relation consisting of a the upper symbol and b the lower symbol
the goal of this paper has been to introduce to the calculus of regular expressions a replace operator with a set of associated replacement expressions that concisely encode alternate variations of the operation
the second objective is to define replacement within a general calculus of regular expressions so that replacements can be conveniently combined with other kinds of operations such as composition and union to form complex expressions
here are three randomly selected translations note that the object of the establishing action is unspecified in the japanese input but penman supplies a placeholder it when necessary to ensure grammaticality savailable from the acl data collection initiative as cd rom NUM
in our machine translation experiences we traced generation disfluencies to two sources NUM NUM incomplete or inaccurate conceptual interlingua structures caused by knowledge gaps in the source lan null guage analyzer and NUM knowledge gaps in the generator itself
when the goal is domain independent generation we need to investigate methods for producing reasonable output in the absence of a large part of the information tradi3including constraints not discussed above originating for example from discourse structure the user models for the speaker and hearer and pragmatic needs
because both equations would assign lower and lower probabilities to longer sentences and we need to compare sentences of different lengths a heuristic strictly increasing function of sentence length f l NUM NUM l is added to the log likelihood estimates
so we can produce the following lattice instead large j j federal deficits a in this case we use knowledge about agreement to constrain the choices offered to the statistical model from eight paths down to six
a ihave the quality of beingl domain p iprocurel agent a2 american patient g gun arm range e jeasy effortlessj
this means that the algorithm generates the most marked licensed form
NUM a generation phase which imposes an order on the bag of target signs which is guaranteed grammatical according to the monolingual target grammar
we report on our experiments to resolve lexical ambiguity in the context of information retrieval ir
the individuality is defined by a forlnative fimctor of name from l in c ind v i in takes the value ind or ci here we discovered two kind of sub objects those which are part of the described object and those which relate the object descl ibed a nd others objects of the world
in this section we describe a general model for lexical choice as part of an overall generation system architecture
finally we compare our approach with other work on lexical choice closing with a summary of our contributions
in contrast nigel is driven solely by the structure of the grammar as encoded in its system networks
it must be able to use the full variety of constraints whether pragmatic semantic lexical or syntactic
the process of the clause is mapped to the relation assignt type and the process roles to the arguments of assignt type
we refer to this stage of processing as phrase planning because of its close relationship to paragraph planning
these commands allow an analyst to access and manipulate all the information stored in the canis prototype
the nltoolset is coded in c and uses the cool object library
all words punctuation numbers etc in a document are processed into tokens
the canis prototype is intended to assist the canis customer with the cable indexing task
if the entity exists then the new information is linked to the existing entities
if the entity does not exist new relational records are created for that entity
the gap head link relation is a subset of the head link relation in which the head category is a possible gap
segmentation breaks the document s token buffer into paragraphs and sentences based on multiple newlines tabs periods etc
indexing captures information about the entities described in the cable their names dates of birth locations etc
analysts will see their tasks change from manually reading and creating index records to verifying and updating automatically generated index records
n represents a restricted range of postmodifiers and the determiner enough following its nominal head
it is always applied with an influence perhaps zero that depends on the weights of the labels
we do not need to assign the compatibility values here since we can estimate them from the corpus
they can refer not only to any fixed context positions also reference to contextual patterns is possible
although the linguistic cg NUM parser does not disambiguate completely it seems to have an almost perfect recall cf
the questionnaire asked the community to rank the features according to how important they thought they were to their particular dialogue manager and to comment on each one
table NUM shows a comparison between the dialogues from the five corpora and the results of this evaluation
on the other hand it correctly predicted all NUM cases where no shift in task initiative occurred
it makes explicit the relationships between predicates and there arguments but does not express any quantifier scope relationships
it works by processing the pas recursively and non deterministically selecting quantifier scopings and focus sets at each level
however the dialogue mode is determined at the outset and can not be changed during the dialogue
this suggests that the bpa s may be somewhat sensitive to application environments since they may affect how agents interpret cues
this dependencyffunction is computed from model NUM and is exhaustively listed in lo below
in appendix NUM clusters are shown their contents sorted by size
the branches in the input graph are assumed to be all symmetric
the constraint is loose when m is small and n is large
all transitive graphs on the horizontal line including g o are complete
most of them are based on the statistical similarity between two words
in addition extracting complete graphs withln a given graph is np complete
however only one anchor branch is sufficient to inhibit the decomposition
the number of the cluster whose topic was inestimable is only NUM
another important improvement is that since the simplified model deviates from the previous larger model only in a small number of constraints we use the parameters of the old model as the initial values of the parameters for the iterative scaling of the new one
where is wi is estimated from the sample of configurations and p w is computed using equation NUM note here that in the second part of the equation we sum over all possible configurations w in the domain
at the threshold NUM NUM cluster NUM is completely merged with cluster NUM
a graph has no ambiguity if its branches co occurrence relations are transitive
the predicates shouldtry and try are related because the appropriateness of a potential interpretation is taken as default evidence that it is in fact the correct interpretation null default NUM intentionalact sl s2 a ts shouldtry s l s2 a ts d try s1 NUM a ts
in order to consider previous states of the context such as before a possible misunderstanding occurred we define a successor relation on turn sequences definition NUM a turn sequence ts2 is a successor to turn sequence ts1 if ts2 is identical to ts1 except that ts2 has an additional turn t that is not a turn of ts1 and t is the successor to the focused turn of ts1
the constraints there is no other coherent interpretation so it is consistent to assume that a misunderstanding occurred selfmisunderstanding m r mistake r askref m r whoisgoing pretell m r whoisgoing inform m r not knowref m whoisgoing ts NUM
the fourth utterance occurring after an exchange such as NUM NUM would be a third turn repair by a the fifth utterance occurring mcroy and hirst the repair of speech act misunderstandings after NUM NUM NUM would be a fourth turn repair by bj NUM closings signal that the participants are ready to terminate the conversation and that they accept the conversation as a whole
to satisfy the second premise of the rule the reasoner must explain try m r pretell m r whoisgoing ts NUM two kinds of explanation are possible a hearer might assume that the act fulfills the speaker s intention to coherently extend the discourse as he has understood it or NUM this oracle thus allows the analyst to test different interpretations
wouldexpect r pretell m r whoisgoing askref r m whoisgoing because he has a linguistic expectation to that effect fact lexpectation do m pretell m r whoisgoing knowsbetterref m r whoisgoing do r askref r m whoisgoing
if shouldtry s1 s2 a ts is true it means that given discourse context ts which corresponds to a particular agent s perspective it would be appropriate for speaker NUM to address speaker NUM with discourse level speech act a i.e. according to social conventions here represented by the linguistic expectations and the meta plans s1 should do a next
that is u is a solution to the following default reasoning problem t u NUM u m meta 3u utter s h u ts in the language of the model the predicate shouldtry is used for discourse actions that are coherent m meta and the predicate try is for actions that are explainable m
example NUM t1 m surface request m r informif r m knowref r whoisgoing t2 r surface request r m informref m r whoisgoing t3 m surface inform m r not knowref m whoisgoing t4 r surface informref r m whoisgoing
an important difference follows from the fact that in the head corner parser only larger chunks of computation are memorized
subsequent improvements to the walk through article performance after a thorough analysis of the problems in the walk through article had been carried out several o f the problems discovered were fixed and a new set of results for this article were produced
apparently this means that loanword hyphenation is independent of the rules governing hyphenation in the original language from which the word was borrowed
the poor named entity scores are caused by a number of factors the most significant of which are the lack of a backup in the case of parsing failures ensures that NUM of the score is lost immediately
in the organization template the org name and orgalias slots are filled by using textrefs attached to the concept which are classified as fullpropernoun the longest one is taken as the name an d the remainder as the aliases
the lolita large scale object based linguistic interactor translator and analyser system is de signed as a general purpose natural language processing nlp system and has been under development a t the university of durham since NUM
in concept slots som e default rules are used to pick the most appropriate one but for situations in which more control i s required the textref slot allows its associated rule to define precisely the textref to be used
some central support facilities are provided to aid application writing such as the genera l template mechanism and the nl generator which translates pieces of the semnet into english or recentl y added into spanish
unlike many of its contemporary nlp systems the lolita system is not designe d as a framework that can be tailored to specific domains but as system that brings its knowledge of specifi c domains to bear as and when appropriate
respectively c1 c2c c3 e is the expression for the set of substrings of lemma NUM b
as it has been previously defined the term vowel refers to a single vowel or vowel character v is the set of vowels
computational linguistics volume NUM number NUM to apply to words containing a maximum of two vowel substrings that are elements of 2v or vc
for example in english common roots is an issue in hyphenation of compounds whereas in greek it is not
the first approach ensures complete and correct hyphenation but it has the disadvantage of being incapable of hyphenating words not on the list
the paper then turns to the problem of vowel splitting and by formally examining prohibitive grammar rules deduces general hyphenation rules
the caller will start a correcting sequence if he notices that the information service gives inappropriate information
note that annotations may denote document structure so that this operator may be used to restrict the match to within a single phrase sentence paragraph section etc
annotatecollection which collection destination collection annotatorname string invokes annotation procedure annotatorname on a subset of collection destination see section NUM NUM for further information
returns the first document within a collection and initializes data structures internal to the collection so that nextdocument can be used to iterate through the documents in a collection
in the present architecture documentcollectionlndexes and querycollectionlndexes are persistent collections are optionally persistent documents are not persistent objects themselves but have persistence as a part of a collection
by accessing these annotations an application can determine the code set employed at a specific position in a document and hence the size of the character at that position
if the id slot of annotation is nil this operation creates a new annotation id unique for this document and assigns it to the id field of annotation
for example a package may declare all the types of annotations used to represent the document structure for one message format header dateline author etc
a set of system specific rules for extracting various classes of objects such as persons or organizations this library could bc used in customizing an extraction system to a particular task
for some systems however an index might just be a normalized copy of the original text in a form which can be scanned by high speed search software
for documents which do not contain text in the form of a sequence of characters the meaning of a span will not necessarily be compatible with this start byte end byte convention
more specifically we propose a novel method for learning a probabilistic model of subcategorization preference of verbs
by taking into account all the occurrences of an oov word in a given text as a whole we propose here a method for automatically extracting a specialised lexicon from a text corpus which is representative of a specific topic
where wk is the first element in the extent of t and wt the last
with these functions in place we proceed to the description of the core algorithm
a table of the probability distribution of words is defined at each terminal transition
computing an inside probability even in an application of moderate size can be impractical
note that the whole of this sequence of words is bracketed off in both corpora
the third type is transitions not committed to stack operation these are terminal and empty transitions
the number of insides counted in chart version also includes the insides computed in preparing the chart
we have presented an efficient re estimation algorithm for a prtn that made use of only valid insides
the method requires the preparation of a chart by running inside computation twice over a whole sentence
experiments on the penn tree corpus show that re estimation can be done more efficiently with charts
the two corpora whose trees are to be aligned contain identifiable structural markup
to test the tagging we compared the results against a previously hand sense tagged corpus of NUM words
no ambiguous coding allowed and found to assign the correct part of speech NUM of the time
NUM conception of a new algorithm besides the primary goal of producing a distinguishing and cognitively adequate description of the intended referent there are also the inherent secondary goals of verbally expressing the chosen descriptors in a natural way and of applying a suitable processing strategy
in this paper we have presented a multiple resource approach for tc
it seems that the pruning method has filtered out some usefifl collocation values that improve classification accuracy such that this unfavorable effect outweighs the additional set of features part of speech and morphological form surrounding words and verb object syntactic relation used
by using a larger value of k the number of nearest neighbors to use for determining the class of a test example and through NUM fold cross validation to automatically determine the best k we have obtained improved disambignation accuracy on a large sense tagged corpus
this is explainable since for a large value of k pebls will tend towards the performance of the most frequent classifier as it will find the k closest matching training examples and select the majority class among this large number of k examples
to our knowledge lexical databases have been used only once in tc
during NUM fold cross validation runs on the training set for each of the NUM words we compared two error rates the minimum expected error rate of pebls using the best k and the expected error rate of the most frequent classifter
in thcory we would expect information such as subject domain and collocations to help part of speech tagging to be more accurate however slightly but we have not yet bccn able to demonstrate this in practice
it appeared plausible although not certain that problems NUM and NUM could be overcome within such an approach by adopting a strategy of conservative parsing
we intend in the near future to expand de fclausepattern to handle parse a richer set of patterns including both sentence modifiers and a wider range of complements
though our initial tests are promising a great deal of work will still be required on this interface to provide the full flexibility needed for creating a wide range of patterns
we have just begun to create such an interface which allows a user to begin the process of pattern creation by entering an example and the correspoding event structure to be generated
in particular we carefully studied the fastus system of hobbs et al NUM who have clearly and eloquently set forth the advantages of this approach
however when specialized constructs did have to be added the task was relatively difficult since these constructs had to be integrated into a large and quite complex grammar
however direct comparison between the numerical results can be misleading since the experiments are carried out on two very different corpora both in size and genre
NUM for disambiguation of polysemous nouns these constraints include the modifiers of these nouns and the verbs which take these nouns as objects etc
NUM initialise the conceptual co occurrence data table ccdt with initial value of NUM for NUM for each sentence s in the corpus do a
NUM the correct sense of each test sample is chosen by hand disambiguation carried out by the author using the sentence as the context
the expanded patterns also include pattern elements for sentence modifiers so that they can analyze sentences such as fred who last year ran ibm
we can expect a computational linguist to consider all syntactic variants although it may be a small burden we can not expect the same of a typical user
in effect we are comparing the score between the sense with the current context and the score between the sense and an artificially constructed average context
the evidence from a polysemous context word is taken to be the evidence from its sense with the highest mutual information score NUM
to provide a better evaluation of our approach we have conducted an informal experiment aiming at establishing a more reasonable upper bound of the performance of such systems
firstly yarowsky s system is trained with the NUM million word grolier s encyclopedia which is a magnitude larger than the brown corpus used by our system
both of the precisions re and rh of the independent frame model are higher than those of any other models
in the partial frame model less restrictions axe put on the definitions of features than in the independent frame model
it receives from the controller a request for the next suggested goal to be undertaken and it returns to the controller its suggestion along with expected results from attempting the test
the goal of the dialog is stated in a prolog style goal and rules are invoked to prove the theorem or achieve the goal in a normal top down fashion
a real possibility in a cooperative interaction is that the user s problem solving ability either on a given subgoal or on the global task may exceed that of the machine
for example the effective perplexity in one test was reduced from NUM NUM to NUM NUM using dialog level constraints while word accuracy recognition was increased from NUM NUM percent to NUM NUM percent
if the user did not know according to the user model how to find the knob the system might have invoked a voice interaction to try to achieve this subgoal
the parser is coded in c speech recognition is performed by a verbex NUM user dependent connected speech recognizer running on an ibm pc and the vocabulary is currently restricted to NUM words
that is the meaning representation from the input no wire is assertion false state exist wire present
thus the task specific expectations for the sample topic would include questions on the location of the object on the definition of the property and on how to perform the action
once the linguistic expectations are produced they are labeled with an expectation cost which is a measure of how strongly each is anticipated at the current point in the dialog
when micro averaging no distinction about document or category orientation can be made
peas involve referring phrases that should help a reader to unambiguously identify an object of a certain type from a pool of candidates
realized where dashed arrows denote a links black circles denote nodes and white circles denote nodes that might be implicit
for the oov common words we reduce the lexicon to the words which have at least NUM occurences in the corpus then we keep for each word only the syntactic labels which represent NUM of all the occurences of the word
initial rules based on surface syntax are refined through incremental experimental tuning
meter which is close to NUM if precision is preferred to recall
table NUM examples of variations from agr
NUM NUM a corpus based method for discovering syntactic transformations
concatenation of several suffixes is accounted for by rule ordering mechanisms
expansion of multi word terms for indexing and retrieval using morphology and syntax
although useful these approaches suffer from two weaknesses which we address
this is a key component in the recognition of type NUM variants
it is therefore necessary to conceive a filtering method for rejecting fortuitous co occurrences
derivational morphology is built with the pers null pective of overgeneration
the second result holds even if we restrict ourselves to iei NUM and irl NUM that is if we use a don t care symbol
in the labeling process when an oov proper name xi appears at position i in the sentence the label which is given to xi represents the class which maximize p t xi the probability of xi belonging to the class t
the notion of matching previously defined is now extended in such a way that for a b e p a matches b if a b
however in order to have a complexity fully independent of the size of the grammar and in particular independent of the number of transitions at each state one should carefully choose an appropriate representation for the transducer
we believe that this finite state tagger will also be found useful when combined with other language components since it can be naturally extended by composing it with finite state transducers that could encode other aspects of natural language syntax
the coherence constraint on elaboration states that an elaborating event must be temporally included in the elaborated event
for instance t4 is not deterministic because d NUM a c1 NUM rcb but it is equivalent to t5 represented figure NUM in the sense that they represent the same function i.e.
the resulting transformation list will first label an item as s if x is true or as s if x is false
this is not the case for decision trees where the outcome of questions asked is saved implicitly by the current location within the tree
manual encoding of linguistic information is being challenged by automated corpus based learning as a method of providing a natural language processing system with linguistic knowledge
given a new decision tree t3 constructed from t1 and t2 as follows brill transformation based error driven learning we construct a new transformation list l3
even if decision trees are applied to a corpus in a left to right fashion they are allowed only one pass in which to properly classify
when the contextual rule learner learns transformations it does so in an attempt to maximize overall tagging accuracy and not unknown word tagging accuracy
in taggers based on markov models the lexicon consists of probabilities of the somewhat counterintuitive but proper form p word i tag
this approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance
on the other hand corpus extension can be supported by sines analyses
as the example shows this may imply the use of underspecified temporal descriptions
hence nl generation is based on a semantic template filled by the client
NUM and expects refinements or counter proposals from the participants
they are ranked according to their informativeness
except for saturdays sundays and holidays
this explains why some highly associated n grams which are not word units are extracted as words by the system
among these n grams only NUM bigrams NUM trigrams and NUM NUM grams are registered as words in a dictionary
where w j mj j are the mj words in the j th alternative segmentation pattern w i
for a large scale system the probabilistic approach is more practical when considering the capability of automatic training and cost
initially the word probability p w i i is estimated from the small tagged seed corpus
a seed corpus of NUM NUM sentences NUM NUM words about NUM k bytes of computer domain is available
initially p tilt i NUM and p w ilti are estimated from the small seed corpus
there are NUM NUM distinct n grams in this corpus including NUM NUM NUM grams NUM NUM NUM grams NUM NUM NUM grams and NUM NUM NUM grams
a node x x about x can only substitute or adjoin into another node with the same label
the possible actions taken by a relational head acceptor m in state qi are left transition write a symbol r onto the right end of the left sequence and eater state qi l
there are three viewsl2 of the fourth gospell3 which have been held14
the cardinal numbers are kept continuous across sentences in the same paragraph
the constituents far apart have less relationship
we assign a cardinal number to each verb and noun in sentences
we analyze the association of noun noun and noun verb pairs in lob corpus
figure NUM comparison of frequency ml
spurious the number of actual fills for which no key was given
this capability allows for flexibility from domain to domain and language to language
NUM like is either a verb to like or a preposition like v p
these articles are distinguishable by unique identifiers called document numbers
the parameter a here is of no practical effect and is chosen to be very small relative to the bq probabilities of lexical translation pairs
sentences NUM and NUM demonstrate why the obvious trick of taking single characters as words is not a workable strategy
knowledge of english bracketing is thus used to help parse the chinese sentence this method facilitates a kind of transfer of grammatical expertise in one language toward bootstrapping grammar acquisition in another
the result is that the maximum likelihood parser selects the parse tree that best meets the combined lexical translation preferences as expressed by the bij probabilities
we also rejected sentence pairs with fewer than two matching words since this gives the bracketing algorithm no discriminative leverage such pairs accounted for less than NUM of the input data
the raw phrasal translations suggested by the parse output were then filtered to remove those pairs containing more than NUM singletons since such pairs are likely to be poor translation examples
as the number of subtrees of an ll constituent grows the number of possible matchings to subtrees of the corresponding l2 constituent grows combinatorially with corresponding time complexity growth on the matching process
NUM the e authority will lcb be c accountable to the financial secretary
consequently we developed a dialogue processing model for task oriented dialogues that when implemented in an electronic repair domain exhibits a number of important behaviors including NUM problem solving NUM coherent subdialogue movement NUM user model usage NUM expectation usage and NUM variable initiative behavior
consequently including them in the computation of the average number of utterances spoken in a given subdialogue phase would distort the averages used in a statistical analysis rcb deg therefore we apply the statistical technique of analysis of variance anova to the data from the first five problems of each session the single missing wire problems
we might expect then that subjects given the initiative in session NUM would behave differently than subjects given the initiative in session NUM furthermore we might expect difficulties for subjects given the initiative in session NUM who then had to work with the system in directive mode in session NUM what do we find in the results
previous work has included analyses of NUM human human dialogues in relevant task domains NUM wizard of oz dialogues in which a human the wizard simulates the role of the computer as a way of testing out an initial model and NUM human computer dialogues based on initial implementations of computational models
this result may prove useful as a possible cue for when the system needs to release task initiative to the user during a mixed initiative dialogue as linguistic control shifts begin to occur more frequently it may be an indicator that a user is gaining experience and can take more overall control of the dialogue
whenever a serious misrecognition caused the computer to interpret the utterance in a way that contradicted what was meant the experimenter was allowed to NUM tell the subject that a misrecognition had occurred and NUM tell the subject the interpretation made by the computer but could say nothing else
a mechanism for gradual accent adaptation might potentially increase recognition accuracies of the speech recognizers of both source and target languages
the current study makes a number of prescriptions for the type of information that such techniques would need to provide to the text planner pursuant to the generation of instructional text but says nothing about how they should be implemented in order to achieve this
the results of this analysis were then implemented in imagene and applied to the full corpus providing a detailed characterization of the instructions found in the original telephone manuals and a quantitative analysis of how well this characterization applies to the other forms of instructions
f adjoined purpose NUM NUM NUM NUM NUM g so that purpose NUM NUM NUM NUM NUM other NUM NUM NUM NUM example 3b uses a for prepositional phrase with a nominalization installation as the complement
di eugenio for example has worked with by purposes and to infinitive tnf purposes in the context of understanding but does not appear to have distinguished the two forms in her analysis of the procedural relations between actions
the current study has used the two aspects of the rst specification that can be mapped onto the procedural structure of the process being expressed namely the hierarchical structure of rst and the subset of rst relations that correspond to procedural relations
compare in detail the output of the system with the text found in the corpus differentiating between the predictions concerning text that was specifically used in the analysis the training set and text that was not the testing set
if a qlf expression contains uninstantiated recta variables the valuation relation can associate more than one value with the expression
this means that the substitutions act as directives controlling the way in which qlf expressions within their scope are evaluated
the initial implementation of the work described here was carried out as part of the clare project dti ied4 NUM NUM
but semantic interpreta tion viewed as building a description of the intended composition is a better prospect
s categories are sets of feature value equations containing syntactic information relevant to determining how uninstantiated meta variables can be resolved
our approach to parallelism is perhaps heavy handed but in the absence of a clear solutions possibly more flexible
it was noted above that substitutions on term indices in scope nodes ensures scope parallelism
the sentence NUM john read abook he owned and so did simon
intuitively the first reading arises from strictly identifying the elliptical book with the antecedentbook
to illustrate an abbreviated qlf for the antecedent john read a book he owned is
we introduce a memory based approach to part of speech tagging
the experimental methodology was taken from machine learning practice e.g.
in a memory based approach a set of cases is kept in memory
on the same training set NUM of word tokens are ambiguous
when tagging a new sentence words are looked up in the lexicon
in this section we report first results on our memory based tagging approach
reasonably good results on unknown words without morphological analysis
these corpus sizes can be easily handled by our system
we will discuss only the reported pos tagging results here
a final compression is obtained by pruning the derived tree
table NUM and intention based discourse segments
table NUM elliptical antecedent in utterance u
table NUM reachability of the anaphoric antecedent
they are centered segmentation algorithm for anaphors and textual ellipses respectively
there is no exhaustive list of names and in german and some related germanic languages street names in particular are usually constructed like compounds rheins ra e kennedyallee which makes decomposition both practical and necessary
ance ui selected by its linear text index i
however once the weights in the performance function have been solved for user satisfaction ratings no longer need to be collected
table NUM summarizes how the NUM avms representing each dialogue with agent a compare with the avms representing the relevant scenario keys
response delay could be measured using seconds while in the example costs were calculated in terms of number of utterances
regression on the table NUM data for both sets of users tests which factors utt rep most strongly predicts us
finally the use of x means that the task success measure in paradise normalizes performance for task complexity providing a basis for comparing systems performing different tasks
in this paper we reviewed the current state of the art in spoken dialogue system evaluation and argued that the paradise framework both integrates and enhances previous work
tagging by avm attributes is required to calculate costs over subdialogues since for any subdialogue task attributes define the subdialogue
a single user satisfaction measure can be calculated from a single question or as the mean of a set of ratings
given similar calculations on a confusion matrix for agent b we can determine whether agent a or agent b is more successful at achievt ing the task goals
the combination of knowledge based and statistical methods resulted in a reliable system
he was furious with tony for being woken up so early
he was furious with him for being woken up so early
continue is preferred to retain is preferred to smooth shift is preferred to rough shift
he wanted tony to join him on a sailing expedition
this passage can be compared to the similar passage in NUM
they redefine rule NUM as follows rule NUM transition states are ordered
it was a store john had frequented for many years
NUM the terms smooth shift and rough shift were introduced in wic
current theories of centering for pronoun interpretation a critical evaluation
he was excited that he could finally buy a piano
on the other hand the lcsr for conseil and conservative is only NUM NUM
however our results suggest that combining filters does not always help and more research is needed to investigate optimal filter combination strategies
these considerations are further complicated by the differences in the tag sets used by taggers for different languages
this statistic was used to estimate dependencies between all co occuring source word target word pairs
next the candidate translations from each pair of training sentences were passed through a cascade of filters
a new objective evaluation measure is used to compare the quality of lexicons induced with different filter cascades
to put bible scores reported here into proper perspective human performance was evaluated on a similar task
as the indexing servers process texts the indexed terms are stored in a relational database with their semantic type information person entity place s t term and alias information along with such meta data as source date language and frequency information
for example using the query interface the user can in effect ask which company was mentioned along with intel in regard to microprocessors and the system will return all the articles which mentions intel microprocessors and one or more company names
thus if the user is searching for documents with the location washington not a person or a company named washington a person clinton not a location or an entity apple not fruit the system allows the user to specify through the gui the type of each query term cf figure NUM
we can compute sable s precision on unfiltered translation lexicons for this corpus by assuming that entries appearing in the collins mrd are all correct
note that these figures are based on translation lexicons from which many valid general usage entries have been filtered out see section NUM
moreover even when these larger text units can be found their size imposes an upper bound on the resolution of the bitext map
for example one would expect to find more cognates between russian and ukrainian than between french and english
these are treatt d in tile linking between syntactic arguments and their corresponding thematic roles
full unification might be problematic because it is possible to add arbitrary information during rule application e.g.
the general form of a transfer rule is given by slsem slconds tau0p tlsem tlconds
lexical decomposition allows us to express generalizations attd to apply transdr rules to parts of the decomposition
the equivalence in 3b relates the german predicate passen with the english predicate suit
type de pos att itude verbs gehen passen
the following example illustrates how conditions are used to enforce selectional restrictions from the domain model
the second rule 7b is more specific because it uses an additional condition
there need not be a NUM NUM relation between semantic entities and individual lexical items
scnlantic entities in NUM are reprc scnted as a prolog list of laimlcd conditions
this made it possible to compare texts written with vs without profet
cases where tile directionality is parallel correspond to complement s
we now describe the interpretation process on b fl rms
constant slope the slope of a tpc chain is rarely much different from the bitext slope
in particular tpcs have the following properties linearity tpcs tend to line up straight
a summarized formal definition of the hyphenation patterns and their associated rules as discussed above is presented in table NUM
there is no a priori reason to believe that one or the other will be easier for simr
during the optimization simr occasionally veered off course when the fixed chain size was NUM or less
the width and height of the rectangle are the lengths of the two component texts in characters
given such a mapping for l1 and l2 it is possible to identify cognates despite incomparable orthographies
two its running time is linear in the number of sentences faster than dynamic programming methods
it would be incorrect to simply connect the dots left to right because the resulting function may not be one to one
one illustration of this difference is that sentence correspondence can express inversions but sentence alignment can not
unfortunately this kind of alignment pattern i.e. 0xl followed by 2xl is surprisingly often correct
below we survey the most common constraints and discuss their relation to itgs
this condition is particularly relevant for many non western european languages such as chinese
lnlerpretation proceeds by propagating semantic translations and their types bottom up
as a second illustration r6 derives the simple p al measure ktab
this step is repeated with two more asynchronous productions yielding figure NUM bottom
the following simplifications can then be made to the parsing algorithm
in the second stage we remove from 7rq all the parse trees not in 7r
again no additional string pairs are generated due to the new productions
the method proceeds depth first sinking each singleton as deeply as possible
consider the following bracketing produced by the algorithm of the previous section
if such constraints are reliable it would be wasteful to ignore them
the vector derivation tree can be seen as representing an outline for the derivation
this might be a valid approach as their number of variables is small but we think that it will lead to frustrating dialogues when several variables are needed
the present paper introduces the otp formalization to the computational linguistics community
f oals which had en set NUM y th inilial planning onfer mt e
the majority of sites had recall and precision over NUM the highest scoring system had a recall of NUM and a precision of NUM
the template element task while superficially similar to named entities it is also based on identifying people and organizations is significantly more difficult
the template has slots for information about the event such as the type of event the agent the time and place the effect etc
we have recently completed the sixth in a series of message understanding conferences which are designed to promote and evaluate research in information extraction
to present it in simplest terms suppose the answer key has nke filled slots and that a system fills neor t
br the text mccann has initiated a new so called global collaborative system omposed of world wide account directors paired with creative partners
another concern which was noted about the mucs is that tile systenls we re tending towards relatively shallow understanding techlfiques based iirimarily on local pa ttern
although one must keep in mind the somewhat limited range of texts in the test set all are from the wall street journal in particular the results are excellent
the final task specification which also involved time currency and percentage expressions used sgml markup to identify the names in a text
horizontal spacing and vertical order are irrelevant
we report on word and sentence accuracy which is an indication of how well we are able to choose the best path from the given word graph and on concept accuracy which indicates how often the analyses are correct
the string comparison on which sentence accuracy and word accuracy are based is defined by the minimal number of substitutions deletions and insertions that is required to turn the first string into the second levenshtein distance
possible path through the word graph based on acoustic scores only possible a combination of acoustic score and bigram score acoustic bigram as reported by the current version of the system
however often no such paths can be found in the word graph due to errors made by the speech recognizer linguistic constructions not covered in the grammar and irregularities in the spoken utterance
this last phase contains among other things some domain specific linguistic knowledge dealing with expressions that may be ungrammatical in other domains e.g. the utterance amsterdam rotterdam does not exemplify a general grammatical construction of dutch but in the particular domain of ovis such an utterance occurs frequently with the meaning departure from amsterdam and arrival in rotterdam
arguments in favor of this kind of shallow parsing is that it is relatively easy to develop the nlp component since larger sentence constructs do not have to be taken into account and that the robustness of the parser is enhanced since sources of ungrammaticality occurring between concepts are skipped and therefore do not hinder the translation of the utterance to updates
in the first experiment the parser is given the utterance as it the number of words of the actual utterances the average number of transitions per word and the average number of words per utterances
NUM otherwise let that element be n s p a NUM if n was already marked as seen then abort this iteration and return to step NUM
the wsj nikkei corpus is the most non parallel type of corpus
figure NUM word relation matrix for administration in both texts
formal redundancy and consistency checking rules for the lexical database wordnet tm NUM NUM
unconstrained use of variables can increase the power
for derivable categories bounded by the maximum number of arguments of a lexical category we add all the instances of wrapping required for simulating the effect of gtrc into the lexicon of g
while proverb s hierarchical planning operators encodes accepted format for mathematical text its local navigation embodies more generic principles of language production
it is possible that these queries are easy to segment
exptyp NUM l0 is done under the same circumstances as exptyp NUM
of course the hierarchical lexical representation does not make a commitment to what levels are true words and which are not about NUM times more internal nodes exist than true words
its ability to capture the statistical and linguistic idiosyncrasies of large structures without sac null rificing the obvious regularities within them makes it a valuable tool for a wide variety of induction problems
furthermore vp v p np may be represented in terms of vp v pp and pp NUM NUM utterances of continuous dictated wall street journal articles
in the case of the chinese which contains no inherent separators like spaces segmentation performance is measured relative to another computer segmentation program that had access to a human created lexicon
naturally each word in the lexicon must be associated with its code and under a near optimal coding scheme like a huffman code the code length will be related to the frequency of the word
the power of the representation is demonstrated by several examples in text segmentation and compression acquisition of a lexicon from raw speech and the acquisition of mappings between text and artificial representations of meaning
since words are built by composing other words and act like their composition a new word can be created from such a pair and substituted in place of the pair wherever the pair appears
average precision is often used as a standard for comparison
given an existing word berry lcb berry rcb the red berry cranberry lcb red berry rcb can be represented c o r o a o n o berry lcb berry rcb red
this process bottoms out in the terminal characters
how the previous two words affects the probabilities of next word
this is very good performance for a purely statistical retrieval system
we are now planning to expand our domain of spoken language understanding as well
his system averages NUM NUM subentries maximum NUM less then half the number produced in our experiment
we hope to exploit this information where possible at a later stage in the development of our approach
due to the wide ranging motivations of funding agencies and the world wide interest in snlds it is not likely that we will find a common task for which everyone will implement their model of dialog processing and then be able to test them all on a common set of problems to see which one performs better
as mentioned previously the trains system demoed at acl NUM did not require any particular training other than being told the task you were trying to complete being given a brief description of the screen layout on the console you were viewing and the encouragement to talk to the machine like you would talk to a human assistant
additionally spontaneous spoken language is often syntactically ill formed yet semantically coherent
it is shallow in that no atof which thetempt is made to fully analyze unbounded dependencies
it also shows in the final column the number of sentences from which classes were extracted
this gives us an estimate of the accuracy of the relative frequencies of classes output by the system
we acquired subcategorization and associated frequency information from the citations in the process successfully parsing 380k words
4in fact NUM of sentences in susanne are assigned only a single analysis by the grammar
each dictionary entry encodes the relative frequency of occurrence of a comprehensive set of subcategorization classes for english
lb is parsed successfully by the probabilistic lr parser and the ranked analyses are returned
their main point was that evaluation needed to be placed within the context of a system s use
we are in the process of experimenting with both possible classifications and their combinations
section NUM briefly describes our method for a verb sense disambiguation system
for example in the scheduling domain numbers could be either dates or times
we have also found noticeable language model perplexity differences between the esst and etd domains
the etd test set contains NUM out of vocabulary tokens out of a total of NUM tokens
final segmentation into sdus is done during parse time guided by the grammar rules
the edit tistance is then a simple extension fi om edit operations to strings
the first two equations show that the terms on the diagonal may be exchanged
8balg pelt skin and iials neck singular biilge and hdlse plural
ill may ex plain m1 flexional paradigms from conjugation to declension
rnathemo tics physics mathematical x figure NUM analogy seen as a rectangle
and no one knows what the meaning of u v could possibly be
however the main purpose of the present paper is to exhibit the use of datr for lexical description iv and the way it makes it relatively easy to capture lexical generalizations and subregularities at a variety of analytic levels v
for example when asked to align greek didomi with latin do it tries only three alignments of which the best two are didomi didomi d o do choosing the right one of these is then a task for the linguist rather than the alignment algorithm
if the anchors are words we simply take their linear order
figure NUM the rda analysis of NUM
figure NUM shows in the form of a tree all of the moves that the aligner might try while attempting to align two three letter words english h ez and german hat
however it would be easy to modify the algorithm to use a lower penalty for skips at the beginning or end of a word than skips elsewhere the algorithm would then be more willing to postulate prefixes and suffixes than infixes
NUM association for computational linguistics computational linguistics volume NUM number NUM the algorithm compares surface forms and does not look for sound laws or phonological rules it is meant to correspond to the linguist s first look at unfamiliar data
many words have several distinct meanings
the search algorithm can be extended to implement special handling of metathesis assimilation or other phenomena that require looking ahead in the string and can return any number of alignments that meet some criterion of goodness not just the one best
our first attempt at a coreference system la hack NUM posited coreference between identical upper case words in the text and was written to test the validity of the system s sgml annotatio n and to test the tokenizer
headline words which were capitalized in the body of the text anywhere other than sentence initial position remained capitalized as did thos e which were frequently capitalized other than in sentence initial position in the treebank wall street journal corpu s NUM
as a result the majority of our efforts went into writing parsers and preprocessing utilities which allowed various pre existing tools to communicate with one another and produce output which could be used by other tools further in th e processing pipeline
in addition rudimentary morphological analysis o f the head of a noun phrase is performed and several databases are consulted to determine whether a particular nou n phrase refers to a male a female or a person of either gender
the combination of the character based nature of the scoring software and the requirements of various tools that punctuation be separated from words forced us t o build a tokenizer which maintains a character offset mapping for all of the tokens in the input messages
this set of scores is presented in order to allow comparison between scores for various system components without having to deal with the adjustment to th e number of correct items which results from different components marking coreference between different numbers of optional elements
the difference in weighting between the two is currently based on intuition though corpus methods migh t yield a more exact estimate of how much weight to give the female reading based on how often such words are actually used to refer to women
as a second source of evidence about the gender or animacy of noun phrase referents two tables of gendered first names compiled by mark kantrowitz and bill ross and freely available from the computing research laboratory of new mexico state university are consulted
we mark one noun phrase called np1 as being coreferent with a second noun phrase np2 because of a n appositive relationship if np1 is the head of a parent noun phrase and np2 is also a direct descendant of this paren t noun phrase
although no time was spent developing tools particularly for the muc task prior t o january many hours went into developing some of the off the shelf components we used such as eric brills part of speech tagger NUM and lance ramshaw and mitch marcus noun phrase detector NUM
we can also extend this abbreviation strategy to cover cases like the following where the path on the right hand side is different but the node is the same come mor root come mor past participle come mor root
the seed word list is thrown away
it is advantageous to express lexical rules in the same formal language as is used to express the lexical hierarchy since lexical rules themselves may well exhibit exactly the kinds of defaulty relations one to another that lexical classes do
this consists of the information that must be exchanged between the agent and the user during the dialogue represented as a set of ordered pairs of attributes and their possible values
and gerbino NUM hirschman and pao NUM polifroni et al NUM simpson and fraser NUM shriberg wade and price NUM
our performance measure also captures information similar to concept accuracy where low concept accuracy scores translate into either higher costs for acquiring information from the user or lower scores
during the dialogue the agent must acquire from the user the values of dc ac and dr while the user must acquire dt
since both and ei can be calculated over subdialogues performance can also be calculated at the subdialogue level by using the values for c and wi as solved for above
given the current state of knowledge it is important to emphasize that researchers should be cautious about generalizing a derived performance function to other agents
instead predictions about user satisfaction can be made on the basis of the predictor variables as illustrated in the application of paradise to subdialogues
user satisfaction is typically calculated with surveys that ask users to specify the degree to which they agree with one or more statements about the behavior or the performance of the system
second smith and gordon s single tag a corresponds to two attribute tags in table NUM which in our scheme defines an extra level of structure within assessment subdialogues
the mth mixed order model was smoothed by discounting the weight of each skip k prediction then filling in the leftover probability mass by a lower order model
the latter indicates how this partial derivation tree combines with other partial trees
agens act goal have to obj have figure 9a
in each user s turn we reported in italian the original user s utterance and its english translation in italics
generally the predicate directly determines the selectional restrictions of its arguments i.e. the discourse referents
to exentl lify l his in the next secl ion the ret resentatioll of lcihe n in its w ria nt to ic nd is emiched by the hmding ltel soll s belief in a return of t he involved object
in this initial dialogue context there were no parameters to be denied and the dialogue module was able to discard this information related to the negative adverb
tile joined representation format proposed he re is likely to facilitate and improve lexieal modelling as well as the automatic construction of text representations l rther investigations ill otller lexical fields and word classes are required in orde r to aehieve a larger lexieal cove rage
this is a bottom up chart parser that records both active and inactive items
the last two parsers of the following list were implemented by mark jan nederhof
in the ovis application these are often simple time or place expressions
this can only be understood in the light of the use of memorization
the first phase combined with the second phase is of course still sound
during the first phase all constraints referring to logical forms are ignored
the solution to this problem is to delay the evaluation of semantic constraints
the model is based on a sorted first order language where every term is either an agent a turn a sequence of turns an action a description or a supposition
the default pickform allows us to account for the fact that the same surface form can perform several discourse acts and the same discourse act might be accomplished by one of several different surface forms
this is given by the following set of theorist defaults NUM intentionalact expectedreply acceptance adoptplan challenge makefourth turnrepair make thirdturnrepair reconstruction othermisunderstanding selfmisunderstanding and done
this approach is similar to one proposed by rayner and samuelsson NUM for tailoring a large grammar to a given corpus
where ep elc log p elc is the total entropy over various contexts of label c
in this experiment we set NUM NUM in equation NUM and k NUM in equation NUM
on the ice and the boy dropped his wallet somewhere NUM replace labels in each label group with a new label in the corpus
finally a statistical parsing model bawd on the acquired grammar is provided and the performance is shown through some experiments using the wsj corpus
this result supports the assumption that local contextual statistics obtained from an unlabeled bracketed corpus are effective for learnln a useful grammar and parsing
where r is an application rule in the tree and is the left and right contexts at the place the rule is applied
in the first place let us consider the following example of the parse strnctures of two sentences in the corpus in figure NUM
NUM match binary vectors to yield a secondary lexicon
fixing details often leads to expected productivity gains
at the same time its expected running time and memory requirements are linear in the size of the input better than any other published algorithm
its expected running time and memory requirements are linear in the size of the input which makes it the algorithm of choice for very large bitexts
the use of feature structures as a semantic representation framework facilitates the specification of partial meanings
in other cases a seed translation lexicon may be used to boost the number of candidate points produced in the generation phase of the search
if the matching predicate uses cognates then every word that might have a cognate in the other half of the bitext should be assigned its own axis position
the error between a bitext map and each tpc can be defined as the horizontal distance the vertical distance or the distance perpendicular to the main diagonal
on the other hand many tokens of a relatively rare type can be concentrated in a short segment of the text resulting in many false correspondence points
the aspectual forms land adverbs are defined as the functions which operate on verbs aspectual features and changes their values
on the other hand the indirect internal argument can provide a temporal terminus for the event described by the verb
in general adverbs focus on the subpart of the event described by a verb and give a more detailed description
by iteration the whole process of a collective event can be taken up regardless of the inherent features of verbs
NUM resultative verbs express a punctual event followed by a new state which holds over some interval of time
strings tagged as names in the collection might also be indexed differently than other strings
in this way advantage was taken of the fact that name terms are ordered and resist interruption by non name terms
however for name terms the proximity searches used the tf and idf of the proximally ordered name terms
efficiency in addition to the points made in the preceding paragraph on ranking we noted earlier that transduction with appropriately restricted source positions for transitions can be carried out with search techniques similar to context free parsing e.g.
we know that some words share similar sorts of linguistic properties thus they should belong to the same class
in the table NUM NUM stative verbs are those that are not dynamic
it has far fewer parameters thus making better use of training data to solve the problem of data sparseness
while for most of the variables our measurements are necessarily apsit should be noted here that the independence assumption of the sign test is mildly violated in these repeated runs since the scores depend on collections of independent samples from a finite population
perplexity is derived from the average log probability that the language model assigns to each word in the test set
however this collection contains only NUM adjectives in NUM pairs some of which can not be used in our study either because they are primarily adverbials e.g. inside outside or not gradable e.g. alive dead
since some of these variables are closely related and their number is so high that it impedes the task of modeling semantic markedness in terms of them we combined several of them keeping NUM variables for the statistical analysis
for each variable and each of the two groups we also performed a statistical test of the null hypothesis that its true accuracy is NUM i.e. equal to the expected accuracy of a random binary classifier
on the other hand tests based on the economy of language principle such as word length and number of syllables perform badly when formal markedness relationships do not exist with lower applicability and very low accuracy scores
for coreference there were problems identifying part whole and set subset relations and distinguishin g the two a proposal to tag more general coreference relations had been dropped earlier a decision was later made to limit ourselves to identity relations
for both decision trees and log linear regression we repeatedly partitioned the data in each of the two groups into equally sized training and testing sets constructed the predictors using the training sets and evaluated them on the testing sets
to solve this problem we adopt the bottom up merging method to the resulting classes
also we use the resulting classes to do experiments on word class based language model
performance on this particular article for some systems was higher than performance on the test set overall reaching as high as NUM recall and NUM precision
for common noun phrases the systems were not required to include the entir e np in the response the response could minimally contain only the head noun
statistically large differences of up to NUM points may not be reflected as a difference in the ranking of the systems
walter thompson last september four systems fallon mcelligott organization category is indicated by context
the reverse is not the case i e org country may be filled even if org locale is not but this situation is relatively rare
the post slot requires a text string as fill and there is no finite list of possible fills for the slot
organization names are varied in their form consisting of proper nouns general vocabulary or a mixture of the two
for organization objects the challenge is greater requiring extraction of location description and identification of the type of organization
as with the sra experiment the only differences in performanc e between the two bbn configurations are with the organization type
they can also be quite long and complex and can even have interna l punctuation such as a commas or an ampersand
this is achieved by collapsing different deriwttions that cover the same subset of input and have the same syntactic potential under a single edge that represents an equivalence class
the c program re calculates f measure recall and precision from raw tallies for higher accuracy than during the approximate randomization comparisons
for evaluation the already annota ted
the annotator can alter the assigned tags
the program automatically assigns grammatical fimetion labels
as bracketing and indentation would be insufficient
if a group or a single system is off by itself then that group or single system i s significantly different from its non members
distinguishing systems at such a strict cutoff as we use i n the statistics may only be justified if variations in human performance are smaller
therefore their intersection is lcb object
at the same time c1 is removed from
the following example illustrates how this rule works
in our example mrs figure NUM displays the generalized mrs mrsg
as with all recognition technologies gesture recognition may result in errors
we call these categories generalized type raised categories gtrc and each ai of a gtrc an argument of the gtrc
intuitively this situation is analogous to long distance movement of c from the position left of skakb kc to the sentence initial position
the main claim of the paper is the following proposition NUM NUM e is weakly equivalent with ta
thus a b c should be read as a b c and returns a b when an argument c is applied to its right
the proof of the sublemma involving the z lcb rcb form can be done by induction on the length of the category
a fairly high level of semantics coverage can be obtained quite quickly when the system i s moved to a new domain
due to the fragmentation produced by fpp top level constituents are typically more shallow and less varied tha n full sentence parses
the discourse component will resolve the reference for the pronoun and will further refin e the relationship between the person and the job situation
these must be converted to succession and in and out objects before the template generation step since this is what the template generator expects
we also believe that it is possible to achieve an f of NUM or better in te using only lightweigh t processing
in this way semantic coverage can be adde d gradually while the rest of the system is progressing in parallel
the te and st systems gather the results of the ne system s processing and incorporate them in the form of lexicon additions
the more complex systems are built on top of the simpler systems in order to minimize duplication of effort and maximize knowledge transfer
james would be person stepping down would be job situation word and chief executive officer would be post
lexical semantic entries indicate the word s semantic type a domain model concept as well as predicates pertaining to it
table NUM unsupervised supervised vs purely supervised training
similarly flat as a noun is defined as a flat tire and the presence of the word in its own definition but with a different part of speech is taken as evidence that the noun and adjective meanings are related
this tradeoff can be manipulated by altering the morphological rules to place more importance on a low deletion rate or a low insertion rate by modifying our post mortem approach to obtain finer control over the process of handling unknown words or by considering additional knowledge sources
with NUM of the open class dictionary missing there are NUM NUM insertions per sentence when using the morphological recognizer in the experimental run as compared to the NUM NUM insertions per sentence when using the baseline method with only NUM of the dictionary missing
another issue that is solved through the logical interpretation of the conditions is determining that the whole input is consumed
the semantic equivalence is established on the basis of the indexing variable and the coverage of facts from the logical form
for example translating john likes it into spanish most naturally comes out as it pleases john
one translation situatiou that the annotated chart approach can address very simply has to do with optional and defeasible specifications
the motivation is to gain more information from the target language in order to improve the quality of the choice
it means that at two or more different nodes only certain combinations of branches can be selected
moved rushed into the room figure NUM branches in nodes NUM and NUM and the right branch at node NUM
one device involves indexing edges on semantic variables and another keeps track of which part of the semantics each derivation expresses
in the semantic representation used in the algorithm each fact is a predicate specifying a relation between events and entities
as noted above many of the uses of morphology for analysis of lexical items require more knowledge than may be possible for some applications especially if the system will be using a limited lexicon but will be expected to cope with words in the language but not found in the lexicon
the deletion rate is in part due to the fact that many words in the complete dictionary are lexically ambiguous whereas many times the morphological recognizer assigns a smaller set of parts of speech which can result in a correct parse being generated but not the entire parse forest
noun modifier is a fifth part of speech that is used in this research to indicate those words that can be used to modify nouns this will eliminate extraneous parses that occur when a word defined as both a noun and adjective is used to modify a head noun
also note spud can use this same meaning of fast and the same reasoning process even when fast does not modify a noun
analogously an entity is either new or old to the discourse according to whether the discourse contains an earlier reference to it
each potential boundary site in our corpus is coded using the set of linguistic features shown in fig NUM
we model these differences by including in each tree a specification of the contextual conditions under which use of the tree is pragmatically licensed
if x is uniquely identifiable then this goal is only satisfied when the overall content planned so far distinguishes x for the hearer
for example in a slightly different context it could describe the state s with this sentence the copier is fasr
furthermore note that the machine learning algorithm used the changes to the coding features that resulted from the tuning methods
with each method we have achieved marked improvements in performance compared to our previous work and are approaching human performance
note that e h represents a set of symbols and so by a slight abuse of notation e h ih denotes ee h a a h ie the sum of a alh over all in e h
step 2b the ak rooted initial trees where the first nonempty frontier node is labeled with ak are then converted into right auxiliary trees as specified by lemma NUM the applicability of schabes and waters tree insertion grammar lemma NUM in this situation is guaranteed by the following facts
the third term partitions the context frequency c w into the available extensions c e w lw and the unallocated frequency c e e w lw c w c e w w in the context w
if the output is less than to the punctuation mark is not a sentence boundary if the output is greater than or equal to tl it is a sentence boundary
it is based in part on cibola a system that supports human translators by providing them with tools that directly support the translation task
figure NUM shows parts of the arclist source file for street name decomposition
number of names at least one system wrong both systems wrong total error rate
annotated text is color highlighted has attributes associated with it such as author or type and can be categorized into groups
these four cities also provide an approximately representative geographical and regional dialectal coverage
table NUM extraction of productive street name components quantitative data
none of these methods were explored and applied in the present study
figure NUM parts of a grammar in arclist format for
we retrieved all available street names from the records of the four cities
as indicated above general purpose pronunciation rules often do not apply to names
the error rates compare favorably with results reported in the research literature
thus we made a binary decision between correct and incorrect transcriptions
the resulting satz error rate was NUM NUM which was still significantly better than the style baseline error rate of NUM NUM which was obtained with a NUM entry abbreviation list
it is to be expected then that the inside outside algorithm favors the suboptimal parse k at its start the inside outside algorithm is guided by tree counting arguments not mutual information between words
to determine the system improvement with more training data we NUM note that the number of items in the were correct column is a subset of those in the not labeled column
such fixed categorization of domain relations in effect prevents a generator from realizing the same domain relation at various linguistic computational linguistics volume NUM number NUM ranks and thus drastically reduces its paraphrasing power
again elhadad mckeown and robin floating constraints in lexical choice since such goals are available to the content planner they can easily be provided as input to the lexical chooser
our architecture positions the lexical choice module between a language generator s content planner and surface sentence generator in order to take into account conceptual pragmatic and linguistic constraints on word choice
the selected entry contains two types of features syntagmatic constraints on daughter nodes in the syntactic tree and paradigmatic choice of alternative lexical entries for the same node in the tree
we detail the nature of input and output to the lexical choice module thus specifying the tasks the lexical choice module performs and the tasks that are expected to be done elsewhere in the system
first the range of constraints on lexical choice covered in this line of work is quite restricted and we have some question about whether it could be extended to include the pragmatic constraints considered here
the second factor accounts for the growth rate proper which is reducible to counting the set of k strings over an n sized alphabet hence n k
by looking at the these frequency lists a language analyst or instructor can improve their coverage and avoid missing prominent words
such a transformation is possible if the attributes of the tokens on the left of the current token are at a fixed position in the stack
in this algorithm no features are checked so it is impossible to establish if a chain is saturated or not until structure building ends
frank does not discuss this issue in detail but it seems that a shift operation must be added to the operations of the parser
on the other hand linguistic concepts operate on different primitives intuitively x theory and principles of argument structure or coreference are different objects
dorr notes that a limited amount of precompilation of the principles speeds up the parse otherwise too many incorrect alternatives are carried along before being eliminated
this is qualitatively similar to the distribution we assumed for verbs nouns and prepositions in configuration a and has entropy rate NUM NUM NUM NUM NUM
a pseudo prolog notation is used which is similar to the output of the parser where chains are represented as lists enclosed in square brackets
theta assignment occurs in the configuration of sisterhood it requires a NUM assigning head and it must occur between a node and its most local assigner
using NUM equally probable words per part of speech we chose a word distribution over the sentences with the following characteristics i a b NUM bit
a greedy approach to grammar acquisition that iteratively hypothesizes relations between the words with highest mutual information will first link v to p then p to n producing exactly the desired result for this example
NUM the learning process terminates when the g set and the s set are both singleton sets which are identical
the latter fact needs to be interpreted in the sense that these data are not correctly describable within the id lp format
all elements of the sibling list are processed and fails when no consistent generalization may be found for some data
4assertions are actually made after he king for consistency with lp s already present in the database
that is a determiner of any grarrniiatical lunnber niust precede a n adjec rive
tile set of most general rules is called the g set and tile set of most specific rules tile s set
oleada a project at crl that seeks to develop computer tools that support language learners and instructors has been developed with this goal in mind
a remark on notation delimits individual l p rules allowing their recovery in terms of prolog structures
NUM 20r are priorly known in the case when the system starts with some lp rules declared by the user
w smen y o xuesh ng hu6 d6 y6u yiyi we want student live csc NUM have meaning we want our students to have a meaningful life computational linguistics volume NUM number NUM b
the fragment e xud shdng hu6 also has overlap ambiguity where the middle character can either combine with the first character to form a word or combine with the last character to form a word
since b n can not be the classifier of the object name k r nsh ng life a special type of codelet known as a breaker codelet is posted to the coderack
for example one codelet may check for the possibility of building an aspectual relation between the words NUM sh ng give birth and tle asp of sentence NUM
in this run for example an affinity relation between the character objects b n and rdn is constructed by an instance of an affinity codelet at cycle NUM figure NUM
china already exploit and yet not kaifa de z yu n dou h n duo exploit struc resource all very many china has many resources which have either been exploited or not yet been exploited
several nodes in the network e.g. agent patient word chunk etc when activated are able to exert top down influences on the types of activities that may occur in the workspace in subsequent processing
at cycle NUM a breaker codelet is executed that examines structures that are introuble namely the words b n and NUM r nsh ng life
coreference co insert sgml tags into the text to link strings that represent coreferring noun phrases
there were no markable time expressions in the test set and there were only a few markable percentage expressions
table NUM ne document subsection scores err metric in order of decreasing overall f measure p r
the evolution and design of the muc NUM evaluation are discussed in the paper by grishman and sundheim in this volume
about half the systems focused only on individual coreference which has direct relevance to the other muc NUM evaluation tasks
the algorithm compares the equivalence classes defined by the coreference links in the manually generated answer key and the system generated response
the answer key for the te task contains one object for each specific organization and person mentioned in the text
the entity types that were involved in the evaluation are the same as those required for the scenario template task
examples of each of these types of error appear below along with the number of systems that committed the error
this question is especially important when dealing with a statistically open source such as natural language
it turns out that the final performance is not terribly sensitive to particular assumptions on priors
in the next sections we present psts and the data structure for the word prediction problem
finally a careful analysis should be made when predicting novel events new words
table NUM the likelihood induced by a pst of maximal depth NUM for different corrupted sentences
this is especially noticeable when phrases longer than a typical n gram order appear repeatedly in the text
the system converts a semantic input into a word lattice sending the result to one of three sentence extraction programs random follows a random path through the lattice
consider the case of time adjuncts that express a single point in time and assume that the generator has already decided to use a prepositional phrase for one of them
given an input semantic pattern we locate the first grammar rule that matches it i.e. a rule whose left hand side features except rest are contained in the input pattern
if a has occurred fifty times and b none at all then we choose a but ifa and b are long sentences then probably we have seen neither
to sum up defaults can help against knowledge gaps but they take time to construct limit paraphrasing power and only return a mediocre level of quality
instead of explicitly constructing all possible renditions of a semantic input and running penman on them we use a more efficient data structure and control algorithm to express possible ambiguities
xn syn if the e structure for the se null mantic material under the xn feature contains syn lat return the word lattice lat otherwise fail
running penman NUM NUM times is expensive but nothing compared to the cost of exhaustively exploring all combinations in larger input representations corresponding to sentences typically found in newspaper text
given that the aim is to classify expressive patterns according to their meaning and function how should this be done
a set of NUM articles was selected in order to test the performance of the method
wheu the clitic climbs certain pronomiual garden path effects deriving from a wrong interpretation initially assigned to the null slibject and later retracted are avoided
for these sets and counters and for NUM NUM the algorithm converges after NUM iterations
the concepts of critical point and critical fragment are fundamental to our sentence tokenization theory
note that in this stage we also include the ambiguous word in each of the sw sets
so for instance in tile crittei i system the three sentences john does not like every woman that peter hates john does not like every woman hated by peter every woman whom peter hates is not liked by john would be assigned the u form of fig NUM
but instead of using sets of lexical signs i.e. morpho syntactic lexemes as in shake and bake we specify translation cquivalences on sets of arbitrary semantic entities
in cases where the semantic representations of source and target language are not isomorphic a nontrivial transfer relation between the two representations is needed
note that we should choose the words for the sw sets such that they are morphologically unambiguous
these examples also illustrates the usefulness of labeled conditions because the negation operator can take such a label as an argument and we can use unification again to achieve the correct coindexation
the other main difference is our nonmonotonic control component whereas tile mrs approach assumes a monotonic computation of all possible transfer equiv lences which are then filtered by the generation grammar
as a result a compositional translation as proposal for a date is possible without stating any additional translation equivalences to the ones for the simplex nouns
the words that exhibit such differences are likely to affect retrieval performance
this constituted the experiment that tested homonymy
NUM temp loc e x sort x time temp loc e x
errors in morphology generally do not hurt performance within the restricted context
but to what extent are such inappropriate matches associated with relevance judgments
they generally improve retrieval performance but the improvements are not consistent
part of speech and phrases as multiple sources of evidence
many retrieval systems represent documents and queries by the words they contain
the third paper by mark seligman takes a broader methodological and architectural perspective and identifies six issues of importance to the field as a whole
therefore corpus based statistical methods for language modelling and automatic acquisition are of special interest for slt as addressed in in amengual et al and frederking eta
the following technique is completely general though it may or may not be practical
jae won lee et al look at words whose translation is particularly dependent on the context a problem which is exacerbated in a language pair such as korean english
the paper describes initial experiments which investigate the parameters of the problem and in particular explores the possibility of constructing recognizers capable of recognizing multilingual input
hutchins somers NUM NUM the incorporation of contextual knowledge into an mt system was just dismissed as impractical or at best uneconomical
the final contribution in this section is mark seligman s which takes a personal perspective in identifying six areas of slt research as particularly interesting
keiko horiguchi discusses meaningful errors in speech which convey contextual meaning or the speaker s attitude and then focuses on the translation of discourse particles from japanese into english
in such cases the dialogue component tries to follow the dialogue by using a keyword spotter
top down predictions are also used to limit the set of applicable grammar rules to a specific subgrammar
we present the dialogue component of the speech to speech translation system verbmobil
the author has implemented a common lisp program which does so correctly based on an algorithm by christian boitet
i.e. when the owner of verbmobil speaks german only
as a result err rises usually slightly in each case
generates a new word list of tagged words not found in the lexicon
the lexical rule processor is an engine which produces a new entry from an existing one such as the new entry compra figure NUM produced from the verb entry comprar figure NUM after applying the lr2event rule NUM the acquirer must check the definition and enter an example but the rest of the information is simply retained
if the system does not know would be helpful it will guess that it is a clarification of the language requirement even if it may not be able to translate it
where a and b are job codes f x returns the number of digits of its argument and n is the number of digits in the job codes i.e.
the purpose of using case based reasoning techniques is to quantify the difference as a metric value between any two instances of a job schema object
it is trivial to implement but the disadvantage is of course that we would have to store one rule for each utterance that we would like our system to produce
problems due to missing equivalent terms in different languages or to slightly different meanings are handled at least in the first stage simply by providing terms nearer in meaning
a third alternative would be to give a phonetic transcription of out of vocabulary items
of more interest perhaps is vocabulary which can be structured since this provides us with an opportunity to allow more sophisticated searching of the database
for example when searching for a job the classification hierarchies inherent in the terminology database allow the user to express general search constraints e.g.
linguistic information about commonly used terms and synonyms used in a given language or more than one to refer to the specific term
we need to recognize dutch english and spanish as being names of languages but these words have terminological status in our system
symbol v ut the source rule must have the verb miss a s a synta ctic
constrahtts c m lm specified in either or both sic h s of the l a tterns
a NUM waters NUM NUM NUM ha s 1ram1 introduced to show t similar possilality
theret ore we can conclude that l t c l g is mtdecidable
NUM most mt systems ca n lm customized only by a ddittg a user dictiona ry
it is however a n olmn qttestion whether these a pl roa hes
is ontologically is a hnma n with optional accusative mill optio al i ossessivc marking obligatory only with ba lilea31s lo waste or demote a person
in addition to the usual sense wu iations due to selectional restrictions on verbal arguments in most cases the meaning conveyed by a case dante is idiomatic with subtle constrmnts
the optional ablative and instrumental objects are defined similarly
case frame approach has been the representation of choice especially for languages with free constituent order explicit case marking of noun phrases and embedded clauses filling nominal syntactic roles
ilowevcr instead of classifying argument structures as simply tr nsit ive intransitive etc we need to consider all relewmt elements of the l ower set of t ossible arguments
a lmssive form wouh not be grammatical fi r the sense convey d all hough syntactic ally yc eat is lcb t transitive verl
for insta nce the verb qa requires ditdrent kinds of argunnmts del endi g m the sense obligatorily exel uding other argmnents i a n ablative casc ma rked
inst obj is optional instrument g sen eat1
in japanese a complement of a verb consists of a noun phrase case filler and its case marker suffix for example ga nominative ni dative or wo accusative
geta clips imag campus bp NUM NUM rue de la chimie NUM grenoble cedex NUM france seligman cer f
consider the following pair of sentences NUM i saw the man with the moustache
each lexical category is associated with a set of structural relations which determine its lexical subtree
we can guarantee this by requiring the lowered node to dominate the last word to be attached
n is accessible iff n dominates w and n does not dominate any unsaturated attachment sites
the result is that the node chosen is lowered or subordinated
we are currently investigating the consequences of changing the search strategy in this way
no two nodes may stand in both a dominance and a precedence relation
painting of the houses that was damaged in the flood
of the two most native english speakers report NUM to be easier
for this we intuitively require some means of inserting one tree description inside another
if a word is not present in the lexicon its ambiguous category can not be retrieved
they are retrieved from the known words case base and the unknown words case base respectively
storage requirements are proportional to n compare o n f for ib1
during tagging each word in the text to be tagged is looked up in the lexicon
the same asymptotic complexity is of course found for memory storage in this approach
examples are represented as a vector of feature values with an associated category label
leaf nodes contain the unique class label corresponding to a path in the tree
already at small corpus size NUM NUM k tagged words performance is good
performance on known words unknown words and total are given in table NUM
then s he can enjoy easyto use interactive operations for translation equivalent selection inflection selection and cd rom dietionary access
in this direction our system would be expanded as a kind of interactive example based translation support system
we implemented the method as an english writing support facility that serves as a translation support front end to an arbitrary application
we first describe the basic model that determines the scope and timing of interaction then the set of interactive operations
in addition some nodes only have a formal role in the grammar and are not meaningful to the user
when the user triggers translation undetermined attributes are calculated then the result replaces the tree under the focus node
figure NUM shows steps to obtain a sentence with an embedded clause i help him to read a book
second the translation equivalent for functional words can be specified which can affect the syntactic structure of the result
while the user is typing english characters the system does nothing special and let them through to the editor window
in the next section we give a simple example of translation steps and provide a general idea of the method
this can be further extended to treat other phenomena of natural language providing that new components are robust and fast
the data extraction phase can be subdivided into a stage of semantic category identification and a stage of lexico semantic pattern extraction
after collocations are collected another tool a generalizer tries automatically deduce regularities and contract multiple patterns into their general representations
the main aim of the analysis and refinement module is to uncover and refine structural generalities found in the previous phases
the matcher evaluates how good a given piece of text matches the pattern and returns matches at various levels of exactness
a fuzzy matcher is a tool which uses a sophisticated pattern matching language to extract text fragments at various levels of exactness
patterns themselves can be quite complex constructions which can include strings words types precedence relations and distance specifiers
body component rcb curly brackets impose a context of a structural group the
figure NUM displays an excerpt from collocations extracted from pds corpus in the original form and after term inclusion checking
this module using semantically driven role filler expectations for verbs provides a more precise attachment of noun phrases to verbs
on the one hand the user s dialogue behavior can not be controlled
but if both genitives are present they must be interpreted according to a thematic role hierarchy
significant contributions to this paper were made by richard schwartz
the phoneme transcription describing the syllable structure in terms of onset nucleus and coda of l he last three syllables of the word
consists of a root node containing the test and a branch for each outcome each bt anch leading to a subset of the original set
we will show that it is possible and useful to make use of unsupervised learning relative to a particular task which is being learned in a supervised way
we will move now to the use of inductive learning algorithms as a generator of generalizations about the domain and compare these generalizations to the analysis of trommelen
several categories relevant for diminutive formation such as liquids nasals the velar nasal semi vowels fi icatives etc are reflected in this hierarchical clustering
the c4 NUM algorithm also contains a value grouping method which on the basis of statistical information collapses different values for a feature into the same category
in c4 NUM this tree to ruh transformation involves additional statistical evaluation resulting sometimes in a rule set more understandable att l
taking one at random will usually result in large decision trees with poor generalization performanee as uninformative tests may be chosen
such a prediction system may be easier for children than for adults
we will describe a case study of using c4 NUM to test linguistic hy1 otheses attd to discover regularities and categories
after all when a lm is applied to a test text to produce a perplexity score this value is a measure of the cross entropy which reflects how well the lm predicts the words in the text
clearly much of the information that humans use to measure textual similarity is found not solely in the individual word frequencies unigrarns but rather in the way they combine n grams
this method may not be as structured as the previous approach but it is more robust in that it involves no manual intervention and does not rely on correct organization or sgml tagging of the background corpus
all calculations were based on spearman s s where d NUM denotes the sum of the squares of the differences between the ranks of each pair of word types and n the number of ranked pairs
the abbot recogniser was run using each combination of the NUM speakers data files as input and each of the four lms email bnc with email vocabulary bnc and the wsi lm
nevertheless the extent to which the email is unlike all the other bnc domains is quite apparent and therefore mitigates any unprincipled approaches to corpus augmentation using crude top down techniques that involve complete domains taken from the bnc
when we construct a feature collocation lattice from a set of samples each sample represents a feature configuration which we must add to the lattice as a node ok if is not already there
we set p w 2n to the word w which are not in the vocabulary
NUM on the design of the dm module how to obey these NUM commandments when designing a dm module
whereby partitioning is explored by unification in the term structure of higher order linear logic piogramming to which we now turn
later we shall see how hierarchical structure can be discovered rather than conjectured by factoring out horizontal structure
this is true in particular of compositional categorial architec null tures and we shall focus on algorithms for showing well formedness
this provides a method of solution to the parsing problem of lambek categorial grammar applicable to a variety of its extensions
it may be that some of these may be automated
applies working back fl om the target sequent right rules before left rules
note that unifications are all one way but even one way associative string unification has expensive worst cases
the linearity resides in the use exactly once per word token of each of the clauses compiled from lexical categorisations
this precedes applications of the res rule hence the uniformity character which corresponds to the left sequent inferences
here when a higher order goal is found on the agenda its precondition is added to the database by dt
moreover the probabilities are assigned to the words to demonstrate how well a word belongs to classes
as said the purpose is to design and implement a voice interface to the berlin driver information system
children who can not speak tend to be socially isolated from their peers
this is a very strong and significant statement
character string fundsand has critical ambiguity in tokenization
by contradiction there must be x y
this should be a fundamental guideline in tokenization research
definition NUM let x and y be word strings
we do not go into the details for brevity reasons but intuitively the minimal tree is computed by taking the underspecified links to be path of length zero when their ends are compatible of length one otherwise figure NUM
the language for expressing the meta rules is very close to the elementary tree language except that meta rules use meta variables standing for subtrees lle proposes to integrate the meta rules to the xtag system which would lead to an efficient maintenance and extension tool
a detailed example let us go back to the tree of figure NUM the next figure shows in detail the super classes NUM introduced at figure NUM for the class w0n0vnl pass representing 7we only show the direct super classes
then if we have the information that the nodes labeled respectively s and v of figures NUM and NUM are the same the conjunction of the two descriptions is equivalent to the description of figure NUM
the generative power of the tool is effective out of about NUM hand written classes the tool generates NUM trees for the NUM families for verbs without sentential complements NUM NUM of which were present in the pre existing grammar
they give no principle about the form of the hierarchy or the lexical rules NUM whereas we believe that addressing the practical problem of redundancy should give the opportunity of formalizing the well formedness of elementary trees and of tree families
weiss kulikowski NUM independent training and test sets were selected from the original corpus the system was trained on the training set and the generalization accuracy percentage of correct category assignments was computed on the independent test set
in doing this the local context also changes to be the new context
the present subsection is only concerned with explicit i.e. nondefault inheritance
we have shown that a memory based approach to large scale tagging is feasible both in terms of accuracy comparable to other statistical approaches and also in terms of computational efficiency time and space requirements when using igtree to compress and index the case base
to see how this global inheritance works consider the query wordl mor form
if we make this change then the verb node will look like this
do verb mor root do mor past did
where node and path are as above and def is an arbitrary descriptor sequence
but in our example of course there are other statements concerning word1
the other improvement introduces one of the most important features of datr specification by default
the first release of gate is now available
for example the module will appear in a graph of all modules available with permissible links to other modules automatically displayed having been derived from the module pre and post conditions
aside from the information needed for ggi to provide access to a module gate compatibility equals tipster compatibility i.e. there will be very little overhead in making any tipster module run in gate
information in these models may be characterised as abstract in our present context as there is no requirement to tie data elements back to the original text these models represent abstractions from the text
in section NUM we detail the design of gate
attributes may be the result of linguistic analysis e.g.
adding a semantic processor to complement a bracketing parser
NUM a related issue is storage overhead
pos tags or textual unit type
in this example one may consider enjinia engineer and akkusu facsimile to be semantically similar to 4in most wsd systems candidates of word sense are predefined in a dictionary
we illustrated the paradise framework by using it to compare the performance of two hypothetical dialogue agents in a simplified train timetable task domain
a general evaluation framework requires a task representation that decouples what an agent and user accomplish from how the task is accomplished using dialogue strategies
we have been inspired to some extent by the premise that everyday person to person dialogues whether it is a booking clerk at a theater responding to a customer s enquiries or a teacher helping a pupil with a mathematics problem are in some sense scripted
the reference answer approach requires canonical responses i.e. a single correct answer to be defined for every user utterance
an analysis of the prototypical patterns that ciaula assigns to these classes suggests that despite the shared wordnet tags verbs in these classes are very different
NUM NUM arguments on the basis of switching reference with structural nonidentity
after one round with spinksi tysonj beat him
for example consider the nonelliptical example 9a
b ivan loves his mother and jamesj loves hisi mother too
NUM a ivan loves his mother and jamesj does too
mother ivan e p ax love x
in this paper it refers to a situation in which a higher level activity is realized through the execution of lower level sub steps
a more detailed analysis of how the situations that give rise to them differ from those for other purposes is yet to be performed
this process continues either until no distinctions can be found or until there are not enough examples on which to base the distinctions
the corpus developed for this study was taken from various types of instructional text including instruction booklets recipes and auto repair manuals
it contains approximately NUM clauses NUM words of instructions taken from NUM different sources representing a diverse array of process types
this analysis technique is designed to identify covariation between elements of the communicative context on the one hand and grammatical form on the other
rather imagene s realization statements allow the insertion of subscripted elements that can correspond to lists of sequential commands multiple preconditions etc
for example if there is a rule vp np verb where verb can be realized as a gap then after compilation a rule of the form vp np will exist
moreover during the consultation of the table we need not worry about modifications to it in contrast to an approach in which the table would be maintained as the value of a prolog variable
also note that goal weakening is complete in the sense that for an answer a to a goal g there will always be an answer a t to the weakening of g such that a t subsumes a
NUM this is not strictly necessary but is often useful because it decreases the size of the tables in this approach tables are redundancy free and hence minimal
it consists of the rule name and two lists of result item references representing the list of daughters left of the head in reverse and the list of daughters right of the head
as a result there will always be at most one matching clause in the linking table for a given goal category and a given head category thus there is no risk of obtaining spurious ambiguities
van noord efficient head corner parsing contains the following two clauses relating categories with functor x NUM and y NUM head link x a b y a b
the problem may seem less acute than that posed by uninstantiated left most daughters for an active chart parser as only a search of the chart is carried out and no additional items are added to it
it is shown that in spite of the fact that bidirectional parsing seemingly leads to more overhead than left to right parsing the worst case complexity of a head corner parser does not exceed that of an earley parser
as discussed in section NUM the senses sharing the same label or cross referencing labels are frequently associated through various linguistic relations
in a broader context this paper promotes the progressive approach to knowledge acquisition for nlp as opposed to the from scratch approach
this evaluation provides an overall picture for the expected success rate of the method when applied to all word senses in the mrd
although our algorithm makes use of defining words with various semantic relations with the sense explicit computation of those relations is not required
we begin by giving the details of material used including the characteristics of definition sentences in ldoce and the organization of words in lloce
the translation proceeds as a cooperative process between the system and the user through interactive operations similar to kana kanji conversion method
a tree for the structure it is possible that coded in the dictionary is synthesized in the generation module
sometimes a verbal command does not include all the information required by the simulation
the global goal is approached by a series of attempts at subgoals each of which involves a set of interactions the subdialogs
for optimal performance these two grammars should as nearly as possible accept exactly the same word sequences
figure NUM flowchart of the xtag system
elementary trees are combined by substitution and adjunction operations
table NUM description of the tuple components
NUM NUM implications of ltag representation for ebl
the size of the fst obtained for each of the
if for example the training example was
figure NUM shows a flowchart of the xtag system
table NUM coverage and retrieval times for various corpora
a robust morphological analyzer for hebrew that gives for each word in the language all its possible analyses
NUM NUM er war auf dem weg nach hause
hence the overall performance of this system is much less promising in hebrew than in other languages
the gemini grammar formalism on the other hand is able to define grammars of much greater computational complexity
however one should balance consistency with commandment v adaptability be consistent but not rigid cf
the paper describes the use of these morpho lexical probabilities as an information source for morphological disambiguation in hebrew
it is currently being extended to provide exercise time control of all simulated u s forces in darpa s stow NUM demonstration
in general there may be more than one instantiation of the sem frame for a given instantiated set of case frame arguments and vice versa
the condition s s t s u u v u NUM is a way to specify that the substring in one but not both languages may be split into an empty string c and the substring itself this ensures that the recursion terminates but permits words that have no match in the other language to map to an instead
the algorithm applied to these sets and counters yielded the following probabilities p1 NUM NUM
ai jk 6ssuu j 5stuv k NUM ai k NUM s u j 6stuv k NUM ai jk 6ssuv j 6stuu k NUM ai k 6ssuv j 5stuu k NUM reconstruction
the same principle applies to nested structures also such as i who have acquired c c new j skills on up to the sentence level
since speed and lexical coverage are most important requirements conventional automatic machine translation systems developed so far are useful for this purpose
when utterance NUM is spoken there are two active task steps NUM performing the voltage measurement and NUM connecting a wire between the v omega a hole and connector NUM
they were given the basic rules on how to speak to the system including the need for carefully enunciated speech the requirement for verbie over bracketing the importance of hearing the acknowledging beep and special requirements for stating numbers
a definite form of a noun the sw set includes the indefinite form of the same noun
for this reason these transcriptions are not suitable for demonstrating the morphological ambiguity problem in the language
for instance the direct object is not obligatory for the basic sense of ye but has to be an edible entity if it is present
statistical approaches to disambiguation offer the advantage of making the most likely decision on the basis of available evidence
entropy between the situations without and with knowledge of the value of that feature equation NUM
there ore ext ensive hse has been inade of ups external liiacros
the possible eombinal ions are given i y the phrase structure rules of l he morl h grammar
in declarative sentences and wh questions the finite element of the verbal complex occupies the second position in the sentence
when specifying cset pred only no recursion wouht be performed n subj
input to i u f is a partially speciiied feature description which constrains the utterance to be generated
phrase la rgely depends o the kind of argutnenl s it s lexical head adtnit s
NUM furthermore since lexical rules in such an approach only serve in a precompilation step the generalizations captured by the lexical rules can not be used at run time
in fact disambiguation of verbs such as mo orneru in which bgh is surpassed by vsm sbl maintains a precision level relatively equivalent to that for vsm
however since the overall precision is biased by frequently appeared verbs such as tsukau and ukeru our word similarity measurement is not necessarily inferior to other methods
the third step of the corpus study intended to show that it was possible to add translational information on the already obtained pragmatic and morpho syntactic information
the corpus we chose to study is the maintenance manual for the super puma helicopter written in french and its attested human translation in english NUM
being enriched by pragmatic and morpho syntactic annotations it considerably helps the evaluator to clearly identify the phenomena well or badly handled by a machine translation system
first it allows the evaluator to have at his disposal a whole set of potential test data which are clearly representative of his real industrial needs
practically we decided to assign to each utterance of the text a label indicating its textual and discursive status according to the ata NUM indications
in particular they allow one to suggest heuristics for the processing of linguistic phenomena which in general are known to be complex problems for nlp
the identification and formalisation of the linguistic constraints and needs illustrated in corpora represent a major step during the evaluation process of machine translation applications by an industrial user
the test set is based on the notion of equivalent textual sequence pairs directly extracted from the aligned french english original studied corpus
infinitive in french has no intrinsic value of imperative all the infinitive verbs in french are not necessarily translated by an imperative verb in english
corpus study is not a new concept in the nlp domain but the methods used can be quite different depending on the expected results and applications
practically the increasing availability of corpora provides the possibilities of creating domain dependent grammars
grammars acquired from different domains or classes and different sizes of the training corpus
however on the evidence provided the guidelines generalise well to a different dialogue and task type el
tit database provides oitv jliotl graphical browsit g and editing of tit data using lmll down menus fbr tinit e hmtain fields s o igure NUM as well as standard import and export fa cilities to exchange data with external applications
test data and am otations in tsni p test suites are organized at four distinct representational levels null ore data the re of the test data c nsists of the individual test items together with all ge neral categorial and structural inforlnation that is indepen lcb lent of a token phenomenon or application
m andoned in fnvor of the notion of a tal al as in which test ilellis tl sl ored l ogether with a rich invenl ory of asso iated liuguisti mm n m linguist i lllllo lcb i iolls
file dit database becord program itun ulilldou bl o se l igurc NUM screen dural o the tsdb NUM test item window the underlying relational d tabase allows parallel browsing and editing of multil le r qai iot s
tim diagnosl ic cvahmtion of a conint rcia nlp product enal led acrosllatiale to give a precise accolllt of t h t yl of informal ion ol tainable from th its of tsnlp
these developments comprise amongst others further extensions of tile test data possibly taking into account aspects of morphology and discourse customization tools which support the adaptation of the test data to specific domains and applications as well as tools and methods which relate the isolated test items to corpora in order to determine their frequency and relevance
q h systolii h 0s n l lltasger l h l hcil ilt ll li of oml lt mentation esl cially iiol ill a ljt tival phrases
while the members of the project will continue this work outside developers and users of nlp applications are invited to contribute to these resources which can become a reference standard only if they are truly public domain
the left part represents the elements involved in the learning of the expanded usst exemplified with a single training pair
because of the limitation of their system to non recursive grammars and the other differences discussed in section NUM global thresholding represents a significant improvement
collins personal communication reports a NUM speedup when this technique is combined with loose beam thresholding compared to loose beam thresholding alone
the full algorithm contains additional checks that our thresholding change had the effect we expected either increased time for decreased entropy or vice versa
also the question of when an agenda based system should stop is a little discussed issue and difficult since there is no obvious stopping criterion
now s x s gives the overall probability of being in s
while this technique is used as part of many search algorithms beam thresholding with pcfgs is most similar to beam thresholding as used in speech recognition
we use one additional optimization keeping track of the descendants of each nonterminal in each cell in prevchart which are in the corresponding cell of chart
for instance the first pass could use regular nonterminals such as np and vp and the second pass could use nonterminals augmented with head word information
each cell in the chart corresponds to a span of the sentence and each cell of the chart contains the nonterminals that could generate that span
to optimize a single threshold we could simply sweep our parameters over a one dimensional range and pick the best speed versus performance tradeoff
such terms play an important role in the approach to be developed
in the eutrans project subsequential transucers are used as the basis of translation systems that accept speech and text input
in figure NUM st is composed of sub l and sub st
undermining linear order for just this assumption
3a sequent f a indicates that the succedent for
our genera goal here is to produce hypotheses about segmentation and dimog acts as early as possible in an incremental manner
each word of an utterance is processed incrementally and passed to tile seg null mentation parser and to the dialog act network
so far we llave concentratcd on single utterances and we do not account for the relationship between utl erances in a dialog
furthermore another class of errors is characterized by time and location specifiers which can occur at the end or start of an utterance
together with each word the segmentation parser receives syntactic and semantic knowledge about this word based on other syntactic and semantic modules in screen
we have chosen the literal word by word trauslation since our processing is incremental and knowledge about the order of the german words matter for processing
for example in our example turu below there arc several utterances and each of them has a particular dialog act as shown below
this is important control knowledge for the dialog act network since without knowing about utterance boundaries the dialog network may assign incorrect dialog acts
we have shown that a symbolic segmentation parser and a learning dialog network can be integrated to perform dialog act assignments for spoken utterances
in spite of many projects in the atis and verbmobil domains there is not a lot of work on learning for the dialog level
l k allows x y c
mixing modes of linguistic description in categorial grammar
please state your departure and your destination
hello this is train enquiry service
dialogue strategies for improving the usability of telephone human machine communication
the output texts are close to detailed proofs in textbooks and are basically accepted by the community of automated reasoning
let us discuss the excerpt shown in figure NUM
figure NUM example of erroneous confirmation
this was interpreted as a departure hour
which hour do you want to leave
sentence boundary is called the prefix and the portion following it is called the suffix
furthermore instead of recalculating the viterbi parse of the training data from scratch when a move is applied we use heuristics to predict how a move will change the viterbi parse
a cosine measure is used to gauge the similarity between constant size blocks of morphologically analyzed tokens
in this manner an exponential model is incrementally built up using the most informative features
the word turps chosen by the algorithm or the quantitative performance of the resulting segmenter
a separate set of s t pairs were extracted from the wsj corpus
the network is trained automatically using a language specific knowledge source a dictionary of definitions
the figures indicate features that are active over a range of sentences
similarly the figures represent features that are active over a range of words
language models expressed as a probabilistic grammar tend to be more compact than n gram language models and have the ability to model long distance dependencies NUM NUM NUM
the mlr achieves better performance than the mdr
NUM der mexikanische verband fiir menschenthe mexican association for human rechte beschuldigt die behsrden
all architects should hand in hand work all architects should work hand in hand
bmb system user bel user replace p34 p104 NUM the system then applies the acceptance rule for refashioning plans rule NUM and so adopts the refashioning as mutually believed
the second is s attrib entity predicate and is used for describing an object in terms of an attribute entity is the discourse entity of the object and predicate is a lambda expression such as x
in the second part of the plan inference process we evaluate each derivation by attempting to find an instantiation for the variables such that all of the constraints hold with respect to the hearer s beliefs about the speaker s beliefs
since there is only one action if it is uttered in isolation it will be ambiguous NUM we use the term clarification since the conversational moves of judging and refashioning a referring expression can be viewed as clarifying it
this involves three tasks first a single candidate referent is chosen second the referring expression is refashioned and third this is communicated to the hearer by way of the action s actions which was already discussed
given an effect the plan constructor finds a plan derivation that has a minimal number of primitive actions that is valid with respect to the planning agent s beliefs and whose root action achieves the effect
if the judgment was reject plan or postpone plan then the evaluation of the judgment plan should enable the hearer to determine the action in the referring plan that the speaker found problematic due to the constraints specified in the action schemas
even if the speaker thought that the referring expression as it stands were adequate since the candidate set cand contains only one member she will construct a non null expansion since the replacement is the recursive version of modifiers
by additionally offering online menus for commands and labels the tool suits beginners as well as experienced users
as we noted earlier the corpus data is eventually represented in our system as a graph with the nodes corresponding to adjectives and the links to predictions about whether the two connected adjectives have the same or different orientation
the compound analysis in automatic indexing aims at the improvement of recall performance by extracting useful component nouns fi om compound nouns
we train our log linear model on l la excluding links between morphologically related adjectives compute predictions and dissimilarities for the links in l and use these to classify and label the adjectives in an c must be at least NUM since we need to leave some links for training
NUM the gourmet insisted that it is done that way at the most fashionable dinners the girl reluctantly agreed
flea these expressions are adjuncts but in the following examples they are comt lelnents since they are
np pp pval to NUM the front part of my head was called a face and i could talk with jr
this tagging task led to the refinement of already existing classes and to the addition of classes that had previously not been defined
tagging also provides statistical data which will allow users to select more common complelnents of a particular verb and ignore rare usage
therefore we concluded that the fact that these verbs can occur without their complements is a fact about the graminar of parentheticals
ipor examt le in relal ive clauses lhe inll l in nt
this last group was estm lished for verbs like define and forecast which do not take members of the original fi ame groups
in contrast both grammatical role and surface position were shown to affect the cf ordering
it furthermore suggests that neither thematic role nor surface position is a determinant of the cb
d tommy likes it better than the bear too although the silly thing is bigger
tm NUM a have you seen the new toys the kids got this weekend
susan told betsy e wine collecting gives her expertise that s fun to share
it fits within a larger effort to provide an overall theory of discourse structure and meaning
b yesterday was a beautiful day and he was excited about trying out his new sailboat
for both data sets the best fmm results are superior to those of cos throughout
centering if u is an utterance of some phrase NUM for which c is the semantic interpretation
NUM a a the vice president of the u s is also president of the senate
in written language recency of mention is known to be an important factor as are syntactic and semantic parallelism the markedness of expressions and constructions and so on
all entities mentioned in a discourse segment purpose and all related entities e.g. parts of mentioned entities are stored in a focus space
some for example needed two questions to find out who is the secretary of the nici others just one of which two subjects indeed used the induced inferential anaphor
for example when the user is entering an input sentence the clause talking to user system is true so the pronoun ik i refers to the user
this rapid reduction in the number of parameters results in a rapid increases in accuracy figure NUM and recall for aic and bic figure NUM relative to the significance tests as they produce models with smaller numbers of parameters that can be estimated more reliably
since the system in this case knows what live in relation does hold it can respond cooperatively with nee hij woont in amsterdam
the sentences with the referring expressions as described in the previous section were processed by edward s referent resolution model and two alternative referent resolution models
by having five subjects two men and three women interact with edward we obtained a total of NUM real user generated referring expressions
we consider our true performance to be the complete responses
consider the sentence in figure NUM two phrase categories are to be determined vp and s
to capture the notion that speakers are normally consistent in the suppositions that they choose to express we need to know how different suppositions relate to each other
NUM speech act names that end with the suffix ref take a description as an argument speech act names that end with if take a supposition
alternatively the conversation might break down leading one participant or the other to decide that a misunderstanding has occurred and possibly attempt to resolve it
the former corresponds to repairs that begin exactly n NUM turns after the problematic utterance while the latter allows an arbitrary number of intervening pairs of turns
the model specifies the relationship between this reasoning and discourse participants beliefs intentions and previously expressed attitudes as well as their knowledge of social conventions
like prolog it applies a resolution based procedure reducing goals to their subgoals using rules of the form goal subgoall a a subgoaln
NUM NUM to keep this example of manageable size we will not assume that he has any expectations regarding testif or testref although in life he would
here we will discuss example NUM from russ s perspective considering in detail russ s reasoning about each turn and showing an output trace from our implemented system
for example while an invitation to visit at 6pm might create an expectation that dinner will be served it does not express an intention to serve it
by suppressing unreliable decisions precision can also be increased to range from NUM to over NUM
it is worth discussing however the different approaches to combining information from non adjacent words
this contrasts with the standard backoff model in which truncation causes significant increases in perplexity
the mixed order smoothing was found to reduce the perplexity of unseen word combinations by NUM
table NUM shows those perplexities for the two smoothed trigram models baseline and backoff
computational linguistics volume NUM number NUM
the root morphemes are lcb ktb rcb and lcb db rj rcb and the vocalism morphemes are lcb a rcb active and lcb ui rcb passive
the analysis appears in NUM NUM appear in three forms i consonantal texts do not incorporate any short vowels but mattes lectionis NUM e.g.
NUM a branch can either lead to the assignment of a class or to another test
the standard deviations in table NUM are often close to NUM NUM or NUM NUM of the reported averages
table NUM results for ml mixed order models m de
then the first branch is taken and the potential boundary site is assigned the class non boundary
all scores improve in condition NUM with precision and fallout showing the greatest relative improvement
reduction of c type errors raises recall and lowers fallout and error rate
in e0 the shifted vowel was analyzed earlier as an omitted stem vowel ore stray whereas in e1 it was analyzed earlier as an omitted spread vowel om sprv
the algorithms are then evaluated by examining their performance in predicting segmentation on a separate test set
we present two methods for developing segmentation algorithms from training data hand tuning and machine learning
ethe actual tree branches on every value of worda the figure merges these branches for clarity
because we have already used cross validation we do not anticipate significant degradation on new test narratives
therefore we currently investigate the benefits of a morphological component and percolation of selected information to parent nodes
in order to simplify intermediate processing semantic interpretation ddo merging pattern matching a flat ddo job situation was defined whic h contains the equivalent of the information in a particular succession in and out pair
fillers found nearby are of high confidence while those farther away receive worse score s low numbers represent high confidence high numbers low confidence thus NUM is the highest confidence score
also the message by message output allowed us to zero i n on messages where our performance was particularly bad and allowed us to add lexical items or semanti c interpretation rules based on the key sentences in the message
the ne task is the simplest task and makes use of only lightweight processes the first three modules of the plum system th e message reader the morphological analyzer and the lexical pattern matcher
the semantic representation of a phrase in the text only includes information contained nearby the discourse module must infer other long distance or indirect relations no t explicitly found by the semantic interpreter and resolve any references in the text
we believed that this was important because the nature of the messages from the dry run was quit e different than that of the test messages because they had been chosen based on their relevance to the st domain
we would incorporate the spatter parser which parses far mor e accurately than fpp does and would look to aid to our domain independent semantic lexicon so that there i s more semantic information to support merging of entity descriptions
a second key feature is partial understanding by which we mean that all components of plum are designed t o operate on partially interpretable input taking advantage of information when available and not failing when it i s unavailable
NUM though we believe that an additional NUM NUM point improvement in f would have been achievable with mor e calendar time than NUM days for the st task to achieve an f above NUM is likely to require significant overall improvement
in other cases tipster prodded and encouraged these r d communities to investigate problems which they might not have considered on their own mluauve
likewise one participant in the information extraction component has participated in all three tipster phase i evaluations muc NUM to muc NUM and met
a series of specific tasks which when successfully accomplished would move the r d community significantly closer to the program s final objective
this briefing has been frequently opened with the observation that multiple agencies have been working closely together on this program since NUM
tipster has just completed its second two year phase and is poised to begin phase iii a three year effort this coming october
during the NUM NUM darpa planning meetings a large number of important yet diverse text handling processing and exploitation requirements surfaced
it is unlikely that any of the individual participating agencies could have started and sustained a program of this magnitude by itself
NUM throughout the entire tipster text program all of the contractors have willing shared data files and software modules with the other participants
these two enabling technology areas are now well known and closely associated with the tipster program document detection and information extraction
almost from day one there has been an underlying current of give and take of teamwork of consensus building
job situations are a flattened version of the succession and in and out objects
for any of the lit application opportunities itemized above a methodology needs to be developed for the selection of the subset of lrs which are applicable to a given lexical entry whether base or derived
section NUM briefly reviews the cost factors associated with lrs the argument in it is based on another case study the adjective related lrs which is especialy instructive since it may mislead one into thinking thai
this observation confirms that categorial information does not reduce nondeterminism
an overview of this design is shown in figure NUM
log n time and space for acyclic structures
this position can occur both in main and embedded clauses
english is headinitial and the specifier precedes the head
NUM in figure NUM i show schematically how these algorithms build chains
NUM there are two routes that we can take to do this efficiently
otherwise the covering grammar would not be sufficiently general
this search for generality is not unique to gb theory
the central idea of our approach that there are systematic paradigmatic meaning relations between lexical items such that given an entry for one such item other entries can be derived automatically is certainly not novel
for example although meanings of many verbs are represented through reference to ontological events and a number of nouns are represented by concepts from the object sublattice frequently nominal meanings refer to events and verbal meanings to objects
to include a new lemma in the dictionary it is necessary to obtain its morphological characteristics
the morpho semantic generator produces all predictable morphonological derivations with their morpho lexico semantic associations using three major sources of clues NUM word forms with their corresponding morpho semantic classification NUM stem alternations and NUM construction mechanisms
an integrated scoring function capable of incorporating various knowledge sources to resolve syntactic ambiguity problems is explored in this paper
also four anonymous reviewers comments on earlier drafts were very helpful to us in preparing the final version
robust learning smoothing and parameter tying function can thus be expressed as follows
both rule based and statistics based approaches have been proposed to attack this problem in the past
with that scoring function various knowledge sources can be unified in a uniform formulation
the geometric mean however fails to fit into the probabilistic framework for disambiguation
the comparison between turing s procedure and the back off procedure thus varies in different cases
this ratio varies with the adopted language models but is always larger than NUM NUM
this assumption is inappropriate because different linguistic information may contribute differently to various disambiguation tasks
in other words the inter level correlation is assumed to be a first order markov process
i would like a room with shower and with view on the garden the spoken input is first processed by the hmm based signal processing component which produces a word lattice which is then mapped into ranked strings of phonetic words
jean told marie that his her book is selling well in such a case a dialogue box specifying all possible sl antecedents is presented to the user who can select the most appropriate one s
the itsvox project aims at a general interactive multimodal translation system with the following characterics i it is not restricted to a particular subdomain ii it can be used either as a fully automatic system or as an interactive system iii i it can translate either written or spoken inputs into either written or spoken outputs and iv it is speaker independent
jean does n t like lawyers advocadoes b avocats homme de loi la lter fruit b uit another common case of interaction that occurs during transfer concerns the interpretation of pronouns or rather the determination of their antecedent
thus g a e dgr lemma NUM let v e k be a possible instance of the vertex cover problem
in order to evaluate our dialogue system with the multi modal interfaces we investigated its performance through the evaluation experiments paying attention to usefulness of our system
obs prop obj propname propvalue propvalue unspecified observing a property
the previous decision rule for utterance verification focused exclusively on the local information about parsing cost and ignores dialog context
recognized be down it be yes displaying be knob flashing seven then figure NUM sample misrecognitions correctly parsed
as can be seen from the example the system usually understood the user utterance but not always
for strategy NUM we explored the impact of raising and lowering the threshold on the over and under verification rates
this of course does not preclude the possibility that domain dependent interaction may be more useful in other domains
this subset consists of the expected meanings that denote a normal continuation of the task
in all cases one main expectation is an acknowledgment that the fact is understood
example a wh question e.g. what is the switch position
the operational way and the order of complexity are similar to the word prediction using grammars
the inclusion of a new lemma in the lexicon might cause some lack of syntactic information
the objective of solving the mir problem is to provide the analyst user with a flexible high performance tool to allow retrieval of relevant information from multilingual corpora without the need for prior translation of large volumes of text
key attributes of the context vector approach are as follows during this effort the initially proposed context vector approach using human defined coordinates and initial conditions was extended and refmed to allow fully automatic generation of context vectors for text symbols stems based upon their demonstrated context of usage in training text
the time requirements for training this set of vectors scale as o nn where n is the number of word stems in the vocabulary and n is the average number of word stems found to co occur and or be related to any given word stem usually on the order of several hundred
NUM the user can enter multi lingual queries based on tie words as well as non tie words
in the spanish portion of this example the window has as its center the word ataque
additionally the cost of development tuning and validation of this approach is a hindrance to widespread use
clearly if all material was translated to a uniform representation say english the problem is solved
this property of quasi orthogonality is important because it serves as the initial condition for the context vector learning algorithm
extend the api to support query by example and cancel search
software can be inserted into any layer with minimal impact on the other layers
prides was designed and developed by logicon and acsiom from june NUM through april NUM
the routing engine and document manager process user profiles and route these incoming documents to mail folders
the user may also search within certain fields identified by the fbis users as particularly content rich
then as new articles are received daily each is compared to the interest profile
pa is responsible for performing any prides specific activity that is not provided by our tipster components
a in paktab measure NUM active and u in puktib measure NUM passive
NUM ule r2 maps the extrmnetrieal consonant in a stem i.e. the last consonant in a stem to i he sm face
standard two levels models can describe some lasses of infixation but resorting to tile use of ad hoc diacritics which have no linguistic significance e.g.
oil remaining measul e has not been discussed measure NUM NUM is derived by prefixing the base template with it
rule NUM NUM handles monomoraie syllables mapping r c v e on the lexical tapes to cv on the surface tape
i1 NUM takes care of measure NUM it represents the operation prefix lcb t rcb and the rule o li c left
two level rules map the two strings the rules are compiled into finite state transducers where lexical strings sit on one tape of the transducers and surface strings on the other
this paper establishes a framework under which various aspects of prosodic morphology such as templatic morphology and infixation can be handled under two level theory using an implemented multi tape two level model
types of information in a document the basic tipster architecture distinguishes between two types of information information conveyed through the conventions of a particular natural language text and information conveyed through other conventions
although the architecture will theoretically be extendible to accommodate the processing of any part type e.g. lisp spreadsheets it is expected that the emphasis for the tipster program will be primarily on processing textual information
application developers may also find that individual icd specifications need to be modified and extended to meet their needs
the relationship between the tipster architecture and tipster applications is expected to be a close and mutually beneficial one
most obviously he will submit information requests if the application is a detection application this will most likely be a retrieval request or a routing request and if the application is an extraction application it will probably be a request to fill templates with information from documents
the end user is expected to be someone in a united states government agency who uses text processing applications
differences between architecture and application as can be seen from the above the differences between the architecture and a tipster compliant application fall into three main areas functionality covered computing environment and internals vs interfaces
this process will result in an enrichment of the architecture with the experience gained from specific implementations as well as the beginnings of a library of information about what tipster compliant modules and components exist throughout the government community
the information service has found a proper travel plan and starts her presentation
figure NUM a scenario employing an information extraction system sub j kinston military rail depot
the second component merges templates created from different phrases in the text that overlap in reference
null we then calculated how often this approach yielded the correct results in each training set
another area where we would like to make changes is in the order of reduction stages
an evaluation of these approaches is then given in section NUM
i NUM description of experiment the parsing system used in this experiment is based on a tomita NUM style parser
since japanese is headfinal the sentence initial case element kare ga he sub j can be the subject of either kau buy or yomu read causing syntactic ambiguity
the moment the first japanese character is typed in the main translation window is opened and all subsequent characters are typed in to that window instead of the editor window
for the je direction they will not be satisfied with the raw output of conventional mt systems but it will be too laborious to write down english sentence from scratch
the user can freely modify the displayed characters at any time and the system responds by invoking an appropriate procedure such as morphological analysis
selecting before translation is much easier than after translation because the word order and understood syntactic structure is that of the user s native language
in our method interactive operations are initiated and guided by the user and all interactive operations are optional except for a small number of translation triggers needed for translating component sentences
third a whole unit with more than one word can be detected and selected in the same interface as translation equivalent for a single word
for example renraku wo toru contact obj take should be translated to make a contact not take a contact nor get a contact
this method seems to leave too much burden to the user since the user must explicitly specify which portions of the text should be translated and in what order
these are often unmarked in japanese and require exceedingly difficult inferences to recover
NUM multiple constraints apply to each lexical decision often in a highly interdependent manner
in other words how often have we seen a and b in the past
NUM NUM NUM the new company plans to establish it in february
so our lattices included paths like him saw i as well as he saw me
our approach is to apply all patterns and insert all results into the word lattice
fortunately the statistical model steers clear of sentences containing nonwords like potatos and photoes
in top down parsing backtracking is employed to exhaustively examine the space of possible alternatives
furthermore lexical constraints are not limited to the syntagmatic interlexical constraints discussed above
the texts are segmented by variable distance algorithm gao j and chen x x
the computational complexity of pa rsing fbr tags however is g ndeg which is t3 r greaw r than tha t of cfg parsing
it should be first noted however that cfg could produce exponentb lly ambiguous parses for some input in which ease we can only apply heuristic or stochastic measurement to select the most promising pa rse
cfg skeletons in q transla tion of an input string s essen null tially consists of the following thre e steps parsing s by using the source cfg skeletons propagating link eonstra ints
this makes possible the mapping of korean scrambled m gument structures into english argument structures
this is reflected in the transfer lexicon of figure NUM
each elementary tree in the derivation is considered with the features given from the derivation through unification
using mc tags allows the scrambled argument structure to be represented as a single set structure
for example a subject e.g. tom would have two korean structures as above
stags are a variant of tags introduced to characterize correspondences between tree adjoining languages
only when the highest priority structure fails will the next available structure be tried NUM
however translating a free order language such as korean to english is complicated
to perform np clustering we prepared two data sets in the first nps are described by their e terminological context in the second one both the e terminological context and the h terminological context obtained with the h link within pus are used
the second motivation was that long terms tend to be more precise than short terms and content words should be as precise as possible
as is often the case our parallel corpus was not precisely of the same domain as the trec document collection for the ultimate evaluation
this includes merging the expectations for james robert l
let us make this point more precise
each ambiguity kernel begins with its header
first they must be fine grained enough to allow the intended operations
usually linguists say that u has several representations with reference to g
the difference is that a turn begins with a speaker s code
in contrast to psg approaches dg requires non projective analyses
email lcb neuhaus nobi rcb coling uni freiburg de
additionally tts is used instead of pre recorded natural speech v vi
the dm module fills the gap between the speech recognition and the controller
however this research program will not be carried out within vodis
this raises an obvious question how applicable are the NUM commandments
the dm module has a modular structure
experiments will be the input for the development of the second prototype
in the section hereafter we discuss the actual implementation in more detail
as mentioned this first prototype will be extensively evaluated by users
we follow the common treatment of resolving ambiguities using this contextual information
if those guidelines were observed in the design of the system s dialogue behavior we assumed this would increase the smoothness of user system interaction and reduce the amount of userinitiated meta communication needed for clarification and repair
a tool which only works or is only known to work on a single system in a highly restricted domain of application or in special circumstances is of little interest to other developers
we model the relationship between s and t as one of the following s and t have identical slot values s is properly subsumed by t s properly subsumes t or s and t are otherwise consistent
the present paper will present ongoing work on one of the tools that are planned to result from disc
these developments highlight the needs for novel tools and methods that can support efficient development and evaluation of sldss
the reason is that it is difficult to realistically simulate the limited meta communication and background understanding abilities of implemented systems
the emerging system seems to understand the following types of domain information NUM departure airport including terminal
in addition to the actions performed by the head transducers this derivation process involves the actions selection of a pair of words w0 e v1 and v0 e v and a head transducer m0 to start the entire derivation
these figures are not very meaningful in themselves however because many identified design guideline violations were identical
it should be noted however that the number of these disagreements has been exaggerated by the data abstraction that went into the creation of a small number of types as shown in figures NUM and NUM
central issues in putting a dialogue evaluation tool into practical use laila dybkj er niels ole bemsen and hans dybkj er the maersk me kinney moiler institute for production technology odense university campusvej NUM NUM odense m denmark emails laila mip ou dk nob mip ou dk dybkjaer mip ou dk phone NUM NUM NUM NUM NUM fax NUM NUM NUM NUM NUM
this formulation allows the interface to reflect each conceptual part of the query the medical terms the diagnosis terms and the software terms
the document whose title begins va automation means faster admissions is quite likely to be relevant to the query and has hits on all three term sets throughout the document
the setting min overlap span refers to the minimum number of tiles that must have at least one hit from each of the three term sets
the building blocks for these theories are phrasal or clausal units and the targets of the analyses are usually very short texts typically one to three paragraphs in length
this article describes a paragraph level model of discourse structure based on the notion of subtopic shift and an algorithm for subdividing expository texts into multi paragraph passages or subtopic segments
description the parsing process is incremental in the sense that the linguistic description attached to a given transducer in the sequence relies on the preceding sequence of transducers covers only some occurrences of a given linguistic phenomenon can be revised at a later stage
sub grammars for technical manuals are specially useful to block analyses that are linguistically acceptable but unlikely in technical manuals a good example in french is to forbid second person singular imperatives in technical manuals as they are often ambiguous with nouns in a syntactically undecidable fashion
unlike aps nps are marked in two steps where the basic idea is the following we first insert a special mark wherever a beginning of an np is possible i e on the left of a determiner a numeral a pronoun etc
in the primary segmentation step we mark segment boundaries within sentences as shown below where np stands for noun phrase pp for preposition phrase and vc for verb chunk a vc contains at least one verb and possibly some of its arguments and modifiers
therefore we use three kinds of tbeginvc to handle different levels of uncertainty a certain tbeginvc tbeginvc1 a possible beginvc tbeginvc2 and an initial tbeginvc tbeginvcs automatically inserted at the beginning of every sentence in the input text
step NUM if there is still a tendvc that was not matched in NUM or NUM then it is matched with a possible tbeginvc2 if any and the sequence is marked with vc and vc
this example represents an interesting case of deixis and at the same time a challenge for the pos tagger as nuit is more likely to be recognized as a noun night than as a verb endan null gers in this particular context
this difference in performance is due to the fact that on the one hand we used the technical manual text to develop the parser and on the other hand it shows much less rich syntactic structures than the newspaper text
the n in the top drs is a mnemonic for now the utterance time
we adopted the break even point as a single measure for comparison which is the one at which precision equals recall a higher score for the break even point indicates better performance
it is able to represent the differences between categories more precisely than hcm and thus is able to resolve the two problems described in section NUM which plague hcm
for the sake of notational simplicity for a fixed i let us write p kjlci as oj and p wlkj as pj w
where f kj cl is the frequency of the cluster kj in ci and f cl is the total frequency of clusters in el
it assumes that a cluster kt is distributed according to p kj ci and calculates the likelihood of each category ci with respect to the document by
for a probabilistic approach to document classification the most important thing is to determine what kind of probability model distribution to employ as a representation of a category
for example because goal is assigned only to ks we use as its frequency within that cluster the total count of its occurrence in all categories
for example named entity annotations which identify and classify proper names e.g.
modules running as external executables might also be recompiled between runs
gate a general architecture for text engineering
the work took around NUM person months
alternatively objects may be developed from scratch for the architecture in either case the object provides a standardised api to the underlying resources which allows access via ggi and i o via gdm
at any point in time the state of execution of the system or more accurately the availability of data from various modules is depicted through colour coding of the module boxes
the ggi has functions for creating viewing and editing the collections of documents which are managed by the gdm and that form the corpora which le modules and systems in gate use as input data
first there is no theory of language which is universally accepted and no computational model of even a part of the process of language understanding which stands uncontested
when using aic the only difference in the feature set selected during fss as compared to that selected during bss is the part of speech feature that is found to be irrelevant during bss l2 is removed and during fss r2 is never added
thus creole developers reuse gate data visualisation code with negligible overhead
the resulting system vie is distributed with gate
in general word senses should be used to supplement word based indexing rather than indexing on word senses alone
an example definition for the lexeme come illustrates all three of these types come
it is important to emphasize again that the judges were not made aware of the purpose of the experiment nor were they told that any of the explanations were computergenerated
by securing the services of such a large number of domain experts we were able to form relatively large panels of NUM writers and NUM judges figure NUM
the edps were used to automatically construct hundreds of explanations the explanation planner used the edps to construct explanation plans and the realization system translated these plans to natural language
in this invocation of apply edp as opposed to the top level invocation by the explain algorithm apply edp is given an elaboration node instead of a topic node
without a realization component the plans produced by an explanation planner would need to be manually translated to natural language which would raise questions about the purity of the experiments
our goal is to develop a representation of discourse knowledge that satisfies two requirements it should be expressive and it should facilitate efficient representation of discourse knowledge by discourse knowledge engineers
hence in addition to discourse knowledge about local content determination an explanation system that produces multiparagraph explanations must also possess knowledge about how to perform global content determination and organization
the kb accessors achieve robust performance in four ways omission toleration they do not assume that essential information will actually appear on a given concept in the knowledge base
each of the nine accessors in our library table NUM can be applied to a given concept the concept of interest to retrieve a view of that concept
let NUM be the set of all rs in a regular grammar p be an auxiliary boundary symbol not in the grammar s alphabets and p ida p
in the present example this mode of default specification can be applied as follows
deftnitional sentences already seen in section NUM include do mor past did
yet it does not use meta rules
figure NUM super classes of w0n0vnl pass
by whom will jean be accompanied
the three dimensions introduced in section NUM NUM NUM
dimension NUM remember we talk about descriptions of trees
default inheritance is often necessary to deal with exceptions
the equations slot is not shown
verb in a wh question on the agent
this pair of super classes defines an actual subcategorization
this is the major reason why word segmentation accuracy levels off or decreases at a certain point as the size of the initial word list increases
one of the most important relation to be extracted from machine readable dictionaries mrd is the hyponym hypernym relation among dictionary senses e.g.
this section discusses extensions to the earley algorithm that go beyond simple parsing and the computation of prefix and string probabilities
this section proposes a way of sharpening our intuition on available readings and re examines traditional linguistic judgments on grammatical readings
comments from the anonymous reviewers have also been very useful in preparing the final version of this paper
NUM u dr smith is not teaching al NUM he is going on sabbatical next year
this paper focuses on the evaluation and modification of proposed beliefs and details a strategy for engaging in collaborative negotiations
focus modification decides whether to attack the evidence proposed to support bel or bel itself step NUM
the belief level of the dialogue model consists of mutual beliefs proposed by the agents discourse actions
collaborative negoti ion occurs when conflicts arise among agents developing a shared plan NUM during collaborative planning
given an unaccept belief bel and the beliefs proposed to support it select focus
the system will try to establish the mutual beliefs NUM as an attempt to satisfy the precondition of modify node
null although this latter task could be seen easier than general wsd NUM genus are usually frequent and general words with high ambiguity
the notational convention will be a s follows
some of the complex transfer issues handled in the transfer phase will be presented in this section first a significant amount of head switching is performed to resolve the lexical and structural differences in the english and turkish languages
in the process of solving a goal there may be many branches that can be taken in an attempt to prove a goal
for example during a medical exam a patient may complain of dizziness nausea fever headache and itchy feet
an agent is said to have dialogue initiative over a mutual goal when that agent controls how that goal will be solved by the collaborators
the project attempts to perform extensive research on turkish which will eventually lead to the development of an english to turkish machine translation system turkish language tutorial system a turkish dictionary and other software tools to be used in further research
singleselection in singleselection mode the more knowledgeable agent defined by which agent has the greater total percentage of knowledge is given initiative
in this case the smooth shift is the least effort transition because only the first element of the c of the preceding utterance has to be checked to perform the smooth shift transition while in the case of continue at least one more check has to be performed
morpho syntaetic type NUM the content words of the original term or one of their derivatives are found in the variant
our rule based algorithm learned a sequence of NUM transformations which improved the score from NUM NUM to NUM NUM a NUM NUM error reduction
thus the error should be seen as a typo
instead it is conveyed implicitly in a topic comment structure NUM
both the lexicon and guesser unambiguously map a surface form of any word that they accept to the corresponding class of tags fig
however improvement in accuracy can be expected since these transducers can be composed with transducers encoding correction rules for frequent errors sec
there are other factors at work as well
additional support has been provided by the nemours foundation
this morphological marking often conveys no extra information
a much different marking than what english requires
mter all of the documents have been class fled the routing scores are sorted with the highest ranking documents being those which are the most like the class profile than any other profde
we define syntactic likelihood as the geometric mean of the length probabilities rather than as the product of the length probabilities in order to factor out the effect of the different number of attachments in the syntactic trees of individual interpretations
for the sentence i ate ice cream with a spoon NUM there are two interpretations one is i ate ice cream using a spoon and the other i ate ice cream and a spoon
there have been a number of methods proposed to perform structural disambiguation using probability models many of which have proved to be quite effective alshawi and carter NUM black et al NUM briscoe and carroll
table NUM shows the breakdown of the result in which lex3 stands for the proportion determined by using lexical likelihood pzex3 lex2 by using lexical likelihood pl x2 and syn by using syntactic likelihood psyn
b to use the notion of length in defining a probabilistic model for the implementation of rap and alpp and c to employ the back off method to combine the use of lexical likelihood with that of syntactic likelihood
for the sentence john phoned a man in chicago NUM there are two interpretations one is john phoned a man who is in chicago and the other john while in chicago phoned a man
the advantage of the syntactic parsing approach is that it mgv embody heuristics principles effective in disambiguation which would not have been thought of by humans but it also risks not embodying heuristics principles already known to be effective in disambiguation
it is known in statistics that the number of samples required for accurate estimation of a probabilistic model is roughly proportional to the number of parameters in the target model and thus the data used for training length probabilities were nearly sufficient
figure NUM leftward complete sequence inside probability
wordnet does not differentiate between binary and n ary opposition n NUM or basically between binary and n ary antonymy n NUM and due to this implicit merging of relations the lexicographers intentions can not be automatically checked by a simple cardinality test for antosemy i.e. we do not know which of the cases of cardinality NUM of antosemy are inconsistent or not
coming across other perhaps more harmful cases see below figure NUM and figure NUM of a concept which is to be represented as a disjunction of concepts and itself a lexical gap we now suggest that the constellation of figure NUM was intended to say arzse NUM is an antosem of the one concept sit down NUM or he down
a practical lesson is that the design of dictionary relations should be such that they are tractable by formal checking and this is severely impeded if different relations are merged of which one has the checkable property p and another lacks it the merged relation has then lost the checkable property p examples from wordnet NUM NUM are meronymy which is treated in this paper only by a short note and antonymy
auto relationships our trainee students in their search for synonyms of a concept c tended to take designations of some superconcept of c whether the superconcept already exists in the thesaurus or not and added them as synonyms of c because these words are also used to denote the concept c
beyond these few slips we found more interesting examples of errors or of redundancies which were not detected by chance but by triggers created by terminologyframework from the specification of the operational semantics of the relations or mostly by queries to the database guided by our methodological interest
formally the heuristic rule of commutativity states for each concept c if antosem c is not empty then the equation hypernyrn antosem c antosem hypernym c or set inclusion in one direction or the other should hold
if we would interpret a set of synonyms as a synset then the rule reads for each synset s the set of the antonyms of the elements of s must be a subset of another synset the subset may even be empty or be the whole synset
this ot eration modularization can reduce exponential alnounts of redtmdant information in a grainmar and can consequently save corresponding alnounts of processing time
other sources of comi lexity are computing the fl ee coinbinadon and testing the result against the original as form
if the cases can be split in this manner we say the cases and by extension tilt group of dependent disjunctions are independent
so in the above examph m lcb NUM NUM rcb where NUM repr sents l he first disjunel ion and NUM represents l he second
indel rcb endent groups of disjunctions can be processed separat ely during unification rathe r than having to try every combination of one group with every combination of every other group
their approach produces a set of feature structures from a satisfiability algorithm such that all of the feature structures have the same shape but the nodes may be labeled by different types
it takes a disjunction of feature structures transforms them into a single feature structure with dependent disjunctions and then pushes the disjunctions down in the structure as far as possible
the need for ef icient inputs has been noted in the literature NUM but there have been few attempts to automatically optilnize gr mnnars tor disjunetiw unification algorithms
the rate at which a structure is built is a function of the urgencies of its dedicated codelets
thus the workbench can easily integrate new tools and upgrade existing ones
in each case the rule sequence learned from the training set resulted in a significant improvement in the segmentation of the test set
then it translates these terminal classes into the relevant elementary tree schemata in the xtag NUM format so that they can be used for parsing
NUM we ran four experiments using this corpus with four different algorithms providing the starting point for the learning of the segmentation transformations
in general a functional role is assigned to a dependency link and specifies the syntactic semantic relation between the head and the dependent
in the n lth column only the lt k eos whose sub sl is null can be inscribed
there have been many efforts to induce grammars automatically from corpus by utilizing the vast amount of corpora with various degrees of annotations
so the search space of dependency grammar may be smaller and the grammar induction may be less affected by the structural data sparseness
with the concept of word class tag the complexity is affected by the class tag size due to the class tag ambiguities of each word
but the construction of vast sized tree corpus or bracketed corpus is very labour intensive and manual construction of such corpus may produce serious inconsistencies
any dependency tree of a sentence always has the dependency z bos z eos as the outermost dependency
given the type lattice of the frame structure tl is the lowest upper bound in t of the paths in c and t2 is the greatest lower bound in tof the paths in c
our methodology is less demanding from the point of view of the required source information and possibly should be compared against one only of the levels mentioned in these works
the t ning phase has been evaluated over the rsd corpus and the resulting average ambiguity of a representative sample of NUM rsd verbs is NUM NUM while the corresponding initial wordnet ambiguity was NUM NUM
the synonymy sto of w in c i.e. the degree of synonymy showed by words other than w in the synsets of the class c in which w appears is modeled by the following ratio
in figure NUM i j the inside probability of lr i j is depicted
in figure NUM and NUM the double slashed link means complete sequence a sequence of null or more adjacent complete links of same direction
all of the tipster information extraction contractors were required to participate in muc NUM where the subject domain consisted of news reports on terrorism events
then in march NUM vice president gore presented the national performance review hammer award to the tipster text program in the reinvention of government
combinal ions of ce rtain fea turcs ll lve imcn spelled out in the type hierarchy
this process is re eaced breadth tirst mltil all onscituents are h aves
fig NUM shows an examph of morphoh gical e ltegorics reslionsible br nominal inlh ction
therefore we are differentiating the degree of compatibility of the different mismatches some mismatches are more serious than others
if cuet is presenl fuf performs recursion on these exl licicly given subscruct ur s only
ianear l rac e hmc c of onstituencs is sl ecitied in the
again all of these tipster data development activities have been previously reported on in the proceedings associated with each of the evaluation programs identified earlier
mil introduction i have been fortunate to have had the opportunity to be associated with the tipster text program since its inception in NUM
computational linguistics volume NUM number NUM with no capital letters
in the discussion of methods of representing context in section NUM NUM NUM
was performed we assume the entire brown corpus was used
depended on a very large lexicon with more than NUM NUM words
ing part of speech frequency data from which the descriptor arrays are constructed
adaptation to other decision tree induced for mixed case english texts
figure NUM decision tree induced when training on difficult cases exclusively
to force semantic determinacy we assign a unique index to those rare instances of categories i e left hand sides of cfg rules that do not have any distinguishing features to account for their differing semantic rule
for the experiments reported in sections NUM NUM we explore and compare different variations of the algorithms we evaluate those on two disjoint pairs of a training set and a test set both subsets of the reuters collection
learning problems in the natural language and text processing domains are often studied by mapping the text to a space whose dimensions are the measured features of the text e.g. the words appearing in a document
the frequency information can be obtained in various ways as discussed in the previous section
this section describes the structure of our adaptive sentence boundary disambiguation system known as satz
but this is not enough as one might need to state equality of any type of nodes like the s nodes in the above example
qzpro quasi zero pronoun is chosen when a sentence has multiple clauses subordinate or coordinate and the zero pronouns in these clauses refer back to the subject of the initial clause in the same sentence as shown in figure NUM
with NUM confidence factor which means no pruning of the tree the tree overfits the examples and leads to spurious uses of features such as the number of sentences between an anaphor and an antecedent near the leaves of the generated tree
because the nlp system is used for extracting information about joint ventures the mdr was configured to handle only the crucial subset of anaphoric types for this experiment namely all the name anaphora and zero pronouns and the definite nps referring to organizations i.e.
for example if a zero pronoun z NUM refers to another zero pronoun z l which in turn refers to an overt np knowing which is the antecedent of z NUM may be important for z NUM to resolve its antecedent correctly
in cue based retrievals we use an occurrence of the cue under investigation as the criterion for retrieving the value of its hypothesized descriptive factors
alternative explanations for the ordering factor will be explored in future work including other types given new distinctions and larger contextual factors such as focus
dnp anaphora are those prefixed by dou literally meaning the same ryou literally meaning the two and deictic determiners like kono this and sono that
each segment originates with an intention of the speaker segments are identified by looking for sets of clauses that taken together serve a purpose
for each tutor explanation in our corpus each coder analyzes the text as described above and then enters this analysis into a database
these sets were then processed by hand to extract the underlying rule patterns from the raw category patterns since these will include instances of serial repetition NUM and lexical breakthrough in cases where phrases are not marked in the original corpus NUM
whilst some of these may be hyl othesized and incorporated it to a formalisation other more obscure pat terns may be missed and so the guidelines postulated in this paper are not necessarily exhaustiw for the whole language
NUM x t lcb nplslal rcb v lcb np s rcb q he rule gencralisation for semicolons is very simi le since the semicolon only separates similar items NUM
as an extension to these results of the analysis it is relatively straight forward to postulate the following simple rules NUM NUM even though the punctuation symbols they refer to are not explicitly searched for ill this analysis and they can in fact be verified in corpora
the second rule NUM extends this rule when applied to the two descriptive categories so that a wider range of categories are permitted within the interpolation again one of the rule patterns permitted by NUM does not actually occur in the corpus but does seem plausible
pp i ni uses the colon merely to introduce a conjunctive structure NUM possibly one which is structurally separated fi om the preceding sentence fi agment in say an itemised list and that has quite linguisti null cally complex items
NUM big red contidentiah inside nebraska football pi pp pp possibly the most productive of the excepted rules this rule pattern provides only for a colon expansion containing a clarifying pp re using the same preposition NUM
we used the clarit commercial retrieval system as a retrieval engine to test the effectiveness of different indexing sets
the objective function for this transformation measures this by computing the difference between the number of unambiguous instances of tag y in context c and the number of unambiguous instances of the most likely tag r in context c where r e x r y adjusting for relative frequency
this is reflected in our reformulation of the two types of thematic progression tp which can be directly derived from centering data the third one requires to refer to conceptual generalization hierarchies and is therefore beyond the scope of this paper cf
8jj adjective md modal nnp singular proper noun nn singular or mass noun pos possessive vb verb base form vbd verb past tense vbn verb past part vbp verb non 3rd person sing
table NUM requires ante to be reachable from the utterance us associated with the segment level s NUM reachability is thus made dependent on the segment structure ds of the discourse as built up by the segmentation algorithm which is specified in table NUM
moreover the knowledge acquired by a statistical method is always consistent because all the data in the corpus are jointly considered during the acquisition process
the reason for this is to provide a tolerance zone with a large margin for better preserving the correct ranking orders for data in real tasks
table NUM gives the experimental results for using the maximum likelihood ml turing tu and back off bf estimation procedures
this over tuning phenomenon happens mainly because of the lack of sufficient sampling data and the possible statistical variations between the training set and the test set
the agency said it will keep the debt under review for possible further downgrade
among the estimators the maximum likelihood estimator provides the best results for the training set but it is the worst on the test set
therefore even though the back off procedure may give better estimates for the parameters it can not guarantee that the recognition result can be improved
this agent offers an api for modsaf that other agents can use
we explored the application of a fast statistical noun phrase parser to enhance document indexing in information retrieval
the development of our language analysis software and our participation in the mucs has been supporte d by the advanced research projects agency under a series of contracts
i want to thank john sterling for his assistance with many of the details of the muc evaluation and allowin g me to survive yet another muc
each is translate d into an event predication in logical form the first two with the predicate leave job the third with the predicate succeeds
because we have a good broad coverage english grammar and a moderately effective method for recoverin g from parse failures this approach held us in fairly good stead
response generatio n for all the tasks we use tipster style annotations as an intermediate representation for the information to be reported
a tipster annotation includes a type a set of start end byte offsets and a set of attributes NUM
tokenization and dictionary look u p processing begins with the reading of the document and the identification of the relevant sgml marke r passages
the current sentence is scanned from right to left i e the most recent anteceden t is preferred
second we generated duplicate spurious instances of th e in and out templates for the chief executive officer position
similarly if it has information about personl it adds information about the job s person2 i s leaving
at the present time i am not aware of any practical implementations using these more complex descriptive devices remotely comparable to the relative efficiency of pure unification based systems when used with wide coverage grammars and large lexica
this enables grammarians to avail themselves of apparently richer notations that allow for the succinct and relatively elegant expression of grammatical facts while still allowing for efficient processing for the analysis or synthesis of sentences using such grammars
rather than computational linguistics volume NUM number NUM use numbers to represent the different subcategorization possibilities we will have an atom with some mnemonic content lcb np np pp np np np pnp rcb
the particular constraints we need to write might figure in the grammar as id c NUM NUM that is everything except c NUM and c NUM
computational linguistics volume NUM number NUM sometimes it happens that although the set of possible values for a feature is very large we only want to write boolean conditions on small subsets of those values
this simple technique can be used to implement many of the kinds of analysis that might be thought to require set valued features although at a small cost of adding some extra features and values to a grammar
for example we might regard for as having these meanings for benefactive with animate np the book is for john for time period with temporal nps he stayed for an hour for directional with locative nps they changed direction for the coast here the pi are the different meanings and the ci are the different types of np
in a context free based formalism they must actually be interpreted as a notation for a rule schema rather than as part of the formalism itself something like a b c d is a shorthand for the infinite set of rules a b d a b c d a b c c d etc
the rule that encodes the combination of subcategorizer and subcategorized has to be identified and feature specifications of the following form added lcb subcat s selectors rest rcb
this unification will only succeed if the verb is subcategorized to finish at that point and we will not have reached this point unless all the other elements subcategorized for have been found in the correct order
it would not be easy to represent a slope in a simple sketch and even less so its characteristic of being steep which the french word magnifique suggests in this context
thus one of the objectives of local analysis has been to determine which types of verbs in the rd express travel actions and which ones serve to introduce landmarks
concerning the ambiguity related to the location of landmarks one can either choose an arbitrary value or try to find a way of preserving the ambiguity in the graphic mode itself
given the differences in capacities of these two means of expression one may expect some problems in trying to encode into a picture the information contained in a linguistic description
computer text image transcription has lately become a subject of interest prompting research on relations between these two modes of representation and on possibilities of transition from one to the other
the most striking case is the information about the tennis courts we do not know on which side of the path right or left they are located
for the sake of the linguistic representation we thought it necessary to carry out an analysis of real examples and elaborate a linguistic model of this particular type of discourse
we propose a model for an automatic text to image translator with a two stage intermediate representation in which the linguistic representation of a route description precedes the creation of its conceptual representation
the taggers whose work is analyzed here were not aware of the frequency ordering of the senses
salient properties are however hard to identify formally as is well known for instance in the scholarslfip on metaphor where salience is the determining factor for the similarity dimension on whicll metaphors and similes are based it is therefore wise to avoid having to search for the salient property and the principle of practical effability offers a justification for tiffs
to derive file semantic part of m adjectival entry from a vel b fl entry first one must identify the case or thematic role such as agent theme beueficiary etc filled by the nolal modifiexl by the adjective ill qnesfion
tile contribution that the adjective makes to the construction of a semantic dependency structure fmr typic ally consists of inserting its meaning a property value p fir as a slot filler in a frame representing the me ming of the noun whidl this adjective syntactically modifies
potentially correct interpretation means that a valid semantic representation has been reached with sthe application task and the recognizer systems are described section NUM NUM
nouns may be easier because they commonly denote concrete imagible referents
each anomaly revealed by the parsing has the trees around it examined to determine whether it is possible to restore a local well formedness by inserting a tree
when wrap up makes a decision about the proper role of a given noun phrase or a possible relationshi p between two noun phrases it considers evidence from a variety of sources
the aim of the robust parser presented here is to build a semantic representation needed by higher layers of the system while faced with possible ill formed sentences
as preposition to exists in the lexicon a sentence in which the words want and to appear calls two lexicon matching thus two parsing branches
for example the verb want is associated in the coven s lexicon with two entries one for the infinitive construction and one for the transitive construction
traditionally word identification has been treated as a preprocessing issue distinct from sentence analysis
all processing is done locally by many simple independent agents that make their decisions stochastically
impossible combinations are gradually filtered out leading to the identification of the most likely combination
in a sentence the mutual information score for each pair of adjacent characters is determined
the activation levels of nodes can be affected by processes that take place in the workspace
computational linguistics volume NUM number NUM table NUM a list of all types of relations
the latter is a prewritten piece of code while the former are instances of the latter
they are merely a reflection of the usage frequencies of chinese characters and words in our dictionary
so future work includes how to evaluate the homogeneity of the semantic space how to locate the non homogeneous areas in the space and how to make them homogeneous
furthermore his formulation of contexts is based on word frequencies while we formalize them with semantic codes given in a thesaurus and their salience with respect to senses
NUM relevant types of management posts are limited to company officers and other top management
no the person did not hold the post as of the date of the article
the organization may be of any type it does not have to be type company
posts related to the chairmanship chairman vice chairman deputy chairman etc
al NUM NUM certainty fill is either yes or no no alternatives
therefore the guidelines concerning consultants are consistent with those outlined above concerning nonrelevant board members
the depart workforce and new post created represent fairly specific reasons for a vacancy
in acting the person s appointment is as an acting officer
oth unk vacancy exists for other reasons or unknown reasons
minimum instantiation conditions this slot must always be filled
brkly7 university of california berkeley experi
this has proven to be a key strength in trec
the database merging task also represents a focussing of the
this was a new track proposed during the trec NUM conference
there is no title field and no narrative field
pircs2 queens college cuny trec NUM ad hoc
twenty five of the routing topics were picked using this criteria
additionally more groups participated in a track for spanish retrieval
trec NUM allowed a continuation of many of these complex experiments
furthermore more ambiguity arises if two objects have selection areas that partially overlap and the user points in this intersection area
where does she live the pronoun she is taken to refer to the last mentioned female in this case hil
moreover pointing gestures can also be combined with other non demonstrative definite nps het rapport over donald zit in claassens
deixis and anaphora ceptual cfs so the multimodal referring expressions are solved in exactly the same way as unimodal referring expressions
after the first update its significance weight drops to NUM and at the next update it becomes NUM notice the difference between selection and indication
depending on the domain edward is being applied to a filter is defined to determine which concepts of the knowledge base should be visually represented on the screen
consequently we could use only a pen and paper analysis of how their model processes the test set of referring expressions
in this section we describe several problems of the three reference resolution models that follow from their design but did not become apparent in the test set evaluation
we present hei e li v methodology with which this flltl ldomental issue in linguistics can t investigated category systems ext racted for difl erent tasks in different languages can be studied to see which categories if any truely have a universal status
exact match criterion and it results in a great deal of improvement over the baseline approach see results in section NUM
v clc2 c c v cl v c2c3 NUM n n n n since the denominator n is canceled it is obvious that the segmentation with larger product of frequencies is preferzed
the longest match string frequency of word w in text t with respect to dictionary d is obtained by counting the number of elements in the set difference lw w for example if the input sentence is g r o
for e ample the phrase NUM gather i c infl and come i past auxv which means came and gathered is segmented into i c topic 1c north because the number of words is fewer
NUM NUM construction of grammar network from parameter settings
in general adjunct nodes are considered to be barriers to movement
these results show that principar compares quite well with both cfg parsers
a trace represents a position from which some element has been extracted
all items at a node must satisfy the node s local constraint
in head final languages such as korean the reverse order is required
the case filter rules out sentences containing an np with no case
barwise and perry argue that there is a proper class of events el in which jackie was not biting molly events that must be classified with so no
in the set theory that is the basis for their development kpu it is elementary that the complement of a set is never a set
in modern situation semantic parlance this is often referred to as an infon or more precisely a basic infon
there is a problem with this analysis that leads barwise and perry to seek a representation of mental states and events with which to augment the interpretation relation
the problem comes about when barwise and perry attempt to characterize attitude reports involving see that in terms of the relation so
this can be rectified by adopting as a set theoretic basis a set theory in which the complement of a set is always a set
most languages that use roman greek cyrillic armenian or semitic scripts and many that use indian derived scripts mark orthographic word boundaries however languages written in a chinese derived writing system including chinese and japanese as well as indian derived writing systems of languages like thai do not delimit orthographic words NUM put another way written chinese simply lacks orthographic words
of course an easy consequence of an axiom of complementation such as we have in nfu is the negation of the axiom of foundation
note that wang li and chang s set was based on an earlier version of the chang et al paper and is missing NUM examples from the a set NUM we note that it is not always clear in wang li and chang s examples which segmented words constitute names since we have only their segmentation not the actual classification of the segmented words
NUM they also provide a set of title driven rules to identify names when they occur before titles such as i t xianlshengl mr or j i lcb tai2bei3 shi4zhang3 taipei mayor obviously the presence of a title after a potential name n increases the probability that n is in fact a name
in processing the subtrees for the left corpus we can simply check whether there is an element in the hash table for the terminal indices of the yield of the tree in the left corpus under the image of the function NUM
formally lin l p e b p a p b swe clarified with the authoz certain parts of the algorithm which we find unclear
overall taggers chose the expert selection less frequently than they agreed on a sense among themselves
with this approximation the time requirements for the current learning law scale as o knn where k is the number of iterations required for convergence
the objective of any learning law used to train context vectors is to minimize the cost function specified in figure NUM subject to the constraints in figure NUM
as can be seen in this figure the symmetric system build uses the unified hash table as the basis for combining the stem sets from both languages
these desired dot products are used in a single pass through the vocabulary of word stems to expand a starting set of quasi orthogonal high dimensional vectors
while the current matchplus learning law has proven to be effective in encoding relationships between words it is computationally intensive and requires multiple passes through the training corpus
the performance of the system using this law can be optimized through parameter sweeps on context vector dimension and free parameter NUM NUM see figure NUM
however as can be seen in the figure the hash table entries for the tie words have been forced to point to a common context vector entry
the purpose of the one step learning law is to approximate the behavior of the original learning law while performing only a single pass through the training corpus
this approach uses a single pass through the training corpus or corpora to obtain desired dot product values for the set of trained context vectors
in the multilingual system a tie word list is used to provide multiple references one word stem ffi om each language for common context vectors
the idea is to label all ambiguity occurrences but only the ambiguity kernels not already labeled
tagger expert matches decreased significantly with increasing number of senses p NUM NUM in both conditions
the value for const elts of the whole will be the const predicate of the portion titus its sem predicate e.g.
the pleposiliou oj is omitted here not in the lkb implelneimltiou since it is unrelewmt its it lacks senianlie collteilt
this way the very same verb iiacer shows two radically different meanings which in principle should be listed scpm alely in the lexicon
the latter focus on shape which is often conceptualised schematically sheet a plane lump ingot brickshaped
similarly the miuimal constitutive distinction to be done is assumed to be that of entaihnent or not about internal structure of things
when appearing in the discourse pns need of further specificalion of the referent either via of compleinenlalion or via ellipsis or anaphora
the case of portions segments and relalivc quantities of objects or substances slices lumps buckets spoon rids
in the default case r will be portion and in more fine grained cases a daughter type of it e.g.
moreover the boundary between literal and metaphoric language seems particularly elusive in the case of verbs
all instances where the two differ have been manually inspected
currently two aac users are taking part in a long term evaluation of the usefulness of the talk prototype in their daily conversations and a third aac user is evaluating a version of talk which has been adapted for people with limited literacy skills
this will be particularly important for meeting immediate social goals such as enjoyment of the interaction and creation of a favourable impression which is essential if the user is to remain motivated to use the aid to have social conversation
the nouns in the text we chose for our analysis had mostly concrete imagible referents
the merging of target fragment nodes in the last condition has the effect of joining the target fragments in a consistent fashion
the categories used in eutrans were seven masculine names femenine names surnames dates hours room numbers and general numbers
a cost function for a search process is a real valued function defined on a pair of equivalence classes of process states
it is possible to apply the subtree search directly to the whole graph starting with the initial runtime entries from lexical matching
c elc in n elc ln n ele
categorial type assigmnent statements are translated into linear logic according to the interpretation of types
we present a language model consisting of a collection of costed bidirectional finite state automata associated with the head words of phrases
a recursive left parent right traversal of the nodes of an ordered dependency tree for a derivation yields the word string for the derivation
in our experimental system we use a more general version of the algorithm to allow input in the form of word lattices
for a consistent probabilistic model the probabilities of all transitions and stop actions from a state q must sum to unity
using the semantic patterns we know that key and keyring are semantically close and through that semantic link between the second and third sentences we prefer to connect the third sentence to the thread begun by the second
for example in 10a the preference technique which allows us to choose the first thread over the second is one which assigns a higher rating to a thread whose tense is parallel to that of the new sentence in this case both sam rang the bell and hannah opened the door are in the simple past tense
on the other hand if temporal expressions indicate an overlap relation and cue words indicate a background relation as in NUM these contributions are consistent and the khet r eln type will contain a background value the more specific value of the two NUM superman stopped the train just in time
in a the third sentence continues the thread about losing the key in b the third starts a NUM in this chart it appears that whether the tense is simple past or past perfect makes no difference and that only aspect affects the possible temporal relations between NUM and NUM
however if there is a rhetorical relationship between two eventualities such as causation elaboration or enablement the temporal defaults can be overridden as in the following examples NUM a john fell
just after NUM just after tf1 precede NUM precede tfi ovedap s1 ovedap tf1 same event i same event tf1 just after st precede NUM overlap NUM same event s1 just after s1 precede s overlap NUM same event s just after s1 just after tf1 precede NUM precede tf1 ovehap i overlap tf1 same event NUM same event tf1 no sam arrived at eight
e2 can occur just after the tempfoc of el if dcu2 describes a simple tense event or dcu1 describes a complex tense clause and dcu2 describes a complex tense event or dcu1 describes an event and dcu2 describes an atelic or a simple tense state or dcu1 describes a state and dcu2 describes a simple tense activity
to better understand where cogenthelp fits in it is instructive to compare it with the closest reference points in the nlg and se communities
first we collected data to compare the autoslog ts dictionary with a dictionary produced by the original version of autoslog
not surprisingly most of the concept nodes in the autoslog dictionary had at least a NUM relevancy rate
again this is not surprising because many useful extraction patterns will be common in both relevant and irrelevant texts
for example autoslog ts produced NUM concept nodes that have a relevancy rate NUM and frequency NUM
for example consider the phrase i the murder in bogota by terrorists
previous work on automated dictionary construction for information extraction has relied on annotated text corpora
the internal operation of modules is left completely to the application
if accepted its specifications would be included in the icd
the high level architecture is described in an architecture design document
this recommendation will be reviewed by the tipster configuration control board
an extraction component is a component that extracts information from documents
support appropriate application response time support incorporation of multi level security
a list of tipster compliant modules will be maintained under configuration management
the tipster architecture design document describes the design underlying the architecture
we have begun to explore the possibility of using an untagged corpus to automatically acquire conceptual patterns for information extraction
the guiding principle behind autoslog is that most role relationships can be identified by local linguistic context surrounding a phrase
in the st task of muc NUM the inference module was used for example to try to infer the corporations involved in a succession event
this phenomenon can be found in swedish too but not very frequently
the formalism allows a value to be a variable drawn from a predefined finite set of possible atomic values
it contains newspaper texts fact and fiction on several stylistic levels
initially the weighted links are disabled
the update factor is given in the following formula
distituency is marked by a mutual information minima
a method of representing a sequence must be chosen
there are about NUM prohibited pairs and NUM triples
in practice the maximum activated is currently about NUM
figure NUM overview of the syntactic pattern recognition process
the update factor is chosen to meet several requirements
null this algorithm differs from some commonly used methods
similarity measures lower than a threshold NUM are considered to be noise and are ignored
the nodes are concepts or synsets as they are called in the wordnet
normally the dependency relationships form a tree that connects all the words in a sentence
the elements in block sij are the similarity measures between the senses of wi and the senses of ii
most wsd algorithms take as input to be defined in section NUM NUM a polysemous word and its local context
a selected sense 8answer is correct if it is similar enough to the sense tag skeu in semcor
we then extracted from the parse trees NUM NUM NUM dependency relationships in which the head or the modifier is a noun
for some emotion adjectives as furieux NUM the manifestation sense is even the only one available llc
we used a NUM million word wall street journal corpus part of ldc dci NUM cdrom to construct the local context database
the texts all consist of written prose published sometime between NUM and NUM
for emotion adjeetlves the second argument is c2 or e3 as they can refer either to the manifestation of the
for example the category popular lore contains an article by the decidedly highbrow harold rosenberg from commentary and articles from model railroader and gourmet surely not a natural class by any reasonable standard
the size of the hidden layer was chosen to be three times as large as the size of the output layer NUM units for binary decisions NUM units for brow NUM units for genre
one important reason is that up to now the digitized corpora and collections which are the subject of much cl research have been for the most part generically homogeneous i.e. collections of scientific abstracts or newspaper articles encyclopedias and so on so that the problem of genre identification could be set aside
by analyzing genres as bundles of facets we can categorize this genre as institutional because of the use of we as in editorials and annual reports and as non suasive or non argumentative because of the low incidence of question marks among other things whereas a system trained on genres as atomic entities would not be able to make sense of an unfamiliar category
for example an editorial is a shortish prose argument expressing an opinion on some matter of immediate public concern typically written in an impersonal and relatively formal style in which the author is denoted by the pronoun we
we propose a treatment of coordination based on the concepts of functor argument and subcategorization
it will be recalled that in the lr models the facets with more than two levels were computed by means of binary decision machines for each level then choosing the level with the most positive score
although table NUM shows that our methods predict brow at above baseline levels further analysis table NUM indicates that most of this performance comes from accuracy in deciding whether or not a text is high brow
in a manual suasxve as in an editorial or de scriptive as in a market survey communication and this facet correlates among other things with a high incidence of preterite verb forms
section NUM gives a brief description of basic data and discusses some constraints and available structures
the parse parse match procedure is susceptible to three weaknesses appropriate robust monolingual grammars may not be available
manual phrasal matching is feasible only for small corpora either for toy prototype testing or for narrowly restricted applications
under this grammar a series of nested constituents with the same orientation will always have a left heavy derivation
this stage is itself divided into two steps partial parsing and combination
however this last case is constrained as examplified hereafter NUM
this should not be taken to be an indication of the full potential of this approach
the final result is selected through interaction with the user
partial analyses for skipped portions of the utterance are also returned by the parser
efforts towards solving the problem of extragrammatieality have primarily been in the direction of building flexible parsers
the system presented here rose1 robustness with structural evolution repairs extragrammatical input in two stages
thus to improve accuracy we should reduce the specificity of the bracketing s commitment in such cases
he is mary s father and proud of it
as agent oriented adjectives refer to the manifestation of the state examples NUM the second argmnent is e3 the event which follows the state
to quantify the effects of pes processing we used the standard ir evaluation measures of recall and precision
where c0 represents the number of times the events occurred in the training data the count
our intuition was that a word preceding the start of a name class such as mr
fable NUM japanese particle translation in je and ik translation
we have also shown that such a system can be gained efficiently and that given appropriately and consistently marked answer keys it can be trained on languages foreign to the trainer of the system for example we do not speak spanish but trained nymble on answer keys marked by native speakers
in particular the firstword feature arises from the fact that if a word is capitalized and is the first word of the sentence we have no good information as to why it is capitalized but note that allcaps and capperiod are computed before fir s tword and therefore take precedence
unfortunately there is rarely enough training data to compute accurate probabilities when decoding on new data
in order to treat this as a generative model where it generates the original name class annotated words
an alternative and more traditional model would have a small number of states within each name class each having perhaps some semantic signficance e.g. three states in the person name class representing a first middle and last name where each of these three states would have some probability associated with emitting any word from the vocabulary
NUM NUM there is also a magical end word so that the probability may be computed for any current word to be the final word of its name class i.e. pr end other l w f nc
figure NUM je translation snapshot by tdmt
figure NUM jk translation snapshot by tdmt
the results of our experiments show that both recall and precision are improved by using extracted subcompounds for indexing
before moving on to example NUM notice that if sentence 2a were not explicitly cued with on the other hand the analysis would proceed somewhat differently
in this paper we have focussed on discourse expectations associated with forward looking clausal connectives sentential adverbs and the imperative verbs suppose and consider
twelve of these correspond to what if questions or negotiation moves which do not raise expectations suppose just suppose this guy was really what he said he was
implicit in our discussion is the view that in processing a discourse incrementally its semantics and pragmatics are computed compositionally from the structure reflected in the coherence relations between its units
in the solution we propose this principle is translated as NUM subcat principle a terminal class must inherit of a canonical subcategorization dimension NUM and a compatible redistribution including the case of no redistribution at all dimension NUM
according to the above literature instruction and corrective feedback dealing with aspects within the zpd may be beneficial instruction or corrective feedback dealing with aspects outside of the zpd will likely have little effect and may even be harmful to the learning process either boring or confusing the student with information s he is unable to comprehend or apply
of course the adopted architecture is not without its difficulties
for pragmatic reasons we have chosen design d
i2 2this proof would have been simpler if we had allowed w to derive the empty string
minimizing manual annotation cost in supervised training from corpora
thus from now on we will simply assume that g is in cnf
we have shown that fast practical cfg parsing algorithms yield fast practical bmm algorithms
valiant showed that boolean matrix multiplication bmm can be used for cfg parsing
in essence we need simply check for the equality of indices k and k
we now prove the following result about the grammar and string we have just described
figure NUM schematic of the derivation process when aik bkj NUM
two thousand documents from different newspapers were processed by human content specialists and th e names to be extracted as companies were tagged
to segment a line of text each possible segmentation alternative is ewduated according to the product of the word fi equencies of the words seglnented
however the production requirements prohibited using a lisp environment and we decided to port it to c
before it is seginented into words a line of text is just a sequence of characters and there are numerous word segmentation alternatives
domains can also be used to separate the generic from the domain specific vocabularies
any word string w in cd s is a critically tokenized word string or simply a critical tokenization or ct tokenization for short of the character string s
the third part of the representation is then linking the nodes
the compere put the contestant to the lie detector test
the compere who put the contestant to the lie detector gained the cheers of the audience
he also claims o that unlike function words the number of instances e of a specific content word is not directly associated with the document length but is rather a function of how much the document is about the concept ex i NUM pressed by that word
the salesman attempted to wear steven down
therefore the formula actually used is correspondence l i k i p i NUM where a is the subset of a which contains only those words that occur in a and not in b similarly b is the subset of b which contains only those words that occur in b and not in a this is shown in figure NUM
there are many other contentious issues which need to be investigated such as the use of the ratio of all the occurrences of a word in a given text to the total length of that text in order to calculate the relative significance measure
this means that a highly significant word occurring only in a has exactly the same effect as an insignificant word occurring only in a in other words the significance biasing is only taking place for words that appear in both a and b
about NUM minutes two orders of magnitude more time for determining the most relevant sentences for an article
4due to space limitations we can not give all tilt details here
we have yet to decide under what circumstances a description needs to be generated at all
the industrial environment required that anything we work on has a fmite development time and is usable in production after the developmen t is over
a description retrieved by the system from the article in NUM is shown in figure NUM
the lexicon generated automatically by the system can be merged with a domain lexicon generated manually
combinations of these methods have also been attempted recently see e.g.
the algorithm performs the following sl et s NUM NUM
the others included a roughly equal number of cases of incorrect np attachment and incorrect part of speech assignment
NUM words alt l NUM sentences on the average range NUM NUM
since neither domain knowledge nor text sort specific heuristics are involved this system provides maximal generality and flexibility
we used the daily telegral h corpus which comprises approx
whereas tile lead method most likely provides a higher readability see brandow et al
c explaining the semantic selection of mental state adjectives
however NUM can have a wider focus if books are contextually given this effect has been called dcaccenting
such a disambiguation procedure is capable of disambiguating with very high reliability about three quarters of the NUM sentence sample instances of the target adjectives we have investigated
all indicators of the not soft sense of hard are concrete so concrete reliably indicates the not easy sense of hard
some noun based disambiguation of adjectives involves the noun s functionality rather than its intrinsic semantic attributes many such nouns relate to relevant attributes of indicator verbs
in the special constructions discussed in section NUM and in particular when they modify the same noun they disambiguate one another with almost perfect reliability
at this point we had extracted a small set of statistically significant nouns that are projected to be indicators for adjective senses in the random samples
compare for example the following sentences from the aphb corpus they were hiding behind the big oak on the left side of the road
katz principled disambiguation statistical process of inferring sense indicators for the corpus at large from the specially selected subcorpora a bias for which we must correct
for this reason the total number of instances is less than NUM for each target adjective varying from NUM for hard to NUM for right
sixty one adjective noun pairs covering NUM instances have NUM or more instances each and thus could admit NUM or more instances in their minority sense
the specificity of nouns in the disambiguated corpus for senses of the target adjectives suggests potentially very high reliability for a noun based procedure to disambiguate common adjectives
will be specific to the errors and difficulties of this learner population our eventual goal is to have the language specific aspects of the system to be excisable allowing modules for different native languages to be inserted so the system would eventually be usable for any learner of english as a second language
assessment against a standard speech and language technology researchers are used to thinking of evaluation in terms of speed and accuracy of system outputs for example success rate of a speech recogniser or syntactic parser in analyzing a standard test corpus
large scale knowledge bases encode information about domains that can not be reduced to a small set of principles or axioms
causal modulatory temporal and locational subtypes participants core connection NUM subevent and temporal step
the biology knowledge base currently contains more than NUM NUM explicitly represented triples and its deductive closure is significantly larger
in addition to the objects and processes the taxonomy includes the hierarchy of relations that may appear on concepts
therefore ease of creation modification and reuse are important goals for the design of a discourse formalism
finally we chose biology because of the availability of local domain experts at the university of texas at austin
when creating content specification expressions the discourse knowledge engineer may name any knowledge base accessor in the kb accessor library
although this approach worked well for small prototype explanation systems it proved unsatisfactory for building fully functioning explanation systems
the realization system collects into a paragraph all of the sentences produced by the views in a particular paragraph cluster
the planner passes the resulting explanation plan to the realization component section NUM for translation to natural language
in much the same way we propose that very unlike rival dmss can be meaningfully compared by assessing how well they match our generic template for dialogue management architecture and using this genericness score to temper any measures of speed accuracy naturalness etc
we leave further investigation of this integration for future work
overgeneration is apparent when we consider translation of german compounds since many do not correspond straightforwardly to english compounds e.g. figure NUM
u z and b member of for otherwise the events in NUM are too disconnected to support ant rhetorical relation
this material is in part based upon work supported by the national science foundation under grant number iri NUM and esrc uk grant number r000236052
in our treatment this involves modification of the constitutive role
stress sometimes disambiguates meaning e.g. with righthand stress cotton bag has the interpretation bag made of cotton while with leftmost stress an alternative reading bag for cotton is available
in this section we ll give a brief overview of the theory of discourse and pragmatics that we ll use for modelling this interaction during disambiguation between discourse information and lexical frequencies
then the rule schema below ensures that the most frequent possible sense that produces discourse coherence is monotonically favored prefer frequent senses
we assume that the grammar lexicon delimits the range of compounds and indicates conventional interpretations but that some compounds may only be resolved by pragmatics and that non conventional contextual interpretations are always available
a elaboration r a NUM a subtype NUM a elaboration o coherence constraint on elaboration
an obvious target inventory is the japanese syllabary itself written down in katakana e.g. or a roman equivalent e.g. hi
the first model generates scored word sequences the idea being that ice cream should score higher than ice creme which should score higher than nice kreem
for example at the end of a word english t is likely to come out as o rather than NUM
NUM for each of the NUM english sounds normalize the scores of the japanese sequences it maps to so that the scores sum to NUM
NUM cowie j guthrie l pustejovsky j waterman s and wakao t the crl bradneis system us used for muc NUM in proceedings of the fifth message understanding conference muc5 baltimore ma morgan kaufmann NUM
collections for name recognitio n in order to apply the id3 algorithm the data needs to be structured into a collection each membe r of which has specific values for a set of attributes and for each of which it is known whether th e member has a specific property or not
for example for location beginnings if word NUM is one of the following milwaukee ridgefild pa st around NUM more words then location beginning else if word NUM is illinois and word NUM is indiana then location beginnin g else if word NUM is northeast and word NUM is in then location beginnning the printed decision table takes about NUM pages
for each of the values of the five attributes words NUM through NUM a count is maintained of the number of times this value contributed to an element holding a proper named occurrence at the middle attribute
NUM for each of the NUM english sounds count up instances of its different mappings as observed in all alignments of all pairs
able to take textual topical syntactic context into account or at least be able to return a ranked list of possible english translations
this provides a mechanism for bootstrapping a sense tagger
these forms are licensed using further phrase structure schemata for english and italian
the function of the modifying noun lemon is to further subtype this argument
the corresponding corpus should include most of the words from the lexicon and be large enough to obtain reliable estimates of word frequency distribution
these kinds of rules are not lexical rules per se since they do not operate on lexical properties of the words
this technique does not require specially prepared training data and uses for training the lexicon and word frequencies collected from a raw corpus
so for every acquired rule we need to estimate whether it is an effective rule which is worth retaining in the final rule set
morphologically ambiguous cases such as NUM are handled by multiple instantiations of the lexical rules
more importantly they should reflect the grammatical or semantic requirements imposed by inflections
for instance the locative case marker has allomorphs de da te ta
this can be seen as the minimal expected value of NUM for the rule if we were to draw a large number of samples
we expand the set of features as more lexical items are added to the lexicon
inflections lexical rules for inflections can check morphotactic constraints for proper ordering of morphemes
w is minimal among such strings
joshi mark liberman and mehryar mohri for valuable discussions
we also proved the correctness and the generality of the methods
we now turn our attention to the implementation of finite state transducers
the example content in fig NUM is that of the verb ageru give used in e.g.
below is the description of the procedure by which the japanese analyzer performs the permutation of the verb subcategorization frame
as anticipated above in tile discussion of the lexical ambiguity involved this conelusioil can be drawn even if other points remain underspecified
the penn treebank corpus contains a sufficient number of part of speech tagged and syntactically parsed sentences to serve as adequate training material for building broad coverage part of speech taggers and parsers
of speech making up the top NUM NUM of word occurrences in the wall street journal corpus
in this view a large sense tagged corpus is critical as well as necessary to achieve broad coverage high accuracy wsd
to shed light on this question it is instructive to examine the distribution of words and their occurrence frequency in a large corpus
m block contains the very surface information and can in general be linked to multiple s blocks
hence i believe that using the current wordnet sense distinction to build a sense tagged corpus is a reasonable approach to go forward
unfortunately an analogous sense tagged corpus large enough to achieve broad coverage high accuracy word sense disambiguation is not available at present
the verb subcategorization frame information plays a major role of disambiguations in many nlp applications
computational linguistics volume NUM number NUM words using different guessers
samuelsson for providing a bit of mathematical elegance
note that the derivation of zipf s recurrence equation in eq NUM of section NUM corresponds to the special case where a NUM i.e. where NUM NUM
to test our derivation of the asymptotic equation NUM from the recurrence equation NUM we will attempt to rederive eq NUM from eq NUM
the remainder if this article is organized as follows
relating turing s formula and zipf s law christer samuelsson
tagging accuracy on unknown words using the cascading guesser was NUM NUM NUM NUM
the second type of mistagging was caused by incorrect assignments by the guesser
there were three types of mistaggings on unknown words detected in our experiments
thus prefix and suffix morphological rules together with their frequencies are produced
we also set the minimum length of the remaining substring to three characters
the appealing feature of these approaches is their extreme simplicity
words with their pos classes are usually kept in a lexicon
after each successful merging the resulting rule is rescored
v i l ma NUM n ary aiternative casea ai c i a vfarm ai ien is satisfiable iff icn icn is satisfiable where each ai is a new propositional variabladeg ai c i v a lcb itere ign are the alternatives and icn are the cases
to resolve this problem we impose the following condition called alternative compactness if a base constraint c rcb equals another base constraint from the same disjunction c then the alternatives variables associated with those base constraints ji and a are also equal
whichever resolution is made the substitution of a for e ensures parallel scoping in the ellipsis for an american flag
giving aa antecedent term wide scope over the ellipsis renders the choice of a strict or sloppy substitution for it in the ellipsis immaterial
basic complete link is a dependency link between adjacent two words
computing mi scores is by now a standard procedure for measuring the co occurrence between objects relative to their overall occurrence
the head of the np book has a number of systematically related senses that are being expressed simultaneously
our research shows however that homonyms only make up a fraction of the whole of the lexicon of a language
types are either simple human artifact or complex e.g. information physical
semantic tags are therefore more like pointers to complex knowledge representations which can be seen as underspecified lexical meanings
brandeis edu paulb cor elex corelex html closed dots consider the underspecified representation for the semantic type actorelation
the difference between types or and cot is in the nature of the objects they denote
the second format is used to present a generated semantic lexicon as a semantic index on a world wide web document
the instances for the type act relation are given in figure NUM covering three different systematic polysemous classes
there is a useful analogy with evaluation of nl parsers typically rival parsers are compared by measuring speed sentences per minute and or accuracy e.g. percentage of sentences parsed e.g.
NUM for each vi v v iei i words vi are assigned class r and governed by the remaining copy of vi in reading ui through valencies rl to rlel l
2german data exist that can not be captured by the more common bounding of discontinuities by nodes of a certain purpose of this paper we need not formally introduce the bounding condition though
NUM each anaphor is given values according to the conditions in the current rule
in the figure z and nz denote zero and nonzero anaphora respectively
this process continues until a rule with promising performance on the data is obtained
this research starts with establishing possible rules for the generation of anaphora in chinese
we further establish a rule for choosing descriptions ira nominal anaphor is decided on
in this paper our goal is the computer generation of anaphora in chinese
thus we will not take it as a constraint to further refine our rule
the result from using full nps for nominal anaphora is shown in table NUM
on average NUM of our annotation markers match those of the speakers
graph fixed phrases and length cut off features
not mean that there are fewer abstract worthy
figure NUM second expenment impact of type of gold
figure NUM second expernnent impact of type of gold
figure NUM first experiment baseline best single
we decided to use two gold standards gold standard a ahgnment
the brkly7 run a manually expanded version of brkly6 used about the same number of terms as the inqi02 run around NUM terms on average but the terms had been manually pulled from multiple sources as opposed to editing an automatic expansion as done by inquery
rutfuai rutgers university decision level data fusion for routing of documents in the trec3 context a best cases analysis of worst case results by paul b kantor used data fusion methods to combine the retrieval ranks from three different retrieval schemes all using the inquery system
etho02 swiss federal institute of technology eth improving a basic retrieval method by links and passage level evidence by daniel knaus elke mittendorf and peter schauble used a completely new method in trec NUM based on combining information from three very different retrieval techniques
vtc2s2 virginia tech combination of multiple searches by joseph a shaw and edward a fox used a combination of multiple types of queries with NUM types of natural language vector space queries and NUM types of manually constructed p norm soft boolean queries
the method of figure NUM modifies the input tree to attach singletons as closely as possible to couples but remaining consistent with the input tree in the following sense singletons can not escape their inmmdiately surrounding brackets
let NUM tu maxp e t e be the maximum probability of any derivation from a that successfully parses both substrings es t and c u v
on the other hand critical tokenization can help significantly in boosting tokenization efficiency
a simple pruning rule was used to get rid of these alternations on the basis of their productivity and only alternations which were observed at least twice were retained
with this new test set the overall performances of our algorithm averages at about NUM NUM of entirely correct words corresponding to a NUM per phoneme correctness
the careful exalnination of the words that can not be pronounced reveals that they are either loan words which are very isolated in an english lexicon and for
this assumption is reflected by the possibility of aligning on a one to one basis graphemic and phonemic strings and these models indeed use this kind of alignment t o initiate learning
we intend to explore several directions to improve this search one possibility is to use a graphotactieal model e.g. a rt gram model in order to make the pruning of the derivation tree more effective
this should be avoided since there are in fact very few words starting with the prefix rl we would therefore like these words to be very poorly ranked
the second strategy implements a kind of compromise between depth first and breadth first exploration of the derivation tree and is best understood if we first look at a concrete example
considering these proportions in terms of orthographical alternations that is in terms of partial fnnctions in the graphemic domain we can see that each proportion involves two alternations
in fact the results reported hereafter use a slightly extended version of this procedure where the pronunciations of more than one a nmog are used for generating and selecting the pronunciation of the unknown word
in both domain however this organization is subject to the same paradigmatic principle which makes it possible to represent the relationships between orthographical and phonological representations in the form of a statistical pairing between alternations
similarity measure that is monotonically related to the dice coefficient would be equivalent
for both tasks general knowledge of the two languages is not sufficient
let n be the size of the corpus in terms of matched sentences
in this case it took a few seconds to compute this information
particularly in technical domains the collocations differ from those in general use
multilingual systems are now being developed in addition to pure machine translation systems
in this case the test is easier to decide using mutual information
we have confirmed the theoretically expected behavior of the similarity measures through testing
there are three main reasons why the word error rate is much higher for etd than esst
with this set up the average word error rate on the etd test set was NUM
another possibility is to give our systems the notion of money value vs
the script also contains pointers to the functions which print each slot fill
any mapping rule can introduce additional semantics and such additions are checked against the lower semantic bound
the use of semantic networks in generation is not new NUM NUM
the counterpart of the unification operation for conceptual graphs is maximal join which is non deterministic
we do not impose any intrinsic directionality on the mapping rules and view them as declarative statements
figure NUM interactions involving the applicability semantics of a mapping rule the following conditions hold
figure NUM mapping rules we start with an initial semantics as given in figure NUM
unlike tags dtgs provide a uniform treatment of complementation and modification at the syntactic level
graphically we will use a dashed line to indicate a d link see figure NUM
these cases have to be singled out separated into a special relationship or simply corrected by introducing e.g. the concept french jlps etc then for the rest transitivity holds and short cut checking and checking for redundancy by inheritance is meaningful
change the tag from nn vb vbp to vbp if the previous tag is nns from nn vb to vb if the previous tag is md from jj nnp to jj if the following tag is nns
NUM the relative likelihoods of tags for words is not known nor is any information about which tags are likely to appear in which contexts
as shown above test set accuracy using the transformation based algorithm described in this paper gives an accuracy of NUM NUM when trained on NUM NUM words
the transformation based tagger captures its learned information in a set of simple rules compared to the many thousands of opaque probabilities learned by markov model based taggers
in order to derive an unsupervised version of the learner an objective function must be found for training that does not need a manually tagged corpus
to score the transformation change the tag of a word from x to y in context c where y e x we do the following
here a transformation will reduce the uncertainty as to the correct tag of a word in a particular context instead of changing one tag to another
initial state annotator the unsupervised learner begins with an unannotated text corpus and a dictionary listing words and the allowable part of speech tags for each word
chinese word segmentation and pos tagging techniques can be found many applications in the real world such as information retrieval text categorization text proofreading ocr speech recognition and text to speech conversion systems
three steps are involved in all the three agents in general a pre processing then finding candidates over tile resulting fragments of characters there are two strategies for seeking candidates in the input sentence
in which premises are consumed in a proof
the notions of substructure occurring in an fstructure
f structure reentrancies are handled correctly without further stipulation
another way is viewing input as word string applying mm segmentation as a pre processing first then trying to find candidates only over the fragments composed of successive single characters
the bigram based agent at the high level for coping with all the remaining ambiguities the conventional pos bigram model and a dynamic programming algorithm are used in this high level agent
the searching space of the algorithm is the complete combination of all possible word and tag sequences and the complexity of it can be theoretically and experimentally proved still polynomial
the size of manually tagged corpus for training the bigram model is about NUM NUM m words and that of the raw corpus for achieving global statistics is 20m characters
the dictionary supporting it contains NUM NUM word entries along with word frequencies parts of speech and various types of information necessary for the purpose of segmentation and tagging
great efforts have been paid to the research in the last decade but unfortunately no practical system with high performance for unrestricted texts is available up to date
we observe from NUM randomly selected sentences that low level agents generate multiple NUM unknown word candidates in NUM NUM of them fig NUM
it features two types of conditions NUM NUM
in addition to maintaining the character offset mapping the tokenizer performs four non standard tasks
a correcting sequence consists of a correction and possibly a negative acknowledgement by the caller and an appropriate answer by the information service
this symbol occurs as the label of the edge in the tree or as a subscript following a parenthesis in the linearized representation 1deg NUM a neighbor gave a boy a book
criteria to decide on these sentence parts being cb or nb will make it necessary to work with a detailed semantic classification of lexical items and to take into account the analysis of preceding co text
and third it requires that there be a unique antecedent for an anaphor
in english the word order is grammatically restricted thus also in NUM the verb occupies the position after the subject in the surface although it is followed by a cb item
let us note that for example the written shape of NUM may also be pronounced with a secondary placement of the intonation center as in NUM with another tfa
NUM note that one can specify the position of the intonation center even with a written sentence the sentence can be read aloud either correctly in accordance with the author s intention or incorrectly
for example a paraphrase of the preferred reading of NUM a would be about everybody in this room i tell you that s he knows at least two languages
we can now formulate rule NUM from section NUM in a more precise form as rule NUM referring to the underlying word order cd rather than to the surface one
plural feminine preterite conditional and semantic distinction within the individual syntactic categories of adverbial modifications such as the meanings of the prepositions in on above under with locative
NUM in our syntactic representations we do not handle the correlates of function words as labels of separate nodes they have the shape of indices accompanying auto semantic lexical units see footnote NUM
in 15t the superscript t denotes the verb as belonging to the topic being cb although this is not in an immediate correspondence with its position in the surface word order
the finite state transducers we use in our system have the property that they can be made deterministic that is there exists a subsequential transducer that represents the same function
finally a rough analysis of the suffix morphology of the word is undertaken
in section NUM i discuss the ovis robustness component and i show that the use of a parser that includes top down prediction is not an obstacle to robustness
here are some problem instances taken from actual newspaper articles NUM itexts used in arpa machine translation evaluations november NUM
as a result various heuristics are used to reduce the number of entities marked
modiffers combine with each other and with argument sentences
the interpretative process must fill these holes
a sample a structure is given in figure NUM
the topical form is selected with a given strength
a structure is a function that takes as many arguments as there are tss s and is defined by using basic functions that are also used for the description of operators and connectives
holding money is considered good in NUM and bad in NUM because of the general structure of the sentence and the opposition between little and a little
we claim that the argumentative structure of sentences is never questioned by the interpretative process that it fully captures the argumentative potential of the sentence and that it is reliable
for instance in NUM and NUM the robbery is considered bad because of the opposition introduced by but to something considered happy because of luckily
connectives and operators contribute to the computation of but unfortunately i had a little money the signification in terms of functional transformations of the signification along the four dimensions of the cells
the word poor contains the negative form of the same topos t that is when you are not rich you may not buy a lot of things
when using the temporal tool on the NUM NUM ap news wire documents weekly cycles are easily identified
it is clear that in terms of visualization one size fits all does not apply
many others are their synonyms and near synonyms
the system converts the free text into a context vector and the same retrieval process is performed
stop list removal refers to the removal of words with high frequency occurrence and little meaning in the training text i.e.
context vectors cvs are high dimensional information representations that encode the semantic content of the textual entities they represent
this discussion raises the question as to how the nodes reflect the amount of information present for the theme they represent
the range of the ontological property size is a numerical and continuous scale
tim membership class has been largely ignored in the literature
the following example illustrates the connection between nominal mid adjectival meanings
the inclusion of neighbor updates results in the organization of the information into a form suitable for visualization
therefore close neighbors will be updated or adjusted more than neighbors that are further away
the directed arc connecting two objects in figure NUM denotes a linguistic relation between the objects connected
it is possible to have unitary constituency whereby one object is the only part of another object
figure NUM shows the state of the workspace at the end of cycle NUM
both test sets were disambiguated by hand
in the initial stage of a run the system constructs relations between characters of a sentence
our experimental results showed that the model is able to address the word boundary ambiguity problems effectively
this is a network of nodes and links representing some permanent linguistic concepts figure NUM
the fragment t xi shen zi in sentence NUM is ambiguous
NUM NUM heuristic NUM entry sense ordering
NUM NUM heuristic NUM monosemous genus term
critical tokenization set since the former can be completely reproduced from the latter
NUM NUM heuristic NUM explicit semantic domain
at cycle NUM the coderack contains the statistics as shown in table NUM
these two values are adjusted by the temperature according to equation NUM
bonsai l NUM planta y arbusto asi cultivado
rigau bat alla is i upc
each equation contained in the simultaneous equation is represented by equation NUM where x is the statistics based length sbl for branch NUM and a NUM is either NUM or NUM as in equation NUM
the neural network achieves NUM NUM accuracy on a corpus of wall str eet journal t ve recommend these articles for a more comprehensive review of sentence boundary identification work than we will be able to provide here
as a result the model in practice tends not to commit towards a particular outcome yes or no unless it ha s seen sufficient evidence for that outcome it is maximally uncertain beyond meeting the evidence
we trained our system on NUM sentences NUM words of wall street journal text from sections NUM through NUM of the second release of the penn treebank NUM marcus santorini and marcinkiewicz we did not train on files which overlapped with pahner and hearst s test data namely sections NUM NUM NUM and NUM
where p yes c p yeslc p yes c i p no c and where c is the context including the potential sentence boundary
the j acl intbrmation used by the maximum entropy model or the potential sentence boundary marked by in col7 in example NUM would be previouswordiscapitalized prefix corp suffix null prefixfeature c orporatedesignator NUM anlp corp chairman dr smith resigned
a l s dr gen whether the candidate is a corporate designator e.g.
surrounding context both of which are denoted by c occurring as an actual sentence i oundary
we describe two semantically oriented dependency structure formalisms t j forms and s forms
also pahner hearst s system requires pos tag information which limits its use to those genres or languages for which there are either pos tag lexica or pos tag annotated corpora that could be used to train automarie taggers
since the subtree of depth NUM is the smallest structural building block of our dop model semantic determinacy of every cfg rule in a subtree means the whole subtree is semantically determinate
if a word refers to something that is strongly associated with members of the category but is not actually a member of the category itself then it deserves a NUM
note that the original seed words were already known to be category members and the new seed words are already in the ranked list because that is how they were selected
of course there is also a law of diminishing returns using a seed word list containing NUM category words is almost like creating a semantic lexicon for the category by hand
by sifting through a large text corpus the algorithm can find many relevant category words that a user would probably not en null ter in a semantic lexicon on their own
if a word refers to a part of something that is a member of the category then it deserves a NUM for example feathers and tails are parts of animals
our goal is to allow a user to build a semantic lexicon for one or more categories using only a small set of known category members as seed words and a text corpus
given the context windows for a category we compute a category score for each word which is essentially the conditional probability that the word appears in a category context
if we consider all the words rated as NUM s NUM s or NUM s then we were able to find about NUM NUM words for every category except energy
the context NUM does n t provide fin tiler information about the identity of the person or persons x to whom the introduced attitudinal state has to be ascribed to the speaker writer to the recipient to peter to someone else or to some group of salient people
the trivial nature of the conversion can be seen by considering the three differences between tig and tag
from the perspective of this difference in approach a tig is also trivially a tag without alteration
third tig imposes a number of detailed restrictions on the interaction of left and right auxiliary trees
step NUM for each nonterminal ai in nt add two more nonterminals wi and zi
we now assume inductively that every ai rooted initial tree t where i k is left anchored
the asserted event presents the first positive outcome to tile test about the instantiation of the ae bac drs type that is connected to the eisequence where each test situation el is characterized by its own specific additional test criterion kx
without further information about the identity of it is ditllcult to say something more precise about the temporm location of the expectation than that an instance s of the corresponding attitudinal state holds at some time i efore the actual now
the type is characterized by the r in tex prcsp strands for classical presuppositions dcf for detlnite descriptions rt for referettce tilne e or re ereucx event et
with respect to the focus adverb use the cases NUM b and NUM c NUM a being an example of the temporal adverb use rnodellings are prevailing that associate ers with different scales cf
we just note that under the conuno assuml tion that the vorfeld in h rmau introduces at most one constituent and under the ensuing assuml tion that ocus adverbs modify tht ir loci in sente nc s
as for the epa reading we consider the case where tile numeral is focussed only 2depending on the tocus structure of tile phrase in the scope of erst in NUM and depending on the contextum restrictions of the admissible alternatives other sets of ps might result
the adjunction of a right auxiliary tree is referred to as right adjunction see figure NUM
first l ter handed the letter to m ri t fhus this tyt e of topicmization disambiguates between the i s reading ou the one hand and the el a and r reading on l hc other
these errors cover ambiguities that are known to be difficult to handle in general such as the already mentioned determiner preposition ambiguity
our rule says that clitic pronouns are attached to a verb and determiners to a noun with possibly an unrestricted number of premodifiers
in this paper we compare two competing approaches to part of speech tagging statistical and constraint based disambiguation using french as our test language
it would be risky to spend say three weeks for writing a corpus and only one week for training
we evaluated the results obtained by the following sequence of operations NUM running the constraint based tagger without the final non contextual rules
our rules were meant to handle such cases but fail some syntactic constructions or word se null quences were omitted
the rules described above are certainly not sufficient to provide full disambiguation even if one considers only the most ambiguous word forms
we first construct two definition vectors to model the definitions of all the words in a cluster and the definitions of w based on the semantic codes of the definition words NUM then determine the sense of w in the context by measuring the similarity between each definition of w and the definitions of all the words in a cluster
for any two senses st sees let cvt xt xe xk cve yt ye yk be their context vectors respectively we define the distance between st and se denoted as dis st se based on the cosine of the angle between the two vectors
here we do n t define the activated cluster as the one which makes disl clu w smallest this is because that the context may contain much noise and the senses in the cluster which makes disj clu w smallest may not be similar with the very sense of the word in the context
suppose sw is the set ofw s senses defined in the dictionary for any sense s sw let cs be the set of all the semantic codes of its definition words we call dvs xl x2 xk definition vector of s where for all i ifci c x l otherwise x NUM
a word string is critical if any other word string does not cover it
moreover c4 NUM produces decision trees and rule sets both often used in text generation to implement mappings from function features to forms
the node is passed to the leaf s parent in the form of a role structure which indicates the role the nod e may play in the semantics of the parent
correction of the bug which gave rise to additional quotation marks vastly improves performance o n the walk through article but would probably have a much smaller affect on the formal evaluatio n results
in this case comments were added to distinguish it from the other two types
however these errors are neither the most frequent nor the most disturbing ones
the user interacts with the system through a windows based interface NUM through which text may either be entered directly or loaded from a file
full nps NUM NUM NUM NUM NUM t NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
entering postulates that each utterance un has associated with it a set of discourse entities the foh wai lcb i loo in cen ters or cfs
relationships between dsps provide the basic structural relationships for the discourse embeddings in the linguistic structure are derived from these relationships
one area of our current work concerns progress toward making an informed choice about which parse tree best represents the student s input
here again where the user is in the acquisition process and thus why s he made the error is crucial
NUM jac9i develops a method to formalise relative shapes including judgements about dimensionality
the difforcuco with bound l ns is that ihe magn value doesu t tend to be minilnal
at their turn i consts be the feature elts e.g.
they are not committed to an agentive process as they may remain attached to the whole
arc fuactious of siiape and magn of the wholes they select
analogously they denote a minimal quantity of the whole
this information is contribuled to tile conslruclion by tile pn
in this paper after motivating our specific application we introduce the architecture of our eventual system and motivate its various components
as discussed above siiape is not relevant measure magn is
they are metonymies of containers expressing a conventionalised measure or quantity of a l b entity
this work has been supported by nsf grant iri9416916 and by a rehabilitation engineering research center grant from the national institute on
NUM lcb rcb u i m q l shltiot
if one looks at the order sensitive nature of the operations of semantic compositions they provide a poor starting point for a treatment of semantics enjoying similar computational success
NUM but the making of these choices does not have to be interleaved in a precise order with the scoping of quantifiers
dsp s account of the first reading of NUM is significantly different from their account of the last two readings
the notion of strict and sloppy identity is usually confined to pronominal items occurring in antecedents and implicitly in ellipses
the index in the scope node means that to semantically evaluate the qlf you get hold of the quantitier restriction and contextual restriction of the corresponding term
during semantic evaluation of the qlf dischaxgo ing the antecedent through scoping will substitute out all occurrence of the term lad its index before ellipsis substitutions are applied
second without additional constraints dsp slightly overgenerate readings for sentences like NUM 3ohn revised his paper before the teacher did and so did bill
in our semantic dop model we modify the constraints on semantic unification as follows a variable can be unified with an expression if the intersection of their respective sets of types is not empty
the user utterances are mostly answers to questions like from where to where do you want to travel at what time do you want to arrive in amsterdam
although dynamic programming can reduce the complexity there remain an exponentially large number of terms to evaluate in each iteration of the em algorithm
to use the dop method not just for syntactic analysis but also for semantic interpretation four steps must be taken NUM decide on a formalism for representing the meanings of sentences and surface constituents NUM annotate the corpus sentences and their surface constituents with such semantic representations
obviously the iteration will always end if we require NUM to be NUM when the algorithm finishes to ti NUM contain the category set of types pairs that took the largest steps towards semantic determinacy and are therefore distinguished in the tree bank
no grammar is used to determine the correct annotation there is a small set of guidelines that has the degree of detail necessary to avoid an anything goes attitude in the annotator but leaves room for his her perception of the structure of an utterance
the poisson fertility model gives the most likely NUM clumpings and alignments which are then restored according to the current general fertility model parameters
in the simplest case the translation model is simply proportional to the product of word pair translation probabilities one per element in the alignment
there was a constraint on the extraction of subtrees from the training set trees subtrees could have a maximum of two substitution sites and no more than three contiguous lexical nodes experience has shown that such limitations improve prob null ability estimations while retaining the full power of dop
that is we accumulate the different formal language patterns seen in the training set and score each of them on the test set
when processing a new input utterance analyses of this utterance are constructed by combining fragments from the corpus the occurrence frequencies of the fragments are used to estimate which analysis is the most probable one
the first two of these can be seen as natural upper and lower boundaries
in the second experiment the parser takes the full word graph as its input
since to s co s the character sting theblueprint has hidden ambiguity in tokenization
the problem is modeled as a supervised learning problem
if s has critical ambiguity in tokenization by definition there is icd s i NUM
this guarantees that every symbol will always have at least one context in every history and that the recursion in NUM will terminate
table NUM break even points comparison
however all other tokenizations can be produced from at least one critical tokenization by further tokenizing words in it
the optimal such list is computed according to criteria to be discussed below
these rules need only specify category information and the relative order of head and complement s
document indexing as a first step the set of documents in the corpus is indexed
sam had arrived at the house
someone had been in the garage
she used two tons of bricks
he had lost the key e
it had fallen through a hole in his pocket
consider NUM mary stared at john
algorithms for analysing the temporal structure of discourse t
NUM a mary built a dog house
he had lost the key e2
a fast and portable realizer for text generation systems
it is therefore both fast and portable cross platform
this reduces the tree matching algorithm to polynomial in n
null coordination of both nouns and clauses
realpro is really a realizer shell which allows for a run time configuration using specially formatted linguistic knowledge bases lkbs which state grammar rules lexical entries and feature defaults
normally the user need not change the two grammar lkbs the dsynt and ssynt grammars unless the grammar of the target sublanguage is not a subset of english or french
each module draws on one or several lkbs
the input to realpro is a syntactic dependency structure
figure NUM input structure for sentence NUM
some relevant extensions are discussed to make it usable for parsing in particular we add verbal selectional restrictions to make lexical discrimination effective
the data in both studies reveal that only a weak correlation between the shift transitions and segment boundaries can be observed
formally let s be a character string over an alphabet e and let d be a dictionary over the alphabet
this distinction arises quite clearly in the glosses of NUM and NUM
in addition to di della is also found for subtyping of arguments in the agentive
however one of the resuits of our method is also to eliminate most of these senses from the hierarchy during the tuning phase so that precision of the two method can not be directly compared
the predicates in the qualia specify the definitional properties of knife
nominals such as hunting race and carving describe activities
there will also be schemata for modification of other default arguments
these are structure shared with the lexical representations for the head noun and the modifying noun respectively
matters become more complex when compounds in which the modifying noun describes an event are considered
this machinery can be used to indicate potential interpretations for compounds
when composed with nominals such as door and breast they specify elements of the constitutive role
intuitively a tokenization is a subtokenization of another tokenization if further tokenizing words in the latter can produce the former
appropriate or inappropriate directives and diag null nostics directives are instructions the system gives to the user while diagnostics are messages in which the system tells the user what caused an error or why it ca n t do what the user asked
however the occurrences of NUM in the cfs of u9 and ux0 are mediated by textual ellipses
the utterances u8 to u10 exhibit a typical thematization of the rhemes pattern which is quite common for the detailed description of objects
in other words shorter word strings do not always cover longer word strings
moreover it has been shown that critical tokenization provides a sound basis for precisely describing various types of tokenization ambiguities
the evaluation semantics described ill the following sections provides a perspicuous treatment of both local and global inheritance in datr
another way of thinking about this is that atom sequences are basic and thus can not be evaluated further
the evaluation relation is now defined as a mapping from elements of cont x desc x atom i.e.
the reason is that the theory already contains an explicit statement about the value of dog lcb root rcb
the following proof serves to illustrate the use of the rules val def and seq
here however the path extension NUM appears as part of the global context in the premise of each rule
together and the node h ft implicit in all but the tirst given sentence
the datrl theory defines the propertie s of two nodes noun and dog
an exami le of a simph datr theory is shown next
sets of definitional sentences with the same node on the left hand side are groupe d
table NUM shows that good translation results a wer of NUM NUM can be achieved with a real time factor rtf of just NUM NUM
NUM vcomposed of list of sentence signsq backgr set of background l 2s sta rus set of social s tatus the value of the feature composei of is a list
by using the intbrmation about social status and the information about sentence external individuals such as speaker and addressee we can explain why a sentence is felicitous in a restricted context and whether a dialogue is coherent or not
chiefsection hon nom go out hon past dec chief section park went out finally it is not possible to compute social status at all just by the information that the subject referent who is mentioned in a sentence is respected
thus the sentence in NUM is felicitous in the context where the social status of the object referent is higher than that of any other individuals involved in the sentence where the social status of the subject referent is higher than that of speaker and addressee and where the social status of speaker is not equal to that of addressee
in their approach it can not be explained why the sentence in NUM instead of the sentence in NUM must be used when speaker has higher social status than the subject referent though the two sentences are equally grammatical
NUM r youngsoo r sungmin m youngsoo m sungmin r m yotmgsoo sungmin likewise from 26b we draw the relative order shown in NUM
NUM k s k l s l similarly the orders shown in NUM and NUM are derived from the sentences 21b and 21c respectively
as shown in the query the first member of the input list is speaker of the input sentence the second lnember of the input list is addressee and the remaining members are the constituents of the input sentence
NUM inds indsp inds indad if the honorific infix si does not occur in a verb the social status of speaker is equal to or higher than that of a subject referent as shown in l NUM
u hfl kkcy since neither an honorific genitive case marker nor an honorific accusative case marker exists the referent of a genitive np or an accusative np is respected when the genitive np or the accusative np contains the honorific suffix nim
in general the similarity between words a and b using sbl sbl a b hereafter is realized by equation NUM where x is the sbl for branch i and path a b is the path that includes thesaurus branches located between a and b
the authors would like to thank mr timothy baldwin titech japan for his comments on the earlier version of this paper mr masayuki kameda ricoh co ltd japan for his support with the qjp parser and mr akira hirabayashi and mr naoyuki sakural titech japan for aiding with experiments
for each of the ten verbs we conducted four fold cross validation that is we divided the corpus into four equal parts and conducted four trials in each of which a different one of the four parts was used as test data and the remaining parts were used as training data the database
let us take figure NUM again and assume that the statistics for w4 are sparse or completely missing
we tentatively use the bunruigoihyo thesaurus in which each word corresponds to a leaf in the tree structure
the crucial concern in this process is how to determine the statistics based length of each branch in a thesanrus
previous methods for word similarity measurement can be divided into two categories statistics based approaches and hand crafted thesaurus based approaches
if such an antecedent for an expression is found earlier in the same paragraph the expression is considered given information i.e. it is not in focus
for instance it can be read off the syntactic structure that the pronoun it is the singular subject of the second sentence and that therefore the finite verb should be was
presentations are generated on the basis of database information by making use of syntactic sentence templates henceforth stemplate structured sentences with variables i.e. open slots for which expressions can be substituted
which sentences should be used in a given situation
many variations of the above presentation are possible
we can now say various things about c and then use the ist formaiism to say that a second sentence for instance it is a sonata is expressed in c
except as enforced by constraints that prefer nas and nas or their edges to overlap in some way
however several obstacles may prevent this from happening
this is done with other smaller s templates
some sentence type ambiguities are also context based
what we really want is to follow the above idea but use a smaller i one that considers just the relevant factors in NUM
we refer to an entry in table with a context c and parse p as incontext c p
in addition to agent factors such as the differences in dialogue strategy seen in dialogues NUM and NUM task factors such as database size and environmental factors such as background noise may also be relevant predictors of performance
we then choose the top scoring rule from any group whose score equals or exceeds the threshold associated with that group
if n NUM has k factors this technique must perform k NUM intersections just as if we had put i n NUM
the distance between two constituents of say a noun phrase that have to agree in various morphosyntactic features may be arbitrar null ily long and this causes occasional mislnatches especially if the right nominal constituent has a surface plural marker which causes a NUM way ambiguity as in masalam
null under certain circumstances where a token has two or more parses that agree in the selected features those parses will be represented by a single projected parse hence the number of parses in the projected training corpus may be smaller than the number of parses in the original corpus
NUM the selected rules are then applied in the matching contexts and ambiguity in those contexts is reduced
we also update the incontext table for the same context and other contexts which contains the disambiguated parse
this however is not a sufficient solution for some very obscure situations where for the foreign word is written using its say english orthography while suffixation goes on according to its english pronunciation which may make some constraints like vowel 8incidentally the correct analysis is the NUM th meaning o my talk show
the reasoning is that we prefer more specific and or high scoring rules high scoring rules are applicable in general in more places while more specific rules have stricter constraints and more accurate morphological parse selections we have noted that choosing the highest scoring rule at every step may sometimes make premature commitments which can not be undone later
another similar example is kurmaya yardlm etti kur ma ya yardlm et ti construct inf dat help make past helped construct something kurmay a yard m et ti milit ary officer dat help make past helped the military officer where again with have a similar problem
cat verb root kos sense pos tam1 opt agr 3sg conv adverb dupi type manner aorist verbal forms with root duplications and sense negation functioning as temporal adverbs
an example is ko a ko a where each lexical item has the morphological parse cat verb root kos sense pos tam1 0pt agr3sg the preprocessor recognizes this and generates the feature sequence NUM
lt nsl in contrast since we have decided on sgml as a common format provides functions such as getnextitem which read the next sgml element
in the first instance this is constrained to situations where element content at one level of one document is entirely composed of elements from another document
details are not relevant here suffice it to say that doc filel resolves to the word level file and establishes a default for subsequent links
one application area where the paradigm of sequential adding of markup to an sgml stream fits very closely is that of the production of annotated corpora
with this approach well below NUM of the tokens remains as unknown in the texts we have experimented with
first we use a large number of context dependent mixing parameters to optimize the overall likelihood of the combined model
all fourteen states presented here at the top level belong to the upper layer of the dialogue
they enforce some very common feature patterns especially where word order is rather strict as in np s or pp s
NUM we also use a set of hand crafted heuristic delete rules to get rid of any very low probability parses
this view of the structure of the dialogue led us to a two layered architecture for the dm
we are therefore investigating an alternative approach making use of cogeneration a novel natural language generation technique which allows the flexible combination of free and fixed text
because the semantic hierarchy does not correspond in a simple way to wordnet a particular category may have to be associated with several disjoint wordnet subhierarchies and it is necessary to allow for exceptions
we should emphasize that a saving in keystrokes will not correspond to an equivalent reduction in time taken to construct a message since there is a cognitive cost in searching a menu for the desired word
for example having a class vehicle part might be counter productive because it would lead to words such as engine being ambiguous between their use in vehicles and in stationary objects which is unwarranted linguistically
we have found that it is possible to use wordnet as a knowledge source to semi automatically derive semantic classes of the appropriate granularity even though our semantic hierarchy does not correspond to the wordnet taxonomy
j obviously the requirements may differ to some extent for other grammatical constructions for instance we have to recognize not just noun noun compounds but conventionalized adjectivenoun combinations e.g. social security
the basic technique behind word prediction is to give the user a choice of the words or words and phrases which are calculated to be the most likely based on the previous input
on the other hand now that the senses in a cluster are similar in meaning their definitions in the dictionary should contain similar words which can be characterized as holding the same semantic codes in the thesaurus
misspellings occur at a rate of roughly NUM in our collected data usually involving words which were not in the lexicon at the time the corpus was collected and were therefore not predicted
it is found in cases where the head noun is an event and the modifier introduces the causal factor which brought about that event
such systems usually ignore phenomena like diectic references expressions of surprise discourse segment shifts etc
uttered whom is this chair chosen by is this chair chosen by the sequence are oo light is spotted by the filtering as a probable substitution
the two models considered in this paper were hidden variable markov models trained by em algorithms for maximum likelihood estimation
only spud s underlying reasoning mechanisms are completely application independent but others are at least partly reusable
consider the example from section NUM the combined convention strings influence pull exert privately
lexicalization allows us to easily specify local semantic and pragmatic constraints imposed by the lexical item in a particular syntactic frame
the official version of alembic for muc NUM did not use any of the rule sequences generated by this phrase rule learner but we hav e since generated unofficial scores
it performs this task in two steps to take advantage of the regular associations between semantics and trees in the lexicon
the meaning of the derived tree is simply the conjunction of the meanings of the elementary trees used to derive it
spud uses a single body of syntactic semantic and pragmatic knowledge to generate both productive and conventional descriptive expressions
this goal is satisfied as long as the overall content en tails p given the shared knowledge of speaker and hearer
the sample sentence is on the first line its initial lexicon based tagging is on the second line the third line shows the final tagging produced by the contextual rules
this configuration actually decreased ou r performance slightly f score down by NUM NUM points of p r trading a slight increase in organization recall for a larger decrease in precision
in this case we failed both to extract organizatio n templates from the headline fields or merge short name forms from headlines with longer forms in the tex t bodies
for the same reason the templates that were generated for the long forms of these names ended up without their alias slot filled accounting for the drop in person alias recall
in addition we were disappointed by the fact that our exhaustive compilation only produced somewhat less than NUM NUM organization names and only led to a piffling improvement in recall
examples such as these abound but by and large alembic s ne output is simply a direct readout of the resul t of running the named entity phraser rules
to package information appropriately requires sensitivity to the knowledge of the hearer and the state of the discourse
the lexicalized tree adjoining grammar ltag formalism provides an abstraction of the combinatorial properties of words
for example pronouns and determiners are members of the closed class set of words it is very rare that a new determiner or pronoun is added to the language
the creation of two separate lists is used by the post mortem parsing approach of our experimental system and the use of the two lists will be detailed in that section
there are many applications that use grammars that do not cover an extensive range of english sentences and these applications would benefit from our mechanisms for dealing with unknown words
data are only available up to NUM of the open class dictionary missing because after this point the program runs out of memory for storing all of the spurious parses
the issue of which of these parses is the correct one would require that we utilize semantic pragmatic and contextual information to select the correct parse a topic beyond the scope of our experiment
morphological recognition uses knowledge about affixes to determine the possible parts of speech and other features of a word without utilizing any direct information about the word s stem
the distinction between closed class and open class words together with morphological recognition appears to be pivotal in increasing the ability of the system to predict the lexical categories of unknown words
one aid in predicting the lexical class of words that do not appear in the lexicon referred to as unknown words is the use of syntactic parsing rules
then for sentence NUM we have two matches a and c two deletions b and d and three insertions e f and g
without the knowledge that butterf is not an acceptable root word and without some notion of legal word structure there is no way to determine that butterfly was not formed by applying ly
the nodes of depth NUM represent an order NUM bi gram model
initial values of weight t are set to NUM
the procedure construct btree which constructs a basic tag context tree is given below
the tagging accuracy of juman for the test corpus was only NUM NUM
which level is appropriate for tl i NUM and wl i
the second step then produces the hierarchical tag context tree
section NUM reports a preliminary evaluation using japanese newspaper articles
equation NUM yields various types of stochastic taggers
we want to ensure that our system aims to be as pure as it can be
it is possible that more that one contiguous syllable will refuse to take stress
a dictionary lookup gives one or several grammatical categories for the most common words
sometimes an NUM phoneme is added between two words
it can be optional removed or replaced as necessary depending on the application
this universal electronic dictionary could also be used for speech recognition and machine translation
the stress pattern for english is difficult to predict and has to be learned
in that case the system must generate the multiple variations of the word
we have adopted this convention because the output could be either phonemic or phonetic
the rules for proper names can generally be derived from the rules for words
first tests has been carried out using a small test set of NUM sentences
but note that through structure sharing the terminal elements will already be constrained by syntactic information
s compared to parsing the corresponding string the factor of speed up is between NUM to NUM
totype of a chart generator has been implemented using the same grammar as used for parsing
next each phrasal template is inserted in the decision tree in the way described above
this is in conflict with the fact that x is an st tokenization
hence many variations have been derived after decades of fine tuning and modification
7a fgh az f gz hz 7b fgh az ay f gzy hzy
this paper shows how the use of abstract syntax permitted by higher order logic programming allows an elegant implementation of the semantics of combinatory categorial grammar including its handling of coordination constructs
abe represents object level abstraction az m by the meta level expression abe i sit is not established if this schema should actually produce an unbounded family of rules
for example the ccg category for a transitive verb s np np would be represented as fs np bs np s
9it is possible to represent the logical forms at the object level without using abs and app so that harry could be simply p p harry
we have shown how higher order logic programming can be used to elegantly implement the semantic theory of ccg including the previously difficult case of its handling of coordination constructs
contrast this with the following NUM computer turn up the switch
if the user was silent for a period of time the system patiently waited for his or her input
this use of universal quantification to extract out c from a term containing c in this case gives the same result as a direct implementation of the rule for cooordination of unary functions 7a would
the situation specific expectations are the most strongly anticipated followed by the other three types
the system has been implemented on a sun NUM workstation with the majority of the code written in quintus prolog
its processing follows the usual mechanisms of prolog style theorem proving and it is modeled by the zmodsubdialog routine given above
statistics were kept on the number of such messages that were delivered during the test sessions as reported below
finally the subjects were asked to fill in a short form and describe their reactions to using the system
people frequently respond in a rather general way make hedging comments to gain time or explicitly defer discussion of the topic to a later occasion
again the annotator has the option of altering the assigned tags cf
the differentiaj entropy z e is defined as follows
is the larger the information fluctuation before aad after merging becomes
NUM repeat NUM until a termination condition is detected
next we give the concept of bayesian clustering in subsection NUM NUM
NUM calculate the similarity of every pair of the derived labels
in this work the grammar acquisition utilizes a lexical tagged corpus with bracketings
pc gh sc g sc g
in this section we conclude the concept of this measure as follows
it was applied to improve the efficiency and the effectiveness of text retrieval categorization
for these remaining polysemous words which account for the last NUM NUM word occurrences with an average of about NUM senses per word we can always assign the most frequent sense as a first approximation in building our wide coverage wsd program
the accuracies for the three test sets are shown in figure NUM precision ranges from NUM NUM to NUM NUM
this feature represents some of the long distance relations between the word and multiple other words which are not its immediate neighbors
online dictionary entries are used as seed words to generate word relation matrices for the unknown words according to correlation measures
due to the large difference in content language writing style we consider this corpus more difficult than others
NUM wall street journal in english and nikkei financial news in japanese from the same time period
NUM seed words are chosen according to their occurrence frequency NUM NUM to minimize the number of function words
a b c and d are computed from the segments in the monolingual text of the non parallel corpus
reason otp currently disallows unranked constraints i know of no linguistic data that crucially require them
g nasvoi nas voi every nasal gesture must be at least partly voiced
as such our sense tagged corpus is still not large enough to enable the building of a wide coverage high accuracy wsd program that can significantly outperform the most frequent sense classifier over all content words encountered in an arbitrarily chosen unrestricted text
thus if v syncopates as in footnote NUM it still violates the parse constraint v v
like the tier rules the constraint automata ci are small and deterministic and can be built automatically
for if all si were polynomial sized in NUM the algorithm would run in polynomial time
thereafter the s automata become smaller thanks to the pruning performed at each step by bestpaths
accordingly otp represents both input and output constituents on the constituent timeline but on different tiers
the lexicon and morphology supply to gen an underspecified timeline a partially ordered collection of input edges
NUM but rather the simplified autosegmental representation in 4b which has no association lines
representations are autosegmental gen is trivial and only certain simple and phonologically local constraints are allowed
let a e d and let NUM be the result of subserting or sister adjoining the d trees NUM 7k into a where NUM 7k are all in ti i g with the subsertions taking place at different substitution nodes in as the footnote
we argue that potential intentions must be able to be discourse segment purposes
some speech acts have weaker forms associated with them in our model
we then subsert this derived structure into the claims tree by substituting the root of the subject component of to adore at the s node of claims and by inserting the s node of the seems d tree as well as the object component of the to adore d tree in the s s d edge of the claims d tree
the elementary d trees of a grammar g have two additionm annotations subsertion insertion constraints and sister adjoining constraints these will be described below but first we define simultaneously dtg derivations and subsertion adjoining trees satrees which are partial derivation structures that can be interpreted as representing dependency information the importance of which was stressed in the introduction NUM
these are negotiation dialogues in which multiple propositions are negotiated in parallel
subsertion can be viewed as a generalization of adjunction in which components of the clausal complement the subserted structure which are not substituted can be interspersed within the structure that is the site of the subsertion
since state constraint is weaker than accept it is counted as acceptable
small spicy small he figure NUM derivation trees for NUM original definition left schabes shieber definition right for instance english sentence NUM gets the deriva
by observing the surface speech action corresponding to the judgment the hearer using plan inference should be able to derive the speaker s judgment plan
in this section we show how plan construction and plan inference fit into a complete model of how an agent collaborates in making a referring action successful
it then substitutes the modifiers subplan that terminates the addition of modifiers with the header of the modifiers recurse action with the chosen object instantiated in
the system then evaluates the constraints of the plan which results in it determining which action in the plan the user found to be in error
it is not until they have been decided upon that they become int
s2 ds2 NUM i could do it wednesday morning too
table NUM presents measures of redundancy
the overall results are shown in the table NUM
this includes the results described earlier to facilitate comparisons
the cases of non anaphorie reference NUM
this is an area for future work
such an antecedent need not be unique
the rule NUM ensures appropriate indexation i.e. via the condition rr c c where t stands for disjoint union ensuring linear usage
its single inference rule allows only a rigid style of combining formulae where order of combination is completely determined by the argument order of functors
the constraint equations for the result of the combination are simply the sum of those for the formulae combined as affected by the unification step
rule NUM allows the non applicative derivation NUM over the formulae from NUM c f the earlier derivation NUM
a natural deduction formulation requires the elimination and introduction rules in NUM which correspond semantically to steps of functional application and abstraction respectively
for example the assumptions i iv of NUM yield the results NUM ignoring semantic terms which remain unchanged
subcategorisation which given the parmlelism of syntax and semantics corresponds to allowing those combinations that establish semantically relevant functional relations amongst lexical meanings
each assumption in NUM is associated with a set containing a single index which serves as the unique 3the point of this maneuver i.e.
lr i j rightward complete link
theoretical linguists and psychologists are interested in morphological generation for its use in linguistic theory or in understanding how people learn a language
but the span slightly differs from our complete sequence and complete link
for convenience we split the field of morphology into three different areas morphological generation morphological reconstruction and morphological recognition
both algoritbrn have o n s time complexities
the user would be given the option of making the output more urgent and less polite which might result in open the kitchen window
section NUM defines the basic units and describes best first parsing algorithm
in this paper we introduce the notion of concept fertility into our translation models p eif to capture this effect and the more general linguistic phenomenon of embedded clauses
the set is composed of j i dependency links
it is difficult to determine bow much saving in utterance generation time results from prediction but it is clear that it considerably reduces physical effort
it is extensively used as reestimation algorithm for phrase structure grammars
the resulting encoding allows the execution of lexical rules on the fly i.e. coroutined with other constraints at some time after lexical lookup
the planner uses the types and values of the data as well as the relational keys but it is mainly goal driven
the feasability of a set of choices depends on the output medium 2d vs 3d color vs greyscale
probabilities were placed on the lexical productions as discussed above with the following additional provisions
it builds on the ideas of mackinlay NUM NUM but extends them in important ways
apt works by allocating the best possible graphical encoding to each variable and then checking if the result is feasible
finally a post optimization phase eliminates redundancies which can occur because the heuristics sometimes miss a compatible grouping of intentions
figure NUM schema points1 correlation be tween profits and spending NUM
the type system s role is to associate to every variable of the input a set of properties and a unit
because of its tempo null ral nature the usual way of presenting this data is the message of evolution
if a unit can not be found using single inheritance the name of the type is used as a unit
it was thus important to keep the data as close as possible to a format compatible with that type of software
toshiba mentioned in this document the user can establish an immediate connection and follow the link from toshiba to other english and japanese documents which contain that term
the web crawler can be used to add textual information from the www it fetches pages from user specified web sites at specified intervals and queues them up for the indexing module to ingest regularly
japanese personal names are translated by finding a combination of first and last names which spans the input then each of the name parts is translated using the japanese english first and last name lexicons
in addition in order to develop a large lexicon of english names and their japanese translations which are transliterated into katakana we have automatically generated katakana names from phonetic transcriptions of english names
we have decided to use commercial databases for our applications as we are not only indexing strings of terms but also adding much richer information on indexed terms available through the use of ie technology
moreover the englishcentric browsing and retrieval mode can be switched according to the users language preference so that for example a japanese user can query and browse english documents in japanese
thus if a term can be translated in one way for one type and in another way for another type the term translation module can output appropriate translations based on the type information
there is a need for a parsing system that can act over less precisely defined domains and still efficiently cope with unknown words
figure NUM translation by a commercial mt system
as discussed in section NUM NUM when the user selects a japanese article they can optionally send the article to a commercial mt system for rough translation by pushing the translate button cf
this research does not consider the meta linguistic use of a word as in this sentence the is a determiner
the implementation of advlsor ii builds on a software environment dedicated to the development of language generation systems the fuf surge package elhadad 1993a NUM here we use the word process in the systemic sense see section NUM NUM NUM
these anchor points are used for compiling a secondary lexicon
note that some high level decisions about sentence structure must be made early on with this architecture i.e. before syntactic realization since for example selecting the verb imposes syntactic constraints on how its arguments can be realized
these vectors are matched against each other by mutual information
figure NUM dtw path reconstruction output and the anchor points obtained after filtering
sentences NUM and NUM illustrate the type of complexity we found the two semantic relations are merged into a single sentence but the second relation is realized as a prepositional adjunct of different types
selecting the verb or a higher level relation such as a connective between two clauses automatically determines overall thematic structure while selecting which concept in the input will serve as head of the sentence directly influences choice of words
floating constraints are handled in both of these stages for example merging two content units in a single linguistic unit is a phrase planning decision whereas picking the appropriate collocate of an already chosen word is a paradigmatic decision
if we rely on monotonic feature inheritance the above question needs a negative answer
here we present results for adding fertility structure to unigram bigram and headword clump generation models on arpa s air travel information service atis domain
the input is a set of three relations each of which is represented similarly by a set of attribute value pairs in the feature structure form shown in the central tier of figure NUM except for cardinality which reduces to an integer
this line can be thought of the text alignment path
a disjunctive hypernym implemented as a set of hypernyms is considered harmful
an employer who is person NUM an employer who is a firm
these include document managers that provide multi source document compatibilities
NUM NUM oleada task oriented user centered design in natural language processing
task analysis demonstrated that the system lacked functionality and resources
plug and play to integrate various kinds of software inside tipster
this enables users to see which resources have relevant entries
this feature can be used to identify important domain specific words
more importantly oleada offers an informational technology alternative to traditional language instruction
crl s work on oleada has been funded by dod contract mda NUM NUM c e086
table i dialog acts and examples
table NUM performance of simple recurrent network
into this system the content of wordnet NUM NUM was downloaded from the dict data files
NUM in words written with both capital and lowercase letters an initial capital letter may have a stress mark
then they continue there is no external criterion of correctness to which these decisions should adhere p
we will also discuss the relationship between parsing accuracy and the size of training corpus
we see the senses of all words in a particular language as forming a space which we call semantic space for any word of the language each of its senses is regarded as a point in the space
when we say that every representative has wide scope we are saying that there is a function which maps r s candidate set onto the power set of s s candidate set
quantifiers and their associated scoping phenomena are ubiquitous in english and other natural languages and a great deal of attention has been paid to their treatment in the context of natural language analysis
it guarantees that r s focus set is maximal in the sense that it contains all possible r s which satisfy the restriction and avoids the above anomoly by failing to allow partition 18b
constraint NUM restricts the range of acceptable partitions by restricting the range of acceptable inner quantifiers for r it also specifies r s outer quantifier as the one which is to be finally generated
NUM saw qr r rep r of r qc c com c qs s sample s
for example to check the consistency of the quantifier at most two in at most two representatives saw a sample assuming r s the following checks need to be made
a set of NUM previously unseen english utterances were translated by the system into french speech using the same kind of subjects as in the previous experiments
on one hand transactional and ideational goals are broadly applicable to contexts in which something external to the conversation is being done e.g.
the input to the algorithm is i a model represented as a collection of facts and ii an abstract description of the target sentence with gaps where the quantifiers should be
instead they have gaps where quantifiers should be where r c s indicates that every representative outscopes a company which in turn outscopes some samples
the segmentation parser provides knowledge about utterance boundaries
the operation obtaining one or more strings is denoted by the symbol
formally this category is defined by rules f6 and f7 table NUM
in addition gjw89 constrains pronominalization such that no element in an utterance can be realized as a pronoun unless the cb is also realized as a pronoun and imposes a preference ordering for operations on cf such that the least reordering is always preferred
proof rule c1 indicates that the strings of expression vlclv2 are always hyphenated as vl clv2
our corpus of NUM words is still medium size
the best performing network is shown in figure NUM
weber for their work on screen
what do we learn from this
in the fourth experiment the classalign algorithm is employed to align both sets of test data again
from russ s perspective these utterances had the following discourse level interpretations at the time each was produced
a significant amount of free translation arises due to the use of four morpheme mandarin idioms for stylistic reasons
similarly the model incorporates only a small number of linguistic expectations these are shown in figure NUM NUM
speech act misunderstandings occur when two participants differ in their understanding of the discourse role of some utterance
however to our knowledge the degree of success of word alignment has not yet been explored
second we believe that for most applications low coverage is just as serious as low precision
the notions of misunderstanding and misconception are easily confounded so we shall begin by explicating the distinction
although these approaches do quite well at preventing certain classes of misunderstandings they can not prevent them all
the language lacks explicit quantification as in prolog variable names are understood to be universally quantified
however mandarin is not an easy language to classify according to this typology for a number of reasons
the two thesauri lloce and cilin are used as the classification systems of source and target words
this symmetry accounts for another problematic case discussed in section NUM 4it is also possible to bail out in coreference between the papers pl and p2 here we would get the strict reading again
l h evokes a scale or ordered set to which the accented constituent belongs l h commits to the salience of the scale and is typically used to convey contrastive stress l h also evokes a scale but fails to commit to its salience e.g. conveying uncertainty about the salience of the scale with regard to the accented constituent
second we attempted experiments on core2 that discriminated between occurrence and placement at the same time and the derived trees were complex and not perspicuous
each data point in our dataset corresponds to a core contributor relation and is characterized by the following features summarized in table NUM
l b is a cluster composed of two units the two clauses related only at the informational level by a temporal relation
NUM c is an example ofsubsegment with its own core contributor structure its purpose is to give a reason for testing part2 first
NUM o core type NUM infor struct NUM inten rel segment as encoded by core type trib type above and below also figures prominently
data for learning should be divided into training and test sets however for small datasets this has the disadvantage that a sizable portion of the data is not available for learning
trees that have trib pos as the root are the most useful for text generation because given a complex segment trib pos is the only attribute that unambiguously identifies a specific contributor
to evaluate this strategy we must do further work to understand whether there are important distinctions among cues e.g. so because apart from their different preferred locations
they proposed heuristics for including and choosing cues based on the rhetorical relation between spans of text the order of the relata and the complexity of the related text spans
segments are internally structured and consist of a core i.e. that element that most directly expresses the segment purpose and any number of contributors i.e. the remaining constituents
this leads to language models whose size grows linearly in the number of words used for each prediction
in particular the em algorithm adapts the matrix elements to the weighting of word combinations in eq
in this way it is easy to check whether the decision points in the algorithm which are illustrated by the examples have been handled adequately
as for the verb it is important to have access to the verb of the preceding utterance and to use a systematic semantic classification of the verbs
the underlying syntactic representations of sentences in our framework can now be illustrated with several simplifications in the form of linearized dependency trees
it is then possible to specify a basic systemic ordering so of the kinds of complementations of every verb noun adjective
to illustrate the notion of contextual boundness we present two additional examples NUM NUM how do you find your neighborhood
in the output of this procedure many ambiguities remain but sentences even in their spoken shape often are ambiguous as to their tfa
thus for example NUM a is a natural answer to what are jane and jim doing or to have you heard about jane and jim recently
natural language processing always requires solutions covering first the typical or most frequent cases and only then more cemplex procedures accounting for peripheral phenomena
the program presupposes that each word form occurring in the text has undergone lexical and morphemic analysis so that it has been assigned the relevant data found in the lexicon
in others specific syntactic constructions allow for an appropriate shape of surface word order such as passivization in NUM or the prepositional expression of addressee in NUM
this can have the added advantage that the list of transformations learned using a mature annotation system as the initial state annotator provides a readable description or classification of the errors the mature system makes thereby aiding in the refinement of that system
as the graph shows the lowest error for the combined system occurs when the filter is loosened all the way and all shogun frames are used
overall shogun had the higher f score because it was optimized for maximal f while plum had been optimized for a mix of high f and low errors
the rationale is straightforward for full templates e.g. st scores have been mired with an f in the 50s ever since muc NUM in NUM
like ne this really is domainindependent though in muc NUM it was evaluated only on documents obtained by a query for documents about change in corporate officers
output record editor ore maintains the output record factbase a data file containing knowledge about how the nlu application output should be formatted
there are several potential contributing factors the merging occurred after output was produced perhaps results would have been better by combining results earlier in linguistic processing
therefore once a linguist had defined the guidelines for correct output potentially less sophisticated less trained speakers of the language could develop the answer keys
however even in the NUM correct detections one still does not know the transcription at present a person would have to transcribe it
the fact that its recall and precision are both in the high 80s represents not just a quantitative improvement in parser performance but also a qualitative improvement
the use of a gui and a database in place of files of source code and data represents a fundamental advance in making natural language technology widely available
here we make this dependency relation explicit
we describe a practical parser for unrestricted dependencies
this kind of rule that starts from word a follows links up to word b and then down to word c introduces a non projective dependency link if word b is between words a and c
NUM NUM NUM NUM NUM NUM NUM NUM
the basic constraint gram null mar idea of introducing the information in a piecemeal fashion is retained but the integration of different pieces of information is more efficient in the new system
in addition the representation is shallow which means that e.g. objects of infinitives and participles receive the same type of label as objects of finite verbs
if in addition the verb does not take indirect objects i.e. there is no sy00 in the same verb ling not NUM sv00 the i NUM bj reading will be discarded
NUM the syntactic relationship between the verbs is established by a rule stating that the rightmost main verb is the clause object of a main verb to the left which mlows such objects
differs from the previous rule in that it leaves the other readings of the noun intact and only adds a possible subject dependency while both the previous rules disambiguated the noun reading also
fliers starting in NUM figure NUM example for automation level NUM the user
the wall street journal corpus wsj is a NUM million word corpus of articles from the newspaper
section NUM explains the construction of the lexical knowledge resources used
thus even though we now use the same parser for an infinite set of input sentences represented by the fsa the parser still is able to come up with a parse forest grammar
the lexicon or machine tractable dictionary wine
where necessary multiple correct senses were allowed in both dictionaries
section NUM describes the test sets and shows the results
this heuristic is applied when the genus term is monosemous
after this short introduction section NUM shows the methods we have applied
it is clear that different dictionaries do not contain the same explicit information
some of them have been fully tested in real size texts e.g.
unlike a simple markov process there are a potentially infinite number of states so there is inevitably a problem of sparse data
this provides a state transition or dynamic model of processing with each state being a pair of a syntactic type and a semantic value
given that our arguments have produced a categorial grammar which looks very similar to hpsg why not use hpsg rather than applicative cg
table NUM shows the various levels of back off for each type of parameter in the model
they increase the set of non terminals by adding semantic labels rather than by adding lexical head words
in the following sections the automation steps NUM and NUM are presented in detail
allowing the model to learn a preference for modification of the most recent verb
as we saw earlier even for cfg it holds that there can be an infinite number of analyses for a given fsa but in the cfg this of course does not imply undecidability
figure NUM a gap feature can be added to non terminals to describe np extraction
figure NUM a lexicalised parse tree and a list of the rules it contains
figure NUM a tree with the c suffix used to identify complements
in rule NUM a trace is generated to the right of the head vb
stz the chancellor but also in english we find evidence for extraposition from vp if we assume that adjuncts adjoin to the vp and hence by default have to follow vp complements NUM florida national said yesterday that it remains committed to the merger
g np internal extraposition and extraposition within fronted vps are captured without the assumption of any further mechanisms
we conclude that the application of the headadjunct schema has to be disallowed on top of a head extra structure
the lci requires that an extraposed element is adjoined at the first maximal projection which dominates its antecedent
apart from leading to spurious ambiguities this assumption is incompatible with the coordination data given in sec
higher order coloured unification and natural language semantics
for english however we assume that all lexical entries are marked per left
ll NUM a man came into the room with blond hair
an ordered list of transformations is then learned to improve tagging accuracy based on contextual cues
the initial state annotator is the tagging output of the previously described one best transformation based tagger
change the tag of an unknown word from x to y if
word w ever appears immediately to the left right of the word
in this section we describe the practical application of transformation based learning to part of speech tagging
we show results when unknown words are included later in the paper
we present a detailed case study of this learning method applied to part of speech tagging
in transformation based learning the entire training corpus is used for finding all transformations
additional comparisons are needed to reject parses other than the lucky NUM NUM
most work on spurious ambiguity has focused on categorial formalisms with substantially less power
yes a different kind of efficient parser can be built for this case
as always a separate lexicon specifies the possible categories of each word
each of NUM through in would be instantiated as either or
we continue these notes on formal versus content based checks at the end of this paper see section NUM by presenting three examples contrasting both types of checks when we already have explicated the formal ones we then can refer to
currently we use two grammatical functions which results in a trigram model
note that the winning assignment probabilities are distributed broadly over the interval NUM
the first two versions are the source and target speech files the third time the form is filled in from the tezt version of the source utterance
has been produced among possibly others where i is the length of the input x
the contexts are smoothed by linear interpolation of unigrams bigrams and trigrams
when selecting the appropriate category judges are instructed only to take into account the actual spoken source utterance and the translation produced and ignore the recognition hypothesis
NUM short cuts were detected all in the part partof hierarchy however these can be judged a priori to be redundancies if and only if transitivity holds for the part partof relation and this depends on definitions not given by wordnet and actual usage
this can be determined as follows
this approach is grossly inefficient however
figure NUM updated disconnected graph after the
the broad outline however is as follows
this work will be reported at a later date
two assumptions are made regarding cxiealsemantic indexing
rather than estimate the relationship between words we measure the mutual information between classes
the speeds of the different parts of our system are shown in figure NUM NUM our system reaches a performance level in speed for which other very low level factors such as storage access may dominate the computation
the quality of the translation itself must also be high in spite of the fact that by the nature of the problem no post editing is possible
antonymy and antosemy in wordnet wordnet NUM NUM permits cardinality NUM for antonymy however a cardinality check of antonymy is blind with respect to fundamental semantic differences of which the cardinality check for the induced relation the antosemy relation is more sensitive
that means the functions gfn1 and sc gen2 have to be linked to the approt riate roles
in such cases it is possible to generate committee members by sampling the posterior distribution for each independent group of parameters separately
for example a transition probability parameter p ti tj has conditioning event ti and conditioned event tj
we can do this in virtually the same way
figure NUM shows an example tree from the treebank
our results suggest applying committee based sample selection to other statistical nlp tasks which rely on estimating probabilistic parameters from an annotated corpus
figure NUM a shows that accuracy increases with batch size only up to a point and then starts to decrease
typically concept learning problems are formulated such that there is a set of training examples that are independent of each other
our results show that this effect is achieved even when using only two committee members to sample the space of likely classifications
the probabilistic model m and thus the score function fm are defined by a set of parameters lcb hi rcb
we denote a particular model by m lcb hi rcb where each ai is a specific value for the corresponding cq
we implemented this effect by employing a temperature parameter t used as a multiplier of the variance of the posterior parameter distribution
most simply we can use a committee of size two and select an example when the two models disagree on its classification
theorem proving is used as the reasoning mechanism for determining when task goals are completed
tm the actual values of NUM NUM and NUM NUM compare favorably with the expected results
zy smith z player y support x y
in NUM of the failures the subject misconnected a wire
only three of the eight subjects successfully completed all possible dialogues
such a methodology allows us to gain clearer insight into the evolving nature of human computer dialogues
these hypotheses are generally supported by the results in table NUM
human human communication frequently contains miscommunication so we should expect it in human computer dialogue as well
declarative mode dialogues are shorter but less orderly consisting of more user initiated subdialogue transitions
assertion the speaker has the initiative unless the utterance is a response to a question
therefore we expect the computer to show strong linguistic control when it has task initiative
confirm no dep city milano arr city roma part day evening do you want to go from milano to roma leaving in the evening
table NUM shows that the grammar coverage for unseen data is about NUM excluding the failures due to unknown words
compared with a lex lzed semantic grammar this grammar achieves a higher parsing coverage without increasing the amount of ambiguity misparsing
this is more frequently the case if the structures of the source and target languages are quite different as in english and korean
namely a misparse of the input often leads to a translation into the target language which has incoherent meaning in the given context
in this paper we have proposed a technique which maximizes the parsing coverage and minimizes the misparse rate for machine translation of telegraphic messages
whereas the language processing is very efficient when a system relies on a lexicalized semantic grammar there are some drawbacks as well
3in the examples nom stands for the nominative case marker obj the object case marker and loc the locative postposition
this introduces a greater degree of syntactic ambiguities than for texts without any omitted element thereby posing a new challenge to parsing
we describe a prototype completion system for english to l y ench translation which is based on simple statistical mt techniques att t give mea stlfenlents el its performance ill terms of rcb laracters saved in a test cortms
applying explanation based learning to control and speeding up natural language generation
we report on our results of disambiguating the verbs in the semantic filters by adding wordnet NUM sense annotations
NUM speech translation is normally an interactive process and it is natural that it should be less than completely automatic
there is another scoping option which instantiates NUM to q h i.e. gives every house wide scope over both antecedent and ellipsis
the implicit pronoun has been sloppily identified with its antecedent to refer to something matching a similar description i.e. the subject or agent of the loving relation simon
each word defines a sequence of order domains into which the word and its modifiers are placed
this forms a generalized quantifier expression whose body is obtained by discharging all occurrences of the term and it index to a variable and abstracting over the variable
dsp block the reading by n more artificial restriction on the depth of embedding of expressions in logical forms they lack the means for distinguishing between coindexed and merely co referential expressions
if this information is not to be lost some way of referring to the structure of the compositions as well as to their results seems to be required
in the case of formulas they may be given both the values true and false corresponding to the formula being true under one possible resolution and false under another
while the treatment of ellipsis is hopefully of some value in its own right a more general conclusion can be drawn concerning the requirements for a computational theory of semantics
but for all non parallel terms we have a choice between a strict or a sloppy substitution s a sloppy substitution involves substituting a new term index for the old one
preserving information focusing exclusively on the results of semantic composition i.e. meanings can ignore differences in how those meanings were derived that can be linguistically significant e.g.
NUM dis2 clu s l cos dvau dv intuitively the distance can be seen as a measure of the similarity between the definitions of the words in the cluster and each definition of the word
in addition it supplies the information that the social status of speaker is not equal to that of addressee
ill order to see values the attribute context may have let us consider the sentence in NUM
thus from an honorific verbal ending we can infer that the social status of speaker and addressee is not equal
thus the dialogue in NUM is not coherent with respect to the honorit ication of the person m
the relationship between speaker and addressee determines whether a formal verbal ending or an informal verbal ending can be used
the occurrence of honorification in a sentence is constrained by relative social status of the individuals involved in the sentence
when a dialogue is processed the inlormation about relative social status is provided in the form of feature structure
after the dialogue is parsed the feature structures in NUM are collected together with other feature structures
this kind of incoherence can be detected only by considering relative social status of the individuals involved in a dialogue
for instance the query for parsing the dialogue in NUM is its illustrated in NUM
standard psg trees are projective i.e. no branches cross when the terminal nodes are projected onto the input string
the way syntax operates such specification may be very complex and independent on the notion of grammatical well formedness
segment medial and segment final utterances are distinguished more clearly by rhythmic features primarily pause
or better except those marked by which were at NUM or better
international squaw it must also be excluded from our test list
if a word is the same in english and in german as e.g.
variation in pitch range has often been seen as conveying topic structure in discourse
the utterances composing the discourse divide into segments that may be embedded relative to one another
it appears that the speech signal can help disambiguate among alternate segmentations of the same text
this could be reduced by automatically accessing translation lists or reliable bilingual dictionaries
average f0 and rms were calculated over the entire intermediate phrase
duration of pause between utterances or phrases has also been identi
in contrast average t scores for group s are NUM
results were calculated using one tailed t tests except where t indicates a two tailed test
the second step consists of turning the transducers produced by the preceding step into transducers that operate globally on the input in one pass
we will see that the final step of the compilation of our tagger consists of transforming a finite state transducer into an equivalent subsequential transducer
dc a5 do you want to leave from torino
doff NUM siegel and castellan NUM
the fourth and final step consists of transforming the finite state transducer obtained in the previous step into an equivalent subsequential deterministic transducer
when a dictionary is represented as a dag looking up a word in it consists simply of following one path in the dag
using the set of contextual rule templates shown in figure NUM after training on the brown corpus NUM contextual rules are obtained
this efficiency is explained by two properties finite state devices can be made deterministic and they can be turned into a minimal form
in this section we prove in general that any transformation based system such as those used by brill is a subsequential function
definition a transformation based system is a finite sequence f fn of subsequential functions whose domains are bounded
NUM user i want to travel from torino to milano
in the example of figure NUM it41 is defined by it4i ah bh and it4i ae ce
the solution that we described is both general and robust
since s can not be governed in any valency it follows that s must be the root
we will describe how non understanding and the effects of misrecognition are dealt with by dialogos a real time spoken dialogue system that allows users to access a database of railway information by telephone
we then applied our marker and clause identification algorithm on the same texts
in this paper we describe evaluations that follow both these avenues
NUM hypothesize a set of relations r between the elements of ur
NUM relations can be partitioned into two classes paratactic and hypotactic
in the first step the marker and clause identification algorithm is applied
in this case the algorithm constructs NUM different trees
in this section we describe the experimental set up used to evaluate and assess the described model of terminological derivation
each time the procedure finds an arc whose input symbol is a category label it expands this arc by the adequate csst producing a new model
in fact whereas stochastic taggers have to store word tag bigram and trigram probabilities the rule based tagger and therefore the finite state one only have to encode a small number of rules between NUM and NUM
since all the processing occurs without any regard to the types of events discussed in the articles the system we have developed here is easily portable across domains
initially we relied on this merging tool to bring together separated org names and descriptors such as nec corp the giant japanese computer manufacturer
any phrase matched is reduced usually but not always to a single multi token o r mtoken
si blow da milano roma setla yes blow from milano roma evening confirm yes dep city milano arr city roma dep time evening
in this version if the sentence can not be parsed a minimum size subset of subtrees that cover the entire sentence is produced
verb argument structures pp disambignation rules ooo the design of the overall process requires a set of modeling principles NUM to focus on the suitable tag system NUM to customize the classification to a corpus NUM to tag the corpus correspondingly
we pop the first the entry from the agenda and since it is not already there we add it to the chart
the semantic domain function the semantic domain function is denoted by c zd and designates a terminal c whose semantic domain is restricted to d
thus the last edge can be extended creating a finished edge so we have created an subtree np that spans the whole sentence
introduction chinese nlp is still greatly impeded by the relative scarcity of resources that have already become commonplace for english and other european languages
the main purpose of the excluded category function is to improve robustness when the grammar coverage inadequacies prevent a full parse tree from being found
though ambiguities remain the smaller number of parses per sentence makes it more likely that most probable parsing can pick out the correct parse
n u e and the left context condition l and the right context condition r are of a form described below
easyenglish identifies a number of structurally ambiguous constructions and supplies suggestions for unambiguous rephrasings
coordination is another source of ambiguity since the scope is not always clear
examples i mean sorry ach nein psh
to illustrate the function of these checks let us look at the checks for passives
however some of the standard stylistic recommendations are not entirely relevant for technical documents at least
the ci has to be in a certain range before the document can be accepted for publication
ibm has developed a number of tools to help writers cope with this task of information development
like most other big corporations today ibm is interested in cost effective yet high quality information dissemination
null i easyenglish also works with the xedit editor on vm and the epm editor on os NUM
in addition to spotting ambiguity and providing terminological support easyenglish also performs more traditional grammar checking
we are currently in the process of defining this rule set
long sentences can distort the results so the weightings awarded to subject domain matches are divided by the number of words in the sentence
but by a conventional phrase such as am i right
examples all right now ja also
discourse particles and routine formulas in spoken utterances can not be translated on a simple lexemeto lexeme basis
applying the algorithm on the above sets and counters yields the following morpho lexical probabilities p1 NUM NUM p2 NUM NUM p3 NUM NUM
in other words the algorithm consists of building a copy of the original transducer and at the same time the identity function that operates on dom t y
the target language generation module is designed to perform a number of linguistic operations such ms enforcing subject verb agreement ensuring that required definiteness information is present such as english determiners quantifiers or possessives and generating the appropriate inflectional morphology
in the parse structures leaf nodes are given tags while there is no label for intermedlzte nodes
the fact that stemplatesare syntactically structured objects makes it possible to formulate various conditions on the form of variable parts
although this method for acquiring morpho lexical probabilities gives very good results for many ambiguous words as will be shown in section NUM we detected two types of inherently problematic cases
both kinds of information are used to find the proper locations for pitch accents
one might try to use a general purpose theory of context to formalize dyds context model
the result of an a marking is that the so called default accent rule cf
an s template indicates how the meaning of a database record can be put into words
consequently the sentence accent trickles down along a path of strong nodes and ends up on hear
dialogues are dynamic constructions and contributions are locally planned and realized so that the communicative requirements of the dialogue us a whole are respected
the differences lie again in our einphasis on rational and cooperative communication as opposed to interaction as a fmlure to prove
NUM if the response would repeat previous information it is considerate to leave this implicit unless the information is assigned a special emphasis
the first step means that the structure is not built according to structuring rules but emerges from local coherence as the dialogue goes on
awumcnts lhlrthermore we follow th el in on that such idiomatic strings m e 1lot llllslrll tlll e lilplexe q bill strll tltre NUM en i ities
len nta einen bock schieben base lexelnes boek schie 3en internal syntactic structure during the parsing process this necessary idiomatic information is extracted from phraseo ll x and mapped into feature structures the parser can handle
therefore it is now widely accepted that we have to distinguish at least two groups of figurative verbal phrasal idioms first there is a group of syntactically frozen idioms as kick the bucket meaning die which are called noncompositional
for the idiom eincn book schicflen this means that schicflcn is a two argument relation with a variable tbr the subject np the noun phrase cincn bock referring to the concept a mistake and the verb schicflen denoting a situation where someone is acting
every part of the idiom is marked with an extra ending in our example vpll this is due to the fact that the same words can occur in different idioms and should not be mixed up during parsing because of the corresponding semantic structures
furthermore if we want to represent the sentence er glaubte ihr die liigcngeschichtc he believes her the tall tale continuing example NUM the connection of the discourse referents can not be made correctly as shown in drs NUM
i eine i iigengeschichte erziihhm o s z tall tale tell in addition it is importmlt that also the seman i i s of the paraphrase and the idiom can be struc i ured in paralm
person two gender mast stem boek vpll3 vpl verb schieflen vpll3 the features val for valency respectively vpl for verbal phraseologism contain the information necessary to find other relevant parts for building the idiom
in the literature two generalisation strategies have been adopted distributional approaches several papers adopt distributional techniques to identify clusters of words according to some defined measure of similarity
in figure 4b the normalised reference performance function and the best fitting scoring function are shown with the estimated values of a x and NUM
we remark that our experiment is on large meaning that we automatically evaluated the performance of the model on a large set of nouns taken from the wall street journal
of these the gunstock and progenitor senses should have been further dropped out but there are NUM senses that are correctly pruned like liquid caudex plant etc
in our experiments we applied a scoring function similar to that obtained for the wall street journal to two other domains a corpus of airline reservations and the unix handbook
on the other hand semantic tagging has a serious drawback which is not solely due to the limited availability of on line resources but rather to the entangled structure of thesaura
although the text collection is not a training collection in the sense of a collection of manually labeled texts for a pre defined text processing task his approach can be regarded as the most similar to ours in the disambiguation task
in this paper we present a method for the selection of the best set of wordnet categories for an effective domaintailored semantic tagging of a corpus
gramb c i card s wi card sc wi card s wi
eliminating noise since the primary lexicon after thresholding is relatively small we would like to compute a secondary lexicon including some words which were not found by dtw
documents comparing government subsidies given to air and bus transportation with those provided to amtrak would also be relevant
table NUM shows which specific information elements generally serve as given and which serve as new information
we see that most utterances with more than one information element contain at least one new element
for more details on the various runs and procedures please see the cited papers in the trec NUM proceedings
this confirms our view that in human human ovr dialogues the travel plan is given in steps
usually they will relate this new information with an entity introduced in the preceding context
metaknowledg e about the dictionary describes its content it lists known features specifies feature applicability to differen t syntactic categories describes possible and default values of different features the default values are no t shown explicitly in the entries
the theoretical complexity of the generator is o n4 where n is the size of the input
first experiments must still continue on the shorter topics since this represents the typical initial input query
compute inferences proofnum meaning meaning phys state prop goalaction actionattribute done true goalaction ach phys state statedes ts goalaction obs phys state statedes ts make inference proofnum phys state statedes ts infer meaning
and any state constraint can attach to the active path as a confirmation because the constraints on confirmation attachments are very weak
program transformation techniques are used to advance the encoding
computational linguistics volume NUM number NUM
a set of four lexical rules
the result is displayed description language
even though we see on the fly application as a prerequisite of a computational treatment of lexical rules it is important to note that a postponed evaluation of lexical rule application is not always profitable
consider the base lexical entry in figure NUM
note that this is not an unfolding step
as opposed to proponents of domain specific information for domain specific applications our approach veztures towards the application of general purpose algor t and resources to our dom i specific s rn tic class disaznbiguation problem
thereafter this chosen concept node is piped througja a semantic distance module which determines the s m c distances between this concept node and all the s m tic class nodes in the domain speci c hierarchy
NUM as relations between word objects
the probability statistics required for resuik s tneematton c umc algoctchm were eonecmd sas this hie zchy is adopted and not created by us occasionally we can only furnish guesses as to the exact meaning of the semantic classes
where p x and p x are the probabilities of the events and p x x2 is the probability of the joint event
before describing the implementation of coordination it is first necessary to mention how ccg categories are represented in the prolog code
although a worthwhile further demonstration of the use of abstract syntax it has been left out of this paper for space reasons
remains constant over different examples we can disregard the term p i in the denominator
solid computational and mathematical framework in tact with linguistic theories null the uno representation offers a solid computational and mathematical framework in tact with linguisti c theories
hybrid analogical translation greatly reduces the number of required examples by relying on the generality of linguistic rules
thus generalized coordination instead of being a family of separate rules can be expressed as a single rule on recursive descent through logical forms
bound variables in aprolog can be either upper or lower case since they axe not logic vaxlables and will be written in lower case in this paper
however our algorithm found both carbon and monoxide to be most likely translated to the single chinese word 4h which is the correct translation for carbon monoxide
it has also shown promise for finding noun phrases in english and chinese as well as finding new chinese words which were not tokenized by a chinese word tokenizer
and we therefore want to cope with word parsing by skipping the p o s tagging step
at stage NUM of our algorithm we try to find anchor points on the dtw paths which divide the texts into multiple aligned segments for compiling the secondary lexicon
if we plot the points on the dtw paths of all word pairs from the lexicon we get a graph as in the left hand side of figure NUM
the second composition operation involving d trees is called sister adjunction
every d tree is a projection from a lexical anchor
dtg involve two composition operations called subsertion and sister adjunction
tion structure shown on the left in figure NUM
we now discuss a possible derivation
figure NUM d trees for NUM
NUM NUM getting word order right kashmiri
this prevents sister adjunction at these nodes
note that components are shown as triangles
section NUM briefly discusses dtg recognition algorithms
social normative requirements that concern the agent s sincerit NUM exchange information which is true or for which evidence can be provided motivation exchange information which is related to one s goals and strategies and consideration exchange information which the
a reliance on detailed information about the context can prove detrimental if such information is often missed by the system
another example might be the assignment of disease noun s of bodypart noun pl to obstruction of arteries function words such as of are usually not further subcategorized since they convey structural information in themselves
also since quite often wordnet gives semantically unrelated in a given domain adjectives together we use a heuristic rule which says that if two adjectives are used together in one phrase they do n t hold synonymy antonymy relation
in our example for the type infarction the following clusters were automatically obtained rest suspected lateral recent further repeated as we see all clusters look fairly plausible except the single adjective old which was misclassified it stands for a temporal property of an infarction rather than its spreading at a myocardium
then we separate pure adjectival modifiers from adjectivized nouns infarction inferior old acute post further anterolateral lateral infero posterior antero septal repeated significant large limited myocardial diaphragmatic subendocardial myocardial infarction anterior first extensive minor small previous posterior suspected
in our notation we refer to singe word semantic categories as uppercase labels which we choose as being descriptive of the class which has been discovered simple sequences of semantic categories by a preceding and a sequence of sequences by a preceding
from the multi word term bank collected by the collocation tool we derive semantic frames by replacing each content word in each phrase by its semantic category derived either empirically from the word level dendrogram in the case of frequent words or derived from wordnet in the case of less frequent words as described above
this pattern covers all strings which have a reference to a person followed by one of the listed verbs in any form followed by a compound noun with the head infarction and followed by a date expression
for example a type oriented structure for eventualities includes their thematic roles agent theme temporal links and properties while a type oriented structure for objects includes their components parts areas and properties
the target domain description consists of words grouped into domain specific semantic categories which can be further refined into a conceptual type lattice ctl and lexico semantic patterns further refined into conceptual structures as shown elsewhere in the paper
the al in our case are members of the power set of possible coreference configurations
this study supports the view that name recognition and matching in the context of information retrieval is a significantly different problem from either name searching or matching in relational databases or name recognition or extraction i.e. tagging names m free text
existing research or commercial software can be used as parts of an overall approach to name searching but there are major adaptations that need to be made and gaps in the architecture to be filled such as how to recognize names effectively in user queries
the performance improvement obtained by proximity searching against a collection which had not had names pre tagged suggests that better retrieval performance improvement gains may be possible using simple name matching heuristics if the query name term is known rather than relying on pre processed name tagging
the muc NUM results imply that recognition accuracy is very high at least for news text but whether this would help retrieval much given that the name to be searched is already known i.e. specified in the query is uncertain
once an effective approach for name searching has been developed there should be large benefits especially for business areas such as newspaper databases where a large proportion of queries contain personal company product or other names
if names are not already identified as such in the database s text records e.g. when they appear as part of a free text field and have not been previously tagged as being names then name recognition is required
in the case of natural language understanding systems there is linguistic context as well perhaps as domain knowledge representation which can be used to help infer that the two naraes being matched refer to the same individual
the following shows rules of derivation that we use
a several men danced with few women
coordination gives an interesting constraint on availability of readings
it seems hard to reconcile quantifying in with these observations
b every man seeks a white unicorn
NUM first consider the following sentences without coordination
some lexical entries for every are shown below
the section following this one will give a detailed example showing all of these mechanisms working together
null the processing of ipsim proceeds with normal theorem proving but is interruptable in two ways
next as specified in figure NUM the system reattempts the computation with this revised rule
appendix a gives the detailed steps required for completing the first part of the NUM utterance sample dialog
the lowest level of this tree is the atomic element that can be addressed in a dialog
additional checking will computational linguistics volume NUM number NUM then make the rounds of all specifications
the set of possible observations provides the situation specific and situationrelated expectations discussed in the section on expectations
1deg consider now sentences including coordination
also has only two grammatical readings
as we have noted dramatic improvements in the worst numbers timex in ne org locale and country in te would have been obtained with very minor changes in the patterns literally a couple hour s worth of work
therefore every new thing reduced is added to a temporary lexicon and another reduction step is applied to look for othe r references with certain allowed variations to those same things for example relatively easy torecognize references to mr
let us tackle the ordering problem first
they saw the ball near the bank
we want to write feature equations like
we soon found however that even with careful use of slot fillers to prevent descriptors for commercial organizations from merging with say th e name of a government organization or a library too many merges were incorrect
assume this set has n members
some examples will illustrate the problem
this was needed to be able to usethat information in name recognition since there did not appea r to be any good way to get the pattern matcher to use the capitalization information contained in th e original tokens
part ial order j eature agr
as a result a unifed parse is obtained as shown in the discourse information if the partial parses are not unified into a single structure in the previous step they are joined together on the basis of the discourse information until a unified parse is obtained
NUM NUM in the operation of the invention an operator loads cartridges into the magazine from 3this structure resulting from an incomplete parse does not indicate that the grammar of the parser lacks a rule for handling a possessive case indicated by an apostrophe and an s
in such cases the default rule of joining the root node of the second partial parse to the last node of the first partial parse was mostly applied since the least restrictive matching patterns in our method were similar to the heuristic rules
in this paper the term discourse is used as a set of words in a text together with the usage of each of those words in that text namely a part of speech and modifiee modifier relationships with other words
NUM when all the sentences have been parsed the discourse information is used to select the most preferable candidate for sentences with multiple possible parses and the data of the selected parse are added to the discourse information
when an identical phrase a set of consecutive words is repeated in different sentences the constituent words of those sentences tend to be associated in identical modification patterns with identical parts of speech and identical modifiee modifier relationships
as we showed in the previous section information that is very useful for obtaining correct parses of ill formed sentences is provided by complete parses of other sentences in the same discourse in cases where a parser can not construct a parse tree by using its grammar rules
the completion procedure consists of two steps step NUM inspecting each partial parse and restructuring it on the basis of the discourse information for each word in a partial parse the part of speech and the rood flee modifier relationships with other words are inspected
thus when a syntactic parser can not parse a sentence as a unified structure parts of speech and modifiee modifier relationships among morphologically identical words in complete parses of other sentences within the same text provide useful information for obtaining partial parses of the sentence
thus in order to evaluate the improvement in the output translation rather than the improvement in the rate of success in syntactic analysis in which only perfect analyses are counted we compared output translations generated with and without the application of our method
the problem is of general importance since practically all methods seem to have accepted the use of a two step approach first tag the words by a part of speech tagger then parse the tags with or without the words by a stochastic parser
NUM NUM the model dop3 a corpus as a sample of a larger population we have seen that the partial parse method employed by dop2 yields very poor predictions for the correct parse of a sentence with an unknown word s
in this paper we have addressed two previously neglected questions about the dop model how does dop perform if tested on unedited penn treebank data and NUM how can dop be used for directly parsing word strings that contain unknown words
NUM but from a performance point of view it is very well acceptable that not all statistical units in our case subtrees have been seen therefore we will put forward the good turing estimator as a statistically and cognitively adequate extension of dop1
this method captures only local discourse structures whereas the plan based approach of verbmobil also allows for the description of global structures
this includes resolving relative time expressions e.g. two weeks ago into precise time descriptions like 23rd week of NUM
the probability of a derivation tl o o tn can be computed as the product of the probabilities of the substitutions that it involves ifli ti t root t root t
section NUM introduces the basic data structures followed by two sections describing some of the tasks which are carried out within the dialogue module
if parts of the structure could not be built we can estimate on the basis of predictions what the gap consisted of
what is different in dop3 is NUM a much larger space of subtrees which is extended to include subtrees in which one or more terminals are treated as wildcards and NUM the frequencies of the subtrees that are now adjusted by the good turing estimator
each character corresponds to one syllable
the algorithm does not cover a number of subcases of relations concerning the ending times
in linear logic programming the rules become resource conscious in this context we write r for the conjunction and o for the implication a c
for these reasons we prefer to stay with a fundamentally transfer based methodology none the less we include some aspects of the interlingual approach by regularizing the intermediate qlf representation to make it as languageindependent as possible consonant with the requirement that it also be independent of domain
a necessary condition for success is that an antecedent type is only selected by p if it yields the succedent atom as its eventual range
a categorial sequent has a translation given by i into a linear sequent of type assignments which can be safely read as predications
the focusing strategy breaks down t i for l vp pp n n pp vp requires switching between configuration types
to eliminate the splitting problem we need some kind of representation of configurations such that the domain of functors need not be hypothesised and then checked but rather discovered by constraint propagation
of the remaining rules each instance of premises has exactly one connective occurrence less than the corresponding conclusion so cut elimination shows decidability through finite space cut free sequent proof search from conclusions to premises
first the notion of subject is not well defined
the algorithm is effective for specific linguistic reasons
alignment at other levels of resolution is obviously useful
basic orientation of the sentence topic vs subject
previously proposed methods rely heavily on word based statistics
finally the success rates are quantitatively evaluated
such translations obviously create problems for word alignment
NUM NUM function words collocation and free translation
the model produces NUM acceptable translations for NUM sentences
we need to determine emax which can be defined ms follows NUM emax maxeeexamples p e p i e the probability distribution over the examples p e encodes the
in considering the significance of these results from a general standpoint the following facts about the tes t set need to be remembered it represents just one style of writing journalistic and has a basic basic toward financial news and a specific bias toward the topic of the scenario template task
at present the architecture includes a very limited amount of such information in the form of the precedencelist argument to writesgml it may be desirable to include in later versions of the architecture an annotationschema more analogous to a dtd
each example is shown in the form of a table at the top of the table is the document being annotated immediately below the line with the document is a ruler showing the position byte offset of each character
one or more white space characters blanks tabs or newlines are required between successive identifiers and alphabetic names zero or more white space characters are allowed before and after the separator characters
the declaration has the form annotation type identifier attribute spec l attribute spec2 rcb where each attribute specification attribute spec has the form attribute name type spec the type spec specifies the type of allowable values of the attribute
id string the identifier of an annotation which is nil when the annotation is created and which is set when the annotation is added to a document the value assigned is unique among the annotations on that document
the functions which perform these conversions will necessarily be specific to the type of data source and hence a tipster application will be required to provide these conversion operations when a new type of data source is to be used
all of these considerations indicate that eventually an opaque type for spans with a subclass being textspan will be needed most annotations will be associated with a single contiguous portion of the text and hence with a single span
in essence retrieval involves the comparison of a single query against a large number of documents while routing involves the comparison of a single document against a large number of queries or user profiles
this section considers the additional object classes and data flow which would be entailed the user would prepare an extractionneed using a combination of formal specification and narrative description comparable to the fill rules for muc NUM
string sequence of string lcb government company other rcb sequence of typed location rcb string lcb country city landregion province waterregion address oth unk rcb rcb this section shows some simple examples of annotated documents
the other reading of this sentence is produced by a derivation in which the adjunct addition rule a adds an adjunct to lijkt re and applies vacuously to ontwijken
null in the categorial grammar example the add adjuncts NUM and division NUM associated with a lexical entry can not be finitely resolved as noted above so e.g. a clause
the abstraction operation should have the property that a b is exactly the same as b except that zero or more constraints in b are replaced with logically weaker constraints
this is the central insight behind the lemma table proof procedure general constraints are permitted to propagate into and out of subcomputations in the same way that earley deduction propagates variable bindings
unfortunately the left recursion inherent in the combinatory rules mentioned earlier dooms any standard backtracking top down parser to nontermination no matter how coroutining is applied to the lexical constraints
in the clp perspective variable binding or equality constraints have no special status informally all constraints can be treated in the same way that pure prolog treats equality constraints
all x NUM literals are classified as memo literals and add adjuncts NUM and division NUM whose second arguments are not sufficiently instantiated are classified as delay literals
the atom x cat left right is true iff the substring between the two string positions left and right can be analyzed as belonging to category cat
this difference points to the notion of idiomatic meaning and not surprisingly the discourse functions introduced above can often also be realized by idiomatic phrases
null we do not use this capability in the categorial grammar example except to pass in variable bindings but it is important in gb and hpsg parsing applications
doing this at run time is slow and additionally there are problems with multiple inheritance
thus this is the relation that needs to be queried to check for grammaticality
next we compute the missing right hand side rhs with the following algorithm
our work addresses a similar problem as carpenter s work on resolved feature structures carpenter NUM ch
its one hiding subtype ne list has different hiding features list has no features appropriate at all
NUM append c arg1 e list append c i arg3arg2 lisq jst
we present a new approach to hpsg processing compiling hpsg grammars expressed as type constraints into definite clause programs
in addition different models are denved in this paper to carry out case identification and word sense discrimination
on the basis of the results of the dry run in which two of the nine systems scored ove r NUM we were not surprised to find official scores that were similarly high but it was not expected that so man y systems would enter the formal evaluation and perform so well
speech act analysis of the current utterance is necessary for translation
ethe reading would be represented as follows which has the first occurrence of the variable c left unbound
together these factors mean that speech translation is currently only practical for limited domains typically involving a vocabulary of a few thousand words
at this stage of this work the adjectives nouns and verbs are considered
the type of context involved on the extraction of candidate terms is also an issue
however further investigation is needed over the context used as it is discussed in the future work
the experimentation on real data will show if this approach actually brings improvement to the results in comparison with previous approaches
these context words have either been found at step NUM and therefore assigned a weight or not
however to identify the dependency relations entailed by a proof we may simply ignore argument ordering and we can trace through the proof to identify those initial assumptions words that are related as head and dependent by each combination of the proof
that is not really considering the weight the importance of each of them
one effect of this bias is simply the number of entities mentioned in the articles for the test set used for the muc NUM dry run which was based on a scenario concerning labor union contract negotiations there were only about hal f as many organizations and persons mentioned as there were in the test set used for the formal run
consequently we can characterise an incremental analysis as being one that at any stage includes the maximal amount of contentful combination of the formulae and hence also lexical meanings so far delivered within the limits of possible combination that the proof system allows
for commandtalk extraction of the recognition grammar is made possible by restricting the gemini syntactic rules to a finite state backbone with finitely valued features
for the left recursive subset we form the disjunction of the expressions that occur to the right of a which we may call right a
the command may be part of a mission to be carried out later or it may be an order to be carried out immediately
the speech recognition sr agent consists of a thin agent layer on top of the nuance formerly corona speech recognition system
the following additional background information from tipster text phases i and NUM can be purchased from the sources indicated
it is available for purchase in electronic form from the linguistic data consortium ldc
tipster text phase i data extraction collection this information includes template definitions and fill rules for joint ventures jv for both english and japanese and is available by ftp from nmsu crl tipster text phase i document detection collection this is the fifll tipster text collection used in trec evaluations and for document detection it is available for purchase in electronic form from the linguistic data
proceedings of the tipster text program this is the fifll tipster text collection used in trec evaluations and for document detection
the second option is to click on the microphone icon with the left mouse button to signal the computer to start listening click to talk
in such cases the ci agent calls modsaf to construct a line through the point and uses that line for the battle position
the process works with a set of subproofs NUM which are initially just the set of assumptions i.e. each of the form n f a and proceeds by combining pairs of subproofs together until finally just a single proof remains
for example in sentence NUM below the prefix zur ick of the verb zuriickweisen to reject follows the object of the verb and a subordinate clause with a subjunctive main verb
in order to overcome this problem to interpolate the plausibility we propose two smoothing techniques
n s f nullis log f s p null s iogf s NUM
toshiba NUM nec NUM compaq NUM apple NUM ibm l NUM toshiba NUM nec NUM compaq NUM apple NUM ibm l
the average scores NUM for the three sentence scoring variations are NUM NUM recall and NUM NUM precision when the system produces extracts of NUM sentences while the random selection method has NUM NUM recall and NUM NUM precision in the same experimental setting and the plain word counting method has NUM NUM recall and NUM NUM precision
however without further work this achievement is of little value because the resulting system will be very computationally expensive due to the problem of derivational equivalence or spurious ambiguity i.e. the existence of multiple distinct proofs which assign the same reading
putting the above methods together we have a complete normal form method for proofs of the first order linear deduction system i.e. for any proof p we can extract its dependency relations and use these to construct a unique maximally incremental alternative proof the normal form of p
the semantic inference component performs two primary functions perform inferences to normalize different but equivalent semantic representations present in the database where the differences may have stemmed from syntactic variations or from incomplete knowledge at the time they were generated and generate more complex representations closer to the template structure from simpler flatter semantics closer to the linguistic structure
this work is currently supported under contract NUM fi57900 NUM from the office of research and development and under contract dabt63 NUM c NUM from the department of the army
the example is analyzed using the low level patterns such as the name and noun group patterns and then translated into a clause level pattern
these issues are hardly new they have been well known at least since the syntactic grammar vs semantic grammar controversies of the NUM s
our preliminary results in learning the named entity extraction task in english and spanish are quite encouraging since they are better than any previously reported scores for a learned system and since they are approaching the scores of the state of the art for manually built rule based systems
thus the derivation is excluded by the independently motivated sits which enforce the notion of projection
with the improvements mentioned above plus a more extensive review of the system for other enhancements the nlu shell could significantly reduce the time and effort needed to build a natural language processing application and make this process available to knowledge engineers who are not programmers as well
a preliminary experiment in information extraction from speech has shown that there are very significant challenges for tipster text extraction technology including the current NUM NUM word error rate of transcription systems the lack of punctuation within sentences the lack of capitalization and the error rate on names
this global goal sometimes led to incorrect local choices of analyses an analyzer which trusted local decisions could in many cases have done better
as a result the process of building a global syntactic analysis involves a large and relatively unconstrained search space and is consequently quite expensive
such weaker systems can be implemented by combining implicational linear logic with a labeling system whose labels are structured objects that record relevant resource information i.e. of sequencing and or bracketting and then using this information in restricting permitted inferences to only those that satisfy the resource requirements of the weaker logic
the annotations are linked to the base texts either by means of character offsets or by a more sophisticated indexing scheme
it consists of three main components gdm an object oriented database for storing information about the corpus texts
sgml has the concept of content models which restrict the allowed positions and nesting 3more precisely by inter byte locations
there is merit in having a high level language to specify tasks which can be translated automatically into executable programs e.g.
it is an object oriented system where one can flexibly associate display and interaction classes to particular sgml elements
in the future however the advent of the dsssl transformation language will undoubtably revolutionise this area
in addition there are a large number of commercial and public domain software packages for transforming sgml
precursors to the lt nsl software were used to annotate the mlcc corpora used by the multext project
in particular we address the advantages and disadvantages of an sgml approach compared with a non sgml database approach
again we will use the slcs to enforce the projection from a lexical anchor to its maximal projection
the experiments showed that our method which is based on the notion of training utility has reduced the overhead for the training of the system as well as the size of the database
the igf will be acquired both from the user and from the context where the nl is to be paraphrased e.g.
it is required great effort to adapt the nl generator to new domains or to extend it without writing new grammar rules
the kind of text produced in this domain is illustrated in the right hand window of vinst in figure NUM
the natural module creates a deep structure from the flat loxy formula by looking up its elements in the dictionary
qlf can be used to direct the generator but it needs to be augmented
in some patent texts of specfic subject tields tile sentences are incredibly long
to give greater power to the preparser pre rule application has been made cyclic
patrans is a running production translation system producing cost effective raw translations of patent texts
the trmisb tion kernel had mechanisms for treating grammar rules dictionary information and mapping rules
all information about doc ument layout is stored separately and taken away from the translation process
chemical biochemical medical etc patents and gradually also a considerable amount of mechanical patents
NUM mi clustering make c classes using the mutual information clustering algorithm with the merging region constraint mentioned in NUM NUM
with pre rules sentences are segmented via pattern matching before they are sent to the parser
sentence b is quite similar to a in meaning and identical to a in sentence structure
the combination is realized by the construction of clusters using the merging method followed by the reshuffling of words from class to class
this indicates the effectiveness of combining automatically created word bits and hand crafted linguistic questions in the same platform i.e. as features
the last pair in the event is a special item which shows the answer i.e. the correct tag of the current word
since h is independent of r the partition that maximizes the ami also maximizes the likelihood l r of the text
experiments we can clearly see the effect of reshuffling for both the word bits only case and the case with word bits and linguistic questions
at lhis level all informalion wilh indetmndenl lexical expressions is t resent
l c c npi log pc npi sj npienp sjes
indeed many perhaps most syntactic phrases have very low frequency and tend to be over weighted by the normal weighting method
the potential parameter space for the probabilistic model can be extremely large when the size of the training corpus is getting larger
first the original database is parsed to form different sets of indexing terms say using different combination of phrases
in particular the indexing set formed solely of single words is used as a baseline to test the effect of using phrases
init prec means initial precision and refers to the highest level of precision over all the points of recall
this could be inefficient and it is too far removed from the ideas of the minimalist framework
however we believe the conclusion for the improvement in performance of the transducer system is valid because the amount of effort in building and training the transfer models exceeded that for the the transducer systems
NUM hc agrs agrsp hc v vp
the projection of the target has two daughters the target itself and an empty position
in this instance one thematic role exists in the main sentence the agent or subject which is further defined by its lexical entry and a modifying prepositional phrase indicated by the keyword qualifier
the central operations of the minimalist program are generalized transformation gt and move
this has proven to be very useful for addressing a critical problem in scaling up explanation generation maintaining a knowledge base of discourse knowledge that can be easily constructed viewed and navigated by discourse knowledge engineers
to explain complex phenomena an explanation system must be able to select information from a formal representation of domain knowledge organize the selected information into multisentential discourse plans and realize the discourse plans in text
in particular it describes knight a robust explanation system that constructs multisentential and multiparagraph explanations from the biology knowledge base a large scale knowledge base in the domain of botanical anatomy physiology and development
there are at least two approaches to achieving semantic unity either packets of propositions must be directly represented in the domain knowledge or a knowledge base accessing system must be able to extract them at runtime
if the message is well formed the fd skeleton processor passes each realizable concept unit found on the message specification to the noun phrase generator which uses the lexicon to create a functional description representing each concept unit
the resulting nondeterminism in the parser implementation leads to a non efficient unification component
although edge does not include a realization system other than simple templates and it was not subjected to a tightly controlled formal evaluation it was sufficiently robust to be used interactively by eight subjects
this calls for the incorporation of an intentional structure into edps but modifying edps to represent intention must be accomplished in a way that preserves the discourse knowledge engineering properties and does not sacrifice text quality
a head corner parser starts the parsing process with a prediction step
one of these two is called the target v
this special status makes them less useful for the parsing process
this would generate symbol table entries corresponding to nps annotated with the
in this paper we describe this improvement in the selection process and the results of evaluation experiments
figure NUM outline of the translation method translation rules can not be completely removed from the dictionary
in the previous selection process the translation rules are evaluated only when they are used in the translation process
the part of this research has been done during the second author kenji araki s stay at csli of stanford university
however this method requires many translation examples to achieve a practical and high quality translation
however the proposed improvement can evaluate all of the produced translation rules by utilizing only the given translation examples
all of the translation examples were processed by the method outlined in figure NUM
however the results of the evaluation experiments show that this method has some problems
methods that use analytical knowledge have some problems such as difficulty in dealing with unregistered words
an improvement in the selection process of machine translation using inductive learning with genetic algorithms
including the distance factor is motivated by the fact that the related events are usually located in the same texthood
we postulate that NUM topic is coherent and has strong relationships with the events in the discourse
finally the parameters pn and pv converge to NUM NUM and NUM NUM respectively
lob corpus of approximately one million words is used to train the basic association norms
the words with tags nc nnu and nnus and ditto tags are not considered
the meaning transition from paragraph to paragraph could be detected by the following way
we are thankful to yu fang wang and yue shi lee for their help in this work
this is to achieve greater delicacy while preserving comparability with the brown corpus
the possible topic n has the high probability ncs n
on the full formal evaluation set the named entity task scored p r NUM NUM
code wise several major extensions have bee n added and much existing code has been improved
this is also the philosophy in designing cseg tagl NUM
here the goal acquire confirmation sval source uval source is omitted and the goals acquire disambiguation of sval destination frankfurt am main acquire disambiguation of sval destination frankfurt an der oder have been abstracted as above
omission means that we leave out a goal altogether for instance if the recognition rate of an ambiguous value is high we take the risk of asking for disambignation right away as in the question is your call from frankfurt am main or frankfurt an der oder
a third example is abbreviation of several acquire confirmation goals e.g. do you want to call darmstadt from magdeburg an abbreviation of acquire con null mation sval destination uval destination
but if we were to designate a single utterance for each communicative goal we would quickly end up with inefficient and annoying dialogues like the following sample dialogue NUM a sys do you want the rate or the total cost of a call
it can be efficient to solicit implicit confirmation of previously recognized values hence we allow a goal for acquiring a new value and a goal for acquiring confirmation of another value to be realized in one utterance as in when do you want to call frankfurt
in addition a heavily semantics based approach such a s this work suffers from a lack of generality due to the absence of linguistic processing
type iv pn vocative term postposition e we call vocative terms ft the following utterances z
when proper names occur as attached to other elements of noun phrases their analysis becomes more complicated
however semantic or syntactic criteria do not allow to distinguish these two categories in an operational way
the lgs will be represented under the form of finite state automata fsa in our system
the nouns considered as proper names according to these conditions do not always correspond to our semantic intuition
nevertheless they usually do not have intrinsic meanings and they do not have explicitly distinct referents
thus they appear even after the plural marker deul s
we are sure that these lists are not illimited ones they will be presented in further studies
type iii such as l bunye father daughter or ns cf
second the dempster shafer theory distinguishes between situations in which no evidence is available to support any conclusion and those in which equal evidence is available to support each conclusion
when multiple pieces of evidence are present dempster s combination rule is used to compute a new bpa from the individual bpa s to represent their cumulative effect
the combine function utilizes dempster s combination rule to combine pairs of bpa s until a final bpa is obtained to represent the cumulative effect of the given bpa s
the first type is perceptible silence at the end of an utterance which suggests that the speaker has nothing more to say and may intend to give up her initiative
first unlike the bayesian model it does not require a complete set of a priori and conditional probabilities which is difficult to obtain for sparse pieces of evidence
furthermore substantial improvement is gained by the use of counters since they prevent the effect of the exceptions of the rules from accumulating and resulting in erroneous predictions
by restricting the increment to be inversely exponentially related to the credit the bpa had in making correct predictions variable increment with counter obtains better and more consistent results than constant increment
we developed three adjustment methods by varying the effect that a disagreement between the actual and predicted initiative holders will have on changing the bpa s for the observed cues
figures NUM a and NUM b show our system s performance in predicting the task and dialogue initiative holders respectively using the three adjustment methods
the better keywords have more idf more variance and less entropy than what would be expected under a poisson with o f d NUM NUM NUM NUM NUM
idf is defined as log2dfw d where d is the number of documents in the collection and dfw is the document frequency the number of documents that contain w
NUM you might have to eat chicken
clearly there are some interesting systematic relationships between idf variance h and f that hold up to replication across multiple years in the ap measurement errors and other sources of noise
we should n t expect to see two or more instances of boycott in the same document unless there is some sort of hidden dependency that goes beyond the poisson
there is a weak tendency for nouns to appear higher on the list than non nouns though tendency is too weak to explain the pattern of the systematic deviations from poisson
np nadvp loc he put the stakes at designated places
pp tie put the stakes every five feet
these noun phrases may be substituted tbr by adverbs or prepositional phrases
NUM he s one hell of a decent boy
np np pred NUM to that rousseau could agree
if a reader inferred that utterance 2a was about john then that reader would perceive a change in the entity which the discourse seems to be about in going from 2a to 2b on the other hand if the reader took 2a to be about the store then in going to 2b there is no change
and said the relation held if either c is an element of the situation described by the utterance u or c is directly realized by some subpart of u we discuss this further in section NUM NUM in the examples in this paper we will be concerned with the realization relationship that holds between a center and a singular definite noun phrase i.e. cases where an np directly realizes a center c
NUM sequences in which a similar pronominalization pattern is used but in which the fourth utterance implies report of a dialogue e.g. she thanked her and told her she appreciated that the wine was quite rare may lead to interpretations in which the subject pronoun is taken as referring to betsy accentuation of the subject may also be used to achieve this result
m is identified with the foot node of ft
the matrix a is updated with every call to this procedure and it is updated with the nodes just realized and also with the nodes in the assoc lists of the nodes just realized
given an input string aza2 an e the recursive algorithm makes use of an n l x n l upper triangular matrix b defined by
formally adjunction is an operation which builds a new tree NUM from an auxiliary tree fl and another tree a is any tree initial auxiliary or derived
figure NUM example of a tag auxiliary tree
secondly the observation we made about an entry
the grammar used generated the tal anbnc n
circuit id corresponds to introduction correct circuit behavior and current circuit behavior correspond to assessment t3they report a of NUM
given values for and wi performance can be calculated for both agents using the equation above
furthermore this task representation supports the calculation of performance over subdialogues as well as whole dialogues
third allows us to measure partial success at achieving the task
c is there a wire between connector eight four and connector nine nine
readers who wish to may substitute the word system wherever agent is used
this paper describes paradise a general framework for evaluating spoken dialogue agents that addresses these limitations
thus provides a basis for comparisons across agents that are performing different tasks
to estimate the performance function the weights and wi must be solved for
for example prolog s built in comparison operator can not be used since that operator requires that its arguments are ground
if the same goal needs to be solved later then we can skip the computation and simply do a table lookup
for some grammars this table simply represents the fact that the head features of a category and its head corner are shared
when a large translation lexicon is not available a small hand constructed translation lexicon for the key terms in a given bitext may suffice to produce a rough map for that bitext
as was argued above prolog backtracking is not used to simulate an iterative procedure to build up a chart via side effects
a point of correspondence inside cell x y indicates that some token in sentence x corresponds with some token in sentence y i.e. the sentences x and y correspond
this means that if simr accepts a chain it should look for others either above and to the right or below and to the left of the one it has just located
simr s errors are smaller than those of the previous front runner by more than a factor of NUM its robustness has enabled new commercial quality applications
next gsa forces all segments to be contiguous if sentence y corresponds with sentences x and z but not y the pairing y y is added
a point of correspondence inside cell x y indicates that some token in sentence x corresponds with some token in sentence y i.e. sentences x and y correspond
note that the error between a bitext map and each reference point can be defined as the horizontal distance the vertical distance or the distance perpendicular to the main diagonal
first it lowers average errors by more than a factor of NUM second it avoids very large errors improving robustness to a level that enables new commercial quality applications
as discussed above NUM percent of the errors could be accounted for by lexical gaps
these entries will contain a pointer to a concept indicated by concept
4available form the acl data collection initiative as cd rom NUM
usually the grammar is derived directly from the work of theoretical linguists
sample concept grammar rules are illustrated in NUM
these metonym definitions were subordinate to the words they defined
table NUM results from automatic scoring using an augmented lexicon
the second problem was human grader misclassification which accounted for
we realize that the results presented in this case study represent a relatively small data set
NUM test item types response sets and lexical semantics
the lexicon developed for this study was based on the training data from all rubric categories
scoring as it is discussed in this paper is a kind of clasgification problem
the parser performs insertions deletions and substitutions in order to transform the input into a grammatical utterance
the following definitions allow us to refer to structural units subtrees within the two corpora
coven collaborative virtual environments addresses the technical and design level requirements of virtual based multi participant collaborative activities in professional and citizen oriented domains
one of those cases is a word that would be not aligned NUM of the time and always surrounded by aligned neighbors
me is a bt igea w d in lhe flrs best figure NUM word scores across n best ranks for the
the specific values for obj propname and propvalue are filled in according to the current goal
nodes x0 x1 or more generally xn are substitution sites they are awaiting a tree whose head symbol is x
a confidence score relates to the word being rightly recognized and not only to the word being acoustically close to an acoustic reference
in the second example table NUM from abbot the word is is inserted but not in all n best hypothesis
by nature it misses some existing information in the sentence and it can be misled in case of errors on informative words
this helps overcome misunderstandings that result from inadequacies in the language model or ungrammatical or ambiguous inputs
for this final decision rule the the over verification rate is NUM NUM while the under verification rate is NUM NUM
with context dependent verification we additionally require that the utterance meaning can not be part of the main expectation
this means that if simr finds one chain it should look for others either above and to the right or below and to the left of the one it has just found
thus they can fool simr into searching the whole bitext space for tpc chains whose slope is close to NUM even though most of the bitext map between linguistic parts of the bitext has a very different slope
simr chooses a fixed chain size k NUM k NUM fixing the chain size at k reduces the number of candidate chains to k n fortypicalvaluesofnandk n k can still reach into the millions
the most difficult problem occurs when an error of omission occurs next to an error of commission like in blocks h and j k i
for each point p x y let x be the number of points in column x within the search rectangle and let y be the number of points in row y within the search rectangle
if a more precise map is desired these larger non monotonic segments can be easily recovered during a second sweep through the bitext space tpc i simr
however these algorithms can fumble in bitext sections that contain many sentences of very similar length like this vote record the only way to ensure a correct alignment in such regions is to look at the words
this point indicates that g h and e f should form a 2x2 aligned block whereas the lengths of the component sentences suggest that a pair of lxl blocks is more likely
such inversions result in chains that contain a pattern like points NUM and NUM in figure NUM simr has no problem accepting the inverted points unlike bitext mapping algorithms that try to minimize the distance between tpcs
apply research results to pilot systems implement robust capabilities in the operational environment reduce development costs and time through software sharing the tipster program sponsors have selected NUM projects to help meet the phase iii goals
as soon as it becomes operational
this function can be performed by existing document managers or commercial off the shelf cots products such as a standard data base management system dbms with the addition of a wrapper to be compatible with the tipster architecture
information about the dates and places of the workshops and other general program information can be found on the tipster program web site http www tipster org
these tools lie outside the architecture but use information about document relevancy relationships between documents phrase lists name lists and relational or object data base records which has been exported by the functionality residing within the tipster architecture
like the named entity task this was also seen as a potential demonstration of the ability of systems to perform a useful relativel y domain independent task with near term extraction technology although it was recognized as being mor e difficult than named entity since it required merging information from several places in the text
section NUM examines the structure of the semantic space and introduces algorithms to merge the senses into a dendrogram and specify the nodes in it which correspond with sets of similar senses
apart from some minor trouble with the suffix of the first item the aligner had smooth sailing
it is quite cumbersome for them to provide an obligatory version
if lower describes the empty set replacement becomes deletion
the abe to xc path in figure NUM is NUM NUM NUM NUM
taking advantage of all these conventions the fully bracketed expression
the optional version of is defined in the same way
they must all be regular expressions that describe a simple language
the full spedfication of the six component relations is given below
for kaplan and kay the primary notion is optional rewriting
requiring that the minimal element under is selected at each stage
globally the profits have gone down despite a strong rise from NUM to NUM
at that point a set instance set NUM consisting of spin report NUM and spinreport NUM is in context as are the two individual file instances though they have lower svs than the set instance
it has to work through numerous alternatives in order to conclude that h ez hat is indeed the best alignment
we classify a potential boundary site as boundary if it was identified as such by at least NUM of the NUM subjects in our earlier study
as can easily be observed the grammar rules are pattern based
manual examination was restricted to those matches having limited frequency of occurrence
the concept pair we present here is satmwood NUM and satinwood NUM linked by a hyponym link
eliminations based on the existence of certain vowel sequences may be possible
the rules in tables NUM and NUM guarantee NUM correct hyphenation
this empirical process resulted in formally expressed rules independent of any exceptions
eliminations of consonant patterns exceeding a maximum length have already been discussed
examined and concrete hyphenation rules were derived
these instances are covered by rule fll
in general NUM NUM NUM NUM NUM NUM remain ambiguous
v4 excessive diphthongs do not split
slide transformations can be triggered by a sequence of one two or three characters over which the boundary is to be moved
most apparent was the fact that the limited context transformations were unable to recover from many errors introduced by the naive maximum matching algorithm
the NUM transformations applied to the test set improved the score from f NUM NUM to NUM NUM a NUM NUM reduction in the error
br system robustness integration of two subsystems is under way i a rule based part of speech tagger to handle tinknown words constructions and if a word for word translator to handle other system failures
in this section we iscnss our or going efforts to overcome these deficiencies integration of a part of speech tagger to handle unknown words constructions mm a word for word translator to cope with other system tsihn es of
in section NUM we discuss the integration of two subsystems for system robustness rule based part of speech tagger to handle unknown words constructions an t a word for word translator to produce partial transbttions in the event of system failure
the structural diffexence between the som ce english and the target korean language is easily captured by the flexible interlingua representation and the strictly modularized target language grammar template external to the core generation system
existing nested major existing subject major observation that referents of subject noun phrases nps are more salient than referents of the other major clause constituents
his paper describes our current work in automatic english to korean text translation of telegraphic military messages u which is an initial step toward the ultimate goat rthis work was sponsored by the defense advanced research projects agency
although these theories are based on dialogue examples rather than texts features used by these theories and those by the decision trees overlap interestingly
consequently the learned classifiers are very domain specific and thus the approach relies on the availability of new filled template sets for porting to other domains
for example if b refers to a and c refers to b c a is a positive training example as well as b a and c b
because the system output is not always perfect especially given the complex newspaper articles however there is some noise in feature values
table NUM where the first applicable orderer ks in the list is used to pick the best antecedent when there is more than one possibility
negative training examples are chosen by pairing an anaphor with all the possible antecedents in a text except for those on the transitive closure described above
instead of a word syntactic approach with separate lexical entries for affixes she describes the formation of baradjectives via a lexical inheritance hierarchy of sorts
i close the door zumachen to close therefore a word syntactic approach and separate lexicm entries for verb prefixes may well be adequate
for example we can specify at the feature structure for verbs of motion that they can only combine with the instance of durch denoting verb through a space
the values for all features of the prefix verb are obtained from the base verb via structure sharing except for basic morphological information and the information to be modified
in this paper i sketch a compositional account of the semantics of german prefix verbs derived from a verbal base concentrating on those verbs that can be generated by a productive word formation rule
for our purposes a rule is productive if it applies to all bases which satisfy a common description such as state or transitive verb
regarding semantics we focus on aspectual classes
for example if the instance of dutch corresponding to NUM is labeled dutch l we get prefix durcti NUM in the lexical entry for eilen
we then illustrate by an example how their interaction can be accounted for
during the query phase these elements are the departure place the arrival place a global indication of the departure or arrival time the day of travel and if the caller wants a direct connection
in the last case the whole dialogue management process will be started again the representation of the query will be updated a new database query will be posed and an appropriate scenario will be chosen
we can roughly distinguish three different situations the standard situation where everything runs smoothly the situation where there is a repair operation by either the client or the operator and the situation where a topic shift occurs
the same will happen when the user corrects the system because it does not give the plan he wants
in such cases the caller will interrupt the presentation by starting a repair sequence to solve the problem the caller will start a reconfirmation sequence if he is not sure that he has heard the operator s utterances well and he wants the information service to repeat to complete or to confirm
in consecutive turns she gives the departure time new at the departure place given then the place where to change new then the departure time new at the place of change given in the previous utterance then the arrival time new at the arrival place given
there will leave at det the train to arp and then you will be there at art the prescribed dialogue act and given new division
the proposals that we will put foreward in this paper will be based on a study of about NUM human human ova dialogues selected from a corpus of NUM telephone conversations recorded at the this work is funded by ovp and senter
vertrek vanuit delft om twintig uur twee nveertig aankomst in rotterdam cs om twintig uur zesenvijftig daar overstappen naar utrecht cs vertrek om eenentwintig uur zeven aankomst in utrecht cs om eenentwintig uur drie nveertig
second the content filter does contribute to the effectiveness of our coreference resolution its absence caused our scores to decline
the majority of descriptors reported were found through association by context even when the longest descriptor selection method is used
NUM NUM NUM NUM NUM NUM NUM the decline in scores adds further confirmation to our hypothesis that the context associated descriptors are more reliable
scores reported here are relevant only as relative measures within this paper and are not meant to represent official performance measu s
scores discussed in this paper measure performance of experimental system reconfigurafions run on the NUM documents used for the final muc6 evaluation
if there is a fie file position is considered as a factor the closest name being the most likely referent
the intuition is that location inftnmation is found frequently within descriptive noun phrases and is extractable once that link has been established
for example if an automarie system has failed to make the link between a descdptor and a name it may create two objects one for each
for every definite noun phrase if a reference can be found it will be associated with that entity otherwise it will become an un named entity
a statement about the underlying purpose for s
i need to know the switch position
in contemporary japanese there are at least five different types of characters other than punctuation maxks kanji hiragana katakana roman alphabet and arabic numeral
we describe an automatic wordcompletion system intended to serve as a vehicle for exploring the feasibility of this new approach and give results in terms of keystrokes saved in a test corpus
it derives the initial estimates from the frequencies in the corpus of the strings of character making up each word in the dictionary whether or not each string is actually an instance of the word in question
no interruptions to other subdialogs are allowed
null in addition to the excellent overall results in chinese segmentation we also showed the rule sequence algorithm to be very effective in improving segmentation in thai an alphabetic language
an accounting for user knowledge and abilities
for an initial experiment segmentation was performed using the maximum matching algorithm with a large lexicon of NUM english words compiled from the wsj
analogical translation relies on a large database of example pairs
and a set of features ifl if2 ifm
table NUM bigram and short word segmentation retrieval results averaged over NUM queries
it appears advisable to keep all stopwords and use them for segmentation purposes
lexicon or rule based stopword removal have negligible effect on retrieval with long queries
any match will result in breaking a sentence into smaller chunks of texts
moreover experiments by others using even simpler bigram representation of text i.e.
besides one also has other tools in ir to remedy the situation
the longest match string frequency lsf method considers all possible longest matches in the text while the greedy longest match lm algorithm considers only one possibility
in general when more documents are retrieved precision falls as recall increases
interest total count NUM money paid for the use of money NUM a share in a company or business NUM readiness to give attention NUM line total count NUM a wire connecting telephones NUM a cord cable NUM an orderly series NUM
this sample can be represented by the NUM x NUM dissimilarity matrix shown in figure NUM in the dissimilarity matrix cells NUM NUM and NUM NUM have the value NUM indicating that the first and second observations in figure NUM have different values for two of the three features
when approximating the maximum of the likelihood function the em algorithm starts from a randomly generated initial estimate of NUM and then replaces NUM by the 9i which maximizes q NUM i NUM this process is broken down into two steps expectation the e step and maximization the m step
for example the incorrect initial segmentation restrain i fr sea bream is correctly subdivided into restrain tr
these values vary since the number of possible values for m varies with the part of speech of the ambiguous word
they indicate the presences or absences of a particular content word in the same sentence as the ambiguous word
the line data comes from both the acl dci wsj corpus and the american printing house for the blind corpus
these methods and feature sets are found to be more successful in disambiguating nouns rather than adjectives or verbs
this is simply the average number of mismatches between each component of the new cluster and the existing cluster
this means that we regard word length as the interval between hidden word boundary markers which axe randomly placed with an average interval equal to the average word length
parsers that can use grammars directly are more likely to have wide coverage and to be valid for many languages they also constitute the most economical model of the human ability to put knowledge of language to use
some words have several functions thus they could belong to more than one class
the clustering process is top down splitting and the binary tree is growin with splitting
the qualitative inspection of the compiled tables is coherent across compilation methods and appears in general to support the icmh as the interaction of structural and lexical information is the cause of repeated patterns of conflicts
on the other hand a precompiled table which keeps track of all the alternative configurations guarantees that incorrect parses are detected as soon as possible and if alternative parses exist they will be found
she finds that precompiling the principles that license empty categories with the phrase structure rules reduces considerably the number of structures that are submitted to the filtering action of the other principles and thus speeds up the parse
if one considers a sentence such as who did you say that john thought that mary seemed to like with four gaps and four heads there are NUM hypotheses about chain formation to explore using nlab
a parser that uses linguistic principles directly must fulfill apparently contradictory demands for the parser to be linguistically valid it must use the grammar directly while a limited amount of off line precompilation might make the parser more efficient
one can describe this sequence of decisions as two problems that must be solved in order to form chains the node labelling problem nlab and the chain selection problem csel formulated below
however structural case can be assigned from left to right since the complementizer which necessarily marks the left edge of an ip is obligatory and the finite complementizer is always different from the infinitival complementizer
tree matching techniques need to be developed for this class of problems which appear often in artificia l intelligence
during the translation we also implemented a new mapping algorithm that is more accurate than the lisp version
ci g and a pa rsing a lgorithm with o g 2n worst case time coml lexity
a nd ollo ttional pa tterns which is exactly what we axe going to a chi ove with pa ttern l a sed
the implemented algorithm for scoring the linkages was discovered by participants a t mitre corporation NUM
also it is computationally inexpensive while being provably equivalent to counting subsequent noun phrases in the chain
very nont rulina NUM syml ol x in p with no head constraint is associa to
to support this breakdown in the score report the named entity scorer had to keep additional tallies
the emacslisp version of the scoring software can handle any alphabets that the emacs text interface can handle
the reasoning behind thi s parallels the notion of connection in the case of unmapped objects
the one that most concerns us here is the scoring option that allows key to response scoring and key to key scoring
the c version will shortly be able to at least batch process extended alphabets and NUM byte character sets
the open test set consists of NUM sentences randomly drawn from the english and chinese versions of the lightship user s guide
the met evaluation has proved that nametag can be ported to other languages with a level of performance similar to english despite various language specific challenges
the first column of the table shows the observed frequencies of np subtrees from zero to six
from the atis training set we derive that only nouns and verbs are actually lexically ambiguous
NUM how can we estimate the probabilities of unknown subtrees
even the extension with a dictionary does not solve the problem
we shall refer to this method as the partial parse method
we also calculated the accuracies according to these metrics for dop1
we conjecture that his results will be even better if larger subtrees are taken into account
it may be evident that it is too rough to apply good turing to all subtrees together
in that case the system should serve as a simple online dictionary
there is nothing interesting about the model above since it constrains only atomic non overlapping i.e. independent features
our approach uses a features collocation lattice and selects the atomic features without resorting to iterative scaling
it is fairly straightforward to implement a top down parser in a functional programming language
an important requirement here is that these dialogues must not force a response
applying the exponential distribution discussed above the maximum entropy approach developed in della pietra et ai
in this paper we present a novel approach to feature selection for the maximum entropy models
here we will refer to w s as configurations meaning that they are mapped into atomic features
each phase takes as its input the output objects produced by the previous phase
this ensures that we account only for that proportion which belongs to xk in the contributing configurations
another is idiosyncratic violation of compositionality assumption such as idiomatic expressions
aic rewards good model fit and penalizes models with high complexity measured in the number of features
here we used about NUM atomic features such as sentence position sentence length q phrases etc
the fourth morpheme translates it to possibly an adverb
translation of denwa as denwa shows this word will simply vanish after translation
the interpretal ions are truth t rese rving
this alternative allows the user to switch from idiomatic interpretation to nonidiomatic interpretation
the patterns of the learned rules match to particular combinations of features in the neighborhood surrounding a word and their action is to change the system s current guess as to the feature for that word
one of those rules whose net score positive changes minus negative changes is maximal is then selected applied to the corpus and also written out as the first rule in the learned sequence
some researchers have applied grammar based methods combining lexical data with finite state or other grammar constraints while others have worked on inducing statistical models either directly from the words or from automatically assigned part of speech classes
we would like to thank eric brill for making his system widely available and ted briscoe and david yarowsky for helpful comments including the suggestion to test the system s performance without lexical rule templates
the method is realized as an english writing support software on personal computers
the set of NUM rule templates used here was built from repetitions of NUM basic patterns shown on the left side of table NUM as they apply to words
in other tests we have explored mixed templates that match against both word and part of speech values but no mixed templates were used in these experiments
the raw percentage of correct chunk tags is also given for each run and for each performance measure the relative error reduction compared to the baseline is listed
this additional class of available information causes a significant increase in the number of reasonable templates if templates for a wide range of the possible combinations of evidence are desired
the users are required to begin each utterance with the word verbie and end with the word over
this supports the intuition that definitive source data at the time of the utterance should be the preferred evidence regardless of expectation
hence correc with respect to an indei endent semantics
almost every statement will change the usevs knowledge base and future interactions will be ill conceived if the appropriate updates are not made
if the user has completed an action by completing each substep then conclude that the user knows how to do the action
in typical dialogs the user modeling system added a net of about NUM NUM prolog style assertions to the user model per user utterance
another important member of the task related expectations are the expectations for topics that are ancestors of the current topic in the discourse structure
a small number of grammar omissions and other minor system shortcomings were noticed in the early subjects and fixed for later subjects
the dialog system being tested was the version that was operative at the time of the test mid february NUM
it began with a reorientation NUM practice sentences on the speech recognizer and some review questions on the general instructions
the implemented domain processor was loaded with a model for a particular circuit assembled on a radio shack NUM in one electronic project kit
hasten has another special module that tries to mutate egraphs in a variety of ways based on their similarity with other egraphs
the collector will also merge the success ion representations from the headline and sentence NUM as described in the nex t section
speakers manage the nonmonotonicity by negotiating with each other to achieve understanding
table NUM a part of the array adverbs
table NUM a part of the array pairs
however the possibility of on line or e mail feedback to the user submitting the job ad plus the fact that the matcher is extremely flexible means that the analysis module can degrade gracefully in the face of such problems
each conjunction token is classified by the parser according to three variables the conjunction used and or bu either or or neither nor the type of modification attributive predicative appositive resultative and the number of the modified noun singular or plural
in order to verify our hypothesis about the orientations of conjoined adjectives and also to train and evaluate our subsequent algorithms we need a 3certain words inflected with negative affixes such as in or un tend to be mostly negative but this rule applies only to a fraction of the negative words
and not modified by process modifiers p nor gradual change indicators g nor end state modifiers e otherwise figure NUM the algorithm for classifying verbs
the feature dynamicity distinguishes between states d and events d and atomicity distinguishes between point events a and extended events a
the baseline and but methods make qualitative distinctions only i.e. same orientation differentorientation or unknown for them we define dissimilarity for same orientation links as one minus the probability that such a classification link is correct and dissimilarity for different orientation links as the probability that such a classification is correct
the whole procedure of constructing the random graph and finding and scoring the groups is repeated NUM times for any given combination of p and k and the results are averaged thus avoiding accidentally evaluating our system on a graph that is not truly representative of graphs with the given p and k
this process is highly accurate but unfortunately does not apply to many of the possible pairs in our set of NUM NUM labeled adjectives NUM NUM possible pairs NUM pairs are morphologically related among them NUM are of different orientation yielding NUM NUM accuracy for the morphology method
p therefore directly represents the precision of the link classification algorithm while k indirectly represents the corpus size
since graph connectivity affects performance we devised a method of selecting test sets that makes this dependence explicit
at the final iteration the cluster assignment of any adjective that violates constraint NUM is changed
these results are extremely significant statistically p value less than NUM NUM when compared with the baseline method of randomly assigning orientations to adjectives or the baseline method of always predicting the most frequent for types category NUM NUM of the adjectives in our collection are classified as negative
a performance of NUM NUM accuracy rate for parse tree selection is obtained for the baseline system when the parameters are estimated by using the maximum likelihood estimation mle method
in addition to the discriminative learning algorithm described above a robust learning procedure is further applied in order to consider the possible statistical variations between the training corpus and the real task
in other words correct recognition will still be obtained if the score of the correct candidate is the highest even though the likelihood values of the various candidates are estimated poorly
hence the robustness issue which concerns the possible statistical variations between the training set and the test set must be taken into consideration when we adopt an adaptive learning procedure
experimental results using this hybrid approach are shown in table NUM where the results using the ml rl mode are also listed for reference
note that according to the definition in equation NUM an error will occur if ddz NUM i.e. NUM ll NUM j i
in this section we present an alternative to nerbonne s analysis based on an extension of the possibilities for domain formation
second the predictor asks the user directly about the information
NUM NUM third approach to solving the prediction problem in an inflected language
the second one includes suffixes and their frequencies ordered by frequencies
NUM NUM second approach to solving the prediction problem in an inflected language
the recency of use may also be included in this approach
finally recursion may be included into the defined rules
the high number of in flexions for each word makes their inclusion in the lexicon impossible
new words are included in the dictionary with a provisional syntactic category deducted from its use
using ontological and lexical information it can reduce the number of propositions by replacing them with fewer propositions with equivalent meanings
generating spoken language from meanings or concepts meaning to speech mts is a new topic and only a few such systems were developed in recent years
language generation in magic is also affected by the fact that language is used in the context of other media as well
currently communication between or caregivers and icu caregivers is carried out orally in the icu when the patient is brought in
by maintaining a correspondence between the referential string generated and the concepts that those referential actions refer to negotiation with graphics has a common basis for communication
the only people who can provide this information are those who were present during surgery and they are often too busy attending to the patient to communicate much detail
in a cardiac intensive care unit icu communication regarding patient status is critical during the hour immediately following a coronary arterial bypass graft cabg
initially we are pursuing three investigations
in all of the language generation components the fact that spoken language is the output medium and not written language influences how generation is carried out
graphical references may include highlighting of the portion of the illustration which refers to the same information as speech or appearance of new information on the screen
these scores placed sri among the leaders
on the training data in NUM out of a total of NUM names NUM NUM at least one of the two systems was incorrect
we evaluated the name analysis system by comparing the pronunciation performance of two versions of the tts system one with and one without the name specific module
name pronunciation is known to be idiosyncratic there are many pronunciations contradicting common phonological patterns as well as alternative pronunciations for certain grapheme strings
if a user is interested in a particular area on the map the region outlines for the nodes of interest can be toggled on or off by the pop up menu provided on each node
the grapheme to phoneme conversion rules were written by experts based on tens of thousands of the most frequent names that were manually transcribed by an expert phonetician
second onomastica did not apply morphological analysis to names while morphological decomposition and word and syllable models are the core of our approach
in short we are not dealing with a memory or storage problem but with the requirement to be able to approximately correctly analyze unseen orthographic strings
names are not equally amenable to morpho null logical processes such as word formation and derivation or to morphological decomposition as regular words are
resources inc s london based european informa
companies can take a company appositive
m step reset the parameters so as to maximize the likelihood relative to the expected rule counts found in the e step
for scfgs the e step involves computing the expected number of times each production is applied in generating the training corpus
exploration of this link then led to the extension of our algorithm to handle e productions as described in section NUM NUM
NUM of course a cyk style parser can operate left to right right to left or otherwise by reordering the computation of chart entries
notice that the definition of p is independent of i as well as the start index of the corresponding earley state
we have presented an earley based parser for stochastic context free grammars that is appealing for its combination of advantages over existing methods
a comparison of the two approaches both in their probabilistic and nonprobabilistic aspects is interesting and provides useful insights
start with the initial state generate the prefix xo xk NUM pass through k x NUM NUM
the estimation procedure described above and em based estimators in general are only guaranteed to find locally optimal parameter estimates
in table NUM precision delineates how many of the aligned pairs are correct and recall delineates how many of the manual alignments we included in systems output
our method performs accurate alignment for such use by combining the detailed word correspondences statistically acquired word correspondences and those from a bilingual dictionary of general use
from many similarity metrics applicable to the task we choose mutual information and t score because the relaxation of parameters can be controlled in a sophisticated manner
first unless the hand crafted dictionary contains domain specific key words the first path yields false alignment which in turn leads to false statistical correspondences
by combining these two types of word correspondences the method covers both domain specific keywords not included in the dictionary and the infrequent words not detected by statistics
for text NUM and text NUM both the combined method and the dictionary method perform much better than the statistical method
statistics merit statistics is robust in the sense that it can extract context dependent usage of words and that it works well even if word segmentation NUM is not correct
our system skillfully combined i and with meg as a result of statistical acquisition
qlf can be described as a contextually set silive logical form
such systems were observed to be difficult to develop a nd maintain
then the chosen representation is mapped into a case frame
these issues are handled with special transfer rules and transfer lexicon entries
some information not extracted in the analysis phase such as the sentence form clause type
consequently the fitting target verb is found to be oksurmek
the system uses a structural transfer approach in translating the domain of ibm computer manuals
the turkish language is characterized as a head final language where the modifier specifier always precedes the modified specified
the system is run on a set of previously unseen speech data the results are stored in text form someone judges them as acceptable or unacceptable translations and finally the system s performance is quoted as the proportion that are acceptable
in contrast a quickset user would say phase line green while drawing a line
for example figure NUM is a fragment of the word net
we now provide the details of step c in our algorithm
not surprisingly spoken interaction with quickset was not feasible although users gestured successfully
by taking account of the unacceptable hypothesis judgements it is possible to evaluate the performance of the system either in a fully automatic mode or in a mode where the source language user has the option of aborting misrecognized utterances
the person category produced mixed results some good category words were found such as rebel advisers criminal and citizen but many of the words referred to organizations e.g. fmln groups e.g. forces and actions e.g. attacks
a better approach is to extract the terms which are statistically significant in the retrieved segments of parallel text in comparison to the corpus as a whole
if a word refers to something that can be associated with members of the category but is also associated with many other types of things then it deserves a NUM for example bowls and parks are weakly associated with animals
they should be solved by the system heuristically and unsurely by using preferences scores or defaults
we found many examples of such ambiguities in atr s transcriptions of wizard of oz interpretations dialogues NUM
in this paper we describe how the translation methodology adopted for the spoken language translator slt addresses the characteristics of the speech translation task in a context where it is essential to achieve easy customization to new languages and new domains
the interpretation of NUM am to obligation or future is solvable reliably only by the speaker
a computable representation system is a representation system for which a reasonable parser can be developed
but if we use f structures with disjunctions u will always have one or zero associated structure s
it is useful to study vatious properties of these ambiguities in the view of subsequent total or partial interactive disambiguation
a representation in a formal representation system is proper if it contains no exclusive disjunction
in practice however developers prefer to use hybrid data structures to represent utterances
further refinements can be made only with respect to the intended interpretation of the representations
for each paragraph or turn we then label the ambiguities of each possible utterance
ongoing work is focusing on improving the performance of query translation techniques while expanding the techniques to work with new languages and search engines including www search services
attached to each token is the result of the lexical lookup
this must be the key criterion for evaluating a candidate translation if apparent deficiencies in syntax or word choice fail to affect subject s ability to understand content then it is hard to say that they represent real loss of quality
a possible scheme for speech translation consists in translating the output of a conventional continuous speech recognition csr front end
the translation model used in this project is the subsequential transducer which is easily integrable in conventional speech recognition systems
in our approach we tackle this problem by calculating the lower confidence limit 7r l for the rule estimate
the neighbor words are people killed rebel and group
NUM NUM well anyway NUM so u m NUM throat clearing NUM tsk NUM NUM all the pears are picked up and he s on his way again figure NUM excerpt from narrative NUM with boundaries
as discussed in section NUM the ability both to segment discourse and to correlate segmentation with linguistic devices has been demonstrated in dialogues and monologues using both spoken and written corpora across a wide variety of genres e.g. task oriented advice giving informationquery expository directions and newspapers
other changes to the definition of ficus pertained to sentence fragments unexpected clausal arguments and embedded speech
we present our results using two sets of statistically validated boundaries those derived using a significance level of NUM
the standard deviations in tables NUM and NUM are often close to NUM NUM or NUM NUM of the reported averages
thus we need to adjust the estimation error in accordance with the length of the affix or ending
a more standard approach is to adopt a rather high confidence value in the range of NUM NUM
this makes the improvements against the base line xerox guesser NUM in precision and NUM in coverage
when the xerox guesser is applied after the e75 guesser no sufficient changes to the performance are noticed
in the second experiment we tagged the same text in the same way but with the small lexicon
the task of unknown word guessing is however a subtask of the overall part of speech tagging process
we expect it to increase the coverage of thesuffix morphological rules and hence contribute to the overall guessing accuracy
some of the research reported here was funded as part of epsrc project ied4 NUM NUM integrated language database
most of these tagging tasks would be improved by making use of methods that preferentially select ambiguous data for manual annotation for example as described in NUM
as the amount of training data increases the performance of the learned rules tends to increase and so the amount of labor saved in pre tagging subsequent training data is further increased
the brill control regime interprets these rules strictly sequentially rule n is applied wherever in the text it can be it is then discarded and rule n l is consulted
then the textref sequences are filtered to leave only properly markable one s nodes connected by is a or identity links are merged copying all of the textrefs from the object to the subject and discarding the object these cases cause particular problems for the lolit a system because the muc NUM definition of what is co referential differs from what the lolita syste m considers as co referential
in the current version of the workbench the user is free to compose these phraser rules and group them into specialized rule sets
the workbench reads and saves its work in the form of sgml encoded files though the original document need not contain any sgml mark up at all
the very same learning procedure that is used to bootstrap the manual tagging process leads eventually to the derivation of tagging heuristics that can be applied in the operational setting to unseen documents
a similar effect seems possible for relatively high precision systems though proper interface design to highlight the type assigned to a particular phrase should be able to mitigate these tendencies
parsing there are four stages in parsing sen sentence branch full propernoun proper noun phrase propernoun john sexed neg copula copula verb phrase ascopulan retire fut comnoun chairman sing per3 a pre parser which identifies and provides structure for monetary expressions
words used john will retire as chairman e5 old chairman u key s subject a action o object ni named ind i individual u universal
two examples may be see n in figure NUM single words are attached to the key words of the sentence only retire is shown and all of the textrefs in the sentence are attached to the node representing the whole event
once the action is known any knowledge available from the prototype event associated with that action can be used to rule out pragmatically implausible readings as well as to aid disambiguatio n of the remaining elements of the event in the spirit of NUM
by re investing the knowledge available in the earliest training data to pre tag subsequent un tagged data the alembic workbench can tralasform the process of manual tagging to one dominated by manual review
in several cases clearly identifiable named entities are not recognized as such because the parser is attempting to produce a full parse of the sentence despite th e fact that some of the grammar is missing and this can only be done by taking an alternative parse for the named entity
to each node will be attached zero or more textref sequences which must be filtered on the basis o f markability eg only proper nouns are markable and then overlaps removed it is possible to find somethin g markable inside a larger entity
these desired dot products are used to perform a quasi linear transformation on an initial set of quasi orthogonal high dimensional vectors
it should also be noted that hnc has implemented a minimal subset of the symmetric approach as a proof of concept
a number of features are incorporated including a pragmatics interpreter dealing with discourse phenomena such as anaphoric resolution and ellipsis a model of the task structure and how it relates to the dialogue structure a model of conversation incorporating an interaction strategy and a recovery strategy and a semantic interpreter which resolves the full interpretation of an utterance in light of its context
as is the predicament of any generic system it is necessarily vague and since it attempts to combine components found in a variety of individual models it may not fit all systems if any in particular
as the focus of this research field shifts from academic study to commercial reality we feel it is important to maintain a theoretical underpinning a generic model for independent qualitative assessment and comparison of practical interactive spoken dialogue systems
the coding described in this paper differs from all of these coding schemes in three important ways
although most moves classified as query w are wh questions otherwise unclassifiable queries also go in this category
speakers often use utterances such as ok and right to serve this purpose
the game coding scheme simply records those aspects of embedded structure that are of the most interest
the replication involved four naive coders and the expert developer of the coding instructions
leaving the coding developer out of the coder pool did not change the results carletta et al
stability can be tested by having a single coder code the same data at different times
however check moves are almost always about some information that the speaker has been told
in any categorization there is a trade off between usefulness and ease or consistency of coding
these categories were chosen to be useful for a range of purposes but still be reliable
it attempts to do this by providing a core platform upon which different applications can be built
NUM the other reason is that there are case postpositions that are not mapped into thematic role
NUM 3c where all case elements x y and z are consistent in these three sentences
john smeared thepaint patient on the window
e g NUM NUM x ga y ga sukina no ha x nom that x likes y is
we then describe the lexicon structure for ambiguity representations in relation to word senses
the only difference is the deep case source added to the agent
the form of the sentence specifications differs depending on the degree of integration between the text planner and the sentence realiser
user modelling in theory the speaker and hearer fields are available for user modelling purposes cf
the realiser operates directly on the kb using the information within the sentence specification to tailor the expression
the statistical tagger introduces NUM errors on the NUM words that remain ambiguous after step NUM
the expression can also be altered by selecting a different entity as the head of the utterance
this is in itself useful 4like bats botta ddrnis ferrasse hersant
the theme field of the speech act specifies the unit id of the ideational entity which is thematic in the sentence
to make the tagger better they should be replaced by writing more accurate heuristic rules
null wag s semantic input improves over that of penman in regards to the relationship between the speech act and the proposition
the remaining errors NUM errors constitute the price we pay for using the heuristics
take for instance a situation where mark owns both a dog and a house and the dog destroyed the house
one may identify various contexts in which either the noun or the adjective can be preferred
in taking this approach wag attempts to extend the degree to which surface forms can be constrained by semantic specification
for instance preposition is preferred to adjective pronoun is preferred to past participle etc
the second non standard task addressed by the tokenizer is the extraction of date information
wordnet is also consulted to tag such nouns as possibly having sets of individuals as their referent
nouns and verbs are the largest categories with approximately NUM NUM and NUM NUM inflected forms respectively
hence such words also count as evidence of a female referent but to a lesser degree
if no data is available for any sense of the word the uniform distribution is assumed
first it was designed to have high precision rather than high recall
bride of cogniac performs resolution on basal noun phrase detected and part of speech tagged text
in computational theories of discourse there are at least three processes presumed to operate under a limited attention constraint of some type NUM ellipsis interpretation NUM pronominal anaphora interpretation and NUM inference of discourse relations between representations a and b of utterances in a discourse e.g.
remaining candidates are ordered according to the following four preference factors NUM recency NUM clausal relations NUM parallelism NUM quotation NUM an anonymous cl reviewer suggests that the filter may be overly restrictive because of examples like the following a it s an important issue and i m very concerned about it
the following sentence whose parse tree is in figure NUM is an example of this NUM get to the corner of adams and clark just as fast as you can in this case the circled vp headed by get is the antecedent for the vpe despite the appearance of containment
consider the following example NUM you vp know what the law of averages vp is do n t you vpe here neither potential antecedent matches the auxiliary category of the vpe and therefore both are penalized by the general auxiliary match constraint
NUM as discussed in section NUM the parallelism preference factor makes an important contribution to the system performance
in table NUM we give results for the blind test and for the entire penn treebank and we report separate figures on the brown corpus and wall street journal corpus rcb NUM as a baseline we also report results table NUM on a simple recency based approach the most recent vp is always chosen
example head match NUM the question is if group conflicts still exist as undeniably they do system output exist coder selection still exist here both the system output and the coder selection have the head verb exist but there is not an exact word for word match
we searched for occurrences of a sentence s with an auxiliary aux but no vp
pronoun resolution systems often incorporate a syntactic filter a mechanism to remove certain antecedents based on syntactic structure
we did this by defining a composite system component consisting of syntactic filter clause rel and post filter
moving rightward toward the vpe the weight of each subsequent vp is multiplied by the recency factor
whereas passage NUM consists of a sequence of continue relations centered on john passage NUM is marked by movements between continuing and retaining which gives the effect that the passage flips back and forth between being about john and being about his favorite music store
g uo i g udeg g un g un or unbound cb un cb unq NUM cp unq NUM cb un l cp unq NUM continue smooth shift retain rough shift
in the current version of slt transfer rules were written directly for neighbouring languages in the sequence spanish french english swedish danish most of these neighbors being relatively closely related with other pairs being derived by transfer composition
we discuss the structure of the grammar the properties of the parser and a method for achieving robustness
nr be the number of events that occur exactly r times
tests based on the productivity of the words as measured through affixation and compounding tend to fall in between their accuracy is generally significant but their applicability is sometimes low particularly for compounds
the third column lists the centering transitions which are derived from the cb c data of immediately successive utterances cf
for example healthy is in most contexts the unmarked member of the opposition healthy sick but in a hospital setting sickness rather than health is expected so sick becomes the unmarked term
if the tree is left to grow uncontrolled it will exactly represent the training set including its peculiarities and random variations and will not be very useful for prediction on new cases
somewhat arbitrarily we mapped this test to the number of grammatical categories parts of speech that each word can appear under postulating that the unmarked term should have a higher such number
a positive negative value indicates that the first second adjective is the unmarked one except for two variables word length and number of syllables where the opposite is true
table NUM test set table NUM and table NUM consider the number of anaphoric and text elliptical expressions respectively
the good news is that the segmentation procedure we propose is capable of dealing even with these more complicated structures
table NUM summarizes the total numbers of anaphors textual ellipses utterances and words in the test set
we want to emphasize from the beginning that our proposal considers only the referential properties underlying the global discourse structure
no constraints or rules are formulated however that account for anaphoric relationships which spread out over non adjacent utterances
as a result of lifting the entire sequence including the final two utterances forms a single segment
only the cp of the utterance at the end point of any of these segments is considered a potential antecedent
let us take the following input enjinia ga fakkusu wo tsukau
the robust learning process continues adjusting the parameters even though the input training token has been correctly recognized until the score difference between the correct candidate and the top competitor exceeds a preset threshold
we now consider how to distinguish between pronouns and nominal anaphora
ing noun dictionary by identifying the remaining string as a no ill ai er eliminating non lloiliil lal part of a word
the tokenizer produces a list of simple and compound nouns by utilizing the noun dictionary and the basic stermning rules
definition NUM i let a and b be sets of word strings
this result helps us in the understanding of character string tokenization ambiguity
compound nouns may contain useful simple nouns that usually refer general contexts and thus will boost the recall of retrieval
there are three well known liter hods NUM weighl ing iitdex l erius
critical tokenization is the most important concept among the second group of findings
we have proven that every tokenization has a critical tokenization as its supertokenization
what makes it even more complicated to handle compound nouns in korean documents lies in the convention of writing compound nouns
in practice each surface form in gen input must contain a silent copy of input so the constraints can score it on how closely its pronounced material matches input
generalized alignment is outside the scope of finite state or indeed context free methods rcb NUM bad news while otp generation is close to linear on the size of the input form
in the case of otp generation the global count to minimize is the degree of violation of ci and the local restrictions are imposed by c1 c2 ci NUM
however not every si can be represented as a single partial order so the approach is quickly complicated by the need to encode disjunction
primitive optimality theory or otp is an attempt to produce a a simple rigorous constraint based model of phonology that is closely fitted to the needs of working linguists
a simpler approach is to represent si as well as inpu and repns as a finite state automaton fsa denoting a regular set of strings that encode timelines
however techniques are discussed for making ellison s approach fast in the typical case including a simple trick that alone provides a NUM fold speedup on a grammar fragment of moderate size
this shows that the salience constraint in tr3 is still effective
observe that for bestpaths to do the correct thing i needs to reflect the sum total of a s constraints on f and x the tiers that c mentions
typically interaction takes the form of clarification dialogues
each time the system reaches a terminal node it has recognized a lexical unit which is inserted into a chart oriented graph which serves as data structure for the syntactic parsing
for simplicity let us consider only the highest candidate in the list which might ideally be something like NUM where stands for voiceless fricatives and NUM for schwas
an example may clarify the problem
then the problem would not occur
suppose we have a history item
with such a structure words are recognized one phoneme at a time
each terminal node specifies one or more lexical entries in the lexical database
as such it is a system for developing particular types of data resource e.g.
ice provides a distribution and communication layer based on pvm parallel virtual machine
vie and its components are being deployed for a number of purposes including ie in french german and spanish
the gdm is fully conformant with the core document management subset of this specification
both architectures are appropriate for nlp but there are a number of significant differences
gate is now available for research purposes see http ul w
parsing tagging morphological analysis and those working on developing end user applications e.g.
documents are grouped into collections each with a database storing annotations and document attributes such as identifiers headlines etc
in this way the information built up about a text by nlp modules is kept separate from the texts themselves
return NUM compute nodes rlr2 rep a
the constraints include selective null and obligatory adjunction constraints
tal recognition in o m n2 time
an example of a tag is given in figure NUM
NUM find the closure of the first NUM NUM i.e.
all nodes spanning trees which are within the first NUM NUM
where n is the initial size of the input string
the minimm nodes can be identified in the following manner
NUM compute nodes rlre rp arl NUM p a r p
the extra cost involved in such a strategy can be made almost negligible
such alignments produced by a human expert it is evident that the mathematical model should try to capture the strong dependence of aj on the preceding alignment a j NUM
where e is the size of t he target language vocabulary and i is the n aximum leng lcb h of the target sentence considered
p fj le using this formulation of the search task we can now use the method of dynamic programming dp to find the best path through the lattice
to be more precise the sentences can be partitioned into a small number of segments within each of which the alignment is monotone with respect to word order in both langaages
we expect t o extend the approach presented by the following methods more systelnatic approaches to local and global word reorderiugs that try to produce the same word order in both languages
on the basis of examples from traveller booklets a prol abilistic gralmnar for different language pairs has been constructed from which a large corpus of sentence pairs was generated
each possible index triple i j e defines a grid point in the lattice and we have the following set of possible transitions fi om one grid point to another grid
since he and him can not select the same referent he requires a cospecifier that is neither john nor bps11
however this is not borne out by the unaccented him which continues to cospecify with bill
by strictest interpretation theories of both centering and intonational meaning fail to predict the existence of pitch accented pronominals
intonational theories would be similarly hard pressed but on grounds of information quality and efficient use of limited resources
this distinction underlies my proposals about the attentional consequences of pitch accents when applied to pronominals in particular that while most pitch accents may weaken or reinforce a cospecifier s status as the center of attention a contrastively stressed pronominal may force a shift even when contraindicated by textual features
as ph90 points out failure to predicate has contradictory sources the proposition has already been predicated as mutually believed or the speaker but not the hearer is prevented from predication perhaps by social constraints or the speaker actively believes the salient proposition to be false
l to the salience of the proposition itself or the relevance of the operation
in contrast propositional salience addressing an item s status in relation to mutual beliefs is qualitative
the local weight of each term is then flattened by taking its log2
a local and global weighting is given to each term in each sentence
the final column lists the training corpus frequency of the given word
thus the comparison between lsa and tribayes is an indirect one
the rows of the matrix correspond to terms and the columns represent documents
recall however that the original representation is expected to be noisy
the bigrams are treated as additional terms during the lsa space construction process
we tried different stemming algorithms and all improved the predictive performance of lsa
we tested the predictive accuracy of the lsa space in the following manner
these seven sets are listed first in all of our tables and figures
lewis et al proposed an example sampling method for statistics based text classification NUM
during the training phase the system selects samples for training from the previously produced outputs
where a is the constant for parameterizing the extent to which ccd influences verb sense disambiguation
our current experiments are based around the japanese word thesaurus bunruigoihyo NUM
to construct a practical size database a considerable overhead for manual sense disambiguation is required
figure NUM shows the relation between the size of the training data and the value of pm
the semantic similarity between two given case fillers is represented by the physical distance between two symbols
let xl and x2 denote different senses of a case filler x
finally we should also take the semantic ambiguity of case fillers noun into account
this is usually assessed with multiple choice questionnaires that ask users to rank the system s performance on a range of usability features according to a scale of potential assessments
based on these results we plan to explore ways to augment the lexicon without consulting the test set
NUM sample from the police item lexicon be iter better good advance improve increase
certainly it would take longer than the NUM hours estimated once the automatic rule generator is implemented
by comparison our scoring lexicon contains a list of base word forms i.e. concepts
NUM the definitions associated with these concepts were typically metonyms that were specific to the domain of the item
lexicons restricted to dictionary knowledge of words are not sufficient for interpreting the meaning of responses for unique items
concept knowledge bases built from an individual data set of examinee responses can be useful for representing domain specific language
our task is to create a system which will score the data using the same criteria used in hand scoring
a prototype was implemented to test our hypothesis that a lexical semantics approach to scoring would yield accurate results
for the most part these types of simplification have been shown to be very helpful to the learner
we have replaced the name of the student s school with xxx to protect the identity of our sources
to illustrate this consider the realization of the verb to be in asl and english
difficulties include both dropping the verb and confusing have and be as main verbs
as a result the marking of that feature in the l2 may seem redundant in the first language
in addition the linguistic model can be used to tailor the system s realization of its response
in particular we discuss two ways in which the system s responses can be tailored to the user
thus we will provide instruction on those aspects of the language that the user is ready to acquire
according to the above literature instruction and corrective feedback dealing with aspects within the zpd may be beneficial
however the arguinents i rcb resented there are not conclusive
precision rtterrnsodterrn8 dterrns and recall i.e.
moreover the application on real data should cover more than one domains
at this point the incorporation of the context information will take place
incorporating context information for the extraction of terms
terms may consist of either one or more words
NUM the comparison of this method with other atr approaches
oj to be part of the extracted candidate terms
as a consequence of this the derivation structures in the left and right grammars are always isomorphic up to ordering and labeling of nodes
alternative fills will be provided in the answer key if the phrases found in the text ar e essentially equivalent e g ceo chief executive officer and chief executive
wei a weight b NUM NUM b cdeg
however could something else also carry useful information
one success for the systems as a group is that each of the six smaller organization objects and four smaller person objects those with just one or two filled slots in the key was matched perfectly by at least one system in addition one larger organization object and two larger person objects were perfectly matched by at least one system
the boolean retrieval method was used in the initial probing of the corpus to identif y candidates for the scenario template task because the boolean retrieval is relatively fast and the unranked result s are easy to scan to get a feel for the variety of nonrelevant as well as relevant documents that match all or some o f the query terms
let k be the number of nearest neighbors to use for determining the class of a test example k NUM
the second step of the mapping is to map the second relation assignt type onto modifiers of the arguments of the head clause
sw NUM lcb at NUM NUM rcb NUM the feminine singular second person nominal personal pronoun at feminine you
example NUM right network of figure NUM perspective alternation with fixed focus NUM ai has six programming assignments
by manually tagging all the occurrences of mwnh in our small corpus we found that the above mentioned analysis is extremely rare its relative weight is NUM NUM
this simple strategy was used in an experiment conducted in order to test the significance of the morpho lexical probabilities as a basis for morphological disambiguation in hebrew
for this particular example it needs information about the speaker s focus and her perspective at this point in the discourse
fuf is the formalism part of the package a language in which to encode the various knowledge sources needed by a generator
subject number NUM verb number NUM
given these numbers we can calculate the relative weights of these two analyses NUM NUM NUM NUM and the test corpus probabilities NUM NUM NUM NUM respectively
formally the mapping from the probability of an analysis to its category is done using two thresholds upper threshold and lower threshold as follows
since many of these sources belong to the systemic linguistic school surge is mostly a functional unification implementation of systemic grammar rules
an architecture for lexical choice the place of lexical choice in the overall architecture of generation systems has varied from project to project
table i gives the errors found in a material of NUM NUM words sorted according to whether they occurred in automatically or manually tagged text or both
for example arer selecting and weighting categories the highfrequency term export shows its largest weight for category trade but it also shows large weights for grain or wheat andsmall weights for belgtan franc and wool
for example the metnorm and metxnorm labels correspond to the task and subtask titles in the text stockage des instruments d stockage des atterrisseurs remise en service de i appareil the illocutionary aim of the metnorm or xnorm is to help the user understanding the topical organization of the document
the information concerning the french sequence is directly attached to it morpho syntactic scheme complete morpho syntactic description and tagged terms the information concerning the english sequence is directly attached to it complete morpho syntactic description and tagged terms and the information concerning the sequence pair pragmatic label factual data is attached to the created test unit
the emphasis is laid on the interest of an approach based on the study of bilingual corpus pragmatic characteristics
the meta utterances are all nominal phrases resulting from the nominalisation of verbal groups stockage des instruments destockage des atterrisseurs or from the topicalisation of an object e 16ments stockfis en containers pressuris6s nominalisation and topicalisation can thus be considered as processes used by the writer to textualise knowledge for its reader
to get these translational information we carried out a contrastive study of our french text with its attested human translation
the following table shows that for each pragmatic value we can find a typical underlying morpho syntactic structure
this article is concerned with the building of a test data set for assisting the industrial user in machine translation evaluation
tile ata NUM specification role is to provide a set of rules for the writing and exploitation of aircraft after sale documents
for us the pragmatic labeling was a first step in the classification of utterances based on their communicative value
when occurring in a tittle a similar infinitive verbal form could be translated by an ing verbal form
analysis of complex np structures such as appositional structures and postponed modifier adjuncts is needed in order t o relate the locale and descriptor to the name in creative artists agency the big hollywood talent agency and i n creative artists agency a big talent agency based in hollywood
therefore the condition on the lnd slot of thc r direct object will be changed to ivan
since the r lndirect object precedes the r post mod van zijn adherenties will be linked correctly with vrijgemaakt
for each verb in the text that is not in the concept lexicon a frame is built
NUM a linking module for linking surgical deeds concepts with other medical concepts witllin the clause
they have a top level which is fixed and represents things that are true of a certain situation
i semantic link q linked wilh cc concept type tag cs surgical deed
NUM suggestions regarding the concept type tbr each word of the generated list
NUM word de peritoneale drain intercutaan eetunneld peritoncale drain cc intervent equipment
for each entry are specilied the possible senmntic links and the possible concept types related with those semantic ianks
the lower level of the surgical deed frames contains sets of slots for the identification of semantic i inks
since the meanings can be nodes which already have textrefs connected then particular nodes can collect textrefs for all occurrences of their mention
the tipoff on the first two events comes at the end of the second paragraph yesterday mccann made official what had been widely anticipated mr james NUM years old i s stepping down as chief executive officer on july NUM and will retire as chairman at the end of the year
collections provide a permanent repository for documents within the tipster architecture
to evaluate the statistical model we made various experiments
it presents plan repair as one example of its use
we examined this corpus for the occurrence of dialogue acts as proposed by e.g.
ts1 and ts2 use the same NUM german dialogues as test data
in that case the average number of predictions goes up to NUM
the responsibility for the contents of this study lies with the authors
the information acquired during dialogue processing is stored in a dialogue memory
figure NUM architecture of the dialogue module
thus the update or prediction time is in that case o 2d
ln s accumulates the likelihood of the node seen as a leaf
for example see cluster NUM NUM in this domain the activity of recording evaluating plotting a property e.g. sea surface temperature wind speed is significant in some cases the weak semantic expectations on the argument structure in wordnet are violated
if gr hi is the number of incoming is a arcs for a supertype that is the number of synsets of vi that point to hi an intuitive algorithm would be to select as the best supertype for a cluster the one that maximizes gr hi values
by using efficient data structures we extend the notion of pst to unbounded vocabularies
table NUM summarizes the results on different texts for trees of growing maximal depth
where ct wo e is the null empty context
in particular the pst prior po t is defined as follows
for example verbs in the second cluster of the previous example i.e. the bad choice have been clustered because they occurred in patterns like the law document indicates establish determine the deadline tameorcaa ent ty for the presentation
rather than inducing verb categories from scratch we augmented the semantic bias of ciaula by preclassifying all the verbs in the rsd using the NUM wordnet semantic domains for verbs which are bodily care change cognition communication computational linguistics volume NUM number NUM competition consumption contact creation emotion motion perception possession social interaction stative and weather
derivation of pp disambiguation rules or verb argument structures have been discussed
towards a bootstrapping framework for corpus semantic tagging
null the typicality depends only on wordnet
ary relay ob satellites ob to ensure cg interme
precision is of about NUM NUM
the value of score depends both on the corpus and on wordnet
no special prides software is needed in the end user s workstation
null NUM stress is irrelevant in predicting l he correct allomorph
and exists x c bone x itlt fore11 xl dog xl gave aaxy x xl exists x flover j x NUM existu xl poiiceman xl iu gave axy x xl
for example the sentence every man found a bone has as a possible lf 8a with the aprolog representation 8b NUM sthis is the same syntax for abstraction as in NUM
this leads to the interpretation of a bound variable as a scoped constant it acts like a constant that is not visible from the top of the term but which becomes visible during the descent through the abstraction
the operational semantics for aprolog state that pi x g is provable if and only if c z g is provable where c is a new variable of the same type as z that does not otherwise occur in the current signature
thus to obtain the lf for john and bill the following query would be made coord fs bs np s s abs p app p john cabs pkcapp p bill m
in first order unification it is simulated as shown in figure NUM NUM the final ccg rule to be considered is the coordination rule that specifies that only like categories can coordinate 2the type raising rules shown are actually a simplification of what has been implemented
as shown in figure NUM cat is declared to be a primitive type and np s conj noun are the categories used in this implementation fs and bs are declared type coord cat tm tm tm o
it unifies with the ta ta function sub walked sub s with harry and m with it s the recta level application of r to s which by the built in fi reduction is walked harry
the tag sequence NUM is selected to niaximize the a posteriori probm ility of tagging NUM by map
in addition a development set of NUM quintuples was also supplied
the i efc tellc distribution is the l rol ability distril ution which is obtaiued directly frolll ill aidillg data
language perplexity in the sense meant here needs to be quantified too before it can be considered a useful feature
since spoken language is characterized by a number of properties that defy interpretation and translation by purely grammar based techniques recent interest has turned to analogical also known as case based or example based approaches
prior to having annotated data the segment boundaries for conversational text data were provided in the form of acoustic segmentations
null to meet this requirement we have designed an architecture for robust practical translation of spoken language in limited domains that integrates morphological and syntactic linguistic processing with an analogical transfer component
for example sato and nagao NUM rcb combine a measure of structural similarity with a measure of word distance in order to obtain the overall distance measure that is used for matching
by this we mean that from the point of view of knowledge representation each knowledge source captures certain aspects of the translation process in its most natural form
in a world in which all the relevant data was already clearly set out in descriptive linguistic work an algorithm that efficiently achieved this kind of induction would be the philosopher s stone to the construction of computational lexicons
this incurs a significant computational cost for searching and matching against all the examples which is proportional to the number of examples multiplied by the average size of the representations of the examples
thus in section NUM above we saw that the node verb defines the default morphology of present forms using global inheritance from the path for the morphological root verb mor present mor root
here the empty path is a leading subpath of every path and so acts as a catch all any path for which no more specific definition at word1 exists will inherit from verb
for example consider the day versus hour ambiguity we discussed earlier
do not make our contribution more infornmtive than is required
graded constraints always return true so they can not eliminate inferences
in all these cases the source of inefficiency stems from the principle based design
the principles are x theory the theta criterion and the case filter
who did you wonder why mary liked figure NUM types of sentences
consider what would result if nlab did not check for all of these factors
the first clause of algorithm NUM starts a new chain whenever a lexical element is seen
if the user changes the selection to another alternative say telephone at the third line in the alternatives window kakeru then the selection in the alternatives window denwa also changes to the third line synchronously
however in spite of abundant information within the dictionary such as inflection verbal case frame idioms and so on the only electronically available part is spelling of translation equivalents through copy paste
since it is easier to obtain an appropriate result for a shorter and simpler structure a result obtained by stepwise conversion tends to be of better quality than a result obtained by translating the whole structure at one step
for example ashi wo arau foot obj wash can be interpreted as either wash one s foot or wash one s hands the latter case losing the original meaning of respective words
the leftmost word make shows that the current translation equivalent for kakeru and the third column shows the current translation equivalent for the whole expression is make a phone call an idiomatic interpretation
then s he changes the underlined area to buy ta book excluding he ga from the region e because this is the correct meaningful phrase in the user s interpretation
wa hi o ta c i gave him a paper change the selection simply by a cursor movement or mouse click on this window then the corresponding translation equivalent on the main window changes synchronously
the function as an add on function to an arbitrary software will be an advantage equally for all users enabling them to work in their familiar environment compared to conventional machine aided translation systems that force them to work in an independent unfamiliar environment
although these idiomatic expressions must be recognized and translated as one thing they can not be registered as one word in the dictionary since their elements can appear in a distant position or they can also have a purely compositional interpretation
the hypotheses produced for each oov word are inserted in the graph of possible categories generated by the language model
the net effect of this definition for wordl can be glossed as wordl stipulates its morphological form to be love ing and inherits values for its syntactic features from verb except for syn form which is present participle
the existential closure of an expression is the result of existentially binding all unfilled arguments of the expression
matchplus focuses on information retrieval from large textual corpora
this training process is described in more detail below
the comparison is a vector dot product operation
one area of interest is developing three dimensional soms
the highlight tool segments the document into NUM line paragraphs
the mechanism used for keeping track of the semantic coverage of each edge consists of a bit array that represents the set of semantic facts
in this example we know that either tile third fact or the fourth l lct but not both can be expressed
this way we reconstruct the logical structure of the disjunctive logical form and select one interpretation at a time from the set of possible paraphrases
in addition to the syntactic composition the boolean arrays of the daughter constituents union to form the semantic array of the resulting mother constituent
a condition identifies a certain partial path in the packed generation forest when this path is selected the corresponding semantic fact is expressed
the method is based on a chart structure with edges indexed on semantic information and annotations that relate edges to the semantic facts they express
the focus in this presentation is on generating multiple paraphrases and the ability to operate on logical forms that contain more than one semantic analysis
when the goal is to generate the second interpretation we reverse the conditions and try to satisfy rl pa lq2
first the regular expressions iz e ing es ed
another approach would be to use the semantics of surrounding words in an utterance to constrain the meaning of an unknown word
machine readable dictionaries do not include much of this information and it is difficult and time consuming to encode it by hand
two words that seem to be derived using the ize suffix but do not conform to the change of state axiom are penalize and socialize with the guests
in addition some affixes are much more reliable cues than others and thus if higher reliability is required then only the affixes with high precision might be used
the result state of an aize predicate is the predicate corresponding to its base this is stated in another axiom
these guesses are cued by the meanings of paper shreds sandwich delicious full and the partial syntactic analysis of the utterances that contain them
instead it makes use of fixed correspondences between surface characteristics of language input and lexical semantic information surface characteristics serve as cues for lexical semantics of the words
english will you ask for a taxi for room number three one oh for us please
refer to the number of different sentences in the training set and the number of different sentences after categorization
implemented as a n entry of a lexicon
no trouble in writing our pa tte
sequence shows tliaps two nontermina l
pa tterns does not necessa rily
another extension is to associa te
formal linguistic analyses are useful for weeding out grammatically unacceptable forms but they do not provide a principled means of determining which of the grammatically acceptable forms should be used in any given communicative context
this problem may be addressed by creating a new classification for punctuation marks the embedded end of sentence as suggested in section NUM
it is able to specify no opinion on cases that are too difficult to disambiguate rather than making under informed guesses
to aid our evaluation we define a lower bound an objective score which any reasonable algorithm should be able to match or better
we investigated the effectiveness of two separate algorithms NUM back propagation training of neural networks and NUM decision tree induction
segmenting a text into sentences is a nontrivial task however since in english and many other languages the end of sentence punctuation marks are ambiguous
the completion rules NUM NUM NUM NUM can apply o igi2n NUM times because they are triggered by pairs of chart states and there can be o igi possibilities for each element of the pair for each i j k
zwischen dem elften und achtzehnten janua bin ich in hamburg
in figure NUM and later in figure NUM which illustrate some semantic aspects of the processing we use a diagrammatic notation to describe semantic structures which are actually encoded using conceptual graphs
table NUM shows for each allomorph the number of errors by the c4 NUM rules trained using corpus nc i.e.
this table is then used in standard clustering approaches to derive categories of values in this case consonmlts
it should be clear that this tree can easily be formulated as a set of rules without loss of accuracy
all information stress onset nucleus coda about the three last syllables NUM syll corpus
frequency frequency of sutfix in the text corpus on which the word list was based
we also experimented with a siml h r alternative to the computationally complex heuristic category orma tion
in other words making category formation del endent oil the task to t e learned unde rmitms
wc chose it because it is an easily available and sophisticated instance of the class of rule induction algorithms
the method used in c4 NUM is based on the concept of mutual information or information gain
she has him praised the indicated f marking follows from the theory there has to be some f marking since the meaning of the complete sentence peter s mother praised peter is not entailed by the context
the function can be spelt out in different ways depending on the choice of a semantic theory la furthermore a function variable is assumed that maps a semantic object to a new variable of the same type
avoiding the computationally ext ensive disjunction of alternative analyses in favor of a single graph rei resentation that is underspecified when based on sentence internal infern constraining the givemmss check to nodes with access to the bg partition makes sure that narrow eontrastive focus on given entities like in NUM is treated correctly
the hpsg type cont the value of the cont ent feature has the following four new features o sem ordinary semantics and i skel f skeleton of the type of a semantic object tile set valued is cstr is constraints and the binary max f for potential maximal focus
in an at plication heuristics may trigger focus closure earlier to aw id unneecessary inferences
l lrthermore schwarzschild s pragmatic condition avoid f that selects the analysis with the least f marking cf
an underspecified is arising from tile prosodic marking of a sentence can be resolved by information from the context
for gegeben and the lowest verb projection ein buch gcgeben there is no such antecedent in the context
a sentence is considered to begin either at turn beginning or after completion of a preceding sentence
for the constituents that are not obligatorily focus marked the underspecified representation requires additional defeasible links to bg NUM background linking principle the o sem value of every sign that is not accented is s linked to bg
i know that hans to otto a book gave let us briefly see how the principles interact to produce the phrase ein 3uch gab for simplicity the np is treated as if it was a word
in effect this fills in the gaps between paths defined at a node on the basis that an undefined path takes its definition from the path that best approximates it without being more specific
2s in our hypothetical example the proper name will have feminine gender either if it ends in a consonant and denotes a female or if it ends in a stop consonant but does not denote a female
he has shown that the same datr theorems can have their values realized as conventional attribute value matrix representations prolog terms or expressions of a feature logic simply by changing the fine detail of the transducer employed
the techniques used in this rather simple treatment of passive can be readily adapted for use in encoding other lexical rules and for grammatical frameworks other than that implicit in the patrish syntax we have adopted in our example
firstly in tr verb we have a double parametrization on syn form the value of syn form is evaluated and used to create a mood path the value returned by this path is then used to route the inheritance
single quotes can be used to form atoms that would otherwise be ill formed as such y is used for end of line comments following the prolog convention is used to introduce declarations and other compiler directives
again we ascend the hierarchy to verb and find ourselves referred to the global descriptor mor past participle this takes us back to word3 from where we again climb first to sew then to en verb
NUM association for computational linguistics computational linguistics volume NUM number NUM show that the language is nonetheless sufficiently expressive to represent concisely the structure of lexical information at a variety of levels of language description
this allows the model to robustly handle the statistics for rare or new words
however model NUM has some advantages which may account for the improved performance
the rest of this paper argues that also parts of speech can be viewed as a rule governed phenomenon possible to model using the linguistic approach
part of speech analysis usually consists of i introduction of ambiguity lexical analysis and ii disambiguation elimination of illegitimate alternatives
of these NUM specify a context that extends beyond the neighboring word in this limited sense NUM of the constraints are global
this morphologically analyzed ambiguous text was then independently disambiguated by two experts whose task also was to detect any errors potentially produced by the previously applied components
though the accuracy of the grammar at the level of syntactic analysis can still be considerably improved the syntactic grammar is already capable of resolving morphological ambiguities left pending by engcg
however the above hybrids still contain a data driven component i.e. it remains an open question whether a tagger entirely based on the linguistic approach can compare with a data driven system
its recall is very high NUM NUM of all words receive the correct morphological analysis but this system leaves NUM NUM of all words ambiguous trading precision for recall
and accept any features within a morphological reading and a finite clause that may even contain centreembedded clauses respectively
compared to the NUM NUM accuracy of the best competing probabilistic part of speech taggers this accuracy achieved with an entirely rule based description suggests that part of speech disambiguation is a syntactic problem
the finite state parser the last module in the system can in principle be forced to produce an unambiguous analysis for each input sentence even for ungrammatical ones
this is the case in corpus dedicated to sub areas of language such as in technical documentation for example
have never been seen in training are replaced with the unknown token
we recall the notion of lr automaton which is a particular kind of pda
precision recall and NUM were longer distance cases recovered with NUM NUM NUM
this results in a space complexity o i q2lri iv NUM
we then count two parsing steps one for ql and one for q2
we define variants of the closure and 9oto functions from the previous section as follows
zwe dispense with the notion of state traditionally incorporated in the definition of pda
the application of a transition NUM NUM is described as follows
furthermore if z a then a is removed from the remaining input
we now specify a grammar transformation based on the definition of a2lr
figure NUM relation to other programs
joined by a subordinate conjunct node because
the third point will be discussed in section NUM NUM
b confirm all the selections are right
we maintain this property throughout the search process that is for every symbol a that we add to the grammar we also add a rule x a i this assures that the sentential symbol can expand to every symbol otherwise adding a symbol will not affect the probabilities that the grammar assigns to strings
we repeat this process parsing the next sentence using the best grammar found on the previous sentences and then searching for the best grammar taking into account this new sentence until the entire training corpus is covered
null the basic shortcoming of the maximum likelihood objective function is that it does not encompass the compelling intuition behind occam s razor that simpler or smaller grammars are preferable over complex or larger grammars
to partially address this we add the move move NUM create a rule of the form a ab b with this iteration move we can construct grammars that generate arbitrary regular languages
using this grammar as the starting point we run the inside outside algorithm on the training data until convergence
as mentioned this work employs the bayesian grammar induction framework described by solomonoff NUM
for example consider the case where the training data consists of the two sentences o lcb bob talks slowly mary talks slowly rcb due to space limitations we do not specify our method for encoding grammars i.e. how we calculate l g for a given g
our move set includes the following moves move NUM create a rule of the form a bc move NUM create a rule of the form a bic for any context free grammar it is possible to express a weakly equivalent grammar using only rules of these forms
the meaning of translation area selection is also clear
in other words instead of using the naive s sxix rule to attach symbols together in parsing data we now use the xi rules and depend on the inside outside algorithm to train these randomly initialized rules intelligently
the analyses proposed alh w tile faetorization of language partieular idiosyncratic information
the predicate is realized as the main verb of the sentence and the arguments are realized as complements of the main verb thus the control information is to a large extent encoded in the tree like semantic structure
em ployer name is seeking job title but these linguistic items do not appear in the lexicon as such
in particular we are being encouraged to broaden the coverage of our system to include many more employment domains
the matching process yields a numeric result representing the distance between two objects
figure NUM examples of job codes and names in french flemish and english
the slash symbol is used to separate the syntax from the semantics
transformations are ordered with later transformations being dependent upon the outcome of applying earlier transformations
different kinds of domain specific information can be found as slot fillers depending on the intended meaning of schema slots
when no more combinations are possible for each phrase spanning the entire input we add the appropriate start of derivation cost to these phrases and select the one with the lowest total cost
unlike first order unification this definition of matching is not commutative and is not deterministic in that there may be multiple matching functions for applying a bilingual entry to an input source tree
the model also includes lexieal parameters p w m qlt for the probability that w is the head word for an entire derivation initiated from state q of automaton m
we will use the notation c zly for the cost of a model event with probability p ziy the assignment of costs to events is discussed in section NUM
a derivation of a pair of symbol sequence thus corresponds to the selection of an initial state a sequence of zero or more transitions writing the symbols and a stop action
here a search process is conceptualized as a non deterministic computation that takes a single input string undergoes a sequence of state transitions in a non deterministic fashion then outputs a solution string
since the model is lexical linguistic constructions headed by lexical items not present in the input are not involved in the search the way they are with typical top down or predictive parsing strategies
the supervised training set comprised around NUM sentences
a morphological rule unlike an ending guessing rule uses information about morphologically related words already known to the lexicon in its prediction
unlike morphological guessing rules nonmorphological rules do not require the base form of an unknown word to be listed in the lexicon
so for example if the guesser had assigned all possible pos tags to the word its recall would have been NUM
NUM precision would mean that the guesser did not assign incorrect pos tags although not necessarily all the correct ones were assigned
we have presented a technique for fully automated statistical acquisition of rules that guess possible pos tags for words unknown to the lexicon
a good way to do this is to decrease it proportionally to a value that increases along with the increase of the length
if a rule is applicable to a word we compare the result of the guess with the information listed in the lexicon
the rule induction process is guided by a thorough guessing rule evaluation methodology that employs precision recall and coverage as evaluation metrics
to see the distribution of the workload between different guessing rule sets we also measured the coverage of a guessing rule set
this was the most frequent type of error which accounted for more than NUM of the mistaggings on unknown words
the eventuality in the when clause is related to this reference time as discussed earlier with respect to narrative progression a state includes its reference time while an event is included in it
the sentence is false in the case where out of ten women one owns NUM cats and is happy while the other nine women own only one cat each and are miserable
the backed off estimate has been demonstrated to work successfully for single pp attachment but the sparse data problem renders it impractical for use in more complex constructions such as multiple pp attachment there are too many configurations too many head words too few training examples
testing in this section we describe how to implement our method
in etmral i he distribution of mull iple nps wil hin
further such approaches are not robust as they can not appropriately handle any unforeseen circumstances
evaluation after a movement the tncb is undetermined as demonstrated in figure NUM
the help messages in the system are context sensitive and are based on the current dialogue state
the original simr implementation for french english included matching predicates that could use cognates and or translation lexicons
NUM NUM the meanings of subtrees and their compositions
in what follows we show how each phenomenon is dealt with
to see how this works consider example NUM again
a sequence of models was trained with increasing subsets of the training set
it is trivial to modify any sort of ccg parser to find only the normal form parses
but it is hardly obvious that NUM eliminates all of ccg s spurious ambiguity
figure NUM derivation and parse for a woman
the varying concentration of identical tokens suggests that more localized noise filters would be more effective
there are two reasons why the tagger has been integrated into the system since the overall translation system is unification based words are disambiguated by the application of all possible rules which is highly inefficient
disambiguadon of individual words the selection of al propriale readings and lhe determinalion of individual xmslituents at a very early stage are rueial in arriving al a l esl tit lmrse
get from kyoto station to your conference center NUM
however it is not enough to consider it in isolation
the second case nmy never occur in representations where all attributes are present in each decoration
in these cases there is no point in trying to arrive at a complete parse of the whole sentence since the parse is most likely to fail and processing will be too space and time consuming
v is an ambiguity scope of an ambiguity if it is minimal relative to that ambiguity
theory and practice of ambiguity labeling with a view to interactive disambiguation in text and speech mt
for example if we represent we read books by the unique decorated dependency free
bracketed numbers are optional and correspond to the turns or paragraphs as presented in the original
we have experimented our technique on various kinds of dialogues and on some texts in several languages
a representation will be said to be ambiguous if it is multiple or u nderspec fied
term reeognition terms and multi word units are also recognized at this stage in this context words are treated as terms if they are subject specific or if they have a unique translation in the given text type
at node NUM we refrain from expressing hydranlic o since we set pl the condition in the third slot to false
in the case of NUM it may still be that the wordnets exhibit language specific differences which have lead to similar differences in the equivalence relations
each sub utterance will then be parsed in parallel by a number of sub domain grammars each of which is faster and less ambiguous than a large grammar would be
we compensate for this by using a more powerful and admittedly more complicated mechanism to relate each constituent to the subset of the semantics it realizes
this check can only be performed reliably for langenscheidt t1 since this is the only system that makes the lexicon transparent to the user to the point that one can access the subject area of every entry
to overcome this drawback we have designed and implemented cosma a novel kind of nl dialogue systems that serves as a german language frontend system to scheduling agents
in addition subsequential transducers can be automatically learned from corpora
table NUM sheds some light on the other cases
neither of tile two functions is in principle restricted to any specific roh
these results are given in table NUM
headers have the same meaning as in table NUM
a dash indicates that an experiment was not run
table NUM classification results for each facet level
we have developed a taxonomy of genres and facets
table NUM tagger evaluation on test data
middle for brow and no for narrative
s sx l e s x x a p a aa a NUM vaen lcb s x rcb vaet n the set of all nonterminal symbols t the set of all terminal symbols
in particular we follow standard practice and take the smoothed igram probability to be a linear combination of the gram frequency in the training data and the smoothed i NUM gram probability that is
part of this discrepancy is due to the fact that we require a smaller number of new nonterminal symbols to achieve equivalent performance but we have also found that our post pass converges more quickly even given the same number of nonterminal symbols
a ls allows a languag iml imlme t
an apparent local minimum in the space rk may no longer be a local minimum in the space k l the extra dimension may provide a pathway for further improvement of the hypothesis grammar
the textual test set consists in a sgml file including the source text sequences aligned with the reference translation sequences and also including the pragmatic formal and translational characteristics in the form of annotations labels and formal descriptions
introduction corpus studies appear to be one of the most appropriate techniques to identify the linguistic constraints and needs which will be used as evaluation measurements and criteria to judge the adequacy of a machine translation system to an industrial user s environment
moreover this format allows an easy exploitation of the contained data provided the evaluator uses sgml tools with which selection and extraction of subset of data become really easy each tagged data is a potential selection criteria
NUM typical morpho syntactic schemes this type of morpho syntactic observations has been carried out for all the utterances of the text and resulted in the definition of twelve morpho syntactic basic schemes presenting the characteristics of the linguistic structures used by the writer
the scheme presentation reflects the results of a textual study and in order to formalise some particular phenomena we had to introduce some specific features such as deverb for the nouns resulting from the nominalisation of verbs
in our corpus we observed that the meta textual indicators usually present a phrasal structure they are not complete sentences and include a large number of brachygraphical signs acronyms codes alpha numerical references etc
we used two kinds of features morphological features tense mode voice derivation etc and functional features manner adverbial complement direct object subject agent etc
the pragmatic study of the corpus resulted in the definition of NUM types of labels the meta textual indicators the topical meta utterances the discursive meta utterances and the illocutionary typed utterances orders definitions etc
we achieve a moderate but significant improvement in performance over n gram models and the inside outside algorithm in the first two domains while in the part of speech domain we are outperformed by n gram models but we vastly outperform the inside outside algorithm
the study of the possible co description of an utterance by a pragmatic label on one hand and by a morpho syntactic scheme on the other hand made it possible to assess compatibilities and incompatibilites between the pragmatic value of an utterance and its linguistic structure
at this stage of the corpus study we described each textual sequence of our text with two labels a pragmatic one indicating the textual and illocutionary status of the sequence and a morpho syntactic one describing its formal behavior
incremental processing is required so as to handle fragmental phrases or incomplete utterances and to realize a real time response
for a given n we create a probabilistic context free grammar consisting of all chomsky normal form rules over the n nonterminal symbols lcb x1 xn rcb and the given terminal symbols that is all rules xi xj xk i j k e lcb NUM n rcb xi a i e lcb NUM
first notice that we do not ever need to calculate the actual value of the objective function we need only to be able to distinguish when a move applied to the current hypothesis grammar produces a grammar that has a higher score on the objective function that is we need only to be able to calculate the difference in the objective function resulting from a move
this paper addresses the problem of spoken language translation and explains the method and its capability to handle spoken language
a number of modifications need to be made however to properly capture the nature of paraphrases the creation of a new type of summary link to compensate for the increased importance of derived trees the allowing of many to many links between trees the creation of partial links which allow some information to be shared and a new notation which expresses the generality of paraphrasing
so for example a mapping between the nodes labeled vp1 in each of the trees of the example described above would be an appropriate place to have such a summary link by establishing a mapping between each subnode of vp1 this covers different types of matrix clauses
this does not however change the properties in any significant way NUM it is also useful to add another type of link which is non standard in that it is not just a link between nodes at which adjunction and substitution occur but which represents shared attributes
the use in machine translation is quite close to the use proposed here hence the comparison in the following section instead of mapping between possibly different trees in different languages there is a mapping between trees in the same language with very different syntactic properties
the new notation has three parts the first part uniquely defines each tree of a synchronous tree pair the second part describes also uniquely the nodes that will be part of the links the third part links the trees via these nodes
the system reports a semantic link between the dd and the np if one of the following is true the np and the dd are synonyms of each other as in the suit the lawsuit
according to different anchor link types and their processing requirements we observed six major classes of bridging dds in our corpus synonymy hyponymy meronymy these dds are in a semantic relation with their anchors that might be encoded in wn
table NUM a sample of the NUM NUM word pairs from
we instead adopt a distribution that focuses on small distances
assigning no boundaries and deterministically placing a segment boundary every NUM p sentences
figure NUM reveals the first several features chosen by the induction algorithm
does the language model degrade in performance in the next two utterances
for an empirically driven example we provide an excerpt from the bn corpus
the wsj model was trained on 325k words of data
and contributes a factor of NUM NUM if the answer is yes
two separate models were built to segment the tdt corpus
the upper verticle lines are boundaries placed by the algorithm
the text content can be specified as syntactic repre null sentations as table specification and or as human authored text for the titles and the object model annotations
the documents generated by modex are always generated dynamically in response to a request and are composed of human authored text generated text and or generated tables
modex does not have access to knowledge about the domain of the oo model beyond the oo model itself and is therefore portable to new domains
once edited this representation can be stored permanently in the library of text plans and can be used to generate descriptions
instead he uses modex to generate fluent english descriptions of the model which uses the domain terms from the model
some previous systems have paraphrased complex modeling languages that are not widely used outside the research community gist ppp
thus we can see that the oo models form the basis of many important flows of information in oo software engineering methodologies
this text structure is a constituency tree where the internal nodes define the text organization while the bottom nodes define its content
classical lr does not model variable interactions
we demonstrate composition of the lcs and corresponding aspectual structures by using exampies from nlp applications that employ the lcs database
semantic content is represented by a constant in a semantic structure position indicating the linguistically inert and non universal aspects of verb meaning cf
the monotonic composition permitted by the lcs templates is slightly different than that perlnitted by the privative feature model of aspect olsen
since the verb classes state activity etc are abstractions over feature combinations we now discuss each feature in turn
the numbers in the lexical entry are codes that map between lcs positions and their corresponding thematic roles e.g. NUM agent
it was shown how linear logic proof nets can be used for efficient natural language meaning deductions in this framework
since NUM of the verbs were classified by automatic means new verbs would receive aspectual assignments automatically as a result of the classification algorithm
we are currently working on a revised version of the system that takes the problems just discussed into account
role etc have to be determined in the beginning of the transfer phase and added to the turkish case frame
we also estimated the lower bound of this evaluation that is we also conducted the same trials using the bunruigoihyo thesaurus
but some usages can not be identified as to case role because of gradation of case role changing
in our corpus NUM NUM NUM of the cases based on events are direct nominalisations for instance changes were proposed the proposals and another NUM were based on semantic relations holding between nouns and verbs such as borrou ed the loan
in this figure the similarity between wl and w2 for example is measured by the sum of x3 and x4
the other NUM cases NUM of dds based on events require inference reasoning based on the compositional meaning of the phrases as in it u ent looking for a partner the prospect these cases are out of reach just now as well as the cases listed under discourse topic and inference
the attribute to be tested first is chosen by computing for each value the relative frequency of positive and negative outcomes for this value
running the autolearn system a pass through the texts is made for each decision tree beginning and end of each named entity
precision appears to tail off at around NUM recall however increases with one exception steadily over the whole range
one of these basic is an improved version of the crl name recognizer developed in phase one of tipster NUM
after all the pattern based procedures have operated on the text a final pass is made to recogniz e abbreviated forms of names
final patterns are used to join together units of the same type which are immediately next to eac h other in the text
we also intend to apply the learning method described here to other nlp tasks such as part o f speech tagging and disambiguation
these new lists are then used as lists of known organizations and persons and any occurrences of these in the text are marked
this article also shows the importance of context in reliably recognizing some names e g an analyst with paynewebber
generating the decision trees as each word of the training data is read it is hashed and stored in a hash table
lexical acquisition based on collocations between terms and not simple lemmas provides more granular information on lexical senses as well as syntactic or semantic selectional constraints
the approximate solution for branch i is given by equation NUM where n is the number of divisions of the equation set
this list does not satisfy any cognitive theory because it is an unstructured index with unique identifiers for concepts that do not have any internal or language independent structure
in these cases we penalized also the recall of the sp method so that the difference between the two methods relies not only in amount of persisting ambiguity i.e.
where ssem ni ssyn lj slex t k stand for the semantic score function syntactic score function and lexical score function respectively they are defined as follows
in addition a robust learning algorithm which has been shown to perform well in our previous work NUM is also applied to the system to minimize the error rate of the testing set
however statistical approaches reported in the literature NUM NUM NUM NUM usually use only surface level information e.g. collocations and word associations without taking structure information such as syntax and thematic role into consideration
therefore in addition i mi to the nf1 representation t i the determination of cases in the case subtree r i m is assumed to be highly dependent on its ancestors and siblings
efirst the tree is decomposed into a number of phrase levels such as t NUM l in fig NUM secondly the transition between phrase levels is formulated as a context sensitive rewriting process
when the parameters of the proposed score function are estimated with the maximum likelihood estimation mle method the baseline system achieves parsing accuracy rate of NUM NUM case identification rate of NUM NUM and NUM NUM accuracy rate of word sense discrimination
the algorithm should be considered as the splitting of both anaphora resolution and pp attachment procedures into several phases and not as the repetition of each procedure
in order to describe the detection of subtopic structure it is important to define the phenomenon of interest
in this case the resolution of the anaphora is postponed to a next call of the anaphora module according to principle b stated above
finally we define the quality of the translation to be i c ce cta get where cm rce cta get in a natural way can be interpreted as the extent to which comprehensibility has degraded as a result of the translation process
the score for each pair of nodes depends only on the closeness of the lexieal entries associated with the nodes and i e.g.
NUM find the largest entry miojo ill the matrix such that neither its row nor its cohmm is already occupied by some pair in toi
the slight increase in precision observed with the 91f there was no correct parse the parscs wil h the fewest errors were used for purposes of nligmnent
we expect to gain greater efficiency if all coi hihoh ilodes between forests are s lare l rather than just the nps
this approach requires a fast tree alignment teehuiqu research has i een ham ered by the lack of efli icnt algorithms
instead we use a greedy approach and choose the d highestscoring mutually disjoint pairs from among the d NUM possible pairs of children of v and v
wc expect that non zero penalties will improve precision with a nonempty bilingual dictionary because they will favor similar structm es
however in the nlp domain the running time is contained becmlse d NUM for most trees encountered in practice
6note if we disregard the arc labels for simplicity and set lex
not is used when it is not unsafe to perform c but may rather be simply inconvenient
as an example of how this can be done we presented an analysis of english preventative expressions
we will now briefly discuss three of the function features we have coded iintentionality awareness and safety
as a collection these texts are the result of a variety of authors working in a variety of contexts
the grammatical forms we found NUM occurrences in all constitute NUM NUM of the expressions in the filll corpus
the corpus from which we take all our coded examples has been collected opportunistically off the intemet and from other sources
NUM the learning algorithm will freely reuse systems i.e. features as various points in the tree
for our example the system sub network shown in figure NUM is produced based on the decision tree shown above
because there are relatively few training examples in our coded corpus we have also performed a NUM way cross validation test
an algorithm to co ordinate anaphora resolution and pps disambiguation process
r rem a a sigma f
the algorithm also demonstrates how the general machinery of a finite state calculus can be usefully applied as a framework for expressing and solving problems in natural language processing
computational linguistics volume NUM number NUM and sgall NUM as well as investigations with native speakers of english we hypothesize that the so of some of the main kinds of complementations in english has the following shape NUM time actor addressee objective origin effect manner directional
this example differs from NUM in that the two groups containing the relevant quantifiers few girls and many problems both are in the topic of the sentence whereas in NUM a and b at least one of them belongs to the focus on all readings
NUM topic and focus in functional generative description in the framework of functional generative description fgd elaborated by the prague research group of theoretical and computational linguistics topic and focus are understood as constituting one of the hierarchies typical for the underlying syntactic structure of the sentence
NUM another point shows the importance of including cd in syntactic representations of sentences on the scale of cd there is always a certain step dividing the sentence its syntactic representation into the topic and the focus as the less dynamic and the more dynamic parts of the sentence respectively
unmarked prototypical values such as singular present and definite are assumed by default
however it was found possible to simplify the calculation by omitting the application of formulae NUM NUM for some of the rules
to locative the core of our experiments has consisted of checking with native informants whether the a or b sentence in such a pair can answer a question in which neither of the two relevant complementations is mentioned or one in which only one of them is mentioned
NUM on the other hand in technical texts which typically are written there is a strong tendency to arrange the words so that the intonation center falls on the last word of the sentence where it need not be phonetically manifested with the exception of course of enclitic words
in the prototypical case the topic theme given information can be understood as that part of the sentence structure that is being presented by the speaker as readily available in the hearer s memory whereas the focus comment rheme is what is being asserted about the topic
juman is a rule based japanese tagging system which uses hand coding cost values that represent the implausibility of morpheme connections and word and tag occurences
the precision value was used as the credit factor of each branch in the morpheme network to be outputted by juman table NUM
the precision of the most plausible segmentation and tag assignment was outputted by the tagger based on each stochastic model estimated either without figs
when estimated without the credit factor fig NUM neither the hmm nor the tag bigram model was robust against noisy training data
in this system the meaning of any particular node is given by its connections its relative position in the net
partial ambiguous sequence given the state of the hmm at the node morpheme candidate in the morpheme network by a node synchronous procedure
the synchronous points are defined as positions of the head character of all morphemes in a morpheme network and are numbered from left to right
the symbols and on q function are defined as follows b the maximum number of synchronous points in a morpheme network
moreover the expressive power remains restricted to that of a cfg so certain constraints simply can not be expressed
we believe progress in chinese parsing technology has been slowed by the excessive ambiguity that typically arises in pure context free grammars
ambiguous coordination possible rephraslags either the cat and the rat or the mat or the cat and either the rat or the mat the above cases illustrate constructions that are definitely ambiguous however some common problems involve modification that may or may not be correct depending on domain knowledge which we do not attempt to make use of at present
a high inter tagger agreement rate would be indicative of the stability of naive inter subject meaning discrimination
we are currently developing a robust grammar of this form for the chinese bracketing application
the key to eliminating the incorrect possibility altogether is that only can also have the part of speech vnn
suppose that the parser is unable to find any full parse tree for some sentence that includes the latter phrase
in such cases our parser will instead return a partial parse tree as discussed further in section NUM
in particular compounding is extremely flexible in chinese allowing both verb and noun constituents to be arbitrarily mixed
the lexicon used was the bdc dictionary containing approximately NUM NUM entries with NUM part of speech categories NUM
the former indicates a question and the latter a negative declaration clearly the parses must differentiate these two cases
figure NUM shows the relation between the size of the training data and the precision of the system
the training test data used in the experiment contained about one thousand simple japanese sentences collected from news articles
the sampling algorithm gives preference to examples of maximum utility by way of equation NUM
it can be seen that as we assumed both restrictions are essential for the estimation of the interpretation certainty
increasing the value of the threshold the precision also increases at least theoretically while the applicability decreases
the next section NUM elaborates on the example sampling method while section NUM reports on the results of our experiment
in this method the system always selects samples which are not certain with respect to the correctness of the answer
unlike rule based approaches corpus based approaches release us from the task of generalizing observed phenomena in order to disambiguate word senses
since there are about one thousand basic verbs in japanese a considerable overhead is associated with manual word sense disambiguation
all verb senses we use are defined in ipal NUM a machine readable dictionary
although this application uses name matching techniques much like those used m conventional relational database name searching and nalne recognition or tagging techniques much like those of information extraction applications text retrieval is sufficiently different from those applications as to present different problems and issues calling for different name searching techniques
the results after duration modelling are input to the intonation module which produces phonetic transcriptions describing both duration and intonation
it is not feasible to do classification experiments on this original corpus
english french is easier than swedish french but substantially more diffcult than any of the others
the problem of low literacy skills among deaf people has been well documented and affects every aspect of deaf students education
our long term goal is to develop a computer assisted language learning call tool to help deaf students leam written english
on the other hand the use of hand crafted thesauri as semantic resources is simple to implement but lacks mathematical rigor
our analysis and intuitions led us to the notion of language transfer to explain many of the errors we were finding
the initial placement of the student on slalom will most likely be based on an analysis of the first input sample
another possible filter could reflect how much and what kind of formal instruction the student has had in written english
our work in this area has included an analysis of writing samples from deaf writers who are proficient in asl
the approach we take is to view the student s leaming of written english as a task in second linda suri cidmac wustl edu
in this respect our effort is similar to other projects geared toward learning english as a second language
first it must have the ability to analyze texts that are input by the student and determine what where errors occur
the possible effects of the student s first language on generated sentences is represented by the language model in figure NUM
this fact was observed in our preliminary experiment despite using statistical information taken from news articles as many as NUM years
and entropy of information is defined as the uncertainty of information source
these models were then used to compute perplexity on the different versions of the test data
but as norvig mentions in passing with parsers defined in the manner just described the memoized versions of programs derived from left recursive grammars fail to terminate
in the next three sections we describe these types of annotations and provide some examples
that is a memoization procedure for a cps procedure should associate argument values with the set of values that the unmemoized procedure passes to its continuation
the following shows the first two turns in the above discourse with both of these annotationss
this is to avoid confusion with the which delimits words and their part of speech
note that while the values cross they are rarely the same for the same word
a transition to the next word through a segment boundary
for example the earley deduction proof procedure is essentially a memoizing version of the top down selected literal deletion sld proof procedure employed by prolog
NUM define alt a b lambda p union a p b p
NUM we are looking at ways of taking advantage of this structure in the language model
also the ling seg model does slightly better at hypothesizing segment boundaries than the acoustic seg model
what meaningful label or name should be given to each word group
in this situation it may be preferred that subsequent references be full descriptions rather than reduced ones or pronouns to emphasize the beginning of discourse segments even if the referents have just been mentioned in the immediately previous utterance
in our current implementation we rely on the hierarchical structure of the message content to be generated as the basis for dividing the message into segments which is effective in improving the texts generated by our chinese natural language generation system
since our annotations were based on intuition we tested them by comparing them with those of other native speakers of chinese to see whether our intuitions about the discourse structures of the test data were reliable for the purpose of the experiments
figure NUM occurrence of referent j in the discourse in figure NUM e ni hui faxian fangwanj zhuangbuxia zhexie shui you will find square bowl fill not in these water you will find that the square bowl ca n t hold this water
it is hard to determine the reason for this though the problems of reliably implementing all the constraints presenting the anaphora within naturalqooking texts and above all coping with the disagreements between native speakers all probably make a contribution
although there are no clear rules delineated in previous linguistic work we nevertheless can summarize a very simple rule rule NUM as shown below and in an associated decision tree in figure NUM for the generation of zero anaphora
in the table the matched rate of the test data is NUM which obviously shows an unpromising performance of the computer employing rule NUM apparently what we need to do is to find more constraints to enhance rule NUM
furthermore there is more ambiguity in travel planning especially because the same utterance can have different meanings in different sub domains
we use average mutual information as global similarity metric to do classification
for chinese word segmentation more self organized approaches have been tried
but the metric does not have transitivity
after all modules have run the constructed spl on the blackboard is passed to kpml for realization
however subgraphs about different topics is merged into the same cluster by two ambiguous words which bridge these two subgraphs figure NUM
for readability we omit these features in the tree diagrams
third different kinds of prompts can be used to mark different contexts
the usefulness of this architecture actually goes beyond the particular domain of application for which it is developed
this commandment essentially says that the system should be a good dialogue partner
one important element is the presence of a vocal undo command
in realpro each transformation is handled by a separate module
the values of these parameters are estimated from a tagged corpus which provides a training set of labeled examples see section NUM NUM
NUM the tree transformer the engine that matches the left hand sides of tree transforawe also foresee an ordering module
i candidate let us illustrate this view diagram via an example
ist c it is a sonata rcb etc
figure NUM shows a strategy develop ed in vodis for that purpose
we use a statistical measure that attempts to capture the likelihood of an sdu boundary between any two words of an utterance
this construct is not covered by the current version of the grammar
the first of the above tests is rather obviously valid and easy to apply
to compile collocations we used xtract on the english version of the hansards
however as the threshold increases the rate of failure can become unacceptable
therefore in the following we give averages over the values of no tried
news stories often relate similar facts but they are not direct translations of one another
NUM conclusion we have presented a method for translating collocations implemented in champollion
this shows up clearly when our evaluation results on the first two experiments are compared
recall that c2 was extracted from a different and larger corpus from db1
this situation is depicted in the column labeled original variables in table NUM
smadja mckeown and hatzivassiloglou translating collocations for bilingual lexicons handling low frequency collocations
tomorrow trains the tennis player the tennis player will train tomorrow
sc NUM is not possible and was not taken into account of the english and the german matrix and the number of non corresponding word positions c for NUM formulas
hereby f i j is the frequency of common occurrence of the two words i and j and f i is the corpus frequency of word i
however for comparison the simulations described below were also conducted using the original co occurrence matrices formula NUM and a measure similar to mutual information formula NUM NUM
future work will deal with the following as yet unresolved problems computational limitations require the vocabu null laxies to be limited to subsets of all word types in large corpora
figure NUM dependency between the mean similarity i
this assumption is reasonable for parallel texts
the satisfaction precedes relation among intentions constrains the order of segments in the discourse but it does not fully determine it
t department of computer science and learning research and development center university of pittsburgh pittsburgh pa NUM
by this definition informational structure is a complex network of domain relations that is defined independently of the intentional structure
the first step involves finding the regions
the automatically generated regions figures 4a and 4b show one of the many
we feel that this technology is far from realizing its full potential
the similarity occurs because the nucleus satellite relation among text spans in rst corresponds to the dominance relation among intentions in g s
neighborhoods can be larger or smaller this is just one example
unfortunately this is not the case for the standard som algorithm
we coin the term core to refer to that part of the segment that expresses the segment purpose
in other words i is the discourse segment purpose dsp of ds
it is therefore surprising to note that all systems in our test seem to lack an elaborate derivation module
one may argue that we could use the word bigzam model
it remains variable until the slasit element becomes bound
yet another paper about partial verb phrase fronting in german
with a separate vcomp feature this problem disappears
a verbal complement of a matrix verb is saturated
the vcomp value of the resulting sign is none
a suificient condition for it to hold is that the joints between prefixes and suffixes minimize some sums of distances
the tagger uses NUM morphosyntactic tags such as noun sg for singular nouns and verb p3sg for verb 3rd person singular
ongoing work includes expansion of the french grammar a wider evaluation and grammar development for new languages
the two operations segmentation and syntactic marking are performed throughout the sequence in an interrelated fashion
we will introduce the notion of compound classes propose a method for constructing them and present results of our approach
but we argue that this incremental view of parsing is instrumental in achieving robust parsing in a principled fashion
this is why we avoid the use of simplifying approximations that would block the possibility of performing delayed assignment
the reductionist approach starts from a large number of alternative analyses that get reduced through the application of constraints
another merit of this approach is that we can avoid the data sparseness problem which is ubiquitous in corpus statistics
the other type repeats merging classes starting from a set of singleton classes which contain only one word
a replace all words in the text except those in c i with their class token
in order to get more robust linguistic descriptions and networks that compile faster segments are not defined by marking sequences that match classical regular expressions of the type det coord det adj noun except in simple or heavily constrained cases aps infinitives etc
the vocabulary is selected as the NUM NUM most frequently occurring words in the entire corpus
however whether no further improvement can be obtained by using texts of greater size is still an unsolved question
note that the dependency of the error rates on the clustering text size is quite similar in the two cases
probability distribution of tags for the root node can be obtained by calculating relative frequencies of tags in the set
although many words are seemingly random words representing million dollars goldman sachs thousand etc are learned
for instance this system would provide the focus has been on those people who are near native users of american sign language
j NUM m a distribution q w kj over its words wekj q w kj NUM and a distribution p wlkj satisfying
since the probability p wt kt does not depend on eate n gories we can ignore the second term yit l p wt ikt in hypothesis testing and thus our method essentially becomes equivalent to hcm c f
we intend to continue improving the existing components while also porting the system to other applications so that we can learn from our porting experiences
perhaps the worst difficulty for the deaf learner is that s he has little to no understandable input in the language s he is attempting to acquire
we then define for each category ci i NUM n a distribution of the clusters p kj ici and define for each category a linear combination of p w kj
for both data sets we evaluated each method in terms of precision and recall by means of the socalled micro averaging NUM when applying wbm hcm and fmm rather than use the standard likelihood ratio testing we used the following heuristics
frame based systems typically have a domain application model to which they map user utterances in an attempt to recognize the nature of the user s query
are deaf the problem of deaf literacy has been well documented and has far reaching effects on every aspect of deaf students education
evaluating classification results on the basis of each individual category we have found that for three of the nine categories in the first data set fmm0 NUM performs best and that in two of the ten categories in the second data set fmm0 NUM performs best
fourteen instances of word codelet are posted to the coderack
the work described here differs from this earlier work mainly in its emphasis on correction and on its model of the user s acquisition process
the process is analogous to the crystallization process in chemistry
rather it is constructed by a sequence of codelets
these codelets reside in a data structure called the coderack
it is an integer ranging between NUM and NUM inclusive
neither are they meant to reflect the preferences of a human
computational activities are a combination of top down and bottom up activities
the cycle in which a structure is built is not preprogrammed
n recl debito publico estero x y log NUM freq debito publico freq estero def
in the sole24ore corpus our method produced both the terms guardia di finanza and aeroporto di fium icino so that the final list of esl reduces to n p n ufficiale della gua rdia di finanz
in our example the early NUM elementary syntactic groups obtained in absence of terminology reduced to NUM with an overall data compression of NUM NUM NUM NUM NUM
the alignment of the section related to the head smaltimento is reported in table NUM x means the presence of the term in the corresponding dictionary while
results are reported in table NUM where separate columns express the scores for the different runs a simple parser sp and a terminology driven parser tp
if this point falls close to the line bss fss then there is little or no difference between the accuracy of the models selected during fss and bss
if a model is selected where there is no edge connecting a feature variable to the classification variable then that feature is not relevant to the classification being performed
statistical analysis of nlp data has often been limited to the application of standard models such as n gram markov chain models and the naive bayes model
the naive bayes classifier uses a model that assumes that each contextual feature variable is conditionally independent of all other contextual variables given the value of the sense variable
such a model can form the basis of a probabilistic classifier since it specifies the probability of observing any and all combinations of the values of the feature variables
for these estimates to be reliable each of the q possible combinations of feature values must occur in the training sample
sthey recommended a model selection procedure using bss and the exact conditional test in combination with a test for model predictive power
a good strategy for developing probabilistic classifters is to perform an explicit model search to select the model to use in classification
also the word errors in the first sentence of each article are not withii our means to tlx
on the other hand very low frequency words soinetimes introduce noise into the retrieval process because of their peculiarity
also there are several things we need to reewduate regm ding our sublanguage model
we will need to use automatic optimization methods and a substantially larger training set
as is the practice in information retrieval we filtered out several types of words
this corpus includes NUM NUM articles or 76m tokens from january NUM to hfly
we olleet the most similar NUM artmes d m the corpus
lie of thenl is the threshold method we adopt here which introduces undesirable discontinuities into our la nguage
for example it might be worthwhile to reconsider how to mix our score with sri s language model score
there are parameters involved in the similarity calculation the size of the sublanguage set the ratio threshold etc
while a variety of distinct approaches have developed most of them can be characterized as constrain based the formalism or formal framework provides a class of structures and a means of precisely stating constraints on their form the linguistic theory is then expressed as a system of constraints or principles that characterize the class of well formed analyses of the strings in the language
where children z yl y y3 holds iff the set of nodes that are children of x are just the yi and vp subcat NUM etc are all members of p NUM a sequence of nodes will satisfy id5 iff they form a local tree that in the terminology of gkp s is induced by the corresponding id rule
o def x nte liicksze hl x where n is a number from the set of alternatives of NUM
these it are required to be alternatives of the event descriptiou in the scope of crst whic h is called e i rs
t disqualified himself at the s r epa i erst drei f unterschriften genfigten
we tackle this problem in the framework of discourse representation theory drt kam81 assuming that discourse representations drss may be augmented by information structure
in this paper we argue that in a number of cases deep semantic analyses can be avoided by taking into account the constraints that the alternative readings impose onto the information structure
to this end we present a study of the arnbigous german adverb erst and point out the particular circumstances under which the given information structure disambiguates the adverb without further semantic analysis
the intelligent construction of the presuppositional sequence of events for the h reading outputs a number of disqualification events that are located al particular places of the ilahnenkamm downhill race in kitzbfihel
sisting of a sequence of events el ek that are related via a non fl n ther specified relation NUM lcb to l redicates i l
in NUM these opportunities may be situations that call be described by the firsle second lhird number is presented to peter
in crman focus adverl s can not be topicmized as such i.e. they can not occur in the vorfeld position without an accompanying constituent cf
here x lc1 rc1 lc2 etc are regular expressions
those sentence readings accepted by all rule automata are proposed as parses
oleada provides users with a consistent networked medium for working with multilingual text and integrates analysis tools using the tipster architecture
the shortest tokenization operation sd is a mapping sd NUM d defined as for any s in sd s lcb w i iw minw etd s iw i rcb every tokenization w in sd s is a shortest tokenization or st tokenization for short of the character string s
with regard to the character string tokenization problem proper this completeness requirement can be translated as given an alphabet a dictionary and a character string the definition should be sufficient to answer the following two questions NUM does this character string have tokenization ambiguity
however unless all parse trees are merged together to form the syntactic graph the only thing feasible is to check every possible position in every parse tree by applying all available knowledge and every possible heuristic since we are unaware of the effectiveness of any checking that occurs beforehand
w a transcript of a conversation is a more concise version than an audio tape which is itself more m concise than a video tape
definition NUM the character string critical tokenization operation cd is a mapping cd NUM d defined as for any s in cd s lcb w i w is a minimal elementoftheposet td s g rcb
let us illustrate with a simplified example
a comprehensive parsing grammar is under development
noun verb and participle past tense were problematic
figure NUM results from a tagging test on a NUM NUM word corpus
in the data driven approach no human effort is needed for rulewriting
the number of word types with more than one meaning was determined
NUM the third issue he addresses is that of how speech recognition and mt techniques should be integrated in particular whether a single set of techniques can or should be used to cover both tasks e.g.
due to the very limited amount of training data available for the travel domain we decided to attempt to build a speech recognition system for etd by a process of adapting the acoustic and language models of our esst recognition system
two strategies address the goal of minimizing the amount of linguistics expertise required to develop applications with the nl assistant toolkit
syntactic information is supplied by a lexical server based on the 50k word comlex dictionary available from the linguistic data consortium
to improve the ease of use of the linguistic development environment several special purpose editors have also been implemented
table NUM animate 3rd l ers n subjects less adjuncts do n t generate indei endent center illg ltllils NUM
the xconcord program is a concordance tool that allows kwic key word in context searches to be done in text in as many as NUM languages
the answer to the first question is probably no although we have made a tremendous progress both from a scientific and from a technological point of view many of the fundamental problems in mt and in speech understanding remain unsolved
annotations are used to store information about a particular segment of the document identified by a span i.e. start end byte offsets in the document content while the document itself remains unchanged
these issues have been and are the topic of a number of nlp projects and programs tsnlp decide tipster muc trec multext multilex genelex eagles etc
finally we present some statistic figures from the results of the lexicon development and confirm that the proposed architecture and the code system can empirically constrain the potential combinatorial explosions of the verb subcategorization frame representation varieties
in the preliminary version of the corelli plug n play layer the choice was made to develop the most general version of the architecture to ensure that any tool can be integrated using this framework
the architecture provides solutions for representing information about a document storing and retrieving this information in an efficient way exchanging this information among all compo null nents of an application
one propitious manner of viewing this model is to imagine that when assigning probability to a word w following a history of words h the model consults a cache of words which appeared in h and which are the left half of some s t trigger pair
each lexical resource is wrapped as a plug n play tool implementing the query interface in order to interface with the databases the java native interface is used to wrap the c database library
this multilingual machine translation system is built out of heterogeneous components such as an english generator written in lisp a spanish morphological analyzer written in prolog a glossary based machine translation engine written in c etc
this architecture also allows the processing load of an application to be distributed by running the components on several machines accessible over the internet thereby enabling the integration of components running on widely different architectures
table NUM presents the costs of parsing the test sentences
the string has been accepted when s NUM u0
the rightmost symbol of NUM represents the top of the stack
one may assume an auxiliary table storing each ui
this problem is in part solved by the filtering function pred
these relationships may be represented implicitly by collocational semantics
this section presents a tabular lr parser which is the main result of this paper
we hope the conceptual framework presented in this paper may at least partly alleviate this problem
dialogue structure coding the information to be confirmed is something the partner has tried to convey explicitly or something the speaker believes was meant to be inferred from what the partner has said
observal ions in lhe corplls which come kern only NUM aulhors are not i otally indct en lent
a i want you to wake us up tomorrow at a quarter past two
o quisiera qua nos despertaran mafiana a las dos y cuarto pot favor
as a result the training procedure amounts to a sequence of iterat ions
figure NUM gives an illustration of the possible alignments for the monotone hidden markov model
we further constrain this model by assigning each source word to exactly one target word
the vocabulary consisted of NUM spanish and NUM english words ineluding punctuatioll marks
for the experiments a trailfing corpus of NUM NUM sentence pairs with NUM NUM spanish and NUM NUM
table NUM effect of the transformation steps on the vocabulary sizes in both languages
in addition the size of both vocabularies is reduced by exploiting evident regularities e.g.
g just curve from the point go right go down and curve into the right til you reach the tip of the pirate ship f so across the bay
p rl rk rk l rn ira q is the product of the probabilities of the actions taken to generate the sequences
the accuracy of the recovery in our robust parser is about NUM NUM
this robust parser can easily be scaled up and applied to various domains because this parser depends only on syntactic factors
entry mapping functions specify how the set of target fragments for deriving a translation are to be combined whenever an entry is applied a global node mapping function is extended to include the entry mapping function
the class of languages defined in this way clearly includes all regular languages since strings of a regular language can be generated for example by a head automaton that only writes a left sequence
here m is the head automaton for w in this derivation the automaton is in state q t is the dependency tree constructed so far and c is the cost of the partial derivation
least errors recognition which is based only on syntactic information was proposed by g lyon to deal with the extragrammaticality
it is important to recover it using only syntactic information although results of recovery are better if semantic factors are considered
the unsupervised training set consisted of approximately NUM NUM sentences it was used for automatic training as described under reflexive training above by translating the sentences into chinese and back to english
a qualitative baseline in this model all choices were assigned the same cost except for irregular events such as unknown words or partial analyses which were all assigned a high penalty cost
b probabilistic counts for choices leading to good translations for sentences of the supervised training corpus together with counts from the manually assigned attachment points were used to compute negated log probability costs
and the military category contains several ordinal numbers e.g. loth 3rd 1st that could be easily identified and removed
modifications to particular attributes of the nodes
figure NUM an architecture for explanation generation
finds structural view of parts of object
question what is a root system
gametogenesis is a step of gametophyte development
embryo sac formation occurs in the ovule
the empty head is described in NUM where the local value is coindexed with the l sl value
multi layer perceptrons mlp were trained to recognize NUM labels based on the features and data as described above
we have thus chosen a bottom up parsing strategy where the introduction of empty verbal heads is constrained by syntactic and prosodic information
condition b rules out a large number of structures but often can not prevent the stipulation of traces in illicit positions
i thought that he yesterday the car fixed i thought that he fixed tile car yester null day
this strategy however will not work for head traces because they do not occur as dependents on a subcat list
why for instance there is perishable but not rottable
we discovered that developing adjective semantics for an application modifies many popular views on the subject
his approach to the problem was much more aeronautical than mine
we will recall this methodological circumstance shortly in section NUM
a large subclass of deverbal adjectives are adjectives that end in able ible cf
abusive is indeed a pretty typical and easy example of lrva
this throws the first monkey s wrench into making the lr fully automatic
an important consideration when discussing related work is the mode of evaluation
the information contained in this statement is NUM x logp geoform
in other words each noun he believes influences the meaning of the adjective
similarly lr e the event itself sub lr places varl in the event position
however coders had a little more difficulty k NUM
the second allows users to manipulate objects in a blocks world using iconic and pantomimic gestures in addition to deictic gestures
quickset provides a portal into leathernet NUM a simulation system used for the training of us marine corps platoon leaders
so case r case must c mtain all f th li mcts in ca sc mm it
for example the flee combination of the two confinements from above have again been replaced by representat ives of their equivalence class
moreover since both confinements at derived fl om the original ease form it is also a suf ficient condition
we an see that this formulation is nearly equivalent to maxwell and kaplan s by substituting p for at and p for a2
lh om these subsets of variables we construct two new cast forms dora the original using the operation of confinement defined below
there are two facts that conspire to make tile treatment of disjunction an important consideration when building a natural language processing nlp system
the first fact is that natural languages are full of ambiguities and in a grammar many of these ambiguities are described by disjunctions
one advalltage of this is that the number of base constraints that must be checked during satisfaction an potentially be exponentially reduced
the generated entries are produ ed by combining stored entries with one or more ext ansion rules and these cnt ri s at more or less elaborate spe ifica ions of actual wor ls
to estimate the performance function the weights c and wi must be solved for
the meaning of a sign is analyzed as a situation involving a number of participants also called arguments and these participants as well as the situation as a whole are modeled in terms of aspectual values semantic roles criterial factors and realizational and selectional properties
the expansion rules fall into five categories depending on what kind of information they insert into the lexical representations NUM morpho syntactic augmentations NUM inflections NUM conceptual expansions NUM syntactic mappings and NUM compositions
if the conceptual structure contains an argument that undergoes some monotonic development the conceptual structure can be expanded with a new argument that serves as the medium for this development and has a dimension matching the criterial property of the monotonic role
in a sentence like jon walked to the school the phrase to the school describes this monotonic development of argument NUM away in gon walked away is another optional constituent that can describe argument l s nmvement along a one dimensional path
dc ac b2 at which time do you want to leave from merano to milano
we discuss the challenges that these differences impose on our translation system and some planned changes in the design of the system
user agent us el utt e2 rep
the avms of the remaining dialogues would differ from the key by at least one value
for the whole dialogue d1 in figure NUM o d1 is NUM utterances
the simt licity of the system enables us to detect problems and provide solutions easily
consequently porting the generation system to a new language is confined to developing these submodules
our source language text is called muc ii data and consists of naval operational report messages
for the whole dialogue d2 in figure NUM cl d2 is NUM utterances
tience we need alternative solutions to deal with unknown words and unknown constructions
given a word w in some context c suppose clu is the set of all the clusters in the semantic space activated by the context the problem is to determine the correct sense of the word in the context among all of its senses defined in the modem chinese dictionary
each data collection should be carefully selected formatted annotated and otherwise prepared to directly support a specific task
finally the graphs show that most of the acquisition curves displayed positive slopes even at the end of the NUM words
table NUM NUM illustrates nymble s performance as compared to the best reported scores for each category
the scoring program measures both precision and recall terms borrowed from the information retrieval community where
in this section we report the results of evaluating the final version of the learning software
typically one holds out NUM NUM of one s training for smoothing or unknown word training
the vocabulary of the system is built as it trains
figure NUM NUM impact of training set size on performance in spanish
the results are shown in a histogram in figure NUM NUM below
the calculation of the above probabilities is straightforward using events sample size
there is almost no change in performance by using as little as NUM NUM words of training data
p number of correct responses and number responses r number of correct responses NUM NUM
this output can be thought of as a form of stand off annotation from which other forms of information about the corpora can be derived
obviously the utility of algorithms such as the one we present here is dependent on the quality and reliability of markup in the corpora we process
thus it is reasonable to select the sense s among all as the correct one in the context such that there exists clu cluw and dis2 clu s gets the smallest value as NUM for clu cluw and s sw
the alignment of the corresponding tree from susanne will be detected by the noting that NUM NUM NUM and NUM NUM NUM
each element in the hash table is a set of numbers to allow for the hashing of multiple unary trees to the same cell in the table
annotation removal and transformation as our procedure works only in terms of terminal elements and structural annotation all other information may be removed from a corpus before processing
corpora we have processed the entire susanne corpus and the corresponding parts of the penn treebank and produced tables of alignments for each pair of markedup texts
it must therefore follow that t is a subtree of t or vice versa and that they are connected by a series of only unary branching trees
in fact the results of the experiments shown in fig NUM give some ground for believing that texts from the newspaper domain as a rule take a rhetorical structure similar to fig NUM such as one in fig NUM where the nucleus appears at the beginning of the text followed by any number of supplementary adjuncts
finally in the development of phrasal parsers our results can be used to obtain a measure of how contentious the analysis of different phrase types is
first in the automatic determination of subcategorization information confidence in the choice of subcategorization may be improved by analyses which confirm that subcategorization from other corpora
for the study described here we developed a coding scheme that supports an exhaustive analysis of a discourse
the corpus study is part of a methodology for identifying the factors that influence effective cue selection and placement
the crucial factor in distinguishing between s ce and because is the relative order of core and contributor
the coders could disagree on whether a relaturn should be further analyzed into an embedded core contributor structure
we have applied rda to our corpus of tutorial explanations producing an exhaustive analysis of each explanation
only by an exhaustive analysis such as ours can hypotheses such as the one discussed here be systematically evaluated
the NUM class analysis allows us to find the label which has the best probability
we verified manually the first NUM most frequent oov words of each filtered lexicon
this shows that the comparison between the reference labels and the labels calculated is a true evaluation
thus NUM NUM of labeling differences with the initial reference were corrected by using the devin
the enhancement of these lexicons can be made automatic as big corpora of specialised texts are available
by applying simple heuristics to a sentence we can separate the oov words into proper names and common words
these labels are distributed amongst NUM syntactic classes adverbs adjectives names verbs
this NUM million word corpus contains a large amount of proper names and technical terms relative to various subjects
for example in an information retrieval context we will want to consider the opinion feature most highly when we are searching for public reactions to the supercollider where newspaper columns editorials
suppose clu is the set of all sense clusters in the space o is the set of all occurrences of the mono sense word in the corpus for any weo let cluw be the sense cluster containing the sense in the space we compute all distances dist cluw w for all weo
hence the problem of fitting the axe parameters is replaced by a term selection task
however due to the inherent gender bias of language words such as chairman can also be used to refer to women
it also relies on proper noun anaphora information provided by la hack NUM and syntactic anaphora information posite d by the parser
lcb thede harper ecn purdue edu
figure NUM average number of deletions
figure NUM average number of insertions
figure NUM average number of matches
the question is how effective can it be
this can lead to an interesting type of error
there has been little research done in this area
the rest of the rules can be read in a similar manner
there are two broaxi approaches to handling unknown words
above results now allow us to compare the accuracy of dop1 with other systems tested on unedited atis data
quirk et al arrange verbs on a scale ranging from modal auxiliaries to main verbs and many of the intermediate verbs particularly those at the higher end of the scale have meanings associated with aspect tense and modality meanings which are primarily expressed through auxiliary verb constructions
the dilemma of any pattern matching approach is in essence a bootstrapping problem if the goal is to induce syntactic information in the form of lexical features then paradoxically some heavy syntactic processing power is needed to parse the training data to mine for evidence that a particular verb subcategorizes for an object option while avoiding false triggers imposter patterns
wf briliposffivbd idiomffitake place i took wf wf pos nn idiomffitake place NUM place wf the first two lines contain the annotation for the first word in the idiom
the string take place l encodes the fact that this is the first word of a take place idiom
the preprocessor performs other miscellaneous tasks to aide in the tagging task such as separating out punctuation marks and contractions
NUM NUM defined as an occurrence whose status is in some degree intermediate between auxiliaries and main verbs
the situation here does not need to be kept in a lofty position but rather maintained
it contains a brill pos tag for take and a wordnet entry for take place
this paper shared our experience in manual annotation of wordnet senses in the wall street journal treebank corpus
we apply a preprocessor to the data which automatically identifies some classes of verb occurrence with good accuracy
NUM NUM lexicalization of grammar rules with semantic categories
part of speech the syntactic ambiguity multiplies
in our experiments the top five nouns were added automatically without any human intervention but this sometimes allows non category words to dilute the growing seed word list
from this eorlmx we seh cted at random NUM lifferent articles for test data each of which onsixts of NUM NUM sentences and has different tel it ilallle wlfich is tagging in the ws i
examining ern ceo and cmd in figure NUM ce and cm1 are grouped together while they have lifferent c tegories with each other
table hea health care providers medicine mtc medicm and biotechnology cmd commodity news farm products NUM shows different senses of word ill bvg and hrd which could be discriminated in dis
our disambiguation method of word senses is based on niwa s method whmt use l the similarit y NUM etween two sentences i.e. a sentevee which contains a polysenmus noun and a sevtenee of dictionarydefinition
stage four clustering method for a set of nouns wl w2 w of a new article we calculate the semantic devi ttion value of all possible pairs of nouns
in table NUM for example security is high freqtlenties and used ill being secure sense ill bvg artme while security is certificate of creditorshii sense in hrd
we used wsj corpus as test artich s in the experiments in order to see how our metho l can effectively classify artmes eacl f whi h beh ngs te the restricted subject domain i.e.
improvements for muc NUM were carried out by one graduate student about one man month
a few of the patterns used require some additiona l context before a name is recognized
the evaluation texts were processed with decision trees generated using subsets of the muc NUM development data
one of the principle drawbacks of the system is its sequen tial application of component tags
this is used to approximate the information content of that attribut e
the main source of error was missing patterns in the system
for example robert l james was partially recognized as l
figure NUM definition of upper lower
the expression x y means that x is computed incrementally as a sum of various y terms which are computed in some order and accumulated to finally yield the value of x NUM NUM transitions are denoted by with predecessor states on the left computational linguistics volume NUM number NUM and successor states on the right
walk through article the performance here was recall NUM and precision NUM
whenever a positive decision is made a new tag is added to th e output stream
to summarize given that one purpose of discourse is to increase the information shared by speaker and hearer it is not surprising that individual utterances convey only partial information
the system is fully implemented in allegro common lisp and runs on different platforms sun workstations pc macintosh
the global weight of a solution is the sum of the c rule weights each divided by the number of times the c rule occurs
it is an utterance i.e. the uttering of a sequence of words at a certain point in the discourse and not a sentence in isolation that has centers
2in the case at hand the grammar writer preferred to ensure availability of the substructure by virtue of the test predicate
failure to apply a tgl rule signals that the rule does not cover the portion of the input structure submitted to it
for instance suitable fragments of context free grammars translated into tgl could be augmented by the domain and task specific properties needed
these factors contribute to the difference in coherence between the following two discourse segments NUM NUM a john went to his favorite music store to buy a piano
additional utterances may provide further constraints on an interpretation and sequences of utterances may not be coherent if they do not allow for a consistent choice of interpretation
similarly if 29b is changed to force the value free interpretation as in 25b then only the value free interpretation NUM is possible
in tg NUM this effort is reduced considerably because it is only necessary to recompute the part licensed by the newly selected rule
partial orderings and even discontinuities can thus be described by allowing a modifier to occupy a position defined by some transitive head
in the latter scheme the text is scanned until a section is found that is deemed to be relevant
disambiguation this section outlines our approach to constraint based morphological disambiguation incorporating unsupervised learning component
NUM as an alternative to the computation of lr transition probabilities from a given scfg one might instead estimate such probabilities directly from traces of parses NUM like earley parsers lr parsers can be built using various amounts of lookahead to make the operation of the parser more deterministic and hence more efficient
overall the information criteria are not greatly affected by a change in the search strategy as illustrated in figure NUM
use artificial intelligence to script pictalk training conversations
when the morphological analyzer detects an auxiliary verb or an equivalent while checking the information contained in the predicate phrase the analyzer develops the verb subcategorization frame from the code in the verb s lexicon and read from the NUM the unique case principle in case grammar and empirical studies is formulated and explained by the lexicalist hypothesis about thematic roles and the x bar theory in the school of universal grammar chomsky88
thus i is really the expected number of occurrences of the given state in state set i having said that we will refer to o simply as a probability both for the sake of brevity and to keep the analogy to the hmm terminology of which this is a generalization
let p be a subset of the entries of p namely only those elements indexed by nonterminals that have a nonempty row in p for example for the left corner computation p is obtained from p by deleting all rows and columns indexed by nonterminals that do not have productions starting with nonterminals
this list is not meant to be comprehensive and new construct specifications can easily be added
words that are unknown are those that could not even be processed by the unknown noun processor
for a valid transition pair between two tags the score is simply calculated by adding the maximum score from the other tagging processes for a sense that can have each grammatical tag to the transition pair weighting usually NUM
NUM the object is already identified uniquely c29 si NUM NUM the descriptor chosen can not be mapped onto a slot of the description generated so far c26 s NUM
NUM NUM the role of pre loaded utterances in
the lack of control over the appearance of the expression to be generated is further augmented by the fact that any kind of feed back is missing that puts the property selection facility in a position to take the needs of ultimately building a referring expression into account
they are responsible for three serious deficits negatively influencing the quality of the expression the first one primarily causing inefficiency NUM applicable processing strategies are restricted because all descriptors of some referent need to be evaluated before descriptors of other referents can be considered
it NUM allows for a widely unconstrained incremental and goal driven selection of descriptors NUM integrates linguistic constraints to ensure the expressibility of the chosen descriptors and NUM provides means to control the appearance of the created referring expression
the string probability p x g x of x given x is the sum of the probabilities of all left most derivations x x producing x from x s the sentence probability p s x of x given g is the string probability given the start symbol s of g
for a word pair x y e.g. has been the tagger is thus able to produce possible scores for x and y as separate words and for x y as a multi word unit throughout each
a number of different tagging process could then adjust any of these scores increasing them for a positive match e.g. a collocation that indicates a particular sense decreasing them for a negative match e.g.
particular difficulties can be expected when a referential description needs to be produced in an incremental style that is portions of a surface expression are built and uttered once a further descriptor is selected that is prior to completion of the entire descriptor selection task
here the cide database gives the possible selectionai classes for head as body part state object human or device for pupil as human or body part for question as communication or abstract
it could also be seen that if yt is the identity function on dom t then locext t tr t
it can be seen in figure NUM that this leads to a transducer that has a copy of the initial transducer and an additional part that processes the identity while making sure it could not have been transformed
table NUM highest p r f measure scores posted
in this example c is compared with the second input token d during the first and second steps and therefore the second step could have been skipped by remembering the comparisons from the first step
figure NUM management succession template structure
etc which appears immediately before the name alias in the text
udurham s use of a world model and sometimes not cf
the number of iterations is bounded by NUM ilz l NUM where t i q is the number of states of the original transducer
if the original state was final as for NUM lcb NUM rcb transduction an transition to the initial state is added to get the behavior of t
the contexts ci are modeled by a fixed number of surrounding elements
each of problems NUM through NUM differed by one missing wire
also the pragmatic phenomena of terminologisation which are not the same in french and in english explain the possible structural non correspondence between some nominal phrases appareil entrepos6 non stock6 aircraft stored no preservation measures or between some sentences
for example a verbal form in the infinitive when occurring in a procedural part of french text identified as such via sgml tags would be analyzed as a sequence with injunctive value and translated into english by a verbal form in the imperative
factored representation of automata may also turn out to help
total divergence to the average a related measure is based on the total kl divergence to the average of the two distributions null
no new input constituents may be added
thus the string encoding is not unique
the solution is given in some detail
phonology has recently undergone a paradigm shift
our results indicate that for similarity based language modeling singletons are quite important their omission leads to significant degradation of performance
translation accuracies are around NUM when only the top candidate is counted
most of the correct translations can be found among the top NUM cand dates
in this paper we hope to shed some light on this question
word relation matrices are then mapped across the corpora to find translation pairs
figure NUM rank of the correct translations for wsj nikkei evaluations
this is another supporting reason for choosing mid frequency content words as seed words
they have about NUM times fewer states and NUM times fewer arcs
unfortunately these methods take time exponential on the size of the grammar
the context vectors of punctuation marks contribute little information about syntactic categorization since there are no grammatical dependencies between words and punctuation marks in contrast to strong dependencies between neighboring words
apparently the NUM most frequent words capture most of the relevant distributional information so that the additional information from less frequent words available from generalized vectors only has a small effect
the motivation is that a word s syntactic role depends both on the syntactic properties of its neighbors and on its own potential for entering into syntactic relationships with these neighbors
where the context is identical and information about the lexical item in question rarely vs will is needed in combination with context for correct classification
hester currently dean of and the conjunction in to add that if united states policies have similar immediate neighbors comma np
even if no automatic procedure can rival the accuracy of human tagging we hope that the algorithm will facilitate the initial tagging of texts in new languages and sublanguages
for example fun sbecause of phrases like i had sweet potatoes forms of have can not serve as a reliable discriminator either
in more detail svd decomposes a matrix c the matrix of left vectors in our case into three matrices to so and do such that
once the cate null gories were defined simple scripts substituted the words in the categories by adequate labels so that the pair ddme la have de la habitaci6n ciento veintitrds give me the key to room one two three became dime is uave de la habitaci6n room give me the key to room sroom where room is the category label for room numbers
we use a weighted average of the evidence provided by similar words where the weight given to a particular word w depends on its similarity to wl
this descriptor is equivalent to wordl mor root and since mor root is not defined at wordl the empty path definition applies causing it to irtherit from love mor root and thereby return the expected value love
this is achieved in one of three ways a value is explicitly stated or it is explicitly inherited or it is implicitly evans and gazdar lexical knowledge representation specified stated or inherited via the default mechanism
this is achieved by declaring datr variables whose use constitutes a kind of macro they can always be eliminated by replacing the equations in which they occur with larger sets of equations that spell out each value of the variables
the following definitions could be used to extend our verb fragment by introducing the path syn args which determines here extensions of syn args first specify properties of the first syntactic argument while extensions of syn args rest specify the others as a first rest list
the conventional inference task presupposes that we have a description such as that given in that section and a query such as love mor past participle the task is to infer the appropriate value for this query namely love ed
in section NUM we will relax the model
we will call them unambiguous ssts ussts
figure NUM an example of the expansion procedure
some examples of sentence pairs are shown in table i
our approach was tested with the three text corpora
this sequence can be represented by a simple chain
automatically learned from corpora of examples
resolve probably did contribute to te processing but its positive effect was overwhelmed by critical weaknesses in noun phrase analysis an area that has not received adequate attention thus far in our quest for trainable technologies
the numbers at the leaf node name NUM yes indicate that there were NUM instances that had the same feature values in the training set and that NUM of these were positive and NUM were negative
the badger sentence analyzer refers to a collection of processes associated with part of speech p o s tagging a trainable decision tree used to locate appositive constructions local syntactic analysis and semantic case fram e instantiation
if phrase NUM is the most recent compatible subject and name NUM no i e phrase NUM has no name information meaning that its probably a pronoun or other anaphoric reference then the phrases are judge d coreferent
the chain of specialists operated in the following order money dates percentages organizations people location s the numeric specialists were reliable and did not interfere with downstream components by claiming false hits
this feature was supposed to apply only to pronouns and generi c descriptions the company but in looking over the code for this feature extractor we see that it wa s not properly constrained
all of the features used by wrap up are extracted using a domain independent mechanism to encode features from cn slot values from the relativ e position of the referents and from verb patterns in which the noun phrase appeared
NUM of those were correctly classified all the misclassified instances were it phrases so a little contextual knowledge would have probably helpe d all it phrases were attempted but many were irrelevant
links from in and out NUM links from in and out NUM i links from in and out NUM i links from in and out NUM i i x says yes i i x says no
the decomposition consists of s reject which takes as its parameter the surface speech actions that are in the yield of the problematic action
s lcb c rcb is a new category representing the situation where c is being passed across categories
and the vocabulary has i034 chinese words which are most frequent
the condition can be viewed in a way comparable to those on rewriting rules to define say context free grammars
with appropriate state transition probabilities the source generates strings where c NUM c NUM e NUM c NUM c NUM c 21e c c and c NUM c NUM c NUM c NUM
an extension w will be added only if the direct estimate of its conditional probability is significantly different from its conditional probability in its maximal proper suffix after scaling by the expansion factor in the context w ie if a alw is significantly different than NUM w c i lwj
since the substring e establish is only followed by m and e the expansion factor e establish is essentially zero after m and e are added to that context and therefore lcb m e rcb l euestablish is also essentially zero
this is illusrated by the three contexts and six extensions shown immediately below where e w includes all symbols in e w that are more likely in w than they were in wj and e w includes all symbols in e w that are less likely in w than they were in l j
syntactically a nnotated corpora of german haze been missing until now
as keyboard input is rnore efficient than mouse input cf
consider the german sentence NUM d tra n
multi stratal representation clear separation of different description levels is desirable
NUM or nominalised a djectives of
theory independence annotations should not be influenced by theory specific considerations
instead the con plete structure should be represented
the tool supports immediate graphical feedback and automatic error checking
it seems to make for a simpler theory of language if case is assigned through the government relation which holds between the preposition and noun in a d but not in e h
for all the tests described here we learn a grammar by starting with an exhaustive set of stochastic context free rules of a certain form and estimate probabilities for these rules from a test corpus
fuzzy expansion can be helpful in cases where the exact form of a search term is not known or where you may not recall the spelling of term
it was observed that translators working with pencil and paper tend to work with a source text alongside the translation in progress rather than above or below it
usability or usefulness may not be the primary concern of developers of new technology whose attention and creative energies are rightly focused on the mechanisms of the software
headless constructions apl ositions temporal expressions etc
what broader implication does this deficiency of scfgs have for context free grammar based and therefore that for many subject and object noun phrases the noun will never enter into a bigram relationship with the verb
to understand this notice that to move from the initially favored parse k to one of the optimal ones i and l three nonterminals must have their most probable rules switched
therefore bcps including sentence and word alignment can benefit from a wealth of effective well established ip techniques including convolution based filters texture analysis and hough transform
a final abbreviated example comes from interlingua expressions produced by the semantic analyzer of japangloss involving long sentences characteristic of newspaper text
this work was supported in part by the advanced research projects agency order NUM contract mda904 NUM c NUM and by the department of defense
the use of different prepositions is an interlexical constraint between the semantic and syntactic heads of the pp that does not propagate outside the pp
the generation lexicon does not mark rare words and generally does not distinguish between near synonyms e.g. finger vs digil
it seems therefore not too preliminary to think that the euz suffix acts as a filter on the head for this kind of adjectives
NUM headless adjectives triste sad heureuz happy furieuz angry furious etc
our hope is that by continuing to be responsive to the needs of the users the designers and implementers of text analysis systems in developing the architecture we can encourage the creation of a wide variety of tipster compliant modules available as cots commercial off the shelf products
this second demo integrated several extraction systems several detection systems and a richer set of interfaces NUM the NUM month demo in turn propelled further developments in the architecture including methods for declaring annotations and for representing information extraction templates as annotations
we recognize that there will be some cost in conformance since we are using general mechanisms in place of ones specially developed for a single application but we need to insure that these costs are not so great that they make the architecture unattractive
we expect that there will be continuing incremental revisions to the architecture driven by the need for efficiency completeness precision and simplicity efficiency we have tried to minimize the loss of efficiency due to conformance to the architecture
we were able to produce these pairs by manipulating a small englishkatakana glossary
this scheme is attractive because japanese sequences are almost always longer than english sequences
some miss the mark nancy care again plus occur patriot miss real
composed together they yield an integrated wfst with NUM states and NUM arcs
a native japanese speaker might be expert at the latter but not the former
people who are expert in all of these areas however are rare
for example people rarely transliterate auxiliary verbs but surnames are often transliterated
we back transliterated these by machine and asked four human subjects to do the same
this might appear in the proof tree as observeposition swl x find swl reportposition swl x that is it is necessary to find swl and then to report its position x
a system can participate in variable initiative dialog if it properly manages NUM the selection of the current subdialog NUM the level of assertiveness in its outputs and NUM the interpretation of its inputs
no account will be given of the treatment of pps like nach hause for the time being
five different situation aspects have emerged which are distinguished using these features and certain temporal schemata
a direct german translation however expresses two subsequent events
at that time it was running on a sun NUM machine which caused computational linguistics volume NUM number NUM table NUM experimental results for eight sclbjects operating at two levels of machine initiative declarative and directive
it leaves open whether the end has been reached or not
7figure NUM shows a simplified representation of the accomplishment event type
we investigate here which viewpoint is appropriate for the german preterite
we will therefore focus on this issue in the next section
aspect and discourse structure is a neutral viewpoint required
neutral viewpoint two viewpoints correspond mainly to the wellknown opposition perfective imperfective
NUM a de tristes enfants sad children to see which cause the sadness of the persons which experience them b
at the present state of the art several stages of speech translation leave ambiguities which current techniques can not yet resolve correctly and automatically
a segment may contain individual utterances as well as embedded segments
moreover this is precisely what the intentional relations capture
ds0 is a segment span designed to achieve the purpose i0
in this way these two determinants of discourse structure can not conflict
as an example consider the pair of rst relations volitional cause and volitional result
b then we can go to the store before it closes
given the nature of segment purposes a coreless segment seems intuitively unlikely
the correspondence suggests a mapping between g s linguistic structure and rst text structure
a class of grammars ccg gtrc is introduced in the next section as an extension to ccg std
a significant decrease in perplexity occurs in moving to the smoothed m NUM mixed order model
table NUM shows the perplexities of the smoothed mixed order models on the validation and test sets
figure NUM rst intentional and informational relations may determine incompatible structures
first how do informational relations fit into the discourse structure
we first develop algorithms using each type of linguistic device in isolation motivated by existing hypotheses in the literature
there are three little boys up on the road a little bit and they see this little accident
the figures illustrate a typical tradeoff between precision and recall where one goes up the other goes down
table NUM presents the average ir scores across the narratives in the training set for the np and ea algorithms
unlike the cue and pause features the np features were thus not directly based on simplifications of existing results
performance on the test set is slightly better overall for t NUM as shown by lower summed deviations
table NUM shows the results of the tuned algorithm on the NUM randomly selected test narratives for np and ea
as a preliminary step for retrieval generally the set of documents must be pre processed
the danish related work reported here was funded by sri international and handelshc jskolen i kebenhsvn
firstdocument and nextdocument must be well behaved in the presence of calls to createdocument and removedocument
we can then choose an optimal parameter setting that minimizes the expected error rate
systems methodological issues there is still no real consensus on how to evaluate speech translation systems
instead the following pair of rules in which the lexical targets have only one character each achieve the desired effect surface b e a u e lexical b e a u e rule def
positions in the rawdata are represented internally in terms of byte offsets rather than characters
NUM adding a new feature to a sort requires one change in a declaration whereas adding an argument to a prolog functor requires changes mostly insertion of anonymous variables to every occurence of the functor
this includes work on compiling grammars into efficient parsers and generators compilation of dcgs into top down prolog programs left corner parsers bup lr parsers head corner parsers and semantic head driven generators
NUM note that these clauses provide a concise notation because uninstantiated features can be omitted and the sorts of structures do not have to be specified explicitly because they can be infered from use of the features
since the iso standard includes neither inheritance hierarchies nor feature terms which are indispensible for the development of large grammars lexicons and knowledge bases for nlp systems a tool like profit that compiles sorted feature terms into prolog terms is useful for the development of grammars and lexicons that can be used for applications
they were both tested on a set of five previously unseen dialogues
we show the use of templates for providing functional notation by a simple example in which the expression c first x stands for the first element of list x and rest x stands for the tail of list x as defined by the following template definition
the expressive power of an n place template is the same as that of an n l place fact
disjunction NUM agr fin dom NUM NUM NUM sg pl
this paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large completely untagged corpus
also instead of just summing more clever combinations can be tried such as training classifiers which use the heuristics as predictor variables
for this example we restrict transformations to terms with n p n structures which represent a full NUM of the binary terms
after the initial expansion is created for a range of structures empirical tuning is applied to create a set of maximum coverage metarules
as for performance the parser is fast enough for processing large amounts of textual data due to the presence of several optimization devices
retrieved variants increase the number of indexing items by NUM NUM NUM NUM type NUM variants and NUM NUM type NUM variants
7as discussed above wh movement requires something more like composition than application
NUM noun verb variations these variations often involve semantic shifts such as process result fixation de l azote fixer l azote to fix nitrogen
another showed on line effects from adjectives and determiners during noun phrase processing
what seems to be needed is some kind of language tuning NUM
the only change needed from aacg notation is
a more appealing alternative is to base the tuning on statistical methods
if x is a syntactic type e.g.
the scenario template task required NUM succession egraphs
the rule for abstractions transforms equations of the form x a t y b to c x a t c y b and ax a t b to c x a t bc where c is a new constant which may not appear in any solution
fortunately the choice of instantiations can be further restricted to the most general terms in the categories above if xc has type f n c and hd has type a then these so called general bindings have the following form g h kzal z a hd h l
for instance introa pa jb xa unifies with introa ya ja sa but not with introa pa ja sa because of the color clash on j
in particular it would be interesting to see whether colored unification can appropriately model the complex interaction of constraints governing the interpretation and acceptability of gapping on the one hand and sloppy strict ambiguity on the other
due to the presence of function variables systematic application of these rules can terminate with equations of the form xc sl s n t hd tl tm
in essence the main function of the por is to ensure that some occurrence occuring in an equation appears as a bound variable in the term assigned by substitution to the free variable occurring in this equation
given the above restriction for well formed colored substitutions such a coloring ensures that any solution containing a primary occurrence is ruled out free variables are pe coloured and must be assigned a pe monochrome term
where all equations are of the form x m such that the variable x does not occur anywhere else in m or g have a unique most general c unifier a that also c unifies the initial equation
g d lcb l j s l x wife rcb that is the hou treatment of focus overgenerates 5a is an appropriate fsv but not 5b
formulate egraphs for examples from the training texts
commandvu virtual display of distributed interactive simulation
at ft bragg during the royal dragon exercise
figure NUM artist s rendition of quickset used with
figure NUM the quickset interface as the user
quickset multimodal interaction for simulation set up and control
however the speed has not taken into account the time required for extracting the noun phrases for training
the em algorithm ensures that l n NUM is greater than l n
the combination of phrases results in only a smaller precision improvement but causes a much greater increase in recall
in this way it is not necessary to split the corpus unless it is extremely large
the difference between the two models can be illustrated by the example compound noun informationsretrieval technique
this may indicate that more experiments are needed to understand how to combine and weight different phrases effectively
we performed the experiments by using the trec NUM ad hoc topics i.e. trec topics NUM NUM
a noun phrase can be assumed to be generated from a word modification structure i.e. a dependency structure
simulation agent the simulation agent developed
in the natural language processing community there has been a growing awareness of the key importance that lexical and corpora resources especially annotated corpora have to play both in the advancement of research in this area and in the development of relevant products
they can be used for instance to reference sophisticated phonological and morphological mechanisms
the choice of composition and perturbation operators captures a particular detailed theory of language
that is the word string the blueprint is the only bt tokenization
define recall to be the percentage of true words that occur at some level of the segmentation tree
therefore the internal representation is free to reorganize at any time it has been decoupled
this allows structure to be built bottom up or for structure to emerge inside already existing parameters
implies that the decisions about linguistic units must be made relative to their representations
no previous unsupervised language learning procedure has produced structures that match so closely with linguistic intuitions
as a consequence data is pooled for estimation and representations are compact
this structure is very much like the class hierarchy of a modern programming language
this is done recursively until the score of the resulting rule does not exceed the threshold in which case it is added to the final rule set
in addition there may be multiple annotators of a single type e.g. multiple tokenizers
in this respect the ability of a tagger to handle both known and unknown words to improve its performance by training and to achieve a high rate of correctly tagged words is the criterion for assessing its usability in practical cases
to evaluate the semantic distance metrics we feed the se tic distance mod e with the correct senses of the entire test corpus and observe the resultant semantic c ss disambiguation accuracy
unlike existing methods which require h d cra fting of lexicon or ual annotation the only human etfort involved in our approach is the mapping of the domain specific semantic classes onto wordneer
this is fflce word sense disambiguation whereby the training set cont features of one word and the algorithm picks one sense for each occurence of this word in the testing set
however they expressed reservations regmrding the use of wordnet to augment their semantic hierarchy automatically citing examples of unintemded senses of words resulting in erroneous semantic cl l sz ation
otherwise the position has no tokenization ambiguity or is an unambiguous token boundary
the probability distributions are far too gappy and even if a huge amount of data were collected the chances that they would provide the desired path for a sentence of any reasonable length are slim
there is no need to explicitly rule out NUM as the transition np hi a n will be vanishingly rare in any corpus of even the most garbled speech while the transition n hi a s rel is commonly met with in both written and spoken english
however if we generalize the schema already obtained for standard coordination by allowing x to be not only a single category but a list of categories it is found to suffice for non constituent coordination as well
of course having allowed such crossed dependencies there is nothing in the formalism itself that will disallow a similar analysis for a discontinuity unacceptable in english such as NUM i saw a yesterday dog
a formalism for dop given that we are attempting to construct a formalism that will do justice to both the statistical and structural aspects of language the features that we would wish to maximize will include the following the formalism should be easy to use with probabilistic processing techniques ideally having a close correspondence to a simple probabilistic model such as a markov process
threw vp np x out prob pl vp x out np prob p2 even if pl were considerably greater than p2 the cumulative negative effect of the longer states in NUM would eventually lead to the model giving the sentence with the shifted np NUM a higher probability
as it is to be used with real data the formalism should be able to characterize the wide range of syntactic structures found in actual language use including those normally excluded by competence grammars as belonging to the periphery of the language or as being ungrammatical ideally every interpretable utterance should have one and only one analysis for any interpretation of it
both nouns introduce a relative clause modifier s rel the difference being that in the discontinuous variant a category has been taken off the stack at the same time as the modifier has been placed on the stack
there is no lack of competing competence grammars available but also no reason to expect that such grammars should be suited to a dop approach designed as they were to characterize the nature of linguistic competence rather than performance
the results of the svstem evaluation on the data set test are given in table NUM
in figure NUM the verb intercepted incorrectly subcategorizes for a finite complement clause
the idea of filtering by finite state transduction of course does not depend on sgml codes
a positive filter that excludes everything else can be expressed as in figure NUM
it forbids any replacement that starts at the same location as another longer replacement
of course the left to right longest match regimen implies that some possible analyses are ignored
no carets are permitted outside the matched substrings and the ignored internal carets are eliminated
note that the four alternatives in figure NUM represent the four factorizations in figure NUM
in effect the input string is unambiguously parsed with respect to the upper language
the effect of the directionality and length constraints is that some possible replacements are ignored
a tokenizer is a device that segments an input string into a sequence of tokens
we will return to this issue in the discussion of tokenizing transducers in section NUM
words in general in the parse tree are represented as vocabulary items in the semantic frame
this is not to say they do not have a semantic form just that in many cases the form is that of identity
a causative suffix changes the subcategofization frame of the verb by adding one more argument and changing the grammatical constraints on the other arguments
some inflections such as case and causative affixes compose semantic form of the stem lfs with that of the affix
person and number do not have any contribution to semantics hence their semantic form or lf is that of identity
a locative case suffix will mark a np as an adjunct which can no longer satisfy subcategorization requirements of the verbs or postpositions
this issue is critical for parsing relatively free word order languages where grammatical relations are often indicated by overt case marking rather than structural position
inflections and derivations can be seen as word based local operations on the root and thus be modelled as lexical rules
i in fact traditional turkish grammar books such as NUM collectively call them substantives 2cf
in the integrated multi dimensional approach the lexicon contains free and bound morphemes they have complete syntactic and semantic specifications
lexical rules handle changes in grammatical roles enforce type constraints and control the mapping of subcategorization frames in valency changing operations
in terms of misparse rate both grammars perform equally well i.e. around NUM NUM
for example sentences of the following form can be generated NUM every boy gave most girls a kiss where there is a different kiss for each boy girl pair
within the committee based paradigm there exist different methods for selecting informative examples
indeed fully unsupervised training may not be feasible for certain tasks
this basic algorithm needs no parameters
this problem is solved by considering each sentence as an individual example
why does committee based sample selection work
all the other systems give the noun reading in such cases
one problem is that some stems occur twice in the list
personal translator does not give a determiner form in these cases
on the other hand all systems employ segmentation on unknown compounds
our tests were run without any selection of a subject area
they are sorted in a hierarchy which is three levels deep
to check these lexicon dimensions new tests need to be developped
power translator on the contrary gives only the most likely readings
in slt however they can not be avoided especially when working with a language rich in particles such as german
we extracted the words for our test from the celex database
figure NUM template element test results
while our current focus is on users of asl and thus some of the modules
the possible effects of asl on the errors identified are captured in the language model
we are developing the initial language learning model and its filters based on acquisition literature
a tutor for teaching english as a second language for deaf users of american sign language
some of these errors will be flagged after syntactic parsing using independent error rules
the following is an example of a mal rule from the grammar currently in implementation
intuitively the knowledge or concepts within the zpd are currently being acquired
edit particular sentences which results in an immediate new analysis of the text
this decision is also affected by information stored in the user model and history module
our implementation to this point has concentrated most heavily on the analysis phase of processing
this work has been supported partly by t he german federal ministry of education
the effect of these transformation steps on the sizes of both vocabularies is shown in table NUM
o cupsnto cuesta una habitacidn doble para cinco noches incluyendo servicio de habitaciones
o explique la faclura de la habitaci6n tres dos cuatro ane
in contrast for model based probability distributions we use the generic symbol p
the goal is the translation of a text given in some source language into a target language
by this reordering our assumption about the monotony of the alignment model is more often satisfied
each of these transitions is assigned a local probability null p ili NUM
a could you ask for my taxi for room number three two two
check the bill for room number eight two one for me please
ntext dependence may enter either at the interpretive mapping from sentence to meaning and or the evaluative mapping from meaning and the world to truth values
the meaning of an ellipsis is composed in essentially the same way and from the same components as the meaning of its antecedent
the antecedent has two possible seopings a single canadian flag in front of all the houses or each house with its own flag
a standard surgical deed frame the lower level of the non surgical deed frame will be discussed below being different from the lower level of both the surgical deed frames
ex NUM b i lcb fuh rcb talked about how a lot of the problems they have to come overcome to lcb f uh rcb lcb a it s a very complex lcb f uh rcb situation rcb to go into space
the student is then informed as to whether the question has been answered correctly depending on how closely the student s response lcs matches the author s prestored lcs
in the following section the notion of finite state transducer and the notion of local extension are defined
of course there are important and less important relations but note if one takes only the important ones or the most important relation the generic relation then formal checking in this type of semantic net is very limited
in fact in wordnet NUM NUM we find NUM NUM subconcepts of object NUM and NUM NUM subconcepts of ltfeform and only NUM concepts in the overlap among them of course satmwood NUM
this nondeterminism is due to the rule vbd vbn nexttag by since this rule has to read the second symbol before it can know which symbol must be emitted
the system must convert this information into meaningful feedback so that the student knows how to repair the answer that was originally given
the lc s for this verb is in the class of this list structure recursively associates logical heads with their arguments and modifiers
for example from state NUM on input symbol vbd two possible emissions are possible vbn from NUM to NUM and vbd from NUM to NUM
in our dug syntax the head of a rule is separated from its body holding the dependents of the word in the head by the binary infix operator
applying a finite state transducer to an input consists of following a path according to the input symbols while storing the output symbols the result being the sequence of output symbols stored
brill s tagger is comprised of three parts each of which is inferred from a training corpus a lexical tagger an unknown word tagger and a contextual tagger
if f is represented by a finite state transducer t and locext f is represented by a finite state transducer t one writes t locext t
for purposes of exposition we will postpone the discussion of the unknown word tagger and focus mainly on the contextual rule tagger which is the core of the tagger
given the initial tagging obtained by the lexical tagger the contextual tagger applies a sequence of rules in order and attempts to remedy the errors made by the initial tagging
NUM taggers further received a dictionary booklet containing the senses for the words to be tagged as they are represented in wordnet
in both conditions performance was significantly p NUM NUM higher for nouns than for the other parts of speech
people s mental representations of noun concepts may be more fixed and stable and less vague than those of verbs and adjectives
p NUM NUM for the frequency condition NUM vs NUM p NUM NUM for the random condition
but whereas lexicographers are trained in drawing fine distinctions naive language users appear to be aware of large grained sense differences only
jorgenson s subjects agreed substantically on discriminating the three most central salient senses of polysemous nouns but did not distinguish subsenses
the most frequently tagged sense also usually represents the most central or core meening of the word in question
highly polysemous words were tagged with less confidence and taggers were more confident when tagging nouns rather than verbs and modifiers
in the frequency condition the most salient core senses usually occurred first or at least fairly high on the list of senses
taggers agreed among themselves significantly more often than they did with the experts NUM NUM in the frequency condition and NUM in the random condition
our user actually works with a menu size of NUM but clearly much larger menu sizes than this are impracticah the graph shows sizes up to NUM because this crudely approximates results that might be achievable with a better predictor with smaller menus
however her model focuses on determining when to include informationally redundant utterances whereas our model determines whether or not justification is needed for a claim to be convincing and ff so selects appropriate evidence from the system s private beliefs to support the claim
in determining whether to accept a proposed befief or evidential relationship the evaluator first constructs an evidence set containing the system s evidence thin supports or attacks bcl and the evidence accepted by the system that was proposed by the user as support for bel
enzyme cuts at every location alternate point about enzyme cutting at specific location detail point
may generate sticky ends part c2 the different results if a mutation occurred at the recognition site for enzyme y
the example output in appendix NUM illustrates matches found between sentences in the essay and the rubric rules from an excellent essay
the original NUM essays were divided into a training set and test set selected arbitrarily from the lowest examinee identification number
a computer based rubric was manually created for the purpose of classifying sentences in essays by rubric category during the automated scoring process
an error analysis of the data indicated the following two error categories that reflected a methodological problem a lexicon deficiency and b concept grammar rule deficiency
to build the lexicon all words and terms considered to contribute to the core meaning of each relevant sentence in an essay were included in the lexicon
in particular these methods could be successfully applied to the analysis of natural language responses for highly constrained domains such as exist in scientific or technical fields
it was used in this study to distinguish the excellent essays with scores of NUM and l0 from essays with lower end scores in the NUM NUM range
the arguments to isa function may be a complex boolean combination of synsets e.g. see selectional restrictions in figure NUM
in the first experimental setting the presence of weaker selectional restrictions just omebody somethzng ymlds more spurious readings
in particular we used the supertype subtype like hierarchy of synsets during the parsing process in order to discard unplausible constituents on a semantic base
the results for the runs without memory limitations are different with an increased preference for unset parameters across all languages but no clear NUM preference for any individual language
in any given instance of course the performer may have a reason to thematize a satellite that overrides the probabilities but in a sophisticated model he she should be able to set this against the knowledge of the general probabiffties for a ziven type of rhetorical relation
we will now look at a typical example of the sort of structure that occurs at the point where the rank based structure of the sfm meets the potential recursion of rst relations using for both a constituency approach that has at each node both elements and units
the goal of the monologue generator is to generate from these data a large variety of spoken texts
while it is possible to construct a translator based on head transduction models without relation symbols using a version of head transducers with relation symbols allowed for a more direct comparison between the transfer and transducer systems as discussed in section NUM we can think of the transducer as simultaneously deriving the source and target sequences through a series of transitions followed by a stop action
it has been part of speech tagged and manually corrected previously cf
table NUM levels of reliability and the percentage ca
various cost functions are possible though in the experiments reported in this paper a discriminative cost function is used as discussed in section NUM in the monolingual models derivation events are actions performed by relational head acceptors a particular type of finite state automata associated with each word in the language
with respect to training effort as noted the amount of supervised training effort in the main experiment was the same for both systems supervised discriminative training for NUM utterauces plus tagging of prepositional attachments for NUM utterances while the transfer system also benefited from unsupervised training with NUM utterances
the additional source of counts used in the transfer system was an unsupervised training method in which NUM training utterances were translated from english to chinese and then back again the derivations were classified as positive otherwise negative if the resulting back translation was sufficiently close to the original english as described in alshawi and buchsbaum NUM
we first describe the transfer and head transducer approaches in sections NUM and NUM and the method used to assign the numerical parameters of the models in section NUM in section NUM we compare experimental systems based on the two approaches for englishto chinese translation of air travel enquiries and we conclude in section NUM
the context for evaluating both the transducer and transfer models was the development of experimental prototypes for speechto speech translation
the first set of counts was derived by processing traces using around NUM sample utterances from the atis corpus
similarly let n elc be the count of taking elc for negative instances
further details of this approach including the analysis transfer and generation algorithms appear in alshawi 1996a
thus the metrical tree for our earlier example looks as follows assume that the verb phrase is in focus and therefore labeled as accented
in this way it is possible to avoid the generation of incorrect sentences such as it were written by him when mozart was only ten years old
only those s templates are selected which are able to convey the relevant information moreover under normal circumstances the same information is presented not more than once
the de predicate plays the role of dyd s so called discourse model noting which objects in the database have been referred to in the monologue
but since k NUM is also referred to in the previous sentence of the discourse k NUM represents given information and is marked a
the first step in measuring semantic entropy is to compute the translational distribution pr t s of each source word s in a bitext
the decision about what to say next falls out as a result of the agent complying with the communicative principles which refer to the agent s rationality sire cerity motivation and consideration
evaluation and response form the agent s reaction
in cdm the dialogue is an instrument to exchange new information on a particular topic to complete a real world task and it is managed locally by reacting to the changed dialogue context
the c goal is then filtered through communicative obligations which impleinent the ethical consideration of ideal cooperation the agent s communicative competence shows in the ways she can realize the same c goal in various situations
if the partner s goal cammt be fulfilled presuppositions are false facts contradictory no information exists it is considerate to inform why explain compensate initiate repair
the content of the user s c goal is inferred from the world model which says that needing a car can be interpreted as wanting to have a cl r
NUM f the partner did not request a piece of related information it is considerate to include this explicitly in the response given that the speaker intends to close the topic
motivation can i say this NUM everything that the speaker wants to know or wants the partner to do is motivated except if the speaker cmmot take the initiative on it
finally the communicative obligation consideration NUM requires that the application service car hire company and location bolton are explicitly expressed in NUM before the list of services
our scenm io for wor NUM completion sul t oses that a translator works on some designated segment of the source text of attproxinmtely sentence size and elaborates its ranslation from left to right
we argue that the conventional approach to interactive machine ih anslation is not the best way to provide assistance to skilled translators and propose an alternative whose central feature is the use of the target text as a medium of interaction
the state transition probabilities horizontal arrows are all NUM NUM for model NUM and depend on the next state for model NUM eg p froms NUM rcb i a NUM
repeat searches when the prefix is extended by one character are ol viated in inost situations by memo zing the results of tile original search with a bestchild pointer in each trie node see figure NUM
in this paper we have covered the most basic parts the easy bits
as an attempt to address the polysemy problem we conducted an exploratory study in which the verbs in levin s semantic classes were disambiguated by hand each verb received as many wordnet senses as were applicable
this leaves NUM for use in evaluating the semantic filter we call these the novel verbs
if given up to NUM assignments the situation 2levin s semantic classes are labeled with numbers ranging from NUM to NUM the actual number of semantic classes is NUM not NUM due to many class subdivisions under each major class these NUM classes cover NUM verbs that occur in the ldoce
the full semantic field contains the union of the related verbs for every verb in the original levin class
these tests are based on grammaticahty of usage in certain well defined contexts e.g. the dative construction
our results clearly indicate that the resolution of polysemy is a key component to developing an effective semantic filter
this arrangement has the advantage of leaving the translator in full control of the translation process of diverting his or her attention very little from the object of its natural focus and of necessitating a minimum of interface paraphernalia beyond those of a word processor
the existing lumping of noun senses in wordnet into coarser sense groups is perhaps a good compromise
2in this paper the notion of anaphor is used more generally
because of its restricted aim however it is nmch simpler
straightforward approaches may fail in cases in which interdependencies between antecedent decisions arise
2b the clienti appreciates that the barbcr shaves himselfi
anaphor resolution and the scope of syntactic constraints
there are however limitations to the scope of syntactic constraints
eandi tates in tie matrix clause
the determination of the substructure describing a local domain iv not always easy
according to accet tability judgements decision introduces a local binding domain
realization of a segment so depends on its similarities to its preceding segment s i occasionally two preceding segments s l and s NUM as well as on the actual realization of the preceding segment s
for example see figure NUM the templates p4 and p8 are siblings because they contain a case role value n2 n3 which represents a node in the conceptual schema see figures NUM and NUM
check is a turn yielding signal prompting the dialogue partner to respond
structuring these functions have received the most attention in the research literature
for instance template p4 see figure NUM can be connected to its corresponding node of the conceptual schema realized as template p1 either through case role value n2 n3 or case role value n4 n3
the contexts are characterized by a existence of matching elements in the two segments b quality of the match c the position in the segments of the matching elements and d the relative position of partially matched strings
templates which were assigned to a cluster through a match against the same string either the label of the conceptual schema node or a case role in one of the templates inside the cluster NUM are grouped into sets of siblings
the subtrees are connected into the text plan tree following the links established in the conceptual schema tree only these links will be between case role values in the templates which are the content of the nodes in the text plan tree
the result will be more fine grained information on discourse particles than is available now in the system
we define four levels of preference based on the quality of match between the node label and a string in a template case role case roles can have a set of strings as their values such values are called compound
in many retrieval contexts being able to retrieve on names whether personal institutional geographic or other names is an important capability
each node of the template contains the rule name used in the corresponding derivation step and a generalization of the local mrs
note that an entry of a table can contain more than one action before continuing in the recognition of the larger slructure headed by category depcat means that the item is not waiting for any completion
the variables of such a pair of items are the two states o igi2 the two sets that contain them o n2 and the two positions o n2
the phases scanner and predictor execute at most o igi actions per item the items are at most o igi n NUM and the cost of these two phases for the whole algorithm is o igl2n2
the mainstream of formalisms consists ahnost exclusively of constituency approaches but some of the original insights of the dependency tradition have found a role in the constituency formalisms in particular the concept of head of a phrase and the use of grammatical relations
in the subsection NUM NUM we describe the data structures and the algorithms for translating the dependency rules into the parse tables the dependency rules for a category are first translated into a transition graph and then the transition graph is mapped onto a parse table
NUM NUM transition graphs and parse tables a transition graph is a pair v e where v is a set of vertices called states and e is a set of directed edges labeled with a syntactic category or the symbol
at each step of the inner hoop the action s given by the entry cat state x lnputcat in the parse table ptca t is are executed where lnputcat is one of the categories of the current word
the firsts are first v first n lcb n a d rcb first p lcb p rcb first a lcb a rcb first d lcb d rcb
t is a set of dependency rules of the form x y1 y2 yi NUM yi l ym where xgc y1gc ym c and is a special symbol that does not belong to c see fig NUM
we extract from sentences a superstructure made of argumentative operators and connectives applying to the remaining set of terminal sub sentences
we see on these examples that tss s are argumentatively ambiguous and modifiers constrain them
we found the argumentative interpretation of utterances on a semantics defined at the linguistic level
he defines an utterance as a concrete occurrence of an abstract entity called a sentence
given a se atence its ambiguous a and structures are computed
as the identification or the contribution of an operator may be ambiguous the structures may contain disjunctions
a tss has a semantics that is described in terms of predications all but one being marked by presupposition
we describe the algorithm various optimizations and onr implementation
tss john stopped smoking its signification is formed of two sets of cells the commitment value being fixed to pc for the cells from the presupposed predication john smoked before and left free for the main predication john does not smoke now
the signification of a complete sentence is computed as the application of what we call the structure
if the dialogue is not in any one of these nine states then there is enough information to issue a query and the dialogue may be in one of the last five states based on the results of the query
figure NUM first several features induced for the wsj corpus presented in order of selection with e x fac tors underneath
it is useful to define for purposes of this paper what is meant by name searching and related terminology and to describe the application areas for which name searching systems have been developed
the baseline refers to our program without any optimiza tions
let d v be the degree of a node v
any of these words taken singly would not necessarily give a strong indication about the passage topic but taken together they can predict with a high degree of certainty the topic of the passage
the intuitive model the mathematical model we use in this paper formalites the intuitive notion that humans can identify the topic of an ulffamiljar article based on the occurrence of topic specific words and phrases
norice that the stemming does not always work perfectly united is shortened to unite but followed is shortened to fouowe
by classifying and routing texts into categories we mean to include a variety of applications categorizing texts by topic by the language the text is written in or by relevance to a specified task
we can see that these probabilities are an excellent measure for determining which of the dice was more likely to be used to generate each of the sets of outcomes
schemes for classification and routing all teild to follow a particular paradigm NUM represent each class or topic or profile or bucket as a numerical object
class NUM is nursery rhymes represented with mary had a little lamb and class NUM is u s documents represented with the the pledge of allegiance
continuing the example with the fair and the loaded die the sets are assigned probabilities that they belong to each of the classes given the fact that they have a certain set of outcomes
in this paper we extend previous work guthde et al NUM on classifying texts into categories and develop a methodology based on the classification technique for routing documents
the sublanguage encoded in tgl only needs a few speech acts about twenty sentential templates and a complete account of german date expressions
version of the algorithni which we have omitted due to space
research on spotting techniques for such expressions would thus seem to be worthwhile
at an intermediate level between phones and words syllables could be used
if they were not they could hardly be useful in simplifying analysis
both require the recognition of the most probable sequences of elements
NUM is the syntax within pause units relatively manageable
multi path finite state automaton without introducing spurious extra paths
where is the frequency of the constituent l cz NUM t in treebank
NUM is translation of isolated pause units a possibility
while stochastic grammars can provide somewhat longer range predictions than ngrams they predict only within utterances
in particular they might not be stemmed since presumably the similarity in meaning assumed to obtain among strings stemming to a common stem for general terms would not apply to names
mds analysis of the e score vectors identifies the major concepts that differentiate the texts
this provides general themes and pointers for identifying the conceptual differences among the texts
the mcca methods suggest further insights based on what purposes we are trying to achieve from tagging
thus figure NUM indicates that sentence e corresponds with sentences g and h
mcca also scores texts in terms of social contexts that are similar to different functions of language
these categories require more detailed analyses several categories correspond well to the hearst schotze model
we are continuing analysis of the mcca categories to characterize them in terms of lexical semantic information
mctavish et al illustrates the simple and the more complex use of these distance metrics
with this sanity check in place manual verification should never be necessary
the etymological similarity is often reflected in the words orthography and or pronunciation
in essence the algorithm has learned three distinct rules NUM b p NUM d t NUM g k because of the inability to refer to previous input symbols it is impossible to make a subsequential transducer that captures the generalization of the rule in NUM
the expanding rectangle search strategy makes simr robust in the face of tbm discontinuities
n c is an averaged value of nca c on a label a
of the psem feature given by the conditional and all other positions will be anonymous variables
most verbs can appear with several different types of complement and some verbs appear with many
the following convention has been adopted
while the notational details vary the basic properties of such formalisms will be very familiar
we could build a term of n i arguments as before where n NUM
type hierarchies are becoming as ubiquitous in computational linguistics as they have been in knowledge representation
but this is still not an interpretation that is likely to be of much practical use
we will illustrate with a partial analysis along these lines of agreement in nps in english
a teenager is both an adult and a child a queen is a monarch etc
whether we assume a flat structure for the vp modifiers vp pp pp
this state may spawn a domain specific sub dialogue in the lower layer one of whose states could be get constraint the objective is to ask the user to specify the least number of constraints that lead to the success state
in this paper we have described the contexts where proper names can occur but the complete lists of the nouns requiring pns have not been done
thus even though we find several entries lkim in the lexicon of nouns such as kira NUM noun steam e.g.
in the case of NUM strings containing ga such as the following ones are detected as common nouns simple or derived ones
as figure NUM shows each preposition has a different saturation accuracy which can not be suttpassed unless a wider sentential context is used
kirn minu has studied in u s a during NUM years the noun phrase in subject position k m minu bagsaneun is composed of three strings
sections NUM and NUM present our algorithm
and the following ones are either nouns followed by a postposition ga or a verb including the inflectional suffix is ga NUM
most of the progress in constructing efficient parsers and generators has been based on logic grammars that make use 1sorted feature structures are sometimes referred to as typed feature structures e.g. in carpenter s logic of typed feature structures
sagr NUM h b c d e NUM lsg 2sg 3sg lpl 2pl 3pl a domain description is translated into a prolog term by unifying the argument pairs that are excluded by the description
we provide a general tool that brings together these developments by compiling sorted feature terms into a prolog term representation so that techniques from logic programming and logic grammars can be used to provide efficient processing models for sorted feature grammars
if the input and output of the program the exported predicates of a module only make use of prolog terms and feature terms are only used for internal purposes then the program file is all that is needed
when two terms are unified which have no element in common i.e. they exclude all domain elements then unification fails because all arguments become unified with each other including the first and last arguments which are different
partial evaluation is achieved when a structure say a principle of a grammar is represented by a template that gets expanded at compile time and does not have to be called as a goal during processing
the following example shows the declaration needed for this finite domain and some clauses that refer to subsets of the possible agreement values by making use of the logical connectives negation conjunction or
as pts do not have inherently vocative functions they can hardly be used alone in the vocative case l gyosu
declarations templates and clauses can be distributed across several files so that it becomes possible to modify clauses without having to recompile the declarations or to make changes to parts of the sort hierarchy without having to recompile the entire hierarchy
in the corresponding prolog term representation below the first argument is a variable whose only purpose is being able to test whether two terms are coreferent or whether they just happen to have the same sort and the same values for all features
interpreted another way the first document listed will be relevant in eight queries out of ten
the first phase of processing is the chinese segmenter developed and supported by new mexico state university
here the contents of the form entries for two versions of the utterance are compared
since the chinese indexing is character based the relevance feedback approach treated characters as query enhancement terms
characters which carry no meaning such as punctuation or grammatical particles should be discarded
all of the involved parties must evaluate the severity of the risks on a successful system outcome
we could segment the relevant documents so that we can use actual words in the feedback query
bbn ported many of the major components of plum to chinese and created named entity identification capabilities
on the average six out of the first ten documents will be relevant to a given query
other systems e.g. dos there are two byte seven bit encodings of these display character sets
some languages like chinese and japanese are written continuously with no spaces between words
note that the predefined information of this module is easily modified without requiring changes to the dialog controller
thus it might pass the following to the parser after a user had spoken no wire
table NUM lists the analogous statistics for the wall street journal corpus
these slow responses were primarily due to the computational costs of parsing long utterances containing many misrecognized words
execution of this rule demonstrates the mechanisms related to the use of the user model and the initiation of voice interaction
the operation of zmodsubdialog and similarly our implemented system becomes clear if a complete example subdialog is carried out
of course many of these trees will invoke associated voice interactions and these constitute the subdialogs of the conversation
each such edit operation has an associated cost depending on the significance of the word being edited
the speech output was done with a dectalk trademark of digital equipment corp dtco1 text to speech converter
all selected subjects were used and all collected data are reported regardless of the level of success achieved
this message was given if the interpreted meaning contradicted the intended meaning or referenced the wrong object
the method for speech translation by analogy described in this paper was designed to overcome the manual knowledge acquisition bottleneck by relying on techniques from symbolic and statistical machine learning while still allowing the kind of manual tunlag that is necessary to produce high quality translations
then the semantic similarity of two words could be estimated from the entropy of their lowest common dominating node lcdn in the absence of distributional information the entropy of a node depends only on the number of words that the node dominates
this product would give us the overall probability that the node is part of the correct parse
in order to be able to provide stylistically and pragmatically adequate translations of spoken language it is not sufficient to merely ignore or tolerate extragrammaticalities in the input in many cases the information carried by such phenomena must be reflected in the target language output
in a traditional rule based system as the knowledge sources such ms grammar rules semantic disambiguation rules transfer rules etc expand in size there comes a point at which the complex interrelationships between the different types of information precludes any further improvement
that result represents nearly a NUM point decrease on the f measure from their official baseline
testing was conducted using wall street journal texts provided by the linguistic data consortium
best and average error per response fill organization object slot scores for te tas k
bbn conducted a comparative test in which the extra configuration gershwin optional
an entire appendix to the scenario definition is devoted to heuristics fo r filling the on the job slot
if an antecedent expression is nonreferential can it nonetheless be considered coreferential with subsequent anaphoric expressions
documentation of the four evaluation tasks is contained in appendices c f to this volume
the example passage covers a broad spectrum of the phenomena included in the task
the work is based on some similarity metrics
text filtering recall and precision for scenario test sets with approximately NUM richnes s
nine sites submitted a total of eleven systems for evaluation on the st task
takes NUM neighbors into account for each word
this may caused by the predefined classification number
how long the dimensions neighbors should be indeed
for example apposition as a markable phenomenon was restrictively defined to exclude constructs that could rather b e analyzed as left modification such as chief executive scott mcnealy which lacks the comma punctuation tha t would clearly identify executive as the head of an appositive construction
NUM the fact that the domain neutral template element evaluation was being conducted led to increased focus on getting the low level information correct which would carry over to the st task sinc e approximately NUM of the expected information in the st test set was contained in the low level objects
miscategorizations of entities as person per name or per alias instead of organization org name or org alias six systems mccann erickson also extracted with the name of mccann one mccann whil e mccann organization category is indicated clearly by context in which full name appears john dooner will succeed james at helm of mccann erickson in headline and robert l
system output james out dooner in as ceo of mccann erickson as a result of a reassignment of james james is not on the job as ceo any more and his new job is at the same as his old job dooner may or may not be on the job as ceo yet and his old job was with the same org as his ne w job
the broad purpose of this alignment is to pair objects that are similar in their slot content thus optimizing the system s scores
after connection any remaining objects will either be all keys or all responses i e all missing or all spurious
the structures may represent varying levels of data abstraction but must be consistent with the levels o f abstraction in the input files
for every document the report display s the score results categorized by object type and subcategorized within each object type by slot
however there are limits in terms of the formal structure of the database object s that we can handle and the available alphabets
so key to key scoring is based on a broader range of comparisons than key to response scoring and is therefore a different and mor e difficult test
in this section we attempt a post evaluation by asking some native speakers of chinese to judge the quality of the anaphora generated by a real system based on the rules
from the classification tree the number of the matched type is the total number of zero and nonzero anaphora associated with zero and nonzero leaf nodes in the classification tree
since we are looking at things from a generation perspective we have considered a zero pronoun to occur when an important semantic element is not overtly specified in the text
therefore a selection of the rules have been implemented in a chinese natural language generation system and their results are further evaluated by means of an experiment using native speakers
c NUM association for computational linguistics computational linguistics volume NUM number NUM anaphora to denote those that are specified in discourse namely pronominal and nominal anaphora
obviously for columns a and b in the table nonzero cases namely the sums of pronouns and nominals are in the minority of the test data
as shown in the classification trees of the test data the numbers of nonzeros are far greater than their counterparts zeros in the long distance cases of anaphora
we also describe the current status how the edr dictionary is utilized
the bilingual dictionary lists the correspondences between headwords in the different languages
the following is an example of english japanese
conbinations used to construct a sentence that is collocational information
we hope this will help refine and extend the edr electronic dictionary
the applications of term recognition specialised dictionary construction and maintenance human and machine translation text categorization etc and the fact that new terms appear with high speed in some domains e.g. in computer science enforce the need for automating the extraction of terms
the sequence of transitions corresponding to john likes sue being a sentence is given in figure NUM
if there is to be no modification of the verb phrase no verb phrase structure is introduced
it is therefore necessary to make various generalisations over the states for example by ignoring the r2 lists
this could be achieved by running the parser over corpora to provide probabilities of particular transitions given particular words
functional types included a list of arguments to the left and a list of arguments to the right
ax likes john x without having to say that john likes is a constituent
i am grateful to patrick sturt carl vogel and the reviewers for comments on an earlier version
the parser does not require fragments of sentences to form constituents and thereby avoids problems of spurious ambiguity
in the current computational and psycholinguistic literature there are two main approaches to the incremental construction of logical forms
this in turn makes it relatively easy to provide proofs of soundness and completeness for an incremental parsing algorithm
the performance of this learned decision tree averaged over the NUM training narratives is shown in table NUM on the line labeled learning NUM
so does an implicit argument as in fig NUM where the missing argument of notice is inferred to be the event of the pears falling
note that learning NUM performance is comparable to human performance table NUM while learning NUM is slightly better than humans
to quantify algorithm performance we use the information retrieval metrics shown in fig NUM recall is the ratio of correctly hypothesized boundaries to target boundaries
researchers have begun to investigate the ability of humans to agree with one another on segmen null tation and to propose methodologies for quantifying their findings
the output of c4 NUM is a classification algorithm expressed as a decision tree which predicts the class of a potential boundary given its set of feature values
the next auxiliary function takes care of those implicit nodes for which the s link is missing
this knowledge is contingent on knowledge of the elusive pr senselword which is currently the subject of much research see e.g.
that s why for example english light verbs have such high entropies even though there are many english verbs that are more frequent
in future work we shall extract the co occurrences directly from the corpora and use other grouping techniques to replace the cd
the co occurrence dictionary consist of a list of NUM NUM NUM dependency relations modifier particle and modificant taken from a corpus
the interesting case is the function shift link which is executed iwl NUM times by the algorithm
compiling these representations for each word is undesirable due to the large amount of training data training time and storage overhead required especially since it is unlikely that such information will be useful to later stages of processing
while the NUM context descriptor arrays present NUM input attributes to the algorithm c4 NUM induced a decision tree utilizing only NUM of the attributes when trained on the same mixed case wsj text used to train the neural network
to disambiguate a punctuation mark given a context of k surrounding words referred to in this article as k context a window of k NUM tokens and their descriptor arrays is maintained as the input text is read
NUM adaptation to other languages since the disambiguation component of the sentence boundary recognition system the learning algorithm is language independent the satz system can be easily adapted to natural languages with punctuation systems similar to english
the induction algorithm proceeds by e caluating the information content of a series of binary attributes and iteratively building a tree from the attribute values with the leaves of the decision tree being the values of the goal attributes
the system called satz makes simple estimates of the parts of speech of the tokens immediately preceding and following each punctuation mark and uses these estimates as input to a machine learning algorithm that then classifies the punctuation mark
the error rates over the sz test set were NUM NUM for mixed case texts and NUM NUM for single case texts both noticeably higher than the best error rate NUM NUM achieved with the neural network on the sz corpus
in addition to the part of speech frequencies present in the lexicon these words are assigned a certain probability of being a proper noun NUM NUM for english with the probabilities already assigned to that word redistributed proportionally in the remaining NUM NUM
style defines a sentence as a string of words ending in one of period exclamation point question mark or backslash period the latter of which can be used by an author to mark an imperative sentence ending
corpora we used a window size of NUM to NUM for n gram data accumulation
we then count the occurrence of each string and sort them in alphabetical order
without applying word segmentation techniques to the inputted plain text we generate n gram data from it
NUM create both rightward table NUM and leftward table NUM sorted strings
let lcb t a be the difference wdue of the string a then
NUM NUM calculate the diiference between the occurrenee of adjoining strings ill the sorted lists
in the experiment we applied thai spelling rules to restrict the search path for string counts
as a result we obtained NUM words NUM fixed expressions and only NUM illegible strings
null NUM it is hard to decide where to segment a string into its component words
this ne system is represented at the core of figure NUM since it was the core for all three tasks
at least half of the utterance has been acceptably translated and the rest is nonsense
o zheyang zai fangfengzheng i shi p piao zai kongzhong de xian j xingcheng yige wanqu de huxing
because only chinese repairs are considered english repairs are lost
repair processing plays an important role in spoken language processing systems
sections NUM through NUM include a description of the morphological ambiguity problem in hebrew followed by the claim that knowing the morpho lexical probabilities of an ambiguous word can be very effective for automatic morphological disambiguation in hebrew
this average matching rate however is lower than the matching rates we obtained in the empirical studies described previously
the results obtained from an implementation of this rule however correlated less well with human performance
for the purposes of this paper we will refer to the given and new parts as before and after meaning before and after the pivot and npc for no pivot complete and npi for no pivot incomplete sentences
the main difference is that our work is not as detailed as shriberg s since we were not planning as fine grained analysis and it covered significantly more data shriberg annotated NUM NUM words whereas this effort annotated NUM NUM million words
to see an example for the convergence of the algorithm consider the neat situation described in section NUM for the word hqph swi lcb hqph NUM hhqph NUM rcb sw2 lcb hqph NUM qph NUM rcb sw3 lcb hqph NUM hqpw NUM hqpm NUM hqpn NUM rcb
the underlying assumption is that the dividing line is the verb or more particularly the first verb that carries content disregarding weak verbs such as is have seems
the vertical lines mark actual article boundaries
in these tables we divide the words into three groups according to the quality of the approximation found for them NUM words with good approximation words for which cat ptest cat papp holds for all their analyses using lower threshold NUM NUM and upper threshold NUM NUM
in order to justify and motivate our approach we must first make the following conjecture although the hebrew language is highly ambiguous morphologically it seems that in many cases a native speaker of the language can accurately guess the right analysis of a word without even being exposed to the concrete context in which it appears
in the corpus we worked with the word hqph appeared NUM times and the number of occurrences of the words in its sw sets were as follows sw1 lcb hhqph NUM rcb sw2 lcb qph NUM rcb sw3 lcb hqpw NUM hqpm NUM hqpn NUM rcb
this strategy requires less communication because a greater amount of information is exchanged in one dialogue step between the participants
range denotes the interval within which a certain appointment has to take place e.g. in NUM
by deriving parts of the grammar directly from corpus annotations maintenance and extension of the grammars are eased considerably
using these features a ccm easily simulates incrementality and realizes intelligent backtracking by providing the computed solutions in a selective manner
procedural core of imas is represented by the transformation of the input sines representation into a set of il expressions
thus be dealt with completely in the server whereas the agents may or may not have a concept of refinement
it includes a declarative feature based representation and task specification language ccl and an object oriented communication and data transfer module cci
taking pasha ii as a representative we describe the requirements for an agent system to connect to the cosma server
by virtue of this mechanism a working day could be defined as an interval from e.g. NUM a m until NUM p m
this way a string like ypx would be split yp x rather than y px dictionary entries being of higher precedence
these topics representing user needs have also been manually judged with respect to the most fruitful part of the collection at nist so that a set of relevant documents for each query is known
the results are summarised in table NUM
NUM rejection of a descriptor because it can be inferred if tl is the intended referent and size tl low is the descriptor selected this time another descriptor must be added since t NUM is also subsumed by this description
this form ensures that for each word position i i NUM i the itmm alignment probabilities satisfy the normmization constraint
in the baseline system the parameters are estimated by using the maximum likelihood estimation mle method
the idea of the model is to make the alignment probabilities dependent on the differences in the alignment positions rather than on the absolute positions
in this case the task of finding the optimal alignment is more involved than in the case of the mixture model lbm2
we use the symbol pr to denote general probability distributions with nearly no sl eeitic assuml tions
among all possible english strings we will choose the one with the highest probability which is given by bayes decision rule
in contrast for modcl t ased prol ability distributions we use the generic symbol v
l ue to the natnre of tile nfixture tnod l there is no interaction between djacent word positions
in mm y cases although not al ways there is an even stronger restriction the differeuce in the position index is smmler than NUM
from the perspective of human behavior it would simply be unnecessary to determine all descriptors of a referent to be described beforehand without even attempting to generate a description usually just a few descriptors are sufficient for this purpose
loss of control means that the partner will select unconditionally the next subgoal
b shows one way of representing it in a gq format
table NUM keywords and their x NUM values in the article
we implemented this model and compared it with our clustering technique
the results of experiments demonstrate the applicability of our proposed method
the results of key paragraphs experiment are shown in table NUM
finally a particular number of sentences are extracted as key sentences
however word237 satisfies the formulae of context dependency
however there still remains the problem of meaningfully applying this criterion in the context of nested descriptions when the intended referent is to be described not only by attributes such as color and shape but also in terms of other referents related to it
for example the deviation value of the word i in paragraph is defined as follows
the deviation value of a word in the paragraph is smaller than that of the article
does it mean that only highly structured dictionaries like ldoce are suitable to be exploited to provide lexical resources for nlp systems
the api then returns this parsed sgml to the calling program as data structures
elements which do not match the query are passed through unchanged to outfile
that was because multext was undecided about the format of its i o
expressions of considerable sophistication can be generated and used successfully by beginners
sgml is a good markup language for base level annotations of published corpora
above the token level annotation being expressed indirectly in terms of links
aspects of the lt nsl library are aimed at supporting this approach
the current release is known to work on unix sunos NUM NUM NUM
one can represent overlapping markup in sgml in a number of ways
the explosion of on lme textual matenal and the advances m text processing
based on this classification the assignment of missing wires to problems in each session was made as follows four wires were used in the four warmup problems of the first session
from a set of NUM other wires NUM were used for the first five problems of session NUM and the other NUM were used for the first five problems of session NUM
a simulation is feasible as long as humans can use their own problem solving skills in carrying out the simulation but when it requires mimicking a proposed algorithm the woz technique becomes impractical
in this methodology human subjects are told they are interacting with a computer when they are really interacting with another human the wizard who simulates the performance of the computer system
in order to balance the difficulty of the problems between the second and third sessions the wires were classified according to the number and type of diagnostic steps required to detect the error
once this expertise is gained and the computer yields task control to the human user it is expected that users will exploit the situation to restrict the dialogue to specific issues of interest
presumably such users have substantial knowledge about the general behavior of the circuit how to determine when NUM due to time constraints not all subjects were able to attempt all possible dialogues
as mentioned in section NUM NUM when the computer made a serious misinterpretation the experimenter was allowed to tell the user about the computer s erroneous interpretation without telling the user what to do
speech recognition technology has improved dramatically since this system was tested but the need for handling miscommunication is still relevant as users and designers will continually test the performance limits of available technology
so p v i the probability of verb v not of class i occurring with a pattern for class i is
the evaluator builds entries by taking the patterns for a given predicate built from successful parses and records the number of observations of each subcategorization class
the system can not provide the user with a car but it can provide information about the services that enable the user to have a car
however the entire approach to filtering needs improvement as evaluation of our results demonstrates that it is the weakest link in our current system
in the next stage of processing patterns are classified in this case giving the subcategorization class corresponding to transitive plus pp with non finite clausal complement
more generally for the manually analyzed verbs almost NUM of the false negatives have only one or two exemplars each in the corpus citations
this gives us an estimate of the parsing performance that would result from providing a parser with entries built using the system shown in figure NUM
the classifier filters out as unclassifiable around NUM of patterns found by the extractor when run on all the patternsets extracted from the susanne corpus
more all analyses are rooted in s so the grammar assigns global shallow and often spurious analyses to many sentences
he defines a number of lexical patterns mostly involving closed class items such as pronouns which reliably cue one of five subcategorization classes
finally games are marked as either occurring at top level or being embedded at some unspecified depth in the game structure and thus being subservient to some top level purpose
if there is a natural set of possible segment boundaries that can be treated as units one can recast segmentation as classifying possible segment boundaries as either actual segment boundaries or nonboundaries
first although the move categories are informed by computational models of dialogue the categories themselves are more independent of the task than schemes devised with particular machine dialogue types in mind
an acknowledge move is a verbal response that minimally shows that the speaker has heard the move to which it responds and often also demonstrates that the move was understood and accepted
they use these speech acts to derive statistical predictions about which speech act will come next within verbmobil a speech to speech dialogue translation system that operates on demand for limited stretches of dialogue
dialogue structure coding purposes to the top level one being played for instance clarification subdialogues about some crucial missing information but the embedding structure is always clear and mutually understood
note that the discovery of phonemic correlates does not require any sort of alignment between the orthographic and the phonemic representations the procedure simply records the changes in the phonemic domain when the mternation applies in the graphemic domain
NUM if the partner s response is unrelated it is considerate to inform of the irrelevance given that the speaker has unfulfilled goals
it also suggests possible problems with the clarify category since unlike explain and instruct moves most clarify moves follow replies and since clarify moves are intended to contain unelicited information
we regard this as a basic strategy and testbed for conducting our system
NUM since this constraint on the binding of an extraposed element is relative to its antecedent we have no fixed site for extraposition which explains the observed interaction between extraposition and fronting
cr NUM it was believed sthat john saw a picture i in the newspaper by everyone of his brother
if the agent has initiated a con municative goal she has the initiative and also the right to pursue the goal until it is achieved or not relevant anymore
sin our experiments m equals to NUM
stz moscow NUM er hat den nerv deutscher nachkriegshe has the nerve of german post war geschichte getroffen mit seiner romanhistory hit with his novel triologie
cf the following examples with extraposition from np NUM an entirely new band rings today several of whom are members of the congregation at great torrington
NUM nobody must vplive here who is earning more than twenty pounds a week and vp benefit from income support
other examples were taken from culicover rochemont NUM cr gu ron NUM cr haider NUM hal nerbonne NUM net and wiltschko NUM wil
NUM ein buch j i wa i erschienen das ihn a book had appeared which him weltberiihmt gemacht hat j
wil has on the other hand we can also observe extraposition from fronted phrases as NUM and NUM show for fronted subjects and objects respectively
NUM NUM s shere loc x denotes a function which takes as x a list of sign and returns a set of loc containing the boc values of the elements of x
cr NUM there is very great public concern in great britain today whether the punishments which the courts are empowered to impose are adequate
to exclude word recognition errors for this paper we only used the spoken word sequence thus simulating NUM word recognition
figure NUM the results of dis experiment
particularly the empty head licenses the realization of the syntactic arguments of the verb according to the rule schemata of german and ih sg s subcategorization principle
here we will only deal with major prosodic phrase boundaries b3 that correspond closely to the intonational phrase boundaries in the tobi approach cf
the results are showit in tm le NUM
the implementation of the principles gives a real generative power to the tool
we have thus chosen monotonicity which gives more transparency and improves declarativity
in case of failure the whole conjunction leads to an unsatisfiable description
and second the generative aspect of these solutions is not developed
adding information to the description reduces monotonically the set of satisfying trees
the inheritance of descriptions of figure NUM and NUM is order independent
these three dimensions constitute the core hierarchy
the generation is made family by family
lexicalized tree adjoining grammars have proved useful for nlp
tree schemata generation respects the predicate functions co occurrence principle
the first step initializes all aspectual values to be unspecified
examples of atelic verbs are given in NUM
levin and rappaport hovav to appear and references therein
in NUM thing NUM is the only argument
though not employed as a mechanism in our database
table NUM featurai identification of aspectual classes
verbal and compositional lexical aspect provide the underlying temporal structure of events
verbs are assigned to lexical aspect classes as in table i cf
this treatment is similar to the partial davidsonian analysis of events due to hobbs NUM
base level phrases i e phrases with no embedded phrases are mapped to unary interpretations
the following example shows the st phrases parsed out of a key sentence from the walkthrough message
in addition most of our rule sequence processors ar e trainable typically from small samples
the genesis of this transformation occurred during a dinner conversation at the last muc conference muc NUM
in addition a phrase s lex tags can encode parts of speech to help guide the p o s tagger
mr dooner is on the prowl for more creative talent and is interested in acquiring a hot agency
as was the case with our muc NUM system the present alembic allows only limited forward inference
indeed template generation consists of nothing more than reading out the relevant propositions from the database
overall performance on the named entity task we obtained an official p r score of NUM NUM
a aircraft were launched at NUM z
figure NUM misparse due to pp attachment ambiguity
misparsing due to incorrect verb subcategorizations iii
muc ii like sentences form data set test
the results are shown in table NUM
second when predicting a left corner y with a production y y1 yi iyi add states for all dot positions up to the first rhs nonterminal that can not expand to e say from x y1 yi i yi through x y1 yi l yi x
such rapid access is likely to be very important in social conversation
repair when something has gone wrong e.g.
figure NUM head transducer m converts the sequences
in particular we do not require formulae to combine under any notion of adjacency but simply as soon as possible
a more efficient choice is selecting the topmost synsets called unique beginners thus eliminating branches of the hierarchy rather than leaves
apparently the task of disambiguating lppl seems easier less polysemy more monosemous genus and high precision of the sense ordering heuristic
note that to devise a back off scheme on the basis of these high dimensional representations each pattern has NUM x NUM features one would need to consider up to NUM ldegdeg smoothing terms
the lcsr threshold was optimized together with simr s other parameters as described in section NUM NUM
let us now see how the algorithm of figure NUM applies step by step to the transducer t7 of figure NUM producing the transducer t8 of figure NUM
the techniques described in this paper are more general than the problem of part of speech tagging and are applicable to the class of problems dealing with local transformation rules
suppose for instance that we have the initial transducer t7 of figure NUM and that we want to build its local extension ts of figure NUM
this leads to two kind of states the transduction states marked transduction in the algorithm and the identity states marked identity in the algorithm
we also show how the application of all rules in brill s tagger is achieved by composing each of these nondeterministic transducers and why nondeterminism arises in this transducer
with this notion if a finite state transducer is deterministic one can apply the function to a given word by deterministically following a single path in the transducer
we shall now prove that the function is rational and then that it has bounded variations this will prove according to theorem NUM that the function is subsequential
let y c NUM and x c n the minimal y decomposition of x is the y decomposition which is minimal in
NUM therefore the right minimal y decomposition function mdy is defined by mdy ty tu o ty which proves that mdy is rational
however the implicit spelling error may still exist and will affect the parser
the work reported in this paper was supported by the national research council of thailand
both filtering and scanning processes use the statistical infomaation collected from the hand tagged corpus
the most likely sequences of correct words arc the ones that maximize chain probabilities
the alning corpus is a set of sentences divided into two groups
we shall denote this poset by td s
accurate tagging of seven european languages has been achieved in the first case error rates of NUM NUM percent for a detailed pos set but an enormous amount of training text is required for the estimation of the parameters for unknown words
if a character string could not be tokenized at all it would be ill formed
in contrast in hmm taggers invalid assignments are biased by the very low value of the corresponding conditional probability of the tags the wrong tag rarely appears in the specific word environment which decreases the overall probability for incorrect tag assignments
in this paper five natural language stochastic taggers that are able to predict pos of unknown words are presented and tested following the process of developing annotated corpora the most recently fully tagged and corrected text is used to update the model parameters
however the truth of the statement below lemma NUM is less obvious
in our experiment we noticed that the merging added NUM NUM new rules to the working rule sets and therefore the final number of rules for the induced sets were prefix NUM suffix deg NUM suffix NUM NUM NUM and ending NUM NUM
for example if the estimate for the word ending o was obtained over a sample of five words and the estimate for the word ending fulness was also obtained over a sample of five words the latter is more representative even though the sample size is the same
if we then find this word in the lexicon as vb vbp base verb or verb of present tense non 3d form we conclude that the unknown word is of the category jj vbd vbn adjective past verb or participle
although the learning process in these systems is fully automated and the accuracy of obtained guessing rules reaches current state of the art levels for estimation of their parameters they require significant amounts of specially prepared training data a large training corpus usually pretagged training examples and so on
the cascading guesser outperformed the guesser supplied with the xerox tagger and the guesser supplied with brill s tagger both on unknown proper nouns which is a relatively easy to guess category of words and on the rest of the unknown words where it had an advantage of NUM NUM NUM NUM
this gave us a search space of four basic combinations the hmm tagger equipped with the xerox guesser the brill tagger with its original guesser the hmm tagger with our cascading prefix suffixdeg suffixl ending c guesser and the brill tagger with the cascading guesser
vbn jj says that if segmenting the prefix un from an unknown word results in a word that is found in the lexicon as a past verb and participle vbd vbn we conclude that the unknown word is an adjective 0j
to collect the ending guessing rules we set the upper limit on the ending length equal to five characters and thus collect from the lexicon all possible word endings of length NUM NUM NUM NUM and NUM together with the pos classes of the words in which these endings appeared
for four items lr ll st and sl of NUM each chart position there can be maximally n searches
the reestimation algorithm computes eight items for each chart box and the computation of each item needs maximally n number of productions and snmrnations respectively
the cooccurrence information vector for a word is collected from the whole dictionary using cooccurrence frequency mutual information or association ratio
figure NUM redundancy by synecdoche or an auto relationship9
addressee honorification is indicated in a verb
NUM hart sensayng nim i o si ess ta
NUM soonchul i minyoung ul manna ss e
NUM park kwacang i naka ss c
within a sentence speaker and addressee do not change
a dialogue is composed of sentences
in particular we will concentrate on how we might the calculation of the intersection of a fsa and a dcg
bernard lang defines parsing as calculation of the intersection of a fsa the input and a cfg
in order to compute the intersection of a dcg and a fsa we assume that fsa are represented as before
to illustrate how a parser can be generalized to accept a fsa as input we present a simple top down parser
in this section we want to generalize the ideas described above for cfg to dcg
this also illustrates that this method is not very useful yet all the work has still to be done
for the senfence a a b b we obtain the parse forest grammar
note that the construction of bar hillel would have yielded a grammar with NUM rules
table NUM effect of different semantic distance metrics on semautic class dls mblguation
a possible way to ensure termination is to remove all constraints from the dcg and parse according to this context free skeleton
table NUM chtstering results of bbk
energy fuel gas gasoline oil power financial bank banking currency dollar money military army commander infantry soldier troop vehicle airplane car jeep plane truck weapon bomb dynamite explosives gun rifle the input to our system is a text corpus and an initial set of seed words for each category
it is implemented in quick set a multimodal pen voice system that enables users to set up and control distributed interactive simulations
for multimodal integration one the most significant challenges facing the development of effective multimodal interfaces concerns the integration of input from different modes
speech and gesture are integrated appropriately even if the integrator agent receives them in a different order from their actual order of occurrence
in contrast the same task can be accomplished by saying phase line green and simultaneously drawing the gesture in figure NUM
this approach embodies a preference for multimodal interpretations over unimodal ones motivated by the possibility of unintended complete unimodal interpretations of gestures
in the example case above both speech and gesture have only partial interpretations one for speech and two for gesture
spoken or gestural input which partially specifies a command can be represented as an underspecified feature structure in which certain features are not instantiated
figure NUM fortified line gesture createjine degbject bj location style fortified fine
the ambiguity of interpretation of the gesture was resolved by integration with speech which in this case required a location feature of type point
the quickset user interface displays a map of the terrain on which the simulated military exercise is to take place figure NUM
examples for french and english include a an on and par
we assume a NUM to NUM nulncfical range for such abstract scales
gsa employs a couple of backing off heuristics to elimninate most of the errors
inversions occur surprisingly often in real bitexts even for sentencesize text units
in a similar manner the coercer relation becomes null
the right context is more complicated
an attribute is an atomic label
all and only the factors of w are represented by paths from the root to some implicit node the statistic of factor u of w is the number of leaves dominated by the implicit node ending the path representing u
we illustrate this process using file lexical entries for abusive and abuse
table NUM some examples of extracted substrings are shown in table NUM
a statistical method for extracting uninterrupted and interrupted collocations from very large corpora
of main interest here are the following properties of suffix trees if node p has children pl pd then d NUM and strings label p pi differ one from the other at the leftmost symbol
the method was applied to newspaper articles involving some NUM NUM million characters
the results for uninterrupted collocations were compared with that of n gram statistics
but successive processes were performed very quickly within one hour
fig NUM example of uninterrupted and interrupted colh cational substring extraction
table NUM number of extracted pairs of substrings
table NUM examples of substrings with high frequency
the results are shown in table NUM
we call these representations lexical space vectors
the results are used in the following stages
in addition nametag will provide foreign language versions including french italian spanish german and japanese
table NUM shows the processing time for the three template element configurations run on a sun sparcstatio n NUM
the three speed configurations show the general trade off between speed and recall with precision remainin g about the same
since the links between aliases are not required for the named entity task the driver suppressed this nametag information
the long term objective of hasten is to provide a system that non developers can easily customize to extract information from text
therefore hasten must be simple flexible robust and trainable and must minimize the customization effort
the matcher also produces an annotated sentence by transferring the extraction annotatio n from the example to the incoming sentence
this feature provides hasten with a central control on extraction performance which will be illustrated in the test results
NUM for each dependency structure multiply the mutual information of its ambiguous dependency relations to obtain the score for that structure
our method on the contrary uses a statistical approach to select the most probable structure or parse of a given sentence
NUM for each relation modifier particle modificant pattern search the concept identifier that generalizes the modifier word and has maximum nmtual information
we point out here that because a dawg is an acyclic graph rather than a tree straightforward ways of defining alignment between two dawgs results in a quadratic number of a links making dawgs much less attractive than suffix trees for factor alignment
NUM for each modificant concept identifer build a taxonomic hierarchy with its modifiers using cd to find the generalizer for each concept identifier
for a working person it happened to be the concept a person itself with mutual information of NUM NUM
given the added derivation information contained within tncbs and the properties mentioned above we can direct this search by incrementally improving on previously evaluated results
we thus see that during generation we formed a basic constituent the dog and incrementally refined it by adjoining the modifiers in place
average case complexity is dependent on the quality of the first guess how rapidly the tncb structure is actually improved and to what extent the tncb must be re evaluated after rewriting
this constraint says that if one constituent fails to combine with another no permutation of the elements making up either would render the combination possible
if the linguistic data that two signs contain allows them to combine it is because they are providing a semantics which might later become more specified
we thus see how the tncbs can mirror the dominance information in the source language parse in order to furnish the generator with a good initial guess
since tncbs are tree like structures if a tncb is undetermined or ill formed then so are all of its ancestors the tncbs that contain it
combination is the commutative equivalent of rule application the linear ordering of the daughters that leads to successful rule application determines the orthography of the mother
in the algorithm presented here we start from observation that the phrases NUM to NUM are not incorrect semantically they are simply under specifications of NUM
after further testing we again re enter the rewrite phase and this time note that brown can be inserted in the maximal tncb the dog barked adjoined with dog figure NUM
the result of the average scores of NUM positions pro sn shown in figure NUM with NUM m NUM and termination maps of the training and test sets confirms two things first correspondences exist between topics and sentence positions in texts such as the ziff davis collection
an important distinction between most of the classical non parametric methods and the learning techniques we study here is that in the former case there was no theoretical work that addressed the generalization ability of the learned classifter that is how it behaves on new data
using this family of algorithms frees the designer from the need to choose the appropriate set of features ahead of time a large set of features can be used and the algorithm will eventually discard those that do not contribute to the accuracy of the classifier
for instance the expansion terminological context eterminological context of a np is the set of the candidate terms appearing in the expansion of the more complex candidate term containing the current np in head position
this definition of the context is original compared to the classical context definitions used in information retrieval where the context of a lexical unit is obtained by examining its neighbors collocations within a fixed size window
it also enables us to study strategic planning and how different roles affect the obligations that the agents want to obey e.g. in conflict situations
we discuss these aspects from the point of view of cooperative goal formulation and present the constructive dialogue model as a new approach to plan system responses
three measures of performance accuracy computation time and memory usage were compared with the results in table NUM showing improvements by the transducer system for all three measures
finally we also looked at the number of choices for which training counts were available i.e. the number of model numerical parameters for which direct evidence was present in training data
null in comparing models for language processing or indeed other tasks it is reasonable to ask if performance improvements by one model over another were achieved through an increase in model complexity
the first was the number of lexical entries
however since japanese sentences tend to be relatively long and the recent japanese dictionary for research is large under flow is sometimes a problem
by comparison the usual logprob cost function using only positive instances would be log n c log n elc
in this paper we describe an experimental machine translation system based on head transducer models and compare it to a related transfer system described in alshawi 1996a based on monolingual head automata
this involves assessing the total cost f
this algorithm can not take advantage of the scaling procedure because it requires the synchronous calculation of all possible sequences in the morpheme network
NUM NUM how much loyalty does dan have to his friends
NUM NUM bill has but one desire to continue working
NUM NUM bill dots not have much desire to continue working
the conversions here all denote that which gives rise to the emotion
NUM NUM conversion from mass noun to count noun
NUM NUM did you order pizza
NUM NUM how many mary s are there in this room
NUM NUM how much effort is required to lift this weight
NUM NUM all ammunition found by the police was fifty caliber
family names become common nouns denoting those in the family of that name
again the parse forest for the sentence ks returned
this type of slot has associated with it a rule which produces a list of concept node s with which the slot should be filled
the sums of the bounds on the values pi for i NUM to m plus the value p1 p2 q q give upper and lower bounds on the total number of candidate translations generated and examined by champollion
to select among the three measures we first observe that for our application NUM NUM matches paired samples where both x and y are NUM are significant while NUM NUM matches samples where both x and y are NUM are not
in fact in cases such as the examples of table NUM where p x NUM i y NUM p y NUM t x NUM the dice coefficient becomes equal to these conditional probabilities
in table NUM we show the number of candidate translations examined by the exhaustive algorithm and the corresponding best worst and average case behavior of champollion for several values of q and m using empirical estimates of the ri s
if p is not empty champollion locates the translation that looks locally the best that is among all members of p analyzed at this iteration the translation that has the highest dice coefficient value with the source collocation
unfortunately the system had no mechanism for recovering from this situation and so the second closin g quote was considered as an opening quote
so we first check whether there is any possibility that this word correlates with the source collocation highly enough to pass the dice threshold by assuming temporarily that the word does not appear at all outside the sentences matching the source collocation
the second cue class discourse cues includes cues that can be recognized using linguistic and discourse information such as from the surface form of an utterance or from the discourse relationship between the current and prior utterances
in the case of evaluation questions errors occur when NUM the result of the evaluation is readily available to the hearer thus eliminating the need for an initiative shift or NUM the hearer provides extra information
to give an idea of speed the final evaluation if run on a single 70mhz sun machine too k approximately NUM hours
rule c NUM will produce a chain which will be verbalized as a c NUM by the definition of transitive closure thus establishing x y e NUM by the definition of subset since x y e NUM note that the rule above is only a simplification of a recursive definition since chaining is not restricted to two derivation steps
in the binary case the choices are property ascription that may be verbalized as x and y are parallel quality relation that allows the verbalization as x is parallel to y or process relation that is the formula x ii y the mapping of upper model objects into the text structure is defined by so called resource trees i.e. reified instances of text structure subtrees
thus for a dialogue system to interact with its user in a natural and coherent manner it must recognize the user s cues for initiative shifts and provide appropriate cues in its responses to user utterances
this analysis shows that me distribution of initiatives varies quite significantly across corpora with the distribution biased toward one agent in the trains and maptask corpora and split fairly evenly in the airline and switchboard dialogues
the tests show that for all corpora the differences between the two algorithms when predicting the task and dialogue initiative holders are statistically significant at the levels of p NUM NUM and p NUM NUM respectively
strictly based on this metric our results indicate that the three coders have a reasonable level of agreement with respect to the dialogue initiative holders but do not have reliable agreement with respect to the task initiative holders
we analyzed the cases in which the system using 6for comparison purposes the straight lines show the system s performance without the use of cues i.e. always predict that the initiative remains with the current holder
this resulted in a reversal of what was considered as normal an d reported speech in the remainder of the document causing numerous problems
the insertion of additional quotes problem caused loss of more than half the correc t co references and more than doubled the number of incorrect ones
for example a word like now has an adverbial sense but only the sub conjunctio n and temporal noun sense were available
in the english implementation for example many such morphemes can be incorporated directly into the letter to sound rule set itself
generation may be performed in the same way as analysis the difference being that the prototype is a tree and pattern matching is performed on trees
the pcp problem however is known to be undecidable
fhis view makes explicit that analogy sets a relation between an unknown on on hand and three terms on the oth hand
a linguistic interpretation is thai analogy involves two orthogonal dimensions reflecting the duality of the lexeme morpheme or root affix or meaning limction etc separation
mathematical on each side of the equality sign both changes in meanings and categories performed at the same time leave the proportion unchanged
for all possible zl tuples of sellt iilees which verify the analogy definition wc eomlmted the analysis of the first sentence by analogy
here we define the precisio as the number of times when the exact structure was computed by analogy divided by the number of solutions delivered
following the linguistic feeling we impose that tile solution of an analogy be built only with the elements of the vocabulary present in the three given terms
there are two methods to loosen the constraint
many automatic clustering methods have been already proposed
we define such a graph as co occurrence graph
figure NUM loose constraint for graph decom
put a triangle graph including e into gi
in that case the system will have a model containing all prior interaction with the user
well with one of the n ost eliichmt algorithm agrcp wu manl er NUM as it is faster in average
i xeeution dines are l eh w one second for the analysis of short chunks of text about NUM words
one such case is that of at least a double consonant sequence whose second consonant is p r followed by a candidate excessive diphthong
the formal rules and the exact definitions of the sets of vowel and consonant sequences compiled in tables NUM and NUM are sufficient to implement the hyphenator program
between the former and the latter is a specialisation link indicating that old chairmen are a subset of chairmen
for example the content of a muc article would come i n this class when analyzed
controls could be represented using links but for efficiency reason s this more compact form is used
if the parse fails analysis is discontinued on that sentence so no semantic result is produced
co reference like the named entity the coref task begins with the set of all nodes created or modified during analysis
identification of diphthongs and excessive diphthongs is a difficult task because of the ambiguity that arises when attempting to make specific designations
the intersection of finite state automata and definite clause grammars
the system has also been ported to different platforms including computational linguistics volume NUM number NUM lotus amipro and a specialized typesetting system of a major greek newspaper
the system allocates new semnet nodes to components of the document words phrases sentences
in this case theorem NUM specifies one non ambiguous hyphen point which will not always be the point preceding vl hence the assumption is again false
the explicit interpretation and formal expression of specific grammar rules led to a formal hyphenation model and further provided a means of expressing the model s limitations
loanwords have been incorporated into greek since ancient times and include words that can not easily be recognized as borrowed because of their adaptation into the above principles
dereferencing must be done explicitly by the application using the property accessors opening the collection accessing the document accessing the annotation in the document etc
a tipster system would have a default stylesheet but it may be necessary to extend the writesgml operation to use a different explicitly specified style sheet
the description may be in natural language expressed with query language operators described below or a combination of natural language and query language operators
a template object may contain information about a real world object such as a person product or organization a relationship or an event
the first example shows a single sentence and the result of three annotation procedures tokenization with part ofspeech assignment name recognition and sentence boundary recognition
annotationsat document or annotationset position integer annotationset returns the set of annotations from document or annotationset which start at the specified position
this segmentation into regions using different character code sets is to be recorded in the tipster architecture as annotations on the document see section NUM NUM
objectreferences are references to names of persistent collections documents etc and not to the object instances created by opening a collection etc
annotations along with attributes provide the primary means by which information about a document is recorded and transmitted from module to module within a system
the final states for this transducer network are kept distinct so that different costs can be assigned by training to the stop actions and modifier transitions at these states
the straightforward approach is to generalize existing recognition algorithms
robustness bottom up lexicalized translation is inherently more robust than top down processing since it allows maximal incomplete partial derivations to be identified when complete derivations are not possible
we define the transition relation using the relation trans NUM
resolvent all of the information in tupre ioua from f on up
in some cases the automata needed to be modified to include additional states and also some transitions with epsilon relations on the english source side
the heart of ore system is a comltletion engine for english to t ench translation which finds the best completion for a y eneh word prefix given the current english source text segment utnler translation attd the words which precede the prefix in the corresponding l y eneh target text segment
the system has not yet been integrated with a word processor st we annot qltantify the anlollnt of a tual time and fl ort it woul t save a translator t nt it seems reasonable to expect this to lie famy well correlated with total character savings
when the translation models were trained invariant tokens in each source text segment were replaced by special tags specific to each class different invariants occuring in the same segment were assigned serial numbers to distinguish them any instances of these tokens found in the corresponding target text segment were also replace by the appropriate tag
it can in principle accomodate a wide range of mt proficien null cies frolil very high in which the system inight be called ut on to propose entire translations and inoditly them in response to changes inade by the translator to very low in which its chief contril ution will be the reduction of typing labor
it has a potential advantage over postedition in that information imparted to the system may help it to avoid cascading errors that would later require much greater effort to correct and it has a potential advantage over preedition in that knowledge of the machine s current state may be useful in reducing the number of analyses the human is required to provide
the simplest case is a graph of three nodes
this is particularly important in the case of speech translation because the input string or word lattice often represents flagmentary illformed or after thought phrases
it suffices that the distances become too large relative to the lengths of the words a thc of x is such a case
the inclusion of such particles often depended on additional distinctions not present in the original english automata hence the requirement for additional states in the bilingual transducer versions
in accumulating discourse information a score of NUM NUM is awarded for each definite modifiee modifier relationship
however as they have limited knowledge of spanish in essence they annotated the english translations
a forward sequential search fss begins by designating the model of independence as the current model
it is this intention in conjunction with the current plan that sanctions the adoption of beliefs and intentions about potential actions that will contribute to the goal rather than just the shared plan
because the input consists of alternative sequences of ilts the system resolves the ambiguity in batches
initially each cue is assigned the following bpa s mt i o i and ma i NUM where lcb speaker hearer rcb
they should form a cluster with relatively lower threshold
the implementation of this was the ipsim interruptible prolog simulator theorem prover which can maintain a set of partially completed proofs and jump to the appropriate one as dialog proceeds
the domain processor algorithm chooses the node subsystem with the greatest suspicion and the specification on that node with greatest suspicion and sends it to the dialog controller for possible checking
in computing the task specific expectations for the user utterance one expectation is for a statement asking about the location of the object of interest in the current topic in this case the switch
it is necessary that the knowledge base store information related to what the user can be expected to do and the system only should make requests that will be within this repertoire
the first eight problems for the two sessions were matched in difficulty as well as possible in order to give balance between sessions and to prevent a varying difficulty from overshadowing important effects
medical topic cluster NUM have too many words
as an example suppose smith hipp and biermann an architecture for voice dialog systems the utterance no wire has been received and the following rules are in the system
a reasonable test of the theory and implementation described here is to bring human subjects to the laboratory and determine whether they can converse sufficiently well with the machine to effectively solve problems
it was defined in section NUM NUM within three nodes
we assume that the machine has selected a new goal that comes from the domain processor tl circuit test2 v where v is a voltage to be returned by the test
in any case after statement NUM the situation specific expectations include an expectation for an affirming utterance indicating completion and this becomes the interpretation given to okay
experimental results table NUM displays exact match parsing results for a normal NUM NUM word test set NUM crucially the amount of tr n ng data here NUM NUM words is only NUM NUM as large as for the models of tables NUM NUM
we argue that haskell allows us to writ e complex code much more easily than say c or lisp
to investigate the generality of our system we applied our training algorithm using the constant increment with counter adjustment method with a NUM NUM on the trains91 corpus to obtain a set of bpa s
technology a probabilistic recursive transition network is an elevated version of a recursive transition network used to model and process context free languages in stochastic parameters we present
a is a transition matrix containing transition probabilities and b is a word matrix containing the probability distribution of the words observable at each terminal transition
for instance the table for storing inside computations takes o n2g2c store where c is the number of terminal and nonterminal categories
the arcs subject act ion and object are used to represent the basic roles of an event
the corresponding figure for the treebank is NUM of NUM terminal elements in the treebank
expected number of transitions from state i to state j expected number of transitions from state i the expectation of each transition type is computed as follows for a terminal transition null
the following charts show the classifmation precision and recall for each of the classes
the techniques used here are not language specific and can be applied to any language or domain
the gain in insides from the chart re estimation algorithm is very clear and in the case of outsides the gain is even more conspicuous see figure NUM
for routing rank the document in the class using some function of the similarity measure
NUM measure the similarity between the new document and each of the classes
p output i classi p classi i output p output
if the last three characters are ies change them to y
if the last three characters are ied change them to y
we now consider how this approach and the use of lexical statistics in general might be naturally extended to handle the more difficult problem of multiple pp attachment
second it specializes in the generation of temporal expressions
clustering analysis is a generic name of a variety of mathematical methods that can be used to find out which objects in a set are s mi sr
the main cause of errors is the polysemous words in dictionary definitions which we will discuss in section NUM
a typical ingress rule made up of macros might look something like this subjphr conjphr in appointvb lcb postorg rcb c reassigning template the binding macros are subjphr appointvb and an optional postorg
corpus based sense disambiguation methods like most other statistical nlp approaches suffer from the problem of data sparseness
this is to compensate for the different lengths of definitions of different senses and different lengths of the context
this is due to the fact that only one of the senses is used in the given sentence
the most serious problem is that many of the words in the controlled vocabulary of ldoce are polysemous themselves
it is based on salient words and may not perform as well on general text as our approach
adding a buffer phrase allows the pattern matcher to jump over part of the conjunctive phrase in the following sentence james NUM years old is stepping down as chief executive officer on july NUM and will retire as chairman a t the end of the year
our system achieves an average accuracy of NUM on a mean NUM way sense distinction over the twelve words
previous lolita applications have relied on the core system s generator NUM to produce output
unlike previous extraction tasks in which the event template is built from lower level relational and primitive elements this specific task requires that informa tion such as in or out status be recognized at the event level but instantiated in the lower leve l relational element the in and out object
the log linear model on the other hand offers an estimate of how good each prediction is since it produces a value y between NUM and NUM
for each run of champollion and for each input collocation we took the final set of candidate translations of different lengths produced by champollion with the intermediate stages driven by the dice coefficient and compared the results obtained using both the dice coefficient and si at the last stage for selecting the proposed translation
even if such lists could be generated it would be impossible to include words such as compounds which can be readily created or all proper names
this calling sequence allows us to selectively apply annotators to subsets of a collection but to keep all the annotations together in the original collection
using distributed rather than centralized this work has been supported by a grant from the german federal ministry of education science research and technology fkz itw NUM
in spite of the noncompositionality of this process the resulting expressions have a clear model theoretic interpretation and could be used by any system accepting first order logic representations as input
the reconstruction of underspecified temporal expressions is performed by a set of template filling functions which make use of parameters specified by the client system at the beginning of the dialogue
as a result the nl server will pass to the client the most informative il expression of the most informative and contextually most relevant sentence of the analyzed text
for a plausible application the server must be complete with respect to a sublanguage all relevant information related to appointments must be analyzed sufficiently robust to deal with inconsistent analysis results
handling underspecified temporal information by offering free time slots see NUM NUM and NUM is among the extensions of pasha ii at the local planning layer
this deep approach to nlu describes nl expressions at general linguistic levels syntax and surface semantics and attempts to capture the complete meanings of all and only the grammatical sentences
we introduce the functor in the following forln
the general plan construction and plan inference processes are essentially the same as those for referring expressions
NUM we refer to the steps in the decomposition that are not action headers as mental actions
the change in mental state can be modeled by the beliefs and goals that a participant adopts
NUM NUM a NUM what s that weird creature over there
the speaker has the goal of the hearer identifying the object that the speaker has in mind
second we assume that agents have mutual knowledge of the mechanisms of referring expressions and collaboration
a accepts the new expression in line NUM and b signals his acceptance in line NUM
in figure NUM fss bic adds too few interactions and does not select as accurate a model as fss aic
perhaps the most obvious would be to extend the planning component of our model
this allows both participants to contribute without being controlled or impeded by the other
however the parallelism between the clauses licenses a sloppy reading via the similarity option
the lazy pronoun reading is not available even though the have before give constraint is not satisfied
example NUM is fairly straightforward so we focus on example NUM
NUM john said that his teacher revised his paper and bill did too
these will be similar if their previously unmatched pair of arguments f and t are similar
NUM john revised his paper before bill did but after the teacher did
we will assume the foot has been identified as the foot of the ladder
the first pair of arguments wl and w2 are similar in that both are weights
obtaining an adequate account of strict sloppy ambiguities has been a major focus of vp ellipsis research
then the co recursion through the arguments and properties normally mirrors the syntactic structure of the sentence
if however these constraints talk about different parts of t s structure then the resulting disjunction will be big and the expansion at compile time should be avoided
while a powerful graphical user interface NUM solves the presentation problem a sophisticated tracing and debugging tool was developed to allow stepwise inspection of the complex constraint resolution process
we extended this mechanism to the universal principles the constraints on a certain type were only checked once certain attributes were sufficiently instantiated w r t the delay statement
offering both more perspicuous grammar code and closeness to linguistic theory it seems well motivated to explore an architecture which allows both relational constraints and universal restrictions to be expressed
modate the hfp call tional constraint encoding the hfp shown in fig NUM constrains only the headed phrases and the non headed ones do not need to be considered
1f2 and fs are conditionally independent given s if p f2lfs s p f21s
this correspondence between global purposes and fronted purpose expressions was already discussed in the corpus analysis section but to give an intuitive feel for this empirical result consider the awkwardness of restating example 3a as hold down flash NUM for about two seconds then release it to end a previous call
such auxiliary relations which will be discussed further in connection with the delay mechanism have turned out to be especially useful in conjunction with principles with complex antecedents
an event is a set of feature value pairs or questionanswer pairs
with this algorithm the time complexity is reduced to o c v
we set the number c of lasses to NUM
the third type of questkms are cmled lingui sl s
as a consequence of this rank ordered approach the best ranked pairing fo r any particular key object may not in fact result in final alignment
the score report reflects th e difference in how we wanted to analyze the scores according to object type subcategories and placement in th e document
after completing the translation of the scorer into c we benchmarked NUM articles on a sparc NUM and found that it took NUM seconds of elapsed time
the speed of the c version is critical during system development especially fo r those evaluation participants that use training in any part of their system
see the appendix entitled muc NUM test scores for information on thes e metrics which included error per response fill undergeneration overgeneration substitution and text filtering
the scorer sectionizes the data files by creatin g groups based on these document numbers each group consists of one each of the three types of input data
in muc5 the number of documents that we wanted to use in a data set caused a memory exhausted message during loadin g of the scorer
to keep costs down saic reuses scoring code from past evaluations as much as possible while respondin g to changing consumer needs and the experience of past evaluations
response text strings are considered matched if they are identical to the key or are identical after ignorin g certain text elements that are considered non substantive
the template element scorer was a low cost adaptation of the scenario template scorer and does not include relevancy judgments because almost all articles contain these low level objects
however the use of morphological recognition can refine this information
is name finding including recognizing unknown names feasible
to date we have only investigated NUM best
figure NUM architecture of information extraction from speech
lvscr transcription places some demands on information extraction
in muc NUM however we chose a path represented in figure NUM below so that there would be domain independent versions of the software for efforts other than muc such as the tipster program
bbn has developed the nlu shell which provides tools that simplify many of the tasks associated with porting information extraction to a new domain and support maintenance once the port is complete
recently bbn has ported part or all of the plum system to new languages chinese and spanish and new domains name finding heterogeneous newswire sources and labor negotiations
thus even though we found fairly good strategies for eliminating bad frames the damage done by eliminating even a few good frames more than outweighed the benefit of eliminating many bad frames
the parallel system increases f score only if the two systems have much better precision than recall while the series case yields improvement only if the two systems have much better recall than precision
an hmm transducer can be composed with one or more of these transducers in order to perform complex text analysis using only a single transducer
the coding developer matched the official map task coding almost entirely
it is then a simple matter of allowing the type of information to determine the appropriate expressional form
information technology research institute university of brighton lewes road brighton bn2 4at uk
this section will discuss the theoretical framework of the implementation and then detail the treatment for purpose expressions
imagene s process structure can be seen as the former level its text structure as the latter
these systems based on the examples in our corpus restrict nominalizations to single non complex arguments
a relational style database is used to represent the rhetorical grammatical and lexical aspects of the corpus
currently the data structures and code necessary to respond to the inquiries automatically have not been implemented
the latter approach has the potential weakness of unsupervised training erasing what was learned from the manually annotated corpus
as detailed in section NUM scoping c discharges the term and its index by substituting a variable for it
the implicit pronoun has been strictly identified with the pronoun in the antecedent to pick out the same referent john
b strict substitution for the book leaves behind an occurrence of the index b in the ellipsis
third though perhaps less importantly higher order unification going beyond second order matching is required for resolving ellipses involving quarttification
this paper presents a treatment of ellipsis which avoids these difficulties while having essentially the same coverage as dsp
this permits a type of memoization not described to my knowledge in the context of functional programming before
changing the order of the definitions will not help as then the variable s will be unbound
as shown in table NUM the increases in matching rates show the effectiveness of the constraint on discourse segments beginning in tr2
scheme was chosen because it is a popular widely known language that many readers find easy to understand
the problem is partly because the test texts used in the former comparison are human created while the test texts used here are computer generated
after the annotations were collected we compared the speakers results with the generated texts to investigate the performance of the test rules
tr2 adds the constraint on discourse structure and tr3 adds to this the salience constraint and is the same as rule NUM
on the basis of the above observations we propose the following preference rule for the generation of descriptions for nominal anaphora in chinese
thus in cases of left recursion memoization does nothing to prevent the ill founded recursion that leads to nontermination
a sequence of rules using independently motivated linguistic constraints is developed until the results obtained are close to those in the real texts
mbt a memory based part of speech tagger generator
we will call this algorithm ib ig
NUM NUM experiment NUM comparison of algorithms
case bases are indexed using igtree
accurate generalization from small tagged corpora
figure NUM learning curve for tagging
the singleton bidirectionality and flipping commutativity equivalences see lemma NUM can also be applied whenever they render the associativity equivalences applicable
instead we employ a more complicated but better constrained grammar as shown in figure NUM designed to produce only canonical tail recursive parses
although this already represents a useful level of accuracy it does not in our opinion reflect the full potential of the formalism
in the sentence pair of figure NUM for example both security bureau and police station are potential lexical matches to j
its amenability to stochastic formulation useful flexibility with leaky and minimal grammars and tractability for practical applications are desirable properties
the time complexity for this constrained version of the algorithm drops from o nbt3v NUM to o tv3
thus we would like to constrain the rank as much as possible while still permitting some reasonable degree of permutation flexibility
for both english and chinese we specify a prepositional bias which means that singletons are attached to the right whenever possible
a random sample of the bracketed sentence pairs was then drawn and the bracket precision was computed under each criterion for correctness
secretarylnn nnn np pp vp vp sp s
by using just this character type heuristics a non stochastic and non dictionary word segmenter can be made
on the source language the main difference between the slsem and conditions is that the former is matched against the input and replaced by the tlsem whereas conditions act as filters on the applicability of individual transfer rules without modifying the input representation
as a consequence the translation of the verb needs to be reduplicated whereas in our approach the translation of the verb can be kept totally independent of this specific translation of tile adverbial because the condition functions merely as a test
the outline of the algorithm is as follows NUM tag the english half of the parallel text
in contrast our new algorithm performs a minimal alignment to facilitate compiling a much larger bilingual lexicon
the rule in 3b might be further abbreviated to NUM by leaving out the unmodified arg3 because it is handled by a single metarule which passes on all semantic entities that are preserved between source and target representation
because both the transfer input and the matching part of the rules consist of sets we can exploit ordered set operations during compilation as well as at runtime to speed up the matching process and for computing common prefixes which axe shared between different rules
here the first rule 7a serves as a kind of default with respect to the translation of terrain in cases where no specific sort information on the marker x is awfilable or the condition in rule 7b nils
for example the quality of a lexicon s noun entries can be compared to the quality of its adjective entries the quality of its entries for frequent words can be compared to the quality of its entries for rare words
if prosperity and occurred in the same eight segments their mutual information score would be NUM NUM
its departure from the diagonal illustrates that the texts of this corpus are not identical nor linearly aligned
for every word pair from this lexicon we had obtained a dtw score and a dtw path
dim v is the dimension of the vector which corresponds to the occurrence count of the word
are plotted against their positions in the text they give characteristic signals such as shown in figure NUM
it is based on the simple heuristic that if a source word s is a cognate of some target word t then t is the correct translation of ss in their sentence pair and there are no other translations of s or t in that sentence pair
bottom and then to pi i tunnel not a very literal translation
prior to a discussion of the algorithmic procedure for hypothesizing discourse segments based on evidence from local centering data we will introduce its basic building blocks
note also that the algorithm does not check the c NUM u10 despite the fact that it contains the antecedent of NUM
the spatial extension and nesting of these discourse segments constrain the reachability of potential antecedents of an anaphoric expression beyond the local level of adjacent center pairs
there have been only few attempts at dealing with the recognition and incorporation of discourse structure beyond the level of immediately adjacent utterances within the centering framework
hence it is unclear how discourse elements which appear in utterances preceding utterance ui NUM are taken into consideration as potential antecedents for anaphoric expressions in ui
the test texts consisted of two types a NUM NUM word section of a novel of which the rest was used in the development of the lexicon of the predictor and a NUM word collection of essays written by students at the stockholm institute of education and not used in the lexicon development
to summarize the findings from this follow up study the use of profet resulted in considerably better spelling not much morphological improvement inclusion of the usually non existent function words and more correct word order as well as positive subjective experiences such as profet helps me write more independently
factors affecting keystroke savings are test text size test text subject lexicon coverage prediction method maximum number of prediction suggestions method for selecting prediction suggestions amount of time needed to write the test text and type of interface
another factor might be the difference in test text style the swedish consisting of adolescent literature with a sizable amount of dialogue the english of newspaper text from the electronic version of the daily telegraph and the danish and norwegian of articles on language teaching
we will now describe the procedure of collecting lexically or morphologically meaningful graphemic substrings that are used productively in name formation
it lists all customers of deutsche telekom by name street address city phone number and postal code
the selection criterion was frequency component types occurring repeatedly within a city database were considered as productive or marginally productive
we wish to acknowledge richard sproat who developed and provided the lextools toolkit this work also benefited from his advice
thus for NUM out of NUM names NUM NUM no correct transcription was obtained by either system
from here we take the arc with the label d ach and a cost of NUM NUM to state first
however these methods require a database that is annotated for all relevant factors and levels on these factors
the cost of the last component platz is zero because this is one of the customary street name markers
applying the syllable model is expensive because we want to cover the name string with as many known components as possible
in conjunction with automatic procedures for learning word translation lexicons sitgs bring relatively underexploited bilingual
for bracketing grammars of the type considered in this paper there is no advantage
on the other elm of the scale we put e.g. the general case of subject verb greement errors
however macron expressed serious worries about the speed of the system should this be really introduced to the market
where the words that have been delivered at any stage in incremental processing are combined to give a single result formula with combinations to incorporate each new lexical formula as it arrives for example in incremental processing of today john sang the first two words might yield after compilation the first order formulae so s and np which will not combine under the rule NUM
each half of the parallel corpus is first parsed individually using a monolingual grammar
transduction grammar models especially of the finite state family have long been known
the ideas contained have been successflfily implemented in a grammar checker for czech a free word order language from the slavic group
pertinent to the substring successfully parsed during the parsing with constraint relaxation to be issued
the assumption that no grammar is available means that constituent categories are not differentiated
or cosy or an excla lnative
we will return to the subject of itgs ordering flexibility in section NUM
hence a simi le finite state a itolua toll
this record is built when the predictions are first entered and updated during the dialogue
the weight function reflects how skewed to the right a tree is
in applications of the lemma table proof procedure to such systems it may be desirable to abstract from a strong type constralnt in the body of a clause to a logically weaker type constraint in the memoized goal
because the x phrase structure rules freely permit empty categories every string has infinitely many wellformed analyses that satisfy the x constraints but the conjoined ecp constraint rules out all but a very few of these empty nodes
in a backtracking parser a natural way of dealing with such constraints is to coroutine them with the other parsing processes reducing them only when the parse tree is sufficiently instantiated so that they can be deterministically resolved
items NUM NUM represent partial alternative analyses of the verb cluster where the two verbs combine using other rules than forward application again these yield no solution items so item NUM is the sole analysis of the verb cluster
this work also employs a heuristic search within a bayesian framework
syntactic and collocational red herrings can add noise too
NUM NUM typical misparses caused by syntactic grammar
for the mug NUM scenario we added a third set for names of executive positions such as executive directo r for recall and precision
there is language interaction about each task goal
each of these problems was balanced for difficulty
the two authors each coded the transcripts independently
this work is currently supporte d under contract NUM fi57900 NUM from the office of research and development and under contract dabt63 NUM c NUM from the department of the army
what general conclusions can we draw from this analysis
this represents a total of NUM completed dialogues
an overview of the experimental design is presented next
further development and testing of this hypothesis are needed
these measures should include NUM speech recognition accuracy NUM the utility of domain independent knowledge about dialog NUM the nature and effectiveness of system error handling and NUM comparisons of effectiveness for multiple interaction styles
consequently they used a wizard of oz study in an information retrieval environment e.g. database query in order to identify the types of natural language inputs a typical user would use in order to gain access to needed information
in particular an implicit recovery occurs when the system only partially understands an utterance but still responds in an appropriate fashion they also define what it means for a response to be appropriate within the context of an information retrieval situation
it is hoped in the next generation of measuring snlds system response time will no longer be a required measure as systems will perform with real time speed and not continually have awkward delays that break up the flow of the dialog
their analysis identified the following requirements for the linguistic coverage of a dialog system in the information retrieval environment NUM operators for specifying the properties of the set of objects for which information would be requested NUM contextual references and NUM references to the actual source of information e.g. the database
consequently comparative measures of perplexity with and without context dependent predictions remain a valid measure for evaluating the performance of a dialog system particularly in a complex linguistic environment where reduction of perplexity is essential for good speech recognizer performance
best results were achieved with a subset of features containing mostly durational features and f0 regression coefficients
illustrations are given below where perceptually labeled but syntactically unmotivated boundaries are denoted with a vertical bar
the classification experiments for this paper were conducted on a set of NUM humanhuman dialogs which are prosodically labeled cf
the overt relationship between the verb reparierlc and its object den wa qe in
the apparent contradiction is resolved by assuming an empty clement which serves as a substitute for tile verb ill second position
in a third experiment finally we were interested in the overall speedup of the processing module that resulted form our approach
the parser will not restrict the stipulation of empty elements until a lexical element containing restrictive information has been processed
if empty elements have to be represented syntactically a top down parsing strategy seems better suited than a bottom up strategy
direct parsing of empty elements can become a tedious task decreasing the efficiency of a system considerably
i believe that you not kill shall i believe that you should not kill
we expect that this will further improve the results of the algorithm although further research is needed on policies of discarding features and avoidance of over fitting
we have exhibited that as expected multiplicative update algorithms have exceptionally good behavior in high dimensional feature spaces even in the presence of irrelevant features
we then show that a quantum leap in performance is achieved when we further modify the algorithms to better address some of the specific characteristics of the domain
learning problems in the text processing domain often map the text to a space whose dimensions are the measured features of the text e.g. its words
the learning algorithms studied here offer a large space of choices to be made and correspondingly may vary widely in performance when applied in specific domains
we use the value NUM d where d is the average number of active features in a document in this way initial scores are close to NUM
in the version we use only weights of active features are being updated this gives a significant computational advantage when working in a sparse high dimensional space
each pair consists of NUM training documents and NUM test documents and was used to train and test the classifier on a sample of NUM topical categories
second the algorithms we study here are mistake driven they update the weight vector only when a mistake is made and not after every example seen
structure is shared the words blackbird and blackberry can share the common substructure associated with black such as its sound and meaning
in particular it argues for a representation of language in which linguistic parameters like words are built by perturbing a composition of existing parameters
in particular a code must be designed that enables a word or a sentence to be expressed in terms of its parts
elhadad mckeown and robin floating constraints in lexical choice model corpus of advising dialogues
these words are themselves decomposed in the lexicon and can be considered to form a tree that terminates in the characters of the sentence
for the purposes of machine translation or information retrieval this sequence is an important idiom but with respect to speech recognition it is unremarkable
this then requires louella to differentiate between the two types of phrases and may lea d her to overgenerate un named organization objects thereby suppressing precision
another argument could be made that since mccann erickson is referred to as world wide in many places it is even more possible that mr
when the evaluation period started the ne person shifte d attention to the te task while the te person shifted to the st task
over four weeks the scenario template task was able to achieve f measure of NUM NUM on the development set and NUM NUM on the blind set
these variables when bound will convey information about vacancy reason on the job an d the other org to the objects involved in the event
this model along with the final template model which guides th e system s template generator is constructed at the beginning of training
when a descriptor is linked to an organization name the syntactic relationship of the descrip tor to the organization name is also stored with the phrase
for example appositives and prenom inal phrases recognized by the ne system are tagged with app and prenom respectively
by possibly making the reference between reinventing himself and lost NUM pounds the system coul d throw out the money tag
additionally louella found even alan gottesman as a person as well as the variatio n even later in the document
the score associated with v is the difference between the positive evidence and the negative evidence of r
NUM NUM word formation and interfacing to syntax
we therefore take the following approach
in these and later examples g denotes the instruction giver the participant who knows the route and f the instruction follower the one who is being told the route
the summation on x is defined only when a NUM or b n i.e. there are words left to be generated
as pointed out in section NUM success in early tool development is not enough if the aim is to be able to recommend the tool to other slds developers on a solid basis
the table contains NUM cases of which NUM are agreed violations of gg1 id c and a one is undecidable ud and one was rejected rej
note that it would not be possible to define unit in the same way for use in kappa because then it would not be possible for the coders to agree on a nonboundary classification
on average the coders marked move boundaries roughly every NUM NUM words so that there were roughly NUM NUM times as many word boundaries that were not marked as move boundaries as word boundaries that were
g this is the left hand edge of the page yeah where the query is asked very generally about a large stretch of dialogue just in case NUM NUM NUM
sg2 provide feedback and gg7 do n t be ambiguous may also overlap in particular cases missing feedback on e.g. time may imply that the utterance becomes ambiguous
example NUM g you go up to the top left hand corner of the stile but you re only say about a centimetre from the edge so that s your line
this paper describes the reliability of a dialogue structure coding scheme based on utterance function game structure and higher level transaction structure that has been applied to a corpus of spontaneous task oriented spoken dialogues
in these simulations once a game has been opened the participants work on the goal of the game until they both believe that it has been achieved or that it should be abandoned
it was funded by an interdisciplinary research centre grant from the economic and social research council u k to the universities of edinburgh and glasgow and grant number g9111013 of the joint councils initiative
for example NUM different as our minds are yours has vp nourished mine pp as no other social influence vpe has
head overlap either the head verb of the system choice is contained in the coder choice or the head verb of the coder choice is contained in the system choice
when success is defined as an exact word for word match with the coder choice the system performs with NUM NUM accuracy and the baseline approach achieves only NUM NUM accuracy
not surprisingly the blind test results are slightly lower than the results on the complete wall street journal corpus since this contains the examples that functioned as training data
note that the insides in the deepest depth are produced first as the recursion is released thus there can be many insides that are not relevant to the given sentence
example head overlap NUM in july par and a NUM owned unit agreed to plead guilty in that inquiry as did another former par official
the results reported here represent the first systematic corpus based study of vp ellipsis resolution and the performance of the system is comparable to the best existing systems for pronoun resolution
NUM however the proposal that similarity of parallel elements can be NUM this reflects the fact that vpe like pronominal anaphora permits the antecedent to follow rather than precede the vpe occurrence
we evaluated subparts in three ways first we began with the baseline recency approach and activated a single additional component to see how the system performance changed based on that component
weights are not kept from the training set only the fist of words is kept
the class scores are then compared to each other to determine the classification and routing results
figure NUM clarifies the correspondences between drt s and set s representation
null subtree indexing and alignment detection we use the following for representation of subtrees and the time efficient detection of aligned trees
drt s fine grained lexical analyses are grounded in inferential behavior
dll l covers much more semantic phenomena than set
matic role of the m gulnent slot under eonsi leralion
kamp and l lcb ot kleuts her
first a definition a tree is maximal if it is not part of another tree within a corpus
finally directions for further research are pointed out in section NUM
these lexical distinctions mark possible starting points for refining set s representations
in cases where markup is more complex other strategies will have to be developed for detecting agreement between corpora
our goal then is to determine those stretches of a text s content which two corpora agree on
do nothing if the word is during or th precedes the ing
the most likely parse under the model is argma zrp t s and the parsing process is a method to find this parse
NUM ad n the contexts by the effectiveness value e some rank higher contexts are selected for elustering the labels instead of all contexts
on the other hand our proposed approach can learn a standard cfg with NUM recall for short sentences and NUM recall for long ones
for instance it is possible to use lexical information and head information in clustering and constructing a probabilistic g l yn tn ly
in other words this task is concerned with the way to cluster the brackets into some certain groups based on their similarity and give each group a label
as defin g contexts by the left and right lexical categories ct is the square of the number of existing lexical categories
where n and n c e are the occurrence frequency of and e respectively
although some statistical nl applications apply backingoff estimation techniques to handle low frequency events our model uses a simple interpolation estimation by adding a lmlform probability to every event
i NUM experimental evaluation to give some support to our su ested grammar acquisition metllod and statistical parsing model three following evaluation experiments are made
the same procedure was used to create a database of NUM tuples conflguratwn v nl pl n2 p2 for the attachment of NUM pps
an implementation of the theory for an english fragment has been written in prolog simulating the 2nd order properties
for example if c is based on NUM observations and c is based on NUM then the c preference is considered stronger
prc is providing tipster expertise from the tipster se cm and the testing and evaluation leadership through fidul
after this backing off becomes unstable so we use the competitive backed off estimate as above but scaled up to handle the three prepositions and fourteen possible configurations
the morphological differences between english and mandarin give rise to many language specific function words
after the evaluation the hearer may find the proposal invalid suboptimal or ambiguous
the second result is roughly confirmed by brill and resnik ignoring the importance of n2 when it is a temporal modifier such as yesterday today
the differences between these results are shown to be statistically significant using cochran s q test
third our prediction mechanism yields better results on taskoriented dialogues
the same considerations apply to the machine oriented perspective neither for a vision system nor for a knowledge based system is it without costs to determine all descriptors of a certain object especially for the vision system the computational effort may be considerable
the third method variable incrementwith counter is a variation of constant increment withcounter
carletta suggests that content analysis researchers consider k NUM as good reliability with NUM
however we leave selecting and annotating such a subset of representative dialogues for future work
this section describes our model for tracking initiative using cues identified from the user s utterances
the above equations incorporate the proposal by collins and brooks that only tuples including the preposition should be considered following their results that the preposition is the most informative lexical item
to carry out the investigation training and test data were obtained from the penn tree bank using the tgrep tools to extract tuples for NUM pp NUM pp and NUM pp cases
in this paper we will concentrate on the improving the presentation of the travel scheme
it must be noted however that counts show a steady increase in the proportion of low attachments for pp further from the verb as shown in the table below
the conceptual expansion of an entry whose headword is not a defining concept is a set of conceptual sets
this adjustment results from the existence of templates a and b the fact that template d has a high probability of coreferring with each combined with the fact that template c is incompatible with each reduces the likelihood that c and d corefer
in this paper we present a novel approach for building maximum entropy models
previous attempts to tackle the data sparseness problem in general corpus based work include the class based approaches and similarity based approaches
these steps use a grammar and a general language dictionary
each document consists of one or two pages of text
in fact many expressions with a noun phrase structure are not terms
the wrong terms are essentially due to problems of polysemy
in figure NUM we see the structure after processing turns b02 and a03
one of the sources of new terms is the corpora
i l analyze statistique module statistique lineaire
shows where utterance boundaries were determined
simr was tested on french and english with two different matching predicates
this subspace of the bitext space will have its own main diagonal
objects may also contain pointers to othe r objects forming a hierarchical structure
the common characters need not be contiguous
simr has no idea that words are often used to make sentences
it builds a tree like structure which we call the intentional structure
more important than gsa s current performance is gsa s potential performance
as time passed the limitations of emacslisp began to outweigh the its convenience
in this evaluation we wrote four scorers one fo r each task
future work includes also more training and the ability to handle sparse data
the numbers after the predicted dialogue acts show the prediction probabilities times NUM
the definition of the database objects is contained in the slo t configuration file
however since reference resolution is difficult erroneous references can hurt precision
table NUM contains the performance measures for the enamex tag and its sub classifications
also the no names configuration excluded the use of personal and organizational names
figure NUM shows the final performance results on the labor negotiation data
elliptical or other highly contextual reference s can not be feasibly encoded
the NUM fmal test data documents have an original size of NUM NUM characters
these points represent the potential extraction performance on the central scenario event
this is the same as non anaphoric case NUM above but the new time is calculated with respect to tupr viou instead of the dialog date
encoding an egraph requires less than a minute using a graphical editor
nametag has three major processing modes that represent trade offs between performance and speed
if our input sentence now is the definition of trans NUM as given above we obtain the following parse forest granunar where the start symbol is p s q0 q2
the reader easily verifies that indeed this grammar generates a isomorphism of the single parse tree of this example assuming of course that the start symbol for this parse forest grammar is p s NUM NUM
viewing the input for parsing as a fsa rather than as a string combines well with some approaches in speech understanding systems in which parsing takes a word lattice as input rather than a word string
note that it is easy to verify that the question whether the intersection of a word graph and an off line parsable dcg is empty or not is decidable since it reduces to checking whether the dcg derives one of a finite number of strings
the arguments in favor of an early discourse segmentation are well known easier coreference of entities a reduced volume of text to be subjected to necessarily deeper analysis and so on
essentially each clause is compared asymmetrically with each other with a NUM denoting a difference in events and a NUM denoting same events
structuring strategies although the legal event assignments for a particular clause may be restricted by constraints there may still be multiple events to which that clause can he assigned
many clauses qulet clauses are free from constraint relationships and it is in these cases that the heuristics are used to determine how clauses should be clustered
for the final evaluation these will be supplied by naive subjects so as to minimize the possibility of any knowledge of the program s techniques influencing the manual segmentation
whether this is because of problems in some aspect of the location analysis module or simply a result of the way we use location descriptions is an area currently under investigation
two of the analysis modules perform a certain amount of island driven parsing one extracts time related information and the other location related information and the third is simply a pattern marcher
the first heuristic operates at the paragraph level
in order to assign more neutral values to the credit factor we can use the estimated model itself
the credit factors can be assigned from this evaluation process and be used in the second phase of estimation
the discourse processo r then tries to merge the new ddo with a previous ddo in order to account for the possibility that the new dd o might be a repeated reference to an earlier one
the ne system is written completely in c and can either be run as a standalone system or as a server which can be queried by the te and st systems which are written in lisp
here we define an ambiguous observation as a lattice structure with a credit factor for each branch
the real and dotted lines in figure NUM represent the correct and incorrect paths of morphemes respectively
perhaps most interesting is out analysis following the walkthrough of what we learned through muc NUM and o f what directions we would take now to break the performance barriers of cur rent information extraction technology
NUM given the already ambitious nature of muc NUM we do not disagree with the decision to consolidate on the fou r evaluation tasks nor are we arguing to make parsing an evalution task in muc NUM
despite this once the new training data arrived we concentrated almost exclusively on it mainly using the older data as a sanity check before making system changes
NUM NUM NUM given the little effort we invested in te we believe that another two person weeks could get plum s scores on blind test material over NUM
precision was improved by the step credit factor function whose threshold is NUM fig NUM
the rationale is straightforward for full templates e g st scores have been mired with an f in the 50s ever since muc NUM in NUM
back transliteration is harder than romanization which is a frequently invertible transformation of a non roman alphabet into roman letters
table NUM illustrates the range of translations which champollion produces
champollion is particularly promising for this purpose for two reasons
direct object have no surface case marking that distinguishes them so word order constraints come into play to force this distinction
in order to present the flavor of word order variations in turkish we provide the following examples
it is straightforward to generalize the mosteller and wallace approach to use katz k mixture or any other mixture of poissons
we then present a discussion comparing our approach with similar work on turkish generation and conclude with some final comments
later we give the highlights of the generation grammar architecture along with some example rules and sample outputs
the topic focus and background information when available alter the order of constituents of turkish sentences
we plan to use this generator in a prototype transfer based human assisted machine translation system from english to turkish
on this account a good keyword is one that behaves very differently from the null hypothesis poisson
note that somewhat is much closer to poisson in almost any sense of closeness that one might consider
this is katz personal communication proposed the following alternative to the poisson
prg k is the probability of k instances of w in a document
the fact that most of the points line up fairly well indicates that idf values are strongly correlated across years
we have used document frequency df a concept borrowed from information retrieval to find deviations from poisson behavior
in general the correlations in tables NUM NUM are larger near the diagonal suggesting that estimates degrade over time
up very clearly in the ap in NUM NUM NUM NUM and NUM dotted lines
note that h t s is not the same as the conditional entropy h tis
to simplify the present analysis let us assume the probability of s cp is held constant at NUM and that the rules not listed above have probability NUM in this case we can write the probabilities of the left three rule as pa ps and pc and the probabihties of the right rhree rules as NUM pa NUM pb and NUM pc
we organize our work depending on the kind and number ofresources involved
so it makes little sense to call it a prepositional phrase or noun phrase as in c or d on ice does not behave as a noun so a is a better description than b
ability threshold can be set to0 NUM NUM NUM and so
it was created in order to rapidly tag large texts and was used to mark the right analysis for each ambiguous word in order to be used later to evaluate the performance of our method
using this method we can find for each ambiguous word w with k analyses a1 ak probabilities p1 p k that are an approximation to the morpho lexical probabilities
table NUM overall results from our ex
across NUM runs the training algorithm converged to three different grammars NUM 4le after the cross entropy had ceased to decrease on a given run the parser settled on one of these strtlctures as the viterbi parse of each sentences in the corpus
NUM if a word appears in several sw sets we calculate its contribution to the total sum according to the proportions between all those sets using the proportions calculated in the previous iteration
using the same set of rules we should be able to deduce for a domain of articles dealing with computer languages that the second analysis is probably much more frequent than the first one
this is done by simply counting for each analysis the number of times that it was the right analysis and using these counters to calculate the probability of each analysis being the right one
an example for this is the word h n vd test group2 and one of its analyses the noun h an hour
by choosing the elements in the sw set carefully so that they meet the requirement of similarity we can study the frequency of an analysis from the frequencies of the elements in its sw set
as we have already noted by saying morpho lexical probabilities we mean the probability of a given analysis to be the right analysis of a word independently of the context in which it appears
terms are selected according to the number of times they occur withincategories
unfortunately the disambiguation of categories with respect towordnet concepts is required
we have also learned that unforeseen technology mismatches can arise complicating user involvement in testing and development
no matter how good the extraction system performs a poor interface can make the entire system unusable
since a program interface to naddis has not been provided all interactions take place through a screen oriented forms interface
dea 6s in softcopy form will arrive daily over the network and will be automatically grouped by case or file number
in addition dea is preparing for initial operational implementation on a small scale to test the feasibility of the system
negation about the worht the type is negated in the present worhl but mq posed valid elsewhere
preprocessor the hookah preprocessor converts incoming electronic dea 6s in a specially coded format to sgml markup
this is performed during off hours to increase user productivity and maximize use of machine resources
a crucial component of the hookah problem is comparing extracted information to a legacy database naddis
all system processing results including user corrections are stored as annotations once the document is complete
in that paper the emphasis was to show that a uniform architecture can be used for both parsing and generation however the conception of the chart was limited and the generation algorithm did not appear to be sufficiently attractive
in the array oqll3j the fact corresponding to the given slot can be rendered either by choosing the ith branch of the oc disjunction or thejth branch of the i disjunction
in this discussion of chart generation we will tbcus on one key advantage of the chart structure the fact that equivalent phrases cml fit into larger structures once regardless of the number of alternatives that they represent
small young dog puppy figure NUM node NUM merges two different adjectives which are indexed on the same variable but express two different facts node NUM merges two nominal phrases with compatible but not completely overlapping meanings
given that exhaustive disambiguation is not always possible the idea is that the choice among the source language analyses will be delayed and the whole set of semantic interpretations will comprise the input to the generation process
this example demonstrates how multiple paraphrases are constructed out of a variety of lexical entries and syntactic constructions and how a record is kept relating the different phrases to the subsets of the semantic facts that they express it shows that the generation method is sensitive to the particular lexicalization patterns that languages use to encode divergent parts of the semantics
our approach is to take a parsing chart as an input read from it an ambiguous logical form encoding multiple source language interpretations and then use it to create a generation chart encoding multiple target language strings
this requirement speaks against the traditional sort of d pendency trees in which heads are represented as non terminal nodes cf
due to the rudimentary character of the argument structure representations a great deal of reformation has to be expressed by gramnlatical functions
just as a parsing chart excels in compact representation of multiple interpretation of a single string the generation chart is designed to represent multiple string realizations of the semantic interpretation and compute them at a minimal cost
in generation this is not available since the semantics is unordered and the formation of subsets is relatively free different lexical entries may cover different parts of the input and different syntactic realizations may choose to pack different facts together
situation is normally state l in ll li grammars by lot stating an lp rule but we shall use it here as we need an explicit hference to it
tile basic algorithm is as follows NUM the g set is instantiated to the most general rule and the s set to the first positive example i.e. a positive is needed to start the learning process
in the learning system to be described for both purposes serves basically a mete interpreter for ll lp grammars which can parse tile concrete grammar given at the outset for both analysis and generation
this is done in order to minilnize the nunnber of training iustanees that need to be generated and hence o nfinimize the number of evmuations that tim teach r lice is to ina ke
if it is positive frorn the g set are removed the rules which do not cover the example and the elements of s set are generalized as little as possible so that they cover the new instance
also if some right hand side is a set which properly includes another right hand side as in rule NUM and rule NUM abowe the latter is not added to the sibling list since we do not want to learn twice the linearization of some two nodes name and vp in our case
its analysis role is needed in processing the firs positive example and the generation role in the production of language examples for all intermediate stages of the learning process which are then evaluated by the teacher
a standard way of expressing tile ordering of nodes in a grammar is i y means of l inear precedence rules in hnme liate l ominance ianear i ocede ce
this fact allows a compact representation of the set of plausible rules hypotheses in the rule space since the set of points in a partially ordered set can be represented by its most general and its most specific elements
in this respect our approach is in sharp contrast to a learning process whose training examples are given en bloc and hence the teacher would of necessity make a great lot of assessions that the learner would never use
the tipoff on the first two events comes at the end of the second paragraph yesterday mccann made official what had been widely anticipated mr james NUM years old is stepping down as chief executive officer on july NUM and will retire as chairman at the end of the year
a type attribute accompanies each tag element and identifies the subtype of each tagged string for enamex the type value can be organization person or location for timex the type value can be date or time and for numex the type value can be money or percent
an algorithm developed by the mitre corporation for muc NUM was implemented by saic and used for scoring the task
the amount of agreement between the two annotators was found to be NUM recall and NUM precision
on the basis of the results of the dry run in which two of the nine systems scored over NUM we were not surprised to find official scores that were similarly high but it was not expected that so many systems would enter the formal evaluation and perform so well
a basic characterization of the challenge presented by each evaluation task is as follows named entity ne insert sgml tags into the text to mark each string that represents a person organization or location name or a date or time stamp or a currency or percentage figure
the results are summarized in table NUM
pruning during online adaptation has two advantages
we tested our algorithm in two modes
within the framework of online learning it is provably see e.g.
a result of such a random walk is given in figure NUM
the average accuracy on unseen tx st data of NUM NUM should be compared to bast line l crforlnan e measures baso d on tnolmbilit y based guessing
the other problem is that we can only specify delays on all constraints on t at once and can not delay individual principles
for the prediction of word associations they achieved best results when modifying each entry in the co occurrence matrix using the following formula
being compared with a heterogeneous one
the question arises on many occasions
a corpus is a collection of texts
the experiments below explore different values for n
noussia hyphenator for modem greek theorem NUM the points immediately preceding vl and immediately following v2 in the strings of expression vlcl c2c cg v2 do not necessarily constitute permissible hyphen points
although phonetics is the ultimate basis for hyphenation our approach is based on the available data which is the orthographic representation of words and not a transcription in a phonetic alphabet such as ipa
this set is i u u x i tj u and although it comprises a relatively great number of elements most of these have low frequency of occurrence in linguistically acceptable words
on the other hand not all diphthong candidates whose second part is stressed split but the candidates in this set that are not simultaneously excessive do always split rule f5 table NUM
however this combination is also considered because such words are regularly used e.g. r pa efivra i invented
NUM the tranformation takes into account the existence of a final s in the uppercase word and tranforms it to the final instead of or according to a corresponding transformation rule
respectively for the case of a final consonant or consonant sequence after v2 according to lemma NUM b a hyphen following v2 is not permitted
it should be noted that the terminology used refers to NUM vowel sequences are sometimes explicitly mentioned in hyphenation rules but usually only in the context of consonant sequences
there are two kinds of repetition adverbs one regulates the whole quantity of the iteration of events such as san kai three times or nandomo many times etc and the other describes the habitual repetition of events such as itumo always or syottyuu very often etc
NUM a body may be divided into paragraphs the p annotation type will be used to identify paragraphs NUM
the specification of these operations is subject to revision based on the experience of implementors in using these sgml representations in applications
the system begins by converting the documentcollection s into documentcollectionlndex es as shown on the left side
however the user may assume that documents that match more arguments are generally ranked higher than documents that match fewer arguments
alternatively an application based on a data base could define an operation for creating a bytesequence from a data base field
to meet this need the architecture defines a class annotationset and a number of operations operating on such sets of annotations
however a set of spans is provided for in order to be able to refer to non contiguous portions of the text
the architecture also has a secondary mission of providing a convenient and efficient environment for research in document detection and data extraction
because annotations are central to the tipster architecture it is expected that applications will have frequent need to access search and select annotations on a document
finally the routing requires a documentcollectionindex which is used to determine weights for the translation of a detectionquery into an routingquery
our approach to disambiguation is to treat the information associated with dictionary this paper is based on work that was done at the center for intelligent information retrieval at the university of massachusetts
there are no morphology routines that can currently handle the problems we encountered with inflectional variants and it is likely that separating related from unrelated forms will make further improvements in performance
the references included two helpful aids during the first three months after total hip replacemenc and aids in diagnosing abnormal voiding patterns
these two senses were found to provide a good separation between relevant and non relevant documents but the distinction is probably not important for machine translation
lexical phrases are generally made up of only two or three words overwhelmingly just two and they usually occur in a fixed order
if we fail to group related senses it is as if we are ignoring some of the occurrences of a query word in a document
to investigate these hypotheses we conducted experiments with two standard test collections one consisting of titles and abstracts in computer science and the other consisting of short articles from time magazine
consider the following description of a search that was performed using the keyword aids unfortunately not all NUM references were about aids the disease
it is likely that different applications will require different types of distinctions and the type of distinctions required in information retrieval is an open question
lexical phrases can be distinguished from a phrases such as sanctions against south africa in that the meaning of a lexical phrase can not necessarily be determined from the meaning of its parts
so the llrob era we address to is to try to detect incoherence
the determination of which items to displace is handled by a cache replacement policy
figure NUM dialogue b is identical to a except for utterances NUM NUM and NUM NUM
next consider the differences between the models with respect to the function of irus
simply reinstantiate an entity in the cache and when they serve as retrieval cues
next consider the differences in status of the entities in completed discourse segments
the occurrence of irus as in dialogue c is one way of doing this
utterance b NUM indicates completion of the embedded segment and signals a pop
the papers by alshawi et a and amengual et al discuss different approaches along these lines
in the rest of the introduction we will introduce very briefly the topics of the four sessions of the workshop
first i contrast the mechanisms of the models with respect to certain discourse processes
if they must be accessible for these inferences to take place as i will argue
if the sentence fails to parse in this pass the parser moves on to pass ve
NUM we are thankful to ken church and the at t bell laboratories for providing us with a prealigned hansards corpus
if an unknown word is encountered the root of that word is likely to also be unknown
database queries are conducted by matching the ideal job as specified by the user against job schemas held in the database
in the example based approach we do not need to be explicit about the structure of the stored example or the inputs
the main advantage of the example based approach is that we do not need to decide beforehand what the linguistic patterns look like
if the string does not return a code it is considered invalid and the user is requested to enter an alternative
the alternative is to allow users to enter a string which is passed to the terminology module to retrieve the appropriate code
the results of a database query are then fed to the generation module for subsequent presentation in the language specified by the user
equation NUM provides a measure of the total distance between two instances by summing the distances between all the constituent parameters
nevertheless our system offers users the possibility of searching in their own language for jobs advertised in a variety of languages
classification of job types along is hierarchies e.g. a wine waiter is type of waiter
regarding the relative evaluation order of f and m structure schema the general principle is all f structure schema are evaluated before any m structure schemata is evaluated i.e. fed to the search operator
each utterance in figures NUM and NUM has been tagged using one or more of the attribute abbreviations in table NUM according to the subtask s the utterance contributes to
fortunately only one of these articles was relevant t o the task
as discussed in section NUM this gives a definite advantage to the dice method over other measures of similarity
for all its complexity this attribute can be extremely important for many of the core problems that computational linguists are concerned with
an important and more relevant set of experiments which deserves careful attention is presented in karlgren and cutting lcb NUM
our goal in this paper has been to prepare the ground for using genre in a wide variety of areas in natural language processing
further practical tests of our theory will come in applications of genre classification to tagging summarization and other tasks in computational linguistics
parsing accuracy could be increased by taking genre into account for example certain object less constructions occur only in recipes in english
another reason for the neglect of genre though is that it can be a difficult notion to get a conceptual handle on
finally nonfiction is a fairly diverse category encompassing most other types of expository writing and fiction is used for works of fiction
the narrative facet is binary telling whether a text is written in a narrative mode primarily relating a sequence of events
this way of looking at the problem allows us to define the relationships between different genres instead of regarding them as atomic entities
our compiler will generate from this a constraint t c v c a c for some appropriate type t
let s w be the prediction of the weighted mixture of all subtrees rooted below s including s itself for w
sproat shih gale and chang word segmentation for chinese table NUM
cai2 neng2 talent followed by j ke4 fu2 overcome
for the examples given in NUM and NUM this certainly seems possible
computational linguistics volume NUM number NUM a set of initial estimates of the word frequencies
these are shown with their associated costs as follows
chinese word segmentation can be viewed as a stochastic transduction problem
and the average agreement between st and the humans is NUM
the first issue relates to the completeness of the base lexicon
the segmentation chosen is the best path through the wfst shown in d
however there are several reasons why this approach will not in general work NUM
instead of converting the case base to a tree in which all cases are fully represented as paths storing all feature values we compress the tree even more by restricting the paths to those input feature values that disambiguate the classification from all other cases in the training material
we did NUM fold cross validation experiments for several sizes of datasets in steps of NUM NUM memory items revealing the learning curve in figure NUM training set size is on the x axis generalization performance as measured in a NUM fold cross validation experiment is on the y axis
this order is fixed in advance so the maximal depth of the tree is always equal to the number of features and at the same level of the tree all nodes have the same test they are an instance of oblivious decision trees cf
the most straightforward distance metric would be the one in equation NUM where x and y are the patterns to be compared and i x y is the distance between the values of the i th feature in a pattern with n features
frequency order is taken into account in this process if there would be words which like once can be rb or in but more frequently in than rb e.g. the word below then a different tag in rb is assigned to these words
consequently only regular expressions were used to collect types the morphological analyzer was not used
the suffix ful marks its base as abstract abstract careful peaceful powerful etc
for example nize could not be stripped from hypothesize because alvey failed to reconstruct hypothesis from hypothes
however for the affixes discussed here NUM percent of the bases were present in the alvey lexicon
p is a place holder for the semantic predicate corresponding to the word sense which has the feature
following this discussion a table of precision statistics for the performance of these surface cues is presented
it was performed for the verbal prefix re and the recall was found to be NUM percent
in addition using many different types of cues should provide a greater variety of information in general
in the first sort the non conformity arises because the cue does not always correspond to the relevant lexical semantic information
will also find it useful to obtain a translation equivalent expression for an idiomatic expression
then the translation of whole expression becomes an one word verb phrase telephone
then the user triggers undo of translation twice returning to b
note that the subject of the relative clause is supplemented by a default element
null figure NUM shows the content of the translation equivalent alternatives window for rareru
as explained in section NUM NUM the method basically assumes simple compositionaiity of translation
figure NUM is a snapshot of the alternatives window for ronbun paper
their post editing function often requires working in a special environment that requires special training
null suppose the input sentence is one shown in figure NUM a
all these operations are optional except for translation triggers to invoke next translation
a a valeney changing transtbrmations as we have already stated we encode senses of verbs in active voice unless a verb has an idiomatic usage with obligatory passive causative and or rllles
NUM constraints on mou hological features that describe any obligatory constraints on the arguments such as case marking verb form in the case of embedded clauses etc
we shall use nth turn to refer to both types allowing intervening exchanges
at the point of misunderstanding the interpretations of the two participants begin to diverge
the sub icct of the senl ences a causative voice marked verb is indicated by causer in the seinani ics fi ame
the last example below illustrales the handling o valency changing ransfortmttions where lexical i ulcs hal dle argument slmllling
NUM the 4note th lt the surface case constraints for these are defined in the ha sic definition of the case fl ame
it is a limitation of the model that we do not distinguish interruptions from clarifications
thus a conversational participant will still need to be able to address actual misunderstandings
for example the axioms representing the linguistic expectations of askref are shown below
there are three important linguistic knowledge relations decomp lintention and lexpectation
the need to account for nonmonotonicity in both the interpretation and production of utterances
figure NUM contains a conversation that includes an example for each of the five types
conversely other misunderstandings are those in which the hearer attributes a misunderstanding to the speaker
this explanation succeeded because each of the conditions of the default for self misunderstanding were explainable
they can also exhibit such differences but represent different concepts such as author authorize
x has property history diabetes property
figure NUM propositions for the first sentence
the key idea of the feature selection is that if we notice an interaction between certain features we should build a more complex feature which will account for this interaction
next we consider narrative progression in quantified contexts as in sentence NUM the basic construction is just the same as in the paradigm structure but now we have narrative progression in the consequent box
the work of the second author was partially supported by a grant from the israeli ministry of science programming languages induced computational linguistics and by the fund for the promotion of research in the technion
however this is not a problem since our analysis of the perfect by the use of the operator perf analyses the eventuality referred to by the main clause as the result state of a previous event
this state holds whenever john is at the beach recorded by the condition that the location time t of sa overlaps the event time tl of john s being at the beach s2 in figure NUM
in our proposed solution the reference time is indeed moved to the right box but it is a different notion of reference time and as will be shown exempt from this criticism
we did not encounter this problem in the drs in figure NUM since although the reference time rl is universally quantified over in that drs as well it is also restricted to immediately follow el
this german system processes spoken input using concept spotting which means that the smallest information carrying units in the input are extracted such as names of train stations and expressions of time and these are translated more or less individually into updates of the internal database representing the dialogue state
given a grammar g and a string w wlw2 wn the parsing or recognition problem asks the question whether w is in l g
in our considerations below we will make heavy use of the well known count invariant for lambek systems benthem NUM which is an expression of the resource consciousness of these logics
but in moortgat s mixed system all the different resource management modes of the different systems are left intact in the combination and can be exploited in different parts of the grammar
a restriction that leaves its proposed linguistic applications intact is to admit a type b o a only as the argument type in functional applications but never as the functor
moreover the notation a k stands for aoao oa k t mes we then define the sdl grammar gr bs l as follows
to obtain the required sequence u we simply choose for the wi terminals the type cs a3 c resp
moreover the fact that only a single formula may appear on the right of make the lambek calculus an intuitionistic fragment of the multiplicative fragment of non commutative propositional linear logic
NUM the claim embodied by sequent u a can be read as formula a is derivable from the structured database u figure NUM shows lambek s original calculus t
kler NUM to show that derivability in the multiplicative fragment of propositional linear logic with only the connectives o and equivalently lambek calculus with permutation lp is np complete
the configuration frequency of the node a now will become the number of times of seeing the node a but not the node ab o
we will see below that this method of distributional tagging although partially successful fails for many tokens whose neighbors are punctuation marks
the unreduced context vectors in the experiment described here have NUM entries corresponding to the NUM most frequent words in the brown corpus
in this paper we will compare two tagging algorithms one based on classifying word types and one based on classifying words plus context
an occurrence of word w is represented by a concatenation of four context vectors the right context vector of the preceding word
the number of total utterances was NUM
NUM NUM touch screen pointing device
the response generator is composed of dialogue manager intention focus analyzer problem solver knowledge databases and response sentence generator as shown in figure NUM lower part
social goals are therefore likely to be particularly salient for them
the domain of our dialogue system is mt
therefore our system displays the history of dialogue
graphical information the system does use these
whole process is carried out as below NUM
on synthesized voice figure NUM spoken dialogue system
as we are essentially interested to np that are actual terms in a domain we will need to decide which nps are actual terms
sometimes however it will be necessary to generate a unique response
figure NUM a simple and incomplete model of conversational interaction
i do n t really remember apology e.g.
a second positive aspect of having an available domain specific terminology is the reduction of the underlying syntactic ambiguity and increase of the parser precision
the suggested model we have arrived at is shown in figure NUM
the initial stage of development and are by no means comprelmnsive
the user could shift these perspectives with one activation of an on screen button
computational linguistics volume NUM number NUM similar to the situation created for topic prominent sentences the sov features of mandarin represent a deviation from the svo order of english
high qtmlity of the obtained clusters are confirmed by the pos tagging experiments
it is based on the n th hypothesis score and the rank so that the amplitude decreases with the rank and with the relative score difference between h1 and h
a number of methods for language driven terminological extraction and complex nominals parsing and recognition have been proposed to support nlp and lexical acquisition tasks
n so we can build a context vector for w as NUM denoted as cvw whose dimension is cr
in this paper we formalize the contexts as a kind of multidimensional real valued vectors so the semantic space can be seen as a vector space
for a particular kind of language we regard its semantic space as the set of all word senses of the language with similarity relation between them
suggesting that agent b may perform better than agent a overall
execute the tests for each of these rules on the input structure and add those passing their test to the conflict set
but it seems to be impossible to ensure that every cluster contains enough words with only mono sense words taken into consideration when building the semantic space
future work can explore the effect of exemplar weighting and feature weighting on disambiguation accuracy
NUM dis st s2 l cos cvt cv2 NUM NUM cvw sal ct w sal c2 w sal ck w where k i cti
nearly NUM NUM example sentences and their translations from the lecdoce were used as training data primarily to acquire rules and to determine mle estimates for the cases of ltp and dp
while the elements john and bill are not within the minimal clauses they are parallel within the main clauses
the case involves coordination in which the coordinated constituents each contain a pronoun as in example NUM
the meaning of the target of sentence NUM is then simply the strict reading derivable by source determined algorithms
we are forced to a view that discourse determined analyses must reduce the issue of vp ellipsis meaning to deaccented vp meaning
finally we note an additional problematic case that to our knowledge has gone unnoticed in the literature
therefore before the ellipsis is resolved the meaning of in john s case must be resolved
b every boyi was hoping that mary would ask himi out but the waiting is over
b john hoped maryk would ask himi out but billj actually knew that shek would ask himj out
we will then argue that this is not so much a discovery as a restatement of the problem
NUM a john s coach thinks he has a chance and jamesj thinks hey has a chance too
in the remove phone text there are six such action expressions listed here in segmented form by firmly grasping top of handset and pulling out
computational linguistics volume NUM number NUM frequently arise in instructions such as the precondition found in the remove phone text when instructed approx
the avoidance nominalization in 11b appears to have been rejected because the argument accidental hangup was itself a nominalization and thus too complex
here is an example of this case 12a the batt low light NUM comes on when the battery is weak
keith vander linden and james h martin expressing rhetorical relations this passage gives an example of the variation of expressional form that is common in instructional text
such expressions could take many conceivable forms all of which are perfectly grammatical la pull out sharply in order to remove the phone
the current study addresses this issue in the context of expressing procedural relations between actions in instructional text that is in written procedural directions
the goal was to allow for the representation of any element of the pragmatic semantic or syntactic context that might be relevant in the analysis
the full analysis of the remove phone text will be given later here we intend to illustrate only the types of manipulations made by the realization statements
in summary we have presented improvements to the exemplar based learning approach for wsd
an interdepemlen y NUM el we it antecedent choi es may arise as well when choosing etween discourse alltece leilts of as a collseqll llc f relative clause attachment which NUM redetermines coindexing
for each anaphoric np y determine the set of possible antecedents x a verify morphosyntactic or lexical agreement with x congruence in person number and gender lexical recurrence ete depending on the type of y b if the antecedent candidate x is intru sentential
also in the actual implementation the x best readings are produced instead of a single best reading
we only changed the handling of zero probability counts to the method just described
NUM for each target node v which has at least one lexical match all of those positions in the score matrix which do nol correspond to a lexicm match o v are set to zero
regardless of the method the retrieval is done via context vector comparisons
cornlen al NUM pp NUM NUM l he expression t NUM rcb lcnotes a tree as a pair of sets v is the set of vertices nodes in he tree and l is the set of edges arcs
in order to inq rovc the precision of alig tment we plan to ext erimenl with w rying the values of the lex fmletions mid penali ies in our scor ing
the user is presented with a ranked list of the most relevant documents
to illustrate in figure NUM there is no lca preserving alignment of the two trees which maps all three of the leaf nodes a b and c into the nodes a b and c
in essence the node vectors are competing to have their values adjusted
intentions encode what the speaker was trying to accomplish with a given portion of discourse
a tool for visualizing information in the time domain is also provided
in this way only three tables would be needed one with the probabilities of the syntactic categories of the lemmas to appear at the starting of a sentence another with the probabilities of the basic suffixes to appear after those words and the third with the probabilities of the basic suffixes to appear after another basic suffix and to make possible the recursion
first of all due to the possibility of recursively composed suffixes concatenating the existing awith n depending on the interaction method ones the system has to again propose a list of suffixes until the user explicitly marks the end of the current word maybe inserting a space character
built as a combination of the previous ones the main idea is to guess the entire current word
the algorithm for region finding and labeling is a two step process
doing this the complexity of operations decrease because there is only the need to treat lemmas and suffixes
this technique called symmetric learning is based upon the use of tie words which provide connectivity between each language s portion of the context vector space
furthermore the additional claims found in only rst or only g s are largely consistent
in most cases the number of words for which aj j is non zero i.e. the co occurring words is several orders of magnitude smaller than the size of the vocabulary
maybe a great number of rules have to be defined to cope with all the variations but in this way the probabilities to guess the rule which is being used are very small because of their variety
to perform this operation the user selects a root word and the trained context vector for that word is determined by a table lookup in the context vector vocabulary
the sections below describe an approach to context vector learning that greatly reduces the amount of computer time and resources required to obtain a trained set of stem context vectors
fully trained vectors have the property that words that are used in a similar context will have vectors that point in similar directions as measured by the dot product
should a coreless segment occur in a g s analysis it can be mapped to a joint schema in rst
since n number of co occurring words is usually much less than n number of vocabulary word stems summing only over co occurring words represents a considerable time savings
finally section NUM presents some concluding remarks and directions for future research
NUM un livre int4ressant et que j aurai du plaisir lire
in these examples the coordinate structure acts as the argument of the verb
2b je sais qu elle a NUM ans et qu elle est venue ici
we do not care of gapping cases as their linguistic properties seem to be different
it remains to integrate right node raising and to extend these cases to more complicated ones
lb hier jean a dans la valse et aujourd hui le tango
i ask them for a bike and for a fishing rod 4d je les leur demande
coordination has mways been a center of academic interest be it in linguistic theory or in computational linguistics
the previous facts argue for the second possibility see also section NUM for criticism of deletion approach
we have been unable to find real examples in our data of constituent arguments undergoing inside out transposition
as the applications below demonstrate the bilingual lexical constraints carry greater importance than the tightness of the grammar
this bracketing is of course linguistically implausible so whether such parses are acceptable depends on one s objective
this new learning law reduces the training time by a factor on the order of NUM over the original context vector learning law with little or no degradation in performance
the formalism s uniform integration of various types of bracketing and alignment constraints is one of its chief strengths
afterwards we introduce a stochastic version and give an algorithm for finding the optimal bilingual parse of a sentence pair
it therefore denotes the mental state of an individual 16a 17a and requires only one argument z of type human
e2 is subtyped as an experiencing event as we consider that the cause of an emotion corresponds to the experiencing of sonlething
since the learning is driven by proximate co occurrence of words the learning results in a vector set where closeness in the space is equivalent to closeness in subject content
if certain adjectives can be restricted to be headed either on the event or the state others can be left underspecified regarding the head
this can however not be generalized to the whole class of mental states adjectives as shown by NUM for example
je suis triste furieux qua tu partes tm sad furious that you are leaving NUM a je suis ing6nieux tm clever b
as a result they will keep this causative sense even when they modify a noun of type human NUM
NUM NUM NUM event structure headed on the state the adjective is projected via the template p el z in the formal role
it therefore selects for an event and gets the causative or manifestation sense examples NUM and NUM
however the formal representation of the suffixes and the way it interacts with the representation of the stem remain to be investigated
the approach to translation of complex nominals described above enables this functionality
the cut act will require the object cut to be a separable object
the resulting representation has a complex telic role with sub qualia
compounds are licensed and interpreted as part of the process of parsing
this limits the set of potential modifiers to those typed as individual
firing for the purpose of performing the activity of hunting
examples of this are given in NUM e f
for example glass door is represented as in NUM
the content of the resulting compound is inherited from the head noun
a consequence of this requirement is that general rule schemata as used in categorial grammar and hpsg can not be used directly in the ovis grammar
the second phase is the selection of the optimal list of maximal projections lying on a single path from the start node to a final node
since semantic analysis is the input for the dialogue manager we have therefore measured concept accuracy in terms of a simplified version of the update language
note that when bigrams are used simply labeling nodes in the graph as seen is nc t a valid method to prevent recomputation of subpaths
this form is a hierarchical structure with slots and values for the origin and destination of a connection for the time at which the user wants to arrive or depart etc
we used a corpus of more than 20k word graphs output of a preliminary version of the speech recognizer and typical of the intended application
the main objective of this paper is to show that our grammatical approach is feasible in terms of accuracy and computational resources and thus is a viable alternative to pure concept spotting
this should enable other slds developers to quickly and efficiently learn to use det at the same level of objectivity as has been achieved during the tests of the tool
NUM NUM a test set of referring expressions
introducing underspecified tags would influence the training and performance of a probabilistic tagger in at least the tbllowing ways a the concerned words would mostly get more alternative tags one for each of the unambigous readings plus one for the underspecified one
if the user enters verwijder die
their initial significance weight is NUM
what is the topic of this e mail
surprising we counted only NUM misses
the original model excluded these double occurrences
NUM NUM inherent limitations of the referent resolution models
result of replacing embedded f marked elements with variables
we further analyze what grammatical components constitute the one tag chunks and find that most of the one tag chunks contam punctuation marks nouns and verbs
we expect each concept node to be activated at least once because these texts were used to create the concept node definitions n this data was handed off to the relevancy signatures algorithm which generates signatures for each text by pairing each concept node with the word that triggered it and calculates statistics for each signature to identify how often it appeared in relevant texts versus irrelevant texts
in these three mappings lob tag in is the most frequent and the only one mapping and in is a candidate for iw
the experimental results demonstrate that definition NUM three parts of speech is more powerful than definition NUM two parts of speech
if the targeted noun phrase is in a prepositional phrase then autoslog uses a simple pp attachment algorithm to attach the prepositional phrase to a previous verb or noun in the sentence which is then used as a trigger word for a concept node
autoslog s heuristics sometimes fail to produce a concept node when the verb is weak e.g. forms of to be when the linguistic context does match any of the heuristics or when circus produces a faulty sentence analysis
we calculate the relevancy rate of each concept node i.e. the number of occurrences in relevant texts divided by the total number of occurrences and the frequency of each concept node i.e. the total number of times it was activated in the corpus
transferable insights are NUM semantic relations which are closely related but differing in a checkable property should be differentiated
figure NUM shows a short cut spanning four levels of the hyponymy hierarchy from noun concept hptd to noun concept triglycerzde
future research will be aimed at determining firstly how we can enrich the information to which the search strategy is sensitive in order to provide a better match with human preferences and secondly which constraints should be relaxed in order to avoid the problem of undergeneration
to evaluate the performance of the chunker susanne corpus which is a modified and condensed version of brown corpus is adopted
t tkakb c d ccg gtrc is defined below where g ta and a rc represent the classes of the instances of ccg std and ccg gtrc respectively definition NUM gatrc is the collection of g s extension of a g e g ta such that l
cases 4hi di have a differently branching derivation in g but can be derived without simulation
the other cases do not require simulation as the same string can be derived in the original grammar
now both of the permutations in NUM can be derived in this extension of ccg std
for the unbounded case we extend the lexicon as in the following example NUM a
since finite features can be folded into a category this can be written as a ccg std without features
the second part choose property is dedicated to test the contextual suitability of the candidate property proposed by next property which may be inappropriate for one of the following reasons criteria NUM and NUM are new ones NUM
partial parser can be directly ported to a new domain
the model was constructed using a set rcb v of the approximately NUM NUM most frequently occurring words in the corpus
assuming robust estimates for the a parameters the resulting model is essentially guaranteed to be superior to the trigram model
conversely when the long range model is consistently assigning higher probabilities to the observed words a partition is less likely
our approach enlists both short range and long range language models to help it sniff out likely sites of topic changes in text
we emphasize that the process of feature selection is completely automatic once the set of candidate features has been selected
the all and none rows include the figures for models which hypothesize all possible segment boundaries and no boundaries respectively
a useful error metric should somehow correlate with the utility of the instrumented procedure in a rem application
so we experimented with a number of different categories fortunately most of them worked fairly well but some of them did not
on the contrary prolog backtracking is used truly for search
for some grammars it turns out that a simplification is possible
since the ego must be recomputed from scratch much less is gained with backtrack points occurring at a higher level e.g.
in fact head corner parsing is a generalization of left corner parsing
clearly underspecification is a concept that arises naturally in prolog
the use of the predicates head link and lex head link is explained below
parse left ds revleftds q0 q e0
furthermore an item of that form is added to the table
for instance in NUM below the verb gehsren to belong takes in one reading a dative np as its object and a nominative np as its subject
for instance in NUM the nominal phrase np der c konom with a masculine head noun is unambiguously nominative identifying it as the subject of the verb
our view of when such a collaborative activity can be entered is very simple the system believes it is mutually believed that one of them has a goal to refer and has a plan for doing so but one of them believes this plan to be in error
in this case the speaker of the acceptance would have inferred by way of rule NUM that the hearer believes the plan to be valid as for the hearer given that he contributed the current plan he undoubtedly also believes it to be acceptable
for some of these upper layer states references are made to the lower layer states that they may spawn to accomplish domain specific sub dialogues
the schema that we give in figure NUM for instance is used to refashion a referring expression plan in which the error occurred in an instance of a modifier action u the decomposition of the schema specifies how a new referring expression plan can be built
NUM the first step pick one object cand chooses one of the objects that matched the part of the description that preceded the error if the speaker is not the initiator of the referring expression then this is an arbitrary choice
bmb system user bel system replace p1 p34 NUM the system on the basis of NUM and NUM applies rule NUM and so assumes that the user will accept the refashioning
bel system achieve p104 knowref system user entity1 antenna1 NUM bel system bel user achieve plo4 knowref system user entityl antennal NUM
c NUM association for computational linguistics computational linguistics volume NUM number NUM sions and identifying their referents to be captured in the planning paradigm but it also allows us to use the planning paradigm to account for how participants clarify a referring expression
NUM bmb system user plan speaker plan goal the system will also add a belief about whether she believes the plan will achieve the goal and if not the action that she believes to be in error
the action headnoun shown in figure NUM has a single step s attrib which is the surface speech action used to describe an object in terms of some predicate which for the headnoun schema is restricted to the category of the object
the variable cand is the candidate set the set of potential referents associated with the head noun that is chosen and it is passed to the modifiers action so that it can ensure that the rest of the description rules out all of the alternatives
we deyme duplicate branch as a branch to be duplicated for graph decomposition such as b c and anchor branch as a branch which i hlbit graph decomposition by duplication such as a d
usability of an sd system refers to the ease with which a user can use the system and the naturalness that it provides
the coverage of the dictionary is high but the degree of ambiguity in swedish is also high actually higher than in english so the texts return from dictionary lookup with NUM of the word tokens carrying more than one analysis
the third type of questions are called linguist s questions and these are compiled by an expert grammarian
an axiomatic treatment is omitted for reasons of space
no foot may cross a morpheme boundary
aggressive minimization and a more compact
NUM partition the factors of NUM
NUM decrement k and return to step NUM
the locality of constraints does not save us here
this is the best intersection problem
no other factors need be involved
there were NUM of such features but they had a very high level of co occurrence and produced the empirical feature collocation lattice of NUM NUM nodes
we did n t specifically evaluate the model but it is about NUM NUM more accurate than a bigram hidden markov model which we used before
together with the constraint language we require a constraint solver that checks constraints for satisfiability usually by transforming them into a normal form also called solved form
as shown in the example many pp ambiguity disappears as soon as a set complex nominals is detected
the result is an overall improvement data compression is around NUM while syntactic ambiguity elimination is about NUM
the main result of this method is to support finer lexicalization in form of complex nominals for lexical acquisition
few attention has been paid to terminology extraction for what concerns the possibilities it offers to corpus linguistics and lexical acquisition
furthermore the detection of a term carried out over single tokens that are morphologically ambiguous improves also the morphological recognition
in table NUM the section headed by attivitd as it has been derived from the enea corpus is shown
note that m1 denotes post nominal adjectives or past participle but also prepositional phrase like dello stato in territorio deilo stato
terminological knowledge consulting a terminologic dictionary before activating a shallow syntactic analyzer is helpful to solve several morphological and syntactic ambiguities
finer lexicalizations like attivit6 antropica are the only way to provide a better input to the target acquisition tasks
the example uses the NUM or nodes a b c and the and nodes NUM through NUM to represent NUM complete parse trees that would use NUM x NUM nodes
finally we also define three types of transition relations across pairs of utterances
i would especially like to thank hiyan alshawi and steve pulman for help and advice on topics relating to this paper
finally in utterance 20e the backward looking center shifts to being mike
cb john cf lcb mike john rcb retain e
NUM in particular for presentational NUM u need not be a full clause
pronouns and definite descriptions are not equivalent with respect to their effect on coherence
but it remains to be seen how readily the equations used for ellipsis here can be integrated into pulman s framework
the global coherence of a discourse depends on relationships among its dp and dsps
for a sequence of utterances to be a discourse it must exhibit coherence
among the goals which were identified were demonstrating domain independent component technologies of information extraction which would b e immediately usefu l encouraging work to make information extraction systems more portabl e encouraging work on deeper understanding each of these can been seen in part as a reaction to the trends in the prior mucs
furthermore while so much effort had been expended a large portion was specific to the particular tasks
it has been claimed that these advantages arise from viewing semantic interpretation as a process of building descriptions of semantic compositions
however from the current perspective of most of the committee these seemed fairly basic aspects of understanding and so an experiment in evaluating them and encouragin g improvement in them would be worthwhile
NUM there were however a number of individual research efforts in information extraction underway before the first muc including the work on information formatting of medical narrative by sager at new york university NUM the formatting of nava l equipment failure reports at the naval research laboratory NUM and the dbg work by montgomery for radc now rome
for eac h executive post one generates a succession event object which contains references to the organiza tion object for the organization involved and the in and out object for the activity involving that pos t if an article describes a person leaving and a person starting the same job there will be two in and ou t objects
last fall we completed the sixth in a series of message understanding conferences which have been organized by nrad the rdt e division of the naval command control and ocean surveillance cente r formerly nosc the naval ocean systems center with the support of darpa the defense advance d research projects agency
the joint venture bridgestone sports taiwan co capitalized at NUM millio n new taiwan dollars will start production in january NUM with production of NUM NUM iron and metal wood clubs a month
however we can simplify this formula once and for all by assuming that for every or node there is only one variable xu that is associated with it and all of its children
parallel terms like john in the example above are those that correspond terms appearing explicitly in the ellipsis
first the precise order in which quantifiers are scoped and ellipses resolved determines the final interpretation of elliptical sentences
for systems using speech recognition the ability to confirm or clarify given information is essential hence system orientated or mixed initiative should exist
tr3 obtains higher matching rates than the other two NUM which shows the effectiveness of the salience constraint in it
the memo table table initially associates every set of arguments with empty caller continuation and empty result value sets
the evaluation result also shows that the rule using all constraints collected from the empirical study performs better than one with simpler constraints
this is true for recognizers defined in the manner just described left recursive grammars yield programs that contain ill founded recursive definitions
note that just as in the first cfg encoding the resulting program behaves as a top down recognizer
nevertheless these models still fail to represent explicitly grammatical structure and semantic relationships even though progress has been made in other work on their statistical modeling
our successful application of mixture psts for word sequence prediction and modeling make them a valuable approach to language modeling in speech recognition machine translation and similar applications
when observing the text long ago and the first the matching path from the root ends at the node and the first
this additional factor is multiplied at all the nodes along the path from the root to the maximal context of this word a leaf of the pst
in that case however the probability of the next word wn l remains independent of this additional prior since it cancels out nicely
for each new observed word wn the likelihood values ln s are derived from their previous values l i s
in those models the length of contexts used to predict particular symbols is adaptively extended as long as the extension improves prediction above a given threshold
in batch mode the structure and parameters are held fixed after the training phase making it easier to compare the model to standard n gram models
parse forests can represent an exponential number of phrase structure alternatives in o n NUM space where n is the length of the sentence
thus the probability of w l is propagated along the path corresponding to suffixes of the observation sequence towards the root as follows
rare words tend to bias the prediction functions at nodes with small counts especially if their appearance is restricted to a small portion of the text
the initial taxonomy was developed from an analysis of forty eight freshman and sophomore writing evaluation samples from gallaudet university a liberal arts university for the deaf seventeen writing evaluation samples from the national technical institute for the deaf ntid a deaf school in delaware and five letters and essays written by asl natives and collected through the biculturai center in washington dc
extensibility of an sd system implies that ad null ditional queries within a given application can be added to the system without much trouble
null we shall call such a generalisation operation simplifying if the normal form of is not larger than any of the input constraints normal form
several of these were particularly interesting
these four pronouns are atypically functional
some of the shortfall in performance on the organization object is due to inadequat e discourse processing which is needed in order to get some of the non local instances of th e org descriptor org locale and org country slot fills
table NUM verbs with the highest semantic entropy
moreover english comas are often lost in translation
table NUM displays a sorted sample of the pronouns
the correction and inconsistent states increase the robustness of the system by making it possible to continue even in the presence of errors
semantically light words are more likely to be paraphrased or translated non literally
entropy is a functional of probability distribution functions pdf s
the openness of these presentations has always been highly commendable
the phase ii NUM month workshop was the 10th such workshop
frequent formal metric based evaluations have been a hallmark of the tipster text program
the interested reader is directed to these sources for additional information and details
evaluation driven research the foundation of the tipster text program
my assessment in a phrase is very well
the relevant evaluations are only highlighted in the following paragraphs
the foundation of the tipster text program dr john d prange
in this way a single success has been quickly multiplied
all four of these tasks were done using english source texts
for instance in our training set the word the was tagged with a number of different tags and so according to our lexicon the is ambiguous
this decision procedure is usually called selective sampling
s is most frequently used as a possessive ending but after a personal pronoun it is a verb john s compared to he s
however not knowref m whoisgoing is among these intentions while active knowref m whoisgoing ts NUM
although the metaplans add flexibility by increasing the number of possible paths they also add to the problem of pruning and ordering the paths requiring additional heuristics
the database contains also a set ps8i cj of case filler examples for each case cj of each sense 8i indicates that the corresponding case is not allowed
let us consider figure NUM with the basic notation as in figure NUM and let us compare the training utility of the examples a b and c
lewis et al estimate certainty of an interpretation by the ratio between the probability of the most plausible text category and the probability of any other text category excluding the most probable one
if the speaker disagrees with a displayed interpretation she can challenge it directly or decide to respond in such a way that the hearer must infer a misunderstanding
given these three subtheories an interpretation of an utterance is a set of ground instances of assumptions that explain the utterance
the influence of ccd i.e. o in equation NUM was extremely large so that the system virtually relied solely on the sim of the case with the greatest ccd
arg max tuf x NUM xex we will explain in the following sections how one could estimate tuf based on the estimation of the certainty figure of an interpretation
in japanese a complement of a verb consists of a noun phrase case filler and its case marker suffix for example ga nominative or o accusative
NUM NUM extending the test to other compilation techniques
many principles regulate the distribution of chains
this proposal is based on two observations
the children are loved t by john
modularity and information content classes in principle based parsing
deleting the prefix suffix x ixl NUM results in a word x is any string of length NUM to NUM
finally only unsaturated chains are chosen
who do you think that john likes
in fact the similarity is superficial
locality information minimality antecedent government e
for instance whether the previous word is tagged as to infinitival or to preposition may be a good cue for determining the part of speech of a word
figure NUM shows the distributions for boycott and somewhat
words are randomly generated by a poisson process n
the data are the same as in figure NUM
the data are the same as in figures NUM NUM
table NUM good keywords have more idf
it compares the observed idf with ii f an estimate based on f assume that a document is merely a bag of words with no interesting structure content
the standard use of the poisson in modeling the distribution of words and ngrams fails to fit the data except where there are almost no interesting hidden dependencies as in the case of somewhat
many applications such as information retrieval text categorization author identification and word sense disambiguation attempt to discriminate documents on the basis of certain hidden variables such as topic author genre style etc
a good keyword like boycott is farther from poisson chance than a crummy keyword like somewhat by almost any sense of closeness that one might consider e.g. idf variance entropy
table NUM performance on training set
na then boundary elseif global pro
table NUM performance on test set
otherwise it is classified as non boundary
cue prosodic s f c s f c true NUM
when the computer asks is the knob position at 10t we have greatest expectation for a response of either yes or no lesser expectation for a sentence answer such as the knob position is five
for example goal user ach prop obj prop narne propvalue NUM denotes the goal that the user achieve the value propvalue for a particular property propname of an object obj
however simulations run on the collected data raised the percentage of utterances that are correctly understood from NUM NUM to NUM NUM NUM unfortunately besides improving understanding through verification of utterances initially misinterpreted the system also verified NUM NUM of the utterances initially interpreted correctly
NUM such a modification might also be appropriate in NUM in actuality a small component of the total parsing cost is the expectation cost based on dialog context but that weighting is negligible compared to the weighting of the parse cost the predominant factor in computing total cost
obs prop obj propname propvalue propvalue specified example a yes no question lcb e.g. is the switch up main expectation NUM yes no response and NUM a direct answer as in the above case
for example the goal of setting the switch position to up may be represented as goal user ach prop switch position up while the goal of observing the knob s color would be goal user obs prop knob color propyalue where propvalue is an uninstantiated variable whose value should be specified in the user input
the fact could concern a piece of state information e.g. that the switch is located in the lower left portion of the circuit that an action needs completing e.g. putting the switch up is desirable or that a certain property should or should not be true e.g. there should be a wire between connectors NUM and NUM
in this example it is a simple encoding in fuf notation of a binary relation class assignt holding between two entities the individual ai and the set assignt set1
NUM one sentence beat as head no lexical optimization the jazz who extended thei r winning streak to three games defeated the bulls
NUM one sentence streak as head with lexical optimization the jazz extended their winning streak to three games with a victory over the bulls
the assignt type is mapped onto the modifier slot of the attribute role of the head clause when it is found that the assignt type and class assignt share an argument
the further constraint equation c lcb i rcb t rr indicates that the argument s index set should include j c f the conditions for using the original indexed formula
using the fuf surge package implementing a generation system thus consists of decomposing nonsyntactic processing into subprocesses and encoding in fuf the knowledge sources for each of these subprocesses
floating constraints have not been addressed in a general way in previous work most systems implicitly hardwire the choices or permit only one or two of many possibilities
a verb with an object pronoun the same verb form with all the other object pronouns forms preserving the person attribute while changing the gender and number ones
these words are assumed similar to the analysis in the sense that we expect them to have approximately the same frequency in the language as the analysis they belong to
this does not mean that the modifier will necessarily be realized by a clause as in the following sentence NUM ai has assignments which involve programming
further syntagmatic choices determine which concepts will function as modifiers of any of these roles ultimately surfacing as relative clauses prepositional phrases or adjectival describers
for b6j the change NUM o rule must apply because the chngo feature for b6j is unspecified and therefore can take any value for zb however the rule is prevented from applying by the feature clash and so the default rule is the only one that can apply
in compilation one may compose any or all of a the two level rule set b the set of affixes and their allowed combinations and c the lexicon see kaplan and kay NUM for an exposition of the mathematical basis
irregular forms either complete words or affixable stems are specified by listing the morphological rules and terminal morphemes from which the appropriate analyses may be constructed for example irreg dit dire present 3s v v affix only
the change e l rule simplified slightly here makes it obligatory for a lexical e to be realized as a surface when followed by t r or l then a morpheme boundary then e as long as the feature cdouble has an appropriate value
it is also possible to view the spelling patterns and production rule tree used to produce a form for chore the trace slightly simplified here is as in figure NUM the spelling pattern NUM referred to here is the one depicted in a different form in figure NUM
however there is often a trade off between run time efficiency and factors important for rapid and accurate system development such as perspicuity of notation ease of debugging speed of compilation and the size of its output and the independence of the morphological and lexical compo null nents
however although the obvious spelling rule spell change au ll ill laui e allows this change it does not rule out the incorrect realization of beau e as e beaue shown in figure NUM because it only affects partitionings where the au at the lexical level forms a single partition rather than one for a and one for u
usability mixed initiative approach helps to promote usability
following is model q0 and fl fl fn
it is an enhanced offspring implemented in c of the preprocessing module of sra s multilingual natural language processing system NUM
in addition to the general differences between met and the muc NUM ne task described earlier there were a few spanish specific issues which had to be tackled for met
for example the system thought valle rivas in olijela del valle rivas was a location as valle also means valley
abcdefg where abc is a person name and defg an organization name with no space or other punctuation in between
as discussed in section NUM nametag can utilize the results of name recognition in subsequent segmentation to partially solve this problem
the met evaluation has revealed several japanesespecific challenges which must be solved in order for the system to achieve even higher performance
nametag is an automated text indexing system that recognizes and classifies names and other key phrases such as time and numeric expressions
here recognizing the person name requires recognizing the adjacent organization name first while recognizing the organization name requires recognizing the person name first
table NUM pairwise probabilities for example coref
templates a and b having previously been merged
we created three splits of training and test data
additionally it is essential that the segmenter be more robust and accurate in order to improve performance on name recognition and other japanese text processing tasks even further
however good segmentation in turn relies on good name recognition as names are usually not in the lexicons and thus tend to cause segmentation errors
this is a very young project operational for only a few months
a sampling of knowledge bank entries illustrating these features is given in table NUM
performance on the october NUM muc NUM test s we did not do well
concerning specific target difficulties we perhaps had the most trouble with organizations
where hollis i s a reference to a prior mentioned company
l his problont is c alh d data slmr sc css
description of the saic dx syste m as used for muc NUM
these considerations motivated the three stage strategy we adopted for the dx project
suggest the nearby occurrence of person names street names and organization names respectively
it might be the requirement that the chicken are eaten by you
that the consumption of the chicken by you is obligatory is possible
this an interesting problem not encountered in otherwise similar speech recognition models
we have also been able to simplify the generation of morphological variants
we took a semantic representation generated automatically from a short japanese sentence
a low complexity model that results in high accuracy disambiguation is the ultimate goal
there are several forms of such adjuncts e.g. at five
existing generation models however select the preposition according to defaults or randomly
NUM the new companies have as a goal the foundation at february
now we need an algorithm for converting generator inputs into word lattices
champollion will correct this error by putting aujourd and hui back together and identifying them as a rigid collocation
spot trw s multi lingual text search tool
key terms in the browser display are highlighted
for this functionality internationalized support is inadequate
predicate big x specifies the size of the thing referenced by x and predicate take x specifies that the person referenced by x takes something then we tag the argument position with attribute
to be able to produce more precise results we distinguish between two attributes that describe the same argument position of the same predicate according to the thing in the other attribute position of the predicate when needed
we also assume that if a certain thing does not occur in any of the nce t s that translates an attribute then the thing can not be combined with the attribute
if fi put and fo tp t contain constants that do not occur in the logic of rldt the generalization rule of fol can be used to derive more general results by replacing the constants by unique variables
given an i ldt t for each pair consisting of the ground lexical atomic formula fi put and the ground database atomic formula fo tput from the dictionary of t we find the set s of conditions a c such that a c fi pu fo p is nce t
we shall call the quadruple a c fi put foutv t nontrivial nce t nnce t iff formula c a a does not imply truth of foutp t in the theory f
the types of queries produced by this system typically showed the repetition of key terminology combined with the elimination of irrelevant terms
a new input sentence like a woman whistles can now be parsed by combining subtrees from this corpus
current work is focusing on improving the performance of mltr methods applying the methods to new languages and making use of new retrieval engines
our ep approach considered the comparative evaluation of document score vectors as an objective measure of the relative fitness of a query to the collection
the resulting queries were given to university of massachusetts amherst who ran them against the spanish trec document collection using spanish inquery
the corpus itself was extremely large however which we hoped would offset the difficulties of using a distinctly different type of text
an added benefit of translating only the query is that queries can be prepared with no special weighting scheme applied to the terms
since inversion is permitted at any level of rule expansion a derivation may intermix productions of either orientation within the parse tree
limiting query size is important because most search engines like infoseek restrict the size of a query to around NUM characters
the counts of the pooled terms are then compared with the counts for the entire un training corpus to evaluate their statistical significance
the input to the wag generator is basically a speech act specification although this specification includes ideational and textual specification
figure NUM the speech act network serving a phatic function for instance greetings farewells and thank yous
what is relevant changes as the text unfolds as the rhetorical structure is realized
the strategies build upon stochastic inversion transduction grammars sitgs a formalism that we have been developing for bilingual language modeling
this approach has been followed with systems such as penman fuf and mumble
NUM textual metafunction how the text is constructed as a message conveying information
the speech act of figure NUM is specified to be and initiate propose
in most systems the sentence realiser has no access to the kb of the text planner
in the stand alone approach the sentence planner needs knowledge of how ideational specifications are formulated in the sentence specification language
in particular it simplifies the representation of speech acts with no ideational content such as greetings thank yous etc
the probability of a derivation tl o o tn is the product of the probabilities of these subtrees
although a linearity assumption such as this breaks down when considering phrasal elements in most languages it is reasonably accurate for many terms and becomes increasingly accurate at the sentence level and above
in terms of language acquisition and parsing if we assume that a sequence of words has been generated from a phrase structure grammar it suggests that we can recover internal structure by grouping sub sequences of words with high mutual information
moreover since these classifications are based on structural properties and the structural properties of natural language can be studied more or less directly there is a reasonable expectation of finding empirical evidence falsifying a hypothesis about language theoretic complexity of natural languages if such evidence exists
whilst each of the above features are important it is not obvious which are more important to naturalness than others
can ask questions and obtain answers via the theorem prover
whereas the high frequency terms extracted in the previous method provide a baseline for examining improved methods high frequency terms are themselves not necessarily the best terms for discriminating the significant features involved in text retrieval
when the training is complete words that are used in a similar context will have their associated vectors point in similar directions
using this approach hash fimction collisions not withstanding each unique stem results in a unique entry and thus a unique context vector
mundial may be accessed at http crl nmsu edu ang ml ml html
cle is a bidirectional unification and feature based grammar written in prolog
from this information it can decide what the deep structure should look like
NUM NUM NUM NUM email hercules dsv su se
the rest of the type of sentences are context dependent i.e. rule etc
steps a re no cessaa y to achieve higher mt a ccura y for a slightly wider ra nge of sentences tha n those included in il however tit de gree of hnprovement in mt a ccura lcb y
it wouhl b difficult for a nyon to dist ute tit id a tha t th world with web www has b tt tit most phertom na l
recall that under dominance a higher subtree index indicates domination by a lower index
system shouhl be combined with liloro powe rful ra llllll r forma lisnls v e l elieve tha t the theory a nd imph mo nta tion of NUM a ttern NUM ased mt will contribut to the realiza tion of con puta tiona NUM linguistic theories
even with the o v a algorithm the calculation is not practical for a large vocabulary of order NUM NUM or higher
the first implementation of a semantic dop model yielded rather encouraging preliminary results on a semantically enriched part of the atis corpus
you can see for instance that among the named entity systems the two lowest scoring systems ar e significantly different from each other and all of the all of the other systems
most single chinese characters can be joined with other character s to form different words
in sections NUM NUM to NUM NUM we will discuss the effects of various factors on our results
its results can be used to bootstrap or refine a bilingual lexicon compilation algorithm
however translators would recognize this error readily and would not consider it as a translation candidate
we had assumed that the trigram word order in chinese and english are similar
context heterogeneity can be used both as a clustering measure and a discrimination measure
the kvec algorithm was previously used to find co occurring bilingual word pairs with many candidates
it is necessary to perform tokenization on the text by using a chinese tokenizer
although we can not say at this point that this result is significant it is to some extent encouraging
to measure the similarity between two context heterogeneity vectors we use simple euclidean distance g
examples of projective relations are in front of between leftmost and beside
empirical evidence shows that deictic gestures are indeed exactly coordinated with their associated verbal expressions
none of them had knowledge of the internal affairs of edward s referent resolution model
the dialogue manager coordinates input and output expressions and controls the linguistic and graphics processes
we have discarded this option because it is unattractive from a computational point of view
no effort is wasted in creating associative cfs for individual instances that are not mentioned
the interpretation of temporal deixis critically depends on the time of speech of the utterance
consequently misunderstanding occurred more often in declarative mode NUM NUM of user utterances than directive mode NUM NUM of user utterances
their results for task oriented dialogues about constructing a water pump showed that experts had control of the dialogue about NUM of the time
an important open question is the degree to which the parameters of the transition model for task oriented dialogues for repair assistance are domain dependent
after reviewing other empirical studies in the next section we will address the impact of these results on future research in section NUM
in section NUM NUM we investigate the relationship of this notion of dialogue control based on linguistic goals to our task goal notion of control
in brief a backward looking center is associated with each utterance in a discourse segment
we analyzed our dialogues using this notion of control with one modification assertions that were a continuation of the current topic left the initiative unchanged
the first and third sessions occurred a week apart and the second session normally occurred three or four days after the first session
and the distance in parse or grammar space between competing proposals is at most one relation switching v p to v n for instance whereas three different rule probabilities may need to be changed in the scfg representation
note that the definitions ensure clause boundedness of quantificational nps lcb l l rcb allow indefinites to take arbitrary wide scope lcb NUM h rcb and assign proper names to the top level of the resulting udrs lcb iv z v h zffj rcb as required
the phrase walking on ice acts like a verb it can conjoin with a verb john walked on ice and sang and takes verbal modifiers john walked on ice slowly
we try to remedy this via rules
this approach has been successfully used for syntactic analysis using corpora with syntactic annotations such as the penn tree bank
in order to do such an analysis a method of computer intensive hypothesis testing was developed by saic for the muc NUM results and has been use d for distinguishing muc scores since that time
finally we discuss an implementation and report on experiments with two semantically analyzed corpora atis and ovis
another method would be to make use of a thesaurus since we have found that human judgement is often based on synonymous information such as real synonyms or anaphora
table NUM distributed frequencies of words
table NUM probability distributions of words
document classification using a finite mixture model
let s be a predetermined number
null robustness partial parser can handle ungrammatical input
NUM and NUM give the details
tool that assists web programmers in creating interactive user driven applications
the dialogue ends when the user decides to quit the system
4one can certainly assume that m n
such conflicts must be resolved before proceeding in the dialogue
this can often be resolved based on the surrounding context
the constraints of the application drive the analysis of utterances
this state usually spawns a sub dialogue which may or may not be domainspecific
natural language query supplying information to lolita and then asking questions about thi s information
this paper presented the paradise framework for evaluating spoken dialogue agents
p the pair is a case where a word changes its part of speech during translation
portability sable was initially implemented for french english then ported to spanish english and to korean english
the objectivity problem then reduces to that of whether different analysers arrive at the same classifications of the identified problems
we found that nearly all dialogue design errors in the user test could be classified as violations of our guidelines
we have no reason to expect this correlation to change across domain specific and general lexicon entries
figure NUM demonstrates the effectiveness of the mrdand corpus based filters with details in table NUM
up to NUM can be considered useful essentially as is the v category alone
the fraction of entries that are useful as is remains roughly the same at NUM
as a first step in addressing the transfer problem we have recently included a det novice in the team
these concordances contained up to the first ten instances of that pair as used in context
for the unfiltered translation lexicons recall on the 3rd likelihood plateau and above was NUM NUM
when all entries on and above the 2nd plateau were considered recall improved to NUM NUM
when done off line these operations of compo null sition and compaction dominate the time corresponding to the construction of the transducer for each individual rule
consider two sets of pronunciation rules from the bell laboratories german text to speech system the size of the alphabet for this ruleset is NUM as noted above
null in order to set the stage for our own contribution we start by reviewing salient aspects of the kaplan and kay algorithm
further many of our applications demand the ability to compile weighted rules into weighted fsts transducers generalized by providing transitions with weights
we also indicated an extension of the theory of rule compilation to the case of weighted rules which compile into weighted finite state transducers
the culprits turn out to be the two intersectands in the expression of rightcontext p in figure NUM
many algorithms used in the finite state theory and in their applications to natural language processing can be extended in the same way
rewrite rules are used in many areas of natural language and speech processing including syntax morphology and phonology NUM
the overall best results are achieved with the most elaborate corpus containing all information about the three last syllables suggesting that eontra trommelen important information is lost by restricting attention to only the last syllable
to test whether the tree has actually learned the problem and has not just memorized the items it was trained on the 9eneralization accuracy is measured by testing the learned tree on a part of the dataset not used in training
we have shown by example that machine learning technique s can profitably be used in linguistics as a tool for the comparison of linguistic theories and hypotheses or for the discovery of new linguistic theories in the form of linguistic rules or categories
o l si t nt i ern categories correctly predit ted over the l en test sets in the ten fold cross validation exlmrililo n lcb
e.g. the so callea major lass features obstruents nasals liquids glides vowels efficiently explain syllable structure eomput ation lint are of little use in the definition of rules describing assimilation
we distinguish between database frequency frequency of a suffix in a list of NUM diminutive forms of nouns we took from the celex lexical database NUM and corpus developed by tile center for lexical ilfformation nijmegen
the set of critical tokenizations was defined as the set of minimum elements in the poset
NUM if t contains different classes then at choose a test feature with a finite num null ber of outcomes values and partition t into subsets of examples that have the same outcome for tim test chosen
an interesting problem is that the etje versus kje problem for words ending in ing couht hot be solved by referring only to the last syllable c4 NUM and any other statistically based induction algorithm overgeneralize to kjc
the simple lexical transfer rule in 3a relates the german intensifier echt with the english real NUM the variables l and a ensure that the label and the argument of the german echt are assigned to the english predicate real respectively
for example the word term could be synonymous with word as in a vocabulary term sentence as in a prison term or condition as in terms of agreement
only the set of semantic conditions is shown in NUM the ot her levels of the multi dimensional vit ret resentation whidt contain additional senmntic 2for l resentaj ion purposes we have simplilied the actmd vit representations
if we have a query about aids tile disease and a document contains aids in the sense of a hearing aid then the word aids should not contribute to our belief that the document is relevant to the query
NUM we were unable to determine whether the results of the experiment were due to the incorrectness of the hypothesis being tested that distinctions in part of speech can lead to an improvement in performance or to the errors made by the tagger
the present success of the statistical approach in part of speech analysis seems then to form an exception to the general feasibility of the rule based linguistic approach
collocations some word pairs such as projects and houses are not direct translations
engcg leaves them pending mainly because it is prohibitively difficult to express certain kinds of structural generalisation using the available rule formalism and grammatical representation
4actually it is possible to define additional heuristic rule collections that can optionally be applied after the more reliable ones for resolving remahdng ambiguities
compared to the NUM NUM accuracy of its best competitors this result suggests the feasibility of the linguistic approach also in part of speech analysis
as will be shown below some filter cascades sift candidate word pmrs so well that training corpora small enough to be hand built can be used to induce more accurate translation lexicons than those induced from a much larger training corpus without such filters
if word a matches word e and word d matches words c and g then d is paired with g so that when the sentences are written one above the other the lines connecting the matching words do not cross
whenever a candidate translation pair s t appeared in the list of translations extracted from the mrbd the filter removed all word pairs s not t and not s t that occurred in the same sentence pair
for example low frequency words are not considered since their positional difference vectors would not contain much information
figure NUM a model linking aac design approaches pragmatic features of conversation and user goals
therefore the whole co occurrence graph obtained from a general corpus cont in q subgraphs each specializing in one topic
when a portion of a corpus specializes in a topic we can sti l extract a co occurrence graph from the portion
figure NUM problematic structure in mscc clustering mblguous word clusters of dffferem toph s figure NUM problematic structure in bicon
star NUM NUM NUM brand NUM NUM NUM a word with the same meaning in the same context NUM words
the same situation is observed for children it would merge topics of childbirth and education into a graph if it was not duplicated
NUM completeness coherence unicity principle the terminal class must inherit exactly one type of realization for each function of the actual subcategorization NUM
the idea is to benefit from a principle based formalism such as NUM ipsg and from computational properties of an i tag
the inheritance hierarchy of tlpsg and its principles are flattened into a lexicalized formalism such as tag
along with the canonical trees a family contains the ones that would be transformationally related in a movement base approach
passive for french NUM and secondly a family may contain the trees with extracted argument or cliticized in french
its classes contain information on the arguments of a predicate their index their possible categories and their canonical syntactic function
equality of nodes can also be inferred mainly using the fact that a tree node has only one direct parent node
then the node constants are translated and the least satisfying tree is computed leading to the target tree of figure NUM
the writing of the hierarchy has been the occasion of updating structures and equations insuring uniform and coherent handling of phenomena
agentless passive restructuration dative shift for english or even augmentation of arguments some causative constructions NUM introducing an agent whose function is subject
the director of company abc sat in the care der kellner begriiflte eine bekannte
in this section tile central composition principles for german are worked out
NUM and NUM can be implemented directly in a sign based formalism like hpsg
an expression t for target is given iff it ha s
s non f marked constituents that contain f marked subconstituents need to be giwm as well
this arrow again will overrule the dashed arrow from begriiflt to ihren besuch
the principles composing the representation are worked out formally in sec NUM
fn NUM NUM applies to integrated constitnents
resolution will be dealt with in more detail in sec
NUM with the nodes in the graph corresponding to the o sem values
if we understand paths to start at their left hand end we can construct a notion of path extension a path p2 extends a path p1 if and only if all the attributes of p1 occur in the same order at the left hand end of p2 so al a2 a3 extends al al a2 and al a2 a3 but not a2 al a3 etc
thus if we assume for example that the lexeme donate is an instance of di verb as defined above and that word5 and word6 are inflected tokens of donate then we will be able to derive the following theorems subcat rest first syn cat pp subcat rest first syn pform to subcat rest rest nil
the value specifications map directly to extensional statements while the global inheritance descriptors operate just as the local ones adding at most one further value statement for each global NUM we continue to oversimplify matters here
because of this the handling of multiple NUM nonfunc3 perhaps comes closest but adding statements about extensions of either a or b quickly breaks the illusion that the two are in some sense unified
if this were the entire definition of wordl the default mechanism would ensure that all extensions of syn including the two that concern us here would be given the same definition inheritance from verb
this is simply the traditional analysis of homonymy encoded in datr there are two entirely distinct lexemes with unrelated meanings that happen both to be nouns and to have indistinguishable mort hological roots
in both these inheritances only one node or path was specified the other was taken to be the same as that found on the left hand side of the statement and come respectively
notice here that each element of a value can be defined entirely independently of the others for mor form we now have an irfl eritance descriptor for the first element and a simple value for the second
this mechanism allows a datr definitional statement to be applicable not only for the path specified in its left hand side but also for any rightward extension of that path for which no more specific definitional statement exists
this means that once distributed in this way the global descriptors form a network of weak equality relationships just as the local descriptors do and distribute the simple values alone in the same way
the second type of mistagging was caused by wrong assignments of boss by the guesser
the accuracy of the tagging on unknown words dropped by about NUM in general
out of NUM NUM words of the text NUM NUM were unknown to the small lexicon
in this paper we describe a new fully automatic technique for learning part of speech guessing rules
in average ending guessing rules were detected to cover over NUM of the unknown words
in the evaluation of tagging accuracy on unknown words we pay attention to two metrics
all other words were considered as unknown and had to be guessed by the guessers
thus an ending guessing rule looks exactly like a morphological rule apart from the class which is always void
thus two different sets of guessing rules prefix and suffix morphological rules together with their frequencies are produced
while tokenization evaluation is important it would be more effective if employed at a later stage
it is very interesting to try the conditional probability model as mentioned in a footnote in section NUM the improvement of the probabilistic model of noun phrase parsing may result in phrases of higher quality than the phrases produced by the current noun phrase parser
it is noticed that accurate segmentation is not essential for good retrieval
such a system should make it easier to retain the child s interest during the rather long training period
seen this way the alep enterprise is orthogonal to ours there is no significant overlap or conflict
additive architectures for managing information about text add markup to the original text at each successive phase of processing
table NUM determines the appropriate discourse segment level s of an utter null assume that they are accessed in the total order given
in place of an sgml dtd an annotation type declaration defines the information present in annotation sets
particular implementations make their own decisions regarding issues such as parallelism user interface or delivery platform
we classify and review current approaches to software infrastructure for research development and delivery of nlp systems
first one may embed the information in the text at the relevant points the additive approach
grammars lexicons and for doing a particular set of tasks in le in a particular way
the task is motivated by a discussion of current trends in the field of nlp and language engineering
a tool selects what information it requires from its input sgml stream and adds information as new sgml markup
the implementation of vie in gate however provides an existence proof that the original conception is workable
until the advent of statistical methods in the mainstream of natural language processing syntactic and semantic representations were becoming progressively more complex
from early trials of the talk system the need for another category of speech act emerged
try to associate different conversational styles with different utterances for later use in predicting which utterances will be appropriate
a decomposition node n is a source tree node for which it is safe to prune suboptimal translations of the subtree dominated by n
in their paper justeson and katz describe some linguistic properties of technical terminology and use them to formulate an algorithm to identify the technical terms in a given document
however like the cases discussed above some speakers find the target clause pronoun in example 11a to require light accent under this interpretation
more generally for source determined analyses the dependency follows from the method for determining anaphoric or coreference relationships in the target uniformly from those in the source
indeed it is the thesis of this paper that discourse determined analyses are not alternatives to source determined analyses but rather are dependent on them
thus while examples such as NUM were not directly addressed in work on the equational method their analysis within the framework is straightforward
it has long been known that anaphoric relationships in the implicit meaning of an elided verb phrase depend on corresponding anaphoric relationships in the source of the ellipsis
hardt argues again that an approach predicated on determining parallelism between source and target would be unable to account for the natural reading of this sentence
in order to counterexemplify a discourse determined analysis it would suffice to provide an elliptical sentence whose pronominal reference possibilities are different for its corresponding unelided form
as we would expect the unelided version shown in NUM also appears to allow this reading without requiring any accent on the target pronoun
this order is created multimodally by drawing the curved route and saying
input signals from each of the modes can be assigned meanings
recent empirical research has shown conclusive advantages of multimodal interaction over speech only interaction for map based tasks
multimodality also offers the potential for input modes to mutually compensate for each others errors
null kehler and shieber anaphoric dependencies in ellipsis NUM ivan likes his mother and hisi father and jamesj likes his mother and his father too
although problematic examples for a source determined analysis of vp ellipsis can be found these do not provide an argument for moving to a discourse determined analysis
the adoption of typed feature structures facilitates the statement of constraints on integration
for c u h occtlrrence which is adjacent to all occnrl elwe of the prior word a new chunk is created or an existing chunk is extended as appropriate
although allowances were made for the words on the stop fist the missing punctuation marks always forced a break in clmnks fl equently limiting the size of chunks which could be found
the corpus is not aligned at any granularity liner than the sentence pair subsententia alignment is perfornled at run time based on the sentence fragments selet ted and the other knowledge sources
panebmt like other example based translation systems uses essentially no knowledge about its source or target languages what little knowledge it does use is optional and is supplied in a eoniiguration file
the engine can not generate a chunk for a word unless it both co occurs with either the preceding or following word somewhere in the corpus and at least one occurrence can be successfiflly aligned
despite all these difficulties panebmt was able to cover NUM NUM of the input it was presented with good chunks and generate some translation for more than 84ordinarily not outpnt at all
input texts are segmented into sequences of words occurring in the corpus for which translations are determined by subsententia alignment of the sentence pairs containing those sequences
the chart is passed through a statistical language model to determine the best path through the chart which is then output as the translation of the original input sentence
alter processing all input words in this tmmner the engine has determined all possible substrings of the input containing at least two words which are present in the corpus
the intention is that the user will be able to access these pre loaded utterances quickly during the conversation
so the algorithm can produce much broader coverage than the original lloce
we also observed that words involved in such inter sense relations are frequently underspecified
including these labels in the mrd based lexical database offers several positive effects
NUM dagan et al NUM dagan and itai NUM
no simple method can solve the general problem of wsd for unrestricted text
we will show that this labeling task is made simplex for several reasons
race track and harbor are situationally related to bank through the location relation
in addition those cases when the algorithm failed can also be analyzed
moreover the proposed algorithm is compared with other approaches in available literature
the NUM parameters i.e. the joint parameters define the joint probability distribution of the feature variables
the model of independence has complexity level i NUM since there are no interactions among the feature variables
the naive bayes classifier is based on a low complexity model that is shown to lead to high accuracy
combined with the other feature variables this results in NUM NUM NUM possible feature vectors or joint parameters
for sparse samples fss is a natural choice since early in the search the models are of low complexity
the rightmost point on each plot for each evaluation criterion is the measure associated with the model ultimately selected
in figure NUM bss bic has gone past much more accurate models than the one it selected
this form reveals another important difference between the confusion probability and the functions d a and l described in the previous sections
the main system grammar is large an d highly ambiguous so a powerful algorithm is required
this has the undesirable result of assigning unseen bigrams the same probability if they are made up of unigrams of the same frequency
we thus consider pairs wl w2 e vi x v2 for appropriate sets NUM NUM and v2 not necessarily disjoint
1to the best of our knowledge this is the first use of this particular distribution dissimilarity function in statistical language processing
also one needs to have training data for which the correct senses have been assigned which can require considerable human effort
pre storage of phrases is in this view seen as suitable only for passing simple frequently used messages for very routine exchanges such as greetings and good byes and for delivery of noninteractional monologue as in giving an address
since the temporal connectire in this sentence is before the relation between these two markers is one of precedence
the situation described by john s always squinting when the sun is shining is analyzed as a complex state s3
the stative clause causes the introduction of sl which includes the reference time rl
we show some applications of this solution to additional temporal anaphora phenomena in quantified sentences
narrative progression is dealt with by using the feature rpt or reference point
the eventuality in the main clause is interpreted with respect to this reference time
this sentence denotes a state sl which includes the then current reference time
we continue in this fashion updating the reference time until the second sentence in the discourse is processed
this is done with the free combination operation shown in definition NUM
many of these can probably be split into new independent groups
the size of the ns however represent the nmnber of cases
v a 6ien and ien are disjunctions and conjunctions
compaction is another operation designed to optimize feature structures for unification
many algorithms for etticient mfitication of lea tare structures with dependent disjunctions have been propose d
at runtime this ineans that all exponential alnount of processing can be saved as well
NUM experimental results indicate that our method of using the finite mixture model outperforms the method based on hard clustering of words
every arc is labeled with the same symbol pair as its destination state with the class symbol in the upper language and the tag symbol in the lower language
this allows us to ignore the meaning of the sentence end position as an hmm barrier because this role is taken by the unambiguous class cu at the sentence end
the upper side of the sentence model usdeg describes the complete but of rare subsequences which would encrease the size of the transducer without contributing much to the tagging accuracy
we call the model an s type model the corresponding fst an s type transducer and the whole algorithm leading from the hmmto the transducer an s type approximation of an hmm
we extract the lower side of this composition where every sequence of n 1l remains unchanged from the beginning up to the first occurrence of an unambiguous class c
however since an s type transducer is incomplete it can not tag sentences with one or more class subsequences not contained in the union of the initial or middle subsequences
the tagger outputs the stored word and tag sequence of the sentence and continues in the same way with the remaining sentences of the corpus
cl t12 or i adj noun noun a state is created and labeled with this same pair fig
NUM x log s NUM NUM x log s NUM NUM NUM hcm can handle the data sparseness problem quite well
training data NUM num of doc in test data NUM num of type of words NUM avg
word based method a simple approach to document classification is to view this problem as that of conducting hypothesis testing over word based distributions
an hpsg based generator for german an experiment in the reusability of linguistic resources
itowever combining resources not designed to work together is not trivial
we view the importation of lcs s from the english lcs database into arabic and spanish as a first approxin ation to the development of complete lexicons for these languages
these same lines a pragmatic component could provide a mechanism for det ermining that certain fully matched responses e.g. john hurled the book inlo the trash are not
with poss head thing NUM the variables in the representation map between lcs positions and their corresponding thematic roles
non leaf node i.e. toward NUM loc in the enc rar definition is filled in through unification at the internal toward node
the extra information is generally ignored although it is recorded in case the instructor decides to program the system to notify the student about this as well
this answer is processed by the system to produce the following lcs the lcs is stored by the tutor and then later matched against the student s answer
the results have been hand checked by native speakers using the class grid lexeme format which is much easier to check than the flfily expanded lcs s
one of the main contributions of our work is that it provides a relation between levin s classes and meaning components as defined in the lcs representation
initially lexicall was designed to support the development of lcs s for english only however the same techniques can be used for nmltilingual acquisition
in addition it is expected that verbs in the same levin class may have finer distinctions than what we have specified in the current lcs templates
this kind of discrimination can be realized with computationally effective algorithms by exploiting the lexical taxonomy of wordnet postponing more complex and expensive computations to the domanin specific analysis
this includes both syntactic information i.e. argumental positions prepositions on indirect objects category type and semantic information such as thematic roles and selectional restrictions
returning to the example of section NUM NUM NUM
null as mentioned in section NUM NUM NUM
aic found seven features to be relevant in both bss and fss
for this set of NUM words the average number of senses per noun is NUM NUM while the average number of senses per verb is NUM NUM
consider a nonterminal x in a cell covering the span of terminals tj tk
ideally we would multiply the inside probability by the outside probability and normalize
both the first and second pass parsing algorithms are simple variations on cky parsing
thus we run our first pass computing this expression for each node
experiments will show that the time it saves easily outweighs the time it uses
our final thresholding measure is p x xfl nj xk
figure NUM optimizing for lower entropy versus op timizing for faster speed olding
because of these issues we chose not to implement an agenda based system for comparison
the methodology and utilities described can be applied to other discourse processing problems such as other forms of ellipsis and anaphora resolution
using the exact match measure the system performs with NUM NUM accuracy and the baseline approach achieves NUM NUM accuracy
this work involves a post hoc evaluation of the system output and it appears that evaluation is based on head match although this is not discussed explicitly
modify the module and its interfaces build a wrapper around it
however because of the approach taken in annotating the penn treebank this nonmaximal vp is not displayed as a vp
this is ruled out by the syntactic filter because the vpe is contained in sbar a sentential complement to said
application development may be expedited by adapting previously developed architecturally compliant modules
there is no comparable work we are aware of dealing with vpe resolution to our knowledge this is the first empirical study of a vpe resolution algorithm
there is a preference for similar parallel elements that is the elements surrounding the ellipsis site and the elements that correspond to them surrounding the antecedent
first the power of the scoring function is enhanced since the smoothing techniques can reduce the estimation errors especially for unseen events
the results show that smoothing the unreliable parameters degrades the training set performance however it improves the performance for the test set
such a definition makes the distance be the difference of the lengths or norms of the score vectors in the parameter space
NUM lex l12 syn l1 this model uses a bigram model in computing lexical
NUM l1 means to consult one left hand side part of speech and l2 means to consult two left hand side parts of speech
therefore a measure namely selection power sp is proposed in this paper to give additional information for evaluation
the current model fails to deal with such problems because only syntactic information from two left contextual nonterminal symbols is consulted for computation
an accuracy rate for parse tree selection is improved to NUM NUM by applying the robust learning procedure to the tung hui chiang et al
on the average there were NUM NUM alternative parse trees per sentence for the training set and NUM NUM for the test set
this results in a recursive process that returns a chain of belief justifications that could be used to support bel
in addition we provide a process for selecting among multiple possible pieces of evidence
this paper has presented a computational strategy for engaging in collaborative negotiation to square away conflicts in agents beliefs
when a node is visited both the belief and the evidential relationship between it and its parent are examined
since agents are autonomous and heterogeneous it is inevitable that conflicts in their beliefs arise during the planning process
discussions with candy sidner stephanie elzer and kathy mccoy have been very helpful in the development of this work
the third heuristic is based on c nice s maxim of quantity and prefers justification chains that contain the fewest beliefs
furthermore the system can better justify a belief in which it has high confidence should the user not accept it
excluding these two types of errors accuracy on word alignment was NUM NUM
b ellipsis ellipsis commonly occurs in a sentence where for reasons of economy style or emphasis part of the structure is omitted
subsequently the constituents of each sentence pair are matched according to some heuristic procedure
lilllst occllr a onllna it a coordina ting
given an input expression the analogical matching algorithm must determine the example expression that is closest in meaning to the input expression
the experimenter was seated in front of the computer console
following this severa NUM possibili ias for the alterna tive
some schematic rules to achieve this might be s lcb if vsem rcb np lcb if s rcb vp lcb if vsem subj s agent a a rcb the sentence semantics is taken from vp
in order to properly combine the meaning of the adjp with that of the nbar as a conjunction say to give the meaning of the mother np some feature on the adjp like sere will at least have to be mentioned in building the np meaning
a category consists of a set of feature equations written lcb fl vl f2 v2 fn vn rcb feature names are atoms feature values can be variables beginning with an uppercase character atoms beginning with a number or a lowercase character or categories
given this it would be nice to be able to have a single entry for the verb send that encapsulated all these alternatives rather than listing them all as separate lexical entries as is done in all grammatical formalisms i am familiar with except of course those that allow explicit disjunction
pp lcb agent a a if and vsem vlf vsem rcb p lcb rcb np lcb rcb a nonagentive pp co oinsitsmeaning to that ofthe ve and passesthe agentthread unchanged
an additional requirement of an automatic translation system is that it should be possible to improve the translation quality by expending additional effort
thus our grammarian might write something like np lcb agr a rcb det lcb agr a rcb adj lcb agr a rcb n lcb agr a rcb this is then compiled into a set of rules as follows
each selector picks out a position in the complement corresponding to the position of the selector in the list the first selector on the list will pick out npl for an np ppl for a pp the second will pick out np2 for an np pp2 for a pp and so on
in fact the unweighted overlap metric specifies exactly the same ordering as the naive back off algorithm table NUM
this approach will try to alleviate the above mentioned problems
the present research is concerned with route descriptions rds and their translation into NUM dimensional graphic sketches
the information is complete enough for a real life situation of finding one s way
tu tombes sur le bpstiment a NUM the inter sequence connections e.g.
using the route model some elements missing in the text can be inferred
for the time being we decided to do with simple symbolic elements without a fine distinction between landmarks
it is difficult to judge whether the downgrade is located right after the turn or a little further
relays are abstract points initiating transfers and may be covered by a turn
we deal with a type of discourse whose informational content may seem quite easy to represent in a graphic mode
it is crucial to predict which morphological or lexical items are likely so that candidates can be weighted appropriately
the internal representation does not matter
this ensures that there is always an output string at the end of the sequence with possibly underspecified segments
the result of these projects is however not available to the research community
after computing for each feature the entropy reduction incurred by splitting the set we choose the best feature which yields maximum entropy reduction
the use of computer computer simulations to study and build human computer dialogue systems is controversial
as in the blackboard architecture a central data structure is maintained which contains selected results of all components
NUM NUM which contains NUM rules and a small lexicon containing only the words that appear in the test sentences
figure NUM shows the top three systems in trec NUM and the top three systems in trec NUM
the fact that passage retrieval which provided substantial improvement of results in trec NUM did not help
a short summary of the techniques used in these runs shows the breadth of the approaches
a soft boolean query is created from the topic but no topic expansion is done
they concluded that improvements from combining results only occurred when the input techniques were sufficiently different
in trec NUM the routing topics corresponded to the trec NUM adhoc topics i.e. topics NUM NUM
the test documents for trec NUM were the documents on disk NUM see section NUM NUM
in trec NUM a slightly different methodology was used to select the routing topics and test data
the median of NUM new relevant documents occurs for a topic with NUM original relevant documents
for example with the generic relation send three semantic case roles are associated called agent goal and recipient
the she of NUM is considered to refer to carla the last mentioned female but the user actually referred to alice
since pointing yields both the screen location pointed to and the object positioned at that location it is the interpreter s job to disambiguate
an additional opportunity in simulated pointing that is not available in normal gesturing is the provision of feedback about the success of a pointing gesture
because the notions of deixis and anaphora make sense only in the language mode we can not apply this distinction to the action mode
usually nl test sentences are made up by evaluators designers themselves but we think made up test sentences may to some extent be unconsciously biased
since there are no benchmarks available to evaluate referent resolution models we had subjects interact with edward to compile a set of referring expressions
this is a general problem for discourse and dialogue segmentation
each arc connects two states and it is associated to an input symbol and an output substring that may be empty
individual coders can come to internally stable views of game structure
a corpora NUM NUM sentences in english extracted from computer manuals and related documents are collected and are parsed by the behaviortran system NUM which is a commercialized englishto chinese machine translation system developed by behavior design corporation bdc
the structural appearance of the resulting referential description can be controlled
since the decomposition of the normal form structures has been carried out in the top down and lefimost first manner the case subtree ft depends on its previously decomposed case subtrees which are either the siblings or the ancestors of the subtree fi m
figure NUM a scenery with tables cups glasses and books
for many natural language processing tasks e.g. machine translation systems usually require to apply several kinds of knowledge to analyze input sentence and represent the analyzed results in terms of a deep structure which identify the thematic roles cases of constituents and the senses of words
to simplify the computation of the semantic score a structure normalization procedure is taken beforehand by the semantic interpreter to convert a parse tree into an intermediate normal form called normal form one nf1 which preserves all relevant information for identification of cases and word senses
several concepts are intended to meet these desiderata i
case identification models to derive the case identification model it is assumed that the required information for case k n l n parts of speech tk l and the word w has be identification from the parse tree j represented by the nf1
we show that instead of enumerating the various syntactic constructions they enter into with the different senses which arise it is possible to give them a rich typed semantic representation which will explain both their semantic and syntactic polymorphism
the situation permits building a large variety of expressions for accomplishing this purpose
in the predecessor algorithms this check is done for each referent separately
null both classes of adjectives exhibit the property of syntactic polyvalency being able to appear in several distinct contexts with optional complement structures as illustrated in NUM NUM and NUM
we plan to give a more detailed analysis of this problem and present a method for choosing the parameters in a future paper
we would therefore expect that our character based transformations would not work as well with thai since a context of more than one character is necessary in many cases to make many segmentation decisions in alphabetic languages
for example an experiment in which we randomly removed half of the words from the english list reduced the performance of the greedy algorithm from NUM NUM to NUM NUM although this reduced english word list was nearly twice the size of the thai word list NUM vs NUM the longest match segmentation utilizing the list was much lower NUM NUM vs NUM NUM
nevertheless even from this flawed initial approximation our rule based algorithm learned a sequence of NUM transformations which nearly doubled the word recall improving the score from NUM NUM to NUM NUM a NUM NUM error reduction
as demonstrated by the experiment with the nmsu segmenter the rule sequence algorithm can also be used to improve the output of an already highly accurate segmenter thus producing one of the best segmentation results reported in the literature
NUM formal triste elje NUM tbmc p e3je agentive exp ev e2je que tu partes NUM formal ing6nieux el je telic intellec act ev e3je partir
from a simple chinese word list the rule based algorithm was thus able to produce asegmentation score comparable to segmentation algorithms developed with a large amount of domain knowledge as we will see in the next section
the score produced using this variation of the maximum matching algorithm combined with a rule sequence NUM NUM is nearly equal to the score produced by the nmsu segmenter segmenter NUM NUM discussed in the next section
since the average word length is quite short in chinese with most words containing only NUM or NUM characters NUM this character as word segmentation correctly identified many one character words and produced an initial segmentation score of f NUM NUM
this is an improvement over previous parameterized approaches in which cross linguistic divergences frequently induced timing discrepancies of one to two orders of magnitude due to the head initial bias that underlies most parsing designs
our model on the other hand is more concerned with efficiency issues broad scale coverage and cross linguistic applicability we produce all possible parse alternatives wherever disambiguation requires extra sentential information
computational linguistics volume NUM number NUM in english the setting would be i in korean the setting would be ii
the current approach provides an alternative to filter based designs that avoids these difficulties by applying principles to descriptions of structures without actually building the structures themselves
each node has a completion predicate that determines whether an item at the node is complete in which case the item is sent as a message to other nodes
we combine the benefits of the message passing paradigm with the benefits of the parameterized approach to build a more efficient but easily extensible system that will ultimately be used for mt
a message containing the attribute value barrier is used to represent an x structure containing a position out of which a wh constituent has moved but without yet crossing a barrier
constituent order the relative order between the head and its complement can vary depending on whether the language in question is i head initial or ii head final
r1 states that any p e lcb cl c2 c3 c4 rcb on the first pattern tape and c on the second root tape with no transition on the third vocalism tape corresponds to c on the surface tape this rule sanctions consonants
in such a case the two level model succeeds in finding two level analyses of the word in question but fails when parsing the word morphosyntactically at this stage the parser is passed a root vocalism and pattern whose feature structures do not unify
the surface lexical restrictions in the contexts could be written out in more detail but both rules make use of the fact that those contexts are analyzed by other partitions which check that they meet the conditions for an omitted stem vowel or omitted spread vowel
operations which are defined on tuples of strings can be extended to sets of tuples and relations
now the incremental cost and benefit of adding a single extension w cr to a model that already contains the extensions w el may be defined as follows
the fact that our model performs significantly better using vastly fewer parameters argues to replace the incremental cost formula ale w a with a constant cost of NUM bits extension
in NUM NUM we present a heuristic model selection algorithm that adds parameters to an extension model only when they reduce the codelength of the data more than they increase the codelength of the model
where dj is the set of all contexts in d that are proper suffixes of another context in d the first term encodes the number n of internal vertices using the elias code
the dictionary d of contexts forms a suffix tree containing ni vertices with branching factor i the m tree contains n i l ni internal vertices and no leaf vertices
null in contrast the substring e establish is overwhelmingly followed by the character m rarely followed by e and never followed by either i or space
given the frequencies of these internal vertices we may calculate the number no of leaf vertices as no NUM n2 2n3 3n4 m NUM am
the third consequence is that the length of the longest candidate context can increase by at most one symbol at each time step which impairs the model s ability to model complex sources
appendix a contains the annotation design specification for these entities
each collection identifier and document identifier pair passed to it
numbers against existing relations and connects them to named entities
we are currently working with the customer to determine evaluation criteria
tokenizatiou creates a buffer of tokens from the document s text
cables are delivered to canis via the cable delivery system server
for each entity the process validates against existing index relations
it validates the address and number information against existing data relations
the types of addresses captured are location residence etc
the types of numbers captured are phone license etc
NUM i.e. an unordered set of attribute val pairs
since the discriminative learning procedure only aims at minimizing the error rate in the training set the training set performance can usually be tuned very closely to NUM when a large number of parameters are available
because the total number of shift actions equal to the number of product terms in equation NUM is always the same for all alternative syntactic trees the normalization problem is resolved in such a formulation
this hybrid tying robust learning approach reduces the number of parameters by a factor of NUM NUM from NUM NUM x NUM to NUM NUM x NUM and achieves NUM NUM accuracy rate for parse tree selection
this is not surprising because starting the robust learning procedure with different initial points would still lead to the same local optimum if the starting region where the initial points are located has only one local optimum
ri where sf i g is the selection factor for the ith sentence ni is the total number of alternative syntactic structures for the ith sentence ri is the rank of the most preferred candidate
currently qe is set to ten times the size of the possible outcomes of xm i.e. qa NUM x the number of possible tags for the part of speech transition parameters
as the parameters are adjusted according to the learning rules described above the score of the correct candidate will increase and the score of the incorrect candidate will decrease from iteration to iteration until the correct candidate is selected
the effects of the parameter smoothing techniques on the robust learning procedure are investigated in section NUM next the parameter tying scheme used to enhance parameter training and reduce the number of parameters is described in section NUM
a random variation was built in to avoid too much repetition of exactly the same words
the easy handling of topic shift is a major problem for users of an aac system
for example suppose agents a and b had different strategies for presenting the value of depart time in addition to different confirmation strategies
the first condition is accomplished by simply placing p to the left and right of r
furthermore this operation need only be done once
matchplus learning equations several points should be noted
stem j co occurs with word stem i tl design parameter chosen to optimize performance a u desired context vector dot product for target i and co occurring stemj figure NUM
desired dot products are found such that words that tend to co occur will have context vectors that point in similar directions while words that do not co occur will have context vectors that tend to be orthogonal
the current matchplus context vector learning law is presented in figure NUM and discussed in section NUM this learning law can be derived as a stochastic gradient descent procedure for minimizing the cost function
adding these solutions to x and x2 yields an expression similar to that of figure NUM of course the fact that the resulting vectors must be normalized makes the analogy only approximate
the former can be gained after applying a certain viewpoint chosen by the speaker and the latter one is stored in the lexical entry of a lexeme l
the figures in the random row were calculated by randomly generating a number of segments equal to the number appearing in the test data
langenscheidts t1 has a total of NUM subject areas
in general useful patterns fall into one of two categories a patterns that frequently extract relevant information and rarely extract irrelevant information or b patterns that frequently extract relevant information but often extract irrelevant information as well
we have put a new spin on the original system by applying it exhaustively to an untagged but preclassified training corpus i.e. a corpus in which the texts have been manually classified as either relevant or irrelevant
on tst4 the autoslog ts dictionary actually achieved higher precision than the hand crafted dictionary for recall levels NUM and produced several data points that achieved NUM precision the hand crafted dictionary did not produce any
it should be noted that although we are using a simple phrase like notation for the patterns they are actually concept nodes activated by an nlp system so the words do not have to be strictly adjacent in the text
to bypass the need for an annotated corpus we created a new version of autoslog that does not rely on text annotations the new system autoslog ts can be run exhaustively on an untagged but preclassified corpus
it seems relatively safe to assume that concept nodes with a relevancy rate below NUM are not highly associated with the domain and that concept nodes with a total frequency NUM are probably not going to be encountered often
autoslog generates a concept node that is triggered by the verb killed and activated when the verb appears in an active construction the resulting concept node recognizes the pattern x killed and extracts x as a perpetrator
the source language input string is first analyzed by a parser which produces a languageindependent interlingua content representation
she also suggests including some of the information captured by various rating heuristics as premises in the rules allowing that these new premises may be assumed by default
an additional class between medium and low frequency
satisfaction is typically calculated with surveys that ask users to specify the degree to which they agree with one or more statements about the behavior or the performance of the system
where ti is the sum of the frequencies in column i of m and t is the sum of the frequencies in m tl tn
this dramatically reduced the codebook size and the dimension of the feature vectors
recall that the claim implicit in figure NUM was that the relative contribution of task success and dialogue costs to performance should be calculated by considering their contribution to user satisfaction
it would be useful to know how users perceptions of performance depend on the strategy used and on tradeoffs among factors such as efficiency speed and accuracy
note that depending on the strategy that a spoken dialogue agent uses confusions across attributes are possible e.g. milano could be confused with morning
his method is supported by a small NUM rule grammar
in those cases only productions that are explicitly admitted are allowed
the results in tables NUM and NUM give an indication of performance levels
with our current size corpus there is not enough data
using this method each tag is in NUM tripos and NUM bipos elements
it should always be positive and asymptotic to maximum and minimum bounds
this level of classification can be done automatically in future
now a partial parse can support such a process
for instance the subject must contain a noun type word
a larger dictionary in later versions will address this problem
speakers knowledge of language must somehow encode such cases with patterns of use of individual nouns in relation to these adjectives emerging on the basis of that knowledge
we have described two new approaches to automatic bracketing of parallel corpora which are particularly applicable to languages where grammar resources are scarce
it should also avoid being too specific since to be effective at bracketing its structure must accomodate chinese to a reasonably broad extent
lexical productions of the form a x y may generate multiple word sequences i.e. x and g may each be more than one word
to indicador indicador ayuda expansi6n previsiones crecimiento comercio comercio narraci6n relaci6n parentesco m4xico ciudad gripe patria campo regidn amor semejante parecido tanto el laca china mar t4 porcelana vitrina coalln corea corea corea mexicana mexicano m xico note that china has been replaced with both china and porcelana as a result of this simple lexical substitution scheme and that relations has included the familial sense parentesco
each of these methods was purposely limited to as simple a scheme as possible however so there is plenty of room for improvement and further experimentation the average precisionrecall curve for all NUM queries is shown in figure NUM
in a pure parse parse match approach however the monolingual parsers must arbitrarily select one bracketing with which to annotate the corpus
what is significant is that due to the relatively small number of parameters being trained convergence is achieved within two or three iterations
this comparison between two outputs can also be used to compare an output and an expected output
people who are interested in getting annotated corpora are invited to contact the authors
filters can be applied in order to show or to hide specified dependency and coordination relations
aunt mind uncle husband cousin mothet daughter brother niec in able NUM and a small number of others like them they represent only a small proportion of the NUM target words subjected to the analysis
such a process would seem to be particularly important when accounting for our understanding of abstract words such as similar and justice which lack concrete the author is supported by the carnegie trust for the universities of scotland referents
travel planning also differs from scheduling in having more types of interactions
since we can obtain from native speakers an assessment of the correct senses of target words in different contexts we do have a means for determining how often a particular technique is able to give the correct sense for a particular target word
whilst it seems reasonable to suppose that children acquiring word meanings would be able to make use of more than this limited amount of context information the analyses were carried out to investigate performance of the system under such crude conditions
despite the existence of the groupings shown c lte wolds wc s d revtion of moving window through text cal cluster analysis was carried out over them using euclidean distance between vectors as a similarity metric
it was found on examination of the dendrograms resulting from the cluster analyses that even using this extremely impoverished source of information about the target words did permit a limited number of semantically coherent groupings of words to be created
secondly the lack of an objective measure of the clusters obtained means that assessments of the success of a particular technique for categorizing language may well be unreliable it is quite possible to focus on the attractive
firstly it is difficult to compare dendrograms rigourously which means that it can be difficult to determine which of a number of alternative approaches or sets of parameters is turning out to be the most successful
scheduling consists almost entirely of negotiation dialogues except for openings and closings
this latter expectation is then satisfied in clause NUM c
for such models the m step in the em algorithm can be carried out exactly and the parameter update formulas are
substitution sites figure NUM grammatical categories
any other substitution would violate the principle
the analysis then continues exactly as in that of example NUM above
the categories of our tag based approach consist of nodes and binary trees
it contains NUM words and NUM discourse units primarily clauses
in the case of wbm the number of parameters is large the training data size however is usually not sufficiently large for accurately estimating them
if we consider the specific case NUM in which a word is assigned to a single cluster and p wlkj is given by lcb NUM
described in linguistic terms a cluster corresponds to a topic and the words assigned to it are related to that topic
from the viewpoint of nlp the task is noun phrase parsing i.e. the analysis of noun phrase structure
racket may however be more indicative of cl than shot because it appears more frequently in cl than shot
in summary fmm always outperforms hcm in some cases it performs better than wbm and in general it performs at least as well as wbm
a human can follow the sequence of words in such a document associate them with related topics and use the distributions of topics to classify the document
these notions lead us to equate practical parsers with the class of c parsers which keep track of c derivations and may also calculate general substring derivations as well
there are at most m NUM a rules since we have an a rule for each non zero entry in a similarly there are at most m NUM b rules
the standard algorithm for converting cfgs to cnf can yield a quadratic blow up in the size of the grammar and thus is clearly unsatisfactory for our purposes
intuitively the use of higher quality phrases might enhance document indexing more effectively but this again needs to be tested
since some parsers require the input grammar to be in chomsky normal form cnf we therefore wish to construct a cnf version g of g
however his results concern on line recognition which is a harder problem than parsing and so do not apply to the general offline parsing case
w i the constant time constraint encodes the notion that information extraction is efficient observe that this is a stronger condition than that called for by lang
at the time of parsing noun phrases the structure of any noun phrase np s np is determined by
all these constellations seem to need correction
deviance from usual distributional behavior of single components can be used both as marker of non compositionality and specific hints of domain relevance
upon recalculation of the free bigram statistics aided design will be demoted in value and the false evidence for aided design as a preferred association in some contexts will be eliminated
for instance one may use corpus specific transducers e.g.
figure NUM vol NUM dhit distribution of the first NUM sen
the sentence is initially represented by a sequence of wordform plus tag pairs
how specific grammars and heuristics can be used is obviously application dependent
the application of each transducer composes it with the result of previous applications
errors between adjectives and past participles and to refine the tagset
finally other syntactic functions are tagged within the segments
in a collection of medical documents for example wilson s disease an actual rheumatological disorder may be used as a lexical atom whereas in a collection of general news stories wilson s disease reference to the disease that wilson has may not be a lexical atom
this 1degnote that the clarit process used as a baseline does not reflect optimum clarit performance e.g. as obtained in actual trec evaluations since we did not use a variety of standard clarit techniques that significantly improve performance such as automatic query expansion distractor space generation subterm indexing or differential query term weighting
a problem may arise when processing a phrase such as program aided design if program aided does not occur frequently in the corpus and we use frequency as the principal statistic we may incorrectly be led to parse the phrase as program aided design
the impossible combinations include pairs such as noun adjective noun adverb adjective adjective past participle adjective past participle adverb and past participle past participle among others
for example one useful heuristic is that we should use a higher threshold of reliability evidence for accepting the pair adjective noun as a lexical atom than for the pair noun noun a noun noun pair is much more likely to be a lexical atom than an adjective noun one
there are three types of vcs infinitives present participle phrases and finite verb phrases
only the following category combinations are allowed for lexical atoms noun noun noun lexatom lexatom noun adjective noun and adjective lexatom where lexatom is the category for a detected lexical atom
this example serves to illustrate the essential observation that motivates our heuristics for identitying lexical atoms in a corpus NUM words in lexical atoms have strong association and thus tend to co occur as a phrase and NUM when the words in a lexical atom co occur in a noun phrase they are never or rarely separated
it is based on the paradigm of representing patterns that express the kinds of descriptions we expect unlike previous work we do not encode semantic categories in the patterns since we want to capture all descriptions regardless of domain
hence they are therefore likely to satisfy the criteria of pertinence to our task such as the likelihood of the sudden appearance of new entities that could n t possibly have been included a priori in the generation lexicon
using circsim tutor one of the important topics which beginning medical students must learn is how blood pressure is regulated in the human body
the negative feedback loop which controls this process known as the baroreeeptor reflex can be a difficult topic for students
this indicates that the probabilistic approach is more suitable than the cosine approach for document classification based on word distributions
as seen in tab NUM our method also helps resolve all of these problems
the finite mixture model we propose is particularly well suited to the representation of a category
we define for each category a finite mixture model based on soft clustering of words
for example the system only generates a hint when the student makes a mistake on the first try at a question
notice that in practice the parameters in a distribution must be estimated from training data
we then applied fmm hcm wbm and cos to conduct binary classification
it conducts a conversation with a student to help the student learn to solve a class of problems in cardiovascular physiology dealing with the regulation of blood pressure
we treat the problem of classifying a document as that of conducting the likelihood ratio test over finite mixture models
we considered the following questions NUM the training data used in the experimentation may be considered sparse
in order to evaluate the performance of the current chinese parser we are using the following measures NUM matched precision mp number of correct matched constituents in proposed parse number of matched constituent in proposed parse number of correct matched constituents in proposed parse number of constituents in treebank parse NUM crossing brackets cbs ffi number of constituents which violate constituent boundaries with a constituent in the treebank parse
NUM boundary prediction precision bpp number of words with correct constituent boundary prediction number of words in the sentence NUM i abeled precisign lp number of correcvi abeled constituents in proposed parse number of correct matched constituent in proposed parse NUM sentence parsing ratio spg number of sentences having a proposed parse by parser number of input sentences table NUM shows the experiment results
for example n p NUM vp NUM NUM pp NUM NUM np NUM NUM indicates that the probability for an open bracket under the context of noun n and preposition p to be the left boundary of a verb phrase vp is NUM NUM a prepositional phrase pp NUM NUM and a noun phrase rip NUM NUM
for each causal relation in the knowledge base the student model keeps a record of whether the student is correct or mistaken about that relationship
for instance i t brackets noun phrases containing genitives in the following way noun phrase NUM s noun phrase NUM
after determining the sense distribution for the target in each set we could project which nouns in the subcorpora are likely to be sense indicators for the target adjectives in these samples
NUM the target adjectives in all NUM test sentences were manually disambiguated both with respect to the antonyms and in some cases with respect to other senses not associated with either antonym
in fact under a deeper analysis these three cases are consistent with the pertinent attributes and should not be treated as errors at all
light pieces of food may be either not dark or not heavy so the noun provides no substantial aid to interpretation
in the aphb corpus at large the new old contrast applied to wine relates chiefly to contexts of production of the wine or of the introduction of a type of wine
the appendix gives the formula needed to project from the disambiguated subcorpora to the corpus at large the probability of each sense of the target adjective given the noun it modifies
in some sentences in fact this noun is the only real basis within the sentence itself for inferring the sense of old the old man answered this time
the young old contrast relates instead to the maturation of some wines or more generally to the developmental phases through which wine passes while aging over a period of years
some nouns have two or more common senses that disagree in the value of relevant attributes and thus were not recovered as indicator nouns their senses might well be reliable indicator features
for our experiments we employed a normal form transduction grammar so a NUM for all f NUM the aproductions used were for all z y lexical translations for all z english vocabulary for all y chinese vocabulary the b z y distribution actually encodes the english chinese translation lexicon
the derivation proceeds as illustrated in figure NUM finally yielding the two structures in figure NUM and figure NUM note that some of the links originating with the np nodes are inherited during the derivation
more precisely we require the following
this structure shapes a text s meaning and assists its readers in deciphering that meaning
these attributes include has basic unit layers fused parts and protective components
next knight visits the location partonomic connection node which is an elaboration of location description
during megasporogenesis the megaspore mother cell divides in the nucellus to form NUM megaspores
hence they are unaffected by inappropriate attributes that were installed on a concept erroneously
the use of linguistic heuristics can assist statistical analysis in several ways
computational linguistics volume NUM number NUM question what happens during embryo sac formation
during embryo sac formation the embryo sac is formed from the megaspore mother cell
there is the time difference and it will take t elve hours
on the contrary ebmt generates all the possible candidates combining suitable phrases
if available information from other occurrences of plant in the discourse may override this classification as described in sec tion NUM
in contrast our algorithm uses automatically acquired seeds to tie the sense partitions to the desired standard at the beginning where it can be most useful as an anchor and guide
our current work differs by eliminating the need for hand labelled training data entirely and by the joint use of collocation and discourse constraints to accomplish this
column NUM shows the performance of schiitze s unsupervised algorithm applied to some of these words trained on a new york times news service corpus
for example l raining examples from same discourse contains a varied plant and animal life the most common plant life the
importantly i do not use one sense per discourse as a hard constraint it affects the classification probabilistically and can be overridden when local evidence is strong
moreover language is highly redundant so that the sense of a word is effectively overdetermined by NUM and NUM above
the use of this property after each iteration is similar to the final post hoe application but helps prevent initially mistagged collocates from gaining a foothold
if cumulative evidence for the majority sense exceeds that of the minority by a threshold conditional on n the minority cases are relabeled
next let us consider an example
multiset ccg contains a small set of rules that combine these categories into larger constituents
these information structure components are successful in describing the context appropriate answer to database queries
adjuncts can also occur in different sentence positions in turkish sentences depending on the context
this captures the fact that simple nps must be continuous and head final in turkish
using these application rules a verb can apply to its arguments in any order
there have been other formalisms that integrate information structure into the grammar for free word order languages e.g.
in most turkish sentences the immediately preverbal position is prosodically prominent and this corresponds with the informational focus
the simple compositional interface described below allows the as and the is of a sentence to be derived in parallel
however i believe my approach is the first to tackle complex sentences with embedded information structures and discontinuous constituents
in this block the word is scanned left to right the number of syllables is counted and pointers are stored in syllable initial position in an array a the number of syllables in the root form is counted and the syllable that forces the primary stress is marked as NUM stress
in bigram part of speech tagging the hmm model m contains three types of parameters transition probabilities p ti tj giving the probability of tag tj occuring after tag ti lexical probabilities p t w giving the probability of tag t labeling word w and tag probabilities p t giving the marginal probability NUM of a tag occurring
in order to instantiate the general algorithm for larger committees we need to define i a measure for disagreement step NUM and it a selection criterion step NUM
taking a bayesian perspective the posterior probability of a model p m s is determined given statistics s from the training set and some prior distribution for the models
the simplest is thresholded seleclion in which an example is selected for annotation if its vote entropy exceeds some threshold NUM the other alternative is randomized selection in which an example is selected for annotation based on the flip of a coin biased according to the vote entropy a higher vote entropy entailing a higher probability of selection
NUM t NUM 2oo0o 15ooo i0000 5ooo i i i i i i i i batch selection ira NUM n i00 thresholded sel lion fi NUM NUM randomized selection g NUM NUM two metnber selection co l training
it is generally assumed that a terminologic dictionary is composed of a possibly structured list of nouns or complex nominals
let lcb ui rcb denote the set of possible values of a given multinomial variable and let s lcb hi rcb denote a set of statistics extracted from the training set for that variable where ni is the number of times that the value ui appears in the training set for the variable defining n i hi
a more general algorithm results from allowing i a larger number of committee members k in order to sample p mis more precisely and it more refined example selection criteria
when the statistics for a parameter are insufficient the variance of the posterior distribution of the estimates is large and hence there will be large differences in the values of the parameter chosen for different committee members
evaluation methodology for bracketing is controversial because of varying perspectives on what the gold standard should be
for each part of the essay the scoring program uses a searching algorithm
the process of removing extraneous concepts from the csrs is currently done manually
the csr in NUM was generated by the concept extraction program
accordingly for the test question studied here the criteria for point
no rule was found to match the csr generated for this test response
from our NUM essays we extracted possible substitutions of the term one fragment
csrs characterize paraphrased information in sentences
these error categories are discussed briefly below
table NUM results of automatic scoring prototype
coverage cov illustrates how many essays were assigned a score
two types of sysl em ewjuatiou have NUM een carried out one for grammar coverage and the other for overall performance
the typicality of the words in the remote sensing domain is captured in the tables some highest relevance words in the classes are reported
4both modules are developed under ai lcb pa sponsorship by the spoken i anguage systems group at the mit laboratory for comlmter science
therefore we are integrating a word for word translator i which provides tools to akl a human translator as a fallback system
these messages tieature incidents involving different platforms such as aircraft surface ships sub muc ii stands for tire second message understanding conference
similarly the so called accusative case marker is realized as ul after a eonsonan and as lul after a w wel
the difficulty lies in what these simulations say about human computer or computer computer dialogues
this scheme is an upper bound on the effectiveness of initiative setting schemes
p v v is the set of all possible pairings of the children of v against the children of v
the development of a glachine translation mt system re luires the lengthy manual preparation of bilingual lexicons an t transfer rules
b is it the case that suspectl0 is the murderer of lord dunsmore
table NUM compares the precision of the alignment procedure with and without the lex match heuristic structure sharing had no eider on the scores
is it the case that suspectl6 had a motive to murder lord dunsmore
procedures which included structure sharing struc share and the lexieal match optimization lmx match as well as with those that did not
the following is an alternative greedy procedure for computing NUM v v NUM vi j s t
in this evaluation certain important factors are examined to weight various branches
bholmes is giving up control of directing the investigation here
example of a transducer not equivalent to any subsequential transducer
this yields to optimal time implementations of transformation based programs
p i r w iti w
let dec be defined by dec w u
ab is a factor of the word resp
however if the thesaurus effect is exploited the coverage can be increased considerably at the cost of a decrease of less than NUM in precision
the formal role introduces an object q of type actorelation
predicate argument structure finally is derived from verb headnoun and headnoun verb constructions
describes in terpretations that can not be separated from each
mandarin sage swede figure NUM a selection of ambiguous classes
each role is typed to a specific class of lexical items
nevertheless the pair as a whole remains active during processing
this leaves not all of the NUM classes are systematically polysemous
further semantic processing steps derive discourse dependent interpretations from this representation
the constitutive introduces objects that are in a part whole relationship with q
ri and the communication relation between two objects r2 and rs
specifically we have defined a probability model to calculate syntactic preference
let us consider some examples of lpr and rap in this regard
the translation lexicon contains an english vocabulary of approximately NUM NUM words and a chinese vocabulary of approximately NUM NUM words
our results indicate that it is preferable to employ the back off method
we have proposed a probabilistic method of disambiguation based on psycholinguistic principles
i2 i1 x1 NUM i2 i1 NUM
we evaluated the results on the basis of the number n accuracy
we call this kind of conditional probability the three word probability
we have conducted experiments to test the effectiveness of our proposed method
these observations motivate us strongly to implement these principles for disambiguation purposes
in this section we briefly describe our lpr based probabilistic disambiguation method
second the order domains must be hierarchically ordered by set inclusion i.e. be projective
in psg on the other hand the phrasal hierarchy separates the scope of precedence restrictions
figure NUM decision tree classification tree and result for rule NUM
next we examine the more complicated texts texts NUM and NUM
figure NUM indicates that a similar situation may happen in chinese discourse
following this idea the context set grows as the discourse proceeds
these figures are further supported by the use of the kappa statistic
NUM it is not mentioned in the third sentence
in general animate objects characterize living things especially animal life
the first structure is the sequence of utterances that comprise the discourse
the subsequent reference has new information in addition to the initial reference
we can classify nominal descriptions into the types shown in figure NUM
manual method for source texi representatlon based on hngmstlc dommn and communlcatlve reformation from the nlp technology point of wew dincourse theory ls the least understood among subfields of hnguistlcs our work addresses challenges encountered in these previous approaches by applying robust and proven nlp techmques such as corpus based statmtlcal nlp
not meanmgful unless a summarization system is trainable to a particular summary style our current work is to identify through training what feature combinations produce an optimal summary for a given user we anticipate that the summary performance will improve with tralmng as dhmsum learns antomatlcally how or whether these different mgna
we describe a scalable summarization system which takes advantage of robust nlp technology such as corpus based statlshcal nlp techmques information extractmn and readily available on hne resources the system attempts to compensate for the bottlenecks of traditional frequency based knowledge based or discourse based summanzatlon approaches by uhhzlng features derived by these robust techniques prehrmnary evaluation results are reported and the multi dimensional summary viewer is described
the dlmsum summarizer exploits our flexible definition of a signature word and sources of domain and duscourse knowledge m the texts through the creation of multiple basehne databases corresponding to multiple definitions of signature words the application of the discourse features in multiple term frequency calculation methods different baseline databases can affect the inverse document frequency ldf
the dm module decides which sub grammar s should be active at any point in the dialogue and sends this information to the speech recognition unit
in the case of an interruption the dm can support the interruption of the main thread of the dialogue and restore the previous context employing stack mechanisms
thus the actual partitioning is a compromise between commandment iv on the one hand and commandments i iii v and vi on the other
of course the many guidelines found in the literature summarized in our NUM commandments are potentially very useful when one designs a dm module
the second step which is addressed in this section is implementing a dm module based on this design as part of the first vodis prototype
suppose the first candidate of the input list received by the dm indicates that the user wants to go to bistro le pot de terre in paris
NUM on the basis 6the recognition of proper names and in particular city names is a major problem to be tackled for the second demonstrator
the main objective of the vodis project is to integrate and further develop the technologies which are required for the design and implementation of voice based user system interfaces
the primary aim of the first vodis prototype is to build a simple but robust spoken language system which can be used effectively in the car
crnlea comell university new retrieval approaches using smart trec NUM by chris buckley amit singhal mandar mitra gerald salton used the smart system but with a non cosine length norrealization method
for example experiments in manual query expansion were done by the university of california at berkeley and experiments in combining information from three very different retrieval techniques were done by the swiss federal institute of technology eth
the experiments included the development of automatic query expansion techniques the use of passages or subdocuments to increase the precision of retrieval results and the use of training information to help systems select only the best terms for queries
the results using new test topics showed significant improvements over the trec NUM results but should be viewed as an appropriate baseline representing the NUM state of the art retrieval techniques as scaled up to handling a NUM gigabyte collection
crnlea comeu university automatic query expansion using smart trec NUM by chris buckley gerard salton james allan and amit singhal used the vector space smart system with term weighting similar to that done in trec NUM
notably for NUM of the NUM topics in which the inqi01 run was superior a NUM or more improvement in average precision to the cityal run the 1nq101 run was also superior to the crnlea run
there is minimal difference in average precision between the two pircs runs but more topics show superior performance for the soft boolean query pircs2 run NUM superior topics versus NUM superior topics for the topic expansion pircsl run
the virginia tech group vtc2s2 combined the results of up to NUM different types of query constraction NUM p norms with different p values and NUM vector space one short and one manually expanded to create their results
as data is accumulated zero frequency could be taken to represent less valid usages
wordnet supplies links between semantically related senses as encoded in synonym sets synsets
by intersecting the resulting sense sets with the output of our cluster based method verb senses can be pruned further
rather than attempting to identify unique word senses we aim for the more realistic goal of pruning sense information
while this might be a limitation of partitioning methods for lexicographical purposes it offers an advantage for our task
we observe that the cluster based method achieves a NUM NUM reduction in the number of senses when measured on types
table NUM valid combinations of syntactic subcategorization frames alternations and senses marked with for the verb appear
for appear which we use as an example throughout this paper we find NUM tokens in the brown corpus
wordnet contains the needed information on permissible combinations of syntactic context and semantic content but its subcategorization information is limited
according to the experimental results although dictalign produces high precision alignment the coverage for both test sets is below NUM
we describe the results of our experiments with the satz system using the neural network as the learning algorithm
these texts require very robust processing methods as they contain a large number of extraneous and incorrect characters
some of the work reported here was done while the author was at the university of california berkeley
on the wall street journal corpus described in section NUM alembic achieved an error rate of NUM NUM
the descriptor arrays representing the tokens in the context are used as the input to a machine learning algorithm
the first k NUM and final k NUM tokens of this sequence represent the context in which the middle token appears
a large and ever increasing source of on line texts is texts obtained via optical character recognition ocr
the german lexicon was built from a series of public domain word lists obtained from the consortium for lexical research
for these reasons we chose to approximate the context in our system by using the prior part of speech information
as an alternative the context could be approximated by using a single part of speech for each word
the cooperativeness and corporateness of the tipster text program participants has been repeatedly demonstrated in a wide variety of ways
three generative lexicalised models for statistical parsing
note that louella has achieved near perfection in four of the six subcategories
louella incorrectly added the word even
likewise references resolved by the reference resolution module are appropriately tagged
below we give a brief description of each of these systems
grieg borodin figure NUM descriptor f measure s
research into this area is currently underway
the best match is considered the referent
this is a true reference resolution task
this binding is then conveyed to the in and out object as its new status
which holds all rules for entering corporate posts egress k
the applicability semantics of this mapping rule is i an mate act on if this structure matches part of the input semantics we explain more precisely what we mean by matching later on then this rule can be triggered if it is syntactically appropriate see section NUM
NUM matching the applicability semantics of mapping rules matching of the applicability semantics of mapping rules against other semantic structures occurs in the following cases when looking for a skeletal structure when exploring an internal generation goal and when looking for mapping rules in the phase of covering the remaining semantics
in the context of generation tags have been used in a number of systems mumble NUM spokesman NUM wm NUM the system reported in NUM the first version of protector NUM and recently spud by stone doran
such covering of the semantic structure avoids some of the limitations of the utterance path approach and is also the general mechanism we have adopted we do not rely on the directionality of the conceptual relations per se the primitive operation that we use when consuming pieces of the input semantics is maximal join which is akin to pattern matching
we use instructions showing how the semantics of a mother syntactic node is computed because we want to be able to correctly update the semantics of nodes higher than the place where substitution or adjunction has taken placc i.e. we want to be able to propagate the substitution or adjunction semantics up the mixed structure whose backbone is the syntactic tree
the use of a non hierarchical representation for the semantics and approximate semantic matching increases the paraphrasing power of the generator and enables the production of sentences with radically different syntactic structure due to alternative ways of grouping concepts into words
sister adjoining constraints associated with nodes in the d trees specify which other d trees can be sister adjoined at this node and whether they will be right or left sisteradjoined null for more details on dtgs see NUM
let gs be a synchuvg dl with g and g its left and right uvg dl components respectively
crucially we block a node of 7rq if we fail in the construction of n
the above transformation will correct this tagging error
the preceding following word is tagged z
in this paper we present such an algorithm
all unsupervised learning results are summarized in table NUM
NUM the preceding following word is w
unsupervised learning of disambiguation rules for part of speech tagging
let us trace the recognition of the sentence i saw a tall old man in the park with a telescope
governor and y1 ym are the dependent of x in the given order x is in position
all the recent constituency formalisms acknowledge the importance of the lexicon and reduce the amount of information brought by the phrasal categories
y is a possible continuation to a new state s that contains the dotted string NUM
the phase completer prevails on the other two phases and the total complexity of the algorithm is o igi NUM n3
the set of the dependency relations that can be defined on a sentence form a tree called the dependency tree fig
in f arley s terms this item corresponds to all the dotted rules of the form cat cz
the third text composed of NUM characters contains NUM occurrences of ssi one of which is a nonpn NUM peulo ssi leumi n n postp figure NUM figure NUM this nonpnwas eliminated after looking up the dictionary of postpositions there is no postposition o leumi
where y k ci is defined as in NUM and c ac are the mean and standard deviation of the dsense w ci over the set of kernel words w in ci NUM the sense c that to assumes in the context k is expressed by
although great efforts have been paid to the related researches by chinese information processing community in the last decade we still have not a practical word segmenter and pos tagger at hand yet
what we can say now is that we believe it is possible to reach this destination in a not very far future and we know more than before about how to approach it
the types of unknown words cseg tagl NUM
the sentence NUM contains a chinese personal
then we still face the problem of part of speech tagging
we will discuss this in depth in another paper
scholars ever tried to do so
it is a kind of pseudo integration
illustrate that the german verb leihen in its variant to lend implies in ontrast to the german vert versehenken in its variant to give as a present the lending t erson s belief in a return of the involved object la calvin lciht iiobbes eine krawatte
the first pair of axioms introduc d below mir i oi s l he fact tlud the configuration a l brevialxxl by eu cul cue ef figure NUM is suitabh i o spec ify a wlriety of h xica l
the event comi lex ec whi h stands tor the verb itself in described as a process e which is caused by an action e of a person p p tel resents l he one wit gives the t resent u to mot her person q
in addition some of the instantiation rules provide temt oral and or spatial constraints that are applicable to tim corresponding parts of a prototypical situation description e.g. etimc is a mapping fl om the set of events or states to the set of temporal entities etime g t
however due to siti s gener l eq pr ach mty fltrther spe ification of its h s rif l ions lea is to an enlargement of the ret resental ion r ther t h m to tt change of the common denominator
since set s l rineipal oriental toil is l owaa ls the systemal i des ription of le cical fields rather than of single lexi al entries it provides ret resentations whit h tend t o NUM e mtdersttecified with respect to e.g.
cause r agens act act r p source have et q goal have bec not have p u u from obj have bec have q u u to obj have
we made use of the theory si e ifie strengths of the single approaches in order to overcome their specific weaknesses and to gain a powerfill means of expression for modelling the semantics of lexical entries
the focus of this article is the integration of two different perspectives on lexical semantics discourse representation theory s drt inferentially motivated approach and semantic emphasis theory s set lexical field based view
let gs be a synchuvg dl g and g its left and right uvg dl components respectively
most important each pair of linked nonterminals generated by gs is represented by g using a compound symbol
in the autumn painters often look for nice sceneries in most different environments
b at least two languages are known by everybody in this room
however at this stage not all combinations of marginal phenomena are covered by our algorithm
the main objective of the paper is to present a procedure specifying the tfa of a sentence
the distribution of tfa may be checked by such means as the question test
b it was john who talked about many problems to few girls
cases in which the intonation center has a secondary non final position must also be considered
NUM a it was john who talked to few girls about many problems
NUM note that we do not discuss the relations of coordination and of apposition in this paper
to NUM again for addressee objective and NUM for the two directionals
for the template element task the set of discourse entities i s scanned for entities of the appropriate type people and organizations plurals and some indefinite reference s are eliminated and the remainder are converted to templates
first we did not have hire among our set of appoint verbs which included appoint name promote and elect this caused us to lose one entire succession event
ideally a parser might learn which decisions could be safely made based purely on syntacti c evidence but building such a parser would be a substantial research project not to be lightly undertaken i n the months leading up to a mug
in order for this to be a viable entry procedure for non specialists this will have to be made int o an interactive interface and difficult issues will have to be addressed about how sentence constituents shoul d be appropriately generalized to create pattern elements
the goal we had set for ourselves was to do a mug using the pattern matching approach in order t o better understand the relative strengths and weaknesses of the pattern matching partial parsing and the full parsing approaches
we did not record interim occupants of positions did not do the time analysis required for on thejob we just used new status and did not distinguish related from entirely differen t organizations in the rel other org slot
there was no shortage of additional patterns to acid i n order to improve performance a few are discussed in connection with our walkthrough message but at that point our focus shifted entirely to the scenario template task
since our goal is to recognize most contexts where pns can occur in order to consn uct a lexicon of fns as complete as possible recall should be more important than precision in our system
dooner NUM the first is an example of an active clause pattern the second an example of a conjoined clause patter n of the form and verb phrase and the third is an example of a passive pattern
verb group recognitio n the third stage of pattern matching recognizes verb groups simple tensed verbs sleeps and verbs with auxiliaries will sleep has slept was sleeping etc
the goal of this work is to study how to generate various kinds of anaphora in chinese including zero pronominal and nominal anaphora from the syntactic and semantic representation of multisentential text
we then generalize the selection scheme allowing more options to adapt and tune the approach for specific tasks
in turkish for instance the nominal paradigm has three affixes number case relativizer and the verbal paradigm has eight for voice tense person aspect and mood
selecting examples on which the committee members disagree contributes statistics to currently uncertain parameters whose uncertainty also affects classification
we also introduce c as the superorcunate subordinate relation of classes in a thesaurus cz c c2 means that cz is subordinate to c2 NUM a subcategorization frame s is represented by a feature structure which consists of a verb v and the pairs of case markers p and sense restriction c of case marked argument adjunct nouns fred
next we assume that the concepts human place and beverege are superordinate to odorao ckild ot en park and juusu juice respectively and introduce the corresponding classes ch n c c and cb
attitudel means that the beneficiary or
in general when learning lexical semantic eollocational knowledge of verbs from corpus it is necessary to consider the following two issues NUM case dependencies NUM noun class generalization when considering NUM we have to decide which cases are dependent on each other and which cases are optional and independent of other cases
the generation of e is denoted as below otherwise if only the two cases ga nom and wo acg are dependent on each other and ude at case is independent of those two cases as in the generation of e in the formula NUM the
each table consists of the order of the feature the feature itself which is represented as a partial subcategorization frame noun class descriptions or example no n in the partial subcategorization frames and the number of the training verb noun collocations for which the feature function returns true
for each case marker pi in s and its noun class c8 there exists the same case marker pi in e and its noun class cce is subordinate to c i.e. cce c cs the subsnmption relation sf is applicable also as a subsumption relation of two subcategorization fraines
swhen applying the learned probabilistic model to the he d out test event e independence of the partial subcategorization frames are judged using the probabilities of partial subcategorization frames estimated from the truini g da as described in section NUM NUM NUM then the set sf e is is constructed
x although we ignore sense ambiguities of case marked nouns in the definitions of this section in the cttrrent implementation we deal with sense ambiguities of case marked nouns by deciding that a class c is superordinate to an ambiguous leaf class cz if c is superordinate to at least one of the possible unambiguous classes of cl
finally we study the effect of sample selection on the size of the model acquired by the learner
the batch selection algorithm executed for each batch b of n examples is as follows NUM
every res and arg feature has an f or p property sign syn NUM plsernj syn and sere are the sources of grammatical g sign and semantic s sign properties respectively
lexical elements may have a phonemes b metephonemes such as h for high vowel and d for a dental whose voicing is not yet determined and c optional segments e.g. y la to model vowel consonant drops in the phon feature
this research is supported in part by grants from scientific and technical research council of thrkey contract no
NUM uzun kol lu g5mlek long sleeve adj shirt two different compositions NUM in ccg formalism are given in figure NUM
eeeag90 nato science for stability programme contract name tu language and metu graduate school of applied sciences
to model interactions between domains we propose a categorial approach in which composition in all domains proceed in parallel
during composition the surface forms of composed elements are mapped and saved in phon
i ei l it j j this model assumes that the position distance relative to the diagonal line of the j i plane is the dominating factor see fig NUM
with the following ingredients sentence length prob d ility p jll mixture alignment probability p ilj i translation probm ility p f e
thereibre we have to resort to dynainic programming for which we have the following typical reeursion formula q i j p fj lel nvax p ili NUM
the smaller the value of g NUM the better the fit of the hypothesized model
therefore the probability of alignment aj for position j should have a dependence on the previous alignment aj NUM p ajiaj l i where we have inchided the conditioning on the total length of the english sentence for normalization reasons
the key component of this approach is to make the alignment probabilities dependent not on the absolute position of the word alignment but on its relative position i.e. we consider the differences in the index of the word positions rather than the index itself
null paramc lcr cstimalion given the position alignment i.e. goiug along the alignment paths for all sentence pairs perform maxitnulu likelihood estimation of the model parameters for model de distributions these estimates result in relative frequencies
case assignment is overt in turkish which allows for scrambling of the constituents all six permutations of the sov order are felicitous if the object np is case marked e.g. 8a and 8b
lexical category changes as described in section NUM we model the nominal use of adjectives in turkish by a single lexical item which may be interpreted as a term or a predicate by a lexical rule
the tipster text program was born out of the best combination of these two camps
it also increases the amount of time used for parsing the sentences
it is transformed into approximately NUM rules of the type a x or a x y where a is a non terminal and x y can be terminals or non terminals
given this equivalence interest in dg as a linguistic framework diminished considerably although many dependency grammarians view gaifman s conception as an unfortunate one cf
this will not only be more cost effective but will also make it possible to combine information from independently created resources making the final database more consistent and reliable while keeping the richness and diversity of the vocabularies of the different languages
given that the less favoure d meanings are rejected at once and no backtracking mechanism is used at present the order of applicatio n of heuristics can have a big effect on the final interpretation
further expanding the dutch wordnet also shows that there is a closely related concept vlees l the stuff where meat products consist of which matches both meat NUM and flesh l the soft tissue of the body
in the spanish english example we see on the other hand that ap6ndice NUM and dedo NUM have complex equivalence relations which are not incompatible with the structure of the language internal relations in the spanish wordnet and in wordnetl NUM situation NUM above
in addition there are relations for complex equivalent relations among which the most important are eq near synonym when a meaning matches multiple ill records simultaneously has eq hyperonym when a meaning is more specific than any available ill record e.g.
our corpus based approach is designed to support fast semantic lexicon construction
the database can be tailored to a user s needs by modifying the top concepts the domain labels or instances e.g. by adding semantic features without having to know the separate languages or to access the language specific wordnets
for the walk through article the succession event is identified on the basis of the event below which has action succeed which is recognized by the fill i n rule as relevant for the management template
but we did perform a few experiments varying the number of seed words
finally we graphed the results from the human judges
the results from this experiment are shown in figures NUM NUM
we then go back to step NUM and repeat the process
for example zoos and nests are strongly associated with animals
more experiments are also needed to evaluate different seed word lists
individual property values temporal intervals and probabilities are represented by the sets al a2
previous strategies for word sense disambiguation mainly fall into two categories statistics based method and exemplar based method
the inference engine module implements the uno algorithm for representing and utilizing knowledg e derived from natural language sentences
we can make further improvements in terms of the perception of our creative work NUM
p NUM asked why he would choose to voluntarily exit while he still i s so young
enamex type person quot james enamex operate d as chairman chief executive officer and president for a period of time
there were three such failures and four completely ungrammatical failures quite significant in an article of NUM NUM sentences NUM since no analysis is produced for these failed parses
for muc NUM we had to redesign our bookkeepin g structures in order to be able to do this
random sampling involves creating a corpus that is representative of a given model distribution q x
let us assume a corpus distribution for the dags in figure NUM analogous to the distribution in figure NUM
the second half of the iis algorithm involves finding the best weights for a given set of features
so future work includes how to add ambiguous words into clusters based on their contexts
amex s ubiquitous av e advertising belongs t onamex type roanization coke enamex
demonstrated by the pre muc6 research and implementation reasoning with explicit negative disjunctive and conjunctive information at all syntactic levels one consequence of this rather unique capability is flat taxonomies complex boolean types need not be stored explicitly which prevents the unnecessary but common exponential growth of a knowledge base
each of these trees has the trees from l g1 that are missing in the training corpus
occasionally i will also use model to refer to the weights themselves or the probability distribution they define
for example a template regarding a takeover will include all the information separated in different slots referring to the takeover itself which represents the main event of the template
the semnet is used to hold several kinds of information concept hierarchies built with arcs such as generalisation concept hierarchies encodes knowledge like man is a mammal is a vertebrate etc
figure NUM shows the muc relevant parts
there are approximately NUM different arcs
this will help in subsequent development
particular points of interest are marked x1 and x2
the walk through article originally scored p r NUM NUM
further work on the pre parser should improve this situation
similar rules are defined for the person template slots
whereas a corpus of NUM NUM words marked in context by part of speech was adequate to give less than a NUM error rate in japanese in chinese with a corpus of NUM NUM words marked the error rate on newswire was still well over NUM predominantly due to the fact that the error rate on unknown words in newswire was near NUM
NUM if un NUM is realized in u otherwise the highest ranked f u l wlfieh is realized in u let s apt ly centering to the constructed exall pl i NUM
the rule based fragment interpreter applies semantic rules to each fragment produced by fpp in a bottom up compositional fashion
even without a grammar semantic entities and relationships are still recognized and created by the lexical pattern matcher
relevant documents were selected and two character sequences common to the relevant documents were automatically added to the original query
hand segmentation requires the user to insert spaces between the chinese words when entering the text of the query
in discourse it keet s track of how local focus varies from one utterance to the next
possible transitions in u that precedes unl i in which a x ntin ie itcurs
all these lransil ions h nt i stab NUM for cf nteil estaih isiimen rcb ecause such refer nces a plle tr lo slaj lish the flew center of local discourse
figure NUM web based interface to profile
the tlllllu hd eled ent es i ell lodes referring expressions that don l refer NUM o a member of cf u i bltl l o all eltlil y ava ilgd le ill lhc discourse
for examph most tevelol menl so l u has been based on simt lc eonslrucl ed xamples to ai ply centering to real text issues such as how possessives and subordimtte clauses al feet re ferring expression resolution lnust t e addressed
the demonstration systems include software components developed under other contracts by new mexico state university including an early version of the tipster document manager tdm a chinese segmenter and a multi lingual motif text widget
this was set quite conservatively for the final run too high and we risked a crash due to garbage collection being unable to reclaim enough heap too low and complex parse s are rejected
l in general parsers of existing principle based interlingual mt systems are exceedingly inefficient since they tend to adopt the filter based paradigm
the nodes in the network are computing agents that communicate with each other by sending messages in the reverse direction of the links
this ordering information is encoded in the grammar network by virtue of the relative ordering of integer id s associated with network links
however the most interesting point is that the trends of the three performance levels relative to sentence length are essentially the same
we have begun negotiations with the ldc for the acquisition of a korean mrd for which we intend to construct similar routines
when a possible expression is recognized we can either collapse it into one unit or leave it otherwise intact except that the most likely interpretation is marked
six errors involve noun adjective ambiguities that are difficult to solve for instance in a subject or object predicate position
the errors are the following the biggest subgroup has NUM errors that re null quire modifications to existing rules
we do not deal with errors produced by the last set of rules the non contextual rules because it is already known that they are not very accurate
the guesser is thus based on productive endings like merit for adverbs ible for adjectives er for verbs
the semantics of both constructions denoting portions aad nouns used to build them arc discussed and eventually formalised in a unification based formalism lkb ia l in terms of pustciovsky s theory of qualia and jackendoff s conceptual semantics
for implicational connectives left and right inferences are interpreted via functional application and abstraction respectively
the identity id and cut rules express the reflexivity and transitivity of the derivability relation
bolh detached iy cii a slice of cake a slice of lemon and modelled mi li portions a lump o f sugar a sheet ofpcq er have been drawn out of the whole and bear a shape straightforwardly determined by such agenlive process
proof a in figure NUM illustrates proof terms are omitted to simplify
this way a rodaja is round because it is a cross cut of either approximately spherical lemon or cylindrical sausage objects a slice of bread will be elliptic or square depending oil whether the bread is the classical lo ff or tile modem ixflyhedfic shapcd one top of a box will show identic d behavior
the above example systems clearly do not exhaust the possibilities for rich lexical encoding
ixo77 makes notice lhal words such as head of cattle sheet of paper or lump of sugar slaiid for exactly tile same function as classifiers in those languages
branching mobiles since order is undermined only within the confines of the given bracketting
however the approach presented there was not easily integrable in a speech recognition system and did not provide for the case in which the categories included units larger than a word
in our example the expected output for me roy alas tres y media could be i am leaving at hour hour half past three
by using the hierarchical tag context tree the constituents of sequential tag models gradually change from broad coverage tags e.g. noun to specific exceptional words that can not be captured by generm tags
gg1 say enough and gg7 do n t be ambiguous are sometimes two faces of the same coin if you do n t say enough what you say may be ambiguous
the data was obtained by analyzing large texts randomly chosen from the hebrew press consisting of nearly NUM NUM word tokens
NUM introduction this paper addresses the problem of morphological disambiguation in hebrew by extracting statistical information from an untagged corpus
calculate the new proportions between the different analyses by computing the proportions between the average number of occurrences of each analysis
thus as has been already mentioned we have incorporated these probabilities into an existing system for morphological disambiguation
the results of the experiment confirm the conjecture we made about the nature of the morphological ambiguity problem in hebrew
the three parameters used for evaluation are as follows no of correct right assignments recall no of ambiguous words
part of speech tagging deciding the correct part of speech in the current context of the sentence has received major attention
even though each sense may have different behavior patterns in practice this did not present a problem for our program
this approach provides highly useful data that can be used by systems for automatic unsupervised morphological tagging of hebrew texts
our algorithm has to handle the frequently occurring case in which a certain word appears in more than one sw set
NUM states what the cooperative speaker should do in case of failure to understand utterances made by the interlocutor
this is why the issue of dialogue cooperativity came to play a central role in our design of the dialogue structure
a word graph is a compact representation for all lists of words that the speech recognizer hypothesizes for a spoken utterance
for obvious reasons dialogue partner asynmietry is important in si ds dialogue design
our system adheres to NUM in that it communicates its failure to understand what the user just said
a generic principle lnay subsunle one or ltlore specific principles which specialise the generic principle to certain classes of phenomena
a irst aim is to demonstrate that a sub set of the principles are roughly equivalent to tile ntaxhns
more vet the principles manifest aspects of cooperative task oriented dialogue which were not addressed by grice
in woz iteration NUM for instance the system did not ask users about their interest in discount fare
provide same fornluhition of the same qucslion or address to users everywhere in the system s dialogue ttlrlls
this means that while zipf s law implies a finite total population turing s formula yields a proper probability distribution also for infinite populations
we present a number of results to indicate how well the nlp component currently performs
from that set of updates one is then passed on to the dialogue manager
figure NUM some german compounds with non compound translations
all experiments were performed on a hp ux NUM NUM machine with more than enough core memory
continuum approximations are useful techniques for establishing the dependence of a sum on its bounds to the leading term and for determining convergence
was actually spoken to simulate a situation in which speech recognition is perfect
this research is being carried out within the framework of the priority programme language and speech technology tst
the temporal relation between these continuations and the portion of earlier text they attach to is constrained along the lines sketched before
clearly the event described by the past perfect sentence must precede the event described by the first simple past sentence
the main advantage of this approach is that it reduces temporal structure ambiguity without having to rely on detailed world knowledge postulates
of course sometimes it is possible to take advantage of certain cue words which either indicate or constrain the rhetorical relation
a basic dcu represents a sentence or clause and complex dcus are built up from basic and complex dcus
representation by using constraints and preferences we can considerably reduce the amount of ambiguity in the temporal rhetorical structure of a discourse
tthis work was supported in part by the european commission s program on linguistic research and engineering through project lre NUM towards a declarative theory of discourse
temporal expressions such as at noon and the previous thursday can have a similar effect they too can override the default temporal relations and place constraints on tense
in example 10b the fact that the key is mentioned only in the second sentence of NUM links 10b with the second thread
c if steps a and b attempt to place conflicting vmues in the i het reln slot the parse will fail
the principal advantage of this approach is that it makes it possible to keep the reference oriented part 1by the reference oriented part we mean the part of a help system which describes the functions of individual windows widgets etc as opposed to more general or more task oriented information about the application other than providing an easy way of linking to and from cogenthelp generated pages cogenthelp leaves task oriented help entirely to the author
now consider a simple top down left to right spatial sort again this would inevitably yield a rather incoherent ordering such as the operators without work list box NUM the hot parts list box NUM the assign button NUM the operators on jobs list box NUM the parts list box NUM the remove assignment button NUM etc
as will be explained below this approach has led us to i make use of what amounts to a large grained phrasal lexicon and ii devise and implement a widget clustering algorithm for recovering functional groupings as part of an intermediate knowledge representation system ikrs
the automatically generated thumbnail images require no intervention on the part of the help author and thus are guaranteed to be up to date furthermore their abstract nature gives them certain advantages over actual bitmaps they do not present information which is redundant since the actual window in question will usually be visible or inconsistent static bitmaps fail to capture widgets which are enabled disabled or change their labels in certain situations
stolcke s algorithm is an order n one that iteratively merges smaller clusters into bigger ones until only one cluster remains new clusters are formed out of the two nearest clusters in the current set ensuring that the results are independent of the order in which clusters are examined
this is in large part due to the fact that drafter s input is centered around task representations which are not typically used for gui level tasks in software engineering practice in contrast nearly every gui builder provides some form of gui resource database which could be used as input to cogenthelp
given this organization consider first arranging descriptions by type and alphabetizing besides cutting across the implicit functional groupings arranging descriptions in this way would end up putting the two view k factors buttons NUM NUM in sequence without any indication of which was which
our users were concerned about the ease of visual navigation of help pages and had experimented with using manually coded and 4with a perfectly intuitive gui the user would never need to know this information as commands would always be enabled when the user expects them to be
in the case of the phrase sized messages each of these fields is accompanied by a syntactic frame which prompts the author to provide consistent syntax for example entries in the when enabled field should fit the frame this element is enabled when to facilitate equality checking and promote text reuse a mechanism is provided enabling the author to alias the message for one widget to that of another
due to the lack of derivation modules words like uneventful unplayable tearless or thievish are either in the lexicon or they are not translated
she uses a test suite with NUM words of authentic texts from an introduction to computer science and from an official journal of the european commission
the situation is straightforward if both corpora share the same number of unary trees for some yield we can pair off subtrees in increasing order of index
all of them know the noun weapon but none is able to translate weaponless although the english derivation suffix less has an equivalent in german los
so the difference between the figures in tables NUM and NUM gives an indication of the precision that we can expect when the translation system deals with infrequent words
it is also interesting to see that the demonstrative pronoun this is translated into different forms of its equivalent pronoun in german
in table NUM we see immediately that there were no unknown words in the high frequency class for any of the systems
we accept only words that are correct sense preservingly segmented or close to correct because of minor orthographical mistakes we obtain the figures in table NUM
this lexical entry does not have any reading without a subject area marker but the word is still found at translation if no subject area is chosen
in this way it has been determined that bank is used as a noun in NUM of all occurrences in NUM it is a verb
we assume that the relatively small number of closed class words like determiners pronouns prepositions conjunctions and adverbs must be exhaustively included in the lexicon
we will first let n x become large and then establish what happens for small but non zero values of ff
we will assign less error values NUM a to the insertion error hypothesis edges of nonterminals which are embraced by comma or parenthesis
when handling sentences the robust parser assings more error values NUM to the error hypothesis edge occurring within a fiducial nonterminal
where is the error cost of a terminal symbol is the error cost of a nonterminal sym null bol
because this algorithm is also syntactically oriented and baaed on a chart it has the same advantzrge as that of mellish s parser
states within a stateset are ordered by ascending value of NUM within a p within a f f takes descending value
so not all the generated edges are processed by the robust parser but the most plausible parse trees can be generated first
an ideal word sequence model would look a bit different
test set first NUM NUM sentences are selected randomly from the wsj corpus which we have referred to in proposing the robust parser
rule we can derive NUM NUM rules and their frequencies out of NUM NUM sentences in the penn treebank tree tagged corpus the wall street journal
edges with more error values are regarded as less important ones so that those edges are processed later than those of less error values
filter NUM gen input where the function gen is fixed across languages and gen input c repns is a potentially infinite set of candidate surface forms
to explain this process we will go through the clause planning of the example input shown in figure NUM step by step
progressivevoicing voi NUM c if the segment preceding a consonant is voiced voicing may not stop prior to the 3such a sequence does alter the meaning slightly
it however is important to consider the limitations of the method
however data sparseness is an inherent problem
we conducted this evaluation in the following way
the edr corpus also provides sense information for each the edit corpus provides sense information for each word based on the edit dictionary which we used as a means of checking the correct interpretation
in case unexpected input occurs repair techniques have to be provided to recover from such a state and to continue processing the dialogue in the best possible way
so the redistribution of the configuration frequencies as on figure NUM c will not bring much advantage and we can safely leave the feature u out of the optimized lattice and therefore use the atomic feature set as on figure NUM b
as mentioned in his paper although his approach was only reported on the disambiguation of words in related noun groupings it can potentially be applied to word sense disambiguation of nouns in r g text
short cuts in the meronymic hierarchy as represented in wordnet NUM NUM by the semantic relations part partof member group and ingredient substance have also been supervised by the system on download
we believe that the framework is also applicable to other dialogue modalities and to human human task oriented dialogues
the approach combines some of the best features of learned rule based and statistical systems small training corpora needed incremental learning understandable and explainable behavior of the system
accordingly the database does not include a verb v NUM which is a direct troponym of a verb v2 and directly entails v2 because the latter link would be redundant
null we briefly illustrate this process for coordination
NUM compounding decompounding in french most terms have a compound noun structure i.e. a noun phrase structure where determiners are omitted such as consommation d oxyg ne oxygen consumption
our results so far indicate that using computational linguistic techniques for carefully controlled term expansion will permit at least a three fold expansion for coverage over traditional indexing which should improve retrieval resuits accordingly
a manual observation of collocations shows that only NUM of the type NUM collocations are correct type NUM variants and that only NUM of the type NUM collocations are correct type NUM variants
text simplification refers to traditional ir algorithms such as NUM deletion of stop words NUM normalization of single words through stemming and NUM phrase construction through dictionary matching
a1lomorphies are obtained through multiple verb stems e.g.
a human reader of the glosses may infer that this or that top concept should be subsumed under a more general one or may have already existing or not existing subconeepts
we assign a score to each alignment based on the labels of the corresponding nodes and the arcs from these nodes as described below
based on our investigation we believe that hearst s original intuition that lexical correspondences can be exploited to identify subject boundaries is a sound one
differences in the kind of equivalence relations of wms with compatible structure are suspect
these ili records are represented by their gloss here all taken from wordnetl NUM
in this paper we will further explain the design of the database incorporating the unstructured multilingual index
the most straight forward relation is eq synonym which applies to meanings which are directly equivalent to some ill record
a proposal for updating the ili is distributed to all sites and has to be verified
future extensions of the database can take place without re discussing the ili structure
our experience with lt nsl has shown that it is a good system for sequential corpus processing where there is locality of reference
on the other hand many tokens of a relatively rare type can be concentrated in a short segment of the text resulting in many false correspondence points
if there were points of correspondence in both hi e and g f the correct alignment would still be the same
null in the other processing mode the dimogue component tries to process the english passages of the dimogue by using a keyword spotter that tracks the ongoing dialogue superficimly
if this is not successful various heuristics are used based on entity typ e and position in text
usually such text is copied as is during translation resulting in regions of bitext space where the slope of the tbm is exactly NUM
NUM computer put the knob to zero one
the specialists were not at fault for that filtering error which failed to follow stated extraction guidelines
on the other hand for the constellation in figure NUM we would assume that the synsets lcb administer dispense rcb and lcb give apply rcb should be merged
this feature is true when an np is a recognized alias of the othe r np e.g.
but we see significant drops in both recall and precision as we move from te to st
the parenthetical numbers indicate how many training instances were encountered at each leaf nodes of the tree
at each step no record of previous partial matches or mismatches is remembered
our experiences with previous muc evaluations gave us a clear understanding of the hurdles that lie ahead
persons and organizations which are not involved in a change of job status are discarded as irrelevant
if a lexicographer wanted to see a word in sentence g in its bilingual context it would be useful to know whether sentence f is relevant
we choose top the tree with the most specific condition top requires r and o while ld requires only r and the unmarked form has no requirements
there does not appear to be an english source of this kind so it is planned to compile one
will shallow processing miss too many of the errors cooperative error processing is aimed at
null the system could possibly make better use of the graph state of its lexicon
the disadvantage remains that generating many correction possibilities with sicstus backtracking is time consuming
at present the word in focus is always the newest word in the purview
this paper is concerned with the detection and correction of sub sentential english text errors
common endings are shared and category information is stored on the first unique transition
the error rules are in two level format and integrate seamlessly into morphological analysis
if this does n t succeed it backtracks to try an error rule at an earlier point in the analysis
correction possibilities are ranked using frequency information on damerau errors and by giving preference to very common words
the closer we get to this ideal the fewer sentences we need to test during parameter optimization
the problem with beam search is that it only compares nonterminals to other nonterminals in the same cell
table NUM is a sample of the case base for the first sentence of the corpus pierre vinken NUM years old will join the board as a nonexecutive director nov
the planner is now in a position to apply the selected edp to the knowledge base
the planner attaches the views to the explanation plan they become the plan s leaves
both the order and grouping of the topic nodes named in an exposition node are significant
this recursive appearance of content specification nodes permits a discourse knowledge engineer to construct arbitrarily deep trees
note that each of these is a multisentential explanation the first is a multiparagraph explanation
during sperm cell generation the pollen generative cell divides to form NUM angiosperm sperm cells
response the root system is part of the plant and is connected to the mainstem
a syntax and semantics analysis for legal sentence without omission of post positions and inversion of word order
invention of tit la st d ca de in the t mlnh ing nvironnwnt
several projects in explanation generation have exploited views to improve the quality of the explanations they provide
finally one of the most fruitful areas for future work is research on animated explanation generation
NUM actual arrival date not verified
see ss4 NUM for the definition of f measure
the result is that each word affects the correspondence measure according to its significance in the text
we ran some of our writing samples from deaf subjects through a few grammar checkers and we judged the results to be consistent with these reports
that is the student may be unaware that the form of the subject has anything to do with the form of the verb in such sentences
another possible filter might reflect how various formal written english instruction programs might alter the model possibly stressing certain features normally acquired after others which remain unmastered
we also expect to seek input from english teachers of deaf students to see how they rank their students abilities based on assignments they correct
it seems clear to us that the difficulties faced by deaf learners of written english require the development of such a tool as the one we envision
the rule shown is a simple sentence rule that states that an s is an np followed by a vp where the vp is the head
we indicate how this affects the system design and the system s correction and explanation strategies and present our methodology for modeling the second language acquisition process
example hmp is a troponym of or a special way to perform walk and snore is an entailment of sleep if simulated snoring is not snoring
some students might prefer this mode of feedback since they would not risk feeling a loss of face as they might with a human tutor
in section NUM we will show that the encoding can be advanced in a way that eliminates the nondeterminism introduced by the multiply defined frame predicates
note that neither clause of the frame predicate needs to specify the features a x and y since these features are changed by lex rule l
thus under the dlr approach no new lexical entries are created but the theory itself is extended in order to include lexical rules
the final output of the compiler constitutes an efficient computational counterpart of the linguistic generalizations captured by lexical rules and allows on the fly application of lexical rules
this is because of the structure sharing between the second lexical rule s inand out specifications which stem from the lexical rule and its frame specification
we therefore automatically group the lexical entries into the natural classes for which the linguist intended a certain sequence of lexical rule applications to be possible
an advantage of the setup presented is that entries that behave according to subregularities will automatically be grouped together again and call the same interaction predicate
note however that unfurling of the first n instances of a cycle does not always allow pruning of transitions i.e. reduce nondeterminism
because of the word class specialization step discussed in section NUM NUM the execution avoids trying out many lexical rule applications that are guaranteed to fail
NUM in order to distinguish the different interaction predicates for the different classes of lexical entries the compiler indexes the names of the interaction predicates
for our user however each movement is not only slow but tiring and somewhat painful so keystroke saving is a useful measurement of performance
a system was developed at csli which would run on a standard laptop while still allowing the use of other software email web browser etc
id deb offer give phone no
a generalized mrs is the abstraction of the liszt value of a mrs where each element only contains the lexical semantic type and handel information the handel information is used for directing lexical choice see below
if passive edges with a wider span are given higher priority than those with a smaller span the tactical generator would try to combine the largest derivations before smaller ones i.e. it would prefer those structures determined by ebl
during the training phase it is recognized for each phrasal template templs whether the decision tree already contains a path pointing to a previously extracted and already stored phrasal template tempi s such that templs templ s
null NUM lexical instantiation in the last step of the application phase the set of selected lexical elements is unified with the constraints of the terminal elements in the order specified by the terminal yield
thus the template templ NUM can now be used to generate e.g. the string kim gives a table to peter as well as the string noam donates a book to peter
a closer look to the four basic ebl generation steps indexing instantiation lexical lookup and terminal matching showed that the latter is the most expensive one up to NUM of computing time
NUM 3it is possible to perform the expansion step off line as early as the training phase in which case the application phase can be sped up however at the price of more memory being taken up
extended training phase the training module is adapted as follows starting from a template templ obtained for the training example in the manner described above we extract recursively all possible subtrees templs also called phrasal templates
such a principle is not immediately clear and may not exist at all be very hard to research be cumbersome and involve a complicated procedure be exception ridden thus coming with a possibly lengthy list or several different lists be not machine tractable
lexicon entries for most open class lexical items represent word and phrase senses which can be either directly mapped into ontological concepts or derived by locally that is in the lexicon entry itself modifying constraints on property values of concepts used to specify the meaning of the given lexical item
more appropriately the victim of the process i.e. the abused person and or the speaker evaluate s the theme of the abuse i.e. the contents of what is said as bad attitude2 means that the agent of abuse v1 thinks of the beneficiary pretty poorly
in the case of continuous scales like size the acquisition of all the adjectives served by this scale is greatly facilitated and expedited after the first one of them gets a lexical entry each new adjective needs only an appropriate range assigned to it and the rest of the information in the semantic zone of the entry as indeed in all the other zones as well remains the same
languages like finnish hungarian and turkish have relatively rich morphology which governs grammatical functions often delegated to syntax in languages such as english
the lexical approach can accomodate both readings provided that lexical rules are invoked with relevant syntactic information e.g. valency of the verb
first table NUM illustrates the generality of our prediction mechanism
from the viewpoint of nlp systems dealing with a particular domain however these thesauri include many unnecessary general words and do not include necessary domain specific words
another alternative is the cluster based approach which first constructs clusters from training data by using some clustering algorithm then calculates similarities between a target document and those clusters
in this section our approach is compared with some previous interesting methods
we also adopt a further yet simplification suggested in ristard NUM to restrict the constraints only to the cases when the overall joint frequency of a feature xk x y is greater than a certain threshold for instance NUM
however the principle itself is not crystal clear in the literature
we have proven that tokenization ambiguity can be categorized as either critical type or hidden type
upon that basis both critical point and critical fragment constitute our first group of findings
the objective in this paper has been to lay down a mathematical foundation for sentence tokenization
that is they possess neither ambiguity nor ill formedness in tokenization
in other words critical ambiguity in tokenization is unquestionably critical
the definitions above however are neither complete nor critical
that is bo s lcb a bcd rcb
ideally we should invest no energy in investigating anything that is irrelevant to these points
the construction of the paper is as follows
NUM have introduced a variable memory length tag model
assume that the training examples consist of a sequence of triples pt st wt in which pt st and wt represent part of speech subdivision and word respectively
up to this section we introduced a new tag model that uses a single hierarchical tag context tree to cope with the exceptional connections that can not be captured by just part of speech level
there were NUM words in the test corpus
this facilitates manual annotation because it is easier to fix a moderate number of errors than to tag the verbs completely from scratch
more precisely this task is concerned with the way to classify the brackets into some certain groups and give each group a label
shirai constructed a japanese grammar based on some simple rules to give a name a label to each bracket in the corpus
node NUM is also omitted although also in the same closed space u5 but it was mentioned one sentence after node NUM and is considered as near concerning textual distance
utilizing divergence as a similarity measure there is a serious problem caused by the sparseness of existing data or the characteristic of language itself
referring to this method we try to make use of baycsiar posterior probability as another similarity measure for grouping the similar brackets
with this corpus the grammar learning task corresponds to a process to determine the nonterminal label of each bracket in the corpus
the second term is generally called a uniform distribution where the probability of an unseen event is estimated to a uniform fixed number
based on the work of reichmann this paper presents a discourse theory that handles reference choices by taking into account both textual distance as well as the attentional hierarchy
for example one heuristic says that the order of ideas in a sentence is not likely to change during translation
for instance explicit translations of a definition may have the pattern by the definition of unit element or by the uniqueness of solution
more precisely this task is concerned with the way to classify brackets to some certain groups and give each group a label
two techniques distributional analysis and hierarchical bayesian clustering are applied to exploit local contextual information for computing similarity between two brackets
this response says in a somewhat roundabout way that due to the mutation the enzyme will not recognize the site and will not cut the dna at this point
for example the representation for the sentence the dna fragment would only have NUM segments was placed in the computer rubric category file for treatment ii
the methods underlying this application could be used in a number of applications involving rapid semantic analysis of textual materials especially with regard to scientific or other technical text
for excellent essays computer based scores would be NUM or NUM points below the NUM point minimum and for poor essays they would be NUM or NUM points above the
for convenience during training and later for scoring essays were divided up by section as specified in the scoring guide see figure NUM and stored in directories by essay section
the prototype accesses information from the lexicon and concept grammars to score essays by assigning a classification of excellent or poor based on the number of points assigned during scoring
and the dna fragment woum only have NUM segments the phrases data segment and dna fragment are paraphrases of each other and NUM pieces and NUM segments are paraphrases of each other
concept grammar rule deficiency in our error analysis we found cases in which information in a test essay was expressed in a novel way that is not represented in the set of concept grammar rules
when we used the cascading guesser with the brill tagger we interfaced them on the level of the lexicon we guessed the unknown words before the tagging and added them to the lexicon listing the most likely tags first as required
we will measure the aggregate by averaging over measures per word micro average i.e. for every single word from the test collection the precision and recall of the guesses are calculated and then we average over these values
unlike many other approaches which implicitly or explicitly assume that the surface manifestations of morpho syntactic features of unknown words are different from those of general language we argue that within the same language unknown words obey general morphological regularities
the performance of the guesser can be measured in recall the percentage of pos tags correctly assigned by the guesser i.e. two jj vbd out of three jj vbd vbn or NUM
evaluation of tagging accuracy on unknown words using texts and words unseen at the training phase showed that tagging with the automatically induced cascading guesser was consistently more accurate than previously quoted results known to the author NUM
we also tried hybrid tagging using the output of the hmm tagger as the input to brill s final state tagger but it gave poorer results than either of the taggers and we decided not to consider this tagging option
future research will look into why the mrbd s contribution to lexicon precision decreases with more training data
relative to the broad language understanding goals of many projects thi s effort is clearly a niche one but perhaps all the more ambitious in its accuracy goals for that reason
to enhance robustness the learning rules from equation NUM to equation NUM are modified as follows
some experimentation was performed to determine the final grouping of the rule sets into the level NUM and level NUM sets but that indicated in the table led to the best results
the example was encoded without regard to the active passive feature and therefore the only structural differenc e between the example and the input is that the input has the preposition by thus resulting in the near perfect similarit y value of NUM NUM
for the muc NUM named entity task a NUM line c driver program used the nametag api to run its name recognition access the table of extracted entities map the nametag classification into the muc NUM specification an d generate the sgml annotated document
the meaning of the prefix symbols is given in the following table an optional operator zero or more occurrences match the minimum
the fastest configuration further drops performance to NUM NUM since it does not apply the ampersand recognition rule to find ammirati pulls as an organization and then individually tags puns as an alias to martin pulls
hasten also defaulted the other org to be the same as the succession org and therefore rel other org was always same org
the next match occurred in sentence NUM resulting in the additional extraction of the organization and post input yesterday mccann made official what had been widely anticipated mr james NUM years old is stepping down as chief executive officer o n july NUM and will retire as chairman at the end of the year
there are five structural elements a noun phrase with a semantic constraint of a non governmental organization a verb group with a head having the root name a noun phrase with a semantic constraint of person an age phrase and a list phrase i e a coordinating conjunctive noun phrase with the semantic constraint of a management post
means a token with any type value and the star indicates zero or arbitrarily many intervening tokens between the two patterns of interest
the reason is that there are now two sources of recursion in the dcg and in the fsa cycles
whereas an ordinary word graph always defines a finite language a fsa of course can easily define an infinite number of sentences
these instantiate the variables speaker and hearer to system or user which is which depends on whether the rule is being used for plan construction or plan inference
finally the rule for s states that in order to construct a succesful top category the a and b lists must match
however if we use that method we will end up typically with an enormously large forest grammar that is not even guaranteed to contain solutions
much research in hpsg focuses on the structure of the lexicon e.g.
in order to illustrate how ordinary parsers can be used to compute the intersection of a fsa and a cfg consider first the definite clause specification of a top down parser
the constraints and mental actions of replace plan do hold and so the system is able to derive the refashioned referring plan which it labels p104
from this it derives a plan whose yield is the s reject action and this plan is an instance of reject plan previously shown in figure NUM
this observation is not very helpful in establishing insights concerning interesting subclasses of dcgs for which termination can be guaranteed in the case of fsa input
we have presented a computational model of how a conversational participant collaborates in making and understanding a referring expression based on the view that language is goal oriented behavior
this would get parsed into two separate surface speech actions an s reject corresponding to no and an s actions corresponding to on the television
as the speaker the system checks whether there is a goal that it can try to achieve and if so constructs a plan to achieve it
among the available choices the first sense of each polysemous word was a significant attractor
word senses were provided as synonym sets along with defining glosses
with increasing polysemy for all parts of speech in both conditions
this effect was also found separately for all pos except nouns
the grounds that each meaning has a fairly clear representation
this is reflected in lower inter tagger and tagger expert agreement rates
we expected less agreement for words that we predicted to be more difficult
no clearly discernible homonyms occurred in the data we analyzed for this report
our definition then is in essence the definition in gb terms of a discrete linear order with endpoints augmented with this closure property
this effect could be attributed at best only partly to the relatively low polysemy of nouns
however the components must all speak a common software language
however using preprocessing in conjunction with stage NUM of our algorithm does improve results
i would like to thank my adviser prof kathleen mckeown and also james shaw and karen kukich for the interaction on plandoc and evelyne tzoukermann for help with reviewing a version of this paper
null data collectors agents that are connected to the real world through filters or use human experts who can feed real time raw data such as sports scores news updates changes in stock prices etc
let s consider the case in which the user has already been notified abo lt a terrorist act a bombing took place on august 23rd NUM in the district of talcahuano chile
since the understanding and generation modules share only language independent templates we would try to implement a limited form of machine translation by summarizing in one language news written in another language
keywords regions in the workl preferences how frequently he wants to get updates and interaction history what information has already been shown to him
in addition we need a separator between the replacement and the context part
figure NUM excerpts from a muc NUM template
a word h may govern a word m in dependency d if h defines a valency b d c such that m isao c and m can consistently be inserted into a domain of h for b or a domain of a transitive head of h for b
he then defines the following rule format for dependency grammars NUM x y y y i y this rule states that a word of category x governs words of category y1 yn which occur in the given order
first of all only simple words are used in the definitions
in this case there are possibility of NUM combinations for NUM types of uninterrupted collocational substrings obtained by chapter NUM out of these combinations NUM combinations were extracted as the combinations which collocate twice or more within the same sentence
to overcome this problems this paper first proposes a method that can automatically extract and tabulate uninterrupted collocational substrings and without omission from the corpora in the order of substring length and frequency under the condition that fractional substrings are excluded
this can be easily performed by restraining the comparison procedure after finding a punctuation mark in procedure NUM second we assume that when a left quote character is found within a sentence all characters are ignored until the right quote character forming a pair with the former character
the case of absorbed relation case NUM can be classified into three sub cases as shown but regardless of which situation the m gram substfing is absorbed in the substring of n gram and therefore there is no need to extract such a m gram substring
here we propose an algorithm which satisfy condition the length of substrings to be extracted are decided from nmc and written in the nsc field of spt NUM according to the method shown in NUM NUM NUM check the validity of the suhstring pointed by the records of the pt NUM in the order of the record number and write the results in the vf field
for example the substring of NUM characters in the string word NUM shown in fig NUM was extracted the substring of string words NUM NUM NUM NUM need to be set as invalid for the length equal or less than NUM NUM NUM NUM characters from the beginning
b well there s a boxcar at dansville
given e a finite alphabet and a finite subset of e we say that a b e x is paradigmatically related to c d e x iff there exits two partial functions f and g from e to e where f exchanges prefixes and g exchanges suffixes and
ii a set lcb bi j rcb j g lcb NUM hi rcb of correlated functions in the phonemic domain and a statistical measure pi j of their conditional productivity i.e. of the likelihood that the phonetic alternation bi j correlates with ai
then by observing translation relations among the english and japanese cue patterns the resulting eriglish and japanese cas were compared
from this we can know that the preprocessed procedure costs much time
the complexity of the rewrite phase is that of locating the two tncbs to be combined
there were NUM subcollections defined corresponding to the various dates of the data i.e. the three different years of the wall street journal the two different years of the ap newswire the two sets of ziff documents one on each disk and the three single subcollections the federal register the san jose mercury news and the u s patents
since it will combine with brown dog no adjunction to a lower tncb is attempted
the complexity of the test phase is the number of evaluations that have to be made
a dominant feature of the adhoc task in trec NUM was the removal of the concepts field in the topics see more on this in the discussion of the topics section NUM NUM many of the participating groups designed their experiments around techniques to expand the shorter and less rich topics
the scheffe tests run by jean tague sutcliffe see paper a statistical analysis of the trec NUM data by jean tague sutcliffe and james blustein in the trec NUM proceedings show that the top NUM category a runs manual and automatic mixed are all statistically equivalent at the a NUM NUM level
the x axis plots the recall values at fixed levels of recall where recall number of relevant items retrieved total number of relevant items in collection the y axis plots the average precision values at those given recall values where precision is calculated by precision number of relevant items retrieved total number of items retrieved these curves represent averages over the NUM topics
in this section we consider a heuristic for producing a motivated guess for the initial tncb
maximal iff it is well formed and its parent if it has one is ill formed
by dominance monotonicity all nodes which were disrupted by the adjunction must become well formed after re evaluation
the top NUM documents were used to locate NUM terms and NUM phrases for expansion as contrasted with using the top NUM documents to massively expand NUM terms NUM phrases the topics as in trec NUM
the addition of secondary tasks tracks in trec NUM combines these strengths by creating a common evaluation for tasks that are either related to the main tasks or are a more focussed implementation of those tasks
it simply takes the last mentioned semantically appropriate referent
section NUM describes the next anchor point finding stage
this ensures that our development stage is still unsupervised
a confidence score is used to threshold these pairs
we obtain the secondary bilingual lexicon from this stage
NUM select a primary lexicon using the scores
NUM find anchor points using the primary lexicon
the chinese translation for prosperity is
the t score was used as a confidence measure
this process eliminated most word pairs
the mmc program is sponsored by spin
the interface to the oleada dictionary resource includes multilingual access to headwords and their definitions and also provides users with examples of usage part of speech information etc further translators often find valuable information by looking at the entries that are close to the target entry
to this end government sponsors contractors and developers are working to design an architecture specification that makes it possible for natural language processing techniques and tools from a variety sources to be integrated shared and configured by end users
oleada s dictionary interface also provides an alternate word list in addition to the headword list for words with multiple entries that match the morphological form of the search word such as accented words or words with alternate spellings
currently oleada provides a multi windowed side by side presentation of source and target language texts as opposed for example to the top bottom presentation of texts on some pc s with editing capabilities in the target text window
our multilingual multi attribute x window text capability is used to format this material to capture and reflect the original printed form complete with all of the lexicographic markup which makes these on line resources at least as useful as their printed counterparts
researchers with the computing research laboratory crl at new mexico state university are interested not only in theoretical aspects of natural language processing but methods for getting the results of this research into the hands of actual users
the system s user interface technology included a bookmark tool so that users could keep track of searches in reference material and an annotation tool that enabled users to highlight and attach comments to text
a preliminary result of our analysis is NUM
oleada user centered tipster technology for language instruction william c ogden and philip bernick the computing research laboratory at new mexico state university box NUM department 3crl las cruces new mexico NUM email ogden i pbemick crl nmsu edu
during the design process it was discovered that translators often like to make notes on documents they are working on either in the margins on the lines themselves or by attaching notes to the document
for each domain we compute the probabilities of partial trees like this
then text of each domain is parsed by the four types of grammar
sometime training corpus in similar domains is useful for grammar acquisition
we found many idiosyncratic structures from each domain by a simple method
for example the domain dependence of lexical semantics is widely known
figure NUM shows the clustering result based on grammar cross entropy data
this strategy is useful to produce better accuracy corrlpared to all non terminal grammar
more importantly shall we prepare different knowledge for these two domain sets
the performance of the last two grammars are very close in many cases
also the issue of the size of training corpus will be discussed
with both automatic speech processing and natural language processing it is necessary to use a lexicon which associates each item with a certain number of characteristics syntactic morphologic frequency phonetic etc
the first row presents the results if the parser is given the actual user utterance obviously wa and sa are meaningless in this case
this topic was treated above see subsection NUM NUM
the argument however is not that the high road toward integrated and maximally automatic systems should be abandoned
we have developed experimental grammars that are compatible with the interlingua design for english parsing phoenix english generation phoenix and glr german generation phoenix and japanese generation phoenix
many thanks also to walter kasper for fruitful discussions
the tests have been performed using a sun ultraspaxc
furthermore we wanted to achieve reasonable coverage of the domain in as short a time as possible
the esst speech recognition training set contains over NUM hours speech data and is composed of NUM utterances
automatic pruning methods will be used to derive each of the sub domain grammars from a manually constructed comprehensive grammar
and the following partition is chosen
these relationships are illustrated in appendix NUM
which are arguments to the same predicate
the work reported in this paper was funded in part by grants from atr interpreting telecommunications research laboratories of japan the us department of defense and the verbmobil project of the federal republic of germany
although the data we have been working with is spontaneous speech the scheduling scenario naturally limits the vocabulary to about NUM words in english and about NUM words in spanish and german which have more inflection
therefore mrsg can be handled like a sequence
we only need to match the small number of words in the corpus
to eliminate this kind of estimation error the parameter smoothing method good turing s formula NUM is adopted to improve the baseline system
figure NUM the template tempi mrs
the application phase is very efficient
neumann df k i uni sb
further pause units are less variable in length than entire utterances the standard deviation is NUM NUM as compared with NUM NUM
with this the semantics of locate with rejspect to the form in NUM which is the left hand side of schemata NUM may be pictorially represented as in figure NUM
the relative evaluation by operating with search order for the sets of m structure schema for different functions is motivated by the default ordering of phrases in a sentence in the target language
agrop agrsp to compare their features with the features that are present in the functional domain
the final solution technique therefore involves first evaluating all f structure schema including those with underspecification metavariables annotating the children nodes of an s dominated c structure tree
if more than one symbol table entry satisfies the m structure schema for a particular function g the one earlier in order of occurrence is chosen
the derivation of this act relies on the rule for intentional action shown earlier in section NUM NUM along with the metaplan for acceptance repeated here NUM the former possibility admits that an utterance that displays a misconception such as a mistaken belief about initial knowledge might still be coherent unless such knowledge has been introduced into the discourse explicitly
computational linguistics volume NUM number NUM default NUM makefourth turnrepair sl NUM areply ts active mistake s l a intended aobserved ts a reconstruction ts tsreconstructed a expected s1 areply tsreconstructed NUM shouldtry sl NUM areply ts
t1 m surface request m r informif r m knowref r whoisgoing t2 r surface request r m informref m r whoisgoing t3 m surface inform m r not knowref m whoisgoing t4 r surface informref r m whoisgoing
NUM from mother s perspective if indeed she did make an askif in t1 t4 can be seen as a display of acceptance of it because a surface informref is one way to do an informif
NUM NUM solution part i initiation of forward reference
gt is a structure building operation that builds trees in a bottom up way as is illustrated in figure NUM
we have relied on other projects the eci and mul text for bilingual corpora although this has involved some work in re aligning the texts
the full structure is not shown for reasons of space
individual applications must convert from their own formats e g
other features include word class noun verb
this is made possible by lemmatizing the entire corpus in a preprocessing step and retaining the results in an index of lemmata
use of this context prevents the semantics bein g purely compositional
we also plan t o improve our parsing and grammar techniques
the slcs considered here enforce the same notion of projection that was used in obtaining the elementary structures
the information about the stem lemma from the morphological parse enables a dictionary lookup and the grammatical information is directly useful
moreover the syntactic score formulation provides a way to consider both intra level contextsensitivity and inter level correlation of the underlying context free grammar
in the semantics we set the type of event to be talk and the talk status to be bargaining
we have implemented a preliminary experimental version of such a system and are currently developing a more advanced one
the first is about ibm and contains numerous subsequent references to the computer company and they
the idea is to apply fastus processing up through coreference resolution to all the documents in the corpus
of persons organizations and locations as well as such special constructions as dates and amounts of money
position titles can be conjoined and a position title can have an of phrase specifying the company
for verb groups it attaches support verbs to their content verb or nominalization complements
in addition locative temporal and epistemic adjuncts are recognized at this stage
they might be called the atomic approach and the molecular approach
thus the agreement will not resolve back to a contract
for instance the english pattern can you vp bare infinitive may express either an action request or a yn question yes no question
the goal of information extraction is to analyze a text an article a message and to fill a template with information about a specified type of event
in particular we propose that besides total compaction and domain union there is a third possibility which we will call partial compaction
thus a sign encodes its internal composition structure via its daughters attribute while its linear composition is available as the value of dom
lemma NUM the cover relation is transitive reflexive and antisymmetric
therefore in figure NUM the internal structure of the relative clause domain becomes opaque once it becomes part of the higher np domain
the fixst tralniug set training NUM is used to make initial word lists of various sizes
the ambiguity rate is reduced to a quarter without any compromise in correctness
then object or complement links are followed downwards bottoh
links formed between syntactic labels constitute partial trees usually around verbal nuclei
the results are not strictly comparable because the syntactic description is somewhat different
as a consequence the output is not optimal in many applications
the syntactic tagset of the constraint grammar provides an underspecific dependency description
some of the most heuristic rules may be applied only after pruning
we pursue the following strategy for linking and dis null ambiguation
the basic implementation of these rules is a compute inferences predicate in prolog that takes the meaning of the user s current utterance and causes inferences to be asserted into the axiom base
if the user knows for example according to the user model how to find the switch the model prevents the useless interaction related to finding the switch from occurring
in the following example it is likely that okay denotes affirmation of completion of the goal to turn up the switch NUM computer turn the switch up
for example the led query might also result in observations about the presence or absence of wires the position of the power switch and the presence or absence of a battery
see for example the first and third branches beginning with if r where respectively the trivial case and the general case for applying a rule are handled
in this case the interpretation of okay could be either that the location description NUM has been understood or that the original goal NUM has been accomplished
NUM recognition is indicated by a beep
note that any finite nonempty poset has at least one minimal element
three experimental sessions were used for each subject
eight subjects were recruited from computer science classes
although our system and systems like it are characterized as pattern matching systems they really are doing a form of parsing they analyze the sentence into a nested constituent structure
this heuristic assigns the maximum score to the hypernym sense which has the same semantic domain tag as the hyponym
this heuristic provides the maximum score to the first sense of the hypernym candidates and decreasing scores to the others
heuristics NUM NUM NUM and NUM and use information present in the entries under study e.g.
bonsai plant and bush cultivated in that way the hyponym hypernym relation appears between the entry word e.g.
conceptual distance provides a basis for determining closeness in meaning among words taking as reference a structured hierarchical net
conceptual distance between two concepts is essentially the length of the shortest path that connects the concepts in the hierarchy
in order to do this the gap between our working languages and english was filled with two bilingual dictionaries
moreover NUM and NUM of times the real solution is between the first and second proposed solution
needless to say several improvements can be done both in individual heuristic and also in the method to combine them
that is the most used and important senses are placed in the entry before less frequent or less important ones
these factors tend to decrease the entropy and increase the other test variables
moreover the recognition unit is trained on the basic command and control language developed for the first phase of the project
the defined pattern can be referred to from other patterns by using the character followed by the pattern name
to develop systems more rapidly tools are needed that will help pattern developers find and define patterns then check the results
these conditions can be the part of speech of the word the word preceding or following the word or the word length
two angle brackets on the right side of the pattern specify the first and last of the words that comprise the identified name or expression
lookup lists text lookup pro gram partially tagged texts ptt semantic lists p t t lookup program marked text marked texts pattern recognizers tagged texts
NUM mr ridley hinted at this motive in answer null ing questions from members of parliament after his announcement finally a database of NUM tuples configuration v nl pl n2 p2 n3 p3 was created for NUM pps
the 1st nearest neighbor of word x is the nearest occurrence of the same word
it is important that the test sets are subsets to ensure that e.g. a pp2 test case does n t appear in the pp1 training set since the pp1 data is used by our algorithm to estimate pp2 attachment and similarly for the pp3 test set
that is for a particular sentence containing a pp attachment ambiguity it is very likely that we will never have seen the precise v nl p n2 quadruple before in the training data or that we will have only seen it rarely
NUM else competitive backed off estimate a use procedure b2 to determine c the configuration of pl and p2 b compute c c c the preferred attachment of p3 w r t nl n2 n3 respectively c determine the best configuration again we back off up to two times always including tuples which contain the three prepositions
we will continue to work towards this goal and plan to improve our pattern matching engine to deal with more complicated patterns that erie can not currently handle
but entity names especially person names were not identified well although time and numeric expressions were identified with a high level of recall and precision
we can broadly distinguish two extreme categories of words content words versus function words
different words have differing semantic functions and relationships with respect to the topic of discourse
if the two sets do not share many words then the correspondence is low
a special characteristic of verbmobil is that both participants are assumed to have at least a passive knowledge of english which is used as intermediate language
the effect of adjoining a 2in these cases the foot node is an argument node of the lexical anchor
current word is w and the preceding following word is tagged z
the first last NUM NUM NUM NUM characters of the word are x
these transformations were learned from a separate NUM NUM word corpus
NUM below we list the set of allowable transformations
department of computer science baltimore md NUM NUM
one of the two preceding following words is w
in this paper it is argued that distributional called also smoothing techniques introduce a certain degree of additional error because co occurrences may be erroneously conflated in a cluster and some of the co occurrences being generalized are themselves incorrect
to address these simulation interface problems and motivated by the above results we have developed quickset see figure NUM a collaborative handheld multimodal system for configuring military simulations based on leathernet NUM a system used in training platoon leaders and company commanders at the usmc base at NUM palms california
in otter to create a unit in quickset the user would hold the pen at the desired location and utter for instance led t72 platoon resulting in a new platoon of the specified type primarily by sri international but modified by us for multimodal interaction serves as the communication channel between the oaa brokered agents and the modsaf simulation system
for example to create a phase line between two three digit x y grid coordinates a user would have to say create a line from nine four three nine six one to nine five seven nine six eight and call it phase line green NUM
this work is supported in part by the information technology and information systems offices of darpa under contract number dabt63 NUM c NUM in part by onr grant number n00014 NUM i NUM and has been done in collaboration with the us navy s nccosc rdt e division nrad ascent technologies mitre corp mrj corp and sri international
corba bridge agent this agent converts oaa establishes two platoons a barbed wire fence a breached minefield and then issues a command to one platoon to follow a traced route the user then adds a barbed wire fence to the simulation by drawing a line at the desired location while uttering oarbed wire
given that numerous difficult to process linguistic phenomena such as utterance disfluencies are known to be elevated in lengthy utterances and also to be elevated when people speak locative constituents NUM NUM multimodal interaction that permits pen input to specify locations and that results in brevity offers the possibility of more robust recognition
more detail on the architecture and the individual agents re provided in NUM NUM
the user can create entities give them behavior and watch the simulation unfold from the handheld
gesture recognition agent ogi s gesture recognition agent processes all pen input from a pc screen or tablet
the user can draw directly on the map in order to create points lines and areas
proof parallel to the proof for lemma NUM
NUM defense advanced research prolects agency fourth message understanding conference muc NUM mclean vlrgtaua NUM software and intelhgent systems
the formalism should be fine grained ie
the previous transitions for dog then become
transition probabilities were generalized in the ways discussed in the previous section
this suggests a radically lexiealist approach cf
two of these will now be sketched
furthermore in the right hand side of each asynchronous production of v we identify a single nonterminal nonterminal called the heir
the parse to forest translation problem for gs takes as input a parse tree r in g and gives as output a parse forest representation for t t
one reason for low performance is that an organization may b e identified in a text solely by a descriptor i.e. without a fill for the org name slot and therefore without th e usual local clues that the np is in fact a relevant descriptor
in addition the short time frame allocated for domain specific developmen t naturally makes it very difficult for developers to do sufficient development to fill complex slots that either ar e not always expected to be filled or are not crucial elements in the template structure
since the semantic end of the link has not been introduced yet the links remains pending until that time
any synchronous links that impinge on a nonterminal rewritten by an asynchronous production are transferred to the heir of the asynchronous production
a third significant reason is that the response fill had to match the key fill exactly in order to be counted correct there was no allowance made in the scoring software for assigning full or partial credit if the response fill onl ypartially matched the key fill
from this table it may be reasonable to conclude that progress has been made since the muc NUM performance level is at least as high as for three of th e four muc NUM tasks and since that performance level was reached after a much shorter time
nyu system kim in as vice chairman of wpp group where the vacancy existed for other unknow n reasons he may or may not be on the job in that post yet and the article does n t say where his ol d job was
the organization pointed to by the event object is the organization where the relevant management post exists the organization pointed to by the relational object is th e organization that the person who is moving in or out of the post is coming from or going to
we see the asynchronous production of the syntactic arrive vector has not only inherited the link to its heir nonterminal but has introduced a link of its own
dl is local with respect to vectors though not with respect to productions since the derivation trees of two synchronized uvg dl derivations need not be isomorphic
examples of possible types are token sentence paragraph and dateline
annotations will typically be organized to describe a hierarchical decomposition of a text
also for simplicity only a single span for each annotation is shown
for the sentence annotation the constituents attribute points to the constituent tokens
her data were the basis for the formulation of the experimental circuit fix it shop system
their key observations include the following transfer of control is often a collaborative phenomenon
the original motivation for the head transducer models was that they are simpler and more amenable to automatic model structure acquisition as compared with earlier transfer models
in only NUM or NUM NUM of these misunderstandings did the experimenter notify the user
furthermore a valid statistical analysis could only be performed on the completed dialogues
there is ongoing work in implementing and testing the collaborative algorithm in human computer interactive environments
the row value represents the initial subdialogue phase and the column represents the new subdialogue
the system can detect errors caused by missing wires as well as a dead battery
the experimenter chose to intervene in NUM of these or NUM of the time
c there is supposed to be a wire between connector NUM and connector NUM
an example can be found in the remove phone text cited above remove phone by firmly grasping top of handset and pulling out
the systemic view is distinctly functional that is it is particularly interested in mapping elements of the communicative context onto the appropriate grammatical forms
example 3g uses a simple imperative for the intended actions with a so that conjoining a present tense action form of the purpose
the exceptions to this are when the scope of the purpose is global the purpose is considered optional or the purpose is considered contrastive
we also received valuable comments from the computational linguistics referees and from members of the itri computational linguistics group including tony hartley c6cile paris richard power and donia scott
when a purpose does not have conditions upon it and the scope is global purpose tnf marks the purpose as a to infinitive tnf
the training portion used in step NUM constitutes approximately one third of the full corpus and consists entirely of keith vander linden and james h martin expressing rhetorical relations cordless telephone manuals
in cases 10a and 11a taken from our corpus there were nominalizations available namely adjustment and avoidance but neither was used
the articles used in the evaluation were drawn from a corpus of approximately NUM NUM articles spanning the period of january NUM through june NUM
alhouhg the disambiguation of the training set is eomputationally the most expensive part of the system it is done only once
NUM take the whole words in vocabulary as one class and take this level in the binary tree as NUM that is level NUM branch NUM else goto end
these types usually depend on the lexical instantiations of a syntactic semantic structure
the semantic domain of all dialogs is the dutch railways schedule
figure NUM size of training set NUM sem synt
the interpretation of an utterance is an update of an information state
there is no conceptual division in the tree bank between pos tags and nonterminal categories
such models therefore maintain large corpora of linguistic representations of previously occurring utterances
figure NUM decomposing a tree into subtrees with uni fication variables
we automatically learn a simpler less redundant representation of the same information
the semantic types of constituents often give rise to differences in semantic structure
only exact matches with the trees and interpretations in the test set were counted as successes
each domain includes a variety of phrases that have specific meanings and translations that apply only in the given domain
table NUM acoustic prosodic correlates of consensus labelings from text and speech
or better indicating a high degree of inter labeler reliability
therefore some words appear at strange frequency positions
table NUM percentage of words translated correctly or incorrectly
in general these studies have lacked an independently motivated notion of discourse structure
it is a necessary but not a sufficient condition
a prosodic analysis of discourse segments in direction giving monologues
table NUM acoustic prosodic correlates of consensus labelings from text alone
a second issue is whether such classification can be done on line
the following preliminary results can be considered for incorporation in such a model
thus the tasks were designed to require increasing levels of planning complexity
level or better except where indicates significance at the NUM
the experiments show that involving larger fragments in the parsing process leads to higher accuracy
for instance if sitakarada NUM is found in the last phrase then the rhetorical relation is reason and if the conjunction sikasi but is found then the rhetorical relation is adverse
these steps select sentences on the basis of their importance value but they also respect the rhetorical structure to some extent step NUM because if the rhetorical structure is totally ignored the output text will be awkward to read
the tense of a sentence is simply determined to be past if it has ta an inflection for the past tense in the last phr e3 the reason why tense is used is that sentences stating about the current fact seem to be more important than ones about the past fact in the context of editorial articles
therefore this paper proposes a method for selecting important sentences by using an equation based on surface features and their weights and a method for determining these weights by multiple regression analysis of abstracts created by humans
insistence tai want to do hosii want someone to do bekida should nakereba naranai must taisetu dearu important hituyouda necessary etc
this paper has proposed a method for creating an abstract by using surface features and their weights to select important sentences and a method for determining these eature weights by multiple regression analysis of abstracts created by humans
in addition a tense filter to be discussed below in section NUM was implemented to heuristically detect subdialogs improving the performance of the seen nmsu ambiguous dialogs
figure NUM uniform framework for data filters
the translation scored NUM NUM on this test
the pos filter is a predicate filter
the mrbd filter is an oracle filter
u s a melamed unagi cis
NUM NUM machine readable bilingual dictionary mrbd
english translation will be mostly parallel
for languages with very similar syntax a linear model will suffice
figure NUM each filter contributes to an improvement in bible scores
in an overwhelming number of cases the last mentioned time is an appropriate antecedent with respect to our model in both the more and the less constrained data
the named entity ne task requires insertion of sgml tags into the text stream
these are the cn definitions that applied here difficulty determining which person to link with a status evidence and tended to attach the status evidence to all persons in the same sentence
null similarity theorem the similarity between a and b is measured by the ratio between the amount of information neededto state the commonality of a and b and the information needed to fully describe what a and b are sirn a b logp common a b logp describe NUM
e.g. a car vs two cars singular vs plural templates
each subsequent reduction has two useful side effects NUM identifying which tokens form the heart of the reduction and therefore should be marked for the ne task and NUM filling the slots of the mtokens wit h appropriate pieces of the text that was reduced for the te task
the results range from NUM the most important to NUM the least important the ratings were allowed to be tied
such filtering reduces the rule sets more than tenfold
more importantly the ambiguity rate is only about a quarter of that in the engcg output
he uses heuristics to relax the constraints of the description and to pick one that nearly fits it
note that the precision in this case is likely to be lower bounded by the weighted precision reported here since we currently assign equal weight to all parses even if they are improbable
then the two sequences will be parsed identically
in keeping with clark and wilkes gibbs we use two discourse plans for refashioning replace plan and expand plan
we have found the notation and the increased expressiveness to be wellsuited for writing large robust grammars for chinese particularly for handling compounding phenomena without incurring the level of parsing ambiguity common to pure context free grammars
the problem is that it becomes quite cumbersome in a pure cfg to specify accurately which types of noun phrases are permitted to compound and this usually leads to excessive proliferation of features and or nonterminal categories
next we pop pron from agenda create an initial edge np pron and find it is also finished and so add the np to the agenda
but though the algorithm is clearly worse than cfg in the worst case in practice the complexity in practice will depend heavily on particular sentences and the grammar
with the rule time phrase np time particle we can parse as a time phrase and since it is a time phrase it will be parsed as the complement of a
however with the substring linking function we can refine the rule to queslion verb vn NUM yg vn NUM now the first vn NUM is defined as in both cases when the first lcb is parsed
with traditional cfgs this is problematic because both i and j have the part of speech up and both and have part of speech nc
the has subconstituent function this function is denoted as a b which means a constituent labeled a with any descendant of category b where a is a nonterminal and b can be either a terminal or a nonterminal
easyenglish comes with a built in general english dictionary of about NUM NUM words
easyenglish a tool for improving document quality
easyenglish is part of ibm s internal information
you must get rid of them
the following two examples illustrate the problem
esg parsing heuristics often arrive at correct attachments in the highest ranked parse
a full parse helps decide on this
every year many pages of online and printed documentation are produced
in the same way easyenglish allows most of standard english syntax
second we have accounted for the conversational moves that participants make during the acceptance process by using meta actions
a c block linked from an s block or more represents an independent word sense and ideally is linked to by other s blocks that are linked to by other m blocks in effect other words of the same or the different language
x gave y to z for a simple japanese analyzer that tries to fill as many slots as possible for a verb the unique case principle is virtually embedded in the subcategorization frame of our architecture for the computational lexicon
NUM NUM a ok x ha y ha z ni age ru b ok y ha x ha z ni age ru practical japanese sentence analyzers would need some semantic inference and default inference to plausibly identify x and y using the semantic restrictions on each case element and the standard word ordering
and so on the n th permutation is performed for the n th auxiliary verb next to the n l th auxiliary verb and the locus moves on from the nl thauxiliary verb to the n th auxiliary verb
the numbers of voice conversion types that affects the surface case pattern was NUM the nonweighted mean number of the case slots tbr each lexicon is counted to NUM NUM NUM of verbs are listed up to take multiple case patterns
the analyzer looks up the slots in surface case frame and find the match of the case postposition for ga ga in the nom case slot matches and the deep case that is stored in the nom slot is taken out from the subcategorization frame
NUM NUM the nominative case marker ga turns into dative case marker ni and the accu ative case marker wo turns into nominative case marker ga when the passive potential auxiliary verb rareru is attached
so the system presupposing that the user understands the system s plan adds the following belief
however many of the collocations that champouion identifies are general domain independent ones
let c i d denote the likelihood that a document d is assigned to or classified by a category c w d a set of words or expressions comprising a text d and s d a set of potential topics for d
it is clear from fig NUM that the initial portion of text is more likely to be chosen as most similar to the title than other parts of text the later a segment appears in text the less chance it has of being selected as most similar to the title
it seems however that longer translations should get a bonus
these tools can then be used by other systems to address more complex tasks
we consider the two extreme cases where the two events are perfectly independent
this apparently different behavior for high threshold values can be traced to sampling issues
in addition we are planning to add a second language for the summaries
thus a list of bilingual collocations would be useful for the summarization process
in the first stage word pairs that co occur with significant frequency are identified
in fact i x y is a completely symmetric measure
some of the chosen verbs can function as both main and auxiliary verbs and some are often used in idioms
to represent pns in the lkb we have made some interpretations for formal aud const quales of the qualia
the effectiveness of this model is estimated in the following section
overgeneral categories may even fail to capture contrastive ambiguities of words
following the algorithm for any w k in wr c i
an interesting point is that although the internal generation goal for the verb referred only to the concept movement in the initial semantics all of the information suggested by the terminal mapping rule iii in figure NUM is consumed
performance figures of sets c i against the reference corpus
sense n NUM stock plant part
the word stock retains only NUM out of NUM senses
in semcor every word is unambiguously tagged with its leaf synset
instead wordnet draws very subtle and fine grained distinctions among words
there are some instances in the corpus that we found to be truly ambiguous
typically about two thirds of the features were filtered for each category significantly reducing the output representation size
as mentioned in section NUM NUM the classical ir literature has addressed this problem using the if and idf factors
the results presented in the third column of table NUM show the improvements obtained when the threshold range is used
many of the techniques previously used in text categorization make use of linear classifiers mainly for reasons of efficiency
in particular we investigate three variations of on line prediction algorithms and evaluate them experimentally on large text categorization problems
the first phase of processing in both the c and lisp systems is assignment of part of speech information e g proper noun verb adjective etc
theref re the entities mentioned and some relations between them are f rocessed in every sentence whethe r syntactically ill formed complex novel or straightforward
their algorithm divides a morpheme network into possible sequences that are then used for the normal baum welch algorithm
NUM from the israeh ministry of science
one promising direction for future work would be an integration of models estimated from tagged and untagged corpora
however as noted above good results of speech recognition is the basic requirement for a voice interface
following the second phase of estimation new credit factors would be decided by evaluation of the new model
in particular the results of the tag bigram model were dramatically improved by using the variable credit factor
the most thoroughly studied application is the information retrieval ir
to do this we would improve ne performance see discussion of ne above an d would work further on the locale country and descriptor slots
similar to reply y a reply to a query with a yes no surface form that means no is a reply n
in contrast the closest points that did not refer to the same boundary were usually five centimeters apart and often much further
calculating as described coders reached promising but not entirely reassuring agreement on where games began NUM n NUM
this amount of agreement seems acceptable given that this was a first coding attempt for most of these coders and was probably done quickly
able uses the kappa coefficient units in this case are moves for which all move coders agreed on the boundaries surrounding the move
the main move and game cross coding study involved four coders all of whom had already coded substantial portions of the map task corpus
the first is stability also sometimes called test rest reliability or intertest variance a coder s judgments should not change over time
english and latin tables NUM and NUM are much harder to pair up since they are separated by millennia of phonological and morphological change including grimm s law
this is clearly true for the dialogue structure coding schemes described here once the dialogues have been segmented into appropriately sized units
the key idea is to abandon any branch of the search tree NUM actually as an anonymous reviewer points out the exact correspondence is between german hat and earlier english hath
finally it automatically derives the frame specification for lexical rules such that following standard hpsg practice only the information changed in a lexical rule needs to be specified
in this sense human noun a semantic concept can nonetheless become an operational term in the formal description of natural languages indispensable many procedures of natural language processing cnlp systems
our principal goal is to allow the system s users and their conversational partners working together to have pleasant social interactions
a compiler is described which translates a set of lexical rules and their interaction into a definite clause encoding which is called by the base lexical entries in the lexicon
meurers and minnen covariation approach to hpsg lexical rules b lex lel ollx framel figure NUM lexical rule predicate representing lexical rule NUM
in the above example this would result in two lexical rules one for words with tl as their c value and one for those with t2 as their c value
our compiler therefore performs what can be viewed as partial unfolding it unfolds the frame predicates directly with respect to the interaction predicates as shown in figure NUM
this is because in order to be able to treat recursive lexical rules producing infinite lexica we perform word class specialization of the interaction predicate instead of expanding out the lexicon
since a family name alone can precede pts the grammar above should be refined figure NUM thus we observe NUM instead of l
in order to recognize automatically pns on a large scale in texts in the absence of a complete lexicon of pns the description of noun phrases containing pns should be necessary
difference between ftand pt nouns of professional title p are different from vocative terms ft not only in syntactic but also in semantic ways
figure NUM figure NUM type i of nominal phrases containing pars postpositions observed on the right side of nouns proper or common ones indicate grammatical
as a result in addition to the information present in the lexical entry syntactic information can be accessed to execute the constraints on the input of a lexical rule
both the input and output of a lexical rule i.e. the mother and the daughter of a phrase structure rule are available during a generation or parsing process
for example the string that includes the sequence z NUM ssi dongsaing neijib in la can hardly be anything else than a noun phrase containing a pn
because NUM is a dialectal adverb and NUM a noun verb string they were not detected in our system
in all other cases the problem is one of inexact string matching i.e. finding the alignment that minimizes the difference between the two words
test group1 consists only of very frequent word types in hebrew but the test corpus probabilities for these word types can be viewed as a reliable estimate of the morpho lexical probabilities
because it knows nothing about place of articulation or grimm s law it can not tell whether the d in daughter corresponds with the th or the g in greek thugat r
another reason is that earlier and later languages are tied together more by the physical nature of the sounds than by the structure of the system
finally table NUM shows how the aligner fared with some word pairs involving latin greek sanskrit and avestan again without knowledge of morphology
phonemic transcriptions are acceptable insofar as they are also broad phonetic but unlike comparative reconstruction alignment does not benefit by taking phonemes as the starting point
this paper presents a guided search algorithm for finding the best alignment of one word with another where both words are given in a broad phonetic transcription
so the understanding of a refashioning does not depend on the understanding of the new proposed referring expression but only on its derivation
as an illustration of how the semantic rules can be simulated in first order unification consider the derivation of the constituent harry found where harry has the category np with lf harry and found is a transitive verb of category s np np with lf NUM aobject asubject found
abeill notes that the stag formalism allows an explicit semantic representation to be avoided mapping from syntax to syntax directly
abstraction of the combinatorial properties of words
in this research the acquired grammar is elr luated
as the result of the grammar acquisition process NUM rtfles are acquized
the differential entropy de is defined as follows
these methods can be classified into non grammar based and grammar based approaches
this is consistent with the grouping result of our approach
NUM NUM as h dai tatsunokuchi nomi ishikawa NUM NUM japan
let cl and c2 be the most similar pair of labels
chart parser tries to find the best parse of the sentence
note that each node corresponds to a bracket in the corpus
one of these measures is divergence which has a symmetrical property
NUM if a probabilistic relation r is replaced by its set theoretic version r i i.e. x y e r iff r x y NUM then the closure operations used here reduce to their traditional discrete counterparts hence the choice of terminology
null in our algorithm computations for tasks NUM and NUM proceed incrementally as the parser scans its input from left to right in particular prefix probabilities are available as soon as the prefix has been seen and are updated incrementally as it is extended
intuitively fli kx a is the probability that an earley parser operating as a string generator yields the prefix xo k NUM and the suffix xi l l while passing through state kx a at position i which is independent of a
empirically matrices of rank n with a bounded number p of nonzero entries in each row i.e. p is independent of n can be inverted in time o n2 whereas a full matrix of size n x n would require time o n3
computational linguistics volume NUM number NUM b the inner probability i k x NUM is the sum of the probabilities of all paths of length i k that start in state k kx
NUM once again it is helpful to compare this to a closely related finite state concept the states of the lr parser correspond to sets of earley states similar to the way the states of a deterministic fsa correspond to sets of states of an equivalent nondeterministic fsa under the standard subset
computational linguistics volume NUM number NUM b the probabilistic left corner relation 1deg pl pl g is the matrix of probabilities p x l y defined as the total probability of choosing a production for x that has y as a left corner
the root node of t NUM explains that both a and b appeared twice in baab when no consideration is taken of previous symbols
our objective is to construct a tag model that precisely evaluates p tiltl i NUM wl i in equation NUM by using the three level tag set
NUM try to analyze data by using the constructed rules and extract the exceptions that can not be correctly handled then return to the first step and focus on the exceptions
the off diagonal cells represent misunderstandings that are not corrected in the dialogue
although we have focused on part of speech tagging in this paper the mistake driven mixture method should be useful for other applications because detecting and incorporating exceptions is a central problem in corpus based nlp
we trained our tag models on the corpora with every tenth sentence removed starting with the first sentence and then tested the removed sentences
they can not be enumerated in any dictionary even with numerous size
another typical application is in text to speech conversion
we have described a new tag model that uses mistake driven mixture to produce hierarchical tag context trees that can deal with exceptional connections whose detection is not possible at part of speech level
in such a ease the first term n sb in equation NUM is enormous for general b and the tree is expanded by using more general symbols
and while the use of a dictionary is more important now that denser and faster memory is available to smaller systems letter to sound still plays a crucial and central role in speech synthesis technology
the letter to sound rule set described above sets lexical stress in a wide variety of cases especially where the word is monosyllabic or the suffixal information is sufficient to place primary or secondary stress
if both contexts are true the rule applies otherwise another rule is searched for first any other rule with the same is and then in decreasing length of is matches
s is pronounced s if preceded by an element of prefix and followed by an element of v a vowel as in t616si6ge
for both languages the spelling has been enforced by dictionaries and laws but the pronunciation has continued to evolve widening the gap between the written and spoken components of the language
if one lists a large number of common morphemes it becomes a simple task to state an accurate set of letter to12 all languages of the world are of an equal degree of complexity
the phonemes for the rule are then placed in the output the current position is advanced over the matched graphemes and the process is repeated until a rule consumes the leftmost grapheme
simr generates candidate points of correspondence in the search rectangle using one of its matching predicates
two tokens in a bitext are cognates if they have the same meaning and similar spellings
however if someone is smuggling it can not be inferred that he is exporting illegally nor that he is importing illegally only the disjunction can be inferred
the heuristic involves three parameters chain size maximum point dispersal and maximum angle deviation
these interactions are also dependent on speech rate
the first is context sensitive and the second context free
the ambiguity level of a given point can change when the search rectangle expands or moves
combinatorial explosion NUM word segmentation candidate space
agents at the low level for treating
when the second dimension set of sub domain classifications is used the classifier correctly identifies NUM of the subdomains
the telic expresses the event p that can be associated with an object of type acterelation
since retrieval is crucially dependent on how well the queries are processed it appears that the NUM are well prepared for retrieval using the original NUM entry lexicon
similarly for exptyps NUM NUM l0 where either rule2 or tag NUM NUM are used for stopword removal effectiveness does not seem to alter much
if one can identify where the single character words occur the rest of the string quite often can be split as such when it is even
the costs of edit operations are based on phonological features we used the NUM binary articulatory features in this feature set was chosen merely because it was commonly used in other speech recognition experiments in our laboratory none of our experiments or results depended in any way on this particular choice of features or on their binary rather than privative or multivalued nature
in this transducer all arcs leaving state NUM correctly lead to the flapping state on stressed vowels except for those stressed vowels that happen not to have occurred before an instance of flapping in the training set
when walking down branches of the tree to add a new input output sample we calculate the longest common prefix n of the sample s unused output and the output of each arc along the path
this behavior is caused by a paucity of training data but even with a reasonably large training set we found it was often the case that some particular strings of segments happened to only occur once
although each particular clump may be correct for the exact input example that contained it it is rarely the case in general that a certain segment is invariably followed by a string of six other specific segments
every article boundary was identified to within an accuracy of two sentences
for each pair of states s t in the transducer the algorithm will attempt to merge s with t building a new state with all of the incoming and outgoing transitions of s and t
however because k is relatively small and because decision trees are induced only after merging states down to a small number decision tree induction in fact takes only a fraction the time of any other step of computation
that a dependent is never separated from its governor by anything other than another dependent together with its subtree or by a dependent of its own
request indirect request interpretation of inputs
these semantic and linguistic expectations can provide the necessary context
the linguistic expectation provides the value of the unspecified object
NUM computer in the lower left corner
the system was later enhanced by moving it to sparc NUM machine
NUM a number must be spoken as digits
the complete session lasted up to two and one half hours
the verbex machine acknowledges each input with a small beep sound
control can also be used to dynamically alter and or suspend proofs
1deg notice that there is no overtraining the difference in accuracies on training and test set remain within a very narrow range throughout with test set accuracy exceeding training set accuracy by a small margin
in this paper we take the former alternative to describe a first order rendering of ccg
NUM 14q every represents every as a quantifier and s every as a set denoting property
note that these two purposes often conflict for example city state references and date ranges were supposed to have pieces marke d separately but were reduced to single mtokens with one set of slot fillers
here the system finds descriptors the big hollywood talent agency and a hot agency but not a qualit y operation and the agency with billings of NUM million
it also found some additional subject boundaries in the middle of articles
on the former a phrase like ad agency fallon mcelligott woul d have caused it to be found but the actual phrase other ad agencies such as fallon mcelligott did not
for the ne task the postprocessing step consists of traversing the token sequences in parallel with the original text writing the original text and inserting markers as the reduction results attached to eac h token indicated
likewise NUM is an abstraction for four readings
NUM NUM other organization errors were getting new york times which in this article is incorrect missing the two descriptors for ammirati puris and the locale for coca cola
we elected to participate despite this conflict but it did limit us to NUM person weeks on muc NUM forcing us to scale bac k from our original plans and only participate in the ne and te tasks
it traverses the token sequences in parallel with the original text using the fact that each token contains information on all the reductions it was involved in to determine where to insert begin and en d brackets
te expectations expectationsextraction merging tokerreduction sequen NUM extraction ist expect the heart of the system is a sophisticated pattern matcher which is used repeatedly in the course o f processing to identify text for reduction or extraction
for a little over two years sterling software itd has been developing the automatic templatin g system ats NUM for automatically extracting entity and event data in the counter narcotics domain from military messages
however mctag dl share a further problem with mctag the derivation structures can not be given a linguistically meaningful interpretation
for this reason we have equipped dtg with an operation sister adjunction that does exactly this and nothing more
the associated sa tree is the desired semantically motivated dependency structure the embedded clause depends on the matrix clause
see the next section for a brief discussion of linguistic principles from which a grammar s sics could be derived
in future work we intend to examine additional linguistic data refining aspects of our definition as needed
in this section we show how an account for the data introduced in section NUM can be given with dtg
the sa tree for e to g consists of a single node labeled by the elementary d tree name for a
sister adjoining constraints specify where d trees can be sister adjoined and whether they will be right or left sister adjoined see below
the algorithm simulates traversal of a derived tree checking for sics and sacs can be done easily
for practical purposes especially for lexicalized grammars it is preferable to incorporate some element of prediction
susan knows NUM the effect of various linguistic constructions on center movement and the interactions of centering shifts with global discourse structure are active areas of research
for example in the utterance the vice president of the united states is also president of the senate the noun phrase the vice president contributes both a value loaded and a value free interpretation
if a discourse is multi party e.g. a dialogue then the dsp for a given segment is an intention of the conversational participant who initiates that segment
in other words the values of all unmodified features of the prefix verb are token identical with the corresponding values of the base verb
this paper presents an initial attempt to develop a theory that relates focus of attention choice of referring expression and perceived coherence of utterances within a discourse segment
derivation is as in figure NUM the unification at line NUM relies on associativity and as always atomic goals on the agenda are ground
this means that matching against the head of a clause and assembly of subgoals does not require any recursion or restructuring at runtilne
the remainder of this paper is organized as follows in section NUM we briefly describe the phenomena motivating the development of centering that this paper aims to explain
the centering framework described above provides the basis for stating a number of specific claims about the relationship between discourse coherence inference load and choice of referring expression
in this way we use the fact that models for nl are given by intersection in the product of rela tional and groupoid models
but in general we have to try subproofs for different unifiers that is we effectively still have to guess partitioning for left rules
furthermore binary relational labeling propagates constraints in such a way that computation of unifiers may be reduced to a subset of cases or avoided altogether
the enamex tag is the most difficult due to the ambiguity between people places and organizations
the no names configuration had little effect on person names illustrating how simple they are to dynamically recognize
hasten consists of NUM NUM lines of code and the development environment consists of NUM NUM lines of code
sra also conducted a test run using the case insensitive mode which is labeled allcaps in the figure
hasten first matches the structural elements and binds the semantic labels of those elements that successfully matched
the resulting egraphs are saved and can be treated in the same way as a manually created egraph
the matcher compares an incomin g text unit such as a sentence to each extraction example
the target operating environment might consist of NUM users trying to use hasten on NUM different extraction scenarios
as an alternative hasten was configured with weights that created a strong preference for the structural match
for muc NUM the process of generalizing the structural elements was done manually using a graphical editor
we will refer to a set of templates that have potential coreference relationships among them as a ture is more telegraphic
in this paper we consider the problem of assigning a probability distribution to alternative sets of coreference relationships among entity descriptions
data for the evidential model the evidential model utilizes the pairwise probabilities between all pairs of templates in a coreference set
the number within parentheses indicates the number of times that the coreference set with the highest probability was the correct one
in this paper we considered the problem of assigning a probability distribution to alternative sets of coreference relationships among entity descriptions
furthermore let a NUM and b NUM
as an example assume that c lcb NUM NUM rcb
figure NUM the encoding for the pcp problem of figure NUM
in that case there would exist an algorithm for solving the problem
bmb system user rep lace p1 p34 NUM this causes the belief module to update the current plan of the collaborative activity NUM
then after the application of rules NUM NUM and most importantly NUM the system adopts the belief that it is mutually believed that the plan achieves the goal of referring
in contrast our clarifications embody both functions in the same actions thus allowing for a simpler approach to inferring the refashioned referring expressions since we need not chain to a meta operator
although they address partial plans they require in order for an action to be part of a partial shared plan that both agents believe that the action contributes to the goal
second the treatment of clarifications could be improved specifically how plan failures are reasoned about how plan failures affect the agent s beliefs and how these failures are repaired
because we need knowledge of the type of action where the error occurred in order to refashion the invalid plan the constraints of this schema are more specific than those of the judgment plans
re present ref judgment judge ref re while judgment accept re refashion ref re judgment judge ref re end while
in the second dialog b implicitly rejects a s initial presentation by replacing it with a new referring expression in line NUM the guy that s pointing to the left again
plan repair techniques can be used to refashion an expression if it is not adequate and clarifications can refer to the part of the plan derivation that is in question or is being repaired
we omit discussion of actions that account for superlative adjectives such as largest that describe an object relative to the set of objects that match the rest of the description
all we need to know is the density of 0s aggregated over all possible combinations of hidden variables
in this table cframeuuuuuu indicates an instance of cursor in the discourse information on the position and on the whole sentence can be extracted from each occurrence of cframe
the first step of the procedure is to extract fi om an input text discourse information that the system can refer to in the next step in order to complete incomplete parses
thus the effectiveness of this method is highly dependent on the source text since it presupposes that morphologically identical words are likely to be repeated in the same text
since this method has a simple framework that does not require any extra knowledge resources or inference mechanisms it is robust and suitable for a practical natural language processing system
two partial parses are joined if the root head node of either parse tree can modify a node in the other parse without crossing the modification of other nodes
thus the system generated a unified parse for each sentence regardless of the discourse information and we compared the output translations generated with and without the application of our method
then if this also fails a modification pattern containing a word that has the same part of speech as the word on one side of the node is searched for
det n n combining dependencies existing within phrases that occur in other sentences of the same chapter in the discourse information the partial parse is restructured according to the discourse information
fig NUM is an isometric view of the magazine taken from the operator s side with one cartridge shown in an unprocessed position and two cartridges shown in a processed position
a particular word might occur in a pattern in which another synonym was seen more often making it the typical choice
we introduced the problem of choosing the most typical synonym in context and gave a solution that relies on a generalization oflexical co occurrence
the result also shows that the sentential marks in the test data closely correlate to the boundaries between discourse segments
the results show that at least second order cooccurrences are necessary to achieve better than baseline accuracy in this task regular co occurrence relations are insufficient
sentences with different word orders reflect different pragmatic conditions in that topic focus and background information conveyed by such sentences differ t information conveyed through intonation stress and or clefting in fixed word order languages such as english is expressed in turkish by changing the order of the constituents
in the absence of any such control information the constituents of turkish sentences have the default order subject ezpression of time ezpression of place direct object beneficiary source goal location instrument value designator path duration expression of manner verb
NUM since the wind m blows the kitc m it makes the kite rise
consider for example two nominal anaphora referring to the same entity occurring at different places in a discourse
in this section we use terms clusters as our output and subgraphs as their candidates
one syntactic and morphological ambiguity remains unresolved until remains ambiguous due to preposition and subordinating conjunction readings
on the other hand an ambiguity remains unresolved if there are no rules for that particular type of ambiguity
the aim of the algorithm is to find a weighted labeling i such that global consistency is maximized
the rules can refer to words and tags directly or by means of predefined sets
next we describe the syntactic tags n represents premodifiers and determiners
we can add more information to our model in the form of statistically derived constraints
two such words were found in the corpora they detract from the performance figures
the algorithms and models were tested against a hand disambiguated benchmark corpus of over NUM NUM words
we will use the algorithm to select the right syntactic tag for every word
the success of the hybrid syntactic disambiguator is evaluated against a held out benchmark corpus
in our experiments the test sentence ps comes from susanne corpus
this NUM word sentence is input to the part of speech tagger and a part of speech sequence
it is clear that definition NUM is the same as definition NUM
the experimental results after applying two heuristic rules are shown as follows
technology is being developed for document detection information retrieval and for data extraction from free text
the abstract class persistent object is introduced which is a superclass of any class of persistent objects
to process real text is indispensable for a practical natural language system
the distribution of chunk length is listed in tables NUM and NUM
this information can be used to implement operations to extract a single character or advance to the next character position
an annotation satisfies the constraint if for each i attribute ai of the annotation has value v i
implementations may choose to supplement these with additional operations for creating and accessing bytesequences for two reasons NUM
NUM a collection of documents needs to be converted into a tipster collection prior to processing within the architecture
at the end of this section the type declaration packages which would be used to describe these annotations is shown
doc attribute name the arguments are to be matched against the value of attribute name
therefore annotation type declarations are introduce here which serve to document the information associated with different types of annotations
finally type spec may be a previously defined annotation type specifying a reference to an annotation of that type
this privileges subsequent information that provides true instantiations for the variables in a salient open proposition
it then applies operators that enrich the content of the description until all intentions are satisfied
we combine this representation with two assumptions about how information states are represented in the grammar
the need to rule out distractor actions can cause information to be added to an expression
this leads to the following axiomatization of the salience of states
consider how the noun phrase the strings she pulled is generated to describe some exerted influence c
researchers in generation rarely address all of these kinds of conventionality
finally the agent of the pulling is described with she
together these assumptions suffice to generate collocations for library parts
table NUM percentage of queries with names
for our experiments we employed the grammar shown in figures NUM and NUM with only NUM syntactic productions and NUM nonterminal categories including NUM part of speech categories
this study consists of three parts
note NUM and the features next to notes NUM and NUM of the figure
lexical choice could be carried out at any number of places within this standard architecture
the modifier description has the same format as a clause in the linguistic structure
the linguistic structure is therefore incrementally expanded as each head is lexicalized in turn
which postmodifiers will be realized as prepositional phrases and which as relative clauses
open classes are large and constantly expanding while closed classes are small and stable
instead the lexical chooser must reason about how different domain entities can be realized
an example of an input conceptual network with paraphrases that can be generated from it
1deg in the case of advisor ii the focus of nonsyntactic processing is lexical choice
lexical choice is performed by unifying the conceptual input with the lexical fug or lexicon
for systems such as win freestyle or targel of west publishing lexis nexis and dialog respectively which take natural language queries as input the approach to take is less clear
1degto see why they are available it is enough to see that a and b below have two readings each
figure NUM example instantiation of state prediction
in a top down manner the transfer lnodule tra nsfbrms the english case frame or adds new infbrmatioll to the turkish case frame in order to generate the equivalent turkish noun phrase clause or sentence with the aid of a transfer dictionary and the transfer rules
one method of dealing with this problem is by breaking the large travel domain into several semantic sub domains
an interpreter without the above mentioned extension would not terminate on this query
figure NUM the head feature principle of hpsgii
in doing this off line we minimize the need for on line inferences
list are abbreviated in the standard hpsg manner using angled brackets
for each node under a feature in hf apply step NUM
the first was that the scoring software development effort would be improved by requesting realistic data from participants as early as possible for software testing instead of waiting until the dry run
after parsing the incoming answer key and system response it determines what piece of information in the response should be scored against each piece of information in the key
frequency and types of expressions vary in the three language sets NUM NUM NUM
in november NUM the message understanding conference NUM muc NUM evaluation of named entity identification demonstrated that systems are approaching human performance on english language texts NUM
e.g. hd should always be expanded before tl in a given structure
for example two of the three sites in chinese shared a word segmentor developed by nmsu crl NUM NUM
dry run test data created by the language teams were analyzed to obtain consistency and accuracy scores as well as timing on the task
a dry run was held in late march and early april and in late april the official test on NUM texts was
prior experience with the languages varied across groups from new starts in january to those with censiderable development history in multilingual text processing
informal and anonymous the met provided a new opportunity to assess progress on the same task in spanish japanese and chinese
the reader can visit http www
because the number of anaphoric nps and intrasentential candidates is bounded by n and the individual a priori verifications of the binding principles contribute costs proportional to the number of nodes in the surface structure tree the worst case time complexity of step NUM is o n3
during inapt lug to the seinanti lf logical form representation the t inding principles s ve as restrictions tbr filtering out the im dex distributions which are considered valid when intert re ted as eorefl rence markers
due to these definitions the acceptability judgements for the data presented above are reproduced by binding principles a b and c for each example the subject demarcating the local binding category is just the ordinary subject of the subordinate clause
with these difficulties in mind questionable antecedent decisions may t e marked as depending on particular local instantiations by this means providing a starting point for more comprehensive considerations which take into account the relation between structural restrictions and the resolution of ellipsis
unless given further information there see ms to i e a strong tendency to choose the antecedents in a way that the syntactic and or semantic case roles of the pronouns re produce the corresponding roles of the it antecedents
where binds is a relation which is defined on the np nodes of the surface phrase structure tree definition NUM the binding relation node x binds node y if and only if x and y arc coindexed and x e commands y
based on this observation an algorithm has been presented which on the one hand is interdependency sensitive but on the other hand avoids computational unfeasibility l y following a strategy according to which the choices with the highest plausibility are considered first
binding theory bt distinguishes three types of np namely type a anaphor comprising reflexives and reciprocals2 type b nonreflexire pronouns and type c referring expressions comprising common nouns and names
where definitions vary slightly definition NUM the c command relation node x e commands node y if and only if the next b nnehing node which dominates x also dominates y and neither x dominates y y dominates x nor x y
this architecture can already be related to some commandments
or more in general what are useful guidelines
the first two reasons are associated with the heuristics used by the stemmer NUM some word forms will be grouped when one of the forms has a combination of endings e.g. ization and ize
the aim of our experiments was to determine how well part of speech differences correlate with differences in word meanings and to what extent the use of meanings determined by these differences will affect the performance of a retrieval system
we manually identified the lexical phrases in four different test collections the phrases were based on our judgement and we found that NUM out of NUM phrases NUM were not found in the longman dictionary
until more is learned about sense distinctions and until very accurate methods are developed for identifying senses it is probably best to adopt a more conservative approach i.e. uses senses as a supplement to word based indexing
we examined all words in the dictionary in which a word ended in y and in which the y could be replaced by e and still yield a word in the dictionary
we therefore omitted idiomatic senses and example sentences from further processing and tagged the rest of the dictionary NUM the result of this experiment is that the dictionary contains at least NUM senses in which the headword was mentioned but with a different part of speech of which NUM were in fact related NUM NUM
this is a systematic class of ambiguity and applies to all verbs of translatory motion e.g. the bottle floated mder the bridge will exhibit the same distinction talmy NUM
for example words can differ in morphology authorize authorized or part of speech diabetic noun diabetic adj or in their ability to appear in a phrase database data base
because of the volume of data analysis only one collection was examined computer science and the distribution of senses was only coarsely estimated there were approximately NUM unique query words and they constituted NUM NUM tokens in the corpus
in an incremental style where parts of the referential description are uttered prior to its completion the slots that can be filled by the descriptor selected are substantially influenced by precedence relations in the ordinary compositional style this is simply identical to the set of yet empty slots
through taking grammatical and lexical constraints into account this process is capable of exposing expressibility problems early expressing a proposed descriptor may require refilling an already filled slot or integrating the mapping result of a newly inserted descriptor may lead to a global conflict such as unintended scope relations
in general there is no guarantee that the set of descriptors chosen can be adequately expressed in the target language given some repertoire of lexical operators conceptual predicates can not always be mapped straightforwardly onto lexemes and grammatical features so that the anticipation of their composability is limited
incidentally wordnet based approach perfomance is comparable with the trainingapproach one
the first part merely comprises two of the algorithm s termination criteria NUM which constitutes the successful accomplishment of the whole task and NUM which reports the failure to do this within the given limits of the linguistic resources and corresponding return statements NUM and NUM
ample is of class ci given feature values vj
where n is the total number of classes
the next three rows show the accuracy figures of pebls using the parameter setting of k NUM k NUM and NUM fold cross validation for finding the best k respectively
even if a sentence pair s translations truly contain structural mismatches that are beyond syntactic accounts the soft constraint optimization permits graceful degradation in the bilingual parse
NUM NUM integrating wordnet and a train
we rest on the property that the parent node of any such implicit node always has an s link when it differs from the root
the ne configuration of identifinder is stand alone and domain independent no information regarding succession of corporate officers is employed
the overall impact has been more robust handling of documents with sgml and less effort to specify this format
yet we believe that we are only beginning to understand techniques for learning domain independent knowledge and domain dependent knowledge
such a tool kit would be unimportant if we did not anticipate the need for creating many extraction applications
under separate darpa jhnding we have applied this approach to the ne problem for english and for spanish
an example appears below text input is first the automatic transcription of a spoken version appears next
the semantic database is used as the repository of semantic information for all the objects mentioned in a message
undergeneration rises at the other points but at each point it more than offsets the gain in overgenerafion
instead of only a NUM NUM word vocabulary which was derived by including all words occurring at least NUM times in seven years of the wall street journal suppose we add roughly NUM NUM vocabulary items focusing on last names rare first names and rare words in organization lists e.g. companies listed by dun bradstreet
this similarity rules out strategies that take advantage of contexts in which one system is superior to the other l we decided to simplify the combining task by using the scoring program which had been used to test the plum and shogun systems before and during the muc NUM competition to match the plum and shogun frames
the rule above works well in an unambiguous context but there is still need to specify more tolerant rules for ambiguous contexts
the distinction between the complements and the adjuncts is vague in the implementation neither the complements nor the adjuncts are obligatory
compared to the engcg syntactic analyser the output not only contains more information but it is also more accurate and explicit
instead it leaves open a possibility to attach a dependency from another syntactic function i.e. the dependency relations remain ambiguous
this anywhere to the left or right may be restricted by barriers which restrict the area of the test
for instance the verb decide has the tag p on which means that the prepositional phrase on is typically attached to it
in tesni re s and mel suk s dependency notation every element of the dependency tree has a unique head
these average yields for each position are plotted in figure NUM which shows the highest yield sentence position to be p2 NUM followed by p3 NUM followed by p4 s1 etc
this evaluation established the validity of the position hypothesis namely that the opp so determined does in fact provide a way of identifying highyield sentences and is not just a list of average highyield positions of the corpus we happened to pick
index pcompl o if NUM obj barrier nphead link NUM up object svoc head o compl
the major improvement over engcg is the level of explicit dependency representation which makes it possible to excerpt modifiers of certain elements such as arguments of verbs
this is excellent news it means that as an upper bound only about NUM of the humans abstracts in this domain derive from some inference processes which means that in a computational implementation only about the same amount has to be derived by processes yet to be determined
paragraphs close to the beginning of texts tend to bear more informative content this is borne out in figure NUM which clearly indicates that paragraph positions close to the end of texts do not show particularly high values while the peak occurs at position p NUM with dhit NUM NUM
both keywords and abstracts contain phrases and words which also appear in the original texts on the assumption that these phrases or words are more important in the text than other ones we can assign a higher importance to sentences with more such phrases or words or parts of them since a topic keyword has a fixed boundary using it to rank sentences is easier than using an abstract
oppo sile side pass thus dat prss past NUM sg l he child was passed to the opposil e side i y i he mtm the olltptl or this sentence is presenl ed on the right
information in rlhu kish and possibly in many other languages verbs often convey several meanings some totally unrelated when they are used with subjects objects oblique objects adverbim adjuncts with certain lexical morphological and semantic features and co occurrence restrictions
the project has completed an operational prototype and the next phase will deploy the system operationally on a small scale
taking letter frequencies into account improves this to a more plausible looking isclim
the same result obtains if we abstract away from the particular implementational details of treelowering and return to the abstract level at which gorrell states his model
of particular interest is the problem of how the parser decides which relations to add to the set at each point in time especially at disambiguating points
in the above case np which will correspond to the subject of the verb will be unified with the left attachment site
though no experimental work has been done on this type of sentence there seems to be an intuitive preference for the lower attachment site np2
in NUM binding constraints force lowering to be applied at np2 while in NUM it must be applied at np1
null in order to capture the minimal expulsion strategy in this class of japanese examples therefore search for the lowering node should be conducted top down
the current implementation shows that the success of an abstract model such as gorrell s depends crucially on the computational details of the processing algorithm used
however this implies that if more than one such node exists the parser must be given a preference for making the requisite decision
in the final section we have seen that the combination of informational monotonicity with the assumption of strict incrementality results in a system which is too constrained to capture all the processing data
in the above gibson et al have manipulated number agreement to force low NUM middle NUM and high NUM attachment of the bracketed relative clause
to see whether lexicalized transformations were contributing to the transformation based tagger accuracy rate we first trained the tagger using the nonlexical transformation template subset then ran exactly the same test
tm the penn treebank tagging style manual specifies that in the collocation as as the first as is tagged as an adverb and the second is tagged as a preposition
many useful relationships such as that between a word and the previous word or between a tag and the following word are not directly captured by markov model based taggers
in each learning iteration the entire training corpus is examined once for every pair of tags x and y finding the best transformation whose rewrite changes tag x to tag y
it is an exciting discovery that simple stochastic n gram taggers can obtain very high rates of tagging accuracy simply by observing fixed length word sequences without recourse to the underlying linguistic structure
the transformation based learner will delay positing a transformation triggered by the tag of the word to until other transformations have resulted in a more reliable tagging of this word in the corpus
if the effect of a transformation is recorded immediately then processing the string left to right would result in ababab whereas processing right to left would result in abbbbb
this was demonstrated with our spanish spontaneous scheduling task database which contained both push to talk and cross talk utterances
in addition the transformation based method learns specific cues instead of requiring them to be prespecified allowing for the possibility of uncovering cues not apparent to the human language engineer
in addition to obtaining high rates of accuracy and representing relevant linguistic information in a small set of rules the part of speech tagger can also be made to run extremely fast
as mentioned above lhe l atrans term NUM ases conlain terms as well as words aim expressions which behave like terms i.e. which have unique translations
dm tagger t rics to determine the t arl of st e w h of the individual words based on local cooccurrence restrictions
on the other hand the nf parse of a b b c c d e uses b2 twice while the non nf parse gets by with b2 and b1 as a magnet for a s semantic class
the spurious ambiguity problem is not that the grammar allows 5c but that the grammar allows both 5f and 5g distinct parses of the same string with the same meaning
one might try a statistical approach to ambiguity resolution discarding the low probability parses but it is unclear how to model and train any probabilities when no single parse can be taken as the standard of correctness
owing to the partial automation the average annotation efficiency improves by NUM from around NUM minutes to NUM minutes per sentence
regular inflection syncope and gemination is accounled for while only completely irregular word forms will have to be coded in their entirety
l a iyans distinguishes two kinds of voealmlm ies the general vocabulary and lhe lerminologi al vocabulm ies
when a term is fomld in one tel in base it is not looked up fllrlher in the subsequenl databases
the integration of the tagger has not only provided for more effecient processing but more importantly also for a higher quality of the translations of fail softed sentences
in this short presentation we will concentrate on the grammar lexicon and translation module and on some of the new features of pa i ans
a complex category like s np s np n may be written as s np s np n under a convention that slashes are left associative
patrans is in everyday use at the translation agency lingtech where it is being used for all texts which are suited for it in its current version i.e.
right transition write a symbol rl onto the left end of r1 write a symbol r to position a in the target sequences and enter state qi l
to keep the human annotator from missing errors made by the tagger we additionally calculate the strongest competitor for each label gi
during the second annotation stage the annotation is enriched with information about thematic roles quantifier scope and anaphoric ret rence
these knowledge bases consist of highly interconnected networks of at least tens of thousands of facts
an exposition node is the top level unit in the hierarchical structure and constitutes the highest level grouping of content
the order specifies the linear left to right organization of the topics and the grouping specifies the paragraph boundaries
inclusion conditions are expressed as boolean expressions that may contain both built in user modeling predicates and user defined functions
for example the process participants description content specification in figure NUM employs a local variable reference process
when the compute inclusion algorithm returns true the applier obtains the children of the edp s topic
the first realizer which was designed and implemented by the first author was a template based generator
functional descriptions encode both semantic information case assignments and structural information phrasal constituent embeddings
although we could not impose hard constraints we made suggestions about how long a typical explanation might be
in NUM either a lexical nominalisation rule for the adjective gliicklichc is stipulated or the existence of an empty nominal head
in other words the subtree terminals are treated as if they are wildcards
a few comments will be given on the section for error analysis though
the viterbi training process for extracting the word list goes through NUM iterations
a smaller seed of NUM NUM sentences is uniformly sampled from the above corpus
NUM NUM postfiltering model viterbi training for words two class classifier postfiltering viterbi
a new set of parameters are then re estimated based on the best path
in general a word n gram should contain characters that are strongly associated
a better extraction model might be more likely to improve the system further
the NUM gram and NUM gram precision rates are quite poor in the above tests
in the first the opening phase the locutors greet each other and the topic of the dialogue is introduced
furthermore unlike in the word identification stage the increase
table NUM performance for part of speech extraction of the two models
parse accuracy for word strings from the atis corpus by dop3
in order to simplify the proofs we will only consider transducers that do not have e input transitions that is e c q x x x q and also without loss of generality
intuitively if f locext f and w e each factor of w in dom f is transformed into its image by f and the remaining part of w is left unchanged
the resulting deterministic transducer yields a part of speech tagger that operates in optimal time in the sense that the time to assign tags to a sentence corresponds to the time required to follow a single path in this deterministic finite state machine
NUM the pronunciation is modified to fit the japanese sound inventory
the basic idea behind the determinization algorithm comes from mehryar mohri is in this section after giving a formalization of the algorithm we introduce a proof of soundness and completeness and we study its worst case complexity
section NUM touches on topic identification in discourse
where p is the number of documents in lob corpus i.e. NUM o i4 is the number of documents with word w and c is a threshold value
the word association norms of noun noun pairs and noun verb pairs which model the meanings of texts are based on three factors NUM word importance NUM pair occurrence and NUM distance
for the example in section NUM the word problem and dislocation are coherent with the verbs and nouns in the discourse
for example an anaphoric reference should be said to be ambiguous if several possible referents appear in the representation several proper representations and also if the referent is simply marked as unknown which causes no disjunction
rather we randomly assign links between nodes so that on average each node participates in k links and NUM x p of all links connect nodes of the same orientation
then we consider these links as identified by the link prediction algorithm as connecting two nodes with the same orientation so that NUM x p of these predictions will be correct
aside from the use of but with adjectives of different orientations there are rather surprisingly small differences in the behavior of conjunctions between linguistic environments as represented by the three attributes
however in all the experiments performed on real corpus data section NUM the system correctly found the labels of the groups any misclassifications came from misplacing an adjective in the wrong group
a strong point of our method is that decisions on individual words are aggregated to provide decisions on how to group words into a class and whether to label the class as positive or negative
the parse tree contains several open ends that might serve as starting points for further dialogue contributions and the state of the task model defines which of these are still relevant before the continuations are calculated
in particular we focused on the system behavior and how the system can respond to the user s cooperation i.e. to newly introduced goals from the user in an equally cooperative manner
we have shown that in order to achieve this behavior one has to define constellations of dialogue acts and given a certain state of the task model which give rise to specific communciative goals
where acquire uval tesadis sub tesadis acquire uval NUM inst price per minute t j inst total cost are abbreviated
if the system continutation ends on a request we get the following map null if a continutation ends on an offer the mappings are stateofx i comm oals
in this way the interaction was restricted to being between the computer and the subject as much as possible given the quality of commercial real time continuous speech recognition devices at the time of the experiment
computational linguistics volume NUM number NUM the expert retains more control in the task oriented dialogues but there are still occasional control changes when the novice has to describe problems that are occurring while completing the task
furthermore they extend the initiative control rules proposed by whittaker and stenton to consider the utterance content by observing that a speaker has control when the speaker makes an utterance relevant to his or her speaker specific domain plan
although she attempted to acquire information concerning user behavior when users were given the initiative she was unable to provide much information because her subjects did not interact with the system enough to evolve from novices to experts
in contrast their results for advisory dialogues where clients talked to an expert over the phone to obtain assistance in diagnosing and repairing various software faults showed that experts had control only about NUM of the time
assessment subdialogue the number of utterances will be reduced slightly in declarative mode as users who take the initiative may exploit their control of the dialogue to carry out some preliminary steps without verbal interaction
in situations where the user carried out a repair without explicitly notifying the computer the computer might think the task was still in one phase when the user had actually moved the task into another phase
to summarize the results in section NUM on the structure of spoken natural language dialogue are based for the most part on planned speech a consequence of the technological limitations of speech recognizers at the time
users would sometimes forget to use the sentinel words or else would not wait for the system s response that would occasionally be delayed up to NUM seconds normal response time was NUM to NUM seconds
we will show that any critically tokenized word string is a minimal element in the partially ordered set of all tokenized word strings on the word string cover relation
recall that in the previous sections the character string tokenization operation was modeled as the inverse of the generation operation
by freezing the problem of token identity determination tokenization ambiguity identification and resolution are all that is required in sentence tokenization
consequently a noncritical tokenization would conflict with the principle of maximum tokenization since it is a true subtokenization of others
computational linguistics volume NUM number NUM the validity of the complete dictionary assumption can also be justified from an engineering perspective
if s has hidden ambiguity in tokenization by definition there is td s cd s
lemma NUM for a complete tokenization dictionary all multicharacter critical fragments and all of their inner positions are ambiguous in tokenization
so did mary we represent the first sentence ignoring tense as a resolved qlf NUM j sleep term j ly name y john
has three readings john and simon read the same book john and simon both read a book belonging to john though not necessarily the same one john reads one of john s books and simon reads one of simon s books
it is hard to see how dsp s analysis could be implemented within a system employing a pipelined architecture that say separates quantifier scoping out from other reference resolution operations this would seem to preclude the generation of some legitimate readings
in this case it will have a category vp tensefinf modalfeill perfectffino progressive no polfneg a more general question is whether all ellipses involve recompositions with variants of linguistic antecedents
drt s use of discourse referents to indicate scope suggests that kamp s treatment may be more readily extended in this manner lists of discourse referents at the top of drs boxes are highly reminiscent of the index lists in scope nodes
he claimed pro her seen to have he claimed to have seen her
this hypothesis however fails when the parser arrives at the participle gelesen
the children have this report read the children have read this report
this kind of projection is limited to some categories and triggered by intrinsic features
she has allowed him pro the book tolook at null she allowed him to look at the book
then the first constituent die kinder is attached as the specifier of the cp
these structures are associated with information concerning traces argument structure and case features
the parser produces a set of gb sstructures trees from an input sentence
if the verb follows the arguments they are also inserted into the argument table with a provisional interpretation
free word order languages raise difficulties for parsing systems based on phrase structure rule grammars where the constituents are ordered
from all this work together with the body of research into discourse and conversation it was apparent that a simple though partial model of conversational interaction could be constructed
by using a pronoun to refer to tony in utterance e the speaker may confuse the hearer
he john c6 john cf lcb john mike rcb continue d
we use subject position as the test because there is no prior sentential context to bias the interpretation
in section NUM we discuss applications of the rules and their ability to explain several discourse coherence phenomena
the intentions provide the basic rationale for the discourse and the relations represent the connections among these intentions
changes in attentional state depend on the intentional structure and on properties of the utterances in the linguistic structure
we conjecture that this is so because they engender different inferences on the part of a hearer or reader
if both susan and betsy were equally likely backward looking centers in the second utterance of these sequences then all of these variants would be equally good or perhaps there would be a preference for variants NUM and NUM which exhibit continuity of grammatical subject and object
dc u4 i want to leave from torino
evolving plans engaging in a task and on the other hand interpersonal and interactional goals tend to predominate when the focus is on social aspects of the conversation itself
the boolean retrieval method was used in the initial probing of the corpus to identify candidates for the scenario template task because the boolean retrieval is relatively fast and the unranked results are easy to scan to get a feel for the variety of nonrelevant as well as relevant documents that match all or some of the query terms
this accords with the observation that hearers have an immediate tendency to resolve subject pronouns based on the existing discourse state before the entire sentence is interpreted
in this work we had to deal with two sets of priors
furthermore data has been presented that shows that in addition to the salience factors utilized by bfp additional types of intersentential relationships must be taken into account
in the previous section we argued that bfp s use of rule NUM along with the transition definitions and definition of cb does not provide the correct utilization
here we adopted two important ideas from machine learning and information theory
centering theory is motivated by two related facts about language that are not explained by purely content based models of reference and coherence cf
therefore the algorithm makes the correct predictions regarding example NUM one of the central motivating examples of centering theory
we conjecture that this is so because they artificial intelligence center NUM ravenswood avenue menlo park ca NUM
we review the fundamental concepts of centering theory and discuss some facets of the pronoun interpretation problem that motivate a centering style analysis
moreover we can determine which tree to use by looking at each tree once per instantiation of its arguments even when the same tree is associated with multiple lexical items
broadly speaking it involves aggregating content into sentence sized units and then selecting the lexical and syntactic elements that are used in realizing each sentence
ifx is uniquely identifiable in the discourse model then this goal is only satisfied when the meaning planned so far distinguishes x for the hearer
the meaning of a tree is just the conjunction of the meanings of the elementary trees used to derive it once appropriate parameters are recovered
elementary trees without foot nodes are called initial trees and can only substitute trees with foot nodes are called auxiliary trees and must adjoin
our s trees specify the unmarked svo order or one of a number of fancy variants topicalization top left dislocation ld and locative inversion inv
we choose tag because it enables local specification of syntactic dependencies in explicit constructions and flexibility in incorporating modifiers further it is a constrained grammar formalism with tractable computational properties
the tree refers to three new entities the object book19 the subject library and the reference point r of the tense of have27
all predicate argument structures are localized within a single elementary tree even in long distance relationships so elementary trees give a natural domain of locality over which to state semantic and pragmatic constraints
is there a sharp change in the video stream in the last NUM frames
two types of semantic composition are basic complement incorlgoration and modifier incorporation
there are five temporal references within subdialogs that recency either incorrectly interprets to be anaphoric to a time mentioned before the subdialog or incorrectly interprets to be the antecedent of a time mentioned after the subdialog
passonneau litman NUM condon cech NUM and hirschberg nakatani NUM where reliability is measured in terms of the amount of agreement among annotators
the error metric counts one error for each aligned block in the reference alignment that is missing from the test alignment
therefore three it is not necessary to manually segment the component texts into smaller units before input to gsa
in figure NUM the vertical range of segment j corresponds to a vertical gap in simr s first pass map
however the expanding rectangle search strategy can miss larger non monotonic segments which can not fit inside one chain
visual inspection of some scatterplots indicated that frequent tokens are often responsible for the lion s share of the noise
for example english adjective noun pairs usually correspond to french noun adjective pairs
in the recognition phase simr calls the chain recognition heuristic to search for suitable chains among the generated points
the stop list of closed class words made the matching predicate more accurate because closed class words are unlikely to have cognates
local slope variation to ensure that simr rejects spurious chains the maximum angle deviation threshold must be set low
several metrics and test collections have been used fordifferent approaches or works
the message to speech mts system described below is specifically designed to function in an environment with seriously restrained computational resources where it is impossible to store large amounts of pre recorded speech
as the system has to function in a very restrictive environment with respect to computational resources a compromise between concept based and template based generation systems had to be found
mu arguments are not necessarily passed on to a carrier slot in a straightforward way the argument can be deleted adapted or swapped
subsequently if alternative surface forms co exist the restriction on the slot see figure NUM is compared with the characteristics of its argument
the input of the duration module is a phonetic transcription in which primary and secondary stress provided by the dictionary or g2p module are indicated
after assimilation has been taken care of the resulting ept for the argument can be inserted without any further action into the ept of the carrier
the singular value decomposition of the resulting NUM NUM by l NUM matrix defines a mapping from the NUM NUM dimensional space of concatenated context vectors to a NUM dimensional reduced space
a major asset of prosody transplantation is the combination of natural sounding speech with a low bit rate for storage less than NUM bit per second
the duration module has access to one or more duration models in order to produce a phonetic transcription that is enriched with a duration value for each phoneme
to be able to predict whole words it is necessary to determine the syntactic role of the next word in the sentence
the parsing algorithm constructs the complete link q and complete sequences for substring and merges incrementally the complete links into larger complete sequence and complete sequences into larger complete link until the lr bos eos with maximum probability is constructed
given the widespread part of speech ambiguity of words this is problematicj how should a word like plant be categorized if it has uses both as a verb and as a noun
the training should be automated whenever possible and where human intervention is required the process should be deskilled to the level where ideally it can be carried out by people who are familiar with the domain but are not experts in the systems themselves
ingcolleetion several ways of integrating wordnet and reuters have occurred to us
for example all references to vowels in rules about splitting of consonants may be augmented with or diphthongs
figure NUM sketch of tile modular tsdbl design tin database kernel is separated from client programs through a layer of interface flmctions
a detailed annotation schema was designed tbr the test data which does not presuppose a specific linguistic theory a particular evaluation situation or application type
verb wdency as a subtype of general complementation each phenoiuenon ix identitied by a phenomenon id and by its supertype s
the tsnlp methodology is designed to optimize i control over test data ii progressivity and iii systematicity
diagnostic results obtained can be stored in the databnsc as part of the set NUM application prwjile for use in contitnlolts progress ewduation section NUM gives mt exainple
to ease the tilne consuming test data construction and to reduce erratic variations in filling in the tsni p annotation schema a graphical test suite construction tool tsct was implemented
the table of conditional probabilities of syntactic categories has a fixed size and it is built before the use of the predictor
similarly etd data is collected in a simulated conversation between a traveller and a travel agent
situation NUM may also indicate a mistake or it may be the case that the meanings are non equivalent and therefore show different language internal configurations
the assumption is that a normal domain utterance can be regarded as a database query involving a limited number of possible categories in the atis domain these are concepts like flight origin and destination departure and arrival times choice of airline and so on
also the esst recording scenario was push to talk whereas the etd recording set up allows for cross talk
as explained in the previous section a lr i j is composed of the dependency link between word i and word j either wi wj or wi wj s i m and sl rn l j for an m from i to j NUM
we begin by presenting the results of tests run in speech to text mode on versions of the slt system developed for six different language pairs english swedish english french swedish english swedish french swedish danish and english danish
an important part of our approach to the travel planning domain is a system of sub domain parsing
these features would also provide the basis for a neural net or deterministic classificatio n approach e.g. c4 NUM for a learning capability to be developed later
our analysis of a wide variety of data classes indicates that the majority of classes and of instances do not require sentence level linguistic information for their detection and identification
an enormous amount of time was spent learnin g how to use dxl and as the rule writers did not know unix learning the linux system
these were time date percent money some references to time involved single word places e g NUM p m chicago time
however it is an empirical question whether the desired accuracy levels particularly NUM misses can truly be achieved without at least a reasonable identification of the case roles of the surface constituents
for the majority of difficult cases it would appear that the semantic features of local adjacent contexts plus some global context e.g. the type of document could resolve the identifications
the first step is to convert the character stream input into a stream of tokens essentially words based primarily on the presence o f blanks between character strings
single tokens bound to vl and v3 are modified by attribute changing actions and al l the bound tokens are finally reinserted back where they were in the token stream
with this new language model we obtained a NUM word error rate
names with commas in the middle commonly law firms e g l f rotchieff unterberg towbin were difficult because we used the commas to suggest phrase boundaries
also recent is the accumulation of the large number of critical for our approach signal words whic h mark the presence of certain data classes for example mr
the same thing happens with the infixes there are few of them in basque and their frequency is not very relevant
mappers mediate between phoenix tree structures and the feature structures of the interlingua design
the boxes with language names spanish english dutch italian and wni NUM represent the language modules and are centered around the ili
their perspective was not based on a specific snlds but a general analysis of the issue of evaluation
we expect that in most cases each sub utterance will not span multiple sub domains
we can clearly identify the role of each resource in this tcapproach
a reserve battery pack supplies the 316lt for approximately NUM minutes with power
tagging errors also caused some translation mistakes
some differences in the etd and esst databases are attributable to the push to talk vs cross talk recording scenarios
auru l indexatlon automatlque imqxexauon automauoue k terms a document with terms and general expressions ii NUM
this is the first step in positioning a term extracted from a corpus in the structure of a thesaurus generic relations synonymy relations
a conceptually simpler translator can be built using head transducer models with only lexical items in which case the distinction between different dependents is implicit in the state of a transducer
the thesaurus is composed of NUM semantic or subject fields included in NUM themes such as mathematics sociology etc
term subject field discrimination uses a representation of the term calculated on the whole corpus in order to classify it into about NUM subject fields in this experiment
this is an important problem in certain disciplines in which knowledge and in particular vocabulary is not very stable over time especially because of neologisms
the experiment described here was used to evaluate different methods for classifying terms from a corpus in the subject fields of a thesaurus
for example terms from computer science which are found in a lot of documents are not highlighted by this indicator
the quality of the indexing process is estimated at NUM percents number of right terms divided by number of terms
this experiment compares different models for representing a term from a corpus for its automatic classification in the subject fields of a thesaurus
this algorithm c tl tl ctes
let us linguistically interpret the previous system of equations
this is a general feature of the technique the lexical chooser incrementally builds a syntactic structure and each time a new linguistic constituent is introduced a subconstituent from the semr is copied under the semr of the syntactic subconstituent representing the mapping between semantic and syntactic constituents
hence the possibility of applying analogy to trees
distau es are given in nodes
a possible application is anmysis and generation by analogy
also transitivity is verified by linguistic examples
we recall tile tbrmal definition of a metric
saussurian analogy a theoretical account and its application
the information service needs these information elements to compose an appropriate database query and to choose the most suitable travel plan
we see that NUM of the turns contain only one utterance and NUM contain two utterances
the maximum of utterances per turn is NUM which is in only NUM NUM of the cases
the vios system presents complete travel plans as a whole while human operators give the information in several chunks
it is more polite to keep these last kind of questions until the information service is ready with her presentation
they are developing an asp system called vios to automate part of its dialogues held at its call centres
this represents how many times we saw this particular configuration in our training samples
it would be more convenient for the caller if the system would provide the information in smaller chunks
likewise if we have some good reason for wanting to put together the various senses of cherry into a value returned by a single path then we can write something like this cherry sem glosses lcb sem gloss i sem gloss NUM sem gloss NUM rcb
NUM this only applies to original source descriptions as we mentioned above the formal inference mechanisms that implement inheritance necessarily add statements to make a description nonfunctional but since these can always be automatically determined they need never appear explicitly in source descriptions
here we see that head NUM represents an intermediate level between human head NUM and externalbody part NUM in wordnetl NUM which is missing between their dutch equivalent lichaamsdeel NUM and hoofd NUM
after the atomic feature selection it was reduced down to NUM NUM nodes
from terminal indices to say byte offsets in a file and the other tree location will give the location in the original corpus of a subtree in terms say of byte offsets of the left and right delimiters
in the second option the structures of both the reference and the source wordnet are compatible and the inter lingual relations are compared relative to this structure
each category was judged by two people independently NUM the judges were asked to rate each word on a scale from NUM to NUM indicating how strongly it was associated with the category
let t be a training set of classified quadruples
equal to NUM figure NUM distance calculation example
let us briefly explain each step of the algorithm
here the value of declension3 accusative inherits from declension2 vocative and then from declensionl accusative using the global path in this case the query path rather than the local path vocative to fill out the specification
a priority value is an integer associated with a given default and all ground instances of it where a default with priority i is stronger than one with priority j if i j
in these cases it is not clear whether we are dealing with different events in which one causes the other or one makes up the other
agents aware of some rule or norm that is relevant to their current situation choose to follow or not follow the rule depending on how they view the consequences of their choice
it is forward looking in the sense that they investigated issues in evaluation independent of building a system
the difficulty in considering misunderstandings in addition to intended interpretations is that it greatly increases the number of alternatives that an interpreter needs to consider because one can not simply ignore the interpretations that seem inconsistent
definition NUM given a theory t and a goal proposition g we say that one can abduce a set of assumptions a from if t u a g and t u a is consistent
NUM in the model it is always possible to begin an embedded sequence without addressing the question on the floor however when the embedded sequence is complete the top level one is resumed
the primary contributions of this work have been to treat misunderstanding and repair as intrinsic to conversants core language abilities and to account for them with the same processing mechanisms that account for normal speech
a speaker sl can expect that making an askref of d to s2 will result in s2 telling sl that s2 does not know the referent of d if s2 does not know it
for all d i such that NUM i n there is no t u d t NUM u u d i NUM that satisfies the priority constraints and is inconsistent with d i
not only does the use of key words in one text slice appear to influence the intensity with which key words are used in the immediately neighboring text slices but as the novel proceeds key words appear with increasing frequency
the presence of inter textual cohesion in addition to intra textual cohesion and the concomitant phenomenon of global lexical specialization suggest that in order to understand the discrepancy between v n and its expectation a more fine grained approach is required
previous analyses have either generated too few or too many readings or have required an appeal to additional processes or constraints external to the actual resolution process itself
the corresponding objects pl and NUM are similar if we take p2 to be a paper and to have a poss property similar to that of pl
we infer end f l and end t z since feet and tops are ends
NUM the antecedent clause is represented in figure NUM and the expansion of the final vp ellipsis is shown in figure NUM
reading NUM results from the second clause receiving a sloppy interpretation from the first and the third clause re
if there are many such words and if these underdispersed words cluster together the resulting deviations from randomness may be substantial enough to become visible as a divergence between the observed and theoretical growth curves of the vocabulary
however this interpretation hinges on the random selection of word tokens and this paper presents ample evidence that once a word has been used it is much more likely to be used again than the urn model predicts
second litman and allen use a stack of unchanging plans to represent the state of the discourse
a word appearing only in the first half of a book enjoys some specialized use but to a far lesser extent than a word with the same frequency that occurs in the first half of the first chapter only
a comparison of the progressive difference scores d k and du k bottom row shows that the underdispersed words are again largely responsible for the large values of d k for small k
they have integrated this into a model of generating utterances a step that we have n t taken
analyses of three novels five consecutive issues of the dutch newspaper trouw and the chronologically ordered samples of the dutch newspaper de telegraaf in the uit den boogaart corpus all revealed systematic overestimation for the expected vocabulary size
next presupposing its partner s acceptance of the plan it applies any rules that it can
a sst is composed of states and arcs
we focus on this conceptual analysis stage describe the data prepared from the results of the morpho syntactic analysis and show the results of the clustering module and their interpretation
by a caret that are instances of the upper language
figure NUM versions of upper that freely allow non
NUM atomic verbs are those that express an atomic event
the algorithm s complexity is cubic when we move one word from one class to another
they focus on the ongoing process of events described by verbs
the number of empty classes is increasing with the tree grows
as shown in figure NUM whether or not contributors could be attributed to the hearer did not correlate with the choice of since or because
applying this idea in our algorithm we create two binary trees to represent different directions
dialogue mt introduces interesting problems beyond the already difficult issues of integrating speech processing with translation
there is a voluminous literature on aspect within linguistics and philosophy
the complening process is described as follows we split the vocabulary into a binary tree
yet we are convinced that the bleak scenario we mentioned as the alternative does not apply either
both papers explore the sorts of approach and architecture that the different sorts of move require
this observation is extremely important when we try to set our goals for spoken translation systems
both types of filters can easily be constructed using the directed replace operator
the approach adopted here is an analogical framework using a cascaded noisy channel model
this raises the question how such systems can be adapted to new domains and vocabulary
the subjects were only allowed to hear each utterance once
their approach is to apply a statistical model of dialogue structure based on trigrams of speech acts
table NUM corpus counts for the NUM structures possible for NUM pp sequences
another efffciency problem is that of acquiring the knowledge for building such a system
the third removes the spaces that are not part of some multiword token
e x e be the homomorphism specified as h a b a a
the application of the transformation might be conditioned by the requirement that some additionally specified pattern matches some part of the string w to be rewritten
we have established the following results transformations with a bounded number of alternations can be learned in polynomial time learning transformations with an unbounded number of alternations is np hard
since the positive evidence of r can not exceed q2 iei r would have a score not exceeding iei q if contrary to our assumption
an occurrence of string u must be rewritten to v in a text whenever u is followed by a substring matching NUM string NUM is called the right context of the transformation
in the remainder of the paper we sometimes identify an implicit node of a suffix tree with the factor represented by the path from the root to that node
at internal node p with children pi NUM i d d NUM we assume that sets r pi s have already been computed
thus e p is the number of different positions at which factors u and v are aligned within lx and hence the positive evidence of transformation u v w r t
if pl is not the root node let p2 s link p1 and return implicit node faslscan p2 NUM abel pl p
regional in the term regional network NUM this analysis allows the organization of all the candidate terms in a network format known as the xall the examples given in this paper are translated from french
there are practical and theoretical reasons for this policy decision the aim of the system is only to distinguish between events and though the ability to represent durations is in a very few situations useful for this task the engineering overheads in incorporating a more complex reasoning mechanism make it difficult to do so within such a shallow paradigm
it is generally the case that such incidents do possess only one location date category or description
our approach is based on the assumption that discourse processing should be done early in the information extraction process
if a slot fill in the key is marked as optional then the scorer treats it in one of two ways NUM if the response provides a slot fill then it is scored as a normal slot fill NUM if the response provides no slot fill then the content of the key is ignored and the response is not penalized for missing the fill
in section NUM we give a short resume of the dop method
even with the current restriction the problem is far from trivial
also other factors that are of interest to consumers such as speed development data requirements and so on need to be considered when making comprehensive comparisons o f systems
the results are reported in a tabular format
for a given verb or noun w and for each category c we evaluate the following function that we call domain sense
how can we derive unknown subtrees
this is lower than our NUM NUM
the root node of the left subtree for b02 is a gree t init operator which belongs to the greeting phase while the partly visible one to the right belongs to the negotiation phase
i am grateful to remko scha for many useful comments and additions
suggest support date hello mrs klein we should arrange an appointment for the team meeting a03 ja ich eiird ihnen vorschlagen im januar zwischen dam ffinfzehnten und neunzehnten
uptake reject date suggest support date oh that is really inconvenient i m in hamburg between the eighteenth of january and the eleventh clarification dialogues are necessary between verbmobil and a user
charniak applies the dop approach to p o s strings from penn s wsj
this shows the importance of taking all subtrees from the training set
misconceptions are a deficit in an agent s knowledge of the world they can become a barrier to understanding if they cause an agent to unintentionally evoke a concept or relation
in misunderstanding a participant obtains an interpretation that she believes is complete and correct but which is however not the one that the other participant intended her to obtain
we allow that individuals might not all share the same taxonomy of speech acts and linguistic intentions and that certain social groups or activities might have their own specialized sets of linguistic expectations
NUM a prioritized theorist reasoner can assume any default d that the programmer has designated as a potential hypothesis unless it can prove d from some overriding fact or hypothesis
we describe a mapping between the utterance level forms semantics and discourse level acts pragmatics and a relation between the discourse acts and the beliefs and intentions that they express
the purpose of this tree structure is to capture the sequential structure of the dialogue and for each state of the dialogue what attitudes the participants are accountable for having expressed
moreover these approaches may actually trigger misunderstandings because they always find some substitution and yet they lack any mechanisms for detecting when one of their own previous repairs was inappropriate
by addressing the problem of repair this work should facilitate efforts to build natural language interfaces that can better recover from their own mistakes as well as those of their users
syntactic variables are zt device which pertnit to quantify jim clauses at an arbitrary time hypassing the normal functional compc sition of lambda terms which requires a strict management of incorporation order
so in the case of sen fence s l tile two readings it is not the case thai john likes every woman hated by peter and lh tl n
thus hate h l h2 which can be glossed as hi hates h2 is given type t while hl and h2 are constrained to be free variables of type e
a further rule removes redundancies in some case analyses see NUM
this is done based on the hierarchical discourse structure as well as on the textual distance
our microplanner takes as input an ordered sequence of pcas structured in an attentional hierarchy
the output of the macroplanner is an ordered sequence of proof communicative acts pcas
it is this intuitive explanation which guarantees the correctness of this rule with respect to meaning
this research is supported by the r o c
to make sentence scoping decisions by singling out one candidate textual semantic category for each constituent
first experiments with pro verb resulted in very mechanical texts due to the lack of microplanning techniques
the u form of fig NUM expresses three predicate argument relations among the nodes like not hate in order to extract the predicate argument relations encoded into tile u form one needs to apply the following rule
second it is flexible attd can tie extended to handle the semantics of sentences extracted fiom a real corpus of texts which it might he perilous to constrain too strongly fi om the starc the mechanism is the following
the tuned hierarchy is then used to guide the disambiguation task over local contexts thus producing a final sense tagged corpus from the source data
we choose to simplify this characterization by using the act primitive rather than introducing yet another level of representation
n cmark noun n n mark noun n
sentence units are followed with p indicating a sentence boundary as shown in example NUM
refine d e n augments the model with all individually profitable extensions of contexts of length n
in figure NUM we see that the deletion rate for the baseline data is zero as expected
the substring o establish occurs most frequently in the gerund to establish which is nearly always followed by a space
this will assign a more liberal set of parts of speech to the word based on its affix
figure NUM the relationship between the number of model parameters and the total codelength l t c
to enhance this approach it seems interesting to try to guess the entire word that is a lemma and its associated suffix
this approach allows the parsing system to find new definitions for words that are already in the dictionary
figure NUM graphs the number of model parameters required to achieve a given total codelength for the training corpus and model
for example while checking the word butterfly the ffix ly matches as a suffix
by combining the syntactic and morphological information the word smolked is identified as a past tense verb
we have proposed and verified from corpus data constraints on the semantic orientations of conjoined adjectives
this process led to the selection of nine predictor variables
the average inter reviewer agreement on labeled adjectives was NUM NUM
table NUM evaluation of the adjective classification and labeling methods
we have NUM possible predictor variables NUM of which are linearly independent
this is a steepestdescent hill climbing method and thus is guaranteed to converge
we will also extend our analyses to nouns and verbs
we make extensive use of this in the next phase of our algorithm
if we choose to satisfy the former we let pl be true and ql be false
the mechanism consists of an array of boolean conditions each corresponding to one semantics fact
note that a forest representing multiple paraphrases can be reentrant as later examples will demonstrate
if oil the other we choose to express the other interpretation we reverse the conditions
the first example demonstrates a disjunction resulting from a structural ambiguity
i am also indebted to the anonymous reviewers of this paper
this is reflected in the third slot of the array
this paper presents a method for generating multiple paraphrases from ambiguous logical forms
see the exan ple lc in the introduction
when these adverbs co occur with verbs the events are understood as instantaneous
they express a continuance of an event or a maintenance of a state
a unique category could be identified for NUM of the target verbs
ve must establish a method which takes into these facts into account
NUM starts with an empty feature space and iteratively tries all possible feature candidates which are either atomic features or complex features produced as a combination of an atomic feature with the features already selected to the model s feature space
NUM each pair of phrase levels in the above equation corresponds to a change in the lr parser s stack before and after an input word is consumed by a shift operation
therefore there are cases in which the same string is reduced under different left contexts to the same symbol at the same state and return to the same state after reduction
because such recursion is not rare for example in groups of adjectives nouns conjunction constructs prepositional phrases in english the estimated scores will be affected by such differences
hence compared with a rule based method the time required for knowledge acquisition and the cost needed to maintain consistency among the acquired knowledge sources are significantly reduced by adopting a statistical approach
again since tigs do not treat the roots of initial trees in any special way there is no problem converting any operation applied to an interior node of t that corresponds to the root of u into an operation on the root of u
NUM NUM miscellaneous errors including extraneous characters dashes asterisks etc ungrammatical sentences misspellings and parenthetical sentences
all the features from the constraint feature space x have corresponding indicator functions fxo w fx w to flag whether a certain constraint feature is active for a particular configuration from the configuration space
after retraining the neural network with the lower case only texts the satz system was able to correctly disambiguate all but NUM NUM of the sentence boundaries
it may turn out that we can achieve our goal with much less annotation effort
most existing boundary disambiguation systems such as the style program depend heavily on abbreviation lists and would be relatively ineffective without information about abbreviations
however this will be described in the author s forthcoming ph d dissertation
e t where t denotes the set of terminal symbols in the domain
where p a a denotes the unsmoothed parameter
however we present the results of our algorithm in three medium sized domains
now let us consider the move of adding the rule
we describe a corpus based induction algorithm for probabilistic context free grammars
the following sequences can then be computed from each corpus w z rcb the terminal elements s lcb t rcb the terminal elements and structural delimiters so s is the corpus retaining structural annotation and w is a text only version of the corpus
repetition of the same syntactic structure and conventional paragraph openings such as on the other hand the above etc
the troponym link from prove NUM to negate l NUM is an error and it may have its origin in a fallacy to prove by negation is a troponym of to prove but this is different from to negate in the sense of to show to be false
we will not provide details of the specific algorithm used they may be found in works referred to above we will simply summarize the main features of the system
within the general properties of taking an inanimate or animate entity as subject and object however ciaula specifies the semantics of the object and subject typical of the domain
basili pazienza and velardi verb classification it appears that much information relevant for the lexical encoding of verbs is domain specific and is completely missing in a general purpose classification like word net
the class NUM NUM for example may be described by an extended argument structure like NUM lcb somebody something rcb makes lcb x rcb where x is an abstraction
on the other hand lexicon builders who have experience of designing taxonomies for real applications claim that in sublanguages there exist very domain dependent similarity relations
to analyze verb similarities we used ciaula a conceptual clustering algorithm for word classification which we applied to the task of verb categorization
the initial part is the reparandum rm which is the part that the speaker is going to repair
we discovered thematic features that are apparently more basic than others with respect to a given semantic domain cognition and a given sublanguage rsd
these results suggest that with appropriate customization it is still possible to exploit the information in general purpose on line thesauri that would be otherwise almost unusable in real nlp applications
the puzzle can lie solved by examining lhe NUM rano siliou preceding he continue ill question
for clarity loop arcs i.e. transitions from a subdialogue back into itself are omitted
because no one promises to repair the bicycle this interpretation will fail since the cp2 zu versuchen can not be attached
these examples are effective to guarantee correctness of the result hence will be useful even for users not very familiar in the target language
these sub objects arc defined by the formative flmetor of nalne whose the argument is the name of object
the same strategy of argument transfer holds for ecm constructions in which a subject is scrambled to the upper clause el
this generates sentences like they plan the statement of the filing for bankruptcy avoiding disasters like they plan that it is said to file for bankruptcy
all these differences need to be taken into account when generating the modsaf command for something like advance in a column to checkpoint NUM at NUM kph depending on what type of unit is being given the command
sometimes explicit indicators may be given as to when the command is to be carried out such as a specific time or after a given duration of time has elapsed or on the commander s order
we built a language model for the english language by estimating bigram and trigram probabilities from a large collection of NUM million words of wall street journal material
we also discuss how the hybrid generation model can be used to simplify current generators and enhance their portability even when perfect knowledge is in principle obtainable
then we rely on the statistical model to correct this approximation by identifying any violations of the compositionality principle on the fly during actual text generation
we then ranked the NUM NUM sentences using the bigram version of our statistical language model with the hope that good renditions would come out on top
commandtalk is a spoken language interface to battlefield simulations that allows the use of ordinary spoken english to create forces and control measures assign missions to forces modify missions during execution and control simulation system functions
second any practical application of speech recognition technology requires a vocabulary and grammar tailored to the particular application since for high accuracy the recognizer must be restricted as to what sequences of words it will consider
the nl agent accepts messages containing word strings to be parsed and interpreted and generates messages containing logical forms or if no meaning representation can be found error messages to be displayed to the user
from the set of atomic categories defined in this way we generate all rules consistent with the original gemini rules except that for daughters that have unconstrained features we use only the corresponding under specified categories
for the right recursive subset we form the disjunction of the expressions that occur to the left of a that is for the rules a ba a ca we generate bc
compiler the sr agent requires a grammar to tell the recognizer what sequences of words are possible in a particular application and the nl agent requires a grammar to specify the translation of word strings into logical forms
the modsaf occupy position and attack by fire tasks require that a line be given as a battle position but users often give just a point location for the position of the unit
if the latter it may be a permanent change to the current mission or merely a temporary interruption of the current task in the mission which should be resumed when the interrupting task is completed
therefore three readings are temporarily possible subject direct object and uninterpreted direct object of a following infinitival complement
NUM weft dp das fahrrad i niemand ep ti zu reparieren verspricht
we must emphasiz tirst that coherence in natm ral language disc urses may result from incoherent parts a t arl o a discern st may NUM c contradictory with what is said in other parts without questiou ing the coh rencc of the whoh
at this stage it is checked whether there are arguments marked as uninterpreted and whether the infinitival complement is available
the parser proceeds as follows the three arguments preceding the main verb verspricht are attached and inserted into the provisional argument table
t a s the resulting sequence of terminal symbols included ill the ta rget
t accepts s and every hea d and link constraint associa ted
the final pair of arguments x and x2 are similar if their properties distance x1 f 20ft and distance x2 t 10ft are similar
seqllen e null generating t from the target cfg deriva tion
fhis includes an undecidable problem l f e
sequence shows a ii english source cfg rule of a pattern used for the deriva tion
mechanism is opo rt to question since the a ddition of tra nsla tion
we will call these words unknown category words
or of a verb with particle the argument structure is available diesen bericht is the direct object and die kinder the subject
b NUM whether it s possible laughter to have honesty in government
student x x is a thing unknown x s x is a thing
we also assume that f in the universal conditional equivalences is a conjunction of atomic formulas rather than arbitrary formula
sections NUM and NUM describe off line processes the normalization process and the extraction of selectional restrictions from normalized rldts respectively
automating the process of finding selectional restrictions reduces nli development time and may avoid errors introduced by hand coding selectional restrictions
contains a dictionary of atomic formulas that specifies which input atomic formulas can be translated into which output atomic formulas
into formula NUM db2ake e x y NUM
we then assign a set of representatives from these classes to things
we shall constrain the expressive power of the ldt to suit tractability and efficiency requirements
the application domain is spontaneous spoken language in face to face dialogs
sortal topic focus constraints etc may be preserw d
4the semantic predicate real abstracts away from the adjective adverbial distinction
in section NUM we introduce transfer rules and discuss examples
currently the transfer conlponent contains about NUM transfer rules
table NUM demonstrates the dimension of the morphological ambiguity in hebrew
this pai er presents a new declarative transfi r
the conlpiler exploits indexing for more efficient search of matching rules
using top down constraints does not necessarily mean sacrificing robustness as discussed in section NUM NUM
for example prefix probabilities conditioned on partial bracketings could be computed easily this way
depends on how you define honesty interesting note that this heuristic does n t always make the correct division
there are two instantiations of this strategy in our current evaluation
rlt is a boolean NUM these figures are not very meaningful for their absolute values
each such loop adds a factor of q to the forward and inner probabilities
it was shown how these matrices could be obtained as the result of matrix inversions
in this respect the earley approach contrasts with both the cnf oriented i o and lri algorithms
among the new states thus produced complete ones are again added to the queue
initially all complete states from the previous scanning step are inserted in the queue
just as english abbreviations and aliases for named entities are formed by selecting letters or subsets of words from the phrase making up the entity name chinese aliases are also formed by selecting one of more characters from the entity
for information retrieval evaluations a reasonably large set of documents is collected a set of queries is prepared by domain experts or collected from users and the relevance of each document to each query is judged
nevertheless the total parse accuracy of NUM is still bad
the use of computers for morphological analysis of hebrew words is nowadays well studied and understood
in japanese foreign names are typically transliterated into a sequence of easily identified kana characters making recognition of foreign names rather easier than in chinese where foreign names are transliterated into the same character set as those used for common words
a consequence of this was that more data labeled by part of speech and word segmentation was needed to process newswire than in any previous language we have worked on english german japanese and spanish
the questions we want to study on ambiguity labeled dialogues and texts are the following what kinds of ambiguities unsolvable by state of the art speech and text analyzers are there in real data to be handled by the envisaged systems
either travel by taxi bus or subway how would you like to go NUM aa i think subway sounds like the best way to me the labeling continues with the next level of granularity paragraphs or turns
if expert system is given and if a disambiguation strategy decides to solve this ambiguity interactively it may ask the expert system if any the interpreter if any or the user speaker
hence in order to define properly what an ambiguity is we must consider the fragment within an utterance and chuify the idea that the fragment is the smallest within the utterance where the ambiguity can be observed
other types concern the acceptions word senses the functions syntactic or semantic etc ambiguity patterns are more specific kinds of ambiguity types usable to trigger actions such as tim production of disambiguating dialogues
in that case it is important that the questions asked from the users are the most crucial ones so that failure of the last step to select the correct interpretation does not result in too damaging translation errors
this is different from the above distance metric used in pebls
we should note that by atr we neither mean dictionary string matching nor term interpretation which deals with the relations between terms and concepts
evaluation of tagging accuracy on unknown words using texts unseen by the guessers and the taggers at the training phase showed that tagging with the automatically induced cascading guesser was consistently more accurate than previously quoted results known to the author NUM
the described rule acquisition and evaluation methods are implemented as a modular set of c and awk tools and the guesser is easily extendable to sub language specific regularities and retrainable to new tag sets and other languages provided that these languages have affixational morphology
since the development time for the muc NUM task was extremely short it could be expected that the tes t would result in only modest performance levels
first we measure the accuracy of tagging solely on unknown words unkownscore correctl ta ledunkownwords totalunknown w ords this metric gives us the exact measure of how the tagger has done on unknown words
for example if a guessing rule strips a particular suffix and a current word from the corpus does not have this suffix we classify these word and rule as incompatible and the rule as not applicable to that word
moreover despite the fact that the training is performed on a particular lexicon and a particular corpus the obtained guessing rules suppose to be domain and corpus independent and the only training dependent feature is the tag set in use
for words which failed to be guessed by the guessing rules we applied the standard method of classifying them as common nouns nn if they are not capitalized inside a sentence and proper nouns np otherwise
for such estimation we perform a statistical experiment as follows for every rule we calculate the number of times this rule was applied to a word token from a raw corpus and the number of times it gave the right answer
for example an ending guessing rule a in s jj nn vbg says that if a word ends with ing it can be an adjective a noun or a gerund
to perform this experiment we take one by one each rule from the rule sets produced at the rule extraction phase take each word token from the corpus and guess its pos set using the rule if the rule is applicable to the word
naturally the performance of the guessers was also lower than in the previous experiment plus the fact that many semiclosed class adverbs like however instead etc were missing in the small lexicon
discourse relations contain not only such relations expressed by subordinate conjunctions as explanation relations because adverse relations though and temporal relations before after etc but also purpose conditional and topic comment relations
the two holes which are contained in each of them partition the sentence in which the element occurs into two parts whereas it will be subordinated to another hole by way of a leq constraint as a unit
this enables us to keep the decision on the one hand that discourse relation elements are in a next to top position in a possible plugging and to keep drss for other parts of the sentence underneath the mode predicate on the other
a big bunch of thanks goes to johan bos bj6rn gambpsck claire gardent christian lieske manfred pinkal and karsten worm for their valuable comments and to feiyu xu and julia heine for a kind help editing the text
a lud representation u is a triple u hu lu cv where hu is a set of holes variables over labels lu is a set of labeled conditions and c r a set of constraints
for a general solution the paper proposes a device to introduce a special kind of predicate mode which has a hole as the only argument for the bottom of a lattice structure which is built by the top hole and discourse relation elements
dakara getsuyoubi de daijoubu des u therefore monday oblwith okay cop pres i am therefore ready for monday though the semantics of so called topic phrases marked by wa goes beyond the scope of this paper we assume that their discourse relations belongs to those whose antecedent part and conclusion part are both plugged sentence internally
NUM wa monday noda node h3 h4 anaphoric NUM noda wa monday node h3 h4 anaphoric NUM noda node wa monday h2 h4 anaphoric NUM noda node h3 wa monday h2 anaphoric
they stipulate relations of those drss which do not come into scope relations to those drss which do leq conditions on the other hand define partial order constraints between holes and labels which give a semi lattice structure on hv uccr with a hole at the top top hole
the three larger organization objects that none of th e systems got perfectly correct are for the mccann erickson creative artists agency and coca cola companies
a reply w is any reply to any type of query that does n t simply mean yes or no
participants can also give other instruct moves such as telling the partner to go through something again but more slowly
NUM other dialogue structure coding schemes a number of alternative ways of coding dialogue are mentioned in the recent literature
in most map task dialogues the participants break the route into manageable segments and deal with them one by one
the basic route giver coding identifies the start and end of each segment and the subdialogue that conveys that route segment
whichever type of reliability is being assessed most coding schemes involve placing units into one of n mutually exclusive categories
thus for both classification and segmentation the basic question is what level of agreement coders reach under the reliability tests
one coder in particular was more likely to mark ready moves indicating either greater vigilance or a less restrictive definition
agreement on whether a move was an initiation response or ready type was good k NUM
supervised learning approaches are useful when a tagged corpus is available as an example of the desired output of the tagger
in ib1 search complexity is o n f with n the number of stored cases
it should be noted that in this experiment we assumed correctly disambiguated tags in the left context
a search among a selection of different context sizes suggested ddfat as a suitable case representation for tagging known words
after determining this ambiguous category the word is disambiguated using context knowledge the same way as known words
in further experiments we studied the performance of our system on predicting the category of both known and unknown words
notice that her algorithm gives no initial preference to training cases that match the test word during its initial case retrieval
the error range indicate averages plus and minus one standard deviation on each NUM fold cross validation experiment
part of speech pos tagging is a process in which syntactic categories are assigned to words
we were also interested in the extent with which a difference in form corresponded to a difference in meaning
the instances of a task are stored in a table as patterns of feature value pairs together with the associated correct output
unlike grishmau et al s approach our application of general word sense disambiguation algorithms and semantic distance metrics allows for an effective use of the rue sense granularity of wordnet
in this regard the zipf mandelbrot law which states that the frequency of the nth most frequent word in a natural language is roughly inversely proportional to n
semantic clustering of words from an underlying corpora allows the knowledge engineer to find out main semantic categories or types which exist in the domain in question and sort out the lexicon in accordance with these types
these co occurrence properties indicate important semantic characteristics of the domain classes of objects and their hierarchical inclusion properties of these classes relations among them lexico semantic patterns for referring to certain conceptual propositions etc
stenosis disease in the right coronary body part artery body part figure NUM this figure shows an excerpt from collocations extracted from pds corpus and the result of term inclusion checking
lexico semantic patterns are structures where linguistic entries semantic types and entire lexico semantic patterns can be used in combinations to denote certain conceptual propositions of the underlying domain and cover certain sequences of words in the text
the word class identification component encompasses tools for the linguistic annotation of texts word clustering tools and tools for access to external linguistic and semantic sources like thesauri machine readable dictionaries and lexical data bases
the workbench supports an incremental process of corpus analysis starting from a rough automatic extraction and organization of lexico semantic regularities and ending with a computer supported analysis of extracted data and a refinement of obtained hypotheses
for example for the type infarction the systems sorts terms as follows myocardial infarction old infarction acute infarction acute myocardial infarction anterior myocardial infarction null further anterior myocardial infarction
modification of the lexicon to conform to aspectual requirements took NUM person weeks requiring NUM decision tasks at NUM minutes each three passes through each of the NUM subclasses to compare the lcs structure with the templates for each feature substantially complete and one pass to change NUM lcs structures to conform with the templates
the parsing component principar processes real world sentences NUM NUM words long from sources such as the wall street journal within a couple of seconds
a practical solution for this is to make a smaller number of buckets for the x i e.g. by clustering see e.g.
in order to handle ungrammatical expressions as well as grammaticm ones we utilized the syntactic domain of locality implicit in categorial lexica l entries under the ccg framework by including known variations of categorial association in the lexical entries
the dateline field is parsed to determine when each article was written
the vast majority of our system was developed in august and september
these patterns are simpler and more intuitive than equivalent surface regular expressions
each of these taggers uses the penn treebank tagset NUM
the table of first names overlaps with place names and time words
in such cases the evidence from the table i s discarded
we used the following scoring method to measure performance
berger et al NUM presented a way of computing conditional maximum entropy models directly by modifying equation NUM as follows now instead of w we will explicitly use x y
NUM is an abstraction for six NUM readings as show has three arguments
a potential drawback of our approach is that we require to build a maximum entropy model for the whole observed feature space which might not be feasible for applications with hundreds of thousands of features
we increment the configuration frequency of a node each time we see in the trair ng samples this particular configuration on its own but not as a part of another configuration
is the center of gravity and i q
data collection mechanisms consisted of the following
probability p w2 c p c wl NUM p clwl w2 c p w2lc p c lwl
this last dimension is itself partitioned according to two parameters the syntactic function and the syntactic construction
some examples from the brown corpus are unfasten unwind decompose defocus reactivate and readapt
we intend the term redistribution in a broad sense for manipulation of the number and functions of arguments
a tree family contains the different possible trees for a given canonical subcategorization or predicate argument structure
e.g. when using the first NUM million words of the wall street journal corpus NUM as t the word once would get the lexical definition rb NUM in NUM i.e. once was tagged NUM times as an adverb and NUM times as a preposition subordinating conjunction
to achieve this in our system one simply needs to name both nodes in the same way
trees the terminal classes representing elementary trees inherit a constructed partial description of tree with meta equations and equations
we could try to generate a grammar with weaker constraints useful for corpora with recurrent illformed sentences
this is a result of the fact that syntactic constraints on coreference can be used to eliminate the possibility of he referring to tony at that time whereas in the other cases it is semantic information that comes later in the sentence that eliminates tony as a referent
furthermore each follow on contains a new subject ross perot who will be NUM in order to model this tendency in the bfp algorithm one might consider a strategy in which provisional referents are assigned to pronouns while proceeding left to right in the current utterance
on the other hand 6e2 should be much worse because two garden paths would be predicted one for changing cb un i from terry to tony when the pronoun him is processed and another for the semantic information subsequently preferring terry
an utterance would be a coherent NUM in the utterance language a yes no question is taken to be a surface request to informif and a wh question is taken to be a surface request to informref
the use of rule NUM is illustrated by the oddness of passage NUM as compared to passage NUM because in lc the cb john is not pronominalized whereas a non cb mike is
finally in sentence 6e3 the assignment of he terry results in a rule NUM violation the cb tony is not pronominalized whereas terry is putting it in the company of highly awkward examples such as passage NUM
it is unlikely that the linguists who developed wordnet had in mind such a narrow use of these verbs when classifying them
it operates in two phases and uses packing
this parser is derived from the head corner parser
this can be done by defining the head relation as a relation between two triples where each triple consists of a category and two indices representing the begin and end position
suppose a hypothetical head corner table NUM this approach also solves another potential problem the linking table may give rise to undesired cyclic terms due to the absence of the occur check
in such an approach the result of lexical analysis may contain clauses such as those in NUM in case there is a rule np np
it uses a table to maintain partial analyses
the use of memorization for only the parse NUM goals implies that the memory requirements of the head corner parser in terms of the number of items being recorded is much smaller than in ordinary chart parsers
this approach may lead to a situation in which the second phase actually filters out some otherwise possible derivations in case the construction of logical forms is not compositional in the appropriate sense
if we want to guarantee that no cyclic structures can be formed then we would need to define goal weakening in such a way that no variable sharing occurs in the weakened goal
while this modification often leads to better termination properties of the parsing method in practice it easily leads to a complete trivialization of the top down prediction step thus leading to inferior performance
for each attribute a we split the set into subsets each associated with attribute value a w and containing samples which were unifiable with value a w belong to the same wordnet class
the procedure is initially called with the sequence NUM n corresponding to the input string aa an
NUM if m has children m1 and me with only me yielding the empty string at its frontier then
solving the recurrence relation we get t n o m ne
steps 5a and le can be computed in o ne pe em pg
NUM a check for all possible adjunctions involving the nodes realized as a result of step NUM
there is no further match for any quadruple and therefore sdt is increased to NUM NUM and the algonthm starts with qi again the quadruples q2 q3 q4 and q6 are already fully disambiguated
the coverage co c i has a rather unstable behavior due to the entangled structure of wordnet
these constructions form a natural class of expressions whose use is licensed by a predicate being available or given in context NUM one can think of the equational analysis then as a source determined method for computing the given predicates made available by a clause in a discourse
the chosen subtask corresponds to a scenario of the hulnan to human communication situations at the registration desk in a hotel see table NUM
for the english sentences we used a bigram language model whose perplexity on the test corpus varied between NUM NUM for the original text
having the above criterion in mind we try t o associate the language model probabilities with the aligmnents j i aj
the dp equation is evaluated recursively to find the best partial path to each grid point i j e
table NUM effect of the global word reordering o original sentence r reference translation a automatic
the result null translation o original sentence reordered a aut omatic translation after reordering
writers systematically choose the particular form from this set that they feel will produce the most effective expression given the communicative context
the details of imagene s treatment of purpose expressions are given as representative of the coverage and form of the full system
it contains among other things two expressions of purpose remove phone and to place calls
these hypotheses may come from an intuitive analysis of the texts as well as from the current literature on the subject
it is intended to demonstrate imagene s breadth of coverage and will not discuss the details of how the forms are motivated
for example the instruct grasp remove and pull nodes are all combined into one sentence in figure NUM
even if a nominalization exists however it still may not be used depending upon the determination of nominal arguments and nominal complexity
the exceptions involved so that and until expressions and expressions of concurrency which were not well represented in the telephone manuals
hence the first step is to identify applicable lexical entries by meaning these items must truly and appropriately describe some entity they must anchor trees that can substitute or adjoin into a node that describes the entity and they must distinguish entities from their distractors or entail required information
rather top is felicitous as long as NUM the fronted np is in a salient poset relation to the previous discourse and NUM the utterance conveys a salient open proposition which is formed by replacing the tonically stressed constituent with a variable NUM c
more studies must be made to determine the most beneficial place of lrs in a computational process
such a purpose clause is expressing a context in which the prescribed sub actions are to be interpreted and thus should be fronted
our planner spud sentence planner using descriptions takes in a collection of goals to achieve in describing an event or state in the world spud incrementally and recursively applies lexical specifications to determine which entities to describe and what information to include about them
but this reasoning is not entirely conclusive
all approaches have NUM errors in common
table NUM numbers of centering transitions
table NUM functional ranking constraints on the c
dartiberhinaus ist die ladezeit mit NUM NUM stunden sehr kurz
they are characterized by the additional 4liebesgeschichte
topic and empathy into the discussion
table NUM costs for transition pairs
we obtained a reference corpus including NUM of the NUM NUM nouns of the wsj corpus
we singled out all cases where the transitivity rule for antosemy was not fulfilled resulting in NUM connection components or NUM involved concepts
following this notation the derivation tree in figure NUM without the addresses of operations is represented as in NUM
head tree the tree anchored by the head word if the current word does not depend on any other word
feature instantiation the values of the features on the nodes of the elementary trees are to be instantiated by a process of unification
this information helps the full parser by reducing the ambiguity of assigning a correct elementary tree sequence for the words of the sentence
further annotation of the proof tree will be required to keep track of dependencies in order to represent the generalized parse as an fst
results of these experiments are summarized in corpora the coverage of the fst and the traversal time per input are shown in this table
the coverage of the fst is the number of inputs that were assigned a correct generalized parse among the parses retrieved by traversing the fst
since these experiments measure the performance of the ebl component on various corpora we will refer to these results as the ebl lookup times
ts or tag s the tag of the s th morpheme
the synchronous point number of the left most word is defined as NUM
the experimental system for model estimation was implemented using the extended reestimation method
each model was estimated both with and without use of the credit factor
such errors can be misleading in the modeling of language
the baum welch reestimation algorithm was also extended in two ways
that is they must be represented by a lattice
in general models with fewer parameters are more robust
also a scaling procedure is defined in the algorithm
this paper has presented a constraint based lexicon architecture for representing and resolving verb senses an t idiomatic usage in a case ffa me a mework
figure NUM q he simplified passivization rule for transitive w r bs l ignres NUM and NUM show two of tit simpler lexical rules
in this paper multitale a system for the semantic tagging of medical neurosurgical texts and for the semi automatic expansion of the medical lexicon will be presented
case fra tne lexicon in which one single mechanism dee ix with both l roblents mtil ormly q hc ess nti d
even though this does not yet explain the difference between NUM and NUM it already provides us with a restriction on the use of prepositions
if a particular concept is encoded in the gl lexical representation language the language specific phrase structure schemata can be employed to generate the corresponding complex nominal in each language
in our approach the qualia structures of the nouns in a compound provide relational structure enabling compositional interpretation of the modification of the head noun by the modifying noun
typestr argstr qualia aag1 phys obj arg2 ljaperture j d arc NUM individual
the availability of compound forms such as bread knife where the modifier specifies an argument in the telic is accounted for by the schema in NUM
we examine data from both english and italian and develop analyses for both languages which use phrase structure schemata to account for the connections between lexical semantic representation and syntactic expression
in addition to the importance of successful translation of complex nominals for full text machine translation this functionality is useful in itself for applications in multi lingual information retrieval and information extraction
the nominal destruction in lla unlike the event nouns hunting and race which denote activities is the nominalization of the transitional event denoted by the verb destroy
in destruction weapon the embedded agentive in the telic is again the telic of the head weapon and the embedded telic is the resulting state from the semantics of destruction
for our initial grammar we choose a grammar that can generate any string to assure that the grammar can cover the training data
we use the term move set to describe the set of modifications we consider to the current hypothesis grammar to hopefully produce a superior grammar
null outperforming n gram models in the first two domains demonstrates that our algorithm is able to take advantage of the grammatical structure present in data
in addition we have genera ted
in their work basic unit of chart entry is span which is also of non constituent concept
when a required preposition is left
with one of the NUM classes
consider the structural divergence between the following english spanish equivalents
in our algorithm it is possible to expand the hypothesis grammar thus increasing the dimensionality of the parameter space that is being searched
if the complete sequence is composed of leftward complete links the complete sequence is leftward and vice versa
semantic data possess an explanatory power that is truly required in specific knowledge domains
however the significant results obtained for verbs are important as several authors e.g.
in pp disambiguation tasks several works based on bi gram statistics collected over syntactic data e.g.
one such morphological analyzer NUM was used to supply the input for the morphological disambiguation project described in this paper
based on the problems of dialogue interaction observed in the woz corpus we established a set of guidelines for the design of cooperative spoken dialogue
this fits well with the syntactic paraphrases described in this paper but it does not as abeill also notes preclude semantic based mappings with shieber and schabes constructing syntax to semantics mappings as the first demonstration of stags
however a different prior probability on grammars is used and the algorithms are only efficient enough to be applied to small data sets
let n be the number of vowels in a word not containing consecutive vowels
computational linguistics volume NUM number NUM table NUM consonant patterns and hyphenation rules
consequently there is at least one character preceding vl and one following v2
note that the stressed u has been computational linguistics volume NUM number NUM
the rules in table NUM are capable of locating all permissible hyphenations of consonant sequences
furthermore three of the sequences as was observed can generate impermissible hyphens
the remaining vowel tokens are characterized explicitly as stressed i nonstressed i and u
there are extreme cases where sequences exist whose assignment as diphthongs is context dependent
figure i shows a NUM x NUM map created as are all the following maps with hexagonal connections in the lattice indicating which units are neighbors
an interesting subset of candidates concerns the intersection of candidate diphthong and excessive diphthong sets
however ad hoc compounds that can be readily created may contain even those sequences
first in any tree there is a unique set satisfying privset x and this contains exactly those nodes not free for f or connected to such a node by propagate
in other words the fact that the performance of the baseline grammar is about the same as that of NUM samples of the non fiction domains means that in the latter grammar the rest of the corpus does not improve or is not harmful for the parsing performance
section NUM summarizes previous approaches and section NUM is devoted to our approach
as seen above the object of the sentence be man is mapped either to a n accusative marked object adami or a dative marked indirect object adama in the target sentence
the project has produced a substantial set of test items for three different languages which are based on a systematic and controlled methodology comprehensively almotated and embedded in an enviromnent allowing for easy access and maintenance of the data
ion j presupposition NUM agreement nilaqrcemc n t j restrictions NUM e ulral interaction none purpose to st comment l transitive oc d
the relation t etween positive an l negative ix st it eros has l een one f the most hallengiug luestions in designing test data and as has l een men tioned is based on the systematic variation of phenomenon specific paraineters
if dialogue acts for deviations are included the prediction rate drops around NUM
currently only the most plausible dialogue act is provided by the semantic evaluation component
as an application example we show how it supports repair when unexpected dialogue states occur
yesterday jean danced the waltz and today the tango
the last argument will result from the coordination of the second and third arguments
a primary goal of abstract syntax is to support recursion through abstractions with bound variables
the aprolog code fragment shown in figure NUM declares how the ccg logical forms are represented
many theories of semantic interpretation use a term manipulation to compositionally compute the meaning of a sentence
type compose tm tm tm o
at this point a further reduction is needed
11there are other clauses not shown here that determine the direction of the ccg rule
nevertheless both can be accounted for in a single entry which selects the agentive role of the complement
we do not address here the further question of how the remaining scoped readings axe derived
this will be used in the implementation of coordination as a test for termination of the recursion
our hypothesis is hypothesis NUM resolving lexical ambiguity will lead to an improvement in retrieval performance
a basic linguistic t ealllre of pns is lhal they as relational predicates bear seleclional restrictions
a portion an individuated bounded object has a shape different from thai of the whole
in the first experiment we investigated the performance of a part of speech tagger for identifying the related forms
we are also examining lexical phrases to decide how to assign partial credit to the component words
the comparatively low number of these cases about NUM but only NUM for nouns and verbs allows for intellectual perusal
we therefore used the arithmetic mean of each interjudge precision recall pair as a single measure of interjudge similarity
llere tp stands for the actual past temporal perspective that holds for the given utteranee text sil uation
modeltheoretic d y the rel ttion between the presuppositional p rt md the asse rtional
in this paper we present a stochastic finite state model wherein the basic workhorse is the weighted finite state transducer
NUM big NUM is the most popular chinese character coding standard in use in taiwan and hong kong
in that work mutual information was used to decide whether to group adjacent hanzi into two hanzi words
personal names such as j zhoul enl lai2 zhou enlai
assuming unseen objects within each class are equiprobable their probabilities are given by the good turing theorem as
to this end we picked NUM sentences at random containing NUM NUM total hanzi from a test corpus
foreign names are usually transliterated using hanzi whose sequential pronunciation mimics the source language pronunciation of the name
the following declaration defines a sort binary tree with subsorts leaf and internalnode
null a coordination of constituents is interpreted as one phrase without any gap
how ever there are some relatively marginal cases that possibly contradict to this assumption
in this paper we can not go into detail with the computation of the alternatiw s of event descriptions
as mentioned the focused elemeut which is marked by the underline is assumed to be the numeral adjective
proving this proposition with ent unbound will cause a unique identifier to be created for ent
modifier absolute pred pred pred is a predicate that an object can be described in terms of
there is a nonnlonotonic but rule independent control strategy based on rifle specificity
this stage of the processing looks at the output from the significance calculation stage and considers every sentence break in turn starting at the top of the document and working down
this type of chain is exemplified in i
no other type of chain link can start a chain
it parses a homogeneous though small set of sentences
the parser described in this paper has been implemented for english
moreover both languages have overt case marking of the subject
although a problem in principle this limitation disappears in practice
NUM the results of some calculations are reported in table NUM
two avenues have generally been pursued to build efficient gb parsers
inefficiency is a problem that can not simply be cast aside
however it is not obvious that this approach is efficient
be a ssociated with a n explmt nonterminal symbol such a s v or a jp e.g. leave v
the deriva tion shown in t le figure wa s the first i.e. the best a nd generates a correct tra nsla tion
the prepositions o a nd de a re merely ttsed to specify that these patterns are for vps and they a re removed when compiled into interna
then we characterize which restrictions of those terms are valid lexicalizations of more specific concepts
the method is very similar to the method for selecting the initial weight for a new feature
in 10b a trace for a verb is modified by an adverb
figures NUM and NUM show the analyses of the sentences in NUM
to further increase portability a proposal was made to standardize the lowest level tenlplates for peoph orgaifizations etc since these basic lasses are involved in a wide variety of actions
the muc NUM tasks in particular had been quite complex and a great effort had been invested by the government in preparing the training and test data and by the participants in adapting their systems for these tasks
for corefcren e ther were problems i hull i ying i art whoh and sei sul s c rela tions
to meet this goal we decided that the infbrmation extraction task for muc NUM wouhl have to involve a relatively simple template more like muc NUM than muc NUM this was duhbed mini null muc
if an entity is mentioned several times possibly using descriptions or different forms of a name these need to be identified together there should be only one template element for each entity in an article
in this section i will address a problem that seems to have gone unnoticed until now
this paper is available via the www http www
the extracted element is licensed by an actually existing verbal projection in the string
there are however examples where a partly saturated verbal complex is fronted
the most promising account so far has been the one of hinrichs and nakazawa
the resulting sign is a verbal complex or a part of a verbal complex
ever on the lookout for additional ewfluation measm es the committee decide d to nlake the creation of telni late eh ments tbr all the people and organizations in a text a separate muc task
on the contrary leftward complete sequence is always composed with a combination of a leftward complete link and a leftward complete sequence
nametag includes a small number of personal an d organization names currently NUM which eliminate the need to dynamically recognize them
nametag performs the majority of the work since it identifies the person and organization names classifies them and resolves the aliases
nametag required NUM person weeks for its engine development NUM person weeks for its data and NUM NUM person weeks for its development interface and utilities
nametag performed very well on the selected walkthrough document achieving a recall precision of NUM NUM fo r the base configuration resulting from three errors
potential areas of experimentation are automatic egraph construction the utilization of negativ e examples the utilization of mutated egraphs and user in the loop feedback
sra submitted two official configurations a base configuration running all components of hasten and a no ref configuration with the reference resolver disabled
these features of the task specification confused the customization and evaluation of extraction system s on the central scenario event namely the management succession
after the initial effort to encode the training examples the training module determined the optimal similarity metric parameters see c
the training module determined the values of the thresholds and also determined the optima l extraction bias which disabled the most over generating egraphs
nothing much in tile analysis below depends on the particular properties of the
if no referent can be found by the interpreter for a particular phrase e.g. no secretary is in context in the case of the nici has NUM employees
upon interpretation of herb and catherine visit the nici the boss of the nici would have some salience owing to three associate cfs that have been created for it
not mentioned individual instances that are in the intersection of the sets of associate individual instances of several consecutively mentioned referents may become more salient than instances that have been mentioned
temporal deixis is realized by the tense system of a language e.g. he lives in amsterdam and by temporal modifiers e.g. in an hour
data from the dialogue memory and from gesture analysis are combined e.g. by taking the intersection of two sets of potential referents suggested by these information sources
this architecture differs from the architectures of related work on multimodal interfaces described in the introduction which all adopt grosz and sidner s approach to modeling referents in context
a visible referent cf has an initial significance weight of NUM so a referent that is visible will be a little more salient than a referent that is not
to prevent uncontrolled growing of the stack we had the system discard the object at the bottom of the stack as soon as the stack length exceeded a certain maximum
to determine the referent s of multimodal referring expressions the interpretation component retrieves the most salient referent that satisfies the semantic restrictions of the input phrase
for comparison the word segmentation accuracy using real word frequency wf computed from the manual segmentation of training NUM not training NUM is shown in the fifth column of table NUM
it gradually improves the accuracy when the initial word list is relatively large d1 and d2 while it worsen the accuracy a little when the initial word list is relatively small d100
we then compared the three frequency estimation methods ira sf and lsf with the initial dictionary augmented by the character type based word identification method ct described in the previous section
it starts at the beg6nning of the sentence finds the longest word starting at that point and then repeats the process starting at the next character until the end of the sentence is reached
if and is in the dictionary the two erroneous word hypotheses and i ik are removed and the correct word t is added to the dictionary after re estimation
in both japanese and chinese one of the most popular non stochastic dictionary based approaches is the longest match method NUM there are many variations of the longest match method possibly augmented with further heuristics
by using sd we then look up all strings iyc in the dictionary that include w as a substring and make list of all their occurrences in the text by using st
in this paper we present a self organized method to build a japanese word segmenter from a small number of basic words and a large amount of unsegmented training text using a novel re estimation procedure
j and k can be any character except j and k respectively
our rule based algorithm is thus able to produce an improvement to an existing high performance system
in this manner a sequence of rules is built for iteratively improving the initial model
a very simple initial segmentation for chinese is to consider each character a distinct word
as a stand alone segmenter we show our algorithm to produce high performance chinese segmentation
as mentioned above lexical resources are more readily available for english than for thai
we used our rule based algorithm to improve the word segmentation rate for several segmentation algorithms in
on our test set chseg produced a segmentation score of f NUM NUM
move from after trigram abc to before abc any figure NUM possible transformations
the most obvious language specific feature is the ordering of head links with respect to complement links in the graphical representation link ordering of this type is indicated by the starting points of links e.g. c precedes ip under cbar since the link leading to c is to the left of the link leading to ip
the phrasal representation assumed in the current framework is the following NUM xp specifier xbar complement x we implement the relative positioning of specifier complement and head constituents by means of dominance links as shown in each of the networks of figure NUM
a dominance link from c to fl is associated with an integer id that determines the linear order between fl and other categories immediatelydominated by c and a binary attribute to specify whether fl is optional or obligatory NUM input sentences are parsed by passing messages in the grammar network
in general it is assumed that the relation between a case assigner and a case assignee is biunique
x theory assumes that a constituent order parameter is used for specifying phrasal ordering on a per language basis
in summary we have shown that the parametric message passing design is an efficient and portable approach to parsing
the generation algorithm must produce structures that satisfy the same set of principles and constraints as the parsing algorithm
we then present the parameterization framework demonstrating the feasibility of handling cross linguistic variation within the message passing framework
in a straightforward realisation of the algorithm this step can be applied o it2lri iv NUM times once for each i k j and each transition each step taking a constant amount of time
in particular for a grammar for which a2lrt is deterministic i.e. for an lr NUM grammar the number of steps performed by j42lr and the number of steps performed by the above algorithm are exactly the same
for a n an a redex is a string qoqlq2 qm m NUM of elements from t lrt satisfying the following conditions i a a
the number of states in NUM lrt is considerably reduced by identifying two states if they become identical after items a cr fl from ilrt have been simplified to only the suffix of the right hand side NUM
for the rest of the cases it seems better to treat the affix in combination with the lemma as a new lemma if this combination is usual
the key benefit of the matchplus context vector approach is its ability to learn the relationships between words
once this process has taken place all stem context vectors are stored in a single dataset
p g NUM g is inexpensive2 however calculating
the resulting dot products are sorted by magnitude where larger means closer in usage
we test on NUM sentences and measure performance by the entropy of the test data
in tables NUM we summarize our results
results can be improved even further by adjusting the dimension of the context vectors
the stem at the center of the window is called the target
matchplus uses an information representation scheme called context vectors to encode similarity of usage
to simply disregard the relationships contained in the foreign data simply does not make sense
linguaiquery context veokx retd al documentsmultilingual figure NUM symmetric approach query processing
as mentioned before with each new symbol a we also create a rule x a
this research represents a step forward in the quest for developing grammar based language models for natural language
first the technology should allow for detection of important information even when fewer cues are present
this approach takes into account the syntactic information inherent to the languages
the european union is a loose geo political organization that has eleven official languages
the system has four key components which are the subject of this paper
html code wrappers can be simply generated around the text
users are expected to explore only links containing information that they need
the tree system provides its output in the form of hyper ext
young energetic experienced phrases describing the location e.g.
to see how this works to our advantage consider the following
our future work will continue to extend the pragmatic approach taken so far
thus we must generate non discriminatory text to avoid running foul of uk law
legal constraints are also a significant issue in the area of job advertising
currently we are using a pause prediction algorithm which utilizes the sentence s semantic structure syntactic structure as well as the linear phrase length constraint to predict the pause position and relative strength
linguistic descriptions and criteria as well as statistical considerations in the sense of frequency distributions derived from a large database were used in the construction of the name analysis component
over NUM NUM v nl p triples were extracted from NUM million words of ap news stories
we will use the symbol f to denote the number of times a particular tuple is seen in training data
when evaluating an algorithm it is useful to have an idea of the lower and upper bounds on its performance
v NUM and hindle and rooth s method gave a definite decision
a quadruple may appear in test data which has never been seen in training data
results on wall street journal data of NUM NUM accuracy are obtained using this method
the result using this modified corpus was NUM NUM an improvement of NUM NUM NUM on the previous result
an example would be if n2 is in the time semantic class choose verb attachment
but again the denominator f wl w2 wn l will frequently be zero especially for large n
semantics even if the whole parse was problematic semantics is sometimes able to extract reasonable analyses fo r some sub trees providing some measure of robustness
ok nounphrase sit s NUM w NUM NUM james NUM years old x2 relate NUM
if such a list is used frequently by several applications it may be added to a persistent knowledge base
the end user is the person who most directly benefits from an operational useful efficient application
the tipster architecture is a flexible cost effective technology independent framework for building text processing applications for government analysts
the availability of standards for describing text processing components and interfaces should greatly facilitate cotr contractor discussions about requirements
each set of conventions requires a different type of processing in order to exploit information represented in it
the specification of those needs is usually made by the end user and the technology transfer officer working as a team
the architecture requirements document provides a standard framework and terminology in which to specify and discuss needs efficiently
seven major groups of participants are identified below according to their interests regarding a text processing application
every application will need at least one component not covered by the icd the user interface component
a detection component is a component that selects documents from a collection or routes documents to a user
so we have embarked on a program of gradual improvement of the architecture
correct response is the sum of data presentation system query alternative plan and rate that the intepreter was unsuccessful in generating a semantic network
NUM repeat until whole sentence is translated
technically it is bracketing by the user
later syntactic transformation making translation steps clearer
suppose after obtaining d the user noticed that this interpretation is not what s he wants and the case element kare ga should be the subject of the verb of the matrix sentence
translation equivalent selection for all content words and the designated region to be translated next is shown in a compact manner allowing the user to examine and change them before translation
the basic interaction model of the method is that the system shows current interpretation in the form of translation equivalents and translation area while the user responds to it by changing these initial selections
this set of operations is essentially the same as the kana kanji conversion method and its obvious advantage is that everybody who can use kana kanji conversion is expected to be well accustomed to these operations
we wanted to see i f our system could be adapted to perform the muc tasks and then to see how well it could do them
we propose in this paper an algorithm that deals with both phenomena in the same analyser
finally the pp attachment procedure has to be called again for the in pp
the disambiguation procedure alms at filling the empty roles using attachment rules
NUM apply the pp attachment procedure
the algorithm we proposed in this
our actual work addresses more the problems inside each module
in this case the anaphora module can be applied before the attachment procedure
to summarize the algorithm is NUM apply the anaphora module first
it concerns rather the way of managing the interaction between the two modules
we introduce NUM a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence pairs and NUM the concept of bilingual parsing with a variety of parallel corpus analysis applications
we describe the design and implementation of the dialogue management module in a voice operated car driver information system
when the first candidate of speech recognition is rejected by the user the system has to initiate a recover strategy
this raises the question whether the dm methods described in this paper are transmissible to the second prototype
to achieve this the system should first of all pay attention to the way prompts are formulated
in our opinion it would be very interesting to find out which specific guidelines are useful in which specific situations
iii why system shall be easy to comprehend put differently the system should have a low threshold for actualusage
such a validation strategy should not be used for harmless actions that would slow down the interaction unnecessarily
in other words recognition errors will occur and this means that a method has to be developed to handle them
the second is the semantic inference procedure discussed in the previous section
on the one hand there is a sense of not having moved beyond the ambiance of their high school
finally figure 4c iv shows the interpretation of clause 3d substituted at NUM satisfying the remaining expectation
the entire structure associated with sentence NUM b is shown in figure 4b iii
these examples show expectations raised by sentential adverbs and the imperative use of the verb suppose
the principle of sequentiality leads to additional constraints on where adjoining and substitution can occur in trees with substitution sites
as in example NUM clause NUM a raises the expectation of learning what is nevertheless the case
but this does not allow for discourse features that express ezpectations about what is to come in the subsequent discourse
this is particularly acute for those who attended midwood high school directly across the street from brooklyn college
they have a sense of marginality at being denied that special badge of status the out of town school
in the figure the numbers in parentheses indicate the number of cases correctly covered by the leaf and the number of expected errors at that leaf
however such a strategy would require the explicit enumeration of all possible semantic equivalences and entalhnents in the relevant domain which seems hardly feasible
in the approach that i propose the contrast between NUM a and b can be derived directly from the data structures they express
since a and b are of the same type the values of their fields can be compared showing which pieces of information are contrastive
because the determination of contrast is based on the data expressed by generated sentences instead of their syntactic structures or semantic reprentations there is no need for separately encoding world knowledge
if two subsequent sentences are generated from the same type of data structure they express similar information and should therefore be regarded as potentially contrastive even if their surface forms are different
according to prevost s theory psvin NUM c should have a contrastive accent because the two teams ajax and psv are obviously in each other s alternative set
a second problem is that there are cases where co occurrence of two items of the same type does not trigger contrast as in the following soccer example NUM a
for example apart from the goal event data type the goalgetter system also has a card event type which specifies at what time which player received a card of which color
the theory of prevost will not predict contrastive accent on ajax in NUM b because NUM a does not contain a member of its alternative set
also a c parser with running time o gn NUM NUM would yield a matrix multiplication algorithm rivalling that of strassen s and a c parser with running time better than o gn h2 could be converted into a bmm method faster than coppersmith and winograd
we use the usual definition of a context free grammar cfg as a NUM tuple g e v r s where e is the set of terminals v is the set of nonterminals r is the set of productions
we prove a dual result cfg parsers running in time o gl w NUM e on a grammar g and a string w can be used to multiply m x m boolean matrices in time o m3 e NUM
element of e we use the notation to denote the substring wiwi l wj lwj we will be concerned with the notion of c derivations which are substring derivations that are consistent with a derivation of an entire string
this description is composed of different types of information text generated automatically section relationships text entered manually by the analyst because the information required is not retrievable from the case tool object model section purpose and tables composed both of information generated automatically and information entered manually section attributes
this loads an object model specification and generates a document displaying the list of classes found in the model
a section must be taught by exactly one ptofee ot and may belong to zero or more cotuses
after saving his modifications he can return to browsing the model and obtain texts with his new specifications
the control that modex gives over the text macro structure is one step toward satisfying different types of text requirements
the analyst then saves the text plan under a new name to use it subsequently for documentation purposes
the domain model undergoes subsequent evolution modification or adjustment by a perhaps different analyst
as mentioned above modex has been developed as a www application this gives the system a platform independent hypertext interface
sity administrator the analyst needs to document it including annotations about the purpose and rationale of classes and attributes
she finds that the intrinsic difficulty of the graphics mode was the strongest effect observed p NUM
in the third case the configuration frequency remaiuing on the parent node will account for the case of having two mutually exclusive higher level nodes
in NUM of these NUM cases the same error occurred and in the other NUM cases the hyponym link was mistaken for a member link also available in wordnet
the noise filter ensures that false points of correspondence are very sparse as illustrated in figure NUM
only one point of correspondence in each row and column can be correct the rest are noise
the objective function for bitext mapping should measure the difference between the tbm and maps produced with the current parameter set
for example when tokenizing german text it is not necessary for the tokenizer to know which words are compounds
matching predicates can take advantage of other information besides cognates and translation lexicons can also be used
most chains of tpcs have the following properties linearity tpcs tend to line up straight
the noise filter described in section NUM NUM prevents simr from being led astray by false points of correspondence
two kinds of information that a matching predicate can rely on most often are cognates and translation lexicons
a complete set of tpcs for a particular bitext is called a true bitext map tbm
the width and height of the rectangle are the lengths of the two component texts in characters
currently we use NUM hand crafted delete rules
table NUM gives some additional statistical results at
such residual ambiguity plagues speech recognition analysis transfer and generation alike
NUM rules learned to choose and delete parses are then applied
preprocessing is common to both the learning and the morphological disambiguation modules
the traveler task text corpora are sets of pairs each pair consisting in a sentence in the input language and its corresponding translation in the output language
in the case of physics a discourse knowledge engineer may need to modify an existing explanation system so that it can produce explanations appropriate for mathematical explanations
in general a node of a particular type in an edp is used by the explanation planner to construct a corresponding node in an explanation plan
each of the nodes in this network is a concept e.g. megaspore mother cell which we refer to as a unit or a frame
pragmatically to represent discourse knowledge for a broad range of queries domains and tasks a formalism must facilitate efficient representation of discourse knowledge
explanation generation is the task of extracting information from a formal representation of knowledge imposing an organization on it and realizing the information in text
in this evaluation knight scored within half a grade of domain experts and its performance exceeded that of one of the domain experts
these inclusion conditions govern the circumstances under which the explanation planner will select particular classes of propositions from the knowledge base when constructing an explanation
rather than merely examining the attribute parts on the given object the substructural accessor examines all known attributes that bear the parts relation to other objects
there is no attempt to provide a balanced survey of the speech translation scene
the analysis shows that a simple test word frequency outperforms more complicated tests and also dominates them in terms of information content
for these words the table below measures the claim s accuracy when the word occurs more than once in a discourse how often it takes on the majority sense for the discourse and applicability how often the word does occur more than once in a discourse
a similar methodology can be applied to identify unmarked positive versus marked negative terms in pairs such as agree dissent
we discourage such behavior in the training algorithm by two techniques NUM incrementally increasing the width of the context window after intermediate convergence which periodically adds new feature values to shake up the system and NUM randomly perturbing the class inclusion threshold similar to simulated annealing
the algorithm uses these properties to incrementally identify collocations for target senses of a word given a few seed collocations 1note that the problem here is sense disambiguation assigning each instance of a word to established sense definitions such as in a dictionary
the variables in this decision are the total number of occurrences of plant in the discourse n the number of occurrences assigned to the majority and minor senses for the discourse and the cumulative scores for both a sum of log likelihood ratios
the first of the above tests compares the text frequencies of the two words which are clearly measurable and easily retrievable from a corpus
we also apply two generic statistical learning methods for combining the indications of the individual methods and compare their performance to the simple methods
let d be the current tree description with root node r let s be the subtree projection of the new word whose left most attachment site a is of identical syntactic category as r the updated tree description is s lj d where a is unified with r
we will call this operation tree lowering intuitively the operation finds a node on the current trec description which matches the left attachment site of the projection of the new word and attaches it while inserting the root of the new projection in its place
this means that in cases such as NUM where on the globally acceptable reading the pp is an adjunct of the np the man this attachment will have to be revised and the pp retrospectively adjoined into the relevant n t node
landis and koch describe these ratings as clearly arbitrary but useful benchmarks p
the following section proposes a filtering system based on syntactic patterns
lcb dom s np dom s vp dom vp v dom v lex prec np vp rcb lexical categories are also associated with lists of left and right attachment sites
adjp ea e jj jealousy nn can md vp breed vb np confusion nn however rb pp in in np np the dr absence nn pp orlon np anyldt auzhorization nnbi11 nn np this dt yearlnn
in gorrell s model unconscious garden paths may be processed via the addition of structural relations to a monotone increasing set at the point of disambiguation but there is no discussion as to how the parser decides which relations to add
the third line represents the second word in the idiom take place t which is a noun nn
declaratively speaking local descriptors simply express equality constraints between definitional values for node path pairs
it also uses the following heuristic initials cause a sentence break only if the next word begins with a capital letter and is found in a dictionary of function words
spreading activation within such networks is often proposed as a method of lexicai disambiguation
in table NUM some results of that experiment are shown
the learning is implemented as a two staged process with feedback
thus the major factor in the learning process is the lexicon
the results of this tagging are summarised in table NUM
several smoothing methods have been proposed to reduce the estimation error
this information can be compiled automatically and also might improve the accuracy of tagging unknown words
there were two types of data in use at this stage
this actually results in about NUM higher accuracy of tagging on unknown words
so our next goal is to extract morphological rules with one letter mutations at the end
the average length of pause units was NUM NUM morphemes as compared with NUM NUM for whole utterances
figure NUM schematic presentation of the algorithm as
experimental results show that this approach reduces the number of examples required for achieving good models with good translation results in acceptable times without using speciaiised hardware
q u e sl this concludes the proof of ii
NUM in order to prove this proposition we need to establish some preliminary notations and lemmas
this same process could be done with any of the other procedural relations e.g. purpose precondition
in drafter technical authors specify the content of instructions in a language independent manner using the drafter specification tool
in fact d aaajad da aaa d
once the input procedure is specified the author may initiate text generation from any node in the procedural hierarchy
we intend to continue this part of the work by applying the technique to larger portions of the planning resources
the interface and actions panes on the left of figure NUM list all the objects and actions defined so far
there is also a waming slot filled with the action reader damage service cover
unaw is used when the agent is perceived to be unaware that a is bad
it is NUM NUM mb in size and is made entirely of written english instructional texts
in this study we used NUM coded examples as input to the learning algorithm
each author independently coded each of the features for all the examples in the sample
the recognition of such prompt response relationships will require analysis of typical speech act sequences
these arcs have to ensure that the output produced in the csst is embraced between c and c being the category label
state application can apply as in figure NUM
function fast scan p u starting at p scan u by iteratively i finding the edge between the current node and one of its children that has the same first symbol as the suffix of u yet to be scanned and ii skipping a prefix of u equal to the length of the selected edge label
the visit to ud on the other hand is charged to the ith symbol of w note that charging the visit to ud to the symbol in w corresponding to the last symbol of ud does not work since in the case of sbi bi the same symbol would be charged again at the next iteration of the for cycle
the transition on input of likes is nondeterministic
in several natural language processing applications it is useful to generalize over some transformations of the form in NUM by using classes of symbols in e let t NUM and let c1 ct be a partition of e each ci o
the positive evidence for such transformation is the number of positions at which factors ux and vx are aligned within the corpus for all possible x x e e with x matching NUM we do not require x x since later transformations can change the right context
consider the execution of shifl link bi l b l for some i NUM i iw NUM assume that correspondingly fast scan visits nodes ul ud of t in this order with d NUM and each uj some factor of w
we then observe that if u is a node of t then factor u is a prefix of some suffi w and either u dominates bi or bi properly dominates u in t if u dominates bi then h u must be an implicit node oft
we next show that if pair p q is found at step NUM then q represents a factor u x v p represents a factor h2 u x v NUM and transformation u7 v has the highest score among all transformations represented by nodes of tx and tr
from table iii we can know that perplexity of hard class based bigram is NUM NUM lower than the word based bigram while perplexity of the soft class based bigram is much lower than the hard class based bigram perplexity reduction is about NUM compared with hard class based bigram
the results are shown in table NUM
var program pl disk loc diskte progralnlar var parts of the case frames for the sentences above are as follows aor l ro r q disk adjs place other problems encountered in the transfer phase are the lexical gaps idiomatic uses of phrases and lexical disambiguation by syntactic or semantic content
the output from eterms is very accurate due to the use of full esg parsing
the model presented there for the stochastic source generating the various zipfian distributions is however linguistically highly dubious a version of the monkeywith typewriter scenario
for example in the even though the word program is used in the plural form in both of the english sentences the transfer module needs to determine the specificity of the noun phrase in question and send it to the generator which will accordingly output either the singular or plural form of the noun
to localize zipf s law we utilize eq NUM and observe that r x in the continuous case
the definition section has one or two of the definitions critical to a human understanding of the topic
the relationship between turing s formula and zipf s law which both concern population frequencies was explored in the present article
in section NUM we summarize the results discuss how they might be used practically and compare them with related work
for any additive constant c and we may approximate x c with x motivating and similar approximations in the following
judging from the feedback from our users this approach seems to have paid off
mar checker tries to deal with it is harder to parse non standard constructions correctly
after signing on the user has access to all objects on the system
the user may specify these words in a specific user dictionary along with preferred alternatives
in addition this category includes slang words a list of which is systemsupplied
it is crucial to be able to determine the applicable part of speech with accuracy
that is to dilute the coordinator s omnipotence a number of demi gods can be created
second the genus terms in metonymical senses are often indistinguishable from each other
sense shifts indicated through a deictic reference are also present in our NUM word test set
we have noticed that zero derivatives are an important knowledge source for resolving pp attachment ambiguity
we sum up the above descriptions and outline the procedure for labeling a dictionary sense
we illustrate how the algorithm functions using the 5th definition of the word interest
since those types of definitions pattern are not considered the labeling algorithm fails in such cases
second indicative words and concepts for each sense are directly available in numbered definitions and examples
initially the candidates are limited to the set labels indicated in lloce for the head word
topfj228 wfj topfb028 wfb topjell2 wje topkao06 wka
the NUM word test set used in the evaluation represents much more difficult cases than average
the parse forest for the sentence is returned
the analysis of how complements influence the aspectual properties of their governing verbs is beyond the scope of this paper
NUM we also expect to seek input from english teachers of deaf students
this also has implications on the system s generation process
of course to be is a standard verb in english
the final sign of the topic is also held slightly longer than usual
how they change depends on the type of comment that follows e.g.
each element of am which represents a sentence pair is updated by adding the number of word correspondences in the sentence pair
the syntactic complexity can no longer be left to chance or indirect constraints
for tarone and others this phenomena is referred to as foreigner talk
at present error identification analyzes only one input sentence at a time
our methodology for developing this knowledge source is described in the next section
it is obvious that only the first context is relevant given that built characterises the dispatching line and not the dispatching
in order to build up the model of the subject field we need to perform a corpus based semantic analysis
a corpus of french texts on any technical subject can be fed into it
it may use the results of any morpho syntactic analyzer which provides dependancy relations e.g.
first a partial morpho syntactic analysis is performed to extract candidate terms
we describe the early stage of our methodology of knowledge acquisition from technical texts
in our case it is not possible to have an a priori reference classification
the four syntactic links of lexter can be used to define this terminological context
lexiclass is a generic clustering module it only needs nominal or verbal compounds described by dependancy relationships
primary is an ellipsis and leads the ke to create the concept primary substation
very high probability an analysis with a probability from this category is the dominant analysis of the ambiguous word and thus given that we can not use any other source of information to disambiguate the given word we would like to select the dominant analysis as the right analysis
portability with regard to domain and language would be emphasized evaluation of complete systems would be encouraged and scheduled periodically and as part of the baseline for these evaluations the government would develop a large corpora for the training and testing of corpus based techniques as well as for system development and evaluation
the concept which the advanced research projects agency arpa put forward at that time was based on the promising results of that conference and on the belief that the technologies being demonstrated at muc for the automated handling of large volumes of text would be of great benefit to a variety of government agencies
a reasonable assumption of this kind would be for instance to say that the masculine form of a verb in a certain tense in hebrew is expected to have approximately the same frequency as the feminine form of the same verb in the same tense
these results demonstrate the effectiveness of morpho lexical probabilities in reducing the ambiguity level in a hebrew text and it seems that by using such information combined with other approaches for morphological disambiguation in hebrew we come very close to a practical solution for this problem
following a series of meetings agreement was reached for sharing the planning funding and execution of the program
in connection with each of the workshops uniform evaluations of system performance were conducted and were reported during the meetings
portions of the information above were originally in papers prepared by roberta h merchant and thomas h crystal and published in the
bidders were judged based on their ideas for research as well as their being potential sources for the demonstration projects
all of the tipster text contractors were required to participate in muc or trec and muc NUM and trec NUM using tipster evaluation techniques were intentionally timed to coincide with the end of phase i the NUM month workshop so as to provide a measure of the state of the art and identify good performers
the architecture stresses functional and knowledge based modularity and uses an sgml iike language for tagging text transferred between the modules
the r d included improvement of algorithms and research into combining the results of the application of diverse extraction and detection techniques
the tipster phase ii prototype systems gave the user document detection tools which feature the algorithms and technology developed in phase i
b NUM but uh other than that i think maybe it just depends on how you define honesty
in written text the definition of a sentence is clear and marked in the text itself by capitalization and punctuation
uh i think it probably is more likely if you have a small government unit where everybody knows everybody
b they re like black corduroy ber bermuda shorts
in annotating switchboard we choose to divide turns into sentences consisting each of a single independent clause
at each stage we conduct experiments that compare the anaphora occurring in the human generated text with those in the texts that would be generated by a computer taking the same syntactic and semantic content as the human texts and generating chinese anaphora according to the rule being tested this has to be simulated by hand
rule NUM if an entity e in the current clause was referred to in the immediately preceding clause does not violate any syntactic constraint on zero anaphora and is not at the beginning of a discourse segment then a zero anaphor is used for e otherwise a nonzero anaphor is used
throwing out the end segment boundary from the n best lists degrades performance by slightly more than an absolute NUM
rule NUM if an entity e in the current clause was referred to in the immediately preceding clause does not violate any syntactic constraint on zero anaphora is not at the beginning of a discourse segment and is salient then a zero anaphor is used for e otherwise a nonzero anaphor is used
ndeg6 this symbol avoids the problem of the reader mistakenly reconstructing argumentation chains criterion NUM presence of tautologtcal sentences
however the fan and mluce protocols when apphed to a test corpus which remains to be defined may nevertheless serve as a base on which to compare systems whtch summartse wa sentence extraction
syntactic ambiguity can lead to hundreds of parses even for fairly simple sentences
table NUM syntbesls of the results for the legibdlty criterion
be an important piece of information nevertheless m order to restrict the effects of interpretation this case was considered to be an anaphora deprwed of a referent in the following sentence
examples of fan and mluce protocols and their results on seraphin
how to appreciate the quality of automatic text summarization
quality of an abstract the results of the mluce protocol illustrate the importance of user
their work relies on the literature and intuitions to identify these features and thus provides an important background for a corpus study by suggesting features to include in the corpus analysis and initial hypotheses to investigate
while the factor of core contributor order accounted for the choice between s ce and be cause this factor could not be explained in terms of whether the contributor can be attributed to the hearer
because the recognition of discourse coherence and structure is complex and dependent on many types of non linguistic knowledge determining the way in which cues and other linguistic markers aid that recognition is a difficult problem
in this section we report two results one from each perspective a comparison of the distribution of sn ce and because in our corpus and the impact of embeddedness on cue selection
because our analysis is exhaustive information about both occurrence and nonoccurrence of cues can be retrieved from the database in order to test and modify hypotheses about cue usage
as with our study their work aims to define each cue in terms of features of the propositions it connects for the purpose of cue selection during text generation
we will closely coordinate the further develolmlent of our corpus with the annotation work in verbmobil and with other german efforts in corpus annotation
in every iteration a tuple consisting of a syntactic category and a set of types is selected
NUM np np np np np or np NUM np each project or activity pp these underlying rule patterns represent all the ways that punctuation behaves in this corpus and are good indicators of how the punctuation marks might behave in the rest of language
we have also seen that the rule patterns we extracted fi om the corpora agreed to a large extent with the descriptions of punctuation use found in publishers style guides suggesting thai reference to these may be usefnl
the mother category of a colon expansion is always the s ujm as the category to which the adjunct is a ttachod the lel t n ost d mghter and this is even t rue of many of the exceptional rule patterns if the constraint is relaxed to allow the daughter to haw a lower bar level
the phrase contained within the colon exl ansion right most daughter nnlst also be descriptive but can be ai jp in addition to np and s although there was no rule pattern found in the corpus that had all adjectival colon expansion with a sentential mother category it is certainly possible to imagine such a sentence NUM
however this will mrnost certainly result in overgeneration of parses as tile rules are still too flexible they accurately describe syntactic situations where punctuation call occur but fail to place any constraints upon those situations
the restriction on this and the reason why there are fewer rule patterns for categories such as pp adjp and adw is that rules with the same daughters but more powerful mother categories e.g.
text classifiers represent a document as a set of features d lcb fl f2 fm rcb where m is the number of active features in the document that is features that occur in the document
the reason is that it is used uniformly in long documents the number of indicative features does not increase significantly but their strength nevertheless is reduced proportionally to the total number of features in the document
while a linear text classifier is a linear separator in the space defined by the features it may not be linear with respect to the document if one chooses to use complex features such as conjunctions of simple features
we have experimented with three alternative ways of adjusting the value of s f d according to the frequency of the feature in the document NUM our default is to let the strength indicate only the activity of the feature
there are other versions of the winnow algorithm that allow the use of negative features NUM littlestone when introducing the balanced version introduced also a simpler version a version of positivewinnow with a duplication of the number of features
past participle and modal verb are all mapped into the more general verb category
in learning mode the descriptor arrays are used to train the parameters of the learning algorithm
the same was true with the decision tree induction algorithm as seen in figure NUM
leaf nodes labeled with NUM indicate that the punctuation mark is determined to be a sentence boundary
in this article we report results using two different learning methods neural networks and decision trees
probabilities were compiled from NUM million words of prelabeled training data from a corpus of ap newswire
there have been two other published attempts to apply machine learning techniques to the sentence boundary disambiguation task
such a distinction might be useful for certain applications that analyze the grammatical structure of the sentence
NUM association for computational linguistics computational linguistics volume NUM number NUM sentence boundaries
NUM somit entsprach ein ecu am NUM NUM NUM NUM NUM us vgl
an intervening adverb for example would simply be represented with its own annotation placed between the annotations for the words in the idiom
the above arguments all support the use of the dice coefficient over either average or specific mutual information
the louella parsing system was de signed with the latest version of the nltoolset
flexible collocations are shown with ellipsis points indicating where additional variable words could appear
in the table the second column represents candidate french word pairs for translating the single word today
champollion considers all pairs of these words and identifies any that are highly correlated with the source collocation
this information is then extracted and reorganized into a lucid account of the events
our algorithm depends on using a measure of correlation to find words that are highly correlated across languages
finally it produces the correct word ordering of the target collocation by examining samples in the corpus
the buffer addition allows two rules to overlap the sentence extracting both succession events
conjphr is a buffer macro allowing the pattern matcher to skip over irrelevant material
once the set of surviving translations p has been computed champollion checks if it is empty
we showed above that filtering is necessary to bring the number of proposed translations down to manageable levels
an auxiliary term a query to a dcp augmenting a rule schema is embedded in a feature structure of a rule schema as the value of goals
the n is a non head daughter of a l hrasal sign i.e. the destination state of the transition and expresses the input condition for the transition
and rewriting rules m ording c l slmcifical ion given by progr mmter
the automatt are augmented with feature structures use l by a partial unification routine and delayed frozen definite el rose programs
a l arsing result is obtaine lcb l by unifying the sub structure for NUM NUM with tim correspon ling core structllre
the append does not terminate at phase NUM because the indices value of non head laughters is
we denote this by f val f p and regard f and f as different entities
by unifying non head dtr values with actual signs to be constructed fl om input sentences a parser can obtain parsing results
the phrasal signs NUM and NUM are invisible until a parser creates the feature structures describing them using expensive unification
after learning NUM NUM transformations and applying them to the training set accuracy increases to NUM NUM
the initial state annotator tags each word in the corpus with a list of all allowable tags
after applying ten iterations of the baum welch algorithm accuracy dropped to NUM NUM
we compare this algorithm to the baum welch algorithm used for unsupervised training of stochastic taggers
if no prior knowledge is available probabilities are initially either assigned randomly or evenly distributed
first a dictionary was created listing all possible tags for each word in the corpus
we can also decrease the number of model parameters by separating the tag model from formulae NUM and NUM
when coml ining cah ulus and i rt two different kinds of abstraction are possible
for semanl ics besides an operator compose for functional composition an operator id for identity is used
therefore internal modifiability of idiolns seems not to be restri ted on the enmm lan guage
NUM conseqnently to guarantee parallelism this also requires a connection nlechanisnl t etween these formalisms is necessary
besides our introspective intuition evidence for the proposed paraphrases is found through text analyses
note that it is now a problem to represent the internal adjectival modifier incredible correctly
in the grammar special rules must he written to handle the idiomatic edges
in these rules it must be checked whether a complete idiom can be constructed
it is important to notice that the information concerning decomposable idioms is distribute d
nevertheless we only have one entry for every idiom in our idiomatic database
the top r ed candidate from the rescored parses is selected as the atr parse
r a s s i z s s i NUM s s i s s i z
however if the system has a capability of falling back and checking if they belong to the same coarser class and if that is the case then the system can take advantage of the class information for the two words
out of the three types of questions basic questions and word bits into the tagger we performed a separate experiment in which a randomly generated bit string is assigned to each word NUM and basic questions and word bits questions are used
this can be done by ordering elements of v with elements of v1 in the first ivll positions and keep merging with a merging region whose width is vll initially and decreases by one with each merging step
suppose we have a text of t words a vocabulary of v words and a partition 7r of the vocabulary which is a function from the vocabulary v to the set c of classes of words in the vocabulary
we first make v singleton classes out of the v words in the vocabulary and arrange the classes in the descending order of frequency then define the merging region as the first c NUM positions in the sequence of classes
are in another class it will not be hard for the system to detect the similarity between a and b and assign the correct sentence structure to b without confusing it with d
the first type is more robust to the local minimum problem but the quality of classes greatly depends on the initial set of classes and finding an initial set of good quality is itself a very difficult problem
the second type of questions word bits questions are on clusters and word bits such as is the current word in class NUM or what is the 29th bit of the previous word s word bits
also any set of nodes in the tree constitutes a partition or clustering of the vocabulary if there exists one and only one node in the set along the path from the root node to each leaf node
in the above examples we looked only at the mutual substitutability of words however a lot of information can also be gained if we look at the substitutability of word compounds for either other word compounds or single words
after a brief description of the corpus and the thesaurus automatic indexing and terminology extraction are described
other relations e.g. synonym translated by etc exist but are not shown in this example
each term is linked to other terms through a generic relation arrow or a neighborhood relation line
the lexical resources general language dictionaries are fairly stable whereas terminologies evolve dynamically with the fields they describe
the difference is that in a corpus a term is generally monosemous and a word is polysemous
the NUM NUM terms thus classified comprise the test sample that is used to evaluate three models for representing terms
generally it appears that the syntactical structure of a term in french language is the noun phrase
the candidate terms are expressions that may become terms and are submitted to an expert for validation
i th6ode des erreurs i analyze discriminante i statistique s ttmation
it can also be shown that li is the same as the result of flattening the characteristic machine for the same grammar modifed so as to fulfil the afore mentioned condition by replacing the right hand side of every e production with a new nonterminal for which there is a single e production
this method yielded NUM NUM city names approximately NUM of all the cities including urban districts covered in the database
neither process requires human intervention nor an external knowledge base
this is done first by computing an inside probability of the input sentence which can return a table of insides used in the computation
hnc has developed docuverse as merely a proof of concept system
docuverse makes use of a rich set of information retrieval functionality
when the current layer c e is completed with the two insides computed the computation extends to the outside
the next step involves finding an appropriate label for each region
therefore each node represents an information theme contained in the corpus
in this paper parses are assumed to be sequences of dark headed transitions see first NUM returns the first state of layer i
nevertheless unlike in the case of big NUM the lex map for NUM does not contain a property value pair that can be attached to the frame of tile modified noun like house in tile tmr
the basic motivation for this organization is the continued inability of the fields of lingnistics and nlp to produce a general coverage unified theory of trealment of language phenomena a failure especially pronoanced in areas beyond computational syntax
equally cruci d is the syntactic sem mtic delrendency nmppiug linking between the syntactic sm eture syn struc and s f m struc zones which in mikrokosmos is canied out with the help of special variables
the snap is ideally suited to determine the computationally intensive solution required by the som algorithms
set2 is the set of all properties of the members of setl set3 is tile set of all properties of var NUM set4 is essentially the intersection of set2 and set3
some temporal adjectives of file kind that levi presents as derived from adverbs rather than nouns examples NUM NUM in levi NUM NUM repeated here as NUM are analyzed in a different manner precisely because they do not modify semantically the nouns they modify syntactically in other words the temporal meaning of the adjective characterizes the proposition
in the tmr the attitudes characterize the whole proposition and thus the semantic link between the modified noun and the adjective is weakened there are other types of adjectives which challenge the conunonsense view that the memfing of the adjective somehow amalgamates with the memfing of the modified noun and most of these types are non scalar or only marginally scalar
this representation is based on the assumption that functioning as a member which differentiates between authentic and nominal in that the former does rod tile latter does not function as a member should is the most salient featttre while sometlfing like physical similarity a fake gun only looks like a gun is the least salient one
they will construct relations between character objects
to address this weran the program with each sentence NUM times
where t denotes the temperature which ranges between NUM NUM
the temperature regulated urgency lit is derived in the following way
therefore at low temperatures codelets with high urgency are preferred
prepositional and participial restrictions can be expressed as term NUM noun a p term
select and extend depend on distributional properties of simple lemmas and complex nominals respectively
we will give a detailed description of this application in this paper
finally some discussions of the model are covered in section NUM
terminology is seen as the acquisition of domain specific knowledge i.e.
note that tagging accuracy is quoted on a per word basis as is customary
NUM if a pause occurs at the beginning of the prosodic phrase after the potential boundary site the potential boundary site is classified as boundary and the phrase is taken to be the beginning of a new segment
we produce two additional scores based on the sublanguage model and the cache model
a cache model was also used in o lr xpetilll ilt
sri provides language model scores for each hyi othesis not for words
here n is the number of tokens in the previously uttere t n best sentences
that is the number of word errors is reduced froin NUM to NUM
another error million or day instead of billion is not a mne
we used a large corpus in the experiment as the source for similar articles
most of these models have ti cussed on relatively local interactions between words
this learning process can be improved by means of categories using the approach detailed in this paper
however the approach has limitations the parser can not handle a large number of candidates so that the number of n best must be limited and hence the correct candidates sometimes missed
the first factor of the re evaluation is the potential defined in equation NUM it is based on the alignments and indicates the type of increase or decrease that a word deserves
the excerpt contains a user question and the system s answer to that question
at the end the two ill recognized words some and armchairs are identified as errors they are classified as substitutions according to their type of alignment in the different n best
both lt and lo are regular languages and their corresponding automata are easily obtainable from the sst
given two strings x y e x xy denotes the concatenation of x and y
given an alphabet x x is the free monoid of strings over x
second to aid in isolating errors due to focus issues the system was evaluated on unambiguous partially corrected input for all the seen data the test sets were retained as unseen test data
after a final ailt has been created for the current utterance the ailt and the utterance are placed together on the focus list where they are now referred to as a discourse entity or de
this is a flexible architecture that accommodates sets of rules targeting different aspects of interpretation allowing the system to take advantage of constraints that exist between them for example temporal and speech act rules
furthermore the lower bound for accuracy NUM is almost NUM lower than the one for the cmu data NUM supporting the claim that this data set is more challenging
for illustration the redundancy is broken down into the case where redundant plus additional information is provided redundant versus the case where the temporm information is just repeated reiteration
system and key agree on non null value system and key differ on non null value system has null value for non null key system has non null value for null key both system and key give null answer accuracy lower bound percentage of key values matched correctly are noticeable gains in performance on the seen data going from ambiguous to unambiguous input especially for the nmsu data
see rule a1 ex how is tuesday january 30th how about NUM see also NUM NUM of the corpus example NUM the current utterance evokes a time that includes the time evoked by a previous time and the current time is less specific
if there is a deictic term dt in tu then return lcb when resolve deictic dt todaysdate certainty NUM NUM rcb rule na2 the starting time cases of non anaphoric relation NUM if most specific starting fields tu
semantic information comes from two servers a kb server based on the noun portion of wordnet 70k concepts and a semantics server containing case frame information for NUM english verbs
when the engine is connected to the servers whenever lexical information for specific words can not be found in the local engine it is requested from the servers
al NUM the tasks these rules have to perform are NUM map the user s utterance into an utterance class which consists of pragmatically equivalent utterances
to reduce the amount of time required to develop an application and the amount of expertise required we have structured the application module into a set of several different types of rules
it used NUM training sentences and achieved a score of NUM first answer correct and NUM first or second answer correct on a live test with NUM queries
for example in our initial applications these have included words such as the verbs oem interface download and customize
when the servers can not supply lexical information for a particular word various heuristics are used to hypothesize the missing information
to reduce the amount of lexical information that the developer must add large scale lexical resources have been integrated with the toolkit
a denotations server using unique concept names generated for each wordnet synset at isi links the words in the lexicon to the concepts in the kb
the goals of this toolkit are to reduce development time and cost for natural language based applications by reducing the amount of linguistic and programming work needed
figure NUM cases and types of dialogue design errors sorted by guideline violated
useless states can be eliminated from a sst without changing the function it defines
the eutrans project aims at developing machine translation systems for limited domain applications
high quality output is essential the speech produced must sound natural if it is to be easily comprehensible
a clique concept is used to define clique fimction that evaluates current state of random variables in clique
a posteriori probat ility is needed to sea rcb for the jrlost likely tag sequence
it is similar to the i ehavior o molecular particles in the rcm world
we ii derive l llc siinldified cquatioil of iimm only wilh bigra m
the mrf provides the base frame to combine various statistical information with maximum entropy me method
is suilicicnt i o find the tag sc ltmnce NUM which sal isiies NUM
let n i denote a set of random variables which are neighbors of ith random variable
the basic information sources which arc used m statistical tagging model are unigram l igrani nnd trigrain
the suffix of a word gives very useful information about the tag of the word in f nglish
two specific guidelines on meta communication sgi0 and sgi NUM had to be added however
NUM for any source node n for which f n and fi n are both defined merge these two target nodes
we can see from table NUM that the choice of method affected translation quality meaning and grammar more than it affected preservation of meaning
there may be other methods to treat the semantic information but their complexity is going to be very great for a real time system as the word predictors are intended to be even the time requirements maybe a few seconds between two consecutive keystrokes of an impaired person are not very strong for the computational capacities of today s equipment
any state of a head automaton can be an initial state the probability of a particular initial state in a derivation being specified by lexical parameters
these themes simple representations statistical modeling and lexicalism form the basis for the models and algorithms described in the bulk of this paper
in context dependent transfer models the cost function takes into account the identities of the labels of the arcs and nodes dominating wi in the source graph
NUM if d is empty apply the subtree transfer search given below to s return the lowest cost solution and stop
that is the ratio of the expected distance for derivations involving the choice and the expected distance for all derivations involving the context for that choice
we refer to an event context pair as a choice for which we use the notation efc borrowed from the special case of conditional probabilities
we will sketch an algorithm for finding the lowest cost ordered dependency tree derivation for an input string in polynomial time in the length of the string
for each relation ri in these sequences select a dependent word wi with dependency probability p l wi w ri
in the sentence below for example two senses of the main verb have are represented simultaneously in the sentence
an effective semantic distance metric is hence needed here
to our knowledge the design of word prediction methods is mainly focused on non inflected languages like english
the question now is does fmm perform better than wbm when NUM is NUM in looking into these issues we found the following null NUM when NUM NUM i.e. when we conduct clustering fmm does not perform better than wbm for the first data set but it performs better than wbm for the second data set
this information is not currently identified or stored during the manual indexing task
incoming cables are processed information is extracted and stored in corporate databases
it validates and connects different types of locations numbers and biographic information
these passes are used to identify specific pieces of information needed for extraction
analyst data setup process csci and the analyst in
biographical entities are connected to the named entity through the odbc sqlserver api
the analyst may review and modify any of this information
the document manager process csci uses microsoft s odbc library
however the prototype canis system is a stand alone system
users can visualite the processed data via the user display
fact lexpectation do sl askref sl s2 d not knowref s2 d do s2 inform s2 sl not knowref s2 d
this gives a total of NUM NUM of trees in the treebank possibly aligned with those in susanne are in fact aligned
however discourse knowledge does not specify what syntactic structure to impose on a sentence nor does it lend any assistance in making decisions about matters such as pronominalization ellipsis or lexical choice
developed by elhadad and his colleagues at columbia fuf is accompanied by an extensive portable english grammar which is the result of five years of intensive experimentation in grammar writing p
if an explanation s length must be limited such as when a user has employed the verbosity preference parameter to request terse explanations an explanation planner should be able to decide at runtime which propositions to include
topic nodes have the atomic inclusion property which enables an explanation planner to make an atomic decision about whether to include or exclude all of the content associated with a topic node
to provide judges with a familiar rating scale they were asked to assign letters grades a b c d or f to each explanation on each of the dimensions
the query interpreter whose capabilities have been addressed only minimally in our work translates the query to a canonical form which is passed along with the verbosity specification to the explanation planner
the knight edge and example generator evaluations employed humans as judges while the ana and streak evaluations had artificial judges in the form of corpora and pauline was evaluated without judges
the feature set for the circum clause indicates the wide range of possibilities for placement of the clause as well as for introducing additional phrasal substructures into the purpose clause
large scale knowledge bases are currently being constructed for many applications and the ability to generate explanations from these knowledge bases for a broad range of tasks such as education design and diagnosis is critical
discourse knowledge engineers build representations of discourse knowledge and this discourse knowledge is then used by a computational module to automatically construct explanation plans which are then interpreted by a realization system to produce natural language
the interpreter has several dozens of template networks which have semantic conditions on some nodes
we can then avoid generating the structures until higher level information can be applied to complete the disambiguation process
consider the following possible continuations of NUM NUM a hannah opened the door
c if the thread currently being followed is among the highest rated threads this thread is continued
the difficulty in these examples is determining whether the third sentence continues the thread begun by the first or second sentence
rhet rein just before overlaps same event precedes no temp reln sequences causes background elaboration results reversesequence contrast list enumeration figure NUM
NUM if more than one possibility exists semantic preferences are used to choose between the possibilities
this algorithm gives the correct results in examples such as the following NUM john entered the room
for example a state can elaborate another state or an event NUM a mary was tired
those in previous threads in order to rate the semantic closeness of the dcu to each thread
tenasp keeps track of the tense and syntactic aspect of the dcu if the dcu is simple
the word interest is reduced to a NUM way ambiguous word
this type of contexts should be filtered out or discounted in decision making
sense NUM is a kind of abstraction
the rows and columns represent word senses
suppose m and n are positive integers
the direct object particle for definite nouns at
table NUM modifiees of new with the highest likeli
NUM determine based on the user s utterance the state of the dialog and any other information relevant to the application such as the state of a database what to do next NUM perform the next action or set of actions
the original technique is based on synchronous calculation with positions of words in the input sentence in left to right fashion
now we already have reached the limits of formal checking because formal checking can not tell us whether all this was intended or is acceptable and if not what to do
thus there is a token boundary after de plus in de plus on ne le fai plus moreover one does n t do it anymore but not in on le air de plus en plus one does it more and more where de plus en plus is a single token
the voice interface is seamless in that the user can choose to use either voice or the traditional keyboard and mouse interface
in italian the canonical prepositions for these three kinds of modification are da di and a respectively
the resulting time series of information themes can be viewed in rapid succession
note that we generalize argmax to the case where maximization ranges over multiple indices by making it vector valued
then the best parse of the sentence pair has probability NUM t NUM v s
for each genre facet it compares our results using surface cues both with logistic regression and neural nets against results using karlgren and cutting s structural cues on the one hand last pair of columns and against a baseline on the other first column
let the input english sentence be el et and the corresponding input chinese sentence be cl cv
we used two architectures a simple perceptron a two layer feed forward network with all input units connected to all output units and a multi layer perceptron with all input units connected to all units of the hidden layer and all units of the hidden layer connected to all output units
generating the full paradigm for a nominal and a verbal root requires NUM NUM and NUM NUM entries in the lexicon respectively
a facet is simply a property which distinguishes a class of texts that answers to certain practical interests and which is moreover associated with a characteristic set of computable structural or linguistic properties whether categorical or statistical which we will describe as generic cues
for example instead of estimating separate weights o NUM and NUM for the ratios words per sentence average sentence length characters per word average word length and words per type token type ratio respectively we express this desired weighting
such a result is expected if we assume that either cue representation is equally likely to do better than the other assuming a binomial model the probability of getting this or a more NUM extreme result is i NUM b i NUM NUM NUM
in english the rewrite rules are used to generate the proper form of the indefinite article a or an
all noun phrases are mapped onto topic and all modifiers as well as verb phrases are mapped onto predicate
figure i head transducer m converts the se quences of left and right relations r r and r NUM r of w into left and right relations NUM NUM NUM NUM rl rj and rj t rv of v linek mercer and roukos NUM
since null values occur quite often these two counts exclude cases when one or both of the values are null
experience we have decided to iml lement the tool in two stages
parsing in the case of an itg means building matched constituents for input sentence pairs rather than sentences
for example suppose the english sentence were i want to fly to memphis please
sb subject mo modifier hd head
fortunately the word have a most accurate most powerful model which will back off to a less powerful model when there is insufficient training and ultimately back off to unigram probabilities
whether a bigram contains an unknown word or not it is possible that either model may not have seen this bigram in which case the model backs off to a less powerful less descriptive model
informally the construction of the model in this manner indicates that we view each type of name to be its own language with separate bigram probabilities for generating its words
all of this modeling would be for naught were it not for the existence of an efficient algorithm for finding the optimal state sequence thereby decoding the original sequence of name classes
in fact many nlp systems suffer from a lack of software and computer science engineering effort runtime efficiency is key to performing numerous experiments which in turn is key to improving performance
reducing the training set size to NUM NUM words would have had a more significant decrease in the performance of the system however the performance is still impressive even with such a small training set
the word feature is a simple deterministic computation performed on each word as it is to or feature computation is an extremely small part of the implementation at roughly ten lines of code
the basic premise of the approach is to consider the raw text encountered when decoding as though it had passed through a noisy channel where it had been originally marked with named entities
informally we have an ergodic hmm with only eight internal states the name classes including the not a name class with two special states the start and end of sentence states
for french an ad hoc programming language has been designed to easily define and modify the rule set
the search could even be done only on phoneme consonants for proper name searches for instance
the rule set for english consists of about NUM NUM rules containing morphs as well as nonsemantic grapheme strings
it is beyond the scope of this paper to discuss letter to sound procedures in languages other than english and french
more importantly new words come into the language every day and from these are generated many derived forms
in some cases the input string is modified to add a morpheme boundary or replace the suffix
there are several rules for phonemic tuning especially to account for morphonemic alternations which are extremely important
the semivowel j can be considered a consonant and the three consonant cluster constraint applies
the current estimate of the parameter is uncertain due to insufficient statistics in the training set
for other languages different orderings may be stated
both of these results are being implemented in our text generator
that is both cue based and factor based retrievals are possible
each word stage NUM hearst treats a text more or less as a bag of words in its statistical analysis
our analysis scheme is coordinated with a system for automatic generation of texts
from this study we are deriving a system of hypotheses about cues
NUM NUM choice of since or because
first the two coders could analyze a contributor as supporting different cores
lcb because since so thus therefoi te rcb
second the coders could disagree on the core of a segment
accompanying these relations were NUM cue occurrences resulting from NUM distinct cues
figure NUM polyphony does not underlie the choice between since and because
consider the properties of those examples that are selected for training
increasing batch size approaching pure batch selection reduces both accuracy and efficiency
if many words are shared by both set a and b then the lexical correspondence between the two sets is high
we agree that although to do so would not be trivial it is nevertheless possible to make the definitions above complete by carefully listing and including all possible cases
the new domain travel planning is still limited but is significantly more complex than the scheduling domain
it would then be possible to bias the calculation of lexical correspondences stage NUM taking into account the higher significance of these words relative to function words
the advantage of this simple statistical method of distinguishing significant content words from non content words is that no words need to be removed before allowing the algorithm to proceed
the algorithm assigns a correspondence measure to each sentence break as follows firstly set a is generated by taking all the words in the previous fifteen sentences
a corresponding speech act analysis might be that the speaker is suggesting a modification of a previous suggestion
on the other hand the features appearing in the tree schemata are common to every lemma selecting these trees
review of our approach a component diagram of our system for the scheduling domain can be seen in figure NUM
nor will it link a person s profile with the profile of the organization of which he is a member
we will be investigating an algorithm that will select a proper ordering of multiple descriptions referring to the same person
only when a suitable stored description can not be found will the sys null tem initiate search of additional text
if a reader tuned into news on this event days later descriptions from the initial articles may be more useful
currently our system does n t have the capability of matching references to the same entity that use different wordings
uary on corruption charges in a ruling that could destroy the media magnate s hope of returning to high office
we have identified several major advantages of using fds produced by the system in generation compared to using canned phrases
this is useful if a summary discusses events related to one description associated with the entity more than the others
work on information extraction is quite broad and covers far more topics and problems than the information extraction problem we address
key john major the database of profiles is updated every time a query retrieves new descriptions matching a certain key
each resulting english corpus has NUM NUM m bytes of data
we define a number of schemata which encode conventional meanings
elaboration a NUM e3 c ea
indicates that the value to its right is default information
an interacting issue is the granularity of meaning of derived forms
applying sdrt to compounds encodes the effects of pragmatics on the compounding relation
for unseen compounds all probabilities depend on schema productivity
figure NUM details of some schemata for noun noun compounds
compounds are attested with meanings which can only be determined contextually
compounds like apple juice seat require marked contexts to be interpretable
table NUM main categories of relationships
this is important to control the ambiguity problem in natural language processing
a final type of flexibility is built in by distinguishing subtypes of relations
the language dependent objects are connected with strings that are words
eliminating spurious cells by hand would be time consuming and error prone but the automatic classification method we report in the next section may help prune them
also we currently assume that words placed in the same group will share relatively few links connecting pairs of competing senses in wordnet
table NUM percentage of correctly translated words same scores
in the case of an utterance that refers to multiple distinct intervals the representation is a list of temporal units
this option is meant to disambiguate between different word senses
the classification of reliability is based on thresholds
starting fields tu returns a list of starting field names for those in tu having non null values
the second distinguishes between discussions about price reservations location time participants directions and general information
in the frequency condition we found the effect of the expert choice being at the top of the list of senses to be particularly strong for the most polysemons words p NUM NUM the overall effect of the expert choice being the first choice for all polysemy classes was significant at the p NUM NUM level
assume x is not a ct tokenization x cd s
values which are always negative are ignored this is primarily to reduce the size of the data being handled
grosz and sidner model the global level component of the attentional state with a stack pushes and pops of focus spaces on the stack depend on intentional relationships
this changing of aboutness in fact flipping it back and forth makes discourse NUM less coherent than discourse NUM
the coherence of a segment is affected by the kinds of centering transitions engendered by a speaker s choices of linguistic realizations in the utterances constituting the segment
a third complication arises in the application of rule NUM in sequences in which the cb of an utterance is realized but not directly realized in that utterance
in all of this work focusing whether global or immediate was seen to function to limit the inferences required for understanding utterances in a discourse
performance the performances of the for the test set and for the walk through article are given in appendix a
the second uses the training data to develop decision trees whic h detect the start and end points of names
the importance of these cases resides in showing that cf u may include more than one entity that is realized by a single np in u
in particular if a given utterance forces either the vf or the vl interpretation then only this interpretation is possible in the immediately subsequent utterance
more training data is perhaps required to make th e system aware of the spread of examples for human names
NUM quinlan j r machine learning easily understood decision rules in computer systems that learn eds
the assumption that names mentioned in the heading wil l be repeated in the body of the text holds almost universally
each element of a collection has a finite number of attributes each o f which may take one of several values
the ed ending on smolked indicates either a past tense verb or a past participle
the information sources in the preceding three sections are combined in our experimental post mortem parsing system
then the recognizer assigns all the parts of speech from the first choice list to that word
for example an unknown word ending in bj is assumed to be an adverb
the use of this approach will limit the possible parts of speech for many unknown words
for each test run the sentences in the corpus are parsed by the system eleven times
this part of speech is also used by cardie NUM in her experiments
the f structures fa fb and fc are for the nps in order
jacobs and zernik use a combination of methods in the scisor system NUM
clearly for a parser to be considered robust it must have mechanisms to process unknown words
the resulting representation for lemon juice is as in NUM
in deciding the set of allowable positions for source and target transitions there are tradeoffs involving model size flexibility for modeling word order changes in translation and computational efficiency of the search for lowest cost transductions
although stochastic taggers usually make use of subdivision level part of speech level is remarkably robust against data sparseness
in this section we will briefly review the basic equations for part of speech tagging and introduce hierarchical tag setting
although the method is very efficient it can not be used to construct hierarchical tag context trees
the bottom level is word level and is indispensable in coping with exceptional and collocational sequences of words
t NUM is constructed from the sequence baab and t NUM from baabab
thus the larger a sb is the more meaningful it is to expand a node by sb
although the variable memory length approach remarkably reduces the number of parameters tagging accuracy is only as good as conventional methods
to tackle this problem we introduce a new tag model based on the mistake driven mixture of hierarchical tag context trees
thus it is reasonable for the tagger to constrain the candidates to frequent open class words and closed class words
the method iteratively performs two procedures NUM constructing a tag model based on the current data distribution and NUM
to minimize the number of parameters needed to specify the deep structure a deep structure representation form called normal form which adopts predicate argument style is used in our system
the problem arises because cyclic solutions can be constructed that would not have been constructed by ordinary sld resolution
it is my experience that a well chosen goal weakening operator may reduce parsing times by an order of magnitude
note that each pair of phrase levels in the above equation corresponds to a change in the lr parser s stack before and after an input word is consumed by a shift operation
on the contrary a constituent with a stative verb would have the case frame in the form of vstat theme goal
therefore once the class of a verb is recognized incorrectly the cases for the verb s arguments and adjuncts will not be identified correctly
during the second phase when a particular derivation is constructed the acoustic scores are combined
in the first phase the parser finds all occurrences of the top category in the input word graph
this notion of word accuracy is an approximation of semantic accuracy or concept accuracy
thus the parse tree of a sentence may be constructed as a side effect of the recognition phase
this allows the use of efficient packing example of a partial derivation tree projected by a history item
root nodes have a nonterminal symbol before the colon and the corresponding rule identifier after the colon
with this learning algorithm NUM NUM error reduction rate for sense discrimination NUM NUM for case and NUM NUM for parsing accuracy are obtained compared with the baseline system
we do not however 4and harmfully restrictive in their unsmoothed incarnations
however us he admits a phrasal lexicon such as atlantic seaboard new england gives a negative influence for clustering since it can not be regarded us units i.e. each word which is the element of a NUM hrasal lexicon is assigned to each semantic code
thin mses the fact theft it is hard to get a higher percentage of correct clustering
for xalnlje l ank3 and banks3 in a new lrticle are rel laced by wordi aud the frequen y of wordi equals to the total nulnl er of fr quency of bank3 and q anks3
we have conducted flmr xl eriments i.e. q req dis link and method in order to exanline how wsd me thod and linking words with their semantically similar words linking method in short atfect the clustering results
for example ern and hrd are both concerned with market news
the alternative al l roach is based on dictionary s infl rlnation as a thesaurus
however in freq each noun corresponds different coordinate and regards to different meaning
results of method show tha t NUM out of NUM sets are cblssified correctly att i the per entage attained was NUM NUM while freq link and din ext eriment att tined NUM NUM NUM NUM NUM NUM renl e tively
in wsd nwthod the o occurrence of x and y f or cah ulating mu is that the two words x y al pear in the training orl us in this order in a window of NUM words i.e. a is folh wed by y within a NUM word distance
that result represents nearly a NUM point decrease on the f measure from their official baseline
testing was conducted using wall street journal texts provided by the linguistic data consortium
nine sites submitted a total of eleven systems for evaluation on the st task
the relational level iih and out objects represent the personnel changes pertaining to that state
performance on the vacancy reason and on the job slots was better for nearly all systems
two systems never filled the other org slot or its dependent slot rel other org
systems scored approximately NUM NUM points lower f measure on st than on te
these two slots caused problems for the annotators as well as for the systems
null definition NUM independence a case orrn is independent iff it is equivalent to j lcb n i6m j in i6m a jcn i6m where m and m partition m
the example passage covers a broad spectrum of the phenomena included in the task
the real challenge of te comes from associating other bits of information with the entity
rare errors are due to incorrect or brittle phonetic models
we thus envision modifying the design of our translation system to facilitate dealing with multiple sub domains simultaneously and or in parallel
the resolution of the anaphor is a new temporal unit that represents the interpretation of the contributing words of the current utterance
here we propose a technique that uses this fact to reduce alternations in syntactic encoding
one concern about greedy algorithms is that if they wander off track they may not be able to find their way back
thus french and russian p rtmone are cognates as are english sistom and japanese isutemu
the points of correspondence in simr s output are sufficiently dense and precise that gsa backs off only for very small aligned blocks
the noise reduction heuristics mentioned in section NUM NUM ensure that very few points of correspondence can be generated away from the tbm trace
if a more precise map is desired these larger non monotonic segments can be easily recovered during a second sweep through the bitext space
therefore to recover non monotonic segments of the tbm simr needs only to search gap intersections that are close to the first pass map
to interpolate injeetive bitext maps non monotonic segments must be encapsulated in minimum enclosing rectangles mers as shown in figure NUM
token types like the english article a can produce one or more correspondence points for almost every sentence in the opposite text
since only one of these correspondence points can be correct all but one of the points in each row and column are noise
their output based on this information is in set b NUM for each japanese term our system proposes the top NUM candidates from the set containing NUM single words plus the nineteen terms
i NUM two pilot non parallel corpora in our experiments we use two sets of non parallel corpora NUM wall street journal wsj from NUM and NUM divided into two non overlapping parts
we expect significantly larger amounts of training data to at least partially alleviate these problems resulting in significant performance gains
the result of human translation based on this candidate list is in set c sets a b and c are all compared to the original translation in the corpus
w w w is the weighted mutual information in our algorithm since it is most suitable for lexicon compilation of mid frequency technical words or terms
we use a smaller segment size between any two punctuations for the segment size for the wall street journal english english corpus since many of the seed words are frequent
NUM evaluation NUM matching english words to english the evaluation on the wsj wsj english english corpus is intended as a pilot test on the discriminative power of the word relation matrix
we showed that humans are able to translate more than twice as many japanese technical terms into english when our system output is used compared to translating a random set of NUM japanese terms without aid
the results are shown in figure NUM evaluators on average are able to translate NUM terms out of NUM by themselves whereas they can translate NUM terms on average with the aid of our output
the horizontal axis has NUM points representing the seed words the vertical axis has the value of the correlation scores between these NUM seed words and our example words
when matching vectors are very similar such as those in the wsj english english corpus a simple metric like the euclidean distance could be used to find those matching pairs
this is another way then that the alembic workbench environment enables and encourages the mixed or cooperative application of human and machine skills to the combined task of developing a domain specific corpus and set of extraction heuristics
the total amount of work is roughly cut in half
with larger trees the saving can be even greater
previous figures br the resulting structure
automatic discovery of non compositional compounds in parallel data
the annotator can alter the assigned tags cf
in addition appropriateness checks are performed automatically
such functors typically are major and minor syntactic category labels such as np vp s s bar verb
in addition we have exploited tagged text documents of the type used in muc NUM
wrap up takes as input the output from badger and forms in and out relations and succession events
we had not anticipated this problem on the basis of the dry run materials
james NUM years old and one that extracts chief executive officer
the crystal dictionary that was trained on the NUM te texts generated NUM cn definitions
this created a significant noise level for same type enough to render the feature questionable
all of our st specific training began in september with the release of the st keys
theorem given an increasing sequence rl r2 rp of symbol positions and given a v gaps rq rq l all nodes spanning trees i j k l rcb with rq i j k l rq l b
v ik heb de volgende verbinding gevonden
in fact the larger number of dictionary sense numbers for verbs in particular may be due less to actual meaning distinctions than to the lexicographer s attempt to account for the great semantic flexibility of many verbs
note however that the amount of search required may grow exponentially if more than one uninstantiated daughter is present
this shows that the salience constraint in tr3 is still effective
as is ususal with stochastic context free grammars every rule has an associated probability and the probabilities of all the rules that expand a single nonteminal must sum to one
the grammar derived from a is optimal under this model of language though c f and h are equally good
the hope is that by searching for a phrase structure or phrase structure grammar that maximizes the likelihood of an observed sequence we will find the generating structure or grammar itself
we have replicated the above experiments on the first NUM sentences of the wall street journal section of the treebank which has a substantially different character than the atis text
the above class of models makes no mention of deletion or movement of phrases and only information about the head of a phrase is being passed beyond that phrase s borders
created a document manager an implementation of the core document management functions in accordance with the architecture
several other contractors NUM provided detection and extraction components which conformed to the architecture and interfaced to this document manager
only the temporal knowledge derived from the situation aspect can provide further information which however may be overridden by the context
schematically she uses an idealised time line where the initial and finishing points of a situation are indicated by i and f respectively
for a bottom up active chart parser for instance this may lead to the introduction of large numbers of active items
central among these ideas was the notion of an annotated document
however there are some areas which are clearly lacking and are needed by many applications
it is obviously a shortcoming of smith s description to define the viewpoint merely as a focus on parts or on the whole situation
the lone experimenter would not need to create an entire application system from scratch to conduct experiments
in particular an explicit specification of the c language interface was added to the document
the principal object classes included the document and the annotation which were mentioned before
an information extraction module would add annotations corresponding to instances of a type of event
on the open sea by late NUM several government systems were being implemented in conformance with the tipster architecture
the word s local frequency is lower than the threshold tf
computational linguistics volume NUM number NUM NUM NUM computational and implementation features
stage NUM step NUM identifying the locally best translation
finally additional information concerning word order is computed and presented
the word groups are treated as sets with no ordering
using si NUM were correctly translated and NUM incorrectly
these groups are identified as collocations for a variety of reasons
computational linguistics volume NUM number NUM le mr
the verb term trust NUM is an antonym of the verb terra mistrust and of the verb term distrust NUM and both as variants are synonyms of each other
we compare the two domains in terms of out of vocabulary rates and linguistic complexity
when more than one grader is used the results are averaged together
this is a good score in comparison with evaluations carried out on full machine translation systems
of course this assumption most likely wo n t be true
on the other hand there are cases one is presented in figure NUM below where the induced relationship has cardinality NUM and the basic antonym relationship has cardinality NUM in figure NUM the term
figure NUM difference between training and test set accuracies
a transformation based system is a processor and not a classifier
NUM the word two before after is w
accuracy increases to NUM NUM after applying the learned transformations
learning stops when no positive scoring transformations can be found
initial state tagging accuracy on the training set is NUM NUM
this means that the test set contains no unknown words
overtraining did not occur when using the original brown corpus either
taggers may often be reluctant to ex mlne a large number of senses when one appears quite appropriate
most probable class assignments of the three hundred most commonly occurring words
the aggregate markov models were also observed to discover meaningful word classes
denoting these posterior probabilities by ck t we have
models assigned non zero probability to all the bigrams in the test set
thus we subdivided the data into three subsets core core contributor relations with the core in first position core core contributor relations with the core in second position impl icit core core contributor relations with an implicit core while this has the disadvantage of smaller training sets the trees we obtain are more manageable and more meaningful
null our last experiment looked at the smoothing of a trigram model
this is found to significantly reduce the perplexity of unseen word combinations
the problem is naturally formulated as one of hidden variable density estimation
thus if a generator uses decision trees such as the one shown in figure NUM to determine where a cuc should bc placed it can then select an appropriate cue from those that can mark the given intentional informational relations and are usually placed in that functional linear location
owing to the unreliability of measuring negative mutual information values between content words in corpora that are not extremely large we have considered that any negative value to be NUM we also set xl x2 to NUM if NUM
it would clearly be desirable to improve our understanding of this fundamental problem
thanks to megan moser for her prior work on this project and for comments on this paper to erin glendening and liina pylkkanen for their coding efforts to haiqin wang for running many experiments to giuseppe carenini and stefll briininghaus for discussions about machine learning
ps1 is significantly better than ps if the upper bound of the NUM confidence interval for ps1 is lower than the lower bound of the NUM confidence interval for g2 for each set of experiments we report the following NUM
this supports our expectation that more choices render the matching task more dii cult making agreement less likely
these methods try to guess what is going to be the current or even the next word the user is trying to type
consider the following alternate forms for expressing purpose 8a sit the person up leaning slightly forward so that blood and saliva can drain from his mouth
the kinds of polysemy and overlap found among the adjectives are carried over to the many derived adverbs in wordnet
as can be seen in the precondition chart in figure NUM imagene s accuracy is lower for preconditions than for purposes particularly in the testing set
in this best l mode the system performs somewhat worse in terms of word accuracy but much faster as seen in the experiments in the next section
all are licensed because of the poset relation r between book19 and books and the salient open proposition o
but a new mechanism is needed to make full use of the structural information provided by multiple rules
a simple rule shows how the subject qsubj is indexed to a finite verb by a link named subj
it maps those features of the communicative context deemed relevant in the corpus analysis performed in step NUM onto the appropriate lexical and grammatical forms for expressing each action
here the first sense was no longer necessarily the most inclusive general one
figure NUM shows the results of the comparison of the engcg syntax and the morphosyntactic level of the dependency grammar
furthermore the pruning mechanism does not contain any language specific statistics but works on a topological basis only
the grammar tries to be careful not to introduce false dependencies but for an obvious reason this is not always possible
j precision and recall is due to the fact that the parser does not force a head on every word
it should have the same head as the existing object if the verb has the proper subcategorisation tag sv0c
but especially in the rule above the contextual test is far from being sufficient to select the subject reading reliably
instead we can apply the rules iteratively and usually some of the rules apply when the ambiguity is reduced
in practice these rules are most likely to cause errors apart from their linguistic interpretation often being rather obscure
we represent operators as elementary trees in ltag and use tag operations to combine them we give the meaning of each tree as a formula in an ontologically promiscuous representation language and we model the pragmatics of operators by associating with each tree a set of discourse constraints describing when that operator can and should be used
more importantly existing techniques for parsing based on strings can be generalized easily by using the names of states in the automaton instead of the usual string indices
intuitively speaking any combination of xs and x4 which satisfies this equation can constitute the sbls for branches NUM and NUM formalizing equations for other pairs of words in the same manner we can derive the simultaneous equation shown in figure NUM that is we can assign the sbl for each branch by way of finding answers for each x
the specification of this algorithm is summarized in the following pseudocode until goals are satisfied determine which uninflected forms apply determine which associated trees apply evaluate progress towards goals incorporate most specific best form tree perform adjunction or substitution conjoin new semantics add any additional goals
i1 is nly the last entailment ritel ion that necessitates some economic semantic infcrencing the t hcrs c rrespon l more v less to structural lookup
ill this paper we cmmu t go into detail with tests that partition tile meaning of a sentence into presui positions assertions proper and inlplicatures the recipient is allowed to drmw from the sentence of
this result further indicates that taggers here were not biased towards the first sense but considered all senses equally
each domain element is associated with a pair of adjacent arguments
subsort declarations have the syntax given in NUM
special thanks for service with a smiley
sentence s n ws such a topicalization o ersl which is marked by the inversion of the basic subj vlin order ersl can only be used as a time adverb i.e. its meaning can only be the li s reading as exemplitied by NUM NUM erst g ab peter maria den brief
it order to avoid interfering effects from the syntacti structure that might eoutplicate matters with regard t deterntine the scol e of crsl we only list examples with verb tinm l osition
compare the following examples NUM well a toml a id er t a aer st eif lml tte if disqualigizierte
f o NUM ipsse 41n NUM further linguistic tests that we must omit here support the assumption that the information about the negative tests is an entailment
in the context NUM b the recipient understands erst as a signal of the speaker writer that the occurrence of the reported event is not preceded by the occurrence of similar alternative events
the execution of this sequence of codelets is interleaved with other codelets that are responsible for building other structures
the sentence containing this fragment has two plausible interpretations as shown in 2a and 2b
nr de bidoqing shff n hudj you struc look very funny you look very funny
when the system was presented with sentences with global ambiguities it produced all the plausible alternative word boundaries
i already go through asp student period i have already gone through the period as a student
in figure NUM the connection between the word objects ta she and h
as a result this method fails to correctly identify the word boundaries in sentence NUM
a statistically emergent approach for language processing application to modeling context effects in ambiguous chinese word boundary perception
the essential features of this model are asynchronous parallelism temperature controlled randomness and statistically emergent active symbols
the system is self organizing with coherent behavior being a statistically emergent property of the system as a whole
if c i lcb cil ci2 ci3 ci4 rcb w2 can not be fully disambiguated by any sense selection algorithm because two of its leaves synsets belong to the same category ci2 with respect to w2 ci2 is overgeneral though nothing can be said about the actual importance of discriminating between such two synsets
in the future we plan to demonstrate that the method proposed in this paper besides reducing the overambiguity of on line thesaura improves the performance of lexical learning methods that are based on semantic tagging such as pp disambiguation case frame acquisition and and sense selection with respect to a non optimal choice of semantic categories
not surprisingly the expert choice was at the top of the list in the frequency condition for most words
to build a reference scoring function against which to evaluate our model parameters we proceeded as follows since our categories are generated for an economic domain wsj while semcor is a tagged balanced corpus the brown corpus we extracted only the fragment of the corpus dealing with economic and financial texts
so far the manual selection of an appropriate set of semantic tags has been a matter of personal intuitions but we believe that this task should be performed in a more principled and automatic way
in other domains see a brief summary in the conclud null ing remarks for which we did not have a reference tagged corpus we used a l NUM NUM z l NUM NUM as model parameters in the NUM and still observed a scoring function similar in shape to that of figure 4b
the coverage co ci is therefore defined as the ratio nc ci w where nc ci is the number of words that reach at least one category of c i discrimination power a certain selection of categories may not allow a full discrimination of the lowest level senses for a word leaves synsets hereafter
ultimately some of the techniques developed here should be able to be extended to more complex formalisms such as hpsg
using the non curried notation of bar hillel it is more natural to use a separate wh list than to mark wh arguments individually
in this paper we propose an automatic method to select from wordnet a subset of domain appropriate categories that effectively reduce the overambiguity of wordnet and help at identifying and categorise relevant language patterns in a more compact way
the cardinality of each set varies but not uniformly from NUM categories for ub NUM remember that words are frequency weighted to i category i.e. the topmost entity for ub NUM NUM
one showed effects before the end of a word when there was no other appropriate word with the same initial phonology
the simplest of these is where the type of argument expected by the state is matched by the next word i.e.
for example the fragment john found a woman who mary can be given the semantics kp NUM x
in both conditions nouns were tagged significantly more often in agreement with the experts choice than verbs and adjectives
given this proposal and some further assumptions about the semantics of only the analysis of tb involves the following equations shared by target and source clause the second solution is clearly incorrect given that it contains information j that is specific to the source clause
NUM hem NUM where the h i are new variables of type f vi and the ei are either distinct color variables if c e ci or ei d c ifc e c
given these assumptions the representation for NUM is ex o we pf ipf ia and the corresponding fsv equation r pf ipf x ex pf x ipf in has two possible solutions
such equations can neither be further decomposed since this would loose unifiers if g and f are variables then ga fb as a solution ax c for f and g but lcb f g a b rcb is unsolvable nor can the right hand side be substituted for x as in a variable elimination rule since the types would clash
binding azw i of since the projection binding leads to a color clash i f t ipf and finally h pf has to be bound to the projection binding azw z since the imitation binding azw ipf is not pf monochrome
for instance the fsv of 4a NUM is 4b the set of formulae of the form l j x where x is of type e and the pragmatic effect of focus is to presuppose that the denotation of this set is under consideration
secondly the framework is purely declarative and focuses on those aspects of language that are more or less directly observable their structural properties
this gives us five mutually exclusive relations which we can combine into a single link relation that must hold between every trace and its antecedent
we analyzed the data from the paid training session that all taggers underwent before they were assigned to work on the semantic concordance cite landesinpress
the response through the speech synthesizer is convenient however user can not memorize the content when the content includes many items
taggers received a specially created booklet with the typed text and a box in which they marked their sense choices
the original impetus for the research was the question are the fiction and jourdali m parts of the longman lancaster corpus and british national corpus i bnc interchangeable
our approach to measuring homogeneity is to randomly divide a corpus into two random halves and measure the similarity of the two halves thus emphasising the relation between the two questions
this result is about the same as for statistical tuggers of english
to obtain the fully unambiguous result we make use of non contextual heuristics
we developed a simple extremely compact and efficient guesser for french
the symbol biases describe what is likely in a given ambiguity class
in the constraint based tagger the rules are represented as finite state transducers
the overall error rate of the tagger increased by over NUM
the latter is a rarely used tense of a rather literary verb
place which can be read as a determiner noun or clitic verb sequence
NUM using the statistical disambiguator independently
often only a minor correction is needed
let fl fin be the old weights for the features
NUM a set of initial weights NUM attached to the rules of g
this added flexibility is welcome but it does make parameter estimation more involved
second the model does not require features to be identified with rewrite rules
for example let us consider a different set of weights for grammar g1
intuitively we begin with the erf distribution and construct a random field to take
in our running example the atomic features are as shown in figure NUM
we also limit our attention to features that actually occur in the training corpus
for the sake of concreteness let us take features to be labeled subdags
let f be the reference to an f structure locate x
from an information theoretic point of view the theoretical answer to the problem is simple entropy is a measure of a corpus s homogeneity and the cross entropy between two corpora quantifies their similarity
except for words with two senses we found more tagger expert matches in the frequency condition than in the random condition
this lends support to the hypothesis that there may be consistent differences among speakers regarding strategies for signaling shifts in global discourse structure
as most of this work im to find good indexing terms for information retrieval it is mostly concerned with middle to low frequency items and differences in topic rather than differences in register
it copies the input until it finds an instance of upper
section NUM identifies some useful applications of the new replacement expressions
first of all a replacement can start at any point
the corresponding paths in the transducer are listed in figure NUM
the first reduces strings of whitespace characters to a single space
the precise definition of the upper lower relation is given in figure NUM
this transducer is unam biguous but can not be sequentialized
device to accumulate an unbounded amount of de layed output
the desired effect can be obtained by constraining the directionality and the length of the replacement
starting in the middle we can replace either b or ba
the system we propose attempts to address these needs as closely as possible within its own constraints i.e. without the ability to converse with the learner in his native language
it provides a central repository or server that stores all the information an le system generates about the texts it processes
industry ref yae NUM NUM NUM and the engineering and physical science research council ref gr k25267
the research reported here has been supported by grants from the u k department of trade and
ac uk for details of hardware and software requirements and licence arrangements
annotation tools currently developed perform text segmentation pos tagging morphological analysis and parallel text alignment
the tei defines standard tag sets for a range of purposes including many relevant to le systems
null availability of gate gate is freely available for research purposes
the multext tools are currently in use and are recommended by the eu
moreover its generalpurpose goals stretching beyond this particular target audience of users could make it a very useful tool for any language classroom
a context free grammar is made to be able to accept sentences with omitted post positions and inversion of word in order to recognize spontaneous speech
i thirdly as will be evident too all workers in corpus based computational linguistics frequency lists are very useful representations of meaning for information retrieval text categorisation and iig numerous other purposes
all communication between the system components goes through gdm thereby insulating parts from each other and providing a uniform api applications programmer interface for manipulating the data produced by the system NUM benefits of this approach include the ability to exploit the maturity and efficiency of database technology easy modelling of blackboard type distributed control regimes of the type proposed by NUM and reduced interdependence of components
we exploit object orientation for reasons of modularity coupling and cohesion fluency of modelling and ease of reuse see e.g.
that to a first approximation all selection methods considered give similar results
this equivalence of the different methods also largely holds with respect to computational efficiency
the parser was fast on the average six times as fast as a parser trained on syntax alone
a tree bank annotated in the manner described above consists of tree structures with syntactic and semantic attributes at every node
committee members are then generated by drawing models randomly from p mis
evaluation was performed using the university of pennsylvania tagged corpus from the acl dci cd rom i
figure l a shows the advantage that sample selection gives with regard to annotation cost
common features are words in the context of w or morphological attributes of it
figure NUM presents the results of comparing the several selection methods against each other
we also find test set comparable to other published results on bigram tagging
the committee based sampling algorithm was initialized using the first NUM NUM words from the corpus
a number of ambiguous words selected for labeling versus classification accuracy achieved
firstly the dialogue manager receives a semantic representation that is semantic network through the semantic interpreter for the user s utterance
the objective in gathering multiple text corpora is to identify a linguistic object in which the individual meanings of texts m are taken out of focus to be replaced by the character of the whole
for example in the sentence the dna segment would be digested only once leaving NUM pieces the csr in NUM was generated
scoring errors can be linked to data entry errors morphological stripping errors parser errors and erroneous rules generated due to misinterpretations of the scoring guide
the example lexical entries in NUM illustrate that the words fragment and segment are metonyms in this domain as well as the words move and travel
our findings suggest that randomly ordered senses would weaken taggers strategy of relying on the first sense being the best match and encourage more scrupulous examination of the available choices NUM confidence ratings reflected the degree of difficulty of the items in that they paralleled the taggers performance as measured by tagger expert and inter tagger agreement
for treatment i the scoring guide indicates that if the sentence makes a reference to NUM fragments that it should receive one point
we consider each of the three cases NUM NUM NUM NUM separately
due to the monotony of our alignnaent model and the bigraln language model
pr e lcb is the language model of the target language
the overall architecture of the statistical translation approach is summarized in figure NUM
o por favor reservamos dos habitaciones dobles con euarto de bafio
for the purpose of concept grammar rule generation each csr from the training data must contain only concepts which denote the core meaning of the sentence
expli que me la factura de la habitacidn tres dos cuatro
wer and sentence error rates ser for different language models
marks before translation and resubstituted t hena by rule into the target sentence
however if we try to preserve robustness by adding such rules whenever we encounter an extragrammatical sentence the rulebase will grow up rapidly and thus processing and maintaining the excessive number of rules will become inefficient and impractical
seeing the node ac is equal of not seeing the node ab when seeing a and this is already presupposed by the configuration frequency when the node ab but not the node ac is in the lattice
since we are building computational models of dialogue it is perfectly reasonable to explore these computational models through computer computer simulations
by using either random mode or continuous mode we can evaluate the effect of those mechanisms in this experimental environment
on average continuous mode results in NUM less branches searched per goal than random
these simulations can help us prune out some mechanisms and suggest mechanisms that may work well in a human computer system
as knowledge is varied between participants we see some significant differences between the various strategies
utterances NUM and NUM indicate that the computer is directing the search for the missing wire in the faulty circuit
our model of mixed initiative dialogue allows either participant to be in control of the dialogue at any point in time
an obvious approach is to ask for help when the agent is unable to satisfy a goal on its own
figure NUM the discourse tree of maximal weight that can be associated with text NUM
in contrast our system uses a mathematical model in which this ambiguity is acknowledged and appropriately treated
this method is similar to a naive pattern matching algorithm
NUM the textual types of the units connected by the discourse marker from clause to multiple paragraph
NUM assign a weight to each of the discourse trees and determine the tree s with maximal weight
hence we manually determined all discourse usages of cue phrases and all discourse boundaries between elementary units
the algorithms described in this paper rely on the results derived from the analysis of NUM of the NUM text fragments
our algorithm found NUM NUM of the discourse markers with a precision of NUM NUM see input a text t
the model includes generic modules for syntactic semantic and speech act constraints these constraints are integrated into spoken input interpretation to compensate for limitations in speech recognition components
the program ends when all functions have been applied
emmanuel roche and yves schabes deterministic part of speech tagging figure NUM
in our implementation transitions can be accessed randomly
emmanuel roche and yves schabes deterministic part of speech tagging figure NUM
this simple algorithm is computationally inefficient for two reasons
however it runs at a much higher speed
section NUM NUM describes an algorithm for determinizing finite state transducers
the effects from the acquisition of english as a second language are captured in the acquisition model described later in this paper
finally the response sentence generator decides a response form from the received inputs and then forms response sentence networks according to this form
answers are generated by an sql program which is a deterministically constructed from the formal language of our system
this is due to the great discrepancy between n gram models of different order
in some forms where the modifier describes an event the appropriate preposition in italian is da as in the forms in NUM while others the preposition is di as in the forms in NUM
for example forms such as coltello d a macellaio literally knife of butcher in which the modifier is an agent using the object described by the head does not translate as butcher knife
in this case the schema NUM specifies that the sequence head noun da modifying noun can be interpreted as having the semantic content of the modifying noun specify one of the arguments within the telic role
for the purposes of this paper we will simplify the representational structure of a gl lexical entry to include four levels of representation type structure argument structure event structure for verbs and qualia structure
typestr arg the type of a l j i d ar61 other arguments in the qualia argstr l j eventstr el events in the qualia l formal isa relation constitutive
in order to generate the proper output in italian it is necessary to determine the relation between the elements in the english compound structure and to determine the appropriate preposition in italian for expression of that relation
the schemata differ with respect to the constraints placed on the content values and the way in which the content values of the head and the modifier are composed to generate the content for the compound as a whole
the optimal arrangement will be to list frequent and idiosyncratic compound forms in the lexicon and use the compositional apparatus for forms which are not listed or in instances when the listed interpretation is ruled out by context
in a multi lingual setting such as information retrieval over the world wide web it may be desirable for a search for a complex nominal from one language to yield documents regarding the same concept in other languages
its search space at this point thus consists of the powerset s of s containing NUM k elements
although the gloss says that smuggle is a troponym of transport we may perhaps redefine export or import illegally
since the labeling is one dimensional this approximates our use of preferred centers of discourse segments
note also that otherwise transitivity of troponymy would be invalidated and short cuts could no longer be deemed a priori to be redundant
in figure NUM the verb concept smuggle is a troponym directly shared by export and by import NUM
NUM analysis of champollion s heuristic filtering stage in this section we analyze the generative capacity of our algorithm
wordnet s database set up program the grinder obviously controls consistency however we are not informed about this
alternative approaches have either avoided defining axioms for mutual belief e.g.
one widely used concept in speech act accounts is mutual belief
in particular the concept of mutual belief seems too strong
rather than compute nested beliefs to some fixed level during comprehension
in particular such techniques assume precomputed nested belief structures
this is the basic principle behind viewgen
we argue that precomputed highly nested belief structures are n t necessary
future work includes the attachment of a robust dialogue parser
in fact it appears that no dialogue exchange required more than a two level belief nesting
belief modelling is the development of techniques to represent the mental attitudes of a dialogue participant
applying this type abstraction strategy on the mrs of figure NUM we obtain where e.g. named is the common supertype of sandyrel and kimrel and actundprep is the supertype of giverel figure NUM shows the template templg obtained from fs using the more general mrs information
the paper presents the constructive dialogue model as a new approach to formulate system goals in intelligent dialogue systems
the services are associated with renting or buying cars thus the disjunction is realized as NUM NUM
work is now in progress to cover other types of task dialogues and to enhance the impleinentation
communicative principles are reasoning rules of the fort0 if cntxtfactl cntxtfactn then cntxtfactm NUM cntxtyactk
NUM the agent has fulfilled goals only but no initiative adopt the partner s goal
both regard natural language as purposeful behavior but differ in how this behavior is to be described
however we also use insights from the huge body of research that exists on dialogue management and natural language planning
if the goal is still unfulfilled and relevant it is resumed otherwise dropped
however if the conflict becomes so serious that it makes any cooperation impossible communication will break down as well
NUM realisation of the goal specifying the goal in regard to the communicative obligations sincerity motivation and consideration
such statements are generally theory specific and therefore are not appropriate for a descriptive approach to annotation
in contrast to conventional phrase structure grammars argument structure annotations are not influenced by word order
for optimal human machine interaction the tool supports immediate graphical representation of the structure being annotated
they are stored together with the corpus which allows easy modification and exchange of tagsets
given a sequence of categories the tagger calculates the most probable sequence of grammatical functions
using the viterbi algorithm and to identify both the phrase category and the respective grammatical functions
by suppressing unreliable decisions precision can be increased to range from NUM to NUM
at earlier stages of annotation the main source of errors was wrong or missing manual annotation
table NUM shows tagging accuracy depending on the category of the phrase and the level of reliability
t department of computer science NUM computer science building columbia university new york ny NUM usa
this is why we distinguish here between true scalars i.e. those adjectives whose meanings are based on a scale that is a property concept in the ontology and all the other adjectives which are gradable and may be also loosely referred to as scalars
this method is simple but its a curacy h lmnds heavily on the accuracy of the usage fi equencies
this results in eight cue types which are grouped into three classes based on the kind of knowledge needed to recognize them
we showed that distinguishing between task and dialogue initiatives allows us to model phenomena in collaborative dialogues that existing systems are unable to explain
more specifically the dialogue manager could generate the concepts to ask could you please turn off the radio
i also owe thanks to afzal ballhn and yoshiki mori for their comments on drafts of this poster
for spoken language input this repertoire of information ensues interactions like the unsuccessful dialogue in figure NUM from a train timetable enquiry system NUM what happens in a dialogue like this is the following the dialogue manager tries to verify a value that is needed for a database query
a bpa assigns a number in the range NUM NUM to each subset of o such that the numbers sum to NUM
we then developed a training algorithm trainbpa figure NUM and applied it on the annotated data to obtain the final bpa s
pma argmaxpj j i count pj incontext c pj
a re eoutl u e i to oloserve how lit preci
we refer to an entry in this table with a given parse p as count p
NUM choose a nominal reading over an adjectival if a three token compound noun agreement can be established with the next two tokens llc lc
on the other hand this very nature introduces another kind of ambiguity where a lexical form can be morphologically interpreted in many ways some with totally unrelated roots and morphological features as will be exemplified in the next section
the unsupervised learning process produces two sets of rules i choose rules which choose morphological parses of a lexical item satisfying constraint effectively discarding other parses and ii delete rules which delete parses satisfying a constraint
pk and for each parse pi we generate a candidate rule of the sort if lc and rc then choose pi NUM every such candidate rule is then scored in the following fashion a we compute
after applying hand crafted rules to a text to be disambiguated we arrive at a state where ambiguity is about NUM NUM to NUM NUM parses per token down from NUM NUM to NUM NUM parses per token without any serious loss on recall
NUM we evaluate the resulting disambiguated text by a number of metrics defined as follows voutilainen in the ideal case where each token is uniquely and correctly disambiguated with the correct parse both recall and precision will be NUM NUM
we are nevertheless rather satisfied with our solution as in our experiments we have noted that well below NUM of the forms remain as unknown and these are usually item markers in formatted or itemized lists or obscure foreign acronyms
in such a case where the right context of an ambiguous token is a derived form one has to consider as the right context both the top level features of final form and the stem from which it was derived
we have implemented this approach and found that it is not very desirable due to two reasons NUM it generates far too many delete rules and NUM it impacts recall seriously without a correspond null ing increase in precision
after each turn new initiative indices are calculated based on the current indices and the effects of the cues observed during the turn
an important class of rules we explicitly have avoided hand crafting are rules for disambiguating around coordinating conjunctions
for instance in both noun phrases are syntactically possible though the second one is obviously nonsense
other cases involve gtrc and require sublemmas which can be proved by induction on the length of the gtrc
description of the umass system as used for muc NUM
an in and out relationship is created with io person mr
tree NUM a portion of the resolve coreference tre e
we made progress in some areas and ignored others completely
the alias feature is the root node of the tree
the tree returns a positive classificatio n for the instance with mr
dates money and percentages were all handled by a single specialist
this customization is handled manually but it is not a difficult task
NUM years old is stepping down as chief executive officer on july NUM
indicate that the admissible structural positions of antecedents for nonretiexive pronouns are distributed complementarily i.e. these pronouns choose their antecedents outside of their local domain
b local sorting for each anai hor y sort their individual antecedent cm didates xj according to decreasing plausibility v y x a
cf section NUM NUM are introduced for which binding principles c and b are verified respectively but for which no antecedent search is performed
k ntext natural language systems dolivostrafie NUM d NUM darmstadt germany stuckar l o darmst adt grad de
of tile matrix clause verb leads to a different judgement while tile syntactic structure is preserved 12a pauli revises the decision for himi
but a change 6in german this kind of interdependency may arise due to lnorphosyntactic ambiguity in case of multiple occurrences of the pronoun sic
to avoid that te sirable antecedent options are ruled out l y interdependency the choices wil h highest plausibility is given preference to
the np barber and the reflexive pronoun himself may be coindexed only indirectly via the possessive pronoun his which is of type b and hence forced to take a nonlocal antecedent
changing from active to passive voice or vice versa retaining the semanti case role should outvote retaining the synta tic as role
in the move class system nedis typically choos s one of the five major classes of move
typically the satellite follows the nucleus but the different types of rhetorical relation have different typicalpatterns in this respect
the pair hmm tag bigram model and tag hmm based on formulae NUM NUM where n NUM and NUM respectively will be investigated in section NUM
in the integration of the two to be described here we model this as the recursion of acts within acts
the architecture should also be modular so that a variety of configurations can be tried it should be possible for instance to exchange competing speech recognition components and it should be possible to combine components not explicitly intended for work together even if these are written in different languages or running on different machines
NUM we use the following conventions with respect to finite state automata to represent lexical rule interaction the state annotated with an angle bracket represents the initial state
the number of lexical entries belonging to a word class is relevant since the interaction predicates are identical for all lexical entries belonging to the same word class
partially be dealt with for example by using a depth bound on lexical rule application to ensure that a finite number of lexical entries is obtained
so on the basis of the signature we can determine which appropriate paths the linguist left unspecified in the out specification of the lexical rule
if the application of a particular lexical rule with respect to a lexical entry fails we know that the corresponding transition can be pruned for that entry
the different steps of the compiler are discussed with emphasis on understandability and not on formal details NUM figure NUM shows the overall setup of the compiler
thus four among the twelve speakers completely agree with tr3 while three agree with tr2
in case of indirect or direct cycles in the automaton however we can not derive all possible lexical entries as there may be infinitely many
however each lexical rule application i.e. each transition in an automaton calls a frame predicate that can have a large number of defining clauses
this hypothesis can be investigated by looking at the extent to which the speakers agree among themselves
null suppose that the referring expression components we wish to compare all adopt the above basic algorithm
this provides a richer and so more informative labeling with the rhetorical relation shown as the class of act
in the more complex use the mds results identify the concepts and themes that are different and similar in the transcripts
with such information we were able not only to provide a more coherent discourse analysis of a text segment but also possibly to summarize the text better
the category prepositions contains NUM words with an average expected fi equency of i i i percent with a range over the four contexts of NUM NUM to NUM NUM percent
however the new goal would still be unsatisfied in the next iteration the pp on reserve would be adjoined into the tree to satisfy it the syntax book we have on reserve
the use of the heuristic normatiw to label this category clearly reflects the presence in these words of a sernrntic component oriented around characterizing something in terms of expectations
we shall shortly see why it is at place NUM rather than place l
the category sanction NUM words has an average expected frequency of NUM percent with a range over the four contexts of NUM to NUM percent
eleven categories such as have prepositions you l me he a an the consist of only a few words from closed classes
in particular we sec the need for encoding derivational and morphological relations finer grained characterization of government patterns feature specifications and primitive semantic components
the reason lies in the fact that the determinization algorithm in the former expression applies on a machine which is by far larger than the small individual machines present in the latter expression is another aspect of rule features concerns the morphotactic unification of lexical entries
finally the results of telicity dynamicity and durativity assignments are returned
even when the cognitive content of the response is indicating that an error occurred the affective feedback should always encourage the learner
NUM the program performs simple bracketing i.e. finds kernel phrases without the user having to explicitly mark phrase boundaries
this work was supported by the dti salt funded project integrated language database and built on work funded by the ec funded project acquilex ii and on background material from cambridge university press
the tagger does not check for p followed by the next tag but rather looks back to what came innnediately before the preceding p and then does the transition pair match on that
the subject codes are arranged in a hierarchy so for example christmas and passover would match at some levels despite not having exactly the same subject code
the cns that crystal induces are designed to locate relevant noun phrases for an extraction task but they do not help us understand where to look inside a given nou n phrase for the relevant information
the processors may thus become black boxes to each other when seamless connection and easy communication might well be preferable
each constraint in the vector is a function that scores possible output representations surface forms NUM ci repns lcb NUM NUM NUM rcb ci e con if c r NUM the output representation r is said to satisfy the ith constraint of the language
because one or more of the ai end i while all the al either end or continue so that we know we are leaving an a NUM thus NUM in all ai some bj in all ai an unusually complex example is shown in NUM
we found that a majority of the pause units in four dialogues gave understandable translations into english when translated by hand
then the constraint targets intervals of the form a c NUM f l c NUM fq each time such an interval ends without any 3j having occurred during it one violation is counted NUM weight NUM arcs are shown in bold others are weight NUM
we use conjunctive predicates where each conjunct lists the allowed symbols on a given tier NUM f 3cr l voi arc label w NUM conjuncts the arc label in NUM is said to mention the tiers f o voi e tiers
it must be emphasized that if the grammar is fixed in advance algorithm NUM is close to linear in the size of the input form it is dominated by a constant number of calls to dijkstra s bestpaths method each taking time o input arcs log input statesl
ellison s method can not directly express the above ga constraint even outside otp because it can not compute a quadratic function NUM NUM NUM on a string like cr f a r r
if all tiers are in a steady state the string need not use any symbols to say so
it has tier rules for NUM tiers and then spends NUM constraints on obvious universal properties of morns and syllables followed by NUM constraints for universal properties of feet and stress marks and finally NUM substantive constraints that can be freely reranked to yield different stress systems such as left to right iambs with iambic lengthening
NUM extends this result to the case where gen mput is the set of parse trees for input under some context free grammar cfg NUM tesar s constraints are functions on parse trees such tha ci a b1 b
otp claims that con is restricted to the following two families of primitive constraints NUM a NUM implication each temporally overlaps some scoring constraint r number of a s in r that do not overlap any NUM
their performances will be compared in the next chapter
hence the estimated performance will be very unreliable
automatic construction of a chinese electronic dictionary
a more extensive survey is being studied
such an approach is being studied
though they are judged as word candidates
figure NUM shows two different derivations of logical forms for the complex np two representatives of three companies
however there is no similar derivation for the fragment of three companies touched as shown below
in one most boys outscopes every man and a few boys which together outscope more than two women
organization names are varied in their form consisting of proper nouns general vocabulary or a mixture of the two
appropriate instructions were given to change the subject expectations to the new mode and ten more sample problems were given
finally they were given four practice problems and allowed to try solving them with the machine operating in directive mode
the te evaluation task makes explicit one aspect of extraction that is fundamental to a very broad range of higher level extraction tasks
the author is turning over government leadership of the muc work to elaine marsh at the naval research laboratory in washington d c
twelve systems from eleven sites including one that submitted two system configurations for testing were tested on the te task
no metrics other than recall and precision were defined for this task and no statistical significance testing was performed on the scores
the management succession template consists of four object types which are linked together via one way pointers to form a hierarchical structure
statistically large differences of up to NUM points may not be reflected as a difference in the ranking of the systems
st results on some aspects of task and on walkthrough article three succession events are reported in the walkthrough article
the post slot requires a text string as fill and there is no finite list of possible fills for the slot
mr t namex type person james enamex will be able to compete in as many sailing race s as a hon
instead we had decided to address temporal information from explicit temporal expressions because we can extremel y reliably recover such expressions via local parsing
this boolean expression can of course be written more compactly by using a few more disjunctions
nevertheless this technique should not be scorned for in other cases there will be some advantage gained
it can be argued that these differences are justified by differences of scale perplexity and meaningfulness
the out value of the subsidiary daughter is passed to the mother category s store
if the pp is an agent it will pass up the daughter vp meaning
next we thread a similar tuple through each category to record which category it is
computational linguistics volume NUM number NUM lcb lex send cat vp subcat npl
a moment s reflection should reveal that this first attempt will not give the correct results for two reasons
in general we will only be dealing with the sum and not the product of the syntactic and semantic ambiguity
this is of course a serious limitation especially for theories of grammar that are largely lexically based
when NUM NUM the outputs are scored in key to response mode as though one annotator s output represented the key and the other the response the humans achieved an overall f measure of NUM NUM and a corresponding error per response fill err score of NUM
the case insensitive results would be slightly better if the task guidelines themselves did n t depend o n case distinctions in certain situations as when identifying the right boundary for the organization name span in a string such as the chrysler division currently only chrysler would be tagged
a basic characterization of the challenge presented by each task is as follows named entity ne insert sgml tags into the text to mark each string that represents a person organization or location name or a date or time stamp or a currency or percentage figure
as a condition for participatio n in the evaluation the sites agreed not to seek out and exploit wall street journal articles from that epoch once th e training phase of the evaluation had begun i e once the scenario for the scenario template task had been disclosed to the participants
umanitoba s production of st output directly fro m dependency trees with no semantic representation per se
some systems completed all stages of analysis befor e producing outputs for any of the tasks including ne
one common problem was the simple failure to recognize hire as a n indicator of a succession
the experience gained from that evaluation will serve as critical input to revising the engish version of the task
in addition there are plans to put evaluations on line with public access starting with the ne evaluation this is intended to make the ne task familiar to new site s and to give them a convenient and low pressure way to try their hand at following a standardized test procedure
the tag elements are enamex for entity names comprising organizations persons and locations timex for tempora l expressions namely direct mentions of dates and times and numex for number expressions consisting onl y of direct mentions of currency values and percentages
first it puts some limit to enumeration of word senses thus keeping limited the search space of any generalization process
past auxv i still i fr copula which means still be taken away
we have presented a self organized method that builds a stochastic japanese word segmenter from a small word list and a large unsegmented text
the first type is not an error but the ambiguity resulting from inconsistent manual segmentation or the intrinsic indeterminacy of japanese word segmentation
it counts the instances of string w1 in text t unless the instance is also a substring of another string w in dictionary d
the estimates of word frequencies by the above string frequency method tend to inflate a lot especially in short words because of double counts
we show that it is very effective to combine a heuristic initial word identification method with a re estimation procedure to filter out inappropriate word hypotheses
we find that the combination of heuristic word identi cation and re estimation is so effective that the initial word list need not be large
table NUM precision and recall for induction based on generalized context vectors
secondly the error analysis suggests that considering non local dependencies would improve results
the black cat vs the cat is black
the tag cd is probably better thought of as describing a word class
structural tags derived from parse trees are marked with NUM classes
the classification was then applied to all natural contexts of the brown corpus
this scheme fails for cases like the soldiers rarely come home
consider two infrequent adjectives that happen to modify different nouns in the corpus
all occurrences of a word are assigned to one class
this classification constitutes the baseline performance for distributional part of speech tagging
figure NUM parallel computation of rule probabilities
here we show how the first paraphrase is generated
for given x xl frt the corr sl onding probal ilities t i xi is nod kllowll
m i lcb i can he sinil lificd t y lhe sunnlml ion of clique un tion as NUM lcb
subsertion as it is defined as a non deterministic operation
subsertion can model both adjunction and substitution in tag
thus generation is in essence a search problem
we have a principled way to constrain this
figure NUM covering the remaining semantics with mapping rules
in this section we informally describe the generation algorithm
figure NUM shows an example of a mapping rule
the borderline between the two paradigms is not clear cut
as mentioned earlier this is a high level algorithm
the effect of refer is that the hearer should believe that the speaker has a goal of the hearer knowing the referent of the referring expression
the hearer might then adopt some goal of his own in response to this and make an utterance that he believes will achieve this goal
cf NUM NUM distance factor tult focuslist
the discourse action accept plan shown in figure NUM is used by the speaker to establish the mutual belief that a plan will achieve its goal
as we mentioned in section NUM once the refashioning plan is accepted the common ground of the participants is updated with the new referring expression
in fact during linguistic realization if the two actions are being uttered by the same person they could be combined into a single utterance
the system next performs plan recognition starting with the second surface speech action s actions which corresponds to the refashioning on the television
our basic representational unit is given in figure NUM
although our work has focused on referring expressions we feel that it is relevant to collaboration in general and to how agents contribute to discourse
first it minimizes the distinction between the roles of the person who initiates the referring expression and the person who is trying to identify it
but this is less preferable from the language point of view
cf NUM NUM distance factor tuft focuslist
backtracking still plays an important role for the implementation of search
the basis probabilities for complete sequence outside probabilities are as follows
table NUM evaluation of system on cmu test data
the basis for inside probabilities of complete sequence are as follows
proverb however can be seen as the first serious attempt for a comprehensive system that produces adequate argumentative texts from nd style proofs
the csr in NUM in which xp dna fragment was removed illustrates the fine tuned version of the csr in NUM
the main objective is to observe the parsing performance based on the grammar acquired from the same domain compared with the performance based on grammars of different domains or combined domains
on the other hand the cross entropies where any of the non fiction domains are models and any of the non fiction domains except p are test have some lower figures
it is a non trivial task to identify a word ill the text of a language which has no specific punctuation to mark word boundaries
educational testing service ets is currently developing computer based scoring tools for automatic scoring of natural language constructed responses responses that are written such as a short answer or an essay
the parsing results show that the best accuracy is obtained using the grammar acquired from the same domain or the same class fiction or nonfiction
the graph between the size of training corpus and accuracy is generally an increasing curve with the slope gradumly flattening as the size of the corpus increases
by adjusting the value of threshold we can extract suitable entries for open compound registration regardless of the size of the input file
in each table n is the number of occurrences and d is the difference in occurrence with the next string
the cross entropies where any of the fiction domains are models and any of the non fiction domains are test are the highest figures in the table
therefore the scoring program looks for matches between concept grammar rules and subsets of csrs if no direct match can be found for the complete set of concepts in a csr
however for practical reasons availability and time constraint we decided to use an existing multi domain corpus which has naturally acceptable domain definition
from the results we can clearly see that fiction domains in particular domains k l and n are close which is intuitively understandable
nns has two common characters with nnj2 so that susanne tag nnj2 is mapped to lob tag nns
table NUM illustrates a twr by two contingency table for words w i and w NUM cell a counts the number of sentences that contain both w i and w NUM cell b c counts the number of sentences that contain w NUM wl but not w i w2
this corpus contains one tenth of brown corpus but involves more syntactic and semantic information than brown corpus
for example the sentence the action of this mutation would nullify the effect of the site so the enzyme y would not affect the site of the mutation
where pi denotes part of speech i f p NUM p2 is the frequency of which p2 follows p NUM f pl and f p2 are the frequencies of pl and p2 and n is the corpus size in terms of the number of words in training corpus
the NUM NUM distribution for these parts of speech is shown in figure NUM position i x axis is the location between parts of speech pi and pi NUM regarded as the boundaries of chunks
where c i denotes chunk i f cl c2 is the frequency of which c NUM follows el f cl and f c2 are the frequencies ofc NUM and c2 and n is the corpus size m terms of the number of words in training corpus
NUM susanne tag lit is mapped to NUM susanne tag iix is mapped to NUM susanne tag io is mapped to susanne tag iw is mapped to NUM susanne tag jb is mapped to NUM susanne tag jbo is mapped to lob tag in
a common assumption held by both approaches is that neighboring words provide strong and consistent clues for the correct sense of a target word in some context
based on this definition and NUM measure consider the sentence the fulton county grand jury said friday an investigation which has tag sequence ati np npl jj nn vbd nr at nn
to replace the conventional multiple questions on standardized examinations choice test items in this paper are copyrighted by educational testing service ets
note that no subcategorization information is used it suffices for a verb to occur in a clause with two nominative accusative ncs for it to be considered testdeg ing training data
specifically the part a s of the essays were stored in a separate directory as were part b s and part c s
NUM recall would mean that the guesser had assigned all the correct pos tags but not necessarily only the correct ones
the system works without subcategorization information it suffices for a verb to occur with a possibly nominative and a possibly accusative nc for it to be considered training test data
errors in training and test data may stem from the morphology component from the grammar specification from the heuristic rule or from actual errors in the text
first we define a relation to express the constraints immediately specified for a type on the argument of the relation a o t vp g
firstly our approach can be extended to handle arbitrarily complex antecedents of implications i.e. arbitrary negation which is not possible using an open world approach
note that the simple type assignment g a leads to a call to the relation atvp imposing all constraints for type a which is defined below
this usually involves a type he list with appropriate features hd and tl where under hd we encode an element of the list and under tl the tail of the list
node bears a simple type and we do nothing but node is again a list and needs to be entered into the rhs
as a result we are able to efficiently process with hpsg grammars without haviog to hand translate them into definite clause or phrase structure based systems
constraining the domain a hpsgii theory consists of a set of descriptions which are interpreted as being true or false of an object in the domain
in the language box the user has the choice of selecting documents in english japanese or both
the indexing module is currently running on a sun platform and is designed to scale for a multi user operational environment
in particular translation quality of names by even the best mt systems is poor
the term translation module uses various resources and methods to translate english and japanese names
the same alias capability is also used in hyperlinking indexed terms in browsing a document
in addition for each indexed term the user can explore co occurring persons entities places and technology
furthermore we plan to apply data mining algorithms to the resulting databases to conduct advanced data analysis and knowledge discovery
elhadad mckeown and robin floating constraints in lexical choice the alt keyword expresses disjunction in fuf
NUM intro to ai has many assignments which consist of writing essays in which you do not have experience
assume that the content planner has decided the focus is on ai and the perspective is on the class assignt relation
a stage of phrase planning first processes the semantic input and determines to which syntactic category it is to be mapped
the intermediate fd corresponds to the features next to notes NUM NUM NUM and NUM in figure NUM
to see how lexicalization works for our simple example sentence ai has six assignments we will only consider semantic constraints
to map the arguments class and assignt of class assignt onto respectively the possessor and possessed roles of the possessive process
null preliminary tests of co oc were carried out on a corpus of japanese japanese dialogues concerning street directions and hotel arrangements at atr interpreting telecommunications laboratories
using a phrasal lexicon however means hand encoding the many mappings of multiple constraints onto multiword phrasings
it records in effect one of three possibilities for each terminal symbol whether it has not yet appeared has appeared and must appear again or has appeared and need not appear again
because files are used as data holding areas in this way components and their managers can be freely distributed across many machines
these requirements may be reconciled by using the more complex grammar to automatically derive a finite state approximation which can then be used as a filter to guide speech recognition or to reject many hypotheses at an early stage of processing
a highly simplified example of a database representation of a recording is a teleshopping system has to be entertaining
some versions of the calculus allow transitions to be labeled with arbitrary prolog terms including variables a feature that proved to be very convenient for prototyping although it does not essentially alter the power of the machinery
the module prosody transforms a syntax tree into a sequence of annotated words the annotations specifying accents and prosodic boundaries e.g.
in the case of some simple examples such as the grammar s a s b i e used earlier the approximation algorithm presented in this paper gives the same result as pereira and wright s algorithm
we would like to thank s ilaack m
the refined automata are encoded as definite relations and each base lexical entry is extended to call the relation corresponding to its class
this means that the fiction domains are not suitable for modeling the syntactic structure of the non fiction domains
figure NUM shows the definite clause representations of lexical rules NUM NUM and NUM and the frame predicates derived for them
this hierarchical structure is imposed by the rule and is not part of the semantic input
furthermore filling in features of the structure below z is unnecessary as the value of z is structure shared as a whole
in the second case when feature c has t2 as its value we additionally have to ensure that z gets transferred
this is not possible though since the values of x and y are specified in the out specification of the lexical rule
note that in case of rejection see NUM pasha ii agents do not use counter suggestions
given an ascii text sines currently produces predicate argument structures containing shallow semantic analyses of pps and nps
as a consequence cosma operates with general and reusable processing modules that interpret domain and task specific data
the world interface realizes the agent s sensing and acting capabilities as well as the connection to its owner
could you please correct the weekday or the date NUM ich meinte natiirlich montag den NUM NUM i h meant of course monday nov NUM NUM am NUM NUM NUM pat3t es bei mir zwischen NUM a und NUM uhr
the observations and an experiment are the following comparison of structure distributions across domains examples of domain specific structures parsing experiment using some domain dependent grammars NUM data and tools the definition of domain will dominate the performance of our experiments so it is very important to choose a proper corpus
the concrete realization of the automata is based on the linguistic annotations of the e mail fragments in the corpus
this way grammar development can be achieved in subsequent feedback cycles between the annotated corpus and sines automata
imas is based on a domain dependent view of semantic interpretation information gathering rules explore the input structure in order to collect all and only the relevant information the resulting pieces of information are combined and enriched in a monotonic non compositional way thereby obtaining an il interface level expression which can be interpreted by the agent systems
obviously the latter idea has a great advantage that you do not have to create a number of grammars for different domains and also do not need to consider which grammar should be used for a given text
head transducers operate outwards from the heads of phrases they convert the left and right dependents of a source word into the left and right dependents of a corresponding target word
the use of lexicalised dtg means that the algorithm in effect looks first for a syntactic head
verbs contains all forms of all irregular verbs
for example the probability of the rule s bought
another obstacle to extracting predicate argument structure from parse trees is wh movement
NUM produce trees without information about wh movement or subcategorisation
in particular the subcategorisation probabilities are smeared by extraction
part of speech tags are generated along with the words in this model
however a pure dependency model omits non terminal information which is important
the probability of the phrase s bought
thus the subcat requirements are added to the conditioning context
they seem like thinly disguised function words that happen to appear in syntactic positions normally reserved for adjectives
in this model the sentences with term weighting are sorted according to their weights and this information is used to extract a certain ratio of highest weighted paragraph in an article
furthermore a particular article for example general signal corp consists of several paragraphs and keywords of the general signal corp article appear throughout paragraphs
we recall that in method a when word i appears in only one article and the frequency of i is one the value of tf idf equals to log50
in table NUM shows the extraction ratio NUM NUM and para shows the mfmber of total paragraphs corresponding to each
according to table NUM we can observe that in paragraph for example some words whose x NUM values are slightly higher than the average NUM NUM exist
in zechner s method the sum over all tf idf values of the content words for each sentence are calculated and the sentences are sorted according to their weights
para shows the number of paragraphs which humans judged to be key paragraphs and correct shows the number of these paragraphs which the method obtained correctly
its major components are top level executive tlx primarily a human interface tool for invoking the other nlu shell components
text description editor tde maintains the text description factbase a data file containing knowledge about the structure of messages i.e.
from the above observation we can estimate that the formulae of context dependency are weak constraints in some domains while they are still effective even in a restricted domain
the denominator of recall is made by three human judges i.e. when more than one human judged the word as a keyword the word is regarded as a keyword
in this paper we have presented some main features of our new framework for dependency syntax
in valency theory usually complements obligatory and adjuncts optional are distinguished
reading from each word belongs to a subtree of which the root is said or suits
figure NUM lists the samples their sizes and the average and maximum sentence lengths
some comments concerning the rules in the toy grammar figure NUM are in order
the sentence is thus disambiguated both morphologically and morphosyntactically and a syntactic phosyntactic alternatives e.g.
once a link is formed between labels it can be used by the other rules
e sometimes the context gives strong hints as to what the correct reading can not be
rules also have contextual tests that describe the condition according to which they may be applied
on the other hand the non finite verb forms functioning as objects receive only verbal labels
this is an area that lt nsl does not address
there is also an api for the python programming language
the lt nsl programs were useful in evaluating these techniques
rude as the textual content reading sgml becomes difficult
the lt nsl system is released as c source code
why did we say primarily for text corpora
it is stated that representing ambiguous or overlapping markup is complex in sgml
null it is claimed that using normalised sgml implies a large storage overhead
using this hei we are developing a generic sgml editor
syntactic sugar apart no special status is given to the attribute word
if the user of verbmobil needs translation she presses a button thereby activating deep processing
this solves the equation since azw w ipf c
as before the focus term is pfand the fsv variable pf coloured
given these constraints the first equation in NUM is reformulated as
this yields a focus semantic value which is in essence kratzer s presupposition skeleton
as dsp themselves note a general theory for the por is called for
for a more formal account of how the unifiers are calculated see section NUM NUM
note that lmizn s is the likelihood of the weighted mixture of trees rooted at s on all past observations where each tree in the mixture is weighted with its proper prior
the general n t ic class disambiguation accuracy on the other hand considers a respouse correct as long as the response class is in the sub hierarchy which originated fz om the same level NUM class as the answer
since the codelength of a word w with probability p w is approximately log p the total estimated change in description length of adding a new word w to a lexicon is
for example if wang is represented in terms of go presumably to save the cost of unnecessarily reproducing syntactic and semantic properties the complex sound change need only be represented once not every time went is used
however in contrast to compression schemes like lz78 that use deterministic rules to add parameters to the dictionary and do not arrive at linguistically plausible parameters it is possible ta perform more sophisticated searches in this representation
erations of the inner loops are usually sufficient for convergence and for the tests described in this paper after NUM iterations of the outer loop there is little change in the lexicon in terms of either compression performance or structure
this tree can have no more than o n nodes for a sentence with n characters though there are o n NUM possible true words in the input sentence thus the tree contains considerable information
to test the algorithm s ability to infer word meanings NUM NUM utterances from an unsegmented textual database of mothers speech to children were paired with representations of meaning constructed by assigning a unique symbol to each root word in the vocabulary
in order to evaluate whether the addition of such a new word is likely to reduce the description length of the input it is necessary to record during the em step the extra statistics of how many times the composed pairs occur in parses
for example kicking the bucket might be built by composing kicking the and bucket NUM of course if a word is merely the composition of its parts there is nothing interesting about it and no reason to include it in the lexicon
a way to get a scoring criterion is to attribute a recognition confidence score to each word in the best sentence hypothesis
the time needed for the task was also measured
NUM the commander produced the campaign plan
of the substructure of these classes cf
the soldier marched across the field
they may not however appear as activities
figure NUM algorithm for aspectual feature determi nation
NUM klavans and chodorow NUM cf
NUM olsen to appear in NUM
the highest ranked analysis and pattern for this example are shown in figure NUM
figure NUM ranking accuracy of classes figure NUM gives the type precision and recall of
n m in the system ranking that are ordered the same in the correct ranking
figure NUM gives the raw results for the merged entries and corpus analysis on each verb
we describe a novel technique and implemented system for constructing a subcategorization dictionary from textual corpora
figure NUM highest ranked analysis and patternset for lb
levin NUM and semantic selection preferences on argument heads
it also needs supplementing with information about diathesis alternation possibilities e.g.
the citations from which entries were derived totaled approximately 70k words
the new dependency parser creates explicit links between the elements of the sentence in figure NUM while still retaining the shallower representation similar to engcg in figure NUM
compilation of the outer domains for these rules took apt roximately NUM minutes and the resulting set occupies 40k of men ory
in t ho lal NUM er il ix argued l hal limimll ive formation ix a local NUM recess in which collcel l s such as word stress and morphological st rll l llre proposed ill l he earlier analyses NUM not play a r le
n linguistics on the other hand it is usually agreed that while computer modeling is a useful or essential tool for enforcing internal consistency completeness and empirical validity of the linguistic theory being modeled its role in formulating or evaluating linguistic theories is minimal
when the coda is not an obstruent the nucleus is short and the coda is ng we have to look at the nucleus again to decide between kje and etje this is where the overgeneralization to kje for words in ing occurs
rule NUM states that words ending in ng get etjeas liminutive alloinorl h when they are monosyllables nucleus of the penultimate syllable is empty or when they have a schwa as t multi mate rainless and kjc othe rwise
contrary to the hyi oth sis of trommelen apart from the rhyme of the last sylbfl le the m eus of the pemfltimate sylhd le is taken to e re levant s well
this can be shown by contradiction proof NUM assume that there is some sign in w to which d is not connected
the updated disconnected graph that ensnes after constructing the clog is shown in figure NUM this np is therefore rejected
the present target is to achieve such a level of performance that the corpus can be extended by hand correction of the parser output rather than hand parsing from scratch
perhaps more interesting was that these expectations appeared to constrain the subsequent discourse until they were resolved
to recapture this discarded aspect of the language it would be sufficient to introduce into the model a probabilistic penalty based on state length
category d denotes religion category f denotes popular lore category g denotes belles lettres biography and essays category h denotes miscellaneous texts category k denotes general fiction category m denotes science fiction and category n denotes adventure and western fiction
these paradigms will corre the spond to a great extent to the word classes of NUM bacon rule based grammars
assuming that we now have a corpus parsed with the state transition grammar how can this information be used to parse fresh text
exactly the same as the hand parse or differing in only relatively insignificant ways which the model could not hope to know s
the prob i ability distribution for individual words can then threw be smoothed by suitably blending in the paradig out matic distribution
this makes nlsynchtag unattractive for applications in theoretical or computational linguistics
we construct a uvg dl g generating the left projection language of gs
we also prove some interesting formal computational properties of our system
in this case the link remains pending
we then apply an asynchronous production from the semantic grammar
figure NUM derivation of 2b in a uvg dl
we give an example of an sdts and a derivation in figure NUM
finally we obtain 7r simply by removing from 7rq all nodes that have been blocked
all other elements of v are referred to as asynchronous productions
for fragment c1 the use of a discourse marker has been determined
if we assume this variant the pre spl expression for the second sentence is
in some instances an implant wears out loosens or fails
the discourse structuring module decides upon the way a discourse relation is communicated
nuances of the predication that are to be encoded in the pre spl
a user specifiable agenda that determines the ordering of module application has been implemented
the types of planning operations required remain the same in both cases
the tree transformer then applies the rules to the current pre spl expression
the output of non parallel modules become the working copy immediately
the ideal indexing terms would directly represent the concepts in a document
figures NUM and NUM show results using a training set size of NUM trees
the semantic formalism employed in the tree bank is the topic of the next section
in updating an information state the notion of a slot value assignment is used
experiments show an increase in semantic accuracy if larger corpus fragments are taken into consideration
it took approximately NUM hours to annotate these NUM NUM utterances supervision included
hence once batch size increases past a point the input distribution has too little influence on which examples are selected and hence classification accuracy decreases
null to illustrate the generation of committeemembers consider a model containing a single binomial parameter a the probability of a success with estimated value a
however the certainty that c is the correct classification is low since there is a NUM NUM chance that c is the wrong class for the example
for bigram tagging comparative evaluation of the different variants of the method showed similar large reductions in annotation cost suggesting the robustness of the committee based approach
in our system we measure d separately for each word and use the average entropy over the word sequence as a measurement of disagreement for the example
in this approach during training the learning program examines many unlabeled examples and selects for labeling annotation only those that are most informative at each stage
committee based selection thus addresses properties NUM and NUM simultaneously it acquires statistics just when uncertainty in current parameter estimates entails uncertainty regarding the appropriate classification of the example
NUM because he thought that the linear order of the words does not belong to the syntactic level of representation which comprises the structural order only
the hierarchical structure of the parse tree is preserved in the semantic frame and therefore a misparse of the input sentence leads to a mistranslation
these total no of sentences NUM no of sentences with no NUM NUM NUM NUM unknown words no of parsed sentences NUM NUM NUM NUM
tl e other is the parsing results on the same sets of data using the grammar which combines lexicalized semantic grammar rules and syntactic grammar rules
since we are interested in getting the accurate interpretation in the given context at the parsingstage we consider parses which are semantically anomalous to be misparses
we adapted the system so that the part of speech tags are used for parsing but are replaced with the original words in the final semantic frame
figure NUM shows that the prepositional phrase i i0 z with at omitted is misparsed as a part of the noun phrase expression hostile raid composition
in addition there is a subcategory headless pp which consists of a subset of noun phrases which typically occur in a locative prepositional phrase with the preposition omitted
such a goal may be achieved by incorporating syntactic rules into the ammar while retaining lexical semantic information to minim ize the ambiguity of the input text
the misparses we find in the muc ii corpus when tested on a syntactic grammar are largely due to the three factors specified in NUM
consider the following text from the november NUM issue of scientific american NUM
there are three kinds of typing errors caused by the carelessness missing
we use the term centers of an utterance to refer to those entities serving to link that utterance to other utterances in the discourse segment that contains it
NUM use a constraint satisfaction procedure to determine all the discourse trees of t
the algorithms use information that was derived from a corpus analysis of cue phrases
NUM the abstract structure of most texts is a binary tree like structure
we conjecture that one psychological reflex of this inference load is a difference in perceived coherence among discourses that express the same propositional content using different linguistic forms
the rhetorical status of each textual unit involved in the relation nucleus or satellite
they can cause a cue phrase to be identified as having a discourse usage
three independent judges graduate students in computational linguistics broke the texts into clauses
we are not aware of the existence of any other rhetorical parser for english
it is also important to note that not all noun phrases in an utterance contribute centers to cf u and not only noun phrases do so
the set of main grammatical classes and an extended set of detailed grammatical categories is the same in all languages
the structure of this paper is as follows in section NUM the stochastic tagging models are presented in detail
in section NUM statistical measurements on the corpora and a short description of the taggers performance is given
thus a long book means a long book to read because of a lexicographic association between books and reading
figure l c shows the topicalized tree anchored by have both of its arguments are substitution sites
a predicate is interpreted as if inside a scope when the predicate takes the corresponding abstract entity as an argument
the status of entities and propositions in discourse varies along at least four dimensions that are relevant to these specifications
environments can collect information about particular topics or when nested can represent the beliefs of particular agents
these disjunctive partof value sets invalidate transitivity and inheritance of meronymic relationships through generic relationships
hence the check must look for non empty overlaps of these two virtual relations
if not why was drill not also a synonym of power drdl
null the database contains only NUM noun homographs selected by the mentioned check
we say that the semantic relation antosemy is mdueedby the lexical relation antonymy
fzgure NUM a case which qualifies as a wolatmn of transitivity of antosemy
human owners own things says that onl y humans can take the subject role in ownership events
our proof of a NUM completeness however does not rely on discontinuity but only requires unordered trees
this has led some dg variants to adopt a general graph structure with multiple heads instead of trees
the input string contains an initial s and for each edge the words representing its end points e.g.
let h be an empty set initially NUM repeat until ihi iol a i
gesture also compensates for errors in speech recognition
iii none of the existing approaches provide a well understood generally applicable common meaning representation for the different modes or iv a general and formally welldefined mechanism for multimodal integration
in the majority of cases gestures will have multiple interpretations but this is rarely apparent to the user because the erroneous interpretations of gesture are screened out by the unification process
for example if the user utters m 1a1 pla toon the name of a particular type of tank platoon the natural language agent assigns this phrase the feature structure in figure NUM
for example if a given speech input can be integrated with a line gesture it can be assigned a feature structure with an underspecified location feature whose value is required to be of type line
when the user draws or gestures on the map the resulting electronic ink is passed to a gesture recognition agent which utilizes both a neural network and a set of hidden markov models
to retrieve information from this collection the user produces a detectionneed
another contributing factor is that users pen input is often sloppy figure NUM and map symbols can be confused among themselves and with route and area gestures
they include various military map symbols such as platoon mortar and fortified line editing gestures such as deletion and spatial features such as routes and areas
in the latter case they are now assigned weight equal to NUM each of these candidate strings is now ready to be assigned a context weight which would be the sum of the weights of its context words
on the other hand if there is a word boundary ambiguity between the characters xy and the character that precedes or follows them say z and these three characters can be grouped into either xy z or x yz then we say that an overlap ambiguity exists
an efficient generation algorithm for lexicalist mt
figure NUM NUM is deleted raising NUM
this allows bottom up evaluation to occur in linear time
consider the tncbs in figure NUM
figure NUM an arbitrary right branching tncb struc ture
the translation process consists of three phases NUM
if not we enter the rewrite phase
figure NUM NUM is adjoined next to NUM inside NUM
tutte alli they wo n t be analyzed in this pat er
we give an informal argument for this
with an uptake the speaker signals a turn taking at the beginning of a turn and a turn holding within a turn
simple additive weightings are also commonly used in the evaluation of chess positions by computers where for example a pawn less could score NUM and an open file for a rook NUM
push and pop mark the beginning of a sub topic or digression and the return to the previous topic respectively
omitting indices xo yo z would yield xo y and z i.e. with the subformula relevant to hypothetical reasoning z effectively excised from the initial formulae to be treated as a separate assumption leaving a first order residue
end state modifiers express the consequent state of events such as mapputatuni into two exact halves konagonani into pieces pechankoni be fiat barabarani come apart etc
notice that english translations use separate lexical items put on for a and wear for b c and different aspectual configurations the progressive for a the perfect progressive for b and another for c while all japanese sentences contain the same verbal form ki te i ru thus
although we can not know how many verbs she tested because she has shown only a subset of the verbs the program was not able to pare down the aspectual category to one in NUM cases out of NUM verbs
note that the sense i an ongoing process has high recall but low precision while iii a resultative state and iv an experiential state show the opposite
a dependent word the relator between the words and supplementary co occurrence item information which is composed of the frequency of the co occurrence relation and a portion of the actual example sentence from which the co occurrence relation was taken
finally observe that the semantics of NUM is handled not by simple application but rather by direct substitution for the variable of a lambda expression employing a special variant of substitution notated e.g.
in this paper we have proposed a method for classifying japanese verbs on the basis of surface evidence from a monolingual corpus and examined the meaning of the form teiru by means of the classifications of verbs and adverbs
step NUM NUM in the ease where the category of the verb can not be uniquely identified in step NUM i.e. other than the category NUM determine it by means of the array modified as follows ture
for example NUM NUM in column a row e shows the cross entropy of modeling by domain e and testing on domain a from the matrix we can tell that some pairs of domains have lower cross entropy than others
the word hmwnh is very frequent in our corpus and for that reason the approximated probability found for this analysis is very high NUM NUM
in this section we describe an experiment that was conducted in order to test the effectiveness of the morpho lexical probabilities for morphological disambiguation in hebrew
the pronoun one may serve as the antecedent of count nouns not of mass nouns
NUM NUM mary gave jill advice and john gave her some one too
let an object formed from one or more members of a given background set be an aggregate
still some mass nouns for virtues are susceptible of conversion namely loyalty and allegiance
no livestock in this pasture weighs more than one hundred kilograms
in other cases the choice is constrained by common knowledge
still another sort of conversion is one which was alluded to above in connection with fear
plan based approaches attempt to recognize the intentions of the entities involved in the discourse and interpret future utterances in this light
cutting it in half one can be said to obtain two rolses of four feet each
indeed minimal pairs such as the following make it abundantly clear that divisity of reference incorrect
the escape character allows letters that have a special meaning in the calculus to be used as ordinary symbols
special cases let us illustrate the meaning of the replacement operator by considering what our definition implies in a few spedal cases
we now extend the notion of simple replacement by allowing the operation to be constrained by a left and a right context
the upward oriented version corresponds to simultaneous rule application the right and left oriented versions can model rightward or leftward iterating processes such as vowel harmony and assimilation
null to make the notation less cumbersome we systematically ignore the distinction between the language a and the identity relation that maps every string of a to itself
some of the operators we use to define them are specific to xerox implementations of the finite state calculus but equivalent formulations could easily be found in other notations
a two level rule always specifies whether a context element belongs to the input lexical or the output surface context of the rule
three other versions of conditional replacement can be defined by applying one or the other or both context constraints on the lower side of the relation
our general intention is to make the conditional replacement behave exactly like unconditional replacement except that the operation does not take place unless the specified context is present
specifically fillers allow the speaker to plan the output avoid undue pauses and help to hold the turn
this process is controlled by the main module of our microplanner the text structure generator tsg which carries out the following algorithm null when the current node is an apo with more than one son apply ordering and aggregation in order to produce more concise and more coherent text
derive reasons a 6f f c g method def subset conclusion a 6g depending on the reference choices the following is a possible verbalization since a is an element of f and f is a subset of g a is an element of g by the definition of subset
we would like to thank ayal shiran and ohad zeliger for programming support of this project and ido dagan for useful discussions concerning this paper
it is worth pointing out that these techniques are required although the input information chunks are of clause size
this means that every step of derivation is translated into a separate sentence and formulae are recursively verbalized
the algorithm takes care of this problem and works as follows initially we assume that the proportions between the different analyses are equal
so far we have investigated three types of aggregation which will be addressed in the next two subsections
they could be understood as some domain specific communicative conventions and must be explored in every domain of application
the type checking mechanism of text structure allows us to achieve paraphrasing by building comparable combinations of linguistic resources
the sinking of the ship is still an ideational event but now presented from an object perspective
the elements of cdun are partially ordered to reflect relative prominence in un
we started out by saying that the objective is to develop a pure spoken dialogue system for information access tasks
the research described here is a further development of several strands of previous research
secondly this technique enforces consis tency a sensible issue in tree bank construction especially if tree banks are to be used to train probabilistic models
the basic operation concatenation is not comnmtative and does not define a group but a relaxed structure that of a monoid
efficient algorithms using dynamic programming have been proposed to pertbrm approximate matching ukkonen NUM and landau vishkin NUM
darpa has a number of information science and technology programs which are driven in large part by regular evaluations
and in fact the machine is now the one required to revise its own copy making use of every keystroke entered by the translator to steer itself in a useful direction
figure NUM contains a detailed record of a completion session that points up one further deficiency in the system it proposes punctuation hypotheses too often
it was trained on 47m words fiom the canadian hansard corpus with NUM o used to make relative fl equency i arameter estintates and NUM used to reestimate interpolation coefticients
this strikes us as the proper place of men and machines in imt and we intend to contiime exploring this promising avenue in our future research
machine translation is usually significantly inferior to human translation and for most applications where high quality results are needed it must be used in conjunction with a human translator
when ewfluating hypotheses a siufilar replacement operation is carried out and the translation probabilities of paired invariants are obtained from those of the tags to which they map
the vahte of was chosen so as to maximize e otnpletion lterforinanee over a test text see s ction NUM
the template element task was harder and the scores correspondingly lower than for named entit y ranging across most systems from NUM to NUM in recall and from NUM to NUM in precision
for instance the extraposed relative clause rc is still treated as part of the subject np
dureheilen to haste through sth is an accomplishment
a compositional account of the semantics of german prefix verbs in hpsg is outlined
each prefix p is assigned a sort p with subsorts pl pn for each potential meaning
for each prefix p the value of the subfeature prefixip points to the adequate prefix meaning
different sorts correspond to different types of verbal bases transitive dative etc
rules are fully productive if they apply to all base verbs which satisfy a common description
tions the frequency of application and acceptability of results also indicate its degree of productivity
movpies ordinal numer ing second als in attributive only at least pivotally coaxially one of each of to cover for moving in on inside movement rotation
verb entries in the system s lexicon consist of a number of zones as follows zone i lists all morphological forms of the verb in which it is expected to occur in patent texts
the second major strength is the loose definition of the two main tasks allowing a wide range of experiments
systems are measured for their performance on distinguishing relevant from nonrelevant texts via the text filtering metric which uses the classic information retrieval definitions of recall and precision
the internal lexicon of the system must include only a detailed specification of predicative words mostly verbs and some closedclass items such as prepositions and conjunctions
we transform every node from the conceptual schema tree into a subtree whose nodes are templates and whose structure is determined by stylistic and rhetorical considerations typical of text planning
in graphical terms this schema can be represented as a tree with nodes representing invention components and arcs the basic meronymic has aspart relations
the architecture of the system is illustrated in figure NUM in what follows we describe each stage of our system in turn and illustrate it with a single example of generating the claim of figure NUM
using common graphical user interface tools such as dialogue boxes menus templates slide bars etc the system guides the user through the paces of describing every essential feature of the invention
the counter of complexity is incremented during the linearization stage and a text chunk is wrapped up at the point when the counter reaches a maximum and linearization starts a new text chunk
schema nodes this step is not straigtltforward because a template can be connected to its conceptual schema through more than one case role value string so that a preference method must be suggested for these cases
the vast majority of cases are simple ones thus some systems score extremely well well enough in fact to compete overall with human performance
fifteen sites participated in the ne evaluation including two that submitted two system configurations for testing and one that submitted four for a total of NUM systems
although the rutgers group rutfual used more elaborate combining techniques they came to the same conclusion
from those candidate articles the training and test sets were selected blindly with later checks and corrections for imbalances in the relevant nonrelevant categories and in article types
thus each of the five person objects in the key and seven of the ten organization objects in the key were matched perfectly by at least one system
errors on the text slot are errors in finding the right span for the tagged string and this can be a problem for all three subcategories of tag
in general she will use the elements that are already known from the query phase or from previous utterances within the information phase as a point of attachment for presenting the unknown elements
one can observe that in utterances with two elements the speakers prefer to mention the new element first while in case of three elements speakers prefer to mention a given element first
the information presentation has a more interactive form
table NUM the given new division in utterances with
this component communicates the plan to the caller
the information is spoken in an unnatural way
the dialogue fragment in figure NUM illustrates this
unsupervised learning of transformations in supervised training the corpus is used for scoring the outcome of applying transformations in order to find the best transformation in each iteration of learning
table NUM the amount of information elements in each
this happens in NUM of the dialogues
NUM separately rather than tagging fiscal NUM s second quarter ended aug NUM as a single expression in accordance with the task guidelines
table NUM ne subcategory scores err metric in order of decreasing overall f measure p r complete and may not match the name as it is
the corpus is pre segrnented into sentences but not pre processed in any other way sense tagged or part of speech tagged
different words in the corpus have different numbers of senses and different senses have definitions of varying lengths
the large number of different words of similar meaning is the major cause of the data sparseness problem
it is well known that some words tend to co occur with some words more often than with others
one possible way is to apply the current method of disambiguation on the defining text of dictionary itself
construct s the set of conceptual expansions of all content words which are defined in ldoce in s
on the other hand the brown corpus is a collection of text with all kinds of genre
NUM negative mutual information score is taken to be NUM NUM
our system constructs a two dimensional table which records the frequency of co occurrence of each pair of defining concepts
for example we had decided to extenn the uno model of natural language to handl e temporal information because virtually all real life tasks involve handling some aspects of time
we ar e sving to have a strong renewed creative partnership wit hnamex type organization coca cola enamex m4 jpsnamex type person dooner enamex says
NUM t t np na d t t np n and the semantic part of NUM and NUM is first order encoding of NUM a and b respectively
whether pre tagging the collection with name recognition software could give even better retrieval performance is an open research question
judgment plans express beliefs about the success of the current plan and refashioning plans update it
the first rule is for judgment moves in which the speaker finds the current plan in error
the first denotes the agent that we are modeling and the latter her conversational partner
the constants in the above conditions are chosen roughly in proportion to the corpus size so that the filtered picture looks close to a clean diagonal line
this will be p56 the modifiers relative action that described the object as being in the corner
third our work is related to the research being done on modeling collaborative and joint activity
so this goal can not be adopted before the goal of expressing judgment has been planned
s attrib entityl x assessment x weird nttll figure NUM
we propose that the content of a referring expression can be accounted for by the planning paradigm
these constraints encode the knowledge of how a description can allow a hearer to identify an object
if they occurred the same number of times then every position i in v1 would correspond to one and only one position j in v2
in this paper we consider one technique which has been successfully applied to this problem backed off estimation and demonstrate how it can be extended to deal with the problem of multiple pp attachment
in other words in solving the pp attachment problem backing off is not advantageous unless the tuple that is being tested is not present in the training set it has zero counts
we do not however use the final noun following the preposition in any of our tuples thus basing our model of pp NUM on three rather than four head words
we then created a test set for pp2 which is a subset of the pp1 test set and approximately NUM of the NUM pp tuples NUM NUM
previous work has focussed on the problem of single pp attachment in configurations of the form iv np pp where both the np and the pp are assumed to be attached within the vp
this method of arriving at slcs not only generalizes for the english and kashmiri examples but also appears to apply to the case of long distance scrambling and topicalization in german
here is a small grammar which implements a kind of set valued subcategorization analysis
john sent a letter out to mary john sent mary out a letter
unfortunately it does not necessarily reduce the amount of nondeterminism during analysis
the agent thread is passed on by unifying in and out values
it corresponds instead to the disjunctive object lcb person plant rcb
pp or adverbial modification of vp is a similar case
in most grammars kleene is used for two different reasons
to begin with we will define a basic unification grammar formalism
each d edge in elementary d trees has an associated subsertion insertion constraint sic
a sic is a finite set of elementary node addresses enas
we start out with the most deeply embedded clause the adores clause
we then subsert this structure and the subject into the to adore d tree
this gives us the derivation structure shown on the right in figure NUM
these components are then inserted into d edges in NUM or above the root of NUM
the direction of complete sequence is determined by the direction of component complete links
these components are added or removed at substitution and insertion
observe that in both trees an argument has been fronted
the classification of ciaula assigns the verb to record to four classes NUM record enter put down make a record of NUM decide make up one s mind decide upon determine NUM create make and NUM investigate look into as shown in table NUM
for example the sense record enter put down make a record of of the verb to record line NUM in table NUM is described by NUM lcb somebody something rcb records lcb something somebody rcb somebody records that clause as shown the information available is mainly syntactic with the exception of the animate inanimate distinction for the arguments
verbs of cognition in the rsd are strongly characterized by a mental object mo or cognitive process co or abstraction abs in the position of direct object affected
in table i the fourth column overlap score is the ratio between verbs in a cluster column NUM that belong to the wordnet synset of column NUM and the cardinality of the cluster
we will refer to this mecl anism as link inheritance
of producing high quality text speech translation output
however they seem very relevant in the sublanguage as for example in sentences like pollutants are recorded and analyzed in surface waters temperature is recorded collected in the bay area
it would be valuable to see how far only lightweight techniques can go not just on te but on other tasks of extracting basic objects and direct relationships between them
this subset of shogun frames had a higher precision than shogun s output alone but its recall was low enough that its f score was also worse than that of shogun alone
recognizing that something is outside the vocabulary is not itself a reliable procedure e.g. perhaps detecting NUM of such segments correctly with a NUM false alarm rate
a training algorithm or module must estimate the parameters of the probability model to be learned from the example data that is from the sentences with their correct annotations
the matching processes for related frames were interdependent so that the removal of a good frame often caused other frames which pointed to it to fail to match
the nlu shell software is a collection of independent editors some tools for managing a set of factbases and a compiler for compiling the factbases into an nlu application
we hoped that by combining the two systems we could produce a system with a high enough recall and precision to yield a higher f score than either system could achieve alone
NUM a learned system may be brought up with far less effort since both manually constructed rules and learned systems assume a substantial collection of annotated textual examples
one other possibility to investigate is to use the two systems in parallel take anything that either produces or in series take only what both accept
consistency checkers cc s report inconsistencies within and between factbases and also report states of the factbases that suggest areas for further work e.g.
instead the spelling patterns are augmented with the fact that certain conditions on certain obligatory rules need to be checked on certain parts of the partitioning when it is fully instantiated
descriptions of french polish and english inflectional morphology have been developed for it and i show how various aspeers of the mechanism allow phenomena in these languages to be handled
as discussed in section NUM we expect the number of affix combinations to be limited but the lexicon is not necessarily known in advance
in particular it should be possible to specify relations among morphosyntactic and morphophonological rules and lexical entries for the convenience of developers this is done by means of feature equations
complications arise in spelling rule application from the fact that at compile time neither the lexical nor the surface form of the root nor even its length is known
however where they do occur the markers unambiguously assign the expressions to one or other of the plan elements body si en and par or goal pour afin de and de fafon it
we also found differing preferences for rhetorical relations in expressing semantic content for example while portuguese expresses generation in over NUM of cases with the relation of purpose french generation divides this relation almost equally between purpose and means
no natural language has an unambiguous mapping from semantics to surface syntax which makes the information encoded by syntax both semantic and pragmatic very difficult to consciously unpack from surface form in the performance of the translation task
i portuguese uses a very small subset of of the available syntactic resources of the language to express enablement only infinitives imperatives together over NUM of the data set and nominals 6we ignore here the singular familiar imperative
there is a strong tendency to present generation in terms of the rhetorical relation of i urpose which is marked almost invariably by para or para que so that both of which can only take a nominal or infinitival expression
which with appropriate temporal markers can appear in both iconic and non iconic order the few non iconic cases NUM are n arked with before follow ling by ed and followed by
although not shown in the figure there is only one case in french generation where the choice of ordering of the elements and the choice of marker plus expression are not mutually constraining this is when the preposition pour is followed by an infinitive
in addition portuguese showed a strong differentiation between ed specific and ing specific forms and therefore little overlap but in french overlap is much greater only one form the gerundive is constrained to one part of the semantic relation generating
while there is a strong preference for infinitive and imperative forms the influence of the part of the semantic relation only extends as in french to a single form the appearance of the infinitive as an expression of generated rather than generating
mvi the first company to announce such a move since the passage of the new international trade agreement is facing increasing demands from unionized workers
approximately NUM of the anaphors are personal pronouns including reflexives and possessives and NUM of the markables anaphors and antecedents are prope r names including aliases
for past muc evaluations the formal ru n had been conducted using the same scenario as the dry run and the task definition was released well before the dr y run
for muc NUM text filtering scores were as high as NUM recall with precision in the 80th percentile or NUM precision with recall in the 80th percentile
but the problems are certainly tractable none of the fifteen te entities in the key ten organization entities and fiv e person entities was miscategorized by all of the systems
viewed from the perspective of the te task th e walkthrough article presents a number of interesting examples of entity type confusions that can result from insufficient processing appendix a
these scores indicate that pronoun resolution techniques a s well as proper noun matching techniques are good compared to the techniques required to determine reference s involving common noun phrases
ms marsh has many years of experience in computational linguistics to offer along wit h extensive familiarity with the muc evaluations and will undoubtedly lead the work exceptionally well
most systems achieved approximatel y the same levels of performance five of the seven systems were in the NUM NUM recall range and NUM NUM precision range
NUM many of the veteran participating sites had gotten to the point in their ongoing development where the y had fast and efficient methods for updating their systems and monitoring their progress
an unknown subtree which has a zero probability in a sample may have a non zero probability in the total population
the above accuracies are the best published results on penn treebank atis word strings to the best of our knowledge
the following table also shows the corresponding sentence accuracy and bracketing accuracy of dop4 for all test sentences together
however the comparison with dop2 may not be fair as dop2 can not deal with unknown category words at all
it remains therefore important to study how to deal with unknown words and unknown category words in a statistically adequate way
as a result we may get subtrees in the parse forest that do not occur in the training set
in our experiments however we need to limit the potential unknown category words as much as possible cf
we therefore pursue an alternative approach and treat the space of subtrees as a sample of a larger population
in our experiments we will therefore limit the potential unknown category words of an input sentence to the nouns and verbs
in a logic programming setting memoization specifically the use of earley deduction avoids the nontermination problems associated with left recursion even when used with the dcg axiomatization of a left recursive grammar
lambda args apply fn args 11b define s vacuous seq np vp figure NUM contains a fragment defined in this way
the recognition process begins by passing the function corresponding to the root category the string to be recognized and a continuation to be evaluated after successful recognition that records the successful analysis
the caller continuation entries are constructed when the memoized procedure is called and the result values are entered and propagated back to callers each time the unmemoized procedure returns a new value
one way to implement this in a functional programming language is to use a continuation passing style cps of programming s it turns out that a memoized
NUM for simplicity the memo procedure presented in NUM stores the memo table as an association list in general resulting in a less than optimal implementation
the cps memo procedure in NUM achieves this by associating a table entry with each set of argument values that has two components a list of caller continuations and a list of result values
for example because of the overhead of table lookup in complex feature based grammars it might be more efficient not to memoize all categories but rather restrict memoization to particular categories such as np and s
as practical results we discuss how our method has been applied to several tasks of language modelling such as sentence boundary disambiguation part of speech tagging and automatic document abstracting
alias enthusiastic scorer mapping new york times spurious entity mapped to NUM inc
we applied the described method to several langnage modelling tasks and proved its feasibility for selecting and building the models with the complexity of tens of thousands constraints
the first model used a lexicon of words associated with one or more categories from the set abbreviation proper noun content word closed class word
2a 2b define vp alt seq v np seq v s define vp seq v alt np s
NUM NUM the cps style of programming can be used to formalize relations in a pure functional language as procedures that can be thought of as returning multiply valued results any number of times
this approach however is not computationaily feasible since the iterative scaling is computationauy expensive and to compute models for many candidate features many times is unreal
sometimes there develops a class of hidden nodes which does not provide good generalizations those hidden nodes that directly support less than two higher level nodes
partition t into subsets ti tm according to the values vl vm which occur for fi in t cases with the same value for this feature in the same subset
the approach is based on the assumption that reasoning is based on direct reuse of stored experiences rather than on the application of knowledge such as rules or decision trees abstracted from experience
if every dialogue followed this model then we would expect to see all transitions out of the introduction phase go to the assessment phase all transitions out of the assessment phase go to the diagnosis phase and all transitions out of the repair phase go to the test phase
the value for this parameter will have to be adapted for other training sets and was chosen here to maximize generalization accuracy accuracy on tagging unseen text
when the computer yielded the initiative the declarative mode dialogues users initiated the transition NUM for example the value of NUM for problem NUM in the assessment phase for subjects who operated in declarative mode in session NUM and directive mode in session NUM is obtained by subtracting the declarative mode average for the number of assessment utterances spoken per dialogue NUM from the directive mode average NUM
we have barely begun to optimise the approach a more intelligent similarity metric would also take into account the differences in similarity between different values of the same feature
for each test pattern its distance to all examples in memory is computed and the category of the least distant instance s is used as the predicted category for the test pattern
the paper discusses our model of initiative and presents quantitative results from an analysis of our corpus on the effect of the computer s level of initiative on aspects of human computer dialogue structure such as NUM utterance classification into subdialogues NUM frequency of user initiated subdialogue transitions NUM regularity of subdialogue transitions NUM frequency of linguistic control shifts and NUM frequency of user initiated error corrections
in some cases short context sizes corresponding to bigrams e.g. are sufficient to disambiguate a focus word in other cases more context is needed
to explain the classification behavior of the system a path in the igtree with associated defaults can be provided as an explanation as well as nearest neighbors from which the decision was extrapolated
some linguistic constraints can not be effectively resolved during parsing at the location in which they are most naturally introduced
as pub lernrna tar z require systematic variable renaming i.e. copying in order to avoid spurious variable binding
if the clause contains a memo literal g then the control rule returns tablei g
add adjuncts i y adv add adjuncts i y
NUM memoization can be selectively applied earley deduction memoizes every computational step
definition NUM a generalized goal is a multiset of relational atoms and constraints
delay add adjuncts x y vat x vat y
new supervised transformations are then learned by comparing the tagged corpus that results from applying these transformations with the correct tagging as indicated in the manually annotated training corpus
experiment ts1 uses NUM hand annotated dialogues with NUM speech acts as training corpus and NUM dialogues with NUM speech acts as test data
these senses were presumably easily understood by the taggers and increased any reluctance to examine the remaining options
the same object can be veridically referred to by a multitude of different terms
this process probably involves a syllabary a store of highfrequent syllabic gestures NUM
from this hazard rate expected retrieval times can be computed for various experimental conditions
in addition the speaker monitors the output and if necessary selfcorrects
during phonological encoding the segmental and metrical features of the word s phonological code are spelled out
the metrical structures of adjacent words may get combined to compute larger size metrical units so called phonological words
its output is a lexical concept i.e. a concept for which there is a word in the speaker s mental lexicon in the computational model lexical concepts figure in a semantic spreadingactivation network
such a task is achieved by applying a x2 1ike statistic
another feature distinguishing mandarin from other languages is topic prominence
e13 the elephant has a very long nose
such free or paraphrased translations are beyond the reach of the proposed method
e20 he abdicated all responsibility for the care of the child
other grammatical functions are either non existent or expressed through an additional function word
unfortunately they have the disadvantage of erroneous overgeneralization from word specific connections
e22 i shall love you as long as i breathe
sue j ker and jason s chang word alignment table NUM
the lloce contains NUM NUM entries and cilin contains NUM NUM entries
a word sequence w is input to the part of speech tagger and a part of null speech sequence p is generated
consider the example attorneys for the mayor said that an amicable property settlement has been agreed upon
to solve this problem a more general definition which considers more parts of speech in contingency table is needed
in their study susanne corpus is used as a trainmg corpus for their chunker
this paper proposes a probabilistic chunker to help the development of a partially bracketed corpus
thus a tag mapper is introduced in the experimental framework shown as figure NUM
the corresponding syntactic structure t is regarded as an evaluation criterion for the probabilistic chunker
only i NUM and NUM are considered
for example an order NUM n gram ie the NUM gram requires more than NUM times as many con null texts and NUM times as many parameters as the order NUM nonmonotonic extension model yet already performs worse on the brown corpus by NUM NUM bits char
rather than using a treebank as our training corpus lob corpus which is tagged with part of speech information only is used
therefore the first step in automatic tag mapping is to collect words from susanne corpus for each susanne tag
modularity thus suffers since it is difficult to assemble a system from components developed separately
concerning translation it is necessary to eldentify the speech acts of the current utterance
it is reasonable to assume similar ratios for german although no precise numbers are currently available
that does not render such an approach unfeasible though as we show in this paper
the most common example is an error in word order produced when the system is forced to back up to the robust translation method
generally speaking nothing ensures correct pronunciation better than a direct hit in a pronunciation dictionary
one of the lextools the program arclist is particularly well suited for name analysis
this transition is defined by a family of arcs which represents common inflectional and derivational suffixes
duced as street name markers making them more likely to be used during name decomposition
for the sake of simplicity we assign a flat cost of NUM NUM in our toy example
a transcription was considered correct when no segmental errors or erroneous syllabic stress assignments were detected
thus the net improvement by the name specific system over the old one is NUM NUM
useful substrings were selected using frequency analysis automatically and native speaker intuition manually
the recall and precision scores together define a two element vector which we will call the comprehensibility of version NUM with respect to version i
quantifiers are then generated based on the cosen focus set
however it raises an obvious question how important in objective terms are the distinctions drawn by the finegrained scale
six uses three related to translation and three to speech processing will be mentioned here
for example in the utterance show flights from boston to atlanta the principal object would be flights
a lexicon of word forms and a n gram language model constitute the linguistic knowledge of this component
similarly speech synthesis researchers can try to provide more natural prosody by exploiting speech act information
figure NUM sample subtree for one class
the judging tool allows a mode in which the text version is displayed instead of an audio file being played
figure NUM effects of reshuffling for tagging
this paper describes the itsvox speech to speech translation prototype currently under development at latl in collaboration with idiap
since a logical form encodes only information that is directly expressed in the utterance the ci agent often must apply contextual information to produce a complete interpretation
the recognizer listens on the audio port of the computer on which it is running and produces its best hypothesis as to what string of words was spoken
the icl communications mechanism is built on top of tcp ip so an oaa based system can be distributed across both local and wide area networks based on internet technology
even worse we need other rules to permit up to eight digits to form a set of coordinate numbers which would give rise to NUM s rules
the ci agent attempts to fill in this missing information by using a combination of linguistic and situational context plus defaults
commandtalk is installed at a number of government and contractor sites including nrad and the marine corps air ground combat center
commandtalk combines a number of separate components developed independently some of which are implemented in c and others in prolog
on the syntactic side this is done using sisteradjunction
while users employ generic verbs like move attack and assault to give verbal commands the corresponding modsaf tasks often differ depending on the units involved
second because the semantic rules are tailored to the application the logical forms they generate require less subsequent processing to produce commands to the application system
also all of the input semantics has been consumed
figure NUM skeletal structure and final structure
this aspect is similar to syntax driven generation NUM
with respect to the morphological level there is a full form dictionary stored in the relational database format currently some NUM NUM
or go to the next paragraph containing a short definition of the labels reprinted from the above me nfionad referancal
as the pseudo html codes are ignored by the browser the rest of the pds is displayed in a neutral way
we can conclude that the application presented above shows the feasibility to integrate electronic medical record emr systems with nlp applications
this semantics can be expressed in a
in another context e.g. test procedure another co occurrence pattern will apply and select the h txproc reading
it focused on the use of static sgml or html code NUM for displaying the results of nlp based checklist screening of clinical documents
as can be seen in figure NUM and thus also in figure NUM the ambiguity for the word procedure in sentence NUM is resolved
mapping rules are a mixed syntactic semantic representation
for the passen example at hand the number of matching predicates in the two competing transfer rules defines the degree of specificity
NUM each completion corresponds to the end of a nonterminal expansion started by a matching prediction step
translation equivalences relating semantic entities of the source and target grammars can be fi mmlated in a grmnmar independent bilingual semantic lexicon
we close this section with a support verb example NUM showing the treatment of head switching in our approa ch
obviously it lua lil y fl he onli e
in our approach grammars can be developed for each language independently of the transfer task and can therefore be reused in other applications
tau0p is an operator indicating the intended application direction one of
figure NUM yi he i ictionall y lookup van i alf hcdendaags frans
hess of he first l wo sorts of informal ion is evident
phc selected word and its lexeme form also the input for the corpora search module
the current implementation of this sear h uses only string matching to find farther tokens
que l on tire au billet ceux que l on doit tire
incidentally a similar interpretation of forward probabilities is required for hmms with non emitting states
in the parsing phase a noun phrase is assigned the structure that has the maximum conditional probability given the noun phrase
consider the following grammar g for the language anbnc n
figure NUM illustrates this proof logical behavior
we consider each direction in turn
parsing for semidirectional lambek grammar is np complete
did bko NUM cs a3 NUM
figure NUM extraction as resource conscious hypothetical reasoning
moreover some languages which are not context free can also be generated
the word we are interested in is v wl w2 w3m
it is pushed onto the trace stack whenever this verb is accessed
the standard unit of spoken language in a dialogue is the turn
table NUM classification results for verb trace
where the parser does not consider the correct trace position at all
these hypotheses were then rated valid or invalid by the grammar writer
null the NUM dialogues described above were labeled according to this scheme
b ich dachte dab er gestern den wagen reparierte
however when we combine the two kinds of phrases the effect is a greater improvement in recall rather than precision
this is borne out by the results
prompt the hearer has the initiative
contact the first author for details
NUM NUM the nature of the spoken dialogue
we have tested it with the n best sentence hypothesis but lattice and word graph could be investigated further
note the following phenomena in these dialogues
command the speaker has the initiative
none were majoring in electrical engineering
it attempts to repair recognition errors and transmits a semantic representation of the sentence to the dialogue module
determine if the led display is correct
however the results from early experiments are encouraging
this allows text markup to proceed very quickly
the alembic workbench interface has been written in tci tk
the annotator was very familiar with the tagging task
figure NUM illustrates this bootstrapping cycle
figure NUM tagging productivity gains with the
incremental application of automatically acquired rule sets
a visualization component for viewing inter annotator or key answer agreement
figure NUM averaged f measure performance figures
with such a record of the discourse agents can refer to alternative interpretations or to the repair process itself potentially enabling them to recover from rejected repairs
this theory includes strategies for expressing beliefs and intentions for displaying understanding and for identifying when understanding has broken down
it looks like it will have to be thursday then
for example the subtree projection for verbs in the english grammar is as follows where lex is a variable which will be instantiated to the actual verb found in the input
NUM NUM solution part ii name binding of forward references
the left attachment site a of smust match a node n accessible in d the root node r of s must be licensed by the grammar in the position occupied by n
this paper describes the author s implementation of a parser aimed at reproducing in a computationally explicit system the constraints of a particular psycholinguistic model gorrell in press
consider the following sentence fragment for example NUM i know np1 the man who believes np2 the countess
each specific guideline is subsumed by a generic guideline
also the satisfying symbol table entry is deleted
we also need to ensure that to avoid crossing branches the lowered node does not dominate any unsaturated attachment sites or dangling nodes we therefore define accessibility for tree lowering as follows
table NUM the corpus used for the experiments
NUM NUM NUM c deb offer give phone no
however a different consideration is pertinent here
ld nob is ld s annotation of that sub corpus
the word based hmm recognizers for english and cantonese use identical features nine mfccs and nine delta mfccs
the first component of the model b represents the beliefs and goals that the participants have expressed during their conversation
symbol table manner than normal projection schema
in addition acts of interpretation and generation update the set of beliefs and goals assumed to be expressed during the discourse
lexical entries of head nouns and verbs in
pps may freely permute among themselves
the evidence from wordnet is then weighted according to how likely it is that the sense for which th e evidence is obtained is the correct sense of the word seen in the input file
for instance rules were learned which involved days of the week but due to sparsity of training data they were learned only for a subset of the seven days of the week
assuming that a semantic feature such as maleness generally will propagate from a parent in the hierarchy to its children one can test the gender of a give n noun by examining its ancestors
for instance mcdonald s when it refers to the fast food chain should be treated as a single token while mary s should be separated into two tokens mary and s
only instances of sentence final punctuation whic h are immediately followed by white space or symbols which may legitimately follow sentence boundaries such a s quotation marks were considered to be potential sentence boundaries
second because of the large number of files and the number of processe s reading each of them file access time accounted for a significant portion of the time required to run the system
thirteen parts of speech are differentiated noun proper noun pronoun verb verb particle adverb adjective preposition complementizer determiner conjunction interjection and noun verb contraction
several verbs function similarly to become and remain but subcategorize for a prepositional phrase headed by as with the object of this prepositional phrase being coreferent with the subject of the verb
this step was necessary because the part of speech taggers and the noun phrase detector required genitive markers t o be tokenized separately while non genitive instances of s or were required to remain attached
fig l shows the word segmentation candidate space for the sentence NUM
the system is designed to provide nlp capabilities to support man y applications in multiple domains
chinese word segmentation and pos tagging are two key techniques in many applications in chinese information processing
this example shows the decomposition of words into their constituents morphs in such a way as to undo the mutations caused by suffixes
english contains a separate preprocessing section for numbers NUM in twenty four acronyms ibm fbi or abbreviations pr
in ongoing work we are investigating extension mixture models as well as improved model selection algorithms
french uses the concept of a class that allows for the grouping of strings having a common property thus reducing the number of rules
concepts with less than two remaining textref sequences are discarded
very little of this work is muc specific so the amoun t of reuse is high
the aim of cseg tag is to be able to process unrestricted running texts
as soon as the original sentence a is entered translation equivalent selection and translation region selection is presented b
there will still be cases where the full power of the richer formalisms is necessary
b some student will investigate two dialects of and collect all interesting examples of coordination in every language
theory of ccg will be described in the next section to show how to derive scoped logical forms for available readings only
we will use s every l man x and its reduced equivalent s every man interchangeably
for instance of has two arguments three comp and two rep and show has three arguments
but first we must consider some apparent counterexamples to the generalization NUM a three hunters shot at five tigers
suppose that the non standard constituent for one of the conjuncts in NUM a has a semantic representation shown below
NUM c has only four available readings where most boys does not intercalate every man and two women
figure NUM illustrates how directly the ccg operations can be encoded NUM o is the type of a meta level proposition and so the intended usage of apply is to take three arguments of type tm where the first should be an object level abstraction and set the third equal to the application of the first to the second
the basic idea of the filtering process is to calculate the probabilities of all possible chains of tagged words by using a trigram of the markov model
a statistical approach to thai morphological analyzer is a part of wpa writing production assistant project supported by the national research council of thailand
one such was to see whether a promising token a capitalized unaccounted for one for example was an element of any of the instances of the three types of data classes that had previously been identified or was a n acronym thereof
this for x is ol ten equated to tim usage fr qu mcy of the word
s gm ntation errors ass ciat d with multi cha ract r
translation equivalent alternatives for the cursor position word focus word are displayed in an alternatives window appearing nearby that word
there are also plans to build an automatic bitext locating spider for the world wide web so that simr can be applied to more new language pairs and bitext genres
it s shorter to bath from avon
8a NUM bone a y ound 8b exists x bone x it forall xl clan xl found xl x
a good place to start is at the beginning since the origin of the bitext space is always a tpc the first search rectangle is anchored at the origin
the lexicon has been updated over six generations after being applied to word segment NUM NUM million characters
for each turn the task and dialogue bpa s for each observed cue are used along with the current initiative indices to determine the new initiative indices step NUM
if it can then one examines if it can be associated with an infix
even il that metals hanging int a shorter w tr
this is l he case because unlike texts in english chinese texl s have no word nlarkers
if it can then one examines if cd can be an infix associated with ab
null about NUM NUM characters are being used in modern chinese and they are the building blocks of all wor ls
of course spud also represents its private knowledge about the domain
so if we want to find chains that are roughly parallel to the main diagonal we should look for chains whose points all have roughly the same displacement NUM from the main diagonal
so gsa s running time is o kn where n is the number of input sentences and k is a small constant proportional to the size of the largest re aligned block
figure NUM discourse model for the example
subgoals of distinguishing these entities are introduced
a set of points of correspondence leads to alignment more directly than a translation model or a translation lexicon because points of correspondence are a relation between token instances not between token types
so i am eager to see whether the geometric approach will compare as favorably to wu s results on english and chinese as it has to simard et al s results on english and french
for instance if the input contains g e h e and h f then gsa adds the pairing g f
in contrast to a correspondence relation an alignment is a segmentation of the two texts such that the nth segment of one text is the translation of the nth segment of the other
a unique bitext map can then be interpolated by using the lower left and upper right corners of the mer map m2 instead of using the non monotonic correspondence points function m1
the original sentence a contains a relative clause with verb kau buy with an antecedent hen book
with the back off method the probabilities of complex conditioning events are approximated by a linear interpolation of the probabilities of more general events
mthough we decided not to add nunitp as a separate np complement we have let the nunitp tags for verb complements remain to reflect the information that in our corpus this type t these art
sentence boundaries are identified using a maximum entropy model developed explicitly for muc NUM
aside fl om presenting these interesting and llilexpected phenomena tagging has tightened ut the classification of some coml lements leading in the direction of combining some complements ghat had been separate and re grouping others
a large corpus about NUM mb of text was selected and examples of NUM fl equently occurring verbs were tagged with their compleinent lass as defined by a large computational syntactic dictionary comlex syntax
these tags which becmne part of the dictionary entry contain tile location of the example and the name of the w rbal complement identified at that location see figure NUM for a sample 1e g
on the other hand the increase type verbs can appear with a whole range of nunitp comt lements cornpleinents which contain an nunitp 1at the price increased 5z to NUM a share
during ore tagging we tound that her are a group of noun phrases that t attern with adverbs and prepositional phrases which we have called nadvi s noun adverbial phrases
pp pval to NUM even if that s all the promise he over gave np arthur williams had to be located they agreed
anote that while ibr some transitive verbs are defined only as verbs which take np complements we consider verbs transitive that take any type of complement including pps NUM he agreed
if be ed is chosen the auxiliary is interpreted as a passive morpheme and treated as such in translation
the most striking fact about this graph is the tremen dous efficiency of the extension model
wanted the woman with this knife the woman wanted to kill the candidate with this knife
dr c4 train NUM leaves torino porto at NUM a m
dc ac dildt please speak after the tone
b i hello this is train enquiry service
9this tagging can be hand generated or system generated and hand corrected
a domain specific task based success measure in the performance model for n
NUM c i am familiar with that circuit
although functional words remain unchanged in the intermediate representation some words provide an alternatives window when the cursor is placed on them
this problem is easily solved by normalizing each factor x to
during the first phase the focus is on almotating correct structures and a coarse grained classification of grammatical functions which represent the following areas of information 2cp stands for conwlementizer oa for accusative object and rc for relative clause
in english structural case is assigned to the subject position if the subject is a sibling of the projection of a finite inflectional node
category syntactic flmction focus the gramrn ticmity of different word permuta null tions does not fit the tr ditional binary rightwrong pattern it rather tbrms a gradual transition between the two poles
parsers for gpsg are particularly interesting because they use a formalism that expresses many grammatical generalizations in paola merlo modularity and information content classes a uniform format
the content available to a pictalk user is very limited for two reasons
NUM NUM using lexical semantics for response representation
NUM NUM test item types and response sets
using lexical semantic techniques to classify free responses
NUM NUM concept grammar rules for the police item
metonyms follow the concepts in a list
NUM NUM additional results using an augmented lexicon
thus the expression abc may denote depending on the context i the string abc ii the language consisting of the string abc and iii the identity relation on that language
in the rightoriented version the left context is checked on the lower side of replacement the first three versions roughly correspond to the three alternative interpretations of phonological rewrite rules discussed in kaplan and kay NUM
for instance in example e24 c24 only one word i is translated literally into
we use four alternate separators i and which gives rise to four types of conditional replacement expressions null we define upper lower l left right and the other versions of conditional replacement in terms of expressions that are already in our regular expression language including the unconditional version just defined
we need six intermediate relations to be defined shortly NUM insertbrackets NUM constrainbrackets NUM leftcontext NUM rightcontext NUM replace NUM removebrackets relations NUM NUM and NUM involve the unconditional replacement operator defined in the previous section
NUM a b x a b a the first two occurrences of ab remain unchanged because neither one has the proper right context on the lower side to be replaced by x
the strategy is first to decompose the complex relation into a set of relatively simple components define the components independently of one another and then define the whole operation as a composition of these auxiliary relations
figure3 a b x o b c x this composite relation produces the same output as the previous one except for strings like abc where it unambiguously makes only the first replacement giving xc as the output
for example maps abc to both ax and xc a b c a b c a x x c the corresponding transducer paths in figure NUM are NUM NUM NUM NUM and NUM NUM NUM NUM where the last NUM NUM transition is over a c arc
the second set of experiments measure the performance improvement obtained by using ebl within the xtag system on the atis corpus
the generalized parse of a sentence is stored indexed on the part of speech pos sequence of the training sentence
an index using the morphological features of the words in the input sentence is computed
however the coverage of this system is limited by the coverage of the ebl lookup
this fst representation is possible due to the lexicalized nature of the elementary trees
in this paper we present some novel applications of explanation based learning ebl technique to parsing lexicalized tree adjoining grammars
the output of the ebl lookup is a sequence of elementary trees annotated with dependency links an almost parse
grammars some aspects of our approach can be extended to other lexicalized grammars in particular to categorial grammars e.g.
the ebl component database under an index computed from the morphological features of the sentence
this is because be rarely conveys much information
the abbreviation test corpus probabilities will be used for this term
similar situations occur in many other ambiguous words in hebrew
a method to acquire morpho lexical probabilities from an untagged corpus has been described
in this case a single analysis can be selected as the right analysis
in hebrew these words are almost always morphologically ambiguous
nominal personal pronoun the other nominal personal pronouns of the same person
the first three words in table NUM belong to this category
sw3 lcb at NUM NUM hat nnn NUM rcb
NUM a hash table that stores all the words in the corpus
this corpus consists of NUM million word tokens taken from the daily newspaper ha aretz
not all of the resulting languages are stringset distinct and some are proper subsets of other languages
this problem was found to occur very rarely for only NUM of the ambiguous words in our test texts the counters found in the corpus were smaller than NUM
to see that consider the word mwnh 1r2 test group2 one analysis of which is the noun mwnh a counter
this paper discusses the design of the eurowordnet database in which semantic databases like wordnetl NUM for several languages are combined via a so called inter lingual index
in case we wish to move to some other domain in hebrew we should be able to use the same set of rules but with a suitable training corpus
the system will respect these boundaries and never try to group goals from different sets
here we apply these ideas and demonstrate that the mixture which can be computed as almost as easily as a single pst performs better than the most likely maximum aposteriori map pst
wildcards allow us to model conditional dependencies of general form p ztlxt il zt i2 zt i in which the indices il i2 il are not necessarily consecutive
ln l s ts wn s c wl wn NUM is d l s i i s otherwise NUM
NUM association for computational linguistics computational linguistics volume NUM number NUM right analysis while all the other k NUM analyses of w are wrong analyses
a similar word for this analysis is the following one rath n t NUM feminine singular third person past tense
as noted earlier the relevance of flexible cgs to incremental processing relates to their ability to assign highly left branching analyses to sentences so that many initial substrings are treated as interpretable constituents
during the definition of the task and when final answer keys have been made key to key scoring is used to compare not only the slot fill data itself between two versions o f an answer key usually versions produced by different annotators but also some slot attributes that may be entere d only in an answer key alternate slot fills optional objects and slot fills and minimal strings
this gives him a subtree frequency list for each corpus and he is then able to investigate which subtree are markedly different in frequency between corpora
on the null hypothesis the expected value for the o e NUM e term would be NUM NUM s and would not vary with word frequency
when cs is too small even when ur is NUM NUM the cluster itself lacks in information
therefore ideal clustering is to decompose graphs into subgraphs which can not be decomposed further by duplication
tion the most basic mathematical laws discussed about relations between elements in a set are reflective symmetric and transitive laws
then cluster NUM and NUM are merged into cluster g when the threshold is lowered again to NUM NUM
however nurse and professor do not co occur so the transitivity between nurse doctor and professor does not hold
this paper discusses a clustering method of the co occurrence graph the decomposition of the graph from a graph theoretical viewpoint
on the other hand when the transitivity holds among three nodes figure NUM NUM the
to sum up when the transitivity does not hold a graph can be decomposed by duplicating the ambiguous node
we do not follow the trend from the sense that our objective is the extraction of clusters of topics
up NUM NUM hence the ambiguity was removed from clusters up to NUM on average
no formal check could prevent the lexicographer to select the wrong link type unless the synecdoche checker would lie in wait and catch this special case because of name equality
profit provides a user interface which accepts queries containing profit terms and translates them into prolog queries converts the solutions to the prolog query back into profit terms before printing them out prints out debugging information as profit terms
applying the rule on the wordnet data has resulted in NUM noun concept and NUM verb concepts fulfilling neither strong equality nor weak set inclusion commutativity
checks may be conceived which are based on an understanding of these definitions whether the reader of the glosses is a human reader or a program based on linguistics
with respect to this check the troponymy hierarchy of wordnet s verb concepts has NUM top verbs however it is surprising to find that NUM of them are isolated
in any case formal checking is only a kind of syntax checking the next step after spelling checking but NUM new pennies will make up an euro
since the output of profit compilation are prolog programs all the techniques developed for the optimisation of logic programs partial evaluation tabulation indexing program transformation techniques etc can be applied straightforwardly to improve the performance of sorted feature grammars
agr NUM NUM x x d e NUM when two such prolog terms are unified the union of their excluded elements is computed by unificatation or conversely the intersection of the elements which are in the domain description
this kind of encoding is only applicable to domains which have no coreferences reaching into them in the example only the agreement features as a whole can be coreferent with other agreement features but not the values of person or number in isolation
judges are allowed to start by writing down as much as they can of the utterance so as to keep it clear ir their memory as they fill in the form
for example the domain description NUM or pl excludes l sg and NUM sg so that the the first and second argument are unified l sg as well as the third and fourth NUM sg
the sort from which to start the feature search must either be specified explicitly NUM or implicitly given through the sortal restriction of a feature value in which case the sort can be omitted and the expression NUM can be used
finally it should be mentioned that mbclassifiers despite their description as table lookup algorithms here can be implemented to work fast using e.g.
the definition of the 2nd senses indicates an a ctionnoun countnoun sense shifts from issue n NUM to issue n NUM through a deictic reference of this
c is a probability function if NUM ih2 h c is a probability function
note that o a c w NUM for all contexts w in the dictionary d
the first goal was to identify from the component technologies being developed for information extraction functions which would be of practical use would be largely domain independent and could in the near ter m be performed automatically with high accuracy
these time slots are blocked until further notice
as the specification finally developed the template element for organizations had six slots for the maxima l organization name any aliases the type a descriptive noun phrase the locale most specific location an d country
beyond these individual problems it was felt that the menu was simply too ambitious and that w e would do better by concentrating on one element of the semeval triad for muc NUM at a meeting held in personal reminder
an available filtering mechanism is valuable but does not replace a find function
intrinsic use of for example left of and right of is assumed to be rare in the current domain none of the now more than NUM users that have interacted with edward used it
when the forms for two versions of an utterance have been filled in and compared the results can be examined for comprehensibility in terms of the standard notions of precision and recall
as a consequence we have a domain independent low level template extraction application in te
for muc NUM some high performing groups invested a small number of person years
this constitutes a qualitative change in the construction and use of language processing software
application generator ag compiles the different factbases into an nlu application
useful dissimilarities in the detailed performance of the two systems proved hard to identify
this paper briefly described a diverse collection of research activities at bbn on information extraction
in retrospect lightweight processes carried most of the burden in our te system
plum had the better precision score it extracted less spurious and incorrect information
we extracted these domain specific kanji characters by x NUM method
we therefore selected wall street journal texts from NUM NUM and NUM which had already been distributed as part of the acl dci cd rom and which were available at nominal cost from the linguistic data consortium
if the thesaurus is constructed well high score is achieved
we describe the development of the message understanding task over the course of the prior mucs som e of the motivations for the new format and the steps which led up to the formal evaluation
usilig the feature vector of doniain specific kanji characters as follows
domain specific kanji characters NUM NUM text representation t y kanji characters
the procedure for combining the specialties is as follows NUM
a method for classifying news stories is significant for distributing and retrieving articles in electronic newspaper
biographical information about a person often falls into this category e.g.
the latter will be explored through automatic acquisition of person name context over a large corpus
this is not surprising in view of our ranked selection system
likewise trigram based mixed order models would be useful complements to NUM gram and NUM gram models which are not uncommon in large vocabulary language modeling
NUM NUM x NUM characters and NUM NUM x NUM kanji characters
this is an extension of traditional coreference but a task we do in many applications
we looked at our system s perfcxlnance in the muc6 template element evaluation in three areas
the family of algorithms we use is introduced in section NUM and the extensions to the basic algorithms along with their experimental evaluations is presented in section NUM in section NUM we present our final experimental results and compare them to previous works in the literature
the goal is to implement a system which detects automatically pns in a given text allowing the construction of an electronic lexicon of pns
however in korean typographical constraint is not a reliable criterion since we can not prohibit writing this phrase in other ways like
in these runs changes in the percentages of absolute default or unset p settings in the population show a marked difference the mean number of absolute principles declined by NUM NUM and unset parameters by NUM NUM so the number of default parameters rose by NUM NUM on average between the beginning and end of the NUM runs
the labels of possible chain links reflect the theoretical distinctions between a movement and a movement and the fact that links of a chain can be either the first element of the chain the head h or an intermediate element i in the case of chains formed by several links or the last element the foot f
to be convenient the sentence boundary markers lcb l
the vocabulary contains NUM NUM NUM and NUM words correspondingly
the abrupt drop in the number of word tokens at the ffirst re estimation step indicates that the inflated inrial estimates of estimation stage word frequencies are adjusted to more reasonable values
section NUM discusses extensions to the basic model to treat linguistic phenomena such as idiomatic expressions
we may expect that these users have basic knowledge and ability to read and understand english
any translation method can be used as long as it is compatible with this general scheme
the system looks for a minimal node marked as above then pauses for user operation
for these users online dictionaries have been used because of the reliability of the result
when the user triggers translation end the result is sent to the original editor window
the main function is divided into two modules the interface module and the translation module
a cd rom online dictionary accessing function is also provided to help user s translation equivalent selection
also we expanded the dictionary and morphological analyzer to allow such multi word translation unit correspondence
at the same time the alternatives window for denwa changes and shows literal translations for denwa
surface request r m informref m r whoisgoing figure NUM
how russ decides after hearing mother s response t3 that his earlier decision was incorrect
because the default has a very weak priority it can be overridden by user input without influencing other defaults
moreover once active a supposition will remain active in all succeeding turn sequences unless it is explicitly refuted
expectation in the speech context refers to what the next word or utterance is likely to be about
grounding acts include initiating clarifying or acknowledging an utterance and taking and releasing a turns
more to the point we need to know when the expressing of two simple suppositions is or is not consistent
nodes that are siblings i.e. that have the same parent correspond to different interpretations of the same turn
the currently active interpretation is defined by its most recent turn which we shall call the focus of the sequence
figure NUM representations for the source of ellipsis paradox
our approach falls into this class
figure NUM representations for simple case
figure NUM example of parallelism establishment
the phenomenon of parallelism pervades discourse
foreign laborer is identified as one word in some places while in others it is divided into two words l m foreigner and laborer
artificial intelligence center sri international NUM ravenswood avenue
it seems the virtue of re estimation lies in its ability to adjust word frequencies and removing unreliable word hypotheses that are added by heuristic word identification
note that r as defined above maximises tile use of tile qlf contextual resolution component quantifier meta variables allow for resolution to logical quantifiers diflbxent fl om surface form e.g. to cover generic readings of indefinites as do predicate variables in e.g.
we define a restricted version t of which switches off the qlf contextual resolution component w maps logical quantifiers to their surface form and semantic forms to qlf formulas or re null o NUM zvl
tags i i are used to repre sent reentrancies and often appear vacuously q he side condition in the second and third clause ensure s that only identical substructures can have identi null cal tags
consider e.g. the f st ructure qo associated with the the control construction most representatives persuaded a manager to support every subsidiary where the object of the matix clause is token identical with tile controlled subject of the embedded clause
let m be g1 with weights NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM and let q be the probability distribution determined by mq then the computation of the kl divergence is as in distribution ql is a better distribution than q in the sense that ql is more similar less dissimilar to the empirical distribution than q is
ly ly a y x min NUM r x g x y in which 7r is the distribution we wish to sample from q in our notation and g x y is the proposal probability the probability that the input sampler will propose y if the previous configuration was x
the precise fln m of these mappings depends on whether the q1 fs and f structures to be mapi ed contain comparable levels of syntactic information and in the case of qlf how this inforination is distributed between term and form categories and the recursive structure of the qlf
our proposal probability is not symmetric but rather independent of the previous configuration and though our acceptance function reduces to a form NUM that is similar to the original metropolis acceptance function it is not the same in general b y b x NUM q y q x
the techniques described in this paper are not restricted to a specific implementation but could be added to many current feature based grammar development systems
the oui comes with a clean backend interface and has already been used as frontend for other natural language applications e.g. in verb mobil
a subcategorization principle applying to phrases for example should be delayed until the valence requirements of the mother or the daughters are known
this evaluation strategy has two advantages it reduces the number of choice points to a minimum and it leads to early failure detection
algorithm a common approach to word segmentation is to use a variation of the maximum matching algorithm frequently referred to as the greedy algorithm
the rule sequence was able to transform this into humana ctivity but was not able to produce the desired human activity
due to space limitations the lexical semantics cued by these affixes can only be loosely specified
this discipline is enforced in part by requiring that the features be axiomatized in a denotational logic
by searching a sufficiently large corpus we should be able to identify a number of telic verbs
for example the verbal prefix un applies to telic verbs and produces telic derived forms
a third approach does not require any semantic information related to perceptual input or the input utterance
automated methods for acquiring lexical semantics could increase both the robustness and the portability of such systems
it concludes by evaluating morphological cues with respect to a list of desiderata for good surface cues
finding the right generalizations and stating them explicitly can be time consuming but is only performed once
a set of NUM affixes were analyzed by hand providing the fixed correspondences between cue and semantics
though context sensitive linguistic phenomena seem to be more naturally expressed in tag formalism from a computational point of view many authors think that ligs play a central role and therefore the understanding of ligs and lig parsing is of importance
moreover the cardinality of v is o n NUM and for any given non terminal say a q there are at most o n a g productions
since derivations in ligs are constrained cf derivations we can think of a scheme where the cf derivations for a given input are expressed by a shared forest from which individual parse trees which do not satisfied the lig constraints are erased
we remark that the stacks of symbols in l constrain the string w to be equal to w and therefore the language ps l is lcb wcw i w NUM lcb a b c rcb
the stack schema a of a primary constituent matches all the stacks whose prefix bottom part is left unspecified and whose suffix top part is a the stack of a secondary constituent is always empty
unhappily this view is too simplistic since the erasing of individual trees whose parts can be shared with other valid trees can only be performed after some unfolding unsharing that can produced a forest whose size is exponential or even unbounded
the second solution is also uncomfortable since it necessitates the reevaluation on each tree of the lig conditions and doing so we move away from the usual idea that individual parse trees can be extracted by a simple walk through a structure
in the sequel we will only consider a restricted 2if x al as the states can be the integers NUM n NUM is the initial state n the unique final state and the transition function NUM is s t
for a lig and an input string all valid parse trees are actually coded into the cf shared parse forest used by this recognizer but on some parse trees of this forest the checking of the lig constraints can possibly failed
the first form is production NUM a c b c a NUM b where three different non terminals in vn are implied i.e.
be used tbr wt rit us dotnains withottt showing sever h gra dation in trans a l ion
jnification of lina ry featl res however is much simphu unification of a t ea ture
the rules produced can be inspected which is useful for gaining insight into the nature of the rule sequence and for manual improvement and debugging of the sequence
the source a nd target rules that is the cfg rules with no constraints are called the cfg skdeton of the patterns
any unifications that succeed are performed
we applied the maximum matching algorithm to the test set using a list of NUM chinese words from the nmsu chseg segmenter described in the next section
slot values and s and t corefer
in general the english recognizer is more robust than our cantonese recognizer even though identical parameter set training and testing mechanisms are used
the robustness results of the satz system in the absence of an abbreviation list and capitalization suggest that it would be well suited for processing ocr texts as well
for example stress can be either a noun the stress on the third syllable or a verb the advisor stressed the importance of finishing quickly
first there can not have previously been any ak rooted auxiliary trees because there were none after step NUM and every auxiliary tree previously introduced in this induction has a root labeled ai for some i k
on the other hand if a grammar has many sets of mutually left recursive rules involving more than one nonterminal even taking advantage of sharing can not stop an exponential explosion in the size of the ltig
upon inspection of the english segmentation errors produced by both the maximum matching algorithm and the learned transformation sequences one major category of errors became clear
to tag a sentence one can split its class sequence at the barriers into subsequences then tag them separately and concatenate them again
these probabilities which are more accurately scaled frequencies are based on occurrences of the words in a pretagged corpus and are therefore corpus dependent
in addition the decision tree induced from NUM french training items by the c4 NUM program produced a lower error rate NUM NUM in all cases
NUM crdd au ddbut des ann es NUM par un gouvernement conservateur cet office s tait vu accorder six ans
finally the learning based system is palmer and hearst multilingual sentence boundary shown to be able to improve the results of a more conventional system on especially difficult cases
as in tag a tig grammar consists of two sets of trees initial trees which are combined by substitution and auxiliary trees which are combined with each other and the initial trees by adjunction
NUM NUM false positive or false negative resulting from presence of ellipsis which can occur at the end of or within a sentence
then g generates exactly the same trees as g further if there is only one way to generate each tree generated by g then there is only one way to generate each tree generated by gc note that when converting the trees in t into trees in t every initial tree is converted into a different auxiliary tree
dialogue system through natural language must be designed so that it can cooperatively response to users
the patterns are specified as semantic representations including variables and the matching algorithm works a unification like
the results of the muc NUM evaluation must be analyzed to determine whether close scores significantl y distinguish systems or whether the differences in those scores are a matter of chance
data presentation is the rate that the system offered the valuable information to user
we developed the robust interpreter that can accept not only spontaneous speech but also misrecognized sentences
and in the text input these rates were NUM and NUM respectively
our recognizer integrates the acoustic process with linguistic process directly without the phrase or word lattice
one is to block semantic networks including the same as the registered networks for wrong patterns
after this process was finished the response sentence generator converts these networks into response sentences
in this case system regard the demonstartive here as a keyword that user has positioned selected
in contrast the method presented in this paper requires a large bitext
tagging a sentence based on a 1st order hmm includes finding the most probable tag sequence t given the class sequence c of the sentence
det at the end of the preceding subsequence since both symbols refer to the same occurrence of this unambiguous class
given these incompatibilities seven possible coreference configurations remain
this improvement is not particularly surprising
good demonstration versions exist for the four pairs english swedish english french swedish english and swedish danish
then we predict that the next word is time with probability NUM NUM and some other word not seen in this context with probability NUM NUM
if this equally fails it gets the label unknown which associates the word with the tag class of unknown words
in contrast the weights that guide abduction correspond to a wider variety of information and do not necessarily correspond to word sense form frequencies
in the third step the major data class recognition rules are applied
third the collector can erroneously merge or split the extracte d semantic representations even if the egraph matches are independently correct
performance of a memory based system accuracy on the test set crucially depends on the distance metric or similarity metric used
igtree combines two algorithms one for compressing a case base into a trees and one for retrieving classification information from these trees
apart from linguistic engineering refinements of the similarity metric we are currently experimenting with statistical measures to compute such more fine grained similarities e.g.
research of the first author was done while he was a visiting scholar at nias netherlands institute for advanced studies in wassenaar
in this experiment numbers were not stored in the known words case base they are looked up in the unknown words case base
the same reasoning applies to the probability of c and d coreferring in light of the existence of a unfortunately the existence of templates other than the pair being modeled is the type of conditional information for which we have little hope of accounting in a general and statistically significant manner
the generator uses an output script to convert the collected semantic information into an arbitrary output format such as a database template
however hasten has a special mode that runs only the egraph keys in order to test the rest of the system
the first four runs use one quarter NUM of the total number of success ion egraphs NUM which were
this limitation makes it difficult for a downstream system to fuse the information with possibly contradictory information from other sources as no information about the ie system s certainty of the results is passed along nor is information about possible alternative states of affairs and their associated levels of certainty
in that case the relevant semantic entropies may be estimated too low
figure NUM shows the name recognition performance for the final test data plus two reference points using the interim test data
furthermore case sensitivity is more significant in the recognition of these names as opposed to the numeric and temporal entities
to make this possible we approximate the probability of the training data p oig by the probability of the single most probable parse or viterbi parse of the training data
ideally in evaluating the objective function for a particular grammar we should use its optimal parameter settings given the training data as this is the full score that the given grammar can achieve
in the first domain we created this grammar by hand the grammar was a small english like probabilistic context free grammar consisting of roughly NUM nonterminal symbols NUM terminal symbols and NUM rules
we have developed a set of triggers for each move in our move set and only consider a specific move if it is triggered in the sentence currently being parsed in the incremental processing
in applications such as speech recognition handwriting recognition and spelling correction performance is limited by the quality of the language model utilized NUM NUM NUM NUM
in n gram models and the inside outside algorithm this issue is evaded by bounding the size and form of the grammars considered so that the optimal grammar can not be expressed
thus to extend our approach to novel word senses i.e. words not occurring in levin we would not be able to use negative evidence
the effects of negative and positive evidence as well as the three ways of handling prepositions show up much clearer here as is clear in table NUM
the choice is of no empiriem consequence but it simplifms the experiment by eliminating the problem of naming the syntactic patterns
in this experiment we attempt to discover whether each class based syntactic signature uniquely identifies a sin null gle semantic class
we have used the results of our first two experiments to help in constructing and augmenting online dictionaries for novel verb senses
we have used the same syntactic signatures to categorize new verbs into lcvin s classes on the basis of wordnet and NUM NUM o NUM NUM
NUM determine where the two ways of grouping verbs overlap null a the semantic classification given by levin
the syntactic behavior of these classes is illustrated with NUM example sentences an average of NUM sentences per lass
given that we are aiming for large scale lexicons of NUM NUM NUM words automation of the acquisition process has become a necessity
therefore for example with respect to an identified coreference set it may be correct to place a referential pronoun in its own cell implying that it does not corefer with anything simply because system error caused its antecedent not to be included in the set
the ill can then be seen as a fund of concepts which can be used in any way to establish a relation to the other wordnets
the som training algorithm is based on a simple iterative comparison process where the n input vectors are compared to each of the node vectors in the map
the combination of these technologies allows users to effectively browse the information space to locate related documents and to discover relationships between different themes in the information space
if the regions are similar enough i.e. if the dot product between the two regions exceeds some threshold the two regions are merged into one region
it also must explicitly represent its decisions as it generates a sentence in order to provide information to the media coordinator for negotiation with graphics
both of these systems took advantage of the richer and more precise semantic information that is available during the process of meaning to speech production
in order to achieve this language generation must provide a partial ordering of spoken references at a fairly early point in the generation process
this paper identifies issues for language generation that arose in developing a multimedia interface to healthcare data that includes coordinated speech text and graphics
from this large body of information the data filter selects information that is relevant to the bypass surgery and patient care in the icu
the inversion transduction grammar formalism skips directly to a context free rather than finite state base and permits one extra degree of ordering flexibility while retaining properties necessary for efficient computation thereby sidestepping the limitations of traditional transduction grammars
the method of figure NUM modifies the input tree to attach singletons as closely as possible to couples but remaining consistent with the input tree in the following sense singletons can not escape their immediately surrounding brackets
the algorithm proceeds bottom up eliminating as many brackets as possible by making use of the associativity equivalences lab c abc and abic abc
moreover if constituents can immediately dominate too many tokens of the sentences the crossing constraint loses effectiveness in the extreme if a single constituent immediately dominates the entire sentence pair then any permutation is permissible without violating the crossing constraint
however inflections provide alternative surface cues for determining constituent roles and growth in number of legal complete subconstituent matchings for context free syntax directed transduction grammars with rank r versus itgs on a pair of subconstituent sequences of length r each
this leaves a very large number of choices if both sentences are of length l then there 2t i are l possible bracketings with rank NUM none of which is better justified than any other
NUM a cet homme est triste furieux de partir c
for mental adjectives two kinds of headedness are possible
je suis triste furieux de partir i m sad furious at leaving c
there is no further specification available for the ezp ev or intellectual aclev variable
these structures encode several different aspects of the semantics for these adjectives
NUM a un homme triste a sad man b
null NUM a je suis triste furieux i m sad furious b
the following section summarizes the problematic behavior of these adjectives
b explaining the links between the different senses of mental adjectives
des enfants tristes sad children which are in a sad state c
and per nil oltly one to ira a lignm ml s
the resulting sets of structureshin lug parse trees form the input to the alignment procedure
by processing a substantial corpus a large set of such corresponding fragments can be collected
phe algorithm uses dynamic programming to score all possible matching nodes between structure sharing trees or forests
1deg we expect line tuning el the pa rameters in our procedures to improve our performance
we io not consider other types of me to many dignlll llts
thus the rule that introduces the end 0f token symbol needs to combine the letter pattern with a list of multiword tokens which may include spaces periods and other delim null the tokenizer in figure NUM is composed of three transducers
this kind of matching techniques will be helpful in our dictionary based approach in the following situation entries of a bilingual dictionary do not completely match the word in the corpus but partially do
if the following three conditions hold add NUM to the i j element of am NUM jsentencei and esentencej contain a bilingual dictionary word correspondence wjpn and w ng
table NUM shows the performance on sentence alignments for the texts in table NUM combined statistics and dictionary represent the methods using both statistics and dictionary only statistics and only dictionary respectively
the texts for the experiment varied in length and genres as summarized in table NUM texts NUM and NUM are editorials taken from yomiuri shinbun and its english version daily yomiuri
the main task of the system is to find anchors from the possible sentence correspondences by using two kinds of word correspondences statistical word correspondences and word correspondences as held in a bilingual dictionary NUM
these word correspondences are given the value of NUM the third operation is introduced because the number of highly confident corresponding words are too small to align all sentences
in the most general case initial anchors are the first and last sentences of both texts as depicted in figure NUM next possible sentence correspondences are generated
a if t is a constrained type enter t x into rhs if it s not already there
the characteristics of the system are as follows
the index has been designed to permit incremental updates allowing new sentence pairs to be added to the corpus as they become awulable for example to implement a translation memory with the system s own output
the previous version had performed very poorly to the point where its results were essentially ignored when combining the outputs of the various translation engines for two main reasons inadequate corpus size and incomplete indexing
if any of the chunks covers the entire input string and the entire source language half of a corpus sentence pair then all other chunks are discarded and the target language half of the pair is prodnced as the translation
together the bilingual dictionary and target language list of roots and synonyms extracted from wordnet when translating into english provide the necessary information to lind associations between source language and target language words in the selected sentence pairs
mo lcb lesl inil ial copi ll8 precisely the i lcb tea l rcb ehin lcb l l r msla i lcb n nlenlory
the output is shown in the format used r r standalone testing which generates only the best translation for each distinct clnmk when integrated with the rest of pangloss panl l mt also includes information indicating which portion of tile input sentence and which pair fi om the corpus were used and can produce multiple translations for each chunk
modification will annotate bel with NUM its focus of modification bel focus which contains a set of beliefs bel and or its descendents which if disbelieved by the user are predicted to cause him to disbelieve bel and NUM the system s evidence against bel itself hel s attack
student x unknown y crept710 NUM from NUM and NUM of the theory f it follows that NUM implies the formula NUM db student x a db course y crept710 NUM
this decision tree was learned under the following conditions all of the features shown in figure NUM were used to code the training data boundaries were classified using a threshold of three subjects and c4 NUM was run using only the default options
the detailed formula blanks de is given by j 2j NUM in our example de is for c then simplifies to 2j NUM m c NUM n0nl this gives NUM
we first introduce our methodology section NUM NUM then evaluate three initial algorithms each based on the use of a single linguistic device frequently proposed in the literature pauses cue words and referential noun phrases respectively section NUM NUM
maximum entropy modeling suppose we wish to model some random process such as that which determines coreference between two templates generated by an ie system based on various characteristics of the context that influence this process such as the content of the templates themselves the form of the natural language expressions from which the templates were created and the distance between those expressions in the text
unlike the previous algorithms in np the potential boundaries are first computed as ordered pairs of adjacent functionally independent clauses ficui ficui i see section NUM NUM NUM then normalized to ordered pairs of prosodic phrases see note NUM
just extended evellts with results whi h become true at their end t oints and agents who inten NUM i hose results NUM o rcb ore true
using the terminology of figure NUM since the algorithm never predicts the class boundary it is necessarily the case that a NUM b NUM recall NUM and precision is undefined in table NUM
the flexibility of a mu is guaranteed by the presence of slots
an entire message can thus be parameterised
conversion table of mus into car
instead of grammatical information being localized to the sentence as a whole it is localized to a particular word in its particular context there is no need to consider a pp as a start of a sentence if it occurs at the end even if there is a verb with an entry which allows for a subject pp
if on the other hand tile mt for eat says that the start and end points of the action must be quite close together then mp simple entails that there must be several such actions in the specified interval
good prosody for the carriers is obtained thanks to the prosody transplantation technique
in this context text to speech tts is an evident alternative
figure NUM example of mapping of message units mu to carriers
each phoneme also has duration and intonation characteristics see figure NUM
figure NUM some relevant lexical productions
the most likely sequences of tagged words are the ones that maximize chain probabilities
so for the verb based experiment our technique for establishing the relatedness between the syntactic signatures and the semantic classes is mediated by the verbs themselves
however it may be an erroneous chain which has an implicit spelling error
for a precise notion of availability we claim that we must appeal to the distinction between referential and quantificational np semantics since almost any referential np can have the appearance of taking the matrix scope without affecting the rest of scope phenomena
the method begins by extracting all of the terms from the sentences that are parallels to the top NUM retrieved english sentences
the plan was to introduce nlp into dialog s technology to add informational value to raw documents
NUM new york times ignored according to dialog s spec
we can make further improvements in terms of the perception o f our creative work
the terminal symbols in the grammar are literal words and combinations of syntactic and dictionary flags
the pattern matching results are collected from the side effect of the actions attached to the rules
agitates v s trans argentine adj noun like argentine n sing adjnoun argentinean n sing adjnoun nationality argentineans n s nationality
we are not interested in the ndfsm output per se
NUM NUM pounds taken for a monetary expression
at first we wanted to use fastus as it was
this approach allows us to restrict non determinism and prevent combinatorial explosion
in the case of a dcg the generalisation operation is anti unify which can be performed in o n
the other route goes one step further and takes into account the content of rule and leaf constraints
fig NUM shows the structure of the f constraint for the or node b in the example parse forest
we investigate the problem of determining a compact underspecified semantical representation for sentences that may be highly ambiguous
observe that because of invariant NUM the input constraints to generalise are bounded by fc as any constraint in sem
in the pp attachment example a compression of the e constraint to polynomial size can not be achieved with factoring alone
the parallelism preference factors aux match and parallel match computational linguistics volume NUM number NUM also make an important contribution when they are activated in the incremental analysis there are NUM additional correct selections in the complete corpus an improvement of NUM NUM
head overlap either the head verb of the system choice is contained in the coder choice or the head verb of the coder choice is contained in the system choice head match the system choice and coder choice have the same head verb
even with the head match criterion there are errors that involve very subtle differences such as the following example NUM we were there at a moment when the situation in laos threatened to ignite another war among the world s giants
this preference is illustrated by the following example NUM someone with a master s degree in classical arts who works in a deli would vp be ideal litigation sciences vp advises
the vpe res system has the following subparts NUM syntactic filter NUM preference factors NUM post filter the candidates for vpe antecedents are all full vps appearing within a three sentence window the current sentence and the two preceding sentences
since the vpe is contained in an adjunct an adverbial phrase there is in fact a nonmaximal vp headed by get that does not contain the vpe this is the vp get to the corner of adams and clark
no examples of this sort have been observed among the NUM vpe examples in the penn treebank and the syntactic filter as currently formulated contributes significantly to the overall performance of the system see section NUM for figures on this
system output plead guilty in that inquiry coder selection agreed to plead guilty in that inquiry according to head overlap the system choice is correct since its head verb plead is contained in the coder selection
the syntactic filter rules out antecedents that improperly contain the vpe occurrence s while the precise definition of improper containment is an active area of theoretical research NUM we rule out antecedents that contain the vpe in a sentential complement
both processes need to look up a statistical information collected from the hand tagged corpus
moreover the dialogue system finds that different chunks of the information supplied by the user could be coherently interpretable
the generation process using this specification produces the sentence shown after the form
then we present the overview of a computational morphological processing in section NUM
however the task of the dialogue state dependent language modeling is more difficult in some application domains
the translations into english of the best decoded sequences are shown in all caps in italics
in this situation the dialogue system should identify the user s detection of miscommunication and provide appropriate repairs
for the sake of simplicity let us assume that any nonterminal node in the parse forest is binary branching
in our task domain most of the recognition errors occur during the recognition of departure city and arrival city
as a consequence the parser output contains another value for the part ofday parameter and a departure hour
in this case the insertion of a concept due to misrecognition was repaired at the dialogue level NUM
as the same slash and likewise sub j value should not be reduced in both trees we state our termination criteria as follows termination criterion the value of an sf f at the root node of a tree is not reduced further if it is an empty list or if it is shared with the value of f at some non trunk node in the frontier
we have generalized the notion of factoring recursion in tag by defining auxiliary trees in a way that is not only adequate for our purposes but also provides a uniform treatment of extraction from both clausal and non clausal complements e.g. vps that is not possible in traditional tag
informally this means that if f is the selector feature for some schema then the value or the element s in the list value of NUM that selects the non sd s is not contained in the f value of the mother node
acknowledgement this work was sponsored in part by arpa and monitored by fort huachuca hj1500 NUM NUM
only NUM words lose the correct tag when almost NUM out of NUM ambiguous words are disambiguated
this is a reduction of the error rate by NUM NUM
these early models were developed for french to english translation
each english word e is generated with probability p ei fc
a headword language model uses two unigram models a headword model and a non headword model
in addition NUM training sentences were manually aligned and included in a separate training experiment
the rules were constructed in less than one month on the basis of NUM newspaper sentences
notice that in a parse considered correct by our second metric the syntax NUM of all tags must be correct
the context in which a model makes its prediction includes any parts of the parse tree which have already been built
the result was a NUM NUM expected rate of disagreement among the team members on the exact choice of tag
it is worth inquiring how well expert humans do at the parsing task that we are attempting here by macblne
the average number of different correct exact syntactic matches 1deg per sentence in our test data is NUM
the estimated probability of any parse state is the product of the probabilities of each step taken to reach that state
the parent of a rule in the grammar often contains feature values that are not derived from any of its children
they employ a heuristic which scores source treebank target treebank parse pairs based essentially on the percentage of identically placed brackets in the two parses
instead we relied on the models to learn the constraints and the conditions for their application directly from the data
in fact the overgeneration typically due to the shallow grammatical approach is significantly limited
extraction the recursive nature of some rules require an iteralive analysis of the corpus
they mainly differ in the emphasis they give to syntactic and statistical control of the induction process
note that the allowed structures are post nominal due to the typical role of specifications in italian
in addition the maintenance of consistency among the inductive rules is by no means easy
the reference term dictionary was manually compiled by a team of three domain experts culturally heterogeneous
feedback of the terminological extraction process to the morphologic analysis has been also designed
the generalized evaluation of mutual information for cn h m1 m2
over the NUM documents mi extracts NUM simple indexes while ti extracts NUM terminological indexes
the level of embedding in a o trlb pos NUM tril type NUM syn rel NUM c0re type NUM above NUM inten rel NUM NUM NUM NUM
coders use rda to exhaustively analyze each explanation in the corpus i.e. every word in each explanation belongs to exactly one element in the analysis
however otir method lifts wa s il ore e lieient than t i gi a lit indexing in l errns o the lluli ber o inde x lerllis mid ti e werage iillltii er of retrieved doeultietlts per query
the operational content of a term is represented as the probabilistic distribution of the term over the document set
compound nouns as index terms that usually subscribe to specific notions tend to increase the precision of retrieval performance
in both identifying and evaluating index terms compound nouns require a different strategy from that for simple nouns
fhe function words are lassified into NUM groups according to their roles and position in sentences
experiments with a set of NUM NUM documents show that our method gains NUM increase of retrieval performance compared to the indexing method without compound noun analysis and is as good as manual decomposition by human experts
NUM for xtelt t conlt osit ion mi o1 a col lpound iiotiii i colnpul e rf d
NUM define the skip k stochastic dependency of w at some position t on w at position t k the parameters ak w are mixing coefficients that weight the predictions from these different dependencies
to do otherwise is an injudicious use of an attentional cue
l h appears to support an impending reordering but does not compel it
furthermore to produce an effective aac system even for just social conversation phrase construction features will need to be incorporated within the basically phrase storage system
this may be sufficient for written discourse
it is calculated through inference chains that link semantic and pragmatic propositions
center list cf and the backward looking center the cb
l h indicates that he no longer cospecifies with john
given the serial and ephemeral nature of speech and the limits of working memory it is most expedient to mark as salient the information rich nonpronominals rather than their semantically impoverished pronominal stand ins
the effect of pitch accenting on pronoun referent resolution
h l evokes an inference path
yet they occur felicitously in spoken discourse
again a held out interpolation algorithm was used to smooth the mixed order markov models from section NUM the ruth mixed order model had mv smoothing parameters NUM k w corresponding to the v rows in each skip k transition matrix
some initial testing of talk was performed in which only pre stored text was used in order to examine the limits of speaking entirely with pre stored material
once decisions about core contributor ordering and cuc occurrence have been made a generator must still determine where to place cues and select appropriate icxical items
each relation node is labeled with both its intentional and informational relation with the order of relata in the label indicating the linear order in the discourse
the perspectives were person me you time past present future orientation where what how when who why
often it is important to provide a comment which does need to be selected from a menu of possibilities because its use is dependent on the context
in that case the relative clause s domain object el is inserted into the domain of the vp together with the domain object consisting of the same synsem value as the original np and that np s phonology minus the phonology of the relative clause
in NUM the probability of generating both dreyfus and fund as subjects NUM np c dreyfus i s vp was t np c fund i s vp was is unreasonably high
NUM the blackboards the main blackboard which contains the latest pre spl expression s and their derivation history and the control blackboard which contains bookkeeping information such as the flags that signal the status running idle etc of each module
verses the system network of each module handles the choice criteria functions typically these criteria pertain either to the input pre spl or to one of the knowledge resources and upon reaching tree transformation rules hands them off to the tree transformer
it is implicit in this approach that the precision of expression attainable with flexible phrase construction at the time a thought occurs is the paramount consideration
NUM modify a pre spl expression r writb x rst relatm NUM rst cause in which the head of a pre spl fragment is changed from rst relation to rst cause
the average length of each dialog is approximately NUM utterances
constraints i and NUM suggest that the intermediate form s of the data during sp operation be some sort of spl in emergence that is a frame continually evolving from the more abstract input to the final spl output s
with the healthdoc sentence planner we are attempting to build an architecture that supports both the addition of new sentence planning tasks in the form of new modules as well as continued growth in sophistication and coverage of individual task performance
in the cmu data the meetings all last two hours
if instead the endophoric module runs first the spl produced is NUM rather than NUM i.e. the endophoric choice module chooses the phrase this happens to refer to an implant wears out loosens or fails
a more specific tsl expression in the more specific tsl expression the deep semantic roles have been replaced by surface semantic roles actor actee and syntactic information tense and textual information theme have been added
step NUM all rules are applied to the normalized input
in this case the auxiliary symbols are dotted rules from the given context free grammar
using these dotted rules as auxiliary symbols we can work with regular languages over the alphabet
a dotted rule is a grammar rule with a dot inserted somewhere on the right hand side e.g.
the two approximations are shown for comparison in figure NUM
it is the axis generator s job to map the two halves of the bitext to positions on the x and y axes of the bitext space before simr starts searching for chains
a method is presented here for calculating such finite state approximations from context free grammars
for example exponential behavior is exhibited by the following class of grammars
the above method constructs translation lexicons containing only word to word correspondences
the work described here used a finite state calculus implemented by the author in sicstus prolog
list fsa x prints out the transition table for the automaton in register x
once the stem learning is complete it is possible to query the vector set to determine the nature of the learned relationships
figure NUM the precision of the models estimated without the credit factor
the credit factor was introduced to redefine the likelihood of training data
such a global iteration is a special version of error correcting learning
formula NUM is the n gram model and formula NUM is the hmm
however their likelihood was the probability with all paths weighted equivalently
table NUM the precision and recall of juman on each cost width
a larger cost width would result in a larger number of output candidates
the yob situation event is a flat object combining information from both succession and in and out objects for ease of processing
NUM in the case of binary part of speech assignment for each possible part of speech the vector is assigned the value NUM if the word can ever occur as that part of speech according to the lexicon and the value NUM if it can not
have appeared more frequently in the training set
bigrams are formed between all adjacent pairs of words
lsa requires the corpus to be segmented into documents
the elimination of the poor local context combined with the larger number of factors increases the performance of lsa to NUM above the baseline predictor compared to NUM for tribayes
lcb mj ones mart in cs colorado edu
s is a diagonal matrix NUM of rank r
it is also called the singular value matrix
what we also need is a list of words used by the user
in this work we use the words directly
a collection of texts is represented in matrix format
NUM automatic lexical tagging viterbi training for pos tags vtt once a word segmented text corpus is acquired the segmented version can be annotated with parts of speech so as to extract a pos annotated electronic dictionary
the numerators in the parentheses are the numbers of correctly identified n grams for precision the denominators are the numbers of n grams in the extracted word lists and for recall they stand for the numbers of n grams in the standard dictionary
given a string of n chinese characters cl c2 c n represented as c NUM a bayesian decision rule requires that we find the if among all possible segmentation patterns wj which maximizes the best word segmentation pattern following probability
with a seed of NUM NUM sentences and an untagged corpus of NUM NUM sentences the performance for bigram word identification is NUM NUM in precision and NUM NUM in recall when the two class classifier is applied to the word list suggested by the viterbi word identification module
NUM automatic word identification a two class classification tcc model the word list acquired through the above reestimation process is based on the optimization of the likelihood value of the word segmentation pattern in a sentence which implicitly takes the contextual words into account
for practical purpose we will only retain n grams that are more frequent than a lower bound lb NUM and only n grams up to n NUM are considered since most chinese words are of length NUM NUM NUM or NUM
furthermore each n gram in the segmented text corpus will be assigned the most frequently encountered n pos tags in the seed corpus in our experiments n is selected as NUM since the most frequently used NUM pos tags already cover over NUM of the tags in the seed
the word precision rate is the number of n grams common to the extracted word list and the standard dictionary divided by the number of n grams in the extracted word list on the contrary the recall is the number of common n grams divided by the number of n grams in the standard dictionary
with the small seed corpus it is observed that the precision for bigram words is improved from the initial precision of NUM NUM to NUM NUM corresponding to an increase of NUM NUM and the recall is dropped from NUM to NUM NUM a decrease of NUM NUM
combining multiple knowledge sources for discourse segmentation
table NUM using NUM fold cross validation
this meant the extraction of only the longest strings in the order of frequency of occurrence
subjects were free to assign any number of boundaries
after sentence final contour sentence flnal contour
the summed deviation for perfect performance is thus NUM
because hei s looking at the girl
a vertex cover of a finite graph is a subset of its vertices such that at least one end point of every edge is a member of that set
an example from the world book related to e1 salvador is shown in figure NUM
such facilitators communicate through kqml performatives and exchange messages written in some content language
the following paragraph was generated by the change of perspective operator on a set of two messages
however the chapel of the church of jesus was damaged
to illustrate we will give as an example an analysis of the quantifier raising example introduced above extending in a natural manner an example given by shieber and schabes
the summarizer is connected with the user model and the user interface
our goal was to expand the model to incorporate natural language interfaces
this stage takes polynomial time in the size of since NUM can be constructed from r in linear time and 7rq can be constructed as in lemma NUM
we parsed the templates figure NUM adding information about the primary and secondary sources of news NUM
the participants were asked to fill templates as shown in figure NUM with information extracted from news articles
to overcome this limitation we might generalise the combination rule to allow composition of functions i.e. combinations akin to e.g.
each rule allows that a proof containing a redex be transformed into one where that occurrence is replaced by its contractum
we can specify a method such that given a set of dependency relations d we can construct a corresponding proof
for example in addition to the proof NUM we have also the equivalent proof NUM
denotes the edge between states i and j
this is not to say that there is no order information available to be considered in distinguishing incremental and non incremental analyses
note that we have not in these comments reintroduced an ordered proof system of the familiar kind by the back door
this normal form is useful only if we can show that every proof has an equivalent normal form
there is a task neutral date slot that is defined as a template element it was used in the muc NUM dry run as part of the labor negotiation scenario but as currentl y defined it fails to capture meaningfully some of the recurring kinds of date information
an occurrence of a nonterminal a in the right hand side of a rule p can be linked to the left hand nonterminal of another rule p in the same vector
in fact they hardly ever are
there are at least two possible tacks
NUM NUM testing the icmh for phrase structure
clearly this is an inelegant solution
they are shown in the appendix
crucial for fast computation of chains
the other table encodes lexical information
however rules that find personal names occur later in our named entities sequence than those which find organizations thus allowing th e phraser to correctly relabel martin purls as a person on the basis of a test for common first names
extracting and processing communicative intentions behind natural language utterances plays an important role in natural language systems see e.g.
figure NUM shows the domain independent dialogue acts and the transition networks which define admissible sequences of dialogue acts
and its evaluation in order to compute weighted dialogue act predictions we evaluated two methods the first method is to attribute probabilities to the arcs of our network by training it with annotated dialogues from our corpus
since the keyword spotter only works reliably for a vocabulary of some ten words it has to be provided with keywords which typically occur in utterances of the same dimogue act type for every utterance the dialogue component supplies the keyword spotter with a prediction of the most likely follow up dialogue act and the situationdependent keywords
the first strategy for error recovery therefore is based on the hypothesis that the attribution of a dialogue act to a given utterance has been incorrect or rather that an utterance has various facets i.e. multiple dialogue act interpretations
this figure gives a rather good impression of the wide variety of material the dialogue component has to cope the dialogue model specified in the networks models all dimogue act sequences that can be usually expected in an appointment scheduling dialogue
in fact the best synset for a cluster does not necessarily cover all the cluster members
the main motivation for this divergence between wordnet tagging and the meanings of ciaula clusters is twofold
natural language is often bursty church this volume that is rare or new words may appear and be used relatively frequently for some stretch of text only to drop to a much lower frequency of use for the rest of the corpus
function 7s u NUM NUM where u c u u lcb c rcb and c represents a novel event that is the occurrence of a word not seen before in the context represented by s
the argument structure of the reached synset is thus too generic
their patterns are very different and show almost no overlap
a possible solution is to use a shorter context one of the ancestors in the pst where the word has already appeared and multiply the probability of the word in that shorter context by the probability that the word is new
looking more in detail the problem with misclassifications is twofold
a further interesting issue is related to identical tags assigned to different clusters
our contribution to this long standing issue will be empirical rather than methodological
let s v be the set of senses of the verb v
verbs in these classes should express similar acts or events
we did so with essentially no guidance from native speakers of any of these languages
besides these rule sequences several languagespecific extensions were required to port alembic to met
any operators encountered are to be treated as text
for manual engineering this allows changes in the model to be implemented and tested efficiently
alembic is a comprehensive information extraction system that has been applied to a range of tasks
finally for chinese we had not even the limited reading ability available for japanese
in addition our spanish system exploited a spanish part of speech tagger that we had developed previously
alembic either exceeded or came near matching its performance on the english name tagging task in muc NUM
these results show gaps between training and testing performance especially in the two asian languages
the preliminary nature of the met task precludes formulating a full assessment of our system s performance
NUM tag insert the appropriate sgml tags around the targets in the input text as calculated by the adjust rules
person organization responsibility period of participatio ave of time approx no of n devoted to project person months
ids are either proper names or technical descriptions of a part of or kind of type
such a rule is only illustrative and would actually not be used because so many optional elements beg for over generalizations
we note that the wall street journal style guide was very useful in reducing the number o f training examples needed
apply dxl rules for data classes which are non interacting and do not require other data classes for their identification also parallelizeable
we envision examples of new data classes first being clustered by hand into high similarity groups but this may not be necessary
the documentcollectionindex will provide progress updates as requested by the monitor
these model structures were coded by hand as monolingual head acceptor and bilingual dependency lexicons for the transfer system and a head transducer lexicon for the transducer system
a hyphenator program takes as input a word and returns the set of points within the word where hyphens are permissible
these limitations were in turn examined through an empirical process which also resulted in formally expressed rules
this observation certainly implies the tendency for words in uppercase to have fewer hyphens than their lowercase equivalents
it was thus decided to examine all theoretically possible cases and not to eliminate a priori any sequences
the patterns flf2 fl f2 c v u 2v u vc of lemma NUM contain exactly NUM such sequences
proof suppose that grammar rules indicate that the points immediately preceding vl and immediately following v2 are also permissible hyphen points
the aim of the present study has been to analytically examine modern greek hyphenation in order to develop a pattern based hyphenator
vowel splitting which traditionally is indicated in terms of prohibitive rather than explicit grammar rules is examined in detail
it should be noted here that some parts of this set have already been covered by other rules
the sets of categories found are not necessarily disjoint whereas all overlaps always lead to consistent hyphenation
where z 0and NUM o l NUM are nonnegative parameters
we chose the former method for our first experiments because it was easier to implement
in section NUM local cohesion in task oriented dialogues is described
in this paper we focus on speech act type based local cohesion
j figure NUM a dialogue with our utterance model
a series of experiments was done to evaluate our method
we described our statistical method of recognizing local cohesion between utterances based on pseudo mutual information
we focused on speech act expressions in calculating the plausibility of local cohesion between two utterances
we conclude that the presented method will be effective for recognizing local cohesion between two utterances
thefand f are modified functions of mutual information in information theory
consequently many initial models can be prototyped and tested before implementation and researchers need not have a fully developed natural language interface
below we discuss two dimensions of information status tif the explicit ideational specification is included in the say form as in figure NUM then the relevance space need not be stated it is assumed that all the entities included within the specification axe relevant and no others
two fields in the semantic specification allow the user to specify the information status of ideational entities and thus how they can be referred to in discourse s these lists will typically be maintained by the text planner as part of its model of discourse context the shared entities field a list of the ideational entities which the speaker wishes to indicate as known by the hearer e.g. by using definite reference
the architecture of realpro is based on meaning text theory which posits a sequence of correspondences between different levels of representation
for example all the input trees used in the tests discussed above have branching factors of no more than NUM
white as well as to three anonymous reviewers for helpful comments about earlier drafts of this technical note and or about realpro
first the input dsynts is checked for syntactic validity and default features from the default feature specification are added
this representation has the following salient features the dsynts is an unordered tree with labeled nodes and labeled arcs
syntactic and lexical knowledge about the tar null get language is expressed in ascii files which are interpreted at run time
the dsynts is a deep syntactic representation meaning that only meaning bearing lexemes are represented and not function words
however the user may want to extend the lexicon if a lexeme with irregular morphology is not in it yet
of course an f measure is calculable but more research is necessary before we can conclude that it will combine recall an d precision in a way that is meaningful for these evaluations
once the new topic is determined appropriate constraints can be exploited e.g. by selecting a relevant subgrammar
for example a in figure NUM acknowledges a fact that might have led the student to make the mistake
it is designed to be easily extended and enhanced as text processing technology advances and as modules with new functionality are developed in response to the needs of particular applications
the cotr program manager s responsibility is to ensure that the most suitable developer s are selected for a project and to oversee the progress of the project
for vendor products if the vendor s product is used in a tipster application the criteria stated above in for tipster application development will apply
an application is a group of modules both internal and external to the tipster architecture that operates on documents to answer a user s request for information from documents
a form NUM document is a form NUM document plus anything done to the document e.g. identifying parts to prepare it for the appropriate tipster algorithms
the researcher operating in an established environment is free to concentrate on specific well defined areas and not be burdened with developing the necessary infrastructure to test the new component
note that for any given document forms NUM NUM may or may not be distinct from one another depending on the amount of processing performed on the document
chinese words have little or no morphological information
table NUM part of the concordance for air
its concordance is shown partly in table NUM
there are no overlapping sentences between the texts
figure NUM results of word matching using context heterogeneity
therefore these words have one to many mappings with english words
a nonparametric time series smoother solid line supports the least squares regression line dotted line
other system capabilities discussed in the sections below allow the user to do a variety of information assimilation and information gathering tasks
as we stated earlier nodes that are near one another on the map have the property that they represent similar themes of information
the dot product for similar direction vectors will be close to NUM NUM while dissimilar vectors will have dot products that are near zero
in order to explore this intuition we need a reliable way to ascertain whether a word is underdispersed
the fastest snap available the snap NUM has NUM processors and delivers an unmatched price to performance ratio of around NUM per megaflop
this type of performance enables hnc to develop and deliver the compute intensive solutions to a wide variety of problems including information visualization
the node vector that is nearest is deemed the winner of this competition and is rewarded by having its vector adjusted
since both terms words or stems and documents are represented in the same flame of reference this allows several unique operations
edward s context model differs significantly from the grosz and sidner model from an engineering and computational point of view
this information is used by the interpretation component to restrict the referent sets of the role fillers of a relation
spoken language adds intonation and when the situational context gets involved various perceptual factors like visibility join in
the program described here is implemented in perl
the conditional probability distribution p i e is modeled as follows
this section presents the probabilistic model that provides a formal basis for this matching step
our results also suggest another possible avenue of future development
in the hybrid analogical approach the example data is categorized by linguistic constituent
the example based approach has certain advantages over traditional rule based approaches to translating spoken language
the analogical matching step is based on a probabilistic formalization of matching by analogy
finally the target language portions of the selected best matches are comhined to form the complete target language expression
if neither type of corpus is available the probabilities can be estimated with the aid of a manually constructed thesaurus
computing recall requires finding all true tokens of a cue
it is assumed that each word sense corresponds to a single semantic predicate
the alvey morphological analyzer is then applied to each type
nonetheless they cue lexical semantics and are easily identified
this sense of the affix will be referred to as aize
in addition such methods might provide insight into human language acquisition
a related but different sense applies to nouns e.g. glamorize
since telicity is a type of non stativity the information is mutually supportive
in the case e.g. of a document originally composed in machine readable form form NUM form NUM
one advantage this important property has is that is allows one to decompose the learning problem from the feature selection problem
some examples are chuckle dangle alleviate and assimilate
some examples are colorful less fearful less harmful less and tasteful less
this system is designed to expand to other lan null guages besides english and japanese and other domains beyond s t terms
as segmentation is not NUM accurate segmentation errors can sometimes cause name tagging rules not to fire or to misfire
we have described an advanced multilingual cross linguistic information browsing and retrieval system which takes advantage of information extraction technology in unique ways
we have written rules which maps phonetic transcriptions to katakana letters and generated possible japanese katakana translations for given english names
the term translation module is bi directional it dynamically translates user queries into target foreign languages and the indexed terms in retrieved documents into the user s native language
the indexing module indexes names of people entities and locations and a list of scientific and technical s zt terms using state of the art ie technology
it translates english query terms into japanese in the query mode and translates japanese indexed terms into english for viewing of a retrieved japanese text in the browsing mode
the model focuses on two convention based sources of expectation
the last utterance in the figure is a closing
it also considers the information used to design repairs
NUM NUM the use of repair in the negotiation of meaning
this type of repair will not be considered here
also at the context in which it was uttered
tables NUM NUM give each of these axioms in detail
it corresponds to the body relation in strips based approaches
NUM NUM collaboration in the resolution of nonunderstanding
this work was supported by the natural
after providing appropriate feedback to the user the system performs a further check to see if any action needs to be carried out on the accessed item s of information
figure NUM overall view of processing
one has to experiment with the web site and study the source pages for the html forms screens in order to create these mappings files and possibly write additional code to generate the appropriate query
accuracy there are four other important design objectives for sd systems portability of an sd system refers to the ability of the system to be moved from one application domain to another
such tasks can be classifted as information access ia tasks where the primary objective is to get some piece of information from a certain place by providing constraints for the search
for example in a library application if the user asks for dickens and the database contains two or more authors with that last name this term is lexically ambiguous
NUM correction this state is reached when the system realizes that the user is attempting to correct either an error the user may have made or an error made by the recognizer
the primary bottleneck in our system at this time is the parser which only identifies partial parses and does not perform appropriate pp attachment conjunct identification or do anaphora resolution or ellipsis handling
in each case the computation of the agreement statistic took into account those cases if any where the annotator could not arrive at a decision for this case and opted simply to throw it out
the v category captures cases that require minimal or no additional effort and the p category covers cases where some additional work might need to be done to accommodate the part of speech divergence depending on the application
in a preliminary evaluation we had three annotators one professional french english translator and two graduate students at the university of pennsylvania perform a version of the annotation task just described they annotated a set of entries containing the output of an earlier version of the sable system one that used aligned sub sentence fragments to define term co occurrence cf
the instructions for the in context evaluation specify that the annotator should look at the context for every word pair pointing out that word pairs may be used in unexpected ways in technical text and words you would not normally expect to be related sometimes turn out to be related in a technical context
our having succeeded there is a very high probability that the specific annotation will be selected by any two annotators because it appears so very frequently as a result the actual agreement rate for that annotation does n t actually look all that different from what one would get by chance and so the values are low
sable s precision on the answerbooks bitext is summarized in figure NUM NUM each of the percentages being derived from a random sample of NUM observations we can compute confidence intervals under a normality assumption if we assume that the observations are independent then NUM confidence intervals are narrower than one twentieth of a percentage point for all the statistics computed
sable was designed with the following features in mind independence from linguistic resources sable does not rely on any language specific resources other than tokenizers and a heuristic for identifying word pairs that are mutual translations though users can easily reconfigure the system to take advantage of such resources as language specific stemmers part of speech taggers and stop lists when they are available
we hired six fluent speakers of both french and english at the university of maryland they were briefed on the general nature of the task and given a data sheet containing the NUM candidate entries pairs containing one french word and one english word and a multiple choice style format for the annotations along with the following instructions
evaluated on a very small NUM NUM word corpus the system shows real promise as a method of processing small domain specific corpora in order to propose candidate single word translations once likely general usage terms are automatically filtered out the system obtains precision up to NUM at levels of recall very conservatively estimated in the range of NUM NUM on domain specific terms
why might we want to do this
this process has been called pseudo feedback
a big difference between the taggers is that the tuning of the statistical tagger is very subtle i.e. it is hard to predict the effect of tuning the parameters of the system whereas the constraint based tagger is very straightforward to correct
we divide the errors into three categories NUM errors due to multi word expressions NUM errors that should could be resolved and NUM errors that are hard to resolve by using the information that is available
we imposed a time limit on our experiment the amount of time spent on the design of our constraint system was about the same as the time we used to train and test the easy to implement statistical model
we used the test sample b after the first step NUM words out of NUM NUM remain ambiguous
thefirst group NUM errors the multi word expressions are difficult for the syntax based rules because in many cases the expression does not follow any conventional syntactic structure or the structure may be very rare
another interesting observation is that the most frequent ambiguous words are usually words which are in general corpus independent i.e. words that belong to closed classes determiners prepositions pronouns conjunctions auxiliaries common adverbials or common verbs like faire to do to make
a very problematic case is the word des which can either be a determiner jean mange des pommes jean eats apples or an amalgamated preposition determiner as in jean aime le bruit des vagues jean likes the sound of waves
the user interface is implemented in tcl tk version NUM NUM
the words are numbered and labeled with part of speech tags
the scoring program is slow in emacslisp and would be slowed further by calculations with higher accuracy
to increase the efficiency of annotation and avoid certain types of errors made by the human annotator manual and automatic annotation are combined in an interactive way
to do this we adapted a standard part of speech tagging algorithm the best sequence of grammatical functions is to be determined for a sequence of syntactic categories cf
accuracy of the unreliable NUM of assignments is NUM i.e. the annotator has to alter the choice in NUM of NUM cases when asked for confirmation
they repre null sent expressions of the type c where c is a regular expression
the perfect linearity indicates an exponential time and space behavior and this in turn explains the observed difference in performance
figure NUM screen dump of the annotation tool
the corpus is stored in an sql database
the assignment of grammatical functions is performed automatically
however the number of paragraphs that more than one human judged the paragraph as a key paragraph was only two
when more than one human judges a paragraph as a key paragraph the paragraph is regarded as a key paragraph
using a term weightmg method every paragraph in an article would be represented by vector of the form
as a result of wsd and linking methods for these articles we have obtained NUM NUM different nouns
this causes the fact that most of nouns do not contribute to showing the characteristic of each domain for given articles
in table NUM each paragraph first mid position and last paragraph includes the paragraphs around it
in formula NUM k is the number of different words and l is the number of the domains
in this paper we propose a method for extracting key paragraphs in articles based on the degree of context dependency
NUM the deviation value of a word in the article is smaller than that of the domain
formulae NUM and NUM shows i and NUM in section NUM respectively
NUM aside from their use in section NUM NUM we will completely ignore such directives in this paper
we start by introducing a verb node verb syn cat verb syn type main
NUM the ability to perform this kind of inference may also be useful in lexicon development and maintenance
secondly active and passive path prefixes are provided for the explicit purpose of controlling the inheritance route
NUM however this modification to our analysis seems to make it less concise rather than more
so the question arises of what we can say about paths for which there is no specific definition
so the statements we previously made concerning wordl and word2 remain true but now only implicitly true
finally an appendix to the paper replies to the points made in the critical literature on datr
the additional arrows between the constituent nodes are introduced by the grammatical principles of f projection irrespective of the actual prosodic marking
the graph in NUM represents the amount of information that is encoded on sentence level without reference to context
where node is a node path is a simple path and ext is a simple value
similarly a quoted node form accesses the globally stored path value as in the following example
this strengthens the arrows pointing towards fo for all unresolved constituents t redicting eiu buch qcgcben
12for examples with several ambiguous accents the modified account collapses some f markings with minimal diffcrm ces in inte rpretation
this is kept track of in the f skel feature assuming independent existential binding of unfilled argmnents and free variables
each subcat frame is a multiset NUM specifying the complements which the head requires in its left or right modifiers
null when the server implementing the idl specification is launched it creates skeleton object references for implemented services objects and publishes them on the orb
however some applications may want to store complex data structures as document annotations for example trees graphs feature structures etc
there would seem no reason why these files should not be replaced by a database implementation however with potential performance benefits from the ability to do i o on subsets of information about documents and from the high level of optimisation present in modern database technology
a disadvantage is that although graph structured data may be expressed in sgml doing so is complex either via concurrent markup the specification of multiple legal markup trees in the dtd or by rather ugly nesting tricks to cope with overlapping socalled milestone tags
our project is to provide a flexible and efficient way to combine le components to make le systems whether experimental or for delivered applications not to provide the one true system or even the one true development environment
for a variety of reasons nlp has recently spawned a related engineering discipline called language engineering le whose orientation is towards the appli null cation of nlp techniques to solving large scale realworld language processing problems in a robust and predictable way
the temple morphological analyzers and the english morphological generator all function as standalone executables and will be easily converted to corelli plug n play tools
the data layer of the corelli document architecture as described above provides a static model for component integration through a common data framework
it is clearly of high utility to those in the le community to whom these theories and formalisms are relevant but it excludes or at least does not actively support all those who are not including an increasing number of researchers committed to statistical corpus based approaches
the major work in converting lasie to vie involved defining useful module boundaries unpicking the connections between them and then writing wrappers to convert module output into annotations relating to text spans and to convert gdm input from annotations relating to text spans back into the module s native input format
in addition the first child following the head of a prepositional phrase is marked as a complement
we believe an average user can easily learn operations of our system
parsers in which the application of a rule is driven by the right most daughter such as shift reduce and inactive bottom up chart parsers encounter a similar problem for rules such as vp as sem vp argias sem arg
in this case the modified head corner relation table will consist of a single clause relating x NUM and y NUM by taking the generalization or anti unification of the two clauses head link x b y b
for example if the goal is to parse a phrase with category sbar from position NUM and within positions NUM and NUM then for some grammars it can be concluded that the only possible lexical head corner for this goal should be a complementizer starting at position NUM
for the current version of the grammar of ovis weakening the goal category in such a way that all information below a depth of NUM is replaced by fresh variables eliminates the problem caused by the absence of the occur check moreover this goal weakening operator reduces parsing times substantially
in practice however this does not seem to be problematic in our experiments the size of the history table is always much smaller than the size of the other tables this is expected because the latter tables have to record complex category information
this solution dramatically improves upon the average case memory requirements of a parser moreover it also leads to an increase in average case time efficiency especially in combination with goal weakening because of the reduced overhead associated with the administration of the chart
also note that even though the first call to the parse predicate has variable extreme positions this does not imply that all power of top down prediction is lost by this move recursive calls to the parse predicate may still have instantiated left and or right extreme positions
common wisdom is that although small grammars may be successfully treated with a backtracking parser larger grammars for natural languages always require the use of a data structure such as a chart or a table of items to make sure that each computation is only performed once
it is useful to consider the fact that if we had previously solved for example the goal parse s NUM x NUM NUM then if we later encounter the goal parse s NUM y NUM NUM we can also use the second table immediately the way in which the extreme positions are used ensures that the former is more general than the latter
in the second and final step of the modification we re arrange the information in the table such that for each possible goal category functor g n there will be a clause head link g al an pg qg head ph qh head link g n head ph qh g ai an pg qg
using the theory embodying the concepts just listed we report on our machine learning program of corpora from ten natural languages
these are perhaps subsumable under more general attributes
the feature concrete is also very widely applicable
for example in the NUM sentence samples course disambiguates short though once it is used for path and once for class because both senses are concrete
evant and indicator nouns are readily recovered section NUM NUM NUM shows that coverage can be increased by exploiting specific indicator nouns in order to infer or to extract automatically general semantic attributes of nouns
in the NUM sentence samples when old modifies a role noun it always applies in its aged sense to the individual and in its former sense to the role
any noun n with sense s can be used to mean a type of s as with family doctor in swedes lament the almost total disappearance of the old family doctor
it was counted directly only in non anaphoric usage
except with respect to the broader discourse context
we refer to this pattern as phrasal substitution
principled disambiguation discriminating adjective senses with modified nouns
for instance in the domain of military equipment maintenance users can be easily classified by rank years of experience equipment familiarity and so on
the large gap between oracle and continuous is due to the fact that continuous initiative selection is only using limited probabilistic information about the knowledge of each agent
we show how to incorporate initiative changing in a task oriented human computer dialogue system and we evaluate the effects of initiative both analytically and via computer computer dialogue simulation
for efficient initiative setting it is also necessary to establish the likelihood of success for one s collaborator s lst ranked branch 2nd ranked branch and so on
while still in prototype development preliminary results suggest that the algorithms that were successful for efficient computer computer collaboration are capable of participating in coherent humanmachine interaction
continuous in continuous mode the more knowledgeable agent defined by which agent s first ranked branch is more likely to succeed is initially given initiative
all initiative changes can be accounted for by explicit initiative changing utterances or by popping of the problem solving stack due to goal resolution as illustrated in figure NUM
there may be some diseases that account for all NUM symptoms others that might account for NUM out of the NUM symptoms and so on
a tutoring training system has the advantage of knowing exactly what lessons a student has taken and how well the student did on individual lessons and questions
an annotated example dialogue from a computer computer collaboration in this domain is presented in figure NUM agents were given partial information through a random process
in fact while the naive bayes classifier is most accurate for only NUM of the NUM words the average accuracy of the naive bayes classifiers for all NUM words is higher than the average classification accuracy resulting from any combination of the search strategies and evaluation criteria
the distribution of g NUM is asymptotically approximated by the x NUM distribution g NUM x NUM with adjusted degrees of freedom dof equal to the number of model parameters that have non zero estimates given the training sample
NUM decomposable models are those graphical models that express the joint distribution as the product of the marginal distributions of the variables in the maximal cliques of the graphical representation scaled by the marginal distributions of variables common to two or more of these maximal sets
each member of this set is a hypothesized model and is judged by the evaluation criterion to determine which model results in the greatest improvement in fit from the current model that model becomes the current model and the search continues
each member of this set is a hypothesized model and is judged by the evaluation criterion to determine which model results in the least degradation in fit from the current model that model becomes the current model and the search continues
multi paragraph subtopic segmentation should be useful for many text analysis tasks including information retrieval and summarization
that suggested here multi paragraph segmentation has many potential applications
in real world text these expectations are often not met
distribution constraint on this term set can be adjusted accordingly
and if so what size unit should be used
although most discourse segmentation work is done at a finer granularity than
another area that models the multi paragraph unit is automated text generation
texttiling is a technique for subdividing texts into multi paragraph units that represent passages or subtopics
the burden of analysis is consequently transferred to identifying the formal markers of topic shift in discourse
such a grammar might be called the parse forest grammar
in the following c denotes a salient concept c c r a salient relationship r of concept c and c r d denotes a related sahent concept d for concept c with respect to the relationship r
hcm first conducts hard clustering of words
that is found in the lexicon as a past verb and participle vbd vbn then the unknown word is a base or non 3d present verb vb vbp
taggers assign a single pos tag to a word token provided that it is known what pos tags this word can take on in general and the context in which this word was used
where b is the number of bootstrap replications NUM b is the mean estimate of the bth bootstrap replication NUM
the two additional types of features used by brill s guesser are implicitly represented in our approach as well one of the brill schemata checks the context of an unknown word
NUM guessing rule induction as already mentioned we see features that our guessing rule schemata is intended to capture as general language regularities rather than properties of rare or corpusspecific words only
coverage is a very important measure for a rule set since a rule set that can guess very accurately but only for a tiny proportion of words is of questionable value
then since we are interested in the application of the rules to word tokens in the corpus we multiply the result of the guess by the corpus frequency of the word
of course not all acquired rules are equally good at predicting word classes some rules are more accurate in their guesses and some rules are more frequent in their application
NUM shows the results of creating clusters
users can specify in a user profile which dictionaries they want to call up
furthermore we discuss approaches to cope with the problem
this is due to the ambiguity of part of speech that is so prevalent in english
NUM summarizing then we conclude that count nouns under conversion give rise to mass nouns with the following shift of sense the denotation of the mass noun is the largest aggregate of the parts of the elements 5however see wierzbicka NUM ch NUM for interesting subtleties
thus it is clear that to accommodate the nonce usage of proper names as common nouns conversion rules with concomitant semantics are required and that to accommodate the fact that some proper names become lexicalized as common nouns requires that they be given special lexical entries
standard treatments of feature can accommodate these two restrictions the features of a count noun are assigned to its first dominating noun phrase node i.e. its maximal projection and the features assigned to a determiner must be consistent with the features of its first dominating noun phrase node
thus in the case of animals and plants the parts are the edible portions in the case of trees the parts are the portions of wood and in the case of stones rocks ropes wires and so forth the parts are pieces
while we have not addressed all claims made on behalf of lexical rules we have provided an alternative explanation in several cases
if the feature assigned to the noun phrase node of the quantified noun phrase is pl then the choice of the aggregation is unconstrained but if it is pl then the choice is constrained to the least aggregation that is the set of all the minimal aggregates of the count noun s denotation which is of course just the count noun s denotation
the situation is not dissimilar for the division within common nouns between mass nouns and count nouns on the one hand nonce formations which give rise to the conversion from mass to count or count to mass requires that these conversion rules have a concomitant semantics on the other hand the very notion of conversion does not stand without an initial specification of membership in one lexical class or the other
fherefore we colnputed a smoothed dialog act plausibility vector for each word w which re lects the i lausilility of the cat egol ies br a particular word
NUM x logp geoform sim hiu coast NUM NUM logp hill logp coast generally speaking 2xlogp n i ci NUM irlz c c iogp c logp c
we except morphological rules which apply to lexical items but we argue that these rules are more productive and the semantics less precise than that suggested by the lexical rule approach which often treat a single morphological process as a number of rules subdivided according to the semantic category of the base or derived item
section NUM provides some background material on lexical rules and their hypothesized grammatical status and contrasts this with our approach to such phenomena
the goal is to enable more efficient handling of long sentences that are otherwise unprocessable given moderate resources
an alternative to sequential selection is batch selection
the rule based tagger trained using this algorithm significantly outperforms the traditional method of applying the baum welch algorithm for unsupervised training of a stochastic tagger and achieves comparable performance to a class based baum welch training algorithm
before the text is passed on to the parser it is subjected to a thorough process of disambiguation
the software component consisted of the t lanslation kernel used tbr analysis transfer and generation
a principle aim would be to determine how large the corpus must be before consistent co occurrence predictions are obtained
table NUM illustrates examples of lrs discovered and used in adjective entries
obviously similar problems occur in real life large scale lexical rules as well
perishable and all together they account for under NUM adjectives
it is true that some of these combinations are extremely rare e.g.
we are also grateful to anonymous reviewers and the mikrokosmos team from crl
it is clear by now that lrs are most useful in large scale acquisition
section NUM presents a fully implemented case study the morpho semantic lrs
the patterns of attachement include unification concatenation and output rules NUM
kupiec ran experiments using the original brown corpus
figure NUM partial entry for the spanish lexical item compra generated automatically
the lr processor applies to all the word senses for a given superentry
yes noun verb noun adj noun det output nodes
in a contextindependent model each entry has a single cost parameter
now in natural language negative correlations are an important source of information the occurrence of some words or groups of words inhibit others from following
since we are working towards a hierarchical language structure we may want the words within constituents correctly tagged ready for the next stage of processing
and though the nets easily train to NUM correct the lower threshold gives slightly better generalisation and thus gives better results on the test data
the number of iterative cycles that are necessary depends on the threshold chosen for the trained net to cross and on details of the vector representation
for instance in sentence NUM above NUM words have NUM alternative tags which will generate NUM possible strings before the hypertags are inserted
in our method the required parse is found by inferring the grammar from both positive and negative information which is effectively modelled by the neural net
the string comparison is defined by the minimal number of deletions and insertions that is required to turn the first string into the second levenshtein distance although it may be worthwhile to investigate other measures
when an argument is a medium for some other argument it means that its monotoni development is manifested or materialized through this other argument
the compositional rules combine two sign stru i ures attd create a new compound structure that includes parts of l oth of them
the conceptual phonological frame which is referred to as a minimal sign is made up of a semantic conceptual part and a realizational part
to evaluate the appropriate combination of the factors determining the scoring function and to evaluate this approach with respect to other approaches we use a corpus of word graphs for which we know the corresponding actual utterances
a single frame supports not only all forms of a word but also words of different categories that are derived from the same semantic basis
this simplifies and speeds up the access of lexical information but also blows up the size of the lexicon and leads to huge maintenance problems
in a similar vein the noun paintingn referring to a painting process is derived from the minimal sign paint and the suffix ingn
consider the minimal sign paint in figure NUM which is the lexical entry underlying the related words paintv paintn paintingn paintablea etc
in our proposal we memo only those categories that are maximal projections i.e. projections of a head that unify with the top category start symbol or with a nonhead daughter of a rule
subjl assigns the subj function to arguments that contain source or controller roles whereas objl requires a completed description and assigns the obj fimction to arguments that have a monotonic role
so if we want to compare the lexicon size of different mt systems we have to find a way to determine the lexical coverage by executing the system with selected lexical items
the linking table information is used to restrict which lexical entries are examined as candidate heads during prediction and to check whether a rule that is selected can in fact be used to reach the current goal
the transfer parameter table specifies costs for the application of transfer entries
some examples of korean translation output are given in the som ee ode is written in c korean translation outputs are displayed on a hangul window running on unix
it would be difficult to make such subtle distinctions rapidly
our system is capable of producing accurate translation of comt lex sentences NUM words and sentence fragments as well as average le ngth NUM words grammatical sentences
we have also discussed ideas on how to make the system robust and proposed two specific solutions integration of a part of speech tagger and a word for word translator section NUM
analysis of discourse structure is needed in order to identify long distance relationships
compiling the complete nlu shell application in order to test changes is time consuming
NUM because the learning algorithm is data driven it only needs to consider a small the former attempts to maximize the probability of a string whereas the latter attempts to minimize the number of errors
for example in grapheme strings such as refuse and produce the default to noun would be to refjuls and prodju s which in unrestricted text are less frequent than the verb forms
this set reads as follows the grapheme c is realized phonemically as k if occurring immediately before the grapheme a or o as in cab cake decal it is realized as s elsewhere cease cigar
ing e with the word relationship the rule decomposes the word into relation ship ship scandalousness is decomposed into scandal ous ness by the following rules
the overall performance on the training set was NUM NUM on the training set and NUM NUM on the test set
this paper NUM association for computational linguistics computational linguistics volume NUM number NUM will outline some of the previous attempts to construct such rule sets and will describe new and successful approaches to the construction of letter to sound rules for english and french
qenteiice and word hypotheses from a speech recognizer are still far fl om optimal for continuously spoken spontaneous speech
as this paper is in english dealing with the french language and in the event that the reader might be not familiar with the idiosyncrasies of french only a few examples will be given to explain the mechanism of the letter to sound rules for french
by examining the left and right words it is possible in most of the cases to get an idea of the grammatical categories of the unmarked words or to reduce to one if possible the set of potential grammatical categories for each word of a sentence
ei has primary stressed marked NUM as in transformation f ii nl is unstressed marked NUM is a syllable boundary
in this approach we integrate a symbolic semantic segmentation parse with a learning dialog act network
in t able i we have described the dialog acts w use in our domain
moreover if we consider the cost of clarifications and repairs in terms of time that is not awfully high giving departure and arrival in less than three turns that is without clarifications or repair takes from NUM to NUM seconds while the entering of repair subdialogues increased this time of an average of NUM seconds on the total average time of the dialogues
during the evaluation of the dialogos corpus we classified the users errors into two main classes NUM the user confirmed a task parameter value that derived from a misrecognition NUM of the users errors after having experienced several recognition errors NUM the user accepted a task parameter derived from a word that was inserted by the recognizer and interpreted by the parser NUM
in order to reduce the number of confirmation turns we use the following strategies the dialogue system avoids confirmation turns when the acquired information is coherent with the dialogue history and with the current focus the dialogue system asks for multiple confirmations of the acquired parameters as in t3 s the dialogue system asks for implicit confirmations whenever it is possible as from milano to roma
for preventing recognition errors the dialogue system sends to the lower levels of analysis information about the domain objects focused during each turn of the interaction this information allows the triggering of context dependent language models that help to constraint the lexical choices at the recognition level see section NUM
the conversation took place on thursday february 27th the dialogue system recognizes the misconception in t2 u because the week day the day and the month are n6t interpretable with respect to its knowledge of the year s calendar and the computer presumes that its calendar is correct
the first one is related to the need for goal management in the task domain of dialogos the goal is fixed during all the dialogue but that is a simplification of the travel inquiry domain introduced to control the complexity of interaction in order to meet the real time requirement
in t6 u the utterance segment mah mi dica se c e qualcosa who knows tell me if there is something was misrecognized as mattino ginosa morning ginosa where ginosa is the name of an italian village
the work of the first and the second authors was partially supported by the le3 NUM project arise automatic railway information system for europe promoted by the commissions of the european communities dg xiii telecommunications information market and exploitation of research
when applied to belief modification modify proposal has two specializations correct node for when a proposed belief is not accepted and correct relation for when a proposed evidential relationship is not accepted
the belief revision mechanism will then be invoked to determine the system s belief about on sabbatical smith next year based on the system s own evidence and the user s statement
in cases where such conflicts are relevant to the t t k at hand the agents should engage in collaborative negotiation as an attempt to square away the discrepancies in their beliefs
although agents involvedin argumentation and non collaborative negotiation take other agents beliefs into consideration they do so mainly to find weak points in their opponents beliefs and attack them to win the argument
the evaluation of proposed beliefs starts at the leaf nodes of the proposed belief trees since acceptance of a piece of proposed evidence may affect acceptance of the parent belief it is intended to support
in selecting the focus of modification the system will first identify the candidate foci tree and then invoke the select focus modification algorithm on the belief at the root node of the candidate foci tree
for each such belief the system could provide evidence against the befief itself address the unaccepted evidence proposed by the user to eliminate the user s justification for the belief or both
select focus modification determines whether to attack bel s supporting evidence separately thereby eliminating the user s reasons for holding bc l to atta bel itself or both
the italian version of wordnet in december NUM included about NUM NUM lemmas NUM NUM nouns NUM verbs NUM NUM adjectives NUM adverbs
as a convenction we decided to describe internal arguments with the symbol while a denotes a verbal adjunct
the top NUM features were selected
the fifth feature concerns the presence of the word mr
the mean document length was NUM p NUM sentences
figure NUM typical segmentations of wsj test data
model a was trained on 2m words of broadcast news
we consider distributions in the linear exponential fam
the first segmenter was built on the wsj corpus
under certain mild regularity conditions the maximum likelihood solution
we think that the main reason this technique was not used sooner is that beam thresholding for pcfgs is derived from beam thresholding in speech recognition using hidden markov models hmms
in the experiments described herein n NUM
in this paper we also explore the integration of selectwnal res mctwng a traditional technique used venezia and the brench of the university of torino at vercelli
some of our concrete diagnoses may be wrong or fall short or become obsolete by a new release but the questions to be posed for this type of semantic net remain
NUM andthefirst world NUM NUM NUM andthefirst time NUM NUM NUM andthefirst boy NUM NUM NUM andthefirst b NUM i
it has been conceived as a computational resource so improving some of the drawbacks of traditional dictionaries such as the circularity of the definitions and the ambiguity of sense references
for instance the clause as long as i breathe in e22 translates into an idiom al zde
these cues are easily computable in contrast to the structural cues that have figured prominently in previous work on genre
a rule only provides patterns for analogical forma1here
in this section we describe the empirical results obtained by coupling a wordnet based lexicon with a parser in our intention the experiment would bring evidence for the following aspects
some judges regard a translation as unacceptable if a single word choice is suboptimal
NUM one of the two preceding following words is tagged z NUM one of the three preceding following words is tagged z NUM
the judge is then asked to decide whether the recognition hypothesis is acceptable
roots and certain relevant features such as subcategorization requirements for closed classes of words such as connectives postpositions etc
we learned rules from ark itself and on the first NUM NUM and NUM sentence portions of c2400
null 13please note for ark in the first two rows the training and the test texts are the same
which selects and adjective parse following a determiner adjective sequence and before a noun without a possessive marker
in the following sections we present an overview of the morphological disambiguation problem highlighted with examples from turkish
for improved disambiguation one has to at least recover any morphological features even if the root word is unknown
one would need serious amounts of semantic or statistical root word and word form preference information for resolving these
NUM if llc ic and stem re then choose delete p
the module takes as input to the system raw turkish text and preprocesses it in a manner to be described shortly
for example num null ber5 in table NUM and sum4 in table NUM arc ahnost the saine sense
the result of the addition of num l s lie or inore eohntlns or rows of numbers to be added
he used the semantic codes of the longmau dictionary of contemporary english in order to determine the subject donmin for a set of texts
in our method the weight of wi is the wdue of mu between v and wi which is calculated in stage one
another interesting t ossibil ity in to use ml altermttive weighting policy such a s the widf weigh te
basic words are sele te l the lo00th most fre luent words in the reference collins english dictionary
lank ix con erned with linking l used experiment i.e. we applied linking method to original artmes
in order to get a reliable statistical data we merged every new article into one and used it to calculate mu
in tal le NUM article means the munber of articles which are sele ted from test data
the output of the system is the ranked list of nouns after the final iteration
the most common reason is again a serious recognition error
this subcomponent allows one to make two semantic individuals co designating i.e. to equate them
note that what counts as a most immediate contextualized fact is itself determined by a separate search procedure
indeed alembic has only a short list of known organizations less than a half dozen in total
what appears to be a spare argument to the has age predicate above is the event individual for the predicate
after the initial tagging contextual rules apply in an attempt to further fix errors in the tagging
the production version of the tagger which we used for muc NUM relies on the even so
first a set of initial phrasing functions is applied to all of the sentences to be analyzed
this seeding process is driven by word lists part of speech information and pretaggings provided by the preprocessors
all of these preprocess components are implemented with lex the lexical analyzer generator and are very fast
for unknown words after a default tag is assigned lexical rules apply to improve the initial guess
NUM a susan gave betsy a pet hamster
NUM a susan gave betsy a pet hamster
it was a store john had frequented for many years
highly ranked element of cf un l
centering a framework for modeling the local coherence of discourse
he was excited that he could finally buy a piano
utterance 2b seems to be about the store
subsequently we revised and expanded the ideas presented therein
he wanted tony to join him on a sailing expedition
however centering also applies to dialogue and multi party conversations
remove the auxiliary symbols to give final result
here atoms are used for the terminal symbols of the grammar a and b and terms of the form are used for the triples representing dotted rules
the goal for scenario templates mini muc wa s to demonstrate that effective information extraction systems could be created in a few weeks
for the tex t mccann has initiated a new so called global collaborative system composed of world wide accoun t directors paired with creative partners
to factor out the influence of the initial configuration of the network the reference vectors are initialized to small random values twenty trials were run on each data set and the map with the lowest quantization error qe was selected as the best
this report describes the theoretical motivations of an experimental system that has been implemented as a set of shell scripts and c programs not all of the technical details of this system have been finalized and it has not been formally tested
the most common approach is some version of the following
for predicate argument structure practically every new construct beyond simple clauses and nou n phrases raised new issues which had to be collectively resolved
the named entity task exceeded our expectation in producing systems which could perform a relativel y simple task at levels good enough for immediate use
the message understanding conferences were initiated by nosc to assess and to foster research on th e automated analysis of military messages containing textual information
the third part extend description takes care of updating some control variables
the last two criteria are additions introduced in the new algorithm
finally we demonstrate the increased functionality of the new algorithm and we evaluate the achievements
NUM adequate control should be provided over the complexity and components of the referring expression
the algorithm is shown in two different degrees of precision
NUM the linguistic aspects are largely simplified and even neglected in parts
after this overview we explain the algorithm in detail
space restrictions do not permit a detailed presentation of the new algorithm at work
even more importantly matters of grammaticality are not taken into account at all by previous algorithms
since the local context database is constructed from wsj corpus which are mostly business news we only used the press reportage part of semcor which consists of NUM files with about NUM words each
one or two minor syntactic or word choice errors otherwise acceptable
however the text often does not clearly indicate that the person must give up the old post
this scenario concerns events that would be of interest to an analyst who tracks changes in company management
NUM all persons in a succession involving a shared post are represented in a single succession even t object
examples of mutually exclusive posts sole ceo versus co ceo chairman versus chairman emeritus president versus vice president
however if the tex t provides a specific title and a more general job description only the specific title will be included in th e answer key thus if both chief executive officer and the top office appear in a text the answer key wil l contain only chief executive officer
related org the person s old and new positions are in organizations that are identified in the tex t as having some corporate relationship with each other e g different units child companies of the same paren t company different companies that have formed a merger a company that is controlled via majority stoc k ownership by a different company
thus it includes all positive evidence for the act of vacating a post except for cases that fit th e definition of depart workforce firing resignation quitting leaving for any reason moving to a ne w position at either the same company e g via promotion or management shuffle or at a different company job hopping
all main verbs are furnished with two syntactic tags one indicating its main verb status the other indicating the function of the clause
uphb shares have been suspended since october g at the firm s request following a surge in its share price on a takeover rumor
because only boundaries separating finite clauses are indicated there is only one sentence internal clause boundary between daughters and that
metrics are then applied to choose a path through this lattice
the similarity between the three senses are all below NUM NUM although the similarity between senses NUM responsibility and NUM assignment chore is very close NUM NUM to the threshold
in buihl ing these structures the generator is e fcctively searching branches of the search space which never lead to a coml lete sentence
arg p wic arg p w NUM
we devised a statistical word formation model for unseen words which can be re estimated
we decompose it into the product of word length probability and word spelling probability
we assume NUM NUM of the segmentation errors belong to this type
first the state of the art has progressed greatly in portability in the last four years
the ninth tenth and eleventh columns of table NUM show the word segmentation accuo facies
this might correspond with the results on unsupervised learning performed by an english part of speech tagger
the morphological description consists of two rule components i the lexicon and ii heuristic rules for analyzing unrecognised words
finally we describe the experiment results of unsupervised word segmentation under various conditions
we then filter out hiragana strings because they are likely to be function words
preliminary discussions among government researchers who were interested in establishing a major new interagency text handling processing and exploitation program began in the summer of that year and continued in earnest during the months that followed
zipf s law concerns the asymptotic behavior of the relative frequencies f r of a population as a function of rank r
combining eq NUM with eq NUM yields dr n1 n1 df n x
in view of the established exponentially declining asymptote of the ideal turing distribution corresponding to NUM NUM we can conclude that the latter is qualitatively different
p r can then be interpreted as the probability of waiting r trials for the first occurrence of the outcome
n n NUM here n is the number of species with frequency count x and x is the improved estimate of x
furthermore the two cases correspond to the upper and lower bounds of this parameter for which the cumulative of the frequency function converges as rank tends to infinity
the major addition here is that the retrieval of chinese language documents has been added to the multilingual track
due to limitations in the amount of available training data the so called sparse data problem estimating probabilities directly from observed relative frequencies may not always be very accurate
each of these evaluations has been reported on in detail in either the proceedings of the tipster text program phase i in this proceedings for phase ii or in the separately pubfished proceedings for the message understanding conferences muc NUM to muc NUM and text retrieval conferences trec NUM to trec NUM
the collection formatting and preparation of appropriate document databases and the creation of topic statements and pooled relevance judgments to support the document detection research tasks and of complex scenario templates detailed fill rule descriptions and appropriate answer keys to support the information extraction research task turned out to be a monumental undertaking
while the job was eventually completed it was only through the tireless and sometimes even heroic efforts of a small number of highly motivated and dedicated government researchers that this data preparation effort was brought to a successful conclusion in phase i to say the least this is not a recommended mode of operation
the main focus was now placed on investigating ways in which the two separate technology areas of document detection and information extraction could synergistically interact within a single modular tipster system architecture on developing and deploying operational prototypes based upon the most promising tipster algorithms and on the continuing advancement of the overall performance of the best tipster algorithms
these included a multiple database merging track a confusion track to examine the effect of corrupted data a multilingual track to examine retrieval of spanish language documents an interactive track and a filtering track
NUM establishing and maintaining a cooperative corporate viewpoint among the program s external participants is made considerably easier if it is evident that there is a similar cooperative and corporate viewpoint being regularly demonstrated by the government sponsors
it is evident that the robust learning procedure is superior to the discriminative learning procedure in the test set
therefore mle fails to provide a reliable result if only a small number of sampling data are available
the accuracy rate for parse tree selection is improved to NUM NUM when the discriminative learning algorithm is applied
resolution of syntactic ambiguity has been a focus in the field of natural language processing for a long time
the investigation described in section NUM has shown that smoothing is essential before the robust learning procedure is applied
on the other hand if the consulted lexical contexts are fixed the performance of the syntactic disambiguation process is improved significantly by using more syntactic contextual NUM the term most preferred candidate means the syntactic structure most preferred by people even when there is more than one arguably correct syntactic structure
robust learning smoothing and parameter tying table NUM performance for lexical and syntactic disambiguation with various estimators
computational linguistics volume NUM number NUM table NUM the decomposed phrase levels associated with the sentence a stack ofpinfeed paper three inches high may be placed underneath it and the corresponding scores with the back off estimation method for a the correct candidate and b the top candidate
fortunately there is such a system the jensen quine system of set theory known as nfu
an event or course of events coe is a function from locations to situation types
NUM that i4 is a proper class therefore e is as well
NUM thus such events can not for example be constituents of other situations
in particular iterated or embedded attitude reports can not be handled in this framework
the problem involves a distinction barwise and perry make between epistemic and non epistemic perception
barwise and perry take individuals properties relations and locations as primitives
thus the situational analysis of attitudinal reports extends to iterated reports such as or2 without violation of set membership dicta
a situation type is a partial function from n ary relations and n individuals to the set lcb NUM NUM rcb p
an evaluation of strategies for selective utterance verification for spoken natural language dialog
every system that uses natural language understanding will sometimes misunderstand its input
NUM NUM strategy NUM using parse cost only
computer did you mean to say that the switch is up
figure NUM sample utterances with word misrecognition
jean is looking at the sails veils b masculin le voile fdminin la voile NUM a
phraser rules do make mistakes but as with other sequence based processors the phraser applies later rules in a sequence to patch errors made by earlier rules
verification operates at the semantic level
we found a combination of knowledge gaps known design problems that had been left unaddressed by the time of the evaluation run and some truly embarrassing bugs
in the template element task our largest performance drop was on the org desriptor siot where we los t ii points of recall and NUM points of precision
to be precise our final dry run p r score prior to the muc NUM evaluation run was NUM NUM a scant o NUM higher than the officially measured evaluation score
for example by not treating embedde d mid word hyphens as white space we failed to process mccann as a shortened form of mccann erickson
specifically this task is the crucible for name coreference i e the process by which short name forms are reconciled with their originating long forms
this merging process takes place by iterating over the semantic individuals in the inferential database tha t are of a namable sort e.g. person or organization
job in pers NUM ttl NUM org NUM j i NUM as with job out this fact is mapped directly by the template generator to an incoming succession template
thus the decision rule for verification may be revised as follows
an examination of the subjects bracketings confirmed that these instructions were satisfactory in yielding plausible word sized units
this very simple approach allows multiple references to the same context vector entity
note that in this example the text chosen is a near literal translation
to perform learning a learning window is used to identify local context
these discussions assume that language NUM is english and language NUM is spanish
these relationships are learned automatically using only the text examples provided for learning
context vector learning is performed in multiple languages simultaneously using multilingual training corpora
obviously text routing and index term assignment could benefit from multilingual technology
additionally it should be noted that all document vectors are unit length
when the system generation is complete mir is ready for query processing
this prevents system biases in retrieval due to document length
by this criteria of statistical efficiency the extension models completely dominate context models and n grams
again the extension model class is the clear winner
the remainder of our article is organized into three sections
the second author is additionally supported by a tuition award from the princeton university research board
figure NUM compares model order to test message entropy for each of the three model classes
the paper has benefited from discussions with the participants of dcc95
the traditional context model maps every history to a unique context
to understand why we can look at an example from a slightly different domain
of course this class of models is strongly equivalent to ordinary context free grammars
note that our figures for bracketed training match very closely to the NUM NUM bracketing accuracy reported in their paper
the number of such statistics grows quadratically with sentence length and is prohibitive over large corpora using our current techniques
this is a strong indication that the algorithm is a poor choice for estimating grammars that have competing rule hypotheses
they could be distinguished from a in longer sentences because they pass different head information out of the phrase
edby is a common english character sequence that occurs in passive constructions fike she was passed by the runner
in this case the module plus the wrapper is equivalent to a single tipster module
such modifications and extensions may be submitted to the ccb as proposed changes to the icd
the end user will need to have the ability to interact with the application in many ways
it is not meant to imply that only those individuals should be able to make those inputs
markups present in form NUM documents require a key before the information they represent can be used
also the maintenance procedures should be more consistent across different types of persistent knowledge repository items
this will facilitate new research ideas increase interest and expand the technology and the associated funding
in addition the architecture will assist the application developer in the actual implementation of the application
examples of persistent knowledge are a lexicon a set of grammar rules and a gazetteer
however it meets only those requirements having to do with document detection and information extraction functions
affixes may appear explicitly in production rules or like roots they may be assigned complex feature valued categories
on backtracking protector returns all solutions
information including the creation of logical forms is passed between constituents in a rule by the sharing of variables
a rule is of the form spell name surface op lexical classes features
if results are cached subsequent attempts to analyze the same word are around NUM times faster still
i describe a compiler and development environment for feature augmented two level morphology rules integrated into a full nlp system
many european languages are of the inflecting type and occupy still another region of the space of difficulty
a rule set embodying a quite comprehensive treatment of french inflectional morphology was developed in about one person month
the original spanish trec queries were also evaluated to establish a reference baseline
a lexicai counts freq t w b bigram counts freq tl t2
figure NUM a simple conceptual graph
a single model can be used to estimate only the second type of uncertainty which does not correlate directly with the utility of additional training
its general operation is to recombine enhance and refine tsl expressions until they are adequately specific spl expressions
the statistics s for such a model are given by n the number of trials and x the number of successes in those trials
if the guessed class is the same as the class stated in the lexicon we count it as a hit or success otherwise it is a failure
and it is unlikely that recognition in the car will lead to better results
figure NUM tuit library integrates xwindows with tipster documents
for example japanese chinese interactive segmentation of documents is possible in the current system using crl s chinese segmentation system and a tipster front end to juman
before tuit tipster applications that needed multilingual text display and edit capabilities required developers to use the motif api and xmat api and write all tipster document browsing functions using the motif and xmat libraries
contact the author for availability or more information
this paper briefly describes tuit and its capabilities
it is not motivated by the presence of the coreference link in the tsl
again the dm can validate the first candidate in the way described above
the actions of the aggregation module result in the following changes within pre spl c1 for
the two subjects with motoric dysfunction have participated in the earlier studies and are well acquainted with computers and writing support
when training a bigram model indeed any hmm this is not true as each word is dependent on that before it
in other words the more statistics there are for estimating the parameter the more similar are the parameter values used by different committee members
a small e constant can be chosen for the probabilities bl and b i so that the optimal matching resorts to these productions only when it is otherwise impossible to match the singletons
as expected fewer of these words appeared in the list of suggestions and no keystroke savings were gained
for these reasons it is best to employ a simple coarse grammar with fallback productions that simulate the generic bracketing grammar when the english productions are too inflexible
NUM the conditional distribution over l ui v productions is estimated from the frequencies for the english part of speech l uniformly distributed over the set of matching chinese words
the part of speech categories were designed by conflating categories in the brown corpus tagset under the following general principle categories should be as broad as possible while still maintaining reasonable discriminativeness for bracketing structure
probabilities were placed on the syntactic productions uniformly but all inverted productions were they are right to do so assigned a slightly smaller probability in order to break ties in favor of straight matchings
parse parse match methods first bracket a parallel corpus by parsing each half individually using a monolingual grammar NUM heuristic procedures are subsequently used to select a matching between the bracketed constituents across sentence pairs
parsing in the context of itgs means to take as input a sentence pair rather than a sentence and to output a parse tree that imposes a shared hierarchical structuring on both sentences
they are recorded in the dialogue intention class or in the relevant domain expert subclass
two subjects that participated in the speed enhancement evaluation study turned out to have se
instanfiation has NUM using figure NUM class relationship model
this is rather similar to prevost s conditions on alternative sets
results from an evaluation study with individuals with motoric dysfunction and or dyslexia will be presented at the workshop in madrid
however contrary to our expectations subject f who had a severe motor disability showed no improvement
we envisage system components that draw on the strength of an object oriented architecture
dialogue intention dialogue intention embodies generic functionality for the furtherance of a dialogue
figure NUM data structures expressed by NUM a and b
there are various ways to determine the filtering range
at the same time there is a good deal of self congratulation at attending a good college
however while mann and thompson maintain that for any two consecutive elements of a coherent discourse one rhetorical relation will be primary i.e. related by an informational or an intentional relation moore and pollack showed that discourse interpretation and generation require that intentional and informational analyses exist simultaneously
because nuclearity can only be determined by consideration of intentions and intentional and informational analyses of a discourse must co exist we argue that the solution to the problem is to properly relegate information about nuclearity intention dominance to the intentional analysis and remove it from definitions of informational relations
in contrast informational relations properly construed should not distinguish between nucleus and satellite in their definitions
the most inclusive definition of informational structure would contain all the domain relations between the things being talked about
by choosing to modify the terminology from simply linguistic computational linguistics volume NUM number NUM structure to intentional linguistic structure we mean to suggest that consideration of something other than speaker intentions for example semantic relations could determine another kind of structure to the discourse
c121 the remaining NUM sentences constitute only about NUM NUM of the brown corpus
c123 alec leaned on the desk holding the clerk s eyes with his
figure 4a iii shows this elementary tree substituted at NUM satisfying that expectation
NUM but the evidence clarifying this local ambiguity may not be available until later in the discourse
be further broken down into the two minimal units b and c where c is a satellite that stands in an evidence relation to b NUM while there is no direct representation of intentions in rst the asymmetry between a nucleus and its satellite originates with the speaker s intentions
as shown on the left in figure NUM i0 dominates i1 which in turn dominates i2 due to these dominance relations the discourse segment that realizes i2 is embedded in the discourse segment for i1 which is in turn embedded within the discourse segment for i0 as shown on the right in the figure
since any multi branching tree can be converted to a binary tree no representational power is lost
in the following i discuss two problems for this approach
tell let has because he has let him tell the story
all analyses that involve argument attraction admit signs with underspecified comps lists
it is marked lex because it can in turn be embedded
i describe a very simple hpsg analysis for partial verb phrase fronting
a solution for the problem of underspecified comps lists was found
figure NUM analysis of vortragen wird cr es morgen
i will not repeat the arguments against these approaches here
a very simple solution for the pvp problem was found
for these sentence pairs the antecedents of the zero pronouns are not explicitly expressed in the human translation but the japanese analyzer still needs the resolution of the zero pronouns because the machine translation system which contains the japanese analyzer can not translate freely like the human translator
once again the embedded base level phrase ends up interpreted as a unary person fact
the incremental finite state parser presented here merges both constructive and reductionist approaches
attempts were made to mark the segments with additional syntactic information e.g.
the mark is called a temporary beginning of np tbeginnp
although edmundson s work is fundamental his experiments used only NUM documents for training and another NUM documents for testing
segments are restricted by their underlying linguistic indeterminacy e.g.
each transducer performs a specific linguistic task
the number of different topic keywords contained in the appropriate sentence in each text and averaging over all texts
after obtaining all the h scores we sorted all the sentences according to their paragraph and sentence numbers
however with window size NUM precision and recall scores drop seriously and more so with even larger windows
for each number of sentences extracted and for each window size we averaged the counts over all NUM NUM texts
one should also prefer sentence positions with smaller sin since paragraphs are generally short
considering that the matching process requires exact match and morphological transformation is not used this result is very encouraging
duplicate matches the same word s in different windows were counted in p but not in r
figure NUM vol NUM dhit distribution for the title sen tence and the first NUM paragraph positions
at the same time information from the response generator will be used to update the recent and long term history of the user
it must then be able to select which of these errors to discuss with the learner and in what order to discuss them
once an error has been identified and chosen for a corrective response the system must also decide on the content of that response
at the end of the woz design phase we began a lllol e theoretical forward looking exercise
the dialogues were recorded transcribed analyzed and used as a basis for inaprovements oil the dialogue model
firstly they were developed on the basis of a simulated human nmchine dialogue corpus collected during dialogue model design
grico however did not develop the inaxinls with the purpose of preveuthlg coinmunication failure in shared goal dialogue
at the end of the dialogue the frustrated user asked whether or not discount had been granted
for instance in the user test a user wanted to order a one way ticket at discount price
provide clear and comprehensible com inunication c NUM what the system can and can not do
it can be soeii as a sl ccial purpose application o1 gp ilonaulbiguity
second in the translation process the system produces several candidates of translation results using translation rules extracted in the learning process
i may without any consequence other than improved clarity be replaced by gp6 and gp7
the following principles have counterparts among the maxims NUM avoid semantical noise in addressing users
this transaction will lead to a negative balance in the checking account or some other domain specific state depend null ing upon the nature of the action involved
NUM status q uo this state is reached if the system determines that the most recent utterance by the user provided no additional query related information to the system
the system is designed to generate either sql queries or cgi script queries which makes it capable of querying the vast amount of information available on the world wide web
the out of bounds state and the meta query state improve usability by informing the user of why a certain utterance was inappropriate and allowing the user to ask about the system s abilities respectively
extensibility additional queries can be added to any ap null plication by specifying the query semantics in the application schema and any new fields that they may need
these results indicate that clustering sometimes does improve classification results when we use our current way of creating clusters
we should note here that a document s probability with respect to each category is equivment to the likelihood of each category with respect to the document and to classify the document into the category for which it has the largest probability is equivalent to classifying it into the category having the largest likelihood with respect to it
this is because in this case a a word will be assigned into all of the clusters b the distribution of words in each cluster will approach that in the corresponding category in wbm and c the likelihood value for each category will approach that in wbm recall case NUM in section NUM
NUM shows the numbers of parameters in our method fmm hcm and wbm where iw is the size of a vocabulary ikl is the sum of the sizes of word clusters m i.e. ikl e i ikil n is the number of categories and m is the number of clusters
suppose entries aik in a and bkj in b are both NUM assume we have some way to break up array indices into two parts so that i can be reconstructed from il and i2 j can be reconstructed from jl and j2 and k can be reconstructed from kl and k2
in the process we also provide a formal definition of parsing motivated by an informal notion due to lang
given two boolean m x m matrices a and b it constructs g and w as described above
this material is based upon work supported in part by the national science foundation under grant no iri NUM
first we would like to be able to retrieve constituent information for all possible parses of a string after all the recovery of structural information is what distinguishes parsing algorithms from recognition algorithms such information is very useful for applications like natural language understanding where multiple interpretations for a sentence may result from different constituent structures
however this reduction converts a parser running in time o iwl NUM NUM to a bmm checking algorithm running in time o m NUM the running time of the standard multiplication method whereas our result says that sub cubic practical parsers are quite unlikely thus our result is quite a bit stronger
bic assesses a greater parameter penalty log n than does aic NUM causing bss bic to remove more interactions than bss aic
the parameter penalty is expressed as x do f where the size of the penalty is the adjusted degrees of freedom and the weight of the penalty is controlled by x
suppose that the graphical representation of a decomposable model is defined by the two cliques i.e. marginals f1 s and f2 f3 s
this paper expands existing model selection methodology and presents the first comparative study of model selection search strategies and evaluation criteria when applied to the problem of building probabilistic classifiers for word sense disambiguation
model selection is presented as an alternative to these approaches where a sequential search of possible models is conducted in order to find the model that best characterizes the interactions among features
at each stage in bss we generate the set of decomposable models of complexity level i NUM that can be created by removing an edge from the current model of complexity level i
at each stage in fss we generate the set of decomposable models of complexity level i NUM that can be created by adding an edge to the current model of complexity level i
for convenience we refer to model selection using for example a search strategy of fss and the evaluation criterion aic as fss aic
we will see that different tokenizations can be linked by the cover relationship to form a partially ordered set
if a character string could only be tokenized in a unique way it would have no tokenization ambiguity
it allows us to reason about the consequences of a theory without hypothesizing a specific mechanism implementing it
each realized human language is just the intersection of the languages selected by the settings of its parameters
note though in contrast to typical applications of default logics a gpsg grammar is not an evolving theory
the agreement principles require pairs of nodes occurring in certain configurations in local trees to agree on certain classes of features
thus while universals in gb are properties of trees in gpsg they tend to be properties of sets of trees
in many cases though references to indices can be eliminated in favor of the underlying structural relationships they express
the first of these feature specification defaults in gpsg are widely assumed to have an inherently dynamic character
more recently it has become clear that in many cases these mechanisms can be replaced with ordinary logical operations
as any node connected to such a node by a sequence of propagate links
the requirement that chains be closed wrt link means that chains can not overlap unless they are of distinct types
the goal is to have both recall and precision as high as possible
theorem NUM every tokenization has a critical tokenization as its supertokenization but critical tokenization has no true supertokenization
in this way a query with terminology banlz will match better with the document than one with bank terminology since the indexing phrase bank terminology provides extra discrimination
with this approach it takes a NUM mhz dec alpha workstation about NUM hours to train the parser over the noun phrases from a NUM megabyte sfor the experiments reported in this paper the threshold is NUM
for example only using the word baals and terminology for indexing is not enough to distinguish bank terminology from terminology baals
the clarit system is configured to accept the indexing set we passed as is to ensure that the actual indexing terms used inside the clarit system are exactly those generated
thus using selective nlp such as the noun phrase parsing technique we proposed is not only feasible for use in information retrieval but also effective in enhancing the retrieval performance deg
pc sj is the probability of structure sj while pc u v is the probability of generating the word pair u v given any word modification relation
if information has a stronger dependency association with retrievap than with technique information retrievat will be grouped first otherwise retrieval technique will be grouped first
correctness for scientific explanations the extent to which the explanations are in accord with the established scientific record
the view that is taken of a concept has a significant effect on the content that is selected for its description
when the accessors encounter attributes with inappropriate values they prevent fatal errors from occurring by employing a rigorous type checking system
NUM finally the explain algorithm passes the paragraph clusters to the realize algorithm which translates them to natural language
for each topic node the edp applier constructs a new corresponding topic node for the evolving explanation plan
recall that the topic nodes and elaboration nodes of an edp are instantiated only when their conditions are satisfied
specification that comes in the form of a qualitative rating expressing the desired length of the explanation figure NUM
though flexible they do not account for extended explanations which require a more global rhetorical structure
NUM conclusion explanation generation is an exceedingly complex task that involves a diversity of interacting computational mechanisms
that is it is often the case that there is more than one correct tag for a word in context where that word could be considered to be functioning as a proper or a common noun n adjective or a noun a participle or an adjective a gerundial noun or a noun e an adverbial particle or a locative adverb and even an adjective or an adverb
given the english alphabet the tiny dictionary d lcb fund funds and sand rcb and the character string s fundsand there is cd s lcb funds and fund sand rcb
given the english alphabet the tiny dictionary d lcb th this is his book rcb and the character string s thisishisbook there is co s lcb this is his book rcb
in fact conjunctive combinational ambiguity as defined above is a special case of hidden ambiguity in tokenization since al aibl bj al ai bl bj
given td s lcb a b c d a b cd a bc d a bcd ab c d ab cd abc d rcb there is co s lcb abc d ab cd a bcd rcb to s
by definition there is x y lemma NUM reveals that a word string is covered by another word string if and only if every word in the latter is realized in the former as a word string
substring cp l cq is a critical fragment of s on d if both p and q are critical points and any other position r in between them p r q is not a critical point
definition NUM the character string s from the alphabet g has tokenization ambiguity on dictionary d if td s NUM s has no tokenization ambiguity if td s NUM
figure NUM depicts the possible combinations of chart entries into a larger lr lt st and sr each
the above iteration is continued until all the probabilities are settled down or the training corpus entropy converges to the minimum
basic complete sequence is null sequence of complete links which is defined on one word the smallest word sequence
figure NUM and NUM depict the cases of lr and ll to become a substructure of larger sr and
this paper presents a reesthnation algorithm and a best first parsing bfp algorithm for probabilistic dependency grummars pdg
in the following section we discuss an extension of the back off estimation model that capitalizes on this property
in the works however the dependency grammar was rather a restricted form of phrase structure grarnrnarss
the inside outside algorithm learns a grammar by iteratively adjusting the rule probabilities to minimize the training corpus entropy
if the verb is being questioned this is a cue that the assertion or negation of the verb will be the focus of the answer no as for ahmet farina did not see him
in addition it is possible to focus the whole vp or the whole sentence which can be determined by the context in this case the database query NUM a bugiin fatma ne yapacak
a sentence can be divided into a topic and a comment where the topic is the main element that the sentence is about and the comment is the main information we want to convey about this topic
after presenting the motivating turkish data in section NUM i present a competence grammar for turkish in section NUM that captures the basic syntactic and semantic relationships between predicates and their arguments or adjuncts while allowing free word order
for example a speaker may use the sov order in 2b to answer the wh question in 2a because the speaker wants to focus the new object ahmet and so places it in the immediately preverbal position
fatma is the focus of the answer while ahmet is the topic a link to the previous context and thus the osv word order is used NUM as for ahmet fatma saw him
null the function above can use the simple application rules to first combine with a focused constituent on its left then a ground constituent on its left then a topic constituent on its left and a ground constituent on its right
the restriction y np on the multiset ccg composition rules prevents the categories for verbs si lcb np rcb and for adjectives np lcb p from combining together before combining with a bare noun
to limit the number of alternative results to one in such cases we must impose a unique factorization on every input
in figure NUM is sequentiable because there the choice between a and a x just depends on the next input symbol
the initial caret is replaced by a and a closing is inserted to mark the end of the match
in the near future we also plan to allow directional and length of match constraints in the more complicated case of conditional context constrained replacement
it is meant to suggest that the mapping from the input into the output strings is guided by the directionality and length of match constraints
the second transducer does a similar transduction on strings that begin with a figure NUM illustrates the effect of the positive filter
left to right longest match replacement can be thought of as a pr ocedure that rewrites an input string sequentially from left to right
it is not obvious at the outset that the operation can in fact be encoded as a finite state transducer for arbitrary regular patterns
the document server consists of three major modules document management service naming service and life cycle service
documents are accessible via a document server which maintains persistent collections documents and their attributes and annotations
corelli document annotations axe essentially the same as tipster document annotations and a similar generic interface is provided
documents annotations and attributes the data layer of the corelli document processing architecture follows the tipster architecture
the lexical knowledge encoded in these systems can truly be called reusable since neither the format nor the content is application dependent
finally we will have to reengineer a portion of the top level application control code in c in java
null an application manipulating only basic data types strings numbers need not define application objects
the emphasis is put on reuse of nlp software components and their integration in order to build large scale applications
the data layer implements the tipster document architecture and enables the integration of tipstercompliant components
engineering reuse and integration in the context of software architectures for natural language processing
pronouns typically refer to these functions e.g.
in the first place if the most specific terms have non zero frequency it still interpolates them with the more general terms
adequate bases are determined via selection restrictions
a robust parser is one that can analyze these extragrammaticalsentences without failure
so the bulk of written sentences are open to the extragrammaticality
yinaertion is the cost of a insertion error for a nonterminal symbol
clearly by writing NUM NUM as different rules the fact that they are instances of the same structure is not captured
at first an input sentence is processed by the normal parser
one module is a normal parser which is the bottom up chart parser
in s i like that of original earley s algorithm
the average frequency of each rule is NUM times in the corpus
in this paper we present a robust parser with a recovery mechanism
above examples show that people are used to write same meaningful sentences differently
NUM one sentence beat as head with lexical optimization the jazz defeated the bulls for their third straight win
in section NUM we will sketch out how this calculation is made present several mode selection schemes based on this factor analysis and show the results of analytical evaluation of these schemes
on the other hand if the resulting dialogues are coherent and exhibit features that are desired in a human computer system this suggests that these mechanisms may work well in a human computer system
suspect7 is the murderer of lord dunsmore
table NUM data on NUM non trivial dialogues from
in our model of dialogue initiative levels for each goal are defined during the interaction based on NUM explicit and implicit initiative changing utterances and NUM competency evaluation
associated with each factor are two weights wi which is the percentage of times a successful branch will have that factor and xi which is the percentage of all branches that satisfy fi
suspectl6 had a motive to murder lord dunsmore
this is the case in NUM and NUM
we also draw on the fact that per is a local feature
for the single pp study vp attachment was coded as NUM and np attachment was coded as NUM a database of quadruples of the form configuration v n p was then created
towards an account of extraposition in hpsg
we will return to this in sec
barring the recording of the set of correct tags for each word and of the set of correct parses for each sentence in a treebank the next best solution to the problem of multiple correct n wers is to at least provide such a recording in one s test set i.e. to provide a gold standard test set with all correct tags and parses for each word in context
both types of constraints also apply for german cf
given the variation in the number of possible configurations across the three cases the performance expected due to chance would be NUM for NUM pp NUM for NUM pps and NUM for NUM pps
fpp averages about NUM fragments for sentences as complex as in the st corpus this number is inflated since punctuation usually results in an isolated fragment
in the present paper we refer to this kind of score as lexical preference
for the same reason we also treat syntactic preference as a kind of score
it is thus guaranteed that the syntactic arguments of the empty head are identical to the syntactic arguments required by the selecting verb
in the experiments we considered only resolving pp attachment ambigui null ties and coordinate structure ambiguities
a number of methods have been proposed however to cope with the data sparseness problem
in this sentence a human speaker would certainly assume the former interpretation over the latter
NUM note that NUM contains NUM and NUM
the necessity of developing a disambiguation method with learning ability has recently come to be widely recognized
interpretations obtained in an analysis can not for example be ranked in their preferential order
NUM and thus the ambiguity of only one of the sentences can be resolved
in our research we implement lpr rap and alpp by means of a probabilistic methodology
the NUM remaining excellent test essays and a set of NUM poor essays used in this study were scored
recall that the lexicon in this study was built from relevant vocabulary in the set of NUM training essays
NUM point maximum recognition binding of enzyme to target sequence curing
the lexicon is composed of words and terms from the relevant vocabulary of the essays used for training
an even larger sample of essays could contain more alternate word or phrase substitutions than those are listed here
during training the csrs of relevant sentences from the training set were placed into computer rubric category files
recall that csrs often have extraneous concepts that do not contribute to the core meaning of the sentence
first all sentences in parts a b and c of each essay were parsed using msnlp
this is based on the criteria for a correct response for the rate size category in the scoring guide
the rules in NUM were used during automated scoring described in the following section
i domain independence e g en es nlggemeyer and
b repeat merging until all the eletuents in vi are i ut in a single lass
algolr thms for the prototype content selectlon ann m order to improve ts success rate in addlt on the more ngourous specflicatlon of the mappings between the surface cues and the
NUM positions on a company s board of directors other than those that are relevant to the scenario see special usage notes in section NUM NUM NUM
a large class of adjectives whose meanings are derived from those of verbs also straddle the scalar non scalar divide
the findings are largely language independent and only english examples are used throughout the paper
lrna n entry adj entry lrva v entry adj entry
another important trade off is between the cost of discovery and the productivity of the rule
victor raskin is grateful to purdue university for permitting him to serve as a consultant for the crl nmsu mikrokosmos project
we do not expect any such lr to be exception free and our methodology is comfortable dealing with those exceptions
our practical view is that a lr is useful for lexical acquisition if it is easily discoverable and very productive
then the ambiguity level of p is x y NUM in particular if p is the only point in its row and column then its ambiguity level is zero
many of the senses as improbable
construct the domain dependent word classification
restriction matrix to eliminate senses that can be excluded
correct sense in the produced tag error rate NUM
the resulting syntaxsemantics restriction matrix for appear is shown in table NUM
figure NUM automatically synthesized lexicon entry
eight of the NUM words are shown
we will assign this predominant sense to all non disambignated occurrences of a verb
in using the clustering method s output we make two further assumptions
dejmal to function of minister of environment
let us remove the word in en ra from the input sentence altogether
the grammar works with unambiguous input data ambiguous words are represented as sets of unambiguous items
the implementation the implementation of our system was to a big extent influenced by the demand of effectiveness
in addition there are also some other techniques used especially in phases b and c
for the purpose of testing and debugging the system we use full parsing even in the first phase
one of them does not contain any syntactic inconsistency remaining NUM has one or two syntactic inconsistencies
for example a typical error in free word order languages is an error in agreement
if the answer is positive the sentence is considered to be correct and no error message is issued
our approach supports a full spectrum of gestural input not just deixis
the geppetto environment allows to edit and debug grammars and lexica linking linguistic data to a parser and or a generator integrating various form of kbs and using specialized processors e.g. morphological analyzers
till now data acquisition has been mostly manual with the help of a graphical interface however a basic goal of the project is the experimentation of techniques for the semi automatic acquisition of data
an empirical verification has been performed which confirms the intuitive hypothesis that selectional restrictions crucially affect lexical disambiguation and that the discrimination rate improves as far as the experiment also brings evidence for a wordnet like sense organization
as far as the first hypothesis is concerned wordnet describes all the english verbs resorting to a set of NUM different syntactic frames which in turn include only two restrictions that is something and somebody
for example the frames provided for the verb write in the synset lcb publish write rcb are somebody s somebody s something the problem arising in using these two restrictions is that they are completely uncorrelated to the noun synsets then they have to be matched with the proper synsets in the noun hierarchy
in the future it may be fruitful to apply a technique that uses such simple information to more complex problems
we used very small training and cross validation sets of NUM and NUM items respectively
thus n provides a basis for comparisons across agents that are performing different tasks
upper lower cap numbers case of word following
but if NUM numbers containing periods acting as decimal points are considered a single token
the sentence boundary module was developed over the course of more than six staff months
occurs at end of sentence probability word following
the third and fourth items demonstrate the difficulty of distinguishing subsentences within a sentence
tm testing is then performed on texts independent of the training and cross validation texts
these data show a very low system error rate on both mixed and single case texts
a major advantage of the satz approach to sentence boundary recognition is its robustness
these rules are semantically conditioned and typically explain how a particular sign can support a variety of subcategorization frames
entries are then generated by expanding the paiutv entry with the different subcategorization frames that are possible for paintv
indexing subj with NUM means that argument NUM of the conceptual structure is to be realized as the subject
the inflectional rules m e grouped together into paradigms that are associated with the appropriate words e.g.
the sign expansion approach forms a basis for creating non redundant lexicon systems that are structured along semantic lines
in section NUM we give a brief introduction to a sign expansion theory called the sign model
in a completed seiise NUM medium arguments of timensionality coloring or existence can be mapped onto the xcomp flmction
we compare the perplexity result of the n gram language model with class based n gram language model
interpretationally the rule in NUM can be applied on a structure z if y is a substructure of z and x unifies with the selection of z specified in s the result of the operation is exactly this unified structure and the operation itself is referred to as a derivation
strucl urc whei cas the cxpansioll i art turns l he whole conceptual structure into an rgulilent k n the basis ot the minimal signs paint and walk l he rule an create i he notms paintingn and walkingn
NUM in contrast the character NUM gram model requires NUM times as many parameters in order to achieve a message entropy of only NUM NUM bits char
the paradise model is based on the structure of objectives rectangles shown in figure NUM
these are related in various ways such as through events who did what to whom and states of affairs properties of the entities
the only difference between the two is that in one case after training i we use a lexicon acquired fi om the muc ii database and in the other case after y aining ii we use a lexicon acquired ffoin a combination of the brown corpus the wall street journal cor pus and the muc NUM database
that is it does not look in the input for nps and aps but rather for say destination phrases and arrival time phrases
these two considerations imply that the handling of speech recognition results by the dm should be a system controlled strategy which is applied to all results given by the speech recognition
in the introduction we noted that the literature does not contain a general theory for the development of a dm module while it does contain a lot of practical guidelines
distinguish novice and expert users and adapt to their levels where possible guide the naive user but also allow the expert user to initiate actions and use short cuts
m since a user which is well aware of the current state of the system is less likely to perform an input which is not allowed at that point
n 3i j l common coalition i emantic i confirmanon j language network i i frame korean lindieilstandinr lllgeor nmok
for instance each user has a personal phone book stored in his gsm telephone and to pronounce the names in this phone book the system can only use tts
tile most desirm3le solution is to i leave the current grammar intact since it eifieiently parses even highly telegral hic messages and ii tackle unknown words and unknown constructions by the same mechanism
summarizing the basic mechanism sketched in figure NUM applies to every spoken input of the user in the same way which complies with commandment i be consistent
of course the step from a limited command and control language to more spontaneous speech is a big one and is likely to affect the dm
the test corpus was the same and we voluntarily removed NUM proper names from the lexicon which represented NUM occurrences in the corpus
of these NUM NUM articles which appeared on may NUM NUM and earlier were used for training and the remaining NUM NUM articles which appeared on june NUM NUM or later were used for testing
the column all classes shows the percentage of correct words which had all their possible syntactic categories in the lexicon
the integration of these lexicons within a linguistic module points out the problem of the dynamic adaptation of the language model
thus the estimation of an out of context probability for each of these classes is independent of the graphical form of the proper names
NUM errors of context as compared to the initial reference were induced by the addition of NUM NUM oov words
as we have mentioned already the syntactic tagger used was trained on a journalistic text corpus from the newspaper le monde
by keeping the NUM most frequent words of each lexicon we reduced by NUM the lack of coverage of our general lexicon on all the test corpus
systemorientated question and answer systems where the system has the initiative throughout the dialogue are the simplest to model since the user is explicitly constrained in their response
on the other hand if we use a tight threshold removing nonterminals that are almost as probable as the best nonterminal in a cell then we can get a considerable speedup but at a considerable cost
if one node is much more likely than the other then it is unlikely that the less probable node will be part of the correct parse and we can remove it from the chart saving time later
this algorithm is given in figure NUM which uses a current pass matrix chart to keep track of nonterminals in the current pass and a previous pass matrix prevchart to keep track of nonterminals in the previous pass
we have found that a minor variation introduced in section NUM in which we also consider the prior probability that each nonterminal is part of the correct parse can lead to nearly an order of magnitude improvement
this can lead to conditions that are the opposite of what we expect for instance loosening thresholds may lead to faster parsing because we do n t need to parse the sentence fail and then retry with looser thresholds
that is rather than just noticing that a particular nonterminal vp spanning the words killed the rabbit is very likely we also note that the production vp v np and the relevant spans is likely
while there exist theoretically efficient o n NUM algorithms for parsing probabilistic context free grammars pcfgs and related formalisms practical parsing algorithms usually make use of pruning techniques such as beam thresholding for increased speed
in particular if we parse using no thresholding and our grammars have the property that for every non zero probability parse in the second pass there is an analogous non zero probability parse in the first pass then multiple pass search is admissible
for instance if in a particular cell in the chart there is some nonterminal that generates the span with high probability and another that generates that span with low probability then we can remove the less likely nonterminal from the cell
john j and the 2the normally controversial term logical form is used loosely here simply to capture the information that the hearer must bear in mind at least implicitly in interpreting texts such as sentence NUM
i step NUM lookup lloce for headword h to obtain a list of sets seth that contains h
step NUM remove all stop words in d to obtain a list of keyword pos pair keyd
after describing the al null gorithm the experimental results for a NUM word test set are presented
for the most part those relations exist conveniently among words under the same topic or across cross referendng topics in lloce
we also describe an implementation of the algorithm for labeling definition sentences in longman dictionary of contemporary english ldoce
step NUM for each definition d ofdefh tag each word in d with pos information
we could use equalities such as y l or since equals can be replaced by equals simply replace y with l however doing this would lose the distinction between y and l under their corresponding descriptions
the sets under which the word is listed in lloce are considered as the initial candidates for labeling
given a knowledge base k representing the mutual knowledge of the participants in the discourse properties p1 and p2 are inferentially independent if neither k NUM i p nor k p2 pi
the elimination cycle is repeated until this summed cross entropy error starts increasing
an advantage that will not justify the additional computational cost in most cases
tells the results when for each level one selects the most discriminating cues
this setup results in different quantitative compositions of training and evaluation set
the same system was used for all four muc tasks ne co te and st the only difference lies in th e information which is generated when the processing of a document is complete
if the noun phrase has an indefinite determiner or quantifier e g a some any most it is assume d to be new information
reference resolution examines each entity and event in logical form and decides whether it is an anaphori c reference to a prior entity or event or whether it is new and must be added to the discourse representation
in most cases once part of speech ambiguities have been resolved using a tagger as we noted above most decision s regarding noun group boundaries and structure can be made deterministically using local syntactic information
the individual tokens in these sentences are gradually aggregated into larger units by the stages of processing as follows dictionary look up dictionary look up combined with part of speech tagging determines the syntacti c features of each word
from mid august to early september we spent several weeks tuning named entity annotation using the dry run test corpus fo r training and pushed our performance to NUM recall NUM precision on that corpus
with a slow system which can analyze only a few sentences per minute it is possible t o perform only one or at best two runs per day over the full training corpus severely limiting debugging
in addition our grammatical approach was not entirely abandoned our noun group patterns were a direct adaptation of the corresponding portion of our grammar just as hobbs patterns were an adaptation from his grammar
a separate set of variables was selected for each binary discrimination task
for each of the four exemplars would be converted to a pattern the system would recognize that this has th e basic form of a clause and generate a corresponding defclausepattern which generates the predicate give n by event
this is done using the inferences add job is used in situations where there is an explicit indication that the position being taken on is an additional position fred was appointed to the additional post of executive vice president
this change is particularly important for the purpose of capturing case variation in word forms in inflecting languages such as russian or german
in the present paper however we have deliberately formulated the general learning axioms of our theory so they do not depend on the robotic framework
in our task of model learning of subcategorization preference each event x y in the training sample is a verb noun collocation e which is defined as in the formula NUM
when the condition on independence judgment becomes more strict the cases in the trig data are judged as dependent on each other more often and then this causes the estimated model to overfit to the training data
in this evaluation task the independent frame model with the independence parameter c NUM NUM performed best in the precision when incorporating the heuristics of case covering as well as in the precision of case covered test events
according to the possible variations of case dependencies and noun class generalization we consider every possible patterns of subcategorization frames which can generate a verb noun collocation and then construct the full set jr of candidate features
more formally we introduce case cover ng relation of a verb noun collocation v e and a feature set s v s iff
a mathematical measure of the uniformity of a conditional distribution p y i z is provided by the conditional entropy hb fc pcu i logpcy ix NUM now we present the principle of maximum entropy maximum entropy principle to select a model from a set of allowed probability distributions choose the model p
if we do not care whether the verb noun collocations satisfy the case covering relation and do not use the heuristics of case covering this means that we use the basic model in 6the reason why the overfitting to the training data occurs in the independent frame one frame models can be explained by comparing the effects of the two values of the independence parameter in the independent model
since the leaf class cc child can be generated from either chum or ni and also the leaf class cj juice can be generated from either cbez or iq e can be regarded as generated according to either of the four formulas the left side formula of NUM and NUM
the subsequent steps can easily be traced by the reader
figure NUM the parse tables for the grammar g NUM
the paper has described a recognition algorithm for dependency grammar
the marked states are the states that were expanded in a previous step
grainmatical relations gained much popularity within the unification formalisms in early NUM
the time complexity of the recognizer is o igi NUM n3
all the possible predictions are precomputed in the transition to a new state
the space complexity of the recognizer is o igi n2
if the signification of tss s reflects their standard or literal potential the normal bottom up process may fail
unclear the information given in the article is not sufficient to determine whether the person held the post as of the date of the article
in a sentence or a combination of sentences some topoi are selected others are not relevant to the discourse context
examples of basic functions that operate cell by cell are the modification of the polyphonic value the direction or the strength
in the normal bottom up process the signification of tss s is computed and the c structure is applied
the signification of tss s connectives and operators may contain instructions referring to the context for the attribution of values
the word rich may be seen as the positive form of t that says when you are rich you may buy a lot of things
we designed this module so that it starts from lexical descriptions which we are able to provide manually and produces a structure whose interpretation can be computed
in this paper we propose for a given utterance the construction of the signification of the underlying sentence which captures its polyphonic and argumentative aspects
other arguments may be instantiated compositionally by the end nlp application as in NUM below
it is clear that there is a problem in subject verb agreement however does it occur because NUM the noun should be in the plural form NUM the verb should be in singular form or NUM the student does n t know that such agreement exists in the language
to appear although these have not been implemented and tested on a large scale
cogeneration involves a combination of grammatical statistical and template based constraints
as NUM shows they may appear in telic sentences with other sentence constituents
our user will not accept any system which does not incorporate prediction
encountered inside the ego of b2
the set of actions is fired from
functions must return an ascii string
gil can represent dag like feature structures
zweig wants to meet you on friday
the interpreter yields all solutions the grammar can generate
now assume that bi also generates a second solution
the drawbacks include a loss of efficiency and runtime
all other material can be reused
word accuracy provides a measure for the extent to which linguistic processing contributes to speech recognition
since we could not bias the subjects towards a particular segmentation and did not presume linguistic sophistication on their part the instructions were simple subjects were to mark all places they might plausibly pause if they were reading the text aloud
subjects topics sets and cross reference between topics in lloce
ill sillllll allot lls c it si oii l n wit h l l st l llcl llr NUM following j
as they can be recognized automatically with high accuracy auxiliaries are automatically annotated by the preprocessor
in addition it would allow for separate evaluation of an automatic classifier tagging have
this is in general very difficult given the extremely free manner in which chinese given names are formed and given that in these cases we lack even a family name to give the model confidence that it is identifying a name
but there is no question that semantic knowledge is essential for many problems in natural language processing
the location category performed very well using seed words such as city town and province
while size of the resulting transducers may seem daunting the segmenter described here as it is used in the bell labs mandarin tts system has about NUM NUM states and NUM NUM arcs recent work on minimization of weighted machines and transducers cf
if a word is clearly a member of the category then it deserves a NUM
most people would probably define words such as car truck plane and automobile
we considered ignoring the zeros but some of the categories would have been severely impacted
since category judgements can be highly subjective we gave them guidelines to help establish uniform criteria
our initial seed words worked well enough that we did not experiment with them very much
but more importantly the ranked list contains many additional category words especially near the top
for example our number processor failed to remove numbers with commas e.g. NUM NUM
fastus s basic architecture shown in figure NUM is unchanged NUM
the met japanese fastus is ready for further development and augmentation toward a full information extraction system
this will be possible by combining the ie technology with suitably constrained applications of machine translation technology
ascii characters are sent to the ascii tokenizer and NUM byte characters are sent to juman
the optimizing compiler constructs an efficient finite state machine allowing a rapid specify compile test cycle
this approach enables a single dictionary to be used for ie and non ie purposes
the met system did not have the last domain event phase that recognizes sentence patterns
figure NUM shows the models selected by the various combinations of search strategy and evaluation criterion for interest
the lower level dialogue states in this sub dialogue could be verify user which asks for the user s account id and password side effects which informs the user of some side effects of the imposed constraints e.g.
most of the names are recognized in the name recognizer phase based on internal patterns
moreover most organization person and location names are unknown to the system
this is unlikely for nlp data samples which are often sparse and highly skewed c f e.g.
at present we are carrying out a preliminary investigation of the use of nlp to support the use of pictalk by children working in structured settings to achieve the following analyse the pragmatic features of children s conversation in a particular setting e.g. a structured news setting
on the other hand bss begins with a saturated model whose parameter estimates are known to be unreliable
we can view an scfg as a stochastic string rewriting process in which each step consists of simultaneously replacing all nonterminals in a sentential form with the right hand sides of productions randomly drawn according to the rule probabilities
clearly for judges j1 and j2 taking j1 as standard and computing the precision and recall for j2 yields the same results as taking j2 as the standard and computing for h respectively the recall and precision
we then present the details of our approach and results
because the association between picture and utterances is imprecise the user will need to experiment with the available items to see what speech is associated with each available picture before deciding whether to load an item into their pictalk system
in any case we believe that the parser oriented view afforded by the earley framework makes for a very intuitive solution to the prefix probability problem with the added advantage that it is not restricted to cnf grammars
note that when summing over all paths starting with the initial state summation is actually over all paths starting with s by definition of the initial state NUM s a follows directly from our definitions of derivation probability string probability path probability and the one to one correspondence between paths and derivations established by lemma NUM
though at present utterances are developed and pre loaded by someone other than the pictalk user the pictalk system has a facility to allow the end user to select from a database of those available utterances and their associated pictures
note that hanzi that are not grouped into dictionary words and are not identified as single hanzi words or into one of the other categories of words discussed in this paper are left unattached and tagged as unknown words
these extensions are all quite straightforward and well supported by the original earley chart structure which leads us to view them as part of a single unified algorithm for solving the tasks mentioned in the introduction
the more familiar t and of i fg are simply shorthand notations of the same idea
this paper describes pa lh ans a fully automatic production mt system designed for producing raw translations of patent texts fl om english into danish
we are interested in investigating ways in which we can assist children in carrying out these activities by using prediction within pictalk s organization structure to reduce the complexity of the process required for finding the next appropriate utterance
prior to sentence deo04 given below l initialized the dialogue requesting a date for a trip s
vorschlag elo07 oh that s too bad i m not free right then
finally yi hei lcb includes e.g.
given tl is formula and the required n grams we can determine the k best predictions for the next speech acts
where ambiguity arises recognition can be biased in the direction of meaningful statements in the current context
additionally we introduced two speech acts necessary for modeling our appointment scheduling dialogues init terminabsprache and bestae tigung
the planner also augments the input sign by pragmatic information i.e. by information concerning its speech act
and that when they are n t an elegant explmiation can be found by looking at a two member sequence of ce ttering tra tsitions rather than at just tie transition
2a john is a nice guy
mill stll je l s lid all l he other referring expressions is also st alistically signilicant x NUM NUM NUM p NUM NUM
while su h lrmlsilions do nol t elong t i entering lhat models how enl ers change h om one cenle ring
the discourse functions of italian subjects a centering approach
the cf list is ranked according to discourse salience
the english wall street journal non parallel corpus gives us an easier test set on which to start
the output of this corpus should consist of words matching to themselves as trans null lations
the organization template element objects are present at the lowest level along with the person objects and they are pointed to not only by the in and out object but also by the succession event object
another set of issues is semantic in nature and includes fimdamental questions such as the validity of including type coreferrence in the task and the legitimacy of the implied definition of coteference versus reference
as a result a few peripheral facts about the event were included that were difficult to define in the task documentation and or were not reported clearly in many of the articles
frequently at least one can be found in close proximity to an organization s name e.g. as an appositive creative artists agency the big hollywood talent agency
however the task does not require the system to extract all descriptors of an entity that are contained in the text it requires only that the system extract one or none
in that evaluation a number of systems scored over NUM on the named entity recall and precision metrics providing a sound basis for good performance on the coreference task for individual entities
note that although named entity coreference and template element are defined as domain independent tasks the articles that were used for muc NUM testing were selected using domain dependent criteria pertinent to the scenario template task
looking at the document section scores in table NUM we see that the error score on the body of the text was much lower than on the headline for all but a few systems
interannotator scoring showed that one annotator missed tagging one instance of coke as an optional organization and the other annotator missed one date expression september
the two sgml based tasks required innovations to tie system internal data structures to the original text so that the annotations could be inserted by the system without altering the original text in any other way
when a new instance of a task is processed a set of relevant instances is selected from memory and the output is produced by analogy to that set
enamex type person james enamex places his hands over hi s mouth
creative assignment for the prestigious coca coll classic account
NUM completed half built semantic hierarchy of structured geographical knowledge
however odds of that happening are slim since word from coke headquarter
if a particular pattern x x is not literally present among the examples all classes have zero ml probability estimates
in summary the back off method does not provide a principled and practical domain independent method to adapt to the structure of a particular domain by determining a suitable ordering between events
first such short forms are very common in written language
he says he feels a great sense of accomplishment
if pattern x is described by a number of feature values xl xn we can write the conditional probability as p classlxl xn
on pages NUM NUM barwise and perry claim a sees that lcb e not d c e rcb c xnvo i.e. those events not in the interpretation of b must be classified as so no
a report such as NUM NUM john saw that joe saw that jackie was biting molly would require that the event e classifying joe s visual state be a constituent of NUM NUM s interpretation NUM
we will demonstrate that theories of discourse which postulate a strict tree structure of discourse on either the intentional or attentional level are not totally adequate for handling spontaneous dialogues
forster and rood sethood and situations the appropriate location i that is a sees that xva c and since xva c NUM c xnvo taking complements we obtain the result
attitudinal reports involving the phrase see that followed by a finite complement involve epistemic perception that is they yield information about the inference an agent has performed after seeing a given coe or situation p
note that the discourse situation d is the situation in which b is uttered and thus is usually distinct from the described situation ic NUM except in cases of self reflexive discourse
whatever reasons caused barwise and perry to desire a set theory with ur elements should presumably still be respected so if we can find a consistent set theory with ur elements and a universal set the outlook will be a lot brighter
an utterance q determines a triple d c q composed of a discourse situation d a speaker connection function c and the utterance q
this framework includes a great variety of different translation scenarios and thus results appropriate for progressive experimentation with increasing level of complexity
furthermore our interpretation relation a function from utterances of the above form to collections of events is given as interpretation of according to d and c lcb e d c c b e holds rcb
c NUM association for computational linguistics computational linguistics volume NUM number NUM also let xnvo lcb e so a e o e s rcb collection of events that a classifies as not being visual options
since the back off models consistently performed worse than the mle models we chose to use only the mle models in our subsequent experiments
we observed that the similarity based methods perform much better on unseen word pairs with the measure based the base language model was mle ol
for this purpose we used a small corpus consisting of more than NUM NUM word tokens taken from the same newspaper
let c c w be the number of times that the symbol followed the string w in the training corpus and let c w be the sum es c crlw of all its conditional frequencies
the substring o establish is also found before the characters m e and i in sequences such as to establishments lcb who ratio also rcb established and lcb to into also rcb establishing
the substring blish is most often followed by the characters e NUM and m corresponding to the relatively frequent word forms publish lcb ed er ing rcb and establish lcb ed ing ment rcb
an adjective the other forms of the same adjective changing the gender and number attributes
a proper noun a particle preposition connective etc the empty sw set
we propose ihal these principles be suhsunlod by il NUM infornialiveness
NUM represents an additional lwccaution against the occurrence of ambigttity in niachine speech
ii the recorded dialogues were plotted onto the graph representing the dialogue model
givca the conllnonalily of purpose it beconies of interest to conlpare principles and illaxinls
separate whenever possihle between tire needs of novice and expert users user adaptive dialogue
computational linguistics volume NUM number NUM the third rule is for judgment moves in which the speaker finds the current plan acceptable
these goals lead to judgment and refashioning moves and so correspond to the rules that we just gave for adopting mutual beliefs
bmb system user plan user pl knowref system user entityl NUM bject
the schema was given in figure NUM rather than realizing the surface speech action immediately the system plans ahead
this additional level might be adopted as the principal one for lexieal specification giving various advantages for linguistic analysis
such relation might be justified in terms of allowing transitions involving forgetting of information i.e.
the speaker uses this schema in order to tell the hearer that the plan is invalid and which action instance the evaluation failed in
note that this effect does not make any claims about whether the new expression will in fact enable the successful identification of the referent
for abstraction over v in a product right inferences are interpreted via system specific pairing
for product left inferences a term such as z vow a
for example using k a fragment of lp may be embedded within l
the left rule kl allows a k marking to be freely discarded
in a purely non associative system such as nl such an analysis is excluded
discussion of such issues however is beyond the scope of the present paper
corresponding transformations may be derived for the connectives of any two appropriately related subsystems e.g.
a dependency parameter p l w lw r is the probability given a head w with a dependent arc with label r that w is the r dependent for this arc
for the stricter measure the differences were statistically significant according to the sign test at the NUM significance level for the following comparisons c and e each outperformed b and d and b and d each outperformed a
specifically it is checked that n is the root node of all source fragments hn of runtime entries in which both n and its node label are included and that fn n is not dominated by i.e.
for this training method to be effective we need a reasonably good initial model i.e. one for which the distance h s is inversely correlated with the probability that t is a good translation of s
we write c al anlbl bk for the cost of the event represented by al a in the context represented by b1 bk
right transition if in state qi NUM m can write a symbol r onto the left end of the current right sequence and enter state qi with probability p qi rlqi NUM m
a particular match of an entry against a dependency tree can be represented by the matching function g a set of arcs a in s and the possibly context dependent cost c of applying the entry
in the table we have grouped together methods a and d for which the parameters were derived without human supervision effort and methods b c and e which depended on the same amount of human supervision effort
these requirements have motivated us to develop robust extensible and trainable anaphora resolution systems
soderland and lehnert s approach relies on a large set of filled templates used for training
table NUM recall and precision of the mlrs and the mdr
table NUM shows ks s employed for the four anaphoric types
we started with the features used by the mdr generalized them and added new features
however the input was only the first paragraphs of newspaper articles which contained relatively short sentences
context dependence is discussed further in section NUM the set of transfer parameters may also include costs for the null transfer entries for wi for use in derivations in which wi is translated by the entry for another word v
NUM run the automaton m0 with initial state q to generate a pair of relation sequences with probability p rl rk rk l r lm q NUM
this speed improvement was possible while also improving memory usage and translation accuracy
forn occurs in a sentence then this forln ca n be only either a snbject or a nominal predicate with copula or a colnparisoli to these adjoined by means of the conjunctions jako jako to
turning now to NUM we have the similar problem that splitting into ma3 horse and lu4 way is more costly than retaining this as one word ma3 lu4 road
on the one hand ciaula clusters are very fine grained as they are built from single observations of verb uses
relevant information for guessing the tag of an unknown word includes contextual information the words and tags in the context of the word and word form information prefixes and suffixes first and last letters of the word as an approximation of affix information presence or absence of capitalization numbers special characters etc
table NUM test data evaluation results on the lexicalized
pra ctically lllol e imtn rtallt was tile question whether there exists a lass of errors with complexity of detection lying between the trivial errors and the errors tor tile detection of which a fldl fledged analysis is necessa ry
every word is first assigned its most likely tag in isolation
NUM the user determines phrase boundaries and syntactic categories s np etc
all components of this kernel are assigned the label nk aml trea ted as sibling nodes
languages differ strongly from each other presenting a challenge to the intended theory independence of the schelne
at this point the mlportance of the underlying argument struc urc is emphasised cf
we describe an annotation scheme and a tool developed for creating linguistically annotated corpora for non configurational languages
in general the resulting interpreted data also are closer to semantic annotation and more netltra l
this extra marking makes it easy to distinguish between normal and coordinated categories
the head of the phrase can be determined in a similar way according to theory specific assumptions
our annotation tool supplies efficient ma nipulation and immediate visualization of argument structures
NUM additionally the program performs simple bracketing i.e. finds kernel phrases
i is tit name of the described object
level of our natured language analysis the pragmatic level
idiomatically combining expressions introduce entities for subsequent reference NUM kim s family pulled some strings on her behalf but they were n t enough to get her the job
these constrainls cxt lain wily the existing knowledge rel re
the name of an ob iect represents the sub object of the denomination
it be composed of several objects world a discourse cart generate worlds
NUM NUM outline of the rei resentation mo h l
n fact class and individual always coexist
these relations include part and whole subset and superset and membership in a common class a number of constructions depend on poset relations to signal their connection with context
following some linguistic research two such error types have been selected for implenlentation and while one of thenr is just a lnarginal subtype of an error in subject verb agreernent the other is an error type of its own and in addition one of really crucial importance tot practical gramma r checking
analogical speech translation does not rely on this presupposition and instead seeks to capture intuitive translation correspondences
obviously this is not possible in a ll cases ibr all senten es but on tile tiler hand it is also clear that ill any language there exists a statistically huge subset of sentences of this language where such techniques are applicable
over the same subset of the corpus we measured a decrement of NUM in the number of morphological derivations produced with terminology against the recognition carried out in absence of any terminological knowledge
for this the test sample is too small
this approach can be extended by taking adjacent words which act jointly as single lexical items as a unit
each element of the vector will represent a feature that is flagged NUM or NUM absent or present
when a string is presented to the network in training mode it activates a set of input nodes
most input nodes are connected to both outputs since most tuples occur in both grammatical and ungrammatical strings
with the most recent improved representation about NUM strings can be trained in NUM second to NUM
the testing process when the trained net is run on unseen data the weights on the links are fixed
the difference between the yes and no activation levels is recorded for each string and this score is considered a measure of grammaticality p the string with the highest i score is taken as the correct one
for example durchlaufen is mostly associated with the meaning passing through all stages of a process NUM er durchlpsuft die schulung
with a simple language of regular expressions the grammar of adjectival 3the set of prepositions that have been selected to introduce typical restrictive descriptions are di a per da
m1 in h m1 structures or m in h m1 m is considered as the right event y and every left incoming substructure i.e.
b probabilistically tag nouns that are not in corelex according to this classifier NUM relating the data obtained in step NUM to one or more qualia roles step NUM is trivial but steps NUM through NUM form a complex process of constructing a corpus specific semantic lexicon that is to be used in additional processing for knowledge intensive reasoning steps i.e.
mn NUM mn is selected as term and thus included in td if the following condition holds cn NUM h NUM
also the capitalization of a word was submerged in the muddiness of part of speech tags which can smear the capitalization probability mass over several tags
the atomic elements of information extraction indeed of language as a whole could be considered the who where when and how much in a sentence
we ran fastus over our development corpus NUM texts of which produced coreference data
we will present the model twice first in a conceptual and informal overview then in a moredetailed formal description of it as a type of hmm
these values are computed in the order listed so that in the case of non disjoint feature classes such as containsdigitandalpha and containsdigitanddash the former will take precedence
none of the formalisms or techniques presented in this paper is new rather the approach to this task the model itself is wherein lies the novelty
these two measures of performance combine to form one measure of performance the f measure which is computed by the weighted harmonic mean of precision and recall
on a spare20 or sgi indy with an appropritae amount of ram nymble can compile in NUM minutes train in NUM minutes and run at 6mb hr
president or other titles preceding the person name class and the word following a name class would be strong indicators of the subsequent and preceding name classes respectively
a good answer is to train a separate unknown word model off of held out data to gather statistics of unknown words occurring in the midst of known words
all parts except the last one were used to create initially and update the model parameters successively
several alternative joker trees are generated when word candidates belong to different categories
we are currently experimenting with additional improvements along the same lines which attempt to defer intersection by keeping tiers separate as long as possible
using factored automata helps ellison s algorithm NUM in several ways the candidate sets si tend to be represented more compactly
using classes of informational relations rather than individual informational relations constitutes a sort of a priori grouping
while the importance of s ln rel for placement seems clear its role concerning occurrence requires further exploration
in section NUM we present our learning experiments and in section NUM we discuss our results and conclude
second we experiment first with each feature individually and then with interesting subsets of features
our goal is to identify general strategies for cue usage that can be implemented for automatic text generation
as an example of the application of rda consider the partial tutor explanation in NUM NUM
table NUM summarizes the distribution of different relations and the number of cued relations in each category
previous attempts to devise rules for text generation were based on intuition or small numbers of constructed examples
a major focus of our future research is to explore the relationship between the selection and placement decisions
three features capture the global structure of the segment in which the current core contributor relation appears
the resulting algorithms are then evaluated by examining their performance on a separate test set of NUM more narratives
if a new ficu is not initiated in pi i values for all three features are na
however the composite algorithms use narrower criteria for boundaries which should reduce the number of false positives
due to coreference of a pronominal np with an np in the preceding ficu from phrase NUM NUM
thus we do not assume that there are correct segmentations against which to judge subjects responses
here we briefly review previous work on characterizing discourse segments and on correlating discourse segments with utterance features
NUM cue2 is assigned true if cue1 is true and the second lexical item is also a cue word
chafe identified three types of prosodic phrases from graphic displays of intonation contours as described in section NUM NUM
by default np assumes that the current segment continues and assigns a boundary under relatively narrow criteria
as shown in figures NUM NUM and NUM np uses more knowledge than pause and cue
the generator includes a micro planner which is responsible for ordering and grouping information into sentences
we note this influence on the generation process throughout the section
the patient has received massive vasotonic therapy massive cardiotonic therapy and massivevolume blood replacement therapy
a media coordinator is responsible for ensuring that spoken output and animated graphics are temporally coordinated
the existing infusion lines are two ivs an arterial line and a swan ganz with cordis
unlike streak magic does not use revision to combine information in a sentence
the speech sentence generator also contributes to the goal of keeping spoken output brief but informative
presently she is NUM minutes post bypass and will arrive in the unit shortly
this output is sent to a speech synthesizer in order to produce final speech
both attempt to gain additional efficiency by a tight integration of analysis and transfer instead of assuming two different processing stages
null language internal relationships language module a language module a interlingual relationships language module a ill module three different types of relationships are necessary in this architecture summarized in the table NUM
the ili is updated and all sites have to reconsider the equivalence relations for all meanings that can potentially be linked to the new ill records
finally section NUM deals with the specific options to compare the wordnets and derive information on the equivalence relations and the differences in wordnet structure
speech act sequence analysis should help fit fragments together since we hope to learn about typical act groupings
this result makes the job of the parser much easier and speeds it up
this work attempts to provide a computational solution called word filtering to those linguistic phenomena
assume a constituent labeled with syntactic tag ph is composed by the syntactic components rp1
operation sm ij or the expanded matching operation em ij
the complete matching principle will guarantee that this algorithm will produce all matched constituents in the sentence
first some common erroneous constituent structures can be enumerated under current pos tagset and syntactic tagset
figure NUM shows the distributions of sentence length in the training and test sets
figure NUM distn bution of sentence length in training and test sets
the second one is how to label the found constituents with suitable syntactic tags
phrase vp verb phrase dj simple sentence panern zj cdegraplete sentence
pet system morphological processing spelling checking
to date grammar checkers and other programs
thus the word filtering also includes the scanning process to detect and correct the error
most importantly the error rules operate over a letter graph of the lexicon so only ever consider lexical words unknown letters are instantiated to the letters associated with the transition options
this should have the advantage over the two level error rules in that it uses a good method of calculating likely error positions and because a set of correction possibilities can be generated fairly cheaply
with the aim of evaluating the effectiveness of shallow processing tests will be carried out to see what proportion of different types of errors can be dealt with elegantly adequately and or efficiently
parameters of the system will be varied for example the breadth of the purview the position of the purview focus the number of correction candidates and the timing of their generation
this paper sets out to study critical tokenization a distinctive type of tokenization following the principle of maximum tokenization
in particular this paper focuses on critical tokenization NUM a distinctive type of tokenization following the maximum principle
these examples seem to imply that unsupervised induction will never converge to ideal grammars and lexicons
during the query phase the caller poses his query and the information service tries to understand this query as clear as possible
in the case of kicking the bucket the perturbation is one of both meaning and frequency
for example the is both an unusually frequent sequence of letters and an english word
this suggests using compression as a means of acquiring underlying properties of language from surface signals
plainly the answers depends on the learning context and not on the signal itself
measure indicates that the algorithm has made very few clear mistakes
the composition operator concatenates the characters and unions the meaning symbols
tions in this case merely frequency changes that distinguish each word from its parts
figure NUM illustrates a recursive decomposition under concatenation of the phrase national football league
the second property that explanation systems should exhibit is robustness
second biology is not a single task subject
the following four techniques operate in tandem to achieve robustness
concept is one which is particularly central to a domain
the spore divides to form NUM plant gametes during gametogenesis
response the spore is a kind of haploid cell
null there are many possible extensions to this work
embryo sac formation is a step of angiosperm sexual reproduction
developing and empirically evaluating robust explanation generators the knight experiments
NUM i z actloc xhi g NUM m by march NUM ii act loc thing NUM by march NUM
it is only at this stage that we touch on the problem of identifying and resolving hidden ambiguity in tokenization
although sometimes the affixation is not just a straightforward concatenation of the affix with the stem NUM the majority of cases clearly obey simple concatenative regularities
note that the task of the rule is not to disambiguate a word s pos but to provide all and only possible poss it can take on
james that attach the age apposition and s o forth
proceeding beyond named entities the phraser next applies its te specific rule sequence
our performance on this task is shown in fig NUM above
embarrassing bugs james in hl treated as location NUM inc
one such phrase for example is in the sample sentence above
this block of sentences is sandwiched between aligned blocks
yields a succession template through the mediation of on e inference rule
some of this information is again gained by fairly straightforward compositional means
the fact that the score dropped so little is encouraging to us
first their respective semantic individuals are equated in the inferential database
this is intended to capture sets of features which are acquired at approximately the same time
NUM the student intended the verb to be in singular form but mistyped
the pattern matching phases of fastus produce templates similar to those shown in figure NUM
setting the threshold NUM at a certain level lets only the rules whose score is higher than the threshold to be included into the final rulesets
or at the very least a crystal dictionary based on so little training should be strengthened with recognition routines for morphological variants
perhaps the most notable of these is the system named ms
in cases NUM and NUM no tutoring should be given
we finish with describing how we propose to model this process
the main difference between the two functions is that there the z value was implicitly assumed to be NUM which corresponds to the confidence of NUM
NUM the mal rules are indexed with the errors that they realize
at this point the user may investigate the individual errors further
figure NUM language complexity in slalom is to capture an ordering on the feature acquisition
we expect to further solidify the model using the writing samples that we have already collected
this is discussed in the next section
unknown words are treated as proper names
results are shown with varying menu sizes
augmented and alternative nlp techniques for augmentative and alternative communication
this basic approach was implemented in the first version of our system
to calculate the probability that a string of words xl x2 x3 xn has the parse represented by the string of category states NUM NUM s3 s we simply take the product of the probability of each transition ie
n np t s rel np t in 3a n np t np t s rel in 4a
we could perhaps also improve results by using trigrams rather than bigrams
we therefore decided to derive transition probabilities by tagging our collected data
based on this notion t is calculated as in equation NUM where is the frequency of w collocating with w3 f3 is the frequency of w3 and t is the total number of collocations within the overall co occurrence data
ken top now next room loc kimono acc puton pres null ken is now putting on kimono in the next room
recently computational linguists also have joined in the act within the context of machine translation or text understanding etc
the form tuzukeru continue can follow the verbs which have duration a
ken top that kimono acc three year before wear pres ken has the experience of wearing that kimono three years ago
in the following section we will present a synchronous system which has local synchronization s formal advantages but handles the scoping data
of public domain german articles distributed internationally by the university of ulm
to clarify what is meant by mesuring out she gives examples of three kinds of measuring out incremental theme verbs eat an apple build a house etc change of state verbs ripen the fruit etc and path objects of route verbs climbed the ladder play a sonata etc
here as in many similar cases in side and outside of this lr we face a dilemma
it is interesting to note that the sense distinctions in abusive seem to be less significant than in abuse
the discovery and use of such relationships and rapid propagation methods based on them lay the foundation for lrs
lr e abuse v1 abusive le NUM lr a abuse v1 abusive la NUM
the treatment of negation in deverbal adjectives is not very difficult the negative quantifier can in most cases be simply added to the non negative adjective and this applies to all of the negative prefices such as un in ira etc dis
an adjective just as a participle is a device to raise a proposition into a higher one
these include adjective taxonomies usually on the basis of more or less consistent external criteria the dichotomy between predicating and non predicating adjectives and the related dichotomy between qualitative and relative adjectives the order of adjectives modifying the same noun the degrees of comparison and scalability and the substantivization of adjectives
as we will see below the former is a somewhat rare bird among the deverbal adjectives ending in ble but on the face of it there is no difference between food products rotting and food products perishing in fact the former sounds more natural than the latter
it becomes clear for instance that many adjectives do not modify semantically the nouns that they modify syntactically adjectival attributive meanings may be delivered by other parts of speech and thus the se1 also of natural language processing laboratory purdue university west lafayette in NUM
the locality of the synchronization becomes clear when we consider a new tree structure which we introduce here called the vector derivation tree
a probabilistic classifier assigns out of a set of possible classes the one that is most probable according to a probabilistic model
in fact it could be argued that any problem with a known set of possible solutions can be cast as a classification problem
our derived corpus contains ten verbs frequently appearing in the edit corpus which are summarized in table NUM in table NUM the column of english gloss describes typical english translations of the japanese verbs the column of of sentences denotes the number of sentences in the corpus while of senses denotes the number of verb senses based on the edit dictionary
it should be clear that the vector derivation trees for two synchronized derivations are isomorphic reflecting the fact that our definition of synchuvg
prepositions in the words occurring only once and in unknown words are minimal in the english text while in the french text one out of ten unknown words is a preposition
three stochastic optimization criteria and seven european languages dutch english french german greek italian and spanish and two pos sets are used in the tests
various sizes of training text and two sets of grammatical categories the main set NUM classes and an extended set described in detail in section NUM were used
therefore the prediction error rates presented in this paper should be regarded only as indication of the probabilistic taggers efficiency in each separate language when small training texts are available
the worst results have been obtained for the greek language because of its significantly greater ambiguity the number of tags requiring significantly greater training text and its freer syntax
the model becomes more complex but tagger speed is slightly higher because of the greater size of the training text which reduces the presence of unknown words in the testing text
computational linguistics volume NUM number NUM the probability p unknown word is approximated in open testing texts by measuring the unknown word frequency
the word occurrence threshold used to define the less probable words and a tag probability threshold used to isolate the less probable tags are estimated experimentally
for instance as long as a character string has multiple tokenizations it is ambiguous
let s cl cn n NUM be a multicharacter critical fragment
dermatas and kokkinakis stochastic tagging another important issue concerns the hmm ability to handle lexicon information e.g. to find how frequently the tags have been assigned to each lexicon entry
example of letter to sound rule that would have assigned primary stress one syllable on the left
the allophonic pass performs some allophonic rules well known to those familiar with phonemic variation
the phoneme string is scanned left to right performing such tasks as vowel reductions
an exception dictionary is automatically defined for words not correctly translated by these NUM rules
french is not a stressed language so there is no need for a syllabification module or a stress module
german for example has a large morphological system yet it is surprisingly simple in terms of letter to sound rules
similarly dr may be doctor or drive and st may be street or saint depending upon the context
example NUM the following is an example of a set of two letter to sound rules for the letter c in english
the greedy nature of the algorithm makes it independent of memory resources
then the ambiguity within b is resolved and the entire graph is divided into two subgraphs the academic one and the medical one
the morphology component ensures that separable prefixes are left in place
instead of a list valued subcat feature the feature args is used
figure NUM lexical entry fl r warren in fuf
we have presented synchuvg dl a synchronous system which has restricted formal power is computationally tractable and which handles the quantifier raising data
the situation however might not bc as simplistic as that because such obvious matches are extremely rare even in a huge corpus
this level is different for different prepositions and we hypothesise that it can be broken only when a wider sentential or discourse context is used
more substan ial changes were required to adapt the hpsg granlmar
firstly if a standard keyboard or equivalent device is used to enter letters then no advantage is being taken of techniques such as huffman encoding which reduce the number of bits required per letter although such encodings are relevant to the use of binary switches
example of annotations for the sentences those
these tokens correspond to spl plans
in the general experimental setting a sentence is given to the parser in a situation characterized by multiple lexical entries for each single word one for each wordnet sense
while most existing systems follow one of the two approaches exclusively proverb uses them as complementary techniques in an integrated framework
this paper describes how reference decisions are made in proverb a system that verbalizes machine found natural deduction nd proofs
each bar represents a step of derivation where the formula beneath the bar is derived from the premises above the bar
in both circumstances this operator first presents the part leading to f v g and then proceeds with the two cases
derive derived formula u iv u reasons unit 1u u u 6u method def semigroup unit
this results in a cleaner treatment of auxiliaries by factoring out morphological wellformedness conditions and allows for the preservation of argurnent structure information in cases like that of the german multiple genitive np eoimtr ction where syntacti ally dissimilar eonstru tions express essentially the same l re ticate argulnent relations
we can illustrate this point using the following type combination which is not an instance of even generalised composition
for present purposes we can take two proofs to be equivalent if they establish identical sets of dependency relations
they show three issues worth discussing
creative artists agency figure NUM te results
the results were more than gratifying
table NUM results on the walkthrough articl e
the only reason it gets painewebber coca cola and coke is because they are in the lexicon the others are all picked up by match various patterns
first we had low precision on timex
org descriptor the big hollywood talent agency
in the present setting however speech generation is helped by the availability of syntactic and semantic information
null NUM a lexical semantic transfer phase which em null ploys the bilingual dictionary to map the bag we wish to thank our colleagues kerima benkerimi david elworthy peter gibbins inn johnson andrew kay and antonio sanfilippo at sle and our anonymous reviewers for useful feedback and discussions on the research reported here and on earlier drafts of this paper
several groups made major changes in their retrieval algorithms and all groups had difficulty working with the very short topics
for each topic it is necessary to compile a list of relevant documents hopefully as comprehensive a list as possible
consider the french sentence NUM le grandchien brun aboya NUM the big dog brown barked the tncb implied by the bracketing in NUM is equivalent to that in figure NUM and requires just one rewrite in order to make it well formed
it is not that the transfer of information per se compromises the ideal such information must often appear in transfer entries to avoid grammatical but incorrect translation e.g. a great man translated as un homme grand
each node must be tested no more than twice in the worst case due to precedence monotonicity as one might have to try to combine its children in either direction according to the grammar rules
they used a simple combination method adding all the similarity values and tested various combinations of query types
now in section NUM we argued that no more than n NUM rewrites would ever be necessary thus the overall complexity of generation even when no solution is found is o n4
if we were continuing in the spirit of the original shake and bake generation process we would now form some arbitrary mutation of the tncb and retest repeating this test rewrite cycle until we either found a well formed tncb or failed
the second trec conference trec NUM occurred in august of NUM less than NUM months after the first conference
the research done by the participating groups in the four trec conferences has varied but has followed a general pattern
we measured for each sense of the NUM words how many of the other words have at least one sense linked with that sense in wordnet in the same toplevel verb sense tree
for a concrete example consider the verb ques tion which can have among others the senses of dispute sense NUM in wordnet or inquire sense NUM in wordnet
the achieved reduction in ambiguity for the NUM ambiguous words ranges from NUM to NUM NUM including cases of full disambiguation and its average for all NUM words is NUM NUM
we are investigating ways to stratify the application of the cluster based method on appropriate groups of tokens identified by the syntactic method by separately clustering tokens of the same verb that appear in different syntactic frames
null for each word z let y lcb yi y2 rcb be the set of other words placed in the same semantic group with z
when the distribution of the words is factored in the corresponding measure on tokens which better describes the applicability of the method in practice is NUM NUM
more exactly if a given major phrase is in focus it is also marked as accented and so is each strong node that is the daughter of a node that is marked as accented
while the syntactic constraints method almost always produces a semantic tag that includes the correct sense for a verb NUM it has no capability to further distinguish the surviving senses in the tag
of two sentences one from each component text
null the comparison of mbl and back off shows that the two approaches perform smoothing in a very similar way i.e. by using estimates from more general patterns if specific patterns are absent in the training data
for instance we can define and query grammatical relations such as clausal subject and main verb
each set of ili records represents the most direct matching of a fragment of a wordnet from the available fund of ili records regardless of the matching of the other wordnet
in this case it is the annotator who has to specify the grammatical function
similarly function tags are assigned to the components of the sentence s
a german newspaper corpus is currently being annotated with a new annotation scheme especially designed for free word order languages
by training here we simply mean assignment of the cost functions for fixed model structures
this attachment information was converted to corresponding counts for head dependent choices involving prepositional phrase attachment
these english test utterances were processed by both systems yielding lowest cost chinese translations
word error rates for direct comparison with the results above are not available
this tiling process has the side effect of creating an unordered target dependency representation
the algorithm analyzes the input frame by frame keeping track of the best path of phones
critic and criticism where critic is pronounced kritik and criticism kritisizrn
table NUM shows the probabilities for the ten phonological rules described in ss2 NUM
p d p be the probability of the derivation d of pronunciation p
we next apply phonological rules to our base lexicon to produce the surface lexicon
since the rules 3the limsi pronunciations already included the syllabic consonants and reduced vowels
we represent pronunciations with the set of NUM arpabet like phones detailed in table NUM
it is important to realize that a allows to specifiy lexical neighborhoods in 12a given a lexical entry x its nearest neighbor is simply f x where f is the most productive alternation applying to x lexical neighborhoods in the paradigmatic cascades model are thus defined with respect to the locally most productive alternations
for instance assuming that factor and reactor respectively receive the pronunciations faekt0r and rii ektor the discovery of the relationship expressed in NUM will lead our algorithm to record that the graphemic alternation f re correlates in the phonemic domain with the alternation f ri
typical examples are synergistically timpani hangdog oasis pemmican to list just a few
these mappings are used to search and retrieve in the lexical database the most promising analog of unseen words
the paradigmatic cascades model achieves quite satisfactory generalization performances when evaluated in the task of pronouncing unknown words
some complementary results finally need to be mentioned here in relation to the size of lexical neighborhoods
for example one pruning procedure detects the most obvious derivation cycles which generate in loops the same derivatives another pruning procedure tries to detect commutating alternations substituting the prefix p and then the suffix s often produces the same analog than when alternations apply in the reverse order etc
in the particular context of grapheme to phoneme transcription it provides us with a more satisfying model of pronunciation by analogy which gives a principled way to automatically learn local similarities that implicitely incorporate a substantial knowledge of the morphological processes and of the phonotactic constraints both in the graphemic and the phonemic domain
this expectation is computed by summing up the productivities of all the alternations that can be applied to an analog y according to p NUM i yedom ai this ranking will necessarily assess any analog starting in rl with a low score as very few alternations will substitute its prefix
in order to take this aspect into account we measure in each experiment the number of words that can not be pronounced at all the silence and the percentage of phonemes and words that are correctly transcribed amongst those words that have been pronounced at all the precision
the crucial observation is that the two constraints left to right and longest match force a unique factorization on the input string thus making the transduction unambiguous if the l01ger language consists of a single string
at the same time we perform some of the checks that a standard grammar checker would perform
it is an operation that combines a target with a moved phrase marker
a projection of the target v is added to the target
the position containing a visible constituent is the so position of that chain
for example instead of agro vp is the head corner of agro
therefore the head is parsed before its sisters in a head driven parser
in the subsection NUM NUM we present the earley type recognizer that equals the most efficient recognizers for contexttree grmnmar
starting from the transition graph for a category cat we can build the parse table for cat i.e.
two phrase markers v and dp are combined into one
vi ca t is an array h x k where h is the number of states of the transition graph and k is the nmnber of syntactic categories in c each row is identified by a pair cat state where state is the label of a state of the corresponding transition graph each column is associated with a syntactic category
the head corner relation is the reflexive and transitive closure of the head relation
hc agro agrop hc n np
james NUM years old hunt NUM
figure NUM parse tree for john will retire as chairman
each heuristic eliminates any meanings which are not the preferred ones
then add a role indicating the action of equality
the preprocessor then adds additional structure to the internal sgml tree where necessary
targets are other nodes and the arc too is just a node
university of durham description of the lolita system as used in muc NUM
the se cm process which is part of the overall tipster program includes the following responsibilities track application conformance with the architecture and the lessons learned are fed back into the architecture for the purpose of refining those details which have been determined and specifying those interfaces which remain under specified
the tipster architecture has been completed to the extent that the basic nctionality of components has been determined
these changes are being managed by a configuration control process administered by the tipster program se cm support contractor
as an example specific annotation types have not yet been defined specified and made available as a tipster standard
the erb review just before the preliminary design review is the first erb to initially examine tipster conformance
all designs are kept on record both in design to and as built form with the tipster se cm
figure NUM shows the basic erb process with two principal gates that a project must pass through
tacad detailing the ways in which the design complies with the architecture and those where it does not
final operating capability review erbprocess tipster program can support the integration of new technology and algorithms
the score of an interpretation is computed by considering the weighted average of the similarity degrees of the input complements with respect to each of the example case fillers in the corresponding case listed in the database for the sense under evaluation
this scale is responsible for the dynamic progression of parts of the sentence from topic proper through intermediate parts to
therefore in our syntactic representations of sentences we work with the scale of cd as with the underlying word order
deeper embedded elements have not yet been properly analyzed and different pronominal forms should be classified in a much more detailed way
NUM we can not discuss here the issues concerning other possible interpretations of sentences such as NUM and NUM
in fgd the correlates of function words in syntactic representations do not take the form of specific nodes in the tree
t institute of theoretical and computational linguistics charles university celetn NUM NUM NUM praha NUM czech republic
for example NUM a may be a full answer to a question such as NUM
the interplay of word order and these other factors allows for a specification of the scale of communicative dynamism cd
in both a and b the most dynamic complementation belongs to the focus on all the readings
in NUM NUM differences in presuppositions are connected with at least some readings of the sentences
we consider maximization of this effect by means of a training utility function tuf aiming at ensuring that the example with the highest training utility figure is the most useful example at a given point in time
given the growing utilization of machine readable texts word sense disambiguation techniques have been variously used in corpus based approaches NUM NUM NUM NUM NUM NUM NUM NUM
we have shown that linguistically fundamental ideas namely subcategorisation and wh movement can be given a statistical interpretation
the bonus program for h equent
in figure NUM a x s located inside a semantic vicinity are expected to be interpreted with high certainty as being similar to the appropriate example e a fact which is in line with restriction NUM mentioned above
NUM NUM the implementation and uses of expectation
a statement about a subgoal of s
the switch can be turned up
recognizes only the literal word forms and nothing else
in order to compare the system s selection with the professional s we identified in the text the sentences that contain the main concepts mentioned in the professional s abstract
this method 1this research was funded in part by arpa under order number NUM issued as maryland procurement contract mda904 NUM c NUM and in part by the national science foundation grant no mip NUM
we require that the branch ratio criterion defined in the previous section can only take effect after the wavefront exceeds the starting depth the first subsequent interesting wavefront generated will be our collection of topic concepts
how now to evaluate the results
we test ratio NUM NUM NUM NUM NUM NUM NUM NUM and depth NUM NUM NUM NUM
let w denote the n gram wl wn and ff w denote the number of times it occurred in a sample text
finally a large number of errors specially in test tuples stems from the fact that soft constraints are used for words unknown to the morphology
since the class scores calculated with this approach are exceedingly small the log of the probability equation is used to avoid computational difficulties
number of classes number of classes with term the class score is calculated by the following equation NUM
the expected probabilities are then the sum of the frequencies of the distinguishing terms in each of the classes divided by the training set lengths
cosine and smart methods the class score is used to rank the documents for the multinomial distribution method the routing score is used
this training set must be of a sufficient size to produce good statistics of the terms in the class and the frequencies of the terms
in modeling a class of text our technique requires that we identify a set of key concepts or distinguishing words and phrases
the method described in the previous section was applied to a text corpus consisting of NUM months of the newspaper frankfurter allgemeine zeitung with approximately NUM million word like tokens
the set lcb nora acc rcb indicates that the first nominal constituent in the structure is ambiguous with respect to case it may be nominative or accusative
one reason for the relatively low coverage is the fact that german compound nouns considerably increase the size of the sample space
expa dinoc this approach to the routing problem we want to fred the most likely class given the probabilities of the outputs
for example facts such as the common cross linguistic occurrence of rules of nasal assimilation which assimilate the place of articulation of nasals to the place of the following consonant suggest a natural class place that groups together at least the labial and coronal features
thus for each state in a transducer we gave the algorithm the set of arcs leaving the state the samples the phonological features of the next input symbol the features and the output transition behaviors of the automaton the decisions
note that if the underlying phone is a t rhotic voice continuant high coronal the machine jumps to state NUM if the underlying phone is an r the machine outputs r and goes to state NUM
these kinds of errors suggest that while a phonological rewrite rule can be expressed as a regular relation the evaluation procedures for the two mechanisms rewrite rules and transducers must be different the correct flapping transducer is in no way smaller than the incorrect one
the muc NUM scenario template task provided and even more rigorous test of sri s domain transportability strategy since participants had only a month to achieve a high level of performance NUM
e.g. variations such as cars are manufactured by gm and gm is to manufacture cars can be generated automatically by the simple active s v o pattern gm manufactures cars
support of transportability two of the key elements in support of transportability are major sri research areas fastspec and metarules NUM
the generic system generic information extraction ie systems today adequately identify in text basic entities such as companies personal names and places
implementation of these metarules enabled sri to bring the fastus system up to a high level of performance in a matter of days for the muc NUM evaluation
in short the goal is to have the system observe users and learn from their annotations rules of maximum generality from the minimum number of examples
sri s excellent results indicate that their approach is on the right track
sri also succeeded in accelerating fastspec s compile time which had positive impact on development time in implementing domain specific applications such as the muc NUM dry run and test scenarios of labor negotiations and management succession
our model differs from theirs in two important aspects
b NUM that s the television aerial
NUM the system is implemented in c prolog under unix
it repeats this until there are no more goals
the system then switches to the role of hearer
used by the modifier relative schema given in figure NUM
participants expect each other to act in this way
the schema for refer is shown in figure NUM
used by the modifier absolute schema given in figure NUM
the relevance score for each document was computed as the sum of the logged products of each term s term frequency if and inverse document frequency id0
what is meant by rule features are satisfied
rule features are incorporated into the right context of rules
let us examine differences among them r
in contrast the second writer would be placed at a very different level within slalom
the situation concerning french is similar to that
unknown words account for only a small percentage of the corpus in our experiments typically two to three percent
ideally we would like to achieve as large an increase in accuracy with as few extra tags as possible
if there are no nearby verbs with known teatures more remote words can be used for deciding on whether a certain feature should apply to the verb being examined especially if a substantial majority of these distant relatives are in agreement
corpus based techniques can help automate this filtering i.e. the source text should be viewed not only as an obstacle to be tamed parsed but as a resource that is best authority on what is grammatical for the domain
however stochastic taggers have the disadvantage that linguistic information is captured only indirectly in large tables of statistics
unlike decision trees in transformation based learning intermediate classification results are available and can be used as classification progresses
for example in applying transformation based learning to parsing a rule can apply any structural change to a tree
adding the character string x as a prefix suffix results in a word ixl NUM
the category space is the arbiter of paradigmatic relatedness and since it is bootstrapped from a training corpus that is representative for the domain sublanguage the resulting lexical entries will be customized for that domain
the net result is that the grammar becomes attuned to the sublanguage parses become possible because the enabling features are present while the search space is pruned of many false positives because unnecessary features are omitted
to a great extent of course these linguists are able to point to regularities because language is first of all a practical thing a means to communicate and there must be a colnmon base for such transfer to take phtce
the existing verbs in the lexicon themselves undergo a similar process whereby they are fitted to the domain some of their generic features which me not appropriate m e dropped whereas gaps in object options are filled
in addition some limited long er distance information is appended to the vector the training corpus has been augmented with bracketing information that is with implicit trees that exhibit binary branching but whose nonterminals are unlabelled
the induction works as follows each verb lms its own neighborhood formed by computing the cosine similarity weight between it and all other verbs in the category space and by retaining those whosc weight excecds a certain threshold
l anz reads a book about ships NUM byanz liest ein buch filler schnq m f f the solution lies in the observation that the latter f skeleton entails the former
note the diiference between NUM and NUM both marked according to NUM and NUM although their maximal focus domain ix identical tin buch is f marked only in NUM
a third slot rel other org required special inferencing on the basis of both linguistics and world knowledge in order t o determine the corporate relationship between the organization a manager is leaving and the one the manager i s going to
as for the f skeleton subclause i of NUM applies at ein buch subclause iii at gab causing the function compose to apply to gab s own semantic value and to its sister s f skel value
at phrase level the indirect head f marking principle NUM and the head f marking principle apply introducing s link g n for the head and s link n in for the phrase respectively
it is not necessary for a system to implement each operator exactly as described below
this includes company names people s names locations currencies and dates
the rules for tokenization for english will follow those used by the penn tree bank
an example of an attribute whose value is another annotation would be a coreference pointer
during our error analysis we were constantly searching for why the errors we found were occurring
it is an error if the document does not have an annotation with that id
if type is nil annotations of all types satisfying the attribute constraints are included
each non terminal node in the parse tree is represented by an annotation of type parse
for example the specifications may include the semantic class of particular slot fills
this means that only the words which are unknown in the training set and the words of the test sentence which are tagged as a noun or a verb in the training set are allowed to mismatch with subtree terminals
the transducers are composed with the sentence in a sequence
here we used the fully disambiguated outputs of the taggers
removing the rules which fail would cause a lot of ambiguity to remain
in our experiment both claims turned out to be wrong
the non contextual rules may be thought of as lexical probabilities
one way to resolve this is to write new biases
this illustrates how difficult it is to write good biases
the first two classes of errors are generally difficult to correct
we selected the xerox tagger that learns from an untagged corpus
i ate an apple the chinese word i and NUM apple has different functions in this sentence
where v is clique function wii h the property that vc depends only oil those randoui variable in clique e
the adaboost method was designed to construct a high performance predictor by iteratively calling a weak learning algorithm that is slightly better than random guess
which does not occur in l rainhig lat l jui lid prohibits even the cvcul
linear interpolation has over estimation problem because it adjusts the model on the training data only and has no policy for untrained data
definil ion of mi lcb f random variable t is markov random field if t salisfies the following two properties
robustness is an important issue for multilingual speech interfaces for spoken language translation systems
a third problem is the complexity of the design of recognizers for multiple languages
an efficient approach is to use recognizers which are identical except for parameter values
however it is quite a daunting task to train every language with every type of accent
our experimental results support the claim that recognition accuracy degrades in the presence of an unmodelled accent
the recognizers were evaluated using native english and cantonese speakers who were not in the training set
the models were evaluated using a separate set of native cantonese and native american english speakers
figure NUM speech recognizers perform better with concatenated pure language model than with mixed lan
these facts fall out of recognizing the parallelism
the problem of unknown subtrees by an unknown subtree we mean a subtree which does not occur in the training set but which may show up in an additional sample of trees like the test set
we will perform more expritnents using larger training and test sets to verify our results
we have performed a set of experiments to compare tile effect of different accents
this section includes examples of imagene s output for the fundamental relations dealt with in the current study that is purpose precondition result and action sequence
the analysis conducted in step NUM has been based primarily on a small subset of the full corpus namely on the instructions for a set of three cordless telephone manuals
for this purpose imagene s system network was re run for all of the approximately NUM action expressions both those from the training set and those from the testing set
these sources include instructions for electronic devices like cordless telephones and clock radios manipulative processes like auto repair and first aid and creative processes like cooking and craft making
highest p r f measure scores posted for muc NUM and muc NUM st task s
the study is then extended by testing the system s predictions on a separate and more diverse portion of the corpus that includes instructions for different types of devices and processes
the ordering realization statements are denoted with the operators and meaning order the clauses in the same sentence and order the clauses in separate sentences respectively
the categories for auxiliary forms we use are do be have can would should to
systems were not penalized if they failed to include such linkages in their output
the test sets used for muc NUM had a much higher proportion of relevant texts
NUM this is illustrated by the following example NUM we vp have good times
we will see that for our applications nr tends to be large for small frequencies r while on the other hand if nr is small r is usually large and needs not to be adjusted
typically there is a many one mapping from compositions onto meanings
again interpretation seen as description building sits easily with this
the approach here gets the four readings identified by them as most plausible
but at the level of incorporating elliptical material once tthe converse also holds
on the strict reading simon and john both love john s mother
this increases the computational complexity of the ellipsis resolution task
pronouns and the mode of combination of other e.g.
subs and otherwise old figure NUM qlf evaluation rules
the numbers of the various types of NUM the sentential mark also has two auxiliaries question and exclamation marks which are used to express sentences with certain tones
although the zero anaphora generated using rule NUM look considerably similar to those in the test data there are nevertheless still a number of overgenerations for the test data
in coconuts this task is carried out by the workflow manager which also manages interdependencies between these activities while avoiding redundant ones and controlling the flow of work among the involved managers e.g. passing subtasks from one manager to another in a correct sequence ensuring that all fulfill their required contributions and taking default actions when necessary
the basic methodology used is to start with a set of human generated chinese texts and the simplest possible anaphor generation rule a rule that only considers the locality of anaphora
in contrast in this paper we use the term nonzero department of computer science and engineering NUM chungshan north road section NUM taipei NUM taiwan
conversely if a zero anaphor is found in some position in the real text while a nonzero anaphor is created by the computer then it belongs to the undergenerated type
the main data structure in these algorithms is a context set which is the set of entities the hearer is currently assumed to be attending to except the intended referent
as illustrated in figure NUM even short sequences of tpcs form characteristic patterns
this left however two important questions unanswered NUM how does dop perform if tested on unediteddata and NUM how can dop be used for parsing word strings that contain unknown words
in that case every word is tested and counted one time even though its occurrence frequency might be very low NUM in terms of percentage of phonemes or of words correctly transcribed
furthermore even in the use of smoothing techniques to deal with sparse data paying close attention to underlying representational issues is often crucial systems that utilize designer encoded linguistic knowledge about representations currently significantly outperform representation poor self organizing smoothing methods
many of the techniques used in statistical parsing derive rather directly from methods used for speech recognition this is particularly true of methods for dealing with sparse data
this result matches our expectation because the syntactic score should provide more discrimination power than the lexical score in the syntactic disambiguation task
since the matching predicate need not be perfectly accurate the translation lexicons need not be either
this use of statistical models was clearly inspired by the use of statistical methods for speech recognition where self organizing systems based on statistics are now beginning to achieve commercial success and greatly outperform systems which attempt to explicitly encode linguistic knowledge
the last section offers some insights about the optimal level of text analysis for mapping bitext correspondence
therefore it can be used in preparing teaching materials such as the structures used by a cai system such as caters saving the instructor of hand coding them
i will claim that the reason these recent systems perform so similarly is that they explicitly encode very similar levels of linguistic representation
the steps NUM NUM find NUM are the processes to determine the target verbs
a few days were then spent on those slots raising the performance slightly see c
for example in mixed case group is not included in the tag for chrysler group
hasten matched person and organization egraphs to extract additional loca l information such as location nationality and descriptors
all the models were compared in terms of how well they represented a held out test set
she gives people the most wonderful presents
an oracle filter is useful when a list of likely translation pairs is available a priori
these four filters fall into three categories predicate filters oracle filters and alignment filters
if only the words in the lexicon are considered bible gives an estimate of precision
figure NUM shows the outline of a bible algorithm for evaluating precision of n best translation lexicons
the advantage of combining data driven mining with the existing lexical knowledgebase over other bootstrapping methods is that this approach does not require the manual identification of appropriate cues for subcategorization features or the involved construction of a pattern matcher that is sophisticated enough to ignore false triggers
the mutual information for any pattern is the maximum mutual information between the sub pattern and the concepts in the taxonomic hierarchy which generalize the moditier in the pattern
fortunately between french and english true cognates occur far more frequently than faux amis
in fact for these three variables the hypothesis of random performance can not be rejected even at the NUM level
while they use data on word groups our method directly uses word co occurrence data to estimate the preferences using the cd to identify the most adequate grouping for each relation
all training cases for which this test succeeds fails belong to the left right subtree of the decision tree
c c z from this the algorithm selects the parse with highest score which is drawn in thick lines
ease of customization trainability and automated knowledge acquisition are widely acknowledged as critica l issues for text extraction systems
the analyzer extracts semantic information from the text using a set of extraction examples and a supporting knowledg e base
this requires new methods of comparative evaluation
a popular method for the automatic construction of such trees is binary recursive partitioning which constructs a binary tree in a top down fashion
we concentrated on checking the general vocabulary
NUM g assistant dieser ist hart
all translations with their tags will be entered into the translation list
for the succession scenario this consists of a few key phrase types most salient among them job a post at an org job in and job out fully specified successions and post in and post out partially specified successions
multiple selections can be made but they can not be prioritized
multiple subject areas from different levels can be selected and prioritized
our next step was to measure the variables described in section NUM which are used in the various cording to morphological relationship and markedness status
thus we can gain a significant amount of boundary information by this simple scheme of hypothesizing segmentations
finally the differentiation test NUM is the one general markedness test that can not be easily mapped into observable properties of adjectives
can we meet instead on tuesday 1agent agent interaction is based on a formal representation language rather than on nl semantic representation for a text or to generate a text from a semantic representation
there is information in the knowledge of segment boundaries that should be incorporated in our language models
in our classifier we employ a maximum likelihood estimator based on the binomial distribution to select the optimal split at each node
instead at every point we picked the most likely path as the history for the next word
the org label on creative artists agency was set by a predicate that tests for org keywords like agency coke was found to be an org elsewhere in the document and the label was then percolated
so far we have described adverbs which concern a single event but some adverbs regulate the multiple events which involves iteration of a single event
the work reported in this paper helps clarify which types of data and tests are useful for such a method and which are not
table NUM plots the NUM most frequent words in the corpus showing their before and after raw totals
the same can be said about the test based on the differentiation properties of the words number of different parts of speech
marked e s and incomplete sentences are marked n s rather than and as described in ss2
null NUM because we focus our efforts on correcting speech repairs the identification of acoustic and prosodic cues does not discuss in this paper
both the precision rate NUM NUM and the recall rate NUM NUM are all better than those in the former models
although this technique can achieve recall rate of NUM NUM it has a relatively low precision rate i.e. NUM NUM
for example NUM repairs are proposed by the simple pattern marcher in conversation NUM but only NUM of them are correct
the discussions above show that the salience constraint in tr3 is sometimes effective in getting small improvements in the output texts
in the cw type a syllable which is correctly converted before repair processing is changed to a wrong one after the repair processing
the problem of chinese homophone disambiguation is defined as how to convert a sequence of syllables s into the corresponding sequence of characters correctly
the first stage of the work is to devise a method of dividing sentences into these two parts
the experimental results show that the precision rate is increased to NUM NUM and the recall rate is decreased to NUM NUM
one type of ambiguity occurs when a conjoined noun phrase premodifies a noun as in this example from an ibm manual it is the number defined in the file or result field definition
the phrase file or result field definition is ambiguous in many ways as is shown by the output from easyenglish ambiguity in the file or result field definition
there is overlap in the kinds of checks made by these three systems but we attempted to evaluate each system on its own terms i.e. on the basis of the collection of checks that it
the specification can include any number of term dictionaries any number of abbreviation dictionaries any number of non allowed word dictionaries and any number of controlled vocabulary dictionaries
the terms file has a format that is directly usable as a user dictionary however to keep terminology consistent and remove misspellings it is necessary that a terminologist approve the content before actual use
it is a delicate balance to process text that has grammatical errors the parser needs to be able to make reasonably good sense of the text in order for the checking component not to overflag problems
the second type of vocabulary check identifies acronyms or abbreviations in the text and checks to see that the first occurrence is properly spelled out according to the definition supplied in the user dictionary for acronyms
the output is its subgraphs with the condition that each subgraph is specialized in a topic
similarly nodes oil the right of a sentence a re suppressed whe n identical to the node on the left
so prefer frequent senses ensures that update a a holds but update a o j3 does not
in datr wflues may be associated with particular node path pairs either explicitly in terms of local or global inheritance or implicitly by default
as an example consider the following simpliiied rule of the operational semantics if n1 l NUM g t the n
the new theory is equivalent to that given previously in the sense that it associates exactly the same values with node path pairs
to understand the way in which global inheritance works it is necessary to introduce datr s notion of global contea t
for c c peso a sequence of descriptors an expression c is called a path descriptor
in contrast the system of inr renee described ill this pal er characterizes a relationship between datr expressions i.e.
this is because one can now assume this bag is one of the bags mentioned in 4a and therefore elaboration can be inferred as before
finally to capture tile way in which values are derived for quoted descriptors three entirely new rules are required one for each of the quoted fi rms
this paper addresses issues in automated treebank construction
indirectly it shows that our rule based segmentation rule d e NUM can define sufficiently good features for retrieval and remedies our deficiency in lexicon size
the word hope is often used in the context of we hope to that or my hope is and quite non content bearing
nhhn NUM e baert NUM n sager NUM g de moor NUM NUM division of medical informatics university hospital gent de pintelaan NUM 5k3 b NUM gent belgium NUM courant institute of mathematical sciences new york university NUM mercer street ny NUM new york usa lcb peter
surgical procedure quintuple coronary bypass the highlighting of medical concepts of interest makes it possible to scan a document quickly focusing on a particular type of information such as symptoms and diagnoses or treatments resolved p NUM
the c program takes the filename and the requested sublanguage label s as parameters and generates a new html file by replacing the occurrences of the concerned label s by a genuine html code strong strong around the relevant words
the earliest systems were prone to frequent hardware and software failures
NUM through NUM below show well formed expressions in ps NUM
on the contrary dialogos was designed to enter clarification subdialogues when faced with input that can not receive a single coherent interpretation in the dialogue context
conversely when users control the task initiative we expect more assertions by the user concerning the user s own task goals rather than direct responses to computer questions or commands
for example if a particular system is always run with a particular speech recognizer it may be difficult to determine what the outcome would have been with a better speech recognizer
when the computer has total control of the dialogue in directive mode it is expected that the computer will initiate the transitions between subdialogues
if the repair process is basically the same once the error is diagnosed few utterances will be required as repairs can be done without discussion
some subjects as part of their expertise developed a somewhat ritualistic style of interaction with the machine which may have lengthened their interactions
NUM the letters correspond to the abbreviations given in section NUM NUM and f represents the finished state i.e. completion of the dialogue
later a faster parsing algorithm was implemented and the system was ported to a sparc ii workstation from the sun NUM used during the experiment
smith and gordon human computer dialogue inputs outp q current computer goal current user focus dialog mode computer response selection algorithm selected task goal
since a noncontrolling participant has the option of seizing control at any moment then the controlling participant must have control because the noncontroller allows it
table the test corpus includes only NUM words of the over NUM NUM in our learning corpus
in figure NUM the average ambiguity is NUM for the set c i lcb cil
in mixed initiative systems however user and system might both lead the dialogue by providing several pieces of information and pursuing several different goals within one utterance
a common feature of a number of current spoken dialogue systems for information retrieval is that little emphasis is placed on the generation of system contributions to the dialogue
as for the wsj corpus a short analysis of the linguistic data may be useful
the method is evaluated against a manually tagged corpus semcor
for each w k in 3remember that words are weighted by their frequency in the corpus
by tagging the corpus with c i we obtain a reduction of ambiguity measured by
the noise produced by the redundant number of tags often overrides the advantage of semantic tagging
this is reasonable in general because our aim is to control overgenerality while reducing overambiguity
this phenomenon is accounted for by the inverse of the average ambiguity a ci
figure NUM two representatives o three companies two sample derivations
d is a collection of texts news articles found in the training corpus
section NUM introduces the idea of bringing an ir technique to the topic identification task
the vertical dimension represents the proportion of words in text against words in title
figure NUM a rhetorical structure for the sample news story in fig NUM
a break even point is a highest point where recall and precision is equal
the length of a segment d is fixed at NUM words in the experiments
section NUM discusses a problem that the proposed method shows poor performance on large documents
then the similarity between a headline and text segment is given by the usual tf
information pertaining to empty heads are projected along the doubli si ash dsl feature instead of the slash feature cf
it is our main result that prosodic information can be employed in such a system to determine possible locations for empty elements in tile input
one of the major tasks of a prosodic component of a processing system is the determination of phrase boundaries between these sentences and free phrases
table NUM shows that in the majority of cases the position with the highest NUM probability turns out to be the correct one
this endof sentence marker will be assigned a higher NUM probability in most cases even if the correct verb trace position is located elsewhere
in particular we find phrase boundaries which are classified according to the perceptual labeling although they did not correspond to a syntactic phrase boundary
when performing this test on NUM sentences the following picture emerged verb trace position within a sentence according to the NUM probability
it is important to note that this approach does not take syntactic boundaries and phonological boundaries to be one and the same thing
the theoretical results differ only slightly in the mistake bounds but have the same flavor
for a given instance sil sia 8ira the algorithm predicts NUM iff
we started by evaluating the basic versions of the three algorithms
training a linear text classifier is a search for a weight vector in the feature space
then there will generally be many linear classifiers that separate the training data we actually see
we address these claims empirically in an important application domain for machine learning text categorization
table NUM recall precision break even point in percentages for different versions of the algorithm
the promotion parameter is a NUM and the demotion is NUM NUM
initially the weight vector is typically set to assign equal positive weight to all features
thus the semi circular shape of the error scores e v n v n shown in figure NUM is a direct consequence of the topical structure at discourse level of alice in wonderland
the most interesting points of comparison with lt nsl are in the areas of query language and underlying corpus representation
as far as corpus annotation goes unix rcs has proved an adequate solution to our version control needs
we are currently in discussion with the gate team about how best to allow the interoperability of the two systems
the first stage of processing normalises the input producing a simplified but informationally equivalent form of the document
no because in practice any corpus is encoded in a way which reflects the assumptions of the corpus developers
the standard distribution is fast enough for use as a search engine with files of up to several million words
it generalises the unix pipe architecture making it possible to use pipelines of general purpose tools to process annotated corpora
it does support the tipster approach of sep null arating base texts from additional markup by means of hyperlinks
the data architecture needs to address not only multiple levels of annotation but also alternative versions at a given level
this operator alters the ith word ewi from the example to the jth word iwj in the input expression
therefore it makes sense to allow for mismatches across wordnets where the same type of equivalence relation holds between a single synset in one language and several synsets with a nearsynonym relation in another language
implementation of this technique requires the grammar writer to declare a particular feature as being able to take values in some boolean combination of atoms for example something like bool comb feature agr i NUM NUM sing plur
in our experience it seems clear that annotated text document s are much less difficult to generate than the key templates used in previous muc evaluations
we also intended to use resolve for coreference resolution within the t e and st tasks where it would have to deal with person and organization references
it is interesting to note that same type is not checked until the fourth layer of the tree if same type no then they are not coreferent
we also needed to create template parsers and text marking interfaces in order to map the muc NUM training documents into data usable by our trainabl e components
if you fail to recognize half of the organizations in a test set it will be difficul t to do well on extraction tasks involving organizations
in an effort to reach beneath the numbers of the score reports we conducted a few informal comparison point experiments which we will report here as well
the conditional probability distribution is estimated using a distortion model of utterances that is de scribed in the next section
given these operators we can view the input i as an example e to which a number of distortion operators have been applied
we hold that a significant body of systematic linguistic regularities has been identified that nlust be accounted for somehow during the process of translation
the hybrid analogical approach is able to model such phenomena using probabilistic operators which are explained in more detail in the next section
this requires an extensive effort to create a body of rules that covers all possible expressions and which can handle extra grammatical
now many researchers are beginning to try to code large dialogue corpora for higher level dialogue structure in the hope of giving their findings a firmer basis
example NUM f go as first move of dialogue poor quality but still an instruction elicited by the partner
of course we are interested in the most straightforward relation between the two which corresponds to the least cost or highest probability
although technically the tree of coding distinctions allows for a check or an align to take the form of a wh question this is unusual in english
is the person who is transferring information asking a question in an attempt to get evidence that the transfer was successful so they can move on
because transaction structure for map task dialogues is so closely linked to what the participants do with the maps the maps are included in the analysis
using just the games for which all four coders agreed on the beginning the coders reached NUM pairwise percent agreement on where the game ended
to determine stability the most experienced coder completed the same dialogue computational linguistics volume NUM number NUM twice two months and many dialogues apart
however individual coders can develop a stable sense of game structure and therefore if necessary it should be possible to improve the coding scheme
a clarify move is a reply to some kind of question in which the speaker tells the partner something over and above what was strictly asked
with no previous mention of blacksmith or any distance straight down so that f ca n t guess the answer carletta et al
the only hand coding involved was in reviewing and revising the tags for the most frequent NUM words in the collected data
furthermore this result demonstrates the need to move beyond small numbers of constructed examples and intuitions formed this included answers that begin with because
we would like to extend this to the potentially much wider range of situations where partially predefined strings are appropriate
evaluation of these automatically generated texts forms the basis for further exploration of the corpus and subsequent refinement of the rules for cue selection and placement
in general achieving this sort of analysis requires some statistical information about the words in the input and their collocations
some of the actions required in the course of accepting and processing this input are NUM predicting words e.g.
consider the example given above where the user inputs open kitchen window as the requested action in a request template
the choice of template is made by the user and the interface provides slots which the user instantiates with text
each relation node has up to two daughters the cue if any and the contributor in the order they appear in the discourse
we thank michael kearns and yoram singer for useful discussions the anonymous reviewers for questions and suggestions that helped improve the paper and don hindle for help with his language modeling tools which we used to build the baseline models considered in the paper
such a concession contributes to the hearer s adoption of the core in b by acknowledging something that might otherwise interfere with this intended effect
the first row corresponds to a bigram model smoothed by a aggregate markov model the second row corresponds to an m NUM mixed order model smoothed by a ml bigram model smoothed by an aggregate markov model the third row corresponds els on the validation and test sets
a corl us integra tion method to verify efficiency of tho gra mmar a cquisition has yo t to i e inlph lnente d
in our experimental results a is assigned with value of NUM NUM which seems to make a good estimate
these rules axe attached with the conditionalprobability based on contexts the left and fight categories of the rules
which is not restricted to chomsky normal form and performs with leas computational cost compared with the approaches applying the inside outside algorithm
however there is still few researches which apply clustering analysis for grammar inference and parsing vior95
n a c is the number of times a label a and a context c are cooccurred
which is a special case of gr mtnar although he claimed that all of cfgs can be in this form
for moderately long sentences NUM NUM and NUM NUM words it works with NUM NUM recall and NUM NUM precision
a two nonterminals x and y are said to be in a left comer relation x l y iff there exists a production for x that has a rhs starting with y x ya
for each x occurring to the right of a dot we generate states for all y that computational linguistics volume NUM number NUM are reachable from x by way of the x l y relation
however the summation in equation NUM can be optimized if the c values for all old states with the same nonterminal z are summed first and then multiplied by r z gl y
a provided that s gl xo k lxv is a possible left most derivation of the grammar for some v the probability that a nonterminal x generates the substring xk xi NUM can be computed as the sum
c the prefix probability p s l x is the sum of the probabilities of all paths NUM starting with the initial state constrained by x that end in a scanned state
where NUM NUM tk are strings of terminals and nonterminals x a is a production of g and u2 is derived from NUM by replacing one occurrence of x with
to guarantee that all occurrences of pns are covered by local grammars it would be necessary to consider a great part of the contexts where common nouns appear
type ii such as janggun general sensu player frs cf
NUM NUM al w NUM officials prices which go way he last they earlier an tuesday there foreign quarter she former federal do n t days friday next wednesday thursday i monday mr we half based part united it s years going nineteen thousand months million very cents san ago u percent billion according in an m NUM mixed order model
numeric weights for NUM a tterns a re axtr moly useful as a mea ns of assigning higher priorities to us r defined NUM a ttevns
so far as we do not have a dictionary which would provide all proper names we need auxiliary tools to analyze them
when they appear in this context there are no ways to sort out proper names only by analyzing their syntactic structures
in fact strings containing frs are necessarily based upon human nouns proper names being only one class of human nouns
NUM experimental results i so far we have examined contexts where we expect to encounter proper names par
we can not distingnish this pn kim jung i from other nouns that can be found in this position such as in the following
all nouns have some semantic and syntactic properties which lead to group them into several classes not by binary distinctions
table ii shows the number of empty classes in different levels in the left right tree when we process the subcorpora containing NUM words
letter to sound rules also known as grapheme to phoneme rules are important computational tools and have been used for a variety of purposes including word or name lookups for database searches and speech synthesis
the rule does not provide for words like batelier bat31je boatman or bachelier bay31je bachelor where elision is not done
first order context can sometimes solve the problem nous notions vs des notions un as vs tu as but generally a parsing of the entire sentence is required
any language with an old writing system that has not undergone a modicum of spelling reform but has undergone dramatic phonological morphonemic and morphological changes will probably fall into this category
of the NUM NUM words NUM NUM are incorrectly processed NUM NUM out of NUM NUM and have to be added to an exception dictionary
memory is increasingly less expensive and we now have the capability to store in memory a large number of words along with their phonetic equivalent grammatical class and meaning
the left and right context patterns are encoded as strings of operators and parameters for a pattern matching procedure and the replacement phoneme string is encoded using the system s internal phoneme codes
in this paper we define the number of steps as equal to the larger number of the classes between two trees resulting classes
so it is difficult to merge the classes obtained by left right binary tree and right left binary tree during the process of growing tree
to obtain the real probabilisy of word w belonging to certain class all belonging probabilities of its ancestors should be multiplied i together
this is true of the phonological phonetic morphological syntactic semantic and letter to sound subsystems of two different languages some are an order of magnitude more complex than others
first the number of syllables in the root is noted and a flag is set on that syllable with the most likely default for the placement of NUM stress
then average mutual information between classes NUM c NUM c n is p c c
they treat the word s neighbors equally without considering the possible different influences of l eft neighbor and right neighbor to the word
although we will experiment with all three modes our primary intention is to employ mode NUM currently for implementational reasons we use mode NUM in its simplest instantiation when the changes made by a module turn out to be incompatible with those of other modules the module starts again
thus tree rewriting rules are realization statements in the sp modules several other realization operators are also supported
the content delimitation module primarily introduces realization statements into the pre spl expression that constrain the exophoric choice module
lack of space prevents us from discussing the criteria and heuristics that are responsible for the individual choices
the sp must transform an underspecified input of deep semantics into a suitably specified output of shallow semantics
the architecture is presented and the interaction of the sentence planning modules within this architecture is shown
it also supports the addition of new modules without requiring the revision of the interfaces of existing modules
the sentence planning process transforms an input tsl expression s into one or more spl expressions
in june NUM a european concerted action disc spoken language dialogue systems and components best practice in development and evaluation will be launched with the goal of systematically addressing this problem
for the examples in this paper we only need type raising and function composition along with function application
in this reading there are three hunters and five tigers such that shooting events happened between the two parties
q every x li s is a term for scoped logical forms
NUM a some student will investigate two dialects of every language
if we generalize over available readings they are only those that have no quantifiers which intercalate over np boundaries
a uill uin r b has a binder NUM that is quaati fving
this section proposes a generalization at the level of semantics for the phenomena described earlier and considers its apparent counterexamples
to illustrate the role of inclusion goals let us suppose that the system also knows that book19 is on reserve in the state have27
spud s grammar currently includes a range of syntactic constructions including adjective and pp modification relative clauses idioms and various verbal alternations
at the same time it exploits linguistically motivated declarative specifications of the discourse functions of syntactic constructions to make contextually appropriate syntactic choices
this work has been supported by nsf and ircs graduate fellowships nsf grant nsf stc sbr NUM arpa grant n00014 NUM and art grant daah04 NUM g0426
we have selected four representative pragmatic distinctions for our implementation however the framework does not commit one to the use of particular theories
prolog is a particularly useful language for the implementation of a head corner parser for constraint based grammars because prolog provides a built in unification operation
crl has investigated the problem of information retrieval against collections of text written in multiple languages
both these products will now be subject to an engineering review board
achievements crl has been heavily involved in the design of the tipster architecture
the goal weakening technique can also be used to eliminate typical instances of the problems concerning the occur check discussed in the previous subsection
this module has asserted clauses for the predicate lexieal analysis NUM where the first two arguments are the string positions and the third argument is the
all the crl graphical user interface tools are now available to government agencies and to other research groups see paper on graphical
it has also been responsible for the integration and delivery of both the six and twelve month tipster demonstration systems and the development of the first tipster document manager
clearly the more often the robustness component uses the information that was actually uttered the more confidence we have in that component
even though we now have a much more general goal the number of different goals that we need to solve is much smaller
the editor supports multiple languages including arabic japanese spanish chinese and russian
this work has been carried out in close compliance with the decisions of the architecture working group
the module generation generates syntax trees on the basis of the mozart database a collection of stemplates and the context model
taxonomic link is a all the metrics detailed above were designed to capture semantic similarity or closeness
for n e f out n denotes the set of all children of n
we have not explored ambiguous situations those in which more than one valid derivation remains or in the absence of validity more than one invalid derivation
header decomposition modifiers recurse entity object cand modifier entity object cand newcand modifiers entity object newcand figure NUM modifiers
computational linguistics volume NUM number NUM between a replacement and an expansion however the speech action resulting from the judgment will provide the proper context to disambiguate its meaning
if a conversant rejects a referring expression or postpones judgment on it then either the speaker or the hearer will refashion the expression in the context of the rejection or postponement
bel hearer goal speaker b el hearer bel speaker error p lan errornode figure NUM postpone plan schema
so the inference process requires only that the proposed referring expression be derived so that it can serve to replace the current plan but not that it be acceptable
first our coverage of referring expressions could be extended to handle references to objects in focus and to descriptions that include a plan of physical actions for identifying the referent
computational linguistics volume NUM number NUM expand plan do not hold since the action in error p56 is not an instance of modifiersterminate so this plan is eliminated
into two steps s refer which expresses the speaker s intention to refer and describe which accounts for the content of the referring expression given next
i use post s correspondence problem pcp as a well known undecidable problem
however the results have been encouraging at least with technical documents such as computer manuals where words with the same lemma are frequently repeated in a small area of text
in this paper we describe a method for completing partial parses by maintaining consistency among morphologically identical words within the same text as regards their part of speech and their modifiee modifier relationship
ni processed position n figure NUM example of an incomplete parse by the esg parser
we have proposed a method for completing partial parses of ill formed sentences on the basis of information extracted from complete parses of well formed sentences in the discourse
in this paper we are concerned with the syntactic analysis phase of a natural language understanding system
after all the sentences except the ill formed sentences that caused incomplete parses have provided data for use as discourse information the parse completion procedure begins
a possible derivation for this grammar constructs the following abbreviated parse tree in figure NUM
figure NUM illustration of a solution for the pcp problem of figure NUM
this skeleton is obtained by removing the constraints from each of the grammar rules
if they are different from those appears in sentences NUM NUM NUM NUM NUM NUM NUM n to find appears in sentences NUM
a naive method for this task would consider all factor pairs appearing at aligned positions in some pair in l the left component of each factor must then be split into a string in e and a string in f to represent a transformation in the desired form
the latter not only requires n complements but also n NUM intersections
the coerce relation for a compound rule can be simply expressed by l
the derivation of katteb is illustrated below length descriptions at some preprocessing stage
to match the feature structures associated with rules and those in the lexicon we proceed as follows
as a way of illustration consider the simplified grammar in figure NUM with j NUM
l proj ctl x lex a gram NUM
secondly we incorporate a feature structure of a rule into the rule s right context p
coerce r insert lcb rcb o NUM k
for all such expressions we subtract coerce from the automaton under construction yielding
in contrast with non local synchronization in local synchronization there is no inheritance of synchronization links
for NUM i iwl bi in algorithm NUM is the node representing the longest prefix of suffi w such that h bi is an implicit node of t is a factor of w
the spt NUM is sorted in the original order of the values of st fields
interrupted collocation which requires a search of the source text by combining the strings thus extracted
to solve this problem this paper proposed a new algorithm that restrains extraction of unnecessary substrings
the case of partially joint relation case NUM can be further classified into two sub cases
and the examples of substrings with high frequency are also shown in table NUM table NUM
next using these results a method for automatically extracting interrupted collocational substrings has been proposed
the lower part of fig NUM shows the application of this method for k NUM
let s consider substrings a and NUM which have been extracted from the same sentence
the problem arises when there is a certain overlap between them as shown in fig NUM
operators sharing the same precedence are interpreted left to right
x b crossproduct cartesian product a
final states are distinguished by a double circle
the state labeled NUM is the start state
the following simple expressions appear frequently in our formulas
any symbol in the known alphabet and its extensions
that do not contain either ab or c anywhere
the first step in the generation process is to convert it into some arbitrary tncb structure say the one in figure NUM
consequently the better formed the initial tncb used by the generator the fewer the number of rewrites required to complete generation
if a maximal tncb is adjoined at the highest possible place inside another tncb the result will be well formed after it is re evaluated
in practice this restriction requires that sufficiently rich information be transferred from the previous translation stages to ensure that sign combination is deterministic
full scale bag generation is not necessary because sufficient information can be transferred from the source language to severely constrain the subsequent search during generation
on the other hand no matter how the sl and tl structures differ the algorithm will still operate correctly with polynomial complexity
we have presented a polynomial complexity generation algorithm which can form part of any shake and bake style mt system with suitable grammars and information transfer
somewhat more surprisingly even for short sentences which were not problematic for whitelock s system the generation component has performed consistently better
there are several ways that name searching could be implemented in a document retrieval context
the two properties required of tncbs and hence the target grammars with instantiated lexicm signs are NUM precedence monotonicity
imagine a bag of signs corresponding to the big brown dog barked has been passed to the generation phase
have NUM make NUM take NUM get NUM add NUM pay NUM see NUM call NUM decline NUM hold NUM come NUM give NUM keep i01 know NUM find NUM lose NUM believe NUM raise NUM drop NUM lead NUM work NUM leave NUM run NUM look NUM meet NUM the basic tags have the following form
the following functional model diagrams are based on the notation used by rumbaugh et al
vector space model vsm we replace s m nc e equation NUM with vsm nc e computed by equa null tion NUM our method base on statistics based length sbl we simply replace sim nc e in equation NUM with sbl nc e computed by equation NUM
figure NUM NUM canis top level process overview figure NUM NUM shows the top level design of the canis
brary routines which provide a standard interface between the canis prototype and the persistent storage of documents
other applications using lockheed marfin s document manager are being built on top of sybase and oracle
our test plan involves subsystem testing of each of the comms process csci extraction process csci
at present there is little or no automation support for the indexing part of the process
extraction process csci ument identifiers to the document manager csci which retrieves and returns the document text
the steps are tokeaization segmentation reduction extraction reference resolution and post processing
symbol id part of speech string case type and character start and end positions
the types of filing and document reference data connected are system folder objects and document ids
canis is compatible with both the input and output systems currently being used by the canis customer
our evaluation across various corpora shows that the use of cues consistently improves the accuracy in the system s prediction of task and dialogue initiative holders by NUM NUM and NUM NUM percentage points respectively thus illustrating the generality of our model
when the unknown words were made known to the lexicon the accuracy of tagging was detected at NUM NUM and NUM NUM respectively
in the case of definite expressions we also consider the recommendations of a distinct coreference module within fas tus
rather the matches between taggers and experts choices reflect the extent to which the ability to match mental representations of meanings with dictionary entries overlap between untrained annotators and lexicographers practiced in drawing subtle sense distinctions and familiar with the limitations of dictionary representations
table NUM distribution of negative imperatives
while the taggers were not told anything about the sense ordering in the dictionary booklet we expected those taggers working in the frequency condition to realize fairly quickly in the course of their annotations that the sense listed at the top was often most inclusive or salient one
for instance consider the alternative responses in utterances 3a 3c given by an advisor to a student s question NUM s i want to take nlp to satisfy my seminar course requirement
we describe an approach that is not quite context free but still admits acceptably fast earley style parsing
although physical cues such as gestures and eye gaze play an important role in coordinating initiative shifts in face to face interactions a great deal of information regarding initiative shifts can be extracted from utterances based on linguistic and domain knowledge alone
some of the functions are simply notational sugar for standard cfgs while others are context sensitive extensions
this mechanism handles search through the net use of inferenc e rules to derive implicit facts and general output formatting
the context structure holds possible referents ordered by recency of occurrence and with semantic and feature information attached to aid disambiguation
normalisation syntax based meaning preserving transformations are applied to the trees to reduc e the number of cases required in semantics
another avenue for further improvement is to introduce the or operation on the nodes of the lattice
our approach uses a feature collocation lattice and selects the atomic features without resorting to the iterative scaling
tu is a set of configuration frequency counts of the nodes NUM of the lattice
apart from the computational overload this will require training data well beyond usually available in the training samples
the newly added feature should improve the model its kuliback leibler divergence from the reference distribution should decrease
our method uses assumptions similar to berger et al NUM but is naturally suitable for distributed parallel computations
we also propose a slight modification to the process of parameter estimation for the conditional maximum entropy models
these nodes in their turn will collocate with each other and with the node ab producing the node abc
figure NUM shows how the configuration frequencies in the optimized lattice are redistributed when adding a new atomic feature
this cluster was proposed as a candidate translation list for debate
proops suppose the problem was decidable
for example mr james in the walk through article is selected because the node NUM belongs to the human family type and has the named individua l rank see figure NUM for the semantic information about mr james
taking into account rule f12 which resolves ambiguity by proposing additional hyphen points under specific contexts the degree of completeness increases
the check for monotone increasing quantifiers is simplest
therefore no additional hyphen point is derived for any word where each vowel sequence is of either the 2v or the vc type
individual ttrrns are represented by a set of facts of the form expressed p t and expressednot p t where p is an unnegated supposition that has not been formed from any simpler suppositions using the function and
this was because the coref algorithm ignores textrefs that refer to more than one concept in case they create a n unintentional linking of two textref chains duplicated textrefs should never occur and so any co referenc e based on them is likely to be wrong
formally the set of expression 0c1 c2c c3 is the set of all maximal prefixes of consonants
their integrity was extensively examined through selection of matching words found in the corpus and in noussia hyphenator for modern greek the lexicon
we saw above that if a tree is not unary branching then its yield is unique
two subtrees are strictly aligned if the above conditions hold and neither tree is a unary branch
the terminal yield of a subtree is then its extent less any occurrences of lsd and rsd
the relation top NUM defines the start symbol
accordingly we will record the existence of a tree spanning NUM to NUM in the treebank
in this case we will say that the unary trees in question are also strictly aligned
the corpora may contain additional markup provided this is distinct from content and structural markup
that is structural delimiters are distinct from other forms of markup and content
in this section we provide a general characterization of agreement in analysis between two corpora
furthermore the paper benefitted from remarks made by the anonymous acl reviewers
among these k closest matching training examples the class which the majority of these k examples belong to will be assigned as the class of the test example with tie among multiple majority classes broken randomly
p ci vl h n NUM is estimated by n1 w ere s the number of training examples with value vl for feature f that is classified as class i in the training corpus and n1 is the number of training examples with value vl for feature f in any class
i would like to thank robert dale and vance gledhill for their helpful comments on earlier drafts of this paper and richard buckland and mark dras for their help with the statistics
in these approaches relationships between a given pair of words are modelled by analogy with other words that resemble the given pair in some way
this affects the accuracy of disambiguating senses which have definitions containing these polysemous words and is found to be the main cause of errors for most of the senses with below average results
however the ability to make use of information in a smaller context is very important because the smaller context always overrules the larger context if their sense preferences are different
NUM for instance the ldoce definitions of both offence and felony contain the word crime and all of the definitions of sentence fine and penalty contain the word punishment
using the definition based conceptual co occurrence data collected from the relatively small brown corpus our sense disambiguation system achieves an average accuracy comparable to human performance given the same contextual information
concept crime will usually be expressed by words like offence or felony etc and punishment will be expressed by words such as sentence fine or penalty etc
n is taken to be the total number of pairs of words processed given by f dc NUM since for each pair of surface words processed
a particular domain for example economic news consists of several articles each of which has different title name
in figure NUM if a word is a keyword in a given article it satisfies the following two conditions NUM
furthermore we linked nouns which are disambignated with their semantically similar nouns mainly in order to cope with the problem of a phrasal lexicon
there are NUM NUM out of NUM NUM nouns whose tf idf value is less than log50 and the percentage attained at NUM NUM
in order to cope with the remaining problems mentioned in section NUM and apply this work to practical use we will conduct further experiments
the ratios of correct judgements in these cases were significantly high i.e. NUM NUM NUM NUM and NUM NUM respectively
null limitations of the method when the ratio of extraction was higher than NUM the results was NUM NUM and NUM NUM
our extraction technique of keywords is based on the degree of context dependency that how strongly a word is related to a given context
the first step to extract keywords is to calculate x NUM for each word in the paragraph the article and the domain
based on the suggested goal and the current state of the dialog select the next goal to be pursued by the computer and determine the expectations associated with that goal
it finds optimum answers in less than two seconds time for most utterances of lengths used in the environment of our system when running on a sun sparc station NUM
the dictionary entries define insertion and deletion costs for individual words as well as substitution costs for phonetically similar words such as which and switch
for example the first new subgoal find knob causes a new entry into zmodsubdialog where it is immediately satisfied by find knob in the user model
an overview of the computation is given here and a detailed trace of all significant details appears in appendix a the following database of prolog like rules is needed for proper system operation
b the meaning of short answers in the implemented system these include such responses as yes no and okay
then it presents information it surmises may be helpful specifically facts computational linguistics volume NUM number NUM from the currently active subdialog that are not in the user model
the user model thus changes on almost every interaction to note new facts that are probably known to the user or to remove facts that the user apparently does not know
as these examples clearly show datr descriptions do not map trivially into sets of standard dags although neither are they entirely dissimilar but that does not mean that datr descriptions can not describe standard dags
the position is figured out from the hypothesis alignment see section NUM NUM the origninal sentence is recovered the third pass differs from previous passes instead of initiating the recovery from the lexical elements at hand it summons predictions from the grammatical expectations
table NUM metarules of type NUM coordination and type NUM noun to verb variations
it is unlikely that our transformational approach with regular expressions could do much better than the results presented here
furthermore we have devised a method for guessing possible suffix combinations from a lexicon and a corpus
and morphology first inflectional morphology is performed in order to get the different analyses of word forms
derivational generation is performed on the lemmas produced by the inflectional analysis and the part of speech information
on a pentium133 with linux the parser processes NUM NUM words min from an initial list of NUM NUM terms
second the term list is dynamically expanded through syntactic transformations which allow the retrieval of term variants
figure NUM example net node text version mr james in the walk through articl e
the on metarule removes the ion suffix and the eur rule adds the nominal suffix eur
pp2 NUM pp3 NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM
rather we propose a technique which makes use of the richer data available from the pp1 training set
NUM to address this problem they employ backed off estimation when zero counts occur in the training data
NUM multiple pp structures are less frequent and contain more words than single pp structures
the first three steps use the standard backed off estimation again including only those tuples containing bolh prepositions
athough as collins and brooks point out this is less of an issue since even low counts are still useful
since at the point of extraction the system knows a great deal about the components o f each sentence it may be possible to have the system itself generate a set of interesting patterns for a particular domain
for multiple pps the space of possible structural configurations increases dramatically placing increased demands on the disambiguation technique
NUM 2we did not consider the left recursive np structure for the NUM pp or indeed NUM pp cases
the xerox tagger was trained on the original brown corpus tag set which makes more distinctions between categories than the penn brown corpus tag set
this paper presents a partial solution to a component of the problem of lexical choice choosing the synonym most typical or expected in context
computing phrasal signs in hpsg prior to parsing
this result shows that our method gets the same level of accuracy as the inside outside algorithm does
using the tagger without lexicalized rules an overall accuracy of NUM NUM and an unknown word accuracy of NUM NUM is obtained
as stated above the initial state annotator for tagging assigns all words their most likely tag as indicated in a training corpus
the second step in lexical analysis is the actual lexicon lookup which attaches information from th e lexicon to the tokens
when allowing more than one tag per word there is a trade off between accuracy and the average number of tags for each word
therefore this method is not subject to the sparse data problems that arise as the depth of the decision tree being learned increases
the text structure contains all the sentence structures obtained from the analysis of a whole message
NUM the discourse memory is structured as a sequence containing all information collected during the dialogue
after all agents accept a proposal the date is confirmed by the initiator NUM
the following times would suit me nov NUM NUM between NUM and NUM a m
conceptually no constraints are made on the number of active virtual systems in the server software
without confusion we will still use the terms of frequency and mutual information throughout the paper
the segmented version is then labeled with pos tags using the viterbi training procedure for pos tagging
concerning insertions the parser checks whether a local analysis is possible without a word suspected to be inserted if so the decision is made to eliminate the word if not the word is considered as substitution and processed as described above
the sizes of the common entries for the various models are around NUM to NUM thousands entries
others include frequently encountered proper names company names city names or productive lexicon entries
therefore we use the frequency measure f xi as the first feature for classification
after segmentation each word in the segmented text is automatically tagged with its part of speech
in the current task we assume that there is only a small segmented seed corpus available
initially the n grams embedded in the unsegmented corpus is gathered to form a word candidate list
it is possible to use any function of the left and right entropies for the classification task
in this paper the average of the left and right entropies is used as a feature
in opposite to this components of decolni osa ble
let wi be the i th word in the best hypothesis the alignment value at rank n is when wi is aligned with itself NUM when wi is not aligned aln wi r when wi is aligned for the r th time with a given word
lob corpus is a million word collection of present day british english texts
that is to say lob corpus is a balanced corpus
however the number of terms to be summed is smaller
the nouns with the strongest connectivity form the preferred topic set
assume there are m nouns and n verbs in a paragraph
the undecidable case is that the assumed topic is a pronoun
discourse analysis is a very difficult problem in natural language processing
this paper proposes a corpus based language model to tackle topi c
besides topic identification the algorithm could detect topic shift phenomenon
they are trained on the paragraph and sentence levels for noun noun and noun verb pairs respectively
einen NUM en aufbinden by jlll hn
the positive effects on consumer capability will b e shown and future enhancements will be briefly outlined
if the minimum were not taken the systems would be unfairly over penalized for mismatched fills
the same may be said of a response object being compared to different answer key objects
a total slot score i s also given for each document
the basic design of the scorers is shown in figure NUM
saic then took over the maintenance of that software for muc NUM
the interfaces do not as yet exist but are planned
we are planning to extend the scorer to handle non english alphabets
overall the scorer has become a much more useful tool for all consumers
object pointers are considered matched if the objects to which they refer are mapped to each other
a linguistic agent can be divided into two main parts its knowledge representation and its knowledge processing
the l ropositional content is tbrmulated in the knowledge ret reselfl ation language of the agellt
to generalize the associational patterns in the category space that was bootstrapped from the physical chcmistry corpus svd was applied with it conservative value for k of NUM
we also did not use the forces agreed and disagreed because our agents only have reliable information
in other cases cooperation between agents is needed when two agents produce different solutions for the same problem
these relations are never rescinded when d trees are composed
disambiguisation we advocate the use of local grammars for some disambiguisation of several solutions produced by a module
architectures based on direct communication between agents allow complete distribution of both knowledge control and distribution of partial results
each window of the context digests tracks co occurrence counts with word types of pos provided these types have a minimum frequency of NUM in the training corpus
certain punctuation characters give constituency indications with high reliability perfect separators include colons and chinese full stops while perfect delimiters include parentheses and quotation marks
without some additional constraints any word position in the source sentence can be matched to any position in the target sentence an assumption that leads to high error rates
however each also has particular disadvantages when researchers attempt to generalize from the findings of previous work
one might even decide to choose nonterminals for an itg that do not match linguistic categories sacrificing this to the goal of ensuring that all corresponding substrings can be aligned
clearly the nonterminals of an itg must be chosen in a somewhat different manner than for a monolingual grammar since they must simultaneously account for syntactic patterns of both languages
let w1 w2 be the vocabulary sizes of the two languages and x lcb a1 an rcb be the set of nonterminals with indices NUM n
however the input sentences do not come broken into appropriately matching chunks so it is up to the parser to decide when to break up potential collocations into individual words
i would like to thank xuanyin xia eva wai man fong pascale fung and derick wood as well as an anonymous reviewer whose comments were of great value
the wires are attached to metal spring like connectors which are identified by numbers on the circuit board
thus a wire is identified by the numbers of the two connectors to which it is connected
learning morpho lexical probabilities table NUM approximated and test corpus probabilities for five ambiguous words from the two test groups
initialize by setting the root of the parse tree to ql NUM t NUM v and its nonterminal label to t ql s
this is a factor of v NUM more than monolingual chart parsing but has turned out to remain quite practical for corpus analysis where parsing need not be real time
the first is to provide syntactic information which guides how the rest of the sentence can be integrated into the tree
the output of this tagger is then passed to the unsupervised learner which learns an ordered list of transformations
here we use the trained unsupervised part of speech tagger as the initial state annotator for a supervised learner
furthermore the shortest tokenization still does not capture all the essences of the principle
given the choice to express an action rhetorically as a precondition imagene is capable of producing four grammatical forms for its expression all of which can be either fronted or not fronted and also linked with various lexical items
for example if imagene specifies the conjunction and for a sequence expression when then occurs in the text the choice of linker would be counted as incorrect in spite of the fact that the resulting text might be quite understandable
although the general philosophy of the approach taken in the current study is to assume that the choices made by the writers of the corpus are correct there are isolated cases in which the forms in the corpus are probably inappropriate
certainly a larger training set is required here but it is not clear at this point how much larger it NUM table NUM indicates NUM but that would be for our corpus with the non procedural portions of text included
because this often produced text that was monotonous or hard to understand they included what was termed a message optimization phase that specifies rules for removing or modifying certain elements of the plan structure that are known to produce poor text
this last operation is shown in figure NUM
however these neighborhoods are not traditional clnsters each verb has its own individual representation in a multi dimensional space i.e. is the center of its own neighborhood
as can be seen purpose expressions occur either before or after the expression of their related sub actions referred to here as the issue of slot and are expressed in a number of grammatical forms the issue of form
briefly the flow of control is as follows during the training phase of the system a new logical form mrs is given as input to the lcb
but it suffers a number of drawbacks especially when viewed from a computational perspective
ellipsis interpretations are represented as simple sets of substitutions on semantic representations of the antecedent
to resolve the ellipsis it needs to be instantiated to some contextually salient predicate
context sensitivity the truth values of many sentences undeniably depend on context
i have also benefited from conversations with claire grover ian lewin and massimo poesio
the index substitution from the primary term reindexes the contextual restriction of the pronoun
the pronominal term h occurs in the restriction of the book term b
non parauel terms are those that do not have an explicit parallel in the ellipsis
the second arises from strictly identifying the pronouns while sloppily identifying the books
consequently any further resolutions to the antecedent are automatically imposed on the ellipsis
product names some acronyms and miscellaneous other upper case words are entered into coreference chains in this stage
to further validate our set of labeled adjectives we subsequently asked four people to independently label a randomly drawn sample of NUM of these adjectives
while no direct indicators of positive or negative semantic orientation have been proposed NUM we demonstrate that conjunctions between adjectives provide indirect information about orientation
it can be seen as a mapping from sentences to strings of tags
running this parser on the NUM million word corpus we collected NUM NUM conjunctions of adjectives expanding to a total of NUM NUM conjoined adjective pairs
combining the constraints across many adjectives a clustering algorithm separates the adjectives into groups of different orientations and finally adjectives are labeled positive or negative
we want to select the partition pmin that minimizes subject to the additional constraint that for each adjective z in a cluster c
for the adjectives where a positive or negative label was assigned by both us and the independent evaluators the average agreement on the label was NUM NUM
the operations of selecting adjectives and assigning labels were performed before testing our conjunction hypothesis or implementing any other algorithms to avoid any influence on our labels
figure NUM algorithm for searching igtrees search ig tree
figure NUM algorithm for building igtrees build ig tree
memory based learning is a form of supervised inductive learning from examples
memory based tagging shares this advantage with other statistical or machine learning approaches
second most important feature followed by the third most important feature etc
igtrees provide an elegant way of automatic determination of optimal context size
table NUM case representation and information gain pattern for unknown words
table NUM case representation and information gain pattern for known words
table NUM elliptical antecedent in centerx
segment NUM finally is continued by ulz
table NUM algorithm for centered segmentation
centering in the large computing referential discourse segments
section NUM for a discussion of evaluation results
for ease of presentation the text is somewhat shortened
table NUM anaphoric antecedent in center
subjective metrics require subjects using the system or human evaluators to categorize the dialogue or utterances within the dialog along various qualitative dimensions
the model further posits that two types of factors are potential relevant contributors to user satisfaction namely task success and dialogue costs
rows represent the data collected from the dialogue corpus reflecting what attribute values were actually communicated between the agent and the user
this is the case in controlled experiments but in field studies determining whether the user accomplished the task requires subjective judgements
given a set of ci it is necessary to combine the different cost measures in order to determine their relative contribution to performance
tagging by avm attributes is also required to calculate the cost of some of the qualitative measures such as number of repair utterances
the notion of a task based success measure builds on previous work using transaction success task completion and quality of solution metrics
p a the actual agreement between the data and the key is always computed from the confusion matrix m
the decompounding variation is the NUM variations are generic linguistic functions and variants are transformations of terms by these functions
i thank an anonymous reviewer for bringing this example to my attention
it was closing just as john arrived
he wanted to meet him quite urgently
gjw exemplify the second of these motivations with passage NUM
NUM preferences and other intersentential relationships
i a john introduced bill to sam
the three candidates had a debate today
he has been acting quite odd
i the pair is a case where a direct translation is incomplete because the computer program only looked at single words
table NUM shows a sample of NUM entries selected at random from the answerbook corpus output on the 3rd plateau and higher
correspondence points are generated using a subset of these matching heuristics the particular subset depends on the language pair and the available resources
a group annotation was obtained for each candidate translation lexicon entry based on agreement of at least three of the six annotators
as noted in section NUM NUM the second filter based on the hansard bitext reduced the overall accuracy of the translation lexicons
simr produces bitext maps a few points at a time by interleaving a point generation phase and a point selection phase
including all entries on the 2nd plateau or higher provides better coverage but reduces the fraction of useful entries to NUM
evaluations were performed on a random sample of NUM entries from each lexicon variation interleaving the four samples to obscure any possible regularities
every condition is prefixed with a lal el serving as a unique identifer
to produce an adequate target utterance additional constraints whirl1 arc important for generation e.g.
handling of verb adverb head switching and does not cleanly separate monolingual from contrastive knowledge either
another advantage of a semantic based t ransfer approach over a pure interlingua apt roach e.g.
a label is a pointer to a semantic predicate nmking it easy t o refer to
this is one of the largest projects dealing with machine translation mt of spoken language
this article presents a new semantic based transfer approach developed and applied within the verbmobil machine translation project
more work is required on the multi lingual design of the architecture before such operations can be incorporated into the architecture itself
our approach is discussed and compared with several other approaches from the mt literature
we give an overview of the declarative transfer formalism together with its procedural realization
therefore the heuristic rule is applied to improve the performance
more precisely for each element x of a generalized mrs mrsg it is checked whether its type tx is subsumed by an upper bound t we assume disjoint sets
means that the words can be distributed in the group means that the component can be both to the left and to the right and since the disease is the first element of the pattern it is assumed to be the head
then the infrequent rules with f NUM are eliminated from the ending guessing rule set
because of the variety of objects which can be referenced the architecture does not provide a single dereferencing operator
therefore only NUM susanne tags are resolved in this experiment
for instance it may need to adjust the gender c est une voiture fantastique or the number a lot of cars
words in these types of languages have a small amount of variation like the ones due to number singular or plural for instance
to face this problem the most frequent verb forms are included in the dictionary and a morphological generator permits their modification or the addition of suffixes when it is necessary
the adaptation of the system would be made updating the first table and while the suffixes would be added to the word the other two tables would be also updated
all three systems use information from passages and whole documents retrieved rather than passage retrieval alone
the chain most parallel to the main diagonal is always one of the contiguous subsequences of this ordering
to interpolate injective bitext maps non monotonic segments must be encapsulated in minimum enclosing rectangles mers
due to the paucity of development resources at my disposal gsa s backing off heuristics are somewhat ad hoc
first simr can wander off the right track only if there is an alternative wrong track
the problem is that these regions can be large enough to severely skew the slope of the main diagonal
the slope of this local main diagonal can be quite different from the slope of the global main diagonal
the slope of the local main diagonal can be quite different from the slope of the global main diagonal
guages it is not difficult to construct an approximate mapping from the orthography to its underlying phonological form
a matching predicate is a heuristic for guessing whether a given point in the bitext space is a tpc
many bracketing errors are caused by singletons
we have found this strategy to be useful for incorporating punctuation constraints
we have made a small preliminary study comparing the quality of easyenglish with that of grammatik and the grammar checker in amipro
the grammatical checks fall into three different categories which we will treat separately syntactic problems lexical problems and punctuation problems
examples are shown in figure NUM
the initial results indicate excellent performance gains
the grammars may be incompatible across languages
the easyenglish editor interface however does allow the user to select an offered rephrasing by mouse clicking and have the selection substituted automatically
stochastic inversion transduction grammars and bilingual parsing of parallel corpora
a family f belongs to n if and only if at least one subderivation in g represented at n induces a forest of vector derivation trees whose root nodes are all and only the nodes in f each n can easily be computed visiting 7rq in a bottom up fashion
we are currently pursuing several directions
existing subject major existing major hetartikelgaatoverzijn NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM NUM vrouw
do not say that for which you lack adequate evidence
the comnfittee felt that it was important to demonstrate that useful extraction systems eouht be created in a few weeks
inatching and that not enough work was being done to build up the mechanisms needed for deeper understanding
we describe some of the motivations for the new format and briefly discuss some of the results of the evaluations
darpa has a number of information science and technology programs which are driven in large part by regular evaluations
two tasks were involved international joint ventures and electronic circuit fabrication in two hmgnages english and japanese
slots correctly and nin or t incorrectly with some other slots possibly left unfilled
the person and organization templates are the temt late element templates which are invariant across scenarios
although we can not explain al tile details of the template here a few highlights shouht be noted
null pushing improvements in the underlying technology was one of tlm goals of semeval and its current survivor eoreference
to understand why let us ignore recursive rules zp zp yp for the moment
responses from the formulating hypotheses item f h were used in this study
among the grounding testbed applications an interior design application is being developed which provides the background of the work described in this article
1as no additional phonetic or temporal information s is used to do the alignment there might be seldom case of bad alignment
as parsing is not always able to reject ill recognized sentences especially when they remain well formed cross checking is required between acoustic and linguistic information
concurrent trees for one item give rise to parallel concurrent branches of parsing but they are taken into account in a local chart parsing
one reasonable means of generating candidates is to look at pairs or triples of words that are composed in the parses of words and sentences of the input
for example the sentence and wha is he painting a plc ure off is paired with the unordered meaning and what be he paint a pic ture of
in particular if each word is associated with a part of speech and parts of speech are permissible terminals in the lexicon then words become production rules
in the first experiment the algorithm received these pairs with no noise or ambiguity using an encoding of meaning symbols such that each symbol s length was NUM bits
this is exactly the result argued for in the motivation section of this paper and illustrates why occasional extra words in the lexicon are not a problem for most applications
this is the best text compression result on this corpus that we are aware of and should not be confused with lower figures that do not include the cost of parameters
for example if water and melon are frequently composed then a good candidate for a new word is water o melon watermelon where o is the concatenation operator
we are currently looking at various methods for automatically acquiring parts of speech in initial experiments some of the first such classes learned are the class of vowels of consonants and of verb endings
however we also saw that obeying all commandments is effectively impossible since some of the guidelines they subsume are inconsistent with each other
one reason for this is that the interaction with the system must never interfere with the user s primary task the actual driving
table NUM variables used in the algorithm
next we illustrate the interfaces to the two major external modules
recognition rate and the overall success rate of the interaction are invariably highly correlated
in particular telephone interactions provide a very challenging environment for speech recognition equipment
an algorithm for generating referential descriptions with flexible interfaces
this material is based upon work supported by the national science foundation under grant no iri9501571
for the most part these systems were greatly limited by the available speech recognition technology
in spite of this dialogos still understood NUM NUM of all sentences a promising result
robustness was measured in terms of how infrequently a system crashed
cat egory verb lcb person number subcat rcb
take into account possible and possibly erroneous user inferences by analogy from related task domains
be relevant i.e. be appropriate to the immediate needs at each stage of the transaction
as to b figure NUM shows that the two analysers produced several alternative classifications
compared to these NUM types analyser a2 only managed to add NUM new guideline violation types
NUM claimed guideline violations were undecidable not agreed upon or jointly rejected by the analysers
table NUM functions used in the algorithm
us user symptoms symptoms of design errors as evidenced from user dialogue behavior
based on the consensus discussion the analysers created two tables one for each sub corpus
london heathrow terminal four at thirteen ten violation ref quot sl NUM NUM quot guide date not mentioned
they participate in linguistic interactions which are successful if their p settings are compatible
a constituent with an action verb tends to prefer the case frame in the form of vactn agent instr theme where agent instr and th ie are the arguments of the action verb assigned by the vactn case
some imt ortmtt linguistic phenomena are covered hy onr proposal for linguistic examples
using these notations analogies are equations with one unknown on NUM u v w x
in doing that we introduce a do n t care symbol representing any possible word
NUM is also the infinite union of all 12n tbr n NUM in
pattern matching with do n t care symbols has already been studied pinter NUM
r cqualily v a b s NUM dist a b 0c a b
for example what is the led showing can represent the action observe the value for the display property of the object led
one weakness of the metric as we have presented it here is that there is no principled way of specifying the distance distribution du
figure NUM provides a small sample of the s t trigger pairs used in most of the experiments we will describe
the set of candidate questions in litman and passonneu s approach however is lacking in features related to lexical cohesion
thus the symbol mr NUM i NUM NUM t represents the feature does the word mr appear in the next sentence
in the example subdialog the user responds ok which corresponds to one of the expected meanings assertion action done
as discussed in section NUM this simple algorithm could be used to adapt the output of an existing segmentation algorithm to different segmentation schemes as well as compensating for incomplete segmenter lexica without requiring modifications to segmenters themselves
here are several examples matches time rule NUM time rule NUM time rule NUM time absolute lcb NUM rcb yesterday mccann made official what had been widely anticipated mr
then if succession events are extracted without an organization bein g directly involved in the event statement the announcing organization can be tied to the organiza tion less events
we also thank jalme carbonell and yiming yang for their input and for encouraging us to build segmentation models on the tdt corpus
expansion in this area will include layering of events as well as an incorporation of time elements and will ultimately improve the system understanding of the texts being processed
these rule packages are applied to the text in successiv e passes to mark primitive text elements such as time money locations person and compan y names
the main improvement to louella for this walk through message was the recognition o f mccann as an alias for mccann erickson instead of as a location
since the ne component is a reusable module it is expected to increase over time in recall and precision as it is exercised over a larger corpus
this type of information may be useful t o the analyst who notices a particular person frequently associated with the purchase of certai n products such as winchester rifles
the te syste m the te system builds an object for each organization and person name that contains all of th e related information it can find in the article
a few additional cases were caught by allowing pairs where the first head word was on a list of honorifics such as president chairman journalist or ceo and the second head was capitalized
we abbreviate ca for comparative adjectives such as larger or smaller sa for superlatives such as greatest or largest ma for modal adjectives such as necessary or uncertain mv for modal verbs like could or will cv for cognitive verbs such as recommended or hoped and cadv and sadv for comparative and superlative adverbs
tagging for gender number and animacy to resolve pronouns which typically select for a gendered antecedent as well as those that typically selec t for an animate antecedent gendered or non gendered the wordnet NUM NUM lexical database NUM for nouns is used to tag each potential antecedent with respect to these semantic features
we experimented with various knowledge sources during system development including wordnet NUM th e xtag morphological analyzer NUM roget s publicly available NUM thesaurus the collins dictionary a version of the american heritage dictionary for which the university of pennsylvania has a site license and the gazetteer
as a result the system is afforded a measure o f robustness if one component fails further components will not necessarily be crippled and no downstrea m component can alter the output of an earlier component
since coreference is allowed between portions of hyphenated words which are themselves words such as apple in the phrase a joint apple ibm venture determining whether a portion of a hyphenated word may participate in coreference is important
to prune ou t descendants of an entry such as man which do not inherit the semantic feature of maleness an and not operator can be used to exclude subclasses of the class of descendants of male
the wall street journal adheres to standard conventions for capitalization of words in headlines but since capitalization is an important cue for coreference resolution w e attempted to eliminate capitalization which resulted solely from these conventions
we found that the following strategy worked remarkably well given the two proposed minimal noun phrases if the first one has a capitalized head and the second head begins with a lower case letter accept the pair as coreferent
it was found that most verb phrases regardless of the verb head which take both a noun phrase np1 and a prepositional phrase headed by as with an object np2 imply coreference between npi and np2
now we can derive the re estimation algorithm for g
until we have a sufficiently big enough word sense tagged corpus we can only hypothesise on the importance of the correct sense disambiguation for the pp attachment
the above experiments confirmed the expectations that using the semantic information in combination with even a very limited context leads to a substantial improvement of nlp techniques
if no match is found for the attribute value of the quadruple at any given node the quadruple is assigned the majority type of the current node
the data contained NUM training and NUM testing quadruples with NUM prepositions and ensured that there was no implicit training of the method on the test set itself
it is divided into four categories nouns verbs adjectives and adverbs out of which we will be using only verbs and nouns
at the beginning of the tree induction the top roots of the wordnet hierarchy are taken as attribute values for splitting the set of training examples
starting with the verb the algorithm searches for other quadruples which have the quadruple distance see below smaller than the current similarity distance threshold
the second problem is that there may in fact be no exact match in the training corpus for the context surrounding words and their relations
this process repeats in all the emerging subnodes using the attribute values which correspond to the wordnet hierarchy moving from its top to its leaves
have to choose an attribute to split the node with NUM adjectival and NUM adverbial quadruples figure NUM choosing an attribute for the decision tree expansion
the translation lexicon is assumed to contain collocation translations to facilitate such multiword matchings
in the previous section we used paradise to evaluate two confirmation strategies using as examples fairly simple information access dialogues in the train timetable domain
NUM consider calculating the performance of the dialogue strategies used by train timetable agents a and b over the subdialogues that repair the value of depart city
u NUM i want to go from torino to roma dc ac c NUM approximately what time of day would you like to travel
performance is modeled as a weighted function of a task based success measure and dialogue based cost measures where weights are computed by correlating user satisfaction with performance
however a critical obstacle to progress in this area is the lack of a general framework for evaluating and comparing the performance of different dialogue agents
the repair utterance in figure NUM is u2 but note that according to the avm task tagging u2 simultaneously addresses the information goals for depart range
first it allows us to evaluate performance at any level of a dialogue since n and ci can be calculated for any dialogue subtask
under such a processing regime the appropriate sense entry for a verb on a particular subcategorization can be simply and cheaply selected since the complete complement will be there and the benefits of the preceding analysis for syntactic processing will be retained
in the t rototyi ical sit m tion des rit tion f
the application of instantiation ruh s has to be regarded as an interpretation of every partial description in a bsf
the most important epsilon rule is part of a gap threading implementation of verb second
note that result items do not keep track of the extreme positions
in correspondence with the theory specific strengths promising subtasks will be reference resolution and the construction of conceptual repre sentations
di t gains basic entries for whole lexical fields and furtherlnore a systematic interface between semantic and syntactic argument structures
interface list of verschenken is given ill figure NUM where the eompoimllts of each pair m e displayed vertically
for example we could assert empty productions as possible lexical analyses
the results of the evaluation give clear evidence of the challenges that have been overcome and the ones that remain along dimensions of both breadth and depth in automated text analysis
computational linguistics volume NUM number NUM NUM NUM NUM integrating the head corner table
the head corner parser is in many respects different from traditional chart parsers
moreover no further work will be done based on those results
the second table represents all solved i.e. instantiated goals
in the mimo2 grammar of english no heads can be gapped
it was taken but the number of propositions in the resulting sentence 7b appears to be too great return taken insert shown push forcing the use of the adjoined form 7a
cstate system user pl knowref system user entityl NUM bject NUM since the system believes there is an error in the current plan it applies rule NUM and so gives itself the communicative goal of informing the user of the error in the current plan
cstate system user p104 knowref system user entity1 NUM bject NUM bmb system user plan user p104 knowref system user entity1 antenna1 NUM the new referring plan will already have been evaluated
bmb system user bel system error p1 p22 NUM then on the basis of NUM and NUM the system applies rule NUM thus adopting the belief that it is mutually believed that there is an error in the plan
even without taking the effects of inter textual cohesion into account and concentrating solely on local specialization and intra textual cohesion formulating lexical specialization in terms of concentration at a particular point in the text is unrealistic it is absurd to assume that all tokens of a specialized word appear in one chunk without any other intervening words
apparently the development of the vocabulary in moby dick can be modeled globally but local fluctuations introducing additional points of inflection into the growth curve are outside its scope a more detailed study of the development of lexical specialization in the narrative is required if the appearance of these points of inflection are to be understood
bmb system user error p1 p22 NUM the system is now able to apply rule NUM on the basis of NUM and NUM and so adopts the goal of refashioning the invalid referring expression plan and of informing the user of the new plan
roughly half of the word types occur only once the so called hapax legomena others occur with higher frequencies let v n NUM denote the number of once occurring types among n tokens and similarly let v n f denote the number of types occurringf times after sampling n tokens
NUM rule NUM bmb system user goal agtl goal bmb system user plan agt l plan goal agtl e lcb system user rcb the next rule concerns the adoption by the hearer of the intended goals of communicative acts
not only does this approach allow the processes of building referring expressions and identifying their referents to be captured by plan construction and plan inference it also allows us to account for how participants clarify a referring expression by using meta actions that reason about and manipulate the plan derivation that corresponds to the referring expression
for the formal evaluation there were NUM organization and NUM perso n objects in the te key versus NUM organization and NUM person objects in the st key
the experiments use texts from the wall street journal wsj corpus and its bracketed version provided by the penn rreebank
with this corpus the grammar learning task corresponds to a process to determ e the label for each intermediate node
by using only effective contexts it is possible for us to improve training speed and memory space without a sacrifice of accuracy
by pr im nary experiments we found out that the following criterion is sufficient for determining the number of contexts
the f measure is used as a combined measure of recall and precision where NUM is the weight of recall relative to precision
here a is a balancing weight between the observed distribution and the uniform distribution and it is assigned with NUM NUM in our experiments
l rthermore we can observe that a high accuracy can be achieved even if not a11 contexts are taxen into account
the results are presented as follows table NUM shows in the column correct the percentage of oov words where all the labels were correct the column wrong indicates the percentage of words which were labeled with at least one incorrect tag
additionally these kinds of distribution rules further contribute to collating facts relevant to template generation onto the individuals for which these facts hold
the crux of our approach is the use of rule sequences a processing strategy that was recently popularized by eric brill for part of speech tagging NUM
the estimated times for building axis generators do not include the time spent to build the english axis generator which was part of the original implementation
the ability to analyze non monotonic points of correspondence over variable size areas of bitext space makes simr robust enough to use on translations that are not very literal
the linearity of the these chains is tested by measuring the root mean squared distance of the chain s points from the chain s least squares line
each test bitext was converted to a set of tpcs by noting the pair of character positions at the end of each aligned pair of text segments
cognates are more common in bitexts from more similar language pairs and from text genres where more word borrowing occurs such as technical texts
the first step in most empirical work in multilingual nlp is to construct maps of the correspondence between texts and their translations bitext maps
engage in a verification suhdialog using this decision rule the over verification rate rises to NUM NUM while the under verification rate falls to NUM NUM
then section NUM explains how to port simr to arbitrary language pairs with minimal effort without relying on genre specific information such as sentence boundaries
for the implementation we used tcl tk version NUM NUM
although the result on the dictionary can not be expected to be as good as the result on the brown corpus due to the smaller size of the dictionary the reliability of further co occurrence data collected and thus the performance of the disambiguation system can be improved significantly as long as the disambiguation of the dictionary is considerably more accurate than by chance
indeed on a held out development test set chinese performance was virtually identical to that on the development training set the learning procedure had thus acquired a very predictive model of the development data overall
moreover the magnitude of mutual information is decreased due to the noise of the spurious senses while the average magnitude of the occurrence probability is unaffected e inclusion of the occurrence probability term will lead to the dominance of this term over the mutual information term resulting in the system flavoring the sense with the more frequently occurring defining concepts most of the time
the classification method used is linear discriminatory analysis based on a learning sample
each sentence is processed on three levels morphologically syntactically and semantically
a combination of these two approaches would also be possible i e tagging names in text and queries as well as handling name queries differently
retrieval performance using proximity based name searching on this test collection as described in section NUM NUM was compared against a baseline provided by the win retrieval algorithm
it investigates name recognition accuracy and the effect on retrieval performance of indexing and searching personal names differently from non name terms in the context of ranked retrieval
participating systems were evaluated on personal organitational and other name recognition as well as on related tasks such as recognizing time and numeric expressions
more interestingly the results of the human subject are found to exhibit a similar pattern to the results of our system the human subject performs better on words and senses for which our system achieve higher accuracy and less well on words and senses for which our system has a lower accuracy
the proximity searches computed relevance for names using the tf and idf of occurrences in which the first name occurred NUM or fewer word positions before the last name
individual rules then refine the initial rough boundaries determine the type of a phrase person location etc or merge fragmented phrases into larger units
NUM the score of a sense with respect to the current context is normalised by subtracting the score of the sense calculated with respect to the globalcs which contains all defining concepts from it see formula NUM the occurrence probabilities of some defining concepts will not be independent in some contexts
by treating joe woods in this manner the proximity search boosted the scores of documents containing references to the person joe woods and thereby improved search performance
this study further shows that the frequency of occurrence of personal and other names in cases is sufficient to warrant their separate treatment in document retrieval
the author expresses his appreciation to d richard hipp for his work on the error correcting parser and for his initial work on context independent verification
nonetheless this is not a very effective indicator for terms that are transverse to the corpus
currently updates are handled by a group of documentalists who regularly examine and insert new terms
we were one of only two sites that attempted all three languages and were the only group that exploited essentially the same body of code for all three tasks
its drawback is that it also highlights large noun phrases in which the terms are included
moreover these expressions are candidate terms that are submitted to experts or documentalists for validation
the strategy NUM decision rule for utterance verification says to engage in a verification subdialog if the parser confidence value falls below the verification threshold
these documented terms like the candidate terms are used in the representation models described below
if some implausible dates are recognized e.g.
in the following example in table NUM the recognizer here abbot has recognized the sentence whom is this chair are too light instead of the actual utterance whom is this chair chosen by
a document is a vector in the document vector space where a dimension is a term
comparison is carried out by a fourth judge
the object pairs that can be considered candidates for mapping are then scored and rank ordered accordin g to some pre determined metric
much less information about the event would be captured but there would be a much stronger focus on the most essential information elements
twelve systems from eleven sites including one that submitted two system configurations for testing were tested on the te task
in the middle of the effort of preparing the test data for the formal evaluation an interannotator variabilit y test was conducted
no metrics other than recall and precision were defined for this task and no statistical significance testing was performed on the scores
some of the site reports in the proceedings may refer to other sites by these code names whe n discussing cross system performance figures
for spanish two of us collaborated to develop a rule sequence by hand to this task one of us brought two semesters of college spanish and the other brought fluency in french
it is evident that the more linguistic processing necessary to fill a slot the harder the slot is to fil l correctly
the management succession template consists of four object types which are linked together via one wa y pointers to form a hierarchical structure
performance on the post slot was not quite as good the lowest error was NUM median of NUM
the te evaluation task makes explicit one aspect of extraction that is fundamental to a very broad range o f higher level extraction tasks
we should of course ask whether the relative succes of the dop approach only holds for such limited domains as atis or whether the approach can be effectively applied to larger corpora such as the wall street journal wsj corpus
all four of the scorers follow this basic design sectionize parse map score and report
null figure NUM shows the dialogue sequence memory after the processing of turn b02
the punchline as we see it is that alembic performed exceptionally well at all three of the met languages despite having no native speakers for any of them among its development team
additionally at the turn level the operators are learned from the annotated corpus
NUM the number of unseen types no is the difference between the total number of distinct np subtrees and the observed number of distinct np subtrees zr o nr which is NUM NUM x NUM NUM NUM NUM NUM x NUM NUM
dialogue processing in a speech to speech translation system like verbmobil requires innovative and robust methods
they extract a probabilistic automaton using an annotated corpus of up to NUM dialogues
fifteenth and the nineteenth b04 oh das ist ganz echlecht
it is a generic structure which mirrors the sequential order of turns and utterances
NUM of all recognized dialogue acts are within the first three predicted ones
stochastic parsing systems either use a closed lexicon or use a two step approach where first the words are tagged by a stochastic tagger after which the p o s tags with or without the words are parsed by a stochastic parser
ldeg in contrast to the low initial thai score the greedy algorithm gave an initial english segmentation score of f NUM NUM
we draw inferences on the basis of our input
secondly systems based on formal representations are brittle a fully interlingual system first needs to translate its input into a formal representation and then realize the representation as a target language string
the scores will not change in these when converted to c becaus e mapping candidacy is based on overlapping offsets
the most important of our findings is that writing constraints that contain more linguistic information than the current statisticm model does not take much time
however since the tagging conventions on the formal test set were not wholly consistent with those in the training set the performance of the model could only be expected to decrease in the final evaluation
they also need considerable training to use the pictalk system effectively
the following example illustrates the effect of this rule
f is the innermost application program concept governing
figure NUM textual variations in form of re
we will also not follow the procedure in detail
furthermore some realization choices block desirable textual reorganisation
following the tradition we call them aggregation rules
figure NUM aggregation rules in proverb
figure NUM architecture of the microplanner
in the name tagging task for example the process begins with an approximate initial labeling whose purpose is simply to find the rough boundaries of names and other met relevant forms such as money
another important class of algorithms is needed for name recognition in applications where the names are not already manually identified
we would like to thank lyn walker diane litman bob carpenter and christer samuelsson for their comments on earlier drafts of this paper bob carpenter and christer samuelsson for participating in the coding reliability test as well as jan van santen and lyn walker for discussions on statistical testing methods
we introduce a representation of aligned corpora that reduces the problem of computing the positive negative evidence of transformations to the problem of computing factor statistics
we believe that suffix tree alignments are a very flexible data structure and that other transformations could be efficiently learned using these structures
function shifl link p p pl up link down p p i up linldown p
we then conclude that NUM has the form denotes the do n t care symbol ajl l aj ji NUM
we have mentioned that the introduction of classes of alphabet symbols allows abstraction over plain transformations that is of interest to natural language applications
in this way we improve space performance of algorithms NUM and NUM avoiding the construction of two copies of the same suffix tree
we then conclude that sets r p for all leaves p of tr can be computed in time o nn2
overall there are o nn NUM possible transformations and we need time o n to read store each transformation
in order to deal with the first problem the following key properties of ccg gtrc must be observed NUM a
case 4a allows direct application of the induction hypothesis for the substructure of smaller height starting with a constant category
in each sublemma the induction hypothesis of lemma NUM is applied mutually recursively to handle the derivations of the smaller substructures from a constant category
evidently some cues provide stronger evidence for an initiative shift than others
b wrapping can occur only when gtrcs are involved in the use of bkx and can only cross at most km arguments
these miscommunications created various problems for the dialog interaction ranging from repetitive dialog to experimenter intervention to occasional failure of the dialog
intuitively all of these phenomena call for a non traditional more flexible notion of constituency capable of representing surface structures including subj v obj in english
if just one ending point is represented all the fields of the other are null
a detailed description of the evaluation method follows
a semantic formula includes a variable e.g. NUM its type and a collection of predicates on that variable
the rule based fragment interpreter encodes defaults so that missing semantic information does not produce errors but marks elements or relationships as unknown
this would more closely match what is stated in the text and would factor out th e issues of data base structure
this gave us more training data though presumably of a lesser quality without violating the integrity of our high quality blind test set
to create the ddos the discourse component processes each semantic form produced by the interpreter adding it s information to the database
a parameter file lists the sgml tags relevant t o the task in this case hl txt dateline and dd
this paper provides a quick summary of our technical approach which has been developing since NUM and wa s first fielded in muc NUM
the te and st systems contai n no lexical patterns of their own relying entirely on the domain independent patterns within the ne system
for example statistical techniques may have suggested th e importance of hire a verb which many groups did not define
given so few messages we felt that there were conventions in filling out the keys in each task that were still not fully clear
a verification subdialog is initiated only if it is believed that the overall performance and accuracy of the dialog system will be improved
for the anaphoric rules the antecedent considered is the most recent one meeting the conditions
null uttered can you give me hop e information about tite company give me more information about the company example table NUM stands for a typical omission recovery
NUM although like expectedreply active is a default active will take precedence over expectedreply because it has been given a higher priority on the assumption that memory for suppositions is stronger than expectation
the model incorporates five strategies or metaplans for generating coherent utterances plan adoption acceptance challenge repair and closing the model treats opening as a kind of plan adoption
a speaker sl can expect that making an askref of d to s2 will result in s2 telling sl the referent of d if s2 knows it
these expectations depend on a speaker s knowledge of social norms her understanding of the discourse so far and her beliefs about the world at a particular time
using a more finely grained representation one could reason about sentence type particles and prosody explicitly instead of requiring the sentence processor to interpret this information cf
preconditions such as know s2 not knowref sl d influence interpretations only to the extent that they provide support for or evidence against a particular abductive explanation
according to the model after russ hears mother s surface request do you know who is going to that meeting he interprets it by attempting to construct a plausible explanation of it
similarly even if russ suspected that mother did not know who was going he might still have chosen to treat her utterance as a pretelling perhaps to confirm his suspicions or to delay answering
categorial selection and functional selection also occur under the same restrictions in the complement configuration i.e. between a head and a maximal projection
the choice of an lr parser then is the result of the icmh with which the parser s organization must be compatible and additional independent factors
using an lr table together with a co occurrence table is equivalent in coverage to a fully instantiated lr table but it is more advantageous in other respects
a parser with this property is incremental in the sense that it does not perform unnecessary work and it fails as soon as an error occurs
this means that for algorithms nlab and nlab p all sentences with more than three relevant links are computed faster if features are checked
as there could always be a licensing head in the right context which would license a left branching structure the shift operation is always correct
NUM these predictions seem to be supported and consequently so is the icmh by two main results which are illustrated below
to see that checking features does indeed pay off the cost of checking these features must be compared to the benefit of reducing the search space
the next sections describe these differences and demonstrate how they can be deployed to enable generation from undisambiguated semantics
each slot in the array corresponds to one fact and indicates whether the fact is expressed by that edge
belbre we do that we will sketch a version of kay s algorithm emphasizing data representations rather than algorithmic details
we hope that by using this approach it will be possible to avoid certain types of disambiguations altogether
the next example shows how an np is generated from a specification that results from a lexical ambiguity
this can happen when the source expression is passive but the corresponding target language verb does not passivize
sincerity do i know this or can provide evidence NUM everything that the speaker asserts or implies is true unless otherwise explicitly stated
let us assume that the verbs enter and rush both decompose as movement verbs
i wish to thank martin kay john maxwell and ronald kaplan for their interest comments and encouragement
it also allows a choice of the verb move since pl rl represents a valid path
however we use trees that in some cases impose coreference requirements between the information states in which different constituents are interpreted
corresponding to the qualia structure of glt we have axioms describing what actions are associated with objects and how salient they are
to guarantee a coherent meaning for a derived structure a node about x can only substitute or adjoin into another node about x
the kb entails the fact fast i s c42 copy action which allows us to incorporate the lexical item fast into the description
precision is the ratio of hypothesized boundaries that are correct to the total hypothesized boundaries
the second method machine learning automatically induces decision trees from coded corpora
the main difference on average performance is the higher precision of the automated algorithm
the third feature global pro is computed from the hand coding
the first method hand tunes features and algorithms based on analysis of training errors
fig NUM shows one of the highest performing learned decision trees from our experiments
other changes pertained to sentence fragments unexpected clausal arguments and embedded speech
then ifcoref na then non boundary elseif coref corer then if after
given that the cross entropy of the uniform distribution and the data is NUM as there are only two possible values for the random variable i.e. s and t are coreferent or not this relatively small reduction suggests that the problem has some amount of difficulty which is consistent with the notable lack of clear signals of coreference characteristic of the texts in our domain
the length of the strings varied from NUM to NUM graphemes
the final list of morphologically meaningful substrings consisted of NUM entries
if there are only two templates in the coreference set then we have the distri null t if there is a chain of preferred referents linking them e.g. if there is a template r that is the preferred referent of t and template s is the preferred referent of r
we also wish to thank an anonymous reviewer for constructive suggestions
this version will be refered to as the new system
the total number of first names in our list is NUM
the number of morphemes collected from the four cities is NUM NUM
most arc labels are weighted by being assigned a cost
the cd l om contains data retrieval and export software
this version will be refered to as the old system
the symbol lcb rcb indicates a morpheme boundary
application model lists car hire companies and car garages as possible services so the communicative goal is formulated as to know which is the preferred service
we divided the trains91 corpus into eight sets based on speaker hearer pairs
due to the varied nature of the constraints on lexical choice exactly how lexical choice is done often depends on the type of constraints a system design accounts for
for example rebound means different things in the basketball domain and in the stock market domain ibm rebounded from a NUM day loss vs magic grabbed NUM rebounds
example NUM in the advi or ii domain shows other options for realizing attached relations as noun noun modifiers ai assignments or premodifier programming assignments
the systemic grammar paradigm also takes this approach where lexical choice is the most delicate of decisions occurring as a by product of many high level syntactic choices
in this fd the notation NUM indicates that the value of the attribute number under subject must be identical to that of the attribute number under verb whatever it may be
once the perspective is chosen focus can shift between the participants of a relation by switching the order of the complements as in sentences NUM and NUM of figure NUM
we apply fuf to lexical choice by representing the lexicon as a fug whose branches specify both constraints on lexical choice as tests and the lexical features selected as a result of the different tests
in fact in the eight domains for which we have implemented generators we have never found a case where other syntactic decisions made during realization force the lexical chooser to undo an earlier decision
thus sentence NUM above is analyzed as being derived from NUM by deletion of the predicate involve and migration of its object programming to a premodifier of the head assignments
although the consistency of the results between the first two training test divisions may suggest that the amount of training data is sufficient for the rather coarsely grained feature set used the size of the test sets are potentially of concern which motivated our inclusion of the third training test division
though inversion transduction grammars remain inadequate as full fledged translation models bilingual parsing with simple inversion transduction grammars turns out to be very useful for parallel corpus analysis when the true grammar is not fully known
viewed top down the hierarchy is in descending order of generality arrows point from specific to general
program now needs to produce a first positive example as required by the version sl ace method
generates a phrase conforming to this description and asks the teacher to re order it correctly if needed
lie sol reliiaills l he same since it covers the positive
theu they are fed again to the version space algo ril hlzl for a second pass
meta interpreter given an lp space representation as input will generate a language expression to be classified as positive i.e.
del nun a ti det sg adj det num adj det nuni adj de t pl adj det sg adj det pl adj do t sg
besides these two spaces two additional processes are needed intermediating them interpretatiot and instance selection
this happens when the human message is either too vague what about a meeting or contains an inconsistent temporal specification as in NUM
table NUM shows the labels assigned to some basic level clusters generated by ciaula for the rsd verbs belonging to the semantic category cognition
with respect to performance on org descriptor note that there may be multiple descriptors or none in the text
umanitoba s production of st output directly from dependency trees with no semantic representation per se
they can also be quite long and complex and can even have internal punctuation such as a commas or an ampersand
this paper surveys the results of the evaluation on each task and to a more limited extent across tasks
table NUM the perplexity of psts for the online mode
in the full experiment NUM of the clusters have a score NUM NUM indicating a good overlap between ciaula and wordnet
highest probability an abstraction as agentive and a manner modifier that may be a property pr or a cognitive process co
in contrast to classical conceptual clustering algorithms ciaula accounts for multiple instances of the same concept that is of the same verb
at the top level is the template object of which there is one instantiated for every document
the interannotator variability test provides reference points indicating human performance on the different aspects of the ne task
in summary the mixture likelihood values are updated as follows
the correct sentence gets the highest probability according to the model
corresponding word lattices are built out of elements that include seq x y create a lattice by sequentially gluing together the lattices x y and
in addition in the absence of external lexical constraints the language model prefers words more typical of and common in the domain rather than generic or overly specialized or formal alternatives
for example there are two ways to pluralize a noun ending in o but often only one is correct for a given noun potatoes but photos
NUM furthermore our two level generation model can implicitly handle both paradigmatic and syntagmatic lexical constraints leading to the simplification of the generator s grammar and lexicon and enhancing portability
but the statistical model studiously avoided the bad paths and in fact we have yet to see an incorrect case usage from our generator
we implemented a medium sized grammar of english based on the ideas of the previous section for use in experiments and in the japangloss machine translation system
we would like to thank yolanda gil eduard hovy kathleen mckeown jacques robin bill swartout and the acl reviewers for helpful comments on earlier versions of this paper
choices between alternative lexical islands for the same concept also become states in the lattice with arcs leading to the sub lattices corresponding to each island
by adhering to general communicative principles cdm provides a new and uniform way to treat various phenomena that have been separately studied in previous research goal formulation coherence and cooperativeness
in most applications the interpolation method is used for tasks with clear orderings of feature sets e.g.
we adopt a statistical approach whereby a probabilistic model is selected that describes the interactions among the feature variables
analysis of the input message results in the user s communicative goal and contains four subtasks determine the explicitness level interpret the propositional content check coherence and verify obligations
examples include cases of categorial and thematic divergences
all semantic entities in udrs are uniquely labeled
NUM a diensta9 ist air lieber
the most striking is the interaction between phrases and morphology
the labeling of all scntantic entities allows a fiat ret resentation of the hierarchical structure of arguinent an NUM ot erator and quantifier scope embeddings as a set of labeled conditions
this approa h contl ines ideas fronl a nunlber of re cnt mt proposals and tries to avoid many of the well known problems of other transfer and interlingua approaches
there are two problems with using words to represent the content of documents
the primary stress is one syllable slleft to the left of graphy so NUM is stressed and the phoneme is there are certain affixes in english that refuse to be assigned stress
therefore need to be aware of this ambiguity and translate the sentence appropriately
the following section will provide an overview of lexical ambiguity and information retrieval
the result is given in table NUM n NUM
the third reason has to do with the nature of morphological variants
this strategy allows the specification of powerfifl defanlt translations
where slsem and tlsem are sets of semantic entities
but you have to change your banana plan
given that the state of the art in speech translation is such that realistic applications are restricted to particular domains this sort of study is clearly of general importance
yumi wakita et al describe a method which attempts to extract the parts of a spoken utterance which have been reliably recognized ignoring those which represent probable recognition errors
let us hope that in years to come the workshop on spoken language translation at the NUM acl eacl meeting in madrid is seen as an important and memorable event in the development of slt techniques
such sub grammars should be far less ambiguous than a grammar for the whole domain would be if parsing proceeds with separate sub grammars in parallel which should also yield benefits in terms of processing speed
dismissed not so long ago as an impossible dream the contributions to this workshop demonstrate that while still perhaps something of a dream it is far from impossible
this conclusion is interestingly at variance with that of lavie et a in the previous paper who argue for an interlingual approach to translation and the use of domain specific semantic grammars
some NUM years ago when machine translation had become fashionable again in europe few people would be prepared to consider seriously embarking upon spoken language translation slt
carter et al argue that the characteristics of the core language engine the language processing component of the system they are describing slt facilitate this customization
or should we rather conclude that all these courageous people are heading for another traumatic experience just as we have seen happen in the sixties and to a lesser extent in the eighties
a theory of parallelism and the case of vp ellipsis jerry r hobbs and andrew kehler
they excluded all terms which did not match in the preposition
naturally occurring collaborative dialogues are very rarely if ever one sided
that is the bilingual sitg parsing algorithm can perform constituent identification and matching using only a generic language independent bracketing grammar
moreover the disjunctive parts are reduced so that a subsequent full fledged search will have considerably less work than when directly trying to solve the original constraint system
two new heavyweight algorithms were developed in the last year
these are the values produced by the muc NUM scoring system
a hybrid scenario of lr use is also plausible where for example lrs apply at acquisition time to produce new lexical entries but may also be available at run time as an error recovery strategy to attempt generation of a form or word sense not already found in the lexicon
the point remains however that each case has to be checked manually well semi automatically because the same tools that we have developed for acquisition are used in checking so that the exact meaning of the derived adjective with regard to that of the verb itself is determined
thus here too given the above job database entry the sentence cafe citrus advertises as vacant a position as chef can be generated
as such it is clear that even in a restricted domain such as that of job ads novel approaches to language engineering are required
this should broaden the utility of information extraction technology and cut its cost
scenario template st a domain specific full template extraction task
plum was employed in both muc NUM template tasks te and st
processes we have begun making a distinction between lightweight techniques and heavyweight processing
the details of the probabilistic model will be documented separately
the effect on information extraction is most notable on names
this seems far less promising than the first alternative above
in a particular system lrs can be applied at acquisition time at lexicon load time and at run time
example NUM o 1o NUM micr means o will be translated as if the syllable is stressed micrometer and as o NUM otherwise microgram
r the input string ca n a lso be scanned to reduce the number of relewmt gramma r
degother ihatures illclude nominative and t ft l NUM tti ts
which are des ril ed by tit notion of unifica tion
sequence null the third step is trivia NUM a s in the case of stag transla tion
this is not a necessary condition for l t c l g
the system has been implemented in a typedfeature system and applied to turkish
we handle these constraints by defining a slightly more complex type hierarchy
verb is ye is it constr fint corresponding to vl itm
h itications before going through all lexical rule s
in recent years there have been several studies on constrmnt based lexicons
as illustrated in these examples verb sense idio m tic
argument noun phrase i case frame i optional
in another study by lascarides et el
the conjunctive part of the output constraint of the algorithm can then be seen as an approximation of the actual result if the output constraint is satisfiable
these nevertheless have to be constrained usually on semantic grounds
this is required to guarantee that the resulting constraint is a kind of solved form actually representing so to speak the free combination of choices it contains
we also investigated a new heuristic to speed up the computation after each pass we disable all rules whose positive score is significantly lower than the net score of the best rule for the current pass
for optimal performance xtract itself relies on other tools such as a part of speech tagger and a robust parser
we call x an ll singleton and c y an l2 singleton
this kind of use of the mode predicate does not seem to be restricted to discourse relations
in this way an ordinary underspecification treatment of multiple discourse relations among each other gets possible
they can all be of the type whose antecedent and conclusion part are both bound sentence internally
the latter s scope domains of the antecedent as well as the conclusion part are sentence internal
in this paper a treatment of multiple discourse relation constructions on the sentential level is discussed
therefore the term for the discourse relation type is basically the same as NUM
at least two problems remain when there are a number of discourse relation elements in a sentence
these relations are determined not only syntactically but also by way of semantics and discourse structure
in order to apply the theory and implementation of lud to japanese some modifications are needed
for example p vnunciar has at least two entries one could be translated as articulate and one as declare the lr generator when ap plied to the superentry would produce among others two forms of pronunciacidn derived from each of those two senses entries
these phonemic symbols in turn are used to feed lower level phonetic modules such as timing intonation vowel formant trajectories etc which in turn feed a vocal tract model and finally output a waveform and via a digital analogue converter synthesized speech
training runs were halted after the first NUM rules rules learned after that point affect relatively few locations in the training set and have only a very slight effect for good or ill on test set performance
is np john vp iv knows is2 gp2 the truth vp hurts with the tree lowering operation so defined the problem of finding which relations to add to the set at a disambiguating point reduces to a search for an accessible node at which to apply this operation
in cases such as NUM assuming the bottom up search when a postclausal noun has been reached in the input the parser starts its search from the node immediately dominating the last word to be incorporated i.e. the verb of what will become the relative clause
the difference is that the foot and root nodes of an auxiliary tree in tag corresponding to the lowered node and the node that replaces it respectively must be of the same syntactic category whereas as we have seen in this example in the model proposed here the two nodes may be of different categories so long as the resulting structure is licensed by the grammar
the two factors determining the syntactic category of a word are its lexical probability e.g.
most work on statistical methods has used n gram models or hidden markov model based taggers e.g.
the final column shows the target category the disambiguated tag for the focus word
in the remainder of this section we will describe each step in more detail
if n is a leaf node output default class c associated with this node
the original case base to NUM of the size of the original case base
nodes are connected via arcs denoting the outcomes for the test feature values
during testing a set of previously unseen feature value patterns the test set is presented to the system
we report two applications of this approach pp attachment and postagging
it can also return the conditional distribution of all the classes
NUM buccleuch place edinburgh eh8 9lw scotland uk
towards a workbench for acquisition of domain knowledge from natural language
for some domains there already exist terminological banks available on line
kawb provides generic facilities for access to such linguistic sources
already existing lexical databases are an important source of information about constituent words of domain texts
among these segments are compound noun phrases verb phrases etc
we also part of speech tag every word in the phrase
figure NUM this figure shows four sub clusters of our hierarchical cluster analysis of the NUM NUM most
it is well known that a general text parsing is very fragile and ambiguous by its nature
let s be the most specific zero mismatches schema
it is difficult and time consuming to place all derived forms in the dictionary including singular and plural forms and all verb affixes especially for a language like french where a verb can expand depending on the conjugation into about fifty strings consisting of the root plus suffixes
for example if the acting in and fu ture variables are all bound in a match then the in and out s new status is in an d on the job is no because the text is reporting that the person will be acting in a position
one problem with the latter is thai they often suggest more than one allomorph the rules are not mutually exclusive
this rule set is useful both in linguistics for evaluation refinement and discovery of theories and in language technology
in this algorithm it is not specitied which test to choose to split a node into sut trees at some point
diminutives are formed by attaching a form of the germanic sntfix tje to t he singular base form of a noun
given are a training set t a collection of examples and a finite number of classes c1 c
this paper shows how machine lem ning techniques can be used to induce linguistically relevant rules and categories fl om data
the k e allomorph on the other hand is learned perfectly on the basis of the last syllable alone
the error rate on etje dramatically increases from NUM to NUM when restricting information to the last syllable
only the rhyme of the last syllable as opposed to an implementation of the rules suggested by ly ommelen
null the comparison shows that c4 NUM did a good job of finding an elegant and accurate rule based description of the problem
those in group b selected important sentences about NUM NUM of the article in NUM editorials and NUM general articles which were different from those used for group used for group b are shown in figures NUM a and NUM a respectively
in addition they are able to improve the performance of a wide range of segmentation algorithms without requiring expensive knowledge engineering
people who are unable to speak tend to be socially isolated
matches pname rule NUM yesterday mccann made official what had been widely anticipated pname lcb NUM rcb mr james NUM years old is stepping down as chief executive officer on july NUM and will retire as chairman at the end o f the year
aac systems aim to help them to communicate using synthesized speech
a number of improvements suggest themselves to further enhance the usability of systems such as talk
this derivational equivalence or spurious ambiguity betrays the permutability of certain rule applications
for l we have the following nl preserves input antecedent configuration in output succedent term structure
the parsing problem is usually construed as the recovery of structural descriptions assigned to strings by a grammar
unfolding of left products would create two positive subfonnulas and thus fall outside the scope of horn clause programming
given denotations for primitive types those of compound types are fixed as subsets of v x v by
i.e. a program clause disappears from the database once it is resolved upon each is used exactly once
for example a casual chat may contain transactional components such as arranging future joint activities
the use of this feature of course involves the user in a significant time penalty
this shows the ira provement of the quality of the hierarchical clusters with increasing size of the clustering text
in the training phase a set of events are extracted from the training texts
in other words it is ambiguous in tokenization
although many sophisticated search and matching methods are available the crucial problem remains to be that of an adequate representation of content for both the documents and the queries
the definitions above contain nothing improper
this description of ambiguity is complete
to hat extent the obtained hierarchical clusters are considered to be portable across domains
figure NUM shows an example of generation of a terminal class corresponding to the tree for french for the full passive of a strict transitive verb in a wh question on the agent see figure NUM it can be illustrated by the sentence je me demande par qui jean sera accompagn6
solutions to the redundancy problem make use of two tools for lexicon representation inheritance networks and lexical rules
following a functional approach to subcategorization see for instance lexical functional grammar bresnan NUM we clearly separate the redistributions of syntactic functions of the arguments from the different realizations of a given syntactic function in canonical extracted cliticized position
as shown in the above example the conjunctkm of two descriptions may require statements of identity of nodes
with it all relevant redistributions dimension NUM and relevant realizations of functions dimension NUM
ira case of failure the whole conjunction fails or rather leads to an unsatisfiable description
due to a lack of space we detail only the following principle useful to understand next section
but for a given predicate we expect the canonical arguments to remain constant through redistribution of functions
NUM for perspicuity we provide these in datr augmented english here
there are at least three distinct but related senses
we can encode this situation in datr as follows NUM
facts that provided our running example in section NUM above
come mor root come mor past participle bare verb
the other major component of datr is definition by default
each of these sentences corresponds directly to a datr statement
we continue until some node path pair specifies an explicit value
syn form present participle mor form love ing
verb mor form mor quot syn form
it is written in c and tcl tk and currently runs on unix sunos solaris irix linux and aix are known to work a windows nt version is in preparation
note that the viewers are general for particular types of annotation so for example the same procedure is used for any pos tag set named entity markup etc see section NUM NUM below
second one may associate the information with the text by building a separate database which stores this information and relates it to the text using pointers into the text the re erential approach
this is also efficient with a small penalty at load time and allows developers to change creole objects and run them within gate without recompiling the gate system
as described in section NUM NUM creoleisation of existing le modules involves providing them with a wrapper so that the modules communicate via the gdm by accessing tipster compliant document annotations and updating them with new information
working with gate the researcher will from the outset reuse existing components and the common apis of gdm and creole mean only one integration mechanism must be learned
typically a creole object will be a wrapper around a pre existing le module or database a tagger or parser a lexicon or ngram index for example
tools in a lt nsl system communicate via interfaces specified as sgml document type definitions dtds essentially tag set descriptions using character streams on pipes an arrangement modelled after unix style shell programming
NUM distributed control is easy to implement in a database centred system like tipster the db can act as a blackboard and implementations can take advantage of well understood access control locking technology
we call this method sense NUM in table NUM
the naive bayes classifier assumes independence of example features so that
we specify the semantics of trees by applying two principles to the ltag formalism
NUM because there are relatively few concurrent expressions in our corpus only NUM those results are not included in this section
upon inspection we find that only three cases of global purpose expressions in our corpus NUM NUM are not fronted
example 3f uses a simple imperative for the purpose with the intended actions in a separate sentence following the purpose
nominal availability will realize a prepositional phrase with a nominalization as the complement whenever the appropriate nominalization exists as in example 9a
the volitionality system determines whether the purpose expresses the desire of the reader to get some inanimate substance to perform in some volitional way
in the general version of the head corner parser gaps are inserted by a special clause for the predict NUM predicate NUM where shared variables are used to indicate the corresponding string positions
consequently the set of constituent structures defined by a grammar can not be read off the rule set directly but is defined by the interaction of the rule schemata and the lexical categories
in a spoken dialogue system it is often impossible to parse a full sentence but in such cases the recognition of other phrases such as temporal expressions might still be very useful
note that unlike the left corner parser the head corner parser may need to consider alternative words as a possible head corner of a phrase for example when parsing a sentence that contains several verbs
depending on the properties of a particular grammar it may for example be worthwhile to restrict a given category to its syntactic features before attempting to solve the parse goal of that category
to move from position p to q you can either use a maximal projection from p to q as constructed by the parser or use a transition from p to q
if bigram scores are not included then this best first search method can be implemented efficiently because for each state in the word graph we only have to keep track of the best path to that state
in section NUM it will be shown that the head corner parser which uses a mixture of bottom up and top down processing can be applied in a similar fashion by using underspecification in the top goal
scenario template contains scores fo r relevancy judgments in the line labeled text filtering and the information retrieval metrics of recall and precision are used
ca NUM minuten vor der entleerung beginnt der rechner NUM sekunden zu beepen
in this paper we intend to make two contributions to the centering approach
the status to date is that the scenario template and template element scorers are completely in c and ca n be run in batch mode only
the fifth column shows the results which are generated by the functional constraints from table NUM
section NUM is limited however to the german middlefield and hence incomplete
the second major contribution of this paper is related to the unified treatment of specific text phenomena
i subject dir object indir object i complement s adjunct s
in section NUM we elaborate on the particular information structure criteria underlying a function based center ordering
instead we claim that functional centering constraints for the c ranking are possibly universal
slot fills are either text strings or pointers to other objects and every slot has a set of fills that may consist of zero one or multiple fills
we will also discuss the result of translating the emacslisp scorers into c including the increased accuracy of the mapping a s well as the increased speed and improved memory management
as long as the number of the resulting classes is less than the pre defined number the splitting process will be continued
an ltag parse of a sentence can be seen as a sequence of elementary trees associated with the lexical items of the sentence along with substitution and adjunction links among the elementary trees
the most efficient method of performing regular expression pattern matching is to construct a finite state machine for each of the stored patterns and then traverse the machine using the given test pattern
in the application phase the pos sequence of the input sentence is used to retrieve a generalized parse s which is then instantiated with the features of the sentence
an alternate representation of the derivation tree that is similar to the dependency representation is to associate with each word a tuple this tree head word head tree number
we did not perform morphological restructuring such as canonicalization to singular nouns verb roots etc or anaphoric resolution replacement of pronouns by originals etc for want of robust enough methods to do so reliably
correct segmentation for lb i i NUM i i this classifier research a ux involve of problem very complex the problems involved in this research are very complex
in addition to take into account word position we based this formula on the fibonacci function it monotonically increases with longer matched substrings and is normalized to produce a score of NUM for a complete phrase match
according to his results the title and location methods respectively scored around NUM and NUM accuracy where accuracy was measured as the coselection rate between sentences selected by edmundson s program and sentences selected by a human
the opposite strategy taking all the shogun frames and adding those plum frames that did not match shogun frames improved recall somewhat but lost on precision and resulted in a decrease in overall f score
NUM applying the procedure of creating an opp to another collection in the same domain should result in a similar opp and NUM sentences selected according to the opp should indeed carry more information than other sentences
the precision and recall scores indicate the selective power of the position method on individual topics while the coverage scores indicate a kind of upper bound on topics and related material as contained in sentences from human produced abstracts
volume NUM of the ziff corpus on which we trained the system consists of NUM NUM newspaper texts about new computers and related hardware computer sales etc whose genre can be characterized as product announcements
so the number of possible cases grows
for instance let us consider these rules
our experience with the basque language in word prediction applied to alternative and augmentative communication for people with disabilities shows that prediction methods successful for non inflected languages are hardly applicable to inflected ones
first of all some relations in eurowordnet have deliberately been defined to give somewhat more flexibility in assigning relations
r5 is the gemination rule it is only triggered if the given rule features are satisfied cat verb for the first lexical element i.e. the pattern and measure pa el for the second element i.e. the root
in addition to applications in natural language understanding machine translation and generation the model of compound interpretation developed here can be applied to multi lingual information extraction tasks
the transitions correspond to dialogue acts
second it allows for interaction between two level rules and word grammar facilitating the formulation of rules for non concatenative morphol actics
the other daughter may be either a cornmap between the lexieal representation of a word arm its surface fl rm
morphology proper on the other hand is viewed as a sirnpte concatenation process governed by a regular grammar
however two problems had to be solved before fui could be used for the planned purpose
the work being described here was done in t he context of a multilingum text generation system
NUM NUM ata NUM a short presentation
each pair of aligned sequences compose what we called a test unit
each maintenance task description should be preceded by a definition of this task
corpus based annotated test set for machine translation evaluation by an industrial user
the pragmatic characteristics of the text apparently implied some translation choices
they actually illustrate the operational organization of the maintenance work to be done by the user
each task and subtask has a precise denomination that takes the form of titles in the document
one example in the case of mt is the translation of french determiners into english
the score is computed by considering the weighted average of the similarity of the input case fillers with respect to each of the corresponding example case fillers listed in the database for the sense under evaluation
a more important reason why the matching rates are lower than before could be that in some circumstances there may be more than one acceptable solution and the speakers may not always choose the same one as the machine
let the statistics based similarity between words a and b be vsm a b and the similarity based on sbl be sbl a b
the distance between two domains is calculated as the average of the two cross entropies in both directions
on the other hands a corpus of similar domains could provide wider coverage for the grammar
then for each pair of domains cross entropy is computed using the probability data
one rationale we can think of is based on the comparison observation described in section NUM
however the observations and the experiment suggest the significance of the notion of domain in parsing
many of them are interesting to see and can be easily explained by our linguistic intuition
the work reported here was supported under contract NUM f145800 NUM from the office of research and development
in particular the differences among fiction domains are relatively small
we can easily imagine that the smaller the training corpus the poorer the parsing performance
to summarize our observations and experiments there are domain dependencies on syntactic structure distribution
the speakers varied greatly in choosing anaphoric forms for these topic shifts among twelve speakers four chose all full descriptions three used all zero anaphors and the other five chose zero pronominal and nominal anaphors
a network database server gives remote though read only access to the test data
the probability mass from these configurations is distributed uniformly over all the possible configurations that have been eliminated
n the other rcb mrml sin o testing or ewdua ting
iq ench grammar checker ho real life c wduation scenario ix
am rrion e.g. of an object np german dcr managcr arbeitet
the timensions hosen in the classificati ena present language specific variation the immbers given are for grammatical vs mlgramnmtical items
the approach has been successfully tested against commercial and research nlp applications and components
change of person l h ench l ingdnieur vient
tlllllllit rcb l oi iis ltils l test
some repetition and overlap will be seen in the textref results these are known bugs
we present a variation on classic beam thresholding techniques that is up to an order of magnitude faster than the traditional method at the same performance level
on the other hand because long nodes will tend to have low inside probabilities taking the minimum of all scores strongly favors sequences of short nodes
ideally as we loosened the threshold every sentence should improve on every metric but in practice that was n t the case
we then computed for each of six metrics how often the metric decreased stayed the same or increased for each sentence between the two runs
finally the output of the parsing module may be only partially determined
some of them may be prevented by using language models during the recognition
the subjects of this experiment called dialogos and another spoken dialogue system
of naturalness of the telephone human machine dialogues
the information about the system dialog act is called dialogue prediction
in this corpus NUM NUM of the dialogues failed for users errors
i mould like to leave from milano milano in the evening
therefore we use a procedure for selecting which of our pool of features should be made active
gralnmatical flinctioxls constitute a complex label system cf
NUM one class of full personal names that this characterization does not cover are married women s names where the husband s family name is optionally prepended to the woman s full name thus j xu3 1in2 yan2 hai3 would represent the name that ms lin yanhai would take if she married someone named xu
other kinds of productive word classes such as company names abbreviations termed suol xie3 in mandarin and place names can easily be NUM note that j in is normally pronounced as leo but as part of a resultative it is liao3
the less favored reading may be selected in certain contexts however in the case of for example the nominal reading jiang4 will be selected if there is morphological information such as a following plural affix meno that renders the nominal reading likely as we shall see in section NUM NUM
for instance for tts it is necessary to know sproat shih gale and chang word segmentation for chinese that a particular sequence of hanzi is of a particular category because that knowledge could affect the pronunciation consider for example the issues surrounding the pronunciation of ganl qian2 discussed in section NUM
the relevance of the distinction between say phonological words and say dictionary words is shown by an example like j t NUM l i zhongl hua2 ren2 min2 gong4 he2 guo2 china people republic people s republic of china arguably this consists of about three phonological words
finally this effort is part of a much larger program that we are undertaking to develop stochastic finite state methods for text analysis with applications to tts and other areas in the final section of this paper we will briefly discuss this larger program so as to situate the work discussed here in a broader context
word frequencies are estimated by a re estimation procedure that involves applying the segmentation algorithm presented here to a corpus of NUM million words NUM using NUM our training corpus was drawn from a larger corpus of mixed genre text consisting mostly of newspaper material but also including kungfu fiction buddhist tracts and scientific material
statistical methods seem particularly applicable to the problem of unknown word identification especially for constructions like names where the linguistic constraints are minimal and where one therefore wants to know not only that a particular sequence of hanzi might be a name but that it is likely to be a name with some probability
insure that this is indeed a family of conditional probability distributions
for simplicity we assume that the features are binary questions
n s richard blystone is here to tell us
this data contains cnn transcripts but no reuters newswire data
the letter c appears among several of the first features
figure NUM gives a striking graphical illustration of this phenomenon
we now present graphical examples of the segmentation algorithm at work on previously unseen test data
we have presented and evaluated a new statistical model for segmenting unpartitioned text into coherent fragments
this will constitute the first step towards generating an interlingua on the basis of a set of aligned language specific semantic networks
dempster s rule can be seen as a very coarse grained approach to conditioning on context in this regard
in order to evaluate the accuracy of the decision algorithm NUM triples were selected from the set of test triples
for instance in sentence NUM all words in the name mexikanische verband iir menschenrechte are capitalized
in doing this the system chooses one of the objects that matched the original description as the likely referent in this case it happens to choose the object in the corner the fern plant which the system represents as fern1
bmb system user bel user error p34 p56 NUM the system then applies the appropriate acceptance rule rule NUM and so adopts the belief that the error is mutually believed
bmb system user replace p34 p104 NUM this causes the belief module to update the current plan of the collaborative activity and to add the belief that the user contributed the new referring expression plan
the subplan corresponding to the television would have been understood without problem NUM and the modifier corresponding to on the television would have narrowed down the candidates that matched weird creature to a single object antenna1
unambiguous examples are provided by sentences in which morpho syntactical information suffices to determine the subject and object of the verb
this dialog shows how collaboration on a referring expression can be embedded in other computational linguistics volume NUM number NUM activities how agents can return back to a collaborative activity and even how agents can take advantage of a mistaken referent
second it would account for why agents would enter into such a mode of interaction how it is initiated how it is carried forward especially how agents beliefs and knowledge influence their actions and how it ends
so when a replan action is part of a plan that is being evaluated the success of this action depends only on whether the plan that is its parameter can be derived but not whether the derived plan is valid
this paper presents a corpus based method to assign grammatical subject object relations to ambiguous german constructs
a high inflation rate this year the economist expects a high inflation rate
such parsers are particularly easy to implement in extended versions of prolog such as prologli sicstus prolog and eclipse which have such coroutining facilities built in
observe that this use of basic constraints generalizes the use of substitutions in ordinary logic programming and the simplification of a conjunction of constraints generalizes unification
like all backtracking parsers they can exhibit non termination and exponential parse times in situations where memoizing parsers such as chart parsers can terminate in polynomial time
in the categorial grammar example a category becomes more instantiated when it combines with arguments allowing eventually the add adjuncts NUM and division NUM to be deterministically resolved
figure NUM the items produced during the proof of x c lijklte on wijkenj using the control and
the modal operator is used to diacritically mark untensed verbs e.g. ontwijken and prevent them from combining with their arguments
in the example discussed above the meaning of the ellipsis is built up in the same way as for the antecedent except that whenever you encounter a term corresponding to john or something dependent co indexed with it you it is treated as though it were the term for mary or dependent co indexed with it
in the course of developing edward s referent resolution model we used hundreds of test sentences made up by ourselves to debug and test the program
put a copy of this experimentor points at leftmost file on screen file in this experimentor points again directory
each referent mentioned in the dialogue is put on a stack and when interpreting a referring expression the stack is processed from top to bottom
as a result the influences on an object s salience are represented distributed and independently which is attractive from a computational point of view
moreover entities and relations are handled in a uniform fashion and syntactic as well as perceptual influences on salience are incorporated into one model
in contrast with anaphors cataphors refer to instances that will be introduced later in the discourse e.g. he will win who
in this paper however we focus on the use of the context model to resolve deictic and anaphoric expressions keyed in by the user
for example suppose herb the brother of the boss of the nici and catherine the boss s sister visit the nici
but any subsequent male pronoun he him his can refer only to herb and not to the not mentioned boss of the nici
as a result the model provides a constrained yet principled account of interpretation it also links social accounts of expectation with other mental states
in this section we will discuss how the model addresses the following concerns the need to control the inference from observed actions to expected replies
that is the hearer displays his understanding and acceptance of the appropriateness of a speaker s utterance independent of whether he actually agrees with it
NUM a detailed example to show how our abductive account of repair works we offer two examples that show repair of self misunderstanding and other misunderstanding respectively
this approach is thus much stronger than most accounts of negotiation such as ours which allow that a participant might choose to forego a complete repair
NUM for example after the computer asks the user to perform some action a it might expect any of the following types of responses NUM
agents interpret utterances on the basis of expectations derived from previous utterances as well as expectations for future actions that are predicted by the utterance under interpretation
NUM the intended meaning of expressednot p t is that during turn t speakers have acted as if the tial assumptions about misunderstandings and metaplanning decisions
presumably this configuration eliminate s the unusual and redundant examples and produces the performance near the level of all egraphs
in effect this mode simulates perfect egraph matching an d predicts the upper bound on extraction performance for those egraphs
the expectation of this reapplieation is allowed for in
the error surface vowels are written in italics
left lexical context lexical form right lexical context left surface context surface form right surface context the special symbol is a wildcard matching any context with no length restrictions
for example the rules sanction both katab m NUM active and kutib m NUM passive as interpretations of ktb as showin in NUM
if no error rules succeed or lead to a successful partition of the word analysis backtracks to try the error rules at successively earlier points in the word
consider the nominal data in NUM
as each root does not occur in all vocalisms and patterns each lexical entry is associated with a feature structure which indicates inter alia the possible patterns and vocalisms for a particular root
NUM error formalism errsurf surf lcb plc prc rcb where plc partition left context has been done prc partition right context yet to be done
conversely at high temperatures it will minimize differences in urgency values
this tree can be converted into the following transformation list assume that two decision trees t1 and t2 have corresponding transformation lists l1 and l2
assume that the arbitrary label names chosen in constructing l1 are not used in l2 and that those in l2 are not used in l1
early into phase i of the tipster text program the decision was made to establish a companion evaluation program based initially on the tipster phase i document detection tasks
this did not happen and as a result the tipster text program phase ii NUM month workshop was treated to several demonstrations of this working prototype system built in compliance with the specifications of the tipster architecture
similarly the information extraction participants were additionally required to automatically locate identify and standardize information contained in newspaper style documents within two distinct subject domains the formation of business joint ventures and microelectronic chip fabrication
rather than running out of steam tipster has continued to pick up momentum and to broaden its area of interest and coverage as it proceeded through phases i and ii and now heads into phase iii
the line usually sparks a snicker or two because those in the audience seem to know that previous joint programs between these agencies have not always been so amicable
inevitably at the next workshop reports are given by several other participants concerning how they were able to successfully and beneficially incorporate these new ideas into their own systems
one of the most challenging information extraction tasks which was first articulated during phase i namely system extensibifity by analyst endusers has still not been completely satisfied
and even though this program manager had spent only slightly more than one year at darpa he clearly understood how darpa established funded and managed new r d programs
appositive modifiers do not correspond to any shared classification but rather to the subjective speaker s point of view
p wil imx p cl ckl unx p k p cl cklk NUM
the f measure was originally developed by the information straining i was used as plain texts that are taken from the same information sou rce as training o
the most notable example of this came from the congressionally funded dual use technology program which provided over NUM million in supplement funds in early NUM about a quarter of the way through phase i
the performance of human analysts in completing their tasks has been routinely measured and have subsequently been used as a benchmark against which the performance of the information extraction and document detection algorithms can be compared
the transformations changing in to wdt are for tagging the word that to determine in which environments that is being used as a synonym of which
with these assumptions in place the time complexity for the algorithm can be estimated to be n sentence length n number of forest nodes o g fc n n o g fa n
if v is a child of or node v then xv xv data structures an array sem of constraints and an array d of names both indexed by node a stack env of def constraints output a constraint representing a packed semantic representation method env nil process nodes in a bottom up order doing with node u if u is a leaf then
we call an order on the nodes of a directed acyclic graph g n e with nodes n and edges e bottom up iff whenever i j e e i is a predecessor to j then j i
target syntactic generation during gelmration the transti r representation is mat ped onto a target syntactic structure through intermediate representational lewfls
they are recognized during text handling and have their translation equivalent attaehed to them along with inorphosyntactic information for both source and target language
a two layered dialogue architecture for spoken dialogue systems is presented where the upper layer is domain independent and the lower layer is domainspecific
smoothing the parameters is thus important in the estimation process
robust learning smoothing and parameter tying smoothed parameters
afterwards the robust learning procedure is applied based on the smoothed parameters
the domain for this corpus is computer manuals and documents
nevertheless they are a little better for the l2 syntactic language model
when the parsing process starts the program is presented with the sentence
furthermore d j k is differentiable with respect to the parameters
the learning rules of the syntactic and lexical weights are modified as follows
finally we discuss our conclusions and describe the direction of future work
then the syntactic score of the tree in figure NUM is defined as
the french speech output uses the m brola synthesizer developed by t dutoit at the university of mons
the speech processing system under development at idiap is speaker independent hmm based and contains models of phonetic units
although a small prototype has been completed the itsvox system described in this paper needs further improvements
for example wordnet NUM NUM lists twenty six senses for the english verb run
once tie word pairs or n toples have been selected all subsequent processing is fully automated
NUM context vector approach will learn second order relationships between the languages used for training
the context vector for the target stem is adjusted in the direction of its neighbors context vectors
in the english portion of this example the window is centered on the word attack
in practice however this summation is performed only over words that co occur with the target word stem i
at the summary level the matchplus system translates flee text into a mathematical representation in a meaningful way
the resulting unified context vector set can be used to identify relationships between words in the two languages
NUM the basic approach described here is extensible and capable of processing more than two languages at once
to start the learning process each stem is associated with a random vector in the context vector space
the user may write queries in natural language or boolean syntax
the document manager software stores and indexes documents coming into prides
pa provides an external message interface to the incoming fbis documents
similarly the search engine and the document manager process user queries and build hit folders
inquery is an information retrieval system based upon a bayesian inference network model of information retrieval
the user may create private collections called save folders storing articles from other folders
the prides document manager is fully tipster compliant and available for use in other tipster systems
the most consistently translated pronouns are the archaic forms thee and thou
applying essentially the same approach to the design of cogenthelp we first determined that for the intended range of generated texts it suffices to associate with each widget to be documented a small number of atomic propositions and properties identifiable by type
the method can be implemented for any language for which a reasonably large bitext is available
users may wander aimlessly in their behaviors without smith hipp and biermann an architecture for voice dialog systems some guidance when things go wrong
how then to gain the benefits of clause level syntax within the context of a partial parsing system
at the first line of both alternatives window the whole original japanese expression is shown with a slash at the boundaries of words like denwa wo kakeru
attributes represent partial translation result for the structure below the node and attribute calculation proceeds from the lexical nodes to the root node in a bottom up manner
the first region is the complement sentence he ga book wo read nd where no is a complement marker
the system provides its prediction as a default selection and other possibilities as second or third choices but the user is free to obey or ignore them
it captures japanese input before they are entered to an application converts it into english and then sends the result to the application figure NUM
when the user triggers translation denwa becomes a phone call kakeru becomes make producing make a phone call in whole
although syntactic cases can be processed by inflection mechanism constraint of sentence styles such as to infinitive or gerund can not be treated in a similar manner
we are also planning to add translation examples to the knowledge base so that translation can be performed either using grammars or examples in the knowledge base
as melby says a post editor will only improve by a certain increment if the result is completely wrong s he will simply abandon the whole result
this is basically a bottom up i ro t NUM i ll silt the numl er m of distinct pa irs x w for NUM
our NUM rototyp took a l out NUM sec ela psed time to transla te this input s mtence a nd produce seve n alterha tire translapsious
soi e of tile assumt tioils on NUM a tt0 rns should be r0 examine d when we extond the l ffinition of pa tterns
it is provable that for any set t of patterns there exists a weakly equivalent cfg grammar f with possibly exponentially more grammar rules such that l t l f
introduced NUM in pretermina NUM rules such a s lea e v v partir where two w rbs leave a nd partir are associated with the heads of the nonterminal symbol v
tool in htd a sy ustomiza tion for div rse h mmns th tfi iency of th tra nsla tion
then pair is substituted into the np op pairs in NUM thus correctly transferring sentence NUM
for handling scrambling the multi adjunction concept in mc tags can be used for combining a scrambled argument and its landing site
and finally the target sentence is generated from the target derivation tree obtained in the previous step
k tom i jerry lul ccossnunta NUM tom nom jerry acc chase e tom chases jerry
in general aat g represents a canonical np structure and flat g represents a scrambled np structure
the root node of the head structure is always mapped to the root node of the target english structure
the transfer lexicon consists of pairs of trees one from the source language and the other from the target language
whereas the case marker in english is implicit in the word case markers are explicit in korean
also the pair NUM shows the links between sov structure of korean to svo structure of english
to analyze it we converted each of the grades to their traditional numerical counterparts i.e. a NUM b NUM c NUM d NUM and f NUM next we computed means and standard errors for both knight s and the biologists grades
the method presented here for measuring semantic entropy is sensitive to ontological and syntactic differences between languages
it is important to emphasize the following distinction between discourse knowledge and explanation plans discourse knowledge specifies the content and organization for a class of explanations e.g. explanations of processes whereas explanation plans specify the content and organization for a specific explanation e.g. an explanation of how photosynthesis produces sugar
we do not currently use any form of stochastic or adaptive techniques i n the main system
although in some cases this may be in error the hope is that automatic stemming of query terms at the search engine will reduce long terms to stems common to many of the keywords that might have been substituted if the entire definition was transferred
using a vector based text retrieval system with no term spreading or other modifications the english queries were translated by performing a lookup on the english side of the parallel corpus collecting the spanish sentences that were parallels to the top NUM retrieved documents filtering the remaining terms to eliminate the top NUM most frequent spanish terms and collecting the next NUM most frequent spanish terms to create the new query
for example if the system applies the participants accessor with photosynthesis as the concept of interest and production as the reference process then the accessor will extract information about the producer chloroplast the raw materials water and carbon dioxide and the products oxygen and glucose
our motivations and goals are explained in more detail in the first part
on the other hand it is counter productive to make too many distinctions
part of these collected ambiguities have been used for experiments on interactive disambiguation
an example of the latter is shown in figure NUM mundial is a query interface to infoseek and yahoo that takes queries in english translates them to spanish and submits the resulting queries to the infoseek and yahoo search engines directly
if not at least two corresponding nodes must differ in their decorations
for example two decorated trees may differ in their geometry or not
take for example NUM the international telephone services many countries
in this sense it plays the role of an initial dictionary
if we could make a set of derived spanish queries retrieve documents in a manner that is similar to the english queries over a training corpus then the spanish query could conceivably produce similar results on a novel corpus
r is multiple if iproper r l l
which combinations are possible should be determined by the person doing the labeling
various methods are possible for testing statistical significance but the method we applied is based on a log likelihood ratio test that assumes a x NUM distribution is an accurate model of the term distributions in text NUM
NUM is another specifi c principle subsumed by gpii
for the coreference task the coreference links created by reference resolution ar e converted to annotations and thence to sgml
the documents were automatically aligned NUM at the sentence level using a procedure that is conservatively estimated to have an NUM accuracy over grossly noisy document pairs which the un documents were not
take into account legitimate partner expectations as to your own background knowledge
no special heuristics are needed for using this example based translation approach the query can be optimized by adding or deleting terms until the target language retrieval results are approximately the same as the source language retrieval results
besides the lack of lexical information the induction of phrase structure grnmmar may suffer from structural data sparseness with medium sized training corpus
below is shown the description for the two problems in NUM
provide sufficient task domain knowledge and inference
the generic principles are expressed at the same
d the system provides insufficient informalion
avoid obscurity of expressio lcb i
for the tagging accuracy we use several measures to estimate the performance
this is the strategy chosen in vodis
figure NUM the handling of sr results
with other blocks via intercommunication protocols
NUM NUM coping with the limitations of speech
NUM NUM how generalizable are the dm methods
4put differently what holds for human speakers cf
a validation process more of which below
thou shalt be aware of the profound
messages from the controller concern state changes of the system
however the emphasis in the traditional interactive theorem proving literature is on giving the user substantial opportunity to propose the notations and individual steps of the proof in a way that is not possible or desirable in our environment
to give a flavor of our system s architecture we include outline descriptions of some of its most important classes dialogue manager dialogue intention find enquiry type and domain expert
an object oriented model for the design of cross domain dialogue systems
previous experience of a situation or explicit tutoring in a particular task means that real life dialogues often consist of elements that have been rehearsed and are therefore predictable
the system has the intelligence to decide in real time which business expertise and which skillsets are required to pursue the user s enquiries and calls upon the services of the appropriate coded objects
the dialogue intention class encapsulates a variety of approaches to phrasing rephrasing and personalising system utterances with the aim of handling in as natural a manner as possible communication errors and processing delays
null the find enquiry type class uses a domain spotter class to identify the domain expert that is best suited to handling the enquiry
in general terms adherence to best practice in object oriented development offers the prospect of systems that can be readily extended and customised in building block fashion
our approach to speech based dialogue modelling aims to exploit in the context of an object oriented architecture dialogue processing abilities that are common to many application domains
handling colors times etc NUM word combinations bigrams or trigrams from the domain to extend the generic capabilities of the recogniser grammar
the game coding shows how moves are related to each other by placing into one game all moves that contribute to the same discourse goal including the possibility of embedded games such as those corresponding to clarification questions
g no i want you to go up the left hand side of it towards green bay and make it a slightly diagonal line towards em sloping to the right
for instance one would obtain different values for kappa on agreement for move segment boundaries using transcribed word boundaries and transcribed letter boundaries simply because there are so many extra agreed nonboundaries in the transcribed letter case
the instruction can be quite indirect as in example NUM below as long as there is a specific action that the instructor intends to elicit in this case focusing on the start point
n NUM k NUM with the largest confusions between NUM check and query yn NUM instruct and clarify and NUM acknowledge ready and reply y
it is sometimes appropriate to consider ready moves as distinct complete moves in order to emphasize the comparison with acknowledge moves which are often just as short and even contain the same words as ready moves
among these tuples only those which included the postposition wo typically marking the accusative case were used
route givers tend to make clarify moves when the route follower seems unsure of what to do but there is n t a specific problem on the agenda such as a landmark now known not to be shared
in some cases such as apposition the anaphoric relation is determined by the syntax
this has two major usages assigning some segments with a default marking at some stage of the process in order to provide preliminary information that is essential to the subsequent stages and correcting the default marking later if the context so requires assigning some segments with very general marking and refining the marking later if the context so permits
regular expression re advp adj comma advp adj comma coord advp adj advp stands for adverb phrase and is defined as adv coord comma adv
then using a replacement transducer we insert the np and np boundaries around the longest sequence that contains at least one temporary beginning of np followed by one temporary end of np
as for linguistic performance we conducted a preliminary evaluation of subject recognition over a technical manual text NUM words NUM sentences and newspaper articles from le monde NUM words NUM sentences
whenever a transducer defines a verb subject construction it is implicitly known at this stage that the initial subject verb construction was not recognized for that particular clause otherwise the application of the verb subject construction would be blocked
this way we implicitly handle complicated nps such as le ou les responsables the sg or the pl person s in charge les trois ou quatre affaires the three or four cases etc
the ordering of the linguistic descriptions is in itself a matter of linguistic description i.e. the grammarian must split the description of phenomena into sub descriptions depending on the available amount of linguistic knowledge at a given stage of the sequence
it should be observed that in real texts not only may one find subjects that do not agree with the verb and even in correct sentences but one may also find finite verbs without a subject
it must NUM appropriately represent a category as well as NUM have a proper preciseness in terms of number of parameters
we treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models and employ the em algorithm to efficiently estimate parameters in a finite mixture model
when applying fmm we used our proposed method of creating clusters in section NUM and set NUM to be NUM NUM NUM NUM NUM NUM NUM because these are representative values
after this conference the same demonstration will be available on internet at the following url
it replaces each word wt in the document with the cluster kt to which it belongs t NUM n
the likelihood used in hypothesis testing becomes the same as that in eq NUM and thus our method becomes equivalent to wbm
using the data in tab l we create two clusters kt and k2 and relate them to ct and c2 respectively
then letting NUM NUM NUM m the finite mixture model in eq NUM may be written as
NUM when NUM NUM fmm performs better than wbm for the first data set and that it performs as well as wbm for the second data set
since creating clusters in an optimal way is difficult when clustering does not improve performance we can at least make fmm perform as well as wbm by choosing NUM NUM
a trace mode enables the step by step display of the construction of the analysis
the outputs will be split into sets in order to focus on the resolution of different kinds of problems i.e.
pictalk has many features that support the above activities
such cases are detected and marked by the pattern matching stages and checked by reference resolution before other tests are made
consider this example coleco failed to come come up with verb NUM up with another winner and filed for bankruptcy law protection
consulting the knowledge base we determine that r is now and that the present tense morpheme applies
domain specific terms are hard to translate because they often do not appear in dictionaries
although the discourse knowledge engineer may write arbitrarily complex specification expressions in which function invocations are deeply nested these expressions can become difficult to understand debug and maintain
to do so the discourse knowledge engineer would ideally take an off the shelf explanation generator and add discourse knowledge about how to explain mathematical interpretations of the behavior of physical systems
for example to build an explanation system for the domain of physics a discourse knowledge engineer could either build an explanation system de novo or modify an existing system
functional description skeleton processor gathers all of the available information from the fd skeleton the lexicon and the noun phrase generator produces the final functional description
we calculated these values for the overall quality and coherence rating as well as for each of the dimensions of content organization writing style and correctness
realization can be decomposed into two subtasks functional realization constructing functional descriptions from message specifications supplied by a planner and surface generation translating functional descriptions to text
the explanation planner should be viewed as an automatic specification writer its task is to write specifications for the realization component which interprets the specifications to produce natural language
for each of the topic s content specification nodes the applier invokes the determine content algorithm which itself invokes kb accessors named in the edp s content specification nodes
because we wanted to gauge knight s performance relative to humans we assigned each of the experts to one of two panels the writing panel and the judging panel
on this analysis the activity features of march durative dynamic propagate to the sentences in NUM
finally we illustrate how access to lexical aspect facilitates lexical selection and the interpretation of events in machine translation and foreign language tutoring applications respectively
in addition factoring out the structural requirements of specific lexical items from the predictable variation that may be described by composition provides information on the aspectual effect of verbal modifiers and complements
for example our machine translation system selects appropriate translations based on the matching of telicity values for the output sentence whether or not the verbs in the language match in telicity
pronouns and auxiliary verbs be have and do are also closed class parl of speech
gweilo in cantonese is actually an idiom referring to a male westerner that originally had pejorative implications
compared to other word alignment algorithms it does not need a priori information
in this section we present some empirical data concerning the centered segmentation algorithm
an example is reported in figure NUM
table NUM is evaluated in order to determine the proper antecedent ante for x
even if one chain of false points of correspondence slips by the chain recognition heuristic the expanding rectangle will find its way back to the tbm before the chain recognition heuristic accepts another are much more dense than false points of correspondence a good signal to noise ratio prevents simr from getting lost
points of correspondence among frequent token types often line up in rows and columns as illustrated in figure NUM token types like the english article a can produce one or more correspondence points for almost every sentence in the opposite text
again we can improve on this
not surprisingly the coverage of morphological rules is much lower than that of the ending guessing ones for the suffix rules it is less than NUM and for the prefix rules about NUM NUM
at the rule extraction phase three sets of word guessing rules morphological prefix guessing rules morphological suffix guessing rules and ending guessing rules are extracted from the lexicon and cleaned from coincidental cases
in this evaluation we tried two different taggers
in comparison with the xerox word ending guesser taken as the base line model we detect a substantial increase in the precision by about NUM and a cheerful increase in coverage by about NUM
for each text we performed two tagging experiments
we obtained quite stable results in these experiments
this concludes our outline of the proof
recall that large rank value denotes low rank
inside test of this proposed algorithm shows NUM NUM
since the user has a warranted belief in on sabbatical smith next year indicated by the semantic form of utterance NUM the system will predict th merely informing the user of the intended mutual belief is not sufficient to change his belief therefore r will select justificatkm from the two available pieces of evidence supporting on sabbatical smith next year presented earlier
each word is accompanied by a counter indicating how many times it appears in the corpus
we denote by iwl the length ofw
now der rechner computer figures as a nominal anaphor already resolved to dell NUM 16lt while akku accumulator is only the antecedent of the elliptical expression der entleerung discharge
transition pairs are called expensive if the backward looking center of the current utterance is not correctly predicted by the preferred center of the immediately preceding utterance i.e. cb ui gp ui l i NUM n
the textual ellipsis ladezeit charge time in 2c has to be resolved to the most preferred dement of the c i of 2b viz the entity denoted by akku accumulator cf
we also augmented the ordering criteria of the forward looking center such that it accounts not only for pro nominal but also for functional anaphora textual ellipsis an issue that so far has only been sketchily dealt with in the centering framework
fx and y both represent the same type of is pattern then the relation c applies to x and y else fx and y both represent different forms of bound elements then the relation rsbo a applies to x and y
since most of the anaphors in these texts are nominal anaphors the resolution of which is much more restricted than that of pronominal anaphors the rate of success for the whole anaphora resolution process is not significant enough for a proper evaluation of the functional constraints
the tagger is represented by a finite state transducer a framework that can also be the basis for syntactic analysis
the resulting deterministic transducer yields a part of speech tagger whose speed is dominated by the access time of mass storage devices
vbd emits the empty symbol c and postpones the emission of the output symbol
we introduce a proof of soundness and completeness with a worst case complexity analysis for the algorithm for determinizing finite state transducers
ab or whether it will be followed by a sequence that makes it impossible to apply the transformation e.g.
for each individual rule the algorithm scans the input from left to right while attempting to match the rule
the resulting part of speech tagger operates in linear time independent of the number of rules and the length of the context
since one wants the transition to be deterministic the actual emission is the longest common prefix of this set
the present paper studies the computational complexity of a variant of the lambek calculus that lies between and tp the semidirectional lambek calculus sdk NUM since tp derivability is known to be npcomplete it is interesting to study restrictions on the use of the i p operator o
first languages may differ in when they mark a particular feature
the second model component captures the process of second language acquisition itself
the two methods described in this paper allow the approximation of an hmm used for part of speech tagging by a finite state transducer
b on the other hand he is very difficult to find
this information is crucial for effective correction
extended class sequences of possible sentences and the lower side of usdeg describes the corresponding extended tag sequences
to accomplish these goals the proposed system must have several components
the current implementation of this phase does not yet include probabilistic information
notice that there are also connections among the hierarchies
some birth controls are side effect
NUM NUM deciding the errors to focus on
at first glance it may NUM
we evaluated the precision of a set of morpheme candidates that have a certain cost
syntactic preference based on rap can be formalized then as a function of cfg rule l h m and length l namely s l l h m
figure NUM shows the transition from an automatically found cluster to a conceptual field the ke constitutes the conceptual fields of the structures
for subproblem c we propose to adopt the back off method i.e. to make use first of a lexical likelihood based on lpr and then a syntactic likelihood based on rap and alpp
specifically in order to perform pp attachment disambiguation in analysis of sentences like NUM we need only calculate and compare the values of p spoonleat with and p spoonlice cream with
then the knowledge engineer assisted by an automatic clustering tool builds the conceptual fields of the domain
null the interpretation by the ke of the results given by the clustering methods applied on the data of table NUM leads him to define conceptual fields
those candidate terms appearing in the head position in a pu containing a given np could denote properties or actions related to this np
lexter performs a morpho syntactic analysis of this corpus and gives a network of noun phrases which are likely to be terminological units
this elementary qualitative scoring allowed the ke to say that the clustering obtained with the second data set is better than the one obtained with the first
we use similar techniques enriched by a preliminaxy morpho synta ztic analysis in order to perform knowledge acquisition and modeling for a specific task e.g.
as there are any number of ways to formulate the function note the fact that syntactic preference is also a function of a cfg rule it is nearly impossible to find the most suitable formula experimentally
therefore when we use only the syntactic likelihood to perform disambiguation we can expect the former interpretation in figure NUM a to be preferred i.e. we have an indication of the functioning of rap
define b a the b count of a a function counting positive and negative occurrences of primitive type b in an arbi null trary type a to be if a b if a primitive and a b
possible relations when NUM expresses a simple past event
the best path was calculated by the viterbialgorithm on the paths of the morpheme network
the overall kappa value for all speakers is about NUM NUM which represents only moderate agreement
for the texts having complicated discourse segment structures tr2 is slightly better than tr3 on average matching rates
also note that a superscript attached to an np is used to represent the index of the referent
this is clearly a very simple rule but it is interesting to see how well it performs
to simplify the work for the moment only one element is stored in the property list
the grammatical structures of the computer generated texts are simplified they are not as sophisticated as human texts
the weight of the string j is due to the attracting power of the earth on the string jr
we examined the nominal anaphora matched by using rule NUM with the ones generated by the preference rule
h t endc nt disjunctions which ontain iild l cm tcn parts
the key to determining that the two disjunctions all be split into different rnote that this requires being able to determine equality of the base constraints
constructing the eontinement of a as form is essentially just throwing out all of the alternative variables that are not in m
c t and c NUM the nature of the base constraints is irrelevant as long as there is a satisfaction algorithm for them
given a morpheme network we can formulate the reestimation algorithm for the hmm parameters
as in the case of hard clustering the way that clusters are created is crucial to the reliability of document classification
this would be especially desirable if system developmeat includes relatively labor intensive linguistic analysis
the following steps were taken to analyze the met chinese test data
for example it treats racket and shot uniformly because they are assigned to the same cluster kt see tab NUM
if for each word w we analyze we require that each of its probable senses be linked to at least a fixed percentage e.g. one third of the total number of words linked to to we can eliminate five verbs on the j part of the brown corpus
so the r bos eos is the same as the sentence probability which is the sum of probabilities of all the possible parses
the basic units of sentence structure in dg the dependency relations are much simpler than the rules in phrase structure grnmmar
the pdg best first parsing algorithm constructs the best dependency tree in bottom up manner with dynamic programrrdng method using cyk style chart
training the training process is as foflow NUM initialize the probabilities of dependency relations between all the possible word pairs
the best parse is maximum lr bos eos in the chart position NUM n NUM
the head of a dependency tree wk can be found in the rightmost lt k eo s of NUM eo s
if lt k eos whose sub st is not null is inscribed into the chart the overall tree structure will have two or more heads
the outside probabilities c r a ar s and are computed and inscribed into the chart in top down and right to left
the authors thank mark gawron david israel and three anonymous reviewers for helpful comments
each of the approaches described above has disadvantages to overcome
the architecture of the system is shown in figure NUM
this eliminates one possible ambiguity of the period at the lexical analysis stage
noun o NUM verb NUM NUM pronoun NUM NUM
she has an appointment at NUM p m saturday to get her car fixed
the satz system works in two modes learning mode and disambiguation mode
it trains and executes quickly without requiring large resources
the number of hidden units in a neural network can affect its performance
words containing an internal period are assumed to be abbreviations
this lexicon was used in testing with two separate corpora
figure NUM an abridged version of the task description
now the relation between c NUM and c NUM could have been expressed because part2 is muted frequently it is more musceptible to damage
in addition in order to study effective explanation we chose experts who were rated as excellent tutors by their peers students and superiors
purpose is to give a reason for testing part2 first namely that part2 is more susceptible to damage and therefore a more likely source of the circuit fault
in addition even when the users locate a possibly relevant text in japanese they will have little idea about what is in the text
matches in and out rule o in and out rule NUM yesterday mccann made official what had been widely anticipated in and out lcb o rcb james NUM years old is stepping down as officertok lcb NUM rcb chief executive officer on july NUM and will retire as officertok lcb NUM rcb chairman at the end of the year
matches organization rule NUM person rule now person lcb NUM rcb james is preparing to sail into the sunset and person lcb NUM rcb dooner is poised to rev up the engines to guide organization lcb NUM rcb interpublic group s organization lcb NUM rcb mccann erickson into the 21st century
content filters the jewelry chain jewelry jewel chain smith jewelers smith jewelers jeweler jewel for example if the organization noun phrase the jewelry chain is identified its content filte r would be applied to the list of known company names
for example if at this point louella knows that a person is leaving one organization and joining another she can conclud e that each organization can be the other org in the in and out object for the other organization s succession event in effect the system swaps succession orgs between succession events to supply their respective in and out objects with other org fillers
as soon as a named entity is recognized it is stored along with its variations on an active token list so that variations of the nam e can be recognized and linked to the original occurrence
for muc NUM louella is comprised of three system modules one for each of the muc NUM tasks addressed named entity ne template element te and scenario template st
as the first step towards a formal definition of upper c lower it is useful to make the notion of ignoring internal brackets more precise
depending on the characteristics of the upper and lower languages the resulting transducers may be unambiguous and even sequential but that is not guaranteed in the general case
some text processing applications involve a preliminary stage in which the input stream is divided into regions that are passed on to the calling process and regions that are ignored
the difference is that our algorithm is based ou substitution not adjoining furthermore it is not clear in their work how offline raising is used to improve ef icicncy of parsing
the intended side effect by unification such as building up logical forms in sere values is comlntted NUM r s l ep where NUM and r arc
therefore it is likely that any computational system that ignores these extra textual cues will suffer a degradation in performance or at the very least a great restriction in the class of linguistic data it is able to process
tile next step towards the devek pnmnt of a theory of punctuation is the study of the interaction of punctuation and the lexical items it separates in particular the way that punctuation will integrate into grammars and syntax
the rules arc shown in table NUM since some of the pal terns only el ply in particular exceptional cases the uulnl er of standar t rules is reduced even tim her
to generalise then l unctuation seems to have adjunctive and conjunctive functions and the theoretical formalisation of these function will form a good method of constraining the l arses produced with the generalised rules above
since many of the rule patterns seem to have a w xy low frequency of occurrence it may also be useflfl to collect such frequencies and use them in the rule generalisations to attach probabilities to various rule expansions
using these ensures a wide range of language is covered since they are hand parsed or checked tile parse will be nominally correct and since there are many parsers editors no individual s intuitions or idiosyncrasies will dominate
NUM meanwhile stations are fuming because many of them say the show s distributor viacom inc is giving an ultimatum either sign new long term commitments to buy future episodes or risk losing cosby to a competitor
if a word has any meaning associated with the given category then only consider that meaning when assigning numbers
one significant difference between this approach and that taken in using the baum welch algorithm is that here the supervision influences the learner after unsupervised training whereas when using tagged text to bias the initial probabilities for baum welch training supervision influences the learner prior to unsupervised training
although less accurate than the taggers built using manually annotated corpora the fact that they can be trained using only a dictionary listing the allowable parts of speech for each word and not needing a manually tagged corpus is a huge advantage in many situations
since manually tagged text is costly and time consuming to generate it is often the case that when there is a corpus of manually tagged text available there will also be a much larger amount of untagged text available a resource not utilized by purely supervised training algorithms
NUM in all cases the combined training outperformed the purely supervised training at no added cost in terms of annotated in this paper we have presented a new algorithm for unsupervised training of a rule based part of speech tagger
null if the word race occurs more frequently as a verb than as a noun in the training corpus the initial state annotator will mistag this word as a verb in the sentence the race was very exciting
however using an unannotated corpus and a dictionary it could be discovered that of the words that appear after the in the corpus that have only one possible tag listed in the dictionary nouns are much more common than verbs or modals
once the system is trained fresh text is tagged by first passing it through the unsupervised initial state annotator then applying each of the unsupervised transformations in order and then applying each of the supervised transformations in order
with this hybrid approach better results are obtained using a more complex language model such as lex l2 syn l2
nevertheless we will show in the next section that the selection of a smoothing method is not crucial after the robust learning procedure has been applied
secondly this tying scheme gives parameters for rare events more chance to be touched in the learning procedure and thus they can be trained more reliably
however a model incorporating more contextual information would have a larger number of null event parameters which will not be touched in the learning procedure
if m left context symbols and n right context symbols are consulted in evaluating equation NUM the model is said to operate in the lmrn mode
and error search by finite automata another important step towards tile apl lication of fsa to error detection was developing a new dimension of cl ussitication of errors to be detected apart fi om the lnore standard criteria of frequency a nd lmrforlnance conqmtence we developed a scale based on the comi lexit y of the forlnal appara tus
usually a more complex model requires more parameters hence it frequently introduces more estimation error although it may lead to less modeling error
each of the probabilities p yi xl xm NUM vyi c v is first assigned a smoothed value in the above step
expression in nominative or vocative case due to rules of czech interl unction any exclanlative expression ha s to be selm ra t ed from the rest of the sentence by collllll lb also due to rules of czech mt erpunction two finite verbs in czech must be sepa rated
we note here a problem that we encountered using semcor s tag format for idioms semcor merges the component words of the idiom into one annotation thereby making it impossible to unambiguously represent information about the individual words
when our method was not applied partial parses of an incomplete parse were joined by means of some heuristic rules such as the one that joins a partial parse with np ill its root node to a partial parse with vp in its root node and the root node of the second partial parse was joined to the last node of the first partial parse by default
parsing possihly also reparsing with relaxed constraints is not started at all and the sentence is iminediately marked as one containing no detectable error if this automaton finds such a lexical trig null ger of an error it
the core of the system broad coverage hpsg based grammars of bulgarian and czech and a single language independent parser was developed m the first three years of the project and was then passed to the industrial partners bulgarian business system imc sofia
finally the last row shows the improvement in percentage points between our prediction method and the baseline 9the expertis assigned as follows in the trains domain the system in the airline domain the travel agent in the maptask domain the instruction giver and in the switchboard dialogues the agent who holds the dialogue initiative the majority of the time
had bee n gradually enhanced at least since muc NUM the concept hierarchy code and reference resolution were essentiall y unchanged from earlier versions
we consider ourselves successful in meeting this goal we implemented the pattern matching scheme quickly and did quite well in generating scenario templates
a conservative parser would perform a reduction only if there was strong usually local syntactic evidenc e or strong semantic support
this approach can be viewed as a form of conservative parsing although the high leve l structures which are created are not explicitly syntactic
clause syntax is now utilized in the metarules fo r defining patterns and in the rules which analyze example sentences to produce patterns
in some cases however the attachment can not be decided locally in such cases we leave the modifie r unattached
we considered carefully whether these difficulties might be readily overcome using an approach which wa s still based on a comprehensive syntactic grammar
in particular in our system we had to separately encode the active passive relative reduced relative etc
we expect that users would enter patterns by example and would answer queries to create variants o f the initial pattern
this most recent explorations also indicate how syntax can creep back into a system from which it was unceremoniously ejected
thus a word is a perturbation of a composition
however it should instead be possible to achieve the same effect by compiling the unification part of an lfg grammar in such a way that completeness and coherence are checked via unifiability of two features one going up saying what a verb is looking for by way of arguments and one coming down saying what has actually been found
exists e see e fred joe in e cambridge on e friday joe was seen on friday by fred in cambridge exists e see e fred joe on e friday in e cambridge
one of the practical advantages of such a regime is that different categories can now be compiled into terms whose functor is the value of the eat feature and whose other feature values can be identified positionally for example lcb cat np number sing person NUM rcb would compile to np NUM sing
x lcb store a rcb y lcb out a in b right b rcb x lcb store b rcb when the distinguished daughter follows the subsidiary the right value of the subsidiary must be unified with its in value and the store of the distinguished daughter
at compile time when we have examined the whole grammar and lexicon we know which values are actually mentioned and we can represent the value space of this feature as c anon1 rcb lcb NUM NUM anon2 rcb where anon1 and anon2 are again atoms standing in for all the other values
with suitable generation of macros by the compile our example might then be written by the grammarian as declaration subcategorization feature subcat lcb cat np rcb lcb cat p rcb lcb cat pp rcb np p pp NUM
figure NUM some words from a lexicon learned from
sentenceyield is dhit score of a sentence
local analysis consisted in the determination of semantic sub units of descriptions and in the definition of the content of different sequences with respect to these sub units
once the whole route has been reconstructed at the conceptum level we start to generate the corresponding graphic map like the one here below
the incompleteness of information will occur on the graphic side this time not all properties of the described element being possible to express in this mode
there is also another kind of ambiguity due to the fact that in a rd the whole path does not have to be linguistically covered
global analysis consisted in dividing descriptions into global units defined as sequences and connections and in categorizing these units on a functional and thematic basis
however instead of trying to create a unique super structure we envisage a dual representation with the linguistic and the conceptual levels
for the purpose of the conceptual representation of rds we need a prototypical model of their referent which is the route
we have used a discourse based approach and analyze local linguistic elements by filtering them through the discourse structure described at the global level
the graphic symbols have been created on the basis of the information accessible from the context rather than the one contained in the names of landmarks
firstly one has to investigate the relationships between the linguistic and the graphic modes the constraints and possibilities which appear while generating images from linguistic descriptions
assessment subdialogue a establish the current behavior
this is done by adding specialized modules into the parsing sequence
figure NUM vol NUM dhit distribution of the last NUM sen
among all the concepts to be introduced critical points and critical fragments are probably two of the most important
among them title and location are related to the position method
the high degree of coverage indicated the effectiveness of the position method
the average number of sentences per summary sps is NUM NUM
each of these NUM wires was assigned to a different problem
the average number of sentences per text in the corpus is about NUM
the length of the window equals the length of the topic keyword
l urax uses lexico syntactic patterns collocational analysis along with information retrieval statistics to find the string of words in a text that is most likely to serve as an answer to a user s whquery
the strictest interpretation is sim sanswer ske l which is true only when 8answer skey
evaluation of the quality of the clustering pro null cedure in the majority of the works using clustering methods the evaluation of the quality of the method used is based on recall and precision parameters
by tagging the corpus using devin modules for common words and proper names we are able to automatically extract a lexicon of oov words which contains for each word its number of occurrences as well as the list of labels which have been attributed to it during the tagging process
however when performing a comparison against the corpus these differences are marked as errors
since the system constructs a lexicalized syntactic functional description fd from the extracted description the generator can re use the description in new contexts merging it with other descriptions into a new grammatical sentence
the fd generation component produces syntactically correct functional descriptions that can be used to generate english language descriptions using fuf and surge and can also be used in a general purpose summarization system in the domain of current news
thus they may range from a simple noun e.g. president bill clinton to a much longer expression e.g. gilberto rodriguez orejuela the head of the cali cocaine cartel
the data was obtained from liang nanyuan beijing university of aeronautics and astronautics
rather it emerges from the statistics of the interaction of all codelets in the coderack
equation NUM is a key factor in deriving the strength of an affinity relation
r n this structure is retained but it becomes dormant in the workspace
it varies according to the amount of coherency in the system s interpretation of a sentence
the number NUM is decided based on intuition and trial and error it is not necessarily optimal
twenty instances of affinity codelet are also posted to identify and construct affinity relations between characters
in each step the longest matched substring is selected as a word by dictionary look up
these nodes in turn cause the posting of codelets that will build relations between word objects
thus in addition to collecting a knowledge source which provides identifying features of individuals profile also provides a lexicon of domain appropriate phrases that can be integrated with individual words from a generator s lexicon to flexibly produce summary wording
in and out in and out vacancy reason lcb depart workforce reassignment new post created
reassignment and oth unk are more general categories
minimum instantiation conditions the article must identify the person by name
for example all of the following triggers in the list minister head administrator and commissioner can be traced up to leader in the wordnet hierarchy
in the unclear cases the following guidelines are to be used a
minimum instantiation conditions this slot must be filled if other org is filled
because of someone resigning with intention not to work again
the complexity of the algorithm is o n NUM when implementing it directly but can be reduced to o n NUM by sorting the distances between all nodes in each previous step
this model called asm for automatic segmentation model is capable of characterizing up to eight classes of different maximum or minimum tokenization procedures
figure NUM a sample recall precision curve
this type of manual modification increased overall average precision by NUM
in trec NUM the trend was reversed
table NUM relationship between completeness and the
applying the learning procedure on each training set required two to three hours of elapsed time on a sun sparc ultra
as a sul class of raising vcrl s e.g.
using a pen and paper analysis of how the grosz and sidner model processes the sentences we think their model resolves all but NUM referring expression correctly
on empirical grounds we conclude that the simplistic model in which anaphoric expressions are considered to refer to the last mentioned semantically appropriate object is inadequate
the output is directed to two devices on the screen a nl output text window and a graphics display and optionally to a speech synthesizer
in this paper we present a single model that accounts for referent resolution of deictic and anaphoric expressions in a research prototype of a multimodal user interface called edward
usually the user points to an object to indicate that it is the argument of the command he wants to perform e.g. a file copy command
to determine the referent of a spatial expression the visible model world is scanned for a referent using the intension of the spatial relation and the relatum
the file system domain filter for instance allows instances of particular file system classes such as directories e mail messages reports and books
was reached when the expert was left out of the pool
when they returned they were given a chance to ask questions
the coders were asked to return with the dialogue extract coded
to simplify the task coders worked from maps and transcripts
op n lcb op NUM i s e n rcb definition3 NUM identity let l be a regu
without such a mechanism writing complex grammars say two level grammars for syriac or arabic morphology would be difficult if not impossible
hence in order for the above two level analysis to be valid the following feature structures must match all the structures must match
the former requires only one complement which results in one determinization since the automata must be determinized before a complement is computed
figure NUM concurrent elementary trees and attached
in other words the machine accepts all centers described by the grammar each center surrounded by p irrespective of their contexts
centers accepts the symbols p followed by zero or more rs each if any followed by p
the study was too small for formal results about transaction category
the validation of a limited set of potential surface forms as actual terms is crucial for lowering the complexity of tile above algorithm
one of the drawbacks to alternative gb based parsing approaches is that they generally adopt a filter based paradigm
our ultimate objective is to incorporate the parameterized parser into an interlingual mt system
the sentence lengths vary from NUM to NUM words with an average of NUM NUM
we have just seen that certain types of syntactic parameterization may be captured in the grammar network
a message can be sent across a link only if it satisfies the link s percolation constraint
her system incorporates a number of psycholinguistic based processing mechanisms for handling ambiguity and making attachment decisions
the current english grammar contains NUM NUM linguistic constraints on the linear order of morphological tags
in this paper a case is made for the syntactic nature of part of speech tagging
though much effort was given to its development it leaves many ambiguities unresolved
this syntactic grammar is able to resolve the pending part of speech ambiguities as a side effect
the complexity of the current version of the system has not yet been formally determined
the central idea behind x theory is that a phrasal constituent has a layered structure
a single rule often resolves all types of ambiguity though superficially it may look e.g.
for example the present way of functionally accounting for clauses enables the grammarian to
the cognitive level is that which concerns the content of the feedback or the part which addresses the intellect of the learner and either enforces the assimilation of the concepts involved or tells the learner to retry his attempt at communication
these data are given in table NUM
table NUM summarizes the test material
formally for a fixed pda NUM
notice that in hcm NUM can not be set less than NUM NUM
the algorithm uses a parse table consisting in a NUM indexed square array u
our algorithm requires fewer lr states
furthermore ordering of elements is restricted e.g. cotton garment bag garment cotton bag
following the system devised under the qing emperor kang xi hanzi have traditionally been classified according to a set of approximately NUM semantic radicals members of a radical class share a particular structural component and often also share a common meaning hence the term semantic
the dictionary sizes reported in the literature range from NUM NUM to NUM NUM entries and it seems reasonable to assume that the coverage of the base dictionary constitutes a major factor in the performance of the various approaches possibly more important than the particular set of methods used in the segmentation
as a first step towards modeling transliterated names we have collected all hanzi occurring more than once in the roughly NUM foreign names in our dictionary and we estimate the probability of occurrence of each hanzi in a transliteration ptn hanzii using the maximum likelihood estimate
the transition from to a final state transduces c to the grammatical tag pl with cost cost unseen lcb cost lcb cost j cost unseen as desired
an example is in i where the system fails to group i lin2yang2gang3 as a name because all three hanzi can in principle be separate words f4 lin2 wood yang2 ocean gang3 harbor
for example the wang li and chang system fails on the sequence nian2 nei4 sa3 in k since nian2 is a possible but rare family name which also happens to be written the same as the very common word meaning year
in this figure eps is e be implemented though such as a maximal grouping strategy as suggested by one reviewer of this paper or a pairwise grouping strategy whereby long sequences of unattached hanzi are grouped into two hanzi words which may have some prosodic motivation
this method one instance of which we term the greedy algorithm in our evaluation of our own system in section NUM involves starting at the beginning or end of the sentence finding the longest word starting ending at that point and then repeating the process starting at the next previous hanzi until the end beginning of the sentence is reached
and classifies the document into that category for which the calculated probability is the largest
both probabilities are set to unity for the initial state NUM s
a few intuitive abbreviations are used from here on to describe earley transitions succinctly
adjective placement is also restricted three english blacksmith s hammers three blacksmith s english hammers
since all context vectors are in the same information space the symmetric learning technique will result in a unified information space for both languages
as a consequence the context vector for this pair has been influenced by the words that have occurred in a similar context in both languages
like the standard matchplus context vector learning algorithm the symmetric learning approach will utilize a convolutional context window with a center and neighbors
the english text in figure NUM comes from the passage four people were killed in the attack by the rebel group shining path
to achieve the desired representation the context vector learning algorithm must take the context vectors for symbols that co occur and move them toward each other
the one step learning law uses a single pass through the training corpus to obtain desired dot product values for the set of trained context vectors
figure NUM shows NUM words in the unified hash table rebel attack ataque and contra
that is the expected value of the dot product between any pair of random context vectors selected fi om the set is approximately equal to zero i.e.
all of language NUM can be presented followed by language NUM alternately documents from the two languages can be presented in intermixed order
builds relations for each of the major entities personnel organizations and associations within the annotations for the document in the collection
the document manager process csci is implemented on top of a relational database with access to the database facilitated through odbc library calls
the comm process csci also transfers the cable delivery system header information to the sql database as it relates to the document in the collection
the result of the abstracting and indexing process is a set of index records about the entities that were described in a cable
although the abstracting portion of the task has been automated it is only a small part of the abstracting and indexing process
the extraction process csci passes the extracted entities to the document manager process csci which stores them as annotations on the document
the comm process csci retrieves the cable data from the cable delivery system server at a constant given rate via a software timer
it then passes the collection and document identifiers to the document manager process csci which retrieves and returns the document and its annotations
it will display a document s text body and allow the analyst to travel through the processing of the information about that document
this formula is derived by maximizing the entropy of the probability distribution pi as satisfying all the constraint given qb search the l s that make pi x satisfy all tile constraints an system with distance NUM iterative algorithm generalized iterative scaling gis exists which is guaranteed to converge to the solution l arroch72
this may ix noticed considering sentences such as h hn rite a slice or i dt attgllt three cul s
coherently we assume const as encoding a relational predicate r x y being r a type taxonomically daughter of part of
in this way selectional restrictions of pns will be basically defined as selection of signs hearing appropriate types lot their formal mid const quales
the following subsections present an overview of each of these technologies
NUM the data for the clustering module the candidate terms extracted by lexter can be nps or adjectives
if the main point of a conversation is simply to enjoy the social interaction it has to be asked whether the conversation will be more enjoyable with long pauses while each ideal utterance is constructed or with roughly appropriate utterances that are delivered without long pauses preceding them
we believe that although it is difficult to predict when an agent may include extra information in response to a question taking into account the cognitive load that a question places on the hearer may allow us to more accurately predict dialogue initiative shifts
e bootstrapped normalized distance the same as method d except that the system used to carry out the reflexive translation was running with parameters from method c
a possible explanation is that the model structure was adequate for most lexical choice decisions because of the relatively low degree of polysemy in the atis corpus
the index sets of a derived formula identify precisely those assumptions from which it is derived
fortunately long sentences typically have several decomposition nodes such as the heads of noun phrases so the search as described is factored into manageable components
stop if in state q m can stop with probability p t31q m at which point the sequences are considered complete
while the intuition seems reasonable the conclusion might be too strong in that it rules out the possibility that natural language itself is adequate for manipulating semantic denotations
while it is true that natural language is ambiguous and under specified out of context this uncertainty is greatly reduced by context to the point where further resolution e.g.
instead because these parameters are independent of surface order they are applied earlier by the transfer component influencing the choice of structure passed to the generator
in the absence of good metrics for comparing translations we employ a heuristic string distance metric to compare word selection and word order in t and s
each analyzed complex candidate term is linked to both its head h link and expansion e link
in NUM the event of mary s standing is understood to occur just after john enters the room while in NUM the state in which mary is seated is understood to overlap with the event of john s entering the room
to handle an example like NUM we employ a preference for relating a sentence to a thread that has content words that are rated as semantically close to that of the sentence NUM sam rang the bell
we presented a brief description of an algorithm for determining the temporal structure is an elaboration of one of the preceding events must not be ruled out because there are cases such as sam arrived at the house at eight
temp relns e2 precedes el also when several structures are possible we can narrow the possibilities by using preferences as in the examples below NUM sam arrived at the house at eight el
for example sentence NUM will be ruled out because the cue phrase as a result conflicts with the temporal expression ten minutes earlier NUM mary pushed john and as a result ten minutes earlier he fell
the problem here is determining which thread in NUM they continue 10a continues the thread in which sam rings the bell but 10b continues the thread in which sam loses the key
consider however that instead of generating all the possible temporm rhetorical structures we could use the information available to fill in the most restrictive type possible in the type hierarchy of temporal rhetorical relations shown in figure NUM
temp center used for temporal centering keeps track of the thread currently being followed since there is a preference for continuing the current thread and all the threads that have been constructed so far in the discourse
this paper presents such a tool the modelexplainer or modex for short and focuses on the customizability of the system NUM automatically generating natural language descriptions of software models and specifications is not a new idea
modex certainly belongs in the tradition of these specification paraphrasers but the combination of features that we will describe in the next section and in particular the customizability is to our knowledge unique
suppose that in browsing through the model using the hypertext interface the university administrator notices that the model allows a section to belong to zero courses which is in fact not the case at his university
in this representation user text indicates free text entered for a title while relations text and examples short are schema names referring to two of the eight predefined text functions found in a c class library supplied with object model loading
once the object model is specified the analyst must validate her model with a university administrator and maybe other university personnel such as dataentry clerks as domain expert the university administrator may find semantic errors undetected by the analyst
for example this method applied on a population of NUM nps data set NUM gives 2this filtering method is mandatory given that the chosen clustering algorithm can not be applied to the whole terminological network several thousands of terms and that the results have to be validated by hand
the end user must be able to control many other aspects of the application as well
the tipster configuration management plan describes the procedures involved
since their interfaces will be standard these pieces can readily be used by other developers
as the tipster architecture grows processing capabilities for additional generic part types can be added
the cotr must identify and focus attention on the more risky parts of his development effort
some previously developed tipster modules may be available which can be easily adapted for a new application
some basic terms as they are used in the rest of this document are given below
this will also allow the cotr to recommend sources for specific technology and encourage teaming arrangements
ensuring that this happens document setup is the responsibility of the application
the following section outlines some of the differences between the architecture itself and an architecturally compliant application
we used paradise to derive a performance function for this task by estimating the relative contribution of a set of potential predictors to user satisfaction
this first stage of the ka process is also the opportunity for the ke to constitute synonym sets the synonym terms are grouped one of them is chosen as a concept label and the others are kept as the values of a generic attribute labels of the considered concept see figure NUM for an example
however the effectiveness of the discard strategy slowly declines as the text length increases
during the dialogue the agent must acquire from the user the values of dc ac and dr while the user must acquire dt
both the objective and the subjective metrics have been very useful to the spoken dialogue community in comparing different systems for carrying out the same task but these metrics are also limited
the use of decision theory requires a specification of both the objectives of the decision problem and a set of measures known as attributes in decision theory for operationalizing the objectives
explicit recovery the proportion of explicit recovery utterances made by both the system system turn correction stc and the user user turn correction utc
our interest however extends to predictions on the scale of several utterances
these perspectives are designed to support the flow of conversation and require only one button press to move from phrases about what i disliked me past sad to phrases about what you disliked you past sad or from phrases about what i liked me past happy to phrases about what i would like me present happy
in our system the local constraints are prohibited tag pairs and triples
it was not found necessary to use number information at this stage
recall that weights are initialised to NUM NUM
the demonstration prototype takes about NUM seconds
the data must be preprocessed by filtering through the prohibition rule constraints
by using these methods the number of candidate strings is drastically reduced
the output is now interpreted differently
we wish to exploit these constraints
these must be converted to binary vectors
the highest output marks the winning node
which predominate in papers like the new brk times latinate affixes which signal certain highbrow registers like scientific articles or scholarly works and words used in expressing dates which are common in certain types of narrative such as news stories
therefore some feeling for the internal functioning of our algorithms can be obtained by seeing what the performance is for each of these binary machines and for the sake of comparison this information is also given for some of the neural net models
the experiments suggest that there is only a small difference between surface and structural cues comparing lr with surface cues and lr with structural cues as input we find that they yield about the same performance averages of NUM NUM surface vs
but a simple machine that always guesses no would perform much better and it is against this stricter standard that we computed the baseline in table NUM here the binomial distribution shows that some numbers are not significantly better than the baseline
the fact that the neural networks have a higher performance on average and a much higher performance for some discriminations though at the price of higher variability of performance indicates that overfitting and variable interactions are important problems to tackle
genre is necessarily a heterogeneous classificatory principle which is based among other things on the way a text was created the way it is distributed the register of language it uses and the kind of audience it is addressed to
we will use the term genre here to refer to any widely recognized class of texts defined by some common communicative purpose or other functional traits provided the function is connected to some formal cues or commonalities and that the class is extensible
in word sense disambiguation many senses are largely restricted to texts of a particular style such as colloquial or formal for example the word pretty is far more likely to have the meaning rather in informal genres than in formal ones
in general cooperative interaction between several participants is required
the user agents may access the dialogue server via internet
the pasha agent system has been developed and extended by sven schmeier
they use the server as their nl front end to human participants
the systems are demonstrated on a sun workstation under unix
the scheduling agent systems act for their respective users
automating nl appointment scheduling with cosma
the demonstration scenario includes three participants
cosma is organized as a client server architecture
these dictionaries are very different in number of headwords polysemy degree size and length of definitions c f
in general it looks as though the induced morphological guessing rules largely consist of the standard rules of english morphology and also include a small proportion of rules that do not belong to the known morphology of english
the volume of data we see in ir tasks also makes it impractical to use sophisticated statistical computations
we used the clarit nlp module as a preprocessor to produce nps with syntactic categories attached to words
for combinations other than noun noun the threshold for passing the second test is high
wilson s severe disease as a phrase though the latter might well occur in the general news corpus
results of experiments show that indexing based on such extracted subcompounds improves both recall and precision in an information retrieval system
four kinds of phrases as indexing terms NUM lexical atoms e.g. hot dog or NUM
the general improvement in precision indicates that small compounds provide more accurate and effective indexing terms than full nps
we used the judged relevant documents from the trec evaluations as the gold standard in scoring the performance of the two processes
to aid us in making such decisions we have developed a metric for scoring preferred associations in their local np contexts
NUM the combination of an adverb with an adjective past participle or progressive verb is given score NUM
there has traditionally been an avoidance of the problem by defaulting to one member of the pair based on blind form class selection default to the noun which of course is less than adequate
this is done in a prepass to ensure that each o or i reduced vowel is accurately adjusted before the main body of the allophonic rules are run
the word six can be pronounced sis j en veux six siz six enfants si sixfi lles
the execution of the set of rules on the NUM NUM unique word dictionary gives NUM NUM of words whose pronunciation is different from the dictionary le petit robert but is acceptable from the authors point of view
the difficulty in developing an accurate algorithm to perform this task is directly proportional to the fit between graphemes and corresponding divay and vitale grapheme phoneme translation phonemes as well as the allophonic complexity of the language in question
NUM introduction and historical background the interest in letter to sound rules goes back centuries and can be found in relatively unsystematic descriptions in many of the older descriptive grammars of languages such as english and french
for example in the us e ending italian names pronounced e in italian is typically pronounced il or even not pronounced
these studies made use of phonetic phonemic or even morphophonemic form such as palatalization credulity cuticle etc morphophonemic alternation symmetry vs symmetric and even morphology singer vs finger
the same mechanism using phonetics is used to retrieve a proper name without knowing how to spell it through the NUM NUM proper names of the phone book of the city of dakar senegal
computational linguistics volume NUM number NUM a syllable boundary and a mark NUM of unstressed syllable for yon as for instance in aggravation
r a s s NUM NUM s s NUM s s NUM o s s NUM NUM a
therefore practical parsers should keep track of c derivations
NUM next we prove the if direction
our result establishes one of the first limitations on general cfg parsing a fast practical cfg parser would yield a fast practical bmm algorithm which is not believed to exist
since fl and f2 are essentially the quotient and remainder of integer division of i by n we can retrieve i from fl i f2 i
let us compute the running time of me
fast context free parsing requires fast boolean matrix multiplication
our index encoding function is as follows
in joint press nfenmce after converting is for s rose president in order for our dort to mcceed amqican roleis indisl ns ble that doing in order to pull back hrael m proton it made that it um ht the ix ifive mediation ofth e united states clear
theoretically such consistency is just what one would expect given a hearer s immediate tendency to resolve subject pronouns based on the existing discourse state
this can be thought of as a clustering of the clusters
such sources include http accessible sites such as the reuters site at www yahoo com
table NUM two word and three word entities retrieved by the system
the purpose of such an initial set of descriptions is twofold
counts for three word noun phrases are shown in the right hand column
they can also specify which sources of news should be searched
currently the system has an interface to reuters at www yahoo com
null to r thtce the size of the vocabulary our system nv rts every word to ul i er ase and runt ales words after the sixth character
for example in our experiments tagging spanish texts for which we had much smaller lexica we have found that lexica l rules play a larger role this can also be partially attributed to the more inflected nature of spanish
at the query time when the user types ibm and chooses the alias option in the search screen see figure NUM the query is automatically expanded to include its variant names both in english and japanese e.g. international business machine international business machine corp and japanese translations for ibm and their aliases in japanese
NUM in which non terminal nodes are indexed for reference purposes the algorithm NUM yields the set of elementary trees of figure NUM l he trees a and c correspond to contiguous words in the original sequence whereas b and d only appear after modifier removal see below
l hc ih sl tie s cltos iu which sl a nds ior t n 7c p7 c
the cmcslm tutor project has published over NUM papers
the NUM aphb sentences that refer to the lifting of a light object all involve the not heavy sense of light
but it is in fact the functional relevance of heaviness versus darkness in the context of its use that is actually involved
relative to a given indicator noun then it makes sense to think of adjective disambiguation in terms of default interpretations
computational linguistics volume NUM number NUM ca n t you see you re on the wrong side of the road
afterward if a target adjective sense was not resolved semantic indicator attributes were applied no individual indicator nouns were used
the following discussion treats the kinds of properties that systematically relate adjective senses to other features of the sentences in which they occur
the indicators do turn out to discriminate as projected between target adjective senses and they do so with NUM reliability
these sentences were manually postprocessed to eliminate all instances in which either the target or its antonym was not being used adjectivally
james NUM years old x2 choose NUM
this results in a call to the as kind of kb accessor which produces a view
by selecting different reference concepts different information about a particular process will be returned
i c this is the circuit fix lt shop
NUM c what is the led displaying
this system adopts english as a dialogue language for human machine interface and makes use of drt based semantic representation units
handling euphemistic expressions NUM there are various types of expressions for politeness modesty and euphemism
reservation objective confirm modest iv go dengon wo o t ut ae moushiage masu
at present the je system has about a NUM NUM word vocabulary and a transfer knowledge from NUM NUM training sentences
for almost two decades computational linguists have studied the problem of automatically inducing this structure from a given text
by using these techniques together we have developed a kb accessing system that has constructed several thousand views without failing
the substructural accessor recognizes that each of these attributes are partonomic relations by exploiting the knowledge base s relation taxonomy
third when problems are detected the nature of the error is noted and reported to the explanation planner
NUM in the tables k denotes the standard error i.e. the standard deviation of the mean
NUM given these results we decided to investigate the differences between knight s grades and the biologists grades
finally to test an equal number of objects and processes we randomly chose NUM objects and NUM processes
when the functional realizer is given a view its first task is to determine the appropriate fd skeleton to use
for example suppose a domain knowledge engineer had erroneously installed an object as one of the subevents of a process
during photosynthesis water and carbon dioxide are converted by the chloroplast into oxygen and glucose figure NUM
the particular issues we consider here are the integration of the statistical and symbolic components and the division of labor between se null mantics and pragmatics in determining meaning
thus for known compounds probabilities of established senses depend on corpus frequencies but a residual probability is distributed between unseen interpretations licensed by schemata to allow for novel uses
instead it must be interpreted as having the less frequent sense given by purposepatient this allows the definite description to be accommodated and the discourse is coherent
we argue here that by utilising probabilities a language specific component can offer hints to a pragmatic module in order to prioritise and control the application of real world reasoning to disambiguation
this underlies the dis null tinct interpretations of cotton bag in NUM vs NUM NUM a mary sorted her clothes into various large bags
on the other hand update a is well defined where t3 is the drs where cotton bag means bag containing cotton
so assume coherence subtype and elaboration yield that sb elaborates sa and the bag in 5b is one of the bags in 5a
his conception addressed didactic goals and thus did not aim at formal precision but rather at an intuitive understanding of semantically motivated dependency relations
first our pragmatic component has no access to word forms and syntax and so it s not language specific whereas hobbs et al s rules for pragmatic interpretation can access these knowledge sources
we believe that the proposed architecture is theoretically well motivated but also practical since large scale semi automatic acquisition of the required frequencies from corpora is feasible though admittedly time consuming
however our letter to sound wfst did not match the performance of japanese translit null erators and it turns out that mispronunciations are modeled adequately in the next stage of the cascade
unfortunately because katakana is a syllabary we would be unable to express an obvious and useful generalization namely that english g usually corresponds to japanese k independent of context
null our wfst is learned automatically from NUM NUM pairs of english japanese sound sequences e.g. s aa k er s a kk a a
for each glossary entry we converted english words into english sounds using the previous section s model and we converted katakana words into japanese sounds using the next section s model
treating these variations as an equivalence class enables us to learn general sound mappings even if our bilingual glossary adheres to a single narrow spelling convention
translating such items from japanese back to english is even more challenging and of practical interest as transliterated items make up the bulk of text phrases not found in bilingual dictionaries
for consistency we continue to print written english word sequences in italics golf ball english sound sequences in all capitals g aa l f b a0 l
it is not the same thing as a context free grammar since each word does not act in the same way as the default composition of its components
it ports easily to new language pairs the p w and p e w models are entirely reusable while other models are learned automatically
for example long occurs much more often than r on in newspaper text and our word selection does not exclude phrases like long island
normally these were the same but not always
for example if the article says fred the president of cuban cigar corp was appointed vice president of microsoft
prior sentences are scanned from left to right this implements in a crude way a preference for the subjects of prior sentences
it also led to ne and te errors since we did n t have th e context pattern hired from
in general it seemed to us that given the limited time adding more patterns yielded greater benefits than focusing on these details
at the end of this paper we return to the questio n of the relation of pattern matching to approaches which use a comprehensive grammar
there are several criteria for deciding which daughter is the head two of which seem relevant for parsing
by starting with the van noord efficient head corner parsing head important information about the remaining daughters is obtained
thus we essentially throw some information away before an attempt is made to solve a memorized goal
suppose we have a very simple program containing the following unit clause x a b
the extra arguments introduce a pair of indices representing the extreme positions between which a parse should be found
as in the left corner parser the flow of information in a head corner parser is both bottom up and topdown
the head corner leaf is special it is a reference to either a lexical entry or an epsilon rule
the table consisting of such partial parse trees is called the history table its items are history items
if we are interested in logical forms rather than in parse trees a similar trick may be used
in the next experiment we evaluated whether the morphological rules add any improvement if they are used in conjunction with the ending guessing rules
after obtaining the optimal rule sets we performed the same experiment on a word sample which was not included into the training lexicon and corpus
this means that the xerox guesser creates more ambiguity for the disambiguator assigning five instead of three boss in the example above
to do that for each rule set produced using different thresholds we recorded the three metrics and chose the set with the best aggregate
taggers assign a single pos tag to a word token provided that it is known what parts of speech this word can take on in principle
unlike morphological guessing rules ending guessing rules do not require the main form of an unknown word to be listed in the lexicon
this is a very labor intensive task the lack of a large scale initiative on lexicography in the manner of ldoce or cobuild is hindering the efforts for automatic extraction of lexical knowledge from on line resources
we believe that as the lexicons of nlp systems become more comprehensive and open ended the trade off will be resolved in favor of using the lexical rules on demand at the expense of slower performance
designating a lexical item as one of the subtypes in the hierarchy will apply all the constraints and incorporate the feature structures of the supertypes along the path to word
the pencils obj his her pencils their pencils it is too early to evaluate the advantages and disadvantages of these approaches in terms of competence grammars and performance issues
one such ii0 case is the denominal verb suffix le which is very productive but has no predictable meaning that can be derived from the lexical semantics of the stem
from a computational point of view the modular approach has efficient lexical access since lexical search is performed on root forms and bound morphemes are not considered lexical items
in this paper we outline a lexical organization for turkish that makes use of lexical rules for inflections derivations and lexical category changes to control the proliferation of lexical entries
the clerks have not been informed of their duties handling inflections and derivations with lexical rules opens us possibilities for encoding semantic and grammatical changes in the lexicon as well
this would account for cases like try tries reduce reducing advise advisable
semantic contribution of inflections seems to be morpheme specific all derivations take part in semantic composition but some inflections such as case and causatives contribute semantically as well
the fourth step tries to turn it into a deterministic machine
this determinization is not always possible for any given finite state transducer
suppose one wants to encode the sample dictionary of figure NUM
the lexicon used in our system encodes NUM NUM words
all taggers were trained on a portion of the brown corpus
the experiments were run on an hp720 with 32mb of memory
tm for instance consider the function fa of figure NUM
idy stands for the identity function on x resp
this corresponds to the formal operation of composition defined on transducers
the transducer obtained in the previous step may contain some nondeterminism
combining situated reasoning with semantic transfer minimally
squibs and discussions dependency unification grammar for prolog
therefore branches to the rule for sleep without accepting the word sleep
this preprocessing step also resolves lexical ambiguities by representing words with alternative meanings through different symbols
we therefore suggest an alternative syntax and translation scheme that produces a more efficient dug parser
accepting mark sleeps as well as mark sleeps well
as a side effect references introduce quasi non terminals to dug
like other contemporary grammar formalisms dug comes with syntactic extensions that code optionality and references
the programming language prolog has proved to be an excellent tool for implementing natural language processing systems
NUM a b c d NUM
the new value of the perplexity and a control parameter cp metropolis algorithm will decide whether a new classification obtained by moving only one word from its class to another both word and class being randomly chosen will replace a previous one
thus for each class in the certain level of the binary tree we multiple the probabilities either NUM or pc to its original l probabilities in which is the other branch class opposite to the j class the word not belonging to
the perplexities pp of n gram for word and class are bigram for class2 exp NUM ln p lc w p c lc t NUM where w denotes the ith word in the corpus and c denotes the class that w is assigned to
this method has its merits top down technique can represent the hierarchy information explicitly the position of the word in class space can be obtained without reference to the positions of other words while the bottom up technique treats every word in the vocabulary as one class and merges two classes among this vocabulary according to certain similarity metric then repeats the merging process until the demanded number of classes is obtained
table NUM average number of senses per polysemous word in the brown corpus for the top NUM top NUM
i only focus on content word disambiguation i.e. words in the part of speech noun t verb adjective and adverb
figure NUM and NUM also confirm that all the training examples collected in our corpus are effectively utilized by lexas to improve its wsd performance
each is associated with a semantic and pragmatic specification as discussed above and illustrated in figures NUM and NUM
we first make a continuum approximation by extending nx from the integer points x NUM NUM
conversely a local reestimation formula in the vein of turing s formula was derived from zipf s law
filtering out the rules with frequency counts of NUM reduced the collections to NUM NUM NUM NUM entries
so although x will be small compared to n it will be large compared to any constant c
implying that there are equally many inhabitants for frequency count x as for frequency count x NUM
although the two equations are similar turing s formula shifts the frequency mass towards more frequent species
the ranking scheme in question orders the species by frequency with the most common species ranked first
this significantly simplifies training data requirements we can induce guessing rules from a general purpose lexicon
therefore we performed an evaluation of the impact of the word guessers on tagging accuracy
takes values depending on the sample size as in figure NUM
then the learner tries to instantiate the generic transformations with word features observed in the text
the learning is performed from a general purpose lexicon and word frequencies collected from a raw corpus
we call such rules ending guessing rules because they rely only on ending segments in their predictions
morphological word guessing rules describe how one word can be guessed given that another word is known
fhis is l mdcularly lain whcli h NUM cn l nl lisjunc l ious arc lnl d hxt
file nexl section will show how an alt ernativecase form for a group of det tndent disjuncl ions can be split into a conjunction of two or more equivalent forms thereby potentially exponentially reducing the munbtr of alternative varial le interactions that must be checked during satisfacl ion null
this means that a nearly linear time operation such as uififieation of imrely conjunctive feature structures becomes an exponential time problem as soon as disjunctions are included t since disjunction is unlikely to dis this work was sponsored by teilprojekt b4 t y om constraints to rules compilation of lips of the sonderforsehungsbereieh NUM of the deutsche forsehungsgemeinschaft
a window is defined as a sequence of minimal segments where a segment is typically a turn but can also be a block delimited by suitable markers in the transcript
other classes consist of both homonyms and systematically polysemous lexical items like the class act log which includes caliphate clearing emirate prefecture repair wheeling vs bolivia charleston chicago michigan
this tag represents the following information a definition of the type of the noun formal a definition of types of possible nouns it can stand in a part whole relationship with constitutive a definition of types of possible verbs it can occur with and their argument structures agentive telic
this process involves a number of consecutive steps that includes the probabilistic classification of unknown lexical items NUM assignment of underspecified semantic tags to those nouns that are in corelex NUM running class sensitive patterns over the partly tagged corpus NUM a constructing a probabilistic classifier from the data obtained in step NUM
recall of the patterns percentage of nouns that are covered is on average among different corpora wsj brown pdgf a corpus we constructed for independent purposes from NUM medical abstracts in the medline database on platelet derived growth factor and darwin the complete origin of species about NUM to NUM
for instance the co occurrence of verbs and the heads of their np objects in size of the corpus i.e. the number of stems n cobj v n log2 v n n n all nouns are now classified by running a similaxity measure over their mi scores and the mi scores of each corelex class
qualia roles distinguish different semantic aspects formal indicates semantic type constitutive part whole information agentive and telic associated events the first dealing with the origin of the object the second with its purpose
disambiguation as the methods described in this paper have been developed for being applied in a combined way each one must be seen as a container of some part of the knowledge or heuristic needed to disambiguate the correct hypernym sense
in this paper we present empirical work on the generation of anaphora in chinese
to see how the speakers agree among themselves we compared speakers annotations
the evaluation task can be divided into an annotation stage and a comparison stage
in this evaluation we chose three chinese natural language generation systems to compare
j reduced referent j in reduced noun phrase sentence boundary
obviously this shows that the choice of full np for nonzeros is not promising
the result of using rule NUM is shown in the table of figure NUM
tins method tries to identify key words that are characteristic for the contents of the document it concentrates on non stop hst words winch occur frequently m the document but rarely m the overall collection in theory sentences cont mg
floe frequency of word w m document fgt number of documents contmnmg word w n number of documents m collectaon the NUM top scoring words are chosen as thematlc words sentence scores are then computed as a weighted count of thematic word m sentence meaned by sentence length the NUM top rated sentences get score NUM all others NUM NUM title method
summary n understand a reference an agent detexmmes his confidence m its adequacy as a means of identifying the referent document an agent understands a reference once he is com dent m the adequacy of its referred p an as a means of identifying the referent our data show an important chfference wlth ku
clusters of such thematlc words should be characteristic for the document we use a standard tenn frequency mverse
cantly fewer ahgnable sentences NUM NUM this does
note that the model used in a is just word unigram and a and b are being done successively denoted as a b
maintenance of flow share in control feedback repair
the evaluation is based on all information from various resources that is the output of it including correct candidates and some unsolved conflicts are then sent to a high level agent for further processing
let e c be any siring pair derivable from a a NUM where e is output on stream NUM and c on stream NUM define e i as the substring of e derived from the ith a of the production and similarly define c i
when using positivewinnow which uses only positive weights we no longer have this advantage and we seek a modification that tolerates the variation in length
name recognizers taggers part of speech taggers etc also demand similar skills
further oleada has been developed using a task oriented user centered design methodology
during a search all dictionaries are searched
further improvements were made to the components for multilingual display and edit
the computing research laboratory crl is a longtime contributor to tipster
this makes it possible for oleada users to display and edit multilingual texts
the searches are fast and all dictionaries are searched each time
typically research objectives drive research in natural language processing nlp
ii structural information nature of characters absolute closure characters for cns they will definitely belong to a chinese surname once falling into the control domain of it relative closure characters for cns in certain conditions they function as absolute the candidates given independently by three agents may contradict each other on some occasions see fig NUM
silence a le NUM pinpoints the main weakness of our model that is its significant silence rate
if a x does not contain any known word we iterate the procedure using x i the top ranked element of NUM
in these models orthographical correspondances are primarily viewed as resulting from a strict underlying phonographical system where each grapheme encodes exactly one phoneme
it is possible for a population to become extinct for example if all the initial lagts go through ten interaction cycles without any successful interaction occurring and successful populations tend to grow at a modest rate to ensure a reasonable proportion of adult speakers is always present
a transitive verb would inherit structure from the type for intransitive verbs and an extra np argument with default directionality specified by gendir and so forth NUM for the purposes of the evolutionary simulation described in ss3 gc u gs are represented as a sequence of p settings where p denotes principles or parameters based on a flat ternary sequential encoding of such default inheritance lattices
again these precision results compare relatively well with the resuits achieved on the same corpus using other selflearning algorithms for grapheme to phoneme trmascription e.g.
for each graphemic alternation we also record their correlated alternation s in the phonological domain and accordingly increment their productivity
nonetheless the system produced the following template for the walkthrough mes sage
in fact this was her next to worst score of al l the messages
which holds all rules for leaving corporate posts and activations k
louella s st application consists of three rule packages ingress k
the next level of complexity is to find the relational object in and out
the following is an example of the first check against th e lexicon
lockheed martin louella parsing an nltoolset system for muc NUM by
the current etd database contains NUM utterances NUM dialogues
utterances will be first segmented into sub utterances by a segmentation procedure
when improvements were made to the named entity task for the walk through message
the secondary factors that hurt louella s performance were unsatisfactory post processin g decisions
in standard textbooks they are defined in the following manner let a be a poset
the foul up system NUM by granger is an example of a method that focuses on the use of context
a major problem that can occur when parsing sentences is the appearance of unknown words words that are not contained in the lexicon of the system
for example assume sentence NUM produces parses a b c and d in the first or control run
it is hoped that these rules could deal with a wide variety of english sentences though they have not been tested on other corpora
combinatorial explosion NUM x combinatorial we find out through experiments that the word segmentation and pos tagging are mutually interacted the performance of the both will increase if they are integrated together NUM
was it by chance that life form did not get the term ammate object which then might associate the used term mammate object as an antonym
de consequence e trofg previous e t opy pcc x p elc logp elc ps pccl x pcelcl logpcelcl pcc2 x pcelc logpce e
we have correct segmentation for NUM cn beautiful cn is beautifuo wrong segmentation for NUM qt i til i i k i
rule base it contains knowledge in rule form including almost all word formation rules in chinese a number of simple but very reliable syntactic rules and some heuristic rules
this research is supported by the national natural science foundation of china and by the youth science foundation of tsinghua university beijing p r china
obviously platinum should be drawn back and added into the tfn candidate such multi syllabic words can be collected from the banks
and the probability of conflicting is about NUM if candidate number is NUM and NUM if it is greater than NUM fig NUM
NUM background and the related issues in chinese there do not exist delimiters such as spacing in english to explicitly indicate boundaries between words
one is simply viewing it as character string finding candidates over whole of it in terms of the relevant character set input text NUM senttobeseg c
can be viewed as a three level multi agent the concept of agent means an entity that can make decision independently and communicate with others system plus some other necessary mechanisms
step NUM further determining boundaries of tile candidates all of the useful information usually languagespecific and unknown word type specific are activated to perform this work
both of them take characters as basic unit of computation because any chinese word is exactly a combination of characters in one way or another
context vector technology was developed at hnc and has been demonstrated to be highly effective for such tasks as text retrieval using a system called matchplus NUM text routing NUM and image retrieval NUM
our prediction work has proven practical utility since our user selects predictions at close to the maximal level i.e. it is rare for a predicted possibility to be missed
at this point the system is ready to process the user s desired corpus note that the corpus to be visualized does not need to be the same corpus that the system was trained with
upon completion of training the som node vectors have the property that node vectors that are close in the high dimensional vector space will be close in the two dimensional grid space or map space
the updates for these neighbor nodes are smaller than the updates for the winning node and the size of the neighbor node update is smaller for neighbors that are farther away in map space ffi om the winning node
the way in which the system measures not only the amount of information for a theme but also the similarity of themes documents words or any flee text is through the use of the vector dot product operation
the docuverse system developed as part of the ic p1000 research effort at hnc is an ongoing research and development effort with a goal of providing users with a tool to quickly and easily assess the information content of large textual corpora
the initial neural network process is shown along the top part of figure NUM the process flow indicates that some set of textual data the training text is used to obtain context vectors for the vocabulary set contained in the training text
the user is presented with another window containing a histogram where each interval of the histogram corresponds to each of the paragraphs in the document and the height of the interval corresponds to the dot product of the paragraph with the query that was issued
key word and linguistically based directed search engines offer some ability to present relevant information to the user and have resulted in useful interact products such as yahoo NUM lycos NUM and alta vista NUM
pragmatics and aac approaches to conversational goals
otherwise i.e. in the unmarked case the verb typically belongs to the focus in which case no subscript is being used in our representations
the item now placed in the penultimate position or that following the intonation center which marks the most dynamic item belongs to the topic in all readings
NUM the notation differs slightly here from that of section NUM the complex symbols are reflected here by subtrees in which the nodes for function words are still present
also verbs with very general lexical meanings such as be have happen carry out and become may be handled as belonging to the topic
thus in he left yesterday the subject is cb and thus less dynamic than its head the verb and also than its nb sister the adverb
this also concerns the cases of group reading e.g. in the three men built those two houses or of j hintikka s branching quantifiers
NUM which teacher do you mean i cb mean cb our cb teacher cb of chemistry nb
we are aware that these formulations do not cover all the possible cases but the more or less marginal exceptions must be left aside for the aim of the present paper
on the other hand NUM b occurs much more probably as an answer however redundant to from where did jane and jim move to chicago
recycled imprecise responses will not do when specific information is requested
significant increases in speed are possible
then other functions within verb segments and at sentence level subject direct object verb modifier etc are considered
all the words within a segment should be linked to words in the same segment at the same level except the head
temporary beginnings of vcs are usually introduced by grammatical words such as qui relative pronoun lorsque et coordination etc
a segment is a continuous sequence of words that are syntactically linked to each other or to a main word the head
to some extent this is reminiscent of the optimality theory in which constraints are ranked constraints can be violated
as french is typically svo the first transducer in the sequence to mark subjects checks for nps on the left side of finite verbs
by identifying the classes of such statements we reduce the overall syntactic ambiguity and we simplify the task of handling less frequent phenomena
if none of these subject pickup constructions applies the final sentence string remains underspecified the output does not specify where the subject stands
this approach led to very broad coverage analyzers with good linguistic granularity the information is richer than in typical chunking systems
however one may construct statements which are true almost everywhere that is which are always true in some frequently occuring context
polyphony is a theory that models utterances with three levels of characters
for instance there is a topos t linking wealth to acquisitions
the signification must finally be interpreted in the context of the utterance
they may give some support for inferences but do not have to
the result is the ambiguous signification of the complete sentence
the signification of a sentence is defined as a disjunction of subsets of c
sentences are chained under linguistic warrants called topoi plural of topos
the signification is then a firm base for the computation of the meaning
the semantics of each predication is described as a set of argumentative cells
the method to identify zero pronouns and their antecedents within japanese and english aligned sentence pairs which was discussed in section NUM was evaluated by automatically identifying zero pronouns and their antecedents from the functional test sentence set which is already aligned sentence by sentence
furthermore i would like to link more powerful and more robust word alignment techniques which can align for unknown words in noisy texts to the proposed method and also would like to link the proposed method with sentence alignment techniques
according to my evaluation for NUM zero p onouns in a sentence set for the evaluation of 3apanese to engllsh machine translation systems NUM NUM of the pairs of zero pronouns in the japanese sentences and their antecedents in the english translations were automatically identified correctly
to avoid any effects caused by problems at the analysis step on the evaluation of the rules to identify antecedents of zero pronouns described in section NUM NUM any incorrect structures in the japanese and english sentences were modified by hand for the evaluation
as shown in this table the accuracy of identified antecedents for three types of zero pronouns using rules described in section NUM NUM is as high as NUM NUM in the test using an english analyzer with anaphora resolution and NUM NUM even in the test using an english analyzer without anaphora resolution
we have identified several morn specific relations
the both cn candidates are reasonable given the isolated sentence only but by cache it is in fact a collection of ambiguous entities unsolved so far in the current input article the algorithm will have more evidence to make decision
table NUM shows the data for the test sets
combining unsupervised lexical knowledge methods for word sense disambiguation
the heuristics applied range from the simplest e.g.
we tested them using different context window sizes
table NUM summarizes the results for polysemous genus
for the rest the difference in size of the dictionaries could explain the reason why cooccurrence based heuristics NUM and NUM are the best for dgile and the worst for lppl
the set of techniques have been applied in a combined way to disambiguate the genus terms of two machine readable dictionaries mrd enabling us to construct complete taxonomies for spanish and french
this paper has presented a general technique for wsd which is a combination of statistical and knowledge based methods and which has been applied to disambiguate all the genus terms in two dictionaries
at any given time one of the existing codelets is selected to execute
the model in a nutshell simulates language understanding as a crystallization process
note that there is no top level executive that decides the order of these activities
the system s high level behavior therefore arises from its low level stochastic actions
i already experience over asp student period i have already experienced student life
however at any run of such a sentence only one alternative is generated
NUM determine the sentence weights for all senten es in tim arl me compltt e the sum over 2clem ly there will be less oherence than in a man made abstract but the extracted passages can t e presented in a way which indicates their relative position in tim text thus avoiding a possil ly wrong inti ression of adjacency
the contextual information deemed useful for sentence boundary detection which we described earlier must be encoded using features
the model also can be viewed under the maximum entropy framework in which we choose a dist ribution
null the abbreviation list is automatically produced from the training data and the contextual questions are also automat ically
table NUM performance on wall ps t vet journal test data a s a flmction of training set
we have described an approach to identifying sentence boundaries which performs comparably to other state of the art systems that require vastly luore resources
tbr different genres of text in english and text in tiler l oma n a lphabet
a system could be quickly built to divide newswire text into sentences with a nearly negligible error rate
where is b c is the observed distribution of sentenceboundaries and contexts in the training data
in comparison our system does not require pos tags or any supporting resources beyond the sentence boundary annotated corpus
the unification of two finite domain terms is successful as long as they have at least one element in common
this avoids potential interface problems between different representations since terms do not have to be translated between different languages
mutually exclusive sorts have different func null tots at the same argument position so that their unification fails
the following declaration states that the sort sign has two mutually exclusive subsorts lexical and phrasal and introduces four features
profit terms can also be output in iatex format and an interface to the graphical feature editor fegramed is foreseen
if a feature s value is only restricted to be of sort top then the sortal restriction can be omitted
profit thus provides a direct step from grammars developed with sorted feature terms to prolog programs usable for practical nlp systems
term NUM the following clause makes use of feature search to express the head feature principle hfp
sign lexical phrasal intro phon synsem qstore retrieved
a NUM error rate is not generally considered a negative result for a statistical tagger but some of the errors are serious
six errors would require more information or the kind of refinement in the tag inventory that would not have been appropriate for the statistical tagger
when building the dendrograrn for the chinese semantic space we found NUM sense clusters in the space
this demonstrates that another main reason for the tagging errors is the sparseness of the clusters in the space
now that word senses are in accordance with their contexts we use the contexts to model word senses
now that word senses can be generally suggested by their distributional contexts we model senses with their contexts
in order to formally represent word senses we formalize the notion of context as multidimensional real valued vectors
in general a word may have several senses and may appear in several different kinds of contexts
the semantic space is a set of multidimensional real valued vectors which formally describe the contexts of words
so in order to make out the sense clusters we only need to determine the level
suppose d be the set of all preliminary nodes the following is the algorithm to construct the dendrogram
for a word in some context the context can activate some sense clusters in the semantic space due to its similarity with the contexts of the senses in the clusters and the correct sense of the word can be determined by comparing its definitions and those of the words in the clusters
such errors have the effect of corrupting the remainder of the parse as the grammar has to use a marginal interpretation to work round the miscategorisation resulting in very strange parses
this performance has been achieved using the glasgow haskell compiler NUM an d with a considerable amouait of assistance from the glasgow haskell group for which we are grateful
this resulted in a semantic output which NUM idicates that a particular phrase referred to more than one concer causing problems for th e coref task
errors in the semnet if the basic lexical information is wrong then a word may not be parsed wit h the right category
first the corpus is checked for stable phrasal collocations for single words and entire semantic clusters by a special tool a collocator
it is important both that information about typology the knowledge engineer adds to the system is accurate and that enough information is added
many commonsense words take their particular meaning only in a context of domain categories and this can be expressed by means of lexico semantic patterns
the questions that arise concern whether there are independent cb s and cf lists for every clause if not how the cb of the complex sentence is computed and how semantic entities appearing in different clauses are ordered on the global cf list
this algorithm is gradually applied to al entries from the term bank and the knowledge engineer is presented with the results
ira cb is mary the only entity belonging to the cf list in 2t thai is realized as mary is also he cp in 2c iii a smootii silift occurs
still since the counters for these two words are substantially smaller than the counter for the word hxwd sw1 the probabilities calculated according to these counters can be considered as a reasonable approximation for the real morpho lexical probabilities
even if these stages yield encouraging results there is a long way to go before the tool can stand on its own and be used as an integral part of best practice in the field
when as in controlled user testing the scenarios used by subjects are available it is relatively straightforward to detect the dialogue design errors that are present in the transcribed corpus using objective methods
the result of this however is that as early as the recognition phase ambiguities will result in a possibly exponential increase of processing time
under the view of how levels are related that i have argued for the linkage between these two levels is such that x r y xey is a theorem alongside which we will find also e.g. x y xo y
z y rany proof of r NUM a may be extended by multiple inferences to give a proof of f v a where f is just like f except all bracket pairs are NUM r
structural modalities are unary operators that allow controlled involvement of structural rules which are otherwise unavailable in a system NUM e.g. a modified structural rule might be included that may only apply where one of the types affected by its use are marked with a given modality
this gives an approach which allows access to a range of different modes of characterising linguistic structure where the specific mode of description that is used in any case can be chosen as that which is appropriate for the aspect of linguistic phenomena that is under consideration
labeling whereby each type is associated with a lambda term giving objects type term in accordance with the well known curry howard interpretation of proofs with the consequence that complete proofs return a term that records the proof s functional or natural deduction structure
this latter transformation suggests x r y x y as a theorem of a mixed logic revealing a natural relation between xqy and x y as if the former were in some sense implicitly modalised relative to the latter
after more than a decade of promises that versatile spoken language dialogue systems sldss using speaker independent continuous speech recognition were just around the comer the first such systems are now in the market place
taken together however the NUM NUM guideline violation types added by the second analyser and the NUM disagreed or rejected types suggest the usefulness of having two different developers applying the tool to a transcribed corpus
the occurrence of a variety of verb idioms semantic units consisting of a verb followed by a particle or other modifying word accounted for a recognizable segment about NUM of the tagged data
utterances which reflected one or more dialogue design problems were annotated with indication of the guideline s violated and a brief explanation of the problem s
depending on the context the fact that the system says too little about what it can and can not do can be a violation of either sg4 or sg8
the battery charge indicator will light
use the redial for frequently busy numbers
the process structure for the remove phone text
two ciaula clusters obtained with different modelparameters
we repeat the most important observations data oriented semantic interpretation seems to be robust of the sentences that could be parsed a significantly higher percentage received a correct semantic interpretation NUM than an exactly correct syntactic analysis NUM
also propose a simpler annotation convention which avoids the need for this procedure and which is computationally more effective an annotation convention which indicates explicitly how the semantic formula for a node is built up on the basis of the semantic formulas of its daughter nodes
before giving the algorithm we need some definitions null 3by cfg rule we mean a subtree of depth NUM without a specified root node semantics but with the features relevant for substitution i.e. syntactic category and semantic type
it is an analysis of the dutch sentence ik i wil want niet not vandaag today maar but morgen tomorrow naar to almere buiten almere buiten
tupleso tuples t is the set of all pairs c s in a tree bank t where c is a syntactic category and s is the set of all semantic types that a constituent of category c in t can have
ambo if t is a tree bank then arab t yields an n e n such that n is the sum of the frequencies of all cfg rules r that occur in t with more than one corresponding semantic rule
the algorithm presented in this section proceeds by grouping semantic types occurring with the same syntactic label into mutually exclusive sets and assigning to every syntactic label an index that indicates to which set of types its corresponding semantic type belongs
null if we decide to view subtrees as identical iff their syntactic structure the semantic rule at each node and the semantic type of each node is identical any fine grained type system will cause a huge increase in different instantiations of subtrees
the decision about the representational formalism is to some extent arbitrary as long as it has a well null defined model theory and is rich enough for representing the meanings of sentences and constituents that are relevant for the intended application domain
they propose that agents are building a shared plan in which participants have a collection of beliefs and intentions about the actions in the plan
its end point gets continuously incremented as the analysis proceeds until this discourse segment ds is ultimately closed i.e. whenever another segment ds exists at the same or a hierarchically higher level of embedding such that the end point of ds exceeds that of the end point of ds
accordingly we define the extension of referential discourse segments over several utterances and a hierarchy of referential discourse segments structuring the entire discourse NUM the algorithmic procedure we propose for creating and managing such segments receives local centering data as input and generates a sort of superimposed index structure by which the reachability of potential antecedents in particular those prior to the immediately preceding utterance is made explicit
under such a generalization the context model would map every history to a set of candidate contexts ie s e NUM d while an extension model would map every history and symbol to a set of candidate contexts ie s e x e NUM d
a close the embedded segment s and continue another already existing segment if ui does not include any anaphoric expression which is an element of the cf s ui o then match the antecedent in the hierarchically reachable segments
NUM nach dem einschalten zeigt das lc display an dab diese praktische hilfsfunktion nicht aktiv ist after switching on depicts the lc display that this practical help function not active is NUM si ge
while our proposal for centered discourse segmentation also requires a data structure of its own it is better integrated into centering than the caching model since the cells of segment structures simply contain pointers that implement a direct link to the original centering data
however the dialogue manager will decide in both cases that this is a correction of the destination town
d interaction strategy at any stage in a dialogue one participant has the initiative of the conversation
we have also discussed the system adaptation to accommodate the proposed technique
for instance the example above as well as the example in section NUM translates as denial destination town leiden corrections destination town abcoude both the updates in the annotated corpus and the updates produced by the system were translated into semantic units of the form given above
whilst it is possible to objectively measure recognition performance evaluation of a dialogue is not as straightforward
the greater freedom the user has to control the dialogue the more complicated this modelling strategy becomes
the user s freedom of expression is implicitly related to the initiative strategy employed by the dialogue manager
for example when the system has the initiative the user s language can be explicitly constrained
the resulting taxonomy is built under a default node
the research community pro7 vides a number of methodologies to the representation of dialogue and its implementation on a computer
table NUM shows the six systems detailed table NUM a summary of the importance of the features to each system
note that where occurs the feature was not ranked and so is omitted from the mean
a rule which specifies that a head daughter may combine with a complement daughter if this complement unifies with the first element on subcat of the head i.e. a version of the categorial rule for functor argument application can not be implemented directly as it leaves the categories of the daughters and mother unspecified
e m f rni log i i NUM the first term represents the encoding of lcb mi rcb while the second term represents the encoding ie w l for each w in d the third term represents the encoding of e w as a subset of e for each w in d
one limitation of the paradise approach is that the task based success measure does not reflect that some solutions might be better than others
words ushig tile tttel ilod for dietiotlary ilial ittg
we report on a version of the robustness component which incorporates bigramscores other versions are substantially faster
in our framework transaction success is reflected in corresponding to dialogues with a p a of NUM
from the experiments we can conclude that almost all input word graphs can be treated fast enough for practical applications
wordnet labels for ciaula classes are somewhat overly general
for each of these graphs we know the corresponding actual utterances and the update as assigned by the annotators
the first matrix summarizes how the NUM avms representing each dialogue with agent a compare with the avms representing the relevant scenario keys while the
first in our scheme figure NUM the greetings turns NUM and NUM are tagged with all the attributes
in u2 the user asks the agent a wh question about the dr attribute itself rather than providing information about that attribute s value
meanwhile jimmy olsen was in trouble
he had entered the roo l
overlap NUM no same event NUM i assembled the desk myself
clearly this would make the processing of text a very expensive operation
however if a third sentence is added an ambiguity results
consider NUM NUM sam rang the bell
finally section NUM NUM explains how performance can be calculated for subdialogues as well as whole dialogues while section NUM NUM summarizes the method
centering assumes that discourse understanding requires some notion of aboutness
temp relns stores the temporal relations between the eventualities in the discourse
for example segment subdialogue NUM in figure NUM is about both depart city dc and arrival city ac
we have presented the paradise framework and have used it to evaluate two hypothetical dialogue agents in a simplified train timetable task domain
performance evaluation for an agent requires a corpus of dialogues between users and the agent in which users execute a set of scenarios
since we use the bgh hierarchy structure instead of constructing a duster hierarchy from scratch in a strict sense this does not coincide with the cluster based approach as described in the previous section
content oontent d compound NUM mod
the prepositions used in italian for this sort of modification are a and al
it could potentially require the cutter to be significantly harder than the object to be cut
in english the appropriate nominal construction in this case uses the possessive butcher s knife
they are schemata which license the availability of complex nominals which we treat as phrasal signs
our target is to have an account which will handle the majority of productive compounding patterns
it corresponds to the english preposition from and it is interpreted as introducing an experiencing relation
the approach described in this paper constrains the interpretation of complex nominals using the type system
another important use of the compositional apparatus described here is in lexical acquisition of compound forms
this is because there can be only one head word for a tree structure
furthermore the information they contain should systematically link various levels of description e.g.
we define non constituent objects complete link and complete sequence as basic units of dependency structure
a redex is any subproof whose final step is a combination of two well ordered subproofs which establishes a dependency that undermines well orderedness
some roles nmst not be verbalized explicitly these are said to be blocked
in n gram modeling it is common to adopt a recursive strategy smoothing bigrams by unigrams trigrams by bigrams and so on
that is the path component of the context is set to tile new global path root
we write cf4 to mean that c evaluates to fl in the global context c
in this and all subsequent examples a nun her of standard abbreviatory devices are adopted
currently datr is the most widely used lexical knowledge representation language in the natural language processing community
the amended rule just captures the most specific sentence wins default mechanism
consider a value descriptor of the form a b NUM
the rule for values states simply that a sequence of atoms evaluates to itself
it is then available to any instance of noun such as dog via local inheritance
to overcome this problem datr provides a second form of inheritance global inheritance
NUM experiments were designed to test each source of evidence independently and to identify areas of interaction
however our approach allows highly nested belief structures to be computed on demand if required for example to understand non conventional language use
it is far more plausible that agents compute nested representations on so that highly nested belief representations are only constructed if required in the dialogue
we believe that by utilising a more sophisticated view of mental attitudes a simpler and more elegant theory of speech acts can be constructed
the use of simplistic belief models has accompanied complex accounts of speech acts where highly nested belief sels accompany any speech act
we have argued that the computation of highly nested belief structures during the performance or recognition of a speech act is implausible
vve are also working on korean to english text translation on the same domain which we do not include in this paper
if there are closely related distinctions that are not to be tagged for such as for example distinctions related to syntactic function what we do is outline a related tagging task to contrast it with the one the taggers are performing and to help them zero in on the particular distinctions they are to make
but commercial mt systems can only be evaluated in a black box setup since the developer typically will not make the source code and even less likely the linguistic source data lexicon and grammar available
in regard to resolving the simultaneous equations we used the mathematical analysis tool matlab NUM
we found that the lower bound was roughly NUM and therefore our framework outperformed this method
to counter this problem we randomly divide the overall equation set into equal parts which can be solved reasonably
the statistics based computation of word similarity has been popular in recent research but is associated with a significant computational cost
let the statistics based vector space model word similarity between wl and w2 be vsm wl w2
when the noun comprised a compound noun it was transformed into the maximal leftmost substring contained in the bunruigoihyo thesaurus
by this we can realize the statistical computation of word similarity based on a thesaurus with optimal computation cost
we showed the effectivity of our method by way of an experiment and demonstrated its application to word sense disambiguation
these linguistic cases work partly thanks to the previous property of distances with prefixes
the proper name falcone is pronounced in anglophone countries as either f elk ni or even f ellcdn bach as either bax or bak
explore more complicated analyses in this general framework
NUM a john went to his favorite music store to buy a piano
the preposed phrase in john s case serves a special function here in a NUM to simplify the discussion the quantifiers in hardt s original example have been replaced by proper nouns
NUM a john went to his favorite music store to buy a piano
he wanted tony to join him on a sailing expedition and left him a message on his answering machine
therefore some of the works described here as extending the work contained therein are dated prior to the published version
d then ross perot reminded him that most americans are also anti null tobacco
center shifting cb un l cb u
it contains segments NUM and NUM within it and consists of utterances u1 u6
during this application the following are also performed a if the application results in an unambiguous parse in the context of the applied rule we increment the count associated with this parse in table count
that is if each token in a sequence of say three ambiguous tokens have a parse matching one of the context constraints in the proper order then all of them are simultaneously disambiguated
the contexts used here are the last NUM in section NUM NUM a score incontext c pi count pi for each parse pi of the still ambiguous token is computed
one obvious approach is to use the formulation described above for learning choose rules but instead of generating choose rules pick the parses that score significantly worse than and generate delete rules for such parses
if the text is to be used for training the learning module then NUM uses an unsupervised learning procedure to induce some additional an possibly corpus dependent rules to choose and delete some parses
it groups any compound verb formations which are formed by a lexically adjacent direct or oblique object and a verb which for the purposes of syntactic analysis may be considered as single lexical item e.g. saygz durmak to pay respect kafay yemek literally t0 eat the head to get mentally deranged etc
for instance in the case of turkish the case feature of a nominal form is only useful in the immediate left context while the poss the possessive agreement marker is useful only in the right context
note that for efficiency reasons rule candidates are not generated repeatedly during each pass over the corpus but rather once at the beginning and then when selected rules are applied to very specific portions of the corpus
the third step combines all transducers into one single transducer
the search for each chain begins in a small rectangular region of the bitext space whose dimensions are proportional to those of the whole bitext space
in geometric terms these two operations arrange all cells that contain points of correspondence into nonoverlapping rectangles while adding as few cells as possible
a unique bitext map can be interpolated by using the lower left and upper right corners of the mer instead of using the non monotonic correspondence points
the rules are matched against the utterance representation and the accumulated weights decide on the most likely discourse function
to avoid backtracking we choose uninflected forms
it just outputs a series of corresponding token positions leaving users free to draw their own conclusions about how the texts larger units correspond
the frame of a lexical rule and lexical rule interaction is automatically determined and the interaction is represented as a finite state automaton
NUM NUM alternative ways to express lexical generalizations lexical rules have not gone unchallenged as a mechanism for expressing generalizations over lexical information
some nouns and most verbal lexical entries fed lexical rules and a single base lexical entry resulted in up to NUM derivations
the automaton allows us to encode lexical rule interaction without actually having to apply lexical rules a possibly infinite number of times
that only the lexical rules in listoflrs can possibly be applied to a word resulting from the application of lexical rule lr
a related problem is that for analyses resulting in infinite lexica the number of lexical rule applications needs to be limited
in this section we briefly discuss some of the more prominent approaches and compare them with the treatment proposed in this paper
the derived lexical entry licenses word objects with as the value of x and y and b as that of a
in the lexicon however the complements of an auxiliary are uninstantiated because it raises the arguments of its verbal complement
in general the granularity of the lexical semantic classes has to be sufficiently coarse to enable reliable statistics to be obtained but also should not introduce unnecessary ambiguity
however we have not investigated this in detail because we would expect improved tagging accuracy to have only a small effect on transition probabilities and hence on prediction performance
however it seems a hard task to assign nontermlnsl labels for a corpus and the way to assign a nonterminal label to each constituent in the parsed sentence is arduous and arbitrary
NUM merge the most m lar pair to a single new label i.e. a label group and recalculate the slmilarity of this new label with other labels
this limits the ambiguity of the times specified and it also leads to a higher level of robustness since additional de s with the same time are placed on the focus list
to our surprise the majority of our errors was du e not to knowledge gaps in the named entity tagger so much as to odd bugs and incompletely thought ou t design decisions
we may thus tentatively conclude that short of being as encyclopedic as the d b listing a larger better integrated organization lexicon may have provided no more than a limite d improvement in f score
in the sentence above the only unknown word dooner is not subject to retagging by lexical rules in fact the default nnp tag assignment i s correct
contextencoding facts are not explicitly present in the database as their numbers would be legion but are instantiate d on demand when a rule attempts to match such a fact
prior to the part of speech tagger however a text to be processed by alembic passes through severa l preprocess stages each preprocessor enriches the text by means of sgml tags
however the rules are in a human understandable form and thus hand crafted rules can easily be combined with automatically learned rules a property which we exploited in the muc NUM version of alembic
we had hoped to avoid full np parsing but the definition of the org descriptor slot clearly requires this and we will need to return to larger scale parsing strategies in the future
once this rule has run the labelings it instantiates become available as input to subsequent rules in th e sequence e.g. rules that attach the title to the person in mr
after the nth rule is applied in this way against every phrase in all the sentences the n lth rule is applied in the same way until all rules have been applied
lexical rules play a larger role when the default tagging lexicon is less complete than our own which we generated from the whole brown corpus plus NUM million words of wall street journal text
kupiec NUM and brill NUM make use of morphology to handle unknown words during part of speech tagging
so unknown words are assumed to be open class restricting them to noun verb adjective adverb or noun modifier
this corpus was specifically chosen for our tests because it has no theme and would thus offer a wider range of sentence types
an experiment is performed to investigate the ability of a parser to parse unknown words using morphology and syntactic parsing rules without human intervention
for example any word ending in ly is now assumed to be a noun verb modifier adjective and adverb
finally we would like to refine the post mortem approach by offering a more elegant solution than the combination of first choice and second choice lists
since the root of an unknown word is assumed to be unknown the recognizer can only consider whether an ffix matches the word
for example any word ending in ly is now assumed to be an a iverb an adjective and a modifier
in each iteration our algorithm must determine the appropriate elementary tree to incorporate into the current description
sec null ond goals of the form communicate p instruct the algorithm to include the proposition p
in particular we treat many types of content as contributing to expressions that refer to semantic objects
the entry may specify additional goals because it describes one entity in terms of a new one
in this section we describe how spud can be made to use words in other conventional combinations
after planning a refemng expression the copier spud has the goal of distinguishing c42 from the other copiers
such differences will allow spud to generate different collocations in different languages even when describing the same entities
this section delineates how a hierarchical tag context tree is constructed from a basic tag context tree
mistake driven mixture sets the weights to NUM for all examples and repeats the following procedures t times
but it does not hurt much to carry the feature along in the principles NUM NUM
most theoretical work on re sampling assumes i i d identically independently distributed samples
next we compared the mixture of bi grams and the mixture of hierarchical tag context trees
tile topmost and the second level of the hierarchy are part of speech level and part of speech subdivision level respectively
by approximating word probability as constrained only by its tag we obtain equation NUM
this is extremely useful in capturing exceptional connections that can be detected only at the word level
if all words are considered context candidates the search space will be enormous
before moving to the next section let us define the basic tag set
in english homographs represent a common problem that can not be solved entirely by letter to sound rules
elision can be done in the first syllable of a word but is considered familiar vs
if the context is false the rules are tested in decreasing order of the longest match
later solutions in our system involved a default to the member with the higher frequency of occurrence
the NUM NUM errors consisted mainly of incorrect morphological analysis and consequent inaccuracies in lexical stress placement
the order in which the rules are written is significant only for those having the same is
primitive languages are a myth perpetrated by early anthropologists missionaries and adventurers
nevertheless some differences exist in the syntax and the interpretation of these rules
we disambiguate and expand all such abbreviations in a separate module that by passes letter to sound
it has been used by various companies producing electronic board speech synthesizers for french
this will tend to increase their fitness over unset learners who do not speak any language until further into the learning period
step NUM is carried out detecting the more significant and less ambiguous words in any class semantic fields of verbs and unique beginners for nouns any of these sets is called kernel of the corresponding class
in NUM pr c is the not uniform probability of a class c given by the ratio between the number of collective contexts for c NUM and the total number of collective contexts
these results show that tuning a classification using word contexts sthose collected around the kernel verbs of c is enough precise to be used in a semantic bootstrapping perspective and by its nature it can be used on a large scale
in this case in fact a statistical significant relationship is not to be detected betwen verb and its lexical arguments but between the verb and a whole class of words that play in fact the role of such arguments
we propose the following strategy NUM tune a predefined general classificatory framework using as source an untagged corpus NUM tag the corpus within the defined model eventually adjusting some of the tuning choices NUM use tagged i.e.
in our current system terminal matching is performed from left to right
in general this will lead to a set of lexical candidates
given a preliminary customization of the wordnet hirerachy according to the set of kernel verbs and nouns exemplified in tables NUM and NUM the described methods allow to apply local semantic taggin to the set of verbs and nouns in the document
figure NUM the more generalized derivation tree dtg of dt
in the non exhaustive mode the longest matching prefixes axe preferred
however these properties are very important for achieving high applicability
in particular i want to thank dan flickinger and ivan sag
these classes define the upper bounds for the abstraction process
mar automatically determined by tm and applied by am
could it be until around twelve NUM NUM there a preference for parallel syntactic roles might be used to recognize that the second utterance specifies an ending time too
the crucial aspect for this paper is that together these features permits the usage of abstract syntax to express the logical forms terms computed by ccg
in other words c is a scoped constant and the current signature gets expanded with c for the proof of c z g
NUM the second part of figure NUM shows declares how quantifiers are represented which are required since the sentences to be processed may have determiners
ccg is a grammatical formalism in which there is a one to one correspondence between the rules of composition NUM at the level of syntax and logical form
a secondary task is to associate with each such query a list of expected answers
smith hipp and biermann an architecture for voice dialog systems sources of expectation
recall that after john is type raised its lf will be abs p app p john and similarly for bill
this is actually a schema for a family of rules collectively called generalized coordination since the semantic rule is different for each case
previously unmentioned information may be assumed to be unknown and may be explained as needed
this assertion prevents the system from giving any explanation about the location of the knob
the example above related to finding the position of a switch shows how this works
a typical task for their system is to properly parse and analyze a given dialog
but the response in this case is negative i do not know
the difference of course is that here the abstraction is a meta level built in construct while in NUM the interpretation is dependent on an extra layer of programming
here the machine diagnoses as well as it can the current subdialog from user comments
second ipsim may be interrupted by the dialog controller to inquire about proof status
in the case of the autolearn system the values are every word occurring in the training collection
we correctly identified the following interesting cases ms washington mr york and ms lansing were not confused with locations
this module also contains functions for creating various structures from strings and lisp s expressions and routines for initializin g global variables used by other modules
this situation can probably be improved by replacing specific words with a null word
we had t o not print the recognized organization names and turn off processing of the hl and dateline parts of the articles
one mccann account NUM ca n t believe it s not enamex type quot person bntter enamex a butter substitute is in NUM countries for example
uno nlp module s the uno nlp system consists of the following modules reader dictionary parser knowledge representation discourse and learning
the boolean algebras module implements th e uno knowledge representation formalisms and some standard boolean algebras such as predicate calculus and the powerset of a finite set
right now our system can reason about geographical region containment in the exact analogous fashion as about type subse t relation and temporal interval containment
this geographical knowledge is encoded in our uniform general purpose uno knowledge representa tion uno nlp system supports geographical reasoning with its general inferencing mechanism
officer of ammirati puri about mccann s acquiring the agenc y with billings of numex type money quot NUM million numex but aothiog has materialised
implemented numbers and personal name s our effort of making the uno system process numbers and improving handling personal names wa s strictly related to muc NUM
plant is still operating plant and animal species plant tissues can be plant located in
in this run around NUM of the initial population converged on a minimal ovs language and NUM others on a vos language
computing minimal differences we use the program diff and interpret its output to compute the function NUM specifically we use the free software foundations gdiff with the options minimal ignore case and ignore all space to guarantee optimal matches of terminals and allowing editorial decisions that result in differences in capitalization
to strain microscopic plant life from the vinyl chloride monomer plant which is
we use logic programming to capture these inferences
figure NUM plots of a training and b test perplexity versus number of iterations of the em algorithm for
they have been displaced by more broadly applicable collocations that better partition the newly learned classes
in this section we give a brief explanation of grammar acquisition using a bracketed corpus
a hand crafted grammar is usually not completely satisfactory and frequently fails to cover many unseen sentences
to reduce the grammar size and ambiguity some hand encoded knowledge is applied in this approach
automatic acquisition of grammars is a solution to this problem
let p1 and p2 be two probability distributions over environments
generally we prefer a small fluctuation to a larger one
NUM assign a unique label to each node of which lower nodes are assigned labels
in this paper we apply this technique to group similar brackets in a bracketed corpus
the environment is a pair of words immediately before and after a label bracket
we asked them to translate these nineteen japanese terms into english without using dictionaries or other reference material
while it is far from ideal this is the first result of terminology translation from non parallel corpora
among these NUM terms shown in figure NUM have their counterparts in the wsj text
they must occur in both sides of the non parallel corpora and have fewer number of candidate translations
NUM debentures is less related to engineering which does not appear in any segments containing debentures
nevertheless top NUM candidate output give a NUM NUM average increase in accuracy on human translator performance
however the cosine measure is also directly proportional to another parameter namely the actual ws
in our case this means that we should choose a large subset of highly discriminative seed word pairs
table NUM lower bounds for both test sets
several strategies for identifying seeds that require minimal or no human participation are discussed in section NUM
in turn dxl is now being used as the specification language for recognizing various components of existing navy tech manuals in the saic ietm project
similarly to allow ii the input representation must specify only c
or the interior excluding edges of a type of labeled constituent written e g a
expansion permits the components of a complex chars string to be broken apart and analyzed using the same rule formalisms employed for multiple tokens
at the same instant says to end nasality raise velum rcb
keeping the constraints small is important for efficiency since real languages have many constraints that must be intersected
the central claim of ot is that the phonology of any language can be naturally described as successive filtering
NUM good yews ellison s algorithm can be improved so that its exponential blowup is often avoided
finally we have considered the prospect of building a practical tool to generate optimal outputs from ot theories
not counting the peculiarities of muc requirements for tagging the identified data classes no par t of this effort has been spent on non reusable muc specific activities
given g v g e g an n vertex directed graph
for instance the natural factors of so are input and all the tier rules see NUM
first attern vi any v2 second pattern v3 v1 actions sv2 sv3 actions
it is also similar to the example in NUM
during generation it is necessary to find appropriate mapping rules
this has the added advantage that the representation has well defined deductive mechanisms
thus the generator does not rely on the strategic component having linguistic knowledge
the final mixed syntactic semantic structure is shown on the right in figure NUM
to get both paraphrases would be hard for generators relying on hierarchical representations
when matching two conceptual graphs we require that their heads be the same
in order to guide the search a number of heuristics can be used
we return to how uppersem and lowersem are actually used in section NUM
so we want builtsem to satisfy lowersern builtsem uppersern
figure NUM subsertion node of a i that dominates the substituted com ponent to t NUM
the finite verb seems also projects to s even though it does not itself provide a functional subject
the modifiers action attempts to ensure that the referring expression that is being constructed is believed by the speaker to allow the hearer to uniquely identify the referent
these take as a parameter the plan that is being judged and for s reject also a subset of the speech actions of the referring expression plan
a dtg is said to be lexicalized if each d tree in the grammar has at least one terminal node
only substitutable components of NUM NUM k can be substituted in these subsertions
we assume that both conversants are sincere and so when such a communicative goal arises both participants will assume that the hearer has adopted the goal
plan agt plan goal agt has the goal of goal and has adopted the plan derivation plan as a means to achieve it
this week looks pretty busy for me
thursday i m free the whole day
simple stack based structure proposed structure int
but thursday and friday are both good
and friday is really tight for me
see figure NUM for a complete list
but clearly this expression refers to wednesday
could be a state constraint or a reject
the separation of dominance and precedence presented here grew out of our work on german and retains the local flavor of dependency specification while at the same time covering arbitrary discontinuities
we leave implicit the fact that use of the rule involves unification of the index variables associated with the two occurrences of b in the standard manner
in the final inference of NUM this method allows the variable z to fall within the scope of an abstraction over z and so become bound
the technique of normal form parsing involves ensuring that only normal form proofs are constructed by the parser avoiding the unnecessary work of building all the non normal form proofs
for the implicational fragment the set of formulae are defined by NUM r a o with a a nonempty set of atomic types
a contraction step modifies the proof to swap this final combination with the final one of an immediate subproof so that the dependencies the two combinations establish are now appropriately ordered with respect to each other
here we simply use an additional feature on the node to capture this figure NUM also shows the semantics and about labels for each tree
3personal communication oct NUM most efficient to use one hand for structural commands with the mouse and the other hand for short keyboard input
under appropriate discourse conditions spud can choose to describe c in terms of the information state bp k c and the lexical item strings
the precedence constraints are formulated as a binary relation over dependency labels including the special symbol self denoting the head
thus we can have any number of basic level terms to describe an object and the appropriate one will be selected on the basis of its specificity
for every si e o r choose a lexicon entry ci e lex si
the solution to these problems involves defining d fault umnarked initial values for some parameters and or ordering the setting of paraineters during learning
saying to someone your goose is cooked is not appropriate as a expression of sympathy the expression conveys a certain amount of disregard for their predicament
resulting inconsistencies e.g. in case of an extracted object are not resolved however
tomatic annotation is useful only if it is absolutely correct while wrong analyses are often difficult to detect and their correction can be time consuming
figure NUM b shows an auxiliary tree representing the modifier syntax which could adjoin into the tree for the book to give the syntax book
the precedence of words within domains is described by binary precedence restrictions which must be locally satisfied in the domain with which they are associated
the original np hand tuned and machine learning algorithms all do relatively poorly on narrative NUM and relatively well on NUM both in the test set under all conditions
a few attempts were made using tools such as x lisp stat for point and line graphs and latex for tables
the justification for these annotations and their prolog syntax are presented in detail in NUM
the decision tree predicts the class of a potential boundary site based on the features before after duration cuel wordl corer infer and global pro
results using cross validation are shown in table NUM and are better than the estimates obtained using the hold out method table NUM with the major improvement coming from precision
word1 is assigned this lexical item if 2we previously used agreement by NUM subjects as the threshold for boundaries for j NUM agreement was significant at the NUM NUM
given a narrative of n prosodic phrases the n NUM potential boundary sites are between each pair of prosodic phrases pi and p i i from NUM to n NUM
take into account the cumulative influence of the secondary messages conveyed by all parts of the profits report
a more complex classification is helpful in general as it allows the classification of other useful properties e.g.
our table is goaloriented instead of type oriented it associates each possible user goal with the schemas that can express it
the user has the option of manually limiting the scope of the grouping process by building sets of related goals
this maximization complicates the exploration of the solutions as it becomes impossible to return the first feasible solution
it creates NUM major problems it is ambiguous at the realization phase and it ignores inter variable phenomena
more details about the organization of our goal system can be found in NUM
for example figures NUM and NUM show NUM NUM graphs that share a subset of intentions
in contrast to a standard part of speech tagger which estimates lexical and contextual probabilities of tags from sequences of word tag pairs in a corpus e.g.
its word order description is much like that of word grammar at least at some level of abstraction and shares the above inconsistency
moreover the system allows interactive information discovery from a multilingual document collection
however monolingual users can currently access information only in their native language
if not specified the query terms are joined by or
NUM NUM the term translation module the term translation module is used by the client
its odbc compliance makes porting of databases from one vendor to another very easy
figure NUM shows the translation result for the japanese document in figure NUM
the result is a truly transparent multilingual document browsing and access capability
l anon and isr e which ereleft negotiatlon
the automated query expansion thus improves retrieval recall without manually creating alias lexicons
currently thesystem offers the user several ways to explore and discover hidden information
we show how standard part of speech tagging techniques extend to the more general problem of structural annotation especially for determining grammatical functions and syntactic categories
the rules mapping from the unordered dependency trees of surface syntactic representations onto the annotated lexeme sequences of deepmorphological representations include global ordering rules which allow discontinuities
we then discuss different types of constraints and the problems they pose presenting the techniques we have developed within fuf to address these issues turning from structural constraints to pragmatic cross ranking constraints and to interlexical constraints
antonymy is equivalent to maximal cardinality NUM for antosemy
sentences NUM and NUM generated by streak show how game result and manner can be realized as two separate surface elements or can be merged into a single element the verb
in particular the type of fd it accepts as input specifies a process in the systemic sense i.e. any type of situation involving a given set of participants or thematic roles
in figure NUM as the default option for possessive verbs english uses a possessive metaphor to refer to the link existing between a class and its assignments a class owns the assignments
and although it is not demonstrated in this paper the ability of a cps procedure to return more than one value at a time can be used to pass other information besides right string position such as additional syntactic features or semantic values
NUM to avoid overloading the word grammar we will use fug to refer to the common representation formalism and syntactic grammar to refer to syntactic data encoded in surce using this formalism
crl s multi attributed text widget was used to provide users with facilities for editing and displaying text
unlike paper dictionaries oleada s on line dictionaries can be searched using partial words and or wildcards
searching is quick and the size of the corpus is limited only by available disk space
both user groups can benefit from the language analysis tools being developed under the tipster program
user protocol task analysis involves an empirical analysis of workers at their jobs and has three goals
this section describes three iterations of the four step iterative design process used to develop oleada
user observation for phase two consisted of users working with the prototype system on example tasks
the spanish morphology component was enhanced and the alignment algorithm for translation memory was improved
instructors can use oleada to support all phases of language training
the result of this work are the collection of tools oleada
the muc converter is just a simple sgml parser
in natural lan lage the effect of left neighbors and right neighbors of a word are asymmetric
the expression seq fa fb evaluates to a function that maps a string position NUM to the set of string positions lcb ri rcb such that there exists an m NUM fa NUM and ri NUM fb rrl
as a more task oriented evaluation we are collaborating with the umverslty of massachusetts to evaluate retrieval etfectiveness for dlmsum system generated and
section NUM NUM marked assignments are to be confirmed by the annotator unreliable assignments are deleted annotation is left to the annotator
from practical point of view word classification addresses questions of data sparseness and generalization in statistical language models
we showed that any transformation based program can be transformed into a deterministic finite state transducer
complex noun groups and verb groups
a better more relaxed view of the problem is that era rectangle
for example we can replace the definition of np in the fragment with the left recursive one given in NUM without compromising termination as shown in NUM where the input string is meant to approximate kim s professor knows every student
thus stated we view tactical generation as the inverse process of parsing
currently a parser is used for processing the test set during training
the overall accuracy for assigning grammatical functions is NUM NUM ranging from NUM to NUM depending on the type of phrase
only if this is the case ts replaces tx in mrsg NUM
such a deviation further worsens our ability to estimate the likelihood of a connection according to translational position
in this paper we have presented an algorithm capable of identifying words and their in context translations in a bilingual corpus
and the sentence computational linguistics volume NUM number NUM e23 translates into k
NUM for russ to have heard t3 as demonstrating mother s acceptance of his t2 i.e. as a display of understanding the linguistic intentions of inform m r not knowref m whoisgoing would need to have been compatible with this interpretation of the discourse
table NUM reveals that the acquired conceptual information compensates for what is lacking in the lecdoce to yield optimum alignment results
the english sentences in this test set range from NUM to NUM words long average sentence length is NUM NUM words
experimental results indicate that classification based on existing thesauri is highly effective in broadening coverage while maintaining a high precision rate
li and thompson s typological description of mandarin is described below from the perspective of the task of word alignment
this encoding is summarized in table NUM
NUM sl has performed action aobserve NUM but the linguistic intentions of a ew are inconsistent with the linguistic intentions of aobservee NUM aobserved and action aintendee can be performed using a similar surface level speech act and NUM s2 may have mistaken aintended for aobserved
this study concentrates primarily on identifying alignment at the word level for a given sentence and its translation
second a topic is the old information of which both the speaker and listener have some knowledge
she can infer this from his acknowledgements but also from his reconfirmations checks and wh questions
automatically trying out all the NUM NUM subsets of features would be possible but it would require manual examination of about NUM NUM sets of results a daunting task
the error rate of a tree obtained by using the whole dataset for training is then assumed to be the average error rate on the test set over the n runs
this indicates that the representations produced by discourse planners should distinguish those elements that constitute the core of each discourse segment in addition to representing the hierarchical structure of segments
e.g. for corel NUM NUM of the relations are not cued thus by deciding to never include a cue one would be wrong NUM NUM of the times
NUM in table NUM three features trib pos in e struck infor s ruct concern segment struc null ture eight do not
for each tree we list the number of nodes and up to six of the features that appear highest in the tree with their levels of embedding
recall that it was only by considering corel and core relations in distinct datasets that we were able to obtain perspicuous decision trees that signifcantly reduce the error rate
for example contributor l d in figure NUM is labeled as bia3 2after as it is the second contributor following the core in a segment with NUM contributor before and NUM after the core
from their study moscr and moore identified several interesting correlations between particular features and specific aspects of cue usage and were able to test specific hypotheses from the hterature that were based on constructed examples
the number of transition probabilities in these models scales as mv NUM as opposed to v m l
the hidden variable in this problem is the class label c which is unknown for each word wl
to circumvent this difficulty we decided to try using strategies that would select among whole joint venture structures rather than selecting individual frames
our results indicated that the matching process used by the scorer was sensitive to perturbations of the internal structure of the joint venture frames
be seen in the drop in performance from NUM word error rate perfect transcription to NUM word error rate
at this point a set of inference rules are applied and any that succeed will add more predicates to the database
the discourse processor when invoked on a sentence first adds all semantic predicates associated with a sentence to the semantic database
a handful of missing features were identified such as the need to find words in the lexicon rather then scrolling
on a more mundane level the nlu shell gui is structured as a set of cascading diagrams depicting the underlying data extraction process
we believe that with a little more effort we could achieve an f score of NUM in te with only lightweight techniques
pruning will be applied once again
the speed roughly corresponds to NUM words in second
no intervening subject labels and clause boundaries are allowed
to the last verbal reading here decide
pruning is then applied agmn and so on
voutilainen and juha heikkil5 created the original engcg lexicon
figure NUM a toy grammar of NUM rules
rules NUM NUM are applied in the first round
all three paradigms of grammar formalisms introduced earlier share similar linguistic judgments for their grammaticality analyses
the first quarter run is connected with these three runs to sho w the gradual improvement in recall NUM to NUM and the minor degradation in precision NUM to NUM as more examples wer e given to hasten
for example the words at the center of a cluster automatically receive a higher score whereas it may be more desirable to have all the members of a cluster assigned a score lying in a narrower range
all this means is that instead of each word counting for NUM in a set it counts for its significance value a value between NUM insignificant and NUM highly significant
in a sense this has intercalating np quantifiers an apparent problem to our generalization
by taking a smaller window size the number of sentences to look at either side of each possible sentence break the resolving power of the algorithm can be increased making it more sensitive to changes in the vocabulary
correspondences stage NUM let us consider two sets of words set a and set b the main aim of this stage of the processing is concerned with calculating a correspondence measure between two such sets depending on how similar they are where similarity is defined as a measure off lexical correspondence
we shall next briefly sketch how normalisation could instead be handled via the standard method of proof reduction
now let a t be the subset of a that contains only those words that occur somewhere in b and let b be the subset of b that contains only those words that occur somewhere in a the lexical correspondence between sets a and b can then be calculated using the simple formula
but here the parser does n t even use the information that the words are to come from a lexicon for a particular language
these are needed to distinguish between cases of real functional arguments of functions of functions and functions formed by state prediction
the first role can be captured using syntactic types where each type corresponds to a potentially infinite number of partial syntax trees
note that if state application is chosen or the first of the state prediction possibilities the fragment john likes sue retains a flat structure
recall that a m a b b and is are not calculated by adding one for each word in each set but by summing the significance values of the words in each set
for example given an input of NUM nps the parser will happily create a state expecting NUM nps to the left
this might be a likely state for say a head final language but an unlikely state for a language such as english
in incremental parsing we can not predict which words will appear in the sentence so can not use the same technique
however if we are to base a parser on the rules given above it would seem that we gain further
note that generation of trace satisfies both the np c and gap subcat requirements
for example rule NUM sbar that gap
the generative process is extended to choose between these cases after generating the head of the phrase
most nlp applications will need this information to extract predicate argument structure from parse trees
but in general the probabilities could be conditioned on any of the preceding modifiers
the second reason for making the complement adjunct distinction while parsing is that it may help parsing accuracy
in rule NUM it is passed on to a right modifier the s complement
evaluation of retrieval results using the assessments from this sampfing method is based on the assumption that the vast majority of relevant documents have been found and that documents that have not been judged can be assumed to be not relevant
the head extra schema together with the pmc has the consequence that elements extraposed from objects are bound at vp level whereas extraposition from subjects involves binding at s level as illustrated in NUM and NUM
intuitively our approach to the phrase structure of extraposition can be formulated as follows an extraposed constituent has to be bound on top of a phrase that introduces intervening material between the extraposed constituent and its antecedent
this phenomenon can be observed with adjuncts such as relative clauses or pps in NUM NUM as well as with sentential and prepositional complements as in NUM NUM NUM NUM an entirely new band rings today at great torrington several of whom are members of the congregation
if we add the assumption that the mother is always marked as per right 1deg then the following data with split antecedents can be accounted for NUM ein mann iut3erte die behauptung und eine a man uttered the claim and a frau leugnete die tatsache daft rauchen woman denied the fact that smoking ungesund ist
the sharing states that the cont value of the output is identical with the cont of the extraposed element which in turn incorporates the semantics of the input via the sharing
we use lpcs to account for multiple extraposition from the same antecedent cf the data in NUM NUM NUM a h e b e head prep e head verb v rel the constraint in 36a orders the extra dtrs e after the head dtr h
we also find evidence for extraposed phases within nps i.e. examples in which adjuncts precede complements NUM in np an interview published yesterday with the los angeles daily news mr simmons said lockheed is actually just a decoy
its value is of type periphery defined as follows a headed phrase is marked per left if it has a daughter d with a non empty inheriextra set and d is a the rightmost daughter and phrasal or b the head daughter and lexical and marked per left
much in the same vein as with np internal extraposition our account accommodates cases of vp internal extraposition which are possible with fronted partial vps in german NUM vp einen hund fiittern der hunger hat a dog feed which hunger has wird wohl jeder diirfen
we find similar data for german where the subject of a finite clause is related to the s projection via a slash dependency and therefore the head extra schema applies on top of the head filler schema NUM is die behauptung iiberrraschte reich und the claim surprised me and erstaunte maria dab rauchen ung
auntil now all texts have been dcliv r d in word perfect lint the conversion program may of oursc l e adat tcd to odmr t t xl processing syst ems
the most slriking example of this is the fact that patrans aims at a very deep analysis of the source text and al the same lime t he formalism alh ws for non lnonotoni mappings l e lweell levels of represenladon
if the parser fijls to assign a wellforme t slr le urc lo the input a path is selected i om tim chart which spans the greatest amount of dm inlml ril l already c reated constituents are collecled
the paterm coding tool provides a screen wilh fiehls lo fill in and in most cases an atlswer is proposed by t he system st lhat lit user llas to make jllst one accet lance ke ysta olce
pre rules have been developed for lexteal disambiguation and for parsing of adverbial phrases complex verb groups coordinated thatclauses indexed lists valency bound prepositional phrases and explicitly marked intervals e.g. from to between and
consequently it is iml ortant thal lhe use r who is nol necessarily a onll htal ional linguisl all elicode l rtns ill a n efficient and precise way
then we give an overview of the trauslat ion process and the basic flmetionality of pa i ans and finally we describe some recent extensions for improving processing efficiency and the translation quality of unexl ected input encountered in real lit texts
create the preconditions needed for application of any other rule while at the same time allowing prioritization of rules the pre rules not only add structure to the input they are also used for lexical disambiguation based on collocatives and immediate context
importantly given the indicated color constraints no other solutions are admissible
in particular c unification will only succeed if comparable formulae have unifiable colors
this is exactly the trait that we will exploit in our analysis
in particular we assume that free variables are colored in all formulae occuring
the effects of pre rules are twofold on tile one hand they assign structure to tile input at a shallow level which nevertheless increases processing efficiency considerably on the other hand they also improve fail soft results since inappropriate readings of words in a given context are discarded at an early stage
patrans adheres to simple transfer i.e. the substitution of source language lexical units with target language lexical units by means of lexical transfer rules NUM while the source language stru tural representation is mapped directly onto the target language transfer representation which is input to tile generation module
such a substitution is called a c unifier of m and n
note that only variables without colors can be abstracted over
pasha ii agents are connected to the unix cm calendar management tool but can easily be hooked up to other calendar systems
note that interface objects are accessible through their tcp ip based internet addresses and can be associated to any component cf figure NUM
managers in the coconuts model are control units which coordinate or perform specific activities and cooperate with each other in a client server form
robust analysis of human e mail messages is achieved through message extraction techniques corpus based grammar development and client oriented semantic processing and representation
each cosma server component is encapsulated by a ccm computing component manager which makes its functionality available to other managers
manual checking of the results reveals gaps in the coverage and leads to further refinement and extension of the automata by the grammar writer
for instance pps and nps were specified further introducing a more fine grained semantic anno sdito and tsdb entries are linked via e mail identifiers
the first dictates that clauses should be assigned to the lowest non conflicting event value the second favors non confllcting event values of the most recently assigned clauses
while this provides a reasonable default the resulting semantic tag has to be considered provisional and validated independently
the task of the taggers was to select appropriate senses from wordnet for these NUM words NUM the number of alternative wordnet senses per word ranged from two to forty one the mean across all pos was NUM NUM
the first senses in the frequency condition which generally express the most salient and central meanings might be most clearly representend in both naive and expert speakers mental lexicons and might show the greatest overlap across speakers
i already over asp student period my student days are over
b ensure that only connected lexical signs are generated and analyzed the following assumt tion must also be made assumption NUM a grammar will only generate or analyze connected lexical signs
in order to apply the algorithm outer domains needed to be compiled from the grammar these are used to discard wfss by ensuring l exical signs outside a wfss can indeed appear outside that string
l urthermore brown1 can not occur as a loaf in a deel er constituent in the vp t ecause such an occurrence would be associated with a different index
however none of these constituents can have brownl as a leaf ill the case of p and vtra this is trivial since they are both categories of a ditferent lexical type
o d the cf ect of hlnda lt sul structnres is not as detrimentm as for parser based generators
the recursive part of the definition states that the translation of an f structure is simply the union of the translation of its component parts
then if cue NUM true then if global pro
a dectalk dtco1 text to speech converter is used to provide spoken output by the computer
we constructed the circuit fix it shop based on the details of our dialogue processing model
the current dialogue processing model considers subdialogues at the lower level of basic domain actions
in the circuit fix it shop the computer misunderstood user utterances NUM NUM of the time
u there is no wire between connector eight four and connector nine nine
the test to repair transition is common when the user makes the repair without mentioning it
subjects attempted a total of NUM dialogues of which NUM or NUM were completed successfully
in reality there are only slight differences in the results when the unsuccessful dialogues are included
the analysis was conducted using the averages by subjects as well as by items problems
for the diagnosis phase the test statistic is NUM NUM with a corresponding p value of NUM NUM
figures NUM and NUM illustrate the parse tree and semantic frame produced by the adapted system for the input sentence NUM z unknown contacts replied incorrectly
lcb at in near off on rcb np NUM a states that a locative prepositional phrase consists of a subset of prepositions and a noun phrase
we then illustrate how these misparses are corrected by lexicalizing the grammar rules for verbs prepositions and some domain specific phrases
for this experiment the subjects were asked to study a number of muc ii sentences and create about NUM muc ii like sentences
the solution lies in a grammar design in which lexicalized grammar rules defined in terms of semantic categories and syntactic rules defined in terms of part of speech are utilized toether
the proposed grammar achieves a higher parsg coverage without increasing the amount of ambiguity misparsing when compared with a purely lexicalized semantic grammar and achieves a lower degree of
as for pp attachment ambiguity lexicalization of verbs and prepositions helps in identifying the proper attachment site of the prepositional phrase cf
misparses due to omission are easily corrected by deploying lexicalized rules for the vocabulary items which occur in phrases with omitted elements
first top level categories such as subjects and the copula verb be are often omitted as in NUM
NUM a haylor hit by a torpedo and put out of action NUM hours for NUM hours b
many studies have been carried out on machine translation and a number of problems has been recognized
therefore many erroneous to resolve this problem we propose an improvement in the selection process
first the user inputs a source sentence in english
figure NUM shows the outline of our proposed translation method
in the experiments NUM translation examples were used as data
we consider that this problem can be resolved by the learning method without any analytical knowledge
third the user proofreads the translated sentences if they include some errors
these translation rules are part of all the translation rules in the dictionary
the system evaluates the translation rules by utilizing the given translation examples directly
the combination may be true when it exists in a given translation example
c value a i where log2 lalf a lal max b log2 lal f a p ro otherwise
in this case the quantifier ranges over objects that are named john and are further restricted to be identical to some contextually salient individual denoted by j smith
in dsp s representation of the antecedent of NUM both nps john and he give rise to two occurrences of the same term a constant j
for instance the scopes of quantifiers or the contextual restrictions on pronouns in the antecedent may not have been resolved this will correspond to the presence of uninstantiated meta variables in the antecedent qlf
this has the effect of reindexing the version of the term occurring in the ellipsis so that it refers to the same kind of thing as the antecedent term but is not otherwise linked to it
null moreover the difference between strict and sloppy readings does not depend on somehow being able to distinguish between primary and secondary occurrences of terms with the same meaning
here t is a form of abstraction for now it will do no harm view it as a form of abstraction though this is not strictly accurate
however were we to do this in this particular case where the antecedent NUM is fully resolved we would successfully capture the intended interpretation of the ellipsis namely
2terms shown abbreviated i.e. tex tj instead of tern wj NUM y name y john y y j maith
this is due to both the verb sense ambiguity write has five senses and to the noun ambiguitms queen has five senses and letter two
this is possible because the french text is produced by a separate micro planning sub network
systran is a system with a development history dating back to the seventies
obviously this results in sometimes bizarre word creations see section NUM NUM
the resulting translations are qualitatively evaluated for lexicon syntax and semantics errors
testing grammatical coverage can be done by using a test suite cp
here we will report on the results for translating from english to german
if we are mainly interested in lexicon size this method has additional drawbacks
shorter longer shorter longer we found strong correlations for consensus sbeg scont and sf phrases for all conditions
group s features for scont were identical to group t except for the absence of a correlation for maximum rms
according to this model at least three components of discourse structure must be distinguished
this paper reports on corpus based research into the relationship between intonational variation and discourse structure
the latter measure proved more robust so we report results based on this metric
although this paper reports results from only a single speaker the findings are promising
x NUM NUM df l and spontaneous p NUM
labels on which all labelers in the group agreed are termed the consensus labels
other than this difference in input modality all subjects received identical written instructions
table NUM acoustic prosodic change from preceding phrase for consensus labelings from text and speech
NUM as opposed to automata a large class of finite state transducers do not have any deterministic representation they can not be determinized
we thank the anonymous reviewers for many helpful comments that led to improvements in both the content and the presentation of this paper
however as proven in section NUM the rules inferred in brill s tagger can always be turned into a deterministic machine
then for each error it is determined which instantiation of a set of rule templates results in the greatest error reduction
it also overcomes the limitations common in rule based approaches to language processing it is robust and the rules are automatically acquired
when applied to the input cdcca the pattern cca is compared three times to the input as shown in figure NUM
in our example at the first occurrence of this line s is instantiated to lcb NUM rcb and type identity
the following state is NUM which is marked as being of type transduction which means that lines NUM NUM should be applied
on line NUM a new state e is created to which the transition labeled a w a e points and n is incremented
for example let cl be the total number of utterances
finally it must be able to generate appropriate corrective tutorial messages concerning the errors keeping in mind both the goal of correcting this sample text and the larger objective of improving the overall literary of the student
they will be wrongly broken into pieces of isolated characters if not processed correct segmentation for NUM accompany tfn1 president visit of i i n NUM b
when the sentence is structurally ambiguous they determine its structure by comparing it to structurally similax patterns taken from a manually generated set of examples and calculating similarity values
the process described above is written in an algorithmic form as follows NUM select the ambiguous relations those with more than one modificant for each structure
for each of those concept identifiers we obtain from the cd all generalizers concept identifiers that express a similar meaning in a more general way and build a taxonomic hierarchy with them
the arrows in the figure indicate the dependency relations
this value is the mutual information for the relation
this is the mutual information for the relation itself
multiply the mutual information for all the dependency relations in each structure
the following figure shows the output from rdg for a given sentence
the selection of the most probable dependency structure in japanese using mutual information
the program reads in a parse tree generated by microsor s natural language processing tools msnlp for each sentence in an essay the program substitutes words in the parse tree with superordinate concepts from the lexicon and extracts the phrasal nodes containing these concepts
for instance while the core concept of the commonly occurring phrase one band is more often than not expressed as one band or one fragment other equivalent expressions existed in the test data some of which did not occur in the training data
since phrasal category does not have to be specified the use of a generalized xp node minimizes the number of required lexical entries as well as the number of concept grammar rules needed for the scoring process
there are at least two domains where this sequential order might be relevant the global domain of the discourse structure of the text as a whole and the more local domain of relatively small sequences of sentences sharing a particular topic
using the proposed technique unknown word guessing rule sets were induced and integrated into a stochastic tagger and a rule based tagger which were then applied to texts with unknown words
it is only by randomly sampling short text fragments as for the data from the uit den boogaart corpus which contains samples evenly spread out over a period of one year that a substantial reduction in overestimation is obtained
thus after encountering the quoted path root rcb in the preceding example tile global context is changed from dog sing to dog root
this leaves the ditransitive production null vp np vnn np as the only possibility forcing the correct subparse to be chosen here
we have found that the primary obstacle has been the syntactic flexibility of chinese coupled with an absence of explicit marking by morphological inflections
otherwise the first complete parse tree is shown preceded by the number NUM indicating that it was the first alternative produced
however in the general case it can be an arbitrarily long string of words spanned by a nonterminal vnl in this example
the least squares regression lines dotted for d k supported by nonparametric scatterplot smoothers solid lines reveal a significant negative slope f NUM NUM NUM NUM p NUM
this list is also a good start on an online bilingual dictionary for an mt system
the terms file may also be sent to the ibm translation centers at an early stage
the increase in the proportions of new underdispersed types and tokens shows that the pattern observed for the absolute numbers of types and tokens observed in the top panels of figure NUM persists with respect to the new types and tokens
this category is obviously the category most sensitive to parsing problems
generality is attained by addressing generally ambiguous constructions rather than restricting ourselves to a specific cl
for example the fill should be unclear if the article says that someon e was named to a post but does not say that the person has succeeded the predecessor which would occasion the fill to be yes or that the person succeeds or will succeed the predecessor which would occasion th e fill to be no
x will be ceo effective immediately if person is identifed as having been hired etc at least two months prior to the date of the article in other words it would be unreasonable to think the person was still in transitio n
in addition in the case of condition NUM it is no t sufficient for a person to have been identified as a candidate for a post he or she must rather be identified as the definite choice for that post either on an acting or permanent basis
since the simulated database design represented by the extraction template is centered on corporate post s rather than on persons instantiate separate succession event objects for each post even in the cas e where a particular person is acquiring or vacating more than one post at a particular organization
if the person is newly appointed to the job yes means that the person is already actually onboard and working in that capacity if the person is vacating the post yes means that the person has not yet been officially relieved of the duties of that post
the construction process is done by a large number of processing agents
these are in turn succeeded by slices in which ahab is hardly mentioned but he reappears in the last part of the book and as the book draws to its dramatic close the frequency of ahab increases to its maximum
for the trouw data this is a matter of stipulation but for texts such as moby dick or alice in wonderland an argument can be made that the novel is the true population rather than a sample from a population
therefore an important system requirement is that a large variety of texts can be produced from the same database structures
in the bilingual duden oxford we find as a translation of drill into german not only the general meaning bohrer wordnet drdl NUM but also bohrmaschme wordnet power drdl in the context carpentry butldmg
starting with an empty discourse model each candidate sentence adds discourse referents and relevant associated information to this model
for example certain points in the discourse are more appropriate for the expression of a certain bit of information
these syntactically structured sentence templates indicate how the information provided by a database object can be expressed in natural language
the notation de c stands for the set of discourse entities roughly earlier introduced individuals associated
but it also raises the question of whether we might have used drt as a backbone for dyd s context model
but it is difficult to see how the requirements of high quality language and speech generation can be reconciled with formal elegance
if the discourse model is found to be well formed the candidate sentence can be used as an actual sentence
null duced from the system log file without any changes other than reformatting
in section NUM we pointed out three important aspects of dialogues which have been insufficiently accounted for in the earlier approaches to dialogue management
maintain the initiative if the response is thematically related follow up old continue take the initiative if unrelated newquestion uotrelated
the context model is represented as a partitioned prolog database and the predicates have an extra argument referring to the contribution whose processing introduced them
the world model uses neo davidsonian event representation and the application model provides mappings from world model concepts to task and role related facts
the roles can be further difl erentiated with respect to social factors such as acquaintance of the addressee and fornlality of the situation
consider the following sample dialogue where tile system s task is to provide service information to the user uh i need a car
it is determined by evaluating the partner s goal with respect to the communicative context expectations initiatives unfulfilled goals and coherence
in 31a it is the prenominal position of the adjective and in 31c the choice of the preposition d versus de as in 5b which give rise to the causative sense versus the stative one
in addition let to s be the tokenization set of s on d
in our view this state of affairs should not be taken as evidence that imt for skilled translators is an inherently bad idea
figure NUM combined trigram translation model per formance versus trigram weight NUM but all punctuation must be manually typed and
we found that simply suppressing punctuation in the generator led to another small increment in keystroke savings as indicated in table NUM
we only ne c l for our i url ose h mmata base lexemes idiom t artmt ating lcxi al words bock schicfl n tim inte rnal syntacti structure encoded as a syntactic tre e the internal sclnantic structure ellcoded as predicate argument structure and the logical tbrm
can be found adjectival modifications in NUM NUM lu mtifieation in NUM and focussing y lemonstr tive determiner NUM and by question in NUM apply c the idioms internal nps
each score produced by the evaluator is an estilnate of p tl lcb st the probability of a target language
when a d tree a is subserted into another d tree fl a component NUM of a is substituted at a frontier nonterminal node a substitution node of j3 and all components of a that are above the substituted component are inserted into d links above the substituted node or placed above the root node of ft
NUM in contrast the use of a non hierarchical representation for the underlying semantics allows the input to contain as few language commitments as possible and makes it possible to address the generation strategy from an unbiased position
like NUM crsl must NUM e interpret d as focus adverl
first the number of senses for each lexeme is on average much smaller than in language as a whole
for instance if the invention is an apparatus it must be described in a static state without reference to its operation
the claim must consist of a single albeit possibly very complex sentence with a wellspecified conceptual syntactic and stylistic rhetorical structure
the classes defined for claims about apparatuses include meronymy spatial connection change state change location apply force purpose and others
this stage takes as input a forest of templates and results in the production of a bracketed string of predicate and case role symbols
the property determining realization is adjacency not hierarchical relations therefore the orientation of the brackets and their nesting is immaterial
text planning in this system can be considered as content preserving revision of a shallow draft representation produced by content specification
the global structure of the claim text plan follows the structure of the conceptual schema of a claim with one important difference
the hierarchical structure among these sets is established based on the position in the tree of the template against which this match occurred
an important goal has been to make our system as parameterizable a s possible so that the same software can meet different demands for recall precision and overgeneration
the final parameter settings for the test were generated by running the systems over all of the data we had and choosing the setting which seemed to maximize the value of the f measure
we look forward to integrating spatter in future information extraction tasks to test the hypothesis that a far more accurate parser could lead to more accurate understanding and to notably higher scores
we wonder what would be an external criterion of correctness even in a system of terminological logic or is there a criterion which checks the adequacy of a manually supplied logic concept definition
the other errors are caused by polysemous verbs such as kakaru hangflie all or ataru hit strike be exposed shine
generally one expects that the sparser the data the more helpful are models that can intervene between different order n grams
this confirms our view that mainly two information elements per utterance are given
the caller needs to know these information elements to carry out this plan
most of the caller s reactions NUM are positive acknowledgements
table NUM shows the frequencies of these three possibilities for each repair act
we see that repair sequences do not occur as frequently as positive acknowledgements
by contrast reconfirmations and corrections mainly occur directly after the problematic utterance
this new information element will often be accompanied by an already given element
the NUM NUM available te texts were randomly partitioned into training set and a blind test set with training size ranging fro m NUM to NUM of the documents
if phrase NUM does have name information name NUM yes but phrase NUM does not have name information name NUM no then they are also judged coreferent
this feature was included specifically to handle pronoun resolution and represents a variation on the well known heuristic of mergin g with any referent found in the most recent compatible phrase
it always helps to look at concrete examples and the designated walk through text provides us with a n opportunity to describe some selected processing as it tackles a specific text
although our recall and precision results are not among the best reported in this evaluation we find the results extremely encouraging given the fact that resolve is a fully trainable system
the inevitable politics of such a situation will be difficult to mediate unless all sites agree to follow the lead of muc annotation conventions fo r their own internal development efforts
some components are more effective than others some have been under developmen t longer than others but in all cases we are working to eliminate manual knowledge engineering
these actions are those that an artificial agent could use in negotiating which actions or beliefs to accept into the shared plan of the agents
NUM NUM lexical rules as unary phrase structure rules
our compiler distinguished seven word classes
to contain no further common factors
NUM NUM lexical rule interaction as definite relations
NUM no additional hand specification is required
conceptual distance between two nodes is defined as the m ir mn number of edges separating the two nodes
the human judges are provided with the NUM nouns surrounding the word to be disambiguated
the other nouns will just be glossed over and do not contribute to the decision
its p fops ance also compares favourably with two supervised learning algorithm q
a walkthrough of the approach with a simple example w l better illustrate it
we let the tree bank data decide which types can be grouped together and which types should be distinguished
interaction into definite relations in figure NUM
finite state automaton representing global lexical rule interaction
this indicates that information theoretically if the topicalization feature is present the semantic class feature is not needed for the classification
in the next section we will elaborate on how we learn the grouping of semantic types from the data
after these operations the shallow syntactic tree is linearized to create an expression in the target language
because the training samples are created withovt this application in mind we may be able to improve the performance by increasing the size of the training samples or by using different samples which have the similar styles and contents to the documents
ewe will discuss the notion of substitutability further in the next section
only components marked as substitutable can be substituted in a subsertion operation
the parser returns a parse forest encoding all parses for an input string
we would like to thank rakesh bhatt for help with the kashmiri data
we first subsert the embedded clause tree into the matrix clause tree
the performance of this parser is sensitive to the grammar and input
a salient feature of tag is the extended domain of locality it provides
in ltag the operations of substitution and adjunction relate two lexical items
but the other day sounds good
then the recognition algorithm consults the parse tables to build the sets of items as in earley s algorithm for context free grammars
the dependency grammar is translated into a set of parse tables that determine the conditions of applicability of the primary parser operations
given the sentence the can will be crushed
the tags are not listed in any particular order
phrases are filtered through a list of general purpose words which is constructed separately for every new domain
in this pattern person and date are patterns themselves and all other constituents are linguistic entries
in general all this is a time consuming process and often requires the help of a domain expert
figure NUM this figure shows main kawb components and modules and sgml marked data flow between them
the pattern finder component makes use of phrasal annotations of texts produced by a general robust partial parser
thus both these activities can be applied iteratively until a certain level of precision and coverage is achieved
these tools are integrated into a coherent workbench with a common inter module data flow interface based on sgml
if instead of infarction we use a type disease we can achieve even broader coverage
the inner context categorization is started with extraction of compound nouns from collected by the collocator noun phrases
however it is not necessarily the case that related adjectives are stated together in one wordnet entry
if the user expresses need for clarification about the previous goal this must be addressed first
this means that the output generator must have a parameter that enables the system to specify assertiveness
add a wire between the minus com hole on the voltmeter and the connector one two one
the paper gives performance data for a series of NUM problem solving dialogs carried out with human subjects
after the semantic expectations are computed they are translated into linguistic expectations according to grammatical rules
the meaning with the smallest total cost is selected to be the final output of the parser
the response received after a given input is likely to be related to the currently active subdialog
example task related expectations would include general requests for help or questions about the purpose of an action
the parser interprets the user statement as a statement asking about the location of an unspecified object
those would have led to substantially better performance if they could have been used
the primary stage picks up interpublic group painewebber coca cola coke creative artist s agency wpp group ammirati puris new york yacht club and new york times
since we were n t using the parser the part ofspeech obtained by a lexical lookup was of interest mainly if it was something like city name or orgname we did also try to prevent the inappropriate inclusion of verbs prepositions etc in names wit h mixed results
we modified the nltoolset supplied tokenizer to try to prevent it from reordering or dropping text in ways that made it difficult to map back to the original text when writing the ne output file we also modified it to preserve upper vs lower cas e information
if a domain required a different set o f template slots than used for muc NUM the patterns would be unchanged but the reduction code that fill s the slots and the postprocessing code that reports them would have to be modified slightly
the system decided mccann was a person based on the mccan n family since it did not recognize mccann erickson as a company every reference to mccann wa s therefore marked as a person
for the te task the postprocessing step consists of traversing the list of expectations and writing a template for each performing final clean ups like removing duplicate aliases combining the person name pieces skipping slots used only to control merging etc
mccreight algorithm uses two basic functions to scan paths in the suffix tree under construction
pair i j denotes the factor of w starting at position
we write q s link p if there is an s link from ptoq
we conclude that these methods use an amount of time o nn3
question is there a transformation that has score greater than or equal to k w r t
stop as soon as a node of t is encountered that already ha s
most important here all of the above results still hold for these generalizations
our results are achieved by exploiting a data structure originally introduced in this work
crucially we assume we can access the s links of t and t
our module contains two instances of a finite state automaton
the black boxes stand for the date currently under consideration
within a uniform computerhuman interaction we resolve these problems
basic notions within verbmobil are tu NUM and utterances
we represented respectively the parse accuracy of the test sentences that contained at least one unknown word the parse accuracy of the test sentences without unknown words and finally the parse accuracy of all test sentences together we will come back to the other accuracy metrics later
in the latter case the correct translation is is that possible for your
what we hope to have shown is that it is possible to extend a stochastic parsing model in a statistically and cognitively adequate way such that it can directly parse and disambiguate word strings that contain unknown category words without the need of an external part of speech tagger
we start with a brief introduction to dialogue processing in the verbmobil setting
thursday NUM is the current date agreed upon
nevertheless processing in the real system creates still new challenges
tile derivation is given in figure NUM
figure NUM groupoid relational compilation of the assignment to are missing
such is the character of our trea tment
composition is now treated as follows
the further step to computing semantics is unproblematic
and deidre wheeler eds categorial grammars
there is another algorithm called viterbi algorithtn to lind ol timal solution
the process of selecting the best tag sequence is called ms optimization process
the second consequence is to attenuate the benefits of context blending because most contexts are equivalent to their maximal proper suffixes
figure NUM most words have a fatter tail than poisson solid line
of course idf is but one of many ways to show deviations from chance
this result for the NUM gram is not honest because knowledge of the test set was used to select the optimal model order
techniques next to natural language processing to search job offer databases is not a new application cf
o nevertheless our experiment has shown that the advantages outweigh the disadvantages at least for this particular formulation of a class based approach to alignment
in a broader sense we have shown that thesauri and corpora can be used in combination to address the critical issues of generality and efficiency
tests are performed on the sentences found in the lecdoce and a user s manual available in both languages to assess the method s robustness and generality
the rest of this paper is organized as follows in section NUM we briefly discuss the nature of text and translation that justifies a class based approach
as stated earlier the compounding effect in mandarin frequently results in a change in the number of words between an english sentence and its mandarin translation
for instance classalign connects the words for and of in e20 erroneously to the morphemes t and in c20 respectively
for most languages other aspects of word order such as that of modifier and modified elements correlate with the order of v and o
for various reasons such as language typology style and cultural differences a translator does not always translate literally on a word by word basis
annotationprecedence which is a list of annotation types is used to resolve conflicts when two annotations cover the same span the tag corresponding to the annotation type which appears first in the list is written out first
length annotationset integer returns a count of the number of annotations in annotationset nth annotationset n integer annotation returns the nth annotation in annotationsct where the first annotation has index NUM
this formal specification is supplemented by a large amount of narrative the fill rules describing the circumstances under which a template object is to be created and the information to be placed in each slot
because attributevalue can take on multiple types including types such as strings which would not use a generic container structure implementations in such languages must provide an explicit type discriminator here accessible through the typeof operator
that annotation would like all annotations have a set of spans referencing the text it would also have a value attribute holding the value of the template slot the slot filler
it is expected that in the future it will be possible for experienced users to customize extraction systems to new scenarios this would be an interactive process which would draw upon a library of predefined template objects
these routingqueries are then stored and indexed in a querycollectionlndex finally this querycollectionlndex can be run against a document to produce a set of relevant queries profiles in the form of a detectionneedcollection
the result of all this preprocessing is a documentcollectionlndex retrieval is then performed by sending a query in the form of an retrievalquery to the documentcollectionlndex the documentcollectionlndex returns a list of relevant documents
for large collections which are already in place on some data store such as a file system or a data base it may be highly desirable to create the tipster collection without copying the document text
for each document in collection which if the same document a document with the same id appears in destination annotate that document in collection destination with the information extraction templates generated for that document
thus untensed verbs must combine with other verbs which subcategorize for them e.g. lijkt re forcing all verbs to appear in a verb cluster at the end of a clause
each memoized goal is associated with a set of bindings for its arguments so in clp terms each memoized goal is a 1this essentially means that basic constraints can be recast as first order predicates
program for each clause p e p such that c resolves with p on s c choose a corresponding resolvent e and add iric c to a
to simplify the interpreter code the prolog code for the selection rule and tableig output of the control rule also return the remaining literals along with chosen goal
because we do not represent variable binding as explicit constraints we can not implement abstraction by means of the control rule and require an explicit abstraction operation
lexical entries are formalized using a two place relation lex w0rd cat which is true if cat is a category that the lexicon assigns to word
experiments led to the hypothesis that the most improvement came by assigning a boundary if the cue prosody feature had the value complex even if the algorithm would not otherwise assign a boundary as shown in fig NUM
we would like to thank andreas eisele pascal van hentenryck martin kay fernando pereira edward stabler and our colleagues at the institut ffir maschinelle sprachverarbeitung for helpful comments and suggestions
that means that any structure subsumed by a solution is also a solution as in prolog for example
yet looking at node i7 the algorithm finds e list a simple type and does nothing
approp to f is a constrained type or a hiding type then t is a hiding type
the compiler finds out exactly which nodes of a structure have to be examined and which do n t
consequently since he list is a subtype of list the value of tl needs to be constrained as well
definition simple type a simple type is a type that is neither a constrained nor a hiding type
for a constrained type t first of all we have to perform constraint inheritance from all types that subsume t
in the following we therefore only deal with implicative constraints with type antecedents the type definitions
first metonymies are not clearly separated and indicated in lloce
call or email ldc unagi cis upenn edu
investigate what applications similar to yours have been implemented and by which contractors
the above figure displays the overall architecture of the ebl learning method
the first stage is i o find a lexical entry in the neighbor null hood of x defined by l
rsuk lcb sock rcb not used in this analysis is eventually discarded from the dictionary for lack of use
the i problem is discussed in the text misanalysis of the role of i also manifests itself on something
in particular the input was phonetically oversimplified each word pronounced the same way each time it occurred regardless of environment
others like t wo and don demonstrate how the system compensates for the morphological irregularity of english contractions
klkt f lcb kick off rcb is later found to be parsable into two subwords and also discarded
automatic grammar extraction is worthwhile because it can be used to support the definition of a controlled domain specific language use on the basis of training with a general source grammar
assuming that the following template index pairs have been inserted into the decision tree ab tl abcd t2 bcd t3
for that reason the elements of mrsg are alphabetically ordered so that we can treat it as a sequence when used as a new index in the decision tree
however in the case of conventional representation form the mechanisms for indexing the trained structures would require more complex abstract data types see sec NUM for more details
noun phrases nonfinite clauses and nominal clauses or it as a deferred preposition is preceded by a passive verb chain pass vchain or a postmodifying clause postmodicl the main verb in a postmodifying clause is furnished with the postmodifier tag n or of a wh question i.e. in the same clause there is a wh word
what is interesting in these hybrids is that they unlike purely data driven taggers seem capable of exceeding the NUM barrier all three report an accuracy of about NUM NUM NUM the success of these hybrids could be regarded as evidence for the syntactic aspects of parts of speech
even though it was originally devised and implemented for dealing with the morphological ambiguity problem in hebrew the basic idea can be extended and used to handle similar problems in other languages with rich morphology
yet the primary point is not to propose a method for morphological disambiguation per se but rather to suggest a method to compute morpho lexical probabilities to be used as a linguistic source for morphological disambiguation
at this step a set of words will be generated by a generating function and be replaced to the detected word
from a given solution set of triples a1 NUM am we can compute in polynomial time a mapping k that sends the index of an element to the index of its solution triple i.e. k i j iff ai e 4j
by noticing that this invariant is true for ax and is preserved by the rules we immediately can state proposition NUM count invariant if i sb l u a then b u b a fo any b t
we verify again via balancedness of the primitive counts that s aj NUM s aj NUM s aj NUM n holds because these are the numbers of positive and negative occurrences of cj in the sequent
let us define the polarity of a subformula of a sequent a1 am a as follows a has positive polarity each of ai have negative polarity and if b c or c b has polarity p then b also has polarity p and c has the opposite polarity of p in the sequent
furthermore due to the subformula property we know that in a cut free proof of u x the mmn formula in abstractions right rules may only be either c o b o x or b o x where x e lcb x y rcb since all other implication types have primitive antecedents
the most outstanding of these benefits is probably the fact that the specific way how the complete grammar is encoded namely in terms of combinatory potentials of its words gives us at the same time recipes for the construction of meanings once the words have been combined with others to form larger linguistic entities
question can NUM be partitioned into m disjoint sets NUM NUM am such that for NUM i m ae a s a n note that each 4i must therefore contain exactly NUM elements from NUM comment np complete in the strong sense
for notational convenience we abbreviate a bi b bn by a b b2 b1 and similarly b o b1 o a by bn b2 b1 o a but note that this is just an abbreviation in the product free fragment
although both frameworks are equivalent in weak generative capacity both derive exactly the context free languages lcg is superior to cg in that it can cope in a natural way with extraction and unbounded dependency phenomena
because of technical reasons we were not able to use the morphological analyzer at this stage and thus we could not identify ambiguous words in the sw sets
whenever a part of a name e.g. a person s last name is outside the vocabulary current lvcsr technology will find the closest transcription within the vocabulary causing one to misrecognize the name and providing errorful input to information extraction
bbn s identifinder tm figure NUM is made up solely of lightweight algorithms and was entereed in the muc NUM named entity task recognizing named organizations named persons named locations monetary amounts dates times etc
the ne task is the simplest task and makes use of only lightweight processes the first three modules of the plum system the message reader the morphological analyzer and the lexical pattern matcher which together form identifinderru
for muc NUM there were three different application dimensions that one could choose to participate in as follows named entity ne recognition of named organizations named persons dates and times monetary amounts and percentages
our first heuristic and one of the best we found was to take only the frames from the system with the best recall shogun that also matched frames from the system with the best precision plum
the views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies either expressed or implied of the defense advanced research projects agency or the united states government
the parallel structure of the muc competition with many different systems attempting to extract the same data from the same material creates an opportunity to achieve even greater performance by combining the results of two or more systems
in order to get a sense of how well we could potentially perform by treating the overall systemcombining task as one of choosing templates we performed an experiment as follows we combined all the frames from both systems and scored the combined result against the key
entity recognition and identification has been recognized by the community as a core problem and was evaluated in muc NUM in november NUM for english and was also evaluated for chinese japanese and spanish in the recently held multi lingual entity task met
where denotes the possible nf1 structures with respect to n i and ly
missing names is an area of on going improvement
a prototype indexing the distribution improves the performance of lt nsl to acceptable levels for much larger datasets
the alternative higher level view lets one treat the nsgml input as a sequence of treefragments
yes because there is in principle no limit on what can be encoded in an sgml document
nsgml is a fully expanded text form of sgml informationally equivalent to the esls output of sgml parsers
sgml architectural forms a method for dtd subsetting could provide a method of formalising these program interfaces
null separating corpus text from annotations is a general and flexible method of describing arbitrary structure on a text
ggi a graphical tool shell for describing pro null cessing algorithms and viewing and evaluating the results
the theme of this paper is the design of software and data architectures for natural language processing using corpora
although disk space is now cheap the cost of preparing and storing the indices for ims cwb is such that the architecture is mainly appropriate for linguistic and lexicographic exploration but less immediately useful in situations such as obtain in corpus development where there is a recurring need to experiment with different or evolving attributes and representational possibilities
although sgml is human readable in practice once the amount of markup is of the same order of magni7this may be a specialised need of academic linguists and for many applications it is undoubtedly more important to provide clean facilities for non hierarchical queries but it seems premature to close off the option of such access
table NUM gives the final perplexities on the validation set the test set and the unseen bigrams in the test set
it follows that algorithm NUM records at step NUM the transformations having the highest score in l among all transformations represented by nodes of tx
if the three cases ga nom vo acc and ade at are dependent on each other and it is not possible to find any division into several independent subcategorization frames e can be regarded as generated from a subcategorization frame contaiuing all of the three cases
in this paper we have focused on bigram models but the ideas and algorithms generalize in a straightforward way to higher order n grams
they can be categorized as follows NUM
unlike the maximum entropy approach which allows one to combine many non independent features ours calls for a careful markovian decomposition
as part of our tipster research program contract number NUM f133200 NUM we have developed a variety of strategies to resolve coreferences within a free text document
of the threehundred most common words table NUM shows the fifty with the lowest and highest values of al w
it is encoded in tile lud as in NUM of
holes are special labels for the slot of an operator s scope domain
lud describes a number of drss and allows for underspecification of scopal ambiguities
the sentence in fig NUM contains at least three different discourse relations
here however the linguistically abstract unit of sentence will be presupposed
they should be maintained in the definition of a consistent subordination relation
discourse relations and discourse markers are abbreviated to discrol and din respectively
in section NUM a representation for multiple discourse relations is proposed
these are common in japanese but cause a problem for the formalism
as a matter of fact the more evzdent problem is that many times argumental positions are not properly filled
this distinction is in accordance with the classical distinction drawn between stative adjectives and dynamic ones which following quirk et al NUM NUM denote qualities that are thought to be subject to control by possessor
in partitular instead of enumerating all syntactic constructions and the different senses for these adjectives we will provide a rich typed semantic representation which explains both the semantic and the syntactic polymorphism associated with these classes
NUM agentive formal and const tel1c in the case of mental adjectives the qualia in NUM and NUM makes explicit that they denote a complex or dotted type written ype type which is the product of basic types st and e2 for agent oriented adjectives and el e2 and e3 for emotive ones
in other words the ll representation for emoti m adjectives NUM stipulates that somebody a is in a state because of an experiencing ewmt e2 which can have a further manifestation e3 NUM that lot agent oriented adjectives NUM specifies that somebody is in n state which can have l manifestation
32b will be impossible in 32a the prenominal position of the adjective forces the causative sense giving rise to an incompatibility as two different nouns namely enfant children and mort de leur m ve death of the mother try to saturate the same variable y i.e. the object of the experiencing
in this last case the adjective can then be projected via the formal or the telic agentive roles and combines the two or three different senses stative causative and or manifestation depending on the number of events it can refer to one for agent oriented adjectives see NUM two for emotional ones see NUM
partir is indeed a subtype of the experiencing event sort parfir ezperiencing event and les dchecs chess of the intellectual act one les dchecs creativeintellectual act by contrast in order for NUM to be an acceptable sentence tre malade be ill must be reconstructed non standardly as an intellectual act
NUM un homme ennuyant pr6occupant admirable effroyable which causes somebody s trouble anxiety admiration fright finally those in NUM will not be specified regarding the head they are headless and will be able to combine the three senses except when the suffix acts as a filter as shown in NUM
NUM semantic selection the case of emotion adjectives on the basis on the headedness configuration we will distinguish three classes of french emotion adjectives exemplified in NUM NUM and NUM NUM adjectives headed on the state fdchg angry ennuyg bored irritd irritated etc
in this experiment accuracy was the igtree version turns out to be better or equally good in terms of generalization accuracy but also is more than NUM times faster for tagging of new words NUM and compresses 4in training i.e. building the case base ib1 and ibi ig NUM seconds are faster than igtree NUM seconds because the latter has to build a tree instead of just storing the patterns
there are both generic requirements for the interface for example viewing extraction results and correcting them and application specific ones providing a database browser and a tool for manual subject matching we therefore spent significant effort in designing the interface to match the needs of the dea analyst
after five rounds of reshuffling the tagging error rates become much smaller than the error rates using the 50mw clustering text with no reshuffling
figure NUM contrasts the tagging results using only word bits against the results with both word bits and linguistic questions NUM for the ws3 text
figure NUM shows the tagging error rates with word bits obtained by zero two and five rounds of reshuffling NUM with a 23mw text
brown et al showed that the likelihood l tr of a bigram class model generating the text is given by the following formula
the root node of the decision tree represents the set of all the events with each event containing the correct tag for the corresponding word
i merge the pair of classes in the merging region merging of which induces minimum ami reduction among all the pairs in the merging region
f elc2 i pr clc2 log pr cl pr c2 NUM cl c2
the qualitative observation supports the numerical results introducing categorial information is not advantageous because it increases the size of the grammar without decreasing significantly the average number of conflicts
if the scope of such a word does not directly correspond to a tree node the word is attached to the lowest node dominating all subconstituents a pl earing ill its scope
figure NUM shows a screen dump of the graphical user interface of our component while processing the example dialogue
uptake suggest support date request comment date well i would suggest in january between the
the robustness of this subcomponent is ensured by dividing the construction of the intentional structure into several processing levels
the precision figure is supported by evidence from the ne evaluation
first configurations and lexical information are precompiled separately into two tables an x table and a table of lexical co occurrence which gives rise to more compact data structures
the following table shows the results of dop4 compared with those of dop3
this produces a total of NUM NUM subdocuments which are segmented into short words as described in section NUM in addition the single characters from each word of length two or greater are also used for indexing purposes to guard against wrong segmentation
thus each different factor that composes a principle can be considered a separate primitive and such primitives can be grouped into classes defined according to their content
the current version makes use of plan operators both hand coded and automatically derived from the verbmobil corpus
the raw counts of k separated bigrams however do give good initial estimates
NUM in the next section i propose and discuss a solution to this problem that builds on other approaches and relates the parser to the grammar in a principled way
the third column shows the adjusted frequencies as calculated by the good tufing formula
after this optimization the word graph contains exactly one start node and one or more final nodes associated with a score representing a measure of confidence that the utterance ends at that point
what is unique in our approach is to integrate system development with the corpus annotation process itself
in particular our approach applies a bootstrapping procedure to the development of the training corpus itself
it is important to note however that there is still noise in the curve
only named entity tags or only tokenization tags or any desired subset of tagsets
an example screen image from a typical session with the workbench is shown above
the new tagging rules were then applied to the next ten documents prior to being manually tagged edited
these rules can then be applied to new unseen texts to pre tag them
to this end we have incorporated a growing number of analysis and reporting features
a full featured interface to the multi stage architecture of the alembic text processing system
ku era NUM pps stands for singular nominative pronoun in third person vbd for verb in past tense np for proper noun vbn for verb in past participle form by for the word by at for determiner nn for singular noun and bedz for the word was
therefore we need to improve how to apply genetic algorithms to be able to remove erroneous translation rules
therefore the translation rules which are not used in the translation process are never removed from the dictionary
for example the combination of words which are i in english and watashi NUM in japanese i in japanese is true when this combination exists in a given translation example
the experiments were carried out with and without the improvement for the selection process described in section NUM in the experiments the precision increased from NUM NUM to NUM NUM and the recall increased from NUM NUM to NUM NUM
on the other hand the combination of words which are volleyball in english and basukettoboru in japanese basketball in japanese is false when this combination does not exist in all given translation examples
state transitions are chosen based on the student s current input whether the student has attempted the question before and domain knowledge
this was illustrated by the experiments on pp attachment and pos tagging data sets
it can also be used to do unranked boolean retrievals
the walkthrough article is an article selected from the test set
line NUM checks that this state does n t already exist and adds it if necessary e n means that the arrival state for this transition i.e. d q w will be the last added state and that the number of states being built has to be incremented
each paragraph has predetermined topics called assumed topics which are determined by a linguist
section NUM shows a series of experiments based on the proposed model and discusses the results
row NUM in table NUM shows the difficulty in finding topics from many candidates
both pn and pv are initialized to NUM NUM and then are trained by using the NUM NUM corpus
the two values are used to screen out the unimportant words whose 1df values are negative
since it is impossible to randomly select candidates as topics we know topic identification is valuable
here a topic set whose members are the first NUM of the candidates is formed
section NUM presents a corpus based language model and discuss how to train this model
the threshold values for nouns and verbs are set to NUM NUM and NUM NUM respectively
with the following ingredients ilmm alignment probability p i i i or p aj laj l i translation probabflity p f e
in most current dialogue systems each of these goals would be realized as a separate utterance i.e. the surface structure of the dialogue would merely be a reflection of the underlying dialogue history see dialogue NUM
l usr frankfurt am maaaaiiin to avoid such uncooperative dialogues a system has to be able to interpret additional information provided by the user as for instance in move d in dialogue NUM
inform part a of figure NUM shows the corresponding parse tree while tree b shows a possible continuation which could result in the utterance did you say hamburg if chosen as the continuation of inform u s and if the recognition rate for hamburg is low
if the interhal structure of adjoining dialogue cycles siblings see figure 4a is identical and the concepts tasks negotiated in these structures either have the same superconcept or are connected to the same concept by means of a relation and if the state of the concepts under consideration is open then the resulting utterance is abbreviated
for instance one can not aggregate between different levels in the dialogue history if the higher level has not yet been satisfied as the following two examples illustrate do you want the rate or the total cost of a call to where or when do you want the rate of a call
our goal however is to generate utterances like those in sample dialogue NUM hence we need to investigate which communicative goals can be satisfied at a time in other words which constellations of dialogue acts given a certain state of the task model can be jointly expressed in one utterance
in our previous example the system employs the same dialogue act request to pursue different goals with the initial request the system realizes the goal acquire specific information with the instantiation of source
since its confidence in the result from the speech recognition is low the continuation is interpreted as acquire confirmation open end NUM which is instantiated to acquire confirmation of recognized source homburg
prefixes can be polysemous and have separate highly underspecified lexical entries
in our implementation the overhead of determining which goals are determinate has turned out to be by far outweighed by the reduction in search space for our linguistic applications
this man a korean is the president of north korea as mentioned above in english or in french proper names could be distinguished from common nouns at least by means of the use of the upper ease for the former even though it is not an absolute criterion
he only drinks bordeaux red wine the derivation of some other categories from pns is also observed in park junghee s manner z france style
this requires a generalization of the above procedure so that instead of using the label seen we attach labels seen i c imes to each node where NUM i x
the results indicate that although the basic probability assignments may be sensitive to application environments the use of cues in the prediction process significantly improves the system s performance
since there is only one instance of the cue in the set of dialogues when the cue is present in the testing set it is absent from the training set
the second method constant increment with counter associates with each bpa for each cue a counter which is incremented when a correct prediction is made and decremented when an incorrect prediction is made
table NUM a also shows that when an analytical cue is detected the system correctly predicted all but one case in which there was no shift in task initiative
in this paper we argue for the need to distinguish between task and dialogue initiatives and present a model for tracking shifts in both types of initiatives in dialogue interactions
in this paper we show how distinguishing between task and dialogue initiatives accounts for phenomena in collaborative dialogues that previous models were unable to explain
first recognizing and providing signals for initiative shifts allow the agents to better coordinate their actions thus leading to more coherent and cooperative dialogues
thus we associate with each cue two bpa s to represent its effect on changing the current task and dialogue initiative indices respectively
the same formalism allows the processing of grammatical categories verb adverb preposition etc instead of characters for transcription
elision often occurs at the end of words petite or in the middle of words emploiera tellement
in the future in text to speech systems some segments and even syllables will disappear entirely and certain functors will be greatly attenuated
in english when the speech rate exceeds a certain threshold in natural speech pauses disappear and segmental durations become shortened
the rule compiler transforms the external form of the rules into an internal form that can be easily used by the rule interpreter
for proper names the correspondence between written names and their pronunciation is even more difficult to specify due to their disparate origins
such an interface can then be used in applications ranging from language pedagogy to the teaching of reading to individuals with learning disabilities
b followed by a point is replaced by b spelled if b is preceded by another point or a space
why not then store all words or certainly all of the words that would be commonly encountered in text in memory
usually in french s between two vowels is pronounced z otherwise s
in the context of the ovis system it is important that the parser can deal with input from the speech recognizer
the lexicalized tree substitution grammar in figure NUM which is constructed by the head corner parser derives exactly these two derivations
james out dooner in as ceo of mccann erickson as a result of a reassignment of james james is no on the job as ceo any more his new job is at the same as his old job dooner may or may not be on the job as ceo yet and his old job was with the same org as his new job
computational linguistics volume NUM number NUM for this approach to work other predicates must expect string positions that are not instantiated
in the second step this table is encoded in such a way that first argument indexing ensures that table lookup is efficient
thus before we try to attempt to solve goal we first check whether a goal item for that goal already exists
null NUM this will typically be much more efficient because in this case gaps are hypothesized in a purely top down manner
the simplified version may be regarded as the run time version whereas parse trees will still be very useful for grammar development
furthermore the start symbol should be defined in such a way that it includes all categories considered useful for the application
let us now look at how the parser of the previous section can be adapted to be able to assert history items
such a local tree representation is an element of a list that is maintained for each lexical head upward to its goal
as defined for muc NUM the st task presents a significant challenge in terms of system portability in that the test procedure requ ed that all domain specific development be done in a period of one month
he put the stakes the table
some systems completed all stages of analysis before producing outputs for any of the tasks including ne six of the seven sites that participated in the coreference evaluation also participated in the muc NUM information extraction evaluation and five of the six made use of the results of the processing that produced their coreference output in the processing that produced their information extraction output
he put the stakes every five feet
on the periphery of the central phenomena are markables whose status as coreferring expressions is determined by syntax such as predicate nominals motor vehicles international is the biggest american auto exporter to latin america and appositives mvi the first company to announce such a move since the passage of the new international trade agreement
poppelsdorfer allee NUM d NUM bonn mwo c asll ikp
NUM in NUM however pragmatic information leads to a preference for the bear not the boat to be the referent of the silly thing in the last utterance this preference is in conflict with the boat s being the most likely cb
that is the centers of an utterance in general and the backward looking center specifically are determined on the basis of a combination of properties of the utterance the discourse segment in which it occurs and various aspects of the cognitive state of the participants of that discourse
the only possible interpretation is that the john referred to in 15c is a second person named john not the one referred to in the preceding utterances in NUM however even under this interpretation the sequence is very odd
NUM this point is connected with the discussion of partial ordering in section NUM NUM it may appear that cb un comes from cf un NUM or prior sets of forward looking centers but then it is only because it is in cf un NUM also
for instance it arises in utterances containing noun phrases that express functional relations e.g. the door the owner whose arguments have been directly realized in previous utterances e.g. a house as occurs in the sequence NUM a
in constructing a computational model we are then left with three choices compute all possible interpretations and filter out possibilities as more information is received choose on some basis a most likely interpretation and provide for backtracking and computing others later compute a partial interpretation
for example if 19b is followed by a sentence with it in the subject position then it is more likely to refer to the doorj NUM this is consistent with the ranking of the door ahead of the house in cf 19b
this measures how well a system processed utterances in isolation but does not give the complete picture of system performance in a dialog where utterances are related through context
descendent relation is both reflexive and transitive thus given a string al a of length n say r NUM NUM the following steps are done
a grown auxiliary tree is defined to be either a tree resulting from an adjunction involving two auxiliary trees or a tree resulting from an adjunction involving an auxiliary tree and a grown auxiliary tree
the measures used for information extraction include two overall ones the f measure and error per response fill and several other more diagnostic ones recall precision undergeneration overgeneration and substitution
the labels of ml and me being the same any node satisfying the above observation will be called a minimal node w r t the symbol positions r r0
assoc list m where m is a node will be useful in obtaining chains of nodes in elementary trees which have children la null NUM
this can also be handled similar to the manner described for case NUM update a i p q s with lcb m rcb u assoc list m
we propose that an attribute value matrix avm can represent many dialogue tasks
each scenario execution has domain a NUM hello this is train enquiry service
note that the ability to calculate performance over subdialogues allows us to conduct experiments that simultaneously test multiple dialogue strategies
u no ft NUM c add a wire between connector eight four and connector nine nine
note that the attributes are almost identical to smith and gordon s list of subtasks
fault type corresponds to diagnosis fault correction corresponds to repair and test corresponds to test
c is the seven on the led displaying for a longer period of time
c is the one on the led displaying for a longer period of time
however the values for and ci are now calculated at the subdia iogue rather than the whole dialogue level
in addition only data from comparable strategies can be used to calculate the mean and standard deviation for normalization
in geometric terms aligned blocks are rectangular regions of the bitext space such that the sides of the rectangles coincide with sentence boundaries and such that no two rectangles overlap either vertically or horizontally
the second matching predicate was just like the first except that it also evaluated to true whenever the input token pair appeared as an entry in a translation lexicon
this task was impossible until now because it can not tolerate even a few wild errors such as those produced by an independent implementation of char al i gn
to accommodate language pairs with vastly different word order it may suffice for simr to increase the maximum point dispersal threshold relaxing the linearity constraint on tpc chains
if the point in cell j i should really be in cell j h re alignment inside the erroneous blocks would not solve the problem
the injectivity property also leads to a heuristic which reduces the number of candidate chains although the chief aim of this heuristic is to increase the signal to noise ratio in the scatterplot
when simr makes its second pass search for non monotonic segments it also searches for sandwiched chains in any space between two accepted chains that is large enough to accommodate another chain
typical errors of omission are illustrated in figure NUM by the complete absence of correspondence points between sentences b c d and b c
concept modified concept f conscious being object q
description of the complete algorithm including a specification of the normalized input representation see section NUM NUM can be obtained from a report available at the project web page http crl nmsu edu research projec
in our work we basically follow the approach of panaget
accuracy measures the degree to which the system produces the correct answers while precision measures the degree to which the system s answers are correct see the formulas in the tables
we only list two variations below b since a
we will only give the verbalization and omit the text structure
in the first rule below and are identical
todaysdate is a representation of the dialog date
tu is the current temporm unit being resolved
our method is to isolate errors according to a scoring criterion and then transmit to the parsing suspected elements with the alternative acoustic candidates
there are about twenty patterns each of them is made to insert the required tree in the form of an underspecified joker tree
n0 person means the substitutor of this node must be of category n noun and must possess a semantic feature person
elements of the design are taken from a set of possible furniture equipment and decoration objects with variable attributes in value domains
to sum up our work described an integration of speech recognition and language processing which is independent from a given recognition system
patterns of anomaly that fits in this case are defined in a compact way thanks to the general tree types used in the grammar
the scoring module can be seen as achieving not so much a filtering than a narrowing of the search space of recognition candidates
this suggests the need for cross checking with other knowledge sources like statistical cues derived from text corpora or from recognition errors corpora
the major parameter for score estimation is the alignment between the word in the best hypothesis and the words in the other hypothesis
we seek methods that can do better
we describe algorithms and show experimental results
i have considered only token based matching predicates which can only return true for a point x y if x is the position of a token e on the x axis and y is the position of a token f on the yaxis
then chains that are most parallel to the main subsequence NUM i mnail j points NUM thru NUM a subsequence NUM points NUM thru NUM bered according to their displacement from the main diagonal
if instead of the point in cell i i e there was a point in cell g f the correct alignment for that region would still be g g e f
total execution time NUM NUM cpu seconds
develop applications to expand the software architecture
the translators have some general knowledge of international news
the motivation for this is that the required reordering of dependents can be achieved with fewer transducer states by accumulating the dependents into subsequences to the left and right of the head
NUM you may have to eat chicken
although this might not seem like a lot of category words NUM NUM words is enough to produce a reasonable core semantic lexicon
an example of the structure of a simplified head transducer for converting the dependents of a typical english transitive verb into those for a corresponding chinese verb is shown in figure NUM
regular languages correspond to simple finite state automata regular relations are modeled by finite state transducers
o b composition square brackets l are used for grouping expressions
examples we illustrate the meaning of the replacement operator with a few simple examples
for example the optional replacement relation maps upper to both lower and upper
thus ai is equivalent to a while a is not
instructional and discourse planning the main components of circsim tutor v NUM are the input understander the student modeler the instructional planner the discourse processor the text generator and the knowledge base problem solver
this hypothesis predicts that sense mismatches will be more likely to appear in documents that are not relevant than in those that are relevant
NUM avoid superfluous or redundant interactions with users relative to their contextual needs
deviations were marked and their causes analyzed whereupon the dialogue model was revised if necessary
i i i i i i l grammar acquisition based on clustering analysis and its application to statistical parsing
it can be paraphrased as they were across the room and they were dancing or as they crossed the tvom as they danced
the column of speech input is the result that experiments was done in practice
for example the word west can be used in the context the east versus the west or in the context west germany
princiljcs subsumed by a new generic pri lcilfle gpi0
finally our experiments with lexical phrases show that it is crucial to assign partial credit to the component words of a phrase
this results in a vocabulary of NUM words
in certain cases a significant increase in speed for training the transformation based tagger can be obtained by indexing in the corpus where different transformations can and do apply
the collection of appropriate sub domains will be determined empirically
then converted into a net many muc ii sentences fail b ased strictlv on tile fact that work structure pr0bability assignments on all arcs in the they contain unknown words i.e. w0rds which are not in the network are obtained autorffatically by parsing each train system s lexicon translate it takes an average of NUM NUM seconds to translate a sentence containing NUM words
there were several reasons for chosing semantic grammars
phenomenon related data based on a hi erarchical classification of linguistic an l extra linguistic phenome a e.g.
interaction with other phenomena as well as the l henom ma which must be presuplmse l are also plements in the case of verb valency are describe t
the tagging has been done using a gui based tool called the discourse tagging tool dttool according to the discourse tagging guidelines we have developed
thus even if there exists a perfect theory it might not work well with noisy input or it would not cover all the anaphoric phenomena
the testing set is composed of NUM utterances
it should be noted that both the training and testing texts are newspaper articles about joint ventures and that each article always talks about more than one organization
we discuss the features used for learning below and go on to discuss the training methods and how the resulting tree is used in our anaphora resolution algorithm
when the anaphoric chain parameter is off only those anaphor antecedent pairs whose anaphora are directly linked to their antecedents in the corpus are considered as positive examples
individual test items can be assigned to one or several phenoluena and annotated ge ording to the eorresl ondii g parameters
anaphora resolution is an important but still difficult problem for various large scale natural language processing nlp applications such as information extraction and machine tr slation
in order to evaluate the performance of the anaphora resolution systems themselves we only considered anaphora whose discourse markers were identified by the nlp system in our evaluation
although use of the confidence values from the tree works well in practice these values were only intended as a heuristic for pruning in quinlan s c4 NUM
in the last three years the janus project has been developing a speech to speech translation system for the appointment scheduling domain two people setting up a time to meet with each other
they make the argument in letters kadane oil co is currently drilling two wells the activity compound nouns this class of dds requires considering not only the head nouns of a dd and its anchor for its resolution but also the premodifiers
to illustrate consider the two atomic features a and b given the null field as old field the best weight for a is fl NUM NUM and the best weight for b is fl NUM
defining distribution q and fl defining q is any set of weights such that q q then d q d fii q
for a set of selected concepts we then generalize their matching concepts using the taxonomy and generate the list of lcb selected concepts matching generalization rcb pairs as english sentences
the table confirms our view that speakers tend to present at least one piece of new information per utterance
in the alparon project we are allowed to try and test out new ideas beyond this next version
the station names and times have a disambiguated form always resulting in a full and uniquely identifying description
when the travel scheme has been determined the dialogue manager sends the entire scheme to the text generator
the system makes the caller feel hunted in processing and copying down the information since it speaks too fast
one of its most important conclusions was that callers appreciate the human operator over all kinds of automated systems
in this case a relatively long period of silence can also be taken as a positive acknowledgement
such a turn will introduce exactly one new information element as happens in most of the ovr dialogues
then the tag may be changed based on contextual cues via contextual transformations that are applied to the entire corpus both known and unknown words
after the presentation and acceptance of a whole travel plan a caller may ask for new travel plans
in the work described in this paper our goal was to evaluate the contributions of various coreference resolution techniques for acquiring information associated with an entity
null for genetic phrases like the company and for pronouns referring to people reference is currently determined solely by file position and entity type
null association by context during name recognition entities are direcdy linked via variable bindings within the patterns with descriptive phrases that make up their context
third the heuristic that caused the system to discard phrases that it deemed too specific for resolution was extremely bad and costly to our performance
this paper investigates a more general view of coreference in which our automatic system identifies not only coreferenfial phrases but also phrases which additionally describe an object
the system will be expanded to rec ize as human those unknown words which are laki g human roles such as participating in family relationships
in the case of name variations it would be helpful to tag the pattern s structural members that can stand alone as variants during the rule binding process
when the ranking is abandoned and the selection is based on the longest descriptor alone NUM of the response descriptors are drawn from those associated by context
first our method of directly linking entities to the descriptive phrases that make up their context via variable bindings within patterns has been very successful
two areas will be improved by better modeling the events which may effect organizational names e.g. the forming of subsidiaries and the changing of names
we also plan to explore probabilistic models for arabic english transliteration
markov model based taggers assign to a sentence the tag sequence that maximizes prob word i tag prob tag i previous n tags
the first is conventions about what attitudes belief desire intention etc each speech act expresses s we call these the linguistic intentions of the speech act
in equation NUM sim stands for the similarity degree between nc and an example case filler e as given by table NUM
animate it would be difficult or even risky to properly interpret the verb sense based on the similarity in the nominative
each of these parameters has a default value in pebls eg k NUM no exemplar weighting no feature weighting etc
our method for disambiguating verb senses uses a database containing examples of collocations for each verb sense and its associated case frame s
the situal ion for ileta1n s and silil l s is nol very lear as none of l he lbur ea t egorics of referring expressions is t redominml
the number of iterations can make a big difference in the quality of the ranked list
the unfilled nodes stand for the nodes in the empirical lattice which do n t have reference in the optimized lattice
backoff test unseen els on the test set and the subset of unseen word combinations
maximum entropy modelling has been recently introduced to the nlp community and proved to be an expressive and powerful framework
the most uniform distribution will have the entropy on its maxim n and the model is chosen accorrllng to
this allows retrieval results to be evaluated against known answers
consequently the category score for a word can be greater than NUM
b rules for performing further segmentation on chunks
this query then practically accounts for all the adverse effect
l0 average precision remains practically the same at NUM NUM
and the normalization constant z x ensures that erp y x NUM
experimentation with more varied queries is needed to verify these findings
the i lcb ackward lookin centei lcb or cb ix the t member of the cf list that lj most centrally con null cerns attd that links u to the previous discourse
we also like to see how lexicon size can affect retrieval
generalized iterative scaling algorithm presented above defines a way for the computing of maximum entropy models for joint probability distributions
NUM the above results indicate that the frequency test essentially contains almost all the information that can be extracted collectively from all linguistic tests
we can define a probabilistic version of av grammars with a correct weight selection method by going to random fields
the better feature is a and a would be added to the field comparing features qa is the best minimum divergence distribution that can be generated by adding the feature a to the field and qb is the best distribution generable by adding the feature b
the only problem that arises not summing to one has an obvious fix normalization
the weight assigned to a configuration is the product of the weights assigned to selected features of the configuration
the sum of the fourth column is the kl divergence d llql between and ql
one reason for adopting minimal kl divergence as a measure of goodness is that minimizing kl divergence maximizes likelihood
in general however adding the new feature may make it necessary to readjust weights for all features
solving NUM yields improved weights but it does not necessarily immediately yield the globally best weights
we compute the score for each candidate feature and add to the field that feature with the highest score
solving equation NUM for fl is easy if l g is small enough to enumerate
the step NUM is a process to register the co occurring forms and adverbs for each verb
the algorithm used for classifying verbs is shown in figure NUM
since there is directional property between words the transitivity will not be satisfied between different directions
travel domain dialogues however will often contain sub dialogues and utterances from different subdomains and will likely shift between one sub domain and another
second because the travel domain database is very small compared to the esst database the esst data dominates the acoustic and language models
and which denote the beginning and end of the sentence or word phrase respectively
shows that the size of a cascade of distinct principles viewed as machines is the size of its subparts while if these same principles are collapsed the size of the entire system grows mulfiplicatively
they use the sum of two relative entropies obtained from neighboring words as the similarity metric to compare two words
word c lassification play an important role in computational i gu s
NUM a no overt case marking b no distinct finite complementizer c verb final d right branching in the projections other than the verb NUM at first sight this might appear as a wild overidealization
a corpus analysis on NUM occurrences of the verb announce in the penn treebank shows that the subject is followed by an aspectual adverb NUM times twice by incidental phrases and NUM times by an apposition
in this paper i investigate the computational problem related to the tension between building linguistically based parsers and building efficient ones which i argue derives from the particular forms linguistic theories have taken recently
when generative grammatical theory in the 70s talked about dative shift topicalization passive it meant that each of these constructions was captured in the grammar by a specific rule
compared to grammar NUM grammar NUM does not show any improvement on either dimension grammar NUM is both larger four times as many lr entries and more nondeterministic than grammar NUM globally one can observe that an increase in grammar size either as a number of rules or number of lr entries does not correspond to a parallel decrease in nondeterminism
the interest in building a parser that is grounded in a linguistic theory as closely as possible rests on two sets of reasons first theories are developed to account for empirical facts about language in a concise way they seek general abstract language independent explanations for linguistic phenomena second current linguistic theories are supposed to be models of humans knowledge of language
in gb parsing there have been two approaches to the implementation of chains one that mirrors directly paola merlo modularity and information content classes features such as case and thematic roles when building chains leads to an exponential growth of the space of hypotheses second i argue that using these features does not restrict the validity of the algorithm to specific constructions or languages
second on positing an empty element the parser must decide to which chain it belongs NUM the two decisions can be seen as instances of the same problem which consists in identifying the type of link in the chain that a given input node can form whether head intermediate or foot abbreviated as h i f in what follows
the symbol v in the target rule must haw the w rb manquer a s a syntactic hea d
it ha s suddenly oltcncd up a window to wtst a mounts of da ta on the ntcrnet
similarly t is sa id to translate s iff there is a synchronized derivation sequence q for s such tha t
5a nonterminal symbol x in a source or target cfg rule x xi xk can only be consl rained
fit into the ont xt fr l a rsing aml g nera tion
m approach however is more ira lined toward the lcb ffg fornlali nl
our aim is to dig out and apply rules which approximate the intended semantics of the links or to populate the inventory of those checks which are based on formal properties of the used relations and attributes and their logical dependencies thus constituting their operational semantics
the terminus causes the event to be delimited as in push the car to a gas station
i illustrate two algorithms to compute chains i show that a particular use of syntactic feature information speeds up the parse and i discuss the plausibility of using algorithms that require strict left to right annotation of the nodes section NUM
the system needs only a small lexicon and training corpus and has been shown to transfer quickly and easily from english to other languages as demonstrated on french and german
however the new chart state produced by the prediction rules does not depend on the identity of the node in the triggering chart element nor on the value of i but rather only on whether there is any chart element ending at j that makes the relevant prediction
the fact that left adjunction can occur any number of times including zero is captured by the fact that states of the form a i j represent both situations where left adjunction can occur and situations where it has occurred
f is said to strongly lexicalize f if for every finitely ambiguous grammar g in f that does not derive the empty string there is a lexicalized grammar g in f such that g and g generate the same string set and tree set
NUM note that constructing a training cross validation or test text simply involves manually disambiguating the sentence boundaries by inserting a unique character sequence at the end of each sentence
if the right hand side of r is empty the initial tree created has a single frontier element labeled with e otherwise the elements of the right hand side of r become the labels on the frontier of the initial tree with the nonterminals marked for substitution
in the presence of sets of mutually left recursive rules involving more than one nonterminal i.e. sets of rules of the form lcb a bfl b ac rcb choosing the best ordering of the relevant nonterminals can greatly reduce the number of trees produced
furthermore the wasson system required nine staff months of development and the riley system required NUM million word tokens for training and storage of probabilities for all corresponding word types
the satz system offers a robust rapidly trainable alternative to existing systems which usually require extensive manual effort and are tailored to a specific text genre or a particular language
it should be noted that while g generates the same strings as g it does not generate the same trees the substitutions in g that correspond to adjunctions in g create trees that are very different from the trees generated by g
adjunction inserts an auxiliary tree t into another tree at a node that has the same label as the root and therefore foot of t in particular is replaced by t and the foot of t is replaced by the subtree rooted at
with the new acoustic models which were trained with esst and etd speech data we obtained a NUM NUM word error rate
first the domain lends itself well to semantic grammars because there are many fixed expressions and common expressions that are almost formulaic
we see these collocations as the contexts of the word senses and evaluate our algorithm automatically
then permuting the relative order of two dependents along the head line corre null o spondm to dttferent scope possibililies wonld have had complex computational conseqttences in the scheme adopted here these cases are handled in a tiniforna way
french and english expiess negation in syntactically different ways rachel does net like claude vs deg rachel n aime pas claude this difference is neutralized in the u fornl representation for both negations are expressed through a single negation predicate in the u feral
in the case where the leaf entity cot tains flee variable arguments the types of these free variables are indicated and the type of the leaf takes into account the fact that these free variables have already been included in the functioned form of the leaf
more generally it can be easily verified that enriching a u form by ordering its nodes and then replacing argument variables by argument names always results in a valid sform null tthe converse is not true not all s forms can be obtained in this way from a u form
knowing thai the head line functions projecting l tlnl it verbal head must he of type t imposes some constraints on wlmt are the possible types for the det endents this can be usefttl in partict lar for constraining the types nf semantically ambiguot s lexical elements
io le gli respeetiw ly him accusative them feminine accusative or her dative him dative and strong t ronouns ui tei ioro respee tively
forms in order to encode tile ordered n ary tree into a binary tree we need to apply recursively the transfotnmtiou ilhlstrated in fig NUM which consists in forming a head line projecting in a north west direction from tile head NUM and in attaching to this line
if we permit re entrancies that is if we permit processes to re merge we generally introduce context sensitivity
with longer contexts taken into consideration there may be too many clusters activated
chinese thesaurus iii a chinese corpus consisting of NUM million chinese characters
enamex type quot person dooner enamex is just gearing up for the headaches of running one o f the largest world wide agencies
it contains six mono sense words whose english correspondences are sad sorrowful etc
nodes NUM and c apart from supporting the node NUM c support some other nodes not represented on figure NUM and thus should be retained in the lattice
it is required for most text processing tasks such as tagging parsing parallel corpora alignment etc and as it turned out to be it is a non trivial task itself
hidden nodes are never observed on their own but only as parts of the reference nodes and represent possible generalizations about domain low complexity constraints x and logically possible configurations w
a grammar equipped with weights and other periphenalia as necessary i will refer to as a model
4note that this is not just the number of atomic features which compose the i th configuration but rather the number of all registered in the model features atomic and complex which are active for it
for instance a joint model can predict how likely it is to generate a capitalized word with suffix ing p capi al ized yes su f ing
we build the optimized lattice by incrementally adding an atomic feature from the empirical lattice together with the nodes which are the minimal collocations of this atomic feature with the nodes already included into the optimized lattice
the application they consider is the induction of english orthographic constraints inducing a grammar of possible english words
NUM 8a c 8b 8c NUM c ok harry i m have a problem h go ahead hank as well as her uh husband
of course the abilities of the user will depend on his or her level of expertise and experience in the current environment
first normal theorem proving may create a new subgoal to be proved and its related voice interactions will yield a subdialog
the various efforts in automatic acquisition of terminologies from a corpus stem from this observation and try to answer the following question how can candidate terms be extracted from a corpus
more typically however the proof fails and the system finds itself in need of more information before it can proceed
then reportposition swl x would become a missing axiom and could be returned to the dialog controller for possible vocalization
thus our system is built around a theorem prover at its core and the role of language is to supply missing axioms
a later publication will propose other uses of voice interactions but our current system uses them for only this purpose
thus the subdialog structure provides a set of expected utterances at each point in the conversation and these have two important roles
the user s affirmation then enables the system to add the assertion find knob back into the user model and proceed
the primary expectations are for an assertion that this request has been done or for a question about how to do this task
the morphosyntactic tags are used to mark ap np pp and vp segments
to classify a term in a subject field can be considered similar to word sense disambiguation wsd which consists in classifying a word in a conceptual class one of its senses
this section will cover the various concepts used by our parser to help in the processing of unknown words
on the one hand linguistic expressions can be used to convey information about the discourse segment structure
an important source of information that is used in this experiment is the distinction between closed class and open class words
though this system performs morphological and syntactic analysis scisor was designed to be used in a single domain
NUM the implicit form similar to the implicit form for the expression of reasons an implicit hint to a domain specific inference method can be given either in the verbalization of the reasons or in that of the conclusion
this problem is one that will only get worse as nlp systems are used for more on line computer applications
the traditional minimum entropy heuristic is a special case of the more effective and more powerful divergence heuristic
the focusing heuristics identify the most coherent relationship between a new inference chain and the discourse inn tree
in two of the tasks the training data is generated by a probabilistic context free grammar and in both tasks our algorithm outperforms the other techniques
in the first two domains we created the training and test data artificially so as to have an ideal grammar in hand to benchmark results
in particular though both algorithms employ a greedy hill climbing strategy our algorithm gains an advantage by being able to add new rules to the grammar
by predicting the differences in the viterbi parse resulting from a move we can quickly estimate the change in the probability of the training data
the values of the parameters p a are set to the smoothed frequency of the x a reduction in the viterbi parse of the data seen so far
in the traditional context model every prefix and every suffix of a context is also a context
for each algorithm we list the best performance achieved over all n tried and the best n column states which value realized this performance
in table we display a sample of the number of parameters and execution time on a decstation NUM NUM associated with each algorithm
in particular solomonoff proposes the use of the universal a priori probability which is closely related to the minimum description length principle later proposed by
in a traditional back propagation network the input to a node is the sum of the outputs of the nodes in the previous layer multiplied by the weights between the layers
description of the thesaurus the edf thesaurus consists of NUM NUM terms including NUM NUM synonyms that cover a wide variety of fields statistics nuclear power plants information retrieval etc
overfitting is also possible in decision tree induction resulting in a tree that can very accurately classify the training data but may not be able to accurately classify new examples
for the log linear model we repeatedly partitioned our data into equally sized training and testing sets estimated the weights on the training set and scored the model s performance on the testing set averaging the resulting scores
this paper concentrates on the use of zero pronominal and nominal anaphora in chinese generated text
in particular if there is a left auxiliary tree pb that can be adjoined on b then the next input item may be matched by p8 rather than b and neither of the shortcuts above can be applied
for example as k or the amount of data increases for a given level of precision p for individual links we want to measure how this affects overall accuracy of the resulting groups of nodes
null classified as positive bold decisive disturbing generous good honest important large mature patient peaceful positive proud sound stimulating straightforward strange talented vigorous witty classified as negative ambiguous cautious cynical evasive harmful hypocritical inefficient insecure irrational irresponsible minor outspoken pleasant reckless risky selfish tedious unsupported vulnerable wasteful tives from set a4 correctly matched adjectives are shown in bold
the trade off for this decreased error rate is a longer training time often more than NUM minutes as well as the extra time required to construct the larger sets
the hybrid disambiguation system reduced the total number of sentence boundary errors by NUM and the error rate for the whole corpus fell from NUM NUM to NUM NUM
in learning mode the input text is a training text with all sentence boundaries manually labeled and the parameters in the learning algorithm are dynamically adjusted during training
in model NUM we give a probabilistic treatment of wh movement which this research was supported by arpa grant n6600194 c6043
underlying this is the intentional structure which shows the relationship between the respective purposes of discourse segments
these include ambiguities derived from speech disfluencies speech recognition errors and the lack of clearly marked sentence boundaries
all of these tables would have fixed sizes even when new lemmas are added to the system
while there are specific modules in magic whose task is concerned with utilizing multiple media media coordination affects the language generation process also
lexical choice for text always selects the full reference but lexical choice for speech must check what expression the text generator is using
as long as the text label on the screen is generated using the full unambiguous reference speech can use an abbreviated expression
the result in the same figure shows that the matched rate increased from NUM to NUM
in this example this is possible in part because the patient s medical history diabetes and hypertension can be realized as adjectives
in particular in order to produce a coordinated presentation magic must temporally coordinate spoken language with animated graphics both temporal media
similarly magic uses balloon pump in speech instead of intra aortic balloon pump which is already shown on the screen
to start the prediction the system tries to anticipate the lemma of the next word
if the dictionary contains n words the dimension of the table will be of n n
in principle the parsing allows storing extracting the information that has influenced in forming the verb
in addition more than one table of probabilities may be necessary to properly make predictions
finally we suspect that the syntactic structure of sentences generated for spoken output should be simpler than that generated for written language
this hypothesis is in conflict with our criteria for generating as few sentences as possible which often results in more complex sentences
maybe the simplest method that can be used is the semantic word prediction by using parsing methods
it is necessary to leave open to the user the possibility of changing the word s ending
however the onomastica results can only serve as a qualitative point of reference and should not be compared to our results in a strictly quantitative sense for the following reasons
NUM some problems in name analysis what makes name pronunciation difficult or special in comparison to words that are considered as regular entries in the lexicon of a given language
for the sake of exemplification let us instead consider the complex fictitious street name dachsteinhohenheckenalleenplatz figure NUM shows the transducer corresponding to the sub grammar that performs the decomposition of this name
in other words no correct transcription was obtained by either system for NUM out of NUM names NUM NUM which is only slightly higher than for the training data
to give a concrete example there is usually only one hauptstrafle main street in any given city but you almost certainly do find a hauptstrafle in every city
however due to the integration of the name component into the general text analysis system of gertts the latter problem has a reasonable solution
on termination the word is tagged with the label name which can be used as part of speech information by other components of the tts system
onomastica was funded by the european community from NUM to NUM and aimed to produce pronunciation dictionaries of proper names and place names in eleven languages
this system was derived in under two hours from the transformation based part of speech tagger described in this paper
this is because is included in the dictionary
crl has employed a task oriented user centered approach to apply natural language technology to the design of interface software that supports working translators language learners and instructors
dialogue participants are able to negotiate the meaning of utterances because in responding to what the hearer decides are the speaker s goals and expectations regarding an utterance the hearer also provides evidence of that decision and hence constraints on what the speaker may do next
if the selected sentence sl has another sentence based on NUM
a the probability of a partial derivation NUM NUM tk is inductively defined by
an earley parser is essentially a generator that builds left most derivations of strings using a given set of context free productions
add the state i kx y move the dot over the current nonterminal
states with empty lhs such as those above are useful in other contexts as will be shown in section NUM NUM
collapsing unit production completions has to be avoided to maintain a continuous chain of predecessors for later backtracing and parse construction
only sentences with multiple ilts at least one of which was correct were used as training and testing data
null lemma NUM the following assumes an earley chart constructed by the parser on an input string x with ixl l
we restrict summation to left most derivations to avoid counting duplicates and because left most derivations will play an important role later
as the need for certain flmctionalities becomes obvious with growing annota tion
multiple coordination as well a s enumerations are annotated in the same way
our algorithm implements a kind of relaxation by gradually reducing flow izow and anc which enables us to find confident sentence correspondences first
however our method will work on larger and noisy sets too by using word anchors rather than using sentence boundaries as segment boundaries
the last pan of the communication is levot d to the taldng into acc ount of negatioll in a st celtic cas of knowledge in our of lc tjowl lge
an english clause might be mapped into a turkish gerund NUM while he was working ken calis tns calisirken another example of a structural transforlnation encountered can be seen in active passive forms of sentences
our iterative method uses two kinds of word correspondences at the same time word correspondences acquired by statistics and those of a bilingual dictionary
their method selects a matching type such as NUM NUM NUM NUM and NUM NUM according to the number of word correspondences per contents word
it is interesting to note that the correct japanese translation of meg is lcb i i
for example nmr meg pet ct mri and functional mri are devices for measuring brain activity from outside the head
in summary the performance of combined is better than either statistics or dictionary for all texts regardless of text length and the domain
among structurally different languages such as japanese and english there is a limitation on the amount of word correspondences that can be statistically acquired
to tackle the problem we describe in this paper a text alignment system that uses both statistics and bilingual dictionaries at the same time
given a formula fting consisting of lexical predicates and an ldt aet tries to find a set of permissible assumptions a and a formula fab consisting of the database predicates such that f u a v fti g fab the translation of fzi g is done one predicate at a time
again the annotator has the option of altering the assigned tags
we distinguish five degrees of automation NUM completely manual annotation
NUM in order to assess the significance of the differences between 4the applicability of all complex methods was NUM in both groups
informally rldt nonrecursivness means that for any set of facts a if there is a prolog like derivation of an atomic formula f in the theory f u a then there is a prolog like derivation of f without recursive calls
a different type of inference is used to generate predictions about what comes next
knowledge about subcategorisation preferences for example that a verb takes exactly one subject is also required
given our assessment of the difficulty of processing chinese this suggests the need for development of basic resources for non european languages e.g. segmenters word part of speech lists lexicons and grammars
next the slots in those objects are filled using information in the ddo the discourse predicate database other sources of information such as the message header e.g. document number or from heuristics e.g. in muc NUM terms the type of an organization object is most likely to be company
in the unix world these two display sets are encoded by the gb guobiao prc and big5 taiwan two byte eight bit encodings although on although it is possible to segment the documents into words automatically and index each word as a term this can cause well posed queries to fail for two reasons words can be improperly joined by the automatic segmentation
to achieve this goal we are examining the pragmatic structures of children s successful unaided conversations and would like to use the relationships between these structures to predict content
expanding the definition of infer also reduces the number of proposed boundaries recall that the algorithm does not assign a boundary if there is an inferential link between an np in the current utterance unit and the prior utterance unit
comparison of tables NUM and NUM shows that as with the error analysis results and as expected average performance is worse when applied to the testing rather than the training data particularly with respect to precision
for ill recognized sentences at least NUM are fully recovered for nuance as well as for abbot this concerns line NUM of table NUM
it does so by evaluating the significance of the differences in column totals tj across the matrix with each row total ui or total number of boundaries assigned by subject i assumed to be fixed
the referent of the underlined noun phrase one in segment y is the most recently mentioned male referent without the segmentation the reasoning required to reject it in favor of the intended referent of he is quite complex
our second method uses machine learning tools to automatically construct segmentation algorithms from a large set of input features features used in our previous experiments enhancements to hand coded features and new features obtainable automatically from our transcripts
the branch of parsing containing this joker is eliminated on syntactic grounds whereas the first branch of parsing turns into a complete analysis figure NUM
for the t NUM boundaries the differences are not statistically significant for condition NUM but for condition NUM precision and error rate are both superior and the difference as compared with ea is statistically significant
this suggests that error analysis is a useful method for understanding how to best code the data while machine learning provides a cost effective and automatic way to produce an optimally performing algorithm given a good feature representation
in addition for both character sets the xat library supports several different input methods for both character sets including both prc and cantonese pinyin and the standard telegraphic code stc NUM digit numeric representation
a format still closer to the tel guidelines may be proposed in the future
if the representations are complex the difference between two representations is defined recursively
what are the possible methods of interactive disambiguation for each ambiguity type
the linguists may define more types and complete the list of values if necessary
their list will be completed in the future as more ambiguity labeling is performed
transitions between two adjacent utterances u i and u can be characterized as a function of looking backward whether cb un is the same as cb un NUM attd of looking forward whether cb ur is the same as cp u
here is an ambiguity pattern of multiplicity NUM corresponding to the example above constituent structures
here p2 and p3 have the same part p2 representing v so that m n
in the case of an anaphoric element q will presumably correspond to one word or term v
consider the utterance i do you know where the international telephone services are located
at one end of the spectrum are the proper names and aliases which are inherently definite and whose referent may appear anywher e in the text
NUM select the sentence that has the highest importance value among the unselected sentences
examination of the score tables in the appendix show that slot level performance on enamex follows a different pattern for most systems from slot level performance on numex and timex
such rules are able to cover more unknown words than morphological guessing rules but their accuracy will not be as high
in comparison with the ending guessing rules the morphological rules have much better precision and hence better accuracy of guessing
as the base line result we measured the performance of the taggers with all known words on the same word sample
another avenue for improvement is to provide the guessing rules with the probabilities of emission of poss from their resulting pos classes
we presented a technique for fully unsupervised statistical acquisition of rules which guess possible parts of speech for words unknown to the lexicon
another source of information which is used and which is not prepared specially for the task is a text corpus
in english as in many other languages morphological word formation is realized by affixation prefixation and suffixation
it can also handle NUM less unknown words which in fact might decrease its performance even lower
therefore it turns out that even at this very early stage the modest pre tagging performance gained from applying the learning procedure provides measurable performance improvement
by supporting multiple ways in which rules can be hypothesized refined and tested the strengths of both sources of knowledge can be brought to bear
another interface is geared towards capturing arbitrary n ary relations between tagged elements in a text these have been called scenario templates in muc
our focus in building the alembic workbench is to provide a natural but powerful environment for annotating texts in the service of developing natural language processing systems
in the absence of complete and deep text understanding implementing information extraction systems remains a delicate balance between general theories of language processing and domain specific heuristics
in our experience of applying the alembic phrase rule learner to named entity and similar problems our errorreduction learning method requires only modest amounts of training data
some of the analysis and reporting utilities available from within the interface as well as unix command line utilities are written in perl c or lisp
then during each learning cycle the learner tries out applicable rule instances and selects the rule that most improves the score when applied to the corpus
originally we tried to buy the technology from outside so that a full fledged partnership could be developed based on the initial project
NUM the chunker 2nd stage of ndfsm brackets noun phrases and verb groups replacing them with thei r head words
the engine provides a lot of tracing information and allows easy definition of which rule s contributed to a specific decision
p p enamex type organization mccann enamex has initiated a new so called global collaborative system composed of world wide account directors paired with creative partners
NUM appendix NUM lexicon and grammar sample s the lexicon contains word forms with flags the flags encode pos and semantic information
but the bragging rights to coke s ubiquitous advertising belongs t o enamex type organization creative artists agency enamex the big enamex type location hollywood enamex talent agency
in this vein we would like to enable virtually any user to be able to compose new patterns rules for performing pre tagging on the data
the results shown in figure NUM give some indication of the ability of the rule sequence learning procedure to glean useful generalizations from meager amounts of training data
there are a number of different forms of anaphora including personal pronouns i you he she it etc spatial anaphora there that etc and temporal anaphora then
but one could get dramatic improvement just by building a machine that always guesses the most populated category nonfict for genre
a generic template to evaluate integrated components in spoken dialogue systems gavin e churcher and eric s atwell and clive souter centre for computer analysis of language and speech ccalas artificial intelligence division school of computer studies the university of leeds leeds ls2 9jt yorkshire england gavin scs leeds ac uk and eric scs leeds ac uk and cs scs leeds ac uk
nine errors could be avoided by refining ex null isting heuristics especially by taking into account exceptions for specific words like point pendant and devant
as we can see bad has the highest plausibility rw NUM he reject dialog act aml l ropose for the 8tty lesl dialog act
in the english network all NUM a trace is represented as ti where i is a unique index referring to an antecedent
these are the figures alter disregarding of course the variation of selectional restrictions
NUM NUM e g NUM NUM a john smeared the window patient with the paint
an empirical architecture for verb subcategorization frame a lexicon for a real world scale japanese english interlingual mt
multiple voice conversions can often occur for a single verb phrase as is shown in e.g.
anatural language analyzer in an mt system is supposed to convert the case elements in e.g.
this distinction may work in the later knowledge based inference phases of the mt system
one quick experiment that motivated the building of the constraint based model was the following we took a million words of newspaper text and ranked ambiguous words by frequency
the english and korean grammar networks in figure NUM are the result of executing this algorithm on the korean x parameter settings
second the category knowledge primarily the abstract semantic knowledge is used for filling the flames so that we get a symbolically accessible structure rather than a tagged word sequence
the NUM NUM word sample of newspaper text has typos and proper names NUM that match an existing word in the lexicon
magn is absolute i.e. not related to that of the whole since that is l b e.g. in broad outline all the sugm paper of the world
since in g every node labeled x where adjunction can occur is the root of an initial tree in it must be the case that in g every node labeled x where adjunction can occur is the root of an initial tree in i because the construction of t t did not create any new nodes labeled x where adjunction can occur
for reference nodes which do not have sub nodes in the optimized lattice at all undecided nodes according to the maximum entropy principle we assign the uniform probability of making an arbitrary prediction
NUM for each feature from the model s constraint space we apply the constraint as in equation NUM and compute how its weight should be adjusted aa to satisfy the constraint
while processing a whole utterance each word is presented with its plausibility vector and at the output layer we can check the incrementally assigned dialog acts for each incoming word of the utterance
cogniac will not commit to a resolution if a unique referent can not be found
this formula shows the interderivability x r y c yqx
parsing enables regular expressions to be written which apply to trees rather than surface text
as a cote for keeping blacks back we consider our butthead to be an endearing
table NUM contain s system performance when optional elements were treated as if required
the fifteen rules used to detect pleonastic it are shown below in table NUM
for the evaluation we usecl the same NUM NUM sentences as in paimerszhearst NUM and palmer hearst NUM ideg which were also used by reynar ratnaparkhi NUM in the evaluation of their system
first as the configuration space we can use only the reference nodes w from the lattice which makes it similar to the method of berger et al NUM described in section NUM NUM
if a participant in a process it will typically be made subject of the sentence
since NUM sra has developed nlp systems mainly in the area of text extraction in both english and foreign languages
shape is not relevant a bathtub may contain a bucket o1 water without ihere being any bucket in it i lements d l tun gaj l li i imon an inncr preexistent division of some fiui of lemon a grain of rice
moreover cat bears the feature count slaildiug as well for ease of exposition for the range of surface gramlnalical belmvior of lexical signs usually refened to as couulability uncounlability see discussiou alxwe
i noted earlier that extensive use of structural modalities tends to result in very complex analyses
to illustrate this point table NUM shows the fraction of words in the test set that were assigned zero probability by the mixed order model
the languages considered were spanish as input and english german and italian as output giving a total of three independent corpora of NUM NUM pairs each
the second sub component of the semantic interpreter module is a pattern based sentence interpreter which applie s semantic pattern action rules to the semantics of each fragment of the sentence
step NUM the previously created by c value r list will now be re ordered considering the weights obtained from step NUM
this means that each constraint can be represented separately and the ordering on how the constraints apply is determined dynamically through unification
forgetting contexts for the moment our basic machine scans sequences of tuples from but requires that any sequence representing a lexical entry be followed by the entry s feature structure from
assume that the above lexical forms are associated in the lexicon with the feature structures as in figure NUM further assume that each two level rule m NUM m NUM above is associated with the feature structure fro
NUM the linguistic intentions of askref are compatible with those that have been expressed so it is consistent to assume that russ is intending to use it as part of a plan
the accuracy of these database answers is measured using arpa s common answer specification cas metric
however in nlu there is a fundamental asymmetry between the natural language and the unambiguous formal language
the models labeled poisson and general use the poisson and general fertility models presented in this paper
this lm has a NUM chance of not including a test pattern and its use leads to pessimistic performance estimates
the alignment is a hidden quantity which is not annotated in the training data and must be inferred indirectly
although these ctumpings are perhaps the most natural neither the clumping nor the alignment is annotated in our training data
the basic underlying intuition is that a single concept may be expressed in english as many disjoint clump of words
rather they rely upon a group of translators with significantly less linguistic knowledge to produce a bilingual training corpus
the clumps form a proper partition of e all the words in a clump c must align to the same f
the views and conclusions contained in this document should not be interpreted as representing the official policies of the u s government
table NUM evaluation of the complex predictors
the probability of obtaining by chance a difference in
we implemented an extractor program to collect the relevant measurements for the adjectives in our sample namely text frequency number of syllables word length and number of parts of speech
the analysis of deviance for this model indicated that for the morphologically unrelated adjectives one of the six selected variables should be removed altogether and another should be replaced by a smoothing spline
this causes the decision tree models to perform badly
table NUM cross classification of adjective pairs ac
sociated with the root node of the tree
table NUM evaluation of simple markedness tests
these studies however lack the fine grained information of the contents of cf lists also needed for proper reference resolution
we have developed a proposal for extending the centering model to incorporate the global referential structure of discourse for reference resolution
note that this test subsumes test NUM
the best predictor overall is the smoothed log linear model
but independent of the use of anaphoric expressions each utterance must have a theme and a c as well
each of the new themes are introduced in the immediately preceding utterance so that local coherence between these utterances is established
whenever a discourse segment is created its starting and closing utterances are initialized to the current position in the discourse
our model due to its hierarchical nature implements a stack behavior that is also inherent to the above mentioned proposals
the statistics of the usage of computationally expensive operations viz intersection quadratic complexity and determinization exponential complexity in both algorithms are summarized in figure NUM kk kaplan and kay ekp grimley evans kiraz and pulman
to estimate a reasonable value for dl we can compute the distance between the context vector of each mono sense word occurrence in the corpus and the context vector of the cluster containing the word then select a reasonable value ford1 based on these distances as the threshold
those recognized as non nominal words but not as unction words are regarded as llol lns
since this method does not exclude invalid or meaningless words it can result in the degradatiou of precision
the quality of the liseriniinating power is the distribution of the tel ill over a document set
processing compound nouns is decomposing them into simple nonns and evaluating the simple nouns as potential index terms
tim more uniform the distril ution is the larger l will be
in this section we describe the algorithm to recognize and evaluate candidate index terms from compound nouns
in korean it is allowed to write compound nouns with or without intervening blanks between constituent nouns
in this paper we propose a method to identify and evaluate the candidate index terms from compound nouns
in the following section a brief review of related work on automatic indexing for korean docnments is made
by eliminating explicit association lines otp eliminates the need for faithfulness constraints on them or for well formedness constraints against gapping or crossing of associations
because repns has 21tiersl NUM states so NUM and NUM each have about NUM states and NUM NUM to NUM NUM arcs
NUM pandq borc p q l b c
for example a weight NUM path would describe a chain of optimal stressed feet interrupted by a single unstressed one where a happens to block stress
fullnasvoi has voi has i voi a nasal gesture may not be only partly voiced
fo support this algorithm a text orims of over NUM millions characters is employed to enrich an NUM o00 words lexi on in terlns of its word entries and word binding forces
maximum matching segmentation of a sequence of characters abcdefghij NUM at the character a starts with matching ab against the NUM character words table
the segmentation error rates over seven generations of the training process are shown in the table below most of these errors occur in proper nouns not included in the lexicon
a structural algorithm resolves segmentation mnbiguities by examining the structural rclationships between words while a statistical algo rithm compares the usage flequencies of the words and their ordered combinations inste ad
in this statistical approach in terms of word frequencies a lexicon needs not only a rich repertoire of word entries lint also the usage frequency of e ach word
in generation i n new blocks of text are picked randomly from the corpus and words segmented using the lexicon enriched in the previous generation
in essence gen generates every way of refining the partial order of input constituents into a total order and decorating it freely with output constituents
on the other hand hiragana and katakana characters are phonograms
for a language we can first make use of its mono sense word to outline its semantic space and produce a dendrogram according to their similarity then word sense disambiguation can be carried out based on the dendrogram and the definitions of the words given in a dictionary
this representation is mapped onto a recursively embedded case frame which is then input to the transfer module
we also believe that sgml and the tei must remain central to any serious text processing strategy
information extraction text summarisation document generation machine translation and second language learning
this model has been adopted by a number of projects including parts of the multext ec project
the tei defines standard tag sets for a range of purposes including many relevant to le systems
of course gate does not solve all the problems involved in plugging diverse le modules together
on platforms which support shared libraries c based wrappers can be loaded at run time dynamic coupling
this is tight coupling and is maximally efficient but necessitates recompilation of gate when modules change
later versions have replaced this model with a pre parsed representation of sgml to reduce this overhead
u k research groups nlp gate for details of how to obtain the system
the competing alternative the affinity relation between the characters b n and rdn is reactivated
of course the idea behind it is that analogy could be considered a similar psychological process as the one intervening in proportions null
for example the agent may be told be careful not to burn the garlic when he or she is perfectly well aware that burning things when cooking them is bad
resolvent the union of the information in the two temporal units
we intend to continue this part of the work by addressing more preventative forms addressing ensurative forms and by extending the analysis to other languages
sl NUM thursday the thirtieth of september
the final corpus sample is made up of NUM examples all of which have been coded for the features to be discussed in the next two sections
we have coded other functional features as well but they have either not proven as reliable as these or are not as useful in text planning
according to this table therefore the awareness and safety features show substantial agreement and the intentionality feature shows moderate agreement
the two select modules pointed to by the main input node select the examples reserved for the training set and the testing set respectively
an empirical approach to temporal reference resolution
note also that we have not distinguished between the various sub forms of dont and neg tc shown in table l this will require yet more features
since turkish has complex agglutinative word forms a separate morphological generator handles the proper morpheme selection vowel harmony etc
paola merlo modularity and information content classes annotation of the category is inspected case distinguishes the foot of an a chain from the foot of an a chain while intermediate traces are characterized by a lack of NUM role and by their configurations i.e. intermediate a empty categories occur in a positions spec of i while intermediate a empty categories occur in a positions spec of c
as the transfer process continues the checklist is referenced in order to block the default translation and handle the exceptions
h l commits to the existence of inference path that would support the proposition as mutually believed indicates that it can be found or derived from the set of mutual beliefs h l conveys uncertainty about the existence of such a path
that is a sequence of continuations 2for simplicity s sake we assume the items in cf to be words and phrases in actuality they may be nonlexical representations of concepts or some hybrid of lexical conceptual and sensory data
this is because the salience most relevant to the attentional state is the proximity of a discourse entity to the head of cf the closer it is the more it is centered and therefore attentionally salient
when intonation and centering collide my synthesis of the claims in ph90 and gjw89 produces an attentional interpretation of pitch accents modeled by operations on cf and derived for each accent from their corresponding propositional effect as described in ph90
after each utterance one of three operations are possible the cb retains both its position at the head of cf and its status as the cb therefore it continues as the center in the next utterance
lsa has thus introduced a NUM error into the writing process
the details of the space construction and testing method are described below
stemming is the process of reducing each word to its morphological root
table NUM baseline performance for NUM confusion sets
table NUM shows the performance of this baseline predictor
the table is divided into confusion sets
contextual spelling correction using latent semantic analysis
tina used probabilistic networks and semantic filtering to reduce perplexity
a singular value decomposition svd is performed on this matrix
the function for selecting cell values will be discussed in section NUM NUM
figure NUM shows a fragment of the entry associated with the japanese verb toru
the database specifies the case frame s associated with each verb sense
in this paper we proposed an example sampling method for example based verb sense disambiguation
figure NUM the relation between the training data size and precision of the system
figure NUM the relation between the training data size and performance of the system
however we should take into account the training effect a given example has on other examples
figure NUM a fragment of a database and the entry associated with the japanese verb toru
the precision is the ratio between the number of correct outputs and the number of inputs
we shall call these two sets the test set and the training set
for this study a concept based lexicon and a concept grammar were built to represent a response set
the results discussed in this paper illustrate the reliability of the lexical semantic methods used in the study
the procedure used to identify conceptual and syntactic information retrieves concepts within specific phrasal and clausal categories
following the syntactic and semantic analysis synthesis which uses the unification based approach the quasi logical form qlf is developed
we expect that once this program is in place our preprocessing time will be cut in half
we hypothesized that our results would improve if more metonyms of existing concepts were added to the lexicon
an additional independent set of NUM test responses from NUM content categories was run through our prototype
therefore we developed a domain specific concept lexicon based on a set of NUM training responses over all categories
therefore the concepts in the lexicon must denote metonyms which can be derived from the training set
user response measures how users felt about using the system
the word business still keeps its ambiguity but the NUM subtle distinctions of wordnet are reduced to NUM senses
we envision a system that would be used by a par ocular student over an extended period of time
figure NUM icicle overall system design figure NUM contains a block diagram of the overall system under development
intuitively the knowledge or concepts within the zpd are ready to be learned by the learner
we have undertaken a project designed to act as a writing tutor for deaf asl signers learning written english
null our decisions concerning both of these aspects stem from work in second language acquisition and educational research
there is a similar principle outlined in kra81 with respect to second language acquisition
such a model should indicate situations where the first language can have either a positive or negative influence
do not expect to see used correctly these are features above the user s level
once a single parse is chosen the errors are identified based on annotations associated with the realrules
NUM this process includes determining which syntactic constructions should be preferred in the actual realization of the response
first the english sentence retrieved from the ibm manual is analyzed by the cle parser alshawi and moore
where k represents the k th input sentence and pk is sum of the probabilities of possible sequences in the k th morpheme network weighted by the credit factors
here i investigate a method that can estimate hmm parameters from an untagged corpus and also a general technique for supressing noise in untagged training data
the algorithm can be applied to an unsegmented language e.g. japanese because of the extension for coping with lattice based observations as training data
second in order to measure semantic similarity over concepts his method requires a concept taxonomy such as the princeton wordnet milg0 which is grounded in the lexical ontology of a particular language
the past participles concerning involving according dealing and regarding are near the top of the list because they occur most often as the heads of adjectival phrases modifying noun phrases as in the world according to np an english construction that is usually paraphrased in translation
this paper describes a reestimation method for stochastic language models such as the n gram model and the hidden maxkov model hmm from ambiguous observations
after analyzing the aggregated results it was time to peek into the semantic entropy rankings within each pos
the numbers of parameters of the tag bigram model tag hmm pair hmm were NUM nd NUM nd and NUM iond respectively
we make extensions to a previous algorithm that reestimates the n gram model from an untagged segmented language e.g. english text as training data
the new method can estimate not only the n gram model but also the hmm from untagged unsegmented language e.g. japanese text
NUM re estimate the likelihood of each lexicon en null try using the number of times n its components co occur the number of times k that they are linked and the probability pr kln model
third resnik s measure of information content is defined in terms of the logarithm of each concept s frequency in text where the frequency of a concept is defined as the sum of the frequencies of words representing that concept in the taxonomy
in section NUM we clarify some subtle aspects of the algorithm presented in section NUM by looking at its application to several ambiguous words in hebrew
using this method one can easily move to a new domain by applying the method to a new untagged corpus suited to this new domain
for this reason we try to construct the sw sets from as many suitable elements as possible in order to be able to detect misleading words of this sort
this observation is also supported by the fact that humans are very often surprised to see the amount of possible analyses of a given ambiguous word
in the same way using the small corpus we found the test corpus probability ptest for each of the analyses in the test groups
victories which are frequently used in sports reports to express historical information can be decomposed semantically into the head noun plus its modifiers
the general design of the translation system and a detailed description of the transfer component is presented in this paper
as such the ata NUM specification defines the document production and use environment
these needs are illustrated in a human attested translation of the chosen source corpora
we thus wrote an sgml dtd in order to formalise the conceptual annotation scheme
we also have information on the corpus specific translational processes used from french to english
the linguistic needs correspond to the minimal level of quality required for the produced translation
firstly these results are potential contributions to the specification of dedicated nlp systems
these constraints are illustrated in the source language corpora to be submitted to machine translation
our morpho syntactic schemes are based on the concept of syntagmatic components which are further specified using a set of features
conclusion the interest of building this kind of annotated test set from corpora is multiple
we work the weights out in the same way as in documents vectors o k log2 wctk where t k is the number of times that term zoccurs within documents assigned to category k and cfiis the number of categories within term occurs
documents in reuters deal with financial topics and were classified in several sets of financial categories by personnel from reuters ltd and carnegie group inc documents vary in length and number of categories assigned from NUM line to more than NUM and from none categories to more than NUM
exploiting an obvious analogy between queries and categories the latters can be represented by term weight vectors then a category canbe assigned to a document when the cosine similarity between them exceeds acertaln threshold or when the category is highly ranked
this measure has been expressed in a variety of ways
as regards the top level three different kinds of frames are defined for multitale
8c part of the entry cs remove of the type lexicon rol
the numbers are based on the occurrences of combinations of concept types in the corpus
NUM a syntactic module lbr the lbrmation of minimal nps and pps
results of the guessing module for ex NUM laterale cc pathology
the performance of the tagger depends rn a great deal on the completeness of the lexicon
NUM NUM NUM ex NUM het aneurysma wordt afgeelipt met een rechte clip
NUM a module for the attribution of concept typc to the minimal nps and pps
the concept type with the highest number is considered the most likely candidate for the filled in clement
other processes are encapsulated as agents that register with the facilitator the types of messages they can respond to
an automatic restart feature keeps agents run null ning in case of machine failure or process death
there is still a need for such definitions in a task assistance environment
in such cases the update derivation remains the same as in the first case above
here we used a pst with maximal depth NUM trained on NUM of the text of paradise lost
yet even in this case the pst of maximal depth three is significantly better than a full trigram model
finally the prediction of the complete mixture of psts for wn is simply given by wn
here we will use the bayesian formalism to derive an online learning procedure for mixtures of psts of words
the prediction function at each node is the trigram conditional probability of observing a word given the two preceding words
an even more fundamental new feature of the present derivation is the ability to work with a mixture of psts
thus in the second case we incur an additional description cost for a new word in the current context
thus a pst being build online may only need to store information about those words for a short period
NUM a s c d NUM
we then iteratively remove all rules that can not participate in a complete parse of an utterance either because they contain daughter categories that can not be expanded into any sequence of words given the particular lexicon we have or because they have a mother category that can not be reached from the top category of the grammar
we then utilize a method for combining the resulting probabilities to form a distribution over all the possible coreference configurations
to make matters worse training data comes at a cost as keys have to be coded by hand
in which yn is the most recently created template in yt yn
the cross entropies of the various approaches as applied to the three sets of test data are shown in table NUM
subsequently s is to be assigned based on the categories of the daughters vp vafin ne and adv
errors of this type may be reduced by introducing finite state constraints that restrict the possible sequences of functions within each phrase
NUM the user selects a substring and a category whereas the entire structure covering the substring is determined automatically cf
we define the relative order of two phrases recursively as the order of their anchors i.e. some specified daughter nodes
these unreliable predictions can be further classified in that we distinguish unreliable sequences as opposed to almost reliable ones
when applied the combined model assigns grammatical functions to the elements of a phrase not knowing its category in advance
we wish to tl ank the universities of stuttgart and tiibingen for kindly providing us with a handcorrected part of speech tagged corpus
the overall accuracy for assigning phrase categories is NUM NUM ranging from NUM to NUM depending the category
we choose the most intuitive alternative and define the anchor as the head of the phrase or some equivalent function
the annotator determines the category of the current phrase and the tool runs the appropriate model to determine the edge labels
as mentioned in section NUM when the ith utterance ui NUM i j NUM and thejth utterance uj in a dialogue have local cohesion with one another speech act i speech act verb i verb and nouns nouns have coherence g
for example desu which represents the speech act type response is often misrecognized as desu ka question or desu ne confirmation on the other hand japanese can easily select the adequate expression desu when the intention of the previous utterance is concerned with a question
this is because they use the coherence relation local cohesion between the two utterances question response
recently statistical approaches have been attracting attention for their ability to acquire linguistic knowledge from a corpus
therefore the plauslbdlty of local cohesion between u and u can be formally defined t j as
of course the reverse is not true an extensionm object may have properties not possessed by the underlying type
the second process is to calculate the plausibility of local cohesion between utterances from the dialogue corpus using a statistical method
in this paper we define the plausibility of local cohesion i.e. equation NUM as follows
we call a series of two endexpr expressions i.e. endexpr i NUM endexpri i an endexpr bigram
indeed discourse structures play a useful role in speech recognition which is an application of nlp
in dependency terms the system allows any set of initial formulae to combine to a single result iff they form a connected graph under the dependency relations that obtain amongst them
in table NUM we show tagging results obtained on a number of different corpora in each case training on roughly NUM NUM x NUM s words total and testing on a separate test set of NUM NUM NUM x NUM s words
we only had to add two specific f rinciples o1 metacontmunication spi0 and spii in tahle NUM
the direct correlation between rules and performance improvement in transformation based learning can make the learned rules more readily interpretable than decision tree rules for increasing population purity s NUM part of speech tagging a case study in transformation based error driven
one difference is that when training a decision tree each time the depth of the tree is increased the average amount of training material available per node at that new depth is halved for a binary tree
the incremental system to be developed in this paper is similarly compatible with a chart like processing approach although this issue will not be further addressed within this paper
note however this is not at all the same problem of writing a reducer in prolog
subject object in the ccg formalism the derivation is as follows harry gets raised with the t rule and then forward composed by the b rule with found and the result is a category of type s rip with lf az found harry z
this work is supported by arc grant daal03 NUM darpa grant n00014 NUM j NUM and aro grant daah04 NUM g NUM
these theories are usually implemented in a language such as prolog that can simulate a term operations with first order unification
in section NUM it will be seen how the use of abstract syntax allows this to be expressed directly
thus for the query apply abe sub walked sub harry n
however this same process of recursive descent via scoped constants will work for any member of the conj rule family
it differs from prolog in that first order terms and unification are replaced with simply typed a terms and higher order unification NUM respectively
compose abs f abs g abs x f g x
the compilation approach however lacks this problem since we have only first order formulae amongst which the dependencies are clear e.g. as in NUM
also since every acceptance expressed as yes is also an affirmative answer affirm is considered to be a weaker form of accept
thus for the above utterance intentional accounts might also consider interpretations corresponding to an attempt to express a need for a pencil a request to be given the pencil an incomplete attempt to fill out a questionnaire and so on
an example is the np the closed bookcase in the case that only one icon resembles a closed bookcase
the results of feeding the test sentences to the three different referent resolution models are given in section NUM NUM
NUM an advantage of this approach is that referent resolution for phrases other than inferential anaphors is not affected
the presence of a visible model world invites the user to generate referring linguistic expressions involving the spatial environment
to identify the correct referent an inference must be made in this case that institutes employ secretaries
in many domains the number of associated individual instances of a mentioned individual instance may be very high
the language interpreter and the language generator consult the context model the knowledge base and the lexicon
at the bottom right the trace window context displays the salience values of some of the discourse referents
we did explicitly encourage the subjects to use the shortest referring expression possible whenever they felt it was appropriate
the referent of dit this one in sentences 2a and 2b would be the most salient report at that moment which would be the report about gr2 in sentence 2a but the report pointed to donald report in sentence 2b
time as pp manner in verb NUM the denver nuggets beat the boston celtics with a narrow margin computational linguistics volume NUM number NUM in these examples the input conceptual constraints time and manner float appearing at a variety of different syntactic ranks here verb and circumstantial and are sometimes merged with other semantic constraints
the system learns synonyms is was am and homonyms read red know no without difficulty
theoretically one should look at all possible groups of goals to see if they can coexist in the same graph and evaluate how efficient each group is both globally and in regards to constraints placed on individual goals by the user
we adopt this model for translation by analogy in the following manner
this operator deletes the ith word ewi from the example
this operator adds the jth word iwj to the input
the system tries to match the input against examples of the largest unit
prior probability of using the different examples to translate expressions in the domain
there are many different sets of distortion operators that could relate the two
the details of this model are described in section NUM
this recursive process continues until all parts have been matched
for this reason the nodes at which the system automatically pauses for interaction are restricted to the node marked as a sentence and the node that dominates a relative clause and its antecedent in short just restricted to contain one predicate
if there are more than one possible translation the system presents them in a similar window as alternatives window as in figure NUM and the user is allowed to change the system s selection by the same interface as translation equivalent selection
the system performs syntactic transformation using syntactic information in the dictionary such as the verbal case frame of the main verb in the area shows the result on the main window and replaces the original sentence with the result c
please note the change in the embedded sentence from a finite form he read a book in c to an to infinitive form him to read a book in d in accordance with the grammatical constraint posed by the verb help
c i glad ga ta node wa c shecame c i glad component sentences are translated first c d then they are combined to produce a complex sentence
figure NUM alternatives for kakeru as an idiom denwa wo kakeru phone call countable make a phone call denwa telephone denwa call up figure NUM alternatives for denwa as an idiom
book read fc ga ta o da f he NUM the book someone bought read fd ga o da g he read the book someone bought figure NUM relative clause and syntactic ambiguity kanojo ga ki ta node watashi wa she subj come past because i top b she come f
a morphological analyzer recognizes word boundaries in the sentence looks up corresponding word entries in the system dictionary and shows the result in the main window b NUM content words are replaced by a translation equivalent assumed most plausible while functional words are left unchanged
this feature allows for the recognition of certain classes of names to impact the recognition of other names
the entire project will be evaluated by the tipster engineering review board erb for documentation and certification as appropriate
the pattern is similar to a regular expression and consists of special operators and operands that match portions of text
we will now show how russ s beliefs might progress this way
if we use the class based syntactic signatures containing t rcposition marked pp s and both positive and negative evidence the NUM example sentences reduce to NUM syntactic patterns just as before
an askref would demonstrate acceptance because it is the expected next act
NUM we might have used priorities to express different degrees of belief
extended inference about goals is usually unnecessary and a waste of resources
t department of computer science toronto canada m5s 1a4 gh cs toronto edu
the need for an alternative to the notion of mutual belief
the results which had the negative evidence are shown in the left hand column of numbers in table NUM and the results which had only positive evidence are shown in the right hand side
two suppositions are equivalent if and only if they are syntactically identical
the snap comes in a variety of configurations
the docuverse temporal analysis tool makes this possible
figure NUM the process of transforming textual data
som technology was originally developed by t kohonen and has been used throughout the neural network community as a method for representing information in a manner suitable for visualization NUM
together these neural network techniques can be utilized to automatically identi the relevant information themes present in a corpus and present those themes to the user in a intuitive visual form
for example in figure NUM the neighborhood of node we NUM is depicted and is defined as those nodes that are within one row or one column of node w2 NUM
if a user has identified a map node representing an interesting theme of information the user can select from the node pop up menu an option to retrieve documents pertaining to the theme
by performing a cumulative dot product operation for each document in the interval with the corpus integral we can obtain a visual summary of the information content of the documents received during that time interval
by stepping through the data in one day increments the weekdays monday through friday are identified by three predominate regions pertaining to themes like banks stocks world news and taxation
figure NUM shows the configuration of our japanese word segmenter
only open class words of intermediate flequeney actually frequency from NUM to NUM in tile corpus of NUM NUM articles are retained as keywords and used in finding the similar articlem also because the n best sentences inevitably contain errors we set at threshold for the appearance of words in tile n best sentences
although the actual improvement in word error rate is relatively small partially because of fa tors we ould not control of which the probleni of mne is the most important the results suggest that the sul language technique may lie useful in improving the si eeeh recognition system
we m expe t that these me he is eliminate less topi relate l words s dtat only str mgly tot i related wor ts are extra ted as the sul language words
fm ch keyword is weighted by t he l rodu t of two factors
this extraction was done ill order to filter out tot i um elated words
we will call the latter kinds of error mne for minimal n best errors
not all of l he words from the prior sentences are used as keywords for retrieving similar articles
there exist systems that verify the morphologic and syntactical correctness of a basque sentence but the complexity of the basque verb avoids its anticipation
we compare the two orthogonal groupings of the inventory of verbs the semantic classes defined by levin and the sets of verbs that correspond to each of the derived syntactic signatures
article scores ascore for all articles in the large ortms are oinputed as the sum of the weighted scores of the selecte t keywords in each arti le an NUM are n irrealized t y the log of the size of each article
cost of one day each in recent weeks continental version suggests only limited announced plans for memory chip plants in worthington project cost of one million each 1note that in our experiment a few errors in tattim sentences were corrected because of the weight optimization based oil the eight scores which includes all of the sri s scores
we are mso in the process of developing bilingual dictionaries for korean and french and we will be porting our lcs acquisition technology to these languages in the near future
the final erb occurs after the completion of the project implementation
this step is based on a lookup table much like mackinlay s algorithm NUM NUM which uses an association between the type of a variable and the most efficient graphical methods to express it
while this framework does not restrict us to a particular grammar formalism in our work we consider only probabilistic context free grammars
one advantage of this method and of other methods that rely only on surface characteristics of language is that the necessary input is currently available
however stress has multiple readings even as a noun it also has the reading exemplified by the new parent was under a lot of stress
natural language generation providing a natural language front end to a database information extraction machine translation and task oriented dialogue understanding all require lexical semantics
nevertheless although the ssts learned by ostia are usually good translation models they are often poor input language models
the derived forms produced by ee er and ant all refer to participants of an event described by their base part in e
a possible disadvantage of surface cueing is that surface cues for a particular piece oflexical semantics might be difficult to uncover or they might not exist at all
however many nlp tasks require at least a partial understanding of every sentence or utterance in the input and thus have a much greater need for lexical semantics
the verbal prefix mis e.g. miscalculate and misquote cues the feature that an action is performed in an incorrect manner incorrect manner
the sizes in the horizontal axis refer to the first three columns in table NUM b
figure NUM evolution of translation wer with the size of the training set spanish to german text
sentence boundaries are easy to detect in the case of read speech where there is a distinctive pause at the end of the sentence and the sentence is usually grammatically complete the second also holds true in case of written speech where in addition a period marks the end of a sentence
the resulting sentences may not correspond to those in the original text as can be seen in example NUM
the two descriptor array flags for capitalization discussed in section NUM NUM allow the system to include capitalization information when it is available
these last two flags allow the system to include capitalization information when it is available without having to require that this information be present
this approach makes multiple passes through the data to find recognizable suffixes and thereby filters out words that are not likely to be abbreviations
there are a few examples of rule based and heuristic systems for which performance numbers are available discussed in the remainder of this subsection
in our test collections the ambiguous punctuation mark is used much more often as a sentence boundary marker than for any other purpose
NUM this issue crosses party lines and crosses philosophical lines said rep john rowland r conn
they use a regular grammar to tokenize the text before training the neural nets but no further details of their approach are available
the most straightforward is to use the individual words preceding and following the punctuation mark as in this example at the plant
since these lattices were built on acoustic segments the models had to deal with implicit acoustic segment boundaries
first NUM NUM news stories are used for training and last NUM are kept for testing
relevant verb classes such as semantic fields or vendler classes are also specified using sorts
among others we show how the assignment of grammatical functions can be automatised using standard part of speech tagging methods
several features of the tool have been introduced to suite the requirements imposed by the architecture of the annotation scheme cf
three tagsets have to be defined by the user part of speech tags phrasal categories and grammatical functions
on the basis of the difference between the best and second best assignment the prediction is classified as belonging to one of the following certainty intervals reliable the most probable tag is assigned less reliable the tagger suggests a function tag the annotator is asked to confirm the choice unreliable the annotator has to determine the
NUM information about the last syllable without stress onc corpus
in the remainder of this t ape r
note that in example NUM the second and is not treated as a coordinating conjunction as the two sentences it conjoins i call him and he comes back are both short and both appear to be modified by the initial if clause
null NUM all information about the last syllable sonc corpus
this produces a table with for each value a distribution over categories
this explains why this rule set looks simi h r
to structure the phoneme inventory of a language linguists define features
unsupervised discovery of phonological categories through supervised learning of morphological rules
correct dimilmtive suffix given the phonological representation of dutch nouns
but matters are not as simple as the example makes them appear if dag1 was really the dag it purports to be then we would also expect to be able to infer dagi v agr gen masc
NUM f projection a f marking of the head of a phrase licenses the f marking of tile phrase
non context based disaml iguation inethods
table NUM examples of context sensitive ambiguities
here for example is a very simple datr fst that will transduce a path such as subj NUM sg futr obj NUM NUM for the sake o and ends in stop are just stipulated in the entries and indeed the second definition in personal name means that ends in stop implies ends in consonant
the passive form is asserted to be the same as the past form the use of global inheritance here ensures that irregular or subregular past forms result in irregular or subregular passive forms as we shall see shortly
as we have already seen there are two basic statement types extensional and definitional and these correspond directly to simple extensional and definitional sentences which are made up from the components introduced in the preceding section
the genetic progranlming and neural net approaches are ideal in this respect
hfformation structure can be of great use in linguistic applications especially in those involving a speech component
hat ein buch gelesen NUM lcb rcb maria hat draussen f geniest
the first feet sing score is a boolean focusing fla l
sentence NUM couhl do it wednesday niol ning too
the idea is to leave the representation underspecified in applications unless resolution is required for a specific reason
NUM focus linking principle the o sem value of a pitch accented word is h linked to foe
however this test can be misleading in cases where the question comes in a wider context ef
the solid line arrows signify obligatory inclusion in the respective is partition the dashed line arrows defeasible inclusion
this sol cc deriwd ion so llol c NUM is actually a compall o l NUM y ils
our work did not use the trigram model since because of the relatively free word order in hebrew it was less promising and also in some cases the different choices are among words of the same part of speech category
we compute the error rate for each k and choose the value of k with the minimum error rate
in other words o r may only do what it is supposed to do extraction and we can directly read off the category assignment which extractions there will be
subset of source cfg skeletons in t snch tha t a source cf skeleton k is in it iff k has no head constraints assoeia ted
in stochastic parsing good turing has to our knowledge never been tried out
the first issue above is solved here by recasting alr in binary form
these devices using a tabular method a grammar transformation and a filtering function
suppose we derive an element q e ui j from an element a
these quantities correlate with the amount of storage needed for naive representation of the respective automata
we see that the number of states is strongly reduced with regard to traditional lr parsing
furthermore all optimizations of the time and space efficiency have been left out of consideration
the following characterization relates the automaton a2lrt and algorithm NUM applied to the 2lr cover
this can be done by adding syntactic information to the entries of the dictionary of lemmas and some weighted grammatical rules on the system
there were a couple of experimental constraints to analyze the aforementioned issues in terms of recognition word error rate
the latter break down a word into two or more words
in alshawi NUM i provide a denotational semantics for a simple under specified language and argue for extending this treatment to a formal semantics of natural language strings as expressions of an under specified representation
the good tufing estimator computes for every frequency r an adjusted frequency r as
a tiling of s yields a costed derivation of a target dependency graph t as follows the cost of the derivation is the sum of the costs ci for each match in the tiling
the independence assumptions implicit in head automata models mean that we can select lowest cost orderings of local dependency trees below a given relation r independently in the search for the lowest cost derivation
a subcollection of NUM of these sentences were translated by the system and the resulting translations manually classified as good NUM translations or bad NUM translations
local context may include the state of local processing components such as our head automata for capturing grammatical constraints or the identity of other words in a phrase for capturing sense distinctions
for example the average sentence length for monte carlo generation with our probabilistic head automata model for atis was NUM NUM words the average was NUM NUM words for the corpus it was trained on
in fact the transfer model is applicable to certain types of source dependency graphs that are more general than trees although the version of the head automata model described here only produces trees
transfer mapping takes a source dependency tree s from analysis and produces a minimum cost derivation of a target graph t and a possibly partial function f from source nodes to target nodes
this prediction system can be adapted to the user by updating the frequency of the word in the dictionary each time this word is used
if a parse tree has kdefivations t11
that is it will have n NUM entries but most of the values in the table will be zero or close to zero
taking into account the previous experience a third approach could be tried
the level of conformance is documented in the tacad
pp NUM NUM after many pages of attempting to pin the concept down they suggest as one alternative investigating topic shift markers instead it has been suggested that instead of undertaking the difficult task of attempting to define what a topic is we should concentrate on describing what we recognize as topic shift
that is between two contiguous pieces of discourse which are intuitively considered to have two different topics there should be a point at which the shift from one topic to the next is marked
the need to pop up additional windows repeatedly made editing multiple entries time consuming
similarly min distribution indicates the minimum percentage of tiles that must have a representative from the term set
by contrast the next subsection will show that lexical co occurrence patterns can be used to identify subtopic shift
a model must be defined that states the mapping from words to the annotations
the top row of each rectangle corresponds to the hits for term set NUM the middle row to hits for term set NUM and the bottom row to hits for term set NUM the first column of each rectangle corresponds to the first texttile of the document the second column to the second texttile and so on
in figure NUM the user has indicated that the diagnosis aspect of the query must be strongly present in the retrieved documents by setting the min distribution to NUM for the second term set NUM when the user mouse clicks on a square in a tilebar the corresponding document is displayed beginning at the selected texttile
there are cases where the form teiru which marks the present tense can co occur with temporal adverbs describing the past
vbn vb vbp says that if adding the segment ed to the end of an unknown word results in a word
in this paper we present a technique for fully automatic acquisition of rules that guess possible part of speech tags for unknown words using their starting and ending segments
we detected that the cascading application of the morphological rule sets together with the ending guessing rules increases the overall precision of the guessing by about NUM
for example these measures assume that all combinations of pos tags will be equally hard to disambiguate for the tagger which is not necessarily the case
the hmm tagger was trained on the brown corpus in such a way that the subcorpus used for the evaluation was not seen at the training phase
because of the similarities in the algorithms with the lisp implemented xerox tagger we could directly use the xerox guessing rule set with the hmm tagger
this is done recursively until the score of the resulting rule does not exceed the threshold at which point it is added to the working rule sets
the major advantage of the proposed technique can be seen in the cascading application of the different sets of guessing rules and in far superior training data
however notice that this bitstring does not correspond to any existing row in the original array
our use of speech as an output medium provides an eyes free environment that allows caregivers the opportunity to turn away from the display and continue carrying out tasks involving patient care
for every input word token the tagger accesses the lexicon determines possible pos tags this word can take on and then chooses the most appropriate one
thus when the index n is set to NUM the result of the application of the v0 operator will be a morphological rule with no mutative segment
in order to ensure that speech is brief and yet still conveys the necessary information the speech micro planner attempts to fit more information into individual sentences thereby using fewer words
since pronouns are used generally to refer to participants in the conversation or things already mentioned whereas articles such as the and a rows NUM and NUM are much more frequent in the after part of the sentence since they are more frequently used in full noun phrases describing new entities
as of now we have neither attempted to determine the etymological or ethnic origin of names nor have we addressed the problem of detecting names in arbitrary text
vp a v x a np x
in this analysis the reference time is an implicit variable which is needed in the interpretation of the temporal relation but is not part of the quantificational structure
NUM splitting the role of reference time our analysis of partee s quantification problem uses a different notion of reference time than that used by the accounts in the exposition above
an important factor in the interpretation of temporal expressions is the classification of situations ihto different aspectual classes or aktionsarten which is based on distributional and semantic properties
the event marker ez is introduced in the antecedent box with the condition that it be temporally included in the current reference time r0 and be prior to n
controls indicate basic information about a node such as its type e g event entity relation its family e.g.
this will not be predicted by the unselective binding of quantifiers in drt which quantify over all the free variables in their scope in this case women cat pairs
thus each leaf is mapped to a set of alternatives varying in category and features which represent all possible interpretations of that leaf
both of tekuru and teiku have usages other than aspect as in mot tekuru bring or mot teiku take
we take lcb p you know rcb whenever we take them to showbiz or they think it s wonderful just to go to mcdonalds coordinating conjunctions occur at the inter sentential level and generally include and but and because
if the meaning of a particular subtree is unambiguous in role the textrefs for th e text in that subtree are connected to that meaning
null coordination with other media the language generation process must produce enough information so that speech and text can be coordinated with the accompanying graphics
in brief the method looks for correspondences in the surface text attached to named individual nodes ie resulting fro m proper nouns
in the paper we show the kinds of modifications that it was necessary to make to the language generator in order to allow the media coordinator to synchronize speech with changing graphics
the procedure can be described as follows after we have merged two classes taken from the left right and the right left trees respectively we use this merged class to replace two original classes respectively
since there are dependences between features only subsets of the combinatorially possible configurations of features are defined as shown in the table NUM
this yields a number of NUM NUM x NUM NUM distinct subtrees
one such problem that has been identified in the analysis of the walk through article is the copying o f the textrefs from one concept to another
textrefs allow the document structure to be fully represe ted in the net and represented uniformly wit h the other information in the system
in this section we shall proceed to an empirical examination of diphthongs and excessive diphthongs
languages that are closely related will often share a large number of cognates
in particular we had labeled journalistic texts on the basis of the overall brow of the host publication a simplification that ignores variation among authors and the practice of printing features from other publications
for example a newspaper story about a balkan peace initiative is an example of a broadcast as opposed to directed communication a property that correlates formally with certain uses of the pronoun you
in a broad sense the word genre is merely a literary substitute for kind of text and discussions of literary classification stretch back to aris null totle
numbers are the percentage of the texts actually belonging to the genre level indicated in the first column that were classified as belonging to each of the genre levels indicated in the column headers
ambigulty of ti lcb e irqmt
because our machines in fact have no prior knowledge of the distribution of genre facets in the evaluation suite but we decided to be conservative and evaluate our methods against the latter baseline
and modeling variance with a binomial random variable i.e. the dependent variable log r NUM NUM is modeled as a linear combination of the independent variables
according to the various functional roles they play
table NUM classification results for all facets
in this paper we describe the issues that arise for language generation in this context conciseness the generation process must make coordinated use of speech and text to produce an overview that is short enough for time pressured caregivers to follow but unambiguous in meaning
for example in the first sentence of the example shown in figure NUM the speech components have a preference for medical history i.e. hypertensive diabetic to be presented before information about the surgeon as this allows for more concise output
during this time there are a number of caregivers who need information about patient status and plans for care including the icu nurses who must prepare for patient arrival the cardiologist who is off site during the operation and residents and attendings who will aid in determining postoperative care
in particular the specification imposes a very strict way of structuring the text in terms of topical and discursive organization and also in terms of practical document production by providing a dedicated sgml dtd for the maintenance manuals ata NUM dtd
we are continuing to work on improved coordination of media use of the syntactic and semantic structure of generated language to improve the quality of the synthesized speech and analysis of a corpus of radio speech to identify characteristics of formal spoken language
sn2 fonction dev obj indicating that the nominal phrase has a function of direct there is no prepositional introducer object of a nominalisation dev obj sav and sp2 fonction compadv and type mani re indicating that the sav and or the sp2 have the function of adverbial complement compadv with a manner semantics type maniere
finally using sgml for the building of the test set file allows one to perform targeted evaluations by giving extraction criteria such as the morpho syntactic schemes or even the pragmatic labels an evaluator can decide to select all the test units including the pragmatic metnorm label in order to carry out a specific evaluation of the mt system performances when translating the task and subtask titles of the super puma helicopter maintenance manual
operative events during surgery are monitored through the lifelog database system modular instruments inc which polls medical devices ventilators pressure monitors and alike every minute from the start of the case to the end recording information such as vital signs
this inconsistency can not be resolved without additional information about the position of the stress mark
j dr smith is not teaching al dr smith is going on sablmutical next year
if the strength of one set of evidence strongly outweighs that of the other the decision to accept or reject bel is easily made
our system will first construct a singleton set for each such justification chain and select the sets containing justification which when presented is predicted to convince the user of bel
in collaborative planning activities since the agents are autonomous and heterogeneous it is inevitable that conflicts arise in their beliefs during the planning process
given a set of newly proposed beliefs the system must decide whether to accept the proposal or m initiate a negotiation dialogue to resolve conflicts
NUM system s own evidence against bel NUM if bel is a leaf node in the candidate foci tree NUM NUM if predict bel bel u evid
once a candidate foci tree is identified the system should select the focus of modification based on the likelihood of each choice changing the user s belief about the top level belief
a collaborative agent is driven by the goal of developing a plan that best satisfies the interests of all the agents as a group instead of one that maximizes his own interest
furthermore some forms can be used regardless of the features and have usages other than aspect as discussed in the previous section
debellis j silver and s sparks of andersen consulting and to f ahmed and b bussiere of raytheon inc for their comments and suggestions made during the development of modex
it is widely believed that graphical representations are easy to learn and use both for modeling and for communication among the engineers and domain experts who tqgether develop the oo domain model
he can add to the text plan specification one or more constituents paragraphs from the list of pre built constituents shown in the lower right corner of figure NUM
modex was developed in conjunction with andersen consulting a large systems consulting company and the software engineering laboratory at the electronic systems division of raytheon a large government contractor
to change the content of the output texts he can go to the text plan configuration window for the text he has been looking at shown in figure NUM
modex lets the user customize the text plans at run time so that the text can reflect individual user or organizational preferences regarding the content and or layout of the output
and may have at most one tad server which receives requests via a standard web cgi interface and returns html formatted documents which can be displayed by any standard web browser
finally the domain model is passed to the designer system analyst who refines the model into a oo design model used as the basis for implementation
this paper presents a glue language account of how negative polarity items e.g.
the licensing takes place precisely at the syntax semantics interface since it is implemented entirely in the interface glue language
finally we noted briefly some similarities and differences between this system and categorial grammar monotonicity marking approaches
we have elaborated on and extended slightly the glue language approach to semantics of dalrymple et al
i m grateful to mary dalrymple john lamping and stanley peters for very helpful discussions of this material
vineet gupta martin kay fernando pereira and four anonymous reviewers also provided helpful comments on several points
furthermore a term conveys a well assessed usually complex meaning as long as a user community agrees on its content
while a learner is acquiring these features we do not expect to see any relative clauses which are beyond that level of acquisition
we thus assume that perspective is given in the input to the lexical chooser figure NUM shows in fd form the input the lexical chooser receives for the example that produces sentences NUM to NUM the network form for this input is shown on the right in figure NUM depending on the values of the additional input features perspective and focus omitted in figure NUM
i know her age and that she came here
NUM NUM NUM fuf a functional unification formalism
a more serious drawback of translational entropy as an estimate of semantic entropy is that words may be inconsistently translated either because they do n t mean very much or because they mean several different things or both
as noted above we put roughly a staff week into customizing the system to handle the scenario templates task bu t chose not to participate in the evaluation because another staff week or so would have been required to achieve performance on a par with other parts of the system
the major differences we noted between training and testing performance lie in the organization alias and descriptor slots and in th e person name and alias fields we have marked these discrepancies with asterisks and will address their caus e later on in this document
head person proxy pers NUM modifiers head has age proxy ha NUM arguments pers NUM head age proxy age NUM the first two fields correspond to the embedded phrase the head field is a semantic sort and the proxy field holds the designator for the semantic individual denoted by the phrase
both the date and title tagger can tag a phrase as either i a single sgml element or NUM individual lexemes with special attributes that indicate the beginning and end of the matrix phrase as i n lex post start chief lex lex post mid executive lex lex post end officer lex we adopted this lex based phrase encoding so as to simplify and speed up the input scanner of the part of speech tagger
but the bragging rights to org coke org s ubiquitous advertising belongs to org org creative artist s agency org corpnp the big location hollywood location talent agency corpnp org after corpnp phrases have been marked another collection of te rules associates these phrases with neighboring org phrases
the rule that accomplishes this is job in pers a ttl org successor pers a succ job out in context succ job out x NUM job out pers b ttl org x NUM the mysterious looking job out in context
aside from this theoretical bound on computation we have found in practice that th e inference system is remarkably fast with semantic interpretation equality reasoning rule application and al l other aspects of inference together accounting for NUM NUM of all processing time in alembic
the correct solution here is to apply all tags in a manner that allows the correct tags to be selected by the pattern processing mechanisms
the biggest problem here is due to overgeneration up from NUM to NUM and partial matches such as the following kraft enamex general foods enamex first e na m ex fidel ity enamex where general foods and fidelity were in the training corpus for the organization lexicon but the longer names above were not
we also separated the pairs into two groups according to whether the two adjectives in each pair were morphologically related or not
overall NUM measurements are computed for each adjective and are subsequently combined into the NUM variables used in our study
such tests are based on intuitive observations and or particular theories of semantics but their accuracy has not been measured on actual data
for comparison nouns the largest class account for NUM NUM and NUM NUM of the words under the two criteria
several distinctions exist for the definition of a variable that measures the number of words that are morphologically derived from a given word
table NUM summarizes the values obtained for some of the NUM variables in our data and reveals some surprising facts about their performance
the analysis of the linguistic tests and their combinations has also led to a computational method for the determination of semantic markedness
to design an appropriate classifier we employed two general statistical supervised learning methods which we briefly describe in this section
however some proposed tests refer to comparisons between measurable properties of the words in question and are amenable to full automation
the problem of polysemy has received much attention when dealing with contentwords but it is just as difficult for discourse particles in spoken language they often perform various functions for dialogue management rather than contributing to propositional content
in figure NUM a screen dump of the graphical interface that supports the interactive validation or removal of terms in td is shown
another type of advantage lies in the flexibility given by the very large dimensionality reductions achievable by kohonen s technique
schematically then resynthesis would take place on the basis of a trajectory across a diphone map
artse NUM has genuine multi value antonymy or in this case ternary antonymy
these assumptions need to be modified in a principled manner rather than by tables of exceptions
however typical purely data driven systems are opaque from a phonetic or phonological point of view
other data sets need to be explored to introduce other kinds of variability
in 4d or above interpretation becomes much more difficult
a monotonically shrinking bubble neighborhood was used in all the maps shown here
the trajectory could be stored simply as a vector of co ordinates that are lit up on the map
figure NUM shows a map resulting from applying the som algorithm to phoneme feature data
under this assumption the semantic score can be simplified as since the normal form comprises the cases of constituents and the senses of content words f i n v i mi rcb w in the representation of the normal form can be thus rewritten as n i si
the lexical score for the k th lexical part of speech sequence t k associated with the input word sequence w is expressed as follows s x t p t iw p tk lw kk l i l k
because the total number of shift actions equal to the number of product terms in the above equation is always the same for all alternative syntactic trees the normalization problem is resolved in such a formulation
therefore slex t p wi tk NUM xp lcb t instead of st x tk is used in our implementation
stahd with arms and legs ipread out figure NUM beyond details to correct we want the reader to see
NUM the matching between the argu null ment table and the argument structure eventually takes place at the time the main verb is attached
as a result the articles are classified correctly
figure NUM fragment of the lexicon for verb selection
the essence or gist of a matter
we apply the parser to the source and target sentences using a spanish and an f nglish grain mar respeetbely
as a formula v1 troponymof v2 v1 entailment v2
comparing the max term in NUM can be mapped into tile maxbnum weight clique problem which is np eomplete of
there are o d such pairings where d is the smaller of the degrees of v and v
j j n where NUM dominates the rem dning no les in l
the justification for this heuristic is that we expect that the high scoring pairs will dominate and will be a priori mutually disjoint
o r parse trees are transformed illl o a regularized format to represent the l redicate argument structure
for each senten e the out put of the parser is a stru tm e sharing tbrest
for each input sent ellee our parser t rodlmes a set of trees corresponding to each possible syntactic analysis
the use of terminology reduces the number of elementary syntactic links from NUM to NUM with a corresponding NUM of overall data compression
we used this to increase the number of never and neg tc examples to match the number of dont examples
drafter s model of procedural relations includes a warning relation which may be attached by the author where appropriate
in this paper we have discussed the use of machine learning techniques for the automatic construction of micro planning sub networks
its output is passed to the c4 NUM node labeled reform which produces the decision tree
in this paper we apply this technique to build micro planning rules for preventative expressions in instructional text
both of these examples involve negation do not and take care not
don sand it or tear it up because this will put dangerous asbestos fibers into the air
in this table we include only an estimate of the full size of that portion of the corpus
we have built no intelligent facility for decomposing the realisation statements and filtering common realisations up the tree
the sub network derived in this section was spliced into the existing micro planning network for the full generation system
recall that each suffix of a multi set of strings is represented by a leaf in the associated suffix tree because of the use of the end marker and that each leaf stores the count of the occurrences of the corresponding suffix in the source multi set
assume now that there exists a transformation r having score greater equal than k NUM w r t l since the replacement of a with b is the only rewriting that appears in pairs of l r must have the form a7 b
for example n sleep verb n n noun n
the fact that a word has no dependent is coded by n well adverb
dg is based on the observation that each word of a sentence has individual slots to be filled by others its so called dependents
we merely add a framework for automatic translation of dug rules to horn clauses that makes dugs as easy to implement as classic dcgs
on the other hand NUM b may be a full answer to NUM in the way that NUM a rather than b may be a full answer to NUM NUM NUM what do you know about john NUM to whom does john speak about many problems
computational linguistics volume NUM number NUM rightmost complementations to few girls and about many problems i.e. addressee and objective is in accordance with so but in NUM b this is not so the objective which was most dynamic rightmost in underlying word order in a does not occupy this position in b
the algorithm has been formulated as follows a after the dependency structure of the sentence has been identified by the parser so that also the underlying dependency relations valency positions of the complementations to the governing verb are known the verb and all the complementations are first assumed to be nb i.e. to belong to the focus which we denote by f
ii such focus sensitive adverbs or focalizers as only also even mostly negation etc see section i and footnote NUM should be considered since their foci may differ computational linguistics volume NUM number NUM from the focus of the sentence as a whole although in the prototypical case such a difference does not occur
however at higher levels of tagging accuracy the reestimation method based on the baum welch algorithm is limited by the noise of untagged corpora
this estimation method can use untagged unsegmented language corpora as training data and build not only the n gram model but also the hmm
in particular it is important to formulate a dynamic method to assign the credit factor based on small sets of tagged data for development
the processes involved in producing templates can be generalised hence the core contains a mechanism to help write templates at an abstract level
the high precision technique is used first and looks for specific relationships between the event node and node s that are connected to it
otherwise compare c with the defined value of aft if not the same return NUM
NUM for all rules whose left corner is b call match b c
however we are also selectively engineering certain resources by hand for both comparison and applications purposes
the excluded category function can help improve the chances of choosing the correct rules within the partial parse tree
some representative examples are shown in figures NUM and NUM the parser produces two kinds of outputs
parsing is somewhat more expensive than for pure context free parsing but is still efficient by both theoretical and empirical analyses
although the time complexity rises compared to the earley algorithm it remains polynomial in the worst case
instead the approach described here augments the cfg rules with a restricted set of contextual applicability conditions
for our applications we therefore feel the effectiveness of the grammar form compensates for the theoretical complications
the parsing strategy is then described in section NUM followed by current experimental results in section NUM
i iue tuniug will require one lcb o two weeks 12o del erlnine reasolls ie word a sses r i okenizal ion along with the required rc indexing of the lcb orlms a nd t lcb adjusl the scoring fllll l ioll weighl s
this l mguage indel rcb endent asl rcb e lcb l of ebmt mm es i rcb mei mt r lpidly retargetm le lcb o other l mguage l rcb mrs and in f t lcb l thcre are dready versions lcb rcb f i rcb mf i mt provi lcb ling serhocro ll ia n i o english
in order to carry out the experiments we used a subset of tile enea corpus in order to measure performance over manually validated documents
we then build a wfst directly from the symbol mapping probabilities
given rills the rule licences the conehlsion that the quoted descriptor oe also evaluates to fl in any context with the same node component n in other words to ewduate a quoted path a in a context n f just evahmte the local descriptor n a in the updated global context n a
it states that if the sequence of value descril tors on the right of the sentence evaluates to the sequence of atoms tt then it may be concluded that the node NUM ath pair ni i also evaluates to a rules of this kind may be used to provide an inductive detinition of an evaluation relation between datr expresskms and their values
this may be due to data sparseness
rio evaluate the quoted path the global context is examined to find the current global node this is dog and the vahle of root is then obtained by evaluating dog root which yields dog as required
high performance bilingual text alignment using statistical and dictionary information
we examine the occurrence of anaphora in human generated text and those generated by a hypothetical computer equipped with anaphor generation rules assuming that the computer can generate the same texts as the human except that anaphora are generated by the rules
a zero anaphor used to refer to some entity in the previous clause might be expected to indicate the continuation of a discourse segment while a nonzero anaphor occurring in the same situation computational linguistics volume NUM number NUM
from the generator s perspective when the decision about the anaphoric form for a phrase referring to some entity in the previous utterance is to be made the factor of discourse segment boundaries must be taken into consideration
thus we assume that there is a distinguished level of structure in a discourse plan that is relevant for this purpose this may be expressible in terms of maybury s distinction between rhetorical acts and speech acts
we consider the case of zero anaphora section NUM first followed by nonzero anaphora section NUM which divides into pronouns section NUM NUM and nominal anaphora sections NUM NUM and NUM NUM
each speaker was given a short description in chinese see the appendix about the idea of discourse structure and the task to be done namely annotate the discourse segment boundaries according to the intentions of the discourse segments
the speakers varied greatly in choosing anaphoric forms for these topic shifts among NUM speakers NUM chose all full descriptions NUM used all zero anaphora and the other NUM chose zero pronominal and nominal anaphora
indexing is used in ensuring general linear use of resources but also notably to ensure proper use of excised subformulae i.e. so that z in our example must be used in deriving the argument of xo y and not elsewhere otherwise invalid deductions would be derivable
the best rule obtained for the choice of anaphor type makes use of the following conditions locality between anaphor and antecedent syntactic constraints on zero anaphora discourse segment structures salience of objects and animacy of objects
the second decision that must be made in the response generator is which kind of correction strategy to use in actually generating the response
in a few runs all unset parameters disappeared altogether
bill likes who the man a present fred gave
figure NUM percentage of each default ordering pa
the wml algorithm ranks sentence types and NUM
NUM NUM random i where NUM
t l and NUM f r
each condition was run ten times
table NUM overall preferences for parameter types
table NUM effectiveness of two learning procedures
rapid development of morphological descriptions for full language processing systems
figure NUM syntactic and semantic morphological production rules
this is useful in checking over and undergeneration
the example in section NUM NUM below clarifies this point
an annotation tool that makes the expanded versions of the formulas visible for the annotator is obviously called for
although the 2this calculation seems be counterintuitive
for example a typical translation lexicon may contain the entries unemployed chaumage and right imm liatement this behavior is quite suitable for our purposes because we are interested only in the degree to which the translational probability mass is scattered over different target words not in the particular target words over which it is scattered
his performance on he brown corpus is NUM NUM using a model learned t rom a corpus of NUM million words
this feature will allow the model to discover that the period at the end of the word mr seldom occurs as a sentence boundary
the ctj s are the unknown parameters of the model and where each c u corresponds to a fj or a feature
we would like to thank david palmer for giving us i he test data he and marti hearst used for their sentence detection experiments
performance figures for our best performing system which used a hand crafted list of honorifics and corporate designators are shown in table NUM
we present the brown corpus performance to show the importance of training on the genre of text on which testing will be performed
the first test set wsj is pahner and hearst s initial test data and the second is the entire brown corpus
now for a given sentence abc there are only two parses with non zero probabilities k and l the probability of abc under parse k is papbpcp c p alc p bla and the probabihty under parse l is NUM pa NUM ps NUM pc p c p blc p alb
we now have an explanation for why the inside outside algorithm converges to the suboptimal parse k so often the first ignorant iteration of the algorithm biases the parameters towards k and subsequently there is an overwhelming tendency to move to the nearest deterministic grammar
however in this paper we will not discuss whether statistical induction is the proper way to view language acquisition our current goal is only to better understand why current statistical methods produce the wrong answer and to explore ways of fixing them
we have argued that there is little reason to believe scfgs of the sort commonly used for grammar induction will ever converge to linguistically plausible grammars and we have suggested a modification namely incorporating mutual information between phrase heads that should help fix the problem
null normalization was conducted in such a way that the suxn of all matrix entries adds up to the number of fields in the matrix
starting from an english vocabulary of six words and the corresponding german translations table la and b show an english and a german co occurrence mat x
in contrast agents using different dialogue strategies can be compared with measures such as inappropriate utterance ratio turn correction ratio concept accuracy implicit recovery and transaction success danieli lwe use the term agent to emphasize the fact that we are evaluating a speaking entity that may have a personality
the word co occurrences were computed on the basis of an english corpus of NUM and a german corpus of NUM million words
the german corpus is a compilation of mainly newspaper texts from frankfurter rundschau die zei and mannl eimer morgen
the dotted curves in figure NUM are the minimum and maximum values in each set of NUM similarity values for formula NUM
in order to estimate this we parsed a corpus of NUM turns in two different settings while in the first round the threshold value was set as described above we selected a value of NUM for the second pass
however these linking relationships are not a by product of the resolution process itself but must be generated separately
ambiguities in word translations can be taken into account by working with continuous probabilities to judge whether a word translation is correct instead of making a binary decision
however the minimum curve in figure NUM suggests that there are some deep minima of the similarity function even in cases when many word correspondences axe incorrect
NUM figure NUM shows for the three formulas how the average similarity j between the english and the german matrix depends on the number of non corresponding word positions c
try and let axe up there because they often serve as mere modal modifiers of a sentential argument
a better model would be to write the probability of each word modification pair as the conditional probability of the modifier i.e. the modifying word given the head i.e. the word being modified
this operation takes only a few seconds to perform and yields a list of a few thousand french words
by setting fy fxy we can efficiently compute the dice coefficient between x and y under this assumption
these words are then combined in a systematic iterative manner to produce a translation of the source language collocation
given a source english collocation champollion first identifies in the database corpus all the sentences containing the source collocation
finally champollion produces as the translation the largest group of words having a high correlation with the source collocation
addition we see that out of the NUM incorrect results produced by si the dice coefficient corrected NUM
however consider if the example had said a paper of his rather than his paper
when the ri s are high the actual number of candidate translations will be close to the lower bound
we do not know how much the merging of parameters affects the parameter estimation but it seems that a majority of phrases are correctly parsed with the merged parameter estimation based on a rough check of the parsing results
such measures can be helpful in determining the usefulness of a theory of dialog processing as well as determining future directions for research
the indexing method was evaluated using the associated press newswire NUM ap89 database in tipster diskl and a general improvement of retrieval performance over the indexing with single words and full noun phrases was reported
thus we optimize the following product over the non aligned word g p eje maxb elg p gie
it can also be seen that when only one kind of phrase either the full nps or the head modifiers is used to supplement the single words each can lead to a great improvement in precision
topic focus identification ii the verb is ambiguous as to its position in the topic or in the focus
because clauses NUM and NUM are vp elliptical we must establish a parallelism between each of them and clause NUM
it is clear that phrases help both recall and precision when supplementing single words as can be seen from the improvement of all phrase runs wd hm set wd np set wd i im np set over the single word run wd set
however because parallelism is also required between clauses NUM and NUM we can not choose these options freely
i1 ne soulnannait pas que qudque jc lrl plus lard une condamnation en r le du laazi me i ou forme d encyc ique t ait introduite clandcstinement en allemagne el h la barbe de autoritt s terait he mlennellement en chaire dens toutcs les glises le dimalache de la f te de rameaux de NUM
for example if bank terminology occurs in the document then we can use the phrase bank terminology as an additional unit to supplement the single words banld and terminology for indexing
learning research and development center and department of linguistics university of pittsburgh pittsburgh pa NUM
the actual contentious ground claims made by one theory that are incompatible with the other is quite small
the satellite provides information that is intended to increase the hearer s belief in or desire to adopt the nucleus
we suggest the synthesis of the two theories would be useful to researchers in both natural language interpretation and generation
this is the broad view of information content for the entire corpus
as discussed in sections NUM NUM and NUM NUM the analysis of informational relations provided by rst is inadequate and incomplete
what is intuitively obvious to one user is unclear and convoluted to another
assume the input space is comprised of n vectors each of dimension n
in the example the nucleus a expresses an action that the speaker intends the hearer to adopt
deictic expressions can be contrasted with anaphors
plural reference is handled by using sets
the first mechanism is called focusing
only NUM pronouns of the NUM return pops have competing referents if a selectional constraint can arise from the dialogue for example if only one of the male discourse entities under discussion has been riding a bike then the verb rode serves as a cue for retrieving that entity passonneau and litman to appear
shall we us then in month march meet should we meet then in march guided by the assumption that only the boundary of the final intonational phrase is relevant for the present purposes we argue for a categorial labeling cf
o most c ucially the m rphological knowl dgc l asc l rovides tim link i el we m tim inlh t ted fornm found in exl s and the il ai ion forms fouml in licl iona ri s sl roa
in figure NUM the tagging error rates seem to be approaching saturation after the clustering text size of 50mw
after several rounds of reshuffling the word bits construction process is resumed from step NUM outer clustering
this dendrogram constitutes a subtree for each class with a leaf node representing each word in the class
therefore we can use the ami as an objective function for the construction of classes of words
an attempt is also made to combine the two types of clustering and some results will be shown
both types are driven by some objective function in most cases by perplexity or average mutual information
one type is based on shuffling words from class to class starting from some initial set of classes
in particular the parallelism in verbal scope between verb final and v2 clauses exemplified in 3a and 3b can be modeled best by assuming that the scope of a verb is always determined w r t the final position
such questions could concern membership relations of words or sets of words or morphological features of words
it is yet to be determined if the effect of reshuffling increases with increasing amount of clustering text
the identity of the action in error will provide context for the subsequent refashioning of the referring expression
hence no substitution will ever contain a primary occurrence i.e. a pe coloured symbol
in addition the scope must not contain an adjunct focused or not that is a uniquely referring temporal location like yesterday
because of this setting it acts as a restriction on the referring expression that helps to pick up the right antecedent from the presupposition line
an examination of all three source pages revealed that a hidden field fltans gets one of three values based on which page invokes the script
the dm may need to know what item of information the user is interested in as this determines the feedback provided to the user
NUM a like dan golf r dan b
the system is designed to be mixed initiative i.e. either the user or the system can initiate a dialogue or sub dialogue at any time
the alternative temporal localization that occurs in the scope of erst is the construction col ula predicative temporal expression which accepts the r reading
it is dso clear as we will argue in the next section that not all of these sets of alternatives can accept the el a reading
however a precise evaluation of the context that can decide about the relevant reading for instance what information defines which scale is still missing
as can be seen from the example the contextual disambiguation not only is needed for understanding the text but is a prerequisite for high quality translation
besides the assertion that peter points to the fourth lucky number at the temporal perspective t the representation presupposes a sum l con
it is clear that depending on this choice the focus conditions may characterize a thematic role as in the described example or the event wriable
geneous deseril tions the description of an event must not subsmne the des ril tion of the following velits
a written chinese sentence consists of a series of evenly spaced chinese characters
these probabilities are updated iteratively by employing the consistency constraints among neighboring words
the sentence is split into two parts by the two characters just grouped
both the pattern matching and the statistical approaches are simple and easy to implement
more promising structures are explored at high speeds and others at lower speeds
the fragment ngmgub also has combination ambiguity
this type of ambiguity is called global ambiguity with respect to the sentence
at any given time the system stochastically selects one action to execute
our proposed method is not limited to document classification it can also be applied to other natural language processing tasks like word sense disambiguation in which we can view the context surrounding a ambiguous target word as a document and the word senses to be resolved as categories
in fact together with the classical notion of recall and precisions we used also data compression as the percentage of incorrect syntactic data that are no longer produced when specific terminology is used
we assume that each word is independently generated according to an unknown probability distribution and determine which of the finite mixture models p w ci i NUM n is more likely to be the probability distribution by observing the sequence of words
ikl iracket stroke shot balll ks kick goal ball we let the number of clusters equal that of categories i.e. m n NUM and relate each cluster ki to one category ci i NUM n
1deg the use of a classifier is in general idiosyncratic
st s NUM t NUM NUM
the number of parameters in fmm is much smaller than that in wbm which depends on iwl a very large number in practice notice that ikl is always smaller than iwi when we employ the clustering method with NUM NUM NUM described in section NUM as a result fmm requires less data for parameter estimation than wbm and thus can handle the data sparseness problem quite well
letting w denote a vocabulary a set of words and w denote a random variable representing any word in it for each category ci i NUM n we define its word based distribution p wici as a histogram type of distribution over w the number of free parameters of such a distribution is thus i w NUM
a word is not disambiguated if the model parameters needed to assign a sense tag can not be estimated from the training sample
figures NUM through NUM show the accuracy and recall NUM for the best fitting model at each level of complexity in the search
the various information criteria are an alternative to using a pre defined significance level a to judge the acceptability of a model
aic corresponds to g NUM bic corresponds to log n where n is the sample size
a saturated model has complexity level i n n t where n is the number of feature vari NUM ables
then fl fq has a multinomial distribution with parameters n NUM 8q
there are q possible combinations of values for the n feature variables where each such combination is represented by a feature vector
shows that bic and g NUM x NUM select lower complexity models than either aic or the exact conditional test
an evaluation criterion that finds models of similar accuracy using either bss or fss is to be preferred over one that does not
we use knowledge about co occurrences implicitly described in some elementary trees elementary trees defined for more than one anchor are now being selected even if all their anchors are not present in the recognized sentence
the repairing capacities at this stage apply for instance to the case mentioned table NUM in sentence can you give me is a budget the word a is marked as a substitution cf
on the other hand if the language model has low confidence sublanguage should have strong weight
however the pur pose of our system is slightly different from that of information retrieval systems
the difference of these nmnbers indicates the possible improvement we an achieve by restoring the hypotheses using additional components
the work reported here was supported by the advanced research projects agency under contract dabt63 NUM c NUM from the department of the army
we would like to thank the collaboration partners at sri in particular mr ananth sankar and mr victor s abrash
we use the n best hypotheses produced by the sill system alon g with their acoustic and language model scores
this research is being done in collaboration wittl sri which is providing the base of the combined speech recognition system
this corpus has no overlap with the evaluation data set which is drawn from august NUM north american business news
st it does not know the correct transcriptions of prior sentences or any information about subsequent sentences in the article
our method integrates the statistics based and thesanrus based approaches
fsds specify conditions on feature values that must hold at a node in a licensed tree unless they are overridden by some other component of the grammar in particular unless they are incompatible with either a feature specified by the id rule licensing the node inherited features or a feature required by one of the agreement just in case it is incompatible with these other components that gives fsds their dynamic flavor
here though the main significance of the definition is that it forms a component of a fullscale treatment of a gb theory of english s and d structure within l NUM this full definition estab k p lishes that the theory we capture licenses a strongly context free language
null finally while gb and gpsg are fundamentally distinct even antagonistic approaches to syntax their translation into the model theoretic terms of l NUM allows us to explore the similarities between k p the theories they express as well as to delineate actual distinctions between them
as a result while these results do not necessarily offer abstract models of the human language faculty since the complexity results do not claim to characterize the human languages just to classify them they do offer lower bounds on certain abstract properties of that faculty
first as should be clear from our brief explorations of aspects of gpsg and gb re formalizations of existing theories within l NUM k p can offer a clarifying perspective on those theories and in particular on the consequences of individual components of those theories
swe could of course skip the definition of privset and define privilegedy x as vx p x z x but we prefer to emphasize the inductive nature of the definition
we can expand this by defining new predicates even higher order predicates that express for instance properties of or relations between sets and in doing so we can use monadic predicates and individual constants freely since we can interpret these as existentially bound variables
first all instances of nonterminals in v are replaced by e furthermore for any instance b of a right hand side nonterminal of v linked to a right hand side nonterminal a of v b is replaced by e and a by a b
other examples also have a similar significance NUM for the pair actor objective NUM for manner directional
in the experiments reported in this paper we split the corpus into chunks of a size of around NUM megabytes
many nlp techniques are simply not efficient enough and not robust enough to handle a large amount of text
long noun phrases especially long compound nouns such as information retrieval technique generally have ambiguous structures
the experiment s results show that supplementing single words with syntactic phrases for indexing consistently and significantly improves retrieval performance
in the table ret rel means retrieved relevant and refers to the total number of relevant documents retrieved
in fact it may not affect the parsing decisions for the majority of noun phrases in the corpus at all
we simply fixed the modification structure parameter and assumed every dependency structure is equally likely
we want to see if a much larger size of collection will make a difference
modification pairs u v being generated when npi is derived from sj
more experiments and analyses are also needed to better understand how to more effectively combine different phrases with single words
an alternative choice would be to mark the scale of cd by specific indexing of the lexical occurrences in the sentence
at this stage slightly over NUM of all analyses were identical
they worked independently consulting written documentation of the grammatical representation when necessary
this suggests that also parts of speech are a rule governed distributional phenomenon
engcg accuracy was close to normal except that the heuristic con
express rules about the coordination of formally different but functionally similar entities
in addition to syntactic tags also morpholog null ical e.g.
before parsing rules and sentences are compiled into deterministic finite state automata
in the data driven approach frequency based information is automatically derived from corpora
at the first flush the linguistic approach may seem an obvious choice
this module employs ordered hand grafted rules that base their analyses on word shape
when hypothesized information is wrong it can be corrected by using the editors
the second strategy is to provide easy to use editors for entering linguistic information
we will be demonstrating a toolkit for developing natural language based applications and two applications
linking the application independent semantic representation to the back end application software is the task of the application module
linguistic work has been reduced by integrating large scale linguistics resources comlex grishman et
the figure below illustrates an editing session in the semantic rule editor for the verb advise
applications we will demonstrate a web based text application on the topic of the nl assistant product
we will also demonstrate a speech recognition application for mortgage quotations our mortgage assistant product
programming work is reduced by automating some of the programming tasks
when all of the observed actions are processed the system switches from the role of hearer to speaker
liberman separated back channel responses from information bearing utterances and created a separate language model
NUM is the counterpart to NUM NUM and NUM reflect clauses 12a and 12b
NUM f skeleton principle the f skin value of a phrase is the function compose applied to i the f skel vahm of a daughter with
in order to resolve the lexical ambiguity of besueh however cf the discussion of NUM above some partial information about is suffices
according to tiles rules the head itself can project focus which appears to be refuted by b ta like the following
the following principles specify the ls cstr set for a sign introducing arrows or links between however such a context is quite intricate to construct
here is an intuitive overview the arrows pointing directly to the foc and bg partition originate from accenting or nonaccenting of the single words respectively
to restrict the optional focus projection from NUM fllrther schwarzschild assumes an additional pragmatic filter avoid f that selects the tree with the least f marking
a pitch accent on the subject karl however can not t roject focus NUM neither do adjuncts t roject focus NUM
if the selection is wrong the translation result becomes wrong which is a feedback to the user
it should be noted that with the final step where all examples in the training set have been provided to the database the precision of both methods is equal
such as corba for defining inter operable interfaces and http for data transport
by using dtgs we can use most of the analysis of xtag while the generation algorithm is simpler
documents can have attributes and annotations and can be grouped into collections
the life cycle service is responsible for creating and copying objects
figure NUM gives a picture of one possible integration solution
the modules are defined in idl and implemented in java
a component server translates an incoming request into a component action
this version provides a concurrency control service and a transaction service
an open distributed architecture for reuse and integration of heterogeneous nlp components
the architecture provides support for component communication and for data exchange
null parame e r eslimalion given the position align null ment
o pot favor me pide mi taxi para la habitacidn tres veintidds
in particular the effect of word ordering is not taken into account appropriately
categorization some particular words or word groups are replaced by word categories
i this maximization is done beforehand and the result is stored in a table
table NUM language model perplexity pp word error rates ins del
r how much does a double room including room service cost for five nights
algorithm the algorithm in section NUM NUM can analyze any input string with the least number of errors
od z is the cost of a deletion error for a terminal symbol
these removed rules are almost for peculiar sentences and the left rules are very general rules
null it is very interesting that parameters of heuristics reflect the characteristics of the test corpus
with heuristics our robust parser can enhance the processing time and reduce the number of edges
therefore we can get better results if the parameter are fitted to the characteristics of the corpus
then we present the extension of this algorithm and the heuristics adopted by the robust parser
we extended this algorithm to recover extragrammatical sentence into grammatical one in running text
the experimental result shows NUM NUM accuracy in error recovery
NUM NUM NUM NUM sudden relation deep heart disease pressure more than NUM NUM was
an extrapst of the hierarchy for is shown in the following figure
we also build a taxonomic hierarchy for the modifier that appears with the particle modificant in the sentence
null NUM calculate the mutual information NUM for all the concept identifiers in the taxonomic hierarchies
we use a statistical method to select the most probable structure or parse for a given sentence
structure rdg identifies all possible dependency structures which consist of modifier modificant relations between elements in a sentence
the following figure shows some modifiers for work with their number of occurrences
since the sentences used are extracted from a newspaper it s also general in its applicability
the correct structures for NUM sentences were found as the second most probable structure by the method
we remark that this mechanism provides a general means to treat translation unit with more than one component word
let ac x s y be the difference in the interpretation certainty of y e x after training with x e x taken with the sense s
the categorization of verb phrases into different aspectual classes can be phrased in terms of which part of the nucleus they refer to
the whenever also causes the introduction of rl a new reference time marker rl lies just after el
finally we deal with sentences such as NUM which contain an iteration of an implicit generic quantifier and always
a new reference time marker rl is then introduced rl lies immediately after r0 recorded as r0 rl
for example the following sentence NUM denotes the state s holding at the present that mary has met the president
sume the following relations for when if both the when clause and the main clause denote states then their respective time indices overlap
this quantification structure does not need to be stipulated as part of the q adverb s meaning but arises directly from the temporal system
ill the worst case this is tnmvoidable el o though we do not expect natural language grainmars to exhibit such behavior
modularizing a group of dependent disjunctions amounts to finding a conjunction of ease forms that in equivalent to the original ease form
tile first is to take the original case form and to construct a pair of possibly indep mdent ease forms from it
tassuming p nil appear from natur fl language gralnlnars controlling its form all save exponential amounts of time
o n l ifl disjunc tions
the modularization algorithm presented in this paper takes existing dependent disjunctions and splits them into independent groups by deterlnining which disjunctions really interact
the second fact is that the introduction of disjmmtion into a grammar causes processing tilne to increase exponentially in the number of disjunets
definition NUM dependency group a dependency group is a conjunction of dependent disjunctions with the same name d where each
to make the formulation completely equivalent we would need to enforce the uniqueness of a solution by conjoining al v g2
table NUM shows the results on NUM NUM polysemous nouns in semcor
an example dependency structure is shown in NUM
using syntactic dependency as local context to resolve word sense ambiguity
the subtle distinctions between different word senses are often unnecessary
we experimented with three interpretations of similar enough
our algorithm treats all local contexts equally in its decision making
we have presented a new algorithm for word sense disambiguation
while we will almost always wish to parse using thresholds it is nice to know that multiple pass parsing can be seen as an approximation to an admissible technique where the degree of approximation is controlled by the thresholding parameter
the word duty has three senses in wordnet NUM NUM
the similarity threshold NUM NUM reduces the number of senses to NUM
our work on adjectives forms a microtheory used by the mikrokosmos semantic analyzer
the research reported in this paper was supported by contract mda904 NUM c NUM with the u s
NUM NUM lrva common cases and complications
for disjunctions of atoms there exists a prolog term representation which is described below
this is probably true but not practical
in fact each such relationship and related method is a lr
both authors feel indebted to the other members of the mikrokosmos team
this abandoned NUM is a deverbal of both abandon v2 see section NUM NUM NUM
3it should be noted however that on bo NUM data kldivergence performed slightly better than the l1 norm
the similarity based methods consistently outperform the mle method which recall always has an error rate of NUM
however with singletons omitted the difference between a and pc is even greater the average difference being NUM
the confusion probability can be computed from empirical estimates provided all unigram estimates are nonzero as we assume throughout
however pml is zero for any unseen word pair which leads to extremely inaccurate estimates for word pair probabilities
we first construct a list of pseudowords each of which is the combination of two different words in v2
rand which is shown for comparison purposes simply chooses the weights w wl w randomly
we wish to model conditional probability distributions arising from the coocurrence of linguistic objects typically words in certain configurations
we next investigate estimates for pr w21wl derived by averaging information from words that are distributionally similar to wl
notes the number of bigrams that were mixed into each prediction
we want to distinguish between items that are unlikely to occur ever and those that have just not happened to turn up in the training data
the sort binary tree introduces the feature label and its subsort adds the features lefl daughler and right daughter
whatever the nature of subcategorization information may be syntactical in gpsg hybrid in hpsg functional in lfg two categories are compatible if they subsume a common denominator in this case a common partial structure
NUM ensures that the skip k predictions are only invoked when the context is appropriate
in a context free transduction grammar terminal symbols come in pairs that are emitted to separate output streams
corpus driven analysis is mainly based on mutual information statistics and the resulting system has been successfully applied to technical documentation e.g.
let us remark that a comma is inserted between marie and sa canne p che in case of extraction before el as in lb indicating the two sentences have not necessarily to be analyzed in the same way 4e je demande ps pierre son v lo et ps marie sa canne p che
appropriate wordnet entries for the individual words can also be stored in the individual word annotations
we claim here that the local combinatory potential of lexical heads encoded in the subcategorization feature explains the previous linguistic facts conjuncts may be of different categories as well as of more than one constituent they just have to satisfy the subcategorization constraints
it can not threshold out an entire cell even if there are no good nodes in it
in a cky chart parser a two dimensional matrix of cells the chart is filled in
and then tries to find the best combination of parameters that works at approximately this level of performance
null an example of an intermediate verb occurrence is the following
our second pass will use an hmm whose states are analogous to the first pass hmm s states
so the mapping between states in the first pass and states in the second pass may be non trivial
groups used the adhoc task guidelines and submitted the top NUM documents retrieved for each of the NUM spanish topics
consider the case in which a particular cell contains only bad nonterminals all of roughly equal probability
in the first pass we now keep track of each production instance associated with a node i.e.
in particular previous work focused on optimizing weights for various components such as the language model component
NUM NUM two kinds of adjective with dotted type the emotional state i and agent orlented ii adjectives will be given the gl representations NUM and NUM respectively
for english there were NUM NUM words of training data
for spanish we had only NUM NUM words of training data
the word feature is the one part of this model which is language dependent
these three steps are repeated until the entire observed word sequence is generated
we only report results on the blind test set for each respective language
point and the period separates groups of three digits in large numbers
longer distance information to find names not captured by our bigram model
it produces one of the fourteen values in table NUM NUM
the probability p c iw is calculated for each word class c and the proper classes for a word w are determined based on it
the preprocessor assists in this task by identifying potential idioms
for the purposes of nlp systems their classification of words is sometimes too coarse and does not provide sufficient distinction between words or is some times unnecessarily detailed
in other words NUM records exact matches between the left and right corpora or mismatches involving only a single element with exact matches to either side
then no terminal element intervenes between wi and t s lsd or between wj and t s rsd and the same condition holds of t
we restricted our co occurrence data to that included the wo postposition which typically marks the accusative case while uramoto used several grammatical relations in tandem
the irregular and nonstationary nature of this corpus poses an exacting test for statistical language models
other perhaps more interesting questions touch on the linguistic content of analyses and whether for example particular linguistic phenomena are associated with divergence between the corpora
normalization we can abstract away from details of the markup used in a particular corpus by providing the following externally defined functions
second the algorithm we have developed is robust in the face of minor editorial differences choice of markup for punctuation and overall presentation of the corpora
abstraction makes it possible to change data structures by changing their definition only at one point
likewise we claim that if a subtree is greater than unary branching then it is uniquely identified by its yield
determine constant tree transformations a set of pairings between aligned subtrees can be used as a bootstrap for semi automatic markup of corpora
we suggest the term tree alignment to indicate the situation where two markup schemes choose to bracket off the same text elements
katz has highlighted many interesting features of the distribution of content words which do not conform to the predictions of statistical models such as the poisson
the different senses of the mental adjectives examples NUM to NUM and their polyvalency examples NUM to NUM follows from the qualia representation
the poset td abcd lcb a b c d a b cd a bc d a bcd ab c d ab cd abe d rcb can be graphically presented in the hasse diagram in figure NUM
breaks are then inserted into the original text at the places corresponding to the local minima if their confidence value satisfies a minimum confidence criterion
the first clause defines the recursive part of the translation function and states that the translation of an f structure is simply the union of the translations of its component parts
the construction of udrss in particular the specification of the partial ordering between labeled conditions in ps is constrained by a set of meta level constraints principles
the formulation of the reverse translation r NUM from udrss back into f structures depends on a map between argument positions in udrs predicates and grammatical functions in lfg semantic forms
those examples determined to be sufficiently informative are selected for training
define a function NUM dependents on referents labels and merged udrss as in figure NUM NUM is constrained to o qi iv c c
the full textual definitions are given in the appendix the u drt construction principles distinguish between genuinely swhere denotes syntactic identity modulo permutation of attribute value pairs
for example c is a subsegment whose tin order to make the example more intelligible to the reader we replaced references to parts of the circuit with the simple labels partl part and part3
consequently an objective human analysis and annotation of all critical tokenizations in a corpus becomes achievable which in turn leads to some important observations
the idea that the hierarchical segment structure of discourse originates with intentions of the speaker and thus the defining feature of a segment is that there be a recognizable segment purpose is due to grosz and sidner
however analysis of our corpus led us to hypothesize that the hierarchical context in which a relation occurs i.e. what segment s the relation is embedded in is a factor in cue usage
there are three types of simpler functional elements NUM units which are descriptions of domain states and actions NUM matrix elements which express a mental attitude a prescription or an evaluation by embedding another element and NUM relation clusters which are otherwise like segments except that they have no core coatributor structure
both types of the parameters we sample have the form ofmultinomialdistributions
conversely mcnealy a high semantic content word which only occurs in a cluster of three receives a high significance value
indeed taking into account all the occurrences of the unknown words of a text corpus permits us to automatically produce lexicons containing for each entry a list of possible syntactic classes with frequency information
in speech recognition these lexicons are necessary in the lexical access phases and the language modelisation as they allow the association between lexical items and recognized sounds while maintaining syntactic coherence within the sentence under analysis
if for a given word the lexical access fails this failure can affect the processing of the word as well as the processing of the contextual words
this lexicon extraction module has been used within the text to speech system developed at lia before the grapheme to phoneme transcription phase we first extract a lexicon of all the oov words of the text to process
finally the repair rr is what the speaker intends to replace the rm with
it is important to point out that the average number of classes which can be attributed to a proper name is very close to NUM NUM NUM in our test corpus and NUM NUM in the general lexicon
a syntactic tagging process based on NUM class and NUM gram language models allows us to automatically allocate possible syntactic categories to the out of vocabulary oov words which are found in the corpus processed
the quicker the evolution of a specialised area the more the dictionary will lack the ability to cover the subject because a dictionary represents the state of a language at a given time
we have implemented a method which assigns an estimated significance score based on a measure of two context dependent properties local burstiness and global frequency
this number is then normalised to between NUM and NUM with NUM indicating a very low significance and NUM indicating a very high significance
later during the generation partial is enriched and at any stage of processing it represents the current syntactic semantic correspondences
the work at rmit the citri2 run was part on their ongoing effort to test various term weighting schemes
other NUM correct relations resulting from the automatic search were found for dds which we have ascribed manually to other classes than syn hyp mer for instance a relation was found for the pair bach the composer in which the anchor is a name
in general we can state that situation NUM is the ideal case
finally we have experienced that some relations tend to overlap for unclear cases
despite the tests for each relation there are always border cases where intuitions will vary
if the analyzer had a passive example its egraph would have matched perfectly and therefore pre empted this erroneous match
the morphological analyser produces about NUM different tag combinations
converting from sentence correspondence to sentence alignment is of dubious practical value
collection the collector receives the semantic representations from all the sentences and merges them into a cumulative semantic representation
section NUM presents results of experiments using both evaluation methods
some of the annotated verbs can function as both main and auxiliary verbs and some are often used in idioms
in related work we have begun to tag nouns and adjectives as well
for example below the annotator tagged take with the first wordnet entry for take place
the annotations for the individual words are delimited by wf and wf
these instances support two completely different interpretations even with the help of the context
the following axe strategies we found useful in dealing with the difficult problem of manually identifying idioms
the purpose of this work is to support related work in automatic word sense disambiguation
on similar reasons we took for english NUM this is adjective
the probability of eventually reaching the final state from any state is always NUM thus the forward probability is all that is needed
the user next utters no on the television
we attempt to identify some common language usage ad hoc rules that can be employed to further split the chunks into short words
the lustering mgorithm whh h is described in stage four is appticd to each NUM ah of articles and t roduces a set of lusters whh h are ordered in the des ending order of heir semantic similarity wdues
ve have rei orted an exl erimentad study for clustering of rticles by using on line lictiom ry deftnitions mid showed how dictionary definitiolt cam use effectively to classify articles ea h of which belongs to the restricted sul ject domain
giw n ml article the procedure for wsd is applied to each word noun in an article i.e. the sense of each noun is estimated using formula NUM and the word is rel laced NUM y its sense
stage three measuring similarity between vectors given a vector representation of nouns in new articles s ill forlnula NUM a dissimilarity between two words noun vl v2 in an article would be obtmned by using formula NUM
exalnilfing the results there are NUM nouns in ern article and NUM nouns in hrd and of these shares stock and share which are semantically similar re included
examining the nouns which arc NUM elonging to ern mid ce plant factory and food senses oil petrohmnl and food order colmn nd md dema nd and interent del t and curiosity whi h are high frequencies re correctly dismnbiguated
in each initial tree the root and interior i.e. nonroot nonleaf nodes are labeled by nonterminal symbols
the nodes on the frontier are labeled with terminal symbols nonterminal symbols or the empty string e
the rules that we use together with their rationale and examples and counter examples are described below below ex NUM NUM
there is a hello menu button to give access to greeting utterances a goodbye menu button to give access to closing utterances and most of the remaining content is accessed through a set of NUM intersecting perspectives namely person with NUM values me and you tense with NUM values past and present future and affect with two values happy and sad
below we examine the simple pca called derive as an example
any derivation in g can be converted into a derivation in g by doing the reverse of the conversion above
to convert a tig into a tag deriving the same trees and no more one has to capture these restrictions
because g does not contain any empty rules the set of initial trees created does not contain any empty trees
to prove the theorem we first prove a somewhat weaker theorem and then extend the proof to the full theorem
for the purpose of experimental evaluation the ltig lexicalization procedure was applied to eight different cfgs for subsets of english
tree insertion grammar a cubic time parsable formalism that lexicalizes context free grammar without changing the trees produced yves schabes merl
the same is true in a tag where the trees in t are allowed to adjoin on each other s roots
a derivation is complete when every frontier node in the tree s derived is labeled with a terminal symbol
however this would help for future checking only if wordnet is modified and updated so that binary and n ary n NUM antonymy would be different lexical relations
this 3in fact it is preterminal rather than terminal elements that are stored in the table in order to account for modified constraints
if there is no such substructures an error is signalled NUM unless an 0ptrule slot for optional rule was executed
any c rule chosen may be the last one in a derivation whereas chosing a non c rule may open up further opportunities of chosing c rules
they can be reused when passing an applicability test that requires the stored category and input structure to be identical to the current ones
if these conditions are met chosing a c rule from every conflict set if possible will lead to a globally best solution first
in figure NUM the first action selects all rules with category np the relevant substructure is the argument filling the patient role cf
for each action part read the category determine the substructure of the input by evaluating the associated function and goto NUM
in this contribution we have introduced tg NUM a production rule based surface generator that can be parameterized to generate the best solutions first
during processing tg NUM can then judge the global impact of chosing the locally best c rule and decide to fulfill or violate a criterion
computing such information from the context free backbone of tgl grammars instead would be less effective since it neglects the drastic filtering effects of preconditions
precision is defined as the proportion of retrieved documents which are relevant and recall that of relevant documents which are retrieved
the tst programme is sponsored by nwo dutch organization for scientific research
NUM the basic ideas were worked out in connection with our tipster ii project
suppose that on average either of two expert analysers find equally many guideline violation types not found by the other
the paper will present first test results on the generality and objectivity of this tool called det dialogue evaluation tool
figure NUM example of error type i
however ambiguity and uncertainty exist at the different levels of analysis
to make the word sense score function feasible for implementation we further assume that the senses of words depend only on the case assigned to the words the parts of speech and the words themselves only
m denotes the case of wi m which is specified by the case subtrees i i simplified model called case dependent cd model is implemented in this paper
let the label t i in fig NUM be the time index for the i th state transition which corresponds to a reduce action and be the i th phrase level
the accuracy rates of NUM NUM for parse tree NUM NUM for case and NUM NUM for word sense are obtained when the baseline system is tested on a corpus of NUM sentences
figure NUM example of error type NUM
parameter smoothing is shown to improve the performance significantly
let us assume an environment consisting of four tables t i to t4 roughly placed in a row as depicted in figure NUM the communicative goal is to distinguish one of the tables uniquely from the other three by a referring expression entailing an adjective a prenominal modifier a category an attribute a postnominal modifier and a relative clause at most
every edge has weight o or NUM
let us do the hardest case first
NUM NUM work in progress factored automata
NUM NUM generation is np complete even in otp
this is a key practical trick
every constituent has width and edges
no ga is simply too powerful
NUM NUM gem input and output in otp
this paper discusses a method automatic extraction of candidates for open compound registration
resolve s decisions are based on a decision tree induced from feature vector representations of noun phrases
it was also unexpected that one of the systems would match human performance on the task
errors due to insufficient model hypotheses
systems were not penalized if they failed to include such linkages in their output
one set of issues concerns the range of syntactically governed correference phenomena that are considered markable
the highest st f measure score was NUM NUM NUM recall NUM precision
however performance of the systems as a group is better on the muc NUM test set
the test sets used for muc NUM had a much higher proportion of relevant texts
each event object captures the changes occurring within a company with respect to one management post
in this article the management succession scenario will be used as the basis for discussion
figures NUM and NUM show the sample size for the various tag elements and type values
this section seeks to disclose an important structure of the set of different tokenizations
NUM the first transformation states that a noun should be changed to a verb if one of the previous three tags is md one of the previous two tags is md one of the previous two tags is dt one of the previous three tags is vbz the previous tag is to as in to to conflict nn vb with
because the more general terms should never overrule the more specific ones the ax e for the more general terms should be quite small
there are always lessons to be learned and a few genuinely new ideas to ponder as well
although crystal was trained on NUM te documents wrap up was only trained on NUM st documents
using this candidate list the translators again translate the nineteen terms
such seed words serve as the textual anchor points in non parallel corpora
i i finding terminology translations from non parallel corpora
in this case the cosine measure would be more appropriate where
figure NUM precisions for the best candidate translation in the wsj nikkei corpus
clear water bay hong kong pas cale ee ust
this non parallel corpus has minimal content and style differences
figure NUM translator improvement on term translation
n top candidates are useful as translator aids
a generalized right context vector v for word w was then formed by counting how often words from these NUM classes occurred to the right of w
for this reason a second induction on the basis of word type and context was performed but only for those tokens with informative contexts
if u no s nondiscount departures
spill initiate clarification recta communication in case of inconsistent user input
in contrast dimensions corresponding to small singular values represent idiosyncrasies like the phonological constraint on the usage of an vs a and will be dropped
an adjective like likely is very sim null ilar to seemed in this respect although its left context is quite different from that of seemed
the main innovation is that the algorithm is able to deal with part of speech ambiguity a pervasive phenomenon in natural language that was unaccounted for in previous work on learning categories from corpora
we became aware of the problem during the post experimental interview
do not say what you believe to be false
second a speaker may adhere perfectly to exchange purpose cf
NUM on which date will the journey start
NUM is near equivalent to gp8 brevity
group NUM p epair and clarification gpno
the maxims are marked with an asterisk in table NUM
initiate repair or clarification lncta connnunication in case of comlntlnication l ailure
although the propositional content expressed by these two passages is the same the only difference being the expression used to refer to john in the subject of the third sentence passage NUM is not jarring in the way that NUM is
hopefully the introduction of the english to turkish mt software into the turkish market will meet the growing demands for accurate fast and highquality translations in the field of computer manuals
second matrix summarizes the information exchange with agent b labels vl to v4 in each matrix represent the possible values of depart city shown in table NUM v5 to v8 are for arrival city etc columns represent the key specifying which information values the agent and user were supposed to communicate to one another given a particular scenario
we then give rules that account for the collaborative process
when a local descriptor is encountered any missing node or path components are filled in from the local context and then control passes to the new context created that is we look at the definition associated with the new node path pair
NUM indeed it will be interpreting the contents of a file if datr has been used to define a lexicon that has subsequently been compiled out rather than being accessed directly by components of the nlp system see section NUM NUM below
by introducing an abstract love evans and gazdar lexical knowledge representation lexeme we can provide a site for properties shared by all forms of love in this simple example just its morphological root and the fact that it is a verb
it can be glossed roughly as the value associated with syn cat at wordl is the same as the value associated with syn cat at verb thus from verb syn cat verb it now follows that wordl syn cat verb
here one starts with a set of known query value pairs love mor past participle love ed love mor pres tense sing three love s etc and the task is to induce a description that has those pairs as theorems under the application of conventional inference
in particular an automaton for lt can be obtained by eliminating the output of the arcs and states and considering the final state set of the automaton being the same as in the sst
in particular the categories chosen do not need very specific rules for recognizing them the translation rules they follow are quite simple and the amount of special linguistic knowledge introduced was very low
for example consider the transducer in figure2 a and a spanish sentence categorised as me voy a hour which corresponds to the categorised english one i am leaving at hour
when translation accuracy is the main concern a more detailed acoustic model and a wider beam in the search can be used to achieve a wer of NUM NUM but with a rtf of NUM NUM
these steps were followed category identification
the differences in wer attributable to the use of lexical categories can be as high as about a NUM in the early stages of the learning process and decrease when the number of examples grows
actually there were only three emi
the precompiled grammar which is used by the chart parsers contains NUM rules
this is because our p o s dictionary derived from wsj articles did n t contain stepping as a verb form interestingly this error does not cause us any difficultie s downstream because the exact same interpretation would have been applied during training
sentences apart NUM sentences apart NUM i i i same noun phrase yes i i i same noun phrase no i i i i obj2 cn type status yes i i i i obj2 cn type status no
another reason for specially marking st texts was a hope that the co training could also be used for coreference resolution in the te and s t tasks unfortunately time constraints prevented us from using this domain specific coreference resolution training in the latter two tasks
in particular we are working with new string recognition specialists for named entities a new part of speech tagger a new sentence analyzer a new and fully automated dictionary construction algorithm a new discors e analyzer and a new coreference analyzer
te and st would probably have benefited from the larger feature set used for the co task but there was not enough time to incorporate all of resolve s features into the systems used for the te and s t tasks
if the cn was of type status evidence as was the case for will retired in the second segment of the sentence the tree branches to a large subtree in which two thirds of the training instances were positive
the irrelevance of crystal s dictionary to noun phrase analysis has been confirmed by an experimental t e run in which we removed the official crystal dictionary containing NUM cn definitions and replaced it with a dictionary of only two cn definitions
space prohibits us from going into much detail about each of the major components in our various muc NUM system configurations so we will concentrate on the trainable components that were present in the te co and s t tasks
the most interesting components in our system are crystal which generates a concept node dictionary NUM wrap up which establishes relational links between entities NUM NUM NUM and resolve the coreference analyzer NUM
if NUM of the recall for the muc NUM co fina l evaluation was based on references to people and organizations as it was for the dry run evaluation then resolve was actually achieving approximately NUM of the total recall for which it was originally intended
if a match can not be made in the current dictionary but occurs in another dictionary that entry will be displayed
in cases where similar spelling or morphological variants are available a fuzzy match list is provided that users can select from
crl s word frequency tool provides users with a simple interface for viewing word statistics for individual documents or large collections of documents
further the current state of the art in traditional machine translation and programmed instruction provide inadequate support for language translators learners and instructors
although task oriented user centered design is not new method for application development it has not previously been applied to natural language processing tasks
these translators would also spend significant amounts of time marking hardcopy comparing source and target language texts and consulting reference material
these include monolingual and bilingual dictionaries and glossaries large collections of source and target language text and other lexical information
here designers observe users working and involve users in the stem design process by having them work with early prototypes
but because they are cumbersome and awkward to learn and use they often go unused by all except computer programmers and developers
learners proceed like researchers as they direct and manage their own training not only in the classroom but also on the job
morphological information another set of labels represents morphological information
syntactic category is encoded in node im els
l he prototype these rea sonings is the at du tiv one in which from a property asserted in the text we infer an object which t ossesses this property m then we consider all the characteristics of tile selected object as valid for i he text
selltal ioll systehts al e tlo collvel li eiii r ollr put pose the information is sl ecific and at eve all the reasonings to l er rni are t rol r o natural language discom ses
after a loop is reached at this factor we lower the factor to NUM then NUM then NUM NUM then NUM NUM
there is a contimmm as in big small and we can not infer small from not big
erkennen da t er weint at it will him anita
a phrase or a lexical item can perform multiple functions in a sentence
the development of linguistically interpreted corpora presents a laborious and time consuming task
in the second phase secondary links and additional structural flmctions are supported
sentences annotated in previous steps are used as training material for further processing
thus each object may hang during this progress we must then listinguish betwe n this punctu l repre sencation and the history of objects which ig is necessary to maintain in the as of a dialogue for exaniple
the lexical and contextual probabilities are determined separately for each type of phrase
for each non empty temporal unit tuii from focuslist starting with most recent if specificity tu11 specificity tu and not empty merge tult tu then
the performance of name org is better than other anaphoric phenomena because the character subsequence feature has very high antecedent predictive power
where p is precision r is recall and NUM is the relative importance given to recall over precision
therefore our initial approach has been to employ quinlan s c4 NUM algorithm at the heart of our classification approach
NUM the tool allows a user to link an anaphor with its antecedent and specify the type of the anaphor e.g.
another approach is to use a hybrid method where a preference trained decision tree is brought in to supplement the decision process
the decision tree approach we have taken may thus predict more than one antecedent to pair with a given anaphor
next we describe the scoring methods used and then the testing results of the mlrs and the mdr
we used recall and precision metrics as shown in table NUM to evaluate the performance of anaphora resolution
mlr NUM NUM NUM NUM and NUM all exceeded the mdr in overall performance based on f measure
if the node m spans i j k l such that the last operation to create this tree is a composition of the form in figure ha then m to assoc list m is minimal
these qlfs are translated into domain specific updates to be passed on to the dialogue manager dm for further processing
a major obstacle for this approach however is the fact that very fine grained semantic distinctions can be made in the update language
the hypotheses were that the system would function acceptably that with a reasonable amount of user training machine directive mode would yield longer completion times and less complex verbal behaviors than a more passive mode and that users would respond positively to using the system
for example after the computer produces an utterance that is an attempt to have a specific task step s performed there are expectations for any of the following types of responses a statement about missing or uncertain background knowledge necessary for the accomplishment of s
the paper describes the target behaviors of the processor the theory that achieves them an implementation of the theory in a system to aid in electric circuit repair and performance statistics gathered as human subjects used it in a series of tests
the led is alternately displaying a NUM and NUM with frequency greater than once per second the led is not on the led is on but not blinking the led is blinking but not alternately displaying a NUM and a NUM the led is damaged etc
the new subdialog finds the rule set knob y find knob adjust knob y which says the way to set the knob is to first find it and then do the adjustment
NUM an overview of the system architecture the architecture of the system is given in figure NUM where five major subsystems are shown the dialog controller the domain processor the knowledge base the general reasoning system and the linguistic interface
thus most users will know how to adjust a knob to a specified level even if they are novices but they may be able to measure a voltage only after they have been told all of the steps at least once in the current situation
for illustration the opening of a chassis cover plate will often evoke comments about the objects behind the cover the measurement of a voltage is likely to include references to a voltmeter leads voltage range and the locations of measurement points
this leads to a number of interactions as traced in detail in appendix a the reader should note in this continued interaction the manner in which the theorem proving drives the dialog and the user model inhibits or enables voice interactions appropriate to the situation
another very important use of formatting tags is checking of revised text only
the conditional distribution over l ui e productions is estimated from the frequencies for each english part of speech l
for example figure NUM shows a parse tree for an english chinese sentence translation
this does not affect the generative power but allows probabilities to be placed on collocation translations
the bii distribution encodes the english chinese translation lexicon with degrees of probability on each potential word translation
on the other hand the present framework incorporates all these aspects within a single probabilistic optimization
the problem of bracketing such corpora is the focus of two new strategies described in this paper
in such cases specific grammatical information about one or both of the languages is needed
the absolute magnitudes are not meaningful since they are largely determined by the fixed lexical translation probabilities
these approaches can encounter difficulties with incompatibilities between the monolingual grammars used to parse the texts
this characteristic gives itgs just the right degree of flexibility needed to map syntactic structures interlingually
for instance the dative shift rule for english changes the second complement the pp into a np which is not semantically satisfying
the three cited solutions give an efficient representation without redundancy of an f tag but have in our opinion two major deficiencies
the structures are lexicalized elementary trees namely containing at least one lexical node at the frontier called the anchor of the tree
figure NUM shows a partial description representing a sentence with a nominal subject in canonical position giving no other information about possible other complements
translation of an input sentence is obtained starting from the initial state following the path corresponding to its symbols through the network and concatenating the corresponding output substrings
the time and speed advantage of igtree grows with larger training sets
as discussed in NUM NUM our first step in parsing is to tag each sentence
the performance of the parser on short sentences of correctly tagged data is extrememly good
subject areas statistical parsing automatic treebank conversion semantic and syntactic analysis of text
for instance complete syntactic and semantic analysis is performed on all nominal compounds e.g.
to do this the label for the category was considered as a variable that acts as a placeholder in the output sentence and whose contents are also fixed by an assignment appearing elsewhere within that sentence
a parse is built up from a succession of parse states each of which represents a partial parse tree
for word nodes this includes membership on vocabulary lists whether the word contains various pref xes
in brief the more sophisticated constraints a rule contains the better it performs
in addition to these pieces of meta information all retrieved descriptions and their frequencies are also stored
it is entirely possible that the correct parse is in fact among the highest scoring parses
cogenthelp itself is a hypertext server written in java making it highly cross platform
they indicate that we can build treebank conversion models of accuracy comparable to the current parser using much less data
as far as its complexity is concerned the algorithm is in some sense even more efficient than its predecessors because it does not require complete lists of descriptors to be produced for each referent
the problems in estimating robust models of this form are well documented
the m step uses these posterior probabilities to re estimate the model parameters
his arguments do not apply to our work for several reasons
figure NUM plot of training set perplexity versus
second order m NUM model in table NUM
the first column shows the perplexities on the training set
let m denote the number of bigram models being combined
we conclude this section with some final comments on overfitting
language models using such combinations have been proposed by huang et al
all sentences were drawn randomly without replacement from the nab corpus
probabilities of the observed distributions range from NUM x NUM NUM to NUM x NUM NUM as given by cochran s q
reliability metrics provide a measure of the reproducibility of a data set for example across conditions or across subjects
jects segmentation task break up a narrative into contiguous segments with segment breaks falling between prosodic phrases
thus in all cases we are able to take into account the full set of correct answers
for t NUM learning NUM is comparable to ea while learning NUM is better
we applied two training methods error analysis and machine learning to the previous test set of NUM narratives
the test rcb NUM a branch can either lead to the assignment of a class or to another test
in learning NUM c4 NUM allows feature values to be grouped into one branch of the decision tree
the deverbal adjective lr falls in between with an output of almost NUM NUM entries
however just as with the early proposals regarding segmentation many of these proposals are based on fairly informal studies
adjoining is a more complicated splicing operation where the first tree replaces the subtree of the second tree rooted at a node called the adjunction site that subtree is then substituted back into the first tree at a distinguished leaf called the foot node
we then present an evaluation of the anaphora in some texts generated by our system
bookl NUM book2 books library patron lcb library patron rcb lcb bookl NUM book2 books rcb book19 member of books book2 member of books library x have y x lcb does doesn t rcb y e books
thus term variant extraction is a significant expansion factor for identifying morphologically and syntactically related multi word terms in a document without introducing undesirable noise
we decompose the generation of the rhs of a rule such as NUM given the lhs into three steps first generating the head then making the independence assumptions that the left and right modifiers are generated by separate 0th order markov processes NUM NUM generate the head constituent label of the phrase with probability NUM h h i p h
h is the head child of the phrase which inherits the head word h from its parent p l1 l and r1 rm are left and right modifiers of h either n or m may be zero and n m NUM for unary rules
the re write rules are either internal to the tree where lhs is a non terminal and rhs is a string NUM t s NUM s is constant hence maximizing is equivalent to maximizing p t s
moreover he found that an actor elaborations node produced information that was intrusive
in each case NUM the final estimate is e ale1 NUM l a2e2 NUM NUM ea where ex e2 and e3 are maximum likelihood estimates with the context at levels NUM NUM and NUM in the table and kl k2 and NUM are smoothing parameters where NUM ki NUM
due to time constraints in carrying out tasks nurses in particular noted that speech takes time and therefore spoken language output should be brief and to the point while text which is used to annotate the graphical illustration may provide unambiguous references to the equipment and drugs being used
while carrying out hypotactic aggregation operators a current central proposition is selected and the system searches through the un aggregated propositions to find those that can be realized as adjectives prepositional phrases and relative clauses and merges them in
finally due to the temporal nature of the speech the language generation module needs to communicate information about the ordering and duration of references to other temporal media such as graphics in order to allow for coordination between media
it is at this critical point when care is being transferred from the operating room or to the icu and monitoring is at a minimum that the patient is most vulnerable to delays in treatment
he has a medical history of transient ischemic attacks pulmonary hypertension and peptic ulcers the medical history can only be realized as noun phrases thus requiring a second sentence and necessarily more words
in our work we have modified the structure of the lexical chooser so that it can record its decisions about ordering using partial ordering for any grammatical variation that may happen later when the final syntactic structure of the sentence is generated
the syntactic constraint is recorded in the intermediate form but the lexical chooser may later decide to realize the proposition by any word of the same syntactic category or transform a modifier and a noun into a semantic equivalent noun or noun phrase
that is quite promising since it is relatively straightforward might halve the error rate on names and has only modest effect on speed and overall accuracy
after all where neither machine translation of written text nor speech understanding or speech production had led to any significant results yet it seemed clear that putting three not even halfway understood systems together would be premature and bound to fail
the end user generally has immediate needs which must be met as quickly as possible
a similar problem appears in the database approach to corpora where the difficulty is not in seeing the original text but in seeing the markup in relationship to the text
it may be possible to provide more general support for variation in style either by picking up the cue to style from the opening utterance selected and then using the corresponding variation of each subsequent utterance selected or by offering suitable prediction when the user selects material from a database of utterances in preparation for an interaction with a particular class of conversation partner e.g.
in lt nsl the corresponding interfaces are less formalised but can be defined by specifying the dtds of a program s input and output files
the software consists of a c language application program interface api of function calls and a number of stand alone programs which use this api
most corpora include a level of representation for words and many include higher level groupings such as breath groups sentences paragraphs and or documents
suppose for example we already had segmented a file resulting in a single document marked up with sgml headers and paragraphs and with the word segmentation marked with w tags
in that case our point would be that sgml is a suitable abstraction for programs rather than a more abstract and perhaps more lim null ited level of interface
the workbench has plainly achieved an extremely successful generalisation of regular expressions and one which has been validated by extensive use in lexicography and corpus building
specialised editors for sgml are available but they are not always exactly what one wants because they are too powerful in that they let all markup and text be edited
the following sections describe the profit language which provides sorted feature terms for prolog and its implementation
the second argument can be further instantiated for the subsorts and the remaining four arguments correspond to the four features
hfp sign head x dtrs head dtr rcb head x
the declaration states that all subi are subsorts of super and that all subi are mutually exclusive
the profit system and documentation are available free of charge by anonymous ftp server ftp coli uni sb de
there can be several template definitions with the same name on the left hand side relational templates
the features introduced by a sort are inherited by all its subsorts which may also introduce additional features
a finite domain with n possible domain elements is represented by a prolog term with n NUM arguments
this is often done by introducing templates whose sole purpose is the abbreviation of a path to a feature
NUM specification of the subsort relationship is more convenient than constructing prolog terms which mirror these subsumption relationships
the combination performs better than isolated heuristics and allows to disambiguate all the genus of the test set with a success rate of NUM in dgile and NUM in lppl
for instance for dgile a lexicon of NUM NUM cooccurrence pairs among NUM NUM word forms was derived stop words were not taken into account
definition performing what is usually called word sense disambiguation wsd NUM in the previous example planta has thirteen senses and arbusto only one
while the average of senses per noun in dgile is NUM NUM the average of senses per noun genus is NUM NUM NUM NUM and NUM NUM respectively for lppl
the vector for a definition vdel is computed adding the cooccurrence information vectors of the words in the definition civ wi
this is the reason for many researchers having focused on the massive acquisition of lexical knowledge and semantic information from pre existing structured lexical resources as automatically as possible
although it is still used for linguistic description e.g.
however there is a major problem
a similar transition occurs for likes
1spivey knowlton et al reported NUM experiments
figure NUM informal description ofa mrr rl rr
all of them must be processed according to basic matching algorithm and two candidate matched constituents mc i ij and mc i j i will be produced
each tag corresponds to one word in the sentence and can value l m or r respectively meaning the beginning continuation or termination of a constituent in the syntactic tree
furthermore replace p w1b and p 2qb by the approximation that each constituent boundary is determined only by a functional word wi or local pos context ci
the arcs show bracket matching operations and the
the probability estimates of the model are based on the boundary distribution data s NUM described in section NUM and can be calculated through maximum likelihood estimation mle method
to capture the fact that those primary occurrences are different from dsp s primary occurrences when dealing with ellipsis we color occurrences that are directly associated with focus rather than a source parallel element in the case of ellipsis pf
furthermore we will see that what counts as a primary occurrence differs from one phenomenon to the other for instance an occurrence directly associated with focus counts as primary w r t focus semantics but not w r t to vp ellipsis interpretation
in this paper we show that higher order coloured unification a form of unification developed for automated theorem proving provides a general theory for modeling the interface between the interpretation process and other sources of linguistic non semantic information
furthermore focus is assumed to trigger the formation of an additional semantic value henceforth the focus semantic value or fsv which is in essence the set of propositions obtained by making a substitution in the focus position cf
this in effect rules out the bound variable reading in NUM if the i 0f occurrence were to become a bound variable the value of r of would then ay x ex of x
thus we have all the known theoretical results such as the fact that reduction always terminates producing unique normal forms and that 3t equality can be tested by reducing to normal form and comparing for syntactic equality
for example in the case of a three word noun phrase only two structures need to be enumerated
specifically we want to obtain the full modification structure of each noun phrase in the documents and query
it is possible to generate three different kinds levels of indexing units from a noun phrase NUM single words NUM head modifier pairs i.e. any word pair in the noun phrase that has a linguistic modification relation and NUM the full noun phrase
with the simplification that generating a noun phrase from a modification structure is the same as generating all the corresponding word modification pairs in the noun phrase and with the assumption that each word modification pair in the noun phrase is generated independently pc npi sj can further be written as
thus at the time of training the parser can first randomly initialize the parameters and then iteratively update the parameters according to the update formulas until the increase of the likelihood is smaller than some pre set threshold s in the implementation described here the maximum length of any noun phrase is limited to six
since word pairs with the least probabilities generally occur quite rarely in the corpus and usually represent semantically illegal word combinations dropping such word pairs does not affect the parsing output so significantly as it seems
in general however the parsing and reestimation involved in em can be considerably more complicated
since the class of possible lexicons is infinite the minimization of description length is necessarily heuristic
thus associating a word with a code substitutes for writing down the frequency of a word
the final lexicon includes extended phrases but meanings tend to filter down to the proper level
the phrase is broken into three words each of which are also decomposed in the lexicon
naturally an unsupervised learning algorithm with no access to meaning will not treat them differently
the simplest reasonable instantiation of the composition and perturbation framework is with the concatenation operator and frequency perturbation
figure NUM a coding of the first few words of a hypo thetical lexicon
the sound model of each phoneme was learned separately using supervised training on different segmented speech
of course there must be some way to alter the default meaning of a word
we further evaluated our word similarity technique in the task of word sense disambiguation wsd
notice that there are several reasons why this error may be generated NUM
in case NUM only the form of the agreement needs to be explained
wilhin cmren lexica NUM n i proacht s a xl m llli tlotl tl l l lill i l NUM NUM g hea d driven i hrase s ruc tm laml m iipsg auxiliaries e.g.
because the subject is third person singular the present tense verb should be likes
NUM the student intended the noun to be in plural form but mistyped
thus the correction should focus on features at or slightly above the student s level of acquisition
there have been some attempts to develop grammar checkers for people who are deaf
the input feedback cycle of icicle begins when the user enters a portion of text into the computer
our method is to develop a model of second language acquisition and use it for this task
the errors were hand counted and categorized leading to the development of the mal rules which represent them
this was averaged over NUM runs
we hence provided NUM h m judges with a randomly selected sample of NUM ex wples from the NUM polysemic nouns of our test corpus of NUM e ples
having identified the particular concept node in word net that plaue corresponds to the distances between this concept node and the three semantic class nodes are then calculated by the semantic distance module
both perceptions of kidnatpping are correct
table NUM sem mtic class dlsambiguation results
take for example the monosemic word kidnapping
the ditransitive verb gave is the common nouns are all n a NUM a n in fact as no intermediate constituents are formed in the analysis an even closer parallel is to a dependency syntax where only rightward pointing arrows are allowed of which the formalism as presented above is a notational variant
using the notation a b to represent a state of basic category a carrying a category b on its stack the hierarchical structure of the sentence intuitively syntactic links between non adjacent words impossible in a standard finite state grammar are here established by passing categories along on the stack through the state of intervening words
this suffices to uniquely determine the order of sibling nodes
however this simple minded approach although easy to implement in other ways leaves much to be desired
not only will this hopefully save a certain amount of drudgery it should also help to minimize errors
NUM were parsed wrongly ie the analysis differed from the hand parse in some non trivial way
for example following the example of gpsg unbounded dependencies can be captured using slashed categories
indeed if the non terminals are viewed as atomic categories then there is no way this can be done
my thanks also for discussion and comments to matt crocker chris brew david milward and anna babarczy
i gave fido a biscuit yesterday in the house and rover a bone today in his kennel
data oriented parsing dop is a model where no abstract rules but language experiences in the form of an analyzed corpus constitute the basis for language processing NUM there is not space here to present full justification NUM for adopting such an approach or to detail the advantages that it offers
in the beginning of aba we can replace either ab or aba
the c operator allows only the last factorization of aba in figure NUM
even if the replacement transducer is unambiguous it may well be unsequentiable if upper is an infinite language
the definition of upper prefix suffix is just as in figure NUM except that the replacement
here matches symbols such as v that are not included in the alphabet of the network
the insertion of end oftoken marks can be accomplished by a finite state transducer that is compiled from tokenization rules
the first reduces to a any string that ends with it and does not contain the a tag
note that only one is inserted even if there are several candidate strings starting at the same location
we make use of the inheritance and type checking mechanism of ale NUM to impose type specific constraints on words
8a and 8c it is not marked morphologically which blocks scrambling and the unmarked sov order is used cf
although this method is not generative in the sense of NUM it allows semantic composition in the lexicon
prominence of morphology puts a greater demand on the information in the lexicon which may grow to an unmanageable size due to heavy use of inflections and derivations
for instance the adverbial suffix ken and the adjectival lu might have phrasal 3a and 3c or lexical scope 3b and 3d
they may be represented uniquely by two metaphonemes da where d is a dental stop unmarked for voice and a is a low unround vowel unmarked for backness frontness
our strategy is to obtain complex forms derivationally if the semantic relation of the bound morpheme to its stem is fairly predictable
in example NUM the purpose of ending a call is stated in contrast to placing a call in the previous sentence
unfortunately descriptive work on turkish linguistics in this regard is very scarce and there is no ontology such as levin s NUM
compilation of all surface forms for a lexicon of only NUM root forms produces around NUM entries and takes about NUM minutes on a sun sparcstation NUM
the parameters whose posterior distributions we wish to estimate are oil p ui
the implicit modeling of uncertainty makes the selection system generally applicable and quite simple to implement
NUM classification of examples is sensitive to changes in the current estimate of the parameter
in figure NUM we investigate further the properties of batch selection
figure NUM evaluating batch selection for m NUM
figure NUM the size of the trained model measured by
the error rate for a category of words was calculated as follows error x wrongly tagged words from set x total words in set x thus for instance the error rate of tagging the unknown words is the proportion of the mistagged unknown words to all unknown words
this can be accounted for by the fact that the unguessed capitalized words were taken by default to be proper nouns and that the brill tagger and the hmm tagger had slightly different strategies to apply to the first word of a sentence
this operator merges two rules with the same affix s mutative segment m and the initial class i into one rule with the resulting class being the union of the two merged resulting classes
for the first system all NUM words will be manually checked
every source sentence was grouped together with all its translations
vb jj vbd vbn advisable jj v1 advise nn vb e able e nn
these findings only relate to translating from english to german
for the extraction of the initial sets of prefix and suffix morphological guessing rules prefix suffix deg and suffix1 we define the operator vn where the index n specifies the length of the mutative ending of the main word
for example the very unusual german verb heuen engl
example NUM shows the different translations for a compound noun
many suggestions have focussed on measuring the translation quality e.g.
another problem is that the frequency count is purely wordform based
no subject area lexicon was activated in our test runs
to find the optimal threshold 0s for the production of a guessing rule set we generated a number of similar rule sets using different thresholds and evaluated them against the training lexicon and the test lexicon of unseen NUM NUM hapax words
first they are the only problems requiring knowledge and heuristics beyond the existing syntax
it is worth noting that similar ideas do exist in natural language derivation and parsing
as compared with its true supertokenization it requires the extra effort of subtokenization
after all these points are the only places where disambiguation decisions must be made
assume the ft tokenization x xl xm is not a ct tokenization
we tackle this problem by calculating the lower confidence limit NUM l for the rule estimate which can be seen as the minimal expected value of for the rule if we were to draw a large number of samples
the corresponding four critical fragments are this is his and book
under this principle only covering tokenizations win and all covered tokenizations are discarded
for example if a guessing rule strips off a particular suffix and a current word from the lexicon does not have this suffix we classify that word and the rule as incompatible and the rule as not applicable to that word
this critical tokenization set contains the unique critical tokenization this is his book
in this paper we are particularly interested in the minimal elements and least elements
a term node label is distinguished from a concept label by a lowercase prefix indicating the part of speech hence n for noun concepts n for noun terms v for verb concepts v for verb terms
this paper does not contain a complete list of checking rules for wordnet NUM NUM whenever we tried to evaluate a rule we got hints for another rule and we have not yet taken into account all wordnet relations and attributes
each abstract contains NUM to NUM sentences on average
the number of antosemy relationships is about NUM for nouns and about NUM for verbs there was no overlap with direct or indirect hypernymy and in only two cases there was a non empty overlap with indirect troponymy
the text structure to be discussed more fully in section NUM NUM is represented in imagene s text representation language trl
the data shown in figure NUM are based on a set of NUM tags since we expanded the initial tagset by adding individual tags for some frequent words
for text input the simplest technique is to use the initial letters of a word as context and to predict words on the basis of their frequencies
emmanuel roche and yves schabes deterministic part of speech tagging q ab q a q r a b
n we can now define precisely what is the effect of a function when one applies it from left to right as was done in the original tagger
the space required by the finite state tagger 815kb is distributed as follows 363kb for the dictionary 440kb for the subsequential transducer and 12kb for the module for unknown words
the difference is that one has to keep track of the set of states of the original transducer one might be in and also of the words whose emission have been postponed
it represents the function f6 t6 such that f6 ab bc and f6 bca dca
in fact the determinization of the transducer is related to the determinization of fsas in the sense that it also involves a power set construction
first the proof of soundness the fact that if the algorithm terminates then the output transducer is deterministic and represents the same function
traditionally natural language generation has been seen as the inverse of parsing where the input is some sort of meaning representation such as predicate calculus expressions
to accomplish this the generator has to be able to order the text units add inflections and insert extra words both function and content words
of course many nouns have abstract referents but as a class we predicted nouns to be easier to annotate than verbs or modifiers
in general the ith nearest neighbor of x is the nearest occurrence of the same word ignoring the 1st 2nd 3rd i NUM th nearest neighbors
we suggest that taggers recognized the most appropriate sense more easily in this condition because they did not use the same strategy as in the random order condition
these senses also had a high chance of being the appropriam ones in the text since we had selected a fiction passage with non technlcal everyday language
however adjectives in wordnet are highly polysemous and show a good deal of overlap so that the context does not always uniquely pick out one sense
our resuits indicate in the case of finer sense distinctions a lack of shared mental representations among the taggers and a decrease in agreement
in the random order condition taggers made their decision with more confidence than in the frequency order condition although was less agreement with the experts
the more alternative senses there were the less likelihood there was that the taggers mental representations of the senses overlapped significantly with those in wordnet
for example an ending guessing rule e ing jj nn vbg says that if a word ends with ing it can be an adjective a noun or a gerund
words unknown to the lexicon present a substantial problem to nlp modules as for instance part of speech pos taggers that rely on information about words such as their part of speech number gender or case
the same situation can be seen with the prefix rule b st nns nns i which is quite predictive but at the same time is not a standard english morphological rule
y i o b b is the mean estimate of the whole bootstrap distribution this way of calculating the estimated standard error for the mean does not assume the normal distribution and hence provides more accurate results
for instance the suffix rule e et nn nn does not stand for any well known morphological rule but its prediction is as good as those of the standard morphological rules
the rule acquisition and evaluation methods described here are implemented as a modular set of c and awk tools and the guesser is easily extendible to sublanguage specific regularities and retrainable to new tag sets and other languages provided that these languages have affixational morphology
another highly ambiguous group is the ing words which in general can act as nouns adjectives and gerunds and only direct lexicalization can restrict the search space as in the case of the word seeing which can not act as an adjective
our initial goal in this study was to develop a prototype scoring system that could reliably assign a classification of excellent to a set of ap biology essays
vb jj the v operator is applied to all possible pairs of lexical entries sequentially and if a rule produced by such an application has already been extracted from another pair its frequency count f is incremented
to do so we perform a statistical experiment as follows we take each rule from the extracted rule sets one by one take each wordtype from the training lexicon and guess its pos class using the rule if the rule is applicable to the word
the decision with regard to whether or not a sentence was relevant was based on information provided in the scoring guide in figure NUM
for this study all concepts in the csr that were considered to be extraneous to the core meaning of the sentence were removed by hand
it also achieves the objectives outlined in section NUM
the syntactic constrains and translation rules are represented by an usst
we used these constraints to automatically construct a log linear regression model which combined with supplementary morphology rules predicts whether two conjoined adjectives are of same NUM links per adjective for a set of n adjectives requires in each figure the last z coordinate indicates the average maximum possible value of k for this p and the dotted line shows the performance of a random classifier
in general it is hoped that by predicting cas we can in turn predict the structural elements of their cue patterns
agreement on move classification was k NUM
it can therefore be extended m a principled manner to incorporate the simpler set of relations of rst
the algorithm made no use of endof paragraph markers
in a dependency derivation an acceptor is associated with a node with word w and the sequences written by the acceptor correspond to the relation labels of the arcs to the left and right of the node
in contrast the head transducer approach is more closely aligned with earlier direct translation methods no explicit representations of the source language interlingua or otherwise are created in the process of deriving the target string
valid transfer mappings are defined in terms of a tiling of the source dependency tree with source fragments from bilingual lexicon entries so that the partial mappings defined in entries are extended to a mapping for the entire source tree
w is taken from the set consisting of the source language vocabulary augmented by the em pty word e and t is taken from the target language vocabulary augmented with e
of left and right relations r r and r l rn NUM of w into left and right relations r r and empty symbol e
from a state qi these actions are as follows left transition write a symbol rl onto the right end of l1 write symbol r2 to position a in the target sequences and enter state qi l
here we take a positive instance to be the derivation of a correct translation and a negative instance the derivation of an incorrect translation where correctness is judged by a speaker of both languages
we can apply a set of head transducers recursively to derive a pair of source target ordered dependency trees this is a recursive process in which the dependency relations for corresponding nodes in the two trees are derived by a head transducer
another justification is that it was not necessary to make difficult comparisons between different aspects of effectiveness the transducer system performed better with respect to all the measures we looked at for accuracy speed memory development effort and model complexity
source language and target language speech was synthesized using commercially available state of the art synthesizers truetalk from entropies and cnetvox from elan informatique respectively
can produce a phonetic pronunciation for each word
table NUM results of the rule probability estimation algorithm
figure NUM automatic vs hand transcribed probabil
the output layer has one unit for each phone
figure NUM shows a schematic of the path computation
the algorithm we use has two steps
now if retrieval of the decision tree is directed by type subsumption the same template can be retrieved and potentially instantiated for a wider range of new mrs input namely for those which are type compatible wrt
this work was supported jointly by the advanced research projects agency and the office of naval research under contract n00014 NUM j NUM and by the national science foundation under contract ger NUM NUM
moreover conventional drss contain plenty of semantic information that is not immediately relevant for current i.e. generative purposes
inspection of the relevant facts suggests strongly that words of very different forms may cause a word to have given status
this information can be exploited when the second utterance it is a sonata is interpreted in the context of c
the theoretical framework and its formalisation as the constructive dialogue model are discussed in section NUM section NUM presents how the system s communicative goal is determined and section NUM provides comparision with related work
in the attitude language the predicates know want and do represent belief intention and action respectively s refers to the system and u to the user
the logical omniscience assumption is tackled by partitioning the context model and focussing on specific knowledge with the hel f of thematic coherence also rationality considerations constrain reasoning
in order to attend the requirements of a particular communicative situation the joint purpose needs to be specified with respect to the agent s role task and communicative obligations
if the expressive attitu null des of the partner s response match the evocative intentions of the agent s contribution the communicative goal of the agent s contribution is fulfilled
NUM however they carry different c goals due to different specification in the application model NUM aims at narrowing down the database search NUM NUM completes the original task
NUM everything that addresses what the partner wanted to know or wanted the speaker to do is motivated except if the speaker emmot disclose the information or do the act
specification of the joint purpose via the application model captures the cognitive consider ation of ideal cooperation the agent plans her response to be operationally appropriate in the current situation
the agent has fulfilled goals only and the initiative finish the dialogue or start a new one depending on the pending task goals finish start continue obj ect spe elf y
NUM le deficit utte alait2 milliards de dollars et selon aires ou ces labarre des NUM milliards a t frar hie en NUM
duns se lem in zijn overtuigingen k enken atteindre subjl pl p3 pin v NUM la balar e des roduits de lati tait encore exc clentajre
atteignissent has been requested on the right from the top are windows for dictionary van dale morphological analysis rank xerox and examples in bilingual corpora of supporting instructional software
the indexed lookup is most satisfactory not only has the absolute time dropped an order of magnitude but the time appears to be constant when corpus size is varied between NUM and NUM mb
the suggestive pronunciation module is not shown
french figure NUM user interface glosser
only the estonian version is complete
for example the broad semantic filter reduced the NUM NUM verbs that passed through the syntactic filter down to NUM assignments NUM of the number of assignments based on syntax and NUM NUM of the potential assignments
the research reported herein was supported in part by army research office contract daal0391 c NUM through battelle corporation nsf nyi iri NUM alfred p sloan research fellow award br3336 and a general research board semester award
to choose a relation to use for the semantic field we looked at verbs semantically related to the prototypical verb in each class and checked how many of the verbs in each class would be included in the filter
our goal throughout the acquisition task is to eliminate as many incorrect assignments as possible while preserving the correct assignments and in this respect we are encouraged by the the behavior of the semantic filter on unknown verbs
NUM for example the change of state verbs of the break subclass class NUM NUM contains the verbs break chip crack crash crush fracture rip shatter smash snap splinter split tear
this includes senses which correspond to the change of state verbs such as sense NUM break bust cause to break the synonyms of which are destroy ruin bust up wreck wrack
it comprises two main components an cvaluatot which assigns scores to completion hypotheses and a generator which produces a list of hyp tthesos that match the current prefix and picks the one with the highest score
the scoring relies on the observation of concurrent hypothesis of the recognizer and their associated acoustic scores
in order to give the system a better chance of getting inflections right we modified the behavior of the hypothesis generator so that it would never produce the same best candidate more than once for a single token in other words when the translator duplicates the first character of a proposal the system infers that the proposal is wrong and changes it
we feel that there is an alternate approach which has the potential to avoid most of the problems with conventional imt in this context use the target text as a medium of communication and have the translator and mt system interact by making changes and extensions to it with the translator s contributions serving as progressively informative constraints for the syste n
we found that we could reliably detect such invariant forms in an english source text using a statistical tagger to identify proper nouns and regular expressions to match immbers and codes along with a filter for frequent names like united states that do not translate verbatim into french and immbers like NUM that tend to get translated into a fairly wide variety of forms
in line with the spirit of truly interactive approaches the translator is called upon early enough to guide the system away from a raw machine translation he or she would rather not have to revise
this is due in large part to the fact that the translation model has already made a contribution in non linear fashion through the dynamic vocabulary which excludes many hypotheses that might otherwise have misled the language model
space requirements for the passive vocabulary were minimized by storing it as a special trie in which conlnlon srl cix patterns are represented only once and variable length coding techniques are used for structural information
in section NUM some basic concepts and the notation are introduced
we consider the worst case score of several empirical cases independent from the two recognizer we tested
analysis of complex np structures such as appositional structures and postposed modifier adjuncts is needed in order to relate the locale and descriptor to the name in creative artists agency the big hollywood talent agency and in creative artists agency a big talent agency based in hollywood
this stage marks the shift from the concelstual to the rhetorical
they must be formulated according to a set of precise syntactic lexical and stylistic guidelines
composing patent claims is a complex task even for experts
if there is a single candidate the procedure finds it
all manipulations at the text planning stage are performed on labels
see figure NUM and compare it with figure NUM
a distinguishing feature of this system is its partially interactive character
therefore its technological sublanguage is that of machines and mechanisms
ostia dr can make use of any kind of finite state model
lexical selection and some other text planning tasks are interleaved with the process of content specification
zone NUM contains the correspondence between the verb s case frame labels and their ranks
beesley for technical help on the definitions of the replace operators and for expert editorial advice
the case insensitive results would be slightly better if the task guidelines themselves did n t depend on case distinctions in certain situations as when identifying the right boundary for the organization name span in a string such as the chrysler division currently only chrysler would be tagged
a dependency grammar is a quintuple s c w l t where w is a finite set of symbols vocabulary of words of a natural language c is a set of syntactic categories preterminals in constituency terms s is a non empty set of root categories c s l is a set of category assignment rules of the form x x where xcc x w and
take a non marked dotted string ds from set of strings mark ds if ds has the form y v and y is starred then set of strings set of strings u lcb f rcb all dotted strings in set of strings are marked star set of strings
sentence w NUM w NUM wn NUM initialization each root category v do e nd each item ternfination j v k o is in s n lh accept g reject file external loop of the algorithm cycles on the sets si NUM i n tile inner loop cycles on the items of the set si of the form cat state j
an item is a quadruple category state position depcat where the first two elements category and state correspond to a row of the parse table ptcategory the third element position gives the index i of the set si where the recognition of a substructure began and the fourth one depcat is used to request the completion of a substructure headed by depcat parse table cat t graphcat
completer when an item is in a final state of the form h the algorithm looks for the items which represent the beginning of tile input portion just analyzed they are the l i ur element items contained in the set referred by j
in s1 the first item n NUM NUM is produced by tire scanner it is the result of advancing on the input string according to the item n NUM NUM in so with an input noun i the entry in the parse table ptn n NUM x n contains scan s2
add new arcs linking the new csst with the usst
let us note in passing a third type of slt message passing for example so called voice mall or real time messages between emergency or security services across linguistic borders e.g. the channel tunnel
in contrast in case b we should use the parent concept computer company as the concept of interest
we call the frequency of occurrence of a concept c and it s subconcepts in a text the concept s weight NUM
we are aware that this evaluation scheme is not very accurate but it serves as a rough indicator for our initital investigation
we define that if a concept s ratio tc is less than NUM t it is an interesting concept
we can draw no conclusion by using word counting method where the topic actually should be john bought some groceries
the higher the ratio the less concept c generalizes over many children i.e. the more it reflects only one child
its small ratio NUM NUM tells us that if we go down to its children we will lose too much important information
topic identification is one of two very important steps in the process of summarizing a text the second step is summary text generation
by repeating this process until we reach the leaf concepts of the hierarchy we can get a set of interesting wavefronts
among these interesting 2according to this a parent concept always has weight greater or equal to its maximum weighted direct children
the cmu data has very little variation in tense and aspect the reason a mechanism for interpreting them was not incorporated into the mgorithm
this pass is meant to detect the other errors and complete the analysis with underspecified elements
developing a letter to sound rule set in software is essentially teaching the computer how to read pronounce a language
scan for and remove any remaining embedded statements
if we treat the output of an existing segmentation algorithm NUM as the initial state and the desired segmentation as the goal state we can perform a series of transformations on the initial state removing extraneous boundaries and inserting new boundaries to obtain a more accurate approximation of the goal tion and prepare appropriate training data
in our work a document is more similar to class NUM than class NUM if the probability of it belonging to class NUM is greater than the probability of it belonging to class NUM
for this example let us choose words which occur more often in one list than in the other list until the sum of the probabilities of the chosen words is at least NUM
the remaining words are separated at blank spaces onto individual lines and stemming is performed to remove embedded sgml syntax possessives punctuation and some suffixes see appendix a
this is the probability that a term is not a distinguiqhlng term and is calculated as NUM NUM minus the sum of the probabilities of all of the distinguishing terms in the training set
the intuition is given in the example above but in this work we want to automate the process of choosing word sets in a way that results in sets of distinguishing concepts
this equation has been modified from the reference by dividing by the sum over the class of the term weights to normalize the results when distinguishing term sets are used which have different lengths
the training set may be as small as the initial query which defined the class or as large as all of the documents which are available which are deemed to be relevant to the class
v NUM our notation can more easily be generalized as it is needed in some transformation systems
in words of two syllables the verb has stress on the second syllable the noun on the first
several aspects of prosody might be exploited pitch contours rhythm volume modulation etc
in analysis sequences of words must be recognized as phrases sentences and utterances
when analyzing in terms of cas we can not expect to recognize all communicative goals
for bottom up parsing based on phones or syllables the number of lexical candidates is explosive
concerning speech processing it is necessary to opredict speech acts to aid speech recognition
if we can predict the coming speech acts we can partly predict their surface patterns
in very practical terms a generator is likely to halt abruptly when it encounters unusual and unexpected knowledge structures if this happens frequently the system will generate too few explanations to enable a meaningful evaluation
figure NUM suffix tree aligmnent for strings w accbacac w acabacba and the identity homomorphism
these propositions are included as constraints in the action schemas as needed
the acceptance of a clarification results in the current plan being updated
replan plan actions complete the partial plan plan
goal agt goal agt has the goal goal
t department of computer science toronto canada m5s 1a4
b NUM the guy that s pointing to the left again
the model is based on the view of language as goal directed behavior
we cast their work into a model based on the planning paradigm
we have defined modifiers as a recursive action with two schemas
this is consistent with our use of plan derivations to represent utterances
here we shall report both the way in which we have successfully modelled the point in the overall discourse grammar at which rst and the sfm meet and the surprising ease with which we were able to do it
thus in a transducer built using the newly induced decision tree for state NUM such as the machine in figure NUM the arc from state NUM to state NUM is taken on seeing any vowel including the six vowels missing from the arc of the machine in figure NUM
since we are generalizing over arcs at a given state of an induced transducer rather than directly from the original training set of transductions the input to the id3 algorithm is limited to the number of phonemes and is not proportional to the size of the original training set
johnson s system while embodying an important insight about the use of positive and negative contexts for learning did not generalize to insertion and deletion rules gildea and jurafsky learning bias and phonological rule induction and it is not clear how to extend his system to modern autosegmental phonological systems
rather since only by adding these biases was a general purpose algorithm able to learn phonological rules and since most theories of phonology assume these biases as part of their model we suggest that these biases may be part of the prior knowledge or state of the learner
NUM augmenting the learner with phonological knowledge in order to give ostia the prior knowledge about phonology to deal with the problems in section NUM we augmented it with three biases each of which is assumed explicitly or implicitly by most if not all theories of phonology
such a constraint is ranked below all other constraints in the optimality constraint ranking since otherwise no surface form could be distinct from its underlying form and is used to rule out the infinite set of candidates produced by gen that bear no relation to the underlying form
computational linguistics volume NUM number NUM we can also provide type declarations for features
value must be an atom in declared set feature lex atom
it is therefore a good idea to eliminate multiple entries as far as is possible
in the example above n NUM so by will have seven arguments
still others are known mostly by word of mouth in the unification grammar community
the vp schema that we need will then have to be of the following form
nondeterminism completely even for verbs capable of appearing with many different types of complement
now one entry for each verb will subsume all the possible subcategorization combinations for it
again macros can be used to make it possible to express all this economically
be done at the second stage of generation i.e. the generation of the structure of discourse
one important class of mechanisms are those which examine the current sentential and discourse context in order to restrict the range of interpretations
this means that ifu appears as a factor of some string w then u should be replaced by v in w
despite this similarity current speech translation systems use quite different techniques for phone word and syntactic recognition
the following clauses based on hpsg state that a structure is saturated if its subcat value is the empty list and that a structure satisfies the head feature principle hfp if its head features are identical with the head features of its head daughter
early processing of utterances may yield fragments which must later be assembled to form the global interpretation for an utterance
there are several issues in query processing besides those encountered by the user interface
for named entities sgml is inserted into a copy of the message text
personnel with linguistic expertise who are also programmers may be rare for some languages
are there sufficient resources available to support moving the technology to a new area
first the ddos found by the discourse module are used to produce template objects
parsing out names of people and locations dovetail nicely with named entity extraction
hopefully this increased understanding will benefit us in future tipster advanced technology transfer efforts
plum s discourse component creates a meaning for the whole message from the meaning of each sentence
determine reasonable cost effective means for evaluating new capabilities in existing technologies
do not rely on evolving standards as a core component of a system
since most methods of handling discontinuous constituents make the fornaalism more powerfifl the efficiency of processing deteriorates too
no combination of expressions was found which gave segments as much as one morpheme shorter than pause units on average
in other words instead of trying to win the war against an enemy we are not even sure we can see we have decided to engage into a series of battles we can be confident of winning
since the purpose is to prompt discussion the treatment is informal and speculative with frequent reference to work in progress
a head transducer reads from a pair of source sequences a left source sequence lt and a right source sequence ri it writes to a pair of target sequences a left target sequence l and a right target sequence r figure NUM
the cases were labeled with the attachment decision as made by the parse annotator of the corpus
the two methods exactly mimic each others behavior in spite of their huge difference in design
the data set consisted of NUM NUM feature value patterns taken from the wall street journal corpus
if we do not have information about the importance of features this is a reasonable choice
the grey colored schemata were effectively left out because they include a mismatch on the preposition
not surprisingly the NUM NUM accuracy they achieve is matched by the performance of ibi ig
for this purpose a large number of probabilities has to be estimated from a training corpus
this metric simply counts the number of mis matching feature values in both patterns
smoothing methods are needed to avoid zeroes on events that could occur in the test material
inductive generalization from observed to new data lies at the heart of machine learning approaches to disambiguation
in such systems all participating components read from and write to a central set of data structures the blackboard
why did n t the method have higher accuracy
it is structured as a tree where each level below the root corresponds to a single turn in the sequence ordered as they occurred in time
continue until node w is a leaf node
in the past few decades automatic speech recognition asr and machine translation mt have both undergone rapid technical progress
word models of NUM NUM english and NUM cantonese simple commands were trained using NUM utterances of each command per speaker
the effort required to acquire and maintain the example database the cost of the space required to store the examples and the cost of the time required to search the database can become prohibitively high since a pure analogical system requires a separate example for every linguistic variation
given an input i and an expression e it is straight forward io determine the isrobability of the feature distortion since the features are indexed by name in order to deternfine the probability of the word distortions we must find the most probable set of distortion operators
to further complicate matters there may not be a single unique set of distortion operators with a unique minimum cost corresponding to a unique maximum probability instead there may be a number of distortion sets that all share the same minimal cost and maximal probability
f d p t n t logp echo ewp j d p n NUM logp add iw d p n min d p l n logp delete ew
in the course of translating an expression a skilled human translator often recalls a similar translation that she has performed or studied before and then carries out the new translation by analogy to the previous case instead of applying a large number of lexical and grammatical rules in her head
in contrast bi gram was constructedby using subdivision level
both ends of the system recognize input speech from human through a common recognition engine comprising of either a concatenated or a mixed language recognizer
in the concatenation model we assume a priori knowledge possibly from a language identifier of the language id of the words
no analysis has been done of the relative difficulty of the muc NUM st task compared to previous extractio n evaluation tasks
the issues with respect to the st task relate primarily to the ambitiousness of the scenario template s defined for muc NUM
figure NUM context tree mixture v s
decreasing f measure p r it was also unexpected that one of the systems would match human performance on the task
coreference co insert sgml tags into the text to link strings that represent coreferring nou n phrases
there were no markable time expressions in the test set an d there were only a few markable percentage expressions
note that nearly NUM of the tags were enamex and that almost half of those were subcategorized as organization names
several systems posted scores under NUM error for locations but none was able to do so fo r oganizations
examples of each of these types of error appear below along with the number of systems that committed the error
and has been disregarded in the tallies thus the total number of systems tallied is eleven NUM
the text filtering results for muc NUM muc NUM tst4 and muc NUM tst2 are shown in figure NUM
once the scenario had been identified the ranked retrieval method was used and the ranked lis t was sampled at different points to collect approximately NUM relevant and NUM nonrelevant articles representing a variety of article types feature articles brief notices editorials etc
commercial systems are available already that include identification o f those defined for this muc NUM task and since a number of systems performed very well for muc NUM it is eviden t that high performance is probably within reach of any development site that devotes enough effort to the task
james out dooner in as chairman of mccann erickson as a result of james departing the workforce james is still on the job as chairman dooner is not on the job as chairman yet and his old job was with the same org as hi s new job
for most events however the fill is one of a large handful of possibilities including chairman president chief executive officer ceo chief operating officer chief financial officer etc
almost without exception systems did more poorly on those two slots than on any other s in the succession event and in and out objects the best scores posted were NUM error on other org median score of NUM and NUM error on rel other org median of NUM
paraphrased summary of st outputs for walkthrough articl e two systems never filled the other org slot or its dependent slot rel other org despite the fac t that data to fill those slots was often present over half the in and out objects in the answer key contain dat a for those two slots
human performance was measured in terms of interannotator variability on only NUM texts in the test set and showed agreement to be approximately NUM when one annotator s templates were treate d as the key and the other annotator s templates were treated as the response
the training set and test set each consisted of NUM articles and were drawn from the corpus using a text retrieval system called managing gigabytes whose retrieval engine is based on a context vector model producing a ranked list of hits according to degree of match with a keyword search query
sra satie base system james out dooner in as chairman of mccann erickson as a result of james departin g the workforce james is not on the job as chairman any more dooner is already on the job as chairman and his old job was with ammirati puris
james out dooner in as ceo of mccann erickson as a result of james departing th e workforce james is still on the job as ceo dooner is not on the job as ceo yet and his ol d job was with the same org as his new job
on average the matching rate of tr3 is NUM compared with the other systems the matching rate of tr1 is NUM and of tr2 is NUM
the intention was to test a range of rules and hence get an indication of how much better if at all the more sophisticated rules are than the simpler ones
the subject of a sentence on the other hand is the np that has a doing or being relationship with the verb in the sentence
the longer the string fluttering in the sky j r the more curved the string i is s and the more difficult it j is to pull straight
after it is initially introduced in b it then appears in zero and nominal forms alternatively in the rest of the discourse as shown schematically in figure NUM
the linguistic principles embodied in our rules were all independently proposed so in some respects the previous data served as both training and test data in the development of the rules
an explicit reference to a premise or an inference method is not restricted to a nominal phrase as opposed to many of the treatments of subsequent references found in the literature
if a zero anaphor is created by the hypothetical computer while the corresponding position in the real text is a nonzero anaphor then it belongs to the overgenerated type
in this data there are NUM zero pronouns NUM pronouns and NUM nominal anaphora making a total computational linguistics volume NUM number NUM of NUM NUM anaphora
the headconcept dictionary contains the concept identifier and the headconcept and the concept explication
the edr corpus is the source for the information described in each of the sub dictionaries
the concept identifier is a numerical expression and the basic constituent of the concept dictionary
the concept description dictionary contains the set of pairs of concepts that have certain semantic relations other than super sub relations
null finally we would like to make a short remark on the new project which edr will launch in fiscal NUM
in this paper we present the specification and the structure of edr electronic dictionary which was developed in a nine year project
this is used in morphological analysis to find the morphemes and also used in morphological generation to produce output sentences
the headconcept is a representative word that is the most appropriate in expressing the corn cept identified by the concept identifier
a number of dictionaries are currentry being developed under the name of electronic dictionaries machine readable dictionaries
one of the users fujitsu released a commercial product using the edr electronic dictionary in NUM
derived at this place and nodes in the form of small boxes are copies of some previously derived nodes circled nodes which are used as premises again
the result of the parse yields labeled bracketings for both sentences as well as a bracket alignment indicating the parallel constituents between the sentences
note that the notion of a chinese word is a longstanding linguistic question that our present notion of segmentation does not address
lemma NUM let x be an ll singleton y be an l2 singleton and a b c be arbitrary terminal or nonterminal symbols
the bracket precision was NUM for the english sentences and NUM for the chinese sentences as judged against manual bracketings
for all i j english chinese lexical translations for all i english vocabulary for all j chinese vocabulary figure NUM a stochastic constituent matching itg
nearly all of these NUM out of NUM can be generated by an itg as shown by the parse trees whose
we now show that every itg can be expressed as an equivalent itg in a NUM normal form that simplifies algorithms and analyses on itgs
we begin in the first part below by laying out the basic formalism then show that reduction to a normal form is possible
in bilingual parsing just as with ordinary monolinguat parsing probabilizing the grammar permits ambiguities to be resolved by choosing the maximum likelihood parse
in experiments with the minimal bracketing transduction grammar the large majority of errors in word alignment were caused by two outside factors
the subjects annotated two training dialogs according to the instructions
for example the category appropriate for relative clauses with a noun phrase gap would be lo o it is then possible to specify operations which act as purely applicative operations with respect to the left and right arguments lists but more like composition with respect to the wh list
among the four reason nodes NUM NUM NUM NUM only node NUM is explicitly mentioned since it is in a closed attentional space u5 and is mentioned five sentences ago
i r h j likes john sue
a simple syntax semantics interface can be retained if the same operation is used in both syntax and semantics
j j application to the left is defined by the rule l r rj the basic grammar provides some spurious derivations since sentences such as john likes mary can be bracketed as either john likes mary or john likes mary
r0 NUM if x is a syntactic type and l and r are lists of categories then application to the right is defined by the rules 6one area where application based approaches to semantic combination gain in simplicity over unification based approaches is in providing semantics for functions of functions
the ability to deal with functions of functions has advantages in enabling more elegant linguistic descriptions and in providing one kind of robust parsing the parser never fails until the last word since there could always be a final word which is a function over all the constituents formed so far
then if duration NUM NUM then non boundary elself duration NUM NUM then boundary elseif after sentence final contour
then machine learning experiment in which one of the default c4 NUM options used in learning NUM is overridden
pause is assigned true if pi l begins with x false otherwise
most b errors correlated with two conditions used in the np algorithm identification of clauses and of inferential links
when multiple types of features are used results approach human performance on an independent test set both methods and using cross validation machine learning
if cj contains a defi null nite pronoun whose referent is mentioned in a previous clause up to the last boundary assigned by the algorithm else global pro
in the future we will extend our dependency grammar into one with functions of dependency lln
superscripts represent whether the object is complete link or complete sequence l for complete link and s for complete sequence
the outermost dependency zoi wj must be replaced with wi wj
the inside probability of complete sequence is the sum of the probabilities that the complete sequences are constructed with
in our system this default is also used as an oracle allowing us to see how different interpretations affect the participants understanding of subsequent turns
in the following expression the oc is NUM if the dependency relation
second we do not require any annotation to be done for the training instead we reuse the information stated in the lexicon which we can automatically map to a particular tag set that a tagger is trained to
then we filtered out words shorter than four characters nonwords such as numbers or alpha numerals which usually are handled at the tokenization phase and all closed class words s which we assume will always be present in the lexicon
a commonly used squashing function due to its mathematical properties which assist in network training is the sigrnoidal function given byf hi NUM where hi is the node input and t is a constant to adjust the slope of the sigmoid
these errors fall into two major categories i false positive i.e. a punctuation mark the method erroneously labeled as a sentence boundary and ii false negative i.e. an actual sentence boundary that the method did not label as such
the part of speech data necessary to construct probabilistic and binary vectors is often present in the lexicon of a part of speech tagger or other existing nlp tool or it can easily be obtained from word lists the data would thus be readily available and would not require excessive storage overhead
if one trains the network too often on the same data overfitting can occur meaning that the weights become too closely aligned with the particular training data that has been presented to the network and so may not correspond well to new examples that will come later
we would also like to thank ken church for making the parts data available and ido dagan christiane hoffmann mark liberman jan pedersen martin r6scheisen mark wasson and joe zhou for assistance in finding references and determining the status of related work
for the english word well for example the lookup module might return the tags jj NUM nn NUM ql NUM rb NUM uh NUM vb NUM NUM the frequencies can be obtained from an existing corpus tagged manually or automatically the corpus does not need to be tagged specifically for this task
xp fragment two the trade off for generating rules automatically in this manner is rule overgeneration but this does not appear to be problematic for the automated scoring process
the above figure shows how many of the NUM evaluated test tuples were assigned subject object based on the values pn and the accuracy of the system at each level
the second element is represented as the value of a saliency importance attitude to the intersection between the properties of the modified noun and those of the set members it is purported to belong to the saliency v due is NUM NUM for authentic still lfigh for nominal and low for fake
the above context would then be represented by the following part of speech sequence preposition article noun pronoun verb verb however requiring a single part of speech assignment for each word introduces a processing circularity because most part of speech taggers require predetermined sentence boundaries the boundary disambiguation must be done before tagging
in analyzing the sources of the errors produced by satz over the raw ocr data it was clear that many errors came from areas of high noise in the texts such as the line in example NUM which contains an extraneous question mark and three periods
for example if a rule was found to apply just once and the total number of observations was also one its estimate p has the maximal value NUM but clearly this is not a very reliable estimate
as the length of a string increases the number of occurrences of that string will decrease
the suitable value of the threshold of difference varies with the size of text corpus file
it needs both of an initial consonant and a final consonant
some types of spelling irregularities can be excluded by this process
table NUM example of a leftward sorted string ta
the results are shown in table NUM and table NUM
following the same reasoning as above we will obtain
this is the problem in assigning priority information for selection
for adjective noun pairs chinese english and even japanese share similar orders whereas french has adjective noun pairs in the reverse order most of the time
although we select the same number of sentences from each language there are NUM unique words from english and only NUM unique words from chinese
with a larger corpus there will be more source words in the vocabulary for us to translate and more target candidates to choose from
we define the context heterogeneity vector of a word w to be an ordered pair x y where a left heterogeneity x
its translation occurred NUM times in the chinese text and part of its concordance is shown in table NUM they are used in totally different sentences
however if one uses context heterogeneity in languages having more function words such as french it is advisable that filtering be carried out on both texts
we applied context heterogeneity measures between debate and the chinese word list with the result shown in table NUM with the best translation at the top
for a noun like house whose appropilate sense NUM is directly mapped into an ontological concepl the me ufing of big house will be represented as it tmr fragment shown in NUM NUM priwlte home size attribute value NUM NUM more complex cases of adjectival moditication are discussed in section NUM
NUM experiment NUM finding the word translation among a cluster of words the above experiment showed to some extent the clustering ability of context heterogeneity
we use the data from NUM NUM taking the first NUM sentences from the english text and the next NUM sentences from the chinese text
the normal algorithm is applied to goals inside each set
test data consists of ambiguous tuples nx v n2 for which it can not be established which noun is the subject object of the verb based on morpho syntacticai information alone
the denominator part does not effect the maximization and it merely serves as a constant multiplier
they are classified into two types wrong to correct wc and correct to wrong cw
the spoken corpus used in this paper consists of two commonplace everyday conversations among friends
since there are NUM repetition repairs in conversation i NUM repetition repairs are not captured
we are grateful to professor kawai chui for her kindly providing the spoken corpus to us
nevertheless the present study includes repairs placed across different turns
the performance of the repair processing can be evaluated as the net gain shown as follows
it contains text of several categories and includes approximately NUM NUM sentences comprising of about NUM NUM NUM characters
this cue can eliminate some implausible repairs so that the precision rate can be increased
NUM NUM in conversation NUM and NUM NUM in conversation NUM of the repairs
null it will turn out to be convenient to use a slightly more complicated notation when the dot is located after the last symbol on the right hand side we use z as the third element of the triple instead of the corresponding integer so the last triple is s NUM z instead of s NUM NUM
in such quick and dirty or low road speech translation systems user interaction is substituted for system integration
once spontaneous data is labeled speech recognition researchers can try to recognize prosodic cues to aid in speech act recognition and disambiguation
learned that then summaries were effecuve enough to support accurate retrieval
NUM instead of l rein re l denotes the result of removing from the language re all terminals that match one of the expressions in the list l the context free language recognized by the original context free grammar is lcb anb n n NUM rcb
thus the sent ence noun sing root states that the singular form of any noun is identical to its root whatever that may be
un dog cat noun root dog sing root plur root noun sufl
the inference rules provides a clear picture of the way in which the different constructs of the language work and should serve as a foundation for future investigations of the mathematical and computational properties of datr
accordingly the value of dog lcb cat is specified implicitly as the value of noun eat and similarly for dog sing and dog surf rcb
however it still remains to provide a suitably general formal theory of inference for datr and it is this objective that is addressed in the present paper
as a point of departure this section provides rules of inference for a restricted variant of datr which lacks both global inheritance and tilt default me hmfism
for example if dog eat rcb evaluates to noun then dog rcb also evaluates to noun given the explicit path extension cat
in particular they fail to provide a full and correct treatment of datr s notion of global inheritance or the widely used evaluable path construct
x m that follows x m n must be either x m NUM a recursive application of the same rule or x m n the next stage in parsing the same rule and there must be such an instance
tr3 performs better than tr2 for texts with simple discourse segment structure
initial references are indicated by bold italics
two sets of considerations determined our selected approach to the data extraction problem our set of performance objectives and the results of our analyses of data classes
figure NUM are admzn ster i and gtve NUM equtvalent
some researchers have proposed investigation of associations beyond the n gram range but the proposed associations remain relatively short range about five words
we then outline an alternative approach currently under development which combines prediction with a constrained technique for natural language generation
reorder is used to change an ordering made earlier in the processing whereas the others establish the order of newly inserted nodes
by a set of synonyms we mean a set of elements which are defined as synonyms
mcca categories have been mapped to wordnet senses
taking into account the previous results it is important to note that the great differences between languages in text ambiguity in the presence of unknown words and in the statistics of the grammatical categories e.g. the different occurrence of prepositions in english and french corpora prevent a direct comparison of languages from the taggers error rate
this way subsystems can on demand be used as servers e.g.
sometimes imas produces an output which can not be used by the pasha ii client
il expressions can be enriched and disambiguated by performing certain inferences involving temporal reasoning
upon receipt of the confirmation the agents fix the date in their calendars
we present in section NUM a third kind of client system the pasha ii user agent
by just describing the verbalizations of relevant information shallow parsing grammars are highly domain specific and task oriented
for cosma the classification has been extended according to semantic information relevant for the appointment domain
for instance verb classification directly leads to the lexical assignment of a corresponding automaton in sines
NUM NUM zwischen NUM und NUM uhr und zwischen NUM und NUM uhr
NUM NUM would suit me between NUM and NUM p m treffen
wordnet does not encode plain logical incompatibility to express disjointness of hierarchies but what about antosemy
the fining characteristics that emerge from the mapping and the statistical techniques used in mcca for analyzing concepts and themes suggest that tagging with wordnet synsets or mcca categories may produce epiphenomenal results that are misleading
null we ran four sets of experiments
section NUM provides an overview of moser and moore s coding scheme
however segment structure still plays an important role via trib pos
table NUM summary of learning results
table NUM cue placement on core2
an explanation may consist of multiple segments
in the following we will not discuss joints and clusters any further
the other constituents help to serve the segment purpose by contributing to it
the set of parsed corpora is sadly very small but still sui icient to yield useflfl results
in this paper we address the problem of introducing structures into the probabilistic dependencies in order to model the string translation probability pr f le lcb
the database contains all the predicates mentioned in the semantic representation of the message
lb train this model we use the naximutn likelihood criterion in the so called ulaximmn al proximation i.e. the likelihood criterion covers only tile most lik ly
using a set of non negative parameters lcb s i i rcb we can write the iimm alignment probabilities in the form
here q i j is a sort of partial probability as in time alignment for speech recognition jelinek NUM
therefore a specific model tbr tile mignment in obabilities is used r i j p ilj NUM l
to describe these word by word aligmnents we introduce the mapping j aj which assigns a word fj in position j to a word el in position lcb aj
the use of credit factors is a useful approach to estimating a reliable stochastic language model from untagged corpora which axe noisy by nature
partial understanding is critical to text processing systems a s missing data is normal
the set of recognized entities is used by the output functions to sgml mark the input
first the ddos found by the discourse module are used to produce template objects
of the NUM cases NUM were string vacuous extraction from subject position recovered with NUM NUM NUM NUM
it would be useful to identify marks as a subject and last week as an adjunct temporal modifier but this distinction is not made in the tree as both nps are in the same position NUM sisters to a vp under an s node
in the left right cases a gap requirement is added to either the left or right subcat variable
NUM cbs NUM cbs are the percentage of sentences with NUM or NUM crossing brackets respectively
it is interesting to note that models NUM NUM or NUM could be used as language models
below is an example of a sentence level rule which looks for the pattern person
probabilistic context free grammar booth and thompson NUM was an early example of a statistical grammar
in rule NUM right is chosen so the gap requirement is added to rc
distinction and subcategorisation the tree in figure NUM is an example of the importance of the complement adjunct distinction
generative models of syntax have been central in linguistics since they were introduced in chomsky NUM
sentential vs adverbial seem to be able to block the application of the less powerful rules
it is not clear to me right now what we loose in practical terms if we give up such cycles
this demonstrates the applicability of the degree of context dependency
total average shows the mean of all keywords
figure NUM shows the structure of wall street journal corpus
we selected NUM different domains and used them as domain
the result of keyword extraction is shown in table NUM
of these three articles are classified into this type of the error
from these we extracted a certain number of paragraphs method a
this shows that method a is not more effective than our method
the extraction ratio described in table NUM is NUM
in the latter case wlog r is a forward rule and nf fl q l fla for some forward composition rule q pure ccg turns out to provide forward rules s and t such that a s ill nf t NUM NUM is a constituent and is semantically equivalent to c
however any de ee of distributed control can also be achieved by providing appropriate programs alongside the coordinator which represent the components from the whiteboard side
first it shows by a simple induction that since c and disagree they must disagree in at least one of these ways a there are trees NUM and rules r r such that r fl NUM is a subtree of a and r NUM NUM is a subtree of a
the insight is that theorems NUM and NUM establish a one to one map between semantic equivalence classes and normal forms of the pure unrestricted ccg NUM two parses a of the pure ccg are semantically equivalent iff they have the same normal form gf a gf a
null detailed proofs of these theorems are available on the cmp lg archive but can only be sketched here
NUM if undefined existingnf the first parse with this nf NUM nf seqno counter counter NUM number the new nf add it to oldnfs NUM oldnfs c nf
the algorithm makes no independence or tny other assumptions on the features in contrast to other parametric estimation techniques typically bayesian predictors which are commonly used in statistical nlp
until recently these properties have remained largely undemonstrated
either condition the proof shows leads to different immediate scope relations in the full trees and in the sense in which f takes immediate scope over NUM in f g x but not in f h g x or g f z
a second important property is being mistake driven
the second concern is discussed in section NUM NUM
however the model does not require equivalent treatment of all confusion sets
the output of a phrase level segmentation might then be stored as follows
this allows us to rapidly prototype editors using the python tk graphics package
this format has the following properties
using sgml as a basis for data intensive nlp
surely that makes pipelines too inefficient
in practice this is extremely useful
so the inference process is allowed to assume that the speaker believes any constraint that the goal of the plan implies
the same could be done for video clips etc
it works with existing corpora without extensive pre processing
it can be shown that the computation of the intersection of a fsa and a cfg requires only a rain null imal generalization of existing parsing algorithms
to support this degree of flexibility the agents that we model form expectations on the basis of what they hear monitor for differences in understanding and when appropriate change their own interpretations in response to new information
however while this work can be seen as an important step in the right direction we are very well aware of fllture developments which will be essential for a widespread acceptance of the system in a broad user coinmunity
the first author was a principal designer of the system while the second author had only watched a videotape of the system in operation and read some of the previous papers about the project
this paper reports work that attempts to address both of these dilemmas through the analysis of human computer dialogues collected in an environment in which smith and gordon human computer dialogue aspects of the system are parameterizable
the two authors compared their coding results as the transcripts for each one of the eight subjects were completed in order to resolve differences and hopefully improve agreement as more transcripts were coded
for example when there are two missing wires the first drt iteration will cause one missing wire to be added but the test phase will show that the circuit is still not working
for repair tasks she identified five primary task subdialogues introduction subdialogue i establish the purpose of the task e.g. to fix the circuit with id number rsl11
furthermore they provide evidence that a spoken natural language dialogue system must be capable of varying its level of initiative in order to facilitate effective interaction with users of varying levels of expertise and experience
NUM u there is no wire from connector nine nine to connector eight four NUM c there is supposed to be a wire between connector nine nine and connector eight four
in the case above each alignment gets a weight of NUM NUM
on the other hand confusion between computer and user was much more likely in declarative mode because the computer would more frequently formulate a response based on its erroneous interpretation of the user s input
their paper concludes that the mode similar to our directive mode is more robust and more likely to succeed but the mode similar to our declarative mode is faster and less frustrating to experienced users
tokens in the patternset are indexed by sequential position in the sentence so that two or more tokens of the same type can be kept distinct in patterns
in addition to evaluating the acquired subcategorization information against existing lexical resources we have also evaluated the information in the context of an actual parsing system
jackendoff NUM by chomsky adjunction to maximal projections of adjuncts xp xp adjunct as opposed to government of arguments i.e.
the filter may well be performing poorly because the probability of generating a subcategorization class for a given verb is often lower than the error probability for that class
our system s recognition of subcategorization classes as evaluated against the merged dictionary entries NUM verbs and against the manually analyzed corpus data NUM verbs
patterns encode the value of the vsubcat feature from the vp rule and the head lemma s of each argument
we also demonstrate that a subcategorization dictionary built with the system improves the accuracy of a parser by an appreciable amount NUM
we illustrate these problems with reference to seem where there is overlap but not agreement between the comlex and anlt entries
our system rankings include all classes for each verb from a total of NUM classes and average NUM NUM correct
these cases must be solved by techniques other than those described here
subjective coding has been described for three different levels of task oriented dialogue structure called conversational moves games and transactions and the reliability of all three kinds of coding discussed
the coders were able to reproduce the most important aspects of the coding reliably such as move segmentation classifying moves as initiations or responses and subclassifying initiation and response types
here extra is shared between the conjuncts and bound at s level
in english fronting from extraposed constituents is disallowed by a language specific constraint
the second measure similar to information retrieval metrics is the actual agreement reached measuring pairwise over all locations where any coder marked a boundary
at most points in task oriented dialogue there is some piece of information that one of the participants is trying to transfer to the other participant
n NUM k NUM on the move segmentation task using word boundaries as possible move boundaries and k NUM
this confusion was general suggesting that it might be useful to think more carefully about the difference between answering a question and providing further information
head extra schema rloolper e ro NUM lextra dtrs i
when she agreed with herself on where a game began she also agreed well with herself about what game it was k NUM
authors jc and ai are responsible for developing the transaction coding scheme and for carrying out the reliability studies all authors contributed to the development of the move and game coding schemes
when all coders were taken together as a group the agreement reached on whether or not conversational move boundaries are transaction boundaries was k NUM
particularly a parser driven by a bottom up strategy has to hypothesize the presence of empty elements at every point in the input
in a verb 2nd clause most of the input follows a finite verb form so that condition a indeed is not very restrictive
the same criticism applies to other parsing strategies with a strong top down orientation such as left corner parsing or head corner parsing
the approach has been implemented in the machine translation project veitbmobii and results in a significant reduction of the work load for the parser t
condition c has the most restrictive effect in that the syntactic potential of the trace is determined by that of the corresponding verb
we describe and evaluate a method for performing backwards transliterations by machine
the computation of the acoustic prosodic features is based oi1 a time alignment of the phoneme sequence corresponding to the spoken or recognized words
in written language phrase boundaries are often determined by punctuation which is of course not available in spoken discourse
obviously the quality of the online dictionary is absolutely essential
figure NUM glosser architecture connects mod
the current corpus size is NUM mb
the decrease in inter tagger agreement with increasing polysemy was especially strong in the case of adverbs
the text the user is reading is displayed in the main window
they may provide a sense of collocation or even nuances of meaning
it is difficult to estimate how long the manual scoring process would take in hours but presumably it would take longer than the approximately NUM hours it took to build the lexicon and concept grammars
finally we illustrate how knowledge of lexical aspect facilitates the interpretation of events in nlp applications
zigzag NUM NUM NUM act loc thing NUM by NUM
with aspectual properties of verbs clearly influencing the alternations of interest
telic verbs denote a situation with an inherent end or goal
for example the original database entry for class NUM NUM NUM is
james NUM years old which is parsed by the phraser as follows
this proposition designates the semantic relationship between a person and that person s age
this can be largely attributed to knowledge gaps in our phrase r rules for organizational noun phrases
org type acronym resolution snafu caa vs creative artists agency NUM inc org
the following example shows a sample sentence from the walkthrough message after initial phrasing
after the final rule of a sequence is run no further processing occurs
this sequence performs manipulations that resemble np parsing e.g. attaching locational modifiers
in addition a subsequence of te rules concentrates on recognizing potential organization descriptors
pressing on the phraser parses the overall org orgnp apposition as an overarching org
this equality machinery is exploited at many levels in processing semantic and domain constraints
given a sample corpus d the estimation procedure finds a set of parameters that represent a local maximum of the grammar likelihood function p d i g which is given by the product of the string
we want exactly the opposite if a more general version of goal is included in the goal table then we can continue to look for a solution in the result table
antosemy relationships and hypernymy or hyponymy relationships are exclusive to each other i.e. both relationships can not hold in conjunction between any pair of concepts
most of the errors made in classifying the data can be accounted for by four error types a lexical gap b human grader misclassification c concept structure problem d cross classification
NUM the experimental results obtained are tabulated in table NUM
cb un can not be from cf u NUM or other prior sets of forward looking centers
summarizer s agents that are concerned with summarizing the data that they have collected over the network from different sources and producing natural language reports for the end user
the transducer is first represented by a two dimensional table whose rows are indexed by states and whose columns are indexed by the alphabet of all possible input letters
the content of the table at line q and at column a is the word w such that the transition from q with the input label a outputs w
relying on algorithms and formal characterizations described in later sections we explain how each rule in brill s tagger can be viewed as a nondeterministic finite state transducer
this was the case for part of speech tagging until brill showed how state of the art part of speech tagging can be achieved with a rule based tagger by inferring rules from a training corpus
this result is achieved by encoding the application of the rules found in the tagger as a nondeterministic finite state transducer and then turning it into a deterministic transducer
once the lexical assignment is performed in brill s algorithm each contextual rule acquired during the training phase is applied to each sentence to be tagged
at line NUM one takes all the possible input symbols w here only a is possible w of line NUM is the output symbol
this means that for a given symbol the set of possible emissions is obtained by concatenating the postponed emissions with the emission at the current state
the filtering process calculates the probabilities of all possible chains of tagged words using a markov model
both filtering and scanning process use a statistical data infonuation collected om the hand ta ed
for example the candidate terms national network regional network dispatching network give the context national regional dispatching for the noun network
this type of error can not be d ectecl by simply using a dictionary approach
this causes the ambiguous pattern in a sentence the example is as shown in the following
rayner et al noticed that a particular node can not be part of the correct parse if there are no nodes in adjacent cells
in the german corpus for example where multiple words are concatenated the words were not separated
these changes can be measured in the training text from the tags distribution of the less probable words
extensive experiments have shown insignificant differences in the tagging error rate when alternative word occurrence thresholds have been tested
specifically the first and the second order mlm mlm1 and mlm2 respectively the first and the second order hmm of the most probable tag sequence criterion hmm ts1 and hmm ts2 respectively and the first order hmm of the most probable tag criterion hmm t1 have been realized
the depth of grammatical analysis and the grammatical structure of each language produce a different number of pos tags
an extended set including common categorization of the grammatical information for all languages as shown in table NUM
with a bigger development bitext more effective backing off heuristics can be developed
admittedly gsa is only useful when a good bitext map is available
tree of this form corresponds almost exactly to the addition of a new leftmost or rightmost subtree below the node that was the site of the adjunction
several automatic methods for this task have been proposed in recent years
the line between the origin and the terminus is the main diagonal
its output can be converted quickly and easily into a sentence alignment
two lines are parallel if the perpendicular displacement between them is constant
the rectangle keeps expanding until at least one acceptable chain is found
third it does not require large amounts of computer memory to run
teacher hon nom come hon past dec teacher han came
first every sentence has a verb
as far as subject honorification is concerned their assertion is correct
the conjunctive however does not contribute anything to a dialogue
NUM a k kkeyse hoyuy ey nom hon meeting postp chamsekha si ess eyo
let us look at the dialogue shown in NUM l
adjunct attachment often gives rise to structural ambiguities or structural uncertainty
let us consider why the dialogue in NUM is not coherent
this research was supported by a scholarship from owoon cultural foundation
let us consider the dialogue shown in NUM
a typical ga constraint is align f l word l which sums the number of syllables between each left foot edge f and the left edge of the prosodic word
depending on the reason for the mistake different kinds of tutorial correction will likely be more helpful
in push to talk dialogues the participants push a key when they start and finish speaking and can not speak at the same time
if we suppose that the modifiers represent specialisations of a head np by giving a specific attribute of it nps described by similar e terminological contexts will be semantically close
p ci v2 is estimated similarly
for any threshold it is the case that the intersection problem of off line parsable dcgs and fsa is decidable
acknowledgments i would like to thank gosse bouma mark jan nederhof and john nerbonne for comments on this paper
section NUM provides a description of the stapler
of course this implies that sometimes the intersection is considered empty by this procedure whereas in fact the intersection is not
a fortiori the problem of deciding whether the intersection of a fsa and a dcg is empty or not is undecidable
i now show that the question whether the intersection of a fsa and an off line parsable dcg is empty is undecidable
in general however the problem whether some pcp has a solution or not is not decidable
the predicate always succoeds and as a side effect asserts that its argument is a rule of the parse forest grammar
figure NUM is tagged with the attributes from table NUM smith and gordon s tagging of this dialogue according to their subtask representation was as follows turns NUM were i turns NUM NUM were a turns NUM NUM were d turns NUM NUM were r and turns NUM NUM were t note that there are only two differences between the dialogue structures yielded by the two tagging schemes
calculations yields performance rb NUM NUM NUM NUM NUM NUM thus the results of these experiments predict that when an agent needs to choose between the repair strategy that agent b uses and the repair strategy that agent a uses for repairing depart city it should use agent b s strategy rb since the performance rb is predicted to be greater than the performance ra
it runs on both desktop and hand held pcs under windows NUM communicating over wired and wireless lans respectively or modem links
the first uses input from hand gestures and eye gaze in order to aid in determining the reference of noun phrases in the speech stream
spoken and gestural input originates in the user interface client agent and it is passed on to the speech recognition and gesture recognition agents respectively
as entities are created and assigned orders they are displayed on the ui and automatically instantiated in a simulation database maintained by the modsaf simulator
however it might also receive an additional potential interpretation as a location feature of a more general line type figure NUM
two factors guide this tagging of speech and gesture as either complete or partial and examination of time stamps associated with speech and gesture
this work adopts the following notation for regular formalisms cf
and the overall feasible feature structures on all sublexica to be
hence restrict becomes after replacing v with w in eq
many of the anonymous reviewers comments proofed very useful
restrict v insert lcb o rcb o NUM k
the above expression is only valid if r consists of only one tuple
these intermediate occurrences can also be recognized with good accuracy and so are also added to the corpus by the preprocessor
for NUM b l NUM working stress the match occurred for force giving a mutual information of NUM NUM
although the size of the test data is small we say that our method provided a way to identify the most probable structure more efficiently than rdg
while kurohashi and nagao compare the sentence with a single sample of patterns we use all occurrehces of the pattern in cod to calculate the mutual information
for the modifiers in the sentence extract their concept identifiers from cod and build the taxonomic hierarchies using cd to find the generalizers for each concept identifier
for each dependency structure we calculate a score by multiplying the mutual information for all ambiguous relations the non ambiguous do not contribute to the evaluation
the detectionquery is specific to the retrieval engine but independent of the collection over which retrieval is to be performed and the operation retrieval or routing to be performed the retrievalquery and routingquery are specific to the retrieval engine to the operation and to a collection they may incorporate for example term weights based on the inverse document frequencies in a collection
with the right linguistic specification this is all the machinery spud needs to generate conventionalized forms
this knowledge produces good results however it is very expensive to build
we describe more precisely how this contribution is evaluated in section NUM NUM
the entry is then substituted or adjoined into the tree at the appropriate node
spud s ontologically promiscuous discourse model offers a natural dimension to represent these distinctions
they outline a model of reasoning in which facts are partitioned into sets called environments
note that spud always chooses a maximally specific licensed form out of equally good alternatives
all predicate argument structures are localized within a single elementary tree even in long distance relationships
first they derive conventionality from relational lexicons that describe only the properties of words
the different constructions that can be described as collocations exhibit an enormous range of conventionalization
a more detailed local grammar of type ii
this top of the list or else the first of these ranked candidate terms will give the weights to the context
intuitively x y implies x yi
NUM nc value a c value a wei a NUM tog
in the following sections we will sketch the basic algorithm consider how to provide it with an initial guess and provide an informal proof of its efficiency
a parsing phase which outputs a multiset or bag of source language signs instantiated with sufficiently rich linguistic information established by the parse to ensure adequate translations
undetermined tncbs are commutative e.g. they do not distinguish between the structures shown in in section NUM we will see that this property is important when starting up the generation process
the order of the orthographies of two combining signs in the orthography of the result must be determinate it must not depend on any subsequent combination that the result may undergo
we will see that this will ensure the termination of the generation algorithm within n NUM steps where n is the number of lexical signs input to the process
whether the constraints will ultimately require monolingual grammars to be enriched with entirely unmotivated features will only become clear as translation coverage is extended and new language pairs are added
generation will fail if all signs in the bag are not eventually incorporated in tile final result but in the naive algorithm the intervening computation may be intractable
we assume a sign based grammar with binary rules each of which may be used to combine two signs by unifying them with the daughter categories and returning the mother
a retrieval operation canceled by the monitor object s monitorprogress operation returns a collection of accumulated documents in routing a set of queries or user profiles in the form of routingqueries are pre processed to create a querycollectionindex routing is then performed by sending a document to a querycollectionindex what is returned is a set of relevant profiles in the form of a detectionneedcollection
all the problems described above lead to the decrease of the recognition performance and of the usability of spoken language systems
the recursion takes place by running a head transducer m in the second action above to derive local dependency trees for corresponding pairs of dependent words w v
in simple head transducers the target positions a can be restricted in a similar way to the source positions i.e. the right end of l or the left end of r
the cost of a solution i.e. a possible translation of an input string is the sum of costs for all choices in the derivation of that solution
the performance comparison above is of course not the whole story particularly since manual effort was required to build the model structures before training for cost assignment
to evaluate the relative performance of the two translators NUM utterances were chosen at random from a previously unseen test sample of atis utterances having no overlap with samples used in model building and cost assignment
one justification for this conclusion is that the systems were closely related having identical sublanguage domain and test data and using similar automata for analysis in the transfer system and transduction in the transducer system
NUM NUM those earnings are insignificant
of course virtues denotes kinds of virtue
consider the first sentence NUM NUM
i sold you two kinds of coffee
NUM NUM conversion from count nouns to mass nouns
the answer i believe is no
these leaves are touching those wires
NUM NUM all information is valuable
now every noun phrase has grammatical number
for instance if tree a should be expanded at node n by tree b the resulting type of b must be compatible to the type restriction attached to n panaget NUM argues however that meteer s semantic categories mix the ideational and the textual dimension as argued in the systemic linguistic theory NUM
an internal document identifier assigned automatically when a new document is created which is unique within an entire tipster system to insure uniqueness in a distributed system an implementation may choose to include a host name as part of the id externalld string r w a document identifier assigned by the application rawdata bytesequence the contents of the document prior to any tipster processing
the contraction relation generates a reduction relation t such that x reduces to y x y if y is obtained from x by a finite series possibly zero of contractions
it shows that while in the majority of turns the task and dialogue initiatives are held by the same agent in approximately NUM NUM of the turns the agents behavior can be better accounted forby tracking the two types of initiatives separately
if the counter is negative the constant increment method is invoked and the counter is reset to NUM this method ensures that a bpa will only be adjusted if it has no credit for correct predictions in the past
due to possible interactions between some rules the generator may have to explore different choices before actually being able to produce a sentence
in generation similar constraints have been used in the generation of referring expressions where the expressions should not be too general by g2
in previous statistics based approaches the similarity between w4 and other words can not be reasonably measured or not measured at all
during the exploration of internal generation goals the applicability semantics of a mapping rule is matched against the semantics of an internal generation goal
one such instance of use of syntactic preferences is avoiding giving lower rating to heavy constituents in split verb particle constructions
our generator is not coherent or complete i.e. it can produce sentences with more general specific semanticj than the input semantics
we also use a notion of headed conceptual graphs i.e. graphs that have a certain node chosen as the semantic head
dtgs are seen as attractive for generation because a close match between semantic and syntactic operations leads to simplifications in the overall generation architecture
the tree like semantics assumption leads to simplifications which reduce the paraphrasing power of the generator especially in the context of multilingual generation
we also consider that a generator can happen to convey more or less information than is originally specified in its semantic input
this rule has an internal generation goal to generate the instantiation of manner as an adverb which yields quickly
for the time being a comprehensive treatment of routine formulas and other idioms does not seem feasible
intuitively the ccd of a case becomes greater when example sets of the case fillers are disjunctive over different verb senses
we have proposed a taxonomy of discourse functions to represent the pragmatic impact of such particles and formulas
when asked to decide their preferences for anaphora in the computer generated texts speakers may find the information shown in the test texts less complete than what they are used to in creating their own texts and hence it may be difficult for them to make decisions
the tagger assigns a score initially NUM to each possible sense of each word
source trees the second tree in fig NUM however can only be combined with another realization of derive resulting in because of the parallelism of line c1 and line c2 b in our current system we concentrat on the mechanism and are therefore still experimenting with heuristics which control the choice of paraphrases
whether restarts and self repairs get translated or are merged into a single coherent utterance is an open question
the second is to loosen the transitivity itself
both NUM and NUM are not interesting because NUM is a graph including all topics and NUM generates graphs of too small topics to check the global trend of topics in the input
since one of the applications for the clustering results is the ambiguity resolution each output cluster is expected to have no ambiguity and be specialized in a single topic
when each property holds for el words a b and c can be explained as follows from the linguistic viewpoint reflective word a co occurs with itself
for example when b is doctor a is nurse and c is professor nurse and professor do not co occur due to the node b s two sided meanings
anchor distance NUM anchor distance n position pneumonia will be included into a cluster if it is connected with these three words even if it is not connected with cancer
having a huge co occurrence graph obtained from a corpus we first tried to decompose it to analyze its graph structure using graph theoretical tools such as maximum strongly connected components or biconnected components
brief specifications were prepared for each task and in the spring of NUM a group of volunteers mostly veterans of earlier mucs annotated a short newspaper article using each set of specifications
this class of pragmatic adverbs loosely corresponds to the discourse usage we have investigated above
the meaning of most leaves is the semantic node associated with the word at the morphology stage
analysis of meanin g this section describes how the parse tree is converted to a disambiguated piece of semnet
this is somewhat better than the formal evaluation scores which were NUM recall and NUM precision
often this is unknown but in cases like verbs it can be determine d as the act
however a small amount of information on common human names was already available in the semantic network
the co reference performance on the walk through article was badly affected by some of the problem s already mentioned
the summary templat e is thus a collection of different kinds of information extracted from the source article
using these counters we can relate the following morpho lexical probabilities to the three analyses of hqph NUM NUM NUM NUM NUM NUM respectively
without going into detail here we merely give a few examples again taken from the verbmobil domain
when we see the word hqph in an untagged corpus we can not automatically decide which of its possible readings is the right one
it is also possible for parses to fail if the sentence ca n t be analyzed with the main grammar
as an example of semnet structure and its meaning is discussed in the section on the semantic net
a representation based on sgml has been selected in order to be able to make use of the large number of existing applications which can operate on sgml documents
for each document in collection which if the same document a document with the same id appears in destination annotate that document in collection destination
similarly in a graphical image of a document such as a fax the most natural definition of a primitive subimage is likely to be a rectangle
the current architecture does not allow for such changes corrections to the text must be recorded as attributes on text elements which are explicitly accessed by subsequent processes
once a customizedextractionsystem is created it can be applied to documents in a collection like other pre existing annotators and will produce templates for the documents
this is done using a relevance recorder which is not part of the architecture but would be part of any application system which wished to support relevance feedback
for most systems this involves the annotation of the documents in the collection with approach specific annotations and then the creation of an inverted index involving these annotations
the header may include a document identifier to be annotated with a docid annotation and such other properties as a title or headline a dateline etc
adopting this approach leaves us with the problem of finding the morpho lexical probabilities for the different analyses of every ambiguous word in the language
the tipster program aims to push the technology for access to information in large multi gb text collections in particular for the analysts in government agencies
most of the operations on annotationsets can also be applied to documents and in that case apply the same operation to the annotationset property of the document
the number of matched cases for zero pronoun and nominal in the test data can be obtained by summing up anaphora of the correct type associated with the leaf nodes labeled z p and n in the classification trees respectively
the solution to this problem involves specifying a normal form for deductions and allowing that only normal form proofs are constructed our route to specifying a normal form for proofs exploits a correspondence between proofs and dependency structures
one method is to restrict the options available to the user for any given field to a number of possible values for a given object attribute i.e.
it seems we have achieved our aim of a linear deduction method that allows incremental analysis quite easily i.e. simply by generalising the combination rule as in NUM having modified indexed formulae using NUM
narr narrative a relevant document will identi a company or institution developing or marketing a natural language processing technology identify the technology and identi one or more features of the company s product
the complexity of this phase is therefore the product of the picking and combining complexities i.e.
the diversity of the participating groups has ensured that trec represents many different approaches to text retrieval while the emphasis on individual experiments evaluated in a common setting has proven to be a major strength of trec
trec NUM required significant system rebuilding by most groups due to the huge increase in the size of the document collection from a traditional test collection of several megabytes in size to the NUM gigabyte tipster collection
there seeme d general agreement however that having prepared code for template elements in advance did make it easie r to port a system to a new scenario in a few weeks
we are currently investigating the mathematical characterisation of grammars and instantiated signs that obey these constraints
the algorithm compares the equivalence classes defined by the coreferenc e links in the manually generated answer key the key and in the system generated output the response
the signs of the affected parts must be recalculated by combining the recursively evaluated child tncbs
in the last section we deliberately illustrated an initial guess which was as bad as possible
inputs from a passive participant can be expected to be more predictable and well behaved than those from a directive one
total no of sentences i NUM i no of sentences which parse NUM NUM NUM NUM
for the formal evaluation there were NUM organization and NUM person objects in the te key versus NUM organization and NUM person objects in the st key
as a pragmatic issue we have found that at least four initiative modes are useful NUM directive
NUM i misparsing due to prepositional phrase attachment hereafter pp attachment ambiguity ii
a series of interactions related to a given subgoal constitute a subdialog and expectations associated with currently active goals are used to predict incoming user utterances
the computer will again select its response according to its next goal for the task but it will allow minor interruptions to subdialogs about closely related goals
the task related expectations for the location of this connector would include all the expectations related to the topics of connecting a voltmeter wire and performing a voltage measurement
the experimenter was allowed to deliver any of the following messages if certain criteria were met NUM due to misrecognition your words came out as
the subjects were provided with a list of the allowed vocabulary words and charts on a poster board suggesting implemented syntax if they wished to use it
they were told not to direct any comments to the experimenter however the experimenter would occasionally give them help as will be described below
the actions may be to assert that the specification is checked to set suspicion flags on other nodes in the tree or to replace parts
a prtn denoted by a is a NUM tuple
two corollaries the following result will be useful later on for two subtrees from a corpus if t t then either t t is a subtree of t or there is no dominance relation between t and t t
a chart outside or re estimation algorithm assumes a refined table of insides that contains only valid insides used in generating the input sentence as discussed earlier and outside computation is done based on the refined table
if the two corpora differ on the number of unary branches relating two nodes there is no principled way of pairing off nodes without exploiting more detailed and probably corpus or markup specific information about the contents of the corpora
han and choi a chart re estimation algorithm
inside computation builds a table of computed insides
linking to original corpus for each of the corpora we assume we can define two functions one terminal location will give the location in the original corpus of a terminal element e.g. a function function will be unique although perhaps empty
susanne word position penn word position the NUM the NUM fulton NUM fulton NUM county NUM county NUM grand NUM grand NUM jury NUM jury NUM note that the function will in this case map NUM to NUM NUM to NUM and so on
verify preservation of analyses across multiple versions of a corpus if all the subtrees of a corpus are aligned with those of another then the second is consistent with the first and represents analyses at least as detailed as those in the first
computational linguistics volume NUM number NUM
under these assumptions the probability 7i of generating a particular candidate translation with i words is the same for all translations with length i the same applies to the probability i that a translation with i words is included in the set of translations of length i that will generate the candidate translations of length i NUM
this is motivated by our observations reported in the previous section that the distribution of high low attachments for specific prepositions did not vary significantly for pps further from the verb
as can be seen the performance for pp1 replicates the findings of collins and brooks who achieved NUM NUM using NUM lexical items compared to our three
the value of the configuration varies over a range NUM NUM corresponding to the NUM structures possible for NUM pps shown in table NUM with their counts in the corpus
thus if f v nl p n2 is zero they back off to an alternative estimation of which relies on NUM tuples rather than NUM tuples
the multiple pp attachment introduces two related problems sparser data since multiple pps are naturally rarer and greater syntactic ambiguity more attachment configurations which must be distinguished
unless clever techniques are developed to deal with ambiguity the number of possible parses for an average sentence NUM words is simply intractable
we present and algorithm which solves this problem through re use of the relatively rich data obtained from first pp training in resolving subsequent pp attachments
moreover we are only considering NUM attachment possibilities for each preposition either it attaches to the verb or it attaches to the lowest nolln
the algorithm used to handle the cases containing 2pps is shown in figure NUM where j ranges over the five possible attachment configurations outlined above
score s c score cs c score cs globalcs NUM where cs is the corresponding conceptual set of s c is the set of conceptual expansions of all content words which are defined in ldoce in c and globalcs is the conceptual set containing all the NUM defining concepts
however deriving these sets of similar words requires a substantial amount of statistical data and thus these approaches require relatively large corpora to start with NUM our definition based approach to statistical sense disambiguation is similar in spirit to the similarity based approaches with respect to the specificity of modelling individual words
it is not very realistic to expect any system which only possesses semantic coherence knowledge including ours as well as yarowsky s to achieve a very high level of accuracy for all words in general text
as an example consider how a german v1 sentence e.g. a question or conditional clause is derived in such a system NUM NUM las karl dasbuch read karl the book e.g. did karl read the book
by virtue of its extra marking the domain object of the relative clause is now ordered last in the higher vp domain while the remnant np is ordered along the same lines as nps in general
this approach is superior to nerhonne s as the extraposability of an item is correlated onlywith its linear properties right peripheral occurrence in a domain via extra but not with its status as adjunct or complement
we will refer to the relation between a sign s and its representation as a single domain object o as the compaction given informally in NUM NUM NUM compaction i el
we have argued for an approach to extraposition from smaller constituents that pays specific attention to the linear properties of the extraposition source s to this end we have proposed a more finegrained typology of ways in which an order domain can be formed from smaller constituents
as a consequence any precedence but not adjacency relations holding of domain elements in one domain are also required to hold of those elements in all other order domains that they are members of which amounts to a monotonicity constraint on deriving linear order
when combining with its verbal head a nominal argument such as das buch in figure NUM in general gives rise to a single domain element which is opaque in the sense that adjacency relations holding within it can not be disturbed by subsequent intervention of other domain objects
but patent documents are also charaeterised by tile frequency of some linguistic phenomena and the absence of others e.g.
interestingly lhere are some flmdamental diilieulties in combining advanced mt with fail soft stralegies
the current maintenance and further development of the system continues this text type specific lille
inlo syntactic and a morphological level and a t ilingual transfer dictionary
for ease of mainlenance and updating patrans has a special coding tool
tim qualily of fail sell output varies considerably and recent work has attempled lo improve the results of fail soft
formula recognition the docmnent handler automatically recognizes certain text typical untranslatable units such as chemical formulas and tables
for all languages the project produced a large grammar and a general language dictionary
it has been trained on a corpus of the wall street journal and on patent texts within the subject field
in NUM h sends by mistake an inconsistent temporal expression to a and b giving rise to clarification dialogues initiated by each of a and b NUM
in these cases imas stores the available information and the server generates a request for clarification in order to recover the necessary temporal specifications or to fix the already available ones
a ccm has among other things a working shortterm memory a long term memory and a variety of buffers for storing and managing computed solutions for subsequent use
in cosma shallow analysis is divided up into an application of the message extraction component sines discussed in section NUM and a semantic analysis component imas section NUM
coordinating internal system activities with respect to parallel dialogue processing including backtracking and failure recovery facilities requires very powerful and flexible mechanisms for task scheduling synchronization and control
a component can be released by a ccm it is bound to when the latter does no longer need its services e.g. if the component has already computed all solutions
since the type of agent system connected to the cosma server is not restricted by its dialogue behavior preference was given to implement application dependent mappings instead of developing a generic formalism
since appointments are often scheduled only after a sequence of point to point connections this will at times necessitate repeated rounds of communication until all participants agree to some date and place
in the server human interactions with multiple machine partners are treated as different nl dialogues in the present case between h and a and h and b
however any instantiation will lead to some constraint being found not to hold
there are two different action schemas for modifier one is for absolute modifiers
peter a heeman and graeme hirst collaborating on referring expressions they are collaborating upon
we need a rule that permits an agent to enter into a collaborative activity
the resulting belief after applying rules NUM and NUM is the following
one result of this is that our surface speech actions are much more fine grained
the proposal state is a subspace of the mutual belief space of the conversants
the first three constraints have only a single solution so they are instantiated
to illustrate this consider the headnoun action which has the following constraints
it is a prime example of a phenomenon at the boundary between syntax and pragmatics
NUM may generate as many as six readings including the missing reading
to interpret this sentence we are more likely to assume an unmentioned transfer event between the two explicit events
NUM john told a man that mary likes him and bill told a boy that susan does
two arguments are similar if their other inferentially independent properties are similar
this is challenging because not all examples are as simple as sentence NUM
we show how seyeral problematic examples are accounted for in a natural and straightforward fashion
if we choose case b then we must choose case d giving us the jbt reading
if we choose case a then we must choose case c giving us the jjj reading
our experience has shown however that delaying universal principles in such a way turns out to be too weak
the addition of a delay mechanism as described in this paper would certainly increase the efficiency of tfs
we show that such an architecture supports a modular encoding of linguistic theories and allows for a compact representation using underspecification
the third statement is of a slightly different form based on the preferred treatment of determinate goals described above
using universal principles on the other hand the global grammar organization does not need to account for every possible distinction
in a typed feature logic NUM this may be expressed by the prin null ciple shown in fig NUM
this prohibits the compile time cross multiplication described above and it allows the user to specify delays for such a principle e.g.
finally a system providing both universal principles and relational constraints at the same level offers a large degree of flexibility
tfs also offered type constraints and relations and to our knowledge was the first working typed feature systems
we compare our work to other approaches before presenting some conclusions and open issues in the last section
san jose state which was making its first ncaa tournament appearance gave kentucky all it could handle in the first half tying the game at NUM NUM with NUM NUM to play
the results of repeated experiments showed that ciaula is able upon an appropriate setting of the model parameters to detect similarity relations in the thematic structure of verbs and to provide a probabilistic and semantic description of the acquired clusters
if the labeller considers only the NUM possibilities aibic d aibicid aib cid and a b cid the following NUM utterances will be labeled a ok a b c ok so go back and is this number three right there b so go back and is this number three b c so go back and is this number three right there c right there c d right there shall i wait here for the bus
figure NUM shows the t agging error rat es plotted against various clustering text sizes
figure NUM shows an example of an evetlt with a current word like
these questions are cmled basic que slio s and always used
therefore the time for the above computation is linear in the tree depth maximal context length
after each actual merge the most frequent singleton class outside of the merging region is shifted into the region
our word bits coijstruction lgormlm is lno tiflotation mid mi extension of the mutual infornm
a nmve computation of NUM would be infeasible because of the size of NUM
in the event that alternative sets of fills are provided by the key then the response set of fills is scored agains t each of the alternate sets in the key
after scoring is completed the scorer provides a slot by slot score report for each document and an overal l summary score report and performance analysis for the set of all documents
we can not afford to parse the training data for each grammar considered indeed to ever be practical for data sets of millions of words it seems likely that we can only afford to parse the data once
most of them hav e defaults but certain ones require user designation such as the filename of the messages keys and responses
figure NUM components of a learning
all three are expressed as percentages
the results of the evaluation follow
the named entity task for english
figure NUM trade off in undergeneration and
many groups chose exactly that route
figure NUM relation of bbn systems
in order to choose the right tagged word combination word tering process will use the statistical association among words couected as a statistical base to eliminate the alternative and or erroneous chain of words which is caused by word boundary and tagging ambiguities and implicit spelling error
that includes a wide variety of types of entities
g lcb ti l ti t NUM ti rcb NUM
this prol erty strictly reflccl s on the cwmt
l aggittg is assigniltg rr NUM
besides several tagging models are developed to show the effect of adding information
NUM p lil wi NUM ti l NUM
this neighborhood system has on dimensional relation and describes the one dimenstional structure of sentence
7onsequent y a i osteriori probability of NUM for giwm w is
null llique function v tj w is described by the
two parts of word filtering there are two parts in word filtering see the figure below a set of tagged word combination the first part of word filtering i.e. the faltering process calculates the strength of each tagged word combination
we have considered the information sources and reasoning processes that agents need to determine their beliefs about the goals and expectations associated with each other s utterances
figure NUM the tree with the complete description of
however the reviewer notes that such examples are no doubt rare and perhaps the proposed containment filter does enough work in correctly excluding ill formed instances of ellipsis to justify the categorical exclusion of these cases
with a couple of minor exceptions the study was performed exclusively on three instruction manuals for cordless telephones approximately one third of our corpus and the results were applied to the remainder of the corpus
NUM we now give a short discussion of how imagene s realization statements can manipulate the evolving text structure making reference to the text structure produced by imagene for the portion of the remove phone text shown in figure NUM which corresponds to the text span remove phone by firmly grasping top of handset and pulling out
the first element of the form selection sub network is conditional status which determines whether the high level purpose being expressed has special conditions pertaining to it such as the expressed precondition in example 6a or other conditions that restrict the applicability of the purpose as in example 7a to wall unit from which it was taken
thompson s study indicated that one common feature of fronted purpose clauses is that their scope is global that is there is more than one expressed proposition that is directly related to the fulfillment of the purpose e.g. to achieve purpose a do b and do c where there are two sub actions b and c
responses to produce the trl structure shown in figure NUM is again this process is not the subject of this paper but is mentioned to provide a more complete discussion of the data structures involved
and so will you the ellipsis is contained within a form expression whose category is vp ellipsis it ense inf modalffivill perf ectffi progressive pol pos this states the syntactic tense aspect and polarity marked on the ellipsis underscores indicate lack of specification
in the case of ellipsis this extra layer of descriptive indirection permits an equational treatment of ellipsis that i is order independent ii can take account compositional distinctions that do not result in meaning differences and also iii does not require the use of higher order unification for dealing with quantitiers
so for example whether two terms with identical meanings are merely co referential or are co indexed is the kind of information that may get lost the difference amounts to two ways of composing the same meaning
there are cases where a degree of inference seems to be required NUM we spent six weeks living in france eating french food and speaking french as we did in austria the year before
each category a is associated with a function ga that represents the relation ra i.e. ga c i reduces in an applicative order reduction in such a fashion that at some stage in the reduction the expression c r is reduced iff a can derive the substring spanning string positions i to r of the input string
informally the expression lambda poe1 np continuation posl is a continuation that specifies what to do if a v is found viz pass the v s right string position posl to the np recognizer as its left hand string position and instruct the np recognizer in turn to pass its right string positions to continuation
the memo procedure defined in NUM is not appropriate for cps programs because it associates the arguments of the functional expression with the value that the expression reduces to but in a cps program the results produced by an expression are the values it passes on to the continuation rather than the value that the expression reduces to
informally the memo table for the procedure corresponding to a category a will have an entry for an argument string position NUM just in case a predictive chart parser predicts a category a at position l and that entry will contain string position r as a result just in case the corresponding chart contains a complete edge spanning from l to r
clearly these definitions could be further simplified with suitable macro definitions or other syntactic sugar NUM specifically if a is a scheme variable bound to the function corresponding to a left recursive category then for any string position p the expression a p reduces to another expression containing a p
NUM define vp p union reduce union map np v p reduce union map s v p if sets are represented by unordered lists union can be given the following definition
NUM specifically when the memoized procedure is called continuation is bound to the continuation passed by the caller that should receive return values and args is bound to a list of arguments that index the entry in the memo table and are passed to the unmemoized procedure cps fn if evaluation is needed
when these functions and procedures are called c is always bound to a procedure called the continuation the idea is that a result value v is returned by evaluating c v
table NUM word classes and lexicon for vertex cover problem from fig NUM
several approaches have been proposed to construct automatic taggers
we start from a training set of tagged sentences t
where possible we used a NUM fold cross validation approach
the information gain values are given as well
storage and time requirements were computed as well
table NUM comparison of three memory based learning techniques
igtree is a heuristic approximation of the ib ig algorithm
in the second part we survey a number of sample applications and extensions of bilingual parsing for segmentation bracketing phrasal alignment and other parsing tasks
we introduce a general formalism for modeling of bilingual sentence pairs known as an inversion transduction grammar with potential application in a variety of corpus analysis areas
guages than might appear at first blush through appropriate decomposition of productions and thus constituents in conjuction with introduction of new auxiliary nonterminals where needed
t into two constituent subtrees deriving e0 s and es t respectively as well as the nonterminal labels j and k for each subtree
however for more complex linguistically structured grammars the more flexible parser does not require the unreasonable numbers of productions that can easily arise from normal form requirements
thus the parse shown in c is preferable to either a or b since it does not make an unjustifiable commitment either way
we could relax the normal form constraint but longer productions clutter the grammar unnecessarily and in the case of generic bracketing grammars reduce parsing efficiency considerably
a left rotation changes a a bc structure to a ab c structure and vice versa for a right rotation
for conciseness we sometimes abuse the notation by writing an index when we mean the corresponding nonterminal symbol as long as this introduces no confusion
the filtering algorithm can be efficiently interleaved with the point generation algorithm so that simr runs in linear time and space with respect to the size of the input bitext
the translation lexicon obtained by running sable on the answerbooks contained NUM french english content word entries on the 2nd plateau or higher including NUM on the 3rd plateau or higher
the sable system was run on a corpus comprising parallel versions of sun microsystems documentation answerbooks in french NUM NUM words and english NUM NUM words
leaving aside the relationship between the two words your choice of p v or i the word pair would be of use in constructing a technical glossary
if you ca n t choose either specific or general chances are that you should reconsider whether or not to mark this word pair invalid
its effects on the proportion of domain specific entries was mixed an NUM increase for the entries more likely to be correct but a NUM decrease overall
i since part of speech tagging was used in the version of sable that produced the candidates in this experiment entries presented to the annotator also included a minimal form of part of speech information e.g.
for example given the pair d lacez drag one instance in that pair s bilingual concordance would be maintenez select enfoncd et d placez le dossier vers l espace de travail
to ensure that interpolation is well defined minimal sets of non monotonic points of correspondence are replaced by the lower left and upper right corners of their minimum enclosing rectangles mers
sable counts two tokens as cooccurring if their point of correspondence lies within a short distance NUM of the interpolated bitext map in the bitext space as illustrated in figure NUM
major parts of the theory have been computer modelled
the mediating process here is called perspective taking
tokenizer error caused some chinese characters to be not grouped together as one word
if carbon monoxide were translated separately we would get k4h
in picture naming for instance there is no hard wired link between the object depicted and the ultimate referential expression
the probability that a lemma is selected within a minimal time interval is its relative activation following luce s choice rule
discovering the sounds of discourse structure
a pattern matching method for finding noun and proper noun translations from noisy parallel corpora
in the multi stage theory of word production NUM the first stage conceptual preparation involves activating a lexical concept given the intention
there is first a stage of conceptual preparation this is followed by stages of lexical selection phonological encoding phonetic encoding and articulation
a speaker self monitors conceptual preparation acoustic output but also an intermediary level of representation namely the syllabified phonological word NUM NUM
the spelled out segments are incrementally from left to right attached to the metrical frame on the fly creating the phonological word s syllabification
example la shows one of the classical verbmobil examples and its possible english translation lb
two contrasts should be emphasized in this regard
those inferences might involve arbitrarily complex inferences like anaphora resolution or the determination of the current dialog act
hence conditions may be viewed as general inferences which yield either true or false depending on the context
rupp and our anonymous reviewers for usefltl feedback a ld discussions on earlier drafts of the paper
another major difference is the addition of coilditions which trigger and block the applicability of individual transfer rules
hence the skolemization prevents unwanted unification of labels and markers while matching individual transfer rules against the semantic representation
the transfer output is also a vit which is based on the semantics of the english grammar el
u12 does not contain any anaphoric expression which co specifies an element of the c NUM u11 hence block NUM of the algorithm applies
moreover the claim is made that the hierarchy of discourse segments implements an intuitive notion of the limited attention constraint as we avoid a simplistic cognitively implausible linear backward search for potentional discourse referents
the cb un the most highly ranked element of c un NUM realized in un corresponds to the element which represents the given information
while only one antecedent of a pronoun was not reachable given the superimposed text structure the remaining eight errors are characterized by full definite noun phrases or proper names
on the one hand centered segmentation may be a part of the cache model since it provides an elaborate nonlinear ordering of the elements within the cache
there is no need for extra computations to determine the segment focus since that is implicitly given in the local centering data already available in our model
hence we may either identify an utterance ui by its linear text index i or if it is accessible with respect to its hierarchical discourse segment index s e.g. cf
in particular the role of shift type transitions is examined from the perspective of whether they not only indicate a shift of the topic between two immediately successive utterances but also signal intention based segment boundaries
morphological features are set wherever possible as a result of the general unification processes in the grammar the inflected form is determined from the lemma and its associated features in a post processing step
the english lexicon was also built from a balanced corpus of some NUM million words while the danish was derived from a conglomerate of some NUM NUM running words of newspaper text prose research reports and legal and it texts
an example is the difference between an interface with automatic row andcolumn scanning which requires two keystrokes to select a letter and an interface with linear scanning and keystrokes on a keyboard which requires only one keystroke per letter
four semantic categories were established for nouns and adjectives inanimate animate human and inanimate behaving as human an example of the latter being company as in the company laid off NUM of its employees
in addition the tipster text program established close ties with the message understanding conference muc beginning with muc NUM
profet a word prediction program has been in use for the last ten years as a writing aid and was designed to accelerate the writing process and minimize the writing effort for persons with motor dysfunction
in a follow up study the potential to use the program as a support for spelling and sentence construction was also investigated by comparing spelling and word choice as well as qualitative aspects such as intelligibility and general style
the most substantial savings are scored by the grammatical bigrams in the four largest texts NUM NUM NUM NUM in the essay texts non lexicon corpus and NUM in each of the novel texts lexicon corpus
for the following five predictors no information on test conditions was available ez keys NUM write NUM NUM predictive linguistic system NUM word strategy NUM and get NUM
vere writing difficulties at different linguistic levels the character level spelling errors morphological level agreement and occasional inflection errors and syntactic level incorrect word order poor grammatical variability and incorrect handling of function words
in the first one which was a preliminary test conducted at our laboratory the swedish british english danish and norwegian versions of profet were run automatically with a statistical evaluation program on text excerpts approximately NUM characters in length
the organization of the paper is as follows
it uses natural language for both input and output and can handle a variety of syntactic constructions and lexical items including sentence fragments and misspelled words
these normal form results for pure ccg lead directly to useful parsers for real restricted ccg grammars
the techniques to be used for gaining overall performance speed and nlemory efficiency etc of a gramma r checking
form vocative case which we shall deal with innnediately below this means that if such a
checking whether between any two finite verb f rllls a collllna or a c of dinating conjunction occurs is a me to detect ma ny cases of the omission of a co tuna a t the end of an embedded subordinated clause which is one of the most dequent errors at all
gain in overall statistical speed of the system which is achieved by adding a preprocessing phase consisting of a finite state automaton pa ssing through the input string and looking for a lexical trigger of a contingent error if this automaton does not find any such trigger tile time consurrfing grammar checking process proper i.e.
the method is also straightforward to employ in tandem with other applications such as those below
we therefore extend the algorithm to optimize the chinese sentence segmentation in conjunction with the bracketing process
the project covering bulgarian and czech two ti eeword order languages from the slavic t rnily was performed between january NUM and mid NUM by a consortium consisting of both academic and industrial partners
tile forlner error to be detected by the l init e state machinery is a particular instance of an error where a plural masculine animate subject is conjoined with a verb in a phlrm feminine tbrm el
then for every NUM i n the production probabilities are subject to the constraint that
the proof closely follows that for standard cfgs and the proofs of the lemmas are omitted
matters are made still worse by unpredictable omissions in the translation lexicon even for valid compounds
segmentation of the input sentences is an important step in preparing bilingual corpora for various learning procedures
the security bureau grante authority to the polic station figure NUM the crossing constraint
phrasal translation examples at the subsentential level are an essential resource for many mt and mat architectures
let e c be any string pair derivable from a b1
the constraint is also useful for computational reasons since it helps avoid exponential bilingual matching times
the rationale is that words with similar left context characterize words to their right in a similar way
c nf leflchild seqno a nf rightchild seqno
transformations such as passivization insertion of auxiliary verbs and whmovement are performed and the final sentence is linearized with the help of an lfg grammar
language specific synsets linked to the same ili record should thus be equivalent across the languages
when something happens to change the blood pressure such as a change in the volume of blood in the body the body must compensate
considering that the travel speech data is only a very small portion of all the available english training data we plan to use adaptation techniques to adapt the current esst acoustic models into models for the travel domain
table NUM shows counts and percentages for the various types of names manually marked in this set of documents
the prediction step does not need to be modified for the viterbi computation
loops are simply avoided since they can only lower a path s probability
for deterministic cfgs the incremental cost is constant NUM l total
predicted os np vp predicted predicted 3vp NUM vt np
in describing the parser it is thus appropriate and convenient to use generation terminology
each prediction corresponds to a potential expansion of a nonterminal in a left most derivation
second recall and precision on organizations was a bit low
these linguistic specifications can apply across many domains
this is followed by the merging step which merges expectations together wherever possible
for example the system currently does all person reductions after organization reductions
in the remaining sections we summarize the janus approach to spoken language translation highlight the differences between the scheduling and travel planning domains present some preliminary results for the travel planning domain and summarize our plans for modifying the design of the system in order to effectively handle a variety of sub domains
we hank hristian 3aequemin irin didier bourigault marie luce herviou jean david sta der edf marie tl NUM ne candito tai ana and sophie aslanides eli for their remarks on a previous version of this l aper
however such a definition would make spurious ambiguity sensitive to the fine grained semantics of the lexicon
even if they are among the most similar adjectives NUM shared contexts and if they belong to the same clique lcb coronaire eoronarien diagonal circonflexe rcb the fact that eoronarien alone is connected to evaluation adjectives severe signifieatif and important shows that they can not always substitute to each other
the search algorithm parses the input as it compresses it and can therefore output a segmentation of the input in terms of words drawn from the lexicon
the goal of this work has been to explain how linguistic units like words can be learned so that other processes can make use of these units
u led is displaying only a flashing seven
NUM c what is the led displaying
we summarize the key features of the model below
NUM c what is the led displaying
NUM c what is the led displaying
for testing purposes spaces will be removed from input text and true words will be defined to be minimal sequences bordered by spaces in the original input
imagine that text utterances are paired with representations of meaning s and that the goal is to find the minimum length description of both the text and the meaning
one way to do this is to explicitly write out any symbols that are present in the word s meaning but not in its components or vice versa
and in speech the three different words to two and too may well inherit the sound of a common ancestor while introducing new syntactic and semantic properties
the basic debugging process consists of the following steps
to update the structure of the lexicon words can be added or deleted from it if this is predicted to reduce the description length of the input
zx c w logp w d l changes z c NUM logp o
and since there are no fixed number of parameters when words do start to have multiple disparate uses they can be split with common substructure shared
for example many of the legitimate weapons e.g. m NUM and ar NUM were not known to the judges
the arguments to be filled in a slot are phonetic transcriptions provided by a dictionary or a grapheme to phomene g2p conversion module
variations on the level of a carrier slot can be merely syntagmatic agreement of all kinds liaison contraction etcetera
this was our strictest test because only true category members or subparts of true category members earned this rating
the purpose of the prosodic integration module is to calculate appropriate prosody for all arguments that are to be filled out in a carrier
for the slot arguments the same technique can be applied or prosody is calculated on basis of specific duration and intonation models
the muc NUM corpus mainly contains reports of terrorist and military events the relatively poor performance of the energy category was probably due to the same problem if a category is not well represented in the corpus then it is doomed because inappropriate words become seed words in the early iterations and quickly derail the feedback loop
semantic knowledge can be a great asset to natural language processing systems but it is usually hand coded for each application
we experimented with larger window sizes and found that the narrow windows more consistently included words related to the target category
our experiments suggest that a core semantic lexicon can be built for each category with only NUM NUM minutes of human interaction
our parser tags unknown words as nouns so sometimes unknown words are mistakenly selected for context windows
the idea of having a wide separation is less clear when there is no perfect separator but we can still appeal to the basic intuition
we have shown that while these algorithms have many advantages there is still a lot of room to explore when applying them to a real world problem
the key feature of winnow is that its mistake bound grows linearly with the number of relevant features and only logarithmically with the total number of features
in this algorithm see in eq NUM the coefficient of the ith feature can take negative values unlike the representation used in positivewinnow
the algorithms differ by whether they allow the use of negative or only positive weights and by the way they update their weights during the training phase
while both these works make use of multiplicative update algorithms as we do there are two major differences between those studies and the current one
they use a more complex representation a multi layer network but this additional expressiveness seems to make training more complicated without contributing to better results
in the future we plan to further investigate the application of our rule based algorithm to alphabetic languages
the following table shows the parse accuracy for subtree depth NUM
table NUM provides a summary of the results using the greedy algorithm for each of the three languages
our english experiments were performed using a corpus of texts from the wall street journal wsj
the algorithm provides a simple language independent alternative to large scale lexicai based segmenters requiring large amounts of knowledge engineering
the expansion of each arc follows these steps eliminate the arc
this expansion can introduce non determinism so these new models are now ussts
the results for the three corpora can be seen on table NUM
this suite of tool s developed over time in cooperation with a subgroup of the management and data systems operations component of general electric aerospace
entities such as dates are found by combining structure format with a list of valid items i e a valid month followed by a number
this heuristic worked well however no allowance had been made for the case in which a succession event was extracted in the absence of any succession org
the nltoolset contains a core knowledge base a large sense disambiguated lexicon and a variety of text processing tools for extracting information organizing information and generating output
the procedure for building an extraction system is currently too labor intensive and haphaz ard a process dependent to a great extent on the abilities of the developer
the most striking effect of a deficient post processing heuristic was the decision to eliminat e any succession events which contained only an in and out object with no other information
dooner could have lost NUM pounds of currency this makes fo r an interesting discussion of how smart our systems should be at the named entity level
developers can then check the scores at the start of the day and determine which area of the system is most in need of improvement at that time
the reference resolution strategies used for muc NUM will be expanded to provide more accuracy in identifying related and unrelated organization descriptors as well as pronoun references
the choice is based on a hierarchy which begins with appositives prenominals and predicate nominatives and ends with references resolved by the reference resolution module
the parser consults the lexicon at the moment in which the so position of a chain is reached
the categorised corpus was used for training a model the initial sst
they are consistent with the context because t1 expresses only that mother does not know whether russ knows and not that she does not herself know NUM thus by NUM NUM and the metaplan for plan adoption shouldtry r m askref r m whoisgoing ts NUM is explainable
this paper describes the use of categories for improving the eutrans translation systems
for example an inform s h p expresses the linguistic intentions whose content is p and intend s know h p i.e. the speaker intends the hearer to believe NUM that p is true and NUM that the speaker intends that the hearer know p
the lexical entries of the head nouns and the verb are given in figure NUM the feature hon is a three valued scalar NUM for honored NUM for casual and NUM for intimate
material which the speaker believes might be inferred given the dialogue context
example NUM g and then have you got the pirate ship
right just move straight down from there then past the blacksmith
in addition higher level segments such as transactions vary in size considerably
the second is some explanation of how games are related to each other
the four coders did not speak to each other about the exercise
kappa for different pairings of naive coders with the expert were NUM
combining categories agreement was also very good k NUM
furthermore other types of parsers will be built to determine if this lexical head corner parser is indeed more efficient
it is difficult to see any difference in meaning between the two choices
grammatical functions gf s are predicted by case inflections markers on the head nouns of noun phrases nps and postpositional particles in postpositional phrases pps
finally vps following the vpe are penalized in a symmetrical fashion
so vpe would someone recently divorced or widowed
here the correct antecedent is the matrix vp headed by go
as mentioned in section NUM we define three criteria for success
we first describe the head transduction approach in general in section NUM in section NUM we explain properties of the particular head transducers used in the experimental english to mandarin speech translator
other criteria for success are also examined
finally we briefly discuss related work
deg clausal relation to the vpe
in the ideal case lexical entries fulfill among others two requirements
first the representations are suitably fine grained such that they capture lexeme speeific distinctions
the link is based on a set of well defined and systematically occurring mappings cf
these theta roles are arran qcd in a fixed hierarchy the theta hierarehy
additionally the rule based interpretation of a bsf delivers a prototypical description of the corresponding situation
van noord efficient head corner parsing note that goal weakening is sound
fmds wherein the s ammti s of the lenw nts
the set of lexemes that are suitable to refer to the same situation constitutes a lexical field
it proceeds in a single phase and does not use packing
the head corner parser was compared with a number of other parsers
the following results should be understood with these reservations in mind
logical forms often implicitly represent the derivational history of a category
these rules are of the form x not x
van noord efficient head corner parsing NUM selective memorization and goal weakening
the first table encodes which goals have already been searched
in practice the difference in space requirements can be enormous
table NUM pleonastic it detection rules
possessive or prepositional modifier conflicts exist
using the specialisation link hierarchies of concepts are specified
from these rules the most similar pair is calculated and merged to a new label the merging process is cached out in an iterative way
figure NUM contains a system flowchart
we chose to include the third sort as well because corpora seemed likely to be valuable in providing examples more concretely and certainly more extensively than other sources
supported by some experiments local contextual information which is left and right categories of a constituent was shown to be useful for acquiring a context sensitive conditional probability context free grammar from a corpus
these transformations generalized on learned ones
we manually added the missing cases
table NUM la hack NUM performance
we tried to use texts from a variety of genres and we attempted with some limited success to find bilingual english bulgarian english estonian and french dutch texts
the text processing techniques employed in glosser are not exotic and likely robust enough to support quick access to corpora on the order of NUM mb in size
for instance for the example on figure NUM b the optimized lattice includes only three nodes but there is just one undecided node c which is not shown in bold
it is done by varying the order of the three middle relations in the composition
by situating word positions in a bitext space the geometric heuristics of sentence alignment algorithms can be exploited equally well at the word level
added to hire as possible succession action
the scenario management template is defined using the hyper template mechanism
pa rser tbr our pa tterns
problems with this have been discussed in the previous sections
graphically we use dotted lines to show the coreference between graphs or concepts
corpus based information presentation for a spoken public transport information system m m m rats and r j van vark and j p m de vreught knowledge based systems
table NUM correlations ofidf across years
the tokenization and associated attribute annotation for the first two words is shown as lists of attribute value pairs in the lisp list format used by the scheme programming language in which th e system is written supplemented by a few c and c modules
the nodes dr and mr then become obsolete as well since they will not support directly any node so we can safely remove them from the lattice too
for example a number of organizations fit the canonical form of a prefix organizational title followed by of or for followed by a location or unknown capitalized words e g bank of new york
there are no immediate plena to replace mr
more var and less entropy than poisson
two first attempts ended in nothing although they were quite useful experiences for dialog and probably for the potential partners as well
that is why a final step of closing off the derivation is needed
piy j ba blnaty eb mg NUM figure NUM example text segmentation
we are therefore making some assumptions about the way reporters structure their articles and part of our work will be to see whether such assumptions are valid ones
we have devised a method of evaluating segmentation grids that seems to closely match our intuitions about the goodness of a grid when compared to a model
areas to investigate are the optimum value for n the effect of normalization on term vector calculation and the potential advantages of using a threshold
if a sentence iuitial clause appears in a sentence that is not paragraph initial then it is assigned to the same event as the first clause in the previous sentence
this heuristic also interacts with the text structuring strategies described above when it is activated it can be used to override the default strategy
consequently and in keeping with the fast and shallow approach we have adopted the range of spatio temporal concepts the program handles has been restricted
the third strategy involves a mix of the above favoring the event value of the previous clause followed by the lowest non conflicting event values
in our generator a mapping rule is represented as a d tree in which certain nodes are annotated with semantic information
we will pursue iv and v in the context of an informal example based introduction to the language and to techniques for its use and we will make frequent reference to the datr based lexical work that has been done since NUM
in this section we illustrate how the algorithm works by means of a simple example
in addition to measuring intercoder reliability we compared each coder s annotations to the evaluation temporal units used to assess the system s performance
although the events and states discussed in the nmsu data are often outside the coverage of this parser the temporal information generally is not
the result of a rule application is a partial ailt pailt information this rule would contribute to the interpretation of the utterance
consider a graph in which the pailts are the vertices and there is an edge between two pailts iff the two pailts are compatible
we currently use NUM narratives for training and NUM narratives for testing
the system is unable to consider correction characters that would be lexical impossibilities
fics are tensed clauses that are neither verb arguments nor restrictive relatives
then the second branch is taken and the feature corer is tested
if t p is the time taken by the procedure compute nodes for an input of size p then
matrix multiplication on the nodes got as a result of step NUM with nodes got as a result of step NUM
interestingly the sparse version is an order of magnitude faster than the ordinary version for strings of length greater than NUM
the main objective of the implementation was to check if matrix multiplication techniques help in practice also to obtain efficient parsing algorithms
NUM the auxiliary tree fl is attached at the copy of m and its root node is identifed with the copy of m
the above implementation results suggest that even in practice better parsing algorithms can be obtained through the use of matrix multiplication techniques
an auxiliary tree has both its root and exactly one leaf called the foot node labeled with the same nonterminal symbol
table NUM performance on test set
table NUM performance on training set
the bracketed numbers will be explained below
a funny thing happened on the way to muc NUM early in the planning of muc NUM an additional dimension for evaluating parsers was planned
as a consequence of needing to limit the effort that we could give we decided to focus on st more than the other two tasks
in this way the system has a placeholder for the information that a certain structura l relation holds even though it does not know what the semantic relation is
in the ne system patterns were used to recognize all three of the expression types which make up the tas k entity expressions temporal expressions and numerical expressions
identifinder i s made up solely of lightweight techniques i e those that rely only on local processing do not involve dee p understanding and can be optimized
the message level representation is a list of discourse domain objects ddos for the top level events of interest in the message e.g. succession events in the st domain
w or word s the spelling of the s th morpheme
we can create a lattice structure from untagged japanese sentences and a japanese dictionary
however he did not apply this algorithm to the estimation of hmm parameters
using the extended forward backward probabilities we can formulate the reestimation algorithm from ambiguous observations
figure NUM the precision of the models estimated with the step credit factor
the other is related to assignment of the credit factor without a rule based tagger
figure NUM the precision of the models estimated with the variable credit factor
the new likelihood was based on the probability with each possible path weighted by the credit factor
a state of the hmm represents an abstract class of a part of the input symbol sequence
null as NUM shows it is possible that a pp in the mittelfeld modifies a fronted verbal complex
lake the named entil y task this was also seen as a potential demonstration of the ability of systelns NUM o pertbrm a useflfl relatively dolnain independent task with near term extraction te hnoh gy although it was recognized as being more dillicult than named entity since it required merging information from several places in the text
mr enamex type quot person quot dooner enamex met with enamex type quot person quot martin puris enamex president and chief executive officer of enamex type organization ammirati puris enamex about enamex type organization mccann enamex s acquiring the agency with billings of numex type money NUM million numex but nothing has materialized
a property consists of a predicate applied to a number of arguments
this is equivalent to the baseline link classification method and provides a lower bound on the performance of the algorithm actually used in our system section NUM
we will uot describe the underlying generation algorithm in detail but we assume that familiarity with chart parsing is sufficient for understanding the proposed method the generator can be thought of as a parser that takes logical forms as input and produces strings as analyses
also let us assume that the meaning of a pp headed by into is into e loc and that quick e is also the semantics of the adverb quickly
let us consider the following logical form that could be produced from analyzing little dog in a lm null guage that interprets little as an ambiguous adjective denoting either smallness in size or youngness in age
usually one daughter will have an array like NUM the other 013j and their combination will yield oql j
the former would be represented as move e agent into e loc and the latter as move e agent quick e
the constraint excludes the path that leads to a selection of the verb rush but it allows a choice of p2 which means that enter can be used to yield john entered the room quickly
if the result is not satisfiable no consistent assignment of truth values or if it is not consistent with the negative constraints then there is no path in the derivation graph that corresponds to an expression of all the facts
this style of seman null tics fits the operation of the generation algorithm very well and it is attractive to translation since it allows for flexibility and simplicity with regard to syntactic realization and treatment of structural mismatches between syntax and semantics
similarly certain psychological verbs come in pairs fear frighten like please etc but not in all languages therefore a specification to express a pro titular argument as the discourse topic might lead to a failure
the crucial advantage that the proposed generation method provides is that it enables considering all of the semantic interpretations at once avoiding the massive duplicated effort that would result from enumerating the logical forms and considering each one of them individually
our coverage of the grammar is substantially higher than the coverage presented in his thesis and we also use a full scale external morphological generator to deal with complex morphological phenomena of agglutinative lexical forms of turkish which he has attempted embedding into the sentence generator itself
we have been influenced by her approach to incorporate information structure in generation but since our aim is to build a wide coverage generator for turkish for use in a machine translation application we have opted to use a simpler formalism and a very robust implementation environment
we then present an overview of the feature structures for representing the contents and the information structure of these sentences along with the recursive finite state machine that generates the proper order required by the grammatical and information structure constraints
turkish in terms of word order turkish can be characterized as a subject object verb sov language in which constituents at certain phrase levels can change order rather freely depending on the constraints of text flow or discourse
tactical generation is the realization as linear text of the contents specified usually using some kind of a feature structure that is generated by a higher level process such as text planning or transfer in machine translation applications
the poisson distribution predicts that lightning is unlike to strike twice in a single document
figure NUM shows the word boycott in five different years of the ap news
a good keyword like boycott picks out a very specific set of documents
maybe not as predictable as idf for the NUM words in table NUM
in the absence of any information regarding the information structure of a sentence i.e. topic focus background etc the constituents of the sentence obey a default order but the order is almost freely changeable depending on the constraints of the text flow or discourse
expected under a poisson log2 NUM e ff d
the problem with somewhat is that it behaves almost like chance poisson
this decision tree was learned under the following conditions all of the features shown in fig NUM were used to code the training data boundaries were classified as discussed in section NUM and c4 NUM was run using only the default options
table NUM shows the results of the hand tuned algorithm on the NUM randomly selected test narratives on both conditions NUM and NUM condition NUM results the untuned algorithm with the initial feature set are very similar to the training set except for worse precision
however froln the current perslmctive of tnost of the eolnmittec ese seenmd fairly asic aspects of unde rstanding and so an experinmnt in evahlating them and encouraging improvem mt in them the committee had l ropos t a ve ry anll itious i rogrmn of cvahu l ions
although the high standard deviations show that the tuned algorithm is not well fitted to each narrative it is likely that it is overspecialized to the training sample in the sense that test narratives are likely to exhibit further variation
we refer to the original np algorithm applied to the initial coding as condition NUM and the tuned algorithm applied to the enriched coding as condition NUM table NUM presents the average ir scores across the narratives in the training set for both conditions
with respect to performance tile bunching of scores suggests that many sites were able to solve a common set of easy problems but were stymied in processing messages which involved hard problems
lex head link sbar p verb
furthermore input must either specify all words or provide enough features so that the syntactic grammar can lexicalize any words that are syntactically determined
the semantic constraints on lexical choice are in effect taken into account in the input knowledge representation i.e. option NUM in figure NUM
a one to one mapping between each domain concept and a word of the language would imply that concepts are represented by words clearly an undesirable situation
however it means the task of lexical choice is computationally complex requiring consideration of a potentially large number of mappings between concepts and words
as the lexical chooser proceeds it adds features to this feature structure representing the syntactic elements of the clause that is to be produced
the lexical chooser first traverses the input conceptual structure which appears under semr to decide what syntactic category will be chosen to realize it
in addition disjunctions can be named using the def alt notation and referred to in other places using the notation name
null surge represents our own synthesis within a single working system and computational framework of the descriptive work of several noncomputational linguists
nevertheless we believe that the results obtained for this restricted set of texts gives a fairly good indication for the success of the method on large texts as well
in a simplistic generation system all semantic relations would be mapped to clauses while entity and set descriptions would be mapped to noun phrases
our research has focused on building into the lexical chooser the ability to realize any choice of perspective on the structures produced by the content planner
this script enables the user to write hebrew texts that are morphologically unambiguous in order to use them later as an input for various kinds of natural language applications
eliminating or reducing the ambiguity at this early stage of automatic processing of hebrew is crucial for the efficiency and the success rate of parsers and other natural language applications
to see the nature of the morphological ambiguity in hebrew consider for example the string hqph ngpn which has three possible analyses NUM
the derivation of this measure is shown below r7 completes the derivation deleting the first vowel on the surfaces l a 101a NUM i oc tism 01ti01b root c v ic2 v ip
a tree meeting these requirements is given below
the deployment effort is being jointly managed by dod and dea with dea responsible for life cycle maintenance
with respect to particular synta ctic theories
grammatical functions encoded in edge labels e.g.
if on the ottmr hand we can split the case forms into a pair of smaller independent as forlns then we can again try to modularize each of those until all groups are modular
this amount of data suffices as training material to reliably assign the grammatical functions if the user determines the elements of a phrase and its type step NUM of the list above
in the second phase of the project verbnmbi a treebank for NUM NUM german spoken sentences a s well a s for the s tllle anlounl of english md apanese
existing treebank annotation schemes exhibit a fairly uniform architecture as they all have to meet the same basic requirements namely descriptivity grammaticm phenomena are to be described rather than explained
dea 6s are organized into case files and are composed of multiple sections with varying amounts of formatting
header fields are normally highly formatted and indicate the subject case date time etc
it also adds markup for certain subfields in the text which are not encoded in the original format
hookah has been supported by congressional dual use funding for transferring tipster technology to civilian agencies
as development has proceeded it has become clear that hookah will change the job description for the analyst
in general our experience suggests there is still a significant gap between laboratory grade extraction software and operational applications
the prototype development effort has been managed by mary ellen okurowski and boyan onyshkevych of the department of defense
disambiguation is based on human processing skills cf
the possibilities for reordering combination steps divide into four cases which are shown in figure NUM
this procedure returns a modified formula plus a set of equations that specify constraints on its indexation
would be allowed by such a rule in the original noncompiled system
from our previous work on word order we despose of a parser generator that can ham dle complex expressions however we shall need to modify or perhaps even replace our learning method with one which is better suited to handle logic constructions like disjunction and negation
if the next instance is negative then fl om the s set are removed the rules that cover the counterexample and the elements of the g set are specialized as little as possible so that the counterexample is no longer covered by any of the elements of the g set
a combination is not allowed if it results in an unsatisfiable set of constraints
the latl er implies an explicit statement oil the part of tlw user of what featm es and values are relevant to the task by ilqmtting the corresponding generalization hierarchies the precedence generalization hierarchy is taken for granted
the learning proceeds in a dialog forni with tile teacher for the learning of each individual l p rule the system produces natural language phrases to be classitied by the teacher mttil it can converge to a single concept rule
the second point is that when it is impossible or soll e structur to be verbalized due to cont radictory lp statelnellts as ill the second row the system itself evaluates this exa ml le
in the higher level tree nodes aux name vp or in the lower level nodes vtr np or in the still lower det num adj n num
the topmost lp rule is most general and covers all the other rules since det num where nnm is a variable covers both det sg and det pl and covers both and
the progranl knows at the outset that the values sg and pl are hoth more specitic than l he varial lc n ran mal ching any mmtber this is tilt bias of the system
knowing these lower lewq lp rules our rneta interpreter would never generate instances like the jonses read thick this book do but only some repositionings of the nodes aux name vp their internal ordering being gua ranteed to be correct
the interpretation process is needed in moving from the instance space to the rule space to interpret the raw instances which may be far removed in form froln the form of the rules so that instances can guide the search in the rule space
common organization names first names of people and location names can be handled by recourse to list lookup although there are drawbacks some names may be on more than one list the lists will not be realized in the text e.g. may not cover the needed abbreviated form of an organization name may not cover the complete person name etc
figure NUM a small example of a pst of
the basis of the third event comes halfway through the two page article in addition peter kim was hired from wpp group s j walter thompson last september as vice chairman chief strategy officer world wide
we then describe and briefly analyze the learning algorithm
lmixn s is calculated recursively as follows
although our current version of crystal does not operate at this level we are currently developing a version of crystal to learn finer grained cn definitions
this will allow the system integrators and customers to more easily identify potential problem areas
the developer and the government must devise a schedule that indicates approximate system delivery dates
she accuses that he steals the auto
procurals of guns by americans were easiness
this leads us to consider other algorithms
also the n gram length is critical
total execution time NUM NUM cpu seconds
is there language expertise available to interpret and explain the novel characteristics of the language
it may not be evident which words will be more frequent and which less if one corpus uses more relative clauses and less passives than another on this hypothesis some will be
co occurring features are then grouped together to give the dimensions of variation and the texts or corpora of di erent registers can be identified by their location with respect to these dimensions
groupings of characters that represent words should be identified either manually or automatically
is a corpus as homogeneous as a subcorpus we produce from it which contains a randomly selected half of its texts or is it as homogeneous as one that contains half of each of its texts
one may include a very small number of texts with a one text corpus as the limiting case another may contain thousands of texts s these factozs present problems for a measure of corpus similarity
in relation to form we should be counting grammatical constructions nnmbers of relative clauses or passives tell us far more about the linguistic character of a text than numbers of occurrences of toho or which
corpus similarity between general corpora will be a matter of whether 5a corpus may contain texts in di erent languages here we only consider corpora which are essentially all in the same language
the standard problem for statistical language modelling is to aim to find the model for which the cross entropy of the model for the corpus is as low as possible
the strategy we adopt is therefore to calculate x NUM for sub corpus pairs and then to use this as the measure of corpus similarity and homogeneity
after arguing the the case for using word frequency lists and describing related work the paper describes the various pitfalls the measure must avoid and presents some first results
in the current context sekine s subtree frequency lists can readily be compared with word frequency lists to determine which lists are better for measuring corpus similarity and homogeneity
a sequence of three or more consonants between two vowels is hyphenated with the succeeding vowel if a greek word exists that begins with the sequence of the first two consonants
the paper expresses these rules which focus mainly on consonant sequences formally and points out their limitations in terms of formal word expressions that can be completely and correctly hyphenated
for all tokens the absolute starting position of the token in the input word is maintained while the length of each token is implicitly defined by the token itself
therefore for all words containing no consecutive vowels precisely n NUM hyphens are derived and thus the rules of table NUM are sufficient to completely hyphenate these words
existing hyphenator programs for modern greek are available as either commercial or research based products and usually work on a minimal basis i.e. finding only hyphenation points of consonant sequences
patterns in table NUM constitute maximal vowel tokens which can be derived by a lexical analysis process while patterns in table NUM consist of single vowels and consonants
nevertheless in both cases the correct point will always be specified thus the assumed rule does not need to be reapplied in order to indicate a potentially impermissible hyphen
if the flow of speech is constrained by the existence of additional difficult or complex phthongs the pronunciation of the excessive diphthong in one syllable becomes impossible
grammar rules v1 and v2 explicitly define NUM of these namely the elements of sets 2v and vc while grammar books refer to NUM diphthongs that never split
this mapping between the english and formal language expressions is called the alignment
this hypothesis was tested by quantifying the ambiguity for a large number of words in such a collection and challenging the assumption that ambiguity does not occur very often
the first experiment was concerned with determining how often sense mismatches occur between a query and a document and whether these mismatches indicate that the document is not relevant
having only one word in common between senses is very weak evidence that the senses are related and it is not surprising that there is a greater degree of error
helm NUM one response to this problem is to use phrases to reduce ambiguity e.g. specifying hearing aids if that is the desired sense
for the phrase experiment we not only had to identify the lexical phrases we also had to identil any related forms such as database data base
we have also provided an explanation for the performance of the porter stemmer and shown it is surprisingly effective at distinguishing variant word forms that are unrelated in meaning
we have shown that natural language processing results in an improvement in retrieval performance via grouping related morphological variants and our experiments suggest where further improvements can be made
the experiment with part of speech tagging also highlighted the importance of polysemy more than half of all words in the dictionary that differ in part of speech are also related in meaning
these words were then manually checked against the words they matched in the top ten ranked documents for each query the ranking was produced using a probabilistic retrieval system
another concern which was noted about the mucs is that the systems were tending towards relatively shallow understanding techniques based primarily on local pattern matching and that not enough work was bein g done to build up the mechanisms needed for deeper understanding
in other words the focus is on the problems of customizing systems for new domains and languages
as the world of mt looks for new directions slt offers a wide range of new challenges
exploiting and exploring dialogue structure slt is the latest frontier for mt research perhaps the last frontier
the common theme for the final three papers in the workshop is an emphasis on methodology and architecture
they present results indicating that their method has an appreciable effect on the performance of a japanese english speech translation system
does that mean that most of the problems involved in speech to text text to text translation and text to speech have been solved
similar heuristics can be used to estimate the benefit of deleting words
in the first two the focus is on the methodology required when one moves from one application domain to another
the approach of frederking et a differs in this respect as it allows for user interaction to improve the translation
thus below the relationship between limited attention and hierarchical recency will be discussed in terms of their stack model but the discussion should also apply to claims about the role of hierarchical recency in other work
if for example the focus is a noun computational linguistics volume NUM number NUM phrase which can be mentioned with an it anaphor then it can not be used to co specify with a stacked focus
in figure NUM dialogue a hierarchical recency supports the interpretation of the proforms in utterance 8a from a radio talk show for financial advice pollack hirschberg NUM c ok harry i m have a problem that uh my with today s economy my daughter is working NUM h i missed your name
in the remaining NUM cases the competing antecedent is not and was never prominent in the discourse i.e. it was never the discourse center suggesting that it may never compete with the other cospecifier
irus at the locus of a return can NUM reinstantiate required information in the cache so that no retrieval is necessary NUM function as excellent retrieval cues for information from main memory
the iru may function this way since NUM the iru reinstantiates the necessary information in the cache or NUM the iru is a retrieval cue for retrieval of information to the cache
in the cache model the entities in these focus spaces would not have a privileged attentional status unless of course they had been refreshed in the cache by being realized implicitly or explicitly in the intervening discussion
NUM NUM irus realize propositions already established as mutually believed in the discourse irus have antecedents in the discourse which are those utterances that originally established the propositions realized by the iru as mutually believed
open class parts of speech are those parts of speech that accept the addition of new items with little difficulty
tree NUM is from the filter stage of wrap up and classifies persons as relevant or irrelevant
this method assumes that most words are known and that all sentences lie in a common semantic domain
such elements are regarded as being properly part of the analysis and generation modules and we describe below how they are handled there
the query engine takes users specifications of their employment interests to identify those job ads held in the database that match their specification
finally generation involves just one single efficient process which is integrated in the sense that no intermediate structures are created during processing
thus whilst most countries in the eu have legislation to prevent race and sex discrimination in job advertising some do not
it is fairly straightforward to extend the grammar to other html constructions such as headers styles lists and tables
if the set of conditions is empty the symbol and what follows it may simply be omitted
although the job titles themselves provide an obvious area of terminology we handle various other areas of vocabulary in a similar way
job ads are submitted as e mail texts analyzed by an example based pattern matcher and stored in language independent schemas in an object oriented database
access to this service will be either through the user s own internet provider or at dedicated terminals located in employment centres
for this reason the domain we have chosen for the prototype development of the tree project is the hotel and catering industry
the normalized rldt allows us to compute which things can be combined with which attributes
informally it means that fi p t can be rewritten to fo tp t if its conjunctive context implies a and does not imply the negation of c a c thus can be viewed as a pattern of conjunctive contexts that justifies translation of finput to foutput
take e x y e is a thing x and y are attributes we can infer that student x can be combined with attribute take x but can not have an attribute take x
definition let t be an rldt and f be a logical part of t the quadruple a c fim t fo put is nce t iff c is a conjunction of input atomic formulas of t a is a conjunction of assumptions of t and formulas
we handle the first argument position of a predicate on x y associated with the condition table y as a different attribute as compared to the condition monday y
to simplify results we divide attributes into equivalence classes where two attributes are equivalent if both attributes are associated with the same set of things that the attributes can be combined with
for each predicate in the formula fting there is a so called conjunctive context that consists of conjuncts occurring together with the predicate in fting meaning postulates in the theory p and the information stored in the database
abbreviated country names for japan and united states in single kanji characters which often occurs in newspapers were sometimes translated by an mt system into their literal kanji meanings day and rice respectively
and the performance appears even better when one considers that the machines do not actually know what the most frequent level is
we consider texts on the one hand as formal objects and on the other as symbols with semantic or referential values
for example the most frequent genre level in the training subcorpus is re portage but in the evaluation subcorpus nonfiction predominates
lr also forces us to simulate polytomous decisions by a series of binary decisions instead of directly modeling a multinomial response
all of the facet level assignments are significantly better than a baseline of always choosing the most frequent level table NUM
the experiments in this paper are based on NUM cues from the last three groups lexical character level and derivative cues
all these ratios are available implicitly while avoiding overfitting and the high computational cost of training on a large set of cues
the word is not in the vocabulary
wu and fung introduce an evaluation method they call nk blind
two measures that can be used to compare judgments are
the first concerns how to deal with ambiguities in segmentation
NUM we are grateful to chao huang chang for providing us with this set
the performance of our system on those sentences appeared rather better than theirs
however we have reason to doubt chang et al s performance claims
this style of naming is never required and seems to be losing currency
there is a costless transition between the nc node and
null finally we wish to reiterate an important point
the performance was NUM NUM recall and NUM NUM precision
note that the lexical forms are distinc t from the concepts they are linked by a concept arc
NUM NUM NUM adoption of mutual beliefs in order to model how the state of the collaborative
there is also a cd rom dictionary access function making translation equivalent selection easier
if a translation is necessary the user needs to go one more step
the user can continue to work in the editor after turning off japanese input
when interaction is finished the system chooses a next node and pauses there
there are some sets of words that acquire special syntactic semantic behavior when appearing simultaneously
the system is placed between the keyboard and an application in the data flow
translation quality needs continuous effort for improvement in both linguistic coverage and precision
we implemented the method as a front end language conversion software to an arbitrary application
in b the translation region is assumed to be he ga buy ta book
then the system pauses showing b as soon as a is input
there are three major types of segmentation errors
conversely the latter is linked to the former with a generalisat ion link representin g a superset
in NUM runs with tagalog vso the same preference emerged in one there was a preference for unset parameters and in the other no clear preference
for example we can express the default that birds normally fly as
the act inform s p asserts that the proposition is true
they must select an utterance form that both parties would agree in the current
department of electrical engineering and computer science milwaukee wi NUM mcroy cs uwm edu
a hearer might then interpret this utterance as an attempt to convey the information
to illustrate the approach we show how it accounts for an example repair
the goal itself must originate within the speaker s non linguistic planning mechanism
terpretations of utterances fincluding recognizing misunderstanding correspond to abductive inference over the theory
the second type is breakdown of unknown words
and in the above example the frequency estimate of w1 becomes NUM although this rarely happens for a large training text we have to smooth the word frequencies
thus we adopt a new method for judging semantic distance between two words
the first drawback results from the character unigram based word model that prefers short words while the second drawback results from the nature of the word tmigram model which prefers fewest words segmentation
when this is the case the head noun of the np realizes the argument of the conceptual relation
successive experiments in which we removed different random sets of half the words from the original list resulted in greedy algorithm performance of NUM NUM NUM NUM and NUM NUM
it allows fine grain connectio n of the analysis results to the sections of the document giving rise to those results
most of our english experiments were performed using training and test sets with roughly the same NUM NUM ratio but in section NUM NUM NUM
this operator simply echoes the ith word ewi from the example to the input
the transformation based algorithm involves applying and scoring all the possible rules to training data and determining which rule improves the model the most
furthermore since all the rules are purely character based a sequence can be learned for any character set and thus any language
now since the counts of this sequent must be balanced the sequence b1 b3m must contain for each NUM j m exactly NUM bj and exactly n cj as subformulae
concerning sd it is straightforward to show that all context free languages can be generated by sdlgrammars null proposition NUM every context free language is generated by some sdl grammar
the following shows an example of an english sentence and its de segmented version about NUM NUM years ago the last ice age ended
let a bo b1 b3m a be a shorthand for and let x stand for the sequence of primitive types c
we use generally capital letters a b c to denote formulae and capitals towards the end of the alphabet t u v to denote sequences of formulae
observe however that such a bottom up synthesis of a new unsaturated type is only required if that type is to be consumed as the antecedent of an implication by another type
t b o a 5in contrast to linear logic girard NUM the order of types in u is essential since the structural rule of permutation is not assumed to hold
from a server nor deiinc tra nsla tions of spe tiff verb object pa irs e g to take advantage of something
although english is not an unsegmented language the writing system is alphabetic like thai and the average word length is similar
affix stripping loses information such as number and case so thi s information is represented using a feature system
null finally we observe that for this reduction the rules r and r are again irrelevant and that we can extend this result to sdi
rules such as a ip np rcb ni when a la rge nulnb0 r of lexica i items axe a ssocia ted
the results of these experiments demonstrate that a transformation based rule sequence supplementing a rudimentary initial approximation can produce accurate segmentation
to return to our example as shown in table NUM there were NUM aize derived words types of which NUM percent conform to the change of state axiom
while thai is also an unsegmented language the thai writing system is alphabetic and the average word length is greater than chinese
this variation of the greedy algorithm using the same list of NUM words produced an initial score of f NUM NUM
after reviewing the standard approach via sequent proof normalisation we outline the relevant features of linear logic programming and explain compilation and execution for associative and non associative calculi in terms of groupoid and binary relational interpretations of categorial connectives
i k b p o j k a k new variable i j b a p constant as p furthermore right product though still not non horn left product unfolding can be expressed
such non determinism is not significant semantically the variants ha ve the same readings the non determinism in partitioning by the binary left rules in l is semantically significant but still a source of inefficiency in its backward chaining generate and test incarnation
categorial type assignment statements comprise a term and a type a we write a a given a set of lexical assignments a phrasal assignment is projected if and only if in every model satisfying the lexical assignments the phrasal assignment is also satisfied
a sequent comprises a succedent type a and an antecedent configuration r which is a binary bracketed list of one or more types we write f a the notation f a here refers to a configuration i with a distinguished subconfiguration a
more problematic are the permutability of rule applications the non determinism of rules requiring splitting of configurations in l and the need in nl to hypothesise configuration structure a priori such hierarchical structure is not given by the input to the parsing problem
we can resolve the first goal on the agenda with the head of a program clause and then continue with the program as before and a new agenda given by prefixing the program clause subagenda to the rest of the original agenda depth first search
have a universal rank specifically named objects have a named individual rank and general individuals an individual rank
the best we could manage seems to be to try different partitionings of NUM at execution time but even if this could work it would still amount to trying different partitionings for r as in the sequent calculus a source of non determinism we seek to reduce
in section NUM we describe the robust interpreter from errorful speech recognition results and illegal sentences and in section NUM we describe the cooperative response generator
part through the experimental result table NUM shows the performance of our system through experiments using mode a system which investigated the performance of the language processing parts
NUM among the systems of ki values learned negative values were learned for the features in which template s properly subsumes template t and in which s and t are otherwise consistent
thirdly the dialogue manager passes a semantic network and contextual information to problem solver to retrieve any information from the knowledge database
system query is the rate that the system queried the user to get necessary conditions and to select the information
the interpreter gives the priority to the interpretation where the number of post position assumed to be wrong is a few as possible
retrieval failure is the rate that the system could not offer the valuable information to user although the interpreter has been successful in generating a semantic network
the touch panel used here is an electrostatic type produced by nissya international system inc and the resolution is NUM x NUM points
therefore the interpreter that receives recognized sentences must cope not only with spontaneous sentences but also with illegal sentences having recognition errors
when one of the steps succeeds go to process NUM if all of the processes fail go to process NUM
further if the system ca n t retrieve any information related to the user s question the generator proposes an alternative plan
each node has a set of links plus a set of control variables or controls
finally we have a characteristic for modeling when the values of the name slot of a template are both multi worded and identical this is a crude heuristic for identifying matching unique identifiers
for example an adverb such as slowly could be given the type NUM lro an unfortunate aspect of bar hillel s first system was that the application rule only ever resulted in a primitive type
the size of the grammar is measured as the number of rules or number of states in the lr table
the meaning of book in this sentence can not be disambiguated between the number of interpretations that are implied the informational content of the book military technicalities its physical appearance heavily weighted and the events that are involved in its construction and use long
schematically this is equivalent to the following rule where r is undetermined to simplify exposition we ignore the quantifier for y
in this account the uniformity is specified by the solving of a certain equation in which roughly speaking the meaning of the source sentence as a whole is equated with the meaning of the target vp as applied to the meanings of the elements in the source that are parallel to overt elements in the target
e mail NUM association for computational linguistics computational linguistics volume NUM number NUM notice that the two sets of readings are disjoint and depend crucially on the antecedent of the pronoun in the source clause NUM past approaches to recovering these readings fall into two categories source determined analyses and discourse determined analyses
as it has never been possible to construe the solutions of ellipsis equations as representing merely the elided material see for instance the solution to example 30b given by dsp it is not clear why this would constitute a departure much less a radical one
if examples like NUM do in fact differ in readings then discourse determined analyses are falsified outright
we should note that while the parallel elements for the ellipsis resolution are determinable from semantic role parallelism the process of identifying the parallel elements in resolving an expression like in john s case is clearly a pragmatic one
for sentence 2a this identity is captured by equation 4a which under suitable assumptions has one solution for the meaning p of the elided vp namely that in 4b
common to these approaches is the idea that at some level of representation surface syntactic deep syntactic or semantic the anaphoric relationships for the source are marked and that the target is interpreted as if it were constructed with relationships determined in some uniform manner by those of the source clause at that level of representation
word reordering this step is applied to the spanish text to take into account cases like the position of the adjective in noun adjective phrases and the position of object pronouns
to show the usefulness of global word reordering
it refers to a previously evoked state or event meant to exemplify or contrast john with respect to some other parallel object or group of objects in this case every other boy in mrs smith s class
this resolution results in a non asserted representation for johnj hoped she would pass himj which serves as the source for the subsequent ellipsis on analogy with cases of cascaded ellipsis discussed by dsp section NUM NUM
the resuhing algorithm is depicted in table NUM
the corpus was generated in a semi automatic way
the details of the search algorithm are described
self organizing maps soms are capable of transforming an input high dimensional signal space into a much lower usually two or three dimensional output space useful for visualization
such problems cause a low error rate to have less significance in ocr texts than in more well formed texts such as the wsj corpus
in the case where n is very large NUM these initial conditions represent a quasi orthogonal state i.e. each unit vector is approximately orthogonal to each other unit vector
we want to build the function that given a word w each time w contains ab i.e.
the total running time for computing the scores of all of the o n NUM nod pairs v and v is o d n2 where d is the lesser of the degrees of the source and target trees
tdeg i ihe dynamic l rogreumniug algorithm accounts for ml approximately NUM iucrc tsc ill speed of align nent a rough estimate sin e much of chc i rogrmn has been r implemcntcd
tiffs offers the potential for acquiring not j ust lcxical but also structural correspondences between the two languages q he specific goal in aligning syntax trees is to identify tile orresponding tree fragments in the source and target trees
we have implemented the greedy lca preserviug algorithm with the following features penalties the penalties for collapsing edges were set to NUM s scores a lex od score of NUM and a lexa
NUM we assume that a hypothetical computer employing the current rule can generate the same text as the test data except for the anaphora which are determined by the rule to be tested
this capability is based upon an approach called context vectors which encodes the meaning and context of words and documents in the form of unit vectors in a high dimensional vector space
transformation based error driven learning and natural language processing a case study in part of speech tagging
in this approach a number of suffixes and important features are prespecified
the problem is that the assumption according to only the constituents of the same category NUM may be conjoined is false indeed coordinations of different categories NUM NUM and of more than one constituent NUM NUM should not be dismissed though being marginal in written texts and must he accounted for NUM
NUM jean danse la vmse et le tango jean dances the waltz and the tango
its value is precisely that it closes off the option of a proliferation of ad hoc notations and the associated software needed to read and write them
a contrario in the case of conjunction reductions wh sentences as well as cliticization are al null lowed referring to what follows the verb as for coordination of constituents and treating the arguments simultaneously on the two parts of the coordination 4a je sais k qui demander un v lo etune canne p che
next in section NUM we describe the implementation of the generation rules in our chinese generation system and show the result of evaluating the anaphora in the text generated by systems employing different rules
on the other hand we have considered the conjunction e and as the head of the coordinate structure so that coordinate structures stem simply from the subcategorization specifications of et and the general schemata of a head saturation
the following eight semantic relations are used object agent goal implement a object place scene cause
accuracy dropped to NUM NUM when contextual probabilities were trained on NUM NUM words
d distinguishing two kinds of adjectives those which denote simple type rouge red grand big etc and those like mental adjectives which denote dotted type
except where noted below the preselected chinese data serves as an independent test of the effectiveness of the different rules which are based on principles that have been independently suggested in the literature
the restr iction relation indicates the temporal precedence between the state and the two events the cause e2 must precede the state and the manifestation e3 must follow it
when training on NUM NUM words a total of NUM transformations were learned
notice however that in some contexts the complement can also refer to the manifestation of the state as for agent orlented adjec null tives 3c
that is the event involved in the agentive role precedes that state existing ill the formal alld the associated constitut1vi vmne shonhl there be one
the qualia structure we proposed in NUM and NUM makes explicit the links between the ditl rent senses of mental adjectives mental state of an individual causative and manifestation
c ta i kanqing lena ren j de zhangxiang he see clear aspect that person gen appearance he saw clearly that person s appearance d oi NUM renchu na renj shi shui
null processing very large plain text or unnormalised sgml corpora where indexing is required and generation of normalised files is a large overhead
for example susan also has a name type attribute with the multiple values of given i female
the following reservations should be made with respect to the numbers given above
the test set was also used during the design of the grammar
timings measure cpu time and should be independent of the load on the machine
semantic accuracy is given in the following tables according to four different definitions
each rule must specify the type of sign of its mother and daughters
this section evaluates the nlp component with respect to efficiency and accuracy
methodologically unsound since no clear separation exists between training and test material
in the third row bigram information is incorporated in the robustness component
as it is not linked at can be adjoined onto any available node in the partially derived korean tree
for notational convenience call the two structures aat s and at gs respectively
this step maps each elementary tree in the source derivation tree to a tree in the target derivation tree by looking in the transfer lexicon
the strength of the present work is that it captures a number of phenomena discussed elsewhere separately and does so within a unified framework
in fact the detection of a chain of tokens that are part of the same term implies a specific choice on the grammatical category of each token thus augmenting the selectivity of pos tagging
to evaluate how well an algorithm predicted segmental structure we used the information retrieval ir metrics described in section NUM
each level of the tree specifies a test on a single feature with a branch for every possible outcome of the test
when the update is completed the window is moved and the process is repeated
the context vector for ataque is moved in the direction of its neighbors
the stem in question is fed to the hashing function and an index is produced
the corresponding number of verb homographs is NUM and they belong to NUM pairs of verb concepts among them five cases which are synecdochical or auto related triplets
with regards to concepts two concepts can be defined to be opposed if at least two of their terms are antonyms
in the case of grammatical language modeling this corresponds to taking
note that the value sets of binary antonymy need not have maximal cardinality NUM although this is true for binary antosemy
these tie words provide connectivity between each language s portion of the context vector space
documents that are similar are close in the space and dissimilar documents are far away
document context vectors are normalized to prevent long documents from being favored over short documents
tested on a representative corpus about NUM of the critical fragments generated are by themselves desired tokens
the actual mappings depend on the properties of words so any tags used in this synchronous manner will necessarily be lexicaiised
standard links are represented by an equal sign other links are represented with the link type subscripted to the equal sign
s s figure NUM paraphrase with partial links the second part of the notation requires picking out important nodes
in stag a link between two nodes specifies that any substitution or adjunction occurring at one node must be replicated at the other
synchronous tags are a useful representation for paraphrasing the mapping between parallel texts of the same language which have different syntactic structure
in this framework a paraphrase is a tool for modifying a text to fit a set of constraints like length or lexical density
similarly more semantically based paraphrases are possible through an indirect application of stags to a semantic representation and then back to the syntax
a more compact definition is to have links of a kind different from the standard stag links between nodes higher in the tree
however for purposes other than sentence simplification where paraphrases like NUM are used a more complex representation is needed
one of the fundamental characteristics of language viewed as a stochastic process is that it is highly nonstationary
central to our approach to segmenting is a pair of tools a short and long range model of language
tive long range language model is on average less accurate than a static trigram model
the procedure that we follow is a greedy algorithm akin to growing a decision tree
figure NUM shows the first several features that were selected by the feature induction algorithm
we thank michael witbrock and alex hauptmann for discussions on the segmentation problem within the context of the inforrnedia project
the fluctuating curve is the probability of a segment boundary according to the exponential model after NUM features were induced
precision and recall statistics are commonly used in natural language processing and information retrieval to assess the quality of algorithms
in the case of the trigger model described above the cache will be filled with relevant words
maintaining a table and doing the table lookup is rather expensive
therefore an np will be a possible head corner of vp
note that we have experimented with a number of different versions of each of these parsers
the second table is represented by the predicate result item
in this subsection we show how the head corner parser can be used in such circumstances
the material is ready to be plugged into the hdrug environment available from the same site
van noord efficient head corner parsing certain common errors are modeled as weighted finite state transducers
in the head corner parser this leads to an alternative to the predicate smaller equal NUM
the predicates to parse a list of daughters are augmented with a list of such references
NUM we already argued above that parse trees should not be explicitly defined in the grammar
bmb speaker hearer category object category subset world ax bmb speaker hearer category x category cand during the first step finding the derivation all co referential variables will be unified
rule NUM bmb system user error plan node cstate system user plan goal bmb system user bel agtl error plan node agtl e lcb system user rcb
NUM so plan construction reasons about the beliefs of the agent in constructing a referring plan likewise plan inference after hypothesizing a plan that is consistent with the observed actions reasons about the other participant s believed beliefs in satisfying the constraints of the plan
to account for how clarification goals arise and how inferred clarification plans affect the agent we propose that the agents are in a certain state of mind and that this state includes an intention to achieve the goal of referring and a plan that the agents are currently considering
so if the referring expression was the weird creature and the hearer could n t identify anything that he thought weird he might say what weird thing thus indicating he had problems with the surface speech action corresponding to weird
goal system bel user bel system replace pl rplan NUM since no further rules can be applied the system checks for goals that it can try to fulfill which will result in choosing NUM
bel system achieve p NUM bel user bel system error p l p NUM NUM then by rule NUM the system adds the belief that it is mutually believed that it has the goal
bmb user system p lan system p NUM NUM be l user be l system error p l p NUM NUM it also adds the belief that this plan will achieve its goal
note that the last element in a training tuple indicates whether the first nc in the structure is the subject of the verb NUM if so NUM otherwise
upon encountering the adjective mexikanische the system takes it to be a noun nouns are capitalized in german followed by the noun verband in apposition
if ni but not nj agrees with v in person and number then nl v n2 g i is a training tuple heuristic rule
tokens will be denoted by the token annotation
since the procedure used to collect training data runs without supervision increasing the size of the training set depends only on the availability of sample text and should be further pursued
the results show that computer aided construction of taxonomies using lexical resources is not limited to highly structured dictionaries as ldoce but has been succesfully achieved with two very different dictionaries
this implies that each maximal projection is computed only once partial projections of a head can be constructed during a parse any number of times as can sequences of categories considered as sisters to a head
NUM applications of the rules the two centering rules along with the partial ordering on the forward looking centers described in section NUM constitute the basic framework of center management
14actually the documents were selected from our main general english treebank of NUM NUM words
in this case an a link is established from u to h u by move link up or move link down depending on whether u dom null inates or is dominated by sbi in t if bi properly dominates u h u does not occur in w
in these examples NUM the speaker uses a description to refer to something other than the semantic denotation of that description i.e. the unique thing that satisfies the description if there is one
figure NUM size of training set NUM
its complexity is o n where n is the number of the leaf nodes in the dendrogram i.e. the number of the mono sense words in the semantic space
john revised a paper of his and bill revised one too
consonant but must be spread onto it
the input fsa constrains only the input tiers
and the generation time drops to NUM NUM seconds
various approaches are possible for choosing such an i
this is good news for comprehension and learning
l as defined in section NUM similarly e is the statistic of factor u u and hence the negative evidence of u v as well as the negative evidence of all transformations having u as left hand side
this notation would ideally be represented in a low dimensional topological space so as to be both perspicuous and flexible enough to use in further nonsymbolic modules
if subject verb agreement is something that the student has not acquired and is not about to acquire case NUM is most likely
a noun i is a term if at least one document j exists for which n wij tij log2 r NUM wi i captures exactly the notion of specificity required in the select step of our algorithm
such an higher discriminating power is required not only for document classification retrieval but first of all for lexical acquisition in this technical domain in fact it seems necessary to rely on the information that attivit6 is typically carried out by humans while attivit6 antropica is not
where freq x y is the frequency of the joint event of x y freq x freq y and n are the frequency of x y and the corpus size respectively
given a term i its inverse document frequency is defined as follows idfi log s n where dfi is the number of documents of the corpus that include term i while n is the total number of documents in the collection
we proceed as follows NUM as terminological units longer than NUM words are very infrequent in any sublanguage we decided to stop after the second iteration NUM select the set of lemmas that by themselves are markers of relevant concepts in the corpus
we will define NUM well formedness principia for term denotations and a description of the different grammatical phenomena related to terms of a language NUM distributional properties that distinguish terms from other accidental forms e.g.
specific nouns are those frequently occurring in a corpus but whose selectivity in sets of documents is very high that is they are very frequent in a possibly small set of documents and very rare in the rest
on the contrary appositive modifiers are used by the speaker writer to add additional details his own point of view or pragmatic information as in la bianca cornice the white frame or la perduta genre the lost people
in figure NUM the head noun debito debt is reported the section related to debito includes all its validated specifications e.g. debito pubblico public debt debito pubblico estero foreign public debt
the extractor program also computes information that is not directly stored in the mrc database
table NUM includes the exact probabilities for obtaining the observed or more extreme values of the test statistic
the various ways of measuring the quantities compared by the tests discussed above lead to the consideration of NUM variables
unlike previous studies the nature of the statistical analysis reported in this paper requires a higher number of pairs
consequently even very sophisticated methods for combining the tests can offer only small improvement
table NUM summarizes the performance of the methods on the two groups of adjective pairs
frequency was selected as the only component of the model for the morphologically related ones
in addition one of the simplest methods text frequency dominates all others
within any single hierarchy the features are ordered according to their difficulty of acquisition reflecting their relative linguistic complexity
in each sequence the a utterance underdetermines what element to add to cf
these loading situations thus constitute a component of the centering constituent of the discourse situation
the book which is fowles s best was a bestseller last year
likewise if no pronouns are used then rule NUM is not applicable
however this is not the case as we show in section NUM
the account given here depends on a semantic theory that permits minimal commitment in interpretations
the open question is which constraints on centers are introduced at which points during processing
he john cb john cf lcb john rcb c
finally c4 NUM is both readily available and is a benchmark learning algorithm that has been extensively used in nlp applications e.g.
to meet this goal the committee developed the name d entity task which basically involves identifying the names of all the people organizations and geographi c locations in a text
to further increase portability a proposal was made to standardize the lowest level objects for people organizations etc since these basic classes are involved in a wide variety of actions
while each individual feature of the template structure adds to the value of the extracted information the net effect was a substantial investment by each participant in implementing the many details of the task
to meet this goal we decide d that the information extraction task for muc NUM would have to involve a relatively simple template mor e like muc NUM than muc NUM this was dubbed mini muc
in contrast most extraction systems did not build ful l predicate argument structures and word sense disambiguation played a relatively small role in extractio n particularly since extraction systems operated in a narrow domain
although called conferences th e distinguishing characteristic of the mucs are not the conferences themselves but the evaluations to whic h participants must submit in order to be permitted to attend the conference
to present it in simplest terms suppose the answer key has nkey filled slots and that a system fills ncorrec t slots correctly and nincorrect incorrectly with some other slots possibly left unfilled
the heuristics are described in detail in NUM
in all the disambiguator uses NUM heuristics based on NUM relationships
NUM heuristic rules are used currently
sense disambiguation using semantic relations and adjacency information
the disambiguation algorithm described by voorhees NUM partitions wordnet into hoods which are then used as sense categories like dictionary subject codes and roget s thesaurus classes
this paper describes a heuristic based approach to word sense disambiguation
this paper has discussed name searching in the context of ranked information retrieval
tence the loss of animal and plant species through extinction the highest ranking collocation found in the target context species is used to classify the example as sensw a a living plant
of these tuples NUM were considered to be partially incorrect based on the judgments of a single judge given the original sentence
in addition to straightforward examples of parallelism like the above there are also contrasts exemplifications and generalizations which are defined in a similar manner
in the test four native speakers of chinese were asked to annotate discourse segment boundaries for five articles selected from the test data
by far the most frequent cases of interaction occur during transfer to a large extent due to the fact that lexical correspondences are all too often of the many to many variety even at the abstract level of lexemes
furthermore red herring restaurant advertises as vacant a position as chef can be generated as well
this approach is the basic mechanism for several dialogue systems young et al NUM smith isince each participant is carrying out initiative evaluation independently there may be conflicts on who should be in control
in information theory inconsistency is called entropy
table NUM pronouns sorted by semantic entropy
responsive to the behavior of individual words as n gram models are
meanwhile table NUM shows that the english verb be is translated much less consistently s than run even though only nine senses are listed for it in wordnet
the table provides empirical evidence for the intuition that function words are translated less consistently than content words the mean semantic entropy of each functionword pos is higher than that of any content word pos
the pos information was not used in the lexicon induction process but after estimating the semantic entropies for all the english words in the corpus the words were grouped into rough part of speech categories
briefly the method works as follows NUM extract a set of aligned text segment pairs from a parallel corpus e.g. using the techniques in g c gla or in me196a
in general the successful achievement of such goals will rely heavily on the accurate transmission of information during the reievant communication episodes
table NUM semantic entropy of punctuation has high
NUM assume that words always translate one to one
this did not happen during tipster phase i
in phase ii the research goals shifted
the importance of these forums and open discussions has been repeatedly demonstrated
why in the process we ve even become friends
these trec tracks are being continued in trec NUM
we also came with numerous challenging problems to be solved along with an understanding and appreciation of the text handling processing and exploitation needs of our individual agency s analysts and linguists
how has it performed in tipster
sufficient quantities of training and testing data
users can search the database in their own language and get customized summaries of the job ads
the timetable which was established for completion of this effort was extremely tight
a number of other participants come close to matching these participation levels
it is well known from the study of complexity theory that the manner in which a class of problems is represented can significantly affect the time or space resources required by any procedure that solves the problem
we also reported on the system s performance by way of experiments
note that this difference would be critical if example data were sparse
figure NUM an input and the database
figure NUM the concept of training utility
several researchers have proposed such an approach
hereafter we will call these examples samples
this data was randomly corrupted at nist using character deletions substitutions and additions to create data with a NUM and NUM error rate i.e. NUM or NUM of the characters were affected
the rationale for these restrictions is given below
this situation is explicitly rejected by restriction NUM
for a more detailed discussion see merlo NUM to appear
the hypothesis space in the three algorithms grows in slightly different ways
for nlab the restriction for active chains no longer holds
hence suppressing feature checks becomes beneficial only if kf n k
they do not discuss specifically the issues of adjunction or rightward movement
the structural licenser and the antecedent need not be the same element
in all other cases the subject and the verb are indeed adjacent
only two verb final languages have postnominal relative clauses persian and turkish
these two problems are not treated here
figure NUM the prediction errors are systematically positive
kim in as vice chairman chief strateg y officer world wide of mccann erickson wher e the vacancy existed for other unknown reasons he is already on the job in the post and his ol d job was with j walter thompson
NUM how robust are these deviations from chance
for the NUM words in table NUM
can we say something more constructive
these crucial deviations from poisson are robust
figure NUM shows one such attempt
its basic strategy for headlines was a conservative one tag a string in the headline as a name only if th e system had found it in the body of the text or if the system had predicted the name based on truncation of name s
but when it rains it pours
the correlations are shown in table NUM
the fact that the above systems all reflect the same translation technique has not always been recognized in the computational linguistics literature
a complementary correction strategy for morphologically sound but morphosyntactically ill formed words is outlined
this section presents a morphographemic model which handles error detection in non linear strings
if the value of before is q sentence final contour
or from NUM to NUM clauses avg NUM NUM
subsection NUM NUM presents a simple two level grammar which describes the above data
the one month limitation on development in preparation for muc NUM would be difficult t o factor into the computation and even without that additional factor the problem of coming up with a reasonable objective way of measuring relative task difficulty has not been adequately addressed
this section deals with morphosyntactic errors which are independent of the two level analysis
the following rules allow for the different possible orthographic vocalisations in semitic texts
cs denote consonants vs denote vowels and a bar denotes complement
they happily utilise the multi tape format and integrate seamlessly into morphological analysis
only the is used error is not obligatory
an algorithm developed by the mitre corporation for muc NUM was implemented by saic and used for scoring the task see a model theoretic coreference scoring scheme and four scorers and seven years ago the scoring scheme for muc NUM in this volume
however due to the myriad of possible led displays and the frequent misrecognition of key words and phrases in these descriptions an effective dialog system would want to be careful to ascertain correctness in interpreting these descriptions
obs behav obs cond observing a behavior where the result of the observation obs depends on the underlying physical conditions present cond when the observation was made
example a wh question e.g. what is the voltage between connectors NUM and NUM main expectation direct answer e.g. seven or the voltage is seven
further study is needed to determine the practical usefulness of this strategy in an actual experimental situation and it is an open question as to whether or not such strategies are feasible for less task specific domains such as advisory dialogs and database query environments
example a wh question e.g. what is the led displaying when the switch is up main expectation a direct answer e.g. the led is displaying only a not flashing seven
for cooperative task assistance dialog making the assumption that the meaning of the user s utterance will belong to a very small subset of the expectations for each abstract goal allows us to define the following context dependent decision rule for utterance verification
this corresponds to a reduction in over verifications from once every NUM NUM user utterances to once every NUM NUM user utterances while under verifications i.e. undetected misunderstandings rises from once every NUM NUM user utterances to once every NUM NUM user utterances
an analysis of the NUM under verifications that occur with the new strategy indicates that while some of the under verifications are due to deficiencies in the grammar there is a a core group of under verifications where misrecognition of the speaker s words is impossible to overcome
this is because in parsing the subentries under the white headed arrows and lr lz in the bold boxes are merged into larger entries which are to be inscribed in the boxes under the black headed arrows
the corpus also contains interesting spelling variants helmut hellmuth as well as peculiarities attributable to regional tastes and fashions maik maia
the multilingual entity task section of this volume is a collection of papers that review the evaluation task and the participating systems
NUM one sentence streak as head no lexical optimization the jazz who defeated the bulls extended their winning streak to three games
computational linguistics volume NUM number NUM example NUM left network of figure NUM NUM two sentences the jazz defeated the bulls
once the verb class is known the transitivity of the clause is determined and the clause skeleton can be extended by specifying the verb s complements
for example sometimes a constraint is stated in the input while at other times it may be derived from the choice of another word in the sentence
because the constraints are represented separately lexical features are added as each constraint apphes thus compositionally constructing the set of features that define the final choice
such components expect as input a specification of the thematic structure of the sentence to generate with the syntactic category and open class words of each thematic role
we focus on the problem of floating constraints constraints that can not be mapped in a systematic way from an input conceptual representation to the linguistic structure
given this organization input to the lexical choice module will be structures from the application domain representation selected during content planning possibly augmented with discourse relations
in the task description for the muc NUM evaluation two events are deemed to be distinct if they describe either multiple types of incident or multiple instances of a particular type of incident where instances are distinguished by having different locations dates categories or perpetrators
secondly arguments in cg are phrasal whereas in dg dependencies are between words
the formulae of NUM for example must combine precisely as shown
the program itself called a functional unification grammar fug is also a feature structure but one which contains disjunctions and control annotations
an important property of fds is their ability to describe structure sharing or reentrancy an fd can describe an identity equation between two attributes
in learning the optimal grouping of types we have two concerns keeping the number of different sets of types to a minimum and increasing the semantic determinacy of syntactic structures enhanced with type information
the text was morphologically analyzed using the engcg morphological analyser
this often indicates a redundancy in the lexicon
NUM the preparation of the syntactic version was the next main step
we present results from part of speech annotation and shallow syntactic analysis
arguments for and against have been given but very little empirical evidence
NUM a consensus version of the tagged corpus was prepared
only three ulsdates were needed to the morphological part of the manual
we show that defining a grammatical representation is possible even relatively straightforward
the eight basic language families are defined in terms of the unmarked order of verb v subject s and objects NUM in clauses
the tla is memoryless in the sense that a history of parameter re settings is not maintained in principle allowing the learner to revisit previous hypotheses
the latter is more optimal with respect to wml and both are of equal expressivity so as expected the vos language acquired more speakers over the next few cycles
the constructions exemplified by each sentence type and their length are equivalent across all the languages defined by the grammar set but the sequences of lexical categories can differ
the results are given in figure NUM next page
null the initial consistency rate was constantly above NUM
we have shown initial results of matching words with their translations in a english chinese non parallel corpus by using context heterogeneity measures
NUM a document may be divided into a header and a body
nl ends the field unless it is escaped see below
t j il second reading of the bill passed the asteriskslin table NUM indicate tokenizer error
in addition one or more attributes may be assigned to each annotation
figure NUM templates representing depots men tioned
the resulting set of templates constitutes a formal description of the state of affairs as described in the text with respect to the application specification which is then fed to the downstream system
on the other hand the x value of the word am is small because it always follows the word i
in the evidential case the fact that all pairs of templates are considered results in a certain amount of washing out of the data due to redundancy in coreference relationships
therefore to address this task in applications with much longer texts mechanisms beyond those that were necessary here will be required for intelligently pruning the search space and subsequently smoothing the distributions
limiting ourselves to modeling probabilities between pairs of templates however leads to inconsistencies because of the failure to take into account the crucial information provided by the existence of other compatible templates
because we have pairwise probabilities for each possibly coreferring pair in the coreference set it turns out that the dempster solution is more easily stated and computed here than in the general case
therefore there would have to be a distribution that assigns NUM NUM of probability mass to a set of configurations that is mutually exclusive from a set that is assigned NUM NUM of probability mass
in a non parallel corpus a domain specific term and its translation are used in different sentences in the two texts
NUM filtering out function words in english there are many function words in english which do not translate into chinese
in the right figure we show the result of filtering out the chinese genitive from the chinese texts
in most cases the hierarchical structure could be recovered from the spans
this can be done by adding a scenario attribute to each annotation
however some allowance must be made for processes such as spelling correction
an consumptions of poulet by you may be the requirements
the procural of the gun by the american is easy
it must also be robust against incomplete or inaccurate inputs
another ploy is to give preference to nominalizations over clauses
many times however the statistical model does not finish the job
it is easy that americans procure that there is gun
she impeach that him thieve that there was the auto
default follows the topmost path in the lattice
among possible alternatives or by explicitly encoding the lexical constraints
support query generation tools users who are not native speakers of the foreign language in which they are submitting a query would like tools to assist in building queries
users might choose to perform a natural language query using the excalibur conquest search engine s concept query and switch to the fast data finder to search chinese text
trw has developed a text search tool that allows users to enter a query in foreign languages and retrieve documents that match the query
it would be very useful for native english speakers to look up relevant words in the japanese thesaurus for assistance in building their queries
other users use paracel s fast data finder search engine due to its powerful search capabilities and are only able to access its power through the fdf search tool user interface
for example an archival database is only available through a legacy text search system that performs its searches very quickly but lacks a great deal in search functionality
trw has developed a text search tool that allows users to enter a query in a number of languages and retrieve documents that match the query
our objective in developing functionslity including multi lingual query generation tools and query functionality has emphasized solutions that work very quickly usually by exploiting the features of a specific search engine
for example if a specific product needs to be marketed to the japanese it might be running under sun s japanese language environment with jle providing support for entering and displaying japanese text
spot currently interfaces to this fdf archival database
one can see that this can only improve the pure form of the model
the basic idea is that whenever an argument is not in a scrambled position it should be substituted into an available empty slot using the aat structure
note that we insert an optional pause between word pronunciations
for example this is an anaphor in print the file about dialogue systems
dependency grammar represents a sentence structure as a set of dependency links between arbitrary two words in the sentence and can not be reestimated by the inside outside algorithm directly
second the new word editor and the lexicon editor were used to add a list of new words to the application s dictionary
if he sends a message a relation instance is created e.g. send NUM
the more complex systems are built on top of the simpler systems in order to minimize duplication of effort and maximize knowledge transfer
the natural and compact modules can be activated and deactivated separately
finally the surface generator generates natural language text from the deep structure
aggregation is also called ellipsis by linguists
the query window where the user
the words are obtained from the parser
this is solved by the compact module
figure 2a normal mode only surface generation
used word list is a list of previous used words
the event window where the user
aggregation is the process which removes redundancy in texts
in figure NUM introduction of linguistic questions NUM is also shown to significantly reduce the error rates for the wsj corpus
the simplest way to construct a tree structured representation of words is to construct a dendrogram from the record of the merging order
out of the three types of questions basic questions and word bits questions are always used in this ext eriment
this teudrogram coust itutes a subtree for each lass with a leaf node rel resenting each word in the class
however since thesecoud term has as much weight as the first terln we used equation NUM to mgke the model complete
by introducing word bits into the atr l ecision tree pos tagger the tagging error rate is reduced by up to NUM
mutum information chlstering niethod enlploys a t ottuni up merging t roce hire with the wel i ge lllul ll
the hierarchical clusters obtained fi orn wsj texts are also shown to be usefld or tagging atr texts which are fi om quite different dommns than wsj texts
we expect there would be a great flumber of words which simply do not have their translations in the other text
the euclidean distance between air and is NUM NUM whereas the distance between air and k is NUM NUM
more vigorous statistical training methods could probably be developed to find out which function words in english have no chinese correspondences
currently simple present and simple past tense are the only two tenses handled
for example in case of a subsequent question like woont koen in amsterdam
in the example this simulates the nps the secretary of the department
to illustrate this suppose edward has just generated het bevat gr2 report en qbgc
their interpretation depends merely on the linguistic expressions that precede them in the discourse
that is the user has prematurely moved from repair to test without notifying tile computer that the repair has actually been made
xtra uses a dialogue memory and a tax form hierarchy to interpret multimodal referring expressions
much like in kl one relations NUM contain role filler class restrictions and role set restrictions
such tracking can be used for recognizing evolving user expertise as well as detecting a lack of mutual understanding about the current situation
the observations reported in this paper are an initial step on the long road to a comprehensive model of actual human computer dialogue structure
these types of control shifts occurred once every NUM NUM user utterances in declarative mode but only once every NUM NUM user utterances in directive mode
it is our conjecture that being able to vary initiative between dialogues is insufficient but further study of this issue is needed
problems NUM and NUM of each session consisted of a missing wire that was also used during the warmup problems of session NUM
NUM in the declarative mode dialogue the subject independently carries out several task goals known to be necessary without any interaction
excluding problem NUM the average number of utterances spoken in the diagnosis phase was NUM NUM in directive mode and NUM NUM in declarative mode
diagnosis subdialogue the number of utterances will change little since all users presumably need the computer s assistance in problem diagnosis
